GATE (Geant4 Application for Emission Tomography) is a Monte Carlo simulation platform developed by the OpenGATE collaboration since 2001 and first publicly released in 2004. Dedicated to the modelling of planar scintigraphy, single photon emission computed tomography (SPECT) and positron emission tomography (PET) acquisitions, this platform is widely used to assist PET and SPECT research. A recent extension of this platform, released by the OpenGATE collaboration as GATE V6, now also enables modelling of x-ray computed tomography and radiation therapy experiments. This paper presents an overview of the main additions and improvements implemented in GATE since the publication of the initial GATE paper (Jan et al 2004 Phys. Med. Biol. 49 4543-61). This includes new models available in GATE to simulate optical and hadronic processes, novelties in modelling tracer, organ or detector motion, new options for speeding up GATE simulations, examples illustrating the use of GATE V6 in radiotherapy applications and CT simulations, and preliminary results regarding the validation of GATE V6 for radiation therapy applications. Upon completion of extensive validation studies, GATE is expected to become a valuable tool for simulations involving both radiotherapy and imaging.

The effects of metoclopramide on the central nervous system (CNS) in patients suggest substantial brain distribution. Previous data suggest that metoclopramide brain kinetics may nonetheless be controlled by ATP-binding cassette (ABC) transporters expressed at the blood-brain barrier. We used 11 C-metoclopramide PET imaging to elucidate the kinetic impact of transporter function on metoclopramide exposure to the brain. Methods: 11 C-metoclopramide transport by P-glycoprotein (P-gp; ABCB1) and the breast cancer resistance protein (BCRP; ABCG2) was tested using uptake assays in cells overexpressing P-gp and BCRP. 11 C-metoclopramide brain kinetics were compared using PET in rats (n 5 4-5) in the absence and presence of a pharmacologic dose of metoclopramide (3 mg/kg), with or without P-gp inhibition using intravenous tariquidar (8 mg/kg). The 11 C-metoclopramide brain distribution (V T based on Logan plot analysis) and brain kinetics (2-tissue-compartment model) were characterized with either a measured or an imaged-derived input function. Plasma and brain radiometabolites were studied using radio-high-performance liquid chromatography analysis. Results: 11 C-metoclopramide transport was selective for P-gp over BCRP. Pharmacologic dose did not affect baseline 11 C-metoclopramide brain kinetics (V T 5 2.28 ± 0.32 and 2.04 ± 0.19 mL⋅cm −3 using microdose and pharmacologic dose, respectively). Tariquidar significantly enhanced microdose 11 C-metoclopramide V T (7.80 ± 1.43 mL⋅cm −3 ) with a 4.4-fold increase in K 1 (influx rate constant) and a 2.3-fold increase in binding potential (k 3 /k 4 ) in the 2-tissue-compartment model. In the pharmacologic situation, P-gp inhibition significantly increased metoclopramide brain distribution (V T 5 6.28 ± 0.48 mL⋅cm −3 ) with a 2.0-fold increase in K 1 and a 2.2-fold decrease in k 2 (efflux rate), with no significant impact on binding potential. In this situation, only parent 11 C-metoclopramide could be detected in the brains of P-gp-inhibited rats. Conclusion: 11 C-metoclopramide benefits from favorable pharmacokinetic properties that offer reliable quantification of P-gp function at the blood-brain barrier in a pharmacologic situation. Using metoclopramide as a model of CNS drug, we demonstrated that P-gp function not only reduces influx but also mediates the efflux from the brain back to the blood compartment, with additional impact on brain distribution. This PET-based strategy of P-gp function investigation may provide new insight on the contribution of P-gp to the variability of response to CNS drugs between patients.

In 18 F-FDG PET, tumors are often characterized by their metabolically active volume and standardized uptake value (SUV). However, many approaches have been proposed to estimate tumor volume and SUV from 18 F-FDG PET images, none of them being widely agreed upon. We assessed the accuracy and robustness of 5 methods for tumor volume estimates and of 10 methods for SUV estimates in a large variety of configurations. Methods: PET acquisitions of an anthropomorphic phantom containing 17 spheres (volumes between 0.43 and 97 mL, sphere-to-surrounding-activity concentration ratios between 2 and 68) were used. Forty-one nonspheric tumors (volumes between 0.6 and 92 mL, SUV of 2, 4, and 8) were also simulated and inserted in a real patient 18 F-FDG PET scan. Four threshold-based methods (including one, T bgd , accounting for background activity) and a model-based method (Fit) described in the literature were used for tumor volume measurements. The mean SUV in the resulting volumes were calculated, without and with partial-volume effect (PVE) correction, as well as the maximum SUV (SUV max ). The parameters involved in the tumor segmentation and SUV estimation methods were optimized using 3 approaches, corresponding to getting the best of each method or testing each method in more realistic situations in which the parameters cannot be perfectly optimized. Results: In the phantom and simulated data, the T bgd and Fit methods yielded the most accurate volume estimates, with mean errors of 2% 6 11% and 28% 6 21% in the most realistic situations. Considering the simulated data, all SUV not corrected for PVE had a mean bias between 231% and 246%, much larger than the bias observed with SUV max (211% 6 23%) or with the PVE-corrected SUV based on T bgd and Fit (22% 6 10% and 3% 6 24%). Conclusion: The method used to estimate tumor volume and SUV greatly affects the reliability of the estimates. The T bgd and Fit methods yielded low errors in volume estimates in a broad range of situations. The PVE-corrected SUV based on T bgd and Fit were more accurate and reproducible than SUV max .

In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods. This work attempts to address these issues by proposing a unified and generic code framework for formatting, processing and reconstructing acquired multi-modal and multi-dimensional data. The proposed iterative framework processes in the same way elements from list-mode (i.e. events) and histogrammed (i.e. sinogram or other bins) data sets. Each element is processed separately, which opens the way for highly parallel execution. A unique iterative algorithm engine makes use of generic core components corresponding to the main parts of the reconstruction process. Features that are specific to different modalities and algorithms are embedded into specific components inheriting from the generic abstract components. Temporal dimensions are taken into account in the core architecture. The framework is implemented in an open-source C++ parallel platform, called CASToR (customizable and advanced software for tomographic reconstruction). Performance assessments show that the time loss due to genericity remains acceptable, being one order of magnitude slower compared to a manufacturer's software optimized for computational efficiency for a given system geometry. Specific optimizations were made possible by the underlying data set organization and processing and allowed for an average speed-up factor ranging from 1.54 to 3.07 when compared to more conventional implementations. Using parallel programming, an almost linear speed-up increase (factor of 0.85 times number of cores) was obtained in a realistic clinical PET setting. In conclusion, the proposed framework offers a substantial flexibility for the integration of new reconstruction algorithms while maintaining computation efficiency.

Positron emission tomography data are typically reconstructed with maximum likelihood expectation maximization (MLEM). However, MLEM suffers from positive bias due to the non-negativity constraint. This is particularly problematic for tracer kinetic modeling. Two reconstruction methods with bias reduction properties that do not use strict Poisson optimization are presented and compared to each other, to filtered backprojection (FBP), and to MLEM. The first method is an extension of NEGML, where the Poisson distribution is replaced by a Gaussian distribution for low count data points. The transition point between the Gaussian and the Poisson regime is a parameter of the model. The second method is a simplification of ABML. ABML has a lower and upper bound for the reconstructed image whereas AML has the upper bound set to infinity. AML uses a negative lower bound to obtain bias reduction properties. Different choices of the lower bound are studied. The parameter of both algorithms determines the effectiveness of the bias reduction and should be chosen large enough to ensure bias-free images. This means that both algorithms become more similar to least squares algorithms, which turned out to be necessary to obtain bias-free reconstructions. This comes at the cost of increased variance. Nevertheless, NEGML and AML have lower variance than FBP. Furthermore, randoms handling has a large influence on the bias. Reconstruction with smoothed randoms results in lower bias compared to reconstruction with unsmoothed randoms or randoms precorrected data. However, NEGML and AML yield both bias-free images for large values of their parameter.