Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Super-Resolution, Unsupervised Learning
Motivation: Multi-contrast MRI super-resolution (MCSR) can effectively shorten the MRI acquisition time. Yet, existing deep learning-based approaches requiring paired low-resolution (LR) and high-resolution (HR) images for training are impractical in clinical settings.
Goal(s): We aim to propose an unsupervised model that can achieve MCSR without the need for ground-truth HR images.
Approach: We construct a network to generate HR images from its LR counterparts and reference images. Meanwhile, a cycle-consistency network and a reciprocal network are proposed to constrain the outputs.
Results: Experiments on two datasets demonstrate that our proposed model effectively restores HR images with clear anatomic details.
Impact: Our model facilitates multi-contrast MRI super-resolution in the absence of ground-truth HR images, which not only substantially reduces MRI acquisition time, but enables the obtaining of certain HR sequences that are difficult to acquire in clinical settings.
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