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.

通过使用卷积神经网络和 MRI 边缘保持正则化方法，提出了一种用于增强 Deuterium 代谢成像（DMI）灵敏度的处理方法（PRECISE-DMI），该方法能够在保持能够检测局部信号变化的能力的同时，将 DMI 的灵敏度提高了 3 倍。