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Updated: Aug 22, 2025

Fiber Type and Subcellular-Specific Analysis of Lipid Droplet Content in Skeletal Muscle

Fiber Type and Subcellular-Specific Analysis of Lipid Droplet Content in Skeletal Muscle

Published on: June 8, 2022

Optimized rectification of fiber orientation density function with background threshold.

Hunter G Moss¹, Andreana Benitez², Jens H Jensen³

¹Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, United States of America; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, United States of America.

Magnetic Resonance Imaging

|November 11, 2022

Summary

This summary is machine-generated.

This study introduces an optimized fiber orientation density function (fODF) rectification method to remove artifacts in diffusion MRI data. The procedure suppresses noise and spurious peaks while preserving essential white matter microstructure features.

Keywords:

Artifacts Axon Rectification White matter fiber ball imaging fiber orientation density function

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Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

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Published on: May 23, 2017

Area of Science:

Neuroimaging
Diffusion MRI
White Matter Tractography

Background:

Diffusion MRI-based fiber orientation density functions (fODFs) can exhibit unphysical negative values and spurious peaks due to signal noise and Gibbs ringing.
These artifacts complicate the accurate interpretation of white matter microstructure and axonal orientation.

Purpose of the Study:

To describe an optimized fiber orientation density function (fODF) rectification procedure.
The procedure aims to remove negative values and absorb features below a specified threshold into a constant background.
To minimize mean square deviation from the original fODF while preserving key directional information.

Main Methods:

A novel fODF rectification procedure is proposed.
This method eliminates negative values and incorporates low-amplitude features into a background.
The procedure minimizes mean square deviation from the original fODF, preserving peak directions and shapes.

Main Results:

The rectification method effectively suppresses artifacts in fODFs derived from diffusion MRI.
Examples demonstrate minimal rectification (η=0), average-level rectification (η≈0.08), and fractional-anisotropy-axonal-based rectification (η≈0.1).
Increasing the threshold (η) enhances artifact suppression with minimal impact on major fODF peaks.

Conclusions:

The optimized fODF rectification procedure effectively suppresses artifactual features in diffusion MRI data.
The method's mean square error minimization makes it suitable for studying fODF fine structure changes in aging and disease.