1.7.1 Motivation
Implant alignment is a critical factor in replicating native kinematics of the elbow and
durability of the artificial components. In order to better position the implant into
medullary canals of the elbow bones, both anatomical understanding of the bones and
biomechanical properties should be considered [Schunid et al., 1995; Figgie et al., 1986].
Brownhill and colleagues [Brownhill et al., 2012a] studied the anatomical perspective of
the distal humerus and derived geometric features of the distal humeral canal, to better
investigate implant positioning. It was shown that the anteriorposterior curvature of
medullary canal of the distal humerus along with FE axis anterior offset from axis of this
canal play an important role in the design and implantation of distal humerus implants
[Brownhill et al., 2012b].
Collision detection can have broad applications in medical area and so many
studies were conducted in this area. In a study by Tutunea-Fatan et al. [Tutunea-Fatan et
al., 2010], collision detection was utilized to assess the insertability of the stem in the
accomplish the insertion. As another application, collision detection was used in virtual
surgery simulators in [Lombardo et al., 1999] to train surgeons on virtual patients.
Nowadays, since non-invasive surgeries contain a majority of surgeries, practicing with
various tools during surgery is essential in which surgical simulators can be a great help.
Successful clinical outcome of surgical joint arthroplasty is decisively influenced
by the pre-operative planning procedures aiming to establish an optimized implant
insertion trajectory into the bone cavity. Since computation of the insertion path of a
body into a cavity represents a traditional instance of a path planning problem often
encountered in robotics field, the proposed research is expected to reinforce the
importance of engineering approaches in the context of Computer-Aided Orthopaedic
Surgery (CAOS). The use of collision detection algorithms – involving advanced
geometric representations and/or computations will enable the determination of optimal
implant insertion trajectory with significant implications with respect to preoperative
prediction of implant alignment and optimal implant design.
1.7.2 Objectives and Hypothesis
The main objective of the proposed research is to develop a library of numerical
algorithms that will constitute the core of a computationally-intensive geometry
visualization module capable of achieving accurate predictions related to implant
insertability into the bone’s endosteal canal as defined by patient-specific CT scans. The methods to be developed within the scope of the proposed research will
permit the replacement of error-prone implant insertion decisions made preoperatively by
least diminish the need for unreliable and undesirable trial and error validation
procedures. Over the long term, it is expected that the knowledge generated through this
study will be incorporated into a complex virtual total arthroplasty training simulator that
will integrate these geometry-based modules with elements of haptic feedback.
The central hypothesis of the proposed research is that by analyzing
preoperatively the implant and medullary canal geometries involved in total elbow
arthroplasty, an accurate prediction can be made with respect to their relative fit. To
address this hypothesis, the objectives are:
1) To develop a computer-aided method capable to reconstruct with minimal user
intervention accurate parametric-based representations of the bone geometry starting
from computer tomography (CT) data;
2) To assess the insertability of particular implant geometry in the context of a
specific humeral specimen by means of numerical techniques; and
3) To use the developed numerical algorithms as validation tools for new implant
stem geometries.
1.7.3 Contributions
The major contributions emerging from this thesis are related to the development
of several numerical techniques of performing aforementioned tasks. Indeed, the
developed techniques within the scope of this study were aimed to automatically
optimal insertion trajectory pre-operatively to serve surgeons have an efficient plan for
intra-operative surgery.
This work is one of the first attempts in the context of implant insertion into the
cavity of bone with minimum malalignment benefiting from a computer-assisted
technique. As such, by utilizing the developed technique surgeons can assess insertability
of different implant sizes while investigating malalignment between native FE axis and
bone implant axis to achieve optimal final position for implant and consequently better
final outcome of TEA.
1.7.4 Outline
Chapter 2 outlines a numerical algorithm developed initially for a highly accurate and
automatic conversion of source CT data into parametric (B-Spline/NURBS-based) data.
The automatic DICOM to B-Spline conversion entails determination of an appropriate
thresholding method, to be followed by an edge detection procedure required to establish
inner and outer cortical bone boundaries.
Chapter 3 contains a numerical algorithm to determine the theoretical/ideal
location of the flexion-extension (FE) axis of the humeral bone based on reconstructed
geometry of the bone. The output of this algorithm was compared and validated against
conventional FE axis determination methods employing marching cube approaches
followed by least square fitting methods through extracted VTK data points.
Chapter 4 is focused on the final posture of the implant to match the natural FE
axis of the bone, provided that this constitutes a feasible solution for analyzed bone canal
initial) in order to reduce the amount of computational time required to detect
inaccessible final implant orientations located – most likely – towards the end of the
insertion trajectory.
Chapter 5 explores new geometry for stems by benefiting from the previously
developed computational tool in conjunction with various implant stem geometries and a
broad variety of humeral bones in an optimization process.
Chapter 6 provides the conclusion of the thesis.