Quantitative accuracy of penalized-likelihood reconstruction for ROI activity estimation

Abstract

Estimation of region of interest (ROI) activity is an important task in emission tomography. ROI quantification is essential for measuring clinical factors such as tumor activity, growth rate, and the efficacy of therapeutic interventions. Accuracy of ROI quantification is significantly affected by image reconstruction algorithm. In penalized maximum-likelihood (PML) algorithm, the regularization parameter controls the resolution and noise tradeoff and, hence, affects ROI quantification. To optimize the performance of ROI quantification, it is desirable to use a moderate regularization parameter to effectively suppress noise without introducing excessive bias. However, due to the non-linear and spatial- variant nature of PML reconstruction, choosing a proper regularization parameter is not an easy task Previous theoretical study has shown that the bias-variance characteristic for ROI quantification task depends on the size and activity distribution of the ROI. In this work, we design physical phantom experiments to validate these predictions in a realistic situation. We found that the phantom data results match well the theoretical predictions. The good agreement between the phantom results and theoretical predictions shows that the theoretical expressions can be used to predict the accuracy of ROI activity quantification and to guide the selection of the regularization parameter.