{"product_id":"statistical-atlases-and-computational-models-of-the-heart-workshop-cmrxrecon-and-mbas-challenge-papers-9783031877551","title":"Statistical Atlases and Computational Models of the Heart. Workshop CMRxRecon and MBAS Challenge Papers.","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- Single-source Domain Generalization for Coronary Vessels Segmentation in X-ray Angiography. \u003c\/p\u003e\u003cp\u003e.- Constraint-Based Model in Multimodal Learning to Improve Ventricular Arrhythmia Prediction. \u003c\/p\u003e\u003cp\u003e.- Automated estimation of cardiac stroke volumes from computed tomography. \u003c\/p\u003e\u003cp\u003e.- Peridevice leaks following left atrial appendage occlusion - analysis with morphology descriptive centerlines and explainable graph attention network. \u003c\/p\u003e\u003cp\u003e.- Improved 3D Whole Heart Geometry from Sparse CMR Slices. \u003c\/p\u003e\u003cp\u003e.- CavityBASNet: Cavity-focused Biatrial Automatic Segmentation on LGE MRI with augmented input channel and left-right myocardium splitting. \u003c\/p\u003e\u003cp\u003e.- A novel MRI-based electrophysiological computational model of progressive doxorubicin-induced fibrosis in the left ventricle. \u003c\/p\u003e\u003cp\u003e.- Quantitative comparison of blood flow patterns from in silico simulations and 4D flow data before and after left atrial occlusion. \u003c\/p\u003e\u003cp\u003e.- Panoramic anatomical context in 3D intracardiac echocardiography (ICE) with 3D registration and geometry-based image fusion. \u003c\/p\u003e\u003cp\u003e.- Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions. \u003c\/p\u003e\u003cp\u003e.- Beyond the standards: Fully-Automated Aortic Annulus Segmentation on Contrast-free Magnetic Resonance Imaging using a Computational Aorta Unwrapping Method. \u003c\/p\u003e\u003cp\u003e.- Coronary Artery Calcium Scoring from Non-Contrast Cardiac CT Using Deep Learning With External Validation. \u003c\/p\u003e\u003cp\u003e.- Effective approach based on student-teacher self-supervised deep learning for Multi-class Bi-Atrial Segmentation Challenge. \u003c\/p\u003e\u003cp\u003e.- Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model. \u003c\/p\u003e\u003cp\u003e.- HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss. \u003c\/p\u003e\u003cp\u003e.- A Multi-Contrast Cardiac MRI Reconstruction Method Using an Advanced Unrolled Network Architecture. \u003c\/p\u003e\u003cp\u003e.- Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle. \u003c\/p\u003e\u003cp\u003e.- Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxil iary Refinement Network. \u003c\/p\u003e\u003cp\u003e.- Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients. \u003c\/p\u003e\u003cp\u003e.- Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs. \u003c\/p\u003e\u003cp\u003e.- An Ensemble of 3D Residual Encoder UNet Models for Solving Multi-Class Bi-Atrial Segmentation Challenge. \u003c\/p\u003e\u003cp\u003e.- Evaluating Convolution, Attention, and Mamba Based U-Net Models for Multi-Class Bi-Atrial Segmentation from LGE-MRI. \u003c\/p\u003e\u003cp\u003e.- On the Foundation Model for Cardiac MRI Reconstruction. \u003c\/p\u003e\u003cp\u003e.- Multi-Loss 3D Segmentation for Enhanced Bi-Atrial Segmentation. \u003c\/p\u003e\u003cp\u003e.- Classification of Mitral Regurgitation from Cardiac Cine MRI using Clinically-Interpretable Morphological Features. \u003c\/p\u003e\u003cp\u003e.- Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI. \u003c\/p\u003e\u003cp\u003e.- Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation. \u003c\/p\u003e\u003cp\u003e.- A self-distillation bi-atrial segmentation network for Cardiac MRI. \u003c\/p\u003e\u003cp\u003e.- Adaptive Unrolling Applied to the CMRxRecon2024 Callenge. \u003c\/p\u003e\u003cp\u003e.- Reducing the number of leads for ECG Imaging with Graph Neural Networks and meaningful latent space. \u003c\/p\u003e\u003cp\u003e.- Rotor Core Projection Ablation (RCPA): Novel Computational Approach to Catheter Ablation Therapy for Atrial Fibrillation. \u003c\/p\u003e\u003cp\u003e.- Automated pipeline for regional epicardial adipose tissue distribution analysis in the left atrium. \u003c\/p\u003e\u003cp\u003e.- Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging. \u003c\/p\u003e\u003cp\u003e.- SBAW-Net: Segmentation of Bi-Atria and Wall Network - Offering Valuable Insights into Challenge Data. \u003c\/p\u003e\u003cp\u003e.- ResNet-based Convolutional Framework for Segmenting Left Atrial Scars and Cavities. \u003c\/p\u003e\u003cp\u003e.- EAT-Mamba: Epicardial Adipose Tissue Segmentation from Multi-modal Dixon MRI. \u003c\/p\u003e\u003cp\u003e.- Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging. \u003c\/p\u003e\u003cp\u003e.- Exploring CNN and Transformer Architectures for Multi-class Bi-Atrial Segmentation from Late Gadolinium-Enhanced MRI. \u003c\/p\u003e\u003cp\u003e.- EigenBoundaries for the temporally regularized segmentation of echocardiographic images. \u003c\/p\u003e\u003cp\u003e.- Dynamic Cardiac MRI Reconstruction via Separate Optimization of K-space and Hybrid-domian Spatial-temporal Feature Fusion. \u003c\/p\u003e\u003cp\u003e.- an Interpretable Learning of Risk Explain Ventricular Arrhythmia Mechanism. \u003c\/p\u003e\u003cp\u003e.- 3D Left Ventricular Reconstruction from 2D Echocardiograms for Reliable Volume Estimation. \u003c\/p\u003e\u003cp\u003e.- Comparing Left Atrial Spontaneous Echo Contrast Intensity with Gaussian Process Emulator Predictions. \u003c\/p\u003e\u003cp\u003e.- UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction. \u003c\/p\u003e\u003cp\u003e.- An All-in-one Approach for Accelerated Cardiac MRI Reconstruction. \u003c\/p\u003e\u003cp\u003e.- Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195464278359,"sku":"9783031877551","price":124.92,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/statistical-atlases-and-computational-models-of-the-heart-workshop-cmrxrecon-and-mbas-challenge-papers-9783031877551","provider":"Book Curl","version":"1.0","type":"link"}