Iterative GRAPPA (iGRAPPA) for improved parallel imaging reconstruction

  title={Iterative GRAPPA (iGRAPPA) for improved parallel imaging reconstruction},
  author={Tiejun Zhao and Xiaoping P. Hu},
  journal={Magnetic Resonance in Medicine},
In this work an iterative reconstruction method based on generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction is introduced. In the new method the reconstructed lines are used to reestimate and refine the weights from all the acquired data by applying the GRAPPA procedure iteratively with regularization. Both phantom and in vivo MRI experiments demonstrated that, compared to GRAPPA, the iterative approach reduces parallel imaging artifacts and permits high‐quality… 

Cross-sampled GRAPPA for parallel MRI

A cross sampling method is proposed to acquire the ACS lines orthogonal to the reduced lines, which can effectively reduce the aliasing artifacts of GRAPPA when high acceleration is desired.

Calibrationless parallel MRI using CLEAR

  • J. TrzaskoA. Manduca
  • Computer Science
    2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)
  • 2011
This work presents a novel image-domain approach to calibration-free parallel imaging (CPI) reconstruction called CLEAR, and investigates its computational complexity and memory footprint, demonstrates its standalone application to undersampled Cartesian acquisitions, and discusses generalizations and future directions of investigation.

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Empirical mode decomposition based GRAPPA reconstruction algorithm for parallel MRI

The results of experimental tests demonstrate that the proposed EMD domainGRAPPA reconstruction method can result in better image quality than that of the conventional GRAPPA, and can be incorporated with other k-space based reconstruction algorithms for further reduction of reconstruction artifacts, in particular, at higher acceleration factor.

A kernel approach to parallel MRI reconstruction

Experimental results demonstrate that the proposed kernel GRAPPA method can significantly improve the reconstruction quality over the existing methods.

Quantitative assessment of the parallel MRI reconstruction using background noise uniformity

  • Fu-Hsing WuHsin-Chih Lo
  • Medicine, Physics
    2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
  • 2015
This relationship between the background noise uniformity of the GRAPPA reconstructed image and the root mean square (RMS) error of this reconstructed image with respect to the reference image was examined and found to be approximately proportional to the RMS error of the same reconstructed image.

Instrument Variables for Reducing Noise in Parallel MRI Reconstruction

A new framework based on errors-in-variables (EIV) model is developed and provides possibilities that noiseless GRAPPA reconstruction could be achieved by existing methods that solve EIV problem other than IV method.

parallel imaging reconstruction using modulation-domain representation of undersampled data

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Experiments on pMRI datasets demonstrate that the proposed calibrationless method outperforms the state-of-the-art pMRI methods even when they can achieve sufficient calibration data, and far better than existing calibrationless pMRI algorithms.

IIR GRAPPA for parallel MR image reconstruction

A two‐dimensional infinite impulse response model of inverse filter is introduced to replace the finite impulse responsemodel currently used in generalized autocalibrating partially parallel acquisitions class image reconstruction methods for accelerated parallel MRI.



Improved data reconstruction method for GRAPPA

An improved data reconstruction method for generalized autocalibrating partially parallel acquisitions (GRAPPA) using multicolumn multiline interpolation (MCMLI), which yields higher‐quality data reconstruction than the original GRAPPA method.

Generalized autocalibrating partially parallel acquisitions (GRAPPA)

This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD‐AUTO‐SMASH reconstruction techniques and provides unaliased images from each component coil prior to image combination.

Parallel magnetic resonance imaging using the GRAPPA operator formalism

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SMASH, SENSE, PILS, GRAPPA: How to Choose the Optimal Method

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Discrepancy‐based adaptive regularization for GRAPPA reconstruction

To develop a novel regularization method for GRAPPA by which the regularization parameters can be optimally and adaptively chosen.