3D isotropic high-resolution fetal brain MRI reconstruction from motion corrupted thick data based on physical-informed unsupervised learning.

Journal: IEEE journal of biomedical and health informatics
Published Date: Jul 15, 2025

Abstract

High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D slices is crucial for precise clinical diagnosis and advancing our understanding of fetal brain development. This necessitates reliable slice-to-volume registration (SVR) for motion correction and super-resolution reconstruction (SRR) techniques. Traditional approaches have their limitations, but deep learning (DL) offers the potential in enhancing SVR and SRR. However, most of DL methods require large-scale external 3D high-resolution (HR) training datasets, which is challenging in clinical fetal MRI. To address this issue, we propose an unsupervised iterative joint SVR and SRR DL framework for 3D isotropic HR volume reconstruction. Specifically, our method conceptualizes SVR as a function that maps a 2D slice and a 3D target volume to a rigid transformation matrix, aligning the slice to the underlying location within the target volume. This function is parameterized by a convolutional neural network, which is trained by minimizing the difference between the volume slicing at the predicted position and the actual input slice. For SRR, a decoding network embedded within a deep image prior framework, coupled with a comprehensive image degradation model, is used to produce the HR volume. The deep image prior framework offers a local consistency prior to guide the reconstruction of HR volumes. By performing a forward degradation model, the HR volume is optimized by minimizing the loss between the predicted slices and the acquired slices. Experiments on both large-magnitude motion-corrupted simulation data and clinical data have shown that our proposed method outperforms current state-of-the-art fetal brain reconstruction methods. The source code is available at https://github.com/DeepBMI/SUFFICIENT.

Authors

  • Jiangjie Wu
  • Lixuan Chen
  • Zhenghao Li
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Taotao Sun
  • Lihui Wang
    Shanghai Mental Health Center, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
  • Rongpin Wang
    Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002 China.
  • Hongjiang Wei
    Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address: hongjiang.wei@sjtu.edu.cn.
  • Yuyao Zhang
    School of Information and Science and Technology, ShanghaiTech University, Shanghai, China.

Keywords

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