Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. This paper gives a detailed description of the design and development of GATE by the OpenGATE collaboration, whose continuing objective is to improve, document and validate GATE by simulating commercially available imaging systems for PET and SPECT. Large effort is also invested in the ability and the flexibility to model novel detection systems or systems still under design. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at http:/www-lphe.epfl.ch/GATE/. Two benchmarks developed for PET and SPECT to test the installation of GATE and to serve as a tutorial for the users are presented. Extensive validation of the GATE simulation platform has been started, comparing simulations and measurements on commercially available acquisition systems. References to those results are listed. The future prospects towards the gridification of GATE and its extension to other domains such as dosimetry are also discussed.

In this study, a measurement protocol is presented that improves the precision of dose measurements using a flat-bed document scanner in conjunction with two new GafChromic film models, HS and Prototype A EBT exposed to 6 MV photon beams. We established two sources of uncertainties in dose measurements, governed by measurement and calibration curve fit parameters contributions. We have quantitatively assessed the influence of different steps in the protocol on the overall dose measurement uncertainty. Applying the protocol described in this paper on the Agfa Arcus II flat-bed document scanner, the overall one-sigma dose measurement uncertainty for an uniform field amounts to 2% or less for doses above around 0.4 Gy in the case of the EBT (Prototype A), and for doses above 5 Gy in the case of the HS model GafChromic film using a region of interest 2 X 2 mm2 in size.

Purpose The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. Approach A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. Findings A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to ...

The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods. We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder–decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Using simulated data derived from a whole-body digital phantom, we randomly sampled the configurable parameters to generate realistic images, which were each augmented to a total of more than 291,000 reference images. Realistic PET acquisitions of these images were simulated, resulting in noisy sinogram data, used for training, validation, and testing the DeepPET network. We demonstrated that DeepPET generates higher quality images compared to conventional techniques, in terms of relative root mean squared error (11%/53% lower than ordered subset expectation maximization (OSEM)/filtered back-projection ( FBP), structural similarity index (1%/11% higher than OSEM/FBP), and peak signal-to-noise ratio (1.1/3.8 dB higher than OSEM/FBP). In addition, we show that DeepPET reconstructs images 108 and 3 times faster than OSEM and FBP, respectively. Finally, DeepPET was successfully applied to real clinical data. This study shows that an end-to-end encoder–decoder network can produce high quality PET images at a fraction of the time compared to conventional methods.

The aim of this study was to develop a clinically applicable noninvasive method to quantify changes in androgen receptor (AR) levels based on 18 F-16b-fluoro-5a-dihydrotestosterone ( 18 F-FDHT) PET in prostate cancer patients undergoing therapy. Methods: Thirteen patients underwent dynamic 18 F-FDHT PET over a selected tumor. Concurrent venous blood samples were acquired for blood metabolite analysis. A second cohort of 25 patients injected with 18 F-FDHT underwent dynamic PET of the heart. These data were used to generate a population-based input function, essential for pharmacokinetic modeling. Linear compartmental pharmacokinetic models of increasing complexity were tested on the tumor tissue data. Four suitable models were applied and compared using the Bayesian information criterion (BIC). Model 1 consisted of an instantaneously equilibrating space, followed by a unidirectional trap. Models 2a and 2b contained a reversible space between the instantaneously equilibrating space and the trap, into which metabolites were excluded (2a) or allowed (2b). Model 3 built on model 2b with the addition of a second reversible space preceding the unidirectional trap and from which metabolites were excluded. Results: The half-life of the 18 F-FDHT in blood was between 6 and 7 min. As a consequence, the uptake of 18 F-FDHT in prostate cancer lesions reached a plateau within 20 min as the blood-borne activity was consumed. Radiolabeled metabolites were shown not to bind to ARs in in vitro studies with CWR22 cells. Model 1 produced reasonable and robust fits for all datasets and was judged best by the BIC for 16 of 26 tumor scans. Models 2a, 2b, and 3 were judged best in 7, 2, and 1 cases, respectively. Conclusion: Our study explores the clinical potential of using 18 F-FDHT PET to estimate free AR concentration. This process involved the estimation of a net uptake parameter such as the k trap of model 1 that could serve as a surrogate measure of AR expression in metastatic prostate cancer. Our initial studies suggest that a simple body mass-normalized standardized uptake value correlates reasonably well to model-based k trap estimates, which we surmise may be proportional to AR expression. Validation studies to test this hypothesis are underway.