Measuring blood flow in stroke using Quantitative Transport Mapping Network (QTMnet)

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.