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dc.contributor.authorAbella, Monica
dc.contributor.authorSerrano, Estefania
dc.contributor.authorGarcia-Blas, Javier
dc.contributor.authorGarcía, Ines
dc.contributor.authorde Molina, Claudia
dc.contributor.authorCarretero, Jesus
dc.contributor.authorDesco, Manuel
dc.identifier.citationPLoS ONE.2017;(12)7:e0180363
dc.description.abstractThe availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden. The development of new flexible X-ray systems could benefit from computer simulations, which may enable performance to be checked before expensive real systems are implemented. The development of simulation/reconstruction algorithms in this context poses three main difficulties. First, the algorithms deal with large data volumes and are computationally expensive, thus leading to the need for hardware and software optimizations. Second, these optimizations are limited by the high flexibility required to explore new scanning geometries, including fully configurable positioning of source and detector elements. And third, the evolution of the various hardware setups increases the effort required for maintaining and adapting the implementations to current and future programming models. Previous works lack support for completely flexible geometries and/or compatibility with multiple programming models and platforms. In this paper, we present FUX-Sim, a novel X-ray simulation/reconstruction framework that was designed to be flexible and fast. Optimized implementation for different families of GPUs (CUDA and OpenCL) and multi-core CPUs was achieved thanks to a modularized approach based on a layered architecture and parallel implementation of the algorithms for both architectures. A detailed performance evaluation demonstrates that for different system configurations and hardware platforms, FUX-Sim maximizes performance with the CUDA programming model (5 times faster than other state-of-the-art implementations). Furthermore, the CPU and OpenCL programming models allow FUX-Sim to be executed over a wide range of hardware platforms.
dc.subject.meshComputer Graphics
dc.subject.meshCone-Beam Computed Tomography
dc.subject.meshTime Factors
dc.subject.meshComputer Simulation
dc.subject.meshRadiographic Image Enhancement
dc.titleFUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems.
dc.identifier.journalPloS one
dc.pubmedtypeJournal Article
Appears in Collections:Fundaciones e Institutos de Investigación > IIS H. General U. Gregorio Marañón > Artículos

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