Towards Universal Learning-based Model for Cardiac Image Reconstruction: Summary of the CMRxRecon2024 Challenge
Fanwen Wang, Zi Wang, Yan Li, Jun Lyu, Chen Qin, Shuo Wang, Kunyuan Guo, Mengting Sun, Mingkai Huang, Haoyu Zhang, Michael Tänzer, Qirong Li, Xinran Chen, Jiahao Huang, Yinzhe Wu, Kian Anvari Hamedani, Yuntong Lyu, Longyu Sun, Qing Li, Ziqiang Xu, Bingyu Xin, Dimitris N. Metaxas, Narges Razizadeh, Shahabedin Nabavi, George Yiasemis, Jonas Teuwen, Zhenxi Zhang, Sha Wang, Chi Zhang, Daniel B. Ennis, Zhihao Xue, Chenxi Hu, Ruru Xu, Ilkay Oksuz, Donghang Lyu, Yanxin Huang, Xinrui Guo, Ruqian Hao, Jaykumar H. Patel, Guanke Cai, Binghua Chen, Yajing Zhang, Sha Hua, Zhensen Chen, Qi Dou, Xiahai Zhuang, Qian Tao, Wenjia Bai, Jing Qin, He Wang, Claudia Prieto, Michael Markl, Alistair Young, Hao Li, Xihong Hu, Lianmin Wu, Xiaobo Qu, Guang Yang, Chengyan Wang
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/cmrxrecon/cmrxrecon2024Officialnone★ 33
Abstract
Cardiovascular magnetic resonance (CMR) imaging offers diverse contrasts for non-invasive assessment of cardiac function and myocardial characterization. However, CMR often requires the acquisition of many contrasts, and each contrast takes a considerable amount of time. The extended acquisition time will further increase the susceptibility to motion artifacts. Existing deep learning-based reconstruction methods have been proven to perform well in image reconstruction tasks, but most of them are designed for specific acquisition modality or dedicated imaging parameter, which limits their ability to generalize across a variety of scan scenarios. To address this issue, the CMRxRecon2024 challenge consists of two specific tasks: Task 1 focuses on a modality-universal setting, evaluating the out-of-distribution generalization of existing learning-based models, while Task 2 follows a k-space sampling-universal setting, assessing the all-in-one adaptability of universal models. Main contributions of this challenge include providing the largest publicly available multi-modality, multi-view cardiac k-space dataset; and developing an open benchmarking platform for algorithm evaluation and shared code library for data processing. In addition, through a detailed analysis of the results submitted to the challenge, we have also made several findings, including: 1) adaptive prompt-learning embedding is an effective means for achieving strong generalization in reconstruction models; 2) enhanced data consistency based on physics-informed networks is also an effective pathway toward a universal model; 3) traditional evaluation metrics have limitations when assessing ground-truth references with moderate or lower image quality, highlighting the need for subjective evaluation methods. This challenge attracted 200 participants from 18 countries, aimed at promoting their translation into clinical practice.