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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1522227

Validation of deep-learning accelerated quantitative susceptibility mapping for deep brain nuclei

Provisionally accepted
Ying Zhou Ying Zhou 1,2Lingyun Liu Lingyun Liu 2Shan Xu Shan Xu 2Yongquan Ye Yongquan Ye 3Ruiting Zhang Ruiting Zhang 2Minming Zhang Minming Zhang 2Jianzhong Sun Jianzhong Sun 2Peiyu Huang Peiyu Huang 2*
  • 1 Taizhou Central Hospital, Taizhou, China
  • 2 Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
  • 3 United Imaging Healthcare of American, Houston, Ohio, United States

The final, formatted version of the article will be published soon.

    Purpose: To test the feasibility and consistency of a deep-learning (DL) accelerated QSM method for deep brain nuclei evaluation. Methods: Participants were scanned with both parallel imaging (PI)-QSM and DL-QSM methods. The PI- and DL-QSM scans had identical imaging parameters other than acceleration factors (AF). The DL-QSM employed Poisson disk style under-sampling scheme and a previously developed cascaded CNN based reconstruction model, with acquisition time of 4:35, 3:15, and 2:11 for AF of 3, 4, and 5, respectively. For PI-QSM acquisition, the AF was 2 and the acquisition time was 6:46. The overall image similarity was assessed between PI- and DL-QSM images using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). QSM values from 7 deep brain nuclei were extracted and agreements between images with different Afs were assessed. Finally, the correlations between age and QSM values in the selected deep brain nuclei were evaluated. Results: 59 participants were recruited. Compared to PI-QSM images, the mean SSIM of DL images were 0.87, 0.86, and 0.85 for AF of 3, 4, and 5. The mean PSNR were 44.56, 44.53, and 44.23. Susceptibility values from DL-QSM were highly consistent with routine PI-QSM images, with differences of less than 5% at the group level. Furthermore, the associations between age and QSM values could be consistently revealed. Conclusion: DL-QSM could be used for measuring susceptibility values of deep brain nucleus. An AF up to 5 did not significantly impact the correlation between age and susceptibility in deep brain nuclei.

    Keywords: Quantitative susceptibility mapping, Acceleration, Brain nuclei, deep learning, parallel imaging

    Received: 04 Nov 2024; Accepted: 10 Jan 2025.

    Copyright: © 2025 Zhou, Liu, Xu, Ye, Zhang, Zhang, Sun and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Peiyu Huang, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.