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Abstract #3279

Quantitative and Qualitative Impact of Magnetic Microstructure on Conventional and Deep Learning-based Quantitative Susceptibility Mapping

Thomas Jochmann1,2, Fahad Salman2,3, Michael G. Dwyer2,4, Niels Bergsland2, Robert Zivadinov2,4, Jens Haueisen1, and Ferdinand Schweser2,4
1Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany, 2Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States, 3Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States, 4Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, Buffalo, NY, United States

Synopsis

Keywords: Susceptibility/QSM, Susceptibility

Motivation: Quantitative susceptibility mapping (QSM) is widely used to estimate iron and myelin contents from MRI phase data but is known to rely on simplistic assumptions about magnetic microstructure.

Goal(s): To assess the impact of microstructure-induced phase shifts on susceptibility maps generated by various QSM algorithms, including recent deep learning methods.

Approach: Using a realistic digital brain model and in vivo data, we compared conventional and deep learning-based QSM algorithms, assessing accuracy and anatomical consistency in susceptibility estimates.

Results: Microstructure-induced phase shifts substantially affect the accuracy of susceptibility maps computed with different QSM techniques.

Impact: This work demonstrates that incorporating microstructure-induced frequency shifts into QSM significantly enhances susceptibility mapping accuracy, particularly in white matter. These findings enable improved study of pathologies with microstructural alterations, providing clinicians and researchers with more reliable maps for disease assessment.

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