Abstract: Coincidence engines follow two main implementation flows: timestamp based systems and AND-gate based systems. The latter have been more widespread in recent years because of its lower cost and high efficiency. However, they are highly dependent on the selected electronic components, they have limited flexibility once assembled and they are customized to fit a specific scanner's geometry. Timestamp based systems are gathering more attention lately, especially with high channel count fully digital systems. These new systems must however cope with important singles count rates. One option is to record every detected event and postpone coincidence detection offline. For daily use systems, a real time engine is preferable because it dramatically reduces data volume and hence image preprocessing time and raw data management. This paper presents the timestamp based coincidence engine for the LabPET(TM), a small animal PET scanner with up to 4608 individual readout avalanche photodiode channels. The engine can handle up to 100 million single events per second and has extensive flexibility because it resides in programmable logic devices. It can be adapted for any detector geometry or channel count, can be ported to newer, faster programmable devices and can have extra modules added to take advantage of scanner-specific features. Finally, the user can select between full processing mode for imaging protocols and minimum processing mode to study different approaches for coincidence detection with offline software.

Abstract: The AX-PET collaboration has developed a novel concept for high resolution PET imaging to overcome some of the performance limitations of classical PET cameras, in particular the compromise between spatial resolution and sensitivity introduced by the parallax error. The detector consists of an arrangement of long LYSO scintillating crystals axially oriented around the field of view together with arrays of wave length shifter strips orthogonal to the crystals. This matrix allows a precise 3D measurement of the photon interaction point. This is valid both for photoelectric absorption at 511 key and for Compton scattering down to deposited energies of about 100 keV. Crystals and WLS strips are individually read out using Geiger-mode Avalanche Photo Diodes (G-APDs). The sensitivity of such a detector can be adjusted by changing the number of layers and the resolution is defined by the crystal and strip dimensions. Two AX-PET modules were built and fully characterized in dedicated test set-ups at CERN, with point-like Na-22 sources. Their performance in terms of energy (Renew approximate to 11.8% (FWMH) at 511 key) and spatial resolution was assessed (sigma(axial) approximate to 0.65 mm), both individually and for the two modules in coincidence. Test campaigns at ETH Zurich and at the company AAA allowed the tomographic reconstructions of more complex phantoms validating the 3D reconstruction algorithms. The concept of the AX-PET modules will be presented together with some characterization results. We describe a count rate model which allows to optimize the planing of the tomographic scans.

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.

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%.

Abstract: A high-resolution silicon detector probe, in coincidence with a conventional PET scanner, is expected to provide images of higher quality than those achievable using the scanner alone. Spatial resolution should improve due to the finer pixelization of the probe detector, while increased sensitivity in the probe vicinity is expected to decrease noise. A PET-probe prototype is being developed utilizing this principle. The system includes a probe consisting of ten layers of silicon detectors, each a 80 x 52 array of 1 x 1 x 1 mm(3) pixels, to be operated in coincidence with a modern clinical PET scanner. Detailed simulation studies of this system have been performed to assess the effect of the additional probe information on the quality of the reconstructed images. A grid of point sources was simulated to study the contribution of the probe to the system resolution at different locations over the field of view (FOV). A resolution phantom was used to demonstrate the effect on image resolution for two probe positions. A homogeneous source distribution with hot and cold regions was used to demonstrate that the localized improvement in resolution does not come at the expense of the overall quality of the image. Since the improvement is constrained to an area close to the probe, breast imaging is proposed as a potential application for the novel geometry. In this sense, a simplified breast phantom, adjacent to heart and torso compartments, was simulated and the effect of the probe on lesion detectability, through measurements of the local contrast recovery coefficient-to-noise ratio (CNR), was observed. The list-mode ML-EM algorithm was used for image reconstruction in all cases. As expected, the point spread function of the PET-probe system was found to be non-isotropic and vary with position, offering improvement in specific regions. Increase in resolution, of factors of up to 2, was observed in the region close to the probe. Images of the resolution phantom showed visible improvement in resolution when including the probe in the simulations. The image quality study demonstrated that contrast and spill-over ratio in other areas of the FOV were not sacrificed for this enhancement. The CNR study performed on the breast phantom indicates increased lesion detectability provided by the probe.