Keywords: Image Reconstruction, Data Processing
Motivation: End-to-End (E2E) trained unrolled algorithms recover MR images with high quality. However, they have large memory demands during training. In addition, these maximum a posteriori methods cannot provide uncertainty estimates.
Goal(s): To develop a memory-efficient framework for E2E learning of the posterior probability distribution.
Approach: We model the posterior distribution as a combination of the data-consistent-determined likelihood term and the prior, represented using a Convolutional Neural Network whose weights are learned in an E2E fashion using maximum likelihood optimization.
Results: The proposed E2E training strategy requires significantly less memory than unrolling. In addition, the model facilitates sampling and provides uncertainty estimates.
Impact: The higher memory efficiency of the proposed E2E scheme makes it an attractive option for image reconstruction problems of large dimensions. The learned posterior model provides a minimum mean square estimate and uncertainty maps, which unrolled approaches cannot offer.
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