Keywords: Image Reconstruction, MR Fingerprinting, Manifold structured data, Locally low-rank
Motivation: Estimating high-quality parameter maps from highly undersampled measurements presents one of the major challenges in MR fingerprinting (MRF).
Goal(s): To propose a novel MRF reconstruction framework based on manifold structured data priors for high-quality parameter maps estimation.
Approach: We propose a novel MRF reconstruction framework leveraging manifold structured data priors to improve the accuracy of the reconstructed parameter maps. Additionally, we integrate a locally low-rank prior into the reconstruction framework to exploit local correlations within each patch and further enhance reconstruction performance.
Results: Experimental results demonstrate that our method can achieve significantly improved reconstruction performance with reduced computational time over the state-of-the-art methods.
Impact: By exploiting the manifold structure prior of MRF data, our method can better reconstruct detailed textures and provide accurate brain tissue characterization, thereby improving the diagnostic accuracy in clinical applications.
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