S. Rit, M. Vila Oliva, S. Brousmiche, R. Labarbe, D. Sarrut, G. C. Sharp
We propose the Reconstruction Toolkit (RTK, http://www.openrtk.org), an open-source toolkit for fast cone-beam CT reconstruction, based on the Insight Toolkit (ITK) and using GPU code extracted from Plastimatch. RTK is developed by an open consortium (see affiliations) under the non-contaminating Apache 2.0 license. The quality of the platform is daily checked with regression tests in […]
Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein
Single Instruction, Multiple Data (SIMD) vectorization is a major driver of performance in current architectures, and is mandatory for achieving good performance with codes that are limited by instruction throughput. We investigate the efficiency of different SIMD-vectorized implementations of the RabbitCT benchmark. RabbitCT performs 3D image reconstruction by back projection, a vital operation in computed […]
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Jonas Adler
A GPU Monte Carlo code for x-ray photon transport has been implemented and extensively tested. The code is intended for scatter compensation of cone beam computed tomography images. The code was tested to agree with other well known codes within 5% for a set of simple scenarios. The scatter compensation was also tested using an […]
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Johannes Hofmann
The computational effort of 3D image reconstruction in Computed Tomography (CT) has required special purpose hardware for a long time. Systems such as custom-built FPGA-systems and GPUs are still widely-used today, in particular in interventional settings, where radiologists require a hard time constraint for reconstruction. However, recently is has been shown that today even commodity […]
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Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein
We examine the Xeon Phi, which is based on Intel’s Many Integrated Cores architecture, for its suitability to run the FDK algorithm–the most commonly used algorithm to perform the 3D image reconstruction in cone-beam computed tomography. We study the challenges of efficiently parallelizing the application and means to enable sensible data sharing between threads despite […]
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Marc Kaseberg, Steffen Melnik, Erwin Keeve
Volume reconstruction in cone-beam CT is a computationally demanding task. Since recent years, the reconstruction is accelerated by utilizing Graphics Processing Units (GPUs). Frameworks for General Purpose Computations on GPUs are proven tool to access the resources of graphics cards. WIth the Open Computing Language (OpenCL) the first open standard for cross-vendor and cross-platform programming […]
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Zilong Pan
The Edinburgh Cancer Centre at the Western General Hospital in Edinburgh is doing research on image analysis for predicting lung fibrosis induced by radiation as part of a treatment plan. They are developing a MATLAB code to analyse three dimensional Computed tomography (CT) images of patients but, because a standard three dimensional CT image is […]
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Davide Montanari, Enrica Scolari, Chiara Silvestri, Yan J. Graves, Hao Yan, Laura Cervino, Roger Rice, Steve B. Jiang, Xun Jia
Cone beam CT (CBCT) has been widely used for patient setup in image guided radiation therapy (IGRT). Radiation dose from CBCT scans has become a clinical concern. The purposes of this study are 1) to commission a GPU-based Monte Carlo (MC) dose calculation package gCTD for Varian On-Board Imaging (OBI) system and test the calculation […]
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Edward S. Jimenez, Laurel J. Orr
This work will present the utilization of the massively multi-threaded environment of graphics processors (GPUs) to improve the computation time needed to reconstruct large computed tomography (CT) datasets and the arising challenges for system implementation. Intelligent algorithm design for massively multi-threaded graphics processors differs greatly from traditional CPU algorithm design. Although a brute force port […]
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Gabor Jakab, Laszlo Szirmay-Kalos
Developing image reconstruction algorithms for diagnostic medical devices requires physically accurate and effective simulation tools. In this paper we present a hybrid Monte Carlo (MC) particle simulation method for Computed Tomography (CT) scanners. To meet the performance requirements, we combine several variance reduction techniques and tailor the algorithms for effective GPU execution. Variance reduction methods […]
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Gabor Jakab, Tamas Huszar, Balazs Csebfalvi
The computing power of modern GPUs makes them very suitable for Computed Tomography (CT) image reconstruction. Apart from accelerating the reconstruction, their extra computing performance compared to conventional CPUs can be used to increase image quality in several ways. In this paper we present our upgraded GPU based iterative reconstruction algorithm, including ML-TR (Maximum Likelihood […]
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Timo Zinsser, Benjamin Keck
Filtered back-projection algorithms are widely used for the reconstruction of volumetric data from cone-beam projections in interventional C-arm computed tomography. Furthermore, general-purpose GPUs have become a popular tool for accelerating the reconstruction during time-critical clinical procedures. In this work, we focus on the systematic performance optimization of cone-beam back-projection on the latest architecture of CUDA-enabled […]
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