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Abstract
This dissertation describes the development of a non-invasive framework for better understanding the motion of bones in knee joints in 3D space using ultrasound and low complexity image registration for applications in the domain of biomedical research and treatment planning related to knee joints. Kinematic analysis allows the motion of individual bones in the knee joint to be measured. These motion measurements provide significant insights into normal and abnormal joint trajectories which can lead to improved artificial joint component design, superior diagnosis for ligament injuries and adequate therapeutic strategies to be formulated. Knee joint kinematics is complex in a sense that the natural motions of flexion, extension and internal, external rotation are coupled. During flexion, the two main bones in a knee joint, the tibia and femur, do not bend like a door hinge, instead they rotate about several specific axes. This type of motion makes it crucial that the joint kinematics be evaluated in 3D space. The existing techniques, whether they use x-ray, optoelectronic, ultrasonic or video motion capture to track the bone movements under the skin, are somewhat prone to invasiveness or ionizing radiation or skin wobbling during dynamic activities. In this thesis, a 2D B-mode ultrasound based multi-slice H shaped array sensor and its calibration technique are proposed. This novel calibrated array is capable of tracking the relative motion of the bones in a knee joint in 3D space without the requirement of any invasive implantation of beads in the knee but it still provides the required accuracy when compared to the current clinical standard Roentgen Stereo Analysis (RSA). Finally, a single element ultrasound sensor based method using image registration for rigid body motion parameter estimation is proposed. This approach not only facilitates joint kinematic determination but also guides pre- and post-surgery planning of the knee joint based non-invasive A-mode ultrasound. The unique feature of the proposed methods are the estimation of the six rigid-body transform parameters with large convergence radius with enough precision, robustness to ultrasound (US) speckle noise and speed of convergence.