A Joint-Constraint Model-Based System for Reconstructing Total Knee Motion

Comprehending knee motion is an essential requirement for studying the causes of knee disorders. In this paper, we propose a new 2-D-3-D registration system based on joint-constraint model for reconstructing total knee motion. The proposed model that contains bone geometries and an articulated joint mechanism is first constructed from multipostural magnetic resonance volumetric images. Then, the bone segments of the model are hierarchically registered to each frame of the given single-plane fluoroscopic video that records the knee activity. The bone posture is iteratively optimized using a modified chamfer matching algorithm to yield the simulated radiograph which is the best fit to the underlying fluoroscopic image. Unlike conventional registration methods computing posture parameters for each bone independently, the proposed femorotibial and patellofemoral joint models properly maintain the articulations between femur, tibia, and patella during the registration processes. As a result, we can obtain a sequence of registered knee postures showing smooth and reasonable physiologic patterns of motion. The proposed system also provides joint-space interpolation to densely generate intermediate postures for motion animation. The effectiveness of the proposed method was validated by computer simulation, animal cadaver, and in vivo knee testing. The mean target registration errors for femur, tibia, and patella were less than 1.5 mm. In particular, small out-of-plane registration errors [less than 1 mm (translation) and 2° (rotation)] were achieved in animal cadaver assessments.