Quantitative methods for tumor imaging with dynamic PET

University dissertation from Umeå : Umeå Universitet

Abstract: There is always a need and drive to improve modern cancer care. Dynamic positron emission tomography (PET) offers the advantage of in vivo functional imaging, combined with the ability to follow the physiological processes over time. In addition, by applying tracer kinetic modeling to the dynamic PET data, thus estimating pharmacokinetic parameters associated to e.g. glucose metabolism, cell proliferation etc., more information about the tissue's underlying biology and physiology can be determined. This supplementary information can potentially be a considerable aid when it comes to the segmentation, diagnosis, staging, treatment planning, early treatment response monitoring and follow-up of cancerous tumors.We have found it feasible to use kinetic parameters for semi-automatic tumor segmentation, and found parametric images to have higher contrast compared to static PET uptake images. There are however many possible sources of errors and uncertainties in kinetic parameters obtained through compartment modeling of dynamic PET data. The variation in the number of detected photons caused by the random nature of radioactive decay, is of course always a major source. Other sources may include: the choice of an appropriate model that is suitable for the radiotracer in question, camera detectors and electronics, image acquisition protocol, image reconstruction algorithm with corrections (attenuation, random and scattered coincidences, detector uniformity, decay) and so on. We have found the early frame sampling scheme in dynamic PET to affect the bias and uncertainty in calculated kinetic parameters, and that scatter corrections are necessary for most but not all parameter estimates. Furthermore, analytical image reconstruction algorithms seem more suited for compartment modeling applications compared to iterative algorithms.This thesis and included papers show potential applications and tools for quantitative pharmacokinetic parameters in oncology, and help understand errors and uncertainties associated with them. The aim is to contribute to the long-term goal of enabling the use of dynamic PET and pharmacokinetic parameters for improvements of today's cancer care.