Precision and accuracy of regional radioactivity quantitation using the maximum likelihood EM reconstruction algorithm
Abstract
The imaging characteristics of maximum likelihood (ML) reconstruction using the EM algorithm for emission tomography have been extensively evaluated. There has been less study of the precision and accuracy of ML estimates of regional radioactivity concentration. The authors developed a realistic brain slice simulation by segmenting a normal subject's MRI scan into gray matter, white matter, and CSF and produced PET sinogram data with a model that included detector resolution and efficiencies, attenuation, scatter, and randoms. Noisy realizations at different count levels were created, and ML and filtered backprojection (FBP) reconstructions were performed. The bias and variability of ROI values were determined. In addition, the effects of ML pixel size, image smoothing and region size reduction were assessed. ML estimates at 1,000 iterations (0.6 sec per iteration on a parallel computer) for 1cm[sup 2] gray matter ROIs showed negative biases of 6% [+] 2% which can be reduced to 0% [+] 3% by removing the outer 1mm rim of each ROI. FBP applied to the fullsize ROIs had 15% [+] 4% negative bias with 50% less noise than ML. Shrinking the FBP regions provided partial bias compensation with noise increases to levels similar to ML. Smoothing of ML images producedmore »
 Authors:
 (National Institutes of Health, Bethesda, MD (United States))
 Publication Date:
 OSTI Identifier:
 6926009
 Alternate Identifier(s):
 OSTI ID: 6926009
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States); Journal Volume: 13:3
 Country of Publication:
 United States
 Language:
 English
 Subject:
 62 RADIOLOGY AND NUCLEAR MEDICINE; BRAIN; POSITRON COMPUTED TOMOGRAPHY; ACCURACY; ALGORITHMS; IMAGE PROCESSING; BODY; CENTRAL NERVOUS SYSTEM; COMPUTERIZED TOMOGRAPHY; DIAGNOSTIC TECHNIQUES; EMISSION COMPUTED TOMOGRAPHY; MATHEMATICAL LOGIC; NERVOUS SYSTEM; ORGANS; PROCESSING; TOMOGRAPHY 550601*  Medicine Unsealed Radionuclides in Diagnostics
Citation Formats
Carson, R.E., Yan, Y., Chodkowski, B., Yap, T.K., and DaubeWitherspoon, M.E. Precision and accuracy of regional radioactivity quantitation using the maximum likelihood EM reconstruction algorithm. United States: N. p., 1994.
Web. doi:10.1109/42.310884.
Carson, R.E., Yan, Y., Chodkowski, B., Yap, T.K., & DaubeWitherspoon, M.E. Precision and accuracy of regional radioactivity quantitation using the maximum likelihood EM reconstruction algorithm. United States. doi:10.1109/42.310884.
Carson, R.E., Yan, Y., Chodkowski, B., Yap, T.K., and DaubeWitherspoon, M.E. Thu .
"Precision and accuracy of regional radioactivity quantitation using the maximum likelihood EM reconstruction algorithm". United States.
doi:10.1109/42.310884.
@article{osti_6926009,
title = {Precision and accuracy of regional radioactivity quantitation using the maximum likelihood EM reconstruction algorithm},
author = {Carson, R.E. and Yan, Y. and Chodkowski, B. and Yap, T.K. and DaubeWitherspoon, M.E.},
abstractNote = {The imaging characteristics of maximum likelihood (ML) reconstruction using the EM algorithm for emission tomography have been extensively evaluated. There has been less study of the precision and accuracy of ML estimates of regional radioactivity concentration. The authors developed a realistic brain slice simulation by segmenting a normal subject's MRI scan into gray matter, white matter, and CSF and produced PET sinogram data with a model that included detector resolution and efficiencies, attenuation, scatter, and randoms. Noisy realizations at different count levels were created, and ML and filtered backprojection (FBP) reconstructions were performed. The bias and variability of ROI values were determined. In addition, the effects of ML pixel size, image smoothing and region size reduction were assessed. ML estimates at 1,000 iterations (0.6 sec per iteration on a parallel computer) for 1cm[sup 2] gray matter ROIs showed negative biases of 6% [+] 2% which can be reduced to 0% [+] 3% by removing the outer 1mm rim of each ROI. FBP applied to the fullsize ROIs had 15% [+] 4% negative bias with 50% less noise than ML. Shrinking the FBP regions provided partial bias compensation with noise increases to levels similar to ML. Smoothing of ML images produced biases comparable to FBP with slightly less noise. Because of its heavy computational requirements, the ML algorithm will be most useful for applications in which achieving minimum bias is important.},
doi = {10.1109/42.310884},
journal = {IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States)},
number = ,
volume = 13:3,
place = {United States},
year = {Thu Sep 01 00:00:00 EDT 1994},
month = {Thu Sep 01 00:00:00 EDT 1994}
}

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