Multi-delay arterial spin-labeled perfusion estimation with biophysics simulation and deep learning
This work addresses the challenge of accurate perfusion estimation in clinical ASL MRI for medical imaging applications, representing a strong specific gain rather than a broad paradigm shift.
The researchers tackled the problem of estimating perfusion from arterial spin labeling (ASL) images by developing a biophysics-based deep learning method (QTMnet), which achieved a relative error of 7.04% for perfusion Q, outperforming conventional models with errors of 25.15% and 12.62%.
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.