  Purpose: To develop biophysics-based method for estimating perfusion Q from
arterial spin labeling (ASL) images using deep learning. Methods: A 3D U-Net
(QTMnet) was trained to estimate perfusion from 4D tracer propagation images.
The network was trained and tested on simulated 4D tracer concentration data
based on artificial vasculature structure generated by constrained constructive
optimization (CCO) method. The trained network was further tested in a
synthetic brain ASL image based on vasculature network extracted from magnetic
resonance (MR) angiography. The estimations from both trained network and a
conventional kinetic model were compared in ASL images acquired from eight
healthy volunteers. Results: QTMnet accurately reconstructed perfusion Q from
concentration data. Relative error of the synthetic brain ASL image was 7.04%
for perfusion Q, lower than the error using single-delay ASL model: 25.15% for
Q, and multi-delay ASL model: 12.62% for perfusion Q. Conclusion: QTMnet
provides accurate estimation on perfusion parameters and is a promising
approach as a clinical ASL MRI image processing pipeline.
