Description
Book Synopsis.- Single-source Domain Generalization for Coronary Vessels Segmentation in X-ray Angiography.
.- Constraint-Based Model in Multimodal Learning to Improve Ventricular Arrhythmia Prediction.
.- Automated estimation of cardiac stroke volumes from computed tomography.
.- Peridevice leaks following left atrial appendage occlusion - analysis with morphology descriptive centerlines and explainable graph attention network.
.- Improved 3D Whole Heart Geometry from Sparse CMR Slices.
.- CavityBASNet: Cavity-focused Biatrial Automatic Segmentation on LGE MRI with augmented input channel and left-right myocardium splitting.
.- A novel MRI-based electrophysiological computational model of progressive doxorubicin-induced fibrosis in the left ventricle.
.- Quantitative comparison of blood flow patterns from in silico simulations and 4D flow data before and after left atrial occlusion.
.- Panoramic anatomical context in 3D intracardiac echocardiography (ICE) with 3D registration and geometry-based image fusion.
.- Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions.
.- Beyond the standards: Fully-Automated Aortic Annulus Segmentation on Contrast-free Magnetic Resonance Imaging using a Computational Aorta Unwrapping Method.
.- Coronary Artery Calcium Scoring from Non-Contrast Cardiac CT Using Deep Learning With External Validation.
.- Effective approach based on student-teacher self-supervised deep learning for Multi-class Bi-Atrial Segmentation Challenge.
.- Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model.
.- HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss.
.- A Multi-Contrast Cardiac MRI Reconstruction Method Using an Advanced Unrolled Network Architecture.
.- Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle.
.- Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxil iary Refinement Network.
.- Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients.
.- Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs.
.- An Ensemble of 3D Residual Encoder UNet Models for Solving Multi-Class Bi-Atrial Segmentation Challenge.
.- Evaluating Convolution, Attention, and Mamba Based U-Net Models for Multi-Class Bi-Atrial Segmentation from LGE-MRI.
.- On the Foundation Model for Cardiac MRI Reconstruction.
.- Multi-Loss 3D Segmentation for Enhanced Bi-Atrial Segmentation.
.- Classification of Mitral Regurgitation from Cardiac Cine MRI using Clinically-Interpretable Morphological Features.
.- Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI.
.- Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation.
.- A self-distillation bi-atrial segmentation network for Cardiac MRI.
.- Adaptive Unrolling Applied to the CMRxRecon2024 Callenge.
.- Reducing the number of leads for ECG Imaging with Graph Neural Networks and meaningful latent space.
.- Rotor Core Projection Ablation (RCPA): Novel Computational Approach to Catheter Ablation Therapy for Atrial Fibrillation.
.- Automated pipeline for regional epicardial adipose tissue distribution analysis in the left atrium.
.- Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging.
.- SBAW-Net: Segmentation of Bi-Atria and Wall Network - Offering Valuable Insights into Challenge Data.
.- ResNet-based Convolutional Framework for Segmenting Left Atrial Scars and Cavities.
.- EAT-Mamba: Epicardial Adipose Tissue Segmentation from Multi-modal Dixon MRI.
.- Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging.
.- Exploring CNN and Transformer Architectures for Multi-class Bi-Atrial Segmentation from Late Gadolinium-Enhanced MRI.
.- EigenBoundaries for the temporally regularized segmentation of echocardiographic images.
.- Dynamic Cardiac MRI Reconstruction via Separate Optimization of K-space and Hybrid-domian Spatial-temporal Feature Fusion.
.- an Interpretable Learning of Risk Explain Ventricular Arrhythmia Mechanism.
.- 3D Left Ventricular Reconstruction from 2D Echocardiograms for Reliable Volume Estimation.
.- Comparing Left Atrial Spontaneous Echo Contrast Intensity with Gaussian Process Emulator Predictions.
.- UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction.
.- An All-in-one Approach for Accelerated Cardiac MRI Reconstruction.
.- Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation.
