Fig. 2.

The proposed 3-D FCNs. Our network architecture consists of three convolutional layers. The input layer is composed of five pulse sequences arranged as channels. The first layer extracts a 64-dimensional feature from input images through convolution process with a $3\times 3\times 3\times 5\times 64$ kernel. The second and third layers apply the same convolution process to find a nonlinear mapping for image prediction.