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Updated: May 17, 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

Comprehensive framework for accurate diffusion MRI parameter estimation.

Jelle Veraart¹, Jeny Rajan, Ronald R Peeters

¹IBBT Vision Laboratory, Department of Physics, University of Antwerp, Antwerp, Belgium.

Magnetic Resonance in Medicine

|November 8, 2012

Summary

This summary is machine-generated.

This study introduces a new framework for diffusion model fitting in diffusion MRI data. It preserves estimator accuracy despite preprocessing by estimating spatially varying noise levels.

Keywords:

diffusion kurtosis imaging diffusion tensor imaging least squares maximum likelihood noise estimation

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Diffusion Imaging in the Rat Cervical Spinal Cord

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Published on: April 7, 2015

Area of Science:

Medical Imaging
Neuroimaging
Diffusion MRI

Background:

Advanced diffusion metric estimators improve accuracy by using prior data distribution knowledge.
Real-world diffusion MRI data is altered by motion and eddy current correction, invalidating assumptions of advanced estimators.
Preprocessing steps can negate the benefits of sophisticated diffusion estimators.

Purpose of the Study:

To present a generic diffusion model fitting framework for diffusion MRI.
To maintain the accuracy of diffusion metric estimators despite preprocessing.
To propose a method for estimating spatially varying noise levels.

Main Methods:

Developed a generic diffusion model fitting framework incorporating diffusion MRI data statistics.
Utilized a conditional least squares estimator as a central component.
Proposed an approach for estimating spatially varying noise levels.

Main Results:

The proposed framework preserves the accuracy of the conditional least squares estimator.
Accuracy is maintained irrespective of applied preprocessing steps when the noise parameter is known.
A method for estimating spatially varying noise levels was successfully developed.

Conclusions:

The generic framework ensures robust diffusion metric estimation in the presence of complex data alterations.
Accurate diffusion MRI analysis is achievable even after significant preprocessing.
Estimating spatially varying noise is crucial for preserving estimator accuracy.