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DYNAMIC PET DATA ANALYSIS
One feature of PET is its ability to capture the dynamics of radiotracer uptake in tissue. The dynamic information has potentials to improve early detection and characterization of cancer and assessment of therapeutic response. We are developing different approaches to extract information from dynamic PET data. In (Liao_MIC06) we proposed a segmentation method using both the spatial and temporal information to improve the classification accuracy. In (Wang_MIC07) we theoretically analyzed the effects of penalized maximum likelihood (PML) methods on the kinetic parameter estimation using dynamic PET. We also developed a practical approach to guide the selection of the regularization parameter in dynamic PET reconstruction. The current direction of the research is on developing statistically efficient methods for parametric imaging using dynamic PET, as well as signal processing method to eliminate the needs of blood sampling in dynamic data analysis. One example is shown in (Wang_PMB) where we proposed a direct reconstruction method for Patlak analysis. Another example is in (Yetik_MIC06) where we avoid the necessity of arterial blood sampling in dynamic PET by joint estimation of kinetic parameters and blood input function at the same time.
Dynamic distribution of radiolabeled glucose [18F-FDG] within a living mouse.
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