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Abstract #3187

Machine Learning for Quantitative IVIM (qIVIM) Cerebral Perfusion Imaging

Mira Liu1, Julian Bertini1, Niloufar Saadat2, Chisondi Simba Warioba1, Donovan Gorre1, Timothy Carroll1, and Gregory Christoforidis2
1University of Chicago, Chicago, IL, United States, 2Interventional Radiology, University of Chicago, Chicago, IL, United States

Synopsis

Keywords: Neurofluids, PerfusionIntravoxel Incoherent Motion (IVIM) is a non-contrast perfusion scan that measures various speeds of molecular movement. Segmentation of IVIM signal origin, i.e. from blood, cerebrospinal fluid (CSF), or tissue water, is crucial. Simulation suggested Inversion Recovery should not be used in IVIM to remove CSF, and analysis of signal patterns suggested differing signal decay as a method of removing CSF during post-processing. A threshold determined by k-fold leave-one-out cross-validation on IVIM diffusion coefficient returned successful CSF segmentation (dice = .69), and CSF removal by supervised machine learning via linear discriminant analysis returned quantitative IVIM with strong agreement to microsphere perfusion.

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Keywords