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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Non-Cartesian parallel imaging reconstruction.

Katherine L Wright¹, Jesse I Hamilton, Mark A Griswold

¹Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

Journal of Magnetic Resonance Imaging : JMRI

|January 11, 2014

Summary

This summary is machine-generated.

Non-Cartesian parallel imaging accelerates MRI scans by efficiently covering k-space. This review details methods like CG SENSE, GRAPPA, and SPIRiT to reduce artifacts from undersampled data.

Keywords:

CG SENSE SPIRiT non-Cartesian non-Cartesian GRAPPA parallel imaging

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Area of Science:

Magnetic Resonance Imaging (MRI)
Medical Imaging Physics

Background:

Non-Cartesian imaging trajectories offer efficient k-space coverage, enabling reduced MRI scan times.
Undersampling these trajectories accelerates acquisition but can introduce aliasing artifacts.

Purpose of the Study:

To review non-Cartesian k-space trajectories and their sampling properties.
To discuss selected non-Cartesian parallel imaging algorithms for artifact mitigation.
To cover potential clinical applications of these advanced MRI techniques.

Main Methods:

Overview of non-Cartesian k-space trajectories and sampling.
In-depth discussion of selected non-Cartesian parallel imaging algorithms.
Detailed description of Conjugate Gradient SENSE (CG SENSE), non-Cartesian GRAPPA, and SPIRiT.

Main Results:

Non-Cartesian parallel imaging leverages multi-coil sensitivities to reconstruct images from undersampled data.
CG SENSE, GRAPPA, and SPIRiT are presented as key methods for artifact reduction.
The review highlights the potential of these techniques to improve clinical MRI.

Conclusions:

Non-Cartesian parallel imaging is crucial for accelerating MRI acquisition.
Advanced reconstruction algorithms effectively address aliasing artifacts in undersampled non-Cartesian data.
This technology holds significant promise for future clinical MRI applications.