mQSM: Multitask Learning-Based Quantitative Susceptibility Mapping for Iron Analysis in Brain

Quantitative analysis of brain iron is widely utilized in neurodegenerative diseases, typically accomplished through the utilization of quantitative susceptibility mapping (QSM) and medical image registration. However, this approach heavily relies on registration accuracy, and image registration can alter QSM values, leading to distorted quantitative analysis results. This paper proposes a multi-modal multitask QSM reconstruction algorithm (mQSM) and introduces a mutual Transformer mechanism (mTrans) to efficiently fuse multi-modal information for QSM reconstruction and brain region segmentation tasks. mTrans leverages Transformer computations on \({\textbf {Q}} \) uery and \({\textbf {V}} \) alue feature matrices for mutual attention calculation, eliminating the need for additional computational modules and ensuring high efficiency in multi-modal data fusion. Experimental results demonstrate an average dice coefficient of 0.92 for segmentation, and QSM reconstruction achieves an SSIM evaluation of 0.9854 compared to the gold standard. Moreover, segmentation-based (mQSM) brain iron quantitative analysis shows no significant difference from the ground truth, whereas the registration-based approach exhibits notable differences in brain cortical regions compared to the ground truth. Our code is available at https://github.com/TyrionJ/mQSM .