There is a growing interest in developing brain PET scanners with high sensitivity and high spatial resolution for early diagnosis of neurodegenerative diseases and studies of brain functions. Sensitivity of the PET scanner can be improved by increasing the solid angle. However, conventional PET scanners are designed based on a cylindrical geometry, which may not be the most efficient design for brain imaging in terms of the balance between sensitivity and cost. We proposed a dedicated brain PET scanner based on a hemispheric shape detector and a chin detector (referred to as the helmet-chin PET), which is designed to maximize the solid angle by increasing the number of lines-of-response in the hemisphere. The parallax error, which PET scanners with a large solid angle tend to have, can be suppressed by the use of depth-of-interaction detectors. In this study, we carry out a realistic evaluation of the helmet-chin PET using Monte Carlo simulation based on the 4-layer GSO detector which consists of a 16  ×  16  ×  4 array of crystals with dimensions of 2.8  ×  2.8  ×  7.5 mm. The purpose of this simulation is to show the gain in imaging performance of the helmet-chin PET compared with the cylindrical PET using the same number of detectors in each configuration. The sensitivity of the helmet-chin PET evaluated with a cylindrical phantom has a significant increase, especially at the top of the (field-of-view) FOV. The peak-NECR of the helmet-chin PET is 1.4 times higher compared to the cylindrical PET. The helmet-chin PET provides relatively low noise images throughout the FOV compared to the cylindrical PET which exhibits enhanced noise at the peripheral regions. The results show the helmet-chin PET can significantly improve the sensitivity and reduce the noise in the reconstructed images.

Comparative study of alternative Geant4 hadronic ion inelastic physics models Comparative study of alternative Geant4 hadronic ion inelastic physics models for prediction of positron-emitting radionuclide production in carbon and oxygen for prediction of positron-emitting radionuclide production in carbon and oxygen ion therapy ion therapy

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The simulation model emulates the behaviour of the unique depth of interaction 26 sensing capability of the scanner without needing to directly simulate optical photon 27 transport in the scintillator and photodetector modules. The model was validated by 28 evaluating and comparing performance metrics from the NEMA NU 2-2012 protocol 29 on both the simulated and physical scanner, including spatial resolution, sensitivity, 30 scatter fraction, noise equivalent count rates and image quality. The results show 31 that the average sensitivities of the scanner in the field-of-view were 5.9 cps/kBq and 6.0 cps/kBq for experiment and simulation, respectively. The average spatial resolutions measured for point sources placed at several radial offsets were 5.2±0.7 mm 34 and 5.0±0.8 mm FWHM for experiment and simulation, respectively. The peak NECR 35 was 22.9 kcps at 7.4 kBq/mL for the experiment, while the NECR obtained via 36 simulation was 23.3 kcps at the same activity. The scatter fractions were 44% and 37 41.3% for the experiment and simulation, respectively. Contrast recovery estimates performed in different regions of a simulated image quality phantom matched the 39 experimental results with an average error of -8.7% and +3.4% for hot and cold 40 lesions, respectively. The results demonstrate that the developed Geant4 model reliably 41 reproduces the key NEMA NU 2-2012 performance metrics evaluated on the prototype 42 PET scanner. A simplified version of the model is included as an advanced example 43 in Geant4 version 10.5. 44 1. Introduction 45 Positron emission tomography (PET) is a non-invasive nuclear medicine technique that 46 is used for the clinical diagnosis of cancer and the study of a range of diseases and 47 biochemical processes in living organisms. The quality of reconstructed PET images 48 is limited by the amount of activity in the object, the duration of the scan, and 49 the performance of the PET scanner -which, in turn, depends on its constituent 50 components, such as the type and size of scintillator material used, the detection efficiency, geometrical arrangement of the detectors and the readout electronics. In 52 addition, the choice of parameters for data acquisition (such as acquisition time, energy 53 window, and coincidence timing window) and reconstruction (choice of algorithm, 54 number of subsets, number of iterations etc.) also affect the quality of the reconstructed 55 image. Experimental optimisation of these parameters is very expensive in terms of time, 56 materials and labour.57 Monte Carlo simulation provides a versatile and low-cost alternative to 58 experimental optimisation of imaging parameters. High-fidelity simulations of existing 59 physical scanners, validated for correctness against experimental measurements, enable 60 the development of new image reconstruction algorithms, segmentation methods and 61 optimised imaging protocols for quantitative evaluation of radiotracer uptake metrics.