Title :
Quantitative accuracy of penalized-likelihood reconstruction for ROI activity estimation
Author :
Fu, Lin ; Stickel, Jennifer R. ; Badawi, Ramsey D. ; Qi, Jinyi
Author_Institution :
Univ. of California, Davis
fDate :
Oct. 26 2007-Nov. 3 2007
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.
Keywords :
cancer; emission tomography; image denoising; image reconstruction; image resolution; maximum likelihood estimation; medical image processing; phantoms; tumours; bias-variance characteristics; emission tomography; image resolution; noise suppression; nonlinear nature; penalized maximum-likelihood reconstruction algorithm; physical phantom experiments; region-of-interest activity estimation; regularization parameter; spatial-variant nature; tumor activity; tumor growth rate; Accuracy; Image reconstruction; Imaging phantoms; Maximum likelihood estimation; Neoplasms; Nuclear and plasma sciences; Reconstruction algorithms; Signal to noise ratio; Spatial resolution; Tomography;
Conference_Titel :
Nuclear Science Symposium Conference Record, 2007. NSS '07. IEEE
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4244-0922-8
Electronic_ISBN :
1095-7863
DOI :
10.1109/NSSMIC.2007.4436966