Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks

TL;DR

This paper introduces deep residual networks with separate magnitude and phase modules for fast, accurate MRI artifact removal, outperforming traditional methods in speed and quality.

Contribution

The paper presents a novel deep residual learning framework with magnitude and phase networks for accelerated MRI reconstruction, enabling rapid and effective artifact removal.

Findings

Successfully removed aliasing artifacts even with strong coherence.

Achieved reconstruction results significantly faster than existing methods.

Performed well with both single and multiple coil data.

Abstract

Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. The proposed deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data is available, the proposed approach works as an image domain post-processing algorithm. Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts. Comparisons using single and multiple coil show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing compressed sensing methods. The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately.