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.

翻译：磁共振波谱（MRS）是一种重要的非侵入性活体生物医学检测技术。然而，由于三个问题，利用质子MRS准确定量代谢物仍具挑战：代谢物信号的严重重叠、非理想采集条件导致的信号畸变以及包括大分子信号在内的强背景信号干扰。最流行的软件LCModel采用非线性最小二乘法进行代谢物量化，并通过引入正则化项、非理想采集条件的不完美因子以及设计代谢物和大分子基函数等若干经验先验来应对这些问题。然而，求解如此庞大的非线性量化问题十分复杂。此外，当输入MRS信号的信噪比较低时，解可能产生较大偏差。本研究引入深度学习以降低求解这一整体量化问题的复杂度。深度学习被设计为直接预测非理想因子和大分子整体信号。随后，量化问题的剩余部分简化为一个更高效拟合过程，可通过线性最小二乘法（LLS）轻松求解，从而显著提升模型对训练数据中未见过代谢物浓度的泛化能力。实验结果表明，与LCModel相比，所提方法在700组模拟测试数据中具有更小的量化误差，并在20组不同信噪比范围的健康活体数据中呈现出更稳定的量化结果。QNet在多数代谢物上的量化误差也低于其他深度学习方法。最后，QNet已部署于云计算平台CloudBrain-MRS，该平台可通过https://csrc.xmu.edu.cn/CloudBrain.html 开放访问。