Method for reconstructing magnetic resonance spectrum based on deep learning
US11782111B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Jul 26, 2021 |
| Grant date | Oct 10, 2023 |
| Priority date | — |
| Expiry date | Jun 8, 2042 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02A90/30
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A new method for reconstructing a full spectrum from under-sampled magnetic resonance spectrum data by using a deep learning network. First, the exponential function is used to generate a time-domain signal of the magnetic resonance spectrum, and a zero-filling time-domain signal is obtained after the under-sampled operation is completed in the time domain. The zero-filling time-domain signal and the full spectrum corresponding to the full sampling are combined to form a training data set. Then, a data verification convolutional neural network model is established for magnetic resonance spectrum reconstruction, where the training data set is used to train neural network parameters to form a trained neural network. Finally, the under-sampled magnetic resonance time-domain signal is input to the trained data verification convolutional neural network, and the full magnetic resonance spectrum is reconstructed.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.