3D Imaging from Video and Planar Radiography

Julien Pansiot 1 Edmond Boyer 1
1 MORPHEO - Capture and Analysis of Shapes in Motion
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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 a novel method that uses a surface motion capture system associated to a single low-cost/low-dose planar X-ray imaging device for dense in-depth attenuation information. Our key contribution is to rely on Bayesian inference to solve for a dense attenuation volume given planar radioscopic images of a moving sample. The approach enables multiple sources of noise to be considered and takes advantage of 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 simulated and in-vivo data.
Type de document :
Communication dans un congrès
Sebastien Ourselin; Leo Joskowicz; Mert R. Sabuncu; Gozde Unal; William Wells. MICCAI 2016 - 19th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2016, Athens, Greece. Springer, 9902, pp.450-457, 2016, LNCS. <10.1007/978-3-319-46726-9_52>
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Soumis le : lundi 24 octobre 2016 - 16:21:31
Dernière modification le : vendredi 28 octobre 2016 - 16:24:00

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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale - Pas de modification 4.0 International License

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Julien Pansiot, Edmond Boyer. 3D Imaging from Video and Planar Radiography. Sebastien Ourselin; Leo Joskowicz; Mert R. Sabuncu; Gozde Unal; William Wells. MICCAI 2016 - 19th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2016, Athens, Greece. Springer, 9902, pp.450-457, 2016, LNCS. <10.1007/978-3-319-46726-9_52>. <hal-01348939v2>

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