pISTA-SENSE-ResNet for Parallel MRI Reconstruction

TL;DR

This paper introduces pISTA-SENSE-ResNet, a deep learning network for parallel MRI reconstruction that combines sparse iterative reconstruction principles with residual structures, achieving faster and more accurate results.

Contribution

The paper proposes a novel deep learning architecture based on sparse iterative reconstruction and residual learning for improved parallel MRI image reconstruction.

Findings

Lower reconstruction error compared to existing methods

More stable performance across different acceleration factors

Faster reconstruction speed with high image quality

Abstract

Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time. Although imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstruction images with a fast reconstruction speed remains a challenge. Recently, deep learning approaches have attracted a lot of attention for its encouraging reconstruction results but without a proper interpretability. In this letter, to enable high-quality image reconstruction for the parallel magnetic resonance imaging, we design the network structure from the perspective of sparse iterative reconstruction and enhance it with the residual structure. The experimental results of a public knee dataset show that compared with the optimization-based method and the latest deep learning parallel imaging methods, the proposed network has less error in reconstruction and is more stable under different acceleration factors.