Keywords: Analysis/Processing, MSK
Motivation: The level of unsaturated fatty acid in bone is an useful biomarker for assessing the health condition of bone.
Goal(s): Predict the fraction of unsaturated fatty acid from a reduced number of gradient echo image data and the uncertainty level simultaneously.
Approach: A Bayesian neural network is trained to refine the coarse parametric map fitted by a reduced number of images to the parametric map fitted by the full number of scans.
Results: Experiments on knee data show that the model can achieve a relative error of less than 5% and the uncertainty prediction reflects the confidence.
Impact: The feasibility of mapping the fraction of unsaturated fatty acid from a reduced number of imaging data in a learning-based way is validated.
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