Apparatus and method that uses deep learning to correct computed tomography (CT) with sinogram completion of projection data
US11039806B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 20, 2018 |
| Grant date | Jun 22, 2021 |
| Priority date | — |
| Expiry date | May 19, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/441
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.