Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-01-013 ISMRM Abstract

A deep learning approach to solve the DECOMPOSE-QSM model for source separation of magnetic susceptibility

Primary: Contrast Mechanisms - Susceptibility/QSM
Secondary: Acquisition & Reconstruction - AI methods
560-01-013 · New Developments in QSM I · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row A
Keywords: Deep learning Quantitative Susceptibility Mapping (QSM) Susceptibility source separation
Accepted
Fábio Otsuka 1,2, Giulia Debiasi3, Chunlei Liu4,5,6, Carlos Salmon7,8, Maria Garcia Otaduy1,2,9
1LIM44-Instituto de Radiologia, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, Sao Paulo, Brazil
2University of Sao Paulo, Ribeirao Preto, Brazil
3Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States of America
4University of California at Berkeley, berkeley, United States of America
5Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States of America
6Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
7Departamento de Física, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Brazil, Ribeirão Preto, Brazil
8Medical Images, Oncology and Hematology, Ribeirao Preto Medical School - University of Sao Paulo, Brazil
9Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, Sao Paulo, Brazil
Presenting Author: Fábio Otsuka

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References

1. [1] Shin, HG et al. χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain. NeuroImage, 2021. https://doi.org/10.1016/j.neuroimage.2021.118371 [doi]
2. [2] Chen, J. et al. Decompose quantitative susceptibility mapping (QSM) to sub-voxel diamagnetic and paramagnetic components based on gradient-echo MRI data. NeuroImage, 2021. https://doi.org/10.1016/j.neuroimage.2021.118477 [doi]
3. [3] Li, Z. et al. APART-QSM: An improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. NeuroImage, 2023. https://doi.org/10.1016/j.neuroimage.2023.120148 [doi]
4. [4] Chen, J. et al. DeepDECOMPOSE: A Deep Learning based framework for solving DECOMPOSE QSM. Abstract 3573. ISMRM Annual Meeting Proceedings, 2023.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-01-013