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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

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The trouble with free-water elimination using single-shell diffusion MRI data: A case study in ageing.

Marta M Correia¹, Rafael Neto Henriques², Marc Golub³

¹MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

This summary is machine-generated.

Free-water elimination modelling in diffusion tensor imaging (DTI) using single-shell data with regularised gradient descent (RGD) introduces biases. Multi-shell data with non-linear least squares fitting avoids these artifacts, suggesting caution when interpreting previous RGD findings.

Keywords:

ageing diffusion MRI free-water elimination

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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

Area of Science:

Neuroimaging
Diffusion Magnetic Resonance Imaging (dMRI)
Biomedical Signal Processing

Background:

Free-water elimination (FWE) modelling in diffusion tensor imaging (DTI) estimates free-water (FW) volume and FW-compensated DTI parameters.
Single-shell (SS) dMRI acquisitions are common clinically, but FWE-DTI is best suited for multi-shell (MS) data.
Regularised gradient descent (RGD) is often applied to SS data, despite methodological limitations.

Purpose of the Study:

To compare the performance of RGD fitting with SS data against non-linear least squares (NLS) fitting with MS data for FWE-DTI.
To evaluate biases introduced by RGD fitting on SS data in simulations and human participants.
To assess the impact of modelling choices on FW-compensated fractional anisotropy (FA) and mean diffusivity (MD) in relation to age.

Main Methods:

Simulations and analysis of dMRI data from 620 participants (aged 18-88 years).
Comparison of RGD fitting on SS data versus NLS fitting on MS data.
Assessment of mean diffusivity (MD) and fractional anisotropy (FA) estimates and their correlation with ground truth and age.

Main Results:

RGD fitting on SS data artificially flattened MD-ground truth relationships and introduced spurious positive correlations between FA and MD.
NLS fitting on MS data did not exhibit these biases.
Fewer significant MD-age correlations were found with RGD SS, consistent with MD flattening.
FW-compensated FA maps differed significantly; RGD SS showed age-related increases not seen with MS NLS, suggesting an artifact.

Conclusions:

RGD fitting with SS dMRI data introduces significant biases in FWE-DTI parameter estimation.
NLS fitting with MS dMRI data provides more reliable FW-compensated DTI parameters.
Previous findings using RGD on SS data for FWE-DTI should be interpreted with extreme caution due to potential artifacts.