Electromagnetically Tracked Personalized Surgical Guides

Electromagnetically Tracked

Personalized Surgical Guides

by

Andrew William Laird Dickinson

A thesis submitted to the Graduate Program in

the School of Computing

in conformity with the requirements for

the degree of Doctor of Philosophy

Queen’s University

Kingston, Ontario, Canada

May 2017

Copyright c© Andrew William Laird Dickinson, 2017

Abstract

This work improves surgical navigation by combining electromagnetic tracking and

personalized guides in a novel system.

Surgical navigation assists a surgeon by tracking instruments relative to the anat-

omy of interest, typically by optically tracking specialized markers. One recent nav-

igation aid is a personalized guide, which is a mechanical device that is customized

to a patient based on preoperative images. A small “negative surface” physically

registers the guide to the patient, and typically one or more through-holes in the

guide constrain a physical path during surgical drilling. A personalized guide may

incorporate a surgical plan into their design but offers no means of intraoperative

adjustment or correction.

Electromagnetic tracking uses a small antenna that is localized within an electro-

magnetic field. Advantages of electromagnetic tracking include small sensor weight

and not being constrained to a line of sight; drawbacks include a lower positional

accuracy than optical tracking, and interference from nearby electrically conductive

objects.

This work is based on a paired-lines registration algorithm that was used to cre-

ate tracked guides for small, delicate bony anatomy. Electromagnetically tracked

i

guides were applied to additively manufactured bone models. The guides were com-

pared to optical tracking in the presence of common metallic surgical instruments.

In every analysis, EM-tracked guides were found to statistically significantly outper-

form optical tracking. A pre-clinical cadaveric case study demonstrated comparable

performance to optical tracking.

This work suggests that EM-tracked guides successfully combined the flexibility

of tracking with the simplicity of physical registration in surgical navigation.

ii

Statements of Co-Authorship and Originality

The work presented in this dissertation was accomplished under the supervision of

Dr. Randy E. Ellis, who provided feedback and direction to code, algorithm, and

experimental development as well as feedback and corrections to the manuscript.

Previous work summarized in the background chapter was co-authored with Dr.

Randy E. Ellis, Dr. Brian J. Rasquinha, Dr. Gabriel Venne, and Dr. John F. Rudan.

Previous work summarized in the methods chapter was co-authored with Dr.

Randy E. Ellis, Dr. David R. Pichora, and Dr. Brian J. Rasquinha.

An early version of part of this dissertation was accepted on March 13, 2017 for

publication in the International Journal of Computer Assisted Radiology and Surgery

(IJCARS), co-authored with Dr. Randy E. Ellis, Dr. Michelle L. Zec, Dr. David

R. Pichora, and Dr. Brian J. Rasquinha.

Statement of Originality

I state that this is an original body of work and that all references used have been

cited.

iii

Acknowledgments

To my supervisor, Dr. Randy Ellis, I cannot thank you enough for the wealth

of knowledge you’ve imparted to me and for the numerous opportunities you helped

make possible. You’ve shown me how to improve as a scientist, a researcher, an

athlete, and a person. It has been an absolute honour to have worked alongside you

these past years and I’m deeply proud of the work we’ve accomplished.

To my unofficial second supervisor, Dr. David Pichora, thank you for your clinical

insight and critical thinking, which has been pinnacle in helping navigate my work.

Your encouragement and willingness in allowing me to observe surgery first-hand

let me better understand your clinical decision-making in ways I would have been

unlikely to gain otherwise. I feel so very fortunate and will be forever grateful.

To my academic counterpart, Dr. Brian Rasquinha, the papers written, experi-

ments performed, hours of discussion, idea-bouncing, laughs, sports played, and ev-

erything in-between kept this odyssey an overwhelmingly positive one. From the

deepest part of me, thank you. It would have been a far harder slog of a journey

without you; I’ll always fondly reflect upon these years.

To my technical mentor, Paul St. John, the self-imposed debt I owe you for the

countless hours of your time I was so lucky to receive will forever remain outstanding.

I owe the overwhelming majority of my technical knowledge and understanding to

iv

your kindness, patient hands, and razor-sharp mind — thank you so very much.

To Dr. Manuela Kunz, the Queen of the Personalized Guide, being able to learn

from your years of experience with guides has been instrumental in my success. Thank

you so very much for all your time, effort, advice, and willingness to listen.

To Dr. Michelle Zec, thank you for your smiles, chipper mood, and your scrupu-

lous attention to the nitty-gritty details: always ensuring each step was carefully

considered and explained before proceeding. It forced me to walk at times I wanted

to run, which helped me immensely through the final stretch.

To Dr. Rick Sellens, Dr. Gabriel Venne, Dr. Sima Zakani, Dr. Mohamed Hefny,

Matt Pearson, Chloe DesRoche, Jacob Peoples, Brandon Chan, and the many others

who were a part of the Medical Computing Laboratory, the School of Computing,

and HMRC during my time: thanks for making my workplaces welcoming, warm,

and full of discussion.

To my wife and better half, Morgan, thank you for being my number one cheer-

leader and support though every step of this journey. You’ve always been there to

encourage me to go for a run and clear my head. Your support meant the absolute

world to me and I’m so lucky to have you at my side.

To my families, Ditch, Maggie, Taylor, Maddy, Dave, Lauren, Liam, and Zoe and

Scott, Paulette, Ben, and Holly, your seemingly endless support helped me more than

you’ll ever know.

My work was supported in part by the Canadian Institutes of Health Research

(CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC),

and the Ontario Graduate Scholarship program.

v

Contents

Abstract i

Statements of Co-Authorship and Originality iii

Acknowledgments iv

Contents vi

List of Tables viii

List of Figures x

Chapter 1: Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Thesis Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.5 Dissertation Structure . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Chapter 2: Background 72.1 Image-Guided Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.1 Surgical Navigation . . . . . . . . . . . . . . . . . . . . . . . . 82.1.2 Spatial Tracking . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Personalized Guides . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3 Relevant Human Anatomy . . . . . . . . . . . . . . . . . . . . . . . . 142.4 Registration Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4.1 Iterative Closest Point (ICP) and Variants . . . . . . . . . . . 172.4.2 Arun’s Method of Spherical Registration . . . . . . . . . . . . 202.4.3 Rasquinha’s Method: Crossing Lines . . . . . . . . . . . . . . 21

2.5 Error Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.6 Tracked Guides Using Crossing Lines . . . . . . . . . . . . . . . . . . 222.7 Other Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

vi

2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Chapter 3: Methods and Materials 293.1 Theoretical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.1.1 Paired-Lines Registration . . . . . . . . . . . . . . . . . . . . 303.2 Evaluating Tracked-Guides Registration . . . . . . . . . . . . . . . . 34

3.2.1 Tracked Glenoid Guides . . . . . . . . . . . . . . . . . . . . . 353.2.2 Tracked Coracoid Guides . . . . . . . . . . . . . . . . . . . . . 39

3.3 Evaluating EM Interference . . . . . . . . . . . . . . . . . . . . . . . 463.3.1 Multimodal Interference Comparison . . . . . . . . . . . . . . 483.3.2 Surgical Navigation: Cadaveric Pre-Clinical Study . . . . . . . 49

3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Chapter 4: Results 584.1 Results for Theoretical Methods . . . . . . . . . . . . . . . . . . . . . 584.2 Results for Tracked-Guides Registration . . . . . . . . . . . . . . . . 63

4.2.1 Tracked Glenoid Guides . . . . . . . . . . . . . . . . . . . . . 634.2.2 Tracked Coracoid Guides . . . . . . . . . . . . . . . . . . . . . 66

4.3 Results for Evaluating EM Interference . . . . . . . . . . . . . . . . . 694.3.1 Multimodal Interference Comparison . . . . . . . . . . . . . . 754.3.2 Surgical Navigation: Cadaveric Pre-Clinical Study . . . . . . . 81

4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Chapter 5: Discussion and Conclusions 845.1 Observations on Simulated and Retrospective Data . . . . . . . . . . 845.2 Observations on Tracked-Guides Registration . . . . . . . . . . . . . . 85

5.2.1 EM-Tracked Glenoid Guides . . . . . . . . . . . . . . . . . . . 855.2.2 EM-Tracked Coracoid Guides . . . . . . . . . . . . . . . . . . 86

5.3 Observations on Evaluating EM Interference . . . . . . . . . . . . . . 865.3.1 Observations on Comparing Multimodal Interference . . . . . 875.3.2 Observations on Surgical Navigation . . . . . . . . . . . . . . 87

5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 885.5 Thesis Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 935.6 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935.7 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

References 96

Appendix 108

vii

List of Tables

4.1 Simulation: Positional FLE Results . . . . . . . . . . . . . . . . . . . 59

4.2 Simulation: Angular FLE Results . . . . . . . . . . . . . . . . . . . . 59

4.3 Femoral Guides: Positional FLE Results . . . . . . . . . . . . . . . . 61

4.4 Femoral Guides: Angular FLE Results . . . . . . . . . . . . . . . . . 61

4.5 Glenoid Guides: Positional and Angular FLE Results . . . . . . . . . 63

4.6 Glenoid Guides: Positional and Angular TRE Results . . . . . . . . . 66

4.7 Coracoid Guides: FLE Results . . . . . . . . . . . . . . . . . . . . . . 67

4.8 Coracoid Guides: Positional TRE Results . . . . . . . . . . . . . . . 67

4.9 Coracoid Guides: Positional TRE U-test Comparison Results . . . . . 67

4.10 Coracoid Guides: Angular TRE Results . . . . . . . . . . . . . . . . . 69

4.11 Coracoid Guides: Angular TRE U-test Comparison Results . . . . . . 70

4.12 EMI Study: EM-tracked Guide FLE Results . . . . . . . . . . . . . . 71

4.13 EMI Study: Positional TRE Results . . . . . . . . . . . . . . . . . . 73

4.14 EMI Study: Positional TRE U-test Retractor Comparison Results . . 74

4.15 EMI Study: Angular TRE Results . . . . . . . . . . . . . . . . . . . . 74

4.16 EMI Study: Angular TRE U-test Retractor Comparison Results . . . 74

4.17 MMI Study: FLE Results . . . . . . . . . . . . . . . . . . . . . . . . 77

4.18 MMI Study: Positional TRE Results . . . . . . . . . . . . . . . . . . 77

4.19 MMI Study: Angular TRE Results . . . . . . . . . . . . . . . . . . . 80

viii

4.20 MMI Study: Positional TRE U-test Comparison Results . . . . . . . 80

4.21 MMI Study: Angular TRE U-test Comparison Results . . . . . . . . 81

4.22 Pre-clinical Study: EM-tracked Guide FLE Results . . . . . . . . . . 81

4.23 Pre-clinical Study: Positional TRE Results . . . . . . . . . . . . . . . 82

4.24 Pre-clinical Study: Angular TRE Results . . . . . . . . . . . . . . . . 82

ix

List of Figures

2.1 Line Drawing of Human Right Femur . . . . . . . . . . . . . . . . . . 14

2.2 Line Drawing of a Human Left Scapula . . . . . . . . . . . . . . . . . 15

2.3 Line Drawing of a Human Left Radius . . . . . . . . . . . . . . . . . 16

2.4 Tracked Personalized Femoral Guide . . . . . . . . . . . . . . . . . . 24

2.5 Using A Tracked Personalized Femoral Guide . . . . . . . . . . . . . 25

3.1 Tracked Personalized Glenoid Guide . . . . . . . . . . . . . . . . . . . 36

3.2 Glenoid Tracked-Guide Data Collection . . . . . . . . . . . . . . . . . 38

3.3 An EM-Tracked Coracoid Guide . . . . . . . . . . . . . . . . . . . . . 41

3.4 Point-Based Optical Data Collection . . . . . . . . . . . . . . . . . . 44

3.5 Point-Based EM Data Collection . . . . . . . . . . . . . . . . . . . . 45

3.6 Electromagnetic Interference Study . . . . . . . . . . . . . . . . . . . 47

3.7 Synthes Six-Holed Plate . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.8 Distal Radius Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.9 Distal-Radius Drill Guide . . . . . . . . . . . . . . . . . . . . . . . . 53

3.10 Drill-Guide Calibrator . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.11 Navigated Surgery on a Distal Radius . . . . . . . . . . . . . . . . . . 56

4.1 Simulation: Positional FLE Histogram . . . . . . . . . . . . . . . . . 60

4.2 Simulation: Angular FLE Histogram . . . . . . . . . . . . . . . . . . 60

x

4.3 Femoral Guides: Data Positional FLE Histogram . . . . . . . . . . . 62

4.4 Femoral Guides: Angular FLE Histogram . . . . . . . . . . . . . . . . 62

4.5 Glenoid Guides: Positional and Angular FLE Histograms . . . . . . . 64

4.6 Glenoid Guides: Positional and Angular TRE Histograms . . . . . . . 65

4.7 Coracoid Guides: Positional and Angular FLE Histograms . . . . . . 68

4.8 Coracoid Guides: Positional TRE Box Plot . . . . . . . . . . . . . . . 69

4.9 Coracoid Guides: Angular TRE Box Plot . . . . . . . . . . . . . . . . 70

4.10 EMI Study: EM-tracked Guide Positional and Angular FLE Histograms 72

4.11 EMI Study: Positional TRE Box Plot . . . . . . . . . . . . . . . . . . 73

4.12 EMI Study: Angle TRE Box Plot . . . . . . . . . . . . . . . . . . . . 75

4.13 MMI Study: Positional and Angular FLE Histograms . . . . . . . . . 76

4.14 MMI Study: Positional TRE Box Plots . . . . . . . . . . . . . . . . . 78

4.15 MMI Study: Angular TRE Box Plots . . . . . . . . . . . . . . . . . . 79

A.1 Letter of ethics clearance for shoulders . . . . . . . . . . . . . . . . . 109

A.2 Letter of ethics clearance for forearms . . . . . . . . . . . . . . . . . . 110

xi

Chapter 1

Introduction

By what means could computer-assisted surgery be carried out on small, delicate

bony anatomy? Computer-assisted surgery has become increasingly prevalent for

orthopedic procedures, especially in the knee and the hip. Although this technology

is highly accurate and repeatable, two drawbacks of computer-assisted surgery are

related to its reliance on optical navigation: large marker arrays make it difficult to

use on small or delicate bones, and requiring a line of sight makes computer-assisted

techniques difficult in deep or small surgical exposures of the underlying anatomy.

This dissertation describes a way to overcome these drawbacks of optical navigation

while preserving its accuracy and repeatability.

1.1 Motivation

These two principal drawbacks of computer-assisted surgery when using optical track-

ing can be viewed as providing research opportunities in other orthopedic applications.

Procedures involving smaller bones incapable of rigidly supporting an optical refer-

ence, or with limited exposures that interfere with a line of sight, may be amenable

to computer-assisted surgery using an alternative tracking technology.

1

1.1. MOTIVATION

Electromagnetic (EM) tracking is a modality that removes both the large-marker

requirement – EM tracking is done with an antenna that is sub-millimeter in diameter

and roughly 10 millimeters in length – and the need to maintain a line of sight. The

less prevalent use of EM tracking for surgical navigation is often attributed to poor

point localization [23, 24, 33], which has been suspected as the cause of point-based

registration methods being unreliable. EM tracking using another way of registering

a tracking system to a patient’s anatomy might be a way of addressing this specific

unreliability.

Personalized guides, containing an embedded surgical plan, have been extensively

used in the hip with great success [41]. A personalized guide is a mechanical de-

vice with a patient-specific “negative surface” design element that mates with an

anatomical region. The use of a personalized guide often requires a larger exposure

to accommodate the guide, which has been accepted because the benefits provided by

the guide, such as reduced ionizing radiation to the patient and operative team [41],

have outweighed the perceived costs. Guides, being imperfect, are susceptible to

misplacement; the intraoperative verification of proper guide placement has been a

difficult surgical problem [41]. Should the guide be difficult to fit, because of poor

imaging or because of growths such as osteophytes that are not properly identified

and incorporated into the guide design, there has to date been no means to intraop-

eratively recover and proceed with the computer-assisted procedure.

One technology that might be used to overcome these drawbacks and unrelia-

bilities is to combine the flexibility of EM tracking with the physical mating of a

personalized guide. The application goal of this dissertation research was to provide

surgical navigation for these difficult orthopedic problems.

2

1.2. OVERVIEW

1.2 Overview

The technical goal of this dissertation research was to create and evaluate an EM-

tracked personalized guide that could provide surgical navigation. The simplicity

and accuracy of physical registration needed to be managed, which required a way to

estimate a transformation between the coordinate frame of the EM localization system

and the coordinate frame of the personalized guide. Studies were conducted that,

through technological iteration, proceeded from development of basic mathematics

for characterizing an EM-tracked guide to extensive laboratory evaluation of potential

orthopaedic navigation.

A previous study, with co-authors, evaluated EM-tracked femoral guides using a

basic algorithm for characterization. The algorithm had mathematical constraints

that produced physical constraints on the design of the EM-tracked guide; how to

adapt the guides from the proximal femur to use in smaller anatomy remained an

open question.

The previous characterization method, which required a set of lines that crossed

in 3D space, was extended to a new registration algorithm that required a set of

at least 2 lines that were not coplanar. This algorithmic improvement removed the

physical constraint of crossing lines for device design. New EM-tracked guides were

developed to use this new algorithm and, when tested using simulated data and

retrospectively processed experimental data, appeared to have sufficient accuracy for

further investigation.

The evaluation of the registration accuracy of these new EM-tracked guides was

investigated using two studies. A small EM-tracked guide for use in the shoulder was

tested in a laboratory using plastic models derived from CT scans. The second study

3

1.3. THESIS STATEMENT

investigated another EM-tracked guide, which also served as the local anatomical

reference, and compared this alternative guide to point-based optical tracking and

to point-based EM tracking. The EM-tracked guides were more accurate than the

alternatives and, in particular, were better than optical tracking which is taken to be

the reference standard for surgical navigation.

The performance of EM-tracked guides was further investigated in three studies.

The first study tested the presence of metallic surgical devices in the EM tracking

volume, looking for degradation of navigation accuracy from objects that were shown

in other studies to cause difficulties. The second study investigated the difference in

performance between EM-tracked guides and optical tracking in the presence of the

same EM-interfering devices. The last study was a cadaveric case study, conducted

to compare the performance of EM-tracked guides and optical tracking for surgical

navigation.

1.3 Thesis Statement

The thesis goal was to demonstrate that EM-tracked personalized guides can provide

reliable orthopedic surgical navigation.

1.4 Contributions

This dissertation will describe three main contributions:

• Demonstrating the reliability of line-based calibration

• Demonstrating the reliability of hybrid EM-tracked personalized guides for sur-

gical navigation

4

1.5. DISSERTATION STRUCTURE

• Demonstrating that the effects of EM interference on EM tracking can be over-

come

1.5 Dissertation Structure

This dissertation is divided into five chapters that present the new paired-lines char-

acterization algorithm and the studies that tested EM-tracked personalized guides for

surgical navigation. The dissertation has a straightforward organization.

Chapter 2 provides relevant background information on image-guided surgery,

surgical navigation, tracking technologies, and examples of registration methods used

to connect the anatomical and tracking coordinate frames. Personalized guides are

presented, as is a brief summary of human anatomy to supplement the descriptions

presented in the various studies.

Chapter 3 describes the theoretical and experimental methods used in the stud-

ies. The theoretical methods are of the paired-lines derivation, a simulation study

comparing crossing-lines to the new paired-lines, and a retrospective comparison of

paired-lines to crossing-lines using experimental data. The registration evaluation was

in two technical studies that investigated the performance of an EM-tracked guide

used to navigate the placement of screws for a surgical plate in the shoulder. The

performance evaluation was two technical studies that investigated the performance

of EM-tracked personalized guides in the presence of material that causes electro-

magnetic interference, and a cadaveric case study navigating screws for implanting a

wrist fracture plate.

5

1.5. DISSERTATION STRUCTURE

Chapter 4 presents comparisons of paired-lines characterized guides to crossing-

lines characterized guides, other tracking methods, and when in the presence of ma-

terial that is known to induce electromagnetic interference.

Chapter 5 presents a practical interpretation of the quantitative results of the

studies performed. The findings of this work are discussed and how they relate to

the field, including strengths and weaknesses. The major contributions are concisely

listed. The chapter concludes with an examination of limitations and recommenda-

tions for future work.

The Appendix includes two approval notices, from the Health Sciences Research

Ethics Board of Queen’s University, for research involving human subjects.

6

Chapter 2

Background

This chapter provides background information for the technologies that facilitate

surgical navigation – which is a form of image-guided surgery – and personalized

guides. Registration algorithms are presented to provide background for the new

registration algorithm that is presented in the Methods chapter. A brief description

of relevant human anatomy is presented described to assist in the understanding of

the experimental studies.

2.1 Image-Guided Surgery

Image-guided surgery is the term used to describe a surgical procedure where the

operating team performs at least part of the intervention using guidance that is based

on preoperative or intraoperative patient imaging; when using preoperative imaging,

it is common to use a tracking device that is physically connected to the patient

[24]. The imaging may also include a superimposed plan for a surgical task, such as

drilling or cutting at a particular trajectory and/or a certain position. Ensuring the

plan is in correspondence with the patient anatomy relies on defining an appropriate

registration, which is a means to transform between the coordinate frames of acquired

7

2.1. IMAGE-GUIDED SURGERY

patient data and the operative field. Tools that have been localized, or tracked, in 3D

space are often used to acquire data used for registration. Tracking is therefore a key

enabling technology for image-guided surgery [16] because a tracked tool will often

be used for collecting registration data and to guide the surgical task. Combining

these elements, so an operative team can follow a preoperative surgical plan, is called

surgical navigation.

2.1.1 Surgical Navigation

Surgical navigation is a form of image-guided surgery where tools, devices, and in-

struments are tracked in relation to the anatomy of interest [78]. Often coupled with

a 3D-rendered environment containing avatars for both the anatomy and any instru-

ments, the tracking information is used to “navigate” the instrument avatar to a

region of interest; the region may be an anatomical landmark, or a pre-determined

location for surgical intervention. Surgical navigation is often of benefit in minimally

invasive surgical procedures where exposures may be small, which can make it diffi-

cult to contextualize an exposed area when targeting a specific region for a surgical

task [45].

Shortcomings of current surgical navigation systems based on preoperative images

include the systems often being difficult and complicated [55,76]. Historically, it has

been difficult or impossible to perform navigation on small anatomy because of the

physical limitations of tracking devices. In procedures that have a reduced surgical

exposure, such as the scapula during a shoulder arthroplasty, a technique that can be

reliably performed via the limited anatomical access has long been desired [80].

8


2.1.2 Spatial Tracking

The most prevalent tracking technology for surgical navigation is optical localiza-

tion [62]. A less common tracking technology uses electromagnetic physics, which

was used extensively in this work.

Optical Tracking

Optical tracking uses cameras to localize visual markers and is the most established

tracking modality, often considered the surgical “gold standard” [62]. The cameras

can determine the position of a visual marker within their field of view. To infer

orientation, multiple markers are often used as an assembly and attached to a tool,

device, or patient [25].

A drawback of optical tracking is maintaining a line of sight, which is a principal

reason that navigation with intraoperative imaging is not more prevalent [37]. For

small or deep anatomy, such as the bones of the shoulder, optical devices are phys-

ically too large to be rigidly affixed to the thin fragile bone [46]. Optical tracking

is impractical for instruments such as flexible endoscopes, catheters and needle tips,

which must be tracked inside the human body [47]. An alternative to optical tracking

is to use properties of electromagnetic fields.

Electromagnetic Tracking

Electromagnetic (EM) tracking has emerged as an appealing method of choice that

enables localization of small sensors in a given field without the requirement for a line

of sight, while providing comparable performance [37,48]. EM tracking localizes small

sensors that are inside a magnetic field that is created by one or more field generators.

9


The use of magnetic positioning for 2D sensor localization was first introduced and

developed into tracking systems in the 1970s [39, 64, 83].

EM tracking has advantages over optical technology that include a small sensor

weight – which, being an antenna with electrical leads, is often sub-millimeter in

diameter – and not being constrained to a line of sight. Most current EM trackers

inherently senses the full pose – both the orientation and the position – of a sensor,

whereas optical tracking must infer angles from the positions of multiple markers.

One major drawback of EM tracking is that, even when used far from electrically

conductive materials, EM tracking has been found to have less positional accuracy

than optical tracking [23,33]. In some cases, the errors have been reported as increased

by an order of magnitude between laboratory and clinical settings [24].

Despite the apparently high technical accuracy of EM localization [24, 32, 33, 58,

71] and equivalent performance to optical tracking [37, 48], particularly for orienta-

tion [49], the application accuracy of point-based EM navigation in cadavers and

patients has been reported to be much poorer, often been attributed to metal arti-

facts [24, 53, 72, 82]. Studies have investigated the effects of various metals on EM-

tracking fields [48,77]. Titanium, cobalt-chromium, and 300-grade stainless steel were

found to leave the EM largely unaffected; 400-grade stainless steel, brass, aluminum,

and copper were found to strongly alter the EM field [48, 77].

Another source of error in EM tracking is imaging equipment. The presence

of a C-arm fluoroscope has been shown to sufficiently perturb the field to cause

navigation errors [63,82]. Attempts to calibrate EM systems to compensate for such

equipment-induced errors have been attempted [85]; to date these compensation have

been for single configurations that required recalibration if the imaging equipment

10


was reconfigured, moved, or adjusted.

The inherent orientation superiority of EM tracking over an optical system [49,58]

may provide benefits in a navigated drilling task. A novel EM system used two EM-

tracked pointers to successfully navigate drilling in the knee and pelvis [30, 46]. One

pointer, with a hooked tip, was positioned at a target location; the other EM pointer

was attached and calibrated to a drill. The navigation interface presented the drill

sensor in a “bulls-eye” view with respect to the hook-tip, so that drilling on the “bulls-

eye” would intersect the target. In a cadaver study, their novel EM system was able

to accurately place screws without fluoroscopic assistance in defined paths [46].

Although it has been observed that EM may be preferable over optical tracking

in some surgical applications [48], there is no clear consensus in the literature. The

preference for a tracking technology appears to have been application-specific [84]. For

example, maxillofacial procedures have been successful using EM navigation [7, 72]

with the authors having stated an ergonomic preference for the small size of the EM

sensors [7].

A previously under-recognized possibility is that the relatively low positional ac-

curacy of EM tracking has been propagated through the coordinate transformations,

which could also have led to the poor performance noted in the literature. Intrigu-

ingly, EM appears to have high inherent orientation accuracy when using common

two-coil 6DOF trackers [49, 58]; this accuracy might be useful for a novel kind of


11

2.2. PERSONALIZED GUIDES

2.2 Personalized Guides

Personalized guides for use in orthopedic procedures are becoming increasingly preva-

lent [38,41,56,59]. Such a guide is a mechanical device that is customized to a patient

based on preoperative images of their anatomy [69]. Small reference regions of the

patient are integrated into the guide as a “negative surface” that allows for a planned

pose of the guide to have a spatial relationship to the anatomy and serve as a means

of physical registration [42]. A personalized guide may incorporate a surgical plan

into their design [6,8,28,70]. As an example for a distal radius osteotomy procedure,

Kunz et al. [41] developed and tested a personalized guide that mated with the distal

end of the radius, thus providing drill paths for the surgical screws of a fixation plate.

Personalized guides have been demonstrated to provide comparable performance

to conventional fluoroscopic techniques [29,42,57,67] while also reducing the exposure

to ionizing radiation to both the patient and operating team [11]. Such guides have

provided a relatively simple [66], low cost [11], and easy-to-use solution [65] that

has facilitated more precise preoperative planning and appropriate intraoperative

implementation [43, 61]. The conventional intraoperative procedure is preserved and

no additional intraoperative registration steps, computerized equipment, space, or

personnel are needed [66]. Personalized guides have also been shown to reduce total

intraoperative time [27, 59] and have led to short-term technical improvements [20].

The use of a guide has not been a guarantee of clinically significant outcomes:

their success has most often been in application to procedures that are technically

difficult and/or have a variable outcome [20]. The literature suggests that, for total

knee arthroplasty (TKA), personalized guides and conventional instrumentation re-

store limb alignment and place components with similar accuracy [13]. TKA guides

12

2.2. PERSONALIZED GUIDES

offer logistical benefits – decreasing the number of required TKA surgical equipment

trays – which reduces total operating room time [38]. This time savings, however, is

contained to surgical set up and clean up times, causing no significant decrease in “in-

cision to closure” time [59], which may not justify the additional costs associated with

using a guide. Thienpoint et al. [79] consulted orthopaedic TKA implant manufac-

turers and surgeons as to whether manufacturer’s guides met surgeon’s expectations;

they concluded that, although personalized guides are of great interest, surgeons re-

main unconvinced of personalized guide surgical accuracy in TKA [79]. Guides were

first developed for TKA in the early 2000s, with refinements to their design and man-

ufacturing made possible by technological advancements, such as improved and more

advanced CAD software [65]. Though 3D imaging has been used to investigate the

anatomy of the shoulder as a surgical aid since the mid-2000s [44], guides for the

shoulder are a more recent development [15,34,50]. It is unclear whether these trends

observed for TKA guides would or will apply to technologically less-mature shoulder

guides.

Some recent success has been shown in creating scapular guides [15,34], but these

offered no means of intraoperative verification beyond tactile feedback to assess the

snugness of fit, which may be misleading if soft tissues were not properly resected [50].

Intraoperatively determining the amount of bone loss when preparing a patient for

a shoulder arthroplasty implant has been a an outstanding challenge [73] for which

a standalone mechanical guide does not appear to provide much assistance. Though

computer-assisted techniques have been shown to improve accuracy and precision for

baseplate positioning of glenoid baseplates in a laboratory study [81], it still remains

unclear as to how to effectively integrate such techniques into a clinical workflow.

13

2.3. RELEVANT HUMAN ANATOMY

2.3 Relevant Human Anatomy

This work makes reference to three human bones: the femur, scapula, and radius.

It may be useful to briefly discuss the anatomy of these bones, to better understand

surgical navigation of these and their nearby anatomy.

Femur

The femur, or thigh bone, is the long bone in the upper half of the lower extremity;

a femur is illustrated in Figure 2.1. The head of the proximal, or upper, end of

the femur articulates with the acetabulum of the pelvis to form the hip joint. The

femoral head may be surgically treated by having its articulating surface and some

underlying bone replaced in a hip resurfacing arthroplasty; it may also be treated by

complete replacement in a total hip arthroplasty. Personalized guides, used as drilling

templates, have been used effectively to navigate these surgeries [42].

Figure 2.1: A line drawing of the anterior aspect of a human right femur. Theproximal femur is on the right side of this drawing; the head is the lower-right partof this drawing.

Scapula

The scapula, a wide flat bone, is part of the thoracic wall. It, with the humerus or

upper arm bone, together constitute the shoulder joint. The humeral head rests in the

14

2.3. RELEVANT HUMAN ANATOMY

glenoid cavity, a shallow articular surface found on the lateral angle of the scapula,

illustrated in Figure 2.2. The articular surface of the glenoid is often replaced during

a shoulder arthroplasty [54, 80]. In part of this dissertation work, the glenoid was

both a surgical target and a region for patient-to-image registration.

The coracoid process is a small hook-like structure also found on the lateral edge

of the superior-anterior aspect of the scapula. It points laterally forward and serves

to stabilize the shoulder joint as an attachment point for muscles. In part of this

dissertation work, the coracoid was a registration region in a study wherein the glenoid

was the surgical target. The coracoid varies considerably among individuals but is in

general a readily identifiable scapular landmark.

Figure 2.2: A line drawing of the anterior aspect of a human left scapula. Theglenoid is the concavity on the upper-right part of this drawing. The coracoid is theforeground portion in the upper-central part of this drawing.

15

2.4. REGISTRATION ALGORITHMS

Radius

The radius is a large forearm bone that extends from the lateral side of the elbow to

the thumb-side of the wrist; a human radius is illustrated in Figure 2.3. The distal

end of the radius, which is furthest from the elbow, articulates with the scaphoid and

lunate bones to create part of the wrist joint. The radius is a commonly fractured

bone that is often surgically repaired. Personalized guides have been successfully used

in distal radius osteotomy [41]. In part of this dissertation work, the radius was both

a surgical target and a region for patient-to-image registration.

Figure 2.3: A line drawing of the anterior, or volar, aspect of a human left radius.The distal portion is on the right side of the drawing.

2.4 Registration Algorithms

The registration of preoperative or intraoperative images, the patient, and any addi-

tional tools or equipment is critical to a successful image-guided intervention. Three

types of registration algorithms are presented here: those that focus on establishing

point-to-point correspondences; those that use features built from points, such as

lines, for correspondences; and those that use lines represented as a point plus a unit

direction vector when calculating a registration transformation.

For clarity, the convention in this chapter will register data in the “moving frame”

to data in the “fixed frame”. The moving coordinate frame “M” will be denoted as

16


{M}, fixed coordinate frame “F” will be denoted as {F}, the ith vector ~v measured in

frame {F} will be denoted as F~vi. The spatial displacement, or rigid transformation,

of a vector in the moving frame to a vector in the fixed frame is

F~v = F

MRM~v + F~t (2.1)

The transformation of Equation 2.1 will be denoted as F

MT .

2.4.1 Iterative Closest Point (ICP) and Variants

The Iterative Closest Point (ICP) algorithm, which was originally presented by Besl

and McKay [9], computes the rigid transformation that aligns two sets of point data

by minimizing the root-mean-squared error (RMSE) between the data points. If the

RMSE is greater than a defined threshold, the algorithm repeats until either the

RMSE is less than the threshold or a specified maximum number of iterations have

been performed.

For two point sets, F~p1...m and M~p1...n, a specified threshold for an acceptable

RMSE value, τ , and a maximum number of iterations, ρ, the algorithm has four

steps:

1. For a given “moving” point M~pi, determine the closest “fixed” point F~pi

2. Calculate the transformation F

MT that will align M~pi with

F~pi

3. Apply F

MT to all M~p1...n

4. Calculate the RMS between each point:

• If the average RMSE < τ , stop

17


• Else if iteration count ≥ ρ, stop

5. GOTO 1

A shortcoming to ICP is its high reliance on an appropriate initialization in its

first iteration: a poor initial alignment may cause the algorithm to converge on a

local minimum that is not the global minimum. This may lead to a sub-optimal, and

perhaps detrimental, solution.

Many ICP variants exist in the literature, some of the more relevant of which may

be useful to understand.

ICP Variants

One simple variant of ICP, the Trimmed ICP (TrICP) algorithm [14], considers only

a user-specified N points in the RMSE calculation. Each point M~p1...n is compared

to its closest F~p1...m; the points are sorted in increasing order by the distance to

their corresponding point. The first N sorted distances are selected and summed. If

this sum is less than a specified threshold, or another stop condition like maximum

iteration count is met, then TrICP stops. Otherwise, the algorithm applies the current

transformation to the moving dataset and continues to iterate until a stop condition

is met.

The Non-rigid ICP (N-ICP) algorithm [3] uses locally affine transformations of

M~p1...n to F~p1...m. The algorithm minimizes a three-term cost function: Ed, a weighted

distance between each M~pi andF~pi; Es, a stiffness term to regularize the deformation;

and, optionally, El, a landmark term used for initialization and registration guidance.

The N-ICP algorithm begins by creating an initial correspondence between {M} and

{F}, then iterates through each specified Es stiffness value until the difference in the

18


cost function between two iterations is beneath a specified ǫ.

The Point-to-Line ICP (PLICP) algorithm [12] matches a moving point to a line

segment in the set of fixed points. For each point M~p1...n, it and the two closest points

in {F}, denoted as F~pi1 andF~pi2 , are stored as the 3-tuple [M~pi,

F~pi1 ,F~pi2 ]. The list of

3-tuples is then trimmed to a specified number, as done in the TrICP algorithm [14].

The sum of the squares of the distances for each M~pi to the line containing the segment

F~pi1 −F~pi2 is iteratively optimized: after each step, {M} is re-registered to {F} using

the updated transformation; the pool of 3-tuples is recalculated; and the sum of the

squares of the distances is tested against a specified threshold.

The Iterative Closest Line (ICL) algorithm [1] matches line segments in the data

sets. In a pre-processing phase, each data set is analyzed to determine a set of line

segments. In the iteration, a weighted sum is measured; this adds a multiple of

angular distances between paired line segments to a multiple of the linear distances

between paired segments. When a sufficient number of paired lines have been found,

an over-determined system of linear equations is formed that can be solved in the

least-squares sense. The angular distance can be re-weighted during the iteration.

The Iterative Most Likely Oriented Point (IMLOP) algorithm [10] incorporates a

probabilistic framework of Gaussian and Fisher distributions to model orientation and

position error, respectively. For moving data {M} and fixed data {F}, a user specifies

the model parameters for the Gaussian and Fisher distributions k0 and σ20, and an

initialization transformation T0. The algorithm computes point correspondences and

registers {M} to {F}, and also updates the model parameters. The model parameter

k acts as a concentration parameter for orientation error and is estimated by an

approximation to the maximum-likelihood estimate. The σ2 parameter is estimated as

19


the RMSE between matches, as in the ICP algorithm. As IMLOP iterates, the moving

data M~p1...n are matched to the fixed data F~p1...m using both position and orientation.

When both k and σ2, as adjusted by the iterations of IMLOP, are beneath specified

thresholds, the IMLOP algorithm terminates and the last computed transformation

is taken as the solution to the problem.

2.4.2 Arun’s Method of Spherical Registration

A frequently encountered problem in 3D geometry, which is needed by ICP and most

variants, is finding the 3D rotation that takes a set of moving points to its corre-

sponding fixed points. This is variously called spherical registration, the orthogonal

Procrustes problem, the orientation problem, the attitude problem, and possibly has

many other names. It has been solved many times in many guises, usually by mini-

mizing the RMSE of absolute Euclidean distances between corresponding points.

Horn [31] used a quaternion solution and Sibson [74] used a method that pre-dates

modern computational linear algebra. Here, the method of Arun et al. [5] is presented

for its simplicity and frequent use in the literature. The data are assumed to have

been processed so that each set has a mean vector of zero.

In Arun’s method, the covariance matrix for each corresponding point pair is cal-

culated. In a covariance matrix, each element (i, j) represents the covariance between

the ith and jth elements of the two compared vectors. These matrices are summed

to create a 3 × 3 matrix A that is generally non-singular (but which may be rank-

deficient if one of the point sets is nearly coplanar). The singular value decomposition

(SVD) [26] of this matrix A is found as A = UΣV T ; the optimal 3D rotation is the

matrix F

MR = UV T that transforms the data of {M} to the data of {F}.

20

2.5. ERROR MEASURES

The translation estimate is calculated as the difference between the mean of the

moving data, and the mean of the moving data after having applied the orientation

estimate.

2.4.3 Rasquinha’s Method: Crossing Lines

The crossing-lines algorithm of Rasquinha et al. [68] takes data that are corresponding

pairs of infinite lines, each data set having lines that ideally intersect at a single

point. The orientation transformation between the data is found by interpreting

the direction vectors as being vectors lying on the surface of a unit sphere; Arun’s

spherical registration method can then be used to compute the orientation, with the

important proviso that the data sets are not zero-mean.

The translation between the data can then be solved, in the least-squares sense, by

creating an over-determined system of linear equations and using a standard numerical

method such as the QR decomposition.

2.5 Error Measures

Here and throughout the rest of this dissertation, the terms fiducial localization error

and target registration error are those introduced and analyzed by Fitzpatrick et

al. [22]. These terms are briefly described below.

Fiducial localization error (FLE) is “the distance of the localized point from the

(forever unknown) actual fiducial position before any alignment is done” [22]. Often,

the FLE represents the reliability of the characterization of a device: a lower value

suggests a more reliable characterization. As observed by Fitzpatrick et al., and

originally demonstrated by Sibson [75], FLE can be pooled across all models because

21

2.6. TRACKED GUIDES USING CROSSING LINES

localization error is the sole reason for a poor registration; it is unrelated to any

universal translation or rotation between point sets [22]. As such, all FLE data in

this work are pooled for analysis.

Target registration error (TRE) is calculated as the average root-mean-squared

distance between a given point and its corresponding point in the latter space after

registration has been performed [22]. The target may be any point in the tracked

space, and is commonly chosen within a point or region of interest, e.g., a preopera-

tively planned drill path. In the production of this work, a target was represented by

a line, not a point, which meant two TRE measurements were required for a proper

comparison to be made: an angular TRE, represented as the difference in angle,

and the minimum distance, as determined by the shortest mutually-perpendicular

distance, between the sensed and planned line.

It is important to note that in later work, Fitzpatrick et al. [21] demonstrated

that these two error metrics are distinct, i.e., an “acceptable” FLE value does not

guarantee – or even suggest – that an “acceptable” TRE can be expected. They

should be treated as independent measurements that assess different aspects of a

surgical navigation system. A low FLE value suggests that a device was reliably

characterized, while a high FLE would suggest a poor characterization. Similarly, a

lower TRE value suggests the entire system is performing reliably as the targets were

successfully reached.

2.6 Tracked Guides Using Crossing Lines

This background section expands on the work of Dickinson et al. [17].

This work introduced the concept of an EM-tracked guide. The motivation was

22


to integrate EM tracking with personalized guides. One goal of this work was to

reduce the size and physical complexity of a personalized guide; a secondary goal

was to provide intraoperative verification and, if needed, re-planning of the surgical

procedure. The apparatus was deemed too cumbersome – the results did not warrant

the additional logistical complications required for tracking during surgery – and was

invasive for human use but serves as an example of how to integrate EM tracking

with a guide.

A tracked guide was instrumented with an EM disc sensor, shown in Figure 2.4.

The guide incorporated a patient-specific “negative surface” that provided physical

registration. Seven intersecting through-holes within the guide served as characteri-

zation features for the crossing-lines algorithm of Rasquinha et al. [68]. Thus, when

an accurately characterized guide was placed on its corresponding region of anatomy,

the EM-to-model registration transformation could be determined by reading the

tracking information.

Seven proximal femur models were derived from CT images that were selected

from a database of hip-resurfacing procedures, which had previously been performed

by a surgical colleague and their team using personalized guides. In place of the

original personalized guide, an EM-tracked personalized guide was designed to fit the

same anatomical region using the same registration geometry that had been used in

the actual surgery.

A 3mm diameter cylinder, which accommodated the manufacturer’s EM probe,

represented the original planned drill-path; the cylinder was computationally sub-

tracted from each femur model. A dovetail was incorporated into each model to allow

for fixation of an EM disc sensor that acted as an anatomical coordinate reference.

23


(a) (b)

Figure 2.4: (A): A photograph of a tracked guide with attached EM disc-style sensor.(B): a photograph of the “negative surface” of the tracked guide, highlighted andbordered, that accomplished the physical registration.

An example of the femoral model with attached disc sensor is shown in Figure 2.5.

All 7 models and their corresponding tracked guides were additively manufactured

in ABS plastic; all drill-holes were hand-reamed to 3mm diameter. Each guide was

characterized by probing each of its seven through-holes with the sharp probe, ac-

quiring 3s of data at approximately 40Hz that were averaged into one representative

pose using the unsigned axial mean [2]. The guide-to-sensor characterization was

calculated using Rasquinha’s crossing-lines algorithm [68].

Each probed guide through-hole, relative to its attached sensor, was transformed

into its guide-model coordinate frame. Each transformed axis was then compared to

its designed through-hole path to measure the position and angle fiducial localization

error (FLE) of the guide characterization. For 7 models each with 7 line fiducials,

this constituted a pool of 49 lines used in the final FLE summary calculations. All

positional and angular FLE values were found to be comparable to previous results

24


(a) (b)

(c) (d)

Figure 2.5: (A): A femoral model with attached EM disc-style sensor and personalizedguide with attached EM sensor. (B): A femoral model with attached anatomicalsensor and its guide; the guide is outlined. (C): a femoral model being probed with thestock probe. (D): A navigation-ready rendering of a model femur with a surgical planfor pin placement in a hip resurfacing procedure; the plan is shown as a translucent,wide cylinder and a tracked probe is shown as a long, thin cylinder.

25

2.7. OTHER RELATED WORK

in the literature [68] and confirmed that characterizations were accurate.

The sharp probe was then placed in the cylindrical plan hole and 3s of data were

recorded. The probe axis, relative to the anatomical sensor, was transformed into the

model coordinate frame and compared to its planned drill-path; this measured the

overall system target registration error (TRE) of the tracked guide as applied to the

physical anatomical models. The 7 placements were pooled for TRE calculations. All

positional and angular TRE values were found to be consistent with the literature [23,

49].

This work demonstrated that an EM-tracked personalized guide could be placed

once, sensed, and then removed from the area of interest to provide registration for


2.7 Other Related Work

There is a small body of work on personalized guides and navigation that is somewhat

relevant to this dissertation work. The Surface Template-Assisted Marker Positioning

(STAMP) device was introduced by Matsumoto et al. [52]; a modification to the

preoperative registration work-flow was presented as the “p-STAMP” device by Oka et

al. [60]. A STAMP is an additively manufactured patient-specific template, with

through-holes that assist point-based registration. The STAMP has a resemblance to

the our EM-tracked guides because both are additively manufactured devices with a

unique patient-specific “negative surface” for mating with a target anatomical feature;

both also have through-holes. However, these devices have different uses and serve

different purposes.

The STAMP device assisted point-based registration in a two-step process. The

26

2.7. OTHER RELATED WORK

first step was the physical mating of the device to the patient via the personalized

mating surface. In the second step, the through-holes in the STAMP that had been

designed to “accommodate a marking pen or 1 mm diamond burr” [52] were used by

a surgeon to physically mark or drill the patient’s anatomy in a reliable manner; they

called these markings the transferred targets. The preoperatively selected virtual

targets, identified in CT imaging, were paired to the transferred targets that were

probed with a tracked tool. The paired points were used to calculate an image-to-

patient registration transformation in a paired-point algorithm.

Unlike the previously described work in EM-tracked guides, the STAMP device

was untracked and was only temporarily placed on the patient while the targets were

transfered; after transfer, an additional optical local coordinate reference had to be

attached to the patient at a second physical site. The STAMP device can be thought

of as a way to improve the accuracy of point-based registration by using a personalized

guide to improve the selection of points on the patient.

It is not possible to completely compare the accuracy of an EM-tracked person-

alized guide with the accuracy of a STAMP device, because only positional target

registration error (TRE) was available for the latter [52, 60]. In the work presented

above, the EM-tracked personalized guides had positional TRE values with a mean

of 1.2mm and a root mean squared error (RMSE) of 0.3mm, using n = 28 measure-

ments. The STAMP devices, in an early study [52], were reported to have a positional

TRE values with a mean of 1.9mm and a RMSE value of 2.2mm, using n = 10 mea-

surements; in a later study [60], the STAMP devices had a mean TRE value of 0.6mm

and a RMSE value of 0.6mm, using n = 3 measurements. The STAMP devices ap-

peared to successfully improve point-based registration of optical tracking in surgical

27

2.8. SUMMARY

navigation.

2.8 Summary

This chapter briefly discussed: surgical navigation, which is a form of image-guided

surgery; two tracking technologies, optical and electromagnetic, that can be used for

localization of anatomy and/or tools; personalized surgical guides; error metrics, and

registration methods. Some previous preliminary work on EM-tracked guides was

discussed, as was the related technology called a STAMP device. The next chapter

describes the methods used in this dissertation work to more fully explore the design

and use of EM-tracked guides.

28

Chapter 3

Methods and Materials

The methods for unifying electromagnetic (EM) tracking with personalized guides, for

the purposes of surgical navigation, have been divided into three sections: the theo-

retical methods, how registration was evaluated, and how performance was evaluated.

In the section on theoretical methods, the crossing-lines algorithm is extended to re-

move the intersecting-line requirement to need only corresponding, or paired, lines.

This modest extension, here called “paired-lines”, relaxed design constraints and al-

lowed for tracked guides to target anatomy that was otherwise found to be difficult

for conventional surgical navigation. The crossing-lines and paired-lines registrations

were compared using simulated data and then by a retrospective comparison that

used the data described in Section 2.6.

The section on registration evaluation describes EM-tracked guides that were de-

signed to mate with two different regions of the human scapula: the superior glenoid

and the superior coracoid, which were chosen because they are known to be dif-

ficult but accessible targets for surgical navigation [49]. The performance of the

EM-tracked guides approach for navigation was first validated using additively man-

ufactured scapular models and EM-tracked guides that were designed to mate with

29

3.1. THEORETICAL METHODS

the superior glenoid. Subsequently, EM-tracked guides that were designed for the

superior coracoid of the scapula were validated by being compared to the current

clinical navigation standard using optical tracking.

The section on performance evaluation describes three studies that investigated

some effects of the presence of interference caused by common surgical instruments,

such as Hohmann-style surgical retractors, on the tracking accuracies of both EM

and optical systems. The coracoid study was first repeated as a validation of EM-

tracked guides, using an increasing number of Hohmann retractors present in the

tracking volume. Next, EM-tracked guides were compared to optical tracking in the

presence of the Hohmann retractors. Last, a pre-clinical human cadaveric study had

a Board-certified orthopaedic surgeon pre-operatively plan and perform a fracture-

plating procedure on the radius bone of the forearm, using both EM-tracked guides

and optical tracking for surgical navigation.

All software was written in MATLAB (MathWorks, Natick, US) and all EM data

were acquired using an Aurora EM system (NDI, Waterloo, CA).

3.1 Theoretical Methods

The paired-lines method was derived using linear algebra. The method was subse-

quently compared to the related crossing-lines method, first by using simulated data

and then by retrospective comparison to experimental data.

3.1.1 Paired-Lines Registration

For this dissertation work, the registration problem was the estimation of the rigid

spatial transformation from the EM coordinate frame to the device coordinate frame.

30


As is usually done in spatial registration [18], the transformation was separated into

orientation and translation estimates, with the orientation estimate calculated first

and applied before calculating the translation estimate. The orientation component

of the transform was calculated using a tracked probe direction that was paired with

a planned device direction, as was done by Rasquinha et al. [68]. The translation

estimate was determined by minimizing the line-line distance between all line pairs.

The orientation component was first found from the line directions, for which a

tracked probe direction E ~di was paired with a planned device direction D ~di. One

way to represent a direction vector is as a point on the unit sphere. This means

that n paired directions could be registered using a spherical registration method. A

least-squares registration transformation D

ER from frame {E} to frame {D}, which

minimizes the residual distances between the sets of points, can be computed by

calculating the covariance matrix for each pair of directions and summating those

matrices into a 3 × 3 data matrix H , as was done by Sibson [74], and using the

singular-value decomposition (SVD) as

H =

n∑

i=1

(

D ~diE ~di

T)

= UΣV T

⇒ D

ER = V UT (3.1)

This representation solved the orientation problem without using points, requiring

only the directions of the lines. It is important to note that the direction vectors

should not be transformed to have zero means, which is a transformation that is often

applied to point sets during spatial registration to place the data from different frames

about the same origin [18]. A zero-mean transformation is not only unnecessary but

may be detrimental by computing a different orientation solution.

31


It is well known that cases where det(V UT ) 6= 1 should be avoided. Such a case

constitutes a geometric singularity in which the points are coplanar [31]. Here, these

singularities were avoided by requiring that all lines have non-coplanar directions.

The translational component was determined by minimizing the line-line distance,

which was the shortest mutually perpendicular distance between each pair of lines,

after applying the optimal rotation to the appropriate line set. Consider the paired

lines

~l1(λ) = ~p+ λ~a ~l2(µ) = ~q + µ~b

Let the vector ~r = ~p− ~q. If the lines are non-parallel, then the mutually orthogonal

direction vector ~m was found from the line directions as

~m =~a×~b

||~a×~b||

For parallel lines, ~m was found from Gram-Schmidt orthogonalization as

~m =~r − (~r · ~a)~a

||~r − (~r · ~a)~a||

The line-line distance was the projection scalar ~m · ~r. Consider translating ~p by a

parameter vector ~t; then the line-line distance would be

d(~t) = ~m · (~p+ ~t− ~q)

= ~mT~r + ~mT~t

32


The two lines intersected when the distance is zero, which implied that

d(~t) = 0 ⇒ ~mT~t = −~mT~r (3.2)

In general, for a set of k lines, instances of Equation 3.2 are not satisfied exactly.

Together, they were formed into the linear equation

~mT

1

~mT

2

...

~mT

k

~t =

−~mT

1~r1

−~mT

2~r1

...

−~mT

k~r1

≡ M~t = ~b (3.3)

Equation 3.3 was solved, in the least-squares sense, by the QR decomposition, to find

the optimal translation vector ~t that brought paired lines into registration.

Crossing-Lines, Paired-Lines Comparison: Simulated Data

A simulation was used to compare the paired-lines and the crossing-lines registration

implementations. The simulation was based on an initial set of 7 lines, represented by

a point and a direction vector, that were each perturbed by independently introducing

up to 5◦ of angular error to each direction vector and up to 2mm of translational

error to each point. The perturbed line-set was then registered to the initial line-

set using both the paired-lines methods and the crossing-lines method, after which

the average line-line distance was then calculated; this was the fiducial localization

error (FLE). Angular differences were not calculated because both methods use the

33

3.2. EVALUATING TRACKED-GUIDES REGISTRATION

same spherical registration method [5], so both methods would compute the same

orientation component.

100000 trials were performed, yielding 2 sets of 700000 FLE values, one for each

registration method. The fiducial localization error (FLE) was calculated and sum-

marized for both methods. A two-sided, non-parametric Mann-Whitney U-test was

performed on the pools of positional FLE produced by each algorithm; this assessed

the statistical differences, if any, between the methods.

Crossing-Lines, Paired-Lines Comparison: Experimental Data

As described in the background chapter, in Section 2.6, there were some experimental

data available that were originally used to characterize the tracked femoral guides

using crossing-lines. These same data were used to retrospectively compare paired-

lines characterizations to crossing-lines characterizations.

The fiducial localization error (FLE) was calculated and summarized for both

methods. A one-sided, one-sample, paired, non-parametric Mann-Whitney U-test

was performed on the paired differences between the transformations produced by

the algorithms; this assessed the statistical differences, if any, between the methods.

Angular FLE was summarized for only one method because both methods used

the same spherical registration computation [5].

3.2 Evaluating Tracked-Guides Registration

The combination of registration by a personalized guide with the flexibility of surgical

navigation was investigated using a smaller guide, characterized with paired-lines.

These guides were intended for physical application to the superior glenoid of the

34


scapula, which was an anatomical region previously found to be difficult to register

with a personalized guide [49].

With approval from the relevant IRB, which was the Health Sciences Research

Ethics Board of Queen’s University, 10 scapula models were derived from axial CT

segmentations of cadaveric specimens and used for experiments. The navigation tar-

get was the cylindrical axis of the central peg of a reverse shoulder arthroplasty [54]

glenoid base-plate. The target cylinder was preoperatively planned by a Board-

certified orthopaedic surgeon and computationally subtracted from the 3D models

of the scapulas. The subtracted path, rather than being the physical sizes of the

peg, was a 3mm diameter cylinder that accommodated a manufacturer’s stock sharp-

tipped probe for cylinder-axis acquisition. Paths for the four fixation screws needed

for baseplate implantation were also planned and subtracted from the models, but

were not used for any summary or statistical calculations in this work. The models

were additively manufactured in ABS plastic at approximately 0.3mm resolution and

each cylindrical hole was hand-reamed to 3mm diameter to ensure a snug fit with the

probe.

3.2.1 Tracked Glenoid Guides

EM-tracked guides were designed to mate with the superior glenoid process.

Each guide consisted of an inverted “U”-shaped base and a raised pillar, to which

an EM 6-DOF disc-style sensor was affixed. The base incorporated a “negative sur-

face” that provided the physical mating and 8 through-holes that were characteriza-

tion features for the paired-lines algorithm. The intended purpose of a guide was that,

when the guide was placed on its corresponding region of anatomy, the EM-to-model

35


registration transformation could be simply read from EM tracking information. A

guide, its negative surface, and line-fiducial probing are pictured in Figure 3.1.

(a) (b)

(c)

Figure 3.1: Procedure for testing an EM-tracked personalized glenoid guide. (A) Aphotograph of a representative personalized glenoid guide with an EM sensor attachedto the upper, non-anatomical surface. (B) A photograph of the personalized guide’s“negative surface” that was designed to mate with the anatomy of the glenoid, high-lighted and bordered. (C) A photograph of a manufacturer’s sharp probe being usedto characterize the guide.

36


Glenoid Guides: Data Collection

Guide characterization began by probing each of the 8 through-holes with a sharp

probe. For each through-hole, a data set was acquired by reading 3s of data at

approximately 40Hz. Each data set was averaged into a representative pose, rela-

tive to the attached EM disc-sensor: points as a Euclidean mean, and directions as

an unsigned axial mean [2]. The guide-to-sensor characterization was calculated by

matching the representative poses with the designed through-hole locations by the

paired-lines method.

A second EM sensor was attached to the coracoid process of the scapula to act

as an anatomical reference. After mating a characterized guide to its corresponding

glenoid region, data were collected that related the reference coordinate frame to the

guide coordinate frame; these data sets were collected for 3s at approximately 40Hz

and averaged. The glenoid guide was then removed, as would be done surgically.

Representative steps of this part of the procedure, performed on a plastic model, are

illustrated in Figure 3.2.

The central cylinder in the glenoid, which represented the plan for the central peg,

was probed for 3s as EM data were collected at approximately 40Hz; this procedure

is also illustrated in Figure 3.2. The data were averaged into one representative pose,

relative to the EM disc-sensor: points as a Euclidean mean, and directions as an

unsigned axial mean [2]. This data collection was repeated for each central cylinder

in each of the 10 models.

37


(a) (b)

(c)

Figure 3.2: Collection of data for an EM-tracked glenoid guide. (A) A photograph ofa representative scapula model with reference EM and and personalized guide. (B)A photograph of the same model with personalized guide; the guide is outlined. (C)A photograph of the same model with a manufacturer’s sharp probe placed in one ofthe planned cylinders.

38


Glenoid Guides: Analyses

Two analyses were performed for glenoid tracked guides, one for the characterization

computations and one for the navigation.

For characterization data, each probed cylinder axis in the guide was transformed

into the guide’s model coordinate frame. Each transformed axis was compared to its

designed through-hole path to measure the positional and angular fiducial localization

error (FLE) of the guide characterization. For 10 models, each with 8 line fiducials,

this provided a pool of 80 lines for the FLE summary calculations.

For navigation data, the probed cylinder axis in the scapula was transformed into

the scapula’s model coordinate frame. The transformed axis was then compared to its

planned axis to measure the positional and angular target registration error (TRE)

of the overall system. For 10 models, each with 1 planned screw path, this provided a

pool of 10 lines for the positional TRE and angular TRE summary calculations. Two-

sided, non-parametric Mann-Whitney U-tests were performed on the TRE values to

compare the glenoid guides and the previous femoral guides to assess the statistical

differences, if any, between the guides.

3.2.2 Tracked Coracoid Guides

One of the major contributions of this thesis was a physically registered EM-tracked

guide. Such a tracked guide served two purposes. First, by means of the “negative

surface” of the guide, it provided a physical surface-to-surface registration of the

guide to the anatomy of interest. Second, because it was tracked, it acted as an EM

local coordinate reference; this meant that an additional sensor was not needed for

navigation.

39


EM-tracked guides were tested by comparing each tracked guide to the clinical

standard of the time that this research was performed. That standard was optical

tracking that used point-based registration to register instruments to the anatomy of

interest. A related navigation method was to use EM-tracked instruments and point-

based registration; studying all three methods meant that differences in navigation

technology and differences in registration methods could potentially be distinguished.

This meant that three methods of surgical navigation were studied: EM-tracked

guides, EM-tracked point-based registration, and optically tracked point-based reg-

istration. Both point-based methods used the same robust, reliable, point-based

registration algorithm [51].

Each method was evaluated using the same concept as for the glenoid guides,

which was to sample and evaluate axes of cylinders.

Coracoid Guide Device Design

An EM-tracked coracoid guide consisted of a base, with a physical size of approxi-

mately 30mm× 20mm× 20mm, and a raised pillar to which a 6-DOF disc-style EM

sensor could be attached. The base incorporated a “negative surface” that mated to

an anatomical surface. Each EM-tracked guide had 5 through-holes for paired-lines

characterization, by which the pose of the EM-tracked guide in the EM frame could

be determined. Each EM-tracked guide was additively manufactured in ABS plastic.

Photographs of a representative EM-tracked guide are provided in Figure 3.3.

A senior surgeon identified the targeted region of the coracoid process as being

clinically feasible for surface mating in shoulder arthroplasty.

40


(a) (b)

(c) (d)

Figure 3.3: An EM-tracked coracoid guide. (A) A photograph of a representativeEM-tracked guide with attached EM sensor. (B) The “negative surface” that matedto the superior coracoid process, highlighted and bordered. (C) A photograph ofa representative scapula model with a mated EM-tracked guide, bordered. (D) Anavigation-ready rendering of a model scapula with a central peg plan shown as atranslucent, wide cylinder and a tracked probe shown as a long, thin cylinder.

41


Data Collection: Surface-Based EM Approach

For the corresponding EM-tracked guide of a given scapula, each of its 5 through-

holes were probed using a manufacturer’s sharp probe. Each through-hole was probed

for 3s at a rate of approximately 40Hz, relative to the EM-tracked guide’s EM disc

sensor. Each data set was averaged: points as a Euclidean mean, and directions as

an unsigned axial mean [2]. The averages were used to characterize the EM-tracked

guide using the paired-lines registration method.

The characterized EM-tracked guide was then mated with the coracoid surface of

its corresponding model and rigidly affixed. Because the EM-tracked guide design

process determined its registered pose a priori, no further registration was required.

With the EM-tracked guide in place, the central planned cylinder was probed

for 3s at a rate of approximately 40Hz, relative to the EM-tracked guide’s EM disc

sensor. Each data set was averaged: points as a Euclidean mean, and directions

as an unsigned axial mean [2]. The averages were used to evaluate the accuracy of

navigation using EM-tracked guides.

Data Collection: Point-Based Optical Navigation

An optical Polaris tracking system (NDI, Waterloo, CA) was used with custom sur-

gical navigation software (iGO Technologies, Kingston, CA). A Polaris tracking de-

vice was attached to the same manufacturer’s sharp EM probe that was used in the

glenoid study designed to accommodate that probe’s diameter; this is illustrated in

Figure 3.4(A). A custom algorithm was used to determine the tip and axis of the

sharp probe relative to the tracking device. The axis of the probe was calibrated as

the axis of a cylinder: a point tracked by the Polaris device was fit to a plane in an

42


ordinary least-squares process, then the projections of the point were fit to a circle;

the normal to the best-plane was the direction of the probe’s axis and the center of

the best-fit circle was a point on the probe’s axis.

A jig held each scapula model and a second Polaris tracking device that served as

a local coordinate reference for the scapula during data collection. This is illustrated

in Figure 3.4(B).

Point acquisition was restricted to the mating region of the tracked guide to en-

sure a fair comparison between the registration approaches. The data collection is

illustrated in Figure 3.4(C).

Optically tracked points were registered to the scapula model, in optically tracked

local coordinates, by a mathematically robust method [51]. After registration, data

were collected for the central planned cylinder: the calibrated probe was placed in

each cylinder and approximately 3s of optical poses were recorded. Each data set was

averaged: points as a Euclidean mean, and directions as an unsigned axial mean [2].

Data Collection: Point-Based Electromagnetic Navigation

A jig held each scapula model and an EM 6-DOF disc-sensor that served as a local

coordinate reference for the scapula during data collection. EM-sensed points were

collected from the EM-tracked guide’s coracoid mating surface region in local coor-

dinates and averaged: points as a Euclidean mean, and directions as an unsigned

axial mean [2]. The same robust point-based registration method that was used for

optically tracked points [51] was used to calculate the EM-to-model transformation.

The data collection is illustrated in Figure 3.5. EM data were collected from the same

physical region as the optical data.

43


(a) (b)

(c)

Figure 3.4: Point-based optical data collection. (A) Photograph of the sharp-tippedprobe with optical local coordinate reference attached. (B) Photograph of the plat-form with scapular model and optical local coordinate reference attached. (C) Pho-tograph of a representative surface point collection from the EM-tracked guide’scoracoid-mating region.

44


(a) (b)

(c)

Figure 3.5: Point-based EM data collection. (A) Photograph of the sharp-tippedprobe. (B) Photograph of the scapular model and the EM local coordinate reference.(C) Photograph of a representative surface point collection from the EM-trackedguide’s coracoid-mating region.

45

3.3. EVALUATING EM INTERFERENCE

Coracoid Registrations: Analyses

For each EM-tracked guide, each sensed cylinder axis in the local coordinate frame

was transformed to the design coordinate frame. Each transformed cylinder axis was

compared to the designed cylinder axis to estimate the guide’s positional and angular

fiducial localization error (FLE). For 10 repetitions of 10 models, each with 5 line

fiducials, this provided a pool of 500 lines for the FLE calculations.

All navigation methods were tested the same way. For each navigation method,

the sensed pose of the probe placed in the central cylindrical hole was transformed

to scapula-model coordinates and compared to its planned cylindrical axis. The

navigation’s positional and angular target registration error (TRE) were calculated

separately. For 10 repetitions of 10 models, in each of the 3 navigation methods, this

provided 3 pools of 100 lines for TRE calculations and statistical tests. Two-sided,

non-parametric Mann-Whitney U-tests were performed between each pool combina-

tion to assess the statistical differences, if any, between the methods.

3.3 Evaluating EM Interference

A frequent clinical question about electromagnetic navigation is its susceptibility to

electromagnetic interference, or EMI. This question was addressed by studying effects

of a small number of common surgical soft-tissue retractors, when the retractors were

placed in clinically realistic poses.

All EM data were acquired as described in Section 3.2.2. The baseline data, in

which the physical region of the scapula model had no metal devices other than the

tracked instruments, will be called the “None” case.

A Board-certified surgeon recommended placements of 1, 2, and 3 Hohmann-style

46


soft-tissue retractors as might be done for a total or partial shoulder replacement

surgery. Representative photographs of a retractor, a scapula model, EM-tracked

guide, and data acquisition with and without the presence of retractors are illustrated

in Figure 3.6.

(a)

(b) (c)

Figure 3.6: Electromagnetic interference study on a model scapula. (A) A photographof a retractor. (B) Photograph of the scapular model and attached EM-tracked guidewith no retractors present. (C) Photograph of a representative experiment with 3retractors in the the physical region of the model scapula.

EMI Analyses

For each case of None, 1, 2, and 3 retractors in the physical region of each model

scapula, each planned guide cylinder was sensed as described in Section 3.2.2. Each

47


transformed cylinder axis was compared to the designed cylinder axis to estimate the

guide’s positional and angular fiducial localization error (FLE). For 10 EM-tracked

guides, each with 5 line fiducials, this provided a pool of 50 lines for the FLE calcu-

lations.

For each retractor case of 10 models, each with 1 line fiducial, this provided 4

pools of 10 lines for TRE calculations and statistical tests. Two-sided, non-parametric

Mann-Whitney U-tests were performed between each data pool combination to assess

the statistical differences, if any, between retractor cases.

3.3.1 Multimodal Interference Comparison

It was conceptually possible that introduction of a soft-tissue retractor might affect

optical tracking of an instrument’s tip, and conceptually likely that a retractor would

affect EM tracking of an instrument’s tip. These potential interferences were studied

in a limited number of cases of retractors in the physical region of each model scapula.

Base on preliminary results from the EMI study, the cases of no retractors and 3

retractors were compared for the 3 navigation methods. Data collected with no

retractors in the field will be referred to as the “None” case. Data collected with 3

retractors placed about the face of the glenoid will be referred to as the “3-Retractors”

case. Data for each navigation method were acquired using the respective method

described in Section 3.2.2.

For each EM-tracked guide, each sensed cylinder axis in the guide coordinate

frame was transformed to the design coordinate frame. Each transformed cylinder

axis was compared to the designed cylinder axis to estimate the guide’s positional and

angular fiducial localization error (FLE). For 10 repetitions of 10 EM-tracked guides,

48


each with 5 line fiducials, this provided a pool of 500 lines for the FLE calculations.

For each case, for each navigation method, the sensed pose of the probe placed in

the central cylindrical hole was transformed to scapula-model coordinates and com-

pared to its planned cylindrical axis. The navigation’s positional and angular target

registration error (TRE) were calculated separately. For both retractor cases, using

the 3 navigation methods, this provided 6 pools of 100 lines for TRE calculations and

statistical tests. Two-sided, non-parametric Mann-Whitney U-tests were performed

between each pool combination to assess the statistical differences, if any, between

the pools.

3.3.2 Surgical Navigation: Cadaveric Pre-Clinical Study

A cadaveric pre-clinical study was conducted to investigate whether a surgeon could

achieve a previously planned drill path. As in the previous studies, the task was to

follow the axis of a cylinder that was planned on a computational model of human

anatomy. The cylinders were planned as the volumes filled by surgical screws that

held a surgical plate. Navigation using EM-tracked guides was compared to the point-

based optical navigation that used mathematically robust estimation for registration.

The radius bone of the human forearm was selected as the anatomical region of

interest. Although the anatomy was not that of the shoulder, the surgical task –

drilling along a planned trajectory – was the same. Because cylinders could not be

physically subtracted from a bone, as was done in additively manufactured models of

scapulas, additional instrumentation was designed and characterized for this study.

With ethics approval from the relevant IRB, which was the Health Sciences Re-

search Ethics Board of Queen’s University, one phenol-embalmed human cadaveric

49


forearm specimen was obtained. Cone-beam computed tomography (CBCT) images

were acquired, from the metacarpal bones to the mid-forearm. The radius bone was

manually segmented using commercially available software (Materialise, Leuven, BE)

and the resulting model was additively manufactured in ABS plastic.

An 85mm long Synthes R© six-holed Small Fragment Locking Compression Plate

was chosen to be physically attached to the distal radius, simulating how a frequently

occurring wrist fracture might be repaired. The plate is shown in Figure 3.7. Trajec-

tories for corresponding surgical screws were planned in the model of the radius bone

as cylinders, the axes of which were to be physically drilled in the bone.

Figure 3.7: A photograph of the Synthes six-holed Small Fragment Locking Compres-sion Plate.

An EM-tracked hand-held drill guide was designed and characterized, as described

below. A single Board-certified surgical fellow performed the tests. Data were post-

procedurally analyzed from CBCT images, also described below.

50


Distal Radius: EM-tracked Guide Design

The EM-tracked distal radius guide had a base, with a physical size of approximately

25mm × 30mm× 15mm, and a raised pillar to which a 6-DOF disc-style EM sensor

could be attached. The base incorporated a “negative surface” that mated to the

anatomical surface. The EM-tracked guide had 5 through-holes for paired-lines char-

acterization, by which the pose of the guide in the EM frame could be determined.

The EM-tracked guide was additively manufactured in ABS plastic. Photographs of

the EM-tracked guide are provided in Figure 3.8.

Drill Guide Design

A dual-axis drill guide, for navigation of the planned screw paths, was designed and

additively manufactured in ABS plastic. The drill guide was T-shaped, with each arm

of the “T” containing a metal-lined through-hole that corresponded to a targeted hole

of the fixation plate. An EM disc-style sensor could be attached to the drill guide.

Alternatively, an adapter for an optical device could be attached at the site for the

EM sensor, making it possible to optically track the guide. The guide and tracking

devices are shown in Figure 3.9.

The drill guide was characterized using a custom-made tracked calibrator, which

had a rectangular base of physical size 55mm × 110mm. An EM disc-style sensor

could be attached to the drill calibrator, as could an optical tracking device. To

accommodate the 3mm diameter EM probe and a preferred 4mm-diameter optical

probe, the platform had 5 through-holes of each diameter for paired-lines calibration.

The calibrator in its optically tracked configuration, and the drill guide as optically

tracked, are shown in Figure 3.10.

51


(a) (b)

(c) (d)

Figure 3.8: Distal radius EM-tracked guide. (A) A photograph of the EM-trackedguide with an EM sensor attached. (B) The radius-mating “negative surface”, high-lighted and bordered. (C) A photograph of the radius physical model mated withits EM-tracked guide. (D) A navigation-ready rendering of the radius, with platescrew plans shown as yellow cylinders and a tracked drill guide shown as long, thincylinders.

52


(a) (b)

(c) (d)

Figure 3.9: Distal-Radius Drill Guide. (A) A photograph of the Drill Guide. (B) Aphotograph of the drill guide with an EM sensor attached. (C) A photograph of theoptical adapter. (D) A photograph of the drill guide with the optical adapter and itstracking device attached.

After characterization, the drill-guide calibrator could be physically mated to the

drill guide. Acquisition of the relative poses produced a rigid spatial transformation,

from which the guide’s drilling axes could be computed.

53


(a) (b)

Figure 3.10: Drill-guide calibrator. (A) a photograph of the drill-guide calibrator,configured for optical tracking. (B) A photograph of the drill-guide calibrator matedto the calibrator, both configured for optical tracking.

Distal Radius: Data Collection

The cadaver forearm was surgically prepared with a standard volar flexor carpi ra-

dialis approach. The incision was extended proximally to accommodate placement

of surgical screws for attachment of the optical tracking device. The superficial tis-

sues were retracted ulnarly. For navigation using the EM-tracked guide, the guide

was physically mated to the volar aspect of the distal radius surface and fixed in

place with two 3mm diameter stainless-steel surgical screws. For point-based opti-

cal navigation, the tracking device was fixed to the distal end of the bone with two

4mm surgical pins; then, data for registration were collected and computed as for the

glenoid models.

For each navigation method, the first technical step was to navigate the proximal

drill hole using the drill guide and the custom navigation software. A surgical pin

was placed through the drill guide and into the newly drilled hole in the bone, which

54


physically constrained the guide as would be done clinically. The distal hole was then

navigated by pivoting the drill guide about the axis of the embedded proximal pin.

After drilling, the pins were removed and replaced with aluminum rods of the same

diameter for post-procedural image analysis.

After both navigation procedures had been done, post-procedural CT images were

acquired with metal rods in place. The post-procedural radius was segmented and

the resulting model was registered to the pre-operative imaging using commercially

available surface-to-surface registration software (Materialise, Leuven, BE). The pins

were segmented, best-fit to cylinders using an ordinary least-squares method, from

which the axis of each cylinder was calculated. The registration was applied to the

segmented axes and compared to the planned axes of the surgical screws.

Distal Radius: Analyses

For the EM-tracked guide, each of the 5 through-holes that had been used for char-

acterization was transformed into the guide model’s coordinate frame. These trans-

formed axes were compared to their designed axes to measure the guide’s characteri-

zation positional and angular fiducial localization error (FLE).

For each navigation method, the cylindrical axes of the two drilled holes were

transformed to model coordinates and compared to the corresponding axes of the

imaged pins in model coordinates. These were used to calculate the navigation’s

positional and angular target registration error (TRE).

55


(a)

(b) (c)

(d)

Figure 3.11: Navigated surgery on a distal radius. (A) A photograph of the cadavericspecimen with volar exposure. (B) A photograph of the specimen with the EM-tracked guide attached. (C) A photograph of the specimen with its EM-tracked guideand EM-tracked drill guide. (D) A screenshot of the custom navigation software withthe model radius; the planned path is rendered as cylindrical tunnel and the path ofthe through-hole of the drill guide is rendered as a pointed cylinder with an extendingtrajectory in blue.

56

3.4. SUMMARY

3.4 Summary

This work required two improvements: paired line-line registration, and adaptation

of anatomical EM-tracked guides from patient-specific guides.

The paired-lines registration, although relatively modest as a mathematical nov-

elty, is a concept that enabled the use of personalized guides on hard-to-reach regions

of the scapula and radius. This registration was first compared to a crossing-lines

method in a simulation study, then retrospectively on previously collected data. The

third assessment used tracked guides that were designed for the superior glenoid.

The fourth assessment compared coracoid-mating EM-tracked guides to the clinical

standard of optical tracking.

Electromagnetic interference was first studied by using an EM-tracked guide with

an increasing number of common surgical instruments within the tracking volume.

The second assessment compared coracoid-mating EM-tracked guides to optical track-

ing with, and without, retractors present nearby.

The final assessment was a pre-clinical cadaveric study on a single forearm, com-

paring an EM-tracked guide to optical surgical navigation.

57

Chapter 4

Results

This chapter presents comparisons of paired-lines tracked guides to crossing-lines

characterized guides, other tracking methods, and when in the presence of material

that is known to induce electromagnetic interference (EMI).

4.1 Results for Theoretical Methods

The paired-lines method, an extension of the crossing-lines method, was intended to

relax physical constraints on guide design. The method was tested using simulated

data and on previously collected empirical data.

The registration transforms that were computed using the paired-lines and the

crossing-lines characterization methods were compared. Comparisons based on a sim-

ulation study are first presented, followed by comparisons using previously collected

experimental data with retroactively calculated paired-lines characterizations.

In the simulation study, a set of 7 lines were each independently deviated by

random values, of up to 5◦ angular error and of up to 2mm positional error, to form

a new set of lines. This was performed 100000 times, creating a pool of 700000 lines

for use in comparison calculations.

58

4.1. RESULTS FOR THEORETICAL METHODS

Positional fiducial localization error (FLE) for each method is summarized as

mean, root mean squared error (RMSE), median, and one-sided 95% confidence in-

terval (CI) values in Table 4.1, and presented graphically as a histogram in Figure 4.1.

Because the two methods used the same computation for angular estimation, only one

angular FLE is reported by its mean, RMSE, median, and one-sided 95% CI values

in Table 4.2 and graphically as a histogram in Figure 4.2.

The paired-lines and crossing-lines positional FLE pools were tested using a two-

sided, non-parametric Mann-Whitney U-test with α = 0.05. A statistically significant

value of p < 0.0001 strongly suggests that the paired-lines method performed better

than the crossing-lines method by 0.1mm.

Table 4.1: Positional fiducial localization error (FLE) of the simulated data, as com-puted using the paired-lines and crossing-lines methods. Measurements reported inmillimeters (n = 700000).

Registration Method Mean RMSE Median 95% CI

Paired-Lines 0.7mm 0.9mm 0.5mm 1.8mm

Crossing-Lines 0.8mm 1.0mm 0.6mm 2.1mm

Table 4.2: Angular fiducial localization error (FLE) of the simulated data, reportedin degrees (n = 700000).

Error Type Mean RMSE Median 95% CI

Angle 3.0◦ 3.6◦ 2.5◦ 7.0◦

The data collected in Background Section 2.6 to characterize the guides with

crossing-lines were used to retrospectively calculate paired-lines registration transfor-

mations. For 7 models, each with 7 through-holes in each guide, this provided a pool

of 49 lines used for comparisons.

59


0 1 2 3 4 5 6 7 80

0.5

1

1.5

2

2.5

3x 10

5

Position Difference (mm)

Fre

quen

cy

Figure 4.1: A frequency histogram of positional FLE values of the simulation studyusing n = 700000 lines. Paired-lines values are shown in green, crossing-lines inmagenta.

0 5 10 150

2

4

6

8

10

12

14

16

18x 10

4

Angle Difference (degrees)

Fre

quen

cy

Figure 4.2: A frequency histogram of angular FLE values of the simulation studyusing n = 700000 lines.

60


Positional fiducial localization error (FLE) for each method is summarized as

mean, RMSE, median, and one-sided 95% CI values in Table 4.3, and presented

graphically as a histogram in Figure 4.3. Only one error report on angular FLE is

summarized as mean, RMSE, median, and one-sided 95% CI values in Table 4.4,

graphically as a histogram in Figure 4.4, because these methods used the same com-

putation for angular estimation.

The positional distance between the paired-lines and crossing-lines registration

transformation matrices were calculated for the 7 models to form a pool. This pool

was tested with a one-sided, one-sample, non-parametric Mann-Whitney U-test at

α = 0.05. The effect size of 0.6mm was the manufacturer-reported position accuracy

RMS for a 6DOF sensor; this value was used to determine whether there was a

detectable difference between the algorithms. A value of p = 0.9922 suggests that the

methods are performing comparably beneath the 0.6mm detectable accuracy of the

Aurora EM measurement system.

Table 4.3: Positional fiducial localization error (FLE) of the experimental data, com-puted using the crossing-lines and paired-lines methods. These were for 7 trackedguides, each with 7 through-holes for a pool of 49 lines. Measurements reported inmillimeters.

Registration Method Mean RMSE Median 95% CI

Paired-Lines 0.2mm 0.3mm 0.2mm 0.5mm

Crossing-Lines 0.3mm 0.3mm 0.2mm 0.5mm

Table 4.4: Angular fiducial localization error (FLE) of the experimental data, reportedin degrees. These were for 7 tracked guides, each with 7 through-holes for a pool of49 lines.


Angle 0.5◦ 0.5◦ 0.4◦ 0.9◦

61


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

1

2

3

4

5

6

7

8

9

10


Fre

quen

cy

Figure 4.3: A frequency histogram of positional FLE values of the experimental datausing n = 49 lines. Paired-lines values are shown in green, crossing-lines in magenta.

0 0.2 0.4 0.6 0.8 10

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5


Fre

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Figure 4.4: A frequency histogram for the angular FLE values of the experimentaldata using n = 49 lines.

62

4.2. RESULTS FOR TRACKED-GUIDES REGISTRATION

4.2 Results for Tracked-Guides Registration

Tracked guides targeted for smaller, more difficult anatomy were created and assessed.

A first study tested tracked guides designed for the glenoid of the scapula. A second

study tested tracked guides designed to mate with the coracoid process of the scapula,

and also compared the guides to point-based tracking.

4.2.1 Tracked Glenoid Guides

This study used 10 scapular models, each with their own glenoid-mating tracked

guide consisting of 8 through-holes, which were EM-sensed, and transformed into

their model’s coordinate frame to create a pool of 80 lines used for assessing FLE.

FLE calculations are summarized as mean, RMSE, median, and one-sided 95% CI

values in Table 4.5, graphically presented as histograms in Figure 4.5.

Each of the 10 models had 1 linear path that was the surgical target for assessing

end-to-end system TRE, which constituted a pool of 10 lines. TRE calculations are

summarized as mean, RMSE, median, and one-sided 95% CI values in Table 4.6 and

presented graphically as histograms in Figure 4.6.

Table 4.5: Positional and angular fiducial localization error (FLE) for the glenoid-mating tracked guides. These were for 10 tracked guides, each with 8 through-holes,constituting a pool of 80 lines. Positions are reported in millimeters; angles in degrees.


Position 0.3mm 0.3mm 0.2mm 0.7mm

Angle 0.7◦ 0.8◦ 0.6◦ 1.4◦

The angular and positional TRE of the glenoid tracked guides and femoral tracked

guides were each compared using a two-sided, non-parametric Mann-Whitney U-test

63


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

2

4

6

8

10

12

14

16

18

20


Fre

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(a)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.60

2

4

6

8

10

12

14


Fre

quen

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(b)

Figure 4.5: Histograms of frequency for positional FLE values (A) and angular FLEvalues (B) for the glenoid guides, using n = 80 lines.

64


0 0.2 0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2


Fre

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(a)

0 0.5 1 1.5 2 2.5 30

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2


Fre

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(b)

Figure 4.6: Histograms of frequency for positional TRE values (A) and angular TREvalues (B) for the scapula models using the glenoid-mating tracked guides, with n =10 lines.

65


Table 4.6: Positional and angular target registration error (TRE) of navigating ashoulder replacement component; positions are reported in millimeters and angles indegrees. These were for 10 models, each with 1 planned hole, constituting a pool of10 lines.



Angle 1.5◦ 1.7◦ 1.6◦ 2.8◦

with α = 0.05 . The statistically significant values of p = 0.0012 for positional TRE

and p = 0.0552 for angular TRE suggest that the glenoid guides performed better

than the femoral guides by less than 1mm and 1 degree.

4.2.2 Tracked Coracoid Guides

This study used 10 scapular models, each with their own tracked coracoid guide that

had 5 through-holes; the cylindrical axes of these holes were sensed, transformed

into their model’s coordinate frame, and used to assessing FLE. For 10 repetitions

of 10 models with 5 through-holes, this constituted a pool of 500 lines used for FLE

calculations. Positional and angular FLE calculations, summarized as mean, RMSE,

median, and one-sided 95% CI values in Table 4.7; the positional and angular data

are graphically presented as histograms in Figure 4.7.

For each registration method – EM-tracked guides, EM-tracked points, and op-

tically tracked points – a pool of 100 lines was used. The overall positional and

angular TRE are summarized as mean, RMSE, median, and one-sided 95% CI val-

ues in Table 4.8 and Table 4.10; the data are graphically presented as box plots in

Figures 4.8 and 4.9, respectively. The results of two-sided, non-parametric Mann-

Whitney U-tests between each registration approach performed on the positional and

66


the angular TRE are presented in Table 4.9 and in Table 4.11 respectively.

Table 4.7: Positional and angular fiducial localization error (FLE) for the coracoid-mating EM-tracked guides. These were for 10 trials of 10 EM-tracked guides, eachwith 5 through-holes, constituting a pool of 500 lines. Positions are reported inmillimeters; angles in degrees.



Angle 0.6◦ 0.7◦ 0.5◦ 1.3◦

Table 4.8: Positional target registration error (TRE) of navigating a shoulder replace-ment component, using 3 registration methods, reported in millimeters. These werefor 10 measurements each from 10 scapula models, constituting a pool of 100 linesfor each method.

Method Mean RMSE Median 95% CI

Guides 1.4mm 1.9mm 1.0mm 4.0mm

EM Points 4.1mm 5.5mm 2.9mm 11.7mm

Optical Points 4.1mm 5.5mm 2.9mm 11.1mm

Table 4.9: Statistical p values for comparisons of positional target registration errors(TRE) of navigating a shoulder replacement component. The p values are from 2-sided Mann-Whitney U-tests of pooled data using n = 100 lines. Values less thanBonferroni-corrected α = 0.05/3, which are statistically significantly different byrejecting Type I errors at a 1.67% rate, are presented in bold-face.

Method Guides EM Points Optical Points

Guides · p < 0.001 p < 0.001

EM Points p < 0.001 · p = 0.780

Optical Points p < 0.001 p = 0.780 ·

EM-tracked guides statistically significantly outperformed both EM and optical

point-based registration methods in position and angle, p < 0.001 in all cases.

67


0 0.5 1 1.5 2 2.5 30

20

40

60

80

100

120

140

160

180

200


Fre

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(a)

0 0.5 1 1.5 2 2.50

10

20

30

40

50

60

70

80

90


Fre

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(b)

Figure 4.7: Histograms of frequency for positional FLE values (A) and angular FLEvalues (B) for the coracoid EM-tracked guides (n = 500 lines).

68

4.3. RESULTS FOR EVALUATING EM INTERFERENCE

Guides EM Points Optical Points

0

2

4

6

8

10

12

14

16

18

Dis

tanc

e (m

m)

Figure 4.8: Data medians, quartiles, and statistical outliers for positional TRE forthe scapula models using coracoid tracked EM-tracked guides. The box is the centralquartiles, within which is a horizontal line at the median value. Whiskers are theupper and lower quartiles and statistical outliers shown as black crosses.

Table 4.10: Angular target registration error (TRE) of navigating a shoulder replace-ment component, using 3 distinct registration methods, reported in degrees. Thesewere for 10 measurements each from 10 scapula models, constituting a pool of 100lines for each method.

Method Mean RMSE Median 95% CI

Guides 2.2◦ 2.4◦ 2.5◦ 3.8◦

EM Points 9.7◦ 11.3◦ 8.6◦ 20.7◦

Optical Points 9.5◦ 12.5◦ 8.0◦ 20.5◦

4.3 Results for Evaluating EM Interference

Tracked guides were tested in the presence of surgical equipment – Hohmann-style

surgical soft-tissue retractors – that are known to induce electromagnetic interference

(EMI) in EM tracking. The first study investigated the presence of multiple retractors

in the EM field. The second study revisited the coracoid study with the addition of

69


Guides EM Points Optical Points0

5

10

15

20

25

30

35

Ang

le (

degr

ees)

Figure 4.9: Data medians, quartiles, and statistical outliers for angular TRE for thescapula models using coracoid-mating EM-tracked guides. The box is the centralquartiles, within which is a horizontal line at the median value. Whiskers are theupper and lower quartiles and statistical outliers shown as black crosses.

Table 4.11: Statistical p values for comparisons of angular target registration errors(TRE) of navigating a shoulder replacement component. The p values are from 2-sided Mann-Whitney U-tests of pooled data using n = 100 lines. Values less thanBonferroni-corrected α = 0.05/3, which are statistically significantly different byrejecting Type I errors at a 1.67% rate, are presented in bold-face.

Method Guides EM Points Optical Points

Guides · p < 0.001 p < 0.001

EM Points p < 0.001 · p = 0.479


70


retractors. The third, pre-clinical, study was performed to investigate whether an

EM-tracked guide could perform comparably to optical point-based navigation for

navigation of the cylindrical axes of fracture-plate screws.

The same scapula models and EM-tracked guides were used as in the coracoid

study, Section 3.2.2. This experiment was performed with an increasing number of

retractors – none, 1, 2, and 3 – present in the sensing field.

The 10 models, each with an EM-tracked guide with 5 through-holes, constituted

a pool of 50 lines used for EM-tracked guide positional and angular FLE calculations.

These are summarized as mean, RMSE, median, and one-sided 95% CI values in

Table 4.12.

For each number of retractors, a pool of 10 lines, 1 per model, was used to as-

sess the overall positional and angular TRE. These are summarized as mean, RMSE,

median, and one-sided 95% CI values in Table 4.13 and Table 4.15; the data are

graphically presented as box plots in Figures 4.11 and 4.12, respectively. The results

of two-sided, non-parametric Mann-Whitney U-tests between each experimental re-

tractor case performed on the position and angle TRE are presented in Table 4.14

and Table 4.16, respectively.

Table 4.12: Positional and angular fiducial localization error (FLE) for the coracoid-mating EM-tracked guides in the electromagnetic interference study, for n = 50 lines.Positions are reported in millimeters; angles in degrees.



Angle 0.6◦ 0.6◦ 0.9◦ 2.2◦

In all cases, values were found to be greater than p = 0.0083, a Bonferroni-

corrected α = 0.05/6, which are statistically significantly different by rejecting Type I

71


0 0.5 1 1.5 2 2.50

2

4

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(a)

0 0.5 1 1.5 2 2.5 30

1

2

3

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5

6

7

8


Fre

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(b)

Figure 4.10: Histograms of positional FLE values (A) and angular FLE values (B)for the coracoid-mating EM-tracked guides in the electromagnetic interference study,for n = 50 lines.

72


Table 4.13: Positional target registration error (TRE) of navigating a shoulder re-placement component for 4 numbers of surgical retractors that induced EM interfer-ence; data are reported in millimeters. These were for 10 scapula models, with 1 lineeach, constituting a pool of 10 lines for each number of retractors.

No. of Retractors Mean RMSE Median 95% CI

None 1.4mm 1.7mm 1.4mm 3.0mm

1 2.0mm 2.4mm 1.8mm 3.9mm

2 1.9mm 2.3mm 1.3mm 3.9mm

3 1.9mm 2.3mm 1.5mm 3.9mm

None 1 2 3−1

0

1

2

3

4

5

6

Dis

tanc

e (m

m)

Figure 4.11: Data medians, quartiles, and statistical outliers for positional TRE of thescapula models using coracoid-mating EM-tracked guides. Up to 3 surgical retractorsthat induced EM interference were present in the tracking volume. The box is thecentral quartiles, within which is a horizontal line at the median value. Whiskers arethe upper and lower quartiles and statistical outliers shown as black crosses.

73


Table 4.14: Statistical p values for comparisons of positional target registration errors(TRE) of navigating a shoulder replacement component, using coracoid-mating EM-tracked guides. Up to 3 surgical retractors that induced EM interference were presentin the tracking volume. The p values are from 2-sided Mann-Whitney U-tests ofpooled data (n = 10). No values were found to be statistically significant usinga Bonferroni-corrected α = 0.05/6, which are statistically significantly different byrejecting Type I errors at a 0.83% rate.

No. of Retractors None 1 2 3

None · p = 0.345 p = 0.427 p = 0.623

1 p = 0.345 · p = 0.970 p = 0.734

2 p = 0.427 p = 0.970 · p = 0.970

3 p = 0.623 p = 0.734 p = 0.970 ·

Table 4.15: Angular target registration error (TRE) of navigating a shoulder replace-ment component for 4 numbers of surgical retractors that induced EM interference;data are reported in degrees. These were for 10 scapula models, with 1 line each,constituting a pool of 10 lines for each number of retractors.

No. of Retractors Mean RMSE Median 95% CI

None 2.7◦ 3.0◦ 2.4◦ 4.8◦

1 3.7◦ 4.0◦ 3.4◦ 5.8◦

2 3.9◦ 4.2◦ 3.5◦ 6.6◦

3 4.5◦ 4.8◦ 4.5◦ 7.0◦

Table 4.16: Statistical p values for comparisons of angular target registration errors(TRE) of navigating a shoulder replacement component. Up to 3 surgical retractorsthat induced EM interference were present in the tracking volume. The p valuesare from 2-sided Mann-Whitney U-tests of pooled data (n = 10). No values werefound to be statistically significant using a Bonferroni-corrected α = 0.05/6, whichare statistically significantly different by rejecting Type I errors at a 0.83% rate.

No. of Retractors None 1 2 3

None · p = 0.162 p = 0.104 p = 0.026

1 p = 0.162 · p = 0.967 p = 0.345

2 p = 0.104 p = 0.967 · p = 0.521

3 p = 0.026 p = 0.345 p = 0.521 ·

74


None 1 2 3−1

0

1

2

3

4

5

6

7

8

9

10

Ang

le (

degr

ees)

Figure 4.12: Data medians, quartiles, and statistical outliers for angular TRE of thescapula models using coracoid-mating EM-tracked guides. Up to 3 surgical retractorsthat induced EM interference were present in the tracking volume. The box is thecentral quartiles, within which is a horizontal line at the median value. Whiskers arethe upper and lower quartiles.

errors at a 0.83% rate. This suggests that the introduction of retractors does not have

a statistically significant effect on TRE.

4.3.1 Multimodal Interference Comparison

For experiments in which the number of surgical retractors that were present in the

EM sensing volume were “None” or “3”, 10 trials of sensing the 5 cylinder guide axes

were performed for each of the 10 models. This required 10 characterizations of each

of the 10 coracoid guides with 5 through-holes was performed, constituting a pool of

500 lines for FLE calculations. The positional and angular FLE are summarized as

mean, RMSE, median, and one-sided 95% CI values in Table 4.17; the positional and

angular data are graphically presented as histograms in Figure 4.13.

75


0 0.5 1 1.5 2 2.5 30

20

40

60

80

100

120

140

160

180

200


Fre

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(a)

0 0.5 1 1.5 2 2.50

10

20

30

40

50

60

70

80

90


Fre

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(b)

Figure 4.13: Histograms of frequency of the positional FLE values (A) and angularFLE values (B) for the coracoid EM-tracked guides, using n = 500 lines.

76


Table 4.17: Positional and angular fiducial localization error (FLE) for the coracoid-mating EM-tracked guides. These were for 10 trials of 10 EM-tracked guides, eachwith 5 through-holes, constituting a pool of 500 lines. Positions are reported inmillimeters; angles in degrees.



Angle 0.6◦ 0.7◦ 0.5◦ 1.3◦

For each registration method – EM-tracked guide, EM points, and optical points a

pool of 100 lines was used. The overall system positional and angular TRE, sum-

marized as mean, RMSE, median, and one-sided 95% CI values in Table 4.18 and

Table 4.19; the data are graphically presented as box plots in Figure 4.14 and Fig-

ure 4.15, respectively. The results of two-sided, non-parametric Mann-Whitney U-

tests between each registration approach performed on the position and angle TRE

are presented in Table 4.20 and Table 4.21, respectively.

The case of “None” surgical retractors, presented above for the coracoid study in

Section 4.2.2, have been repeated below to clarify the comparisons.

Table 4.18: Positional target registration error (TRE) of navigating a shoulder re-placement component, using 3 distinct registration methods with “None” or 3 surgicalretractors present in the EM sensing volume. Data are reported in millimeters. Thesewere for 10 measurements each from 10 scapula models, constituting a pool of 100lines used to compute each value.

No. of Retractors Method Mean RMSE Median 95% CI

None




3




77



0

2

4

6

8

10

12

14

16

18

20

22

Dis

tanc

e (m

m)

(a)


0

2

4

6

8

10

12

14

16

18

20

22

Dis

tanc

e (m

m)

(b)

Figure 4.14: Data medians, quartiles, and statistical outliers of positional TRE for thescapula models using coracoid-mating EM-tracked guides. There were (A) “None” or(B) 3 retractors present in the EM sensing volume. The box is the central quartiles,within which is a horizontal line at the median value. Whiskers are the upper andlower quartiles and statistical outliers shown as black crosses.

78



5

10

15

20

25

30

35

40

Ang

le (

degr

ees)

(a)


5

10

15

20

25

30

35

40

Ang

le (

degr

ees)

(b)

Figure 4.15: Data medians, quartiles, and statistical outliers for angular TRE for thescapula models using coracoid-mating EM-tracked guides. There were (A) “None” or(B) 3 retractors present in the EM sensing volume. The box is the central quartiles,within which is a horizontal line at the median value. Whiskers are the upper andlower quartiles and statistical outliers shown as black crosses.

79


Table 4.19: Angular target registration error (TRE) of navigating a shoulder replace-ment component, using 3 distinct registration methods with “None” or 3 surgicalretractors present in the EM sensing volume. Data are reported in degrees. Thesewere for 10 measurements each from 10 scapula models, constituting a pool of 100lines used to compute each value.

No. of Retractors Method Mean RMSE Median 95% CI

None

Guides 2.2◦ 2.4◦ 2.5◦ 3.8◦

EM Points 9.7◦ 11.3◦ 8.6◦ 20.7◦

Optical Points 9.5◦ 12.5◦ 8.0◦ 20.5◦

3

Guides 3.7◦ 4.0◦ 3.6◦ 6.6◦

EM Points 10.9◦ 12.4◦ 10.6◦ 20.9◦

Optical Points 9.7◦ 11.5◦ 8.1◦ 22.1◦

Table 4.20: Statistical p values for comparisons of positional target registration errors(TRE) of navigating a shoulder replacement with “None” or 3 retractors present in theEM sensing volume, for 3 registration methods. The p values are from 2-sided Mann-Whitney U-tests of pooled data (n = 100). Values less than Bonferroni-correctedα = 0.05/3, which are statistically significantly different by rejecting Type I errors ata 1.67% rate, are presented in bold-face.

No. of Retractors Method Guides EM Points Optical Points

None

Guides · p < 0.001 p < 0.001

EM Points p < 0.001 · p = 0.780


3

Guides · p < 0.001 p = 0.036

EM Points p < 0.001 · p = 0.200

Optical Points p = 0.036 p = 0.200 ·

EM-tracked guides outperformed EM and optical point-based registration meth-

ods, in positional and angular TRE, for both “None” and “3 Retractors” experimental

cases, with statistically significant values of p < 0.001 in all cases, noting one excep-

tion.

In the case when 3 retractors were present, and EM-tracked guides were compared

80


Table 4.21: Statistical p values for comparisons of angular target registration errors(TRE) of navigating a shoulder replacement with “None” or 3 retractors present in theEM sensing volume, for 3 registration methods. The p values are from 2-sided Mann-Whitney U-tests of pooled data (n = 100). Values less than Bonferroni-correctedα = 0.05/3, which are statistically significantly different by rejecting Type I errors ata 1.67% rate, are presented in bold-face.

No. of Retractors Method Guides EM Points Optical Points

None

Guides · p < 0.001 p < 0.001

EM Points p < 0.001 · p = 0.479


3

Guides · p < 0.001 p < 0.001

EM Points p < 0.001 · p = 0.040


to Optical Points in terms of positional TRE, a value of p = 0.036 was found.

4.3.2 Surgical Navigation: Cadaveric Pre-Clinical Study

One EM-tracked guide with 5 lines was used for FLE calculations. Data are sum-

marized as mean, RMSE, median, and one-sided 95% CI values in Table 4.22. Two

lines, one per screw cylindrical axis, were navigated using each method and used for

position and angle TRE calculations; these data are summarized as mean, RMSE,

median, and one-sided 95% CI values in Table 4.23 and Table 4.24, respectively.

Table 4.22: Positional and angular EM-tracked guide fiducial localization error (FLE)for the pre-clinical trial of plating a distal radius in a single cadaver for n = 5 lines.Positions are reported in millimeters; angles in degrees.



Angle 1.2◦ 3.2◦ 0.9◦ 2.3◦

Though only based on n = 5 lines, it appears that sub-millimeter positional FLE

81

4.4. SUMMARY

Table 4.23: Positional target registration error (TRE) for the pre-clinical trial ofplating a distal radius in a single cadaver, reported in millimeters. There were n = 2lines for each registration method.

Method Mean RMSE Median CI% 95



Table 4.24: Angular target registration error (TRE) for the pre-clinical trial of platinga distal radius in a single cadaver, reported in degrees. There were n = 2 lines foreach registration method.

Method Mean RMSE Median CI% 95

Guides 7.1◦ 7.3◦ 7.1◦ 8.6◦

Optical Points 8.6◦ 8.7◦ 8.6◦ 10.1◦

and sub-degree angular FLE is a continuing trend.

Similarly, the similar TRE summary values between the methods suggest that

EM-tracked guides are able to navigate a drilling task comparably to point-based

optical tracking during surgical navigation.

4.4 Summary

This chapter presented performance analyses of paired-lines tracked guides as com-

pared to crossing-lines characterized guides, other tracking methods, and when in the

presence of EMI-generating equipment. The paired-lines characterization algorithm

was found to be to be statistically equivalent to the crossing-lines algorithm. Tracked

guides that were characterized with the paired-lines algorithm were found to consis-

tently outperform point-based registrations in laboratory settings, and were found to

be comparable in a pre-clinical case study. In summary:

• Paired-lines performance was found to be better than crossing-lines performance

82

4.4. SUMMARY

by 0.1mm using both simulated and experimental data.

• Positional and angular fiducial localization error for calibrating EM-tracked

guides were consistently, respectively, less than one millimeter and less than

one degree.

• For EM-tracked guides, the positional target registration error was unaffected

by the presence of certain metals.

• For EM-tracked guides, the angular target registration error was affected by the

simple presence of a single metal retractors; presence of additional retractors

made no statistically significant difference.

• EM-tracked guides statistically significantly outperformed point-based registra-

tion methods, regardless of tracking technology.

• EM-tracked guides statistically significantly outperformed point-based registra-

tions regardless of the presence of metallic surgical retractors, regardless of

tracking technology.

• EM-tracked guides showed promise of being at least comparable to optical tech-

nology in orthopedic navigation, based on a single cadaveric case.

83

Chapter 5

Discussion and Conclusions

This chapter presents a practical interpretation of the quantitative results of the

studies performed. The findings of this work are discussed and how they relate to

the field, including strengths and weaknesses. The major contributions are concisely

listed. The chapter concludes with an examination of limitations and recommenda-

tions for future work.

5.1 Observations on Simulated and Retrospective Data

Paired-lines registration for device calibration, which was an extension of the crossing-

lines registration, was intended to relax physical constraints on guide design. Sim-

ulation results suggest that, as measured by fiducial registration error (FLE), the

paired-lines registration performed better than crossing-lines. This was evident from

the similarity of the respective summary calculations on simulated data, which had

no statistically significant difference according to the non-parametric Mann-Whitney

test.

Similar results were found when using retrospective data from a previous study.

The means and medians of the positional FLE values were sub-millimeter, and of the

84

5.2. OBSERVATIONS ON TRACKED-GUIDES REGISTRATION

angular FLE values were sub-degree. The translational differences between the paired-

lines and crossing-lines registrations were statistically significantly indistinguishable

beneath 0.6mm, which was the manufacturer-reported accuracy of the measurement

system under ideal conditions [36]. This suggested that, within the manufacturer-

stated tracking accuracies of the electromagnetic sensing equipment, the paired-lines

and crossing-lines registration algorithms were comparable.

5.2 Observations on Tracked-Guides Registration

EM-tracked guides targeted for smaller, more difficult anatomy were created and

assessed.

5.2.1 EM-Tracked Glenoid Guides

The calibration tests in the glenoid study also measured FLE of paired-lines regis-

tration. As for the simulated data, the positional FLE was sub-millimeter and the

angular FLE was sub-degree. This strongly suggested that using paired lines, without

the restriction that the lines intersect, was reliable for device calibration using EM

tracking.

The overall accuracy was measured by a combination of physical registration, using

an EM-tracked guide, and sensing with an EM-tracked probe. The positional target

registration error (TRE) was sub-millimeter and the angular TRE was less than 2

degrees. These values are comparable to TRE values reported in the literature [17,23,

49]. These were computed from 80 readings and appeared to be statistically reliable.

The glenoid tests were consistent with the hypothesis that EM-tracked guides

provided a usable way of blending personalized surgical instrumentation with EM

85

5.3. OBSERVATIONS ON EVALUATING EM INTERFERENCE

tracking.

5.2.2 EM-Tracked Coracoid Guides

The coracoid, which is an anatomically variable process of the scapula, has been used

as a location for optically tracked navigation in shoulder surgery [19,35]. This study

examined whether the coracoid could also provide a registration surface; doing so

would avoid difficulties that are present in the glenoid, principally the osteophytes

that are known to add error to the physical registration step [34].

As for the glenoid guide, for the calibration step the positional FLE was sub-

millimeter and the angular FLE was sub-degree. These were computed from 500

readings and were taken to be statistically reliable.

Because the coracoid was selected as the most likely clinical use of an EM-tracked

guide, the overall accuracy of its use was compared to uses of point-based EM tracking

and point-based optical tracking. The target registration error (TRE) values for the

EM-tracked guides outperformed the other methods in all analyses, as shown by

the summary calculations. This further supported the hypothesis of the usability of

EM-tracked personalized guides.

5.3 Observations on Evaluating EM Interference

The performance of using an EM-tracked guide for surgical navigation was tested in

the presence of surgical equipment – Hohmann-style surgical soft-tissue retractors –

that are known to induce electromagnetic interference (EMI) in EM tracking. As

expected, the characterization FLE values were excellent.

The first part of this study compared effects of none, 1, 2, or 3 surgical retractors

86

5.3. OBSERVATIONS ON EVALUATING EM INTERFERENCE

in the EM sensing volume. From the TRE values, it appeared that the introduction

of retractors did not have a statistically significant effect on either the positional or

angular TRE. This result may surprise some readers because EM tracking is frequently

criticized for its unreliability in the presence of metallic objects causing EMI [24, 72,

82].

5.3.1 Observations on Comparing Multimodal Interference

As expected, the characterization FLE values were excellent.

The target registration error (TRE) values for the EM-tracked guides were lower

than those of the point-based EM tracking and those of the point-based optical track-

ing in all analyses, for all cases. All of the summary calculations showed the EM-

tracked guides as providing the highest tracking accuracy. In all but one comparison,

the TRE values of the EM-tracked guides were strongly statistically significantly

better that the TRE values of the other tracking methods. In one comparison, of

EM-tracked guides to point-based optical tracking with 3 surgical retractors present

in the EM sensing volume, the TRE values were different with p = 0.036 which is not

statistically different after applying the Bonferroni correction for multiple hypotheses.

It is unclear whether this inability to detect a statistical difference would amount to

a substantial difference in actual tracking.

5.3.2 Observations on Surgical Navigation

As expected, the characterization FLE values were excellent.

The number of cylindrical axes available for comparison was deemed too small to

provide statistical significance, so human observation must suffice. The TRE values

87

5.4. DISCUSSION

suggested that the EM-tracked guide enabled a surgeon to perform navigated drilling

tasks on a single cadaveric specimen with accuracy that were comparable to naviga-

tion using point-based optical tracking. These preliminary results demonstrated the

feasibility of the clinical use of EM-tracked personalized guides for surgical navigation.

5.4 Discussion

This work demonstrated the integration of two disparate technologies – electromag-

netic (EM) navigation and personalized guides – in a working prototype for surgical

navigation. The integration was initially tested by adapting an un-tracked mechani-

cal personalized guide used for hip resurfacing arthroplasty (described in Background

Section 2.6), and demonstrating equivalent performance. Part of the motivation for

using EM-tracked guides was that un-tracked personalized guides have been observed

to be difficult to use, in part “due to joint contracture, inadequate soft tissue release,

and a smaller exposure” [15]. Part of the motivation in using paired lines for charac-

terization is that EM tracking is known to have degraded performance in the presence

of EMI-generating surgical equipment [58,72,77,82]. The integration of the technolo-

gies into EM-tracked guides was tested in an ordinary laboratory environment, in the

presence of many sources of EM interference such as desks, chairs, tables, and other

common equipment.

This work found that additively manufactured devices can be reliably character-

ized using a line-based algorithm. The modestly novel paired-lines algorithm pre-

sented was statistically not different from its intellectual predecessor, the crossing-

lines algorithm. The superiority of the paired-lines concept is that it removed the

88

5.4. DISCUSSION

requirement that the line fiducials had to physically intersect. The relaxed design con-

straint made it possible to design EM-tracked guides for otherwise difficult anatomical

regions, such as the glenoid and coracoid process of the scapula.

The characterization was posed and solved as a registration problem that, as

usual, was decomposed into separate optimizations of a translational component and

an orientation component [18]. The orientation problem was solved by using only the

directions of the lines; the unit direction vectors were represented as points on the unit

sphere, which was a formulation that could be optimized by any of many standard

methods such as the singular-value decomposition [5, 74] or quaternions [31]. The

translational problem was solved by combining line directions and reference vectors

on the lines into an over-determined linear equation that could be solved by standard

methods. As a minor mathematical observation, only modern linear algebra was

needed and direct optimization using calculus was unnecessary.

The characterizations were assessed as fiducial localization error (FLE), which

compared line-line directions as angles and the minimum distance between lines as

linear magnitudes. On simulated data and on empirical data, the paired-lines algo-

rithm was sufficiently accurate for the proposed surgical application.

The characterizations were then used for EM tracking of personalized surgical

guides. One study examined the most conceptually straightforward application, which

was to use EM tracking of a guide that is physically applied to the target anatomy;

here, additively manufactured models of cadaveric shoulders were used and the glenoid

was the anatomical registration region. The next study examined a less obvious appli-

cation, which was to physically apply an EM-tracked guide to a surgically accessible

site that was near the target anatomy; here, the same shoulders were used and the

89

5.4. DISCUSSION

coracoid was the anatomical registration region. For the latter study, point-based

registration and tracking was also performed using an EM localization system and an

optical localization system.

The studies consistently found that line-based anatomical EM navigation was

more accurate than traditional optical navigation and than point-based EM navi-

gation. All point-based registrations were performed using the same robust algo-

rithm [51]; the EM point-based results were comparable to the optical point-based

results, an unsurprising finding that is commonly observed in the literature [24, 32,

33, 58]. That the EM-tracked guides were able to outperform both modalities us-

ing a point-based approach suggested that the improved performance was related to

the combination of physical registration and line-based characterization, and was not

related to the tracking method.

The final study for this dissertation was a single cadaveric application for fracture-

plate fixation. A Board-certified orthopedic surgeon was able to navigate surgical

screws with comparable accuracy using an EM-tracked guide and an optical naviga-

tion system. There were too few data for statistically reliable conclusions but this

provided a proof of the concept.

Previous reports [58, 72, 77, 82] have found that EM tracking may not be well

suited to surgical navigation, with many papers raising concerns of electromagnetic

interference (EMI). The results from these studies suggested that, if points were to be

replaced by lines, then EM tracking performed well and that concerns of EMI might

be overcome when a small number of surgical instruments were present in the EM

tracking volume.

It can be tentatively concluded that one fundamental difference between previous

90

5.4. DISCUSSION

accuracy studies and these studies is that, here, care was taken to incorporate direc-

tional information as often as possible in the entire process. Lines, not points, were

used to calibrate the personalized guide to the EM tracker. Physical surfaces, not

points, were used for registration. Lines, not points, were used as surgical targets.

This consistency took advantage of the inherently high directional accuracy of EM

tracking and placed reduced emphasis on the less-accurate position sensing of EM

tracking.

The experiments that used the hybrid EM-tracked personalized guides demon-

strated that it is possible to “virtually link” a registration device to a modifiable

surgical plan, unlike a purely mechanical personalized guide that have a single un-

alterable surgical plan that is physically integrated into the guide [41]. This major

improvement in surgical applications was not exploited in these experiments, but

the virtual linkage is a potentially important component of future integrated surgical

navigation systems.

Navigation with an EM-tracked guide had some of the ergonomic advantages of

personalized templates – principally, physical registration – while maintaining much

of the flexible decision-making available when using a surgical navigation system. EM

tracking also had usability advantages not provided by optical tracking, such as no

need to maintain a line of sight and having smaller sensors that can be physically

attached to relatively fragile bone. One minor clinical implication was that these

EM-tracked guides can be integrated into the work flow of surgery with greater ease,

especially because registration is a task that can be quickly accomplished by the

physical mating of a guide to the anatomy. The larger clinical implication was that

navigation may in future be possible for previously inoperable anatomy: small bones,

91

5.4. DISCUSSION

limited surgical exposure, and impossibility of maintaining a line of sight are optical

obstacles that are overcome with a small, accurate EM system. This and related

work have demonstrated that EM-tracked guides can perform comparably to their

un-tracked mechanical analogs in the hip [17], with likely applications in the shoulder

and the wrist.

A unique ergonomic advantage of these EM-tracked personalized guides is a re-

duction of the the number of required reference sensors. An EM-tracked guide was

able to also serve as the anatomical, or patient, local coordinate reference. A typical

optical navigation requires a reference sensor – which is physically large – that is

impractical for smaller anatomy or for procedures with a difficult or minimal surgical

exposure. EM-tracked guides were demonstrated to be an effective solution for both

registration and as the anatomical reference.

Some insight into anatomical registration arose from consideration of the experi-

mental results. From the experiments that compared the physical surface registration

of EM-tracked guides to point-based registration, it was found that the EM-tracked

guides were more accurate. It seems unlikely that the accuracy arose from EM track-

ing alone: the EM-tracked and optically tracked point-based methods used the same

mathematically robust estimation method on data from the same anatomical region,

so the data source is unlikely to be the cause of the accuracy improvement. A dis-

tinct possibility is that the mechanical averaging effect of the physical registration

is superior to the computational optimization of the robust estimation, and that

the line-based characterizations of the EM-tracked guides transformed this improved

registration accuracy to overall system accuracy.

92

5.5. THESIS CONTRIBUTIONS

5.5 Thesis Contributions

This dissertation has described three main contributions:

• Demonstrated the reliability of line-based calibration

• Demonstrated the reliability of hybrid EM-tracked personalized guides for sur-

gical navigation

• Demonstrated that the effects of EM interference on EM tracking can be over-

come

5.6 Limitations

For most of these studies, simple additively manufactured plastic models were used.

Although the models were derived from CT data of cadaveric specimens, and this

method has been shown to be reliable [4], additional studies using human cadavers

would be of benefit.

These studies were also limited by small sample sizes. There were 10 scapula

models that were used throughout the studies, with EM-tracked guides designed to

mate with the specified anatomical region in each study. Repetitions were performed

in the latter studies to compensate for the small sample size, but adding further

models to create a larger sample sizes is an opportunity for future work.

The use of a single cadaveric specimen in the pre-clinical study provided limited

testing of these EM-tracked guides. From the high accuracy found in the studies

using EM-tracked guides on plastic models, and the surgically acceptable results

from the single cadaveric specimen, further studies using additional specimens are

recommended.

93

5.7. FUTURE WORK

The effects of EMI on the EM tracking system were explored to only a preliminary

stage. It in unclear what effects might arise from the presence of additional surgical

equipment; multiple EMI configurations may be needed for a deeper understanding

of the effects that EMI has on EM tracking performance. This work tested only the

Aurora EM tracking system (NDI, Waterloo, CA) and it is unknown whether other

tracking technologies have better or worse behavior when using EM-tracked guides

for surgical navigation.

5.7 Future Work

Further cadaveric studies are a natural continuation of this work. The cadaveric pre-

clinical study investigated the performance of EM-tracked guides on a single forearm.

Expanding the study to additional forearm specimens, or investigating an alternative

plating technique such as dorsal-side plating, are possibilities. Other anatomical

regions, such as the glenoid or the acetabulum of the hip, could be topics for a

future pre-clinical study. Based on the extensive findings for the scapula in this work,

navigating drilling tasks – such as those for the implantation of a glenoid base-plate for

shoulder arthroplasty – on cadaveric shoulder specimens may be of particular interest.

If warranted by future cadaveric studies, human trials are a natural extension of this

work.

The combination of line fiducials and an EM-tracked probe led to high accuracy

in these studies. Future work might extend the line-fiducial idea to optically tracked

personalized guides; this work was motivated in part by desires to avoid some surgical

difficulties with optical tracking but tracking of additively manufactured items may

be of interest in non-surgical applications. Future work might also investigate other,

94

5.7. FUTURE WORK

inherently orientation-based sensors – such as magnetometers and accelerometers –

which may benefit from the use of lines as fiducials.

Although not specifically tested here, a potential advantage of of an EM-tracked

guide is intraoperative verification of proper guide placement. A surgical tracking

system can track a tool, such as a sharp-tipped probe, which can be used to identify a

known landmark in the operative field that can also be identified in the preoperative

medical image; if such a sensed landmark does not align with the navigation, the

operative team may be able to use this information to establish the accuracy of the

physical registration and, if needed, to adjust the placement of the EM-tracked guide.

This is a desirable property that personalized templates do not currently have [40].

This work aimed to demonstrate that computer-assisted surgery can be performed

on small, delicate bony anatomy. Though computer-assisted surgery has become in-

creasingly prevalent for numerous orthopaedic procedures in the hip and knee, perhaps

smaller joints will be as prevalent in the near future.

95

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Appendix

Figure A.1 reproduces a letter of ethics clearance for the research use of medical

images of shoulders.

Figure A.2 reproduces a letter of ethics clearance for the research use of medical

images of forearms, and forearm use as specimens in experiments.

108

QUEEN'S UNIVERSITY HEALTH SCIENCES AND AFFILIATED TEACHING HOSPITALS

RESEARCH ETHICS BOARD ANNUAL RENEWAL

Queen's University, in accordance with the "Tri-Council Policy Statement 2, 2010" prepared by the

Interagency Advisory Panel on Research Ethics for the Canadian Institutes of Health Research, Natural

Sciences and Engineering Research Council of Canada and Social Sciences and Humanities Research Council

of Canada requires that research projects involving human participants be reviewed annually to determine their

acceptability on ethical grounds.

A Research Ethics Board composed of:

Dr. A.F. Clark, Emeritus Professor, Department of Biomedical and Molecular Sciences, Queen's University

(Chair)

Dr. H. Abdollah, Professor, Department of Medicine, Queen's University

Dr. C. Cline, Assistant Professor, Department of Medicine, Director, Office of Bioethics, Queen's University,

Clinical Ethicist, Kingston General Hospital

Dr. R. Brison, Professor, Department of Emergency Medicine, Queen's University

Dr. M. Evans, Community Member

Ms. J. Hudacin, Community Member

Mr. D. McNaughton, Community Member

Ms. S. Rohland, Privacy Officer, ICES-Queen's Health Services Research Facility, Research Associate,

Division of Cancer Care and Epidemiology, Queen's Cancer Research Institute

Dr. M. Sawhney, Assistant Professor, School of Nursing, Queen's University

Dr. A. Singh, Professor, Department of Psychiatry, Queen's University

Dr. J. Walia, Assistant Professor and Clinical Geneticist, Department of Paediatrics, Queen's University and

Kingston General Hospital

Ms. K. Weisbaum, LL.B. and Adjunct Instructor, Department of Family Medicine (Bioethics)

has reviewed the request for renewal of Research Ethics Board approval for the project “Proper Positioning

of Glenoid Screws in Reverse Shoulder Arthroplasty, An Anatomical Study” as proposed by Dr. R.

Bicknell of the Department of Surgery, at Queen's University. The approval is renewed for one year,

effective June 14, 2014. If there are any further amendments or changes to the protocol affecting the

participants in this study, it is the responsibility of the principal investigator to notify the Research Ethics

Board. Any unexpected serious adverse event occurring locally must be reported within 2 working days or

earlier if required by the study sponsor. All other adverse events must be reported within 15 days after

becoming aware of the information.

____________________________Date: June 14, 2014

Chair, Health Sciences Research Ethics Board

Renewal 1[ ] Renewal 2 [ ] Extension [x] Code# SURG-232-11 Romeo file# 6006038

Figure A.1: Letter of ethics clearance from the relevant IRB, which was the HealthSciences Research Ethics Board of Queen’s University, for the research use of medicalimages of shoulders.

109

QUEEN'S UNIVERSITY HEALTH SCIENCES & AFFILIATED TEACHING HOSPITALS

RESEARCH ETHICS BOARD (HSREB)

HSREB Initial Ethics Clearance

October 23, 2015

Dr. Michelle Zec

Department of Surgery

Kingston General Hospital

ROMEO/TRAQ: #6016516

Department Code: SURG-335-15

Study Title: The Development of Teaching and Assessment Tools in Hand and Wrist Anatomy and

Reconstruction.

Co-Investigators: Dr. D. Pichora

Review Type: Delegated

Date Ethics Clearance Issued: October 23, 2015

Ethics Clearance Expiry Date: October 23, 2016

Dear Dr. Zec,

The Queen's University Health Sciences & Affiliated Teaching Hospitals Research Ethics Board (HSREB) has

reviewed the application and granted ethics clearance for the documents listed below. Ethics clearance is

granted until the expiration date noted above.

Appendix 3: Detailed Checklist and Global Rating Scale Appendix 4: Pass/Fail Assessment Letter of Information

Amendments: No deviations from, or changes to the protocol should be initiated without prior written

clearance of an appropriate amendment from the HSREB, except when necessary to eliminate immediate

hazard(s) to study participants or when the change(s) involves only administrative or logistical aspects of the

trial.

Renewals: Prior to the expiration of your ethics clearance you will be reminded to submit your renewal report

through ROMEO. Any lapses in ethical clearance will be documented on the renewal form.

Completion/Termination: The HSREB must be notified of the completion or termination of this study

through the completion of a renewal report in ROMEO.

Reporting of Serious Adverse Events: Any unexpected serious adverse event occurring locally must be

reported within 2 working days or earlier if required by the study sponsor. All other serious adverse events

must be reported within 15 days after becoming aware of the information.

Reporting of Complaints: Any complaints made by participants or persons acting on behalf of participants

must be reported to the Research Ethics Board within 7 days of becoming aware of the complaint. Note: All

documents supplied to participants must have the contact information for the Research Ethics Board.

Investigators please note that if your trial is registered by the sponsor, you must take responsibility to ensure

that the registration information is accurate and complete.

Yours sincerely,

Chair, Health Sciences Research Ethics Board

The HSREB operates in compliance with, and is constituted in accordance with, the requirements of the Tri-

Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS 2); the International

Conference on Harmonisation Good Clinical Practice Consolidated Guideline (ICH GCP); Part C, Division 5

of the Food and Drug Regulations; Part 4 of the Natural Health Products Regulations; Part 3 of the Medical

Devices Regulations, Canadian General Standards Board, and the provisions of the Ontario Personal Health

Information Protection Act (PHIPA 2004) and its applicable regulations. The HSREB is qualified through the

CTO REB Qualification Program and is registered with the U.S. Department of Health and Human Services

(DHHS) Office for Human Research Protection (OHRP). Federalwide Assurance Number: FWA#:00004184,

IRB#:00001173

HSREB members involved in the research project do not participate in the review, discussion or decision.

Figure A.2: Letter of ethics clearance from the relevant IRB, which was the HealthSciences Research Ethics Board of Queen’s University, for the research use of medicalimages of forearms, and forearm use as specimens in experiments.

110

Electromagnetically Tracked Personalized Surgical Guides

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