Coupled Dictionary Learning for Multi-contrast MRI Reconstruction

TL;DR

This paper introduces a novel coupled dictionary learning method for multi-contrast MRI reconstruction that leverages guidance contrast to improve image quality, demonstrating superior results over existing techniques.

Contribution

The proposed approach uniquely combines coupled dictionary learning, sparse denoising, and k-space consistency to enhance multi-contrast MRI reconstruction using guidance contrast.

Findings

Improved MRI reconstruction quality with guidance contrast.

Outperforms state-of-the-art methods in numerical experiments.

Effective in handling under-sampled clinical MRI data.

Abstract

Medical imaging tasks often involve multiple contrasts, such as T1- and T2-weighted magnetic resonance imaging (MRI) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities. In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast. Our approach consists of three stages: coupled dictionary learning, coupled sparse denoising, and $k$-space consistency enforcing. The first stage learns a group of dictionaries that capture correlations among multiple contrasts. By capitalizing on the learned adaptive dictionaries, the second stage performs joint sparse coding to denoise the corrupted target image with the aid of a guidance contrast. The third stage enforces consistency between the denoised image and the measurements in the $k$-space domain. Numerical experiments on the retrospective under-sampling of clinical MR images demonstrate that incorporating additional guidance contrast via our design improves MRI reconstruction, compared to state-of-the-art approaches.