An Iterative Method for Parallel MRI SENSE-based Reconstruction in the Wavelet Domain

TL;DR

This paper introduces a novel wavelet domain regularization method for SENSE-based parallel MRI reconstruction, improving image quality and reducing artifacts especially under high reduction factors and low magnetic field conditions.

Contribution

The paper proposes an iterative SENSE reconstruction method with wavelet domain regularization and local convex constraints, enhancing image quality in challenging MRI scenarios.

Findings

Reduced artifacts in high reduction factor MRI reconstructions

Improved image quality at low magnetic field strength

Effective in vivo results on GRE and EPI MRI data

Abstract

To reduce scanning time and/or improve spatial/temporal resolution in some MRI applications, parallel MRI (pMRI) acquisition techniques with multiple coils acquisition have emerged since the early 1990s as powerful 3D imaging methods that allow faster acquisition of reduced Field of View (FOV) images. In these techniques, the full FOV image has to be reconstructed from the resulting acquired undersampled k-space data. To this end, several reconstruction techniques have been proposed such as the widely-used SENSE method. However, the reconstructed image generally presents artifacts when perturbations occur in both the measured data and the estimated coil sensitivity maps. In this paper, we aim at achieving good reconstructed image quality when using low magnetic field and high reduction factor. Under these severe experimental conditions, neither the SENSE method nor the Tikhonov regularization in the image domain give convincing results. To this aim, we present a novel method for SENSE-based reconstruction which proceeds with regularization in the complex wavelet domain. To further enhance the reconstructed image quality, local convex constraints are added in the regularization process. In vivo experiments carried out on Gradient-Echo (GRE) anatomical and Echo-Planar Imaging (EPI) functional MRI data at 1.5 Tesla indicate that the proposed algorithm provides reconstructed images with reduced artifacts for high reduction factor.