Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network

TL;DR

This paper introduces a deep learning-based dual-domain framework for multi-contrast MRI reconstruction that minimizes cross-contrast misalignment, improves accuracy, and outperforms existing methods at high acceleration rates.

Contribution

The novel synthesis-based fusion network effectively combines data synthesis, registration, and reconstruction in dual domains, enhancing multi-contrast MRI quality.

Findings

Outperforms state-of-the-art methods at up to 8-fold acceleration

Effective reduction of inter-scan motion artifacts

Demonstrates robustness through extensive ablation studies

Abstract

Purpose: To develop an efficient dual-domain reconstruction framework for multi-contrast MRI, with the focus on minimising cross-contrast misalignment in both the image and the frequency domains to enhance optimisation. Theory and Methods: Our proposed framework, based on deep learning, facilitates the optimisation for under-sampled target contrast using fully-sampled reference contrast that is quicker to acquire. The method consists of three key steps: 1) Learning to synthesise data resembling the target contrast from the reference contrast; 2) Registering the multi-contrast data to reduce inter-scan motion; and 3) Utilising the registered data for reconstructing the target contrast. These steps involve learning in both domains with regularisation applied to ensure their consistency. We also compare the reconstruction performance with existing deep learning-based methods using a dataset of brain MRI scans. Results: Extensive experiments demonstrate the superiority of our proposed framework, for up to an 8-fold acceleration rate, compared to state-of-the-art algorithms. Comprehensive analysis and ablation studies further present the effectiveness of the proposed components. Conclusion:Our dual-domain framework offers a promising approach to multi-contrast MRI reconstruction. It can also be integrated with existing methods to further enhance the reconstruction.