Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and detail loss in reconstructing the DTI-derived parametric maps especially when sparsely sampled q-space data are used. This paper proposes a novel method, AID-DTI (Accelerating hIgh fiDelity Diffusion Tensor Imaging), to facilitate fast and accurate DTI with only six measurements. AID-DTI is equipped with a newly designed Singular Value Decomposition (SVD)-based regularizer, which can effectively capture fine details while suppressing noise during network training. Experimental results on Human Connectome Project (HCP) data consistently demonstrate that the proposed method estimates DTI parameter maps with fine-grained details and outperforms three state-of-the-art methods both quantitatively and qualitatively.

翻译：深度学习在加速扩散张量成像（DTI）方面展现出巨大潜力。然而，现有方法在重建DTI参数图时，特别是在使用稀疏采样的q空间数据时，往往受限于莱斯噪声和细节丢失问题。本文提出了一种新方法AID-DTI（加速高保真扩散张量成像），仅需六次测量即可实现快速且准确的DTI。AID-DTI配备了一种新设计的基于奇异值分解（SVD）的正则化器，该正则化器能够在网络训练过程中有效捕捉精细细节，同时抑制噪声。在人类连接组计划（HCP）数据上的实验结果一致表明，所提方法能够估计出具有精细细节的DTI参数图，并在定量和定性上均优于三种现有最优方法。