Keywords: DWI/DTI/DKI, DWI/DTI/DKI, Denoising, Deep Learning
Motivation: Diffusion Tensor Imaging (DTI) is limited by the long acquisition time and low signal-to-noise ratio. Noisy images hinder the observation of the anisotropy of water molecule diffusion, which further impacts clinical assessment.
Goal(s): To design a deep learning-based method for DTI signal-to-noise ratio improvement.
Approach: The proposed Dual-domain Tensor Denoising (DuTD) network leverages the structural, diffusion, and tensor information of DTI for denoising and fractional anisotropy (FA) generation.
Results: Extensive results show that DuTD can improve the signal-to-noise ratio and remain more diffusion information efficiently compared with other advanced methods.
Impact: The proposed DuTD network effectively enhances the SNR of images and describes diffusion information more accurately in both public datasets and private Parkinson's datasets.
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