Deep-learning-based scatter estimation and correction for X-ray projection data and computer tomography (CT)
US10937206B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 18, 2019 |
| Grant date | Mar 2, 2021 |
| Priority date | — |
| Expiry date | Jul 20, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/452
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.