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. IMAGE-GUIDED SURGERY
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
2.1. IMAGE-GUIDED SURGERY
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
2.1. IMAGE-GUIDED SURGERY
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
surgical navigation.
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
2.4. REGISTRATION ALGORITHMS
{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
2.4. REGISTRATION ALGORITHMS
• 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
2.4. REGISTRATION ALGORITHMS
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
2.4. REGISTRATION ALGORITHMS
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
2.6. TRACKED GUIDES USING CROSSING LINES
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
2.6. TRACKED GUIDES USING CROSSING LINES
(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
2.6. TRACKED GUIDES USING CROSSING LINES
(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
surgical navigation.
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
3.1. THEORETICAL METHODS
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
3.1. THEORETICAL METHODS
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
3.1. THEORETICAL METHODS
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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
(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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
(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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
(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
3.2. EVALUATING TRACKED-GUIDES REGISTRATION
(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
3.3. EVALUATING EM INTERFERENCE
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
3.3. EVALUATING EM INTERFERENCE
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
3.3. EVALUATING EM INTERFERENCE
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
3.3. EVALUATING EM INTERFERENCE
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
3.3. EVALUATING EM INTERFERENCE
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
3.3. EVALUATING EM INTERFERENCE
(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
3.3. EVALUATING EM INTERFERENCE
(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
3.3. EVALUATING EM INTERFERENCE
(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
3.3. EVALUATING EM INTERFERENCE
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
3.3. EVALUATING EM INTERFERENCE
(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
4.1. RESULTS FOR THEORETICAL METHODS
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
4.1. RESULTS FOR THEORETICAL METHODS
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.
Error Type Mean RMSE Median 95% CI
Angle 0.5◦ 0.5◦ 0.4◦ 0.9◦
61
4.1. RESULTS FOR THEORETICAL METHODS
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
Position Difference (mm)
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
Angle Difference (degrees)
Fre
quen
cy
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.
Error Type Mean RMSE Median 95% CI
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
4.2. RESULTS FOR TRACKED-GUIDES REGISTRATION
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
Position Difference (mm)
Fre
quen
cy
(a)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.60
2
4
6
8
10
12
14
Angle Difference (degrees)
Fre
quen
cy
(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
4.2. RESULTS FOR TRACKED-GUIDES REGISTRATION
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
Position Difference (mm)
Fre
quen
cy
(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
Angle Difference (degrees)
Fre
quen
cy
(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
4.2. RESULTS FOR TRACKED-GUIDES REGISTRATION
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.
Error Type Mean RMSE Median 95% CI
Position 0.5mm 0.6mm 0.4mm 1.0mm
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
4.2. RESULTS FOR TRACKED-GUIDES REGISTRATION
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.
Error Type Mean RMSE Median 95% CI
Position 0.3mm 0.4mm 0.2mm 0.8mm
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
4.2. RESULTS FOR TRACKED-GUIDES REGISTRATION
0 0.5 1 1.5 2 2.5 30
20
40
60
80
100
120
140
160
180
200
Position Difference (mm)
Fre
quen
cy
(a)
0 0.5 1 1.5 2 2.50
10
20
30
40
50
60
70
80
90
Angle Difference (degrees)
Fre
quen
cy
(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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
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
Optical Points p < 0.001 p = 0.479 ·
70
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
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.
Error Type Mean RMSE Median 95% CI
Position 0.3mm 0.5mm 0.6mm 1.3mm
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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
0 0.5 1 1.5 2 2.50
2
4
6
8
10
12
14
16
Position Difference (mm)
Fre
quen
cy
(a)
0 0.5 1 1.5 2 2.5 30
1
2
3
4
5
6
7
8
Angle Difference (degrees)
Fre
quen
cy
(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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
0 0.5 1 1.5 2 2.5 30
20
40
60
80
100
120
140
160
180
200
Position Difference (mm)
Fre
quen
cy
(a)
0 0.5 1 1.5 2 2.50
10
20
30
40
50
60
70
80
90
Angle Difference (degrees)
Fre
quen
cy
(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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
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.
Error Type Mean RMSE Median 95% CI
Position 0.3mm 0.4mm 0.2mm 0.8mm
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
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
3
Guides 2.2mm 2.8mm 1.7mm 6.7mm
EM Points 4.6mm 6.3mm 2.9mm 14.1mm
Optical Points 3.9mm 5.5mm 2.3mm 11.8mm
77
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
Guides EM Points Optical Points
0
2
4
6
8
10
12
14
16
18
20
22
Dis
tanc
e (m
m)
(a)
Guides EM Points Optical Points
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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
Guides EM Points Optical Points0
5
10
15
20
25
30
35
40
Ang
le (
degr
ees)
(a)
Guides EM Points Optical Points0
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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
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
Optical 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
4.3. RESULTS FOR EVALUATING EM INTERFERENCE
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
Optical Points p < 0.001 p = 0.479 ·
3
Guides · p < 0.001 p < 0.001
EM Points p < 0.001 · p = 0.040
Optical 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.
Error Type Mean RMSE Median 95% CI
Position 0.1mm 0.3mm 0.1mm 0.2mm
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
Guides 0.4mm 0.5mm 0.4mm 0.7mm
Optical Points 0.7mm 0.8mm 0.7mm 0.9mm
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
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