Human Bone Typing Using Quantitative Cone-Beam Computed Tomography

Abstract

Introduction: Bone typing is crucial to enable the choice of a suitable implant, the surgical technique, and the evaluation of the clinical outcome. Currently, bone typing is assessed subjectively by the surgeon.

Objective: The aim of this study is to establish an automatic quantification method to determine local bone types by the use of cone-beam computed tomography (CBCT) for an observer-independent approach.

Methods: Six adult human cadaver skulls were used. The 4 generally used bone types in dental implantology and orthodontics were identified, and specific Hounsfield unit (HU) ranges (grey-scale values) were assigned to each bone type for identification by quantitative CBCT (qCBCT). The selected scanned planes were labelled by nonradiolucent markers for reidentification in the backup/cross-check evaluation methods. The selected planes were then physically removed as thick bone tissue sections for in vitro correlation measurements by qCBCT, quantitative micro-computed tomography (micro-CT), and quantitative histomorphometry.

Results: Correlation analyses between the different bone tissue quantification methods to identify bone types based on numerical ranges of HU values revealed that the Pearson correlation coefficient of qCBCT with micro-CT and quantitative histomorphometry was R = 0.9 (P = .001) for all 4 bone types .

Conclusions: We found that qCBCT can reproducibly and objectively assess human bone types at implant sites.

Keywords: Bone types; CBCT; Dental implantology; Human; Quantification.

Fig. 1

Graphical abstract of the experimental design.

Fig. 2

Cone-beam computed tomography (CBCT) instrument calibration by K₂HPO₄ and data analysis by 2 different software products. A, By using a defined concentration series of K₂HPO₄ (dipotassium hydrogen phosphate) electrolyte solution calibration was attained. The solutions were used in the range of 50 mg/mL to 1000 mg/mL K₂HPO₄, yielding corresponding Hounsfield unit (HU) values; control reference values were obtained using air and water. The representative CBCT images of these radiographs illustrate the corresponding grey value levels. B, CBCT analysis of the calibration solutions of K₂HPO₄ was performed by using 2 different software products: blue line = Planmeca Promax 3D (Planmeca Oy) and red line = Simplant 16 (Densply). These 2 analytical tools, based on product specific reconstruction algorithms, delivered identical results, thus providing a basis for data comparison irrespective of the equipment used.

Fig. 3

Illustration of the quantitative bone-type measurements. Bone-type measurements were obtained by (A) cone-beam computed tomography (CBCT), (B) histomorphometry, and (C) micro–computed tomography (CT) (representative sample specimen).

Fig. 4

Graphic presentation of the quantitative bone type results. A, Presentation of the quantitative (q) cone-beam computed tomography (CBCT) data for each bone type. X-axis: bone types 1 to 4; y-axis: qCBCT data in Hounsfield units (HUs). Pearson correlation coefficient = 0.9. B, Quantitative micro–computed tomography data (% mineralised bone density) are represented on the y-axis. Pearson correlation coefficient = 0.9. C, Quantitative histomorphometric data (% mineralised bone volume density) are represented on the y-axis. Pearson correlation coefficient = 0.9. The Pearson correlation coefficients for each method are thus 0.9.