A radial self‐calibrated (RASCAL) generalized autocalibrating partially parallel acquisition (GRAPPA) method using weight interpolation

Codella, Noel; Spincemaille, Pascal; Prince, Martin R.; Wang, Yi

doi:10.1002/nbm.1630

Cited by 9 publications

(8 citation statements)

References 31 publications

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“…For example, undersampled data may be acquired along interleaved trajectories that are then merged in a sliding window manner to form a calibration dataset with reduced temporal bandwidth . There are also methods that synthesize a full calibration dataset from only the Nyquist sampled region at the center of k ‐space or that interpolate GRAPPA weights directly from undersampled data .…”

Section: Non‐cartesian Parallel Imagingmentioning

confidence: 99%

Non‐Cartesian parallel imaging reconstruction

Wright

Hamilton

Griswold

et al. 2014

Magnetic Resonance Imaging

102

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Non-Cartesian parallel imaging has played an important role in reducing data acquisition time in MRI. The use of non-Cartesian trajectories can enable more efficient coverage of k-space, which can be leveraged to reduce scan times. These trajectories can be undersampled to achieve even faster scan times, but the resulting images may contain aliasing artifacts. Just as Cartesian parallel imaging can be employed to reconstruct images from undersampled Cartesian data, non-Cartesian parallel imaging methods can mitigate aliasing artifacts by using additional spatial encoding information in the form of the non-homogeneous sensitivities of multi-coil phased arrays. This review will begin with an overview of non-Cartesian k-space trajectories and their sampling properties, followed by an in-depth discussion of several selected non-Cartesian parallel imaging algorithms. Three representative non-Cartesian parallel imaging methods will be described, including Conjugate Gradient SENSE (CG SENSE), non-Cartesian GRAPPA, and Iterative Self-Consistent Parallel Imaging Reconstruction (SPIRiT). After a discussion of these three techniques, several potential promising clinical applications of non-Cartesian parallel imaging will be covered.

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Section: Non‐cartesian Parallel Imagingmentioning

confidence: 99%

Non‐Cartesian parallel imaging reconstruction

Wright

Hamilton

Griswold

et al. 2014

Magnetic Resonance Imaging

102

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“…2. A GRAPPA kernel of size 2 × 3 in the spiral arm × read direction was used; the 2 × 3 kernel has been suggested previously by Seiberlich et al (8) and Codella et al (14). It is important to note that other direct, noniterative spiral GRAPPA methods such as those described by Heberlein and Hu (6) and Heidemann et al (7) would be more difficult to implement with this type of data due to the variable density, and thus irregularity, of the spiral data.…”

Section: Methodsmentioning

confidence: 99%

Improved temporal resolution in cardiac imaging using through‐time spiral GRAPPA

Seiberlich

Lee

Ehses

et al. 2011

Magnetic Resonance in Med

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Previous work has shown that the use of radial GRAPPA for the reconstruction of undersampled real-time free-breathing cardiac data allows for frame rates of up to 30 images / s. It is well known that the spiral trajectory offers a higher scan efficiency compared to radial trajectories. For this reason, we have developed a novel through-time spiral GRAPPA method and demonstrate its application to real-time cardiac imaging. By moving from the radial trajectory to the spiral trajectory, the temporal resolution can be further improved at lower acceleration factors compared to radial GRAPPA. In addition, the image quality is improved compared to those generated using the radial trajectory due to the lower acceleration factor. Here we show that 2D frame rates of up to 56 images / s can be achieved using this parallel imaging method with the spiral trajectory.

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“…The GRAPPA category, where unsampled raw data is calculated from nearby samples in the raw data, depends on the accurate estimate of the “weighting factors” from sampled to unsampled points; for trajectories over the raw data such as radial and spiral sampling, the estimation of the GRAPPA weights is complicated due to non-equidistant spacing between k-space points. Alternative strategies for calculation of the GRAPPA weights have been proposed to rectify this difficulty for GRAPPA [ 86 – 88 ]. Recently 'through-time' calibration techniques for radial GRAPPA [ 89 ] and spiral GRAPPA [ 90 ] have been developed, which calculate the weights using multiple fully-sampled prescans.…”

Section: Acceleration Through Sub-nyquist Reconstructionmentioning

confidence: 99%

A review of 3D first-pass, whole-heart, myocardial perfusion cardiovascular magnetic resonance

Fair

Gatehouse

DiBella

et al. 2015

Journal of Cardiovascular Magnetic Resonance

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A comprehensive review is undertaken of the methods available for 3D whole-heart first-pass perfusion (FPP) and their application to date, with particular focus on possible acceleration techniques. Following a summary of the parameters typically desired of 3D FPP methods, the review explains the mechanisms of key acceleration techniques and their potential use in FPP for attaining 3D acquisitions. The mechanisms include rapid sequences, non-Cartesian k-space trajectories, reduced k-space acquisitions, parallel imaging reconstructions and compressed sensing. An attempt is made to explain, rather than simply state, the varying methods with the hope that it will give an appreciation of the different components making up a 3D FPP protocol. Basic estimates demonstrating the required total acceleration factors in typical 3D FPP cases are included, providing context for the extent that each acceleration method can contribute to the required imaging speed, as well as potential limitations in present 3D FPP literature. Although many 3D FPP methods are too early in development for the type of clinical trials required to show any clear benefit over current 2D FPP methods, the review includes the small but growing quantity of clinical research work already using 3D FPP, alongside the more technical work. Broader challenges concerning FPP such as quantitative analysis are not covered, but challenges with particular impact on 3D FPP methods, particularly with regards to motion effects, are discussed along with anticipated future work in the field.

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A radial self‐calibrated (RASCAL) generalized autocalibrating partially parallel acquisition (GRAPPA) method using weight interpolation

Cited by 9 publications

References 31 publications

Non‐Cartesian parallel imaging reconstruction

Non‐Cartesian parallel imaging reconstruction

Improved temporal resolution in cardiac imaging using through‐time spiral GRAPPA

A review of 3D first-pass, whole-heart, myocardial perfusion cardiovascular magnetic resonance

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