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 and Qi 2010) we presented a segmentation method using both the spatial and temporal information to improve the classification accuracy. In (Wang and Qi 2009) we theoretically analyzed the effects of penalized maximum likelihood (PML) methods on the kinetic parameter estimation using dynamic PET. Current work has been focused on developing statistically efficient methods for parametric imaging using dynamic PET. We have developed methods to reconstruct parametric images directly from sinogram data for both linear kinetic models (Wang and Qi 2009) and (Wang and Qi 2008) and nonlinear models (Wang and Qi 2009b). In addition, we also worked on methods to eliminate the needs of blood sampling in dynamic data analysis. One example was presented in (Yetik and Qi 2006) 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.
Bias images Variance images
Direct reconstruction Indirect reconstruction Direct reconstruction Indirect reconstruction
Direct reconstruction can substantially reduce variance of parametric images without increasing bias. See (Wang and Qi 2009b) for details.

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