Positron Emission Tomography (PET) is a widely-used imaging modality for medical research and clinical diagnosis. Imaging of the radiotracer is obtained from the detected hit positions of the two ...positron annihilation photons in a detector array. The image is degraded by backgrounds from random coincidences and in-patient scatter events which require correction. In addition to the geometric information, the two annihilation photons are predicted to be produced in a quantum-entangled state, resulting in enhanced correlations between their subsequent interaction processes. To explore this, the predicted entanglement in linear polarisation for the two photons was incorporated into a simulation and tested by comparison with experimental data from a cadmium zinc telluride (CZT) PET demonstrator apparatus. Adapted apparati also enabled correlation measurements where one of the photons had undergone a prior scatter process. We show that the entangled simulation describes the measured correlations and, through simulation of a larger preclinical PET scanner, illustrate a simple method to quantify and remove the unwanted backgrounds in PET using the quantum entanglement information alone.
Component based normalization is a well-established method to calculate correction factors for unbiased and reliable PET reconstruction. Several methods have been studied and validated for ...cylindrical PET scanners. In this work we adapted a method already presented for cylindrical scanners to rotating dual head PET scanners. The model included corrections for detector efficiency, axial and transaxial geometric effects, crystal interference and attenuation corrections. Results from a simulated realistic dual head PET showed that the adaptation is valid. The images are significantly improved in terms of homogeneity, resolution and background contribution.
Commercial clinical and preclinical PET scanners rely on the full cylindrical geometry for whole body scans as well as for dedicated organs. In this study we propose the construction of a low cost ...dual-head C-shaped PET system dedicated for small animal brain imaging. Monte Carlo simulation studies were performed using GATE toolkit to evaluate the optimum design in terms of sensitivity, distortions in the FOV and spatial resolution. The PET model is based on SiPMs and BGO pixelated arrays. Four different configurations with C- angle 0°, 15°, 30° and 45° within the modules, were considered. Geometrical phantoms were used for the evaluation process. STIR software, extended by an efficient multi-threaded ray tracing technique, was used for the image reconstruction. The algorithm automatically adjusts the size of the FOV according to the shape of the detector's geometry. The results showed improvement in sensitivity of ∼15% in case of 45° C-angle compared to the 0° case. The spatial resolution was found 2 mm for 45° C-angle.
Low count PET data is a challenge for medical image reconstruction. The statistics of a dataset is a key factor of the quality of the reconstructed images. Reconstruction algorithms which would be ...able to compensate for low count datasets could provide the means to reduce the patient injected doses and/or reduce the scan times. It has been shown that the use of priors improve the image quality in low count conditions. In this study we compared regularised versus post-filtered OSEM for their performance on challenging simulated low count datasets. Initial visual comparison demonstrated that both algorithms improve the image quality, although the use of regularization does not introduce the undesired blurring as post-filtering.
The study aims to evaluate the performance of different empirical soil erosion models (EPM, USLE, Koutsoyiannis and Tarla, RUSLE) in mountainous Mediterranean-type catchments. The study area ...comprises the Arachthos, Kalamas, Upper Acheloos and Venetikos river basins, located in northwestern Greece. The methodology followed includes both qualitative and quantitative analyses. The former refers to the specific attributes of the models and the latter to the estimated sediment yield results. The results were initially validated against observed sediment yield values. The ambiguous reliability of such measurements led to their replacement by simulated ones, estimated using the sediment rating curve methodology. In the latter analysis, the models performed better, with more accurate results. Overall, the RUSLE corresponded best to such basins. Finally, the performance of seven empirical equations (Syvitski, Avendano Salas et al., Dendy and Bolton, Lu et al., Webb and Griffiths, Zarris et al.) was assessed, yielding relatively poor results.
Purpose: In the present study a patient‐specific dataset of realistic PET simulations was created, taking into account the variability of clinical oncology data. Tumor variability was tested in the ...simulated results. A comparison of the produced simulated data was performed to clinical PET/CT data, for the validation and the evaluation of the procedure. Methods: Clinical PET/CT data of oncology patients were used as the basis of the simulated variability inserting patient‐specific characteristics in the NCAT and the Zubal anthropomorphic phantoms. GATE Monte Carlo toolkit was used for simulating a commercial PET scanner. The standard computational anthropomorphic phantoms were adapted to the CT data (organ shapes), using a fitting algorithm. The activity map was derived from PET images. Patient tumors were segmented and inserted in the phantom, using different activity distributions. Results: The produced simulated data were reconstructed using the STIR opensource software and compared to the original clinical ones. The accuracy of the procedure was tested in four different oncology cases. Each pathological situation was illustrated simulating a) a healthy body, b) insertion of the clinical tumor with homogenous activity, and c) insertion of the clinical tumor with variable activity (voxel‐by‐voxel) based on the clinical PET data. The accuracy of the presented dataset was compared to the original PET/CT data. Partial Volume Correction (PVC) was also applied in the simulated data. Conclusions: In this study patient‐specific characteristics were used in computational anthropomorphic models for simulating realistic pathological patients. Voxel‐by‐voxel activity distribution with PVC within the tumor gives the most accurate results. Radiotherapy applications can utilize the benefits of the accurate realistic imaging simulations, using the anatomicaland biological information of each patient. Further work will incorporate the development of analytical anthropomorphic models with motion and cardiac correction, combined with pathological patients to achieve high accuracy in tumor imaging. This research was supported by the Joint Research and Technology Program between Greece and France; 2009–2011 (protocol ID: 09FR103)
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