Keywords: Machine Learning/Artificial Intelligence, Elastography, Stiffness Maps
Motivation: AI has proven itself in improving MRI reconstructions, yet its potential in estimating MRE stiffness maps with rapidly acquired data remains unexplored.
Goal(s): Investigating the untapped potential of AI in MR Elastography, promising advancements in diagnostic accuracy and efficiency of this modality.
Approach: 3D FEM was used to create the dataset, and Deep Learning was used to reconstruct the stiffness maps from sparse wavefield data
Results: The Deep Learning model was able to effectively reconstruct the MRE-Stiffness maps at high acceleration rates. The model's performance was reported in terms of SSIM.
Impact: This innovative study leverages deep learning and finite element modeling to reconstruct liver stiffness maps from under-sampled MR Elastography data. The proposed AI approach demonstrates robustness and potential for accelerating stiffness estimation, paving the way for improved tissue stiffness estimation.
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