Slice Profile Estimation From 2D MRI Acquisition Using Generative Adversarial Networks

Abstract

To save time and maintain an adequate signal-to-noise ratio, magnetic resonance (MR) images are often acquired with better in-plane than through-plane resolutions in 2D acquisition. To improve image quality, recent work has focused on using deep learning to super-resolve the through-plane resolution. To create training data, images can be degraded in an in-plane direction to match the through-plane resolution. To do this correctly, the slice selection profile (SSP) should be known, but this is rarely possible since precise details of signal excitation are usually unknown. Therefore, estimating the SSP of an image volume is desired. In this work, we first show that a relative SSP can be estimated from the difference between in- and through-plane image patches. We further propose an algorithm that uses generative adversarial networks (GAN) to estimate the SSP. In this algorithm, the GAN’s generator blurs in-plane patches in one direction using an estimated relative SSP then downsamples them. The GAN’s discriminator distinguishes the generator’s output from real through-plane patches. The proposed method was validated using numerical simulations and phantom and brain scans. To our knowledge, it is the first work to estimate the SSP from a single MR image. The code is available at https://github.com/shuohan/espreso.