Advanced modeling in positron emission tomography using Monte Carlo simulations for improving reconstruction and quantification

Positron Emission Tomography (PET) is a medical imaging technique that plays a major role in oncology, especially using ¹⁸F-Fluoro-Deoxyglucose. However, PET images suffer from a modest spatial resolution and from high noise. As a result, there is still no consensus on how tumor metabolically active volume and tumor uptake should be characterized. In the meantime, research groups keep producing new methods for such characterizations that need to be assessed. A Monte Carlo simulation based method has been developed to produce simulated PET images of patients suffering from cancer, indistinguishable from clinical images, and for which all parameters are known. The method uses high resolution PET images from patient acquisitions, from which the physiological heterogeneous activity distribution can be modeled. It was shown that the performance of quantification methods on such highly realistic simulated images are significantly lower and more variable than using simple phantom studies. Fourteen different quantification methods were also compared in realistic conditions using a group of such simulated patients. In addition, the proposed method was extended to simulate serial PET scans in the context of patient monitoring, including a modeling of the tumor changes, as well as the variability over time of non-tumoral physiological activity distribution. Monte Carlo simulations were also used to study the detection probability inside the crystals of the tomograph. A model of the crystal response was derived and included in the system matrix involved in tomographic reconstruction. The resulting reconstruction method was compared with other sophisticated methods for modeling the detector response in the image space, proposed in the literature. We demonstrated the superiority of the proposed method over equivalent approaches on simulated data, and illustrated its robustness on clinical data. For a same noise level, it is possible to reconstruct PET images offering a significantly better spatial resolution than this observed in standard clinical images. This might lead to an improved accuracy in tumor characterization from PET images. (author)