Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
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
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 leads to necessitate extensive training data in many
deep learning reconstruction methods. This work proposes a novel and efficient
approach, leveraging a dimension-reduced separable learning scheme that excels
even with highly limited training data. We further integrate it with
spatiotemporal priors to develop a Deep Separable Spatiotemporal Learning
network (DeepSSL), which unrolls an iteration process of a reconstruction model
with both temporal low-rankness and spatial sparsity. Intermediate outputs are
visualized to provide insights into the network's behavior and enhance its
interpretability. Extensive results on cardiac cine datasets show that the
proposed DeepSSL is superior to the state-of-the-art methods visually and
quantitatively, while reducing the demand for training cases by up to 75%. And
its preliminary adaptability to cardiac patients has been verified through
experienced radiologists' and cardiologists' blind reader study. Additionally,
DeepSSL also benefits for achieving the downstream task of cardiac segmentation
with higher accuracy and shows robustness in prospective real-time cardiac MRI.