Apparatus and method for sinogram restoration in computed tomography (CT) using adaptive filtering with deep learning (DL)
US11176428B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 1, 2019 |
| Grant date | Nov 16, 2021 |
| Priority date | — |
| Expiry date | Aug 23, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.