Fourier volume rendering (FVR) is a significant visualization technique that has been used widely in digital radiography. As a result\nof its O(N2 log N) time complexity, it provides a faster alternative to spatial domain volume rendering algorithms that are O(N3)\ncomputationally complex. Relying on the Fourier projection-slice theorem, this technique operates on the spectral representation of a\n3D volume instead of processing its spatial representation to generate attenuation-only projections that look like X-ray radiographs.\nDue to the rapid evolution of its underlying architecture, the graphics processing unit (GPU) became an attractive competent\nplatform that can deliver giant computational raw power compared to the central processing unit (CPU) on a per-dollar-basis.\nThe introduction of the compute unified device architecture (CUDA) technology enables embarrassingly-parallel algorithms to\nrun efficiently on CUDA-capable GPU architectures. In this work, a high performance GPU-accelerated implementation of the\nFVR pipeline on CUDA-enabled GPUs is presented. This proposed implementation can achieve a speed-up of 117x compared to\na single-threaded hybrid implementation that uses the CPU and GPU together by taking advantage of executing the rendering\npipeline entirely on recent GPU architectures.
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