Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based framework for compressive sensing MRI reconstruction that preserves image structure and details, achieving high-quality, fast, and noise-robust results suitable for real-time clinical applications.

Contribution

The work presents a novel GAN architecture with structural similarity loss and dense residual connections for improved CS-MRI reconstruction.

Findings

Outperforms state-of-the-art methods in image quality

Provides reconstruction in milliseconds for real-time use

Demonstrates robustness to noise

Abstract

Compressive sensing magnetic resonance imaging (CS-MRI) accelerates the acquisition of MR images by breaking the Nyquist sampling limit. In this work, a novel generative adversarial network (GAN) based framework for CS-MRI reconstruction is proposed. Leveraging a combination of patch-based discriminator and structural similarity index based loss, our model focuses on preserving high frequency content as well as fine textural details in the reconstructed image. Dense and residual connections have been incorporated in a U-net based generator architecture to allow easier transfer of information as well as variable network length. We show that our algorithm outperforms state-of-the-art methods in terms of quality of reconstruction and robustness to noise. Also, the reconstruction time, which is of the order of milliseconds, makes it highly suitable for real-time clinical use.