Volume 10, Issue 2

We discuss computing issues for data analysis and image reconstruction of positron emission tomography based on time-of-flight medical scanner or other medical scanning devices producing large volumes of data. Service architecture based on grid and cloud concepts for distributed processing is proposed and critically discussed.

A method of the determination of a γ-quantum absorption point in a plastic scintillator block using a matrix of wavelength-shifting (WLS) strips is proposed. An application of this method for the improvement of position resolution in newly proposed positron emission tomography (PET) detectors based on plastic scintillators is presented. The method enables to reduce parallax errors in the reconstruction of images, which occurs in the presently used PET scanners.

A method for depth of interaction (DOI) estimation with temperature gradient was considered. Preliminary results covered measurements of bismuth germanate oxide (BGO) monolithic crystal light pulse dependence on temperature. The proof of concept for using temperature gradient as a way to encode DOI incident γ in monolithic BGO crystal was presented.

This article describes the simulations of the scattering of annihilation γ quanta in a strip of a plastic scintillator. Such strips constitute the basic detection modules in a newly proposed positron emission tomography (PET), which utilizes plastic scintillators instead of inorganic crystals. An algorithm simulating the chain of Compton scatterings was elaborated and a series of simulations have been conducted for the scintillator strip with a cross-section of 5×19 mm. The results indicate that secondary interactions occur only in the case of about 8% of the events and only 25% of these events take place in the distance larger than 0.5 cm from the primary interaction. Also, the light signals produced at the primary and secondary interactions overlap with the delay, the distribution of which is characterized by a full width at half-maximum (FWHM) of about 40 ps.

The complexity of the hardware and the amount of data collected during the PET imaging process require application of modern methods of efficient data organization and processing. In this article, we will discuss the data structures and the flow of collected data from the novel TOF-PET medical scanner that is being developed at the Jagiellonian University. The developed data format reflects the registration process of the γ quanta emitted from positron electron annihilation, front-end electronic structure, and required input information for the image reconstruction. In addition, the system database fulfills possible demands of the evolving J-PET project.

A novel PET detector consisting of strips of polymer scintillators is being developed by the Jagiellonian Positron Emission Tomograph (J-PET) collaboration. The map of efficiency and the map of geometrical acceptance of the two-strip J-PET scanner are presented. The map of efficiency was determined using the Monte Carlo simulation software GEANT4 Application for Tomographic Emission (GATE), which is based on GEANT4. Both maps were compared using a method based on the χ 2 -test.

Current statistical methods and technologies used for speaker identification via dynamic formant frequency often involve classic multivariate analyses that must meet a number of criteria in order to be considered trustworthy. The authors propose more advanced classification techniques, including artificial neural networks. Owing to iterative learning algorithms, neural networks can be trained to detect highly complex, nonlinear relations hidden in input data. This study specifically considers feed-forward multilayer perceptron and radial basic function network models. The investigation involves an analysis of the Polish vowel (stressed or unstressed) in selected contexts described by the four lowest formant frequencies. Results indicate high accuracy of neural networks as a speaker identification tool reaching up to 100%. In addition, the authors have determined that the accuracy of classification is similar when based on a single context to when input data are aggregated over several different contexts.