Fast multi-contrast MRI using joint multiscale energy model

TL;DR

This paper presents a CNN-based multiscale energy model for joint reconstruction of multi-contrast MRI data from undersampled scans, significantly improving image quality and detail preservation.

Contribution

It introduces a novel joint multiscale energy model that leverages redundancies across contrasts for enhanced MRI reconstruction, using a maximum a posteriori framework.

Findings

Sharper reconstructions with preserved fine details.

Effective utilization of multi-contrast redundancies.

Generalizable approach for various multi-contrast MRI settings.

Abstract

The acquisition of 3D multicontrast MRI data with good isotropic spatial resolution is challenged by lengthy scan times. In this work, we introduce a CNN-based multiscale energy model to learn the joint probability distribution of the multi-contrast images. The joint recovery of the contrasts from undersampled data is posed as a maximum a posteriori estimation scheme, where the learned energy serves as the prior. We use a majorize-minimize algorithm to solve the optimization scheme. The proposed model leverages the redundancies across different contrasts to improve image fidelity. The proposed scheme is observed to preserve fine details and contrast, offering sharper reconstructions compared to reconstruction methods that independently recover the contrasts. While we focus on 3D MPNRAGE acquisitions in this work, the proposed approach is generalizable to arbitrary multi-contrast settings.