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 language | English (US) |
|---|---|
| Article number | 4782150 |
| Pages (from-to) | 167-172 |
| Number of pages | 6 |
| Journal | IEEE Transactions on Nuclear Science |
| Volume | 56 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 2009 |
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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
Cite this
Quantitative accuracy of penalized-likelihood reconstruction for ROI activity estimation. / Fu, Lin; Stickel, Jennifer R.; Badawi, Ramsey D; Qi, Jinyi.
In: IEEE Transactions on Nuclear Science, Vol. 56, No. 1, 4782150, 02.2009, p. 167-172.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Quantitative accuracy of penalized-likelihood reconstruction for ROI activity estimation
AU - Fu, Lin
AU - Stickel, Jennifer R.
AU - Badawi, Ramsey D
AU - Qi, Jinyi
PY - 2009/2
Y1 - 2009/2
N2 - 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.
AB - 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.
KW - Emission tomography
KW - Image reconstruction
KW - Maximum a posteriori
KW - Penalized likelihood
KW - Quantification
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U2 - 10.1109/TNS.2008.2005063
DO - 10.1109/TNS.2008.2005063
M3 - Article
AN - SCOPUS:60449117535
VL - 56
SP - 167
EP - 172
JO - IEEE Transactions on Nuclear Science
JF - IEEE Transactions on Nuclear Science
SN - 0018-9499
IS - 1
M1 - 4782150
ER -