Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
Motivation: MRI reconstruction models are typically trained for specific acceleration factors. Multiple model variants are required to accommodate varying acceleration factors in practice.
Goal(s): We aim to develop a unified reconstruction model capable of handling arbitrarily accelerated MRI scans.
Approach: We first use self-supervised learning to obtain feature representations of fully-sampled and arbitrarily sub-sampled data. Then, we employ latent diffusion model to map feature representations of sub-sampled data to those of fully-sampled data.
Results: Experiments show that the use of feature transferring in our unified model brings an average performance gain of 3.86dB in PSNR for acceleration factors of 2x, 3x, and 4x.
Impact: We adopt feature representation transfer in the field of MRI reconstruction to address a practical issue largely overlooked by existing studies. Our adaptive reconstruction model can significantly simplify the deployment of MR reconstruction model and reduce the development costs.
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