Generalized autocalibrating partially parallel acquisitions (GRAPPA)

By Mark A. Griswold, P. Jakob, R. Heidemann, M. Nittka, V. Jellús, Jianmin Wang, B. Kiefer, A. Haase. Magnetic Resonance in Medicine, 2002

5,643 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a79e019871750d3102b2b00e647c012fc0f39d19

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In Atomic and Molecular Physics, and Optics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

k‐t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI (2009, methodology): “For example, SMASH (SiMultaneous Acquisition of Spatial Harmonics) (4), GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions) (5), PILS (partially parallel imaging with localized sensitivities) (6), and SENSE (SENSitivity Encoding) (7) belong to parallel imaging techniques; whereas UN”
SEMAC: Slice Encoding for Metal Artifact Correction in MRI (2009, methodology): “The accelerated data acquisition can also benefit from recent advances in parallel imaging [26,27] and/or compressed sensing [28].”
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data (2019, methodology): “The forward model presented in Equation (1) is usually illconditioned due to sub-Nyquist sampling; therefore, regularizers that induce prior information are incorporated into the objective function for the reconstruction.”
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021, methodology): “This minimum requirement can be circumvented by incorporating other techniques such as parallel imaging [36]–[38] and compressed sensing [39].”
Second order total generalized variation (TGV) for MRI (2011, methodology): “Thus, TGV can be used in combination with all parallel imaging reconstruction methods that first generate individual coil images, which are then combined to a single sum of squares image in the last step, like GRAPPA (25).”
A survey on the magnetic resonance image denoising methods (2014, methodology): “, SiMultaneous Acquisition of Spatial Harmonics (SMASH), Autocalibrating SMASH (AUTO-SMASH), GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) [100].”
Ultra-Fast MRI of the Human Brain with Simultaneous Multi-Slice Imaging (2013, methodology): “It uses a separate GRAPPA-like kernel set [21] to estimate the k-space data of individual slices from the collapsed slice data.”
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018, methodology): “The raw data acquired from these independent channels can be combined using either a sensitivity encoding (SENSE) technique [8] or a generalised auto-calibrating (GRAPPA) method [9].”
Magnetic Resonance Fingerprinting (2013, neutral): “We have recently published data indicating that we can achieve ~10× reduction in imaging time for a 2D slice using parallel imaging alone 43,45,46 .”
Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging (2010, methodology): “Echo train shortening achieved by reducing the phase encoding steps by factor R using partial parallel acquisition techniques such as SENSE [11] or GRAPPA [10] and their derivatives, which is referred to as acceleration, or the use of partial Fourier sampling [9] can also be incorporated in this seq”
Improving diffusion MRI using simultaneous multi-slice echo planar imaging (2012, methodology): “Conventional accelerated 2D parallel imaging approaches (Griswold et al., 2002; Pruessmann et al., 1999; Sodickson and Manning, 1997) can greatly reduce the EPI readout or the echo train length by reducing the number of phase encoding steps by a factor of 2 to 4.”
Golden-Angle Radial Sparse Parallel MRI: Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling for Fast and Flexible Dynamic Volumetric MRI (2013, methodology): “The k-t acceleration methods, such as k-t BLAST/k-t SENSE (9), k-t GRAPPA (10), and SPEAR (11), are based on the fact that the representation of dynamic images in the combined spatial (x) and temporal Fourier (f) domain is typically sparse, which reduces the signal aliasing in x-f space due to regul”
ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA (2014, methodology): “[3] for the unknown variables gri at different positions in k-space, where the xi are known.”