Adaptive GPU Ray Casting Based on Spectral Analysis

Stefan Suwelack 1 Eric Heitz 2 Roland Unterhinninghofen 1 Rüdiger Dillmann 1
2 ARTIS - Acquisition, representation and transformations for image synthesis
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : GPU based ray casting has become a valuable tool for the visualization of medical image data. While the method produces high- quality images, its main drawback is the high computational load. We present a novel adaptive approach to speed up the rendering. In contrast to well established heuristic methods, we use the spectral decomposition of the transfer function and the dataset to derive a suitable sampling criterion. It is shown how this criterion can be e ciently incorporated into an adaptive ray casting algorithm. Two medical datasets, which each represent a typical, but di erent material distribution, are rendered using the proposed method. An analysis of the number of sample points per ray reveals that the new algorithm requires 50% to 80% less points compared to a non-adaptive method without any quality loss. We also show that the rendering speed of the GPU implementation is greatly increased with reference to the non-adaptive algorithm.
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Stefan Suwelack, Eric Heitz, Roland Unterhinninghofen, Rüdiger Dillmann. Adaptive GPU Ray Casting Based on Spectral Analysis. MIAR 2010 - 5th International Workshop on Medical Imaging and Augmented Reality, Sep 2010, Beijing, China. pp.169-178, ⟨10.1007/978-3-642-15699-1_18⟩. ⟨hal-00686463⟩

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