AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with
  Detail-Preserving Model-based Deep Learning
Wenxin Fan Jian Cheng Cheng Li Xinrui Ma Jing Yang Juan Zou Ruoyou Wu Qiegen Liu Shanshan Wang
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.