Apparatus and method using physical model based deep learning (DL) to improve image quality in images that are reconstructed using computed tomography (CT)
US10925568B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 12, 2019 |
| Grant date | Feb 23, 2021 |
| Priority date | — |
| Expiry date | Jul 13, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/441
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and apparatus is provided that uses a deep learning (DL) network to improve the image quality of computed tomography (CT) images, which were reconstructed using an analytical reconstruction method. The DL network is trained to use physical-model information in addition to the analytical reconstructed images to generate the improved images. The physical-model information can be generated, e.g., by estimating a gradient of the objective function (or just the data-fidelity term) of a model-based iterative reconstruction (MBIR) method (e.g., by performing one or more iterations of the MBIR method). The MBIR method can incorporate physical models for X-ray scatter, detector resolution/noise/non-linearities, beam-hardening, attenuation, and/or the system geometry. The DL network can be trained using input data comprising images reconstructed using the analytical reconstruction method and target data comprising images reconstructed using the MBIR method.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.