Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
431-03-009 / 431-03-009 ISMRM Abstract

HiFi-QSM: A deep learning framework for high-resolution QSM reconstruction

Primary: Contrast Mechanisms - Susceptibility/QSM
Secondary: Contrast Mechanisms - Electromagnetic Tissue Properties
431-03-009 · Image Reconstruction · Tuesday, 12 May, 4:00 PM–5:36 PM · Roof Terrace
431-03-009 · Image Reconstruction · Tuesday, 12 May, 4:00 PM–5:36 PM · Roof Terrace
Keywords: Deep learning High-Resolution MRI Data Scarcity
Accepted
Chungseok Oh 1, Taechang Kim1, Jiye Kim1, Hyeong-Geol Shin2,3,4, Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of
2Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States of America
3Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, United States of America
4F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, United States of America
Presenting Author: Chungseok Oh

Synopsis

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References

1. Jung, Woojin, Steffen Bollmann, and Jongho Lee. "Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities." NMR in Biomedicine 35.4 (2022): e4292.
2. Oh, Chungseok, et al. "A fair comparison in deep learning-based QSM reconstruction." IEEE Access (2025).
3. Liu, Juan, and Kevin M. Koch. "Meta-QSM: an image-resolution-arbitrary network for QSM reconstruction." arXiv preprint arXiv:1908.00206 (2019).
4. Gao, Yang, et al. "xQSM: quantitative susceptibility mapping with octave convolutional and noise‐regularized neural networks." NMR in Biomedicine 34.3 (2021): e4461.
5. Oh, Gyutaek, et al. "Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization." Medical Image Analysis 79 (2022): 102477.
6. Xiong, Zhuang, et al. "Affine transformation edited and refined deep neural network for quantitative susceptibility mapping." NeuroImage 267 (2023): 119842.
7. Graf, Simon, Walter A. Wohlgemuth, and Andreas Deistung. "Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution." Frontiers in Neuroscience 18 (2024): 1366165.
8. Ji, Sooyeon, et al. "Successful generalization for Data With Higher or Lower Resolution Than Training Data Resolution in Deep Learning Powered QSM Reconstruction." Joint Annual Meeting ISMRM‐ESMRMB & ISMRT 32nd Annual Meeting, Toronto, Canada. 2023.
9. Zhang, Ming, et al. "Diffusion-QSM: diffusion model with timetravel and resampling refinement for quantitative susceptibility mapping." IEEE Transactions on Biomedical Engineering (2025).
10. Zhang, Jinwei, et al. "Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction." Neuroimage 211 (2020): 116579.
11. Zhang, Ming, et al. "A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation." Medical Image Analysis 95 (2024): 103173.
12. Xiong, Zhuang, et al. "Quantitative susceptibility mapping through model-based deep image prior (MoDIP)." NeuroImage 291 (2024): 120583.
13. Ravishankar, Hariharan, et al. "UATTA-QSM: Uncertainty-Aware Test Time Adaptation for Improved Quantitative Susceptibility Mapping." 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025.
14. Yoon, Jaeyeon, et al. "Quantitative susceptibility mapping using deep neural network: QSMnet." Neuroimage 179 (2018): 199-206.
15. Lai, Kuo-Wei, et al. "Learned proximal networks for quantitative susceptibility mapping." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer International Publishing, 2020.
16. Kim, Minjun, et al. "χ‐sepnet: deep neural network for magnetic susceptibility source separation." Human Brain Mapping 46.2 (2025): e70136.
17. Li, Wei, et al. "A method for estimating and removing streaking artifacts in quantitative susceptibility mapping." Neuroimage 108 (2015): 111-122.
18. Kim, Jiye, et al. "In-vivo high-resolution χ-separation at 7T." NeuroImage 308 (2025): 121060.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/431-03-009