Iterative Self-consistent Parallel Magnetic Resonance Imaging Reconstruction based on Nonlocal Low-Rank Regularization

TL;DR

This paper introduces a novel MRI reconstruction method called NLR-SPIRiT that combines nonlocal low-rank regularization with the SPIRiT model, leveraging image self-similarity and calibration consistency for improved results.

Contribution

It proposes integrating nonlocal low-rank regularization into the SPIRiT framework using weighted nuclear norm and NE-ADMM, enhancing MRI reconstruction performance.

Findings

Outperforms state-of-the-art methods in objective metrics

Demonstrates superior visual reconstruction quality

Effectively utilizes nonlocal self-similarity in MRI images

Abstract

Iterative self-consistent parallel imaging reconstruction (SPIRiT) is an effective self-calibrated reconstruction model for parallel magnetic resonance imaging (PMRI). The joint L1 norm of wavelet coefficients and joint total variation (TV) regularization terms are incorporated into the SPIRiT model to improve the reconstruction performance. The simultaneous two-directional low-rankness (STDLR) in k-space data is incorporated into SPIRiT to realize improved reconstruction. Recent methods have exploited the nonlocal self-similarity (NSS) of images by imposing nonlocal low-rankness of similar patches to achieve a superior performance. To fully utilize both the NSS in Magnetic resonance (MR) images and calibration consistency in the k-space domain, we propose a nonlocal low-rank (NLR)-SPIRiT model by incorporating NLR regularization into the SPIRiT model. We apply the weighted nuclear norm (WNN) as a surrogate of the rank and employ the Nash equilibrium (NE) formulation and alternating direction method of multipliers (ADMM) to efficiently solve the NLR-SPIRiT model. The experimental results demonstrate the superior performance of NLR-SPIRiT over the state-of-the-art methods via three objective metrics and visual comparison.