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Updated: Apr 26, 2026

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

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

Published on: November 8, 2012

Smoothing fields of weighted collections with applications to diffusion MRI processing.

Gunnar A Sigurdsson¹, Jerry L Prince¹

¹Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.

Proceedings of Spie--The International Society for Optical Engineering

|July 31, 2014

Summary

This study introduces a novel spatial smoothing framework for diffusion MRI data. It improves neuronal tract analysis by reducing errors caused by noisy voxels, enhancing tract tracking and segmentation accuracy.

Keywords:

Correspondence Diffusion MRI HARDI Multi-tensor Multiple directions Multiple orientations Smoothing

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

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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
Biomedical Engineering
Data Analysis

Background:

Diffusion MRI enables estimation of multiple neuronal fiber orientations per voxel.
Current analysis methods like fiber tracking are limited by spatial smoothness issues in orientation data.
Noisy voxels can disrupt accurate tract reconstruction in complex geometries.

Purpose of the Study:

To develop a generalized spatial smoothing framework for diffusion MRI data.
To address limitations in neuronal tract analysis caused by lack of spatial smoothness.
To improve the accuracy of fiber tracking and tract segmentation.

Main Methods:

Proposed a generalized spatial smoothing framework for diffusion MRI.
Estimated optimal fuzzy correspondences of orientations and fractional contributions between voxels.
Applied smoothing based on these fuzzy correspondences, avoiding exact matches.

Main Results:

Demonstrated visual and quantitative improvements in postprocessing steps using phantom experiments.
Showcased enhanced smoothing in the measurement domain with both phantom and in vivo human data.
Reduced smoothing anomalies caused by erroneous correspondences around noisy voxels.

Conclusions:

The proposed framework effectively smooths neuronal orientation data in diffusion MRI.
This method enhances the reliability and accuracy of subsequent tract analysis.
Offers significant improvements for postprocessing diffusion MRI data, particularly in complex fiber crossing regions.