System and method for deep learning-based chemical shift artifact mitigation of non-Cartesian magnetic resonance imaging data
US12406412B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 30, 2023 |
| Grant date | Sep 2, 2025 |
| Priority date | — |
| Expiry date | Mar 7, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2210/41
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A computer-implemented method for generating a chemical shift artifact corrected reconstructed image from magnetic resonance imaging (MRI) data includes inputting into a trained deep neural network an image generated from the MRI data acquired during a non-Cartesian MRI scan of a subject. The method also includes utilizing the trained deep neural network to generate the chemical shift artifact corrected reconstructed image from the image, wherein the trained deep neural network was trained utilizing a tissue mixing model that models interactions between different tissue types to mitigate chemical shift artifacts. The method further includes outputting from the trained deep neural network the chemical shift artifact corrected reconstructed image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.