Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
630-01-001 ISMRM Abstract

ANISO-QSM: An Anisotropic Nonlinear Inversion for Susceptibility-separation Optimization algorithm.

Primary: Contrast Mechanisms - Susceptibility/QSM
Secondary: Contrast Mechanisms - Electromagnetic Tissue Properties
630-01-001 · Quantitative Susceptibility Mapping: From Phase to Tissue Properties · Thursday, 14 May, 8:30 AM–10:20 AM · Meeting Room 2.60
Keywords: Quantitative MRI Susceptibility source separation Anisotropy White matter microstructure Myelin mapping
Accepted
Daniel Ridani 1, Youssef Diouane2, Benjamin De Leener1,3,4, Eva Alonso Ortiz1,4,5
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Canada
2Department of Mathematical and Industrial Engineering,, Polytechnique Montréal, Montréal, Canada
3Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montréal, Canada
4CHU Sainte-Justine Research Center, Montréal, Canada
5Polytechnique Montréal, Montréal, Canada
Presenting Author: Daniel Ridani

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Shin HG, Lee J, Yun YH, et al. χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain. Neuroimage. 2021;240:118371. doi:10.1016/j.neuroimage.2021.118371 [doi]
2. Dimov AV, Nguyen TD, Gillen KM, et al. Susceptibility source separation from gradient echo data using magnitude decay modeling. J Neuroimaging. 2022;32(5):852-859. doi:10.1111/jon.13014 [doi]
3. Li Z, Feng R, Liu Q, et al. APART-QSM: An improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. Neuroimage. 2023;274:120148. doi:10.1016/j.neuroimage.2023.120148 [doi]
4. Chen J, Gong NJ, Chaim KT, Otaduy MCG, Liu C. Decompose quantitative susceptibility mapping (QSM) to sub-voxel diamagnetic and paramagnetic components based on gradient-echo MRI data. Neuroimage. 2021;242:118477. doi:10.1016/j.neuroimage.2021.118477 [doi]
5. Dimov AV, Gillen KM, Nguyen TD, et al. Magnetic Susceptibility Source Separation Solely from Gradient Echo Data: Histological Validation. Tomography. 2022;8(3):1544-1551. doi:10.3390/tomography8030127 [doi]
6. Zhu Z, Naji N, Esfahani JH, et al. MR Susceptibility Separation for Quantifying Lesion Paramagnetic and Diamagnetic Evolution in Relapsing-Remitting Multiple Sclerosis. J Magn Reson Imaging. Published online February 2, 2024. doi:10.1002/jmri.29266 [doi]
7. Emmerich J, Bachert P, Ladd ME, Straub S. On the separation of susceptibility sources in quantitative susceptibility mapping: Theory and phantom validation with an in vivo application to multiple sclerosis lesions of different age. J Magn Reson. 2021;330:107033. doi:10.1016/j.jmr.2021.107033 [doi]
8. Wharton S, Bowtell R. Fiber orientation-dependent white matter contrast in gradient echo MRI. Proc Natl Acad Sci U S A. 2012;109(45):18559-18564. doi:10.1073/pnas.1211075109 [doi]
9. Daniel Ridani, Benjamin De-Leener, and Eva Alonso-Ortiz. (ISMRM 2024) A realistic in-silico brain phantom for magnetic susceptibility-separation algorithm validation. doi:10.58530/2024/4826 [doi]
10. Sibgatulin R, Güllmar D, Deistung A, Ropele S, Reichenbach JR. In vivo assessment of anisotropy of apparent magnetic susceptibility in white matter from a single orientation acquisition. Neuroimage. 2021;241:118442. doi:10.1016/j.neuroimage.2021.118442 [doi]
11. Radwan AM, Sunaert S, Schilling K, et al. An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI. Neuroimage. 2022;254:119029. doi:10.1016/j.neuroimage.2022.119029 [doi]
12. Shi Y, Feng R, Li Z, Zhuang J, Zhang Y, Wei H. Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset. Neuroimage. 2022;261:119522. doi:10.1016/j.neuroimage.2022.119522 [doi]
13. Cao S, Wei H, Chen J, Liu C. Asymmetric susceptibility tensor imaging. Magn Reson Med. 2021;86(4):2266-2275. doi:10.1002/mrm.28823 [doi]
14. Yeh FC. Population-based tract-to-region connectome of the human brain and its hierarchical topology. Nat Commun. 2022;13(1):4933. doi:10.1038/s41467-022-32595-4 [doi]
15. Li W, Liu C, Duong TQ, van Zijl PCM, Li X. Susceptibility tensor imaging (STI) of the brain. NMR in Biomedicine. 2017;30(4):e3540. doi:10.1002/nbm.3540 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/630-01-001