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Med Image Anal. 1998 Dec;2(4):379-93.

An approximate bootstrap technique for variance estimation in parametric images.

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  • 1Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, USA. maitra@math.umbc.edu

Abstract

Parametric imaging procedures offer the possibility of comprehensive assessment of tissue metabolic activity. Estimating variances of these images is important for the development of inference tools in a diagnostic setting. However, these are not readily obtained because the complexity of the radio-tracer models used in the generation of a parametric image makes analytic variance expressions intractable. On the other hand, a natural extension of the usual bootstrap resampling approach is infeasible because of the expanded computational effort. This paper suggests a computationally practical, approximate simulation strategy to variance estimation. Results of experiments done to evaluate the approach in a simplified model one-dimensional problem are very encouraging. Diagnostic checks performed on a single real-life positron emission tomography (PET) image to test for the feasibility of applying the procedure in a real-world PET setting also show some promise. The suggested methodology is evaluated here in the context of parametric images extracted by mixture analysis; however, the approach is general enough to extend to other parametric imaging methods.

PMID:
10072204
[PubMed - indexed for MEDLINE]
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