Reflection of an ultrasonic wave on the bone−implant interface: Effect of the roughness parameters

Abstract

Quantitative ultrasound can be used to characterize the evolution of the bone-implant interface (BII), which is a complex system due to the implant surface roughness and to partial contact between bone and the implant. The aim of this study is to derive the main determinants of the ultrasonic response of the BII during osseointegration phenomena. The influence of i) the surface roughness parameters and ii) the thickness W of a soft tissue layer on the reflection coefficient r of the BII was investigated using a two-dimensional finite element model.When W increases from 0 to 150 μm, r increases from values in the range [0.45; 0.55] to values in the range [0.75; 0.88] according to the roughness parameters. An optimization method was developed to determine the sinusoidal roughness profile leading to the most similar ultrasonic response for all values of W compared to the original profile. The results show that the difference between the ultrasonic responses of the optimal sinusoidal profile and of the original profile was lower to typical experimental errors. This approach provides a better understanding of the ultrasonic response of the BII, which may be used in future numerical simulation realized at the scale of an implant.

Assessment of dental implant stability using resonance frequency analysis and quantitative ultrasound methods

“…The ISQ is related to the resonance frequency of the bone-implant system, which depends on properties of the entire host bone that vibrates when excited mechanically (24). However, QUS measurements are only sensitive on bone tissue located at a distance lower than around 30 µm from the implant surface (28)(29)(30), which corresponds to the region of interest where osseointegration phenomena are known to occur (31,53). Therefore, QUS are likely to be more sensitive to the properties of the BII and to osseointegration phenomena.…”

“…The interaction between an ultrasonic wave and a cylindrical implant was modeled numerically (26,27) at the scale of the implant. Then, a realistic 3-D geometry was considered (28) and two studies carried out at the microscopic scale and taking into account the implant surface roughness and the implant threading allowed to quantify the effect of osseointegration phenomena on the ultrasonic response of the BII (29,30).…”

Ultrasonic assessment of osseointegration phenomena at the bone-implant interface using convolutional neural network

“…Therefore, numerical studies considering 2-D and 3-D finite element modeling (Vayron et al, 2015;Vayron et al, 2016;Dorogoy et al, 2020;Kwak et al, 2020) have been used to understand wave propagation in a dental implant but considered the BII as fully bonded, neglecting the effect of partial contact and of surface roughness. More recently, a finite element model was developed in the time domain to investigate the sensitivity of the ultrasonic response to surface roughness properties of the BII and to osseointegration processes (Hériveaux et al, 2018;Hériveaux et al, 2019a;Hériveaux et al, 2020). The implant roughness was first modeled with an idealized sinusoidal profile (Hériveaux et al, 2018) and actual implant roughness profiles (Hériveaux et al, 2019a), and the 3D case was eventually considered (Hériveaux et al, 2020).…”

“…More recently, a finite element model was developed in the time domain to investigate the sensitivity of the ultrasonic response to surface roughness properties of the BII and to osseointegration processes (Hériveaux et al, 2018;Hériveaux et al, 2019a;Hériveaux et al, 2020). The implant roughness was first modeled with an idealized sinusoidal profile (Hériveaux et al, 2018) and actual implant roughness profiles (Hériveaux et al, 2019a), and the 3D case was eventually considered (Hériveaux et al, 2020). However, it still remains difficult to perform the inversion of the ultrasound signal in order to determine the biomechanical properties of the BII.…”

“…Osseointegration phenomena are determined based on the estimation of the soft tissue thickness present at the BII that is obtained by analyzing the interaction of the ultrasonic waves with the BII. To do so, the forward model was modified compared to (Hériveaux et al, 2018;Hériveaux et al, 2019a;Hériveaux et al, 2020) since it was derived in the frequency domain and then translated into the time domain, which has the advantage of significantly reducing the computation cost, allowing to construct an annotated large dataset. The CNN based-assessment method was then applied to the aforementioned dataset to assess the osseointegration at the microscopic and macroscopic roughness scales.…”