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.

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Bibtex reference

@inproceedings{pansiot16xrays3d,
    author     =  {Julien Pansiot and Edmond Boyer},
    title      =  {{3D} Imaging from Video and Planar Radiography},
    booktitle  =  {International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)},
    year       =  2016,
    month      =  Oct,
    address    =  {Athens},
    publisher  =  {Springer},
    editor     =  {S. Ourselin et al.},
    series     =  {LNCS},
    volume     =  9902,
    chapter    =  52,
    pages      =  {450-457},
    doi        =  {10.1007/978-3-319-46726-9\_52},
    url        =  "http://dx.doi.org/10.1007/978-3-319-46726-9_52",
    eprint     =  "http://julien.pansiot.org/papers/2016_Pansiot_MICCAI_Xrays3d_HALv2.pdf",
    video      =  "http://julien.pansiot.org/suppl/2016_Pansiot_MICCAI_Xrays3d.mp4",
}