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Impact of system design parameters on image figures of merit for a mouse PET scanner

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5 Author(s)
Kisung Lee ; Univ. of Washington, Seattle, WA, USA ; Kinahan, P.E. ; Miyaoka, R.S. ; Jae-Seung Kim
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In this study, an analytical simulation model was developed to investigate how system design parameters affect image figures of merit and task performance for small animal positron emission tomography (PET) scanners designed to image mice. For a very high resolution imaging system, important physical effects that may impact image quality are positron range, annihilation photon acollinearity, detector point-spread function (PSF) and coincident photon count levels (i.e., statistical noise). Modeling of these effects was included in an analytical simulation that generated multiple realizations of sinograms with varying levels of each effect. To evaluate image quality with respect to quantitation and detection task performance, four different figures of merit were measured: 1) root mean square error (RMSE); 2) a region of interest SNR (SNRROI); 3) nonprewhitening matched filter SNR (SNRNPW); and 4) recovery coefficient. The results indicate that for very high resolution imaging systems, the increase in positron range of C-11 compared to F-18 radiolabeling causes a significant reduction of quantitation (SNRROI) and detection (SNRNPW) accuracy for small regions. In addition, changing the shape of the detector PSF, which depends on crystal thickness, causes significant variations in quantitation and detection performance. However, while increasing noise levels significantly increase RMSE and decrease detectability (SNRNPW), the quantitation task performance (SNRROI), is less sensitive to noise levels. These results imply that resolution is more important than sensitivity for quantitation task performance, while sensitivity is a more significant issue for detection. The analytical simulation model can be used for estimating task performance of small animal PET systems more rapidly than existing full Monte Carlo methods, although Monte Carlo methods are needed to estimate system parameters.

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Nuclear Science, IEEE Transactions on  (Volume:51 ,  Issue: 1 )