www.elsevier.com/locate/cagd A local fitting algorithm for converting planar curves to B-splines

In this paper we present a local fitting algorithm for converting smooth planar curves to B-splines. For a smooth planar curve a set of points together with their tangent vectors are first sampled from the curve such that the connected polygon approximates the curve with high accuracy and inflexions are detected by the sampled data efficiently. Then, a G1 continuous Bézier spline curve is obtained by fitting the sampled data with shape preservation as well as within a prescribed accuracy. Finally, the Bézier spline is merged into a C2 continuous B-spline curve by subdivision and control points adjustment. The merging is guaranteed to be within another error bound and with no more inflexions than the Bézier spline. In addition to shape preserving and error control, this conversion algorithm also benefits that the knots are selected automatically and adaptively according to local shape and error bound. A few experimental results are included to demonstrate the validity and efficiency of the algorithm

Computational Techniques to Predict Orthopaedic Implant Alignment and Fit in Bone

Among the broad palette of surgical techniques employed in the current orthopaedic practice, joint replacement represents one of the most difficult and costliest surgical procedures. While numerous recent advances suggest that computer assistance can dramatically improve the precision and long term outcomes of joint arthroplasty even in the hands of experienced surgeons, many of the joint replacement protocols continue to rely almost exclusively on an empirical basis that often entail a succession of trial and error maneuvers that can only be performed intraoperatively. Although the surgeon is generally unable to accurately and reliably predict a priori what the final malalignment will be or even what implant size should be used for a certain patient, the overarching goal of all arthroplastic procedures is to ensure that an appropriate match exists between the native and prosthetic axes of the articulation. To address this relative lack of knowledge, the main objective of this thesis was to develop a comprehensive library of numerical techniques capable to: 1) accurately reconstruct the outer and inner geometry of the bone to be implanted; 2) determine the location of the native articular axis to be replicated by the implant; 3) assess the insertability of a certain implant within the endosteal canal of the bone to be implanted; 4) propose customized implant geometries capable to ensure minimal malalignments between native and prosthetic axes. The accuracy of the developed algorithms was validated through comparisons performed against conventional methods involving either contact-acquired data or navigated implantation approaches, while various customized implant designs proposed were tested with an original numerical implantation method. It is anticipated that the proposed computer-based approaches will eliminate or at least diminish the need for undesirable trial and error implantation procedures in a sense that present error-prone intraoperative implant insertion decisions will be at least augmented if not even replaced by optimal computer-based solutions to offer reliable virtual “previews” of the future surgical procedure. While the entire thesis is focused on the elbow as the most challenging joint replacement surgery, many of the developed approaches are equally applicable to other upper or lower limb articulations