Quantitative accuracy of penalized-likelihood reconstruction for ROI activity estimation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 [1] 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.

Original languageEnglish (US)
Title of host publicationIEEE Nuclear Science Symposium Conference Record
Pages3881-3885
Number of pages5
Volume5
DOIs
StatePublished - 2007
Event2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC - Honolulu, HI, United States
Duration: Oct 27 2007Nov 3 2007

Other

Other2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC
CountryUnited States
CityHonolulu, HI
Period10/27/0711/3/07

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering

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  • Cite this

    Fu, L., Stickel, J. R., Badawi, R. D., & Qi, J. (2007). Quantitative accuracy of penalized-likelihood reconstruction for ROI activity estimation. In IEEE Nuclear Science Symposium Conference Record (Vol. 5, pp. 3881-3885). [4436966] https://doi.org/10.1109/NSSMIC.2007.4436966