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Updated: Oct 10, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Gradient nonlinearity correction in liver DWI using motion-compensated diffusion encoding waveforms.

Sean McTavish¹, Anh T Van², Johannes M Peeters³

¹Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. sean.mctavish@tum.de.

Magma (New York, N.Y.)

|December 11, 2021

Summary

This summary is machine-generated.

Gradient nonlinearity correction effectively reduces ADC bias across various diffusion encoding waveforms in phantom studies. However, in vivo, this correction can exacerbate ADC overestimation caused by motion and bright vessels.

Keywords:

ADC mapping Diffusion-weighted imaging Gradient nonlinearity Liver imaging Motion compensation

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Area of Science:

Magnetic Resonance Imaging
Diffusion Weighted Imaging
Biomedical Engineering

Background:

Diffusion Weighted Imaging (DWI) is crucial for assessing tissue microstructure.
Gradient nonlinearity can introduce bias in apparent diffusion coefficient (ADC) measurements.
Motion-compensated diffusion encoding waveforms aim to improve image quality and reduce artifacts.

Purpose of the Study:

To evaluate the efficacy of a gradient nonlinearity correction method.
To assess its performance with different motion-compensated diffusion encoding waveforms.
To investigate its impact on ADC bias in phantom and in vivo experiments.

Main Methods:

Utilized standard monopolar Stejskal-Tanner pulsed gradient spin echo (pgse) waveform.
Employed symmetric bipolar velocity-compensated (sym-vc), asymmetric bipolar velocity-compensated (asym-vc), and asymmetric bipolar partial velocity-compensated (asym-pvc) waveforms.
Applied a gradient nonlinearity correction method based on spherical harmonic expansion of the gradient coil field.
Tested the method in a phantom and in four healthy subjects.

Main Results:

The gradient nonlinearity correction method successfully reduced ADC bias in phantom experiments for all tested waveforms.
Significant reductions in ADC value ranges were observed across different waveforms post-correction.
In vivo results indicated that the correction method can amplify ADC overestimation due to motion or bright vessels.

Conclusions:

The gradient nonlinearity correction method is effective in mitigating ADC bias with various motion-compensated diffusion encoding waveforms.
Caution is advised when applying this correction in coronal liver Diffusion Weighted Imaging (DWI) due to potential amplification of motion- and vessel-related ADC errors.