Abstract #4992

A Fully Automated Pipeline for the Determination of the Iron Microstructure Coefficient (IMC) from Multi-Echo GRE Data

Saleha Mir¹, Fahad Salman¹, Dejan Jakimovski¹, Cheryl McGranor², Robert Zivadinov^1,2, and Ferdinand Schweser^1,2

¹Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ²Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Susceptibility mapping

Motivation: This work aims to automate analysis of the newly introduced Iron Microstructure Coefficient (IMC) to facilitate understanding of iron cellular distribution in neurological diseases for large cohort studies.

Goal(s): The goal is to develop and test an automated and widely applicable pipeline for IMC analysis using quantitative susceptibility and R₂^* maps obtained from the same multi-echo GRE sequence.

Approach: The pipeline inputs magnetic susceptibility and R₂^*maps, T1-w data, templates, and regions of interest (ROIs). The output is the IMC value per ROI per subject.

Results: The pipeline successfully executed in 50 minutes without segmentation failure for a cohort of 28 test subjects.

Impact: The automated pipeline accelerates data processing for IMC, providing enhanced standardization in a robust, reproducible, and user-friendly manner. It facilitates large-scale research, driving significant advancements in our understanding of neurological diseases, with the goal of improving accurate diagnosis for patients.

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