Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Engineering Science

Program

Electrical and Computer Engineering

Supervisor

McIsaac, Kenneth

Abstract

Throughout the procedure of a computer-navigated total knee arthroplasty (TKA), there are many opportunities for sources of error to be introduced. Identifying these errors can improve surgical outcomes. There is also a lack of accessible methods in available literature for clinicians to perform research in this area using engineering analysis techniques. This thesis aims to provide a greater understanding of the sources of error that can occur pre-bone cut. Possible sources of error include the bony landmark selections and the placement of the cut guide. Using artificial bone models and a 3D point capture system concurrently with a computer-navigation system, the data points collected during the procedure are mimicked. It was found that variability of point selection varied between landmarks with some being more precise than others. Bone reference frames can be calculated using these landmark points. By painting the surface of the saw blade, the cut plane values, and a reference frame for the cuts, can also be estimated. These frames are easily represented with homogeneous transformation matrices. One method of comparing transformation matrices is with a metric on SE(3), simplified in this thesis to be the Frobenius norm. It was found that bone reference frames with the highest metric were the ones with the highest error in femur or tibia center points. It was also found that there was no clear correlation between the bone reference frame error and cut plane error, implying that other sources must be taken into account. Sensitivity analyses were performed to observe the outcome error of the bone reference frame and cut plane in regards to error in the landmark selection. The results from this support other results in this thesis: that landmark points used for the origin of the reference frames have the greatest effect on the system output. The methods in this thesis can easily be applied to other computer-navigated systems for analysis.

Summary for Lay Audience

The use of technology to assist orthopedic procedures such as computer-navigated total knee arthroplasty (TKA) has shown to improve accuracy of the implant placement, leading to higher patient satisfaction. Computer-navigated procedures are when a camera and trackers are used to create a digital model of the bone in order to assist surgeons in the placement of their cuts. However, longer operation times and a steeper learning curve for surgeons remains a concern for these procedures. As well, there are various aspects of the procedure where error can occur. Identifying and explaining these errors can help surgeons be more confident in their decisions during surgery. The main step of computer-navigation is selecting bony landmarks, which the computer then uses to create its digital model of the bone, called a reference frame. To analyze the impact of selecting landmarks incorrectly, a computer-navigated surgery was simulated with another point capture system concurrently on artificial bones. It was found that the same surgeon did not always choose certain landmark points in the same spot every time. The bone reference frames were represented with a homogeneous transformation matrix, which identifies its position and orientation in 3D space. When analyzing the difference between these frames, it was found that certain landmark points had a greater effect on them than others. Also, there are other places where errors can be introduced such as when the surgeon is placing the cut guide manually. Two methods of sensitivity analysis were also performed which analyze how an error in input affects the error in output. It was found that certain landmarks, particularly the center points of the knee, had a much larger effect on the output of the model than others. This work shows that there are multiple sources of error that surgeons must consider, and some sources have a greater affect than others.

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