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Oliver, J. F., & Rafecas, M. (2010). Improving the singles rate method for modeling accidental coincidences in high-resolution PET. Phys. Med. Biol., 55(22), 6951–6971.
Abstract: Random coincidences ('randoms') are one of the main sources of image degradation in PET imaging. In order to correct for this effect, an accurate method to estimate the contribution of random events is necessary. This aspect becomes especially relevant for high-resolution PET scanners where the highest image quality is sought and accurate quantitative analysis is undertaken. One common approach to estimate randoms is the so-called singles rate method (SR) widely used because of its good statistical properties. SR is based on the measurement of the singles rate in each detector element. However, recent studies suggest that SR systematically overestimates the correct random rate. This overestimation can be particularly marked for low energy thresholds, below 250 keV used in some applications and could entail a significant image degradation. In this work, we investigate the performance of SR as a function of the activity, geometry of the source and energy acceptance window used. We also investigate the performance of an alternative method, which we call 'singles trues' (ST) that improves SR by properly modeling the presence of true coincidences in the sample. Nevertheless, in any real data acquisition the knowledge of which singles are members of a true coincidence is lost. Therefore, we propose an iterative method, STi, that provides an estimation based on ST but which only requires the knowledge of measurable quantities: prompts and singles. Due to inter-crystal scatter, for wide energy windows ST only partially corrects SR overestimations. While SR deviations are in the range 86-300% (depending on the source geometry), the ST deviations are systematically smaller and contained in the range 4-60%. STi fails to reproduce the ST results, although for not too high activities the deviation with respect to ST is only a few percent. For conventional energy windows, i.e. those without inter-crystal scatter, the ST method corrects the SR overestimations, and deviations from the true random rate are of the order of 1% or less. In addition, in the case of conventional energy window STi results reproduce ST results and therefore the former can be used to obtain the true random rate.
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Aguiar, P., Rafecas, M., Ortuño, J. E., Kontaxakis, G., Santos, A., Pavia, J., et al. (2010). Geometrical and Monte Carlo projectors in 3D PET reconstruction. Med. Phys., 37(11), 5691–5702.
Abstract: Purpose: In the present work, the authors compare geometrical and Monte Carlo projectors in detail. The geometrical projectors considered were the conventional geometrical Siddon ray-tracer (S-RT) and the orthogonal distance-based ray-tracer (OD-RT), based on computing the orthogonal distance from the center of image voxel to the line-of-response. A comparison of these geometrical projectors was performed using different point spread function (PSF) models. The Monte Carlo-based method under consideration involves an extensive model of the system response matrix based on Monte Carlo simulations and is computed off-line and stored on disk. Methods: Comparisons were performed using simulated and experimental data of the commercial small animal PET scanner rPET. Results: The results demonstrate that the orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions yield better images in terms of contrast and spatial resolution than those obtained after using the conventional method and the multiray-based S-RT. Furthermore, the Monte Carlo-based method yields slight improvements in terms of contrast and spatial resolution with respect to these geometrical projectors. Conclusions: The orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions represent satisfactory alternatives to factorizing the system matrix or to the conventional on-the-fly ray-tracing methods for list-mode reconstruction, where an extensive modeling based on Monte Carlo simulations is unfeasible.
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Oliver, J. F., Fuster-Garcia, E., Cabello, J., Tortajada, S., & Rafecas, M. (2013). Application of Artificial Neural Network for Reducing Random Coincidences in PET. IEEE Trans. Nucl. Sci., 60(5), 3399–3409.
Abstract: Positron Emission Tomography (PET) is based on the detection in coincidence of the two photons created in a positron annihilation. In conventional PET, this coincidence identification is usually carried out through a coincidence electronic unit. An accidental coincidence occurs when two photons arising from different annihilations are classified as a coincidence. Accidental coincidences are one of the main sources of image degradation in PET. Some novel systems allow coincidences to be selected post-acquisition in software, or in real time through a digital coincidence engine in an FPGA. These approaches provide the user with extra flexibility in the sorting process and allow the application of alternative coincidence sorting procedures. In this work a novel sorting procedure based on Artificial Neural Network (ANN) techniques has been developed. It has been compared to a conventional coincidence sorting algorithm based on a time coincidence window. The data have been obtained from Monte-Carlo simulations. A small animal PET scanner has been implemented to this end. The efficiency (the ratio of correct identifications) can be selected for both methods. In one case by changing the actual value of the coincidence window used, and in the other by changing a threshold at the output of the neural network. At matched efficiencies, the ANN-based method always produces a sorted output with a smaller random fraction. In addition, two differential trends are found: the conventional method presents a maximum achievable efficiency, while the ANN-based method is able to increase the efficiency up to unity, the ideal value, at the cost of increasing the random fraction. Images reconstructed using ANN sorted data (no compensation for randoms) present better contrast, and those image features which are more affected by randoms are enhanced. For the image quality phantom used in the paper, the ANN method decreases the spill-over ratio by a factor of 18%.
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Torres-Espallardo, I., Diblen, F., Rohling, H., Solevi, P., Gillam, J., Watts, D., et al. (2015). Evaluation of resistive-plate-chamber-based TOF-PET applied to in-beam particle therapy monitoring. Phys. Med. Biol., 60(9), N187–N208.
Abstract: Particle therapy is a highly conformal radiotherapy technique which reduces the dose deposited to the surrounding normal tissues. In order to fully exploit its advantages, treatment monitoring is necessary to minimize uncertainties related to the dose delivery. Up to now, the only clinically feasible technique for the monitoring of therapeutic irradiation with particle beams is Positron Emission Tomography (PET). In this work we have compared a Resistive Plate Chamber (RPC)-based PET scanner with a scintillation-crystal-based PET scanner for this application. In general, the main advantages of the RPC-PET system are its excellent timing resolution, low cost, and the possibility of building large area systems. We simulated a partial-ring scannerbeam monitoring, which has an intrinsically low positron yield compared to diagnostic PET. In addition, for in-beam PET there is a further data loss due to the partial ring configuration. In order to improve the performance of the RPC-based scanner, an improved version of the RPC detector (modifying the thickness of the gas and glass layers), providing a larger sensitivity, has been simulated and compared with an axially extended version of the crystal-based device. The improved version of the RPC shows better performance than the prototype, but the extended version of the crystal-based PET outperforms all other options. based on an RPC prototype under construction within the Fondazione per Adroterapia Oncologica (TERA). For comparison with the crystal-based PET scanner we have chosen the geometry of a commercially available PET scanner, the Philips Gemini TF. The coincidence time resolution used in the simulations takes into account the current achievable values as well as expected improvements of both technologies. Several scenarios (including patient data) have been simulated to evaluate the performance of different scanners. Initial results have shown that the low sensitivity of the RPC hampers its application to hadron
Keywords: PET; in-beam; RPC; particle therapy; TOF; range deviation; partial-ring
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Gillam, J. E., Solevi, P., Oliver, J. F., & Rafecas, M. (2013). Simulated one-pass list-mode: an approach to on-the-fly system matrix calculation. Phys. Med. Biol., 58(7), 2377–2394.
Abstract: In the development of prototype systems for positron emission tomography a valid and robust image reconstruction algorithm is required. However, prototypes often employ novel detector and system geometries which may change rapidly under optimization. In addition, developing systems generally produce highly granular, or possibly continuous detection domains which require some level of on-the-fly calculation for retention of measurement precision. In this investigation a new method of on-the-fly system matrix calculation is proposed that provides advantages in application to such list-mode systems in terms of flexibility in system modeling. The new method is easily adaptable to complicated system geometries and available computational resources. Detection uncertainty models are used as random number generators to produce ensembles of possible photon trajectories at image reconstruction time for each datum in the measurement list. However, the result of this approach is that the system matrix elements change at each iteration in a non-repetitive manner. The resulting algorithm is considered the simulation of a one-pass list (SOPL) which is generated and the list traversed during image reconstruction. SOPL alters the system matrix in use at each iteration and so behavior within the maximum likelihood-expectation maximization algorithm was investigated. A two-pixel system and a small two dimensional imaging model are used to illustrate the process and quantify aspects of the algorithm. The two-dimensional imaging system showed that, while incurring a penalty in image resolution, in comparison to a non-random equal-computation counterpart, SOPL provides much enhanced noise properties. In addition, enhancement in system matrix quality is straightforward (by increasing the number of samples in the ensemble) so that the resolution penalty can be recovered when desired while retaining improvement in noise properties. Finally the approach is tested and validated against a standard (highly accurate) system matrix using experimental data from a prototype system-the AX-PET.
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