Motivation: Multi-contrast MR images contain complementary information critical for quantitative MRI and disease diagnosis. Efficiently harnessing both contrast-variant and contrast-invariant information is vital for enhancing reconstruction quality and reducing acquisition times. Goal(s): We aim to develop a joint reconstruction model to efficiently reconstruct multi-contrast MR images. Approach: We propose a coarse-to-fine network architecture to effectively utilize inter-contrast information through decoupling, alignment, and fusion modules, while also leveraging intra-contrast information via multi-scale fusion. Results: Our model achieves a 1.39dB improvement in PSNR and a 2.48% increase in SSIM over the state-of-the-art methods for acceleration on an in-house multi-contrast dataset. Impact: Our joint reconstruction model significantly reduces acquisition times for multi-contrast MRI and shows promise for quantitative MRI, improving parametric map estimation. It also holds potential for other clinical applications requiring multiple imaging modalities.
Dan et al. (Tue,) studied this question.