Assessment of Bone Density Measurements Using Cone Beam Computed Tomography and Multislice Computed Tomography (Experimental Study) Thesis Submitted to the Faculty of Oral and Dental Medicine, Cairo University, for Partial Fulfillment of the Requirements of Masters Degree in Oral Radiology. By Yara Rabia Helaly (B.D.S) 2005 Major Area of Interset: Diagnostic Research Minor Area of Interset: Role of CBCT in diagnosis and follow-up of maxillofacial abnormalities and lesions Code: 1102 Faculty of Oral and Dental Medicine, Cairo University. 2012
Supervisors Dr. Mushira Mohamed Dahaba Professor and Head of Oral Radiology Department, Faculty of Oral and Dental Medicine, Cairo University. Dr. Mohamed Khalifa Zayet Lecturer of Oral Radiology, Faculty of Oral and Dental Medicine, Cairo University. II
Acknowledgement First and foremost I would like to thank God for helping me to achieve this work. I am heartily thankful to Dr. Mushira Mohamed Dahaba, Professor and Head of Oral Radiology Department, Faculty of Oral and Dental Medicine, Cairo University, for her valuable instructions, great help and constant support during the study. I offer my regards and blessings to Dr. Hany Mahmoud Omar, Assistant Professor of Oral Radiology, Faculty of Oral and Dental Medicine, Cairo University, for his initial support. Last but not the least; I would like to thank Dr. Mohamed Khalifa Zayet, Lecturer of Oral Radiology, Faculty of Oral and Dental Medicine, Cairo University, for his encouragement, guidance, patience and support from the initial to the final level of this work. I would like also to thank all the staff members and technicians at the Oral Radiology Department, Faculty of Oral and Dental Medicine as well as those at the Radiodiagnosis Department, Faculty of Medicine, Cairo University. III
Dedication This thesis would not have been performed without throwing the light on how my parents and my brother helped me get through difficult times during this work. Thank you for all the emotional support and care that you provided me. IV
List of Contents Chapter Page 1- Introduction 1-2 2- Review of Literature 3-84 3- Aim of The study 85 4- Material and Methods 86-95 5- Results 96-132 6- Discussion 133-146 7- Summary and Conclusion 147-148 8- References 149-172 9- Arabic Summary v
List of Figures Figure Page Figure (1): CT data acquisition. 5 Figure (2): Diagrammatic presentation of the process involved in CT 6 image reconstruction. Figure (3): Diagrammatic presentation of the principle of CT. Diagram shows the x-ray attenuation through a specific material of finite thickness (A) and through a material considered as a stack of voxels 6 with each voxel of finite thickness (B). Figure (4): Pre-patient and post-patient (detector) collimation. 9 Figure (5): Types of CT detectors. 10 Figure (6): Horizontal movement of the CT couch allowing examination of the patient without being repositioned. 12 Figure (7): Spiral CT data acquisition. 15 Figure (8): CT gantry with an x-ray tube, an x-ray beam, and detectors for a multisection scanner (four-section system shown). 21 Figure (9): Cone-shaped x-ray beam rotates around a stationary patient with the area X-ray detector on the opposite side of the patient s head 28 (left picture) (Macleod & Heath 2008).Cone-shaped beam rotates about head one time (right picture). Figure (10): Schematic presentation of the sequence for cone beam computed tomography image acquisition. Figure (11): Novel method of acquiring an extended FOV using a flat panel detector. (A) Conventional geometric arrangement whereby the central ray of the x-ray beam from the focal source is directed through the middle of the object to the center of the flat panel detector. (B) Alternate method of shifting the location of the flat panel imager and collimating the x-ray beam laterally to extend the FOV object. Figure (12): Different sizes of FOV. 32 Figure (13): Isotropic CBCT voxels (left) in comparison to anisotropic conventional CT voxels (right). 37 Figure (14): Initial presentation of cone beam computed tomography image slices in three orthogonal planes. 44 29 32 VI
Figure (15): Bilateral linear oblique multiplanar reformation through lateral and medial poles of the mandibular condyle on the axial image (a) providing corrected coronal, limited field-of-view, thin-slice temporomandibular views (b) demonstrating right condylar hyperplasia. Figure (16): Curved MPR simulated panoramic image from CBCT showing CBCT applications in temporomandibular joint assessment. Reformatted panoramic image (top) showing right side condyle differences in shape compared with normal left. Figure (17): Reformatted panoramic image (a) providing reference for multiple narrow trans-axial thin cross-sectional slices (b) of radiolucent bony pathology in the left mandible, demonstrating buccolingual expansion and location of the inferior alveolar canal. Figure (18): Ray sum simulated lateral cephalometric projection. 49 Figure (19): Visualization options for a cone-beam computed tomography volume. A. Visualization of the full data volume by means of a shaded surface display method with thresholds set to show the soft tissues. B. Visualization of the full data volume by means of a shaded surface display with the threshold set to show hard tissues (bone and teeth) only. C. The volume rendering method. The data attenuation 51 values corresponding to the soft tissues were made partially transparent, allowing for visualization of the underlying skeleton and teeth. Figure (20): Maximum intensity profile rendering of a reformatted panoramic image. 53 46 47 48 Figure (21): CBCT images of a patient with a mandibular cyst. A. Mesial view of the right half of the mandible in a surface mode. B. Anterior view of the mandible in the surface mode (measurements in mm).c. Lingual view of the mandible in surface mode (measurements in mm). D. Radiographic cross-sectional view of the maxilla and mandible. E, Panoramic view. 55 VII
Figure (22): (A) Typical implant planning image set shows a generic implant fixture orientation in relation to the inferior alveolar nerve. (B) A close-up image of the case above isolating the 3D color volume and proposed implant placement visualization. (C) A 3D colorized view to show the submandibular fossa in relation to the implant site. (D) A colorized slab rendering allows the clinician to actually see the canal and the desired position of the intended implant fixture. Precise measurements can now be made. 58 Figure (23): CBCT images of a patient with a cleft palate. A, Anterior view of the maxilla in the surface mode. B, Anterior view of the maxilla in the radiographic mode. C, Occlusal view of the maxilla in the surface mode. D, Occlusal view of the maxilla in the radiographic mode. Figure (24): Image layout of a typical examination of the right and left temporomandibular joint. (a) Axial image, (b) lateral images perpendicular to the long axis of the condyle (closed mouth), (c) central lateral images (closed and open mouth), (d) coronal views parallel to the long axis of the condyle (closed and open mouth) and (e) three-dimensional reconstructions (closed and open mouth). Figure (25): (A) Periapical radiograph displaying an impacted tooth #11. (B) CBCT axial image showing the palatal position of tooth #11. (C) CBCT image of the relationship between teeth # 9 and #11. (D) Sagittal CBCT image displaying the proximity of #11 to the root of #10. Figure (26): CBCT airway view displaying the volume of the airway and sinuses. 71 Figure (27): A group of bone pieces in an arch-shape with the guttapercha markers on them cemented to a thin foam sheet. 87 Figure (28): The bone pieces on the sheet were placed in a watertight clear container to be immersed in water. 87 Figure (29): Scanning of the bone specimens using a computed tomography scanner. 88 Figure (30): Scanning of the bone specimens using a cone beam computed tomography scanner. 89 60 63 70 Figure (31): Simplant software preview screen demonstrating the axial image, reformatted panoramic image, cross-sectional image and 3D image. VIII 91
Figure (32): One of the investigated pieces assessed by the Simplant software using method one. Eight measurements were taken for the cortical bone in the whole slice. Figure (33): One of the investigated pieces assessed by the Simplant software using method one. Eight measurements were taken covering the cancellous bone. Figure (34): A different investigated piece assessed by the Simplant software using method one. One measurement was taken for the cancellous bone in the whole slice. Figure (35): One of the investigated pieces assessed by the Simplant software using method two. One measurement was taken for the cancellous bone in each half. Figure (36): Another investigated piece assessed by the Simplant software using method two. Two-three measurements were taken for the cortical bone in each half. Figure (37): Bar chart representing Cronbach s alpha results for the agreement between (CT WS 80 kvp) and (CT Simplant 80 kvp) in cortical bone. Figure (38): Bar chart representing Cronbach s alpha results for the agreement between (CT WS 100 kvp) and (CT Simplant 100 kvp) in cortical bone. Figure (39): Bar chart representing Cronbach s alpha results for the agreement (CT WS 120 kvp) and (CT Simplant 120 kvp) in cortical bone. Figure (40): Bar chart representing Cronbach s alpha results for the agreement between (CT WS 80 kvp), (CT WS 100 kvp) and (CT WS 120 kvp) in cortical bone. Figure (41): Bar chart representing Cronbach s alpha results for the agreement (CT Simplant 80 kvp), (CT Simplant 100 kvp) and (CT Simplant 120 kvp) in cortical bone. Figure (42): Bar chart representing Cronbach s alpha results for the agreement between (CT WS 80 kvp) and (CT Simplant 80 kvp) in cancellous bone. 92 92 93 94 94 100 102 103 105 106 108 IX
Figure (43): Bar chart representing Cronbach s alpha results for the agreement between (CT WS 100 kvp) and (CT Simplant 100 kvp) in cancellous bone. Figure (44): Bar chart representing Cronbach s alpha results for the agreement between (CT WS 120 kvp) and (CT Simplant 120 kvp) in cancellous bone. Figure (45): Bar chart representing Cronbach s alpha results for the agreement (CT WS 80 kvp), (CT WS 100 kvp) and (CT WS 120 kvp) in cancellous bone. Figure (46): Bar chart representing Cronbach s alpha results for the agreement (CT Simplant 80 kvp), (CT Simplant 100 kvp) and (CT Simplant 120 kvp) in cancellous bone. 109 111 112 114 Figure (47): Bar chart representing Cronbach s alpha results for the agreement (CBCT Simplant) and (CBCT i-vision) in cortical bone. 115 Figure (48): Bar chart representing Cronbach s alpha results for the agreement (CBCT Simplant) and (CBCT i-vision) in cancellous bone. 117 Figure (49): Bar chart representing Cronbach s alpha results for the agreement between (CBCT i-vision) and (CT WS 80 kvp) in cortical bone. Figure (50): Bar chart representing Cronbach s alpha results for the agreement between (CBCT i-vision) and (CT WS 100) in cortical bone. Figure (51): Bar chart representing Cronbach s alpha results for the agreement between (CBCT i-vision) and (CT WS 120 kvp) in cortical bone. Figure (52): Bar chart representing Cronbach s alpha results for the agreement between (CBCT i-vision) and (CT WS 80 kvp) in cancellous bone. Figure (53): Bar chart representing Cronbach s alpha results for the agreement between (CBCT i-vision) and (CT WS 100 kvp) in cancellous bone. 118 120 121 123 124 X
Figure (54): Bar chart representing Cronbach s alpha results for the agreement between (CBCT i-vision) and (CT WS 120 kvp) in cancellous bone. Figure (55): Bar chart representing Cronbach s alpha results for the agreement between (CBCT Simplant) and (CT Simplant 120 kvp) in cortical bone. Figure (56): Bar chart representing Cronbach s alpha results for the agreement between (CBCT Simplant) and (CT Simplant 120 kvp) in cancellous bone. Figure (57): Bar chart representing Cronbach s alpha results for the agreement between (CBCT Simplant) and (CT WS 120 kvp) in cortical bone. Figure (58): Bar chart representing Cronbach s alpha results for the agreement between (CBCT Simplant) and (CT WS 120 kvp) in cancellous bone. 126 127 129 130 132 XI
List of Tables Table (1) The effect of interkilovoltage variations, and the effect of using a third party software on the mean values of CT and CBCT measurements in cortical and cancellous bone. (2) Results of Cronbach's alpha coefficient for the agreement between (CT WS 80 kvp) and (CT Simplant 80 kvp) in cortical bone. (3) Results of Cronbach's alpha coefficient for the agreement between (CT WS 100 kvp) and (CT Simplant 100 kvp) in cortical bone. (4) Results of Cronbach's alpha coefficient for the agreement between (CT WS 120 kvp) and (CT Simplant 120 kvp) in cortical bone. (5) Results of Cronbach's alpha coefficient for the agreement between (CT WS 80 kvp), (CT WS 100 kvp) and (CT WS 120 kvp) in cortical bone. (6) Results of Cronbach's alpha coefficient for the agreement between (CT Simplant 80 kvp), (CT Simplant 100 kvp) and (CT Simplant 120 kvp) in cortical bone. (7) Results of Cronbach's alpha coefficient for the agreement between (CT WS 80 kvp) and (CT Simplant 80 kvp) in cancellous bone. (8) Results of Cronbach's alpha coefficient for the agreement between (CT WS 100 kvp) and (CT Simplant 100 kvp) in cancellous bone. (9) Results of Cronbach's alpha coefficient for the agreement between (CT WS 120 kvp) and (CT Simplant 120 kvp) in cancellous bone. (10) Results of Cronbach's alpha coefficient for the agreement between (CT WS 80 kvp), (CT WS 100 kvp) and (CT WS 120 kvp) in cancellous bone. (11) Results of Cronbach's alpha coefficient for the agreement between (CT Simplant 80 kvp), (CT Simplant 100 kvp) and (CT Simplant 120 kvp) in cancellous bone. (12) Results of Cronbach's alpha coefficient for the agreement between (CBCT Simplant) and (CBCT i-vision) in cortical bone. (13) Results of Cronbach's alpha coefficient for the agreement between (CBCT Simplant) and (CBCT i-vision) in cancellous bone. Page 98-99 100 101 103 104 106 107 109 110 112 113 115 116 XII
(14) Results of Cronbach's alpha coefficient for the agreement between (CBCT i-vision) and (CT WS 80 kvp) in cortical bone. (15) Results of Cronbach's alpha coefficient for the agreement between (CBCT i-vision) and (CT WS 100 kvp) in cortical bone. (16) Results of Cronbach's alpha coefficient for the agreement between (CBCT i-vision) and (CT WS 120 kvp) in cortical bone. (17) Results of Cronbach's alpha coefficient for the agreement between (CBCT i-vision) and (CT WS 80 kvp) in cancellous bone. 122 (18) Results of Cronbach's alpha coefficient for the agreement between (CBCT i-vision) and (CT WS 100 kvp) in cancellous bone. 124 (19) Results of Cronbach's alpha coefficient for the agreement between (CBCT i-vision) and (CT WS 120 kvp) in cancellous bone. 125 (20) Results of Cronbach's alpha coefficient for the agreement between (CBCT Simplant) and (CT Simplant 120 kvp) in cortical bone. 127 (21) Results of Cronbach's alpha coefficient for the agreement between (CBCT Simplant) and (CT Simplant 120 kvp) in cancellous bone. 128 (22) Results of Cronbach's alpha coefficient for the agreement between (CBCT Simplant) and (CT WS 120 kvp) in cortical bone. 130 (23) Results of Cronbach's alpha coefficient for the agreement between (CBCT Simplant) and (CT WS 120 kvp) in cancellous bone. 131 118 119 121 XIII
List of Abbreviations AC BMC BMD CBCT CCD CT DC DICOM DPA DXA DSR FBP FDA FOV FPD HU II kev kvp kw lp mas MDCT μsv MIP MPR MRI MSCT OSA PC PET/CT Alternating Current Bone Mineral Content Bone Mineral Density Cone Beam Computed Tomography Charged-Coupled Device Computed Tomography Direct Current Digital Imaging and Communications in Medicine Dual Photon Absorpiometry Dual-energy X-ray absorptiometry (DXA) Digital Subtraction Radiography Filtered Back Projection Food and Drug Administration Field of View Flat Panel Detector Hounsfield Unit Image Intensifier Kilo electron Volts kilo Voltage peak Kilo Wattage Line Pair milli Ampere second Multi-Detector Computed Tomography micro Sievert Maximum Intensity Projection Multi-planar Reformatting Magnetic Resonance Imaging Multi-Slice Computed Tomography Obstructive Sleep Apnea Personal Computer Positron Emission Tomography/Computed Tomography XIV
QCT QUS ROI SPSS TMDs TMJ VCT WS 2D 3D Quantitative Computed Tomography Quantitative Ultrasonography Region of Interest Statistical Package for the Social Sciences Temporo Mandibular Disorders Temporo Mandibular Joint Volumetric Computerized Tomography Work Station Two-Dimensional Three-Dimensional XV
Introduction Introduction Evaluation of bone density is essential to assist the dental practitioner in many clinical situations. The most common method for estimating bone quality is X-ray evaluation. Quantitative computed tomography (CT) or dual photon X-ray absorptiometry could be used to measure the bone density. Other methods for assessing bone quality have included histomorphometry of bone, digital image analysis of microradiographs and ultrasound. Most of these techniques provide a reliable quantitative measure of bone density but in the clinical circumstances of the dental clinic, their usefulness is limited due to the lack of proper equipments and the difficulty of carrying out their procedures (Turkyilmaz et al 2007 and Lee et al 2010). Computed tomography (CT) was first developed in the early 1970s. Hounsfield received a Nobel Prize for his achievement and also gave his name to the measure of radiodensity commonly applied in CT. The density of structures within the image is absolute and quantitative and can be used to differentiate between tissues in the examined area. Computed tomography permits the evaluation of proposed implant sites and provides diagnostic Information that other imaging methods could not (Dawood et al 2009). Cone beam computed tomography (CBCT) scanners were first developed for use in angiography. Mozzo et al 1998 reported the first CBCT unit developed specifically for dental use. CBCT proved superiority to CT regarding minimizing the radiation as well as 1
Introduction reducing the size of the irradiated area as most Cone Beam CT units can be adjusted to scan small regions for specific diagnostic task, low cost, convenient size, ease of operation and relatively quick scans (Mah and Hatcher 2003). The data collected by CT and CBCT scanners can be used to determine the density of scanned tissues or objects. Images of both modalities provide X-ray attenuation information for specific sized image pixels/voxels in terms of Hounsfield units (HU) (Lagravère et al 2008). Based on the previously mentioned merits of cone beam computed tomography, it can be considered a promising modality to be widely used in dentistry. The available research on CBCT-based bone quality assessment is scarce and hampered by the inherent technical constraints of CBCT image data sets. Bone quality assessment using it needs to be further investigated for confirmation (Hua et al 2009 and Mah et al, 2010). That s why this study was undertaken to throw the light on the validity of assessing bone density using CBCT when compared with CT. 2
Computed Tomography (CT) Review of Literature Since the invention of X-rays by Roentgen in 1895, technology has led to a revolution in diagnostic medicine, making it possible to see the inner workings of the body. Within the last 20 years, diagnostic digital imaging modalities in dentistry, including periapical, bitewing, panoramic and cephalometric imaging, have been replacing conventional (film-based) radiography. In spite of being digital modalities, but still have the drawbacks of two-dimensional (2D) imaging which include superimposition with overlap of anatomical structures, inherent magnification and distortion (Dunn 2001, Howerton & Mora 2008 and Swain & Xue 2009). Computed tomography (CT) imaging was first developed in the early 1970s. CT provides a digital as well as three-dimensional imaging that has been used to overcome the inherent problems with conventional two dimensional radiographic techniques. In CT, the patient is scanned and digital processing is used to generate image data. This pioneering imaging system was developed by Sir Godfrey Hounsfield. Hounsfield received a Nobel Prize for his achievement and also gave his name to the measure of radiodensity commonly applied in CT (Swain & Xue 2009 and Dawood et al 2009). 3
Review of Literature Computed tomography is a medical imaging method employing tomography where digital geometry processing is used to generate a three-dimensional image of the internals of an object from a large series of two-dimensional X-ray images taken around a single axis of rotation. CT comprises thin section tomographic imaging, electronic image acquisition, and computerized image generation (William 2007). Technical Consideration in CT Imaging Computed tomography literally means slice imaging. Like radiography, CT is based on differential attenuation of X-rays by the body. Unlike plain radiography, CT generates cross-sectional images with exquisite detail without the problem of overlapped tissues. It could be said that a CT scanner makes many measurements of attenuation through the plane of a finite-thickness cross section of the body (Mahesh 2002 and Shekhar et al 2010). In the scanning phase of CT, an X-ray source and an array of detectors located opposite to each other with the body in between, rotate to produce a number of projections. Projection data sets are acquired throughout the 360 rotation angles of the X-ray tube around the patient. The projections (also referred to as the raw data) are processed to form a cross-sectional image representing a map of tissue attenuation coefficient (Budoff et al 2005 & Shekhar et al 2010) (Figure 1). 4
Review of Literature Figure (1): CT data acquisition (Miracle & Mukherji 2009a). The second step during CT imaging is the tomographic reconstruction, which describes the mathematical process of calculating the image from the acquired data. A filtered back projection (FBP) mathematical algorithm converts the projection data points (raw data) into a CT image. It determines how much attenuation of the narrow X- ray beam occurs in each voxel of the reconstruction matrix. These calculated attenuation values are then represented as gray levels in a twodimensional image of the slice (Goldman 2007 and Engelke et al 2008) (Figure 2) and (Figure 3). 5