Abstract #2808

DeepSepSTI: Improved Susceptibility Tensor Reconstruction by Anisotropic Susceptibility Source Separation

Zhenghan Fang¹, Hyeong-Geol Shin^2,3, Blake E. Dewey⁴, Peter A. Calabresi⁴, Peter van Zijl^1,2,3, Jeremias Sulam¹, and Xu Li^2,3

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, ²Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ⁴Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Brain, Susceptibility Tensor Imaging, Susceptibility Source Separation

Motivation: Magnetic susceptibility source separation has potential for characterizing pathological tissue changes in disease. However, existing source separation methods assume isotropic susceptibility, ignoring anisotropy in white matter.

Goal(s): To develop a method for anisotropic susceptibility source separation for better susceptibility tensor reconstruction.

Approach: The paramagnetic susceptibility, modeled by an isotropic scalar, and the diamagnetic susceptibility, modeled by an anisotropic tensor, are jointly estimated in each voxel from local frequency and R2’ measurements using a deep learning model, named DeepSepSTI.

Results: DeepSepSTI shows generally improved estimation of susceptibility tensors, anisotropy and PEV than DeepSTI. DeepSepSTI can better describe tissue characteristics in multiple sclerosis lesions.

Impact: The proposed DeepSepSTI approach may help better measure changes in iron, myelin, and susceptibility anisotropy in various neurological diseases such as multiple sclerosis, potentially providing improved biomarkers for better characterization of disease stage and progression.

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