Realistic and Efficient Modeling of Radiotracer Heterogeneity in Monte Carlo Simulations of PET Images With Tumors

Abstract

Monte Carlo simulations are extensively used in PET to evaluate the accuracy with which PET images can yield reliable estimates of parameters of interest. For such applications, the simulated images should be as realistic as possible so that conclusions can be extrapolated to clinical PET images. In this work, we describe a method for introducing realistic modeling of radiotracer heterogeneity into Monte Carlo simulations of patient PET scans. The modeling of the complex physiological activity distribution in healthy regions is directly based on real patient PET/CT images, and realistic tumor shapes can be included into these regions. This method represents a competitive alternative to the use of complex anthropomorphic phantoms such as the XCAT, that require a fixed activity per structure. The method is extended to the simulation of serial PET scans with tumor changes, as acquired in the context of therapy monitoring, and this extension is validated using a patient study. Using the proposed method, very realistic patient PET images can be produced for evaluation purposes.In addition, a strategy to efficiently simulate many sets of pathological cases, based on a unique background physiological activity distribution, is described and carefully assessed using a numerical phantom. The background activity is simulated only once, while tumors are simulated separately. The data are then recombined in a specific way so that the final image has the same properties as images produced by simulating pathological and tumor activities at the same time.

Variability and Uncertainty of 18F-FDG PET Imaging Protocols for Assessing Inflammation in Atherosclerosis: Suggestions for Improvement

“…On the basis of these activity and tissue composition distributions, PET scans were simulated using the GATE V6.1 Monte Carlo simulation software (12), without any variance reduction technique to properly reproduce the statistical properties of real data. They corresponded to 1 bed position (18-cm long) located on the neck acquired with a Gemini GXL PET/CT scanner (Philips) (13). The acquisition duration was set to 8 min.…”

Comparison of five cluster validity indices performance in brain [18F]FET‐PET image segmentation using k‐means

“…This is due to the unknown tumor structure (e.g., perfusion, distribution of different cell types, vessels, etc.) and the tumors are most often simulated as spheres with uniform activity . In addition, such phantoms typically can neither reproduce the complexity of the brain and nor both the normal and pathological anatomical variability observed in clinical data …”

“…These characteristics include partial volume artifacts (e.g., different percentages of blood vessels in each voxel), poor spatial resolution and noise; these make the construction of adequate digital phantoms a difficult task . In addition, one needs to generate phantoms with various anatomies and complex shapes and different levels of activity in the tumors to generate realistic tumors …”

“…and the tumors are most often simulated as spheres with uniform activity. 38,39 In addition, such phantoms typically can neither reproduce the complexity of the brain and nor both the normal and pathological anatomical variability observed in clinical data. 40 Therefore, using digital phantom as surrogate for real PET data is a common approach to know the ground truth.…”

A 5D computational phantom for pharmacokinetic simulation studies in dynamic emission tomography

“…However, the proposed methodology counterbalances this by benefiting from a fast simulation environment, with semi-automatic segmentation techniques, making it possible to simulate personalized dynamic protocols [39]. Having the MR data available, construction of the anatomical phantom, kinetic modelling simulation, motion field estimation and application, and forward projection to generate the raw emission and attenuation data should take between 20 and 25 hours [15].…”