Quantitative accuracy of penalized-likelihood reconstruction for ROI activity estimation

Lin Fu, Jennifer R. Stickel, Ramsey D Badawi, Jinyi Qi

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Estimation of the tracer uptake in a region of interest (ROI) 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 algorithms. In penalized maximum-likelihood (PML) algorithm, the regularization parameter controls the resolution and noise tradeoff and, hence, affects ROI quantification. To obtain the optimum 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. Our previous theoretical study (Qi and Huesman, IEEE Trans. Med. Imag., 2006) 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.

Original languageEnglish (US)
Article number4782150
Pages (from-to)167-172
Number of pages6
JournalIEEE Transactions on Nuclear Science
Volume56
Issue number1
DOIs
StatePublished - Feb 2009

Keywords

  • Emission tomography
  • Image reconstruction
  • Maximum a posteriori
  • Penalized likelihood
  • Quantification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Nuclear Energy and Engineering
  • Nuclear and High Energy Physics

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