Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation

TL;DR

This paper introduces structure-guided total variation methods for multi-contrast MRI reconstruction, leveraging structural similarities between contrasts to improve image quality and edge definition over traditional total variation approaches.

Contribution

It proposes two novel modifications of total variation that incorporate structural prior knowledge, enhancing multi-contrast MRI reconstruction quality.

Findings

Exploiting structural information improves reconstruction metrics like PSNR and SSIM.

Directional priors produce images with sharper, well-defined edges.

The methods outperform separate total variation reconstructions across tested datasets.

Abstract

Magnetic resonance imaging (MRI) is a versatile imaging technique that allows different contrasts depending on the acquisition parameters. Many clinical imaging studies acquire MRI data for more than one of these contrasts---such as for instance T1 and T2 weighted images---which makes the overall scanning procedure very time consuming. As all of these images show the same underlying anatomy one can try to omit unnecessary measurements by taking the similarity into account during reconstruction. We will discuss two modifications of total variation---based on i) location and ii) direction---that take structural a priori knowledge into account and reduce to total variation in the degenerate case when no structural knowledge is available. We solve the resulting convex minimization problem with the alternating direction method of multipliers that separates the forward operator from the prior. For both priors the corresponding proximal operator can be implemented as an extension of the fast gradient projection method on the dual problem for total variation. We tested the priors on six data sets that are based on phantoms and real MRI images. In all test cases exploiting the structural information from the other contrast yields better results than separate reconstruction with total variation in terms of standard metrics like peak signal-to-noise ratio and structural similarity index. Furthermore, we found that exploiting the two dimensional directional information results in images with well defined edges, superior to those reconstructed solely using a priori information about the edge location.