Whole-brain functional MRI registration based on a semi-supervised deep learning model.

Journal: Medical physics
PMID: 33583029

Abstract

PURPOSE: Traditional registration of functional magnetic resonance images (fMRI) is typically achieved through registering their coregistered structural MRI. However, it cannot achieve accurate performance in that functional units which are not necessarily located relative to anatomical structures. In addition, registration methods based on functional information focus on gray matter (GM) information but ignore the importance of white matter (WM). To overcome the limitations of exiting techniques, in this paper, we aim to register resting-state fMRI (rs-fMRI) based directly on rs-fMRI data and make full use of GM and WM information to improve the registration performance.

Authors

  • QiaoYun Zhu
    School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
  • YuHang Sun
    School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
  • Yi Wu
    School of International Communication and Arts, Hainan University, Haikou, China.
  • HuoBiao Zhu
    School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
  • GuoYe Lin
    School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
  • Yujia Zhou
    Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, United States.
  • Qianjin Feng
    Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: qianjinfeng08@gmail.com.

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