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Magnetic Resonance Imaging

Magnetic Resonance Imaging

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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Updated: Sep 11, 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

Objective QC for diffusion MRI data: Artefact detection using normative modelling.

Ramona Cirstian^1,2, Natalie J Forde^1,2, Jesper L R Andersson³

¹Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

This summary is machine-generated.

This study introduces a semi-automated pipeline using normative modeling to detect diffusion MRI artifacts and processing errors. The method efficiently identifies issues in white matter imaging, improving data quality and participant selection for research.

Keywords:

Eddy QC artefacts diffusion MRI normative modelling quality control

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Last Updated: Sep 11, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Published on: July 28, 2013

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Published on: June 13, 2025

Area of Science:

Neuroimaging
Biomedical Engineering
Radiology

Background:

Diffusion MRI is crucial for mapping brain white matter microstructure.
Image artefacts and processing errors can compromise diffusion MRI data integrity and analysis.
Robust quality control is essential for reliable neuroimaging research.

Purpose of the Study:

To develop and validate a semi-automated pipeline for detecting diffusion imaging artefacts and processing errors.
To leverage normative modelling for enhanced quality control in diffusion MRI.
To improve the reliability of white matter microstructure analysis using UK Biobank data.

Main Methods:

Utilized normative modelling on 24 white matter imaging-derived phenotypes from the UK Biobank dataset.
Modeled microstructural features (e.g., fractional anisotropy, mean diffusivity, NODDI parameters) across six key white matter tracts.
Compared the normative modelling approach against traditional visual and quantitative quality control methods.

Main Results:

The normative modelling framework effectively detected diffusion imaging artefacts from sources like distortions and motion.
The method successfully identified processing errors, including inaccurate spatial registrations.
Demonstrated superior comprehensiveness and efficiency compared to traditional quality control approaches.

Conclusions:

Normative modelling provides a robust and efficient method for identifying both image artefacts and processing errors in diffusion MRI.
This approach enhances data understanding and informs participant inclusion/exclusion criteria in neuroimaging studies.
The developed pipeline contributes to improving the quality and reliability of diffusion MRI research.