Title: Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks,
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.