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• W2913504910 abstract "In this paper, we consider dense volumetric modeling of moving samples such as body parts. Most dense modeling methods consider samples observed with a moving X-ray device and cannot easily handle moving samples. We propose instead a novel method to observe shape motion from a fixed X-ray device and to build dense in-depth attenuation information. This yields a low-cost, low-dose 3-D imaging solution, taking benefit of equipment widely available in clinical environments. Our first innovation is to combine a video-based surface motion capture system with a single low-cost/low-dose fixed planar X-ray device, in order to retrieve the sample motion and attenuation information with minimal radiation exposure. Our second innovation is to rely on Bayesian inference to solve for a dense attenuation volume given planar radioscopic images of a moving sample. This approach enables multiple sources of noise to be considered and takes advantage of very limited prior information to solve an otherwise ill-posed problem. Results show that the proposed strategy is able to reconstruct dense volumetric attenuation models from a very limited number of radiographic views over time on synthetic and in-situ data." @default.
• W2913504910 created "2019-02-21" @default.
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• W2913504910 date "2019-02-01" @default.
• W2913504910 modified "2023-10-15" @default.
• W2913504910 title "CBCT of a Moving Sample From X-Rays and Multiple Videos" @default.
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• W2913504910 doi "https://doi.org/10.1109/tmi.2018.2865228" @default.
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