Model-based extraction of input and organ functions in dynamic scintigraphic imaging

Abstract

Image-based definition of input function (IF) and organ function is a prerequisite for kinetic analysis of dynamic scintigraphy or positron emission tomography. This task is typically done manually by a human operator and suffers from low accuracy and reproducibility. We propose a probabilistic model based on physiological assumption that time–activity curves (TACs) arise as a convolution of an IF and tissue-specific kernels. The model is solved via the Variational Bayes estimation procedure and provides estimates of the IF, tissue-specific TACs and their related spatial distributions (images) as its results. The algorithm was tested with data of dynamic renal scintigraphy. The method was applied to the problem of differential renal function estimation and the IF estimation and the results are compared with competing techniques on data-sets with 99 and 19 patients. The MATLAB implementation of the algorithm is available for download.