Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.

Journal: IEEE transactions on bio-medical engineering
Published Date: May 28, 2025

Abstract

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.

Authors

  • Zi Wang
    Clinical Medical College, Yangzhou University, 225009 Yangzhou, Jiangsu, China.
  • Min Xiao
  • Yirong Zhou
  • ChengYan Wang
  • Naiming Wu
  • Yi Li
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Yiwen Gong
    Department of Cardiovascular Medicine, Heart Failure Center, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Shufu Chang
  • Yinyin Chen
  • Liuhong Zhu
    Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
  • Jianjun Zhou
    Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University Xinjiekouwai Street No. 19 Beijing 100875 P. R. China hhuo@bnu.edu.cn.
  • Congbo Cai
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
  • He Wang
    Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, China International Neuroscience Institute, Beijing, China.
  • Xianwang Jiang
    Neusoft Medical System, China.
  • Di Guo
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Xiaobo Qu

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