Deep learning systems and methods of removal of truncation artifacts in magnetic resonance images
US12045917B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 22, 2020 |
| Grant date | Jul 23, 2024 |
| Priority date | — |
| Expiry date | Sep 8, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30004
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method of removing truncation artifacts in magnetic resonance (MR) images is provided. The method includes receiving a crude image that is based on partial k-space data from a partial k-space that is asymmetrically truncated in at least one k-space dimension. The method also includes analyzing the crude image using a neural network model trained with a pair of pristine images and corrupted images. The corrupted images are based on partial k-space data from partial k-spaces truncated in one or more partial sampling patterns. The pristine images are based on full k-space data corresponding to the partial k-space data of the corrupted images, and target output images of the neural network model are the pristine images. The method further includes deriving an improved image of the crude image based on the analysis, wherein the derived improved image includes reduced truncation artifacts and increased high spatial frequency data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.