IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules

TL;DR

This paper introduces IR2QSM, a deep learning method with iterative reverse concatenations and recurrent modules, significantly improving the accuracy and artifact reduction in quantitative susceptibility mapping from MRI data.

Contribution

The novel IR2QSM approach enhances QSM reconstruction by integrating iterative reverse concatenations and recurrent modules within a U-net architecture, outperforming existing methods.

Findings

IR2QSM achieved the lowest NRMSE of 27.59% in simulated experiments.

IR2QSM outperformed traditional and deep learning methods in accuracy.

IR2QSM reduced artifacts in in vivo QSM images.

Abstract

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases. However, the ill-conditioned nature of dipole inversion makes QSM reconstruction from the tissue field prone to noise and artifacts. In this work, we propose a novel deep learning-based IR2QSM method for QSM reconstruction. It is designed by iterating four times of a reverse concatenations and middle recurrent modules enhanced U-net, which could dramatically improve the efficiency of latent feature utilization. Simulated and in vivo experiments were conducted to compare IR2QSM with several traditional algorithms (MEDI and iLSQR) and state-of-the-art deep learning methods (U-net, xQSM, and LPCNN). The results indicated that IR2QSM was able to obtain QSM images with significantly increased accuracy and mitigated artifacts over other methods. Particularly, IR2QSM demonstrated on average the best NRMSE (27.59%) in simulated experiments, which is 15.48%, 7.86%, 17.24%, 9.26%, and 29.13% lower than iLSQR, MEDI, U-net, xQSM, LPCNN, respectively, and led to improved QSM results with fewer artifacts for the in vivo data.