Keywords: Image Reconstruction, Image Reconstruction, Multi-Contrast, Joint Optimization, Optimized Sampling
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords