Apparatus and method for dual-energy computed tomography (CT) image reconstruction using sparse kVp-switching and deep learning
US10945695B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 21, 2018 |
| Grant date | Mar 16, 2021 |
| Priority date | — |
| Expiry date | Apr 8, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/436
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A deep learning (DL) network reduces artifacts in computed tomography (CT) images based on complementary sparse-view projection data generated from a sparse kilo-voltage peak (kVp)-switching CT scan. The DL network is trained using input images exhibiting artifacts and target images exhibiting little to no artifacts. Another DL network can be trained to perform image-domain material decomposition of the artifact-mitigated images by being trained using target images in which beam hardening is corrected and spatial variations in the X-ray beam are accounted for. Further, material decomposition and artifact mitigation can be integrated in a single DL network that is trained using as inputs reconstructed images having artifacts and as targets material images without artifacts with beam-hardening corrections, etc. Further, the target material images can be transformed using a whitening transform to decorrelate noise.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.