Keywords: Stroke, Perfusion, Stroke, DSC MRI, Ischemia, Blood Flow Quantification, Deep Learning
Motivation: To assess the ability of quantitative transport mapping (QTM) to estimate blood flow in stroke from DSC MRI through a deep learning model.
Goal(s): To use an automated deep learning based method to measure blood flow in stroke using DSC MRI.
Approach: A deep learning network (QTMnet) is trained on synthetic MR data generated using realistic vascular models to learn the mapping between DSC MR data and underlying tissue blood flow.
Results: QTMnet demonstrates decreased perfusion in ischemic lesion compared to contralateral healthy tissue (p=0.0006), similar to results using traditional modeling. QTMnet performed well without needing to select an appropriate AIF or regularization.
Impact: QTMnet may identify hypoperfused tissue following stroke in an automated manner. Accurate blood flow estimation may assist in determining whether reperfusion therapy is beneficial.
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