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Through-time non-cartesian GRAPPA calibration

A whole-process, calibration data technology, applied in the direction of measuring devices, measuring magnetic variables, instruments, etc., can solve the problems of unrealistic, limited radiation GRAPPA maximum undersampling, etc.

Active Publication Date: 2011-05-04
CASE WESTERN RESERVE UNIV
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  • Application Information

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Problems solved by technology

In addition, since only a single fully sampled dataset is used for calibration, and due to its inherent sensitivity to variation in point structure across regions, traditional radiographic GRAPPA relies on high quality fully sampled training data, which may require large Scale Signal Averaging
Traditionally, this acquisition may be impractical for certain applications such as contrast-enhanced dynamic learning
In addition, errors caused by collecting rays too widely spaced limit the maximum possible undersampling of radiographic GRAPPA

Method used

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  • Through-time non-cartesian GRAPPA calibration

Examples

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Embodiment Construction

[0023] Example systems and methods acquire data at different time points and perform global calibration for radiological GRAPPA. The calibration data can be a fully sampled calibration set, but can also be less than a fully sampled calibration data set. By collecting calibration data throughout, multiple copies of each point can be collected. Using these multiple copies, reconstruction kernels can be derived separately for each desired reconstruction point in the original data. Since the exact kernel configuration can be computed for each point, the resulting reconstruction kernel will support a higher speedup factor for undersampling than previously thought possible for radial GRAPPA. The radiometric calibration data is acquired according to a plan to acquire radiographic rays of the same configuration as the rays to be used in the reconstruction. Since the data is collected in the whole process, the reconstruction and verification of rays collected multiple times in the wh...

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Abstract

The application relates to through-time non-cartesian GRAPPA calibration. Example systems and methods control a parallel magnetic resonance imaging (pMRI) apparatus to acquire radial calibration data sets throughout time. Example systems and methods also control the pMRI apparatus to acquire an under-sampled radial data set from the object to be imaged. Example systems and methods then control the pMRI apparatus to reconstruct an image of the object to be imaged from the under-sampled radial data set. The reconstruction depends, at least in part, on a through-time radial GRAPPA calibration where a value for a point missing from k-space in the under-sampled radial data set is computed using a GRAPPA weight set calibrated and applied for the missing point. The GRAPPA weight set is computed from data in the radial calibration data sets.

Description

technical field [0001] The present invention relates to generalized auto-calibrating partially parallel acquisition (GRAPPA) technology, in particular to through-time radial GRAPPA calibration. Background technique [0002] Conventional Generalized Self-Aligning Partially Parallel Acquisition (GRAPPA) produces an uncombined coil image for each coil in a receive coil array used in a parallel magnetic resonance imaging (pMRI) setup. GRAPPA reconstructs missing lines in each coil element by forming linear combinations of adjacent lines to reconstruct individual missing data points. The weights used for these linear combinations were derived from the fits formed between the otherwise acquired lines using a pseudo-inverse operation. GRAPPA is described in Griswold, et al., Proceedings of the ISMRM, Vol. 7, Issue 6, Pg. 1202-1210 (2002). [0003] Traditional Radial GRAPPA (Radial GRAPPA) acquires data and generates a reconstruction kernel including GRAPPA weights. This reconstr...

Claims

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Application Information

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IPC IPC(8): G01R33/565
CPCG01R33/4824G01R33/5611
Inventor 马克·A·格里斯沃尔德杰弗里·杜尔克尼科尔·赛博尔里奇
Owner CASE WESTERN RESERVE UNIV
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