3D Imaging from Video and Planar Radiography

International audienceIn 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

CT from Motion: Volumetric Capture of Moving Shapes with X-rays and Videos

International audienceIn this paper, we consider the capture of dense volumetric X-ray attenuation models of non-rigidly moving samples. Traditional 3D medical imaging apparatus, e.g. CT or MRI, do not easily adapt to shapes that deform significantly such as a moving hand. We propose an approach that simultaneously recovers dense volumetric shape and motion information by combining video and X-ray modalities. Multiple colour images are captured to track shape surfaces while a single X-ray device is used to infer inner attenu-ations. The approach does not assume prior models which makes it versatile and easy to generalise over different shapes. Results on synthetic and real-life data are presented that demonstrate the approach feasibility with a limited number of X-ray views. The resulting dense 4D attenuation data provides unprecedented insights for motion analysis