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

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 concentrati...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Hu, Renjiu, Zhang, Qihao, Spincemaille, Pascal, Nguyen, Thanh D, Wang, Yi
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.11.2023
Subjects
Angiography
Biophysics
Brain
Deep learning
Delay
Estimation
Image acquisition
Image processing
Magnetic resonance
Medical imaging
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422