Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network

TL;DR

This paper introduces DCRNet, a deep learning approach that significantly accelerates quantitative susceptibility mapping in MRI by recovering phase and magnitude images from undersampled data, outperforming existing methods in accuracy and speed.

Contribution

The study presents a novel deep complex residual network (DCRNet) that enables rapid and accurate QSM reconstruction from undersampled MRI data, reducing reconstruction time from hours to seconds.

Findings

DCRNet outperforms existing methods in PSNR and SSIM metrics.

Achieves 4-8.8% higher susceptibility accuracy in deep grey matter.

Reduces reconstruction time from 80 hours to 30 seconds.

Abstract

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing method that quantifies tissue magnetic susceptibility distributions. However, QSM acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition. Magnitude, phase, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM) and region-of-interest susceptibility measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR and SSIM on the magnitude, phase, local field, and susceptibility maps. It led to 4.0% to 8.8% accuracy improvements in deep grey matter susceptibility than some existing methods, when the acquisition was accelerated four times. The proposed DCRNet also dramatically shortened the reconstruction time by nearly 10 thousand times for each scan, from around 80 hours using conventional approaches to only 30 seconds.