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Updated: Jul 13, 2025

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Outliers in diffusion-weighted MRI: Exploring detection models and mitigation strategies.

Viljami Sairanen¹, Jesper Andersson²

¹Baby Brain Activity Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Department of Radiology, Kanta-Häme Central Hospital, Hämeenlinna, Finland.

|October 11, 2023

Summary

This summary is machine-generated.

Diffusion-weighted MRI (dMRI) processing benefits from outlier correction. Gaussian Process outlier replacement offers similar tensor fit results to downweighting, making it ideal for single tensor model estimation.

Keywords:

Diffusion-weighted MRI Outlier detection Precision of model parameters Robust modelling

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Neuroimaging
Medical Physics
Biomedical Engineering

Background:

Diffusion-weighted MRI (dMRI) is crucial for studying brain microstructure and connectivity.
dMRI data processing is complex and susceptible to motion-induced signal dropout artefacts.
Accurate artefact correction is vital for clinical dMRI research.

Purpose of the Study:

To compare outlier replacement and downweighting methods for dMRI data processing.
To guide the dMRI community in selecting optimal data processing tools.
To evaluate the impact of these methods on motion correction and tensor modeling.

Main Methods:

Simulated realistic whole-brain dMRI data with varying dropout artefacts.
Applied Gaussian Process (GP) and Spherical Harmonic (SH) based outlier replacement.
Implemented outlier downweighting techniques.
Evaluated motion correction, registration, and single tensor model fitting.

Main Results:

GP-based outlier replacement yielded tensor fit results comparable to GP-based outlier downweighting.
Both methods effectively addressed signal dropout artefacts in simulated and infant dMRI data.
Outlier downweighting may offer improved model precision estimates.

Conclusions:

Outlier replacement is recommended when the primary interest is the least-squares estimate of the single tensor model.
Outlier downweighting is potentially more suitable for applications requiring precise model estimation, such as probabilistic tractography.
The choice between methods depends on the specific analytical goals in dMRI research.