Section: New Results

Bayesian 3D imaging from X-rays and video

A new method for estimating 3D dense attenuation of moving samples such as body parts from multiple video and a single planar X-ray device has been devised [13]. Most dense modeling methods consider samples observed with a moving X-ray device and cannot easily handle moving samples. We proposed 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, as illustrated in Figure 4.

Figure 4. Results of the proposed method on a forearm phantom (2 selected slices). Left-to-right: ground-truth CT scan, proposed method, without optical flow, without TVL${}_1$prior, ART. Without optical flow, artefacts are visible, for example in the bone cavities. The ART method produces much noisier results.