Towards Universal Learning-based Model for Cardiac Image Reconstruction: Summary of the CMRxRecon2024 Challenge

Cardiovascular magnetic resonance (CMR) offers diverse imaging contrasts for assessment of cardiac function and tissue characterization. However, acquiring each single CMR modality is often time-consuming, and comprehensive clinical protocols require multiple modalities with various sampling patterns, further extending the overall acquisition time and increasing susceptibility to motion artifacts. Existing deep learning-based reconstruction methods are often designed for specific acquisition parameters, which limits their ability to generalize across a variety of scan scenarios. As part of the CMRxRecon Series, the CMRxRecon2024 challenge provides diverse datasets encompassing multi-modality multi-view imaging with various sampling patterns, and a platform for the international community to develop and benchmark reconstruction solutions in two well-crafted tasks. Task 1 is a modality-universal setting, evaluating the out-of-distribution generalization of the reconstructed model, while Task 2 follows sampling-universal setting assessing the one-for-all adaptability of the universal model. Main contributions include providing the first and largest publicly available multi-modality, multi-view cardiac k-space dataset; developing a benchmarking platform that simulates clinical acceleration protocols, with a shared code library and tutorial for various k-t undersampling patterns and data processing; giving technical insights of enhanced data consistency based on physic-informed networks and adaptive prompt-learning embedding to be versatile to different clinical settings; additional finding on evaluation metrics to address the limitations of conventional ground-truth references in universal reconstruction tasks.

DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography

Three-dimensional coronary magnetic resonance angiography (CMRA) demands reconstruction algorithms that can significantly suppress the artifacts from a heavily undersampled acquisition. While unrolling-based deep reconstruction methods have achieved state-of-the-art performance on 2D image reconstruction, their application to 3D reconstruction is hindered by the large amount of memory needed to train an unrolled network. In this study, we propose a memory-efficient deep compressed sensing method by employing a sparsifying transform based on a pre-trained artifact estimation network. The motivation is that the artifact image estimated by a well-trained network is sparse when the input image is artifact-free, and less sparse when the input image is artifact-affected. Thus, the artifact-estimation network can be used as an inherent sparsifying transform. The proposed method, named De-Aliasing Regularization based Compressed Sensing (DARCS), was compared with a traditional compressed sensing method, de-aliasing generative adversarial network (DAGAN), model-based deep learning (MoDL), and plug-and-play for accelerations of 3D CMRA. The results demonstrate that the proposed method improved the reconstruction quality relative to the compared methods by a large margin. Furthermore, the proposed method well generalized for different undersampling rates and noise levels. The memory usage of the proposed method was only 63% of that needed by MoDL. In conclusion, the proposed method achieves improved reconstruction quality for 3D CMRA with reduced memory burden.