Principles of computerized tomographic imaging
Principles of computerized tomographic imaging
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Computer Architecture: A Quantitative Approach
Computer Architecture: A Quantitative Approach
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Proceedings of the 30th international conference on Software engineering
Parallel implementation of a X-ray tomography reconstruction algorithm based on MPI and CUDA
Proceedings of the 20th European MPI Users' Group Meeting
Proceedings of the 2014 Workshop on Programming models for SIMD/Vector processing
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We present an evaluation of state-of-the-art computer hardware architectures for implementing the FDK method, which solves the 3-D image reconstruction task in cone-beam computed tomography (CT). The computational complexity of the FDK method prohibits its use for many clinical applications unless appropriate hardware acceleration is employed. Today's most powerful hardware architectures for high-performance computing applications are based on standard multi-core processors, off-the-shelf graphics boards, the Cell Broadband Engine Architecture (CBEA), or customized accelerator platforms (e.g., FPGA-based computer components). For each hardware platform under consideration, we describe a thoroughly optimized implementation of the most time-consuming parts of the FDK algorithm; the filtering step as well as the subsequent back-projection step. We further explain the required code transformations to parallelize the algorithm for the respective target architecture. We compare both the implementation complexity and the resulting performance of all architectures under consideration using the same two medical datasets which have been acquired using a standard C-arm device. Our optimized back-projection implementations achieve at least a speedup of 6.5 (CBEA, two processors), 22.0 (GPU, single board), and 35.8 (FPGA, 9 chips) compared to a standard workstation equipped with a quad-core processor.