Self-constraint non-iterative GRAPPA reconstruction with closed-form solution
US9310452B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 14, 2013 |
| Grant date | Apr 12, 2016 |
| Priority date | — |
| Expiry date | May 12, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01R33/5612
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Parallel magnetic resonance imaging (pMRI) reconstruction techniques are commonly used to reduce scan time by undersampling the k-space data. In GRAPPA, a k-space based pMRI technique, the missing k-space data are estimated by solving a set of linear equations; however, this set of equations does not take advantage of the correlations within the missing k-space data. All k-space data in a neighborhood acquired from a phased-array coil are correlated. The correlation can be estimated easily as a self-constraint condition, and formulated as an extra set of linear equations to improve the performance of GRAPPA. We propose a modified k-space based pMRI technique call self-constraint GRAPPA (SC-GRAPPA) which combines the linear equations of GRAPPA with these extra equations to solve for the missing k-space data. Since SC-GRAPPA utilizes a least-squares solution of the linear equations, it has a closed-form solution that does not require an iterative solver.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.