Keywords: DWI/DTI/DKI, Diffusion/other diffusion imaging techniques, Denoising, Self-Supervised Learning, Spatio-Angular Domain
Motivation: Diffusion MRI (DMRI) suffers from heavy noise. The noise issue reduces the accuracy and reliability of the derived diffusion metrics.
Goal(s): Existing Deep Learning (DL) methods for DMRI denoising usually rely on training with paired noisy-clean data, which are unavailable in a clinical setting. Therefore, we propose a self-supervised DL denoising method, called Spatio-Angular Noise2Noise, for DMRI denoising.
Approach: We stem from the fact that a network trained with paired noisy data can capture the essential information of underlying clean data for noise reduction.
Results: Extensive experiments on simulated and real datasets demonstrate the superiority of SAN2N over existing DMRI denoising methods.
Impact: SAN2N can reduce the noise effectively and improve the quality of fiber ODFs and tractography.
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