Computation of exact g‐factor maps in 3D GRAPPA reconstructions

@article{RabanilloViloria2019ComputationOE,
  title={Computation of exact g‐factor maps in 3D GRAPPA reconstructions},
  author={Inaki Rabanillo-Viloria and Ante Zhu and Santiago Aja‐Fern{\'a}ndez and Carlos Alberola-L{\'o}pez and Diego Hernando},
  journal={Magnetic Resonance in Medicine},
  year={2019},
  volume={81},
  pages={1353 - 1367}
}
To characterize the noise distributions in 3D‐MRI accelerated acquisitions reconstructed with GRAPPA using an exact noise propagation analysis that operates directly in k‐space. 

References

SHOWING 1-10 OF 42 REFERENCES

General formulation for quantitative G‐factor calculation in GRAPPA reconstructions

In this work a theoretical description for practical quantitative estimation of the noise enhancement in generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstructions,

Multipeak fat‐corrected complex R2* relaxometry: Theory, optimization, and clinical validation

TLDR
To develop R2* mapping techniques corrected for confounding factors and optimized for noise performance, researchers at the Massachusetts Institute of Technology (MIT) used EMMARM, a state-of-the-art machine learning system, to solve the challenge of systematically cataloging individual neurons in the response to noise.

Real valued diffusion‐weighted imaging using decorrelated phase filtering

TLDR
A new phase correction (PC) technique is adopted that yields real valued diffusion data while maintaining a Gaussian noise distribution in diffusion‐weighted imaging.

The importance of correcting for signal drift in diffusion MRI

To investigate previously unreported effects of signal drift as a result of temporal scanner instability on diffusion MRI data analysis and to propose a method to correct this signal drift.

Noise estimation in parallel MRI: GRAPPA and SENSE.

Interleaved variable density sampling with a constrained parallel imaging reconstruction for dynamic contrast‐enhanced MR angiography

TLDR
An interleaved variable density (IVD) sampling method that pseudorandomly undersamples each individual frame of a 3D Cartesian ky–kz plane combined with parallel imaging acceleration achieves improvements in temporal fidelity of the depiction of the contrast bolus passage relative to the clinical standard.

2D‐GRAPPA‐operator for faster 3D parallel MRI

TLDR
A new approach based on the generalized autocalibrating partially parallel acquisitions (GRAPPA) technique that allows Fourier‐domain reconstructions of data sets that are subsampled along two dimensions that is compared with an extension of the conventional GRAPPA method.

Exact Calculation of Noise Maps and ${g}$ -Factor in GRAPPA Using a ${k}$ -Space Analysis

TLDR
An accurate characterization of the noise distribution for self-calibrated parallel imaging in the presence of arbitrary Cartesian sampling patterns is developed by exploiting the symmetries and separability in the noise propagation process, which is computationally efficient and does not require large matrices.

Blipped‐controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g‐factor penalty

TLDR
The method to create interslice image shifts in the phase encoding direction to increase the distance between aliasing pixels achieves the desired shifts but avoids an undesired “tilted voxel” blurring artifact associated with previous methods.

Comprehensive quantification of signal‐to‐noise ratio and g‐factor for image‐based and k‐space‐based parallel imaging reconstructions

TLDR
A simple Monte Carlo based method is proposed for all linear image reconstruction algorithms, which allows measurement of signal‐to‐noise ratio and g‐factor and is demonstrated for SENSE and GRAPPA reconstructions for accelerated acquisitions that have not previously been amenable to such assessment.