3D knee-motion tracking from sequences of radiographs

In this paper, a 3D model-based approach is proposed for tracking 3D motion (positions and orientations) of the knee from sequences of 2D radiographs. Conventional methods using external skin markers or body model do not accurately reflect motion of the underlying bone. In contrast, our method is to use sequences of radiographs for direct visualization of bone motion during activities. A 3D texture-mapped volume rendering is used to simulate a radiograph image, a 2D projected image of the 3D model data. A Quadtree-based normalized correlation algorithm is employed to measure similarity between the projected 2D model image and the pre-processed radiograph image. An optimization routine iterates the six motion parameters until the optimal similarity is obtained. This method has been evaluated using test data collected from an anatomically accurate radiographic knee phantom, specifically femur part of the phantom. Further testing is underway using in-vivo radiograph image sequences of a canine hindlime during treadmill walking.