Department of Control Engineering and Information Technology
Budapest University of Technology and Economics
Although Monte Carlo Volume Rendering (MCVR) is an efficient point-based technique for generating simulated X-ray images from large CT data, its practical application in medical imaging systems is limited by the relatively expensive preprocessing. The quality of images is strongly influenced by the transfer function, which maps a data value onto a sampling probability. An appropriate transfer function concentrates the point samples onto the region of interest. Since it is data dependent, a fine parameter tuning is necessary. However, the costly preprocessing has to be repeated whenever the transfer function parameters are modified. In this paper a new preprocessing algorithm is proposed for MCVR, which allows for an interactive transfer function control in the rendering phase, providing a visual feedback in a couple of seconds. In order to rapidly recompute point samples according to the modified transfer function, an efficient hybrid sampling strategy is applied, which combines the advantages of the probabilistic Monte Carlo sampling and the deterministic quasi-Monte Carlo sampling.
X-ray volume rendering, Monte Carlo integration, Importance sampling, Progressive refinement.
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