Evaluation of Single-Chip, Real-Time Tomographic Data Processing on FPGA - SoC Devices

A novel approach to tomographic data processing has been developed and evaluated using the Jagiellonian PET (J-PET) scanner as an example. We propose a system in which there is no need for powerful, local to the scanner processing facility, capable to reconstruct images on the fly. Instead we introduce a Field Programmable Gate Array (FPGA) System-on-Chip (SoC) platform connected directly to data streams coming from the scanner, which can perform event building, filtering, coincidence search and Region-Of-Response (ROR) reconstruction by the programmable logic and visualization by the integrated processors. The platform significantly reduces data volume converting raw data to a list-mode representation, while generating visualization on the fly.Comment: IEEE Transactions on Medical Imaging, 17 May 201

J-PET Framework: Software platform for PET tomography data reconstruction and analysis

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.Comment: 14 pages, 5 figure

Processing optimization with parallel computing for the J-PET tomography scanner

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.Comment: 8 page

Flexible data input layer architecture (FDILA) for quick-response decision making tools in volatile manufacturing systems

This paper proposes the foundation for a flexible data input management system as a vital part of a generic solution for quick-response decision making. Lack of a comprehensive data input layer between data acquisition and processing systems has been realized and thought of. The proposed FDILA is applicable to a wide variety of volatile manufacturing environments. It provides a generic platform that enables systems designers to define any number of data entry points and types regardless of their make and specifications in a standard fashion. This is achieved by providing a variable definition layer immediately on top of the data acquisition layer and before data pre-processing layer. For proof of concept, National Instruments’ Labview data acquisition software is used to simulate a typical shop floor data acquisition system. The extracted data can then be fed into a data mining module that builds cost modeling functions involving the plant’s Key Performance Factors

A GPU-based Implementation for Improved Online Rebinning Performance in Clinical 3-D PET

Online rebinning is an important and well-established technique for reducing the time required to process Positron Emission Tomography data. However, the need for efficient data processing in a clinical setting is growing rapidly and is beginning to exceed the capability of traditional online processing methods. High-count rate applications such as Rubidium 3-D PET studies can easily saturate current online rebinning technology. Realtime processing at these high-count rates is essential to avoid significant data loss. In addition, the emergence of time-of-flight (TOF) scanners is producing very large data sets for processing. TOF applications require efficient online Rebinning methods so as to maintain high patient throughput. Currently, new hardware architectures such as Graphics Processing Units (GPUs) are available to speedup data parallel and number crunching algorithms. In comparison to the usual parallel systems, such as multiprocessor or clustered machines, GPU hardware can be much faster and above all, it is significantly cheaper. The GPUs have been primarily delivered for graphics for video games but are now being used for High Performance computing across many domains. The goal of this thesis is to investigate the suitability of the GPU for PET rebinning algorithms

Optical simulation study for high resolution monolithic detector design for TB-PET

Background The main limitations in positron emission tomography (PET) are the limited sensitivity and relatively poor spatial resolution. The administered radioactive dose and scan time could be reduced by increasing system sensitivity with a total-body (TB) PET design. The second limitation, spatial resolution, mainly originates from the specific design of the detectors that are implemented. In state-of-the-art scanners, the detectors consist of pixelated crystal arrays, where each individual crystal is isolated from its neighbors with reflector material. To obtain higher spatial resolution the crystals can be made narrower which inevitably leads to more inter-crystal scatter and larger dead space between the crystals. A monolithic detector design shows superior characteristics in (i) light collection efficiency (no gaps), (ii) timing, as it significantly reduces the number of reflections and therefore the path length of each scintillation photon and (iii) spatial resolution (including better depth-of-interaction (DOI)). The aim of this work is to develop a precise simulation model based on measured crystal data and use this powerful tool to find the limits in spatial resolution for a monolithic detector for the use in TB-PET. Materials and methods A detector (Fig. 1) based on a monolithic 50x50x16 mm3 lutetium-(yttrium) oxyorthosilicate (L(Y)SO) scintillation crystal coupled to an 8x8 array of 6x6mm2 silicon photomultipliers (SiPMs) is simulated with GATE. A recently implemented reflection model for scintillation light allows simulations based on measured surface data (1). The modeled surfaces include black painted rough finishing on the crystal sides (16x50mm2) and a specular reflector attached to a polished crystal top (50x50mm2). Maximum Likelihood estimation (MLE) is used for positioning the events. Therefore, calibration data is obtained by generating 3.000 photo-electric events at given calibration positions (Fig. 1). Compton scatter is not (yet) included. In a next step, the calibration data is organized in three layers based on the exact depth coordinate in the crystal (i.e. DOI assumed to be known). For evaluating the resolution, the full width at half maximum (FWHM) is estimated at the irradiated positions of Fig. 2 as a mean of all profiles in vertical and horizontal direction. Next, uniformity is evaluated by simulating 200k events from a flood source, placed in the calibrated area. Results For the irradiation pattern in Fig. 2 the resolution in terms of FWHM when applying MLE is: 0.86±0.13mm (Fig. 3a). Nevertheless, there are major artifacts also at non-irradiated positions. By positioning the events based on three DOI-based layers it can be seen that the events closest to the photodetector introduce the largest artifacts (Fig. 3b-d). The FWHM improves for Layer 1 and 2, to 0.69±0.04mm and 0.59±0.02mm, respectively. Layer 3 introduces major artifacts to the flood map, as events are positioned at completely different locations as the initial irradiation. A FWHM estimation is thus not useful. The uniformity (Fig. 4) degrades with proximity to the photodetector. The map in Fig. 4c shows that the positioning accuracy depends not only on DOI but also the position in the plane parallel to the photodetector array. Conclusions A simulation model for a monolithic PET detector with good characteristics for TB-PET systems was developed with GATE. A first estimate of the spatial resolution and uniformity was given, pointing out the importance of depth-dependent effects. Future studies will include several steps towards more realistic simulations e.g. surface measurements of our specific crystals for the optical surface model and inclusion of the Compton effect