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Abstract #3506

Deep learning prediction of retrieved stroke thrombus RBC content using quantitative, multiparametric MRI

Spencer D. Christiansen1,2, Junmin Liu2, Maria Bres Bullrich3, Manas Sharma4, Sachin K. Pandey4, Mel Boulton3, Luciano A. Sposato3, and Maria Drangova1,2
1Medical Biophyics, Western University, London, ON, Canada, 2Robarts Research Institute, London, ON, Canada, 3Clinical Neurological Sciences, London Health Sciences Centre, London, ON, Canada, 4Department of Medical Imaging, Western University, London, ON, Canada

Thrombus red blood cell (RBC) content has been associated with ischemic stroke etiology and responsiveness to recanalization therapies, yet currently can only be analyzed through retrospective histological analysis. We evaluated the ability of a convolutional neural network for predicting thrombus RBC content using multiparametric (R2*, QSM, late echo GRE) MR image slices of retrieved stroke thrombi ex vivo. The network predicted thrombus RBC content with an accuracy and mean absolute error of up to 71 and 8%, respectively, when data augmentation was applied. This technique holds potential for in vivo RBC content prediction and improving acute stroke care.

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