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Updated: May 8, 2026

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

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Interslice leakage artifact reduction technique for simultaneous multislice acquisitions.

Stephen F Cauley¹, Jonathan R Polimeni, Himanshu Bhat

¹Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.

Magnetic Resonance in Medicine

|August 22, 2013

Summary

Split slice-GRAPPA (SG) improves magnetic resonance imaging reconstruction accuracy by reducing artifacts. This robust optimization enhances image quality and temporal efficiency for diffusion-weighted and functional MRI studies.

Keywords:

CAIPIRINHA leakage parallel imaging simultaneous multislice

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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

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Published on: December 18, 2016

Area of Science:

Magnetic Resonance Imaging (MRI)
Image Reconstruction
Signal Processing

Background:

Controlled aliasing techniques enhance temporal efficiency in echo-planar imaging for diffusion-weighted imaging (DWI) and functional MRI (fMRI).
Slice-GRAPPA (SG) is a common method for reconstructing simultaneously acquired echo-planar imaging slices.
Image reconstruction accuracy is crucial for reliable MRI data analysis.

Purpose of the Study:

To investigate robust optimization techniques for slice-GRAPPA (SG) to improve image reconstruction accuracy.
To reduce leakage artifacts in SG reconstructions.
To enhance the temporal efficiency of diffusion-weighted imaging and functional MRI.

Main Methods:

Proposed Split SG as an alternative kernel optimization method.
Compared Split SG to standard SG using phantom and in vivo data at 3 Tesla.
Quantified performance using signal leakage metrics and time-series SNR for slice-accelerated spin-echo diffusion-weighted acquisitions.

Main Results:

Split SG significantly reduced leakage artifacts in both phantom and in vivo data.
Observed a significant increase in time-series SNR for in vivo diffusion-weighted acquisitions with Split SG.
Demonstrated improved performance with in-plane 2x and slice 3x accelerations.

Conclusions:

Minimizing leakage artifacts during SG kernel training improves reconstruction accuracy.
The robust kernel fitting strategy enhances image reconstruction quality.
This approach enables higher slice-acceleration and improved accuracy across various MRI applications.