BibTex format
@article{Wang:2025:10.1109/TBME.2025.3574090,
author = {Wang, Z and Xiao, M and Zhou, Y and Wang, C and Wu, N and Li, Y and Gong, Y and Chang, S and Chen, Y and Zhu, L and Zhou, J and Cai, C and Wang, H and Jiang, X and Guo, D and Yang, G and Qu, X},
doi = {10.1109/TBME.2025.3574090},
journal = {IEEE Trans Biomed Eng},
pages = {3642--3654},
title = {Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.},
url = {http://dx.doi.org/10.1109/TBME.2025.3574090},
volume = {72},
year = {2025}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: DeepSSL is efficient under highly limited training data and adaptive to patients and prospective undersampling. SIGNIFICANCE: This approach holds promise in addressing the escalating demand for high-dimensional data reconstruction in MRI applications.
AU - Wang,Z
AU - Xiao,M
AU - Zhou,Y
AU - Wang,C
AU - Wu,N
AU - Li,Y
AU - Gong,Y
AU - Chang,S
AU - Chen,Y
AU - Zhu,L
AU - Zhou,J
AU - Cai,C
AU - Wang,H
AU - Jiang,X
AU - Guo,D
AU - Yang,G
AU - Qu,X
DO - 10.1109/TBME.2025.3574090
EP - 3654
PY - 2025///
SP - 3642
TI - Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.
T2 - IEEE Trans Biomed Eng
UR - http://dx.doi.org/10.1109/TBME.2025.3574090
UR - https://www.ncbi.nlm.nih.gov/pubmed/40434852
VL - 72
ER -