Accelerated Chemical Exchange Saturation Transfer Imaging With Deep Unrolling Networks and Synthetic Brain Tumor Datasets.

Journal: Magnetic resonance in medicine
Published Date: Nov 5, 2025

Abstract

PURPOSE: To develop a model-based deep unrolling network for high-quality image reconstruction of accelerated multi-channel chemical exchange saturation transfer (CEST) imaging. THEORY AND METHODS: Inspired by the theory of model-based deep learning (MoDL), we unrolled the alternating direction method of multipliers (ADMM) optimization for image reconstruction into a network, named MoDL-ADMM. Additionally, we designed a CEST image synthesis pipeline (BraTS-CEST) to obtain large-scale brain tumor training data using open BraTS and fastMRI datasets and Bloch-McConnell simulations. The performance of the proposed MoDL-ADMM method was evaluated on data from healthy volunteers and brain tumor patients using retrospective and prospective undersampling with various acceleration rates. We compared the reconstruction results of MoDL-ADMM with the original MoDL and other methods, including the state-of-the-art CEST-VN. RESULTS: The proposed BraTS-CEST dataset yielded high-quality CEST images compared to previous methods and reduced the reconstruction error of the trained networks. As the acceleration rates increased from 3 to 6, MoDL-ADMM consistently reconstructed accurate source images and amide proton transfer-weighted (APTw) maps, outperforming GRAPPA, L + S, the original MoDL, and CEST-VN. The ablation studies further validated the effectiveness of the structural design, particularly the selective kernel networks and the learnable sparse transformation. CONCLUSIONS: The proposed MoDL-ADMM, trained with the BraTS-CEST synthetic dataset, effectively reconstructed high-quality CEST source images and APTw maps from undersampled multi-channel data.

Authors

  • Yuyan Wang
    State Key Laboratory of Polymer Materials Engineering, Polymer Research Institute, Sichuan University, Chengdu 610065, China.
  • Junjie Wen
    Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Jianping Xu
    Department of Biology, McMaster University, Hamilton, ON L8S 4K1 Canada. [email protected].
  • Zhechuan Dai
    Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Yi-Cheng Hsu
    MR Collaboration, Siemens Healthineers Ltd, Shanghai, China.
  • Kannie W Y Chan
    Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Yi Zhang
    Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals