Document Detail


An optimized wild bootstrap method for evaluation of measurement uncertainties of DTI-derived parameters in human brain.
MedLine Citation:
PMID:  18302985     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Evaluation of measurement uncertainties (or errors) in diffusion tensor-derived parameters is essential to quantify the sensitivity and specificity of these quantities as potential surrogate biomarkers for pathophysiological processes with diffusion tensor imaging (DTI). Computational methods such as the Monte Carlo simulation have provided insights into characterization of the measurement uncertainty in DTI. However, due to the complexity of real brain data as well as different sources of variations during the image acquisition, a robust estimator for DTI measurement uncertainty in human brain is required. Recent studies have shown that wild bootstrap, a novel nonparametric statistical method, can potentially provide effective estimations of DTI measurement uncertainties in human brain DTI data. In this study, we further optimized the DTI application of the wild bootstrap method for typical clinical applications. We evaluated the validity of wild bootstrap utilizing numerical simulations with different combinations of DTI protocol parameters and wild bootstrap experimental designs, and quantitatively compared estimates of uncertainties from wild bootstrapping with those from Monte Carlo simulations. Our results demonstrate that a wild bootstrap implementation using at least 1000 wild bootstrap iterations with a type II or type III heteroskedasticity consistent covariance matrix estimator provides robust evaluations of most DTI protocols.
Authors:
Tong Zhu; Xiaoxu Liu; Patrick R Connelly; Jianhui Zhong
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-01-26
Journal Detail:
Title:  NeuroImage     Volume:  40     ISSN:  1053-8119     ISO Abbreviation:  Neuroimage     Publication Date:  2008 Apr 
Date Detail:
Created Date:  2008-03-24     Completed Date:  2008-06-17     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9215515     Medline TA:  Neuroimage     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1144-56     Citation Subset:  IM    
Affiliation:
Department of Biomedical Engineering, University of Rochester, Rochester, NY 14642-8648, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Brain / anatomy & histology*
Computer Simulation
Diffusion Magnetic Resonance Imaging / statistics & numerical data*
Humans
Image Processing, Computer-Assisted / statistics & numerical data*
Models, Statistical
Monte Carlo Method
Reproducibility of Results

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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