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.

本文提出了一种基于深度学习的 IR2QSM 方法，通过四次迭代的反向连接和中间循环模块增强 U-net 的设计，极大地改善了潜在特征利用效率，实验结果显示，IR2QSM 相比其他方法能够更准确地获取 QSM 图像，并减轻了伪影问题。