A 3D Convolutional Neural Network with Gradient Guidance for Image Super-Resolution of Late Gadolinium Enhanced Cardiac MRI.

Abstract

In this paper, we describe a 3D convolutional neural network (CNN) framework to compute and generate super-resolution late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) images. The proposed CNN framework consists of two branches: a super-resolution branch with a 3D dense deep back-projection network (DBPN) as the backbone to learn the mapping of low-resolution LGE cardiac volumes to high-resolution LGE cardiac volumes, and a gradient branch that learns the mapping of the gradient map of low resolution LGE cardiac volumes to the gradient map of their high-resolution counterparts. The gradient branch of the CNN provides additional cardiac structure information to the super-resolution branch to generate structurally more accurate super-resolution LGE MRI images. We conducted our experiments on the 2018 atrial segmentation challenge dataset. The proposed CNN framework achieved a mean peak signal-to-noise ratio (PSNR) of 30.91 and 25.66 and a mean structural similarity index measure (SSIM) of 0.91 and 0.75 on training the model on low-resolution images downsamp led by a scale factor of 2 and 4, respectively.