CT-based visualization and quantification of bone microstructure in vivo

Probing the Influence of Hybrid Thread Design On Biomechanical Response of Dental Implants: Finite Element Study and Experimental Validation

OBJECTIVE

To perform quantitative biomechanical analysis, probing the effect of varying thread shapes in an implant for improved primary stability in prosthodontics surgery.

METHODS

Dental implants with square (SQR), buttress (BUT) and triangular (TRI) thread shapes or their combinations. Cone-beam computed tomography images of mandible molar zones in human subjects belonging to three age groups were used for virtual implantation of designed implants, to quantify patient-specific peri-implant bone micro-strain, using finite element analyses. The in silico analyses were carried out considering frictional contact to simulate immediate loading with a static masticatory force of 200 N. In order to validate computational biomechanics results, compression tests were performed on 3D printed implants having investigated thread architectures. Bone/implant contact areas were also quantitatively assessed.

RESULTS

Bone/implant contact was maximum for SQR implants followed by BUT and TRI implants. For all the cases, peak micro-strain was recorded in the cervical cortical bone. The combination of different thread shapes in the middle or apical part (or both) was demonstrated to improve peri-implant micro-strain particularly for BUT and TRI.

CONCLUSIONS

Considering 1500-2000 micro-strain generates in peri-implant bone during regular physiological functioning, BUT-SQR, BUT-TRI-SQR, TRI-SQR-BUT, SQR, SQR-BUT-TRI design concepts were suitable for younger; BUT-TRI-SQR, BUT-SQR-TRI, TRI-SQR-BUT, SQR-BUT, SQR-TRI for middle-aged, and BUT-TRI-SQR, BUT-SQR-TRI, TRI-BUT-SQR, SQR, SQR-TRI for older group of human patients.

Assessing fracture risk using gradient boosting machine (GBM) models

Advanced bone imaging with quantitative computed tomography (QCT) has had limited success in significantly improving fracture prediction beyond standard areal bone mineral density (aBMD) measurements. Thus, we examined whether a machine learning paradigm, gradient boosting machine (GBM) modeling, which can incorporate diverse measurements of bone density and geometry from central QCT imaging and of bone microstructure from high-resolution peripheral QCT imaging, can improve fracture prediction. We studied two cohorts of postmenopausal women: 105 with and 99 without distal forearm fractures (Distal Forearm Cohort) and 40 with at least one grade 2 or 3 vertebral deformity and 78 with no vertebral fracture (Vertebral Cohort). Within each cohort, individual bone density, structure, or strength variables had areas under receiver operating characteristic curves (AUCs) ranging from 0.50 to 0.84 (median 0.61) for discriminating women with and without fracture. Using all possible variables in the GBM model, the AUCs were close to 1.0. Fracture predictions in the Vertebral Cohort using the GBM models built with the Distal Forearm Cohort had AUCs of 0.82-0.95, while predictions in the Distal Forearm Cohort using models built with the Vertebral Cohort had AUCs of 0.80-0.83. Attempts at capturing a comparable parametric model using the top variables from the Distal Forearm Cohort resulted in resulted in an AUC of 0.81. Relatively high AUCs for differing fracture types suggest that an underlying fracture propensity is being captured by this modeling approach. More complex modeling, such as with GBM, creates stronger fracture predictions and may allow deeper insights into information provided by advanced bone imaging techniques.

Determinants of forearm strength in postmenopausal women

Bone strength at the ultradistal radius, quantified by micro-finite element modeling, can be predicted by variables obtained from high-resolution peripheral quantitative computed tomography scans. The specific formula for this bone strength surrogate (-555.2 + 8.1 × [trabecular vBMD] + 19.6 × [cortical area] + 4.2 × [total cross-sectional area]) should be validated and tested in fracture risk assessment.

INTRODUCTION

The purpose of this study was to identify key determinants of ultradistal radius (UDR) strength and evaluate their relationships with age, sex steroid levels, and measures of habitual skeletal loading.

METHODS

UDR failure load (~strength) was assessed by micro-finite element (μFE) modeling in 105 postmenopausal controls from an earlier forearm fracture case-control study. Predictors of bone strength obtained by high-resolution peripheral quantitative computed tomography (HRpQCT) in this group were then evaluated in a population-based cohort of 214 postmenopausal women. Sex steroids were measured by mass spectrometry.

RESULTS

A surrogate variable (-555.2 + 8.1 × [trabecular vBMD] + 19.6 × [cortical area] + 4.2 × [total cross-sectional area]) predicted UDR strength modeled by μFE (R(2) = 0.81), and all parameters except total cross-sectional area declined with age. Evaluated cross-sectionally, the 21% fall in predicted bone strength between ages 40-49 years and 80+ years more resembled the change in trabecular volumetric bone mineral density (vBMD) (-15%) than that in cortical area (-41%). In multivariable analyses, measures of body composition and physical activity were stronger predictors of UDR trabecular vBMD, cortical area, total cross-sectional area, and predicted bone strength than were sex steroid levels, but bio-available estradiol and testosterone were correlated with body mass.

CONCLUSIONS

Bone strength at the UDR, as quantified by μFE, can be predicted from variables obtained by HRpQCT. Predicted bone strength declines with age with changes in UDR trabecular vBMD and cortical area, related in turn to reduced skeletal loading and sex steroid levels. The predicted bone strength formula should be validated and tested in fracture risk assessment.

Extracting 3D information on bone remodeling in the proximity of titanium implants in SRμCT image volumes

Bone-implant integration is measured in several ways. Traditionally and routinely, 2D histological sections of samples, containing bone and the biomaterial, are stained and analyzed using a light microscope. Such histological section provides detailed cellular information about the bone regeneration in the proximity of the implant. However, this information reflects the integration in only a very small fraction, a 10 μm thick slice, of the sample. In this study, we show that feature values quantified on 2D sections are highly dependent on the orientation and the placement of the section, suggesting that a 3D analysis of the whole sample is of importance for a more complete judgment of the bone structure in the proximity of the implant. We propose features describing the 3D data by extending the features traditionally used for 2D-analysis. We present a method for extracting these features from 3D image data and we measure them on five 3D SRμCT image volumes. We also simulate cuts through the image volume positioned at all possible section positions. These simulations show that the measurement variations due to the orientation of the section around the center line of the implant are about 30%.

Assessing forearm fracture risk in postmenopausal women

A diverse array of bone density, structure, and strength parameters were significantly associated with distal forearm fractures in postmenopausal women, but most of them were also correlated with femoral neck areal bone mineral density (aBMD), which provides an adequate measure of bone fragility at the wrist for routine clinical purposes.

INTRODUCTION

This study seeks to test the clinical utility of approaches for assessing forearm fracture risk.

METHODS

Among 100 postmenopausal women with a distal forearm fracture (cases) and 105 with no osteoporotic fracture (controls), we measured aBMD and assessed radius volumetric bone mineral density, geometry, and microstructure; ultradistal radius failure load was evaluated in microfinite element (microFE) models.

RESULTS

Fracture cases had inferior bone density, geometry, microstructure, and strength. The most significant determinant of fracture in five categories were bone density (femoral neck aBMD; odds ratio (OR) per standard deviation (SD), 2.0; 95% confidence interval (CI), 1.4-2.8), geometry (cortical thickness; OR, 1.5; 95% CI, 1.1-2.1), microstructure (structure model index (SMI); OR, 0.5; 95% CI, 0.4-0.7), and strength (microFE failure load; OR, 1.8; 95% CI, 1.3-2.5); the factor-of-risk (applied load in a forward fall / microFE failure load) was 15% worse in cases (OR, 1.9; 95% CI, 1.4-2.6). Areas under receiver operating characteristic curves (AUC) ranged from 0.62 to 0.68. The predictors of forearm fracture risk that entered a multivariable model were femoral neck aBMD and SMI (combined AUC, 0.71).

CONCLUSIONS

Detailed bone structure and strength measurements provide insight into forearm fracture pathogenesis, but femoral neck aBMD performs adequately for routine clinical risk assessment.