Statistical Atlases and Computational Models of the Heart. Regular and Cmrxrecon Challenge Papers: 14th International Workshop, Stacom 2023, Held in C (Paperback, 2024)

CardiacSeg: Customized Pre-Training Volumetric Transformer with Scaling Pyramid for 3D Cardiac Segmentation.- Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics.- Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy.- Patient Stratification Based on Fast Simulation of Cardiac Electrophysiology on Digital Twins.- Deep Conditional Shape Models for 3D cardiac image segmentation.- Global Sensitivity Analysis of Thrombus Formation in the Left Atrial Appendage of Atrial Fibrillation Patients.- Sparse annotation strategies for segmentation of short axis cardiac MRI.- Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI.- FM-Net: A Fully Automatic Deep Learning Pipeline for Epicardial Adipose Tissue Segmentation.- Automated quality-controlled left heart segmentation from 2D echocardiography.- Impact of hypertension on left ventricular pressure-strain loop characteristics and myocardial work.- Automated segmentation of the right ventricle from 3D echocardiography using labels from cardiac magnetic resonance imaging.- Neural Implicit Functions for 3D Shape Reconstruction from standard Cardiovascular Magnetic Resonance views.- Deep Learning-based Pulmonary Artery Surface Mesh Generation.- Impact of catheter orientation on cardiac radiofrequency ablation.- Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net.- Effects of Fibrotic Border Zone on Drivers for Atrial Fibrillation: An In-Silico Mechanistic Investigation.- Exploring the relationship between pulmonary artery shape and pressure in Pulmonary Hypertension: A statistical shape analysis study..- Type and Shape Disentangled Generative Modeling for Congenital Heart Defects.- Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network.- Inherent Atrial Fibrillation Vulnerability in the Appendages Exacerbated in Heart Failure.- Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images.- Automatic Landing Zone Plane Detection in Contrast-Enhanced Cardiac CT Volumes.- A Benchmarking Study of Deep Learning Approaches for Bi-atrial Segmentation on Late Gadolinium-enhanced MRIs.- Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction.- Learnable objective image function for accelerated MRI reconstruction.- Accelerating Cardiac MRI via Deblurring without Sensitivity Estimation.- T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer.- T1 and T2 mapping reconstruction based on conditional DDPM.- $k$-$t$ CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction.- Cardiac MRI reconstruction from undersampled k-space using double-stream IFFT and a denoising GNA-UNET pipeline.- Multi-Scale Inter-Frame Information Fusion Based Network for Cardiac MRI Reconstruction.- Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction.- A Context-Encoders-based Generative Adversarial Networks for Cine Magnetic Resonance Imaging Reconstruction.- Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace Models.- DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models.- C3-Net: Complex-Valued Cascading Cross-Domain Convolutional Neural Network for Reconstructing Undersampled CMR Images.- Space-Time Deformable Attention Parallel Imaging Reconstruction for Highly Accelerated Cardiac MRI.- Multi-level Temporal Information Sharing Transformer-based Feature Reuse Network for Cardiac MRI Reconstruction.- Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement.- ReconNext:A Encoder-Decoder Skip Cross Attention based approach to reconstruct Cardiac MRI.- Temporal Super-Resolution for Fast T1 Mapping.- NoSENSE: Learned Unrolled Cardiac MRI Reconstruction Without Explicit Sensitivity Maps.- CineJENSE: Simultaneous Cine MRI Image Reconstruction and Sensitivity Map Estimation using Neural Representations.- Deep Cardiac MRI Reconstruction with ADMM.