CBCT of a Moving Sample From X-Rays and Multiple Videos

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 3D 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.

Bone Position and Ligament Deformations of the Foot From CT Images to Quantify the Influence of Footwear in ex vivo Feet

“…To observe the 3D position of each foot bone, the approaches based on medical images, such as X-rays, are privileged. For dynamic and static cases, 2D images from biplanar fluoroscopy can be used (Ito et al, 2015(Ito et al, , 2017Pansiot and Boyer, 2019). For static case only, images from computed tomography (CT) scanners directly provide 3D information but usually involve a manual processing or interpretation which limits their usage to small sample size (Ferri et al, 2008;Colin et al, 2014;Lintz et al, 2018).…”