Virtual Coil Augmentation Technology for MR Coil Extrapolation via Deep Learning

TL;DR

This paper introduces a deep learning-based virtual coil augmentation method for MRI that enhances image quality and reconstruction speed by expanding receive coils in both image and k-space domains using dummy variables.

Contribution

It presents a novel approach combining dummy variable technology and a sum of squares loss to improve MRI coil extrapolation and parallel imaging reconstruction.

Findings

Effective in super-resolution of MR images

Accelerates parallel imaging reconstruction

Improves high-dimensional prior information utilization

Abstract

Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challenging to obtain high-quality MR images. In this article, we propose a method of using artificial intelligence to expand the channel to achieve the goal of generating the virtual coils. The main characteristic of our work is utilizing dummy variable technology to expand/extrapolate the receive coils in both image and k-space domains. The high-dimensional information formed by channel expansion is used as the prior information to improve the reconstruction effect of parallel imaging. Two main components are incorporated into the network design, namely variable augmentation technology and sum of squares (SOS) objective function. Variable augmentation provides the network with more high-dimensional prior information, which is helpful for the network to extract the deep feature information of the data. The SOS objective function is employed to solve the deficiency of k-space data training while speeding up convergence. Experimental results demonstrated its great potentials in super-resolution of MR images and accelerated parallel imaging reconstruction.