Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-01-010 ISMRM Abstract

Physics-informed deep image prior for susceptibility source separation

Primary: Contrast Mechanisms - Electromagnetic Tissue Properties
Secondary: Analysis Methods - Data Processing
560-01-010 · New Developments in QSM I · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row A
Keywords: Machine Learning/Artificial Intelligence Quantitative Susceptibility Mapping (QSM) Susceptibility source separation
Accepted
Maneesh John1,2, Alexandra G Roberts1,2, Benjamin Weppner2,3, Dominick Romano2,3, Mert Sisman1,2, Pascal Spincemaille 2, Ilhami Kovanlikaya2, Alexey Dimov2, Yi Wang 2,3
1Electrical & Computer Engineering, Cornell University, Ithaca, United States of America
2Radiology, Weill Cornell Medicine, New York, United States of America
3Biomedical Engineering, Cornell University, Ithaca, United States of America
Presenting Author: Yi Wang

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References

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2. Dimov AV, Nguyen TD, Gillen KM, et al. Susceptibility source separation from gradient echo data using magnitude decay modeling. Journal of Neuroimaging. 2022;32(5):852-859. doi:10.1111/jon.13014 [doi]
3. Ulyanov D, Vedaldi A, and Lempitsky V. Deep Image Prior. International Journal of Computer Vision. 2020;128:1867-1888. doi:10.1007/s11263-020-01303-4 [doi]
4. Qayyum A, Ilahi I, Shamshad F, et al. Untrained Neural Network Priors for Inverse Imaging Problems: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2023;45(5):6511-6536. doi:10.1109/TPAMI.2022.3204527 [doi]
5. Xiong Z, Gao Y, Liu L, et al. Quantitative susceptibility mapping through model-based deep image prior (MoDIP). NeuroImage. 2024;291:120583. doi:10.1016/j.neuroimage.2024.120583 [doi]
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9. Pei M, Nguyen TD, Thimmappa ND, et al. Algorithm for fast monoexponential fitting based on Auto-Regression on Linear Operations (ARLO) of data. Magnetic Resonance in Medicine. 2015;73(2):843-850. doi:10.1002/mrm.25137 [doi]
10. De Rochefort L, Liu T, Kressler B, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging. Magnetic Resonance in Medicine. 2010;63(1):194-206. doi:10.1002/mrm.22187 [doi]
11. Liu Z, Spincemaille P, Yao Y, Zhang Y, Wang Y. MEDI+0: Morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping. Magnetic Resonance in Medicine. 2018;79(5):2795-2803. doi:10.1002/mrm.26946 [doi]

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http://echo.ismrm.org/p/ISMRM2026/560-01-010