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Related Experiment Video

Updated: Sep 11, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Optimisation and impact of gradient waveform modulation on Non-uniform Oscillating Gradient Spin-Echo sequences for

Melisa L Gimenez¹, Pablo Jimenez², Leonardo A Pedraza Pérez¹

¹Centro Atómico Bariloche, CNEA, Av. Bustillo 9.500, S.C. de Bariloche, 8400, Río Negro, Argentina; Instituto Balseiro, CNEA, Universidad Nacional de Cuyo, Av. Bustillo 9.500, S.C. de Bariloche, 8400, Río Negro, Argentina.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)

|August 16, 2025

Summary

Non-uniform Oscillating Gradient Spin-Echo (NOGSE) MRI effectively estimates microstructural sizes in the nervous system. Optimized NOGSE parameters, especially with smooth gradient modulations, enhance diagnostic potential for neurological diseases.

Keywords:

Diffusion MRI Gradient waveform Microstructure phantom OGSE Optimal parameter estimation Quantitative imaging Tissue microstructure

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Area of Science:

Biomedical Imaging
Neuroscience
Medical Physics

Background:

Neurological diseases cause microstructural changes, necessitating advanced non-invasive imaging for biomarker discovery.
Diffusion-weighted magnetic resonance imaging (DWI) probes molecular diffusion to reveal tissue microstructure.
Modulated Gradient Spin-Echo (MGSE) sequences, including Non-uniform Oscillating Gradient Spin-Echo (NOGSE), offer enhanced microstructural contrast.

Purpose of the Study:

To evaluate the performance of NOGSE for estimating microstructural sizes using a preclinical MRI scanner.
To optimize NOGSE parameters for accurate microstructural size estimation.
To assess the impact of gradient modulation types (sharp vs. smooth) on NOGSE performance.

Main Methods:

Utilized a preclinical MRI scanner to acquire NOGSE data.
Conducted phantom experiments and numerical simulations.
Performed information-theoretic analysis to optimize NOGSE parameters.
Investigated both sharp and smooth gradient modulations.

Main Results:

NOGSE successfully estimates microstructural sizes, with sharp modulations maximizing signal decay-shift.
Smooth gradient modulations provide meaningful contrast and expand scanner compatibility.
Optimal NOGSE parameters were identified compatible with preclinical hardware.
Smooth NOGSE shows potential for in-vivo applications.

Conclusions:

NOGSE is a viable technique for characterizing microstructural features in diffusion-weighted MRI.
Optimized NOGSE parameters enable reliable microstructure size estimation.
Smooth gradient NOGSE broadens the applicability of this quantitative imaging approach for neurological disease research.