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arXiv · 2004.06281

xQSM: Quantitative Susceptibility Mapping with Octave Convolutional and Noise Regularized Neural Networks

Yang Gao·Xuanyu Zhu·Bradford A. Moffat·Rebecca Glarin·Alan H. Wilman·G. Bruce Pike·Stuart Crozier·Feng Liu·Hongfu Sun
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Abstract

Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing modified state-of-the-art octave convolutional layers into the U-net backbone. The xQSM method was compared with recentlyproposed U-net-based and conventional regularizationbased methods, using peak signal to noise ratio (PSNR), structural similarity (SSIM), and region-of-interest measurements. The results from a numerical phantom, a simulated human brain, four in vivo healthy human subjects, a multiple sclerosis patient, a glioblastoma patient, as well as a healthy mouse brain showed that the xQSM led to suppressed artifacts than the conventional methods, and enhanced susceptibility contrast, particularly in the ironrich deep grey matter region, than the original U-net, consistently. The xQSM method also substantially shortened the reconstruction time from minutes using conventional iterative methods to only a few seconds.

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Yang Gao, Xuanyu Zhu, Bradford A. Moffat, Rebecca Glarin, Alan H. Wilman, G. Bruce Pike, Stuart Crozier, Feng Liu, Hongfu Sun. 2020-11-26. xQSM: Quantitative Susceptibility Mapping with Octave Convolutional and Noise Regularized Neural Networks. https://doi.org/10.1002/nbm.4461

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