Fig. 5.

Visual comparison of noise reduction performance between DnCNN, SCNN and dDLR in a 38-year old male volunteer. Top-left is a ground-truth (10-number of image acquisition [NAQ]-like) image, and the others are magnified images of the rectangular annotated area in the ground-truth image. Top-center: magnified ground-truth image, top-right: magnified noise-added image without denoising, bottom-left: magnified image denoised with DnCNN, bottom-center: denoised with SCNN, and bottom-right: denoised with dDLR. Denoising was applied to the artificially noise-added images as follows: (a) T₁WI with 3% noise, (b) T₂WI with 4% noise, and (c) fluid-attenuated inversion recovery (FLAIR) with 2% noise. dDLR unambiguously reduced image noise while preserving intrinsic structures and structural boundaries (arrows) compared with DnCNN and SCNN images. dDLR, deep learning-based reconstruction; DnCNN, denoising convolutional neural network; SCNN, shrinkage convolutional neural network; T₁WI, T₁-weighted image; T₂WI, T₂-weighted image.