Purpose: Common to most MRSI techniques, the spatial resolution and the minimal scan duration of Deuterium Metabolic Imaging (DMI) are limited by the achievable SNR. This work presents a deep learning method for sensitivity enhancement of DMI. Methods: A convolutional neural network (CNN) was designed to estimate the 2H-labeled metabolite concentrations from low SNR and distorted DMI FIDs. The CNN was trained with synthetic data that represent a range of SNR levels typically encountered in vivo. The estimation precision was further improved by fine-tuning the CNN with MRI-based edge-preserving regularization for each DMI dataset. The proposed processing method, PReserved Edge ConvolutIonal neural network for Sensitivity Enhanced DMI (PRECISE-DMI), was applied to simulation studies and in vivo experiments to evaluate the anticipated improvements in SNR and investigate the potential for inaccuracies. Results: PRECISE-DMI visually improved the metabolic maps of low SNR datasets, and quantitatively provided higher precision than the standard Fourier reconstruction. Processing of DMI data acquired in rat brain tumor models resulted in more precise determination of 2H-labeled lactate and glutamate + glutamine levels, at increased spatial resolution (from >8 to 2 $\mu$L) or shortened scan time (from 32 to 4 min) compared to standard acquisitions. However, rigorous SD-bias analyses showed that overuse of the edge-preserving regularization can compromise the accuracy of the results. Conclusion: PRECISE-DMI allows a flexible trade-off between enhancing the sensitivity of DMI and minimizing the inaccuracies. With typical settings, the DMI sensitivity can be improved by 3-fold while retaining the capability to detect local signal variations.

翻译：目的：与大多数MRSI技术类似，氘代谢成像（DMI）的空间分辨率和最短扫描时间受限于可达到的信噪比。本文提出一种用于增强DMI灵敏度的深度学习方法。方法：设计了一种卷积神经网络，用于从低信噪比和畸变的DMI自由感应衰减信号中估算²H标记代谢物浓度。该网络使用涵盖活体典型信噪比水平的合成数据进行训练。通过为每个DMI数据集引入基于磁共振成像的边缘保持正则化对网络进行微调，进一步提高了估算精度。所提出的处理方法——保留边缘卷积神经网络的灵敏度增强DMI（PRECISE-DMI）——被应用于仿真研究和活体实验，以评估预期的信噪比改善并探究潜在不准确性。结果：PRECISE-DMI在视觉上改善了低信噪比数据集的代谢图，在定量上提供了比标准傅里叶重建更高的精度。与标准采集相比，对在大鼠脑肿瘤模型中采集的DMI数据进行处理后，能以更高空间分辨率（从大于8微升到2微升）或更短扫描时间（从32分钟到4分钟）更精确地测定²H标记乳酸和谷氨酸+谷氨酰胺水平。然而，严格的SD偏差分析表明，过度使用边缘保持正则化可能会损害结果的准确性。结论：PRECISE-DMI允许在增强DMI灵敏度与最小化不准确性之间灵活权衡。在典型设置下，DMI灵敏度可提升3倍，同时保留检测局部信号变化的能力。