Structure-preserved denoising of MRI images is a critical step in medical image analysis. This is particularly critical in diffusion MRI where higher spatial and angular resolutions required to map tissue microstructure in low SNR (especially at higher b-values) situations, if longer acquisition times are not used. Denoising using deep convolutional neural networks (DCNN) can reduce noise without requiring extensive averaging, enabling shorter scan times and high image quality, especially in the resulting tensor-derived maps. Preliminary results using DCNN based denoising on multi-shell diffusion data demonstrates improved image quality and reduced noise, without compromising on structural integrity and tensor derived metrics.
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