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

Sub-millimeter nuclear medical imaging with high sensitivity in positron emission tomography using beta-gamma coincidences

We present a nuclear medical imaging technique, employing triple-gamma trajectory intersections from beta^+ - gamma coincidences, able to reach sub-millimeter spatial resolution in 3 dimensions with a reduced requirement of reconstructed intersections per voxel compared to a conventional PET reconstruction analysis. This '$\gamma$-PET' technique draws on specific beta^+ - decaying isotopes, simultaneously emitting an additional photon. Exploiting the triple coincidence between the positron annihilation and the third photon, it is possible to separate the reconstructed 'true' events from background. In order to characterize this technique, Monte-Carlo simulations and image reconstructions have been performed. The achievable spatial resolution has been found to reach ca. 0.4 mm (FWHM) in each direction for the visualization of a 22Na point source. Only 40 intersections are sufficient for a reliable sub-millimeter image reconstruction of a point source embedded in a scattering volume of water inside a voxel volume of about 1 mm^3 ('high-resolution mode'). Moreover, starting with an injected activity of 400 MBq for ^76Br, the same number of only about 40 reconstructed intersections are needed in case of a larger voxel volume of 2 x 2 x 3~mm^3 ('high-sensitivity mode'). Requiring such a low number of reconstructed events significantly reduces the required acquisition time for image reconstruction (in the above case to about 140 s) and thus may open up the perspective for a quasi real-time imaging.Comment: 17 pages, 5 figutes, 3 table

System Response Kernel Calculation for List-mode Reconstruction in Strip PET Detector

Reconstruction of the image in Positron Emission Tomographs (PET) requires the knowledge of the system response kernel which describes the contribution of each pixel (voxel) to each tube of response (TOR). This is especially important in list-mode reconstruction systems, where an efficient analytical approximation of such function is required. In this contribution, we present a derivation of the system response kernel for a novel 2D strip PET.Comment: 10 pages, 2 figures; Presented at Symposium on applied nuclear physics and innovative technologies, Cracow, 03-06 June 201

Event-Driven Motion Compensation in Positron Emission Tomography: Development of a Clinically Applicable Method

Positron emission tomography (PET) is a well-established functional imaging method used in nuclear medicine. It allows for retrieving information about biochemical and physiological processes in vivo. The currently possible spatial resolution of PET is about 5 mm for brain acquisitions and about 8 mm for whole-body acquisitions, while recent improvements in image reconstruction point to a resolution of 2 mm in the near future. Typical acquisition times range from minutes to hours due to the low signal-to-noise ratio of the measuring principle, as well as due to the monitoring of the metabolism of the patient over a certain time. Therefore, patient motion increasingly limits the possible spatial resolution of PET. In addition, patient immobilisations are only of limited benefit in this context. Thus, patient motion leads to a relevant resolution degradation and incorrect quantification of metabolic parameters. The present work describes the utilisation of a novel motion compensation method for clinical brain PET acquisitions. By using an external motion tracking system, information about the head motion of a patient is continuously acquired during a PET acquisition. Based on the motion information, a newly developed event-based motion compensation algorithm performs spatial transformations of all registered coincidence events, thus utilising the raw data of a PET system - the so-called `list-mode´ data. For routine acquisition of this raw data, methods have been developed which allow for the first time to acquire list-mode data from an ECAT Exact HR+ PET scanner within an acceptable time frame. Furthermore, methods for acquiring the patient motion in clinical routine and methods for an automatic analysis of the registered motion have been developed. For the clinical integration of the aforementioned motion compensation approach, the development of additional methods (e.g. graphical user interfaces) was also part of this work. After development, optimisation and integration of the event-based motion compensation in clinical use, analyses with example data sets have been performed. Noticeable changes could be demonstrated by analysis of the qualitative and quantitative effects after the motion compensation. From a qualitative point of view, image artefacts have been eliminated, while quantitatively, the results of a tracer kinetics analysis of a FDOPA acquisition showed relevant changes in the R0k3 rates of an irreversible reference tissue two compartment model. Thus, it could be shown that an integration of a motion compensation method which is based on the utilisation of the raw data of a PET scanner, as well as the use of an external motion tracking system, is not only reasonable and possible for clinical use, but also shows relevant qualitative and quantitative improvement in PET imaging.Die Positronen-Emissions-Tomographie (PET) ist ein in der Nuklearmedizin etabliertes funktionelles Schnittbildverfahren, das es erlaubt Informationen über biochemische und physiologische Prozesse in vivo zu erhalten. Die derzeit erreichbare räumliche Auflösung des Verfahrens beträgt etwa 5 mm für Hirnaufnahmen und etwa 8 mm für Ganzkörperaufnahmen, wobei erste verbesserte Bildrekonstruktionsverfahren eine Machbarkeit von 2 mm Auflösung in Zukunft möglich erscheinen lassen. Durch das geringe Signal/Rausch-Verhältnis des Messverfahrens, aber auch durch die Tatsache, dass der Stoffwechsel des Patienten über einen längeren Zeitraum betrachtet wird, betragen typische PET-Aufnahmezeiten mehrere Minuten bis Stunden. Dies hat zur Folge, dass Patientenbewegungen zunehmend die erreichbare räumliche Auflösung dieses Schnittbildverfahrens limitieren. Eine Immobilisierung des Patienten zur Reduzierung dieser Effekte ist hierbei nur bedingt hilfreich. Es kommt daher zu einer relevanten Auflösungsverschlechterung sowie zu einer Verfälschung der quantifizierten Stoffwechselparameter. Die vorliegende Arbeit beschreibt die Nutzbarmachung eines neuartigen Bewegungskorrekturverfahrens für klinische PET-Hirnaufnahmen. Mittels eines externen Bewegungsverfolgungssystems wird während einer PET-Untersuchung kontinuierlich die Kopfbewegung des Patienten registriert. Anhand dieser Bewegungsdaten führt ein neu entwickelter event-basierter Bewegungskorrekturalgorithmus eine räumliche Korrektur aller registrierten Koinzidenzereignisse aus und nutzt somit die als "List-Mode" bekannten Rohdaten eines PET Systems. Für die Akquisition dieser Daten wurden eigens Methoden entwickelt, die es erstmals erlauben, diese Rohdaten von einem ECAT Exact HR+ PET Scanner innerhalb eines akzeptablen Zeitraumes zu erhalten. Des Weiteren wurden Methoden für die klinische Akquisition der Bewegungsdaten sowie für die automatische Auswertung dieser Daten entwickelt. Ebenfalls Teil der Arbeit waren die Entwicklung von Methoden zur Integration in die klinische Routine (z.B. graphische Nutzeroberflächen). Nach der Entwicklung, Optimierung und Integration der event-basierten Bewegungskorrektur für die klinische Nutzung wurden Analysen anhand von Beispieldatensätzen vorgenommen. Es zeigten sich bei der Auswertung sowohl der qualitativen als auch der quantitativen Effekte deutliche Änderungen. In qualitativer Hinsicht wurden Bildartefakte eliminiert; bei der quantitativen Auswertung einer FDOPA Messung zeigte sich eine revelante Änderung der R0k3 Einstromraten eines irreversiblen Zweikompartment-Modells mit Referenzgewebe. Es konnte somit gezeigt werden, dass eine Integration einer Bewegungskorrektur unter Zuhilfenahme der Rohdaten eines PET Systems sowie unter Nutzung eines externen Verfolgungssystems nicht nur sinnvoll und in der klinischen Routine machbar ist, sondern auch zu maßgeblichen qualitativen und quantitativen Verbesserungen in der PET-Bildgebung beitragen kann

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

High-Level Programming for Medical Imaging on Multi-GPU Systems Using the SkelCL Library

Application development for modern high-performance systems with Graphics Processing Units (GPUs) relies on low-level programming approaches like CUDA and OpenCL, which leads to complex, lengthy and error-prone programs. In this paper, we present SkelCL – a high-level programming model for systems with multiple GPUs and its implementation as a library on top of OpenCL. SkelCL provides three main enhancements to the OpenCL standard: 1) computations are conveniently expressed using parallel patterns (skeletons); 2) memory management is simplified using parallel container data types; 3) an automatic data (re)distribution mechanism allows for scalability when using multi-GPU systems. We use a real-world example from the field of medical imaging to motivate the design of our programming model and we show how application development using SkelCL is simplified without sacrificing performance: we were able to reduce the code size in our imaging example application by 50% while introducing only a moderate runtime overhead of less than 5%