The relationship between dental implant stability and trabecular bone structure using cone-beam computed tomography

Abstract

Purpose: The objective of this study was to investigate the relationships between primary implant stability as measured by impact response frequency and the structural parameters of trabecular bone using cone-beam computed tomography(CBCT), excluding the effect of cortical bone thickness.

Methods: We measured the impact response of a dental implant placed into swine bone specimens composed of only trabecular bone without the cortical bone layer using an inductive sensor. The peak frequency of the impact response spectrum was determined as an implant stability criterion (SPF). The 3D microstructural parameters were calculated from CT images of the bone specimens obtained using both micro-CT and CBCT.

Results: SPF had significant positive correlations with trabecular bone structural parameters (BV/TV, BV, BS, BSD, Tb.Th, Tb.N, FD, and BS/BV) (P<0.01) while SPF demonstrated significant negative correlations with other microstructural parameters (Tb.Sp, Tb.Pf, and SMI) using micro-CT and CBCT (P<0.01).

Conclusions: There was an increase in implant stability prediction by combining BV/TV and SMI in the stepwise forward regression analysis. Bone with high volume density and low surface density shows high implant stability. Well-connected thick bone with small marrow spaces also shows high implant stability. The combination of bone density and architectural parameters measured using CBCT can predict the implant stability more accurately than the density alone in clinical diagnoses.

Keywords: Bone and bones; Cone-beam computed tomography; Dental implants; X-ray microtomography.

Figure 1

Measurement of implant stability in bone samples using an inductive sensor. The implant (A) with an adaptor (B) was placed into the swine bone sample (C) and was tapped using the tapping rod of the Periotest (D). An inductive sensor (E) measured the movement of the implant-adapter assembly.

Figure 2

Examples of impulse response signals (A, B) and their power spectra (C, D) in two different bone samples, and CT images using micro-CT (E, F) and using CBCT (G, H). The implant (B, D) placed at the bone sample (F, H) with higher volume density and well-connected thick trabeculae shows higher stability than that that (A, C) at the bone sample (E, G).

Figure 3

Relationships between SPF and 3D bone microstructural parameters of BV/TV (A), BV (B), BS (C), BS/BV (D), BSD (E), Tb.Th (F), Tb.Sp (G), Tb.N (H), Tb.Pf (I), SMI (J), and FD (K) for 23 bone specimens using micro-CT (P<0.01) (SPF, stability according to peak frequency; BV/TV, percent bone volume; BV, bone volume; BS, bone surface; BSD (BS/TV), bone surface density; Tb.Th, trabecular thickness; Tb.Sp, trabecular separation; Tb.N, trabecular number; Tb.Pf, trabecular bone patternfactor; SMI, structural model index; FD, fractal dimension).

Figure 4

Relationships between SPF and 3D bone microstructural parameters of BV/TV (A), BV (B), BS (C), BS/BV (D), BSD (E), Tb.Th (F), Tb.Sp (G), Tb.N (H), Tb.Pf (I), SMI (J), and FD (K) for 23 bone specimens using CBCT (P<0.01, P<0.05 for Tb.Sp and FD) (SPF, stability according to peak frequency; BV/TV, percent bone volume; BV, bone volume; BS, bone surface; BSD (BS/TV), bone surface density; Tb.Th, trabecular thickness; Tb.Sp, trabecular separation; Tb.N, trabecular number; Tb.Pf, trabecular bone patternfactor; SMI, structural model index; FD, fractal dimension).