Data Driven Tight Frame for Compressed Sensing MRI Reconstruction via Off-the-Grid Regularization

TL;DR

This paper introduces a novel off-the-grid regularization method for compressed sensing MRI that leverages low-rank Hankel matrices and data-driven tight frames, improving reconstruction quality over existing methods.

Contribution

It proposes a new off-the-grid regularization model using low-rank Hankel matrices and data-driven tight frames for CS-MRI reconstruction, with a guaranteed convergent algorithm.

Findings

Outperforms existing CS-MRI reconstruction methods.

Utilizes low-rank Hankel matrix structure for regularization.

Employs a convergent proximal alternating minimization algorithm.

Abstract

Recently, the finite-rate-of-innovation (FRI) based continuous domain regularization is emerging as an alternative to the conventional on-the-grid sparse regularization for the compressed sensing (CS) due to its ability to alleviate the basis mismatch between the true support of the shape in the continuous domain and the discrete grid. In this paper, we propose a new off-the-grid regularization for the CS-MRI reconstruction. Following the recent works on two dimensional FRI, we assume that the discontinuities/edges of the image are localized in the zero level set of a band-limited periodic function. This assumption induces the linear dependencies among the Fourier samples of the gradient of the image, which leads to a low rank two-fold Hankel matrix. We further observe that the singular value decomposition of a low rank Hankel matrix corresponds to an adaptive tight frame system which can represent the image with sparse canonical coefficients. Based on this observation, we propose a data driven tight frame based off-the-grid regularization model for the CS-MRI reconstruction. To solve the nonconvex and nonsmooth model, a proximal alternating minimization algorithm with a guaranteed global convergence is adopted. Finally, the numerical experiments show that our proposed data driven tight frame based approach outperforms the existing approaches.