Figure 1.

Architecture of the U-Net-based CNN scheme. In the contraction part, the low-quality images generated from TSE images were used as input images. Each blue box includes a convolutional layer with a size of 3 × 3, a BN layer, and a PReLU layer. Each purple box represents a max pooling layer for downsampling. Each white box represents a concatenate layer. Each green box represents a deconvolution layer. A yellow box represents a final convolutional layer. The TSE images were used as ground truth images. The image size and the number of feature channels from each convolutional layer are listed near each blue box. In the test process, the trained CNN model was applied to the datasets of SSTSE images, which were used as input images. The output images were denoted as DL-SSTSE images. BN = batch normalization layer, CNN = convolutional neural network, Conv = convolutional layer, DL-SSTSE = deep learning-based single-shot turbo spin-echo, PReLU = parametric rectified linear unit, SSTSE = single-shot turbo spin-echo, TSE = turbo spin-echo.