000			08257cam a2200661 a 4500
001			on1420627363
003			OCoLC
005			20250707095504.0
006			m o d
007			cr un|---aucuu
008			240210s2024 sz o 101 0 eng d
040			_aEBLCP _beng _cEBLCP _dGW5XE _dYDX _dOCLKB _dOCLCO _dEBLCP _dOCLCQ _dUKAHL _dEMRUN _dOCLCO _dOCLCQ
019			_a1419902470 _a1420008506
020			_a9783031524486 _q(electronic bk.)
020			_a3031524489 _q(electronic bk.)
020			_z9783031524479
020			_z3031524470
024	7		_a10.1007/978-3-031-52448-6 _2doi
029	1		_aAU@ _b000076148152
035			_a(OCoLC)1420627363 _z(OCoLC)1419902470 _z(OCoLC)1420008506
050		4	_aRC683.5.I42
082	0	4	_a616.1/0754 _223/eng/20240214
049			_aMAIN
111	2		_aSTACOM (Workshop) _n(14th : _d2023 : _cVancouver, B.C.) _9979466
245	1	0	_aStatistical Atlases and Computational Models of the Heart : _bRegular and CMRxRecon Challenge Papers : 14th International Workshop, STACOM 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Revised Selected Papers.
246	3		_aRegular and CMRxRecon Challenge Papers
246	3		_aSTACOM 2023
260			_aCham : _bSpringer, _c2024.
300			_a1 online resource (507 p.).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aLecture Notes in Computer Science ; _v14507
500			_aDescription based upon print version of record.
505	0		_aCardiacSeg: 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.
520			_aThis book constitutes the proceedings of the 14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023, as well as the Cardiac MRI Reconstruction Challenge, CMRxRecon Challenge. There was a total of 53 submissions to the workshop. The 24 regular workshop papers included in this volume were carefully reviewed and selected from 29 paper submissions. They deal with cardiac segmentation, modelling, strain quantification, registration, statistical shape analysis, and quality control. In addition, 21 papers from the CMRxRecon challenge are included in this volume. They focus on fast CMR image reconstruction and provide a benchmark dataset that enables the broader research community to promote advances in this area of research.
500			_aIncludes author index.
588	0		_aOnline resource; title from PDF title page (SpringerLink, viewed February 14, 2024).
650		0	_aHeart _xImaging _vCongresses. _916188
650		0	_aHeart _xComputer simulation _vCongresses. _916189
650		6	_aCœur _xImagerie _vCongrès. _9966811
650		6	_aCœur _xSimulation par ordinateur _vCongrès. _9966812
700	1		_aCamara, Oscar _q(Oscar Camara Rey) _1https://id.oclc.org/worldcat/entity/E39PCjK4dyQpYWRGbKggxcrtXd _940382
700	1		_aPuyol Anton, Esther. _1https://id.oclc.org/worldcat/entity/E39PCjtBhFQVcppppF96BKDJ8P _9915078
700	1		_aSermesant, Maxime. _1https://id.oclc.org/worldcat/entity/E39PBJrGwBmHBpvwpfFmKx7vHC _935098
700	1		_aSuinesiaputra, Avan. _1https://id.oclc.org/worldcat/entity/E39PCjMg9D63BFJy9fcJHVMvBP _969236
700	1		_aTao, Qian. _9600063
700	1		_aWang, Chengyan. _9979467
700	1		_aYoung, Alistair _q(Alistair Andrew) _1https://id.oclc.org/worldcat/entity/E39PCjqVCjB3VpYKKtwh9pJfYP _963404
711	2		_aInternational Conference on Medical Image Computing and Computer-Assisted Intervention _n(26th : _d2023 : _cVancouver, B.C.)
776	0	8	_iPrint version: _aCamara, Oscar _tStatistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers _dCham : Springer International Publishing AG,c2024 _z9783031524479
830		0	_aLecture notes in computer science ; _v14507.
856	4	0	_uhttps://link.springer.com/10.1007/978-3-031-52448-6
938			_aProQuest Ebook Central _bEBLB _nEBL31102338
938			_aYBP Library Services _bYANK _n305972302
938			_bOCKB _zkortext.alltitles,28eee68f-1179-4c48-9eb0-6b7762d69c67-emi
994			_a92 _bATIST
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