AID-DTI：使用保留细节的基于模型的深度学习加速高保真扩散张量成像
AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-Preserving Model-based Deep Learning
AI总结：
本文提出AID-DTI方法，通过引入基于SVD的正则化器，仅需6次测量即可实现快速且高保真的DTI参数图重建，有效抑制噪声并保留细节，在HCP数据上定量与定性均优于三种现有方法。
摘要：
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衍生的参数图时往往容易受到Rician噪声影响并丢失细节，尤其是在使用稀疏采样的q空间数据时。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6次测量即可实现快速且准确的DTI。AID-DTI配备了一个新设计的基于奇异值分解（SVD）的正则化器，能够在网络训练期间有效捕捉精细细节并抑制噪声。在Human Connectome Project（HCP）数据上的实验结果一致表明，该方法估计的DTI参数图具有细粒度细节，并在定量和定性上均优于三种最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
