标题： 磁共振谱分析方法的发展，对静态磁场不均匀性具有鲁棒性 显示英文标题

标题： Development of MR spectral analysis method robust against static magnetic field inhomogeneity

摘要： 目的：开发一种方法，以提高在静态磁场B0不均匀性存在下的光谱分析的准确性。 方法：作者提出了一种新的光谱分析方法，利用深度学习模型进行训练，该模型使用一致表示由B0不均匀性引起的光谱变化的建模光谱。 这些建模光谱是从健康人脑的B0图和代谢物比值生成的。 将B0图划分为子区域的块大小，并分别估计的代谢物和基线成分被平均然后整合。 建模光谱的质量通过与测量光谱的视觉和定量评估进行评估。 分析模型使用测量、模拟和建模光谱进行训练。 使用代谢物比值的均方误差（MSEs）评估所提出方法的性能。 在分析两种类型的B0不均匀性下获取的幻影光谱时，还比较了代谢物比值的平均绝对百分比误差（MAPEs）与LCModel。 结果：根据B0不均匀性，建模光谱表现出展宽和变窄的光谱峰，并且在定量上接近测量光谱。 使用测量光谱和建模光谱进行训练的分析模型相比仅使用测量光谱训练的模型，MSEs提高了49.89%，相比使用测量光谱和模拟光谱训练的模型，MSEs提高了26.66%。 随着建模光谱数量从0增加到1,000，性能有所提高。 在两种类型的B0不均匀性下，该模型的MAPEs明显低于LCModel。 结论：开发了一种使用建模光谱的新型经过光谱分析训练的深度学习模型。 结果表明，所提出的方法有潜力通过增加光谱的训练样本数量来提高光谱分析的准确性。 显示英文摘要

摘要： Purpose:To develop a method that enhances the accuracy of spectral analysis in the presence of static magnetic field B0 inhomogeneity. Methods:The authors proposed a new spectral analysis method utilizing a deep learning model trained on modeled spectra that consistently represent the spectral variations induced by B0 inhomogeneity. These modeled spectra were generated from the B0 map and metabolite ratios of the healthy human brain. The B0 map was divided into a patch size of subregions, and the separately estimated metabolites and baseline components were averaged and then integrated. The quality of the modeled spectra was visually and quantitatively evaluated against the measured spectra. The analysis models were trained using measured, simulated, and modeled spectra. The performance of the proposed method was assessed using mean squared errors (MSEs) of metabolite ratios. The mean absolute percentage errors (MAPEs) of the metabolite ratios were also compared to LCModel when analyzing the phantom spectra acquired under two types of B0 inhomogeneity. Results:The modeled spectra exhibited broadened and narrowed spectral peaks depending on the B0 inhomogeneity and were quantitatively close to the measured spectra. The analysis model trained using measured spectra with modeled spectra improved MSEs by 49.89% compared to that trained using measured spectra alone, and by 26.66% compared to that trained using measured spectra with simulated spectra. The performance improved as the number of modeled spectra increased from 0 to 1,000. This model showed significantly lower MAPEs than LCModel under both types of B0 inhomogeneity. Conclusion:A new spectral analysis-trained deep learning model using the modeled spectra was developed. The results suggest that the proposed method has the potential to improve the accuracy of spectral analysis by increasing the training samples of spectra.