Titlebook: Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers; 13th International W Oscar Camara,Esthe

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Towards Real-Time Optimization of?Left Atrial Appendage Occlusion Device Placement Through Physics-Inite element simulation for training. To this end, we leveraged physics-informed neural networks (PINN), which embed the physical laws governing the domain of interest into the model, exhibiting far superior generalization capabilities than conventional data-driven models. Several device types and p

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Haemodynamic Changes in?the?Fetal Circulation Post-connection to?an?Artificial Placenta: A Computatir altering heart rate and AP’s resistance and compliance. We also added a simple wave reflection model and studied its effect on pressure and flow traces. We found that reducing AP’s resistance increased mean flow, and reducing compliance decreased velocity PI. When adding the reflection model, the

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Going Off-Grid: Continuous Implicit Neural Representations for?3D Vascular Modelingnts on the surface. Second, we simultaneously fit nested vessel walls in a single INR without intersections. Third, we show how 3D models of individual arteries can be smoothly blended into a single watertight surface. Our results show that INRs are a flexible representation with potential for minim

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Comparison of?Semi- and Un-Supervised Domain Adaptation Methods for?Whole-Heart Segmentationility across modalities and patients. Hence, the aim of this work was to develop a pipeline to perform automatic heart segmentation of multiple cardiac imaging scans, addressing the domain shift between MRs (target) and CTs (source). We trained two Domain Adaptation (DA) methods, using Generative Ad

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An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallotess the effect of perturbations in these ED shape modes on corresponding components of SWM. Perturbations to ED shape modes and myocardial contractility explained observed variation in SWM to varying degrees. In some cases, shape markers were partial determinants of systolic function and, in other c

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Review of?Data Types and?Model Dimensionality for?Cardiac DTI SMS-Related Artefact Removaltuition, our experiments show that, for a fixed number of parameters, simpler 2D real-valued models outperform their more advanced 3D or complex counterparts. The best performance is although obtained by a real-valued model trained using both the magnitude and phase components of the acquired data.

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Improving Echocardiography Segmentation by?Polar Transformationhe segmentation model is trained on both .-. and .-. images. During inference, the predictions from the .-. and .-. images are combined using max-voting. We verify the efficacy of our method on the CAMUS dataset with a variety of segmentation networks, encoder networks, and loss functions. The exper

Introduction

Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approachs a data-driven approach inspired by the PSM method to learn population-level spatiotemporal shape changes directly from shape data. We introduce a novel SSM optimization scheme that produces landmarks that are in correspondence both across the population (inter-subject) and across time-series (intr