Magnetic resonance image reconstruction with deep reinforcement learning
US10573031B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 6, 2017 |
| Grant date | Feb 25, 2020 |
| Priority date | — |
| Expiry date | May 7, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2210/41
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Deep reinforcement machine learning is used to control denoising (e.g., image regularizer) in iterative reconstruction for MRI compressed sensing. Rather than requiring different machine-learnt networks for different scan settings (e.g., acceleration of the MR compressed sensing), reinforcement learning creates a policy of actions to provide denoising and data fitting through iterations of the reconstruction given a range of different scan settings. This allows a user to scan as appropriate for the patient, the MR system, the application, and/or preferences while still providing an optimized reconstruction under sampling resulting from the MR compressed sensing.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.