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Bangkang Fu

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YNIMG Journal 2026 Journal Article

PI-uMSS: Prior information-based unsupervised magnetic source separation in quantitative susceptibility mapping

  • Junjie He
  • Bangkang Fu
  • Cen Pan
  • Lisha Nie
  • Rui Xu
  • Zi Xu
  • Rongpin Wang

Magnetic source separation (MSS) in quantitative susceptibility mapping (QSM) provides a powerful tool to disentangle paramagnetic and diamagnetic contributions, enabling more accurate quantification of brain iron and myelin alterations. However, existing MSS approaches typically depend on approximations derived from reversible transverse relaxation (R2'=R2∗-R2) or extrapolate from a limited number of brain regions to perform whole-brain separation. Furthermore, current deep learning-based methods often require extensive and high-quality labels, which are difficult to obtain. To address these limitations, we propose an unsupervised MSS framework guided by prior information and constrained by physics-informed loss functions to improve separation fidelity. The proposed model directly processes whole-brain QSM and R2∗ data, infers intermediate parameters, and reconstructs the spatial distributions of paramagnetic and diamagnetic sources via biophysical modeling. Experimental results show that the method achieves high structural similarity (SSIM = 0.9945 for paramagnetic and 0.9942 for diamagnetic components) and a low normalized mean square error (0.11) relative to the original QSM, demonstrating robust and consistent source decomposition performance. Code is available at https://github.com/TyrionJ/PI-uMSS.

Details DOI

YNIMG Journal 2023 Journal Article

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping

  • Junjie He
  • Yunsong Peng
  • Bangkang Fu
  • Yuemin Zhu
  • Lihui Wang
  • Rongpin Wang

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.

Details DOI
v2026.09.13