3D deformable motion reconstruction from fluoroscopy images based on articulating statistical shape and intensity models.

Motion patterns are crucial markers for the health of a horse. The lack of accurate motion analysis systems leads to subjective diagnoses with a negative effect on the treatment outcome. The current most accurate systems are based on X-ray radiographs of the subject during motion. The 3D reconstruction from the planar images requires a prior CT-scan of the subject. The techniques are not suitable for medical applications because they require placement of invasive markers into the subject and require a lot of manual processing. Moreover, they can not handle deformable motions. As a result, the cushions on horse hoofs which deform during landing, can not be imaged. We propose a motion reconstruction technique that replaces the CT-scan by a statistical shape and intensity model. Omitting a CT-scan reduces examination costs, time and radiation dose. A statistical model describes the variation in shape and densities present within the population. To describe moving subjects, such a model needs to be articulated over time. This will be extended with a model for the deformable dynamics of soft tissues. The motion reconstruction technique will autonomously find the right shape and pose of the model based on the X-ray images by comparing them with a simulated X-ray image of the model. The novel technique will serve as an objective diagnostic tool for diagnosis, follow-up and validation of innovative orthopedic products by means of motion analysis, both for animals and humans.

Keywords:MODEL SYSTEMS

Disciplines:Biomechanics

Project type:Collaboration project