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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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Brzezinski, K., Oliver, J. F., Gillam, J., Rafecas, M., Studen, A., Grkovski, M., et al. (2016). Experimental evaluation of the resolution improvement provided by a silicon PET probe. J. Instrum., 11, P09016–13pp.
Abstract: A high-resolution PET system, which incorporates a silicon detector probe into a conventional PET scanner, has been proposed to obtain increased image quality in a limited region of interest. Detailed simulation studies have previously shown that the additional probe information improves the spatial resolution of the reconstructed image and increases lesion detectability, with no cost to other image quality measures. The current study expands on the previous work by using a laboratory prototype of the silicon PET-probe system to examine the resolution improvement in an experimental setting. Two different versions of the probe prototype were assessed, both consisting of a back-to-back pair of 1-mm thick silicon pad detectors, one arranged in 32 x 16 arrays of 1.4mm x 1.4mm pixels and the other in 40 x 26 arrays of 1.0mm x 1.0mm pixels. Each detector was read out by a set of VATAGP7 ASICs and a custom-designed data acquisition board which allowed trigger and data interfacing with the PET scanner, itself consisting of BGO block detectors segmented into 8 x 6 arrays of 6mm x 12mm x 30mm crystals. Limited-angle probe data was acquired from a group of Na-22 point-like sources in order to observe the maximum resolution achievable using the probe system. Data from a Derenzo-like resolution phantom was acquired, then scaled to obtain similar statistical quality as that of previous simulation studies. In this case, images were reconstructed using measurements of the PET ring alone and with the inclusion of the probe data. Images of the Na-22 source demonstrated a resolution of 1.5mm FWHM in the probe data, the PET ring resolution being approximately 6 mm. Profiles taken through the image of the Derenzo-like phantom showed a clear increase in spatial resolution. Improvements in peak-to-valley ratios of 50% and 38%, in the 4.8mm and 4.0mm phantom features respectively, were observed, while previously unresolvable 3.2mm features were brought to light by the addition of the probe. These results support the possibility of improving the image resolution of a clinical PET scanner using the silicon PET-probe.
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Cabello, J., Torres-Espallardo, I., Gillam, J. E., & Rafecas, M. (2013). PET Reconstruction From Truncated Projections Using Total-Variation Regularization for Hadron Therapy Monitoring. IEEE Trans. Nucl. Sci., 60(5), 3364–3372.
Abstract: Hadron therapy exploits the properties of ion beams to treat tumors by maximizing the dose released to the target and sparing healthy tissue. With hadron beams, the dose distribution shows a relatively low entrance dose which rises sharply at the end of the range, providing the characteristic Bragg peak that drops quickly thereafter. It is of critical importance in order not to damage surrounding healthy tissues and/or avoid targeting underdosage to know where the delivered dose profile ends-the location of the Bragg peak. During hadron therapy, short-lived beta(+)-emitters are produced along the beam path, their distribution being correlated with the delivered dose. Following positron annihilation, two photons are emitted, which can be detected using a positron emission tomography (PET) scanner. The low yield of emitters, their short half-life, and the wash out from the target region make the use of PET, even only a few minutes after hadron irradiation, a challenging application. In-beam PET represents a potential candidate to estimate the distribution of beta(+)-emitters during or immediately after irradiation, at the cost of truncation effects and degraded image quality due to the partial rings required of the PET scanner. Time-of-flight (ToF) information can potentially be used to compensate for truncation effects and to enhance image contrast. However, the highly demanding timing performance required in ToF-PET makes this option costly. Alternatively, the use of maximum-a-posteriori-expectation-maximization (MAP-EM), including total variation (TV) in the cost function, produces images with low noise, while preserving spatial resolution. In this paper, we compare data reconstructed with maximum-likelihood-expectation-maximization (ML-EM) and MAP-EM using TV as prior, and the impact of including ToF information, from data acquired with a complete and a partial-ring PET scanner, of simulated hadron beams interacting with a polymethyl methacrylate (PMMA) target. The results show that MAP-EM, in the absence of ToF information, produces lower noise images and more similar data compared to the simulated beta(+) distributions than ML-EM with ToF information in the order of 200-600 ps. The investigation is extended to the combination of MAP-EM and ToF information to study the limit of performance using both approaches.
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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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Blume, M., Martinez-Moller, A., Keil, A., Navab, N., & Rafecas, M. (2010). Joint Reconstruction of Image and Motion in Gated Positron Emission Tomography. IEEE Trans. Med. Imaging, 29(11), 1892–1906.
Abstract: We present a novel intrinsic method for joint reconstruction of both image and motion in positron emission tomography (PET). Intrinsic motion compensation methods exclusively work on the measured data, without any external motion measurements. Most of these methods separate image from motion estimation: They use deformable image registration/optical flow techniques in order to estimate the motion from individually reconstructed gates. Then, the image is estimated based on this motion information. With these methods, a main problem lies in the motion estimation step, which is based on the noisy gated frames. The more noise is present, the more inaccurate the image registration becomes. As we show both visually and quantitatively, joint reconstruction using a simple deformation field motion model can compete with state-of-the-art image registration methods which use robust multilevel B-spline motion models.
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