Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Compressed sensingWe present a Bayesian optimization approach to find the optimal regularization parameters for undersampled MRI reconstructions such as Compressed Sensing (CS). Bayesian optimization can find optimal points for expensive to evaluate functions by efficient sampling the next points to try by maximizing the expected improvement. Additionally, the use of pruning can speed up optimization by stopping early unpromising trials. We demonstrate the effectiveness of this optimization technique by finding optimal parameters for undersampled MRI using Total Variation and CS-Wavelet regularization. The parameters found with the proposed approach are comparable to does found by grid search but requiring shorter computational times.
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