Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Overall Macromolecular Signal

• URL: http://arxiv.org/abs/2306.09681v1
• Date: Fri, 16 Jun 2023 08:24:18 GMT
• Title: Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Overall Macromolecular Signal
• Authors: Dicheng Chen, Meijin Lin, Huiting Liu, Jiayu Li, Yirong Zhou, Taishan Kang, Liangjie Lin, Zhigang Wu, Jiazheng Wang, Jing Li, Jianzhong Lin, Xi Chen, Di Guo and Xiaobo Qu
• Abstract summary: It is still challenging to accurately quantify metabolites with proton MRS. Deep learning is introduced to reduce the complexity of solving this overall quantitative problem. Qnet has been deployed on a cloud computing platform, CloudBrain-MRS.
• Score: 16.574504002037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic Resonance Spectroscopy (MRS) is an important non-invasive technique for in vivo biomedical detection. However, it is still challenging to accurately quantify metabolites with proton MRS due to three problems: Serious overlaps of metabolite signals, signal distortions due to non-ideal acquisition conditions and interference with strong background signals including macromolecule signals. The most popular software, LCModel, adopts the non-linear least square to quantify metabolites and addresses these problems by introducing regularization terms, imperfection factors of non-ideal acquisition conditions, and designing several empirical priors such as basissets of both metabolites and macromolecules. However, solving such a large non-linear quantitative problem is complicated. Moreover, when the signal-to-noise ratio of an input MRS signal is low, the solution may have a large deviation. In this work, deep learning is introduced to reduce the complexity of solving this overall quantitative problem. Deep learning is designed to predict directly the imperfection factors and the overall signal from macromolecules. Then, the remaining part of the quantification problem becomes a much simpler effective fitting and is easily solved by Linear Least Squares (LLS), which greatly improves the generalization to unseen concentration of metabolites in the training data. Experimental results show that compared with LCModel, the proposed method has smaller quantification errors for 700 sets of simulated test data, and presents more stable quantification results for 20 sets of healthy in vivo data at a wide range of signal-to-noise ratio. Qnet also outperforms other deep learning methods in terms of lower quantification error on most metabolites. Finally, QNet has been deployed on a cloud computing platform, CloudBrain-MRS, which is open accessed at https://csrc.xmu.edu.cn/CloudBrain.html.

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