Synthetic resampling methods for variance estimation in parametric images

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 procedures in a diagnostic setting. Unfortunately, the complexity of the radio-tracer models used in the generation of a parametric image makes analytic variance expressions intractable. A natural extension of the usual resampling approach is infeasible because of the 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. 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.

Research supported in part by the National Institutes of Health grant CA-57903 at the University of Washington, Seattle, USA.