AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-Preserving Model-based Deep Learning
This work addresses a domain-specific problem in medical imaging for researchers and clinicians, offering an incremental improvement in DTI reconstruction.
The paper tackles the problem of noise and detail loss in accelerated diffusion tensor imaging (DTI) with sparse sampling, proposing AID-DTI to achieve fast and accurate DTI using only six measurements, with experimental results showing it outperforms three state-of-the-art methods on HCP data.
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.