A Determination of the Accuracy of Cone Beam Computed Tomography and Digital Orthopantomography for the

Determination of Bone Quantity in the Mandibular Ramus

by

Dr Robin Gallardi

A thesis submitted in conformity with the requirements for the degree of Masters of Science

Faculty of Dentistry University of Toronto

© Copyright by Dr Robin Gallardi 2013

ii

A Determination of the Accuracy of Cone Beam Computed

Tomography and Digital Orthopantomography for the

Determination of Bone Quantity in the Mandibular Ramus

Robin L Gallardi

Master of Science

Graduate Department of Dentistry University of Toronto

2013

Abstract

Objective: The purpose of this study was to compare the accuracy of cone beam CT (CBCT)

imaging with digital orthopantomograms for determining bone quantity in the mandibular ramus.

Methods: Twenty-nine cadaveric mandibles marked bilaterally with three fiducial markers were

imaged using both CBCT and digital orthopantomography. After sectioning, four cross sectional

measurements were made on the specimens and on the CBCT images. Two corresponding

linear measurements were made on the orthopantomograms. Statistical analysis was used to

compare the CBCT and orthopantomogram measurements with measurements from the

anatomic specimens. Results: CBCT measurements were found to significantly differ from

those made on the anatomic specimens (P<0.05). Linear measurements from the

orthopantomograms varied by 15.9 percent compared to the anatomic specimens. Conclusion:

CBCT and orthopantomogram measurements were significantly different from those of the

anatomic specimens suggesting inaccuracies in the radiographic technology or a lack of

precision in landmark identification.

iii

Acknowledgements

I wish to express my deepest gratitude to Dr. Cameron Clokie for all your guidance and

patience throughout the last four years. I could not have done this without your

continued support and encouragement.

I would like to thank my advisory committee members Dr. Ernie Lam, Dr. Mike Wiley

and Dr. Howard Holmes for your constructive revisions and continued support.

A special thank you for Joseph Hasso and Fouad Ebrahim for your tremendous

contribution to this thesis. Your hard work and eagerness to learn is contagious and I

wish you both amazing success in your dental careers.

I wish to thank my family who have supported and encouraged me throughout my

training. It is because of your love and support that I have been able to succeed and

reach the goals that I have set. My accomplishments in life are the result of the values

and morals you have taught.

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Table of Contents

Abstract ii

Acknowledgements iii

Table of Contents iv

List of Tables vii

List of Figures viii

List of Abbreviations xii

Chapter 1: Introduction 1

1.1 Bone Quality and Quantity 1

1.2 Bone Grafting 4

1.3 Autogenous Bone Grafts 4

1.4 Intraoral Graft Harvest 7

1.5 Ramus Graft Harvest 8

1.6 Orthopantomograms in Pre-Surgical Planning 15

1.7 Computed Tomography 18

1.8 Cone Beam Computed Tomography 20

1.9 Cone Beam Computed Tomography and Implant Therapy 20

1.10Cone Beam Computed Tomography Technology 22

1.11Limitations of Cone Beam Computed Tomography 27

v

1.12 Cone Beam Computed Tomography in Clinical Practice 28

Chapter 2: Statement of Problem 31

Chapter 3: Objectives and Hypotheses 32

3.1 Objectives 32

3.2 Hypotheses 33

Chapter 4: Significance 35

Chapter 5: Materials and Methods 36

5.1 Anatomic Specimen Preparation 36

5.2 Cone Beam Computed Tomography Images 37

5.3 Orthopantomography Images 38

5.4 Anatomic Measurements 38

5.5 Cone Beam Computed Tomography Measurements 39

5.6 Orthopantomogram Measurements 39

5.7 Data Analysis 40

Chapter 6: Results 42

6.1 Anatomic Data 42

6.2 Inter- and Intra-observer Variability 44

6.3 Comparision of Orthopantomogram and Cone Beam Computed

Tomography Measures to Anatomic Measures 50

vi

Chapter 7: Discussion 54

7.1 Cadaveric Specimens 54

7.2 Anatomic Gold Standards and Surgical Planning 55

7.3 IAN Canal Identification 59

7.4 Distortion in Orthopantomography 59

7.5 Cone Beam Computed Tomography and Pre-Surgical Planning 63

7.6 Landmark Identification 64

7.7 Image Quality with Cone Beam Computed Tomography 71

7.71 Image contrast and FOV 71

7.72 Noise 72

7.73 Artifacts 74

7.74 Summary 79

7.8 Access to Radiographic Modalities 80

7.9 Clinical Applications in Pre-Surgical Planning 81

Chapter 8: Conclusion 83

Chapter 9: Future Direction 84

Chapter 10: References 85

Appendices 115

Appendix I 115

Appendix II 116

vii

List of Tables

Table 1 Cadaveric measurements in millimeters of the mean distances

and standard deviations from the alveolar crest to IAN canal,

buccal cortex to IAN canal, average mandible width and height

at the following anatomic locations: ascending ramus,

second molar and first molar sites for all of the cadaveric

mandibles. 46

Table 2 Cadaveric measurements in millimeters. The mean distance

and standard deviations from the buccal cortex to the IAN

canal at various vertical positions of the IAN canal. 47

Table 3 Mean and Standard Deviation (+/- SD) of percent error of 51

measurements from 29 mandibles from CBCT

and Orthopantomogram images compared to anatomic

measures.

viii

List of Figures

Figure 1 Classification of Bone Quality, type D1 to D4. 2

Figure 2 Caywood and Howell classification of alveolar bone loss. 4

Figure 3 Intra-oral harvest sites of the mandible. Area A is a

symphyseal graft, Area B is a mid-body graft and Area C

is a ramus graft. 6

Figure 4 Outline of a graft from the external oblique ridge. 9

Figure 5 Clinical photo of the outline of a ramus harvest site. 10

Figure 6 Landmark measurement of a ramus graft. Sg, sigmoid

notch; Ct , tip of coronoid process; Cf, anterior point of

coronoid process; Mc, mandibular canal; R, 3 mm posterior

to distal root of third molar; a, point 3mm anterior to beginning

of the mandibular canal; b, point 3 mm anterior to the

mandibular canal; 1, anterior side length of the ascending

ramus; 2, posterior side length of the anterior part of

the ascending ramus; 3, upper horizontal side length

of the ramus; 4, lower side length of the anterior

ascending ramus. 13

Figure 7 Various ramus harvest techniques to maximize bone

quantity. 14

ix

Figure 8 Sequence of CBCT image acquisition to surgical guide

fabrication in implant pre-surgical planning. 21

Figure 9 A CBCT machine for an office setting. 22

Figure 10 CBCT fan shaped beam. 23

Figure 11 X-ray beam projection scheme comparing acquisition

geometry of conventional or “fan” beam (right) and “cone”

beam (left) imaging geometry and resultant image production. 28

Figure 12 Cadaveric mandible with fiducial markers at the first molar,

second molar and ascending ramus sites. 37

Figure 13 A cross-section of a cadaveric mandible is displayed

on the left. The schematic on the right demonstrates the

four measurements that were made at each section: the

distance from the buccal cortex to IAN canal(black), mandibular

width(red), mandibular height(yellow) and the distance from

the alveolar crest to the IAN canal (blue). 39

Figure 14 A cros-section of a CBCT image is displayed on the left. The

schematic on the right demonstrates the four measurements

that were made at each section; the distance from the

buccal cortex to the IAN canal (black), mandibular width (red),

mandibular height (yellow) and the distance from the alveolar

crest to the IAN canal (blue). 41

Figure 15 An orthopantomogram image. The yellow line represents

the measurement from the alveolar crest to the inferior

cortex and the blue line represents the measurement from

the alveolar crest to the superior cortex of IAN canal. Both

measurements were made at each pin location. 42

x

Figure 16 Boxplots showing the percent error of measurements of the

buccal cortex to IAN canal, made on 29 mandibles comparing

dental students and oral surgeons. Boxes enclose the middle

50% of observations. Vertical lines extend to include

approximately 90% of observations. Circles and asterisks denote

outlying and extreme data points. 48

Figure 17 Boxplots showing the percent error of measurements of

mandibular width made on 29 mandibles comparing dental

students and oral surgeons. Boxes enclose the middle 50%

of observations. Vertical lines extend to include approximately

90% of observations. Circles and asterisks denote outlying

and extreme data points. 49

Figure 18 Boxplots showing the percent error of measurements of

mandibular height made on 29 mandibles comparing dental

students and oral surgeons. Boxes enclose the middle 50%

of observations. Vertical lines extend to include approximately

90% of observations. Circles and asterisks denote outlying

and extreme data points. 50

Figure 19 Boxplots showing the percent error of measurements of the

alveolar crest to IAN canal made on 29 mandibles comparing

dental students and oral surgeons. Boxes enclose the middle

50% of observations. Vertical lines extend to include approximately

90% of observations. Circles and asterisks denote outlying and

extreme data points. 51

Figure 20 Boxplots showing differences between replicate measurements

based on CBCT and Orthopantomogram images of 29 mandibles.

xi

Boxes enclose the middle 50% of observations. Vertical lines extend

to include approximately 90% of observations. Circles and

asterisks denote outlying and extreme data points. 52

Figure 21 Boxplots showing the percent error of measurements based

on Orthopantomogram images of 29 mandibles. Boxes enclose

the middle 50% of observations. Vertical lines extend to include

approximately 90% of observations. Circles and asterisks denote

outlying and extreme data points. 55

Figure 22 Boxplots showing the percent error of measurements based

on CBCT images of 29 mandibles. Boxes enclose the middle

50% of observations. Vertical lines extend to include approximately

90% of observations. Circles and asterisks denote outlying and

extreme data points. 56

xii

Abbreviations

IAN Inferior alveolar nerve

CBCT Cone Beam Computed Tomography

CT Computed Tomography

2D Two Dimensional

3D Three Dimensional

FOV Field of View

mm Millimeters

cm Centimeters

1

Chapter 1

Introduction

Dental implants are becoming a popular choice for the replacement of missing

teeth. With an aging population and ease of access to this technology, more and more

patients are choosing dental implants as a restorative option. Successful outcomes in

implant dentistry are dependent on the integration of the implant with the surrounding

bone.

1.1 Bone Quality and Quantity

Bone availability, as determined by the quality and quantity of cortical and medullary

bone, and the locations of adjacent anatomic structures, is an important determinant of

implant success. When planning for implant placement, each site must be carefully

assessed for the amount of available bone. Once this has been determined one can

select the appropriate implant size so that it may be positioned in the optimal bucco-

lingual and mesio-distal orientation at the proposed site. It can therefore be appreciated

that without an adequate knowledge of bone quantity and quality, the success of implant

therapy can be negatively affected.

Determination of bone quality at a potential implant site is a critical step in predictable

outcomes in implant therapy. Several classifications are available to assist clinicians in

determining bone quality (Misch, 1997; Seibert, 1983). The most frequently utilized

system classifies bone into four categories (Figure 1). D1 bone is composed almost

entirely of cortical bone and is more commonly identified in the anterior mandible. D2

2

bone is composed of a thick crestal layer of cortical bone surrounding dense trabecular

bone. This bone type can be found commonly in the anterior and posterior mandible.

D3 bone is composed of a porous crestal layer of cortical bone with fine trabecular bone

beneath. This bone type is found primarily in the maxilla. Finally, D4 bone consists of

fine trabecular bone with only a thin layer of cortical bone. When present, this is almost

entirely found in the posterior maxilla (Misch, 1989). It is the thickness of the cortical

bone and the density of the surrounding cancellous bone that determines the suitability

of a site for dental implant placement. It would therefore stand to reason that sites

having D4 bone will provide the greatest challenge for implant osseointegration, while

those sites having D2 bone would be superior (Wang and Al-Shammari, 2002).

Figure 1. Classification of Bone Quality, Types D1 to D4 (Misch, 1989).

The placement of endosseus dental implants requires adequate bone quantity both in

height (vertically) and width (horizontally). Frequently, patients having had long

standing edentulism, demonstrate less than adequate amounts of bone (i.e. quantity) for

implant placement. Once teeth are lost or removed, resorption of the alveolar ridges

3

occurs in a relatively predictable pattern and rate. Tallgren (1972) observed that

although the greatest proportion of bone loss occurred within the first year following

tooth extraction, the process continued at a slower rate over 25 years. The rate of

bone loss was noted to be four times faster in the mandible than in the maxillae

(Tallgren, 1972). Investigations by Cawood and Howell (1988) quantified bone loss

horizontally and vertically and showed variations with different locations within the jaws

(Figure 2). The maxillae and anterior mandible demonstrated patterns of loss in both

alveolar process bone height (vertical) and width (horizontal). In the posterior mandible,

loss of the alveolar process was mainly in height.

Frequently, the pattern and amount of alveolar ridge resorption interferes with the ability

to proceed to dental implant placement without adjunctive grafting to augment the

alveolar ridge (Jensen and Sindet-Pedersen, 1991). The goal of alveolar bone grafting

is to establish a ridge height and width that enables stable placement of a dental implant

that will eventually form the foundation upon which a prosthesis will be delivered to

restore patient function.

4

Figure 2. Caywood and Howell classification of alveolar bone loss (Cawood and Howell,

1988)

1.2 Bone Grafting

Numerous options for bone augmentation are available for alveolar ridge grafting.

Xenogeneic bioimplants, allogeneic and alloplastic implants, and autogenous bone

grafts all been extensively studied in the literature as options for augmenting deficient

alveolar sites (Becker et al., 1994). Xenogeneic bioimplants are materials that have

been obtained from a member of a non-human living source such as bovine (cow). In

contrast, allogeneic implants are tissues obtained from one human and then transferred

to another human while autogenous grafts involve using bone obtained from the same

5

human individual that will be receiving the graft. Alloplastic biomaterials are

synthetically made implantable bone substitutes.

The morphology and nature of a bony defect are important considerations in the

selection of the appropriate type of bone graft material and grafting technique. Bone

grafts obtained directly from the patient (autogenous) and transferred to another site are

thought to consistently provide predictable outcomes and as such, are considered the

“gold standard” in alveolar ridge augmentation (Hammack and Enneking, 1960;

Lundgren et al., 1999; Triplett and Schow, 1996).

1.3 Autogenous Bone Grafts

Branemark was the first to described the use of autogenous bone grafting to augment

deficient alveolar ridges for endosseus dental implant placement (Branemark et al.,

1975). Autogenous bone grafts can be harvested from a large number of anatomic

sites such as the illiac crest (anterior and posterior), tibia, calvarium, rib, ramus,

tuberosity and zygoma. Both gnathic and extragnathic origins for autogenic graft

material have been described for use in ridge augmentation. The choice of donor site

largely depends on the size of the defect and the type of bone desired. For large

defects or for reconstruction of areas requiring bridging of a bone defect, autogenous

grafts are frequently harvested from the anterior or posterior iliac crest (Tolman, 1995).

Alveolar ridge reconstruction typically requires less bone volume, and as a result

gnathic (intraoral) sources for bone harvest have been used extensively (Figure 3).

Autogenous grafts can be harvested in both block and particulate forms. Block grafts

can be used to reconstruct large bony defects or can be used to augment the alveolar

ridge width as an onlay graft. Particulate forms can be used for ridge augmentation

procedures such as sinus lifts and when combined with a mesh framework can also be

used to bridge small bony defects.

6

Once a graft is placed in the recipient site, complete stabilization is necessary to allow

for successful graft healing. Any movement of the graft material may result in failure of

the grafted bone to integrate with the recipient bone (Raghoebar et al., 2006). A large

number of techniques for securing the graft to the recipient site have been described in

the literature (Burger et al., 2011; Degidi et al., 2003; Louis, 2010; Quereshy et al.,

2010). Membranes, titanium and resorbable mesh have been used to stabilized

particulate grafts while fixation screws and plates have been traditionally used for block

stabilization (Beckers and Freitag, 1980; Her et al., 2012).

Figure 3. Intra-oral harvest sites of the mandible. Area A is a symphyseal graft, Area B

is a mid-body graft and Area C is a ramus graft (Kosaka et al., 2004).

Both cortical and cancellous bone can be used for ridge augmentation. Autogenous

bone grafts can be composed completely of cortical bone, cancellous bone or a

combination of both. Depending on the site of bone harvest, variable amounts of each

7

bone type will be obtained. Most intraoral harvest sites will provide primarily cortical

bone, whereas extraoral sites such as the anterior or posterior iliac crest will be a

source of both cortical and cancellous bone.

Bone quality plays an important role in implant stability both at the time of implant

placement and over the long-term in the final implant reconstruction phase. Studies

have demonstrated that cortical thickness plays a greater role in primary implant

stability than increases in implant length (Miyamoto et al., 2005). The amount of time

required for a bone graft to integrate at the recipient site is also dependent on the

quality of bone grafted. Cortical bone requires more time as it has a prolonged healing

phase when compared to cancellous bone (Marx, 2007). The quality of the bone

grafted also influences the amount of bone resorption that will occur following graft

placement. Investigators have demonstrated that cortical ramus grafts have a

volumetric resorption rate of approximately 17.5% (Proussaefs et al., 2002). Misch

(2000) suggested that despite the prolonged healing phase, cortical bone grafts have

been found to exhibit minimal resorption and consistent gains in bone volume when

compared to cancellous grafts. It is generally accepted that the use of cortical bone for

alveolar ridge grafting provides predicable results and will allow for optimal outcomes

during implant placement.

Autogenous bone can be harvested from osseous structures formed either by

membraneous (i.e. derived from mesenchyme cells) or endochondral (i.e. derived from

a cartilaginous model) ossification. Examples of membraneous harvest sites include

the calvarium and ramus, where endochondral sites commonly used include the tibia,

the iliac crest and the rib. Investigators have suggested that corticocancellous block

grafts harvested from a membranous donor site showed decreased rates of resorption

compared with those of endochondral donor sites (Misch, 1997; Smith and Abramson,

1974). Early revascularization of the membranous grafts, biochemical similarity and

increased inductive capacity are thought to be the reasoning behind such differences

(Zins and Whitaker, 1979).

8

1.4 Intraoral Graft Harvest

Numerous intraoral sites have been described in the literature for harvesting of bone in

order to augment deficient alveolar ridges (Hoppenreijs et al., 1992; Misch, 1996;

Tolstunov, 2009), most of which provide a source of high quality cortical bone. Those

sites most commonly used include, the symphysis, the maxillary tuberosity, the ramus

region and the retromolar trigone. Not withstanding the biological advantages, there are

several operative advantages of using an oral donor site. First, both bone harvest and

grafting can be accomplished in a single surgical site, and second, the procedure can

be performed with the use of local anesthesia without or with some form of sedation.

With the limited availability of operating room time, the ability to harvest bone using local

anesthesia negates the need for a general anesthetic team. Third, the close proximity

of donor and recipient sites reduces operative and anesthesia time, making it a simple

straight forward outpatient procedure. And finally, there can be a significant reduction in

post-operative morbidity given the ease of harvest from a single surgical site (Jensen

and Sindet-Pedersen, 1991; Misch, 1999; Sindet-Pedersen and Enemark, 1990).

1.5 Ramus Graft Harvest

Misch (1996) was the first to describe a technique for bone harvest from the mandible in

the area of the external oblique ridge (Figure 4). Use of the retromolar region has since

been reported for alveolar grafting (Buser et al., 1995; Misch, 1997), sinus grafting

(Wheeler et al., 1996; Wood and Moore, 1988), orthognathic surgery (Braun and

Sotereanos, 1984) and reconstruction after tumor resection (Muto and Kanazawa,

1997). Specifically, this donor area has been used extensively for mandibular

reconstruction (Muto and Kanazawa, 1997), and the lateral plate of the mandibular body

has been used in the repair of complex orbital fractures (Laskin and Edwards, 1977).

Being composed primarily of high quality cortical bone, the ramus region is a preferred

9

site of graft harvesting for ridge augmentation prior to implant placement (Jensen and

Sindet-Pedersen, 1991; Sindet-Pedersen and Enemark, 1990).

Figure 4. Outline of a graft from the external oblique ridge (Martini et al., 2001).

The anatomical limits of the ramus graft harvest include the coronoid process of the

mandible posteriorly, the molar teeth anteriorly, the inferior alveolar nerve (IAN) canal

inferiorly and the thickness of the mandible buccal-lingually. The technique for ramus

graft harvest as originally described by Misch (1996) begins with an incision in the

buccal vestibule medial to the external oblique ridge. The incision extends from the

ascending ramus to the mid-molar region, no higher than the level of the occlusal plane

to minimize chances of severing the buccal artery. Once the lateral aspect of the ramus

is exposed, a posterior vertical osteotomy is placed in the area of the ascending ramus

perpendicular to the external oblique ridge (Figure 5). The anterior osteotomy is

positioned in the body in the second molar region. The length of the graft is determined

by the requirements of the recipient site. A superior osteotomy joins both the posterior

10

and anterior cuts, and the inferior osteotomy is performed to complete this step in the

harvest procedure. Ideally the position of an inferior osteotomy should remain above

the IAN canal but can be placed below the canal provided care is taken during

procurement of the graft. Chisels are used to gently harvest the bone paying close

attention to ensure protection of the IAN (Misch, 1996).

Figure 5. Clinical photo of the outline of a ramus harvest site (arrow demarcates the

posterior vertical osteotomy).

Harvesting bone from the ramus region is not without concern, and as such, careful

case selection must be undertaken when choosing this surgical procedure. The

potential for negative outcomes range from wound dehiscence to sensory deficits and

jaw fracture (Honig, 1996; Khoury, 1999; von Arx and Kurt, 1998). A study comparing

donor site complications using different harvest sites, determined that the harvest of

bone from the ramus was associated with the lowest percentage of complications

(Scheerlinck et al., 2013). The literature suggests that injury to the inferior alveolar

nerve (IAN) is rarely reported as a complication with ramus grafts. Leong et al. (2010)

reported no incidence of nerve injury and Nkenke et al. (2002) encouraged the use of

the ramus for harvest due to “low strain on the patient and minimal complications”.

11

However, the morbidity associated with nerve injuries is significant and techniques to

reduce this risk are essential. A clinician must accurately quantify the amount of ramus

bone available for harvest and the proximity of the IAN canal.

During ramus graft harvest, nerve damage is more likely to occur when the IAN canal is

closer to the buccal cortex near the position of the osteotomies. The path of the IAN

canal is often used as a guide to the osteotomy positioning in pre-surgical planning

(Rachel et al. 1986). Anterior osteotomies are frequently made in the third or second

molar region due to the thickness of medullary bone in this area (Rachel et al. 1986).

Despite numerous descriptions of the anatomic course of the IAN, there is no true

consensus on its path and pattern of distribution in the region of the posterior mandible.

Oliver (1928) first described two typical patterns for the course of the IAN. The first

pattern consists of a single nerve trunk with branches to the individual teeth while the

second pattern is that of a plexus of branches (Oliver, 1928). Further studies classified

the IAN distribution into three separate patterns (Carter and Keen, 1971). More

recently, the first 3-dimensional (3D) reconstructions of the IAN canal position were

created (Kieser et al., 2004). These investigators found sixty-nine percent of

neurovascular bundles were positioned in the middle or lower third of the body of the

mandible. This study did not, however, investigate the proximity of the nerve canals to

the buccal cortex. In a cadaveric study, a close analysis of the IAN canal position,

demonstrated significant heterogeneity among the cadaveric mandibles (Verdugo et al.,

2009).

The amount of buccal bone found between the cortex and the IAN canal is a critical

measurement when considering harvesting bone from the ramus region. As ramus bone

availability is difficult to assess pre-operatively there is the potential risk of damage to

the IAN during harvest. Several anatomic studies have attempted to determine the

average thickness of cortical bone at various sites in the posterior mandible. Cadaveric

studies have demonstrated buccal bone thickness in the retromolar area to be

1.98±0.81 mm to 2.06±0.41 mm (Katranji et al., 2007). Rajchel et al. (1986) determined

the average distance of the IAN to the buccal cortex in the retromolar region to be

3.4±0.9 mm. The data also demonstrated that the position of the canal was closer to

the buccal cortex in the third molar region (Rajchel et al., 1986).

12

There have been a limited number of studies directed towards the quantification of bone

in the ramus region. Earlier descriptions of harvest techniques, claim a graft thickness of

4.0 mm can easily be obtained (Misch, 2000). Recent studies present significant

evidence that ramus grafts should be harvested with a thickness of no greater than 3.0

mm (Leong et al., 2010). Many believe that if the ramus width is less than 10 mm then

other harvest sites should be investigated.

One must also take into consideration the vertical height of bone above the IAN canal.

Investigators demonstrated that a vertical cut of 10 mm can be safely performed without

injury to the neurovascular bundle (Smith et al., 1991b). Others studies have since

demonstrated that 10 mm of bone height is frequently not available above the IAN canal

(Nkenke et al., 2002). Misch (2000) described a typical graft size of 15 mm in height

with lengths of up to 40 mm for areas requiring multiple implants. Cadaveric studies by

Gungormus and Yavuz (2002) found that the average length of the various arms of the

osteotomies to be 33.17 mm (superior osteotomy), 37.60 mm (inferior osteotomy), 9.15

mm (anterior osteotomy) and 22.48 mm (posterior osteotomy) (Figure 6). In a similar

study, intra-surgical quantification of graft volumes in 10 patients found the average

volume of bone to be 2.5 mL (Verdugo et al., 2009). While the potential to harvest such

large grafts may exist, there are limitations to access of the entire ascending ramus

from an intraoral approach.

13

Figure 6. Landmark measurement of a ramus graft. Sg, sigmoid notch; Ct , tip of

coronoid process; Cf, anterior point of cornoid process; Mc, mandibular canal; R, 3 mm

posterior to distal root of third molar; a, 3mm anterior to beginning of the mandibular

canal; b, point 3 mm anterior to the mandibular canal; 1, anterior side length of the

ascending ramus; 2, posterior side length of the anterior part of the ascending ramus; 3,

upper horizontal side length of the ramus; 4, lower side length of the anterior ascending

ramus (Gungormus and Yavuz, 2002)

Several technique modifications have been reported to maximize the amount of bone

harvest. These include harvesting the full height of the vertical mandible and a J-

shaped graft that extends over the oblique ridge (Figure 7) (Clavero and Lundgren,

2003; Moghadam, 2009). Modifications in harvesting techniques reported produced

ramus bone volumes comparable to those that could be harvested from the mandibular

symphysis (Clavero and Lundgren, 2003). It was suggested that unlike the symphyseal

region, the amount of bone harvested from the ramus is not directly proportional to

patient morbidity. In certain cases, the use of bilateral ramus grafts can alleviate the

14

need for larger grafts from a single site or from extra-oral sites like the ilium (Khoury,

1999; Wood and Moore, 1988).

Figure 7. Various ramus harvest techniques to maximize bone quantity (Clavero and

Lundgren, 2003).

It is difficult to accurately quantify the amount of bone available at harvest. Clinical

examination and sounding of bone combined with radiographs has historically been

used for pre-operative treatment planning. Clinical examination involves measurement

of the alveolar width and determination of osseus ridge shape and height in the region

of the proposed implants. Bone sounding using a periodontal probe or a similar device

can be used to directly measure the alveolar bone level and to measure the thickness of

the ridge through the overlying gingiva. Intra-oral radiography can be used as an

adjunct and more recently medical computed tomography (CT) and cone beam

computed tomography (CBCT) have been utilized in more complex clinical scenarios

(Lofthag-Hansen et al., 2009; Makris et al., 2010; Verdugo et al., 2009).

15

1.6 Orthopantomograms in Pre-surgical Planning

Panoramic radiography (orthopantomograpy) has been the standard for imaging in the

pre-surgical planning for intraoral bone harvest. Numata and Paatero (1948) were the

first to describe the concept of panoramic radiography involving two adjacent disks

rotating at the same speed but in opposite directions, while an x-ray beam passes

through their center of rotation. Unlike the initial technology, newer machines use a

continuously moving center of rotation, which is generally located near the lingual

surface of the mandible. Some of the more contemporary designs can now vary the

shape of the center of rotation to better conform to the patient’s anatomy. The image

itself is created from a focal trough or image plane. Structures within this layer are well

defined and those objects lying outside this layer are blurred, magnified and/or

foreshortened. The position of any desired anatomic structure in relation to the focal

trough is critical. When landmarks of interest are positioned lingual to the focal tough,

they will appear elongated. Alternatively, structures that lie buccal to this image layer

will appear smaller or narrower. Improper patient positioning can lead to distortion of

the area of interest on the final images (White and Pharoah, 2009).

The introduction of digital panoramic radiography offers several advantages to

conventional techniques. These include faster processing time, elimination of a

darkroom with chemical processing, and a variety of image manipulation tools. The

digital images are captured using either a charge-coupled device (CCD) or a phosphor

imaging plate.

Charge-coupled devices capture the image in an incremental manner that converts the

analog x-ray signal to a digital one, which is delivered to a computer. As the x-rays

strike the detector, x-ray information is converted to visible light in a scintillator, and this

light is collected by a fibre optic plate which, in turn, converts the light to electrons. The

electrons are then captured by the charge-coupled device itself, and this information is

relayed to a computer. Digital images are composed of elements termed pixels that are

arranged in a two-dimensional grid or matrix. Each pixel has a dimension, an intensity

16

value and a specific coordinate that identifies its location in the matrix. The absorption of

electrons by a CCD pixel element generates a small amount of electrical energy

(quantization). The value assigned during quantization represents a shade of gray that

is displayed on the computer screen (Angelopoulos et al., 2008). Contemporary digital

radiographic images can have an intensity value of 12 or more (i.e. 212 or 4096 shades

of gray) (Hatcher and Aboudara, 2004).

Similar to film based panoramic imaging systems, storage phosphor imaging systems

use standard cassettes without intensifying screens, but the traditional film is replaced

with a phosphor plate. The x-ray energy is captured by a phosphor layer, and a latent

image is created similar to conventional films (Hildebolt et al., 2000). A laser scans the

plate, and the energy stored is released. An analog-to-digital converter produces an

electrical signal and assigns a number to the intensity of that signal (Angelopoulos et

al., 2008). Again, this number represents pixel intensity and an image in grey scale is

created (Parks and Williamson, 2002). The scanning process is slightly more time

consuming compared to the charge-coupled device, and the phosphor plates require

exposure to visible light after image acquisition to erase the latent image and prepare

them for reuse.

Orthopantomography while used extensively for pre-surgical planning, can only provide

two-dimensional (2D) anatomical information. Determination of bone quantity using

orthopantomograms with radiologic markers has been used historically (Bartling et al.,

1999). Buccal-lingual width of the alveolar ridge and buccal-lingual positioning of the

IAN canal cannot be assessed (Schwarz et al., 1987). When horizontal distances are

critical for treatment decisions, supplementary intraoral radiographs will need to be

obtained. Despite these limitations, orthopantomography has been used for measuring

the height of residual bone (Tal and Moses, 1991; Vazquez et al., 2008) and after

adjusting for patient positioning, measurements are deemed sufficiently accurate for the

determination of vertical dimensions (Frei et al., 2004).

In the pre-operative stages, orthopantomograms have been fundamental in assessing

the location of certain landmarks specifically, the IAN and the relationship of the nerve

to the roots of teeth and the alveolar ridge crest. Clinicians routinely use the findings on

17

these images to identify cases with the potential for risk of injury to the IAN. Some have

suggested that panoramic images may be more reliable in excluding a surgical risk in

the absence of radiographic evidence than in confirming the location of a particular

landmark (Atieh, 2010).

The diagnostic accuracy and validity for landmark identification of 2D films can be

underestimated due to projection errors and overlapping of structures (Elefteriadis and

Athanasiou, 1996). Superimpositions of bilateral landmarks may detract from the true

anatomy of the patient (Dudhia et al., 2011). Images can be complicated by overlap of

soft tissues, adjacent air spaces, and by “ghost” images of the spine and mandible

(Rushton and Horner, 1996).

Magnification of objects can occur in both horizontal and vertical dimensions with

orthopantomography. The magnification varies with both position and object depth

(Tronje et al, 1985), and the degree of distortion makes direct measurements inaccurate

(Tammisalo et al., 1992). More specifically, these inherent distortions can lead to

considerable variability in the dimensions and magnitude of angular measurements

(Dudhia et al., 2011). Traditional orthopantomograms have shown extreme variability

with errors in measurement of up to 7.5 mm or 27% distortion (Sonic et al, 1994). These

distortions have been confirmed by other investigators who found measurements

performed using an orthopantomogram are overestimated (Georgescu et al., 2010).

Evaluation of accuracy using cadaveric skulls determined magnifications of 18 to 21% in

all vertical measures (Larheim and Svanaes, 1986), with magnification ratios of 1.09 to

1.28 (Yim et al., 2011). Significant differences in magnification occur depending on the

type of equipment used and the location of the desired landmarks during image

acquisition. Adopting a standard magnification ratio for evaluation during treatment

planning is difficult and should be avoided (Yim et al, 2011).

It has been suggested that safety zones of 2 to 6 mm will assist clinicians to overcome

misrepresentations regarding the exact position of a landmark (Worthington, 2004). The

use of such wide margins of error are not clinically acceptable, and could demonstrate a

lack of precision of the instrument. The shortcomings of 2D imaging, even when

combined with clinical qualitative interpretations can lead to suboptimal treatment

18

outcomes. As a result, in more complex surgical procedures the findings on 2D imaging

may warrant further investigation with 3D imaging techniques if there are questionable

issues that arise (Monaco et al., 2004). The application of a more discriminating and

effective imaging modality may be necessary when complex treatment planning

requires further precision (Mah et al., 2003).

1.7 Computed Tomography

Advances in computed tomographic imaging have made it a more important modality for

dentists. CT has always been an essential diagnostic tool in maxillofacial surgery and is

now revolutionizing pre-surgical treatment planning. The popularity in craniofacial

imaging has increased dramatically since the development of higher resolution third

generation helical CT scanners. In basic terms, a CT scanner consists of a fan shaped

x-ray beam which passes through a subject to a series of detectors located on the

opposite side. Images are captured electronically as the linear array of solid-state

detectors and the x-ray source rotate about the patient. The detectors measure the

number of photons exiting the patient and use this information to formulate an image.

In earlier designs, both the x-ray tube and the detector row formed a continuous ring

around the supine patient, who was slowly moved forward a few millimeters at the

completion of each rotation. Newer technology introduced in 1989 allowed for scanners

to acquire image data continuously in a helical or spiral manner. Furthermore,

contemporary systems incorporate multiple detector rows (4, 8, 12, 16, 32 and 64) so

that multiple slices can be acquired with a single rotation of the radiation source. The

photons recorded by the detectors represent the absorption characteristics of all the

elements of the patient in the path of the beam. Computer algorithms use this

information to create cross sectional images.

Image reconstruction is complex and a large number of one-dimensional projections

create a single image. The CT image is a matrix of 3D blocks called voxels, which are

19

the smallest elements of the 3D image volume. A voxel can be compared with the pixel

of 2D images, but with the added dimension of depth. The size of each voxel is

determined by its height, width and thickness, and each pixel is assigned a Hounsfield

number or unit which, represents a tissue’s ability to attenuate the x-ray beam when

compared with water. Medical CT voxels are anisotropic in dimension, meaning their in-

and out-of-plane dimensions are different, the latter being determined by the thickness

of adjacent slices. Today, helical CT scanners are typically standard of care but more

recently multidetector helical CT scanners with many detector rows have been

introduced. With the newest generation of machines, images are captured quickly with

a reduction in patient acquisition time and motion artifact, but at an increased radiation

dose cost to the patient.

There are several disadvantages to this technology, namely limited access (because

these systems are found in hospital settings) and increased radiation dose to the

patient. Radiation doses from medical grade CT scanners are in the range of 830 to

1263 micro-Sieverts, µSv (Mah et al., 2003). When compared to conventional

orthopantomogram radiographs with exposures in the range of 14 to 24 µSv, exposure

doses are significantly higher (Ludlow et al., 2008; Mah et al., 2003). Alternative CT

protocols have been developed in an attempt to reduce exposure without significant

loss of image quality (Hagtvedt et al., 2003).

The advantages of CT over conventional projection radiography include higher contrast

images and the elimination of superimposition of structures in an area of interest. Also,

the ability to select specific slices or regions of an image and the ability to view

landmarks in three dimensions provides more diagnostic information and improved

planning capabilities. Medical grade CT also has the option of the use of contrast

agents to improve visualization of soft tissues and soft tissues pathology.

20

1.8 Cone Beam Computed Tomography

Although developed in late 1990s, cone beam computed tomography has only recently

become available for use by the oral and maxillofacial surgeon. Originally developed for

angiography in 1982, cone beam systems have subsequently been adapted for use in

maxillofacial imaging. Use of this technology in the maxillofacial region was first

reported by Mozzo et al. (1998), and the first commercially available CBCT system was

the NewTom 9000. Since this time, applications in the maxillofacial region have

expanded to include, the evaluation of pathological lesions, orthognathic surgery

planning, implant pre-surgical planning, guided implant surgery and pre-prosthetic

grafting.

Guidelines for preoperative radiographic planning for implant placement, were published

by the American Academy of Oral and Maxillofacial Radiology in 2000 and 2012, and by

the European Association for Osseointegration (Harris et al., 2002; Harris et al., 2012).

CBCT was a relatively new and unexplored technology, and as such these guidelines

did not include the use of this radiographic technique. More recently, investigations

have suggested that CBCT should be considered as a “standard of practice” in implant

pre-surgical treatment planning (Lofthag-Hansen et al., 2009).

1.9 Cone Beam Computed Tomography Technology

CBCT systems can be divided into those capable of imaging a large portion of the

maxillofacial and cranial complex and those that acquire image volumes less that the

size of the entire head. The former are termed large field of view (FOV) systems while

the latter are termed small FOV systems. Hybrid digital panoramic/CBCT units are

available with separate sensors for both large and small FOV systems. Some systems

21

can even provide a 2D digital cephalogram option. The remainder of this review will

primarily focus on a discussion of the large FOV craniofacial machines.

CBCT 3D volumes can be acquired with the patient in a standing, sitting or supine

position. Seated units are the most popular, being comparable in size to a conventional

orthopantomogram radiographic machine, and can similarly be installed in a clinical

office setting (Figure 9). Images are captured using a rotating solid-state flat panel or

image intensifier detector and an x-ray source that are mounted in parallel on a rotating

gantry. The CBCT x-ray beam produces a cone-shaped beam with a circular projection

that falls onto the detector. The cone shaped x-ray beam and the digital detector move

synchronously and in the same direction (Figure 10). Cone beam technology utilizes

less electrical energy and uses the x-ray energy much more efficiently (Sukovic, 2003).

The time for image acquisition is rapid with an exposure as low as 9.6 seconds.

Figure 8. A CBCT machine for an office setting (IlumaTM GE Healthcare).

22

Figure 9. CBCT fan shaped beam (Kau et al., 2005).

Unlike medical CT scanners, CBCT systems reconstruct an entire image volume from a

single rotation of the gantry and not from individual slices (Figure 11). After a complete

revolution around the subject, data can be collected either for the entire maxillofacial

region or for limited areas of interest (Danforth et al., 2003). This technology allows for a

high spatial resolution and a field of view in excess of 40 cm for medical CBCT

purposes and up to 23 cm in diameter anterior-posterior and mesio-distally for dental

purposes (Jaffray and Siewerdsen, 2000).

A single rotation produces 100 to 600 individual frames each with more than one million

pixels and up to 16 bits of data assigned to each pixel. The information obtained is then

used in a process called primary reconstruction to create a volumetric data set. The

initial images are corrected for inherent pixel imperfections and uneven exposure, and

then formatted on a computer. The computer uses the voxel data to reconstruct the

X-Ray Source

Detector

23

volume using a rendering algorithm (Scarfe and Farman, 2008). All available voxels are

compiled into a single volume for visualization.

Figure 10. X-ray beam projection scheme comparing acquisition geometry of

conventional or “fan” beam (right) and “cone” beam (left) imaging geometry and

resultant image production(Scarfe and Farman, 2008).

CBCT voxels are isotropic; that is, their dimensions are equal in the x, y and z planes.

Theoretically, isotropic voxels should yield more accurate measurements in all

dimensions (Scarfe et al., 2006). Some CBCT voxel sizes may be as low as 0.076

mm. In contrast, most medical CT units have voxel sizes of between 0.5 and 1 mm

(Pinsky et al., 2006). The use of megapixel solid-state detectors provides sub millimeter

pixel resolution. Literature exists suggesting that the level of resolution of CBCT images

is higher than that of medical CT (Naitoh et al., 2004). However, a recent investigation

24

found limited differences between both imaging modalities for the depiction of fine

anatomical structures in the mandible (Naitoh et al., 2010).

Patient radiation doses vary between different types of CBCT units, but in general, are

lower than those for medical CT (Tsiklakis et al., 2005). It should be noted that

adjustments in collimation, beam geometry and most importantly FOV size can all affect

the absorbed dose distribution, and hence, the overall radiation dose to the patient. The

effective dose can vary even within the same machine depending on the technique and

parameters that are used. Current guidelines regarding the set up of radiation detectors

and the geometrical calculation of true dose exposure have yet to be agreed upon (De

Vos et al., 2009).

The effective radiation dose from a CBCT scan of the maxillomandibular volume has

been measured to be in the range of 53 to 1073 µSv, depending on the type, model and

imaging protocol used (Jadu et al., 2010). When the radiation dose provided by CBCT is

compared to those of panoramic machines, exposure times can be up to 44 times

greater. However, when compared with medical CT scans for common oral and

maxillofacial radiographic imaging tasks, CBCT has been recommended as a dose-

sparing alternative technique. When one compares the effective dose for a medium

FOV dental CBCT scan, the medical grade CT provide a 1.5 to 12.3 times greater dose

(Ludlow and Ivanovic, 2008). This makes the diagnostic benefit of a medical grade CT a

trade off, as the patient exposure doses can be higher.

In 2007, the ICRP updated the method for calculating effective dose on the basis of the

latest available scientific information regarding the biology and physics of radiation

exposure (Streffer, 2007). Utilizing the 2007 ICRP guidelines, researchers assessed

the risk associated with dental radiography and found it to be 32 to 422% higher than

that estimated according to the 1990 ICRP guidelines (Ludlow et al., 2008). The results

reflect newly available cancer incidence and mortality data, whereas the 1990 ICRP

guidelines were based solely upon mortality data (Ludlow et al., 2008).

Current guidelines published by major scientific societies recommend compliance with

principles of justification and optimization in the prescription of any radiographic imaging

25

study (Harris et al., 2012; Ludlow et al., 2006). Finding a balance between the ideal

diagnostic modality and reduction of patient risk can be challenging for the clinician in

complex surgical cases.

1.11 Limitations of Cone Beam Computed Tomography

While there are many ideal features of CBCT images, there are still limitations to the

technology that can compromise diagnostic image quality (Chan et al., 2010).

Investigators have found that medical CT technology is superior to CBCT in imaging

cortical bone (Loubele et al., 2007). The beam projection geometry of both CBCT and

medical CT combined with the image reconstruction processes can produce varying

types of artifacts. Those artifacts most commonly in found in these 3D imaging

techniques can result from partial volume averaging, undersampling, cone beam effect

and scatter.

Partial volume averaging occurs when voxel size is greater than the spatial resolution of

the object to be imaged. The voxels that are created are not representative of the

tissue, but represent a weighted average of the different CT values contained within the

area (Scarfe and Farman, 2008). This can lead to two or more adjacent tissues of

differing attenuation being averaged together to produce a single voxel value. Definition

between these areas becomes blurred and indiscernible. Partial volume averaging

artifacts often occur in surfaces that are rapidly changing such as along a bony edge or

margin.

Undersampling occurs when too little data is provided for reconstruction leading to the

creation of images with poor signal-to-noise ratios (Scarfe and Farman, 2008). Although

this effect may not severely degrade the images, when resolution of fine detail is

important, undersampling artifacts may negatively impact image quality. By maintaining

the number of basis projection images, this effect can be reduced (Schulze et al., 2004)

The cone beam effect is a potential source of image artifact especially in the peripheral

26

portions of the scan volume. As the number of sections acquired per rotation increases,

the x-ray beam becomes more cone-shaped (Barrett and Keat, 2004). The total amount

of information for peripheral structures is reduced because the outer row detector pixels

record less attenuation. More information is recorded for objects projected onto the

more central detector pixels. The cone beam effect will degrade image quality and

results in image distortion, streaking artifacts, and greater peripheral noise (Schulze et

al., 2004). Clinically, it can be reduced by positioning the region of interest adjacent to

the horizontal plane of the x-ray beam and also collimation of the beam to an

appropriate field of view.

Due to the nature of the cone shaped beam, a potentially large area of interest is

irradiated and this will lead to scattered radiation. The amount of scatter depends on

the size of the field and thickness of the object. The scatter radiation is still processed

by the detector but does not represent actual attenuation of the beam. Scatter creates

noise that degrades the image quality and reduces contrast. Compared with medical

CT, CBCT can have up to 15 times higher scatter levels (Siewerdsen and Jaffray,

2000). The presence of certain metallic structures can affect the quality of the final

CBCT image, leading to increased scatter and streaking artifacts across the film. These

artifacts represent more than a nuisance in that they can affect how a scanner interprets

and reconstructs the surrounding data (Zhang et al., 2007).

1.10 Cone Beam Computed Tomography and Implant Therapy

A distinct advantage of CBCT technology is the ability to plan implant therapy virtually,

with the use of specifically designed 3D software. In the last decade, several computer

based software programs for pre-surgical implant planning have been developed that

utilize images derived from CBCT. Using these programs, clinicians have the ability to

select and virtually “try in” implants of different diameters and lengths in order to select

the implant best suited for a given location (Chan et al., 2010; Jeffcoat et al., 1991).

Using these virtual images, implants can be displaced, rotated and tilted on any axis,

27

and their positions can then be evaluated in a 3-dimensional space. This pre-operative

planning allows for optimal 3D diagnosis and accurate transfer of virtual implant

positions to the corresponding anatomical sites in the patient (Chan et al., 2010). In

2000, rapid prototype medical modeling manufactured from CBCT data became

available to the dental profession. With this advancement, information from CBCT

images could be transferred directly to the patient via a prefabricated surgical stent

(Figure 8). It has been suggested that CBCT guided surgery is superior to non-CT

guided surgery due to its potential to eliminate errors with manual placement (Veyre-

Goulet et al., 2008). The use of software systems with CBCT imaging has become one

of the primary tools used for dental pre-surgical implant treatment planning. Not only

can one select for a particular implant size and length, but alveolar ridge height and

width as well as the proximity of adjacent anatomic structures can be determined.

Areas of inadequate ridge height or width can be identified and then considered for

ridge augmentation procedures.

Having the ability to locate vital anatomic structures in the proximity of the surgical site

can reduce the risk of injury to those structures during the operative procedure.

Specifically, the identification and localization of the IAN canal has been the focus of a

multitude of radiographic studies. A recent cadaveric study by Kamburoglu et al.

(2009), determined that measurements taken from CBCT images were very accurate

when compared with those found when digital calipers were used to measure the actual

dimensions of an anatomic specimen. It was found that CBCT was a useful

preoperative diagnostic tool for identification of the neurovascular bundle (Kamburoglu

et al., 2009b).

According to Widmann and Bale (2006), long-term clinical studies are necessary to

confirm the value of this technology and to justify the additional radiation dose, effort

and costs. Other studies have found that CT guided surgery is not always accurate as

one might expect, with differences of 1 to 2 mm between the planned and actual

placement positions being reported (Drago et al., 2011). Consequently, clinicians must

still be careful to use their expertise and clinical skills when placing implants utilizing 3D

technology and adjunctive computer software.

28

Figure 11. Sequence of CBCT image acquisition to surgical guide fabrication in implant

pre-surgical planning (Jeffcoat et al., 1991).

1.12 Cone Beam Computed Tomography in Clinical Practice

Since the introduction of CBCT in clinical practice, bias still exists towards conventional

films due to the fact that most clinicians are well acquainted with the use of these

imaging methods. Accuracy in the determination of bone thickness and identification of

anatomic landmarks using CBCT is still not certain. When used in combination,

CBCT  Image  Acquisition  

Regions  of  interest  De7ined  

Virtual  Implant  Placement  (Dimension,  angulation,  position)  

Treatment  Plan  Finalized  

Surgical  Guide  Fabrication  and  Implant  Placement  

29

traditional 2D images and CBCT together provide increased information and improved

detail.

The accuracy of landmark identification is essential to treatment planning and for the

prevention of unnecessary morbidity during routine surgical procedures. Specifically, a

particular craniofacial landmark must be easily identifiable with a high degree of

precision and accuracy. Landmark identification and concise measurements will be

made difficult if the image quality and contrast are poor. The variables that have

significant influence on the quality of a CBCT image include voxel size, number of gray

levels, signal, and noise. In general, the best quality image is composed of small

voxels, a large number of gray levels, high signal, and low noise (Hatcher, 2012). In

vivo, patient related factors that can reduce image quality include increased soft tissue

thickness and patient movement (Pinsky et al., 2006). As a result of these potential

inherent limitations and patient related factors, the routine use of CBCT technology to

improve landmark identification has yet to be determined and its utilization for everyday

pre-surgical planning as a replacement to traditional 2D films is promising but still ill

defined.

The literature has documented numerous studies comparing CBCT and traditional

radiographic techniques but few compare these modalities in the pre-surgical planning

phase. For the harvesting of a ramus graft and in dental implant planning, identification

of the IAN is critical and the use of radiographic adjuncts such as CBCT are essential to

optimize results. Pawelik et al. (2002) found CBCT images offered a significantly

clearer perception of both the spatial resolution and delineation of the IAN canal when

compared to conventional 2D panoramic images. However, the conventional panoramic

images did score higher overall in visual grading scores of all anatomic landmarks

studied (Pawelzik et al., 2002). In a similar investigation, CBCT images were found to

be superior to panoramic images for identification of the IAN canal (Angelopoulos et al.,

2008) while noting that the posterior third of the canal was always better depicted

irrespective of the imaging modality used (Angelopoulos et al., 2008). A study

comparing CBCT with digital orthopantomograms for linear measures of root resorption,

found panoramic images to be less reliable than CBCT in terms of measurement

accuracy and agreement between observers (Alqerban et al., 2009). Similarly, the

30

accuracy of determination of linear root angulation was found to be less reliable on

digital orthopanotomograms when compared to CBCT images, particularly in the

anterior maxilla (Peck et al., 2007).

Given the number and multiplicity of issues, CBCT images may not necessarily be an

ideal replacement for orthopantomogram images in pre-surgical dental implant or graft

harvest planning. Only more recently have studies investigated whether CBCT

measures accurately reflect clinical findings in a given area of interest. The literature in

this area has demonstrated a wide range of results. CBCT has been shown to be

accurate to within 0.1 to 0.2 mm for measures over long distances between anatomic

landmarks, however, the literature is equivocal for linear accuracy of objects in close

proximity (Molen, 2010). Lascala et al. (2004) used a series of metal spherical markers

to identify measured distances. They found that CBCT imaging underestimated

anatomic linear measurements. In a similar study, Fuhrmann et al. (1995) found that

CBCT overestimated measurements by 0.2 mm. This overestimation increased to 2.2

mm for all intra-bony measurements (Fuhrmann et al., 1995). Conversely, Hilgers et al.

(2005) found no statistical difference between iCat CBCT measurements and those

made on anatomic specimens, and concluded that 3D reconstructions from CBCT

acquired images provided accurate and reliable linear measurements. Similarly, data

comparisons by Baumgaertel et al. (2009) demonstrated reliable and accurate results

with CBCT images having a slight tendency to underestimate the actual measures.

Several studies examining the accuracy of measurements between reference points

comparing CBCT machines and an anatomic gold standard, found mean deviations to

be insignificant (Kamburoglu et al., 2009b; Stratemann et al., 2008). Another

investigation assessed and quantified the accuracy of linear measurements provided by

CBCT with those of dry skulls. This report concluded that CBCT images provided

reliable information and could be used for pre-operative implant planning in the posterior

maxilla (Veyre-Goulet et al., 2008). The wide range of results documented when direct

comparisons are made between measures taken from anatomic specimens and those

taken from CBCT images, suggests that further inquires are warranted to better define

the limitations of this technology.

31

Chapter 2

Statement of Problem

It has been stated that the use of CBCT may provide improved surgical

outcomes by converting a clinical scenario of suspected high surgical risk to one with a

low likelihood of complications (Susarla and Dodson, 2007). By improving our

understanding and therefore reducing the risk of injury to vital anatomic structures, we

should be able to improve patient safety and acceptance rates of complicated surgical

procedures for both patients, and the treating clinicians. Pre-operative evaluation prior

to ramus graft harvest requires intimate knowledge of adjacent and related anatomic

structures, specifically the location of IAN, to reduce post-operative morbidity. The

emergence of CBCT for craniofacial imaging has lead to the evaluation of this

technology when considering bone graft harvest from this anatomic region. The ability of

this modality to further reduce patient risk and therefore improve outcomes has yet to be

determined and requires investigations comparing it to conventional radiographic

techniques. Determination of the utility of CBCT as an alternative to or replacement of

traditional radiography with respect to accuracy for pre-surgical planning was the

purpose of this investigation.

32

Chapter 3

Objectives and Hypotheses

3.1 Objectives

1. To establish anatomic measurements for buccal bone thickness in the molar and

retromolar region of the mandible and to use this information to determine

average bone thickness available for harvesting.

2. To determine the position of the inferior alveolar nerve canal both in a superior-

inferior and buccal-lingual direction.

3. To compare measurements made on CBCT and digital orthopantomogram

images with those of anatomic specimens with respect to the following:

mandibular height, mandibular width, distance from the alveolar crest to the

inferior alveolar nerve canal and the distance from the buccal cortex to the

inferior alveolar nerve canal.

4. To determine the accuracy of measurements made from CBCT and digital

orthopantomograms with those of anatomic specimens.

33

3.2 Hypotheses

Hypothesis #1

H0: The thickness of buccal bone does not vary with location in the posterior mandible.

H1: The greatest thickness of buccal bone can be found in the retromolar region of the

posterior mandible.

Hypothesis #2

H0: The position of the IAN nerve in a superior-inferior direction does not correlate with

a specific thickness of buccal bone.

H1: A superiorly positioned IAN canal coincides with a greater thickness of buccal bone.

Hypothesis #3

H0: Measurements made on digital orthopantomograms accurately reflect those made

on anatomic specimens.

H1: Measurements made on digital orthopantomograms overestimate the true anatomic

measures.

34

Hypothesis #4

H0: Measurements made on CBCT images accurately reflect those made on anatomic

specimens.

H1: Measurements made on CBCT images underestimate the true anatomic gold

measures.

35

Chapter 4

Significance

Typically an appreciation of the anatomic relationships in the posterior mandible

is established in the pre-surgical planning phase using various radiographic and clinical

techniques. A better understanding of the anatomic relationships of the IAN canal

within the posterior aspect of the mandible and the thickness of buccal bone will help to

establish a ramus graft harvest design that maximizes bone quantity while at the same

time reduces the risk to vital anatomic structures. By studying the relationship of the

superior-inferior position of the IAN canal and the associated quantity of buccal bone

may allow clinicians to use the position of the canal on an orthopantomogram image to

deduce the quantity of buccal bone available for harvest. The precision and accuracy of

linear measurements using both CBCT and digital orthopantomograms were compared

to cadaveric anatomic specimens to ascertain the most suitable diagnostic technique for

pre-operative surgical treatment planning.

36

Chapter 5

Materials and Methods

5.1 Anatomic Specimen Preparation

This study was conducted according to the ethical principles on human

experimentation and was approved by the University of Toronto Health Sciences

Research Ethics Board. Twenty-nine formalin fixed mandibles from twenty-nine human

cadavers were obtained from the Division of Anatomy at the University of Toronto. The

mandibles were dissected free of all soft tissues. Of the twenty-nine mandibles, six

mandibles were edentulous and twenty-three were dentate. No demographic

information was obtained on the human remains and the cadavers were not identified

by age, sex or ethnicity.

Twenty-four gauge straight stainless steel fiducial markers were milled to precise

lengths of 10 mm. A slot was prepared in the buccal cortical bone on the right and left

sides of the mandibles using a #4 round bur and a surgical handpiece. Each 10 mm

metal marker was attached with orthodontic wax to the following locations: 1) ascending

ramus; 2) site of the mandibular second molar or approximate site in the edentulous

mandibles; 3) site of mandibular first molar or approximate site in the edentulous

mandibles (Figure 12). The markers were oriented perpendicular to the mandibular

plane. Each marker was labeled sequentially A to F beginning on the right side of

mandible with marker A corresponding to the location of the ascending ramus on the

right side and marker F corresponding to the location of the ascending ramus on the left

side.

37

Figure 12. Cadaveric mandible with fiducial markers at the first molar, second molar and

ascending ramus sites.

5.2 Cone Beam Computed Tomography Images

Cone beam computed tomograms were obtained using the CB MercuRay system

(Hitachi Medical Corporation, Tokyo, Japan). Two technologists following a

standardized protocol with parameter settings of a 9” field of view, 80 kVp and 10 mA

made all images. Each mandible was placed in a 6” x 12” plexiglass cylinder filled with

water to simulate attenuation by the soft tissues normally present. Each mandible was

aligned with respect to the mid-sagittal positioning laser of the CBCT unit. A lateral

anterior-posterior positioning laser was used to adjust table height till the laser was

centered on the mid-ramus region. Images were captured using a flat panal detector.

Primary reconstruction of the data using 1 mm axial slices was performed automatically

after acquisition taking approximately 60 s. CB Works v2 software program (Cybermed,

Seoul, Korea) was used to reconstruct the cone beam volumes.

38

5.3 Orthopantomography Images

The orthopantomogram images were made using a Kodak 9000 3D Imaging Unit

(Carestream Kodak, Rochester, NY, USA). The occlusal plane of the mandible was

oriented horizontally, and the midline was centered corresponding to the midsagittal

laser of the unit. The mandibles were held in place with a prefabricated jig to ensure

reproducible positioning between specimens and no movement during the exposure.

The images were acquired at 62 kVp, 2 mA with a 15 s exposure time and were

captured digitally with a charged coupled device.

5.4 Anatomic Measurements

After the completion of all image acquisitions, each mandible was sectioned vertically

using a band saw (Butcher Boy, Model 8A20, Ayshire Scotland, UK) in the Division of

Anatomy at the University of Toronto. Sectioning was performed at each of the fiducial

markers dividing the mandibles into 6 posterior segments and one anterior segment.

The following linear distances were measured on the medial surfaces of the vertical cuts

in millimeters: 1) distance from the buccal cortex to the buccal cortex of the IAN canal;

2) horizontal width of alveolar crest buccal to lingual at the widest point; 3) vertical

dimension from alveolar crest to inferior cortex; and 4) distance from alveolar crest to

superior cortex of the IAN canal (Figure 13). All measurements were made by six

independent observers (three senior dental students and three senior oral surgery

residents) using a high precision digital microcaliper, calibrated to the nearest 0.1 mm

(Salvin Dental, Charlotte, NC, U.S.A.). To establish the intraobserver agreement, one

observer performed the anatomic measurements twice, one week apart.

39

Figure 13. A cross-section of a cadaveric mandible is displayed on the left. The

schematic on the right demonstrates the four measurements that were made at each

section: the distance from the buccal cortex to IAN canal (black), mandibular width

(red), mandibular height( yellow) and the distance from the alveolar crest to the IAN

canal (blue).

5.5 Cone Beam Computed Tomography Measurements

The cone beam CT images were uploaded into E-Film 2 X 2.1.2 (Merge Healthcare,

Chicago, IL,USA) and then subsequently transferred into the program CB Works 2.1.2.

Coronal reconstructions were used to perform four measurements at the site of each of

the six fiducial markers. Six independent observers (three senior dental students and

three senior oral surgery residents) measured 4 linear distances in millimeters: 1)

40

distance from the buccal cortex to the cortex of the IAN canal; 2) horizontal width of

alveolar crest buccal to lingual at the widest point; 3) vertical dimension from alveolar

crest to inferior cortex; and 4) distance from alveolar crest to the superior cortex of the

IAN canal (Figure 14). All measurements were made to the nearest 0.1 mm. To test for

intra-observer agreement, one observer performed all measurements twice, one week

apart.

Each investigator was trained to manipulate the software and perform measurements

using the software enhancement tools according to their own preference. Training was

performed until the examiner felt comfortable with the use of the program. All

measurements were performed under standardized conditions in a radiology reading

room with dimmed light using Dell (Dell Corporation, Round Rock, TX, USA) 21 and 23

inch Ultrasharp monitors. All of the data collected was complied into an Excel

spreadsheet (Microsoft, Palo Alto, CA, USA) for analysis.

41

Figure 14. A cross-section from a CBCT image is displayed on the left. The schematic

on the right demonstrates the measurements made at each of the sections: the distance

of the buccal cortex to the IAN canal (black), the mandibular width (red), the mandibular

height (yellow) and the distance from the alveolar crest to the IAN canal (blue).

5.6 Orthopantomogram Measurements

The digital images were uploaded into Axium Dental Software (Exan, Coquitlam, BC).

At the site of each fiducial marker, six independent investigators (three senior dental

students and three senior oral surgery residents) measured 3 linear distances in

millimeters: 1) distance from alveolar crest to superior cortex of IAN canal; 2) distance

from the alveolar crest to inferior cortex; and 3) length of the fiducial marker (Figure 15).

All measurements were made parallel to the fiducial markers, to the nearest 0.1 mm.

To test for intra-observer agreement, one observer performed all measurements twice,

one week apart. Each investigator was trained to manipulate the software and perform

42

the measurements using the software enhancement tools according to their own

preference. Training was performed until the examiner felt comfortable with the use of

the program. All measurements were performed under standardized conditions in a

radiology reading room with dimmed light using Dell 21 and 23-inch Ultrasharp

monitors. All of the data collected was complied into an Excel Spreadsheet (Microsoft,

Palo Alto, CA, USA) for analysis.

Figure 15. An orthopantomogram image. The yellow line represents the measurement

from the alveolar crest to the inferior cortex and the blue line represents the

measurement from the alveolar crest to the superior cortex of IAN canal. Both

measurements were made at each pin location.

43

5.7 Data Analysis

The mean distance from the buccal cortex to the IAN canal and the alveolar crest to the

IAN canal was calculated from the anatomic measurements at each of the fiducial

markers. The mean height of the mandible from alveolar crest to inferior cortex and

average width buccal to lingual was calculated from the anatomic measurements at

each fiducial marker site. Intra-examiner reliability was assessed using intra-class

correlation coefficients for all repeated measures. Systematic errors of the inter-

examiner analysis were assessed using paired t-tests. Differences between groups

were calculated for each of the measures. A level of significance was set to P<0.05.

The ratio of measured length to actual length for each fiducial marker was used to

calculate vertical magnification at each site. This magnification factor was then applied

to the panoramic linear measurements at each site to establish the actual length in

millimeters.

The data was analyzed using SAS statistical software (SAS 9.13, SAS Institute, Cary,

NC, USA). Repeated measures models were used to compare mean percent error

between the data of interest. Mixed models were used to analyze the data. All outcomes

were log transformed to meet the assumptions of the models (log percent error).

The outcome variables were defined as percent error, as one of the objectives of this

study was not only in the actual measurements of the mandibles (anatomic measures)

but also in the error (or variance) in measurements made by CBCT and the

orthopantomograms. For each of the four measurements, the true lengths were defined

as the overall average of all of the anatomical measurements. The percent error was

the deviation of a single measurement from the true anatomical measure expressed as

a percentage.

For measurement i then,

Percent error i = ((xi – x ¯ ) / x ¯ )

44

This provided the percent error in measurements made by CBCT and

orthopantomograms, and this result could be equated to the accuracy of the

measurements.

45

Chapter 6

Results

6.1 Anatomic data

The measurements collected from the cadaveric mandibles were used to

calculate anatomic averages for each of the measured distances. The average distance

of the IAN canal to the alveolar crest, buccal cortex to IAN canal, average mandibular

width and mandibular height in the following areas; ascending ramus, second molar and

first molar sites, are displayed in Table 1.

46

Anatomic Location

Alveolar Crest to IAN Canal (mm)

Buccal Cortex to IAN Canal

(mm)

Mandibular Width (mm)

Mandibular Height (mm)

Ascending Ramus

13.7±3.4 3.2±1.5 10.4±2.2 31.5±4.4

Site of Second Molar

13.2±5.3 5.3±1.7 13.1±2.5 22.8±5.1

Site of First Molar

11.9±4.8 5.6±1.6 12.1±2.6 20.6±6.5

Table 1. Cadaveric measurements in millimeters of the mean distances and standard

deviations from alveolar crest to IAN canal, buccal cortex to IAN canal, average

mandible width and height at the following anatomic locations: ascending ramus,

second molar and first molar sites for all of the cadaveric mandibles.

The greatest thickness of buccal bone is located in the area of the first molar while in

the area of the ascending ramus the amount of bone buccal to the IAN canal is the

narrowest. Irrespective of the location in the posterior mandible, the IAN canal on

average can be found approximately 12.3±4.5 mm from the height of the alveolar crest.

The mandibular width ranges from 10.4±2.2 to 13.1±2.5mm. The mandibular width is

greatest in the area of the second molar and is 13.1±2.5 mm. The mandibular height

ranges from 20.6±6.5 mm in the first molar region to 31.5±4.4 mm in the area of the

ascending ramus. The overall average height of the mandible is 24± 5.3mm.

47

Alveolar Crest to IAN Canal Buccal Cortex to IAN Canal

0-10 mm 4.2±1.5

11-15 mm 4.9±1.6

16-20 mm 4.8±1.4

Table 2. Cadaveric measurements in millimeters. The mean distance and standard

deviations from the buccal cortex to the IAN canal at various vertical positions of the

IAN canal.

In attempts to determine if a relationship existed between the vertical positioning of the

IAN canal and the thickness of bone from the buccal cortex to the canal, mean and

standard deviation of buccal bone thickness was calculated at various vertical positions

of the IAN canal (Table 2). The average distance from the buccal cortex to the IAN

canal is 4.2±1.5 mm when the distance of the IAN canal to the alveolar crest is between

0-10 mm. The average buccal bone thickness is 4.9±1.6 mm when the average

distance of the IAN canal to the alveolar crest is between 11-15 mm and when the

position of the IAN canal from alveolar crest is between 16 to 20 mm, the average

buccal bone thickness is 4.8±1.4 mm. Irrespective of the position of the IAN canal the

average buccal bone thickness is approximately 4mm.

6.2 Inter and Intra-observer Variability

To test for inter-observer variability paired t-tests were used to compare differences

between the two groups for each of the measures. None of the differences were

statistically significant (P>0.73). Figures 16 to 19 are box plots comparing the two

48

observer groups. The individual mean and standard deviations for each measurement

location for each group of observers are displayed in Appendices I and II.

Figure 16. Boxplots showing the percent error of measurements of the buccal cortex to

IAN canal, made on 29 mandibles comparing dental students and oral surgeons. Boxes

enclose the middle 50% of observations. Vertical lines extend to include approximately

90% of observations. Circles and asterisks denote outlying and extreme data points.

49

Figure 17. Boxplots showing the percent error of measurements of mandibular width

made on 29 mandibles comparing dental students and oral surgeons. Boxes enclose

the middle 50% of observations. Vertical lines extend to include approximately 90% of

observations. Circles and asterisks denote outlying and extreme data points.

50

Figure 18. Boxplots showing the percent error of measurements of mandibular height

made on 29 mandibles comparing dental students and oral surgeons. Boxes enclose

the middle 50% of observations. Vertical lines extend to include approximately 90% of

observations. Circles and asterisks denote outlying and extreme data points.

51

Figure 19. Boxplots showing the percent error of measurements of the alveolar crest to

IAN canal made on 29 mandibles comparing dental students and oral surgeons. Boxes

enclose the middle 50% of observations. Vertical lines extend to include approximately

90% of observations. Circles and asterisks denote outlying and extreme data points.

Intra-observer variability was assessed against both radiographic method and measure

location in the model. Three-way and all two-way interactions were not significant

(P>0.05) and hence differences between replicate measures are not statistically

significant. Figure 20 displays the results for replicate measures of CBCT and

Orthopantomogram measurements.

52

Figure 20. Boxplots showing differences between replicate measurements based on

CBCT and Orthopantomogram images of 29 mandibles. Boxes enclose the middle 50%

of observations. Vertical lines extend to include approximately 90% of observations.

Circles and asterisks denote outlying and extreme data points.

53

6.3 Comparision of Orthopantomogram and Cone Beam Computed

Tomography Measures to Anatomic Measures

Analysis of the orthopantomogram measures compared with the anatomic standards

demonstrated an average difference of 2.6 ± 0.8 mm or 15.9% (P<0.05) at all three

sites: the ascending ramus, area of the second molar and area of the first molar.

Measures were overestimated 91% of the time compared to the anatomic

measurements.

The percent error for the measurements from the orthopantomograms are shown in

Figure 21. The mandibular height was on average less than that found with the

anatomic measures and the distance of the alveolar crest to the IAN canal was on

average greater than (with a larger standard deviation of 23.7) those of the anatomic

measures.

Analysis of the CBCT measures compared with the anatomic measures demonstrated

an average difference of 2.9 ± 0.5 mm or 24.9%(P<0.05). Forty-eight percent of

measures were less than their anatomic equivalents and 50% were greater than the

anatomic measurements. Only 2% of the measures were equal. The mean percent

difference for each individual measurement at each pin location, are displayed in

Appendices I and II.

The percent error of measurements based on the CBCT images are displayed in Figure

22. Mandibular height and distance from the alveolar crest to the IAN canal were on

average less than the anatomic measures with a similar percent error of 7.0 and 7.2

respectively. The distance of the buccal cortex to the IAN canal was generally greater

than the anatomic measures with a large standard deviation of 26.6. The measures of

mandibular width were on average greater than the anatomic measures and had the

largest percent error of all measurements at 27.9. The largest standard deviations were

found for the measures of the alveolar crest to the IAN canal and the buccal cortex to

the IAN canal at 38.5 and 26.6 respectively.

54

The percent error and standard deviations of the CBCT and Orthopantomogram

measures differed significantly from the anatomic standards P< 0.01 and P< 0.05. This

is shown in Table 3. The average for each measurement at each pin location and the

mean percent difference of the Orthopantomogram measures and CBCT measures

from the anatomic measures for each observer group independently, are displayed in

Appendices I and II. Appendix I displays the results for the Oral Surgery observers and

Appendix II displays the results the Dental Student observers.

MEASURE CBCT ORTHOPANTOMOGRAM

Buccal cortex to IAN Canal 7.8 (26.6) **

Mandible Width 27.9 (16.5) **

Mandible Height -7.0 (8.6) ** -2.9 (5.1) *

Alveolar crest to IAN canal -7.2 (38.5)* 8.2 (23.7) **

* *mean is significantly different from zero P<0.01

* mean is significantly different from zero 0.01<P<0.05

Table 3. Mean and Standard Deviation (+/- SD) of percent error of mandible

measurements from 29 mandibles from CBCT and Orthopantomogram images

compared to anatomic measures.

55

Figure 21. Boxplots showing the percent error of measurements based on

Orthopantomogram images of 29 mandibles. Boxes enclose the middle 50% of

observations. Vertical lines extend to include approximately 90% of observations.

Circles and asterisks denote outlying and extreme data points.

56

Figure 22. Boxplots showing the percent error of measurements based on CBCT

images of 29 mandibles. Boxes enclose the middle 50% of observations. Vertical lines

extend to include approximately 90% of observations. Circles and asterisks denote

outlying and extreme data points

57

Chapter 7

Discussion

A number of radiographic modalities are available for clinicians to use for pre-

surgical planning. Several studies have addressed the reproducibility, validity and

accuracy of available radiographic techniques (Brown et al., 2009; Cavalcanti et al.,

2004; Chien et al., 2009; Greiner et al., 2007; Titiz et al., 2012). The ideal choice would

be one which provides accurate information at a low cost with limited patient risk.

7.1 Cadaveric Specimens

In order to validate new craniofacial imaging modalities, the use of dry human skulls

have traditionally been used. Cadaveric specimens have the advantage of allowing for

direct anthropometric measurements. These can then be used to evaluate the accuracy

of various imaging techniques (Cavalcanti et al., 1999; Hildebolt et al., 1990; Lascala et

al., 2004). Most studies testing geometrical precision of 3D data sets have been

performed on prepared bone (Cavalcanti et al., 2004; Liang et al., 2010; Periago et al.,

2008; Suomalainen et al., 2008), physical test cadavers and measurement phantoms

(Eggers et al., 2008). In order to compare the results of this study to others, similar

parameters were used to determine the accuracy of the imaging modalities to the

anatomical specimens. When evaluating the intra- and inter-observer variability no

statistically significant differences were seen. This supports the use of direct cadaveric

measurements as a reliable standard from which to compare the CBCT and

orthopantomogram measures. As measures were collected from both the right and left

sides of the mandibles, ample data decreased the risk that individual outliers could have

biased the results.

58

Some have speculated that the use of dry skulls might affect the translation of study

findings to clinical applications. Previous research has suggested that clinically the soft

tissue drape may negatively affect image quality and the accuracy of 3D volume images

(Kwong et al., 2008; Periago et al., 2008). This is interesting in that discrepancies were

observed in this study in the absence of soft tissues. The suggestion is that clinically the

presence soft tissues may further increase such distortions, which could affect any final

diagnostic measures. As cadaveric specimens were used, the effects of embalming

may also need to be considered. However, studies have shown no apparent qualitative

effects on resolution and contrast compared with ante-mortem scans for gross anatomic

features (Chew et al., 2006). It has still been recommended that additional studies be

performed to determine the effects of post-mortem processing on visualization of

microstructures.

7.2 Anatomic Gold Standards and Surgical Planning

Averages obtained in this investigation from analysis of the anatomic data sets were

consistent with previous anatomic studies found in the literature. The IAN canal was

found to be 10 to 15 mm inferior to the alveolar crest in the majority of the mandibles.

These findings are in agreement with those of Rajchel et al. (1986). An attempt was

made to establish a relationship between the superior-inferior positioning of the IAN

canal and the thickness of bone from the buccal cortex to the canal. The specimens

evaluated for this investigation demonstrated that irrespective of the position of the IAN,

the average distance of the buccal cortex to the lateral cortex of the IAN canal ranged

from 4.2±1.5 mm to 4.9±1.4 mm in the posterior mandible. Variations in canal position

on 2D radiographic imaging techniques like orthopantomograms, will therefore not

provide any additional diagnostic information in regards to the quantity of buccal bone.

The limits of harvesting bone from the ramus area are dictated by clinical access,

presence and position of molar teeth and the location of the IAN canal. A common

treatment planning error is to overestimate the quantity of bone available for harvesting.

59

It is crucial to obtain accurate measurements of the distance from the buccal cortex to

the IAN canal in order to maximize harvest quantity, while at the same time protecting

the neurovascular bundle. Although the position of the canal is variable, anatomic

averages are helpful when surgical planning. Past studies have found the mean vertical

distance between the superior edge of IAN canal and the external oblique ridge to be

7.0 mm in the second molar region and 11.0 mm in the third molar region (Rajchel et al.,

1986). As a result, it has been suggested that the maximum size of block that can be

harvested is 40.0 mm in length, 10.0 mm in height and 4.0 mm in thickness (Misch,

1997; 2000). The technique originally described used the course of the IAN canal as a

guide for the position of the osteotomies and hence the size of the final graft (Misch

1996). The rationale for this approach is that the thickness of the medullary bone

between the IAN canal and the buccal cortex was considered to be greatest in the areas

of the proposed osteotomies (Misch, 1996; 1997).

Despite the variability in the IAN canal position, the greatest thickness of buccal bone

has been reported in the area distal to the second molar (Rajchel et al., 1986).

Specifically, it was determined that the thickness of bone in the area of the second

molar was 4.0 mm and in the retromolar area it was 3.0 mm. As a result of these

findings it was suggested that the anterior vertical osteotomy for a ramus graft be

placed in the region of the second molar (Smith et al., 1991a). In a similar study, buccal

bone was found to be the narrowest in the area immediately posterior to the third molar

with an average thickness of 1.9 mm±0.3 mm (Verdugo et al., 2009). Similarly in this

study, the thickness of the buccal bone in area of the ascending ramus was measured

to be 3.2 mm and 5.2 and 5.5 mm respectively in the second and first molar regions.

These results are similar to the findings of Levine and colleagues who found the

average thickness of buccal bone in the first molar region to be 5.0 mm and the IAN

position to be 4.9 mm from the buccal cortex (Levine et al., 2007). Graft harvest is

generally taken from the area distal to the third molar and anterior to the ascending

ramus. These results would suggest that the thickness of buccal bone is least in the

region distal to the third molar, which is where the graft is generally harvested. As a

greater dimension of bone has been identified in the area of the first and second molars,

one might consider modifying traditional harvest techniques to extend anteriorly into

60

these regions of the mandible. Several investigators have suggested this alternative

harvest technique as a superior approach (Leong et al., 2010). From the results of

Leong and colleagues, it was inferred that a safe thickness to harvest in the molar

region would be 2.5 to 3.0 mm. However, the IAN was exposed in all 34 cases with no

permanent damage being reported (Leong et al., 2010). The results of the current study

would support this approach and would suggest limiting graft harvest thickness to 3.0

mm distal to the third molar while allowing an increased graft thickness of 4.0 to 5.0 mm

in the first and second molar areas.

In the edentulous mandible, the expected resorption of the alveolar crest leads to less

bone height over the IAN canal. As such, this could mean the potential of increased

injury to the neurovascular bundle when procuring a ramus bone graft (Leong et al.,

2010). When considering the use of the ramus as a graft harvest technique for an

edentulous patient, there are those who suggest that only the anterior part of the

ascending ramus be used for alveolar reconstruction (Muto and Kanazawa, 1997).

Pre-operative planning on a case-by-case basis is required to determine the safest

location for the placement of the desired osteotomies to reduce surgical morbidity.

Based on prior studies the ramus could provide grafts of up to a length of 30.0 mm

(Smith et al., 1991b). It has been reported that a graft size of 37.6 mm in length by

22.48 mm in width with a 9.1 mm thickness could be obtained using a significant portion

of the anterior part of ascending ramus (Gungormus and Yavuz, 2002). However, when

each individual case was carefully scrutinized, it was determined that the thickness and

morphology of the grafts was not homologous. As demonstrated the site of the anterior

ramus can therefore be a source of a large quantity of cortical bone but use of the entire

area is often limited by inadequate intraoral access.

Studies have also demonstrated that the average width of the mandible is 14 mm in the

area from the second molar to the ascending ramus. Some have suggested that the

average width of the mandible may be is a predictor for the thickness of bone available

for harvest. Knowledge of the average width of the mandible in the retromolar area is

not sufficient to predict the available thickness of bone that can be obtained from a graft

harvest. Coupling this information with an understanding of the buccal positioning of the

61

IAN canal is essential. Several studies have tried to correlate mandibular width with

IAN canal position (Rajchel et al., 1986). It has been suggested that if the ramus is less

than 10 mm wide in the retromolar region, then an alternative donor sites should be

considered (Misch, 2000). The results of this investigation determined that the mean

width of the mandible is 11.9 mm, which suggests that width’s greater than 10 mm are

not always present. If a clinician uses the 10 mm width as a guideline to the suitability

of a ramus graft harvest, as often as not this technique will be abandoned. In these

instances modifying the procurement technique may be a more appropriate strategy for

patients having limited mandibular width.

Another area for harvest is the inferior mandible. Clavero and Lundgren (2003)

modified Misch’s technique by harvesting bone along the inferior mandibular body in a

location below the IAN canal. They found that a greater volume of could be harvested

from this location without the increase in sensory morbidity that can be seen when

grafts are harvested from the anterior mandible (Clavero and Lundgren, 2003). Using

the lateral plate of the mandible from the retromolar region to 3 mm distal to mental

foramen, the average bone size that can be harvested was reported at 15 mm in width

by 30 mm in length (Li and Schwartz, 1996). This report recommended that the

thickness of the block harvested remain less than 3.0 mm in order to reduce injury to

the neurovascular bundle.

Soehardi and colleagues (2009) described a surgical technique that involved bone

harvest from the horizontal ramus region for pre-prosthetic surgery. Ninety seven

percent of patients were satisfied with the results, with only two suffering from

temporary hypoesthesia of IAN (Soehardi et al., 2009). Partial cortical bone harvesting

as described by Hwang et al. (2008), minimized invasion into the marrow space with the

intention of reducing nerve damage. They suggested that only a block of 25 mm in

length by 15 mm in width with a maximum thickness of up to 3.5 mm should be

harvested (Hwang et al., 2008). In contrast, the present anatomic study demonstrated

that thicknesses of 3.5 mm may be all of the available buccal bone as opposed to the

partial thickness graft as described by Hwang et al (2008).

62

7.3 Inferior Alveolar Nerve Canal Identification

Recognition of the cortical boundaries of the IAN canal can be challenging and

frequently the appearance of this landmark is not clear. Various explanations have been

given to explain this limitation: 1) the mandibular neurovascular bundle is not always

surrounded by an ossified canal; 2) the cortical margins surrounding the IAN might have

“burned out”; and 3) the resolution might be insufficient to clearly demarcate the

cortication (Liang et al., 2001; Miller et al., 1990).

Several studies in the literature have stressed that the canal is not always surrounded

by a corticated border making it difficult to locate (BouSerhal et al., 2002; Carter and

Keen, 1971; Stella and Tharanon, 1990). The proximity of the canal to the cortical bone

in the projected plane, and the variation in radiodensity (compactness and thickness) of

closely apposed anatomic structures will affect identification of the cortical boundaries of

the canal (Mehra and Pai, 2012). One explanation for difficulty in localization of this

landmark is a dependence upon the geometry of the radiographic projection used

during image acquisition. The angulation of the x-ray beam in panoramic radiography

allows for a parallel projection of the beam on the canal walls, potentially improving

visualization of this landmark when compared to other radiographic techniques (Mraiwa

et al., 2003). In a study evaluating the ease of identification of the IAN canal using

panoramic radiographs, results demonstrated that the canal was found to be identifiable

in almost all cases but the majority of time it lacked a corticated border (Mehra and Pai,

2012). Clinicians must be aware that visibility of the IAN on 2D images is technically

dependent on both the projection geometry and the amount of cortication of the canal

walls (Dharmar, 1997).

There is a significant amount of heterogeneity in the positioning of the mandibular canal

from the lingula to the mental foramen. When this landmark cannot be identified in an

image, clinicians often use their knowledge of the average position of the canal as a

means of identifying the location. Anatomic averages can be helpful while at the same

63

time reliance on averages alone can lead to inaccurate localization of the neurovascular

bundle due to the true lack of homogeneity in the population.

7.4 Distortions in Orthopantomography

Pre-operative surgical planning requires not only an understanding of the relative

positioning of anatomic landmarks, but depends on accurate correlation of this

information with measurements from various radiographic modalities. Once an

understanding of the distances from the operative area to vital anatomic structures has

been determined, the final surgical plan can be established. Traditional image analysis

has been limited to linear and angular measurements between landmarks

superimposed on a 2D image. Orthopantomography remains a first line diagnostic tool

for pre-prosthetic surgical planning, providing information on overall jaw shape, position

of the maxillary sinuses, nasal cavity as well as the position of the IAN canal and mental

foramina. Harvesting of bone from the region of the mandibular ramus requires an

appreciation of the location of the IAN canal and for this purpose linear measurements

are made on panoramic images and represent the first step in the treatment planning

and ultimately in the design of the graft.

The image created by an orthopantomogram is created by linking the rotation of an x-

ray beam and a detector around a patient’s head. Objects outside of the field of view or

center plane are reproduced with distortions. Objects outside the plane that are closer

to the rotational center will be magnified. Due to the image distortion produced in

panoramic images, (Tronje et al., 1981a; b) it must be cautiously used when measuring

bone, especially when attempting to determine bone height above the IAN canal

(Batenburg et al., 1997; Bolin et al., 1996; Lindh et al., 1995). Tronje et al. (1981)

suggested that when greater accuracy is required, measurements on

orthopantomograms are not recommended. Panoramic image distortion can be caused

by inappropriate head position, location of area of interest in relation to the focal trough,

overlapping landmarks and by the type of orthopantomogram equipment which is

64

utilized.

The fact that panoramic images have inherent magnification is generally appreciated.

To determine the exact magnification in a particular area, reference objects with known

dimensions are placed in situ when taking an image (McDavid et al., 1993; Stramotas et

al., 2002). A true magnification can then calculated from the ratio of the projected to true

length of the reference object (Schulze et al., 2000). This resultant magnification must

be taken into consideration when orthopantomograms are used for surgical planning.

The standard of care has been the use of radiologic markers with these images from

which an accurate determination of all measurements, including that of bone height

above the IAN canal can be obtained.

Objects are seen to be enlarged by 15 to 25% in panoramic images and further

distortion occurs with poor patient positioning (Sanderink et al., 1991). A review of 210

implants placed in 80 patients found an average horizontal magnification of 1.20 to 1.32

and an average vertical magnification of 1.23 and 1.31 (Choi et al., 2004). A similar

study investigated the enlargement ratios of implants placed in an edentulous mandible

and it was determined that the mean vertical enlargement in the lateral region of the

mandible was in the range of 1.21 to 1.26 (Gomez-Roman et al., 1999). Others have

found that vertical measures on panoramic images were 2.4 mm greater (12%

magnification) than those of the anatomic specimens (Laster et al., 2005). Vasquez et

al. (2011) found that the magnification factor was constant both in the premolar

(1.28±0.01) and molar regions (1.27±0.01). Differences between the sites were not

found to be statistically significant. (Vazquez et al., 2011). When metal balls were used

for calibration on panoramic radiographs, examinations reported a mean magnification

factor of 1.27±0.03 (range 1.23 to 1.31) in the premolar region, and 1.26±0.03 (range

1.23 to 1.30) in the molar region (Schropp et al., 2009).

Clinically magnification is not easy to predict. Despite the available aides the clinician

can still have difficulty positioning the patient and the area of interest accurately in the

focal trough of the x-ray beam. Standardization of patient head position in the machine

is almost impossible. Freedman and Matteson believe that when a patient’s teeth are

carefully positioned to ensure that they are within the focal trough the result is negligible

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magnification (Freedman and Matteson, 1977). Variations in sizes and shapes of

patients, makes positioning all aspects of the alveolus within the focal trough very

challenging. Inherent magnification will exist in certain regions of the film. Even small

variations in horizontal positioning can cause image distortions. However, several

investigators believe that if patient positioning is appropriately adjusted, that vertical

dimensions can be sufficiently accurate for measuring the height of residual alveolar

bone prior to implant placement (Frei et al., 2004; Larheim and Svanaes, 1986). Devlin

and Yuan (2013) attempted to determine how image magnification and distortion are

influenced by object size and position. They were able to establish that only certain

places in focal trough achieve zero magnification. Only one type of panoramic machine

was used and they surmised that the trajectory of the moving center of the rotation is

machine specific and as a result, universal standards of magnification could not be

determined (Devlin and Yuan, 2013).

Modifications in object position during image acquisition can further increase distortion

and blurring due to the interposition and overlap of osseous structures, teeth, filling

materials and soft tissues. Fine anatomic structures can become difficult to discern.

Asymmetry of the mandible and alveolus can lead also to increased difficulty in

landmark identification on orthopantomograms that is unrelated to patient positioning.

Correction for these distortions can be challenging if the true cause cannot be

determined or if the asymmetry is not appreciated (Farman 1999).

While vertical measures on orthopantomograms present certain challenges, horizontal

measurements have limited accuracy (Gomez-Roman et al., 1999; Tronje et al., 1981a;

Yeo et al., 2002). Mesio-distal dimensions can become distorted on panoramic

radiographs,(BouSerhal et al., 2002) and it is understood that the distortion in a

horizontal plane follows no consistent pattern and varies widely with location within the

jaw (Kim et al., 2011). Despite the inability to obtain information in a mesial-distal

dimension on panoramic images, it has been deemed to be a safe method of evaluating

posterior mandibular implant placement. In a review of 2584 implant placements in the

posterior mandible, no permanent sensory disturbances were reported with the use of

panoramic images in pre-surgical planning (Vazquez et al., 2008). The results of the

present investigation suggested an overestimation of linear measures in panoramic

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images when compared to those of the anatomic specimens. If a clinician fails to

consider these potential variations between the actual dimensions and those seen on

the radiographic images, the result could be implant placement or surgical osteotomy

placement near the IAN leading to sensory injury. This is especially important if

orthopantomography is the sole imaging modality being used in the pre-prosthetic

planning phase.

7.5 Cone Beam Computed Tomography and Pre-Surgical Planning

The introduction of CBCT imaging to surgical implant treatment planning has allowed

clinicians to make measurements in dimensions not previously available. These images

are thought to have less distortion with less superimposition as seen with 2D

radiographic imaging. The ability to remove overlapping structural landmarks with

CBCT by selecting for only a specific image layer is to be a distinct advantage over

orthopantomograms particularly in critical areas such as the posterior mandible. The

main advantage for the use of 3D imaging in implant dentistry, is to provide an

assessment of bone height and width of the alveolar crest and the spatial relationship of

the IAN canal or maxillary sinus (Frederiksen, 1995). Today, the accuracy in placing

implants or surgical osteotomies in close proximity to vital structures is more frequently

dependent on the diagnostic measures taken from these imaging modalities.

There is a large body of literature on the accuracy of measurements made on CBCT

images, the results of which are variable. Several investigators have reported that linear

measures made on CBCT are sufficiently accurate for use in pre-surgical planning. A

study evaluating the accuracy of linear measures of bony defects on CBCT images,

found minor differences between the CBCT and the anatomic measures (0.01 to 0.27

mm for width and height) and deemed CBCT to be an accurate diagnostic tool (Pinsky

et al., 2006). Others have discovered larger differences (2.0 mm with a mean percent

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error of 2.31%) between CBCT and anatomic specimens. Interestingly, although the

error appeared higher, once again those that reported considered the results to be

clinically acceptable for craniofacial studies (Periago et al., 2008). Moreira et al. 2009

and Stratemann et al. (2008), determined that CBCT images demonstrated a high level

of accuracy with respect to linear distances when compared to physical measures made

at the time of surgery. They found errors in precision that ranged from 0.01to 0.19% and

having means that varied from 0.04 to 0.31 mm. The differences identified in this study

were considered not to be of clinical significance (Moreira et al., 2009; Stratemann et

al., 2008). CBCT cephalograms were found to be more accurate compared to traditional

lateral cephalograms for linear measures in a sagittal plane (Moshiri et al., 2007).

Timock et al. (2011) found inter-rater reliability with an absolute difference equivalent to

0.3 mm when comparing buccal bone thickness on CBCT images versus anatomic gold

standards. Overall measurement accuracy showed mean differences to be nearly zero

with no trend to overestimate or underestimate the linear distances (Timock et al.,

2011). In a study by Suomalainen and collegues, linear measures made on CBCT

images of both height and width, were found to be reliable and accurate when

compared with those of the cadaveric mandibles. They concluded that CBCT should be

considered as a reliable tool for implant planning (Suomalainen et al., 2008).

Results presented in this investigation found statistically significant differences when the

CBCT linear measures were compared with those measures from the anatomic

specimens. Several studies have demonstrated similar results regarding accuracy and

reduced image quality with CBCT. One group reported that CBCT measurements were

consistently less than those taken from the corresponding direct anatomic specimens,

particularly over longer dimensions (30 to 100mm). Differences were reported in the

range of 3.43 to 6.59 mm less than their anatomic counterparts (Lascala et al., 2004).

Similarly, Baumgaertel et al. (2009), demonstrated a trend for underestimating CBCT

measurements when compared to anatomic equivalents, particularly when calculating

multiple measures. In opposition to the results previously discussed by Moshiri et al.

(2007), a more recent study demonstrated statistically significant measurement errors

when comparing cephalometric landmarks on CBCT cephalograms to anatomic

specimens. The significance became even greater when several measurements were

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combined (Berco et al., 2009). Of further interest, several studies have shown that the

smaller the distance measured, the greater the inter-examiner variability and the greater

the deviation from the measurements taken from the anatomic equivalents (Cavalcanti

et al., 2004; Periago et al., 2008). These discrepancies from the true anatomic findings

can lead to inaccurate treatment planning when CBCT is used. Careful attention has to

be made when solely relying on CBCT technology for surgical planning, as the potential

for inaccuracy exists. Clinically, measures with even a 5 to10% potential error could be

of great significance in any pre-surgical planning especially when considering implant

placement or ramus graft harvest in the posterior mandible (Dalessandri et al., 2012).

7.6 Landmark Identification

When significant discrepancies exist between measurements made on CBCT images

and corresponding anatomic specimens, consideration must be given to the accuracy of

anatomic landmark identification as a cause for this variance. Inconsistency in landmark

identification has been suggested as the main source of errors in inaccurate

measurements. Measurement errors as a consequence of inappropriate landmark

identification can lead to unexpected surgical complications. The clinical significance of

such errors depends on the level of precision required for the surgical procedure.

Most research examining the accuracy of landmark identification, focuses on variability

in discerning cephalometric landmarks. Van Vlijmen et al. (2010) reported the

reproducibility of landmarks on conventional cephalograms was higher compared with

those of CBCT images of the same skull. Investigations by Chang and others, found

landmark identification errors to be higher with CBCT for certain anatomic points, while

other landmarks were more easily identified using this imaging modality. This was

attributed to specific characteristics of the landmark itself and differences in contrast

(Chang et al., 2011). Several additional reports confirm that variability in landmark

identification follows characteristic patterns and is directly associated with measurement

inaccuracies (Chen et al., 2004; Kamoen et al., 2001; Lou et al., 2007).

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Landmark variability can be structure or image related. The shape and position of the

landmark in relation to any surrounding anatomic boundaries can affect the ease with

which certain structures are identified (Chen et al., 2004; Lou et al., 2007). Landmarks

found on anatomically formed edges or crests are easier to identify when compared with

those on curves with wide radii (Baumrind and Frantz, 1971). Medelnik and colleagues,

found similar results, stating any landmark on a curve or prominence will have a greater

standard deviation and hence any measurements made involving such landmarks will

also have greater inaccuracies (Medelnik et al., 2011). Bilateral landmarks have been

found to be more easily identified on CBCT as opposed to traditional cephalograms

(Ludlow et al., 2009). Delmare et al. (2010) attempted to reduce the superimposition of

bilateral landmarks by adjusting image acquisition parameters. The results suggested

that there was no benefit of this approach for landmark identification and they surmised

that position and shape of the structure itself was most likely the cause for the

inconsistencies (Delamare et al., 2010).

Similarly, the inaccuracies found in the measures of the present study could be due to

an inability to consistently identify the start and end points of the linear measures. As

the difficulty in identification of a landmark increases, the standard deviations of

repeated measures involving a given landmark increase as well. Measures involving

the IAN canal demonstrated the largest standard deviations. These results suggest that

the location IAN canal is more difficult to identify with consistency, leading to a higher

variability in the final measures irrespective of the observer and their level of training.

When examining CBCT images, Ludlow et al. (2009) found standard deviations for

repeated measures between landmarks in the posterior mandible to be greater than in

any other maxillofacial region. This variability was deemed largely due to an increased

difficulty in landmark identification (Ludlow et al., 2009). Other investigators found high

variability and large standard deviations when landmarking and measuring small

periodontal defects on CBCT images (Grimard et al., 2009).

Several studies have used radiopaque markers to aid in landmark identification

(Lagravere et al., 2008; Matteson et al., 1989). With the use of titanium markers on

cadaveric mandibles, one group evaluated the accuracy of linear measurements in

CBCT, and found a mean measurement error less than 1.0 mm (Lagravere et al., 2008).

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Matteson et al. (1989) similarly found improved accuracy of 3D images with use of

metallic markers as landmarks. Radiolucent markers are much more difficult to identify

and are the reason many studies on accuracy rely on the use of a radiopaque or

metallic landmarks (Berco et al., 2009). The accuracy of the aforementioned results

may be a reflection of the ease of identification of the measurement start and end

points. The presence of an easily identifiable metallic marker can provide for a more

consistent location from which to begin the measures. Although this approach does

demonstrate accuracy of the technology, it does not simulate a true clinical environment

where precision can be influenced by the ease or difficulty in true landmark identification

(Kragskov et al., 1997).

Landmarks that provide good contrast, such as tooth enamel are more easily detected

as compared to landmarks at the junction of tissues with similar radiopacities. Smaller

structures can be more difficult to visualize and those that have a grey scale similar to

the surrounding anatomy can be difficult to discern from the neighboring landmarks. The

accuracy of identifying the intersection between two materials of different densities is

limited by the size of each voxel in the image (Leung et al., 2010). In a study of CBCT

imaging, identification of the cemeto-enamel junction (CEJ) was found to be more

accurate than that of a bony margin (Leung et al., 2010). The CEJ is the junction

between the enamel and the cementum, two tissues with differing densities. Large

standard deviations were found for the measures of mandibular height in the present

study. One would expect less variability in this measure due to an increased likelihood

of identifying the crest and the inferior cortex due to the increased density of these

landmarks. This variability may be attributed to fact that images of mandibles without

soft tissues can suffer burn out of thin anatomical structures. This is termed burn out

effect. One such area is the alveolar crest, making the exact identification of this

relatively dense landmark, potentially impossible (Liang et al., 2001).

The accuracy in determining the junction between two tissues with similar opacities is

limited not only by voxel size but also by the physical spatial resolution of the image.

The measure of how closely individual lines can be resolved in an image, or the ability

to differentiate between two objects in close proximity, is termed spatial resolution.

Spatial resolution depends on the properties of the system creating the image and not

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simply on the voxel size. Ideally, spatial resolution would be equal to the voxel size in an

image, however, obtaining this level resolution can be difficult due to noise and other

image artifacts.

Landmarks or anatomic structures with dimensions that fall below the threshold for

resolution, may not be detected by the machine and therefore may be missed. Such

landmarks may not be visible on the final image and any measures involving these

landmarks will be inaccurate. Leung et al. (2010) found CBCT images had a higher

false negative rate for the detection of bony dehisences and attributed the results to a

thickness of bone that was less than the detectable resolution of the machine. Low

spatial resolution might explain the difficulty in distinguishing the interface between the

bony landmarks in the present study. The fact that the IAN canal and the surrounding

bone have similar radiopacities can make locating this landmark less reliable. A lack of

cortication and medullary bone with large trabeculations can also contribute to difficulty

in the identification of the IAN canal.

Despite the provision of the third dimension, the spatial resolution of CBCT images is

approximately 1.2 to 6.5 line pairs per mm-1 and is inferior to both conventional films

(approximately 20 lp mm-1) and digital images (ranging from 8 to 20 lp mm-1) (Scarfe et

al., 2010). Ballrick and colleagues demonstrated that the average image resolution for

the clear separation of 4 lines on a CBCT (with a flat panel detector) was 0.622 mm for

a 6 cm FOV and 0.860 mm for a 13 cm FOV(Ballrick et al., 2008). Others demonstrated

that the resolution of a CBCT with an image intensifier detector and charge-coupled

device was about 0.6 mm for a 25 cm FOV (Bab et al., 2001). The superior spatial

resolution of plain films may suggest that these modalities are a better choice when

surgical planning requires accurate measures of fine anatomic structures. Other

investigations have suggested that CBCT offers comparable or somewhat superior

spatial resolution in comparison to medical CT images, but noted that specifically soft-

tissue contrast resolution is reduced (Xu et al., 2012). Studies by Yu et al. (2010)

demonstrated that compared to medical CT, CBCT has higher spatial resolution only if

high-resolution modes are used. The use of high settings can improve image resolution

but can lead to clinically unacceptable image noise (Yu et al., 2010). (Bab et al., 2001;

Ballrick et al., 2008). Maximizing resolution while minimizing noise can be a difficult

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balance, and can lead to sacrifices in overall diagnostic image clarity.

An important consideration is the distinction between measurement accuracy and

spatial resolution. Investigators have suggested that studies reporting measurements

should include resolution and voxel size (Molen, 2010). The accuracy and precision of

any measurements are ultimately limited by the spatial resolution of the scan (Pinsky et

al., 2006). Measurements that approach the spatial resolution of the image may be less

consistent than measures that largely exceed it. This may explain the inaccuracies and

variability presented in this paper, associated with the smaller measures from the buccal

cortex to IAN canal.

Landmark identification in 3D is more time consuming than in conventional 2D imaging,

requiring identification in coronal, sagittal and axial views (Ludlow and Ivanovic, 2008).

Some landmarks may be easily identified in one or two planes, but with more difficulty in

the third plane. Results of studies by several investigators have established that the

reliability of landmark identification differed within the three planes of CBCT images

being more or less reliable in certain planes (de Oliveira et al., 2009; Kusnoto et al.,

1999). Examination in the three planes of space takes additional time and leaves room

for introduction of errors. In attempts to reduce the effects of this variable, the use of

fiducial markers in the present study identified a consistent slice where measurements

were to be taken by each of the observers. It has been stated that measurement

accuracy depends on the landmark itself and the direction or orientation of that

landmark within the image slice (Medelnik et al., 2011). When conducting an evaluation

of the suitability of a 3D imaging modality for measuring distances and angles, it is

essential to first check the reproducibility and reliability of the measured landmarks in all

axes (x, y and z-axes).

The data set generated from 3D image acquisition can be accessed in many different

ways and the operator can visualize the images through series of sections. 3D images

can be reformatted into 2D images allowing the viewer to scroll through in many planes

and directions depending on the section thickness. The selection of processing

parameters including cross-sectional slice thickness and interslice interval, are chosen

by the operator and depend on the imaging study being performed. Thinner slices may

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aide in identification of fine anatomic landmarks whereas thicker more widely spaced

sections may be sufficient for gross diagnosing (Chadwick and Lam, 2010). Recent

investigations have found that slice thickness affects the appearance of CBCT

reconstructed images and statistically significant differences in bone height measures

were found when only slice thickness was varied (Chadwick and Lam, 2010). It is

suggested that small interslice intervals may introduce noise that can affect the

accuracy of identifying areas like fine boney crests leading to inaccurate information.

This can be especially vital when quantification is necessary.

Variability in locating landmarks will always be a limitation whenever human-based

systems of identification are utilized. It should be assumed that human based

approaches will likely never be capable of reproducing precise anatomic

measurements. In contemporary clinical practice, one should always consider that

operator measurements are subjective and error is part of the process (Kamoen et al.,

2001). The acceptable degree of error depends on the type and complexity of the

treatment procedures (de Oliveira et al., 2009). Variables, including individual

perception and lack of professional training, may have an influence on the magnitude of

error in landmark identification. Therefore there must be a minimum acceptable level of

variability and tolerable margins of error must be discerned. Mean measurement errors

of 0.1 to 4.0 mm were found in an investigation by Lagravere et al. (2010). They stated

that differences up to 1.0 to 2.0 mm are clinically acceptable, but that any measures

with differences greater than 2.0 mm should be used with caution (Lagravere et al.,

2010). Reliability studies on evaluating cephalometric landmark identification in CBCT

images considered differences from the anatomic specimens below 1.0 mm to be

precise (Chen et al., 2000; Richardson, 1981). Chien et al. (2009) found dispersion

errors of less than 1.0 mm for mean estimates of differing landmarks comparing 2D and

3D imaging. These differences were considered to be clinically insignificant, and

suggested that errors in 3D be resolved through better manipulation of the image layers

or by increasing observer experience (Chien et al., 2009).

Variability in study methods, the use of different radiographic machines and a variety of

measures provides for a large body of literature on radiographic measurement

accuracy. As a result, there is a wide range in differences from the anatomic standards,

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which makes determination of a universal margin of error nearly impossible. In the past,

a safety limit of 2 mm above the IAN canal was considered to be sufficient in implant

surgery to avoid nerve injury (Bartling et al., 1999). Others have stated that in the

preoperative evaluation for implants, measures are considered to be acceptable within

an error range of 1.0 to 2.0 mm (Kamburoglu et al., 2009b; Nasel et al., 1999). Results

of this study have found a discrepancy of 2.4 to 2.9 mm between measures made on

CBCT generated images, digital orthopantomograms and the anatomic specimens.

These findings would suggest that clinicians should assume up to a 3 mm margin of

error when making any pre-surgical measures. Risk can be elevated if a practitioner

decides to neglect this margin of error and studies still have not fully determined the

extent of this limitation. Reliable margins of error for linear measurements need to be

determined, yet with the variability in study methods, assumptions of definitive accuracy

from any one study may not be applicable to all clinical situations.

7.7 Image Quality with Cone Beam Computed Tomography

The amount of variability found in the present study between the radiographic measures

and the anatomic measures suggests that image quality may have been a factor

preventing accurate interpretation. The ability to appropriately interpret a radiograph not

only depends on the inherent characteristics of the anatomic landmarks of interest but

also on many additional factors that affect image quality (Sayinsu et al., 2007; Yu et al.,

2008). Poor image quality prevents the clinician from clearly identifying the area of

interest and is a major source of inappropriate interpretation of a film. Without the ability

to clearly identify anatomic landmarks pre-surgical measurements cannot be made.

Past research has demonstrated significant variability in image quality between CBCT

and medical CT as expressed through the ability to depict various anatomical structures

in the maxillofacial region. Specifically, when compared to CBCT, medical CT was

deemed to have improved image quality for the detection of cortical bone (Loubele et

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al., 2007). Image quality is determined by the signal-to-noise ratio of the image, and

this is affected by kVp, mA, scatter and artifacts. It is important to expand on each of

the above factors to demonstrate the effects that each can have on clinical image

quality.

7.71 Image Contrast and FOV

Radiographic image quality is defined as the amount of information within the image

that allows the radiologist to make a diagnostic decision with a particular level of

certainty (Martin et al., 1999). Image quality is affected by two factors; contrast and

image definition. Contrast is the difference in optical density in a radiograph and is

influenced by spatial variation of the x-ray photon intensities that are transmitted

through the patient. Image contrast is affected by quantum noise, object absorption and

scatter radiation. Image definition depends on the size of the anatomical detail,

contrast, spatial resolution and noise (Medelnik et al., 2011). Ideally diagnostic images

also need to posses fidelity and diagnostic image clarity (Kundel, 1986). Fidelity is the

degree to which a radiographic technique accurately reproduces the image of its input

signal. It can be expressed in terms of signal to noise ratio, spatial resolution and the

absence of distortion. Image clarity is generally expressed in terms of diagnostic

accuracy (Kundel, 1986).

It is generally assumed that by increasing image quality one can increase the diagnostic

accuracy. It is known that image quality is degraded when the imaging dose

decreases. The diagnostic quality of an image can be increased when greater kVp and

mA settings are chosen (Kwong et al., 2008). This does however occur at the expense

of increased exposure to the patient (Jadu et al., 2011). Generally speaking, low dose

scanning can be achieved by either fixing the number of projections while decreasing

mAs, or fixing the mA level while decreasing the number of projections. The visibility of

an object relies on contrast and dimension and hence the clinically acceptable lowest

imaging dose level is task dependent. A recent investigation suggested that 72.8 mAs

is a safe dose for visualizing low-contrast objects and that imaging doses lower than 40

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mAs will lead to dramatic image degradation and should be used cautiously (Yan et al.,

2012). Clinically we must optimize not only image quality but also radiation dose to the

patient (Tanimoto, 2009). Current radiation safety philosophies are based on the

assertion that every radiation dose of any magnitude can produce some level of

detrimental effects, which may be manifested as an increased risk of genetic mutations

and cancer. Needlessly increasing exposure dose to decrease noise and improve

image quality is a contradiction to the ALARA (As low as reasonably achievable)

principle (Dykstra, 2011). As compared to 2D radiographic techniques, CBCT imaging

provides additional information but with the disadvantage of an increased radiation dose

to the patient. The necessity of such advanced imaging techniques for everyday use,

considering the concern of patient exposure is unknown. Technical advances have

reduced patient radiation exposure but the constant desire to obtain higher-quality

images creates a complex interplay among these variables. The principle of data

collection in CBCT does allow for a partial volume of the region of interest, which can

reduce overall patient exposure (Scarfe et al., 2006). In the selection of a pre-surgical

imaging modality, clinicians must consider the ability of the radiographic technique to

have adequate diagnostic quality to clearly depict the landmarks of interest. Selection

of a diagnostic technique with a higher patient exposure may be necessary to achieve

the desired surgical results.

The FOV used during image acquisition is one of the most important factors in image

quality. It is the FOV that determines the size of the voxel used, and therefore, the

image resolution. Most studies using medium to large FOV use a voxel size of 0.4 mm

in their scans, whereas others assessing limited FOV may use a 0.2 mm or smaller

voxel size (Hilgers et al., 2005; Kobayashi et al., 2004; Mischkowski et al., 2007). No

improvement in image resolution was found by Tanimoto and others, below a voxel size

of 80 microns and these workers surmised that this was the lower limit for improvement

of image quality (Tanimoto, 2009). A recent study has demonstrated that fine anatomic

structures like trabecular bone, the periodontal ligament and the lamina dura, were the

least visible anatomic landmarks when comparing CBCT machines with various FOV

settings. The machine using the smallest FOV was superior to all of the others in

demonstration of the aforementioned structures (Liang et al., 2010). The size of the

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FOV also correlates to image noise. The smaller the FOV or the smaller the voxel size,

the higher the noise and artifacts that can be found within the final image (Liang et al.,

2010). As such, a smaller FOV with a thin slice setting does not necessarily directly

translate to better image quality or higher spatial resolution.

7.72 Image Noise

Image quality and hence resolution, are also highly correlated with image noise. In all

imaging procedures that use x-ray or gamma photons, most of the image noise is

produced by the random manner in which the photons are distributed within the image.

This is designated as quantum noise. The amount of noise is determined by the

variation in photon concentration from point to point within a small image area. Noise

represents itself as inconsistent attenuation or grey values in the projection images, i.e.

large standard deviations, in areas where a constant attenuation should be present

(Schulze et al., 2011). Quantum noise can be reduced by increasing the concentration

of photons (i.e., the exposure) used to form an image (Gies et al., 1999). The signal-to-

noise ratio which is often used as a parameter for the image quality, reaches higher

values within the high-dose modes (Seeberger et al., 2012). However, they did,

demonstrate that the use of low-dose modes are possible without a significant reduction

in image quality.

Noise diminishes the ability to depict small landmarks and those of low contrast that are

close to the visibility threshold. Image noise has been shown to be higher in CBCT

compared to medical CT(Medelnik et al., 2011). When compared to medical CT, CBCT

has been known to produce images with reduced contrast resolution and higher ‘‘noise’’

(more graininess) due to the projection geometry, and limitations in detector sensitivity

(Scarfe et al., 2010). Despite these findings many still believe that the influence of

noise on CBCT images is clinically acceptable. (Liang et al., 2010; Mozzo et al., 1998).

When selecting CBCT for pre-surgical planning, a clinician may find a higher noise level

in the final images compared to other imaging modalities (Fast et al., 2012; Kamburoglu

et al., 2009a) The effects of noise on identification of landmarks and on linear

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measures will likely vary from case to case and will depend on the specific landmarks of

interest.

7.73 Artifacts

Image noise is only one type of artifact that can degrade the diagnostic quality of a

radiographic image. CT images are inherently more prone to artifacts than conventional

radiographs, as the image is reconstructed from up to a million independent detector

measurements (De Vos et al., 2009). An image artifact may be defined as a visualized

structure in the reconstructed data that is not present in the object under investigation.

Artifacts may be considered as a source, or type, of noise and can further compromise

image quality and the ability to accurately identify anatomic landmarks.

In CT terminology the term artifact refers to any systematic discrepancy between the CT

numbers in the reconstructed image and the true attenuation coefficients of the object

(Barrett and Keat, 2004). Although there are a vast number of artifacts found, the

following are considered to be the most common and the most disruptive to image

quality: extinction artifacts; beam hardening artifacts; partial volume effect; ring artifacts;

motion artifacts (misalignment artifacts) and scatter(Schulze et al., 2011).

Artifacts arise as a result of the interaction between the subject and the machine; it is

therefore also useful to classify them by the nature of the error made in the scanning

process. Sources for CT and CBCT image artifacts include: (a) Physics-based artifacts,

which result from the physical processes involved in the acquisition of CT data. Artifacts

caused by beam hardening and x-ray scatter are examples of physics-based artifacts;

(b) Detector-based artifacts, which result from imperfections in detector function. The

ring artifact is the most prominent artifact in this category; (c) Patient-based artifacts,

which are caused by such factors such as patient movement or the presence of metallic

materials in or on the patient and (d) Artifacts which are produced by the image

reconstruction process (Schulze et al., 2011).

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Extinction artifacts are often termed ‘‘missing value artifacts’’. If the object under study

contains a highly absorbing material, e.g. prosthetic gold restorations, then the signal

recorded in the detector pixels behind that material may be close to zero or actually

zero. No absorption can be computed and severe artifacts are induced as these zero

entries are backprojected into the volume (Prell et al., 2009). Several studies have

produced improved images for patients with metallic devices, using pre-processing

algorithms (Bechara et al., 2012). By increasing the milliampere per second factor

levels, higher-quality images will result because the increased beam energy is not

absorbed entirely by the metallic structures (Vande Berg et al., 2006).

Beam hardening is a major source of artifact in CBCT and is caused by the lower

wavelength rays of the emitted x-ray source that suffer substantial absorption when

passing through the object under study. An x-ray beam is composed of individual

photons with a range of energies. As the beam passes through an object, it becomes

“harder,” which means its average energy increases, because the lower energy photons

are absorbed more rapidly than the higher energy photons. The more dense the latter

and the higher the atomic number of its composition, the larger the share of absorbed

wavelengths (Barrett and Keat, 2004). The result is a series of dark streaks on the final

image. Beam hardening can contribute to grey level non-uniformity in CT images, as it

is not included as a factor in the mathematics of image formation (Schulze et al., 2011).

The presence of metallic bodies within the maxillofacial complex causes beam

hardening and streak artifacts (El-Khoury et al., 2004). Ultimately this leads to a limited

diagnostic field by obscuring anatomical structures especially fine anatomic landmarks.

It reduces the contrast between adjacent objects and can impair the detection of the

areas of interest (Draenert et al., 2007).

In reconstructed images, dental metallic artifacts may be observed as hypodensities

surrounded by hyperdense areas and can be found near any type of metal, including

titanium implants and metallic restorations (Perrella et al., 2010). It has been noted that

beam hardening caused by dental metallic restorations does not influence

measurements performed at various boney sites. However, the presence of such

artifacts can make it more difficult to accurately locate the alveolar crest (Cremonini et

al., 2011). Accurate detection of the alveolar crest was essential to several

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measurements performed in the current study and may be the reason for the variability

found compared to the anatomic measures. It is well known that beam hardening

artifacts in particular make CBCT imaging unsuitable for dental caries diagnosis (Scarfe

et al., 2010). As such, it may also be an unsuitable technology for identification of other

fine anatomic details.

A partial volume effect is a common artifact in CT imaging and can introduce

imprecision into the digital image. When tissues of widely different absorption occupy

the same voxel, the beam attenuation is proportional to the average value of the

attenuation (Glover and Pelc, 1980). An average is computed for such voxels and is

called volume averaging which can lead to the artifacts. If a voxel lies completely within

an object, it will reflect the true object density. However, if a voxel is at the junction of

two objects of different densities, than it will represent an average between the true

values for both. This voxel can be interpreted as being part of either area, potentially

misrepresenting the data set (Baumgaertel et al., 2009). Volume averaging may reduce

the visibility of a landmark within an image layer and could result in underestimation of

the edges of structures such as cortical bone surfaces (Kumar et al., 2007).

Consideration and reduction of this artifact is necessary when imaging any part of the

body where the anatomy is rapidly changing. Partial volume artifacts can best be

avoided by using a thin acquisition section width and selection of the smallest

acquisition voxel (Scarfe and Farman, 2008).

A ring artifact is a circular artifact that is produced when any detector is out of calibration

leading to a consistently erroneous reading at each angular position. These artifacts can

impair the diagnostic quality of an image, and this is particularly likely when the central

detectors are affected. The result is the creation of a dark smudge at the center of the

image(Barrett and Keat, 2004). These artifacts are quite visible and often when

detected a correction is made and the scan is repeated.

Patient motion can have significant effects on image quality and was the reason for the

development of the current scanners that can acquire images in a shorter period of

time. The acquisition time of current CBCT machines roughly ranges between 6 and 20

seconds. There is still however, sufficient time for the patient to have some minor

81

movement (Ritchie et al., 1992). The smaller the voxel size (i.e. the higher the spatial

resolution), the smaller the movement necessary to demonstrate artifacts on the film

(Schulze et al., 2011). It has been reported that images will suffer a distortion of the

volume data sets when translation movements of the head are greater than 5 mm

rotations are more than 2 degrees (Wagner et al., 2003). Although this is a concern in

the clinical setting, this type of artifact can be ruled out as a contributor to poor image

quality in this study since no live patients were imaged.

Scatter is caused by those photons that are diffracted from their original path after

interacting with matter. The amount of scatter increases with object thickness and field

size and is proportional to the tissue density and atomic number. Scatter reduces

subject contrast by adding background signals that are not representative of the

anatomy thereby reducing image quality. This artifact known to affect the density values

of all tissues, but in particular it can reduce soft tissue contrast (Tofts and Gore, 1980).

Considering the geometry of large area detectors, it is known that the larger the

detector, the higher the probability that scattered photons will be detected. Thus, the

image-degrading effect of scattered radiation will affect CBCT machines more than

classical highly-collimated fan-beam CT (Kalender and Kyriakou, 2007). In conventional

medical grade CT, collimation at the x-ray source restricts the coverage of the beam,

only allowing scatter from a thin axial volume of tissue to reach the detector elements

during section acquisition. In contrast, CBCT expands the coverage of the beam,

allowing x-ray scatter generated from the entire volume of coverage to reach the

detector elements as the image is acquired (Miracle and Mukherji, 2009; Zhang et al.,

2007). Artifact formation by scatter is very similar to beam hardening owing to the fact

that both will reduce the measured attenuation coefficients (Meganck et al., 2009; Zhu

et al., 2009). Scatter radiation has been shown to reduce the accuracy in reconstructed

values, which in turn will degrade accuracy when preforming any associated measures

(Siewerdsen and Jaffray, 2001).

The imaging of any metallic body will show strong beam hardening and scattering effect

artifacts which can lead to unsuitable images for diagnostic purposes (Draenert et al.,

2007). Scatter reduces image quality by degrading the CT number linearity, and

reducing the contrast-to-noise ratio (Ren et al., 2012). Scatter can present as (a) spatial

82

low frequency grey value deformations, known as cupping; (b) streaks, bars, or

shadows, particularly in the vicinity and between highly absorbing regions; and (c)

decreased soft tissue contrasts.

The cupping and shadowing artifacts look similar to those from beam hardening but are

often more severe (Ruhrnschopf and Klingenbeck, 2011). Cupping occurs due to a

decrease in grey levels in the center of an object owing to the increase in transmitted

intensity to the detector from the presence of beam hardening during image acquisition

(Hunter and McDavid, 2012). Upon reconstruction, the attenuation coefficients and the

CT numbers will appear to have been decreased. This results in CT images showing

less dense materials in the center of the object and hence artifacts (Barrett and Keat,

2004).

Finally, image degradation can result from image lag affecting the final diagnostic

quality. Image lag is mainly due to the trapping of charges in the sensitive area of the

imager. It delays the signal reading and causes a portion of the signal from the current

image to appear superimposed on the images taken in later time frames(Mail et al.,

2008). The superimposed images can affect the clarity of fine anatomic structures.

Any one of the aforementioned artifacts alone or in combination can degrade diagnostic

image quality. Attempts should be made to reduce the occurrence of all artifacts

through adjustment of pre-processing parameters. By eliminating these effects, the final

image will be more highly representative of the true anatomic structures.

There seems to be a knowledge transfer gap between the technical understanding of

these artifacts or image limitations and those clinicians reading the films. This lack of

knowledge may introduce diagnostic errors that could be avoided by a better

understanding of the causative factors and the error effects (Barrett and Keat, 2004).

Design features incorporated into modern CT scanners minimize some types of

artifacts, and some can be partially corrected by the scanner software during post-

processing. However, in many instances, careful patient positioning and optimum

selection of scanning parameters are the most important factors in avoiding CT artifacts.

New advancements are continuously being created to reduce image artifacts. Many of

83

them are post-processing algorithms operating on the 3D volume data set (Barrett and

Keat, 2004). Although this may result in considerable reduction of some apparent

artifacts, from a physical point of view post-processing is like putting the cart before the

horse since the error has already been integrated into the volume. In these cases the

final image does not fully reflect the true anatomic structures in study. Consequently,

more modern approaches should be attempting to avoid reconstruction errors through

alterations in image acquisition (Schulze et al., 2011). It has been suggested that until

such time that image quality can be improved with these newer imaging modalities,

traditional films should remain the gold standard in pre-operative diagnostic planning.

7.74 Summary

Any of the aforementioned variables can have a significant effect on image quality and

the clarity of anatomic structures. Despite adjustments to the image acquisition

parameters some inherent noise and artifacts will be present that clinically can affect the

ease by which a clinician can interpret a film. Any of the factors discussed could have

been a reason for the variability found in this investigation in the measures made on the

radiographic images as compared to those of the anatomic specimens. A potential

reduction in image clarity may have made it more difficult to identify the fine anatomic

landmarks with consistency, which would have led to variability in the final associated

measures. Clinicians must appreciate and consider the effects of these variables on

images prior to their use in any pre-surgical planning.

7.8 Access to Radiographic Modalities

The ease of accessibility and handling of dedicated CBCT scanners raises some

important concerns as it has caused a major shift in the user group of highly

sophisticated 3D CT imaging. Most purchasers of CBCT scanners are specialist

dentists and maxillofacial surgeons and not radiologists (De Vos et al., 2009). The

errors and confusion found in the clinical literature on CBCT imaging can be partially

84

attributed to the limited technical knowledge about medical imaging devices of this new

user group (Honey et al., 2007). Currently, in regions where CBCT technology is in

regular use, images are read by clinicians at various levels of training and not

necessarily by radiologists. Most clinicians rarely revise their basic concepts of image

reading and the resultant errors occurring during clinical practice may be caused by

modifications in personal perception acquired overtime. One might speculate that

landmark identification becomes improved with pattern recognition, which is more

applicable to experienced observers. One study reported that a standardized period of

training reduced structure related causes of variability among observers (Delamare et

al., 2010). One could surmise that multiple repeat readings of the same images might

improve accuracy of landmark identification, irrespective of additional training. If

accuracy is however not improved, then the errors in identification will simply be

repeated. Consistency in locating anatomic structures would decrease the variability of

any repeated measures involving such landmarks.

The results of the present study demonstrated that those observers with less

experience reading orthopantomograms and CBCT images did not perform any

differently than the more experienced clinicians. Paired t-test comparisons

demonstrated no statistically significance difference between groups. Similarly, Berco

and others, found the accuracy of linear measurements was uninfluenced by operator

experience (Berco et al., 2009). Contrary to this, one study has reported that the major

influence on reliability of a landmark is inter-observer variation and that this can affect

accuracy outcomes overall (Chen et al., 2004). CBCT technology may provide images

with more diagnostic information, but users have to be aware of their responsibility to

interpret the data thoroughly and appropriately. A complete understanding of the

potential inherent limitations of these new radiographic modalities is essential. It must

be noted that measures performed by radiologists who read such images more regularly

may more closely match those of the anatomic standards when compared to other

clinicians.

85

7.9 Clinical Applications In Pre-Surgical Planning

The choice of the ideal radiographic technique for pre-operative surgical planning can

be quite confusing for the clinician. The introduction of 3D imaging choices combined

with computer planning software is becoming the mainstay of pre-surgical planning,

particularly in the field of implant placement. Resorption of bone, position of the IAN

canal and the location of the maxillary antrum have been reported as being more clearly

depicted on CBCT than on conventional films (Nakagawa et al., 2002). The impact that

CBCT technology has had on maxillofacial imaging, cannot be underestimated. This

does not however imply that CBCT be the first or only choice in imaging modalities in

clinical practice. 3D images of skeletal and dental structures without superimpositions

do provide much more information than a conventional 2D radiograph. The clinician

must decide whether this extra information will facilitate the patient's diagnosis and

treatment to avoid unnecessary radiation exposure for no therapeutic advantage.

Provisional guidelines for the use of CBCT were created by SEDENTEXCT project in

2009. These evidence-based guidelines include referral criteria, quality assurance

guidelines and optimization strategies (Simeonov, 2011).

Imaging modalities, although being an integral component to the surgical planning

phase, as evidenced, all have inherent limitations that can affect image quality and

hence the accuracy of landmark identification and associated linear measures. Despite

the fact that 3D imaging provides more detailed information compared to traditional

films, the accuracy within these images is in not necessarily superior to traditional 2D

imaging techniques. The variability in 3D measures may be due to inaccuracies with the

technology itself or more likely errors in interpretation of the images due to their lack of

clarity, contrast and overall quality. Irrespective of the reasoning, it has been

demonstrated that CBCT technology has limitations that can lead to variability in any

final measures. The American Academy of Oral and Maxillofacial Radiology defends

the position that the success of any surgical treatment is, in part, dependent on

adequate diagnostic information about the bony structures of the oral region, including

accurate linear measurements (Carter et al., 2008). With this potential for inaccuracy

86

and the increase in radiation dose to the patient, it would suggest that the use of

orthopantomograms remain an adequate preoperative adjunct for the surgical planning

of ramus graft harvest or any other surgical procedure.

How these study results and others can be extrapolated to clinical scenarios is

unknown. The variations in structures, imaging parameters and subjects of interest that

arise in clinical scenarios make it difficult to apply results found in in-vitro to everyday

clinical use. Errors found under ideal laboratory conditions should be applied with

caution to in-vivo applications, particularly since all clinical situations have yet to be

investigated.

There is no question that a combination of traditional films and CBCT together provide

the best anatomic detail, but orthopantomograms alone can be invaluable in identifying

the need for further imaging studies (Pawelzik et al., 2002). An optimized examination

algorithm is necessary for each individual case. In more complex scenarios and those

demonstrating concerning findings on the orthopantomogram, the adjunctive use of

CBCT imaging would likely be of merit.

87

Chapter 8

Conclusions

The purpose of this study was to determine the utility of CBCT technology as an

alternative to digital orthopantomograms with respect to pre-surgical planning accuracy.

The conclusions as they relate to the original hypotheses are as follows;

Hypothesis #1

H0 : The thickness of buccal bone does not vary with location in the posterior mandible.

Conclusion: The greatest thickness of bone from the buccal cortex to the lateral aspect

of the IAN canal can be found in the first and second molar area. Ramus graft harvest

design should therefore extend into these areas to maximize harvest quantity. The

maximum thickness of the ramus graft should not exceed 4 mm to avoid injury to the

IAN.

Hypothesis #2

H0: The position of the IAN nerve in a superior-inferior direction does not correlate to a

specific thickness of buccal bone.

Conclusion: The superior-inferior position of the IAN does not correlate with a particular

thickness of buccal bone in the posterior mandible. In the majority of the mandibles the

superior most aspect of the IAN canal was found 10 to 15 mm inferior to the alveolar

crest in the posterior mandible.

88

Hypothesis #3

H0: Measurements made on digital orthopantomograms accurately reflect those made

on anatomic specimens.

Conclusion: The linear measurements made on digital orthopantomograms

demonstrated 15.9 % variation as compared to the measures on the anatomic

specimens.

Hypothesis #4

H0: Measurements made on CBCT images accurately reflect those made on anatomic

specimens.

Conclusion: The linear measurements made on the CBCT images demonstrated 24.9%

variation as compared to the measures on the anatomic specimens.

The results have demonstrated that both digital orthopantomograms and CBCT images

have inherent variability from the anatomic specimens. The use of both of these images

during the pre-surgical planning phase could introduce errors leading to inaccurate

surgical plans.

89

Chapter 9

Future Direction

The use of CBCT imaging coupled with computer imaging software is becoming the

standard in pre-operative surgical planning for dental implant treatment. This

investigation determined that there is variability in measures made with this imaging

modality when compared to those taken from the corresponding anatomic specimens.

A potential lack of diagnostic quality within the images was deemed as a possible

contributor to this variability. Future studies, which control for selected image

acquisition and/ or pre or post-processing parameters would be of merit in an attempt to

determine if improved diagnostic quality will increase linear accuracy. Examination of

the CBCT and digital orthopantomograms after correction for inherent artifacts could be

compared to measures from corresponding anatomic specimens and those of the

original films. Results may demonstrate less variability as a result of improved image

quality and thereby improving diagnostic abilities.

The variability of the measured results may also be related to the clinical experience of

the observers reading the films. An important study would be one designed to compare

the results of linear measures made by both surgically trained clinicians and trained

radiologists. A study design that assesses any potential correlation between years of

experience with diagnostic accuracy of measures would be beneficial.

Finally, it is important that clinically acceptable margins of error in pre-surgical planning

be agreed upon. Using the anatomic averages calculated in this study, ramus harvest

could be preformed on cadaveric mandibles to determine the risk of injury to the inferior

alveolar nerve. The study design could include the application of a variety of margins of

error to determine which one would most reduce surgical risk.

90

Chapter 10

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Appendices

Appendix I

Anatomic  

Site  

Distance  

Measured  

Anatomic

Measure  

Orthopantomogram  

Measure  

(Mean  ±  SD)  

Mean  

Percent  

Difference  

CBCT  

Measure  

(Mean±SD)  

Mean    

Percent    

Difference  

Ascending  

Ramus  

 

Buccal  

cortex  to  

IAN  canal  

 

3.2±1.5  

     

4.1±1.7  

 

21.9  

  Width   10.4±2.2       14.7±2.5   29.3  

  Alveolar  

crest  to  IAN  

canal  

 

13.7±3.4  

 

13±3  

 

5.1  

 

13.6±4.9  

 

0.7  

  Height   31.5±4.4   28.1±4.3   10.8   28.4±4.87   10.8  

Area  of  

Second  

Molar  

Buccal  

cortex  to  

IAN  canal  

 

5.3±1.7  

     

5.1±1.9  

 

3.8  

  Width   13.1±2.5       17.3±2.3   24.2  

  Alveolar  

crest  to  IAN  

canal  

 

13.2±5.3  

 

12.7±3.7  

 

3.7  

 

9.9±3.6  

 

25  

  Height   22.8±5.1   22.8±5.1   0   20.7±4.4   9.2  

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Area  of  

First  Molar  

Buccal  

cortex  to  

IAN  canal  

 

 

5.6±1.6  

     

5.1±1.9  

 

8.9  

  Width   12.1±2.6       14±2.6   14.2  

  Alveolar  

crest  to  IAN  

canal  

 

11.9±4.8  

 

14.3±4.7  

 

20.1  

 

10.2±3.5  

 

14.2  

  Height   20.6±6.5   23.1±6.3   10.8   19.5±4.9   5.3  

Mean of each measurement at the locations of the ascending ramus,

second molar and first molar and the mean percent difference of the

orthopantomogram measures and CBCT measures from the anatomic

measures for the Oral Surgery observers.

118

Appendix II

Anatomic  

Site  

Distance  

Measured  

Anatomic  

Measure  

Orthopantomogram  

Measure  

(Mean  ±  SD)  

Mean  

Percent  

Difference  

CBCT  

Measure  

(Mean±SD)  

Mean  

Percent  

Difference  

Ascending  

Ramus  

 

Buccal  

cortex  to  

IAN  canal  

 

3.2±1.5  

     

4±1.4  

 

20  

  Width   10.4±2.2       15±2.6   30.1  

  Alveolar  

crest  to  IAN  

canal  

 

13.7±3.4  

 

13.4±2.8  

 

2.2%  

 

13.8±3.9  

 

0.7  

  Height   31.6±4.4   28.3±4.1   10.4%   28.1±4.0   11.1  

Area  of  

Second  

Molar  

Buccal  

cortex  to  

IAN  canal  

 

5.3±1.7  

     

5.3±1.8  

 

0  

  Width   13.1±2.5       14.8±2.3   11.4  

  Alveolar  

crest  to  IAN  

canal  

 

13.2±5.3  

 

11.7±3.8  

 

11.3%  

 

10.2±3.2  

 

 

22.7  

119

  Height   22.8±5.1   22.7±5.3   0.4%   20.3±4.6   10.9  

Area  of  

First  Molar  

Buccal  

cortex  to  

IAN  canal  

 

 

5.6±1.6  

     

5.3±1.6  

 

5.4  

 

  Width   12.1±2.6       15.1±2.5   19.8  

  Alveolar  

crest  to  IAN  

canal  

 

11.9±4.8  

 

12.4±4.6  

 

4.0%  

 

10.3±3.8  

 

13.4  

  Height   20.6±6.5   22.3±5.9   7.6%   20.5±5.5   0.4  

Mean of each measurement at the locations of the ascending ramus,

second molar and first molar and the mean percent difference of the

orthopantomogram measures and CBCT measures from the anatomic

measures for the Dental Student observers.

120