Method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction
US11756161B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 7, 2021 |
| Grant date | Sep 12, 2023 |
| Priority date | — |
| Expiry date | Apr 2, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/464
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present application relates to a method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction, and relates to the field of deep learning. The method includes connecting layers of the encoder with layers of the decoder by skip connection to provide a U-Net type picture generator; generating a group of generative adversarial networks by matching a plurality of picture generators with a plurality of discriminators in one-to-one manner; obtaining a first multi-task learning-type generative adversarial network; designing a joint loss function 1 for improving image quality; and training the first multi-task learning-type generative adversarial network according to the joint loss function 1 in combination with an optimizer to provide a second multi-task learning-type generative adversarial network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.