Publications

Abstract

Lack of available data, ease of clinical use and lack of evidence for prognostic benefit are arguably the key limitations to clinical uptake for any model. This paper reports the development of an image-based analysis protocol, coupling 0D left heart and systemic circulation components with a 3D aortic valve model to measure and to predict the pressure gradient across the aortic valve at rest. The model is personalized using routine clinical data available for aortic valve patients, augmented by additional image data (transesophageal echo and/or CT) to support valve characterization. Computed aortic pressure gradient both pre- and post-intervention was compared with clinical measurements based on Doppler ultrasound for a cohort of 21 patients with aortic valve disease. Correlation for the diseased state measures were adequate (𝑅2= 0.81) for those cases for which associated image data was deemed to be of acceptable quality for segmentation to support a 3D computational fluid dynamics analysis but poor otherwise. Post treatment correlation was reasonable (𝑅2= 0.47) for all cases. Importantly, the personalized model presented here describes the interaction between the patient’s cardiovascular system, including the heart and circulation, and the valve, rather than evaluating the valve in isolation.

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Aerosol-generating treatments in dental practice carry the risk of releasing potentially infectious body fluids (PIBF), resulting in possible disease transmission from one patient to others and to the dental staff. The SARS-CoV-2 pandemic has revealed the urgent need to provide precise and comprehensible safety measures for all kinds of diseases. Dentists and dental staff are more exposed to potentially infectious aerosols than the general population. However, there is no established method to quantify the infectivity of aerosols and splatter as encountered by dentists. This study presents a proof-of-principle setup to differentiate PIBF and drill coolant within dental aerosols and splatter.

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In vivo verification for the development of cardiac MRI and CFD simulations is limited by scan time and motion artifacts. We developed a subject-specific, MRI-compatible left ventricle (LV) phantom within a closed-loop circulation system driven by an MRI-safe pump to reproduce cardiac motion and provide robust data for high-fidelity model validation.

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The role of cardiovascular magnetic resonance (CMR)-imaging-based pulse wave velocity (PWV) and aortic distensibility (AD) in population-based cohorts as a risk stratification tool remains unclear. The purpose of this study was the CMR-based quantification of PWV and AD in the context of cardiovascular risk factors (CVRF) and/or diseases (CVD) in the Hamburg City Health Study (HCHS).

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To introduce and validate optimized multifrequency cardiac time-harmonic elastography (THE) with portable drivers and multiple-heartbeat acquisitions for detecting abnormal diastolic stiffness using in silico testing and investigating participants with confirmed cardiac wild-type transthyretin amyloidosis (ATTRwt), a model disease for myocardial stiffness, compared to healthy controls.

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The contribution of clonal hematopoiesis (CH) and disease-initiating precursors in B-cell non-Hodgkin lymphomas (B-NHLs) remains underexplored. Such precursors may drive clonal evolution, contributing to disease progression and relapse. Here, we systematically profiled genetic precursor lesions in 43 patients with B-NHL using complementary whole-exome, targeted, and single-cell sequencing approaches. CH-associated mutations with a variant allele frequency of ≥1% were detected in the peripheral blood of 55% of patients, with significantly higher frequencies in indolent compared with aggressive B-NHL (P =.03). Quantification of allele burden in flow-sorted cell populations revealed a B-cell-skewed expansion of CH clones, contrasting the myeloid differentiation bias reported in individuals without hematologic malignancies. Gene-specific expansion patterns were evident among the most frequent CH lesions, with DNMT3A-mutant clones exhibiting impaired hematopoietic differentiation and TET2-mutant clones having multilineage propagation. Notably, identical CH clones were detected in 41% of corresponding lymphomas, displaying distinct clonal dynamics: tumor-promoting CH (expansion in B-NHL; 10/16 clones; mainly TP53) and tumor-infiltrating CH (no expansion; mainly DNMT3A). Moreover, we identified lymphoma-associated mutations in flow-sorted hematopoietic progenitors from patients with indolent but not aggressive B-NHL and observed a stepwise accumulation of mutations along the lymphoid differentiation path. Single-cell genotyping confirmed the presence of mutated progenitors in 3 follicular, 2 mantle cell, and 2 marginal zone lymphoma patients, providing direct evidence of a preneoplastic state in disease pathogenesis. Our findings offer novel insight into the cellular origin of nodal B-NHLs and highlight a previously underappreciated role for early clonal events involving the stem/progenitor cell compartment.

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Aortic stiffness (AS) parameters as pulse wave velocity (PWV), aortic distensibility of the ascending and descending aorta (AD AoAsc, AD AoDesc) and pulse pressure (PP) are marker for vascular ageing, whereas data concerning obstructive sleep apnea (OSA) in population-based cohorts are sparse. Thus, we aimed to identify possible associations of AS with OSA in the Hamburg City Health Study (HCHS) cardiovascular magnetic resonance (CMR) cohort.

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Autologous stem cell transplantation (ASCT) involves harvesting hematopoietic stem and progenitor cells (HSPCs) prior to chemotherapy and subsequent repopulation of the bone marrow. This process imposes a bottleneck, providing a framework to dissect the unresolved short- and long-term clonal dynamics during hematopoietic reconstitution. By integrating bulk error-corrected targeted sequencing of clonal hematopoiesis (CH)-associated genes with mitochondrial single-cell Assay for Transposase-Accessible Chromatin sequencing (mtscATAC-seq), we characterized mutational trajectories in frequently altered hematological genes and traced clonal evolution through somatic mitochondrial DNA variants, revealing post-transplant cellular heterogeneity and clonal architecture. Among 60 patients (multiple myeloma, n = 51; non-Hodgkin lymphoma, n = 6; Hodgkin lymphoma, n = 3), CH-associated mutations were identified in 53% pre-ASCT, predominantly involving DNMT3A. A transient increase in mutation counts and gene diversity occurred 10-25 days post-ASCT, with a gradual clonal expansion two years post-transplantation. Tandem ASCT amplified clonal complexity, with a twofold increase in mutation count and gene-level diversity, while preserving clonal trajectories across both transplant courses. Mitochondrial single-cell profiling in longitudinal samples of 3 patients showed patient-specific immune reconstitution and clonal dynamics, with balanced multilineage output from graft HSPCs. Collectively, our findings provide a firsthand comprehensive view of ASCT-induced clonal dynamics and immune reconstitution, paving the way for targeted gene-specific post-transplant monitoring.

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Background: Three-dimensional velocity-encoded cardiovascular magnetic resonance (4D Flow CMR) is a valuable method for assessing cardiac hemodynamics. It enables detailed evaluation of blood flow patterns, wall shear stress, and intracardiac kinetic energy, as well as precise quantification of forward and regurgitant valve flow. The technique has shown strong inter-observer reliability (1,2) and the integration is already realized in pediatric cardiology (3). This study aimed to investigate intracardiac flow dynamics and assess the comparability and reliability of these measurements across various sites.
Methods: A cohort of 20 healthy volunteers was recruited and underwent CMR imaging at three different sites within the Berlin research network for-CMR (BER-CMR) (site I: 3T MAGNETOM Skyra fit; sites II and III: 3T MAGNETOM Prisma fit, all manufactured by Siemens Healthineers, Forchheim, Germany). (4,5,6). All sites employed the same 4D Flow research sequence with the following scan parameters: Echo Time Range of 2.4-2.38ms, Temporal Resolution Range of 40.8-40.64 ms, flip angle 8° and voxel size of 2.3x2.3x2.3 mm. A compressed sensing acceleration technique was used with an acceleration factor of 7.6. Three- dimensional velocity-encoding was set to 150–200 cm/sec. Site II used a 32-channel coil, other sites used an 18-channel coil.
Assessment of Kinetic Energy (KE), Peak and Mean Velocity (PV and MV) across the left and right ventricle were conducted using MEVISFlow (Fraunhofer Institute for Digital Medicine MEVIS, software version 11.4, Bremen, Germany). Tolerance ranges for reliability assessment were defined through a scan-rescan analysis conducted at site I. Additionally, intra- and interobserver analyses for the measurements at site I were conducted to assess consistency.
Results: The final analysis included 19 healthy volunteers (11 males and 8 females). Baseline demographic and clinical characteristics are summarized in Table 1. One traveling volunteer was excluded from the study due to a newly diagnosed cardiac condition. A total of 11 scans were excluded due to technical issues, motion artifacts, or suboptimal image quality (Table 1). Despite these exclusions, inter-site comparability analysis across the right and left ventricle demonstrated strong reliability for all flow parameters in both ventricles (Figure 1). Inter- and intra-observer analyses showed that the differences in measured values were minimal, with all flow parameters displaying a narrow range of variation (Figure 2).
Conclusion: Whole-heart 4D Flow CMR measurements in healthy volunteers showed consistent results across all three sites. Despite variations such as different surface coils, there was strong agreement in intracardiac flow parameters. These results are especially significant for supporting the successful implementation of future multicenter studies.

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In the past decade, virtual reality (VR) and augmented reality (AR) have seen a second wave of activity due to consumer-level availability of devices. In the medical realm, use of VR and AR has been investigated extensively and successfully for training, teaching, rehabilitation, and therapy. However, extended reality (XR) and its intraoperative use in the operating room has yet been underexplored and underdeveloped. Intelligent XR environments that integrate advanced visualization, intuitive interaction, and AI-assisted decision support have the potential to transform clinical care. Responsive, context-aware spaces could enhance medical teams’ capabilities by enabling real-time access to critical information, enhancing situation awareness, and supporting remote collaboration and access to clinical expertise across geographical boundaries—all while preserving the natural flow of procedures. But to realize the potential benefits of XR in the operating room requires a significant and focused effort by an interdisciplinary community. It is the intention of this paper to help to catalyze such an effort. In this paper, we identify pressing issues in clinical care, the potential of XR to address them, and the further research that is needed to realize that potential. We look at a broad range of areas where XR can play an important role, including direct support for the surgeon, support for the surgical team, support for the patient, and remote collaboration. We also consider ways in which XR can be integrated with AI to provide enhanced information and interaction. Discussions are underpinned by considerations of methods to evaluate contributions and progress. This paper is the result of a Dagstuhl Seminar on Extended Reality for the Operating Room (XR4OR), held at the Leibniz Center for Informatics, Schloss Dagstuhl, Germany, in February 2025. It gathered twenty-four researchers working in the areas of virtual reality, augmented reality, medical informatics, human-computer interaction, and surgery.

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Abstract

Background: Sex-specific differences in patients with aortic stenosis (AS) are subject to ongoing research. While distinct pathogenesis, presentation and prognosis have been reported1, and 4D phase-contrast (PC) MRI patterns described2, a characterization in 2D PC MRI, a faster technique to quantify aortic flow, is desirable. We aim to differentiate flow patterns in AS patients using radiomics extracted from 2D PC MRI and study interactions with clinical profile and proteomic expression.
Methods: We analyzed 2D PC MRI velocity maps of AS patients before aortic valve replacement surgery3, and of healthy controls4.
The ascending aorta (AAo) in a plane just distal of the ostia was segmented in corresponding magnitude images. To minimize effects of imaging resolution, each map was resampled to an individual voxel size to yield 605 voxels within the AAo. We extracted 101 radiomics features from the AAo region at peak systole, and 10 features quantifying throughflow, flow eccentricity and relative aortic movement.
Distinct flow patterns were discovered with non-negative matrix factorization (NMF) on normalized radiomics features of the AS cohort and the resulting groups compared. In addition to clinical parameters, we analyzed proteomic profiles of an AS subcohort, obtained from left-ventricular biopsies during valve replacement, and non-AS controls as previously described3.
Results: We included 60 AS patients and 19 age-matched healthy controls in the 2D PC MRI analysis (Tab. 1). 41 of AS patients had proteomic profiling, along with 17 non-AS controls (distinct from imaging controls).
Resampling to uniform AAo voxel count decouples radiomics parameters that are resolution-sensitive from patients’ aortic size (Fig. 1b). While men and women did not differ in AAo area in our study, size-sensitive resampling precludes confounding radiomics analyses by the choice of FOV and voxel-size relative to the size of the aorta.
NMF resulted in two stable clusters. Flow profiles of patients in cluster A show more homogeneous flow velocities, but higher eccentricity, commensurate with mostly bicuspid valves. Cluster B is characterized by central peak flow, higher backflow and heterogeneous velocity distributions (Fig. 1c).
Clusters do not align with sex: cluster A contains more women, cluster B more men. Men in A have low BSA and high AAo area, women in B vice versa. Patients in B have lower ejection fractions, higher heart failure markers, and more ventricular scar tissue.
Of proteins relevant in AS3, serpinE2, involved in cardiac fibrosis, is upregulated in men and in cluster B women, while STAT3, relevant in cardiac remodelling, is differentially downregulated between A and B in both sexes.
Conclusion: Radiomic profiling of 2D PC MRI velocity maps identifies two AS subgroups. Notably, flow patterns do not align with sex, indicating the value of personalized over sex-specific approaches in AS characterization, which complement clinical and proteomic findings with the potential to inform treatment strategies.

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Assessment of right ventricular volume is crucial for monitoring patients with congenital heart defects. However, due to the right ventricle's complex geometry, 2D echocardiography is challenging and MRI is commonly used to evaluate right ventricular volume. However, MRI has several limitations: it lacks bedside imaging, is time-consuming, and often requires sedation in patients with congenital heart defects. Therefore, we aimed to develop a reliable and simple method for calculating right ventricular volume using 2D echocardiography.

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Despite clear perioperative guidelines, adherence in clinical practice remains suboptimal, particularly in the preoperative cardiovascular risk assessment of non-cardiac surgical patients. Clinical decision support (CDS) systems may enhance structured, guideline-based care. The KIPeriOP project evaluated the impact of a CDS tool on perioperative guideline adherence and diagnostic decision quality.

To assess whether the implementation of a perioperative CDS tool improves guideline adherence in preoperative risk assessment and diagnostics for patients undergoing non-cardiac surgery.

This multicenter randomized controlled interventional study was conducted at two German hospitals. Adult patients (ASA III–IV) scheduled for non-cardiac surgery were randomized to either standard preoperative evaluation (control) or CDS-supported evaluation (intervention). The CDS system (ZAZA-Pro) provided structured risk assessment and evidence-based diagnostic recommendations. Primary endpoints were adherence to guideline-based diagnostic recommendations and diagnostic decision quality. Secondary endpoints included ASA classification concordance and estimated additional time required for guideline implementation.

A total of 229 patients were randomized (CDS: 123, control: 106). Baseline characteristics were comparable. Concordance between CDS-suggested and user-assigned ASA classification was highest for ASA IV (OR 5.58 [95% CI 2.35–13.22], p < 0.001), but low for ASA III (OR 0.35 [0.18–0.67], p = 0.002). Adherence to guideline-recommended diagnostics was low in both groups and did not differ significantly (e.g., ECG OR 0.78 [0.40–1.56], p = 0.518; echocardiography OR 1.33 [0.37–5.10], p = 0.774). The proportion of correct diagnostic decisions was similar (CDS 67 % vs. control 66 %, OR 1.06 [0.83–1.36], p = 0.679). Underdiagnosis was the main deviation (82.9 % of recommendations not followed in CDS group), whereas overdiagnosis was rare (3.4 %). Full implementation of CDS recommendations would have added a mean additional workload of 20 minutes per patient.

While CDS provided structured, evidence-based recommendations, it did not significantly improve guideline adherence or diagnostic accuracy compared with standard practice. High rates of underdiagnosis suggest that barriers are behavioral and organizational rather than informational. Targeted implementation strategies and workflow integration are needed to leverage the full potential of perioperative CDS.

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Acute rejection remains a leading cause of morbidity and mortality early after pediatric heart transplantation (HT). Current guidelines recommend only endomyocardial biopsies (EMB) as reliable for rejection surveillance. We purposed to explore clinical outcomes of less invasive rejection surveillance in a low-intensity biopsy center in pediatric heart recipients.

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The visual assessment of X-ray coronary angiography (XCA) videos is challenging due to the complex and dynamic structure of the coronary tree. Automated interpretation can improve confidence in identifying stenoses and estimating their severity, thereby supporting clinical decision-making. However, most existing approaches rely on static images or single-view videos, limiting the use of spatial and temporal information.

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Shear-induced hemolysis remains a major challenge in pediatric mechanical circulatory support (MCS), where hemolysis-related complications occur more frequently than in adults. Despite known hematologic differences between pediatric and adult blood, it is unclear whether pediatric red blood cells (RBCs) are intrinsically more susceptible to mechanical stress than those from adults. This study compared shear-induced hemolysis of pediatric and adult blood under identical conditions and assessed the predictive accuracy of established power-law hemolysis models.

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Rotary blood pumps (RBPs), used as Ventricular assist devices (VADs), feature a suspended impeller (rotor) within a housing (stator). Titanium alloys are mostly used for those parts, but their limited wear resistance in critical contact areas, leading to scratches and promoting thrombus formation. Therefore, hard material coatings (HMCs) can be applied to increase wear resistance and bonding agents ensure stable coatings on the bulk material. Still, coating damage may occur and expose the material to blood, requiring hemocompatibility assessment. Therefore, their hemocompatibility must be evaluated as well as that of the HMCs. Platelet adhesion as thrombus formation indicator was investigated using an in vitro flow chamber and fluorescence microscopy for the following materials: Silicon diamond-like carbon (SiDLC), titanium nitride with and without droplets (TiN_D, TiN), bonding agents chrome (Cr), chrome nitride (CrN), uncoated Ti6Al4V (Ti), and aluminum (Alu) as positive control. CFD simulations determined wall shear rates, averaging 5730.5 1/s on the evaluated area. The normalized percentage of the covered surface (NCSA) area was statistically evaluated ( p -values, Wilcoxon effect size). NCSA analysis showed that Alu had the highest value (8.7), significantly exceeding CrN and SiDLC (both 0.3, p  < 0.05). Cr (3.6) exhibited significantly more platelets than CrN with a medium effect compared to CrN, Ti (0.2), TiN (0.3), and SiDLC, and a weak effect compared to TiN_D (0.4) and Alu. No significant differences were observed among HMCs, Ti, and CrN. This study highlights Cr's elevated thrombogenicity, whereas the other surfaces (except Alu) showed hemocompatibility comparable to Ti, supporting their use in VADs.

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Aortic coarctation (CoA) is a major cause of arterial hypertension in young individuals, with recurrence occurring in up to one-third of patients throughout life despite guideline-directed management.

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Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure for treating aortic valve stenosis. It is associated with potential complications, including paravalvular leak (PVL) and conduction disturbances, such as atrioventricular block (AVB). We present a fully automated pipeline to predict these complications using radiomics features automatically extracted from convolutional neural network (CNN) segmented computed tomography (CT) images. Radiomics features contribute to interpretability by quantifying shape, intensity, and texture patterns in anatomically defined regions. Our approach focuses on the aortic root, left ventricular outflow tract (LVOT) and proximal ascending aorta (pAAo). Using a gradient-boosted decision tree algorithm (XGBoost), we calculate patient-specific risk scores for overall complications as well as AVB and PVL. We compare our radiomics-based model to (i) a model trained on conventional image-based biomarkers, (ii) a DenseNet-based CNN trained on CT cropped to a bounding box around the aortic root, and (iii) a dual-channel DenseNet using the cropped CT and corresponding segmentation. We also evaluate whether combining radiomics with conventional parameters enhances predictive performance. Model performance varied across complication types and models, with AUCs on the test set ranging from 0.42 (DenseNet_CT+Seg) to 0.54 (combined) for any complication, 0.40 (radiomics) to 0.59 (DenseNet_CT) for AVB, and 0.35 (DenseNet_CT) to 0.63 (combined) for PVL. Although predicting TAVI complications remains challenging, our approach mitigates operator-dependent variability in image segmentation and feature extraction, promoting fully automated, reproducible, and interpretable risk stratification for TAVI-related complications.

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Developments in fusion imaging (FI) software have facilitated easy use of three-dimensional (3D) roadmaps based on preregistered computed tomography (CT) or magnetic resonance imaging (MRI) datasets for guidance of cardiac catheterizations.

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Background: Global longitudinal strain (GLS), derived from long-axis cine images (LAX), is a sensitive marker for myocardial dysfunction and a strong predictor for clinical events and future ventricular deterioration. In patients with complex congenital heart disease (CHD) transverse-oriented cine imaging is part of the standard cardiac magnetic resonance (CMR) protocol. We aimed to study the feasibility and reproducibility of strain measurements derived from transverse-oriented cine images (Transverse Strain (TrS)) and compare them with standard GLS. Methods: We retrospectively analyzed CMR cine images from 40 patients (n = 20 Fontan, n = 20 Tetralogy of Fallot (ToF)) and 10 healthy controls. Strain analysis was performed in every subject using both the conventional GLS and the TrS approach. Results: TrS showed high intra- and interobserver reproducibility in patients with CHD (intraclass correlation coefficient (ICC) > 0.75, p < 0.05). Intermethod agreement between TrS and GLS was strong in Fontan patients and in the right ventricle (RV) of ToF patients (ICC > 0.75) but showed a positive bias for TrS in the left ventricle (LV) of ToF patients (mean difference = 5.03) and in both ventricles of healthy controls (mean difference LV = 5.36, RV = 4.01). Conclusions: TrS is feasible and reproducible and may offer a new methodological approach for strain assessment, especially in CHD patients with univentricular physiology and ToF patients. Further studies are needed to validate this new approach and perform correlations to clinical outcomes.

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Pediatric patients with myocarditis present with heterogeneous symptoms, disease courses and outcomes. Late Gadolinium Enhancement (LGE) in cardiovascular magnetic resonance imaging (CMR) is a routine diagnostic tool, but relationships between LGE patterns and patient characteristics are typically assessed qualitatively. We investigate the use of radiomic features to quantify LGE texture and location to identify signatures that stratify pediatric myocarditis cases.

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Background: Artificial intelligence (AI) increasingly automates medical imaging tasks such as segmentation and classification (1,2). However, defining ground truth labels can be difficult and may be influenced by inter-reader variability. These inter-reader differences and label uncertainties may be integrated into datasets such that they bias AI tools or manifest unclear decision criteria within the dataset and in consequence the AI tools (3–5).
The AIM of this work is to quantify the impact of different labels and inter-reader agreement on AI development and testing results in CMR.
Methods: 1411 late gadolinium enhancement MagIR short axis images from 161 cases were labeled independently by two readers (reader A, reader B) as ‘no artifact’, ‘infolding’, and ‘motion’. Only artifacts overlapping the left myocardium were labeled (Fig. 1). The label ‘artifact’ is the union of the labels ‘infolding’ and ‘motion’. Reader AB is the intersection of the labels where A and B agree. Label distribution by reader is presented in Fig. 1. Accuracy was calculated to assess inter-reader agreement.
The cases were randomly separated into training, validation and test datasets (112/24/25 cases respectively). Convolutional neural networks (CNN) with equivalent parameters were trained for three classification scenarios: i) classify an image as ‘no artifact’ or ‘artifact’, ii) classify an image as ‘no artifact’ or ‘infolding’ and iii) classify an image as ‘no artifact’ or ‘infolding’ or ‘motion’, with ‘no artifact’ always being exclusive. For each scenario labels of readers A, B and AB were used to train CNNA, CNNB and CNNAB respectively leading to nine CNNs.
Recall (correctly classified images per labeled images per class) was calculated to evaluate the readers and the CNNs on the test dataset for labels A, B and AB respectively.
Results: Inter-reader accuracy between reader A and B was 55.6% (785/1411 images). Qualitative investigation revealed that disagreement largely stemmed from deciding whether the artifact affected the myocardium (no artifact ⟷ artifact) and only minorly from differentiating between artifact types (see Fig. 1).
Averaged class-wise recall of the CNNs ranged between 55.4% (CNNA on B for no artifact – infolding – motion) and 92.2% (CNNB on AB for no artifact – infolding) depending on the test data (Fig. 3). Recall analysis shows that infoldings were easier to detect than motion for human and AI readers alike (Fig. 3). In all scenarios testing the AI only on images with agreement of labels increased the mean recall. Indicating that classifying images with lack of inter-reader agreement might also be more challenging for AIs.
Conclusion: This experiment highlights the impact of label uncertainties on developing AIs as the mean recalls differ significantly between different labels. Considering only images with inter-reader agreement in training can increase AI reliability. Identifying inconsistent or misleading labels by inter-reader reproducibility assessments can support stronger AI tools.

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Extracellular volume (ECV) quantification involves time-consuming multi-step post-processing and a blood draw for hematocrit analysis. This study aimed to develop a fully automated blood draw-free, machine learning-based approach for synthetic ECV assessment for non-invasive assessment of diffuse myocardial fibrosis. We retrospectively evaluated a large clinical cohort of 1092 patients who underwent CMR and ECV measurement at 1.5T or 3T. Participants were divided into training (n = 767) and validation (n = 325) cohorts. Manual contouring of T1 maps was used to iteratively develop a neural network segmentation model, which was then applied for automated analysis. Fully-automated synthetic ECV was calculated using validated sex- and field strength-specific models. Agreement was assessed using Student's t-test, Pearson correlation, Bland-Altman analysis, and classification analysis. Fully-automated synthetic ECV showed strong correlation with conventional ECV (r = 0.79, p < 0.001), with no significant differences (26.9% ± 4.9% vs. 27.3% ± 6.4%, p = 0.056). Bland-Altman analysis indicated minimal mean difference of 0.4% with moderate limits of agreement (LoA) spanning - 7.24% to + 8.07%, with good agreement for values of up to 35% (mean difference 0.1%, LoA: - 5.38% to + 5.23%). Fully automated synthetic ECV offers a blood-free proof-of-concept for large-scale post-processing, supporting consistent and efficient assessment of myocardial fibrosis in research settings, pending further validation for clinical use at higher ECV ranges.

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The CARE-WHS challenge focuses on achieving precise whole-heart segmentations across various computed tomography (CT) and magnetic resonance (MR) images, addressing the complexities arising from anatomical variability and different scanning protocols. Previous efforts have highlighted limitations in both model-based and deep-learning-based approaches to cardiac segmentation. This study evaluates the performance of two state-of-the-art architectures, nnU-Net and SwinUNETR, in the context of the CARE-WHS challenge. We explore three training strategies: a basic configuration using only challenge data for training, a model trained on public CT data, and a transfer learning approach, where the model trained on public data is finetuned to the available challenge data. Additionally, a random smooth gray value transformation is implemented as data augmentation for nnU-Net and SwinUNETR to mitigate discrepancies between CT and MR scans. Our results indicate that while nnU-Net consistently outperforms SwinUNETR, the SwinUNTER in combination with random smooth gray value transformation (GVT) shows the highest potential for unseen intensities.

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This study compared long-term outcomes of surgical management for Ebstein's anomaly before and after the introduction of an individualized surgical approach at our centre, including Cone repair.

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Motivation: Despite their quantitative nature, multi-parametric mapping (MPM) of the brain may exhibit scanner bias in multi-centric studies. Calibration with travelling subjects is challenging.
Goals: We aim to retrospectively harmonize MPMs without the need of matched images of identical participants from all centers and scanners.
Approach: We train an unsupervised deep learning model to learn disentangled representations of anatomy and intensity using intrinsically paired R1 and R2* maps of individual subjects, as previously shown in CALAMITI [Zuo 2022] for non-quantitative MRI.
Results: Harmonization with CALAMITI trained on MPMs shows promising results for R1 maps, with further improvements needed to decode maps in higher detail.

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Complicated carotid artery plaques (cCAP), characterized by intra-plaque haemorrhage (IPH), thin or ruptured fibrous caps, and/or superimposed thrombi pose a high stroke risk. The role of individual carotid geometry and local haemodynamics-such as wall shear stress (WSS) and oscillatory shear index (OSI)-in the development of cCAP is insufficiently understood. This study aimed to identify imaging- and blood-based biomarkers associated with the presence of cCAP to improve individual risk stratification.

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Background/Objectives: Liquid biopsy is a minimally invasive alternative to tissue biopsy and is used to obtain information about a disease from a blood sample or other body fluids. In the context of cancer, circulating tumor cells (CTC) can be used as biomarkers to determine the nature of the tumor, its stage of progression, and the efficiency of the administered therapy through monitoring. However, the low concentration of CTCs in blood (1-10 cells/mL) is a challenge for their isolation. Therefore, a minimally invasive medical device (BMProbe™) was developed that isolates CTCs via antigen-antibody binding directly from the bloodstream. Current investigations focus on the process of detaching bound cells from the BMProbe™ surface for cell cultivation and subsequent drug testing to enable personalized therapy planning. Methods: This article presents two approaches for detaching LNCaP cells from anti-EpCAM coated BMProbes™: enzymatic detachment using TrypLE™ and detachment through enzymatic pretreatment with supplementary flow-induced shear stress. The additional shear stress is intended to increase the detachment efficiency. To determine the flow rate required to gently detach the cells, a computational fluid dynamics (CFD) simulation was carried out. Results: The experimental test results demonstrate that 91% of the bound cells can be detached enzymatically within 10 min. Based on the simulation, a maximum flow rate of 47.76 mL/min was defined in the flow detachment system, causing an average shear stress of 8.4 Pa at the probe edges. The additional flow treatment did not increase the CTC detachment efficiency. Conclusions: It is feasible that the detachment efficiency can be further increased by a longer enzymatic incubation time or higher shear stress. The influence on the integrity and viability of cells must, however, be considered.

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Cardiovascular magnetic resonance (CMR) offers state-of-the-art volume, function, fibrosis and oedema imaging. Quality assurance (QA) tasks, such as quantitative parameter reproducibility assessments, the evaluation of AI methods, and the assessment of trainees have become essential to CMR. However, the explainability of how qualitative differences impact quantitative differences remains underexplored. Our aim is to demonstrate a semi-automated QA tool, Lazy Luna's (LL) applicability to typical CMR QA application cases.

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In the field of cardiovascular device development, new devices such as heart valves, stents or pressure probes for long term heart failure monitoring are subject to animal trials to evaluate their safety and efficacy. For such applications, swine and sheep are the animal models of choice owed to their similarities to humans with regards to heart size, weight and ventricular kinetics. However, clinical aspects regarding the choice of animal model revolve mainly around anatomical similarities as well as the ability to induce the desired pathology. In the case of pulmonary artery pressure sensors, both swine and sheep appear to be suitable candidates for animal trials since both animals have been used for pre-clinical evaluation. Hemodynamic aspects however, although equally important for device performance, appear rather underrepresented in current research and it remains uncertain whether anatomical similarities between humans and animal model in the region of interest translate to hemodynamic similarities. To provide insight whether pulmonary artery hemodynamics in large animal models are indeed comparable to those in humans, this work presents a computational fluid dynamics-based study on pulmonary artery hemodynamics for humans, swine and sheep. A total of 28 human, 41 porcine and 14 ovine transient simulations of pulmonary artery hemodynamics were performed based on subject-specific geometries reconstructed from computed tomography data. The distributions of wall shear stress (WSS) and oscillatory shear index (OSI) within the cohorts were then compared to assess hemodynamic similarity. Distributions of time averaged WSS were found to be similar between humans and sheep (median 1.2 vs. 1.5 Pa, interquartile range (IQR) 0.8 Pa vs. 0.6 Pa, Wilcoxon rank sum test p = 0.42) but were significantly different for swine (median 1.7, IQR 0.5, p < 0.05), whereas OSI was significantly different for sheep and swine (0.17 ± 0.04 vs. 0.14 ± 0.03 and 0.09 ± 0.02). between sheep and humans. In summary, pulmonary artery vessel wall stresses of both animal models appear broadly similar to humans, however, sheep seem to have a notable edge over swine in our study.

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The conventional approach to developing medical treatments and medical devices usually covers pre-clinical and in-vitro investigations, in-vivo animal studies and clinical trials with humans. In-silico trials and virtual cohorts present a promising avenue for addressing the challenges inherent in clinical research and improving its efficiency. Despite considerable advancements in the field of in-silico trials, several notable gaps and challenges still need to be addressed, one is the limited availability of open and user-friendly statistical tools to support the specific analysis of virtual cohorts and in-silico trials. In the EU-Horizon funded project SIMCor we have developed a web application, providing a R-statistical environment supporting the validation of virtual cohorts and the application of validated cohorts for in-silico trials. It provides a practical platform for validating cohorts and has implemented existing statistical techniques that can be applied to compare virtual cohorts with real datasets. It is fully open, generic and menu driven and provides user guidance and help ( https://github.com/ecrin-github/SIMCor, https://zenodo.org/records/14718597).The tool has been developed according to specified user requirements and has been extensively tested and validated. Important next steps are to gain more experience with the tool in other domains and research environments and to extend its functionality.

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ShuttlePump is a novel total artificial heart (TAH) recently introduced to potentially overcome the limitations associated with the current state-of-the-art mechanical circulatory support devices intended for adults. In this study, we adapted the outflow cannulation of the previously established ShuttlePump TAH and evaluated the anatomical compatibility using the virtual implantation technique.

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Limited magnetic resonance imaging (MRI) data on autograft dilatation following the Ross procedure in congenital cohorts presents challenges in understanding its evolution and impact on clinical outcomes. This study, spanning from February 2003 to December 2022, included patients under 40 years at the time of the Ross procedure, with MRI follow-ups assessing dimensions at key aortic sites. Among 307 patients, 132 MRIs were analyzed from 76 individuals, revealing that autograft z-scores increase primarily with time post-procedure (Coef. 0.13; 95% CI:0.051-0.216; P = 0.002). Additionally, older patients at the time of surgery showed larger ascending aortic dimensions (Coef. 0.13; 95% CI:0.099-0.165; P = 0.001). Notably, autograft dilation at the sinus of Valsalva significantly predicted higher reintervention risks (HR 1.57; 95% CI:1.21-2.04; P = 0.001). Surgical reinforcement techniques of the autograft, via subcoronary implantation or external support, prevented such dilation (P < 0.001) and mitigated aortic regurgitation. In conclusion, our model predicted autograft dilation over time in patients after Ross procedure, aiding clinicians in making data-driven decisions regarding the optimal timing of the procedure and the selection of the most effective surgical strategy.

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2,3-bisphosphoglycerate mutase (BPGM) is traditionally recognized for its role in modulating oxygen affinity to hemoglobin in erythrocytes. Recent transcriptomic analyses, however, have indicated a significant upregulation of BPGM in acutely injured murine and human kidneys, suggesting a potential renal function for this enzyme. Here we aim to explore the physiological role of BPGM in the kidney.

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The purpose of this study is to support stroke risk analysis, evaluation of therapy effectiveness, and lesion progression through a comprehensive assessment of carotid atherosclerotic lesions in 3D based on automatic segmentation and visualization of quantitative parameters.

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Although left ventricular assist devices (LVADs) are an alternative to heart transplantation, their artificial surfaces often lead to serious thrombotic complications requiring high-risk device replacement. Coating blood-contacting surfaces with antithrombogenic endothelial cells is considered an effective strategy for preventing thrombus formation. However, this concept has not yet been successfully implemented in LVADs, as severe cell loss is to be expected, especially on the impeller surface with high prothrombogenic supraphysiological shear stress. This study presents a strategy that exploits the magnetic attraction of the impeller on ECs loaded with iron oxide nanoparticles (IONPs) to minimize shear stress-induced cell detachment from the rotating magnetic impeller while ensuring antithrombogenic EC adhesion, especially as a bridge until they formed their adhesion-promoting matrix. In contrast to polyvinylpyrrolidone (PVP)-coated IONPs, more efficient and safer cell loading is achieved with sodium citrate (Cit)-stabilized IONPs, where incubation with 6.6 µg iron mL-1 Cit-IONPs for 24 h resulting in an average internalization of 23 pg iron per cell. Internalization of Cit-IONP significantly improved cell attraction to the highly magnetic impeller surface without affecting cell viability or antithrombogenic function. This protocol is key for the development of a biohybrid LVAD impeller that can prevent life-threatening thrombosis and hemorrhage in a future clinical application.

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Perturbations of myocardial metabolism and energy depletion are well-established hallmarks of heart failure (HF), yet methods for their systematic assessment remain limited in humans. This study aimed to determine the ability of computational modelling of patient-specific myocardial metabolism to assess individual bioenergetic phenotypes and their clinical implications in HF.

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The significant impact of coronary artery disease on cardiovascular health underscores the need for precise diagnostic methods. The SYNTAX score, based on the locations of stenoses in the coronary tree, provides a quantitative estimation of the disease’s complexity, allowing to settle on a treatment strategy. However, expert experience is required for visual localization of stenoses, which would be facilitated by automated labelling of the coronary segments. In this work we represent the connected structure of the coronary tree as a graph, extracted from the angiography image, onto which we apply a graph convolutional network to label the coronary segments according to the SYNTAX score scheme, before creating a multi-class segmentation mask. The method was trained and evaluated using the ARCADE dataset. The segment classification of graph nodes reached an F1-score of 53.68. Our approach achieved a mean F1-score of 45.43 for multiclass segmentation of the image after applying the entire pipeline. Although a baseline nnU-Net model achieves better performance (mean F1-score of 62.53), the results are comparable to other existing approaches and show that graph-based approaches are suitable for the complex task of coronary segment labelling.

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Heart failure with preserved ejection fraction (HFpEF) has become the predominant form of heart failure and a leading cause of global cardiovascular morbidity and mortality. Due to its heterogeneous nature, HFpEF presents substantial challenges in diagnosis and management. Given the limited treatment options and lifestyle-associated comorbidities, early identification is crucial for establishing effective preventive strategies. Here, we introduce and validate a machine learning-based multi-omics approach that integrates clinical and molecular data to detect and characterize HFpEF.

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Magnetic Resonance Elastography (MRE) quantifies soft tissue stiffness by measuring induced shear waves. MRE inversion techniques for parameter reconstruction are often affected by noise and compression waves. Neural network-based inversions have emerged as a possible solution to address these challenges. However, current approaches lack generalizability and do not provide uncertainty estimates. Therefore, we propose ElastoNet, a novel neural network-based approach for MRE wave inversion that analyzes multiple wave components independently of resolution and vibration frequency and provides uncertainty quantification maps. ElastoNet was trained on synthetically generated wave patches of 5 × 5 pixels. Uncertainty quantification was implemented using evidential deep learning. ElastoNet was evaluated on synthetically generated plane waves, finite element simulations of abdominal MRE, phantom MRE data, and a prospective wideband multifrequency abdominal MRE study (excitation frequencies of 20 to 80 Hz) in 14 healthy volunteers. ElastoNet was compared with established inversion methods LFE and k-MDEV, as well as neural network-based TWENN. ElastoNet generated shear wave speed maps as a proxy of stiffness with comparable or better accuracy than established methods and did not require retraining for different resolutions and vibration frequencies. ElastoNet achieved a lower root mean square error relative to ground truth values in finite element simulations and phantom data than other inversion methods and provided uncertainty maps. ElastoNet is a promising method for universal neural network-based inversion in MRE, effectively overcoming current challenges and expanding the potential use of neural networks in diagnostic MRE applications.

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Although flow simulations have become more accurate, hemolysis prediction models still show large deviations from measured values in blood-carrying devices. To develop and validate more accurate models, specific hemolysis experiments are needed to determine the influence of parameters on hemolysis, such as stress type and exposure time typical for rotary blood pumps (RBPs).

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Heart failure (HF) is a complex clinical syndrome with high morbidity and mortality, influenced significantly by sodium balance. Recently, magnetic resonance imaging (MRI) has emerged as a non-invasive method to evaluate tissue sodium load in HF patients. This proof-of-principle study investigates the association between tissue sodium content, assessed by MRI, and HF-related baseline parameters in an outpatient cohort of patients with chronic heart failure, including those with reduced ejection fraction (HFrEF), mildly reduced ejection fraction (HFmrEF), and preserved ejection fraction (HFpEF).

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Complicated American Heart Association (AHA) lesion type VI plaques and cCAPs, characterized by the presence of intraplaque hemorrhage and a surface defect or thrombus, are strongly associated with ischemic stroke and stroke recurrence. Hemodynamics seem to play a relevant role in their development. Thus, we investigated the association of 4D-flow MRI-derived local wall shear stress (WSS) and the oscillatory shear index (OSI) with the presence of cCAPs in patients with mild-to-moderate ICA stenosis.

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Gastrointestinal bleeding (GIB) is the most common and potentially life-threatening bleeding complication in pediatric patients on left ventricular assist devices (LVAD), particularly under anticoagulation. We report a 13-year-old female with dilated cardiomyopathy on HeartMate 3 support who developed acute GIB after participating in a "Hot Chip Challenge," consuming excessive spicy chips. The bleeding required blood transfusion and intensive management. This case highlights the potential impact of lifestyle factors, especially dietary choices, on GIB risk in pediatric LVAD patients.

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Transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of severe aortic stenosis, yet paravalvular leakage (PVL) remains a significant complication, associated with increased mortality. Clinical studies have identified correlations between PVL and both anatomical features and calcification patterns. Numerical simulations, particularly patient-specific models, offer valuable insights into PVL, but the limited scale of these studies hinders robust statistical analysis. This study introduces a novel in silico clinical trial (ISCT) framework to investigate the correlation between calcification severity, localization and PVL. For this purpose, a synthetic cohort of calcified aortic roots was generated. A conditional convolutional variational autoencoder was used to create calcification patterns for an existing virtual cohort of the aortic root. The workflow includes finite element analyses for pre-dilation and deployment simulations as well as computational fluid dynamic simulations for PVL calculations of 243 virtual TAVR patients. The results show that the absolute amount of calcification in the device landing zone has no significant influence, but its regional distribution does, especially in the combined leaflet regions. In addition, sinotubular junction diameter, annular eccentricity index, oversizing as well as the combination of aortic angle and calcification in the combined non and left coronary leaflet region influence the occurrence of PVL. This framework not only advances our understanding of PVL mechanisms but also demonstrates the potential of ISCT to complement traditional clinical studies, enabling systematic exploration of complex factors influencing TAVR outcomes.

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Surgical ventricular restoration (SVR) excludes scarred myocardium after myocardial infarction to restore shape and contractility of dilated, aneurysmal left ventricles (LVs). Detailed changes in intracardiac hemodynamics following the surgery are not fully investigated. In this study, digital replicas of the patient's LV were used to study the hemodynamic impact of successful SVR. The digital replicas were built based on pre-operative and post-operative cardiac computed tomography data of nine patients (3females, 60 ± 13years) who underwent successful SVR (significant reduction in heart failure symptoms). The computational framework was used to calculate LV morphology, dynamics, and intracardiac hemodynamics using image-based computational fluid dynamics (CFD). SVR successfully reduced the LV volumes. Morphological analysis showed restoration of myocardial wall thickness in aneurysmal regions (5.5 ± 2.0 vs. 8.6 ± 3.0 mm) and an increased end-diastolic sphericity (sphericity index 0.39 ± 0.07 vs. 0.46 ± 0.07). No distinct flow alterations could be linked thereto. CFD revealed a higher post-operative kinetic energy level (diastolic maximum 10.0 ± 7.6 vs. 16.8 ± 9.1mJ) and an improved global washout (29.5 ± 9.7 vs. 10.3 ± 6.4% after five cycles), which correlated to increases in volume-curve-derived diastolic energy gain and ejection fraction, respectively. Flow efficiency improved by means of an increased end-diastolic surface-averaged vortex strength (16.2 ± 5.1 vs. 30.0 ± 15.01/s) and a decreased normed diastolic energy loss (18.9 ± 3.9 vs. 15.0 ± 3.7%). The hemodynamic filling forces in diastole were aligned with the LV long axis before and after surgery and correlated with LV contractility. In summary, the digital patient replicas facilitated a detailed analysis and showed favorable flow changes with successful SVR.

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Transcatheter edge-to-edge repair (TEER) is an effective treatment for mitral valve regurgitation in patients with high surgical risk, but predicting the hemodynamic outcomes is challenging. Reduced-order models (ROMs) show promise for post-TEER hemodynamic outcome predictions, but personalization of the ROM equations is essential for accurate simulations of mitral valve blood flow rates and pressure gradients during both diastole and systolic regurgitation. While the mitral valve orifice area is a common parameter used for ROM personalization, other aspects of the mitral valve shape are usually not considered. In this in-silico study, we investigated the influences of mitral valve shape on transmitral hemodynamics using a combination of computational fluid dynamics (CFD) simulations and geometrical analyses. Mitral valves from ten TEER patients were analyzed at three valve states: early diastole (pre- and post-TEER) and systolic regurgitation (pre-TEER). The orifice-to-annulus area ratio and the orifice orientation were identified as key shape parameters impacting mitral valve hemodynamics. Based on these findings, we developed shape-based ROM equations that are personalized using routine echocardiographic data. The ROM estimates agreed well with CFD simulation results (mean relative differences <1 % and limits of agreements <13 % for both flow rates and pressure gradients). Application of the ROM equations to patient-specific data revealed distinct hemodynamic differences between the three valve states, aligning with expectations from both physiological and fluid dynamics perspectives. Our results suggest that incorporating mitral valve shape parameters into ROMs could improve the accuracy of patient-specific simulations, thus enhancing their potential for supporting TEER planning and predicting intervention outcomes.

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Minimally invasive surgery is the state-of-the-art approach for repairing the mitral valve, which controls the blood flow into the left heart chamber. The surgeons rely on camera and sensor technologies to support visualization, navigation, and measurement. As patients are connected to a cardio-pulmonary bypass, the anatomy is severely deformed by the altered pressure conditions. We developed a technique that combines stereo-endoscopic video with three-dimensional transesophageal echocardiography (3D TEE) to improve anatomic visualization and measurement accuracy during mitral valve repairs. Our methodology includes stereo camera calibration, image segmentation, and 3D model reconstruction. Anatomical landmarks are used to align the imaging modalities. This approach allows the visualization of pre-operatively determined mitral valve properties, e.g., overlaying heat maps in stereo endoscopic data. Our validation results showed high precision and accuracy within an error range of 0.5 ± 0.1 mm. The effectiveness of the heatmap visualization in complex prolapse cases varied. In the future, this approach can also be used to visualize local tissue properties or the optimal positions of implants.

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We propose a novel approach that uses sparse annotations from clinical studies to train a 3D segmentation of the carotid artery wall. We use a centerline annotation to sample perpendicular cross-sections of the carotid artery and use an adversarial 2D network to segment them. These annotations are then transformed into 3D pseudo-labels for training of a 3D convolutional neural network, circumventing the creation of manual 3D masks. For pseudo-label creation in the bifurcation area we propose the use of cross-sections perpendicular to the bifurcation axis and show that this enhances segmentation performance. Different sampling distances had a lesser impact. The proposed method allows for efficient training of 3D segmentation, offering potential improvements in the assessment of carotid artery stenosis and allowing the extraction of 3D biomarkers such as plaque volume.

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Mitral regurgitation (MR) affects approximately 10% of the population and can lead to serious cardiac complications. Mitral annuloplasty, a surgical repair method involving a ring-shaped implant, is a common treatment option. However, the impact of annuloplasty on the left ventricle shape and motion remains underexplored. We introduce a deep learning-based approach that enables analyzing local shape and motion properties for the assessment of the effect of mitral valve annuloplasty. The approach is based on a template mesh that gets deformed to match the lumen of the left ventricle visualized in the image. Thereby, the original topology remains unchanged, and it is assumed that vertices move to anatomically similar locations. This enables the subsequent analysis of the deformations caused by annuloplasty. We utilized a dataset of 110 3D+t transesophageal echocardiography (TEE) images from 64 patients, suffering of MR. There are pre- and post-procedure images of most patients available, and the procedure always included a mitral annuloplasty. All images were annotated by medical experts using a self-developed tool. Our model, inspired by the voxel2mesh approach, employs a U-Net architecture for segmentation and graph neural networks for mesh deformation. To compare our approach with literature, we train it on the publicly available MITEA data and reach results comparable to those reported in the original paper. When trained and evaluated on our own data, we reached a Dice Score of 0.83 and a Mean Surface distance of 2.94 mm between annotations and predicted ventricles. The deformation analysis revealed significant alterations in ventricle geometry post-annuloplasty, especially a reduced deformation between systole and diastole. We thereby demonstrated the feasibility of using mesh generation algorithms for detailed deformation analysis in echocardiographic images, potentially aiding in better understanding and treatment of MR.

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Cardiovascular magnetic resonance imaging (CMR) offers state-of-the-art myocardial tissue differentiation. The CMR technique late gadolinium enhancement (LGE) currently provides the noninvasive gold standard for the detection of myocardial fibrosis. Typically, thresholding methods are used for fibrotic scar tissue quantification. A major challenge for standardized CMR assessment is large variations in the estimated scar for different methods. The aim was to improve quality assurance for LGE scar quantification, a multi-reader comparison tool "Lumos" was developed to support quality control for scar quantification methods. The thresholding methods and an exact rasterization approach were implemented, as well as a graphical user interface (GUI) with statistical and case-specific tabs. Twenty LGE cases were considered with half of them including artifacts and clinical results for eight scar quantification methods computed. Lumos was successfully implemented as a multi-level multi-reader comparison software, and differences between methods can be seen in the statistical results. Histograms visualize confounding effects of different methods. Connecting the statistical level with the case level allows for backtracking statistical differences to sources of differences in the threshold calculation. Being able to visualize the underlying groundwork for the different methods in the myocardial histogram gives the opportunity to identify causes for different thresholds. Lumos showed the differences in the clinical results between cases with artifacts and cases without artifacts. A video demonstration of Lumos is offered as supplementary material 1. Lumos allows for a multi-reader comparison for LGE scar quantification that offers insights into the origin of reader differences.

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Aortic stenosis (AS) is a common valvular disease becoming more prevalent globally due to the aging of the population. Transcatheter aortic valve implantation (TAVI) is a minimally invasive intervention indicated for AS patients as alternative to surgical replacement. TAVI is to date an established procedure. However, it has been often associated with complications such as paravalvular leakage (PVL) or conduction abnormalities. Evidence of associations between morphological features of the aortic root, valve calcification measurements and suboptimal procedural outcomes have been suggested but the analyses were limited by availability and reproducibility of clinical measurements. In this work, we aim to enrich the characterization of AS patients referred for TAVI by analyzing the clinical findings in conjunction with advanced morphological analysis of the implantation site including aortic root, left ventricular outflow tract and 3D calcification patterns. A population of consecutive patients with AS (n = 130) who underwent TAVI at our clinical centre were retrospectively selected for this study. Demographic and clinical measurements were collected before and after TAVI. Pre-operative CT images were used to reconstruct 3D models of patient-specific anatomies. Statistical shape modelling was carried out and outcomes were analyzed in conjunction with clinical outcomes. The 3D modelling of the valve calcification rate matched previous clinical descriptions; including the crescent shapes visible on each leaflet and the higher calcification rate of the non-coronary cusp. Higher calcification rate was found in larger valves together with a positive association between each coronary height and the calcification of their respective leaflet. Sexual dimorphism, on both shape and calcification, was recorded beyond the size differences with straighter aortas and higher calcification rate at the junction between the left and right coronary leaflets for males compared to females. Morphological differences were significantly associated (p = 0.005) with PVL assessments based on post-operative echocardiograms. Larger aortas and shorter left coronary sinus were associated with less leakage. The outcome distribution appeared to be directly affected by sexual differences and device design. Female phenotypes, smaller and more conic aortic root, were associated with worse outcome. Different patterns in calcification distribution on the leaflets were identified but the association with outcomes is not conclusive. In the future, the presented morphological characterization of patients with AS could contribute to predict post-TAVI PVL and design and test improved TAVI devices.

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Magnetic Resonance Elastography (MRE) is a non-invasive imaging modality quantifying soft tissue stiffness. The reconstruction of stiffness maps is based on solutions of an inverse problem, which poses challenges in balancing accuracy, computational resources, and robustness. To stabilize the reconstruction, many inversion techniques, and most recently neural network-based inversion techniques, have explored multifrequency acquisition and reconstruction. However, these techniques typically perform separate single-frequency inversions followed by multifrequency aggregation. In this work, we propose a fully multifrequency neural network-based inversion trained on synthetically generated data that directly incorporates the relationship between multifrequency acquisitions, assuming a viscoelastic material model. Our proposed approach provides flexibility with respect to the acquisition frequencies, ensuring its practical applicability in the clinical and research setting. We evaluated our method using finite element simulations and in vivo abdominal MRE datasets, achieving increased accuracy and providing a more reliable and effective solution for MRE-based tissue characterization than standard reconstruction approaches.

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Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance.Methods DICOM-structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration with interactive filtering capabilities, thereby simplifying the process of creation of patient cohorts over several sites with consistent multi-modal data.Results In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data include imaging and waveform data (i.e., computed tomography images, electrocardiography scans) as well as annotations (i.e., calcification segmentations, and pointsets), and metadata (i.e., prostheses and pacemaker dependency).Conclusion Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for multi-centric data analysis. The graphical interface as well as example structured report templates are available at https://github.com/Cardio-AI/fl-multi-modal-dataset-creation.

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Liquid biopsy, particularly the isolation of circulating tumor cells (CTCs) from blood, is a promising approach in the fight against cancer. However, the main reason why CTCs are hardly used as biomarkers in the clinic is their complicated isolation from the patient's blood. Existing ex vivo systems use a small volume of blood and can therefore only isolate very few CTCs. To overcome this problem and increase the number of isolated CTCs, a new in vivo method-the BMProbe was introduced, which can isolate CTCs directly from the patient's bloodstream. This study investigates the efficiency of the BMProbe by using Computational Fluid Dynamics simulations to evaluate parameters influencing the attachment probability of CTCs to the probe surface. The analyzed parameters include screened blood volume, residence time, and wall normal rate. Additionally, the impact of probe geometry, vein diameter, and blood flow velocity on probe efficiency was examined. The numerical data suggest that the geometry has a strong influence on cell binding efficiency. Increasing the number of windings from 4 to 32 improves the transport of cells to the surface (negative wall normal rate) from 0 to -29 [mm2 /s] and the screened blood volume by 138% but decreases the residence time of particles in the close vicinity of the probe by 77%. When compared to experimental data, the screened blood volume and the wall normal rate indicate cell attachment very well, whereas the residence time does not show a significant impact on the attachment of cells. For the 32-windings BMProbe, the screened blood volume is determined to be 130-313 mL, depending on the vein diameter, which is a multiple of the volume achieved by common CTC isolation techniques.

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The Ross procedure with autograft reinforcement has been proposed as a strategy to prevent autograft failure in adults, but outcome data in children during somatic growth remain limited. We investigated long-term outcomes following an individualized autograft reinforcement protocol to evaluate survival and reintervention rates.

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The majority of patients recovers from severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) coronavirus disease 2019 (COVID-19) without obvious sequelae, but a significant proportion suffers long-term consequences which have been termed post COVID syndrome (PCS). Despite a wide range of considerations on treatment options in PCS and a significant number of trials initiated, only very few results from randomized controlled trials are currently available. In conclusion, there is an evident medical need to identify treatments for patients with PCS.

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Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data unused. Leveraging these could enhance transformer architectures' ability in regimes with small and diversely annotated sets. We conduct the largest federated cardiac CT analysis to date (n = 8, 104) in a real-world setting across eight hospitals. Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. This improves predictive accuracy and enables simultaneous learning of all partial labels across the federation, and outperforms UNet-based models in generalizability on downstream tasks. Code and model weights are made openly available for leveraging future cardiac CT analysis.

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The establishment of protective immune responses relies on the ability of terminally differentiated B cells to secrete a broad variety of antigen-specific antibodies with different effector functions. RIF1 is a multifunctional protein that promotes antibody isotype diversification via its DNA end protection activity during class switch recombination. In this study, we showed that RIF1 ablation resulted in increased plasmablast formation ex vivo and enhanced terminal differentiation into plasma cells upon immunization. Mechanistically, this phenotype is independent from RIF1's role in DNA repair and class switch recombination, and reflects its ability to modulate the transcriptional status of a subset of BLIMP1 target genes. Therefore, here we show that, in addition to promoting antibody diversification, RIF1 fine-tunes the kinetics of late B cell differentiation, thus providing an additional layer of control in the establishment of humoral immunity.

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With 2 different Ross autograft implantation techniques: subcoronary versus full-root evolving, the question remains which strategy demonstrates the better early/mid-term performance, especially concerning autograft durability.

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Coronary artery disease (CAD) is caused by pathological narrowing of the coronary vessels and requires treatment with stent or bypass placement in order to restore the blood flow. X-ray coronary angiography (XCA) is the gold standard imaging modality for CAD
assessment and therapy decision-making. However, the analysis of XCA projections remains a difficult task and requires experience and expert knowledge. In XCA multiple recordings from different angles are required to capture the complex structure of the coronary arteries
with 2D projections.
CT angiography (CTA) shows the vessel tree in 3D space, but is applied les frequently in clinical practice. Reconstructing the coronary tree from XCA projections can assist physicians in the diagnosis and treatment planning of CAD, when only XCA is available.

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Implantable pulmonary artery pressure sensors (PAPS) might impose a flow-induced risk of thrombus formation in the pulmonary artery (PA). To assess this risk, an in silico study-enhanced animal study with 20 sensors implanted in 10 pigs had previously been conducted. In the in silico study, PAPS were virtually implanted mimicking real implantations, based upon data acquired by CT. This animal in silico study investigated changes in hemodynamics caused by PAPS using image-based computational fluid dynamics (CFD). However, porcine and human PA differ significantly in geometry and hemodynamics. To investigate the transferability of animal in silico study findings toward human conditions, we propose a parallel in silico human study. Based on a similarity analysis (L1 norm for 8 geometric features) human PA geometries with the least difference to 10 porcine PA were selected. PAPS were virtually implanted in human PA as close as possible, mimicking the implantation configuration of the animal study. Finally, a numerical flow analysis of the hemodynamic changes due to PAPS implantation was done. Comparing human and porcine PA, we found significantly larger left and right PA diameters in humans, whereas no differences were found for main PA diameters and bifurcation angle. Comparing hemodynamic boundary conditions, we found a significantly smaller heart rate and a significantly higher peak systolic main PA flow rate in humans, whereas no significant differences for cardiac output were found. The human in silico PAPS study found no relevant changes in hemodynamics increasing the risk of thrombus formation after sensor implantation. This is also valid for PAPS that were non-optimally implanted. Thus, despite differences between species, findings of the in silico animal study were confirmed by the human in silico study.

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The visual interpretation of X-ray coronary angiography, the primary imaging modality for coronary stenosis evaluation, is a difficult task and requires experience and expert knowledge. Automating stenosis assessment can improve confidence in stenosis identification and severity estimation, facilitating decisions regarding revascularization strategies. However, existing methods are predominantly limited to static images or single-view videos, which increases the risk of missing crucial information due to the complex structure of the coronary tree and movement of the heart. We propose a five-step workflow for automated stenosis detection, localization and severity estimation in X-ray angiography videos. For evaluation at the patient-level, multiple videos per patient, captured from different views, were considered. The workflow achieved an overall sensitivity of 58.98% and specificity of 84.15% for stenosis prediction per coronary segment. Sensitivity and specificity for stenosis severity classification were 62.75% and 59.72%, respectively. To assess the impact of multi-view analysis, we compared severity estimation performance for stenoses detected in single- and multi-view projections, demonstrating that only one view is associated with the highest uncertainty. Our findings encourage further refinement and development of the workflow and highlight the importance of multi-view consideration for accurate stenosis evaluation.

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Recent studies suggest that any degree of patient-prosthesis mismatch (PPM) increases morbidity and mortality after surgical aortic valve replacement (SAVR). We used computational fluid dynamics simulations to test the influence of prosthesis size and physical activity after SAVR.

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Total artificial hearts (TAH) serve as a temporary treatment for severe biventricular heart failure. The limited durability and complication rates of current devices hamper long-term cardiac replacement. The aim of this study was to assess the feasibility of a novel valveless pumping principle for a durable pulsatile TAH (ShuttlePump).

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Fair allocation of funding in multi-centre clinical studies is challenging. Models commonly used in Germany - the case fees ("fixed-rate model", FRM) and up-front staffing and consumables ("up-front allocation model", UFAM) lack transparency and fail to suitably accommodate variations in centre performance. We developed a performance-based reimbursement model (PBRM) with automated calculation of conducted activities and applied it to the cohorts of the National Pandemic Cohort Network (NAPKON) within the Network of University Medicine (NUM). The study protocol activities, which were derived from data management systems, underwent validation through standardized quality checks by multiple stakeholders. The PBRM output (first funding period) was compared among centres and cohorts, and the cost-efficiency of the models was evaluated. Cases per centre varied from one to 164. The mean case reimbursement differed among the cohorts (1173.21€ [95% CI 645.68-1700.73] to 3863.43€ [95% CI 1468.89-6257.96]) and centres and mostly fell short of the expected amount. Model comparisons revealed higher cost-efficiency of the PBRM compared to FRM and UFAM, especially for low recruitment outliers. In conclusion, we have developed a reimbursement model that is transparent, accurate, and flexible. In multi-centre collaborations where heterogeneity between centres is expected, a PBRM could be used as a model to address performance discrepancies.Trial registration: https://clinicaltrials.gov/ct2/show/NCT04768998; https://clinicaltrials.gov/ct2/show/NCT04747366; https://clinicaltrials.gov/ct2/show/NCT04679584.

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Numerical phantom methods are widely used in the development of medical imaging methods. They enable quantitative evaluation and direct comparison with controlled and known ground truth information. Cardiac magnetic resonance has the potential for a comprehensive evaluation of the mitral valve (MV). The goal of this work is the development of a numerical simulation framework that supports the investigation of MRI imaging strategies for the mitral valve.

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Conventional mock circulatory loops (MCLs) cannot replicate realistic hemodynamic conditions without inducing blood trauma. This constrains in-vitro hemocompatibility examinations of blood pumps to static test loops that do not mimic clinical scenarios. This study aimed at developing an atraumatic MCL based on a hardware-in-the-loop concept (H-MCL) for realistic hemocompatibility assessment.

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The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) pandemic causes a high burden of acute and long-term morbidity and mortality worldwide despite global efforts in containment, prophylaxis, and therapy. With unprecedented speed, the global scientific community has generated pivotal insights into the pathogen and the host response evoked by the infection. However, deeper characterization of the pathophysiology and pathology remains a high priority to reduce morbidity and mortality of coronavirus disease 2019 (COVID-19).

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Extensive research has established cardiovascular magnetic resonance (CMR) as a powerful tool for diagnosing and monitoring various cardiovascular diseases (CVDs). However, CMR has yet to reach its full potential in routine clinical care, which is mainly due to reimbursement issues. Among other factors, overcoming this gap requires adequate awareness among healthcare professionals and potential patients, the extent of which is currently unknown. Therefore, we conducted a survey to assess awareness and identify knowledge gaps regarding the clinical role and socio-economic factors associated with CMR.

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Due to aging of the population, the prevalence of aortic valve stenosis will increase drastically in upcoming years. Consequently, transcatheter aortic valve implantation (TAVI) procedures will also expand worldwide. Optimal selection of patients who benefit with improved symptoms and prognoses is key, since TAVI is not without its risks. Currently, we are not able to adequately predict functional outcomes after TAVI. Quality of life measurement tools and traditional functional assessment tests do not always agree and can depend on factors unrelated to heart disease. Activity tracking using wearable devices might provide a more comprehensive assessment.

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Patients with congenitally corrected transposition of the great arteries may require left ventricular training before the double switch operation. We evaluated the effects of combined pressure and volume loading.

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The COVID-19 pandemic demonstrated that reliable risk assessment of venues is still challenging and resulted in the indiscriminate closure of many venues worldwide. Therefore, this study used an experimental, numerical and analytical approach to investigate the airborne transmission risk potential of differently ventilated, sized and shaped venues. The data were used to assess the magnitude of effect of various mitigation measures and to develop recommendations. Here we show that, in general, positions in the near field of an emission source were at high risk, while the risk of infection from positions in the far field varied depending on the ventilation strategy. Occupancy, airflow rate, residence time, virus variants, activity level and face masks affected the individual and global infection risk in all venues. The global infection risk was lowest for the displacement ventilation case, making it the most effective ventilation strategy for keeping airborne transmission and the number of secondary cases low, compared to mixing or natural ventilation.

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To better understand physiological and pathological movement patterns in the equine thoracolumbar spine, investigation of the biomechanics on a segmental level requires a constant moment. A constant moment along the spinal column means that the same torque acts on each vertebral segment, allowing the range of motion of different segments to be compared. The aims of this study were to investigate the range of motion of the equine thoracolumbar spine in horses with and without spinal pathology and to examine whether the pressure between the spinous processes depends on the direction of the applied moment. Thoracolumbar spine specimens (T8-L4) of 23 horses were mounted in a custom-made mechanical test rig to investigate spinal biomechanics during lateral bending, axial rotation, flexion and extension using computed tomographic imaging. Results were compared between horses with spondylosis, overriding spinous processes and specimens free of gross pathology. The interspinous space pressure was additionally determined using a foil sensor. The median lateral bending between T9 and L3 was 3.7°-4.1° (IQR 5.4°-8.0°). Maximum rotational movement with inconsistent coupled motion was observed at T9-T16 (p < 0.05). The dorsoventral range of motion was greatest in segments T9-T11 (p < 0.05). Spondylosis and overriding spinous processes restricted spinal mobility, depending on the severity of the condition. There was no significant difference in interspinous pressure during motion (p = 0.54). The biomechanical study confirmed that the range of motion of intervertebral joints depends on the anatomical position of the joint and the direction of the moment applied. Restricted mobility was evident in the presence of different grades of overriding spinous processes or spondylosis. A better understanding of equine spinal biomechanics in horses with spinal pathology facilitates individual rehabilitation.

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The utilization of numerical methods, such as computational fluid dynamics (CFD), has been widely established for modeling patient-specific hemodynamics based on medical imaging data. Hemodynamics assessment plays a crucial role in treatment decisions for the coarctation of the aorta (CoA), a congenital heart disease, with the pressure drop (PD) being a crucial biomarker for CoA treatment decisions. However, implementing CFD methods in the clinical environment remains challenging due to their computational cost and the requirement for expert knowledge. This study proposes a deep learning approach to mitigate the computational need and produce fast results. Building upon a previous proof-of-concept study, we compared the effects of two different artificial neural network (ANN) architectures trained on data with different dimensionalities, both capable of predicting hemodynamic parameters in CoA patients: a one-dimensional bidirectional recurrent neural network (1D BRNN) and a three-dimensional convolutional neural network (3D CNN). The performance was evaluated by median point-wise root mean square error (RMSE) for pressures along the centerline in 18 test cases, which were not included in a training cohort. We found that the 3D CNN (median RMSE of 3.23 mmHg) outperforms the 1D BRNN (median RMSE of 4.25 mmHg). In contrast, the 1D BRNN is more precise in PD prediction, with a lower standard deviation of the error (±7.03 mmHg) compared to the 3D CNN (±8.91 mmHg). The differences between both ANNs are not statistically significant, suggesting that compressing the 3D aorta hemodynamics into a 1D centerline representation does not result in the loss of valuable information when training ANN models. Additionally, we evaluated the utility of the synthetic geometries of the aortas with CoA generated by using a statistical shape model (SSM), as well as the impact of aortic arch geometry (gothic arch shape) on the model's training. The results show that incorporating a synthetic cohort obtained through the SSM of the clinical cohort does not significantly increase the model's accuracy, indicating that the synthetic cohort generation might be oversimplified. Furthermore, our study reveals that selecting training cases based on aortic arch shape (gothic versus non-gothic) does not improve ANN performance for test cases sharing the same shape.

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Properties of the pulmonary artery play an essential role in the diagnosis and treatment planning of diseases such as pulmonary hypertension. Patient-specific simulation of hemodynamics can support the planning of interventions. However, the variable complex branching structure of the pulmonary artery poses a challenge for image-based generation of suitable geometries. State-of-the-art segmentation-based approaches require an interactive 3D surface reconstruction to prepare the simulation geometry. We propose a deep learning approach to generate a 3D surface mesh of the pulmonary artery from CT images suitable for simulation. The proposed method is based on the Voxel2Mesh algorithm and includes a voxel encoder and decoder as well as a mesh decoder to deform a prototype mesh. An additional centerline coverage loss facilitates the reconstruction of the branching structure. Furthermore, vertex classification allows for the definition of in- and outlets. Our model was trained with 48 human cases and tested on 10 human cases annotated by two observers. The differences in the anatomical parameters inferred from the automatic surface generation correspond to the differences between the observers’ annotations. The suitability of the generated mesh geometries for numerical flow simulations is demonstrated.

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Accurate and efficient segmentation of anatomical structures in medical images, e.g. ultrasound images, is crucial for diagnosis. Deep Learning methods can provide automatic reproducible segmentation, and simulation of medical images with their intrinsic ground truth could help to develop and tune these methods. We introduce a simulation pipeline for the example of mitral valve segmentation in Transesophageal Echocardiography (TEE) images including different valve opening states. As anatomical ground truth, we used a CT based patient phantom with simulated mitral valve closure. For each region within the phantom, scatter intensities and reflections between tissue boundaries were set, and ultrasound images were simulated with incorporation of attenuation and noise. To further improve realism of the simulated images a speckle reduction filter was used. The adjustments applied to improve realism were assessed by testing the segmentation performance (including Dice score) of a deep learning method trained on real TEE data. The initial Dice score for the simulation was 31 %. This value increased with image postprocessing (37 %), exclusion of surrounding cardiac structures (45 %) and the combination of both (46 %). In comparison, the initial Dices score for real TEE was 72 %. On both simulated and real TEE images, the deep learning method performed better on fully closed valve states (42 % and 77 %) than on fully open valves (27 % and 66 %). This work introduced a novel pipeline for the realistic simulation of TEE images with different valve opening states. Our analysis demonstrated feasibility of the proposed pipeline and highlighted the importance of accurate and dynamic valve phantoms, comprehensive simulations and specific post-processing for the simulation of realistic TEE images. In the future, with further improvements of the simulation, we will evaluate the pipeline for the training of Deep Learning methods on simulated data for the application on real data.

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The study of blood trauma, such as hemolysis in blood-carrying devices, is crucial due to the high incidence of adverse events like alteration of blood function, bleeding, and multi-organ failure. The extent of flow-induced hemolysis, predominantly influenced by stress duration and intensity, is described by established model parameters based on the power law approach. In recent years, various parameters were determined using different Couette shearing devices and donor species. However, they have not been validated due to limited experimental data.

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Bland-Altman analysis of the ECV values between manual and automatic segmentation showed an excellent agreement (mean 0.11, Limits of agreement(LoA) 1.32, -1.09). ECV based on the septum also showed an excellent agreement (mean: -0.08, LoA -1.26, 1.1). Comparing the ECV values of the automated ECV with Otsu thresholding to the clinical ECV, showed a good agreement in ECV, both for the myocardium (mean 2.23%, LoA: -2.66, 7.111) and the septum (mean 2.73%, LoA -1.72, 7.19). Without the thresholding, the agreement is reduced for both myocardium (mean 4.19%, LoA -1.89, 10.27), and septum(mean 4.74%, LoA -1.14, 10.61). Figure 3 shows examples of sources of errors.
Conclusion ECV values can be assessed automatically by the proposed pipeline with a good agreement compared to ECV values assessed according to current guidelines.

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In silico trials are a promising way to increase the efficiency of the development, and the time to market of cardiovascular implantable devices. The development of transcatheter aortic valve implantation (TAVI) devices, could benefit from in silico trials to overcome frequently occurring complications such as paravalvular leakage and conduction problems. To be able to perform in silico TAVI trials virtual cohorts of TAVI patients are required. In a virtual cohort, individual patients are represented by computer models that usually require patient-specific aortic valve geometries. This study aimed to develop a virtual cohort generator that generates anatomically plausible, synthetic aortic valve stenosis geometries for in silico TAVI trials and allows for the selection of specific anatomical features that influence the occurrence of complications. To build the generator, a combination of non-parametrical statistical shape modeling and sampling from a copula distribution was used. The developed virtual cohort generator successfully generated synthetic aortic valve stenosis geometries that are comparable with a real cohort, and therefore, are considered as being anatomically plausible. Furthermore, we were able to select specific anatomical features with a sensitivity of around 90%. The virtual cohort generator has the potential to be used by TAVI manufacturers to test their devices. Future work will involve including calcifications to the synthetic geometries, and applying high-fidelity fluid-structure-interaction models to perform in silico trials.

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Regular physical activity is highly recommended for patients with Fontan hemodynamics. Our aim was to investigate the effects of a long-term individualized home-based endurance training (IHET) on a bicycle ergometer in combination with inspiratory muscle training (IMT) in pediatric and adult patients after Fontan palliation. Additionally, factors influencing the trainability of Fontan palliated patients were analyzed.

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Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow CMR segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies.

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Three-dimensional stacks acquired with confocal or two-photon microscopy are crucial for studying neuroanatomy. However, high-resolution image stacks acquired at multiple depths are time-consuming and susceptible to photobleaching. In vivo microscopy is further prone to motion artifacts. In this work, we suggest that deep neural networks with sine activation functions encoding implicit neural representations (SIRENs) are suitable for predicting intermediate planes and correcting motion artifacts, addressing the aforementioned shortcomings. We show that we can accurately estimate intermediate planes across multiple micrometers and fully automatically and unsupervised estimate a motion-corrected denoised picture. We show that noise statistics can be affected by SIRENs, however, rescued by a downstream denoising neural network, shown exemplarily with the recovery of dendritic spines. We believe that the application of these technologies will facilitate more efficient acquisition and superior post-processing in the future.

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Thromboembolic complications still arise on blood contacting surfaces. Surface charge and topography influence the subsequent deposition of proteins and platelets, potentially leading to thrombi. Research showed a correlation of surface charge and nanoscale roughness, and a negative surface charge as well as a smooth surface finish are associated with lower thrombogenicity. The aim of this study was to compare the platelet adhesion on titanium with different nanoscale roughnesses and to examine if those roughness variations caused a change in surface charge. Titanium samples were polished and roughened to four different nanoscale roughness levels. Platelet adhesion (covered surface area (CSA), N  = 8) was tested in flow chambers with human whole blood using fluorescence imaging. ζ-potential was measured over a broad range of pH-values and interpolated to obtain the ζ-potential for pHBlood (7.4). Platelet adhesion tests were evaluated in terms of p -values and the Wilcoxon test effect size and the trend of the ζ-potential at pHBlood and the CSA was compared. Ra -values ranged between 35 (polished) and 156 nm. Regarding platelet adhesion, the polished sample showed the lowest mean CSA with a medium or strong effect size compared to the roughened samples. The interpolated ζ-potentials for pHBlood follow a similar trend as the CSA, with the lowest ζ-potential measured for the polished surface. These findings suggest that the decreasing ζ-potential due to lower nanoscale roughness might be an additional explanation for the improved hemocompatibility besides the smoother topography.

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To quantify the effects of CSF pressure alterations on intracranial venous morphology and hemodynamics in idiopathic intracranial hypertension (IIH) and spontaneous intracranial hypotension (SIH) and assess reversibility when the underlying cause is resolved.

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Virus infection triggers inflammation and, may impose nutrient shortage to the heart. Supported by type I interferon (IFN) signalling, cardiomyocytes counteract infection by various effector processes, with the IFN-stimulated gene of 15 kDa (ISG15) system being intensively regulated and protein modification with ISG15 protecting mice Coxsackievirus B3 (CVB3) infection. The underlying molecular aspects how the ISG15 system affects the functional properties of respective protein substrates in the heart are unknown.

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Cortical spreading depolarization (SD) imposes a massive increase in energy demand and therefore evolves as a target for treatment following acute brain injuries. Anesthetics are empirically used to reduce energy metabolism in critical brain conditions, yet their effect on metabolism during SD remains largely unknown. We investigated oxidative metabolism during SD in brain slices from Wistar rats. Extracellular potassium ([K+ ]o), local field potential and partial tissue oxygen pressure (pti O2) were measured simultaneously. The cerebral metabolic rate of oxygen (CMRO2) was calculated using a reaction-diffusion model. By that, we tested the effect of clinically relevant concentrations of isoflurane on CMRO2 during SD and modeled tissue oxygenation for different capillary pO2 values. During SD, CMRO2 increased 2.7-fold, resulting in transient hypoxia in the slice core. Isoflurane decreased CMRO2, reduced peak [K+ ]o, and prolonged [K+ ]o clearance, which indicates reduced synaptic transmission and sodium-potassium ATPase inhibition. Modeling tissue oxygenation during SD illustrates the need for increased capillary pO2 levels to prevent hypoxia. In the absence thereof, isoflurane could improve tissue oxygenation by lowering CMRO2. Therefore, isoflurane is a promising candidate for pre-clinical studies on neuronal survival in conditions involving SD.

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Mathematical modeling is a promising strategy to fill the experimentally unapproachable knowledge gaps about the relative contribution of various molecular processes to cellular metabolic function. To this end, we developed detailed kinetic models of the central metabolism of different cell types, comprising multiple metabolic functionalities. We used the model to simulate metabolic changes in several cell types under different experimental settings in health and disease. In this way, we show that it is possible to decipher and characterize the relative influence of various metabolic pathways and enzymes to overall metabolic performance and phenotype.Quantitative Systems Metabolism (QSM™) allows quantitative assessment of metabolic functionality and metabolic profiling based on proteomic data. Here, we describe the technique, namely, molecular resolved kinetic modeling, underlying QSM™. We explain the necessary steps for the generation of cell-specific models to functionally interpret proteomic data and point out some unresolved challenges and open questions.

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Atherosclerosis of the carotid artery is a major risk factor for stroke. Quantitative assessment of the carotid vessel wall can be based on cross-sections of three-dimensional (3D) black-blood magnetic resonance imaging (MRI). To increase reproducibility, a reliable automatic segmentation in these cross-sections is essential.

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Analyzing the anatomy of the aorta and left ventricular outflow tract (LVOT) is crucial for risk assessment and planning of transcatheter aortic valve implantation (TAVI). A comprehensive analysis of the aortic root and LVOT requires the extraction of the patient-individual anatomy via segmentation. Deep learning has shown good performance on various segmentation tasks. If this is formulated as a supervised problem, large amounts of annotated data are required for training. Therefore, minimizing the annotation complexity is desirable.

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Epilepsy is characterized by hypersynchronous neuronal discharges, which are associated with an increased cerebral metabolic rate of oxygen and ATP demand. Uncontrolled seizure activity (status epilepticus) results in mitochondrial exhaustion and ATP depletion, which potentially generate energy mismatch and neuronal loss. Many cells can adapt to increased energy demand by increasing metabolic capacities. However, acute metabolic adaptation during epileptic activity and its relationship to chronic epilepsy remains poorly understood. We elicited seizure-like events (SLEs) in an in vitro model of status epilepticus for eight hours. Electrophysiological recording and tissue oxygen partial pressure recordings were performed. After eight hours of ongoing SLEs, we used proteomics-based kinetic modeling to evaluate changes in metabolic capacities. We compared our findings regarding acute metabolic adaptation to published proteomic and transcriptomic data from chronic epilepsy patients. Epileptic tissue acutely responded to uninterrupted SLEs by upregulating ATP production capacity. This was achieved by a coordinated increase in the abundance of proteins from the respiratory chain and oxidative phosphorylation system. In contrast, chronic epileptic tissue shows a 25-40% decrease in ATP production capacity. In summary, our study reveals that epilepsy leads to dynamic metabolic changes. Acute epileptic activity boosts ATP production, while chronic epilepsy reduces it significantly.

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Microglia, brain-resident macrophages, can acquire distinct functional phenotypes, which are supported by differential reprogramming of cell metabolism. These adaptations include remodeling in glycolytic and mitochondrial metabolic fluxes, potentially altering energy substrate availability at the tissue level. This phenomenon may be highly relevant in the brain, where metabolism must be precisely regulated to maintain appropriate neuronal excitability and synaptic transmission. Direct evidence that microglia can impact on neuronal energy metabolism has been widely lacking, however. Combining molecular profiling, electrophysiology, oxygen microsensor recordings and mathematical modeling, we investigated microglia-mediated disturbances in brain energetics during neuroinflammation. Our results suggest that proinflammatory microglia showing enhanced nitric oxide release and decreased CX3CR1 expression transiently increase the tissue lactate/glucose ratio that depends on transcriptional reprogramming in microglia, not in neurons. In this condition, neuronal network activity such as gamma oscillations (30-70 Hz) can be fueled by increased ATP production in mitochondria, which is reflected by elevated oxygen consumption. During dysregulated inflammation, high energy demand and low glucose availability can be boundary conditions for neuronal metabolic fitness as revealed by kinetic modeling of single neuron energetics. Collectively, these findings indicate that metabolic flexibility protects neuronal network function against alterations in local substrate availability during moderate neuroinflammation.

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We present a geometric deep-learning method for reconstructing a temporally continuous mitral valve surface mesh from 3D transesophageal echocardiography sequences. Our approach features a supervised end-to-end deep learning architecture that combines a convolutional neural network-based voxel encoder and decoder with a graph neural network-based multi-resolution mesh decoder, all trained on sparse landmark annotations. Key elements of our methodology include a tube-shaped prototype mesh with labeled vertices, a specialized loss function to preserve the known inlet and outlet, and a rigid alignment system for anatomical landmarks. A custom term in the loss function prevents self-intersecting geometries within the valve mesh, promoting point correspondence and facilitating a continuous representation of valve anatomy over time. An ablation study evaluates the impact of different loss term configurations on model performance, highlighting the effectiveness of each individual loss term. Our Mitral Valve Graph Neural Network (MV-GNN) outperforms existing deep-learning methods on most distance metrics for the annulus and leaflets. The continuous valve motion representations generated by our approach (3D+t) exhibit distance measures comparable to our 3D solution, demonstrating its potential for analyzing mitral valve dynamics and enhancing personalized simulations for hemodynamic assessment and therapy planning.

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Patients affected by neurofibromatosis type 1 (NF1) frequently show muscle weakness with unknown etiology. Here we show that, in mice, Neurofibromin 1 (Nf1) is not required in muscle fibers, but specifically in early postnatal myogenic progenitors (MPs), where Nf1 loss led to cell cycle exit and differentiation blockade, depleting the MP pool resulting in reduced myonuclear accretion as well as reduced muscle stem cell numbers. This was caused by precocious induction of stem cell quiescence coupled to metabolic reprogramming of MPs impinging on glycolytic shutdown, which was conserved in muscle fibers. We show that a Mek/Erk/NOS pathway hypersensitizes Nf1-deficient MPs to Notch signaling, consequently, early postnatal Notch pathway inhibition ameliorated premature quiescence, metabolic reprogramming and muscle growth. This reveals an unexpected role of Ras/Mek/Erk signaling supporting postnatal MP quiescence in concert with Notch signaling, which is controlled by Nf1 safeguarding coordinated muscle growth and muscle stem cell pool establishment. Furthermore, our data suggest transmission of metabolic reprogramming across cellular differentiation, affecting fiber metabolism and function in NF1.

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Oxygenators are a lifesaving technology used for blood oxygenation and decarboxylation in case of acute respiratory failure, chronic lung disease, and during open-heart surgery. Devices typically consist of a bundle of thousands of fibremembranes in a housing, with gas flowing inside the fibresand blood flowing in the opposite direction outside the fibres. Both ends of the fibre-membranes are fixed with an adhesive to separate gas and blood. The shape of the volume through which the blood flows is determined by the housing of the oxygenator and the internal end surfaces of the fixed ends of the fibre-membrane bundle. The traditional potting process results in a volume shape that is associated with stagnation zones, which are known to promote thrombus formation. In this study, an adapted potting process is proposed which results in a blood compartment with bevelledend faces of the glued bundle parts. Using a numerical study, we have demonstrated that the novel oxygenator design results in optimized flow conditions

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This repository contains instructions on downloading and installing a pretrained nnU-Net model [1] for the segmentation of the left myocardium and blood-pool in short-axis Late Gadolinium Enhancement (LGE) Cardiac Magnetic Resonance Images (SAX LGE MRI). The training data consists of 268 phase-sensitive inversion recovery (PSIR) images collected in the MYKKE Registry of children with suspected myocarditis [2], which have been annotated by medical staff at the Charité Berlin as part of the DHZK Shared Expertise Project 81X2100274, as well as 100 training images from the EMIDEC dataset [3], with scar and no-reflow areas counted towards the total myocardial segment.

The multi-site, multi-age pediatric training cohort includes images from patients of various ages, from 1.5T and 3.0T scanners, with various voxel-sizes and fields-of-view. LGE is present in a variety of patterns and extents, with some MRI presenting as normal.

The model is intended to be used in the nnUNet eco-system.

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Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.

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Mutation of the PRDM16 gene causes human dilated and non-compaction cardiomyopathy. The PRDM16 protein is a transcriptional regulator that affects cardiac development via Tbx5 and Hand1, thus regulating myocardial structure. The biallelic inactivation of Prdm16 induces severe cardiac dysfunction with post-natal lethality and hypertrophy in mice. The early pathological events that occur upon Prdm16 inactivation have not been explored.

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Abstract

Pregnancy places a metabolic burden on the body including the liver, which is responsible for ensuring adequate nutrition for the maternal and fetal systems. To gain a better understanding of liver adaptation, this study investigates metabolic shifts occurring in livers of pregnant rats. Metabolic capacities of the livers of pregnant and non-pregnant female Wistar rats were assessed using comprehensive metabolic models. Kinetic metabolic models were generated for each animal based on protein abundance data from proteomics analysis allowing for a subject-specific assessment of hepatic metabolic functions. Data are available via ProteomeXchange with identifier PXD050758. Additionally, tissue stiffness, viscosity, and water diffusion obtained from magnetic resonance imaging and elastography were correlated with metabolic capabilities to study the relationship between metabolic function and biophysical properties. Proteome profiling revealed differences in protein expression in the livers of pregnant and non-pregnant animals. Functional analysis showed significant variations in metabolic capacities. Livers of pregnant rats had reduced capacities in carbohydrate and fatty acid metabolism, along with altered urea synthesis. Additionally, there were associations between metabolic functions and biophysical properties highlighting potential links between changes in liver structure and metabolic capacities during pregnancy. In summary, our work reveals extensive hepatic metabolic changes in pregnant rats. The liver adapts its metabolic capacities to ensure whole-body metabolic homeostasis but may struggle to counteract nutritional challenges, such as hypoglycemia. The study, employing a personalized approach combining proteomics, kinetic modeling, and advanced imaging, sheds light on the intricate interplay between hepatic adaptations and medical imaging markers, providing a foundation for further investigations into the implications for maternal and fetal health.

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Abstract

4D-flow MRI is a promising technique for assessing vessel hemodynamics. However, its utilization is currently limited by the lack of reference values, particularly for pulmonary vessels. In this work, we have analysed flow and velocity in the pulmonary trunk (PT), left and right pulmonary arteries (LPA and RPA, respectively) in Landrace pigs at both rest and stress through the software MEVISFlow. Nine healthy Landrace pigs were acutely instrumented closed-chest and transported to the CMR facility for evaluation. After rest measurements, dobutamine was administered to achieve a 25% increase in heart rate compared to rest. 4D-flow MRI images have been analysed through MEVISFlow by two independent observers. Inter- and intra-observer reproducibility was quantified using intraclass correlation coefficient. A significant difference between rest and stress regarding flow and velocity in all the pulmonary vessels was observed. Mean flow increased 55% in PT, 75% in LPA and 40% in RPA. Mean peak velocity increased 55% in PT, 75% in LPA and 66% in RPA. A good-to-excellent reproducibility was observed in rest and stress for flow measurements in all three arteries. An excellent reproducibility for velocity was found in PT at rest and stress, a good one for LPA and RPA at rest, while poor reproducibility was found at stress. The current study showed that pulmonary flow and velocity assessed through 4D-flow MRI follow the physiological alterations during cardiac cycle and after stress induced by dobutamine. A clinical translation to assess pulmonary diseases with 4D-flow MRI under stress conditions needs investigation.

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In congenital aortic valve disease, quantifying aortic regurgitation (AR) varies by the measurement site. Our study aimed to identify the optimal site for AR assessment using 2D and 4D MR flow measurements, with a focus on vortices.

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Tumours are heterogeneous tissues containing diverse populations of cells and an abundant extracellular matrix (ECM). This tumour microenvironment prompts cancer cells to adapt their metabolism to survive and grow. Besides epigenetic factors, the metabolism of cancer cells is shaped by crosstalk with stromal cells and extracellular components. To date, most experimental models neglect the complexity of the tumour microenvironment and its relevance in regulating the dynamics of the metabolism in cancer. We discuss emerging strategies to model cellular and extracellular aspects of cancer metabolism. We highlight cancer models based on bioengineering, animal, and mathematical approaches to recreate cell-cell and cell-matrix interactions and patient-specific metabolism. Combining these approaches will improve our understanding of cancer metabolism and support the development of metabolism-targeting therapies.

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Abstract

Exercise intolerance is a debilitating symptom in heart failure (HF), adversely affecting both quality of life and long-term prognosis. Emerging evidence suggests that pulmonary artery (PA) compliance may be a contributing factor. This study aims to non-invasively assess PA compliance and its dynamic properties during isometric handgrip (HG) exercise in HF patients and healthy controls, using cardiovascular magnetic resonance (CMR). We prospectively enrolled 36 subjects, comprising 17 HF patients (NYHA class II and III) and 19 healthy controls. Participants performed an HG test, and we assessed changes in PA compliance and hemodynamic flow parameters using advanced CMR techniques. We also explored the relationship between CMR-derived PA compliance metrics and established clinical indicators, ensuring the validity of our findings through intra- and interobserver agreements. HF patients had significantly lower resting PA compliance compared to controls (28.9% vs. 50.1%, p < 0.01). During HG exercise, HF patients exhibited a dampened adaptability in PA compliance. Hemodynamic responses, including heart rate and blood pressure, were not significantly different between the groups. Further analyses revealed a significant correlation between changes in PA compliance and functional capacity, and an inverse relationship with NYHA class. Our study demonstrates a marked difference in PA vascular responses during HG exercise between HF patients and healthy controls. The compromised adaptability in PA compliance in HF patients is correlated with diminished functional capacity. These findings have significant clinical implications and may guide future interventional strategies in HF management.

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Abstract

Background
Pediatric myocarditis is a heterogeneous disease that can present with asymptomatic courses up to severe heart failure. Next to endomyocardial biopsy (EMB) as the diagnostic gold standard, late gadolinium enhancement (LGE) in cardiac magnetic resonance imaging (CMR) is an important non-invasive diagnostic marker [1]. Whereas analysis of LGE is often limited to qualitative assessment, radiomics offers quantitative characterization of LGE intensity and texture patterns. We aimed to characterize pediatric patients enrolled in the multicenter registry for suspected myocarditis “MYKKE” [2] using first-order and texture features resolved spatially into the 17-segment AHA model [3].

Methods
Short axis CMR LGE sequences were analyzed, selected on image availability and sufficient image quality for analysis. Epi- and endocardial borders were detected automatically [4], corrected by a radiologist, and divided according to the AHA model. To account for different anatomical size, field of view and spatial sampling, each case was resampled in 2D to the median myocardial voxel count (extent of blood pool / voxel size), resulting in similar numbers of voxels covering the myocardium. We extracted 48 radiomics features for each AHA segment with pyradiomics v3.0.1, including first-order intensity value statistics and second-order texture features. Where segments extend over multiple slices, feature values were averaged. Clustering with Gaussian Mixture modelling on radiomics features was used to find groups with similar signatures, and clinical parameters of these patient groups were compared.

Results
We included 175 patients in our analyses. We explored differences in features between age groups <5 and >12 years of age in patients with EMB confirmed myocarditis (n=14 and n=66, respectively). Patients <5 years of age had significantly more LGE in inferior segments compared to patients >12 years, reflected in higher first-order histogram values. The septum in the younger cohort is more homogeneous, as indicated by gray-level co-occurrence (GLCM), run-length (GLRLM), size-zone (GLSZM) and neighboring gray tone difference matrix (NGTDM) statistics [Figure 1]. Gaussian Mixture modelling on 182 non-correlated radiomics features divides cases into three subgroups, which do not align with sex, age, or myocarditis diagnosis in EMB. However, we found differences between
clusters in LV ejection fraction and NT-proBNP among cases with EMB confirmed myocarditis (n=91).

Conclusion
We show a spatially resolved radiomics characterization of pediatric patients with proven myocarditis using individually resampled LGE CMR images. The two age groups identified in previous work [1] to have different disease courses also present with different radiomics profiles. Clustering further suggests that patients with similar radiomics features also share some clinical characteristics. Using radiomics features for stratifying patients from their first MRI thus is a promising approach for future evaluation of the disease.

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Accumulating evidence implicates the activation of G-protein-coupled PARs (protease-activated receptors) by coagulation proteases in the regulation of innate immune responses.

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Major challenges for clinical applications of in silico medicine are limitations in time and computational resources. Computational approaches should therefore be tailored to specific applications with relatively low complexity and must be verified and validated against clinical gold standards.

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X-ray coronary angiography is the primary imaging modality for evaluating coronary artery disease. The visual assessment of angiography videos in clinical routines is time-consuming, requires expert experience and lacks standardization. This complicates the calculation of the SYNTAX score, a recommended instrument for therapy decision making. In this work we propose an end-to-end pipeline for segment-wise stenosis prediction in multi-view angiography videos to facilitate the calculation of the SYNTAX score. While recent approaches mainly focus on stenosis detection on frame- or video-level, our method is developed and evaluated for stenosis prediction on patient-level. The pipeline is composed as follows: (1) Selection of frames showing arteries filled with contrast medium using a convolutional neural network, (2) Stenosis detection and segment labelling on selected frames using a region-based convolutional neural network for object detection, (3) Linkage of detected regions showing the same stenosis by tracking the optical flow of the detections in the angiography video, (4) Segment assignment to the detected and tracked stenosis to predict stenotic segments on patient-level. The workflow is adjusted and evaluated using the image data and diagnostic annotations of 219 patients with multi-vessel coronary artery disease from the German Heart Center of the Charité University Hospital (DHZC), Berlin. To fine-tune the models, we used manually flagged frames for the frame classification model and bounding box annotations provided by a cardiac expert for the stenosis detection model. For the segment-wise prediction of all patients, we achieved a total sensitivity of 56.41, specificity of 85.88, precision of 52.81 and F1 score of 54.55 with varying results for the 25 coronary segments. The established workflow can facilitate visual assessment of CAD in angiography videos and increase accuracy and precision in clinical diagnostics.

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Abstract

Bicuspid aortic valve (BAV) disease leads to deviant helical flow patterns especially in the mid-ascending aorta (AAo), potentially causing wall alterations such as aortic dilation and dissection. Among others, wall shear stress (WSS) could contribute to the prediction of long-term outcome of patients with BAV. 4D flow in cardiovascular magnetic resonance (CMR) has been established as a valid method for flow visualization and WSS estimation. The aim of this study is to reevaluate flow patterns and WSS in patients with BAV 10 years after the initial evaluation.

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Complicated carotid artery plaques (cCAPs) are associated with an increased risk of rupture and subsequent stroke. The geometry of the carotid bifurcation determines the distribution of local hemodynamics and could thus contribute to the development and composition of these plaques. Therefore, we studied the role of carotid bifurcation geometry in the presence of cCAPs.

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We comprehensively studied morphological and functional aortic aging in a population study using modern three-dimensional MR imaging to allow future comparison in patients with diseases of the aortic valve or aorta. We followed 80 of 126 subjects of a population study (20 to 80 years of age at baseline) using the identical methodology 6.0 ± 0.5 years later. All underwent 3 T MRI of the thoracic aorta including 3D T1 weighted MRI (spatial resolution 1 mm3) for measuring aortic diameter and plaque thickness and 4D flow MRI (spatial/temporal resolution = 2 mm3 /20 ms) for calculating global and regional aortic pulse wave velocity (PWV) and helicity of aortic blood flow. Mean diameter of the ascending aorta (AAo) decreased and plaque thickness increased significantly in the aortic arch (AA) and descending aorta (DAo) in females. PWV of the thoracic aorta increased (6.4 ± 1.5 to 7.0 ± 1.7 m/s and 6.8 ± 1.5 to 7.3 ± 1.8 m/s in females and males, respectively) over time. Local normalized helicity volumes (LNHV) decreased significantly in the AAo and AA (0.33 to 0.31 and 0.34 to 0.32 in females and 0.34 to 0.32 and 0.32 to 0.28 in males). By contrast, helicity increased significantly in the DAo in both genders (0.28 to 0.29 and 0.29 to 0.30, respectively). 3D MRI was able to characterize changes in aortic diameter, plaque thickness, PWV and helicity during six years in our population. Aortic aging determined by 3D multi-parametric MRI is now available for future comparisons in patients with diseases of the aortic valve or aorta.

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In aortic valve replacement (AVR), the treatment strategy as well as the model and size of the implanted prosthesis have a major impact on the postoperative hemodynamics and thus on the clinical outcome. Preinterventional prediction of the hemodynamics could support the treatment decision. Therefore, we performed paired virtual treatment with transcatheter AVR (TAVI) and biological surgical AVR (SAVR) and compared hemodynamic outcomes using numerical simulations.

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To facilitate pre-clinical animal and in-silico clinical trials for implantable pulmonary artery pressure sensors, understanding the respective species pulmonary arteries (PA) anatomy is important. Thus, morphological parameters describing PA of pigs and sheep, which are common animal models, were compared with humans. Retrospective computed tomography data of 41 domestic pigs (82.6 ± 18.8 kg), 14 sheep (49.1 ± 6.9 kg), and 49 patients (76.8 ± 18.2 kg) were used for reconstruction of the subject-specific PA anatomy. 3D surface geometries including main, left, and right PA as well as LPA and RPA side branches were manually reconstructed. Then, specific geometric parameters (length, diameters, taper, bifurcation angle, curvature, and cross-section enlargement) affecting device implantation and post-interventional device effect and efficacy were automatically calculated. For both animal models, significant differences to the human anatomy for most geometric parameters were found, even though the respective parameters' distributions also featured relevant overlap. Out of the two animal models, sheep seem to be better suitable for a preclinical study when considering only PA morphology. Reconstructed geometries are provided as open data for future studies. These findings support planning of preclinical studies and will help to evaluate the results of animal trials.

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Maintenance of ion homeostasis is essential for normal brain function. Inhalational anesthetics are known to act on various receptors, but their effects on ion homeostatic systems, such as sodium/potassium-adenosine triphosphatase (Na+/K+-ATPase), remain largely unexplored. Based on reports demonstrating global network activity and wakefulness modulation by interstitial ions, the hypothesis was that deep isoflurane anesthesia affects ion homeostasis and the key mechanism for clearing extracellular potassium, Na+/K+-ATPase.

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Abstract

Standardized 4D PCMRI postprocessing protocols could enable comparable bloodflow quantification. We propose an automatic segmentation of aortic cross section over time with a residual trained data from different imaging sequences, scanner types, pathologies and position of cross section planes. Dice score, Hausdorff metric as well as flow and velocity curves for the segmented areas show good performance both in the validation and test sets.

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In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

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Abstract

Properties of the pulmonary artery play an essential role in the diagnosis and treatment planning of diseases such as pulmonary hypertension. Patient-specific simulation of hemodynamics can support the planning of interventions. However, the variable complex branching structure of the pulmonary artery poses a challenge for image-based generation of suitable geometries. State-of-the-art segmentation-based approaches require an interactive 3D surface reconstruction to prepare the simulation geometry.
We propose a deep learning approach to generate a 3D surface mesh of the pulmonary artery from CT images suitable for simulation. The proposed method is based on the Voxel2Mesh algorithm and includes a voxel encoder and decoder as well as a mesh decoder to deform a prototype mesh. An additional centerline coverage loss facilitates the reconstruction of the branching structure. Furthermore, vertex classification allows for the definition of in- and outlets.
Our model was trained with 48 human cases and tested on 10 human cases annotated by two observers. The differences in the anatomical parameters inferred from the automatic surface generation correspond to the differences between the observers' annotations. The suitability of the generated mesh geometries for numerical flow simulations is demonstrated.

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Abstract

Pressure overload in patients with aortic valve stenosis and volume overload in mitral valve regurgitation trigger specific forms of cardiac remodeling; however, little is known about similarities and differences in myocardial proteome regulation. We performed proteome profiling of 75 human left ventricular myocardial biopsies (aortic stenosis = 41, mitral regurgitation = 17, and controls = 17) using high-resolution tandem mass spectrometry next to clinical and hemodynamic parameter acquisition. In patients of both disease groups, proteins related to ECM and cytoskeleton were more abundant, whereas those related to energy metabolism and proteostasis were less abundant compared with controls. In addition, disease group-specific and sex-specific differences have been observed. Male patients with aortic stenosis showed more proteins related to fibrosis and less to energy metabolism, whereas female patients showed strong reduction in proteostasis-related proteins. Clinical imaging was in line with proteomic findings, showing elevation of fibrosis in both patient groups and sex differences. Disease- and sex-specific proteomic profiles provide insight into cardiac remodeling in patients with heart valve disease and might help improve the understanding of molecular mechanisms and the development of individualized treatment strategies.

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Abstract

A lab-on-a-chip multichannel sensing platform for biomedical analysis based on optical silicon nitride (SiNx) microring-resonators (MRR) was established. The resonators were surface functionalized and finally combined with a microfluidic chamber for validation using an avidin-biotin ligand-binding assay. The results with a limit of detection (LOD) of 2.3∙10-5 and a mean intra-assay coefficient of variation (CV) of ±10.0 %, also under consideration of FDA guidelines, show promising future applicability for a wide variety of targets in the field of outpatient medical diagnostics and life science.Clinical Relevance- Biomarkers play a crucial role in physiological processes of the human body. To enable instantaneous and decentralized analysis of these markers, systems are needed that can be used in a laboratory-independent environment with minimal amounts of biofluid. An example is the utilization of such systems for neonates or infants.

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Abstract

With the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), global researchers were confronted with major challenges. The German National Pandemic Cohort Network (NAPKON) was launched in fall 2020 to effectively leverage resources and bundle research activities in the fight against the coronavirus disease 2019 (COVID-19) pandemic. We analyzed the setup phase of NAPKON as an example for multicenter studies in Germany, highlighting challenges and optimization potential in connecting 59 university and nonuniversity study sites. We examined the ethics application process of 121 ethics submissions considering durations, annotations, and outcomes. Study site activation and recruitment processes were investigated and related to the incidence of SARS-CoV-2 infections. For all initial ethics applications, the median time to a positive ethics vote was less than two weeks and 30 of these study sites (65%) joined NAPKON within less than three weeks each. Electronic instead of postal ethics submission (9.5 days (Q1: 5.75, Q3: 17) vs. 14 days (Q1: 11, Q3: 26), p value = 0.01) and adoption of the primary ethics vote significantly accelerated the ethics application process. Each study center enrolled a median of 37 patients during the 14-month observation period, with large differences depending on the health sector. We found a positive correlation between recruitment performance and COVID-19 incidence as well as hospitalization incidence. Our analysis highlighted the challenges and opportunities of the federated system in Germany. Digital ethics application tools, adoption of a primary ethics vote and standardized formal requirements lead to harmonized and thus faster study initiation processes during a pandemic.

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Both single- and double-outflow cavopulmonary assist devices (CPADs) were recently proposed for the Fontan population, whereas single-outflow configurations were evaluated in large animal trials and double-outflow concepts are lacking an equivalent in vivo assessment. The aim of this study was to test the hemodynamic properties of a double-outflow CPAD device in an acute sheep model. The two inflow cannulae of a CPAD were anastomosed to the caval veins. Outflow graft connection was performed via end-to-side anastomosis to the right (RPA) and main pulmonary artery (MPA). Speed ramp protocols were conducted, and hemodynamic effects were monitored in terms of caval flows, cardiac output (CO), central venous pressure (CVP), pulmonary artery pressure (PAP), and left atrial pressure (LAP). Six experiments were conducted (53.35 ± 5.1 kg). In three experiments, the animal model was established, the CPAD was examined, and restoration of biventricular equivalency in terms of venous return was achieved. Venous pressures (CVP) declined linearly with increasing pump speed (r > 0.879), whereas caval flow (r > 0.973), CO (r > 0.993), PAP (r > 0.973), and LAP (r > 0.408) increased. Despite the considerable complexity of the sheep model caused by the sheep pulmonary arterial anatomy that requires substantial graft bending, the CPAD was evaluated in three acute experiments and showed the potential to completely substitute a subpulmonary ventricle.

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Abstract

Obesity and associated nonalcoholic steatohepatitis (NASH) are on the rise globally. NASH became an important driver of hepatocellular carcinoma (HCC) in recent years. Activation of the central metabolic regulator mTOR (mechanistic target of rapamycin) is frequently observed in HCCs. However, mTOR inhibition failed to improve the outcome of HCC therapies, demonstrating the need for a better understanding of the molecular and functional consequences of mTOR blockade. We established a murine NASH-driven HCC model based on long-term western diet feeding combined with hepatocellular mTOR-inactivation. We evaluated tumor load and whole-body fat percentage via µCT-scans, analyzed metabolic blood parameters and tissue proteome profiles. Additionally, we used a bioinformatic model to access liver and HCC mitochondrial metabolic functions. The tumor burden was massively increased via mTOR-knockout. Several signs argue for extensive metabolic reprogramming of glucose, fatty acid, bile acid and cholesterol metabolism. Kinetic modeling revealed reduced oxygen consumption in KO-tumors. NASH-derived HCC pathogenesis is driven by metabolic disturbances and should be considered separately from those caused by other etiologies. We conclude that mTOR functions as tumor suppressor in hepatocytes especially under long-term western diet feeding. However, some of the detrimental consequences of this diet are attenuated by mTOR blockade.

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Abstract

Numerical simulations of pulsatile blood flow in an aortic coarctation require the use of turbulence modeling. This paper considers three models from the class of large eddy simulation (LES) models (Smagorinsky, Vreman, -model) and one model from the class of variational multiscale models (residual-based) within a finite element framework. The influence of these models on the estimation of clinically relevant biomarkers used to assess the degree of severity of the pathological condition (pressure difference, secondary flow degree, normalized flow displacement, wall shear stress) is investigated in detail. The simulations show that most methods are consistent in terms of severity indicators such as pressure difference and stenotic velocity. Moreover, using second-order velocity finite elements, different turbulence models might lead to considerably different results concerning other clinically relevant quantities such as wall shear stresses. These differences may be attributed to differences in numerical dissipation introduced by the turbulence models.

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Abstract

Radiomics has been applied in cardiovascular imaging with promising results. Still, the effect of imaging parameters on the reproducibility of radiomics features needs to be well understood for these to be used in clinical diagnostics. We conduct a retrospective study on short-axis cine CMR images of healthy volunteers to assess the impact of different temporal sampling parameters on the reproducibility of shape, first-order, texture, and deformation field-based features, and contextualize the findings with inter- and intra-observer variability. We find that the reproducibility of features is dependent on temporal sampling, warranting the introduction of parameter standardization for radiomics analyses.

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To assess whether in-silico models can be used to predict the risk of thrombus formation in pulmonary artery pressure sensors (PAPS), a chronic animal study using pigs was conducted. Computed tomography (CT) data was acquired before and immediately after implantation, as well as one and three months after the implantation. Devices were implanted into 10 pigs, each one in the left and right pulmonary artery (PA), to reduce the required number of animal experiments. The implantation procedure aimed at facilitating optimal and non-optimal positioning of the devices to increase chances of thrombus formation. Eight devices were positioned non-optimally. Three devices were positioned in the main PA instead of the left and right PA. Pre-interventional PA geometries were reconstructed from the respective CT images, and the devices were virtually implanted at the exact sites and orientations indicated by the follow-up CT after one month. Transient intra-arterial hemodynamics were calculated using computational fluid dynamics. Volume flow rates were modelled specifically matching the animals body weights. Wall shear stresses (WSS) and oscillatory shear indices (OSI) before and after device implantation were compared. Simulations revealed no relevant changes in any investigated hemodynamic parameters due to device implantation. Even in cases, where devices were implanted in a non-optimal manner, no marked differences in hemodynamic parameters compared to devices implanted in an optimal position were found. Before implantation time and surface-averaged WSS was  Pa, whereas OSI was , respectively. Areas affected by low WSS magnitudes were  cm, whereas the areas affected by high OSI were  cm. After device implantation, WSS and OSI were  Pa and , respectively. Surface areas affected by low WSS and high OSI were  cm, and  cm, respectively. This in-silico study indicates that no clinically relevant differences in intra-arterial hemodynamics are occurring after device implantation, even at non-optimal positioning of the sensor. Simultaneously, no embolic events were observed, suggesting that the risk for thrombus formation after device implantation is low and independent of the sensor position.

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Abstract

Computational fluid dynamics (CFD) is a powerful tool for the in-silico evaluation of rotodynamic blood pumps (RBPs). Corresponding validation, however, is typically restricted to easily accessible, global flow quantities. This study showcased the HeartMate 3 (HM3) to identify feasibility and challenges of enhanced in-vitro validation in third-generation RBPs. To enable high-precision acquisition of impeller torques and grant access for optical flow measurements, the HM3 testbench geometry was geometrically modified. These modifications were reproduced in silico, and global flow computations validated along 15 operating conditions. The globally validated flow in the testbench geometry was compared with CFD-simulated flows in the original geometry to assess the impact of the necessary modifications on global and local hydraulic properties. Global hydraulic properties in the testbench geometry were successfully validated (pressure head: r = 0.999, root mean square error [RMSE] = 2.92 mmHg; torque: r = 0.996, RMSE = 0.134 mNm). In-silico comparison with the original geometry demonstrated good agreement ( r > 0.999, relative errors < 11.97%) of global hydraulic properties. Local hydraulic properties (errors up to 81.78%) and hemocopatibility predictions (deviations up to 21.03%), however, were substantially affected by the geometric modifications. Transferability of local flow measures derived on advanced in-vitro testbenches toward original pump designs is challenged by significant local effects associated with the necessary geometrical modifications.

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Abstract

Cardiovascular Magnetic Resonance (CMR) imaging is a growing field with increasing diagnostic utility in clinical routine. Quantitative diagnostic parameters are typically calculated based on contours or points provided by readers, e.g. natural intelligences (NI) such as clinicians or researchers, and artificial intelligences (AI). As clinical applications multiply, evaluating the precision and reproducibility of quantitative parameters becomes increasingly important. Although segmentation challenges for AIs and guidelines for clinicians provide quality assessments and regulation, the methods ought to be combined and streamlined for clinical applications. The goal of the developed software, Lazy Luna (LL), is to offer a flexible evaluation tool that is readily extendible to new sequences and scientific endeavours.

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Abstract

Myelination enhances the conduction velocity of action potentials (AP) and increases energy efficiency. Thick myelin sheaths are typically found on large-distance axonal connections or in fast-spiking interneurons, which are critical for synchronizing neuronal networks during gamma-band oscillations. Loss of myelin sheath is associated with multiple alterations in axonal architecture leading to impaired AP propagation. While numerous studies are devoted to the effects of demyelination on conduction velocity, the metabolic effects and the consequences for network synchronization have not been investigated. Here we present a unifying computational model for electrophysiology and metabolism of the myelinated axon. The computational model suggested that demyelination not only decreases the AP speed but AP propagation in demyelinated axons requires compensatory processes like mitochondrial mass increase and a switch from saltatory to continuous propagation to rescue axon functionality at the cost of reduced AP propagation speed and increased energy expenditure. Indeed, these predictions were proven to be true in a culture model of demyelination where the pharmacologically-induced loss of myelin was associated with increased oxygen consumption rates, and a significant broadening of bandwidth as well as a decrease in the power of gamma oscillations.

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Background: Pressure measurement in total cavopulmonary connection (TCPC) patients is a domain of cardiac catheterization. 4D velocity encoded cardiovascular magnetic resonance (4D–flow MRI) offers an alternative for assessment of even minor pressure differences. The scope of this study was to measure even minor pressure differences in the anastomosis of TCPC patients, who are clinically uncompromised. Methods: Twenty-four patients (median 15 years [8;34]) with TCPC were studied prospectively by 4D-flow MRI. Pressure differences between superior vena cava (SVC) and extracardiac conduit (C) to both right pulmonary artery (RPA) and left pulmonary artery (LPA) were assessed. Small fluid obstructions as vortices within the anastomosis were detected by flow pathlines from 4D-flow MRI. In two patients pressure differences were calculated also by computational flow dynamics (CFD) as a plausibility check for the order of magnitude. Results: Median values of pressure differences in the anastomosis between SVC and RPA were 0.63 (0.21–2.1) mmHg, between C and RPA 0.67 (0.3–2.2) mmHg, between SVC and LPA 0.8 (0.3–2.4) mmHg and between C and LPA 0.7 (0.2–1.9) mmHg. Patients with potential flow obstruction (stents, occluder, vortices) had significantly higher gradients at the anastomosis (p < 0.05) than patients without potential obstructions, although the absolute values were small. CFD-and measure-ment-based pressure difference showed good agreement. Conclusion: 4D-flow MRI is able to detect minor pressure differences within the Fontan circuit even in patients with apparently satisfactory TCPC. Slightly higher pressure differences are due to the presence of small flow obstruction.

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Abstract

The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS).

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Abstract

Heart failure (HF) presents manifestations in both cardiac and vascular abnormalities. Pulmonary hypertension (PH) is prevalent in up 50% of HF patients. While pulmonary arterial hypertension (PAH) is closely associated with pulmonary artery (PA) stiffness, the association of HF caused, post-capillary PH and PA stiffness is unknown. We aimed to assess and compare PA stiffness and blood flow hemodynamics noninvasively across HF entities and control subjects without HF using CMR. We analyzed data of a prospectively conducted study with 74 adults, including 55 patients with HF across the spectrum (20 HF with preserved ejection fraction [HFpEF], 18 HF with mildly-reduced ejection fraction [HFmrEF] and 17 HF with reduced ejection fraction [HFrEF]) as well as 19 control subjects without HF. PA stiffness was defined as reduced vascular compliance, indicated primarily by the relative area change (RAC), altered flow hemodynamics were detected by increased flow velocities, mainly by pulse wave velocity (PWV). Correlations between the variables were explored using correlation and linear regression analysis. PA stiffness was significantly increased in HF patients compared to controls (RAC 30.92 ± 8.47 vs. 50.08 ± 9.08%, p < 0.001). PA blood flow parameters were significantly altered in HF patients (PWV 3.03 ± 0.53 vs. 2.11 ± 0.48, p < 0.001). These results were consistent in all three HF groups (HFrEF, HFmrEF and HFpEF) compared to the control group. Furthermore, PA stiffness was associated with higher NT-proBNP levels and a reduced functional status. PA stiffness can be assessed non-invasively by CMR. PA stiffness is increased in HFrEF, HFmrEF and HFpEF patients when compared to control subjects.Trial registration The study was registered at the German Clinical Trials Register (DRKS, registration number: DRKS00015615).

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Abstract

We aimed to assess symptoms in patients after SARS-CoV-2 infection and to identify factors predicting prolonged time to symptom-free.

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Abstract

Objective. This study assesses age-related differences of thoracic aorta blood flow profiles and provides age- and sex-specific reference values using 4D flow cardiovascular magnetic resonance (CMR) data. Approach. 126 volunteers (age 20-80 years, female 51%) underwent 4D flow CMR and 12 perpendicular analysis planes in the thoracic aorta were specified. For these planes the following parameters were evaluated: body surface area-adjusted aortic area ( A '), normalized flow displacement (NFD), the degree of wall parallelism (WPD), the minimal relative cross-sectional area through which 80% of the volume flow passes (A80) and the angle between flow direction and centerline ( α). Main results. Age-related differences in blood flow parameters were seen in the ascending aorta with higher values for NFD and angle and lower values for WPD and A80 in older subjects. All parameters describing blood flow patterns correlated with the cross-sectional area in the ascending aorta. No relevant sex-differences regarding blood flow profiles were found. Significance. These age- and sex-specific reference values for quantitative parameters describing blood flow within the aorta might help to study the clinical relevance of flow profiles in the future.

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Abstract

4D PC MRI of the aorta has become a routinely available examination, and a multitude of single parameters have been suggested for the quantitative assessment of relevant flow features for clinical studies and diagnosis. However, clinically applicable assessment of complex flow patterns is still challenging. We present a concept for applying radiomics for the quantitative characterization of flow patterns in the aorta. To this end, we derive cross-sectional scalar parameter maps related to parameters suggested in literature such as throughflow, flow direction, vorticity, and normalized helicity. Derived radiomics features are selected with regard to their inter-scanner and inter-observer reproducibility, as well as their performance in the differentiation of sex-, age- and disease-related flow properties. The reproducible features were tested on user-selected examples with respect to their suitability for characterizing flow profile types. In future work, such signatures could be applied for quantitative flow assessment in clinical studies or disease phenotyping.

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Abstract

Different software programs are available for the evaluation of 4D Flow cardiovascular magnetic resonance (CMR). A good agreement of the results between programs is a prerequisite for the acceptance of the method. Therefore, the goal was to compare quantitative results from a cross-over comparison in individuals examined on two scanners of different vendors analyzed with four postprocessing software packages.

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