Osteoporosis imaging: effects of bone preservation on MDCT-based trabecular bone microstructure parameters and finite element models

Textural and geometric measures derived from digital tomosynthesis discriminate women with and without vertebral fracture

Vertebral fractures are a common and debilitating consequence of osteoporosis. Bone mineral density (BMD), measured by dual energy x-ray absorptiometry (DXA), is the clinical standard for assessing overall bone quantity but falls short in accurately predicting vertebral fracture. Fracture risk prediction may be improved by incorporating metrics of microstructural organization from an appropriate imaging modality. Digital tomosynthesis (DTS)-derived textural and microstructural parameters have been previously correlated to vertebral bone strength in vitro, but the in vivo utility has not been explored. Therefore, the current study sought to establish the extent to which DTS-derived measurements of vertebral microstructure and size discriminate patients with and without vertebral fracture. In a cohort of 93 postmenopausal women with or without history of vertebral fracture, DTS-derived microstructural parameters and vertebral width were calculated for T12 and L1 vertebrae, as well as lumbar spine BMD and trabecular bone score (TBS) from DXA images. Fracture patients had lower BMD and TBS, while DTS-derived degree of anisotropy and vertebral width were higher, compared to nonfracture (p < 0.02 to p < 0.003) patients. The addition of DTS-derived parameters (fractal dimension, lacunarity, degree of anisotropy and vertebral width) improved discriminative capability for models of fracture status (AUC = 0.79) compared to BMD alone (AUC = 0.67). For twelve additional participants who were imaged twice, in vivo repeatability errors for DTS parameters were low (0.2 % - 7.3 %). The current results support the complementary use of DTS imaging for assessing bone quality and improving the accuracy of fracture risk assessment beyond that achievable by DXA alone.

A Comparative Analysis of International Classification Systems to Predict the Risk of Collapse in Single-Level Osteoporotic Vertebral Fractures

INTRODUCTION

Various classifications for osteoporotic vertebral fractures (OVFs) have been introduced to enhance patient care and facilitate clinical communication. However, there is limited evidence of their effectiveness in predicting vertebral collapse, and very few studies have compared this association across different classification systems. This study aims to investigate the association between OVF categories, according to the most widely used classification systems, and vertebral collapse.

PATIENTS AND METHODS

A retrospective single-center study was conducted involving patients diagnosed with acute OVFs at the emergency department of a tertiary-level academic hospital with a minimum follow-up of 6 months. Vertebral fractures were independently classified by two radiologists according to several classification systems, including those proposed by Genant, Sugita, the German Society for Orthopedics and Trauma (DGOU), and the AO Spine. Associations between vertebral collapse and OVF classification systems were analyzed using bivariate and logistic regression analyses.

RESULTS

This study included 208 patients (82.7% females; mean age of 72.6 ± 9.2 years). The median follow-up time was 15 months, with L1 being the most common fracture site (47.6%). The most frequent OVF types observed, according to Genant's morphological, Genant's quantitative, Sugita 's, DGOU's, and AO Spine's classifications, were biconcave (50%), grade 0.5 (47.6%), bow-shaped (61.5%), OF2 (74%), and A1 (61.5%), respectively. All classifications, except for Genant's quantitative system, were significantly associated with vertebral collapse in bivariate analyses. Logistic regression analyses showed a significant association (p = 0.002) between the AO Spine classification and vertebral collapse, with 85.7% of A4 fractures developing collapse on follow-up.

CONCLUSIONS

The AO Spine classification showed the highest predictive capacity for vertebral collapse. Specifically, A4 fracture types showed a very high risk of vertebral collapse, confirming the need for non-conservative management of these fractures. Further multicentric and prospective studies are warranted to confirm these findings.

Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients

OBJECTIVE

This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images.

METHODS

A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA).

RESULTS

BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction.

CONCLUSION

This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.

Femoral osteoporosis prediction model using autosegmentation and machine learning analysis with PyRadiomics on abdomen-pelvic computed tomography (CT)

Background

With the advancement of artificial intelligence technology and radiomics analysis, opportunistic prediction of osteoporosis with computed tomography (CT) is a new paradigm in osteoporosis screening. This study aimed to assess the diagnostic performance of osteoporosis prediction by the combination of autosegmentation of the proximal femur and machine learning analysis with a reference standard of dual-energy X-ray absorptiometry (DXA).

Methods

Abdomen-pelvic CT scans were retrospectively analyzed from 1,122 patients who received both DXA and abdomen-pelvic computed tomography (APCT) scan from January 2018 to December 2020. The study cohort consisted of a training cohort and a temporal validation cohort. The left proximal femur was automatically segmented, and a prediction model was built by machine-learning analysis using a random forest (RF) analysis and 854 PyRadiomics features. The technical success rate of autosegmentation, diagnostic test, area under the receiver operator characteristics curve (AUC), and precision recall curve (AUC-PR) analysis were used to analyze the training and validation cohorts.

Results

The osteoporosis prevalence of the training and validation cohorts was 24.5%, and 10.3%, respectively. The technical success rate of autosegmentation of the proximal femur was 99.7%. In the diagnostic test, the training and validation cohorts showed 78.4% vs. 63.3% sensitivity, 89.4% vs. 98.1% specificity. The prediction performance to identify osteoporosis within the groups used for training and validation cohort was high and the AUC and AUC-PR to forecast the occurrence of osteoporosis within the training and validation cohorts were 90.8% [95% confidence interval (CI), 88.4-93.2%] vs. 78.0% (95% CI, 76.0-79.9%) and 94.6% (95% CI, 89.3-99.8%) vs. 88.8% (95% CI, 86.2-91.5%), respectively.

Conclusions

The osteoporosis prediction model using autosegmentation of proximal femur and machine-learning analysis with PyRadiomics features on APCT showed excellent diagnostic feasibility and technical success.

Sliding on cortical shell: Biomechanical characterization of the vertebral cannulation for pedicle screw insertion

BACKGROUND

Pedicular screws pull-out has been well studied unlike their insertion. A need for characterizing cannulation before pedicle screw implantation is highlighted in literature and offers promising prospects for future intra-operation instrumentation. A reliable cannulation protocol for ex-vivo testing in swine and cadaver vertebrae is presented in this work to predict extra pedicular perforation.

METHODS

An MTS Acumen 3 A/T electrodynamic device, with a tri-axis 3 kN Kistler load cell mounted on a surgical tool was used to reproduce surgeon's gesture by moving at a constant rotational speed of 10°/mm and performing a three-section test. Perforation of the pedicle's cortical shell was planned through a design of experiment on the surgical tool angle at the entry point. Samples were scanned before and after mechanical tests and reproducibility of the protocol was tested on synthetic foam. Computation of the angle between cannulation tool and pedicle cortical shell was performed as well as cannulation coefficient of each perforation section.

FINDINGS

A total of 68 pedicles were tested: 19 perforated and 21 non-perforated human pedicles, 17 perforated and 16 non-perforated swine pedicles. The reproducibility of the protocol for cannulation coefficient computation resulted in an intraclass correlation coefficient of 0.979. Cannulation coefficients results presented variability within spinal levels as well as between swine and human model. Correlation between bone density and cannulation coefficient was found significant (p < 0.005). Torque measurement was found to be the best predictor of perforation. Threshold of angle for prediction of perforation was found to be 21.7°.

INTERPRETATION

Characterizing pedicle cannulation enables to predict extra pedicular perforation. Influence of bone mineral density and patient-specific morphology on pedicle cannulation has been highlighted together with a comparison of swine and cadaver models.

Towards an effective sensing technology to monitor micro-scale interface loosening of bioelectronic implants

Instrumented implants are being developed with a radically innovative design to significantly reduce revision surgeries. Although bone replacements are among the most prevalent surgeries performed worldwide, implant failure rate usually surpasses 10%. High sophisticated multifunctional bioelectronic implants are being researched to incorporate cosurface capacitive architectures with ability to deliver personalized electric stimuli to peri-implant target tissues. However, the ability of these architectures to detect bone-implant interface states has never been explored. Moreover, although more than forty technologies were already proposed to detect implant loosening, none is able to ensure effective monitoring of the bone-implant debonding, mainly during the early stages of loosening. This work shows, for the first time, that cosurface capacitive sensors are a promising technology to provide an effective monitoring of bone-implant interfaces during the daily living of patients. Indeed, in vitro experimental tests and simulation with computational models highlight that both striped and circular capacitive architectures are able to detect micro-scale and macro-scale interface bonding, debonding or loosening, mainly when bonding is weakening or loosening is occurring. The proposed cosurface technologies hold potential to implement highly effective and personalized sensing systems such that the performance of multifunctional bioelectronic implants can be strongly improved. Findings were reported open a new research line on sensing technologies for bioelectronic implants, which may conduct to great impacts in the coming years.

Reliability of 3D image analysis and influence of contrast medium administration on measurement of Hounsfield unit values of the proximal femur

PURPOSE

To evaluate the reliability of 3D image analysis and the effect of an iodine contrast agent on the computed tomography (CT) Hounsfield unit (HU) values of the proximal femur.

MATERIALS AND METHODS

Fifty female patients (mean age, 61.3 years; age range, 50-79 years) who underwent both pre- and post-enhancement abdominopelvic CT scans were included in this retrospective study. Whole 3D volumes of the left proximal femur from the head to the lesser trochanter were extracted using the regional growth technique with commercial 3D software. Total volume, mean HU, and HU histogram analysis (HUHA) values of the extracted femur were calculated. HUHA distribution was classified into HUHAfat for the assumed fatty marrow (percentage of negative HU values) and HUHAdense-bone (percentage of HU values ≥ 126 HU). Reliability was assessed by calculating intra- and interobserver correlation coefficients (ICCs) and by drawing Bland-Altman plots. The effect of contrast medium administration was evaluated by the paired t-test.

RESULTS

All intra- and interobserver ICCs of 3D volume measurements showed excellent reproducibility (all ICCs > 0.90). On Bland-Altman analysis of two observers' 3D volume measurements, the differences in the mean total volume, HUHAfat, HUHAdense-bone, and mean HU were 2.4 cm3, 0.17%, 0.6%, and 1.9 HU, respectively. The mean difference in HU after contrast agent administration (-2.2 HU) was not significant (P = 0.27). The mean difference in HUHAfat and HUHAdense-bone after contrast agent administration were -1.1% and -2.2%, respectively, on the Bland-Altman plot. HUHAfat and HUHAdense-bone showed significant differences (P < 0.05). The 95% limits of agreement for HUHAfat, HUHAdense-bone, and mean HU were -3.6% to 1.3%, -6.5% to 2.1%, and -30.0 to 25.5 HU, respectively.

CONCLUSION

Image analysis based on 3D volume measurement of the proximal femur showed excellent reliability, with the contrast agent administration showing negligible influence on the mean HU.

Effect of the intervertebral disc on vertebral bone strength prediction: a finite-element study

BACKGROUND CONTEXT

Osteoporotic vertebral fractures (OVFs) are a prevalent skeletal condition in the elderly but the mechanism behind these fractures remain unclear due to the complex biomechanical interplay between spinal segments such as the vertebra and intervertebral discs (IVDs).

PURPOSE

To investigate the biomechanical influence of IVDs by (1) comparing finite element (FE)-predicted failure load with experimentally measured failure load of functional spinal units (FSUs) and (2) comparing this correlation with those of FE-predicted failure load and bone mineral density (BMD) of the single central vertebra with experimentally measured failure load.

STUDY DESIGN

A computational biomechanical analysis.

PATIENT SAMPLE

Ten thoracic FSUs consisting of a central vertebra, the adjacent IVDs, and the upper and lower halves of the adjacent vertebrae were harvested from formalin-fixed human donors (4 males, 6 females; mean age of 82±9 years).

OUTCOME MEASURES

The outcome measures included the prediction of vertebral strength and determination of BMD in FSUs and the single central vertebra and the correlation of both measures with experimentally measured vertebral strength of the FSUs.

METHODS

The FSUs underwent clinical multidetector computed tomography (MDCT) (spatial resolution: 250×250×600 μm³). BMD was determined for the FSUs from the MDCT images of the central vertebrae. FE-predicted failure load was calculated in the single central vertebra of the FSUs alone and the entire FSUs. Experimentally measured failure load of the FSUs was determined in a uniaxial biomechanical test.

RESULTS

BMD of the central vertebrae correlated significantly with experimentally measured failure load (R²=0.66, p<.02), whereas FE-predicted failure load of the central vertebra showed no significant correlation with experimentally measured failure load (p=.07). However, FE-predicted failure load of FSUs best predicted experimentally measured failure load of FSUs (R²=0.93, p<.0001).

CONCLUSIONS

This study demonstrated that routine clinical MDCT images can be an accurate and feasible tool for prediction of OVFs using patient-specific FE analysis of FSU models.

CLINICAL SIGNIFICANCE

Improved management of OVFs is essential amidst current clinical challenges. Implementation of a vertebral strength assessment tool could result in more accurate prediction of osteoporotic fracture risk and aid clinicians with better targeted early treatment strategies.

Association of bone mineral density with bone texture attributes extracted using routine magnetic resonance imaging

OBJECTIVE

Dual-energy X-ray absorptiometry (DXA)-derived bone mineral density (BMD) often fails to predict fragility fractures. Quantitative textural analysis using magnetic resonance imaging (MRI) may potentially yield useful radiomic features to predict fractures. We aimed to investigate the correlation between BMD and texture attributes (TAs) extracted from MRI scans and the interobserver reproducibility of the analysis.

METHODS

Forty-nine volunteers underwent lumbar spine 1.5-T MRI and DXA. Three-dimensional (3-D) gray-level co-occurrence matrices were measured from routine sagittal T2 fast spin-echo images using the IBEX software. Twenty-two TAs were extracted from 3-D segmented L3 vertebrae. The estimated concordance coefficient was calculated using linear regression analysis. A Pearson correlation coefficient analysis was performed to evaluate the correlation between BMD and the TAs. Interobserver reproducibility was assessed with the concordance coefficient described by Lin.

RESULTS

The results revealed a fair-to-moderate significant correlation between BMD and 13 TAs (r=-0.20 to 0.39; p<0.05). Eight TAs (autocorrelation, energy, homogeneity 1, homogeneity 1.1, maximum probability, sum average, sum variance, and inverse difference normalized) negatively correlated with BMD (r=-0.20 to -0.38; p<0.05), whereas five TAs (dissimilarity, difference entropy, entropy, sum entropy, and information measure corr 1) positively correlated with BMD (r=0.29-0.39; p<0.05). The interobserver agreement was almost perfect for all significant TAs (95% confidence interval, 0.92-1.00; p<0.05).

CONCLUSION

Specific TAs could be reliably extracted from routine MRI and correlated with BMD. Our results encourage future evaluation of the potential usefulness of quantitative texture measurements from MRI scans for predicting fragility fractures.

Correlation between vertebral bone microstructure and estimated strength in elderly women: An ex-vivo HR-pQCT study of cadaveric spine

PURPOSE

A vertebral fracture is the most common complication of osteoporosis, and various factors are involved in its occurrence. The purpose of this study was to investigate the role of trabecular and cortical bone microstructure on vertebral strength using high-resolution peripheral quantitative computed tomography (HR-pQCT).

METHODS

Three female cadaveric spines were investigated (average age: 80.3 years). The whole spine (T1-L4) was scanned by second-generation HR-pQCT at a voxel size of 60.7 μm. Bone microstructure analysis and micro finite element analysis were performed after excluding the upper and lower endplates and posterior elements of a total of 48 vertebrae. Correlations between trabecular and cortical bone microstructure parameters and estimated vertebral strength were analyzed by univariate and multivariate regression models.

RESULTS

Cortical thickness (Ct.Th) and trabecular thickness (Tb.Th) were strongly correlated with estimated failure load on univariate analysis (r = 0.89, 0.82). Trabecular volumetric bone mineral density (Tb.vBMD), bone volume fraction (BV/TV), trabecular number (Tb.N), and Ct.Th were correlated with estimated failure load on multivariate regression analysis.

CONCLUSIONS

It was suggested that, in addition to trabecular bone (Tb.vBMD, BV/TV, Tb.N), cortical bone (Ct.Th) contributed significantly to vertebral strength in elderly women.

Effect of Statistically Iterative Image Reconstruction on Vertebral Bone Strength Prediction Using Bone Mineral Density and Finite Element Modeling: A Preliminary Study

Statistical iterative reconstruction (SIR) using multidetector computed tomography (MDCT) is a promising alternative to standard filtered back projection (FBP), because of lower noise generation while maintaining image quality. Hence, we investigated the feasibility of SIR in predicting MDCT-based bone mineral density (BMD) and vertebral bone strength from finite element (FE) analysis. The BMD and FE-predicted bone strength derived from MDCT images reconstructed using standard FBP (FFBP) and SIR with (FSIR) and without regularization (FSIRB0) were validated against experimental failure loads (Fexp). Statistical iterative reconstruction produced the best quality images with regard to noise, signal-to-noise ratio, and contrast-to-noise ratio. Fexp significantly correlated with FFBP, FSIR, and FSIRB0. FFBP had a significant correlation with FSIRB0 and FSIR. The BMD derived from FBP, SIRB0, and SIR were significantly correlated. Effects of regularization should be further investigated with FE and BMD analysis to allow for an optimal iterative reconstruction algorithm to be implemented in an in vivo scenario.

Multidetector Computed Tomography Imaging: Effect of Sparse Sampling and Iterative Reconstruction on Trabecular Bone Microstructure

Multidetector computed tomography-based trabecular bone microstructure analysis ensures promising results in fracture risk prediction caused by osteoporosis. Because multidetector computed tomography is associated with high radiation exposure, its clinical routine use is limited. Hence, in this study, we investigated in 11 thoracic midvertebral specimens whether trabecular texture parameters are comparable derived from (1) images reconstructed using statistical iterative reconstruction (SIR) and filtered back projection as criterion standard at different exposures (80, 150, 220, and 500 mAs) and (2) from SIR-based sparse sampling projections (12.5%, 25%, 50%, and 100%) and equivalent exposures as criterion standard. Twenty-four texture features were computed, and those that showed similar values between (1) filtered back projection and SIR at the different exposure levels and (2) sparse sampling and equivalent exposures and reconstructed with SIR were identified. These parameters can be of equal value in determining trabecular bone microstructure with lower radiation exposure using sparse sampling and SIR.

Effect of radiation dose reduction on texture measures of trabecular bone microstructure: an in vitro study

Osteoporosis is characterized by bone loss and degradation of bone microstructure leading to fracture particularly in elderly people. Osteoporotic bone degeneration and fracture risk can be assessed by bone mineral density and trabecular bone score from 2D projection dual-energy X-ray absorptiometry images. However, multidetector computed tomography image based quantification of trabecular bone microstructure showed significant improvement in prediction of fracture risk beyond that from bone mineral density and trabecular bone score; however, high radiation exposure limits its use in routine clinical in vivo examinations. Hence, this study investigated reduction of radiation dose and its effects on image quality of thoracic midvertebral specimens. Twenty-four texture features were extracted to quantify the image quality from multidetector computed tomography images of 11 thoracic midvertebral specimens, by means of statistical moments, the gray-level co-occurrence matrix, and the gray-level run-length matrix, and were analyzed by an independent sample t-test to observe differences in image texture with respect to radiation doses of 80, 150, 220, and 500 mAs. The results showed that three features-namely, global variance, energy, and run percentage, were not statistically significant ([Formula: see text]) for low doses with respect to 500 mAs. Hence, it is evident that these three dose-independent features can be used for disease monitoring with a low-dose imaging protocol.

Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis

This study investigated the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. The results showed an acceptable reproducibility of texture features, and these features could discriminate healthy/osteoporotic fracture cohort with an accuracy of 83%.

INTRODUCTION

This aim of this study is to investigate the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis.

METHODS

We performed texture analysis at the spine in routine MDCT exams and investigated the effect of intravenous contrast medium (IVCM) (n = 7), slice thickness (n = 7), the long-term reproducibility (n = 9), and the ability to differentiate healthy/osteoporotic fracture cohort (n = 9 age and gender matched pairs). Eight texture features were extracted using gray level co-occurrence matrix (GLCM). The independent sample t test was used to rank the features of healthy/fracture cohort and classification was performed using support vector machine (SVM).

RESULTS

The results revealed significant correlations between texture parameters derived from MDCT scans with and without IVCM (r up to 0.91) slice thickness of 1 mm versus 2 and 3 mm (r up to 0.96) and scan-rescan (r up to 0.59). The performance of the SVM classifier was evaluated using 10-fold cross-validation and revealed an average classification accuracy of 83%.

CONCLUSIONS

Opportunistic osteoporosis screening at the spine using specific texture parameters (energy, entropy, and homogeneity) and SVM can be performed in routine contrast-enhanced MDCT exams.

Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship

PURPOSE

Cortical thickness and density are critical components in determining the strength of bony structures. Computed tomography (CT) is one possible modality for analyzing the cortex in 3D. In this paper, a model-based approach for measuring the cortical bone thickness and density from clinical CT images is proposed.

METHODS

Density variations across the cortex were modeled as a function of the cortical thickness and density, location of the cortex, density of surrounding tissues, and imaging blur. High resolution micro-CT data of cadaver proximal femurs were analyzed to determine a relationship between cortical thickness and density. This thickness-density relationship was used as prior information to be incorporated in the model to obtain accurate measurements of cortical thickness and density from clinical CT volumes. The method was validated using micro-CT scans of 23 cadaver proximal femurs. Simulated clinical CT images with different voxel sizes were generated from the micro-CT data. Cortical thickness and density were estimated from the simulated images using the proposed method and compared with measurements obtained using the micro-CT images to evaluate the effect of voxel size on the accuracy of the method. Then, 19 of the 23 specimens were imaged using a clinical CT scanner. Cortical thickness and density were estimated from the clinical CT images using the proposed method and compared with the micro-CT measurements. Finally, a case-control study including 20 patients with osteoporosis and 20 age-matched controls with normal bone density was performed to evaluate the proposed method in a clinical context.

RESULTS

Cortical thickness (density) estimation errors were 0.07 ± 0.19 mm (-18 ± 92 mg/cm(3)) using the simulated clinical CT volumes with the smallest voxel size (0.33 × 0.33 × 0.5 mm(3)), and 0.10 ± 0.24 mm (-10 ± 115 mg/cm(3)) using the volumes with the largest voxel size (1.0 × 1.0 × 3.0 mm(3)). A trend for the cortical thickness and density estimation errors to increase with voxel size was observed and was more pronounced for thin cortices. Using clinical CT data for 19 of the 23 samples, mean errors of 0.18 ± 0.24 mm for the cortical thickness and 15 ± 106 mg/cm(3) for the density were found. The case-control study showed that osteoporotic patients had a thinner cortex and a lower cortical density, with average differences of -0.8 mm and -58.6 mg/cm(3) at the proximal femur in comparison with age-matched controls (p-value < 0.001).

CONCLUSIONS

This method might be a promising approach for the quantification of cortical bone thickness and density using clinical routine imaging techniques. Future work will concentrate on investigating how this approach can improve the estimation of mechanical strength of bony structures, the prevention of fracture, and the management of osteoporosis.

Effects of dose reduction on bone strength prediction using finite element analysis

This study aimed to evaluate the effect of dose reduction, by means of tube exposure reduction, on bone strength prediction from finite-element (FE) analysis. Fresh thoracic mid-vertebrae specimens (n = 11) were imaged, using multi-detector computed tomography (MDCT), at different intensities of X-ray tube exposures (80, 150, 220 and 500 mAs). Bone mineral density (BMD) was estimated from the mid-slice of each specimen from MDCT images. Differences in image quality and geometry of each specimen were measured. FE analysis was performed on all specimens to predict fracture load. Paired t-tests were used to compare the results obtained, using the highest CT dose (500 mAs) as reference. Dose reduction had no significant impact on FE-predicted fracture loads, with significant correlations obtained with reference to 500 mAs, for 80 mAs (R²  = 0.997, p < 0.001), 150 mAs (R² = 0.998, p < 0.001) and 220 mAs (R² = 0.987, p < 0.001). There were no significant differences in volume quantification between the different doses examined. CT imaging radiation dose could be reduced substantially to 64% with no impact on strength estimates obtained from FE analysis. Reduced CT dose will enable early diagnosis and advanced monitoring of osteoporosis and associated fracture risk.