System and method for super-resolution of magnetic resonance images using slice-profile-transformation and neural networks

Abstract

A system for super-resolution of magnetic resonance (MR) images includes an input for receiving a two-dimensional (2D) multi-slice MR dataset of a subject, a pre-processing module coupled to the input and configured to generate a convolved input from the received 2D multi-slice MR dataset by applying slice-profile convolution to the received 2D multi-slice MR dataset, a through plane super resolution neural network coupled to the pre-processing module and configured to generate a through-plane super-resolution imaging volume based on the convolved input, and a post-processing module coupled to the through plane super resolution neural network and configured to generate a three-dimensional (3D) isotropic super resolution imaging volume by applying slice-profile deconvolution. The through plane super resolution neural network can be trained using a training input dataset generated by applying slice-profile downsampling to a 2D multi-slice MR training dataset.

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