Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

• URL: http://arxiv.org/abs/2402.15939v2
• Date: Wed, 02 Oct 2024 16:42:35 GMT
• Title: Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
• Authors: Zi Wang, Min Xiao, Yirong Zhou, Chengyan Wang, Naiming Wu, Yi Li, Yiwen Gong, Shufu Chang, Yinyin Chen, Liuhong Zhu, Jianjun Zhou, Congbo Cai, He Wang, Di Guo, Guang Yang, Xiaobo Qu,
• Abstract summary: MRI plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled. This challenge necessitates extensive training data in deep learning reconstruction methods. We propose a novel and efficient approach, leveraging a dimension separable learning scheme that can perform exceptionally well even with highly limited training data.
• Score: 22.7085949508143
• License:
• Abstract: Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI.

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