Multi-delay arterial spin-labeled perfusion estimation with biophysics simulation and deep learning

TL;DR

This paper introduces QTMnet, a deep learning model that accurately estimates brain perfusion from ASL MRI images using biophysics-based simulations, outperforming traditional models in synthetic and real data.

Contribution

The study presents a novel deep learning approach, QTMnet, trained on biophysics-based simulations to improve perfusion estimation from ASL images.

Findings

QTMnet achieved 7.04% error in synthetic brain ASL images.

QTMnet outperformed single-delay and multi-delay models in accuracy.

The method shows promise for clinical ASL MRI processing.

Abstract

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.