2006
DOI: 10.1016/j.neuroimage.2006.07.001
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Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters

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Cited by 112 publications
(142 citation statements)
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“…Previous studies have shown that the uncertainty in DTI-based parameters decreases with increasing SNR, while the accuracy remains largely unaffected (3). To assess the quality of the DTI data, wild bootstrapping has been developed to determine the uncertainty in fitted DTI parameters (11).…”
mentioning
confidence: 99%
“…Previous studies have shown that the uncertainty in DTI-based parameters decreases with increasing SNR, while the accuracy remains largely unaffected (3). To assess the quality of the DTI data, wild bootstrapping has been developed to determine the uncertainty in fitted DTI parameters (11).…”
mentioning
confidence: 99%
“…Assuming that same DTI acquisition protocol was used for two time points, which is a typical scenario since it is always strongly encouraged to use the same protocol in the same scanner for follow-up MRI studies, the simplest approach satisfying exchangeability is to permute only the DWIs that have the same diffusion encoding (diffusion gradient direction and strength). This stratified permutation scheme shares similarity with permutation of scans only within an exchangeability block in functional neuroimaging (Nichols and Holmes, 2002), and with stratified (or repetition) DTI bootstrap (Chung et al, 2006a;Pajevic and Basser, 2003). While multiple repeated acquisitions are mandated for stratified DTI bootstrap, scans for stratified DTI permutation do not need to be repeated at each time point since pooling two time points naturally guarantees at least two repetitions.…”
Section: Dti Permutation Testingmentioning
confidence: 98%
“…The simulation of noisy diffusion weighted signals and DTI processing was done in a similar manner as described elsewere (Chung et al, 2006a). From D, noise-free diffusion weighted signals were calculated by S(g) = S 0 exp(-bg T Dg) where S 0 is the signal without diffusion weighting, b is the diffusion weighting factor, and g is 3×1 unit vector of the diffusion-encoding gradient direction.…”
Section: Monte Carlo Simulationmentioning
confidence: 99%
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“…It is, therefore, not an attractive approach in the busy clinical environment. 30 Alternatively, if multiple signalintensity averages are used in routine image acquisition but are stored separately in the memory space, a permutation of the dataset can be performed to equivalently form multiple sets of single-average data (the so-called bootstrap ap-proach). 31 Moreover, when Ͼ6 directions are used for the diffusion-sensitizing gradients with a single signal-intensity average, it is possible to estimate the diffusion tensor fitting uncertainties, from which the probability of fiber tracts passing through certain regions can also be computed.…”
Section: Bootstrap Probabilistic Tractographymentioning
confidence: 99%