Posterior image sampling using statistical learning model
US10672153B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 13, 2018 |
| Grant date | Jun 2, 2020 |
| Priority date | — |
| Expiry date | Nov 13, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/441
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Image reconstruction can include using a statistical or machine learning, MAP estimator, or other reconstruction technique to produce a reconstructed image from acquired imaging data. A Conditional Generative Adversarial Network (CGAN) technique can be used to train a Generator, using a Discriminator, to generate posterior distribution sampled images that can be displayed or further processed such as to help provide uncertainty information about a mean reconstruction image. Such uncertainty information can be useful to help understand or even visually modify the mean reconstruction image. Similar techniques can be used in a segmentation use-case, instead of a reconstruction use case. The uncertainty information can also be useful for other post-processing techniques.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.