Reconstruction of Dynamic Perfusion and Angiography Images from Sub-sampled Hadamard Time-encoded ASL Data using Deep Convolutional Neural Networks

摘要

Arterial spin labeling (ASL) is a non-invasive technique for acquiring quantitative measures of cerebral blood flow (CBF)1. Hadamard time-encoded(te) pCASL allows time-efficient acquisition of dynamic ASL-data and when done with and without flow-crushing, 4D MRA and arterial input function measurements can be obtained~2. While improving quantification, this approach is also a factor two slower. In this study, we propose an end-to-end 3D convolutional neural network (CNN) in order to accelerate CBF quantification from sparse sampling (50%) of te-pCASL with and without flow crushers. For training and evaluation of the CNN, we propose a framework to simulate the te-PCASL signal.