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Imaging Studies IV: Magnetic Resonance Imaging

Imaging Studies IV: Magnetic Resonance Imaging

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Related Experiment Video

Updated: May 22, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Optimized inversion-prepared gradient echo imaging.

Albert Kir¹, Alan McMillan

¹Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

Journal of Magnetic Resonance Imaging : JMRI

|May 1, 2012

Summary

This study optimized inversion-prepared gradient echo sequences using extended phase graph (EPG) simulations. The EPG method accurately predicts signal and contrast, enabling faster, high-quality quantitative T1-weighted brain imaging.

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Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

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Published on: December 9, 2010

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Last Updated: May 22, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

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Published on: December 18, 2016

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

Area of Science:

Magnetic Resonance Imaging (MRI)
Biomedical Engineering
Quantitative Imaging

Background:

Rapid gradient-echo imaging faces challenges with residual transverse magnetization.
Spoiling schemes are used to mitigate these effects, but are not always perfect.
Extended Phase Graph (EPG) provides a more accurate model for gradient-echo imaging.

Purpose of the Study:

To develop and implement an EPG-based simulation for optimizing inversion-prepared gradient echo sequences.
To achieve desired signal and contrast characteristics with minimal acquisition time.
To enable quantitative T1-weighted human brain imaging.

Main Methods:

Utilized EPG-based simulations to analyze and predict image signal and contrast.
Applied the simulation to optimize inversion-prepared gradient echo sequences.
Validated the simulation accuracy through phantom and in-vivo human brain experiments.

Main Results:

EPG-based simulations accurately predicted signal and contrast in biological phantoms and human brains.
Experiments confirmed the simulation's effectiveness in optimizing sequence parameters.
Demonstrated improved image quality in cadaver brain imaging.

Conclusions:

EPG-based simulation is a powerful tool for optimizing 3D magnetization-prepared rapid gradient-echo imaging sequences.
Sequence parameter manipulation via EPG allows for specific signal and contrast generation.
This method facilitates quantitative T1-weighted brain imaging with enhanced image quality.