Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers

LearningFramework 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.