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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

Quality assessment of high angular resolution diffusion imaging data using bootstrap on Q-ball reconstruction.

Julien Cohen-Adad¹, Maxime Descoteaux, Lawrence L Wald

¹Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA. jcohen@nmr.mgh.harvard.edu

Journal of Magnetic Resonance Imaging : JMRI

|April 22, 2011

Summary

A new bootstrap method reliably assesses High Angular Resolution Diffusion Imaging (HARDI) data quality. This technique uses Q-Ball imaging (QBI) reconstruction to evaluate HARDI data, enhancing tractography and clinical reliability.

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Area of Science:

Neuroimaging
Diffusion MRI
Computational Neuroscience

Background:

High Angular Resolution Diffusion Imaging (HARDI) provides detailed microstructural information about brain tissue.
Assessing the quality of HARDI data is crucial for accurate analysis and reliable results.
Existing methods may not fully capture the nuances of HARDI data quality.

Purpose of the Study:

To develop and validate a novel bootstrap method for assessing HARDI data quality.
To utilize Q-Ball imaging (QBI) reconstruction within the bootstrap framework.
To establish a quantitative measure of HARDI data reliability.

Main Methods:

Employed a bootstrap resampling technique with jackknife sampling on HARDI data.
Estimated the diffusion Orientation Distribution Function (ODF) using QBI reconstruction with spherical harmonics.
Assessed ODF reproducibility via Jensen-Shannon divergence (JSD) and derived angular confidence intervals.
Evaluated method sensitivity by manipulating image signal-to-noise ratio (SNR), echo time, and b-value.

Main Results:

Demonstrated a direct correlation between JSD and image SNR.
Showcased the influence of echo time and b-value on JSD and angular confidence intervals.
Confirmed the bootstrap method's utility in evaluating specific HARDI data characteristics.

Conclusions:

The developed bootstrap method effectively assesses HARDI data quality.
This method can aid in evaluating new imaging hardware and pulse sequences.
It supports multifiber probabilistic tractography and provides reliability metrics for clinical applications.