Diffusion MRI super-resolution reconstruction via sub-pixel convolution generative adversarial network

Abstract

To solve the problem of long sampling time for diffusion magnetic resonance imaging (dMRI), in this study we propose a dMRI super-resolution reconstruction network. This method not only uses a three-dimensional (3D) convolution kernel to reconstruct the dMRI data in the space and angle domains, but also introduces an adversarial learning and attention mechanism to solve the problem of the traditional loss function not fully quantifying the gap between high-dimensional data and not paying more attention to important feature maps. Experimental results from the comparison of peak signal-to-noise ratio, structural similarity, and orientation distribution function visualization show that these methods bring better results. They also prove the feasibility of using an attention mechanism in dMRI reconstruction and the use of adversarial learning in a 3D convolution kernel.