Joint Optimization Sampling and Reconstruction of Multi-Contrast MRI under Specific Scan Sequence Combination

Motivation: The current multi-contrast MRI sampling and reconstruction methods cannot efficiently collect complementary information to achieve better reconstruction performance. Goal(s): A method was designed to generate corresponding sampling masks for each contrast image in a multi-sequence clinical scanning scenario, collect the optimal complementary information for better application in multi-contrast joint reconstruction. Approach: We jointly optimized the sampling and reconstruction of multi-contrast images, and designed learnable acceleration ratio and decoder feature fusion for images with different contrasts. Results: The PSNR and SSIM metrics of reconstructed images have significantly improved, and different sampling masks can be generated for different contrasts and sampling order. Impact: The method of jointly optimizing the sampling and reconstruction of multi-contrast images in a single scan may provide a powerful tool for accelerating and optimizing the MRI scanning process and improving the reconstructed quality of the multi-contrast images.