Keywords: Image Reconstruction, AI/ML Image Reconstruction
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.
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