Apparatus and method for medical image reconstruction using deep learning for computed tomography (CT) image noise and artifacts reduction
US11517197B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 6, 2017 |
| Grant date | Dec 6, 2022 |
| Priority date | — |
| Expiry date | Oct 6, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/441
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
A method and apparatus is provided that uses a deep learning (DL) network to reduce noise and artifacts in reconstructed medical images, such as images generated using computed tomography, positron emission tomography, and magnetic resonance imaging. The DL network can operate either on pre-reconstruction data or on a reconstructed image. The DL network can be an artificial neural network or a convolutional neural network (e.g., using a three-channel volumetric kernel architecture). Different neural networks can be trained depending on the noise level, scanning protocol, or the anatomic, diagnostic or clinical objective of the reconstructed image (e.g., by partitioning the training data into noise-level range and training respective DL networks for each range). Further, the DL networks can be trained to mitigate artifacts, such as the cone-beam artifact.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.