Multi-task learning based regions-of-interest enhancement in PET image reconstruction
US12367622B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 22, 2022 |
| Grant date | Jul 22, 2025 |
| Priority date | — |
| Expiry date | Dec 12, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/441
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed is a method for region-of-interest enhanced PET image reconstruction based on multi-task learning, which comprises the following steps: firstly, acquiring a backprojection image of the PET original data, and designing a main task of establishing a mapping between the backprojection image and a reconstructed PET image by using a three-dimensional deep convolution neural network. A new auxiliary task 1 is designed to predict a computerized tomography (CT) image with the same anatomical structures as the PET image reconstructed from the backprojection image, so as to reduce the noise in the reconstructed PET image by using the local smoothing information of the high-resolution CT image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.