Chinese Journal of Magnetic Resonance

   

Research on Transformer super-resolution reconstruction algorithm for ultrafast spatiotemporal encoding magnetic resonance imaging

NING Xinzhou1, HUANG Zhen1, CHEN Xiqu1, Liu Xinjie2,3, CHEN Gang2,3, Zhang Zhi2,3, BAO Qingjia2,3*, LIU Chaoyang 2,3,4#   

  1. 1. School of Electrical & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China; 2. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan (Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences), Wuhan 430071, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China; 4. Optics Valley Laboratory, Wuhan 430074, China
  • Received:2024-04-16 Revised:2024-05-16 Published:2024-05-16 Online:2024-05-16
  • Contact: BAO Qingjia;LIU Chaoyang E-mail:qingjia.bao@apm.ac.cn;chyliu@apm.ac.cn

Abstract: Spatio-temporal encoding(SPEN) MRI is an ultra-fast magnetic resonance imaging technique. The resolution of the original image acquired with SPEN is relatively low, so super-resolution reconstruction based on sequence physics principles is required to improve the spatial resolution of the original image. Existing SPEN super-resolution reconstruction algorithms based on deep learning have limited ability to capture long-range dependencies. To solve such problems, this paper proposes a transformer-based SPEN MRI super-resolution reconstruction algorithm. An encoder-decoder structure is adopted, and a transformer module is introduced to extract local context information and long-range dependencies of feature maps. Experimental results show that the proposed reconstruction method can reconstruct a super-resolution image with high spatial resolution and no aliasing artifacts from the low-resolution SPEN image without adding additional sampling points. Compared to existing super-resolution methods, the proposed method achieves better results on both clinical and preclinical datasets.

Key words: utrafast MRI, spatio-temporal encoding, deep learning, super-resolution, image reconstruction

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