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Phys Med Biol. 2012 Feb 7;57(3):609-29. doi: 10.1088/0031-9155/57/3/609. Epub 2012 Jan 12.

Selection of weighting factors for quantification of PET radioligand binding using simplified reference tissue models with noisy input functions.

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  • 1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.


Input function noise contributes to model-predicted values and should be accounted for during parameter estimation. This problem has been examined in the context of PET data analysis using a noisy image-derived arterial input function. Huesman and Mazoyer (1987 Phys. Med. Biol 32 1569-79) incorporated the effect of error in the measured input function into the objective function and observed a subsequent improvement in the accuracy of parameters estimated from a kinetic model of cardiac blood flow. Such a treatment has not been applied to the reference region models commonly used to analyze dynamic positron emission tomography data with receptor-ligand tracers. Here, we propose a strategy for selection of weighting factors that accounts for noise in the reference region input function and test the method on two common formulations of the simplified reference tissue model (SRTM). We present a simulation study which demonstrates that the proposed weighting approach improves the accuracy of estimated binding potential at high noise levels and when the reference tissue and target regions of interest are of comparable size. In the second simulation experiment, we show that using a small, homogeneous reference tissue with our weighting technique may have advantages over input functions derived from a larger (and thus less noisy), heterogeneous region with conventional weighting. A comparative analysis of clinical [(11)C]flumazenil data found a small but significant increase in estimated binding potential when using the proposed weighting method, consistent with the finding of reduced negative bias in our simulation study. The weighting strategy described here accounts for noise in the reference region input function and may improve the performance of the SRTM in applications where data are noisy and the reference region is relatively small. This technique may offer similar benefits to other models using reference region inputs, particularly those derived from the SRTM.

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