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Abstract #2110

A Semi-Automated Analysis Pipeline for Reproducible SWI Analysis of Multiple Sclerosis Pathology

Michael G. Dwyer1, Niels Bergsland2, Claudiu Schirda2, Mari Heininen-Brown, Ellen Carl, David Wack, Guy U. Poloni, Robert Zivadinov3

1Buffalo Neuroimaging Analysis Center; 2University at Buffalo, Buffalo Neuroimaging Analysis Center, Buffalo, NY, United States; 3Neurology, Buffalo Neuroimaging Analysis Center, Buffalo , NY , United States

Susceptibility-weighted imaging (SWI) has gained much interest recently as a sensitive means for detecting iron deposition in a variety of diseases, including multiple sclerosis (SM). We propose a fast and reproducible analysis pipeline to extract detailed quantitative SWI data and to combine it with other established indicators of disease state (including magnetization transfer and perfusion imaging).