Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization

TL;DR

This paper introduces a fast MRI reconstruction framework leveraging low-rank Hankel regularization on 1D vectors, significantly reducing computation time while maintaining high image quality, especially in undersampled scenarios.

Contribution

It proposes a novel low-rank Hankel regularization method that avoids large matrix operations, enabling faster MRI reconstructions with improved artifact removal.

Findings

8x faster than STDLRSPIRiT in in-vivo tests

Effective removal of undersampling artifacts

Enhanced image quality with additional information incorporation

Abstract

The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time. However, the low-rank structured approaches demand considerable memory consumption and are time-consuming due to a noticeable number of matrix operations performed on the huge-size block Hankel-like matrix. In this work, we proposed a novel framework to utilize the low-rank property but meanwhile to achieve faster reconstructions and promising results. The framework allows us to enforce the low-rankness of Hankel matrices constructing from 1D vectors instead of 2D matrices from 1D vectors and thus avoid the construction of huge block Hankel matrix for 2D k-space matrices. Moreover, under this framework, we can easily incorporate other information, such as the smooth phase of the image and the low-rankness in the parameter dimension, to further improve the image quality. We built and validated two models for parallel and parameter magnetic resonance imaging experiments, respectively. Our retrospective in-vivo results indicate that the proposed approaches enable faster reconstructions than the state-of-the-art approaches, e.g., about 8x faster than STDLRSPIRiT, and faithful removal of undersampling artifacts.