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

A Multi-GPU Programming Library for Real-Time Applications

We present MGPU, a C++ programming library targeted at single-node multi-GPU systems. Such systems combine disproportionate floating point performance with high data locality and are thus well suited to implement real-time algorithms. We describe the library design, programming interface and implementation details in light of this specific problem domain. The core concepts of this work are a novel kind of container abstraction and MPI-like communication methods for intra-system communication. We further demonstrate how MGPU is used as a framework for porting existing GPU libraries to multi-device architectures. Putting our library to the test, we accelerate an iterative non-linear image reconstruction algorithm for real-time magnetic resonance imaging using multiple GPUs. We achieve a speed-up of about 1.7 using 2 GPUs and reach a final speed-up of 2.1 with 4 GPUs. These promising results lead us to conclude that multi-GPU systems are a viable solution for real-time MRI reconstruction as well as signal-processing applications in general.Comment: 15 pages, 10 figure

GPU acceleration of brain image proccessing

Durante los últimos años se ha venido demostrando el alto poder computacional que ofrecen las GPUs a la hora de resolver determinados problemas. Al mismo tiempo, existen campos en los que no es posible beneficiarse completamente de las mejoras conseguidas por los investigadores, debido principalmente a que los tiempos de ejecución de las aplicaciones llegan a ser extremadamente largos. Este es por ejemplo el caso del registro de imágenes en medicina. A pesar de que se han conseguido aceleraciones sobre el registro de imágenes, su uso en la práctica clínica es aún limitado. Entre otras cosas, esto se debe al rendimiento conseguido. Por lo tanto se plantea como objetivo de este proyecto, conseguir mejorar los tiempos de ejecución de una aplicación dedicada al resgitro de imágenes en medicina, con el fin de ayudar a aliviar este problema