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The Jagiellonian-PET (J-PET) collaboration is developing a prototype TOF-PET detector based on long polymer scintillators. This novel approach exploits the excellent time properties of the plastic scintillators, which permit very precise time measurements. The very fast, FPGA-based front-end electronics and the data acquisition system, as well as, low- and high-level reconstruction algorithms were specially developed to be used with the J-PET scanner. The TOF-PET data processing and reconstruction are time and resource demanding operations, especially in case of a large acceptance detector, which works in triggerless data acquisition mode. In this article, we discuss the parallel computing methods applied to optimize the data processing for the J-PET detector. We begin with general concepts of parallel computing and then we discuss several applications of those techniques in the J-PET data processing.
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A novel Positron Emission Tomography system, based on plastic scintillators, is being developed by the J-PET collaboration. In this article we present the simulation results of the scatter fraction, representing one of the parameters crucial for background studies defined in the NEMA-NU-2-2012 norm. We elaborate an event selection methods allowing to suppress events in which gamma quanta were scattered in the phantom or underwent the multiple scattering in the detector. The estimated scatter fraction for the single-layer J-PET scanner varies from 37% to 53% depending on the applied energy threshold.
In this paper, we present a complete Data Acquisition System (DAQ) together with the readout mechanisms for the J-PET tomography scanner. In general detector readout chain is constructed out of Front-End Electronics (FEE), measurement devices like Time-to-Digital or Analog-to-Digital Converters (TDCs or ADCs), data collectors and storage. We have developed a system capable for maintaining continuous readout of digitized data without preliminary selection. Such operation mode results in up to 8 Gbps data stream, therefore it is required to introduce a dedicated module for online event building and feature extraction. The Central Controller Module, equipped with Xilinx Zynq SoC and 16 optical transceivers serves as such true real time computing facility. Our solution for the continuous data recording (trigger-less) is a novel approach in such detector systems and assures that most of the information is preserved on the storage for further, high-level processing. Signal discrimination applies an unique method of using LVDS buffers located in the FPGA fabric.
J-PET Framework is an open-source software platform for data analysis, written in C++ and based on the ROOT package. It provides a common environment for implementation of reconstruction, calibration and filtering procedures, as well as for user-level analyses of Positron Emission Tomography data. The library contains a set of building blocks that can be combined by users with even little programming experience, into chains of processing tasks through a convenient, simple and well-documented API. The generic input-output interface allows processing the data from various sources: low-level data from the tomography acquisition system or from diagnostic setups such as digital oscilloscopes, as well as high-level tomography structures e.g. sinograms or a list of lines-of-response. Moreover, the environment can be interfaced with Monte Carlo simulation packages such as GEANT and GATE, which are commonly used in the medical scientific community.
The purpose of the presented research is estimation of the performance characteristics of the economic Total-Body Jagiellonian-PET system (TB-J-PET) constructed from plastic scintillators. The characteristics are estimated according to the NEMA NU-2-2018 standards utilizing the GATE package. The simulated detector consists of 24 modules, each built out of 32 plastic scintillator strips (each with cross section of 6 mm times 30 mm and length of 140 cm or 200 cm) arranged in two layers in regular 24-sided polygon circumscribing a circle with the diameter of 78.6 cm. For the TB-J-PET with an axial field-of-view (AFOV) of 200 cm, a spatial resolutions of 3.7 mm (transversal) and 4.9 mm (axial) are achieved. The NECR peak of 630 kcps is expected at 30 kBq/cc activity concentration and the sensitivity at the center amounts to 38 cps/kBq. The SF is estimated to 36.2 %. The values of SF and spatial resolution are comparable to those obtained for the state-of-the-art clinical PET scanners and the first total-body tomographs: uExplorer and PennPET. With respect to the standard PET systems with AFOV in the range from 16 cm to 26 cm, the TB-J-PET is characterized by an increase in NECR approximately by factor of 4 and by the increase of the whole-body sensitivity by factor of 12.6 to 38. The TOF resolution for the TB-J-PET is expected to be at the level of CRT=240 ps (FWHM). For the TB-J-PET with an axial field-of-view (AFOV) of 140 cm, an image quality of the reconstructed images of a NEMA IEC phantom was presented with a contrast recovery coefficient (CRC) and a background variability parameters. The increase of the whole-body sensitivity and NECR estimated for the TB-J-PET with respect to current commercial PET systems makes the TB-J-PET a promising cost-effective solution for the broad clinical applications of total-body PET scanners.
Modern TOF-PET scanner systems require high-speed computing resources for efficient data processing, monitoring and image reconstruction. In this article we present the data flow and software architecture for the novel TOF-PET scanner developed by the J-PET collaboration. We discuss the data acquisition system, reconstruction framework and image reconstruction software. Also, the concept of computing outside hospitals in the remote centers such as Swierk Computing Centre in Poland is presented.
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