A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction

TL;DR

This paper introduces RECGAN-GR, a novel GAN-based method for MRI reconstruction that combines a new generator, multi-modal loss functions, and k-space correction to significantly improve image quality and speed up acquisition.

Contribution

It presents a new GAN architecture with a specialized generator and dual-domain loss functions, including a k-space correction block, for enhanced MRI image reconstruction.

Findings

PSNR improved by 4 dB over existing GAN methods

PSNR improved by 2 dB over traditional CNN methods

Achieves 5-10 times faster MRI acquisition

Abstract

Parallel imaging accelerates MRI data acquisition by acquiring additional sensitivity information with an array of receiver coils, resulting in fewer phase encoding steps. Because of fewer data requirements than parallel imaging, compressed sensing magnetic resonance imaging (CS-MRI) has gained popularity in the field of medical imaging. Parallel imaging and compressed sensing (CS) both reduce the amount of data captured in the k-space, which speeds up traditional MRI acquisition. As acquisition time is inversely proportional to sample count, forming an image from reduced k-space samples results in faster acquisition but with aliasing artifacts. For de-aliasing the reconstructed image, this paper proposes a novel Generative Adversarial Network (GAN) called RECGAN-GR that is supervised with multi-modal losses. In comparison to existing GAN networks, our proposed method introduces a novel generator network, RemU-Net, which is integrated with dual-domain loss functions such as weighted magnitude and phase loss functions, as well as parallel imaging-based loss, GRAPPA consistency loss. As refinement learning, a k-space correction block is proposed to make the GAN network self-resistant to generating unnecessary data, which speeds up the reconstruction process. Comprehensive results show that the proposed RECGAN-GR not only improves the PSNR by 4 dB over GAN-based methods but also by 2 dB over conventional state-of-the-art CNN methods available in the literature for single-coil data. The proposed work significantly improves image quality for low-retained data, resulting in five to ten times faster acquisition.