Parallel Optimisation and Implementation of a Real-Time Back Projection (BP) Algorithm for SAR Based on FPGA.

This study conducts an in-depth evaluation of imaging algorithms and software and hardware architectures to meet the capability requirements of real-time image acquisition systems, such as spaceborne and airborne synthetic aperture radar (SAR) systems. By analysing the principles and models of SAR imaging, this research creatively puts forward the fully parallel processing architecture for the back projection (BP) algorithm based on Field-Programmable Gate Array (FPGA). The processing time consumption has significant advantages compared with existing methods. This article describes the BP imaging algorithm, which stands out with its high processing accuracy and two-dimensional decoupling of distance and azimuth, and analyses the algorithmic flow, operation, and storage requirements. The algorithm is divided into five core operations: range pulse compression, upsampling, oblique distance calculation, data reading, and phase accumulation. The architecture and optimisation of the algorithm are presented, and the optimisation methods are described in detail from the perspective of algorithm flow, fixed-point operation, parallel processing, and distributed storage. Next, the maximum resource utilisation rate of the hardware platform in this study is found to be more than 80%, the system power consumption is 21.073 W, and the processing time efficiency is better than designs with other FPGA, DSP, GPU, and CPU. Finally, the correctness of the processing results is verified using actual data. The experimental results showed that 1.1 s were required to generate an image with a size of 900 × 900 pixels at a 200 MHz clock rate. This technology can solve the multi-mode, multi-resolution, and multi-geometry signal processing problems in an integrated manner, thus laying a foundation for the development of a new, high-performance, SAR system for real-time imaging processing.

On challenges in clinical assessment of hip fracture risk using image-based biomechanical modelling: a critical review.

INTRODUCTION: Hip fracture is a common health risk among elderly people, due to the prevalence of osteoporosis and accidental fall in the population. Accurate assessment of fracture risk is a crucial step for clinicians to consider patient-by-patient optimal treatments for effective prevention of fractures. Image-based biomechanical modeling has shown promising progress in assessment of fracture risk, and there is still a great possibility for improvement. The purpose of this paper is to identify key issues that need be addressed to improve image-based biomechanical modeling. MATERIALS AND METHODS: We critically examined issues in consideration and determination of the four biomechanical variables, i.e., risk of fall, fall-induced impact force, bone geometry and bone material quality, which are essential for prediction of hip fracture risk. We closely inspected: limitations introduced by assumptions that are adopted in existing models; deficiencies in methods for construction of biomechanical models, especially for determination of bone material properties from bone images; problems caused by separate use of the variables in clinical study of hip fracture risk; availability of clinical information that are required for validation of biomechanical models. RESULTS AND CONCLUSIONS: A number of critical issues and gaps were identified. Strategies for effectively addressing the issues were discussed.

Comparison of femur stiffness measured from DXA and QCT for assessment of hip fracture risk.

Femur stiffness, for example axial and bending stiffness, integrates both geometric and material information of the bone, and thus can be an effective indicator of bone strength and hip fracture risk. Femur stiffness is ideally measured from quantitative computed tomography (QCT), but QCT is not recommended for routine clinical use due to the public concern about exposure to high-dosage radiation. Dual energy X-ray absorptiometry (DXA) is currently the primary imaging modality in clinic. However, DXA is two-dimensional and it is not clear whether DXA-estimated stiffness has adequate accuracy to replace its QCT counterpart for clinical application. This study investigated the accuracy of femur stiffness (axial and bending) estimated from CTXA (computed tomography X-ray absorptiometry) and DXA against those directly measured from QCT. Proximal-femur QCT and DXA from 67 subjects were acquired. For each femur, the QCT dataset was projected into CTXA using CTXA-Hip (Mindways Software, Inc., USA). Femur stiffness at the femoral neck and intertrochanter were then calculated from QCT, CTXA and DXA, respectively, and different elasticity-density relationships were considered in the calculation. Pearson correlations between QCT and CTXA/DXA measured stiffness were studied. The results showed that there were strong correlations between QCT and CTXA derived stiffness, although the correlations were affected by the adopted elasticity-density relationship. Correlations between QCT and DXA derived stiffness were much less strong, mainly caused by the inconsistence of femur orientation in QCT projection and in DXA positioning. Our preliminary clinical study showed that femur stiffness had slightly better performance than femur geometry in discrimination of hip fracture cases from controls.

Fracture Risk Indices From DXA-Based Finite Element Analysis Predict Incident Fractures Independently From FRAX: The Manitoba BMD Registry.

OBJECTIVE: Finite element analysis (FEA) is a computational method to predict the behavior of materials under applied loading. We developed a software tool that automatically performs FEA on dual-energy X-ray absorptiometry hip scans to generate site-specific fracture risk indices (FRIs) that reflect the likelihood of hip fracture from a sideways fall. This longitudinal study examined associations between FRIs and incident fractures. METHODS: Using the Manitoba Bone Mineral Density (BMD) Registry, femoral neck (FN), intertrochanter (IT), and subtrochanter (ST) FRIs were automatically derived from 13,978 anonymized dual-energy X-ray absorptiometry scans (Prodigy, GE Healthcare) in women and men aged 50 yr or older (mean age 65 yr). Baseline covariates and incident fractures were assessed from population-based data. We compared c-statistics for FRIs vs FN BMD alone and fracture risk assessment (FRAX) probability computed with BMD. Cox regression was used to estimate hazard ratios and 95% confidence intervals (95% CIs) for incident hip, major osteoporotic fracture (MOF) and non-hip MOF adjusted for relevant covariates including age, sex, FN BMD, FRAX probability, FRAX risk factors, and hip axis length (HAL). RESULTS: During mean follow-up of 6 yr, there were 268 subjects with incident hip fractures, 1003 with incident MOF, and 787 with incident non-hip MOF. All FRIs gave significant stratification for hip fracture (c-statistics FN-FRI: 0.76, 95% CI 0.73-0.79, IT-FRI 0.74, 0.71-0.77; ST-FRI 0.72, 0.69-0.75). FRIs continued to predict hip fracture risk even after adjustment for age and sex (hazard ratio per standard deviation FN-FRI 1.89, 95% CI 1.66-2.16); age, sex, and BMD (1.26, 1.07-1.48); FRAX probability (1.30, 1.11-1.52); FRAX probability with HAL (1.26, 1.05-1.51); and individual FRAX risk factors (1.32, 1.09-1.59). FRIs also predicted MOF and non-hip MOF, but the prediction was not as strong as for hip fracture. SUMMARY: Automatically-derived FN, IT, and ST FRIs are associated with incident hip fracture independent of multiple covariates, including FN BMD, FRAX probability and risk factors, and HAL.

Long-Term Proton Pump Inhibitor Use Is Not Associated With Changes in Bone Strength and Structure.

OBJECTIVES: Multiple studies have reported an association between proton pump inhibitor (PPI) use and fracture. However, the causality of this association is questionable, as there is not a well defined mechanism of action, nor is there evidence of an effect on PPIs on areal bone mineral density (aBMD) using dual photon X-ray absorptiometry (DXA). It is possible that PPIs may induce changes in bone structure which would predispose to fracture in the absence of changes in aBMD. We used three-dimensional quantitative computed tomography (3D-QCT) imaging to determine if long-term PPI use was associated with structural changes in bone independent of aBMD. METHODS: We enrolled a sample of long-term (≥5 years) PPI users matched to a similar cohort of persons with no PPI use in the previous 5 years. All subjects underwent assessment of aBMD using DXA, volumetric BMD using 3D-QCT, as well as markers of bone metabolism. Measures of bone strength, including buckling ratio and section modulus, were also compared between the two samples. RESULTS: 104 subjects were enrolled (52 PPI users and 52 PPI non-users). There were no differences detected in standard BMD, volumetric BMD, markers of bone metabolism or measures of bone strength between the two groups. CONCLUSIONS: Long-term PPI use is not associated with any changes in bone mineral density or bone strength that would predispose to an increased risk of fracture. These findings provide further evidence that the association between PPI use and fracture is not causal.

Study of the variations of fall induced hip fracture risk between right and left femurs using CT-based FEA.

BACKGROUND: Hip fracture of elderly people-suffering from osteoporosis-is a severe public health concern, which can be reduced by providing a prior assessment of hip fracture risk. Image-based finite element analysis (FEA) has been considered an effective computational tool to assess the hip fracture risk. Considering the femoral neck region is the weakest, fracture risk indicators (FRI) are evaluated for both single-legged stance and sideways fall configurations and are compared between left and right femurs of each subject. Quantitative Computed Tomography (QCT) scan datasets of thirty anonymous patients' left and right femora have been considered for the FE models, which have been simulated with an equal magnitude of load applied to the aforementioned configurations. The requirement of bilateral hip assessment in predicting the fracture risk has been explored in this study. RESULTS: Comparing the sideways fall and single-legged stance, the FRI varies by 64 to 74% at the superior aspects and by 14 to 19% at the inferior surfaces of both the femora. The results of this in vivo analysis clearly substantiate that the fracture is expected to initiate at the superior surface of femoral neck region if a patient falls from his/her standing height. The distributions of FRI between the femurs vary considerably, and the variability is significant at the superior aspects. The p value (= 0.02) obtained from paired sample t-Test yields p value ≤ 0.05, which shows the evidence of variability of the FRI distribution between left and right femurs. Moreover, the comparison of FRIs between the left and right femur of men and women shows that women are more susceptible to hip fracture than men. CONCLUSIONS: The results and statistical variation clearly signify a need for bilateral hip scanning in predicting hip fracture risk, which is clinically conducted, at present, based on one hip chosen randomly and may lead to inaccurate fracture prediction. This study, although preliminary, may play a crucial role in assessing the hip fractures of the geriatric population and thereby, reducing the cost of treatment by taking predictive measure.

Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps.

Personalized orthopedic devices are increasingly favored for their potential to enhance long-term treatment success. Despite significant advancements across various disciplines, the seamless integration and full automation of personalized orthopedic treatments remain elusive. This paper identifies key interdisciplinary gaps in integrating and automating advanced technologies for personalized orthopedic treatment. It begins by outlining the standard clinical practices in orthopedic treatments and the extent of personalization achievable. The paper then explores recent innovations in artificial intelligence, biomaterials, genomic and proteomic analyses, lab-on-a-chip, medical imaging, image-based biomechanical finite element modeling, biomimicry, 3D printing and bioprinting, and implantable sensors, emphasizing their contributions to personalized treatments. Tentative strategies or solutions are proposed to address the interdisciplinary gaps by utilizing innovative technologies. The key findings highlight the need for the non-invasive quantitative assessment of bone quality, patient-specific biocompatibility, and device designs that address individual biological and mechanical conditions. This comprehensive review underscores the transformative potential of these technologies and the importance of multidisciplinary collaboration to integrate and automate them into a cohesive, intelligent system for personalized orthopedic treatments.

Achieving the ideal balance between biological and mechanical requirements in composite bone scaffolds through a voxel-based approach.

Achieving successful bone regeneration necessitates the design of scaffolds that meet diverse biological and mechanical requirements, often leading to conflicts in the design parameters. A key conflict arises between scaffold porosity and stiffness. Increasing porosity facilitates cell infiltration and nutrient exchange, promoting bone regeneration. However, higher porosity compromises scaffold stiffness, which is crucial for providing structural support in the defective region. Furthermore, appropriate scaffold stiffness is crucial for preventing stress shielding. Conventional geometry-based design methods utilizing single-phase materials have limited flexibility in resolving such conflicts. To address this challenge, we propose a voxel-based method for designing composite scaffolds composed of hydroxyapatite (HA) and polylactic acid (PLA). Our strategy involves first satisfying primary biological requirements by selecting appropriate porosity, pore shape, and size. Subsequently, scaffold stiffness requirements are met by selecting suitable phase materials and tuning their contents. The study demonstrates that the voxel-based approach effectively balances both biological and mechanical requirements in scaffold design. This method addresses the limitations of traditional designs by achieving an optimal balance between porosity and stiffness, which is crucial for scaffold performance in biomedical applications. Moreover, the scaffolds designed using this method can be manufactured using voxel-based 3D printing technology, which is emerging in the field. Future advancements in voxel-based 3D printing technology will further enhance the feasibility and practicality of this approach for bone tissue engineering applications.

Impact of Regular and Irregular Pore Distributions on the Elasticity of Porous Materials: A Microstructure-Free Finite Element Study.

Conventional analytical formulas for predicting the effective Young's modulus of porous materials often rely on simplifying assumptions and do not explicitly incorporate microstructural information. This study investigates the impact of regular versus irregular pore distributions on the stiffness of porous materials using microstructure-free finite element modeling (MF-FEM). After conducting a convergence study, MF-FEM predictions were validated against experimental data and used to assess the accuracy of commonly employed analytical models. The results demonstrate that materials with irregular microstructures exhibit a rapid decrease in Young's modulus, approaching zero at porosities slightly greater than 50%. In contrast, regular microstructures show a more gradual decline, maintaining significant stiffness until the porosity exceeds 90%. Additionally, the study reveals that some analytical formulas align better with irregular microstructures while others are more suited to regular ones, attributable to the underlying assumptions of these models. These findings underscore the necessity of considering pore distribution patterns in modeling to accurately predict the mechanical behavior of porous materials.

Finite Element Modeling of Microstructures in Composite Materials: A Special Issue in Materials.

This Special Issue of the journal Materials aims to gather recent advancements and novel developments in the field of finite element modeling of microstructures in composite materials [...].

Characterization and Design of Three-Phase Particulate Composites: Microstructure-Free Finite Element Modeling vs. Analytical Micromechanics Models.

Three-phase particulate composites offer greater design flexibility in the selection of phase materials and have more design variables than their two-phase counterparts, thus providing larger space for tailoring effective properties to meet intricate engineering requirements. Predicting effective elastic properties is essential for composite design. However, experimental methods are both expensive and time intensive, whereas the scope of analytical micromechanics models is limited by their inherent assumptions. The newly developed microstructure-free finite element modeling (MF-FEM) approach has been demonstrated to be accurate and reliable for two-phase particulate composites. In this study, we investigate whether the MF-FEM approach can be applied to three-phase particulate composites and, if applicable, under which conditions. The study commences with a convergence analysis to establish the threshold ratio between the element size and the RVE (representative volume element) dimension. We then validate the MF-FEM approach using experimental data on three-phase composites from the existing literature. Subsequently, the MF-FEM method serves as a benchmark to assess the accuracy of both traditional and novel analytical micromechanics models, in predicting the effective elasticity of two distinct types of three-phase particulate composites, characterized by their small and large phase contrasts, respectively. We found that the threshold element-to-RVE ratio (1/150) for three-phase composites is considerably smaller than the ratio (1/50) for two-phase composites. The validation underscores that MF-FEM predictions align closely with experimental data. The analytical micromechanics models demonstrate varying degrees of accuracy depending on the phase volume fractions and the contrast in phase properties. The study indicates that the analytical micromechanics models may not be dependable for predicting effective properties of three-phase particulate composites, particularly those with a large contrast in phase properties. Even though more time-intensive, the MF-FEM proves to be a more reliable approach than the analytical models.

Finite-element study of cerebrospinal fluid in mitigating closed head injuries.

The mechanism of cerebrospinal fluid in mitigating closed head injuries caused by mild impacts was investigated by finite-element modeling. Three biomechanical models were constructed. In these models, cerebrospinal fluid was considered as a soft solid material, an inviscid fluid without intracranial pressure, and an inviscid fluid with normal intracranial pressure, respectively, while other conditions such as the finite-element mesh, the impact, and the boundary conditions were kept the same. The recently developed nearest nodes finite-element method was adopted to deal with large deformations in brain tissue. Results obtained from the numerical studies showed that cerebrospinal fluid was able to remarkably reduce the maximum peak strains, especially the shear strains induced by impacts and transmitted to the brain. Cerebrospinal fluid with intracranial pressure was able to further buffer relative oscillations between the skull and the brain.

Prediction of backside micromotion in total knee replacements by finite element simulation.

The micromotion at the interface between the polyethylene tibial insert and metal tibial tray [corrected] in modular total knee replacements [corrected] has been shown to contribute to wear particle-induced osteolysis and may [corrected] cause implant failure. Therefore, studying the design parameters that are involved in the backside wear process is an important task that may lead to improvement in new total knee replacements. In the present study, a finite element model was developed to predict the backside micromotion along the entire modular interface. Both the linear elastic constitutive model and non-linear J2-plasticity constitutive model were considered in the finite element model for polyethylene and were corroborated against published results obtained from displacement controlled knee simulator wear tests. The finite element simulation with the non-linear J2-plasticity constitutive model was able to predict backside micromotion [corrected] more accurately than the simulation with the linear elastic constitutive model. [corrected] The developed finite element model (including the non-linear J2-plasticity constitutive model) was then applied to assess the effects of the tibial tray locking mechanism design (dovetails versus fullperipheral [corrected] design) and different levels of interference fit on insert micromotion. The developed finite element model, implementing the non-linear J2-plasticity constitutive model, was shown to successfully predict clinical amounts of backside micromotion and could be used for the design and development of total knee replacements for the reduction of backside micromotion and polyethylene [corrected] wear.

An Accuracy Comparison of Micromechanics Models of Particulate Composites against Microstructure-Free Finite Element Modeling.

Micromechanics models of composite materials are preferred in the analysis and design of composites for their high computational efficiency. However, the accuracy of the micromechanics models varies widely, depending on the volume fraction of inclusions and the contrast of phase properties, which have not been thoroughly studied, primarily due to the lack of complete and representative experimental data. The recently developed microstructure-free finite element modeling (MF-FEM) is based on the fact that, for a particulate-reinforced composite, if the characteristic size of the inclusions is much smaller than the composite representative volume element (RVE), the elastic properties of the RVE are independent of inclusion shape and size. MF-FEM has a number of advantages over the conventional microstructure-based finite element modeling. MF-FEM predictions have good to excellent agreement with the reported experiment results. In this study, predictions produced by MF-FEM are used in replace of experimental data to compare the accuracy of selected micromechanics models of particulate composites. The results indicate that, only if both the contrasts in phase Young's moduli and phase Poisson's ratios are small, the micromechanics models are able to produce accurate predictions. In other cases, they are more or less inaccurate. This study may serve as a guide for the appropriate use of the micromechanics models.

Improved Voigt and Reuss Formulas with the Poisson Effect.

The Poisson effect, measured by the Poisson's ratio, plays an important role in the regulation of the elastic properties of composite materials, but it is not considered in the conventional Voigt (iso-strain) and Reuss (iso-stress) formulas, which explains why the formulas are inaccurate even if the iso-strain or the iso-stress conditions are satisfied. To consider the Poisson effect, we derived a set of new formulas based on the governing equations of elasticity. The obtained formulas show greater mathematical complexity. To further understand how the Poisson effect influences composite elastic properties, two types of finite element models (FEM) were constructed to simulate the situations where the Poisson effect does or does not have an influence. The results show that the conventional Voigt and Reuss formulas are special cases of the newly derived ones. The Poisson effect induces secondary strains and stresses into the phase materials, which demands more strain energy to achieve the same deformation in the primary (loading) direction, and thus increases composite stiffness; the magnitude of the increase is dependent on the contrast of phase properties. The findings may have significant impact on the study of the emerging nanocomposites and functionally graded materials, where the conventional Voigt and Reuss formulas have wide applications.

Study of the significance of parameters and their interaction on assessing femoral fracture risk by quantitative statistical analysis.

Early assessment of hip fracture helps develop therapeutic and preventive mechanisms that may reduce the occurrence of hip fracture. An accurate assessment of hip fracture risk requires proper consideration of the loads, the physiological and morphological parameters, and the interactions between these parameters. Hence, this study aims at analyzing the significance of parameters and their interactions by conducting a quantitative statistical analysis. A multiple regression model was developed considering different loading directions during a sideways fall (angle [Formula: see text] and [Formula: see text] on the coronal and transverse planes, respectively), age, gender, patient weight, height, and femur morphology as independent parameters and Fracture Risk Index (FRI) as a dependent parameter. Strain-based criteria were used for the calculation of FRI with the maximum principal strain obtained from quantitative computed tomography-based finite element analysis. The statistical result shows that [Formula: see text] [Formula: see text], age [Formula: see text], true moment length [Formula: see text], gender [Formula: see text], FNA [Formula: see text], height [Formula: see text], and FSL [Formula: see text] significantly affect FRI where [Formula: see text] is the most influential parameter. The significance of two-level interaction [Formula: see text] and three-level interaction [Formula: see text] shows that the effect of parameters is dissimilar and depends on other parameters suggesting the variability of FRI from person to person.

Efficacy and safety of naotaifang capsules for hypertensive cerebral small vessel disease: Study protocol for a multicenter, randomized, double-blind, placebo-controlled clinical trial.

Background: Hypertensive cerebral small vessel disease (HT-CSVD) is a cerebrovascular clinical, imaging and pathological syndrome caused by hypertension (HT). The condition manifests with lesions in various vessels including intracranial small/arterioles, capillaries, and small/venules. Hypertensive cerebral small vessel disease has complex and diverse clinical manifestations. For instance, it can present as an acute stroke which progresses to cause cognitive decline, affective disorder, unstable gait, dysphagia, or abnormal urination. Moreover, hypertensive cerebral small vessel disease causes 25-30% of all cases of ischemic strokes and more than 50% of all cases of single or mixed dementias. The 1-year recurrence rate of stroke in cerebral small vessel disease patients with hypertension is 14%. In the early stage of development, the symptoms of hypertensive cerebral small vessel disease are concealed and often ignored by patients and even clinicians. Patients with an advanced hypertensive cerebral small vessel disease manifest with severe physical and mental dysfunction. Therefore, this condition has a substantial economic burden on affected families and society. Naotaifang (NTF) is potentially effective in improving microcirculation and neurofunction in patients with ischemic stroke. In this regard, this multicenter randomized controlled trial (RCT) aims to furtherly evaluate the efficacy and safety of naotaifang capsules on hypertensive cerebral small vessel disease. Methods: This study is a multicenter, randomized, double-blind, placebo-controlled clinical trial. A total of 388 eligible subjects were recruited from the First Hospital of Hunan University of Chinese Medicine, Hunan Academy of Chinese Medicine Affiliated Hospital, the First Hospital of Shaoyang University, the First Traditional Chinese Medicine Hospital of Changde, and Jiangmen Wuyi Hospital of Traditional Chinese Medicine from July 2020 to April 2022. After a 4-week run-in period, all participants were divided into the intervention group (represented by Y-T, N-T) and control group (represented by Y-C, N-C); using a stratified block randomized method based on the presence or absence of brain damage symptoms in hypertensive cerebral small vessel disease (represented by Y and N). The Y-T and N-T groups were administered different doses of naotaifang capsules, whereas Y-C and N-C groups received placebo treatment. These four groups received the treatments for 6 months. The primary outcome included Fazekas scores and dilated Virchow-robin spaces (dVRS) grades on magnetic resonance imaging (MRI). The secondary outcomes included the number of lacunar infarctions (LI) and cerebral microbleeds (CMB) on magnetic resonance imaging, clinical blood pressure (BP) level, traditional Chinese medicine (TCM) syndrome scores, mini-mental state examination (MMSE) scale, and safety outcomes. Fazekas scores, dilated Virchow-robin spaces grades, and the number of lacunar infarctions and cerebral microbleeds on magnetic resonance imaging were tested before enrollment and after 6 months of treatment. The clinical blood pressure level, traditional Chinese medicine syndrome scores, mini-mental state examination scale and safety outcomes were tested before enrollment, after 3-month, 6-month treatment and 12th-month follow-up respectively. Conclusion: The protocol will comfirm whether naotaifang capsules reduce Fazekas scores, dilated Virchow-robin spaces grades, and the number of lacunar infarctions and cerebral microbleeds, clinical blood pressure, increase mini-mental state examination scores, traditional Chinese medicine syndrome scores of Qi deficiency and blood stasis (QDBS), and improve the quality of life of subjects. The consolidated evidence from this study will shed light on the benefits of Chinese herbs for hypertensive cerebral small vessel disease, such as nourishing qi, promoting blood circulation and removing blood stasis, and dredging collaterals. However, additional clinical trials with large samples and long intervention periods will be required for in-depth research. Clinical Trial registration: www.chictr.org.cn, identifier ChiCTR1900024524.

Bone Organic-Inorganic Phase Ratio Is a Fundamental Determinant of Bone Material Quality.

BACKGROUND: Bone mineral density is widely used by clinicians for screening osteoporosis and assessing bone strength. However, its effectiveness has been reported unsatisfactory. In this study, it is demonstrated that bone organic-inorganic phase ratio is a fundamental determinant of bone material quality measured by stiffness, strength, and toughness. METHODS AND RESULTS: Two-hundred standard bone specimens were fabricated from bovine legs, with a specialized manufacturing method that was designed to reduce the effect of bone anisotropy. Bone mechanical properties of the specimens, including Young's modulus, yield stress, peak stress, and energy-to-failure, were measured by mechanical testing. Organic and inorganic mass contents of the specimens were then determined by bone ashing. Bone density and organic-inorganic phase ratio in the specimens were calculated. Statistical methods were applied to study relationships between the measured mechanical properties and the organic-inorganic phase ratios. Statistical characteristics of organic-inorganic phase ratios in the specimens with top material quality were investigated. Bone organic-inorganic phase ratio had strong Spearman correlation with bone material properties. Bone specimens that had the highest material quality had a very narrow scope of organic-inorganic phase ratio, which could be considered as the "optimal" ratio among the tested specimens. CONCLUSION: Bone organic-inorganic phase ratio is a fundamental determinant of bone material quality. There may exist an "optimal" ratio for the bone to achieve top material quality. Deviation from the "optimal" ratio is probably the fundamental cause of various bone diseases. This study suggests that bone organic-inorganic phase ratio should be considered in clinical assessment of fracture risk.

Femoral bone mineral density distribution is dominantly regulated by strain energy density in remodeling.

BACKGROUND: It is well known that there is a relationship between bone strength and the forces that are daily applied to the bone. However, bone is a highly heterogeneous material and it is still not clear how mechanical variables regulate the distribution of bone mass in a femur. METHODS: We studied the role of four mechanical variables, i.e. principal tensile/compressive stress, von Mises stress, and strain energy density (SED), in the regulation of bone mineral density (BMD) distribution in the human femur. The actual BMD in a femur was extracted from quantitative computed tomography (QCT) and used as a reference for comparison. A finite element model of the femur was constructed from the same set of QCT scans and then used in iterative simulations of femur remodeling under stance and walking loading. The finite element model was initially assigned a homogeneous BMD distribution. During the remodeling, femur BMD was locally modified according to one of the four mechanical variables. The simulations were stopped when BMD change in two consecutive iterations was adequately small. The four simulated BMD patterns were then compared with the actual BMD. RESULTS: It was found that the BMD pattern regulated by SED had the best similarity with the actual BMD. The medullary canal was successfully reproduced by simulated remodeling, indicating that in addition to its biological functions, the medullary canal has important biomechanical functions. CONCLUSIONS: Both the actual and simulated BMD distributions showed that the proximal femur has much lower BMD than the femur shaft, which may explain why hip fractures most often occur at the proximal femur.

Assessment of hip fracture risk by cross-sectional strain-energy derived from image-based beam model.

BACKGROUND: Clinicians have been looking for a simple and effective biomechanical tool for the assessment of hip fracture risk. Dual-energy X-ray absorptiometry (DXA) is currently the primary bone imaging modality in clinic, and the engineering beam is the simplest model for a mechanical analysis. Therefore, we developed a DXA-based beam model for the above purpose. METHODS: A beam model of the proximal femur was constructed from the subject's hip DXA image and denoted DXA-beam. Femur stiffness was calculated at cross-sections of interest using areal bone-mineral-density profile. Impact force induced in a sideways fall was applied as a critical loading. Fracture risk index at a cross-section was defined as the ratio of strain-energy induced by the impact force to the allowable strain-energy. A clinic cohort was used to study the discriminability of DXA-beam, which was measured by the area under the curve and odds ratio, both with 95% confidential interval. FINDINGS: Fracture risk measured by DXA-beam model at the femoral neck [odds ratio 2.23, 95% confidence interval (1.83, 2.57)], inter-trochanter [2.49, (2.14, 3.25)] and sub-trochanter [2.82, (2.38, 3.51)] were strongly associated with hip fracture. The area under the curve by DXA-beam at the femoral neck [0.74, 95% confidence interval (0.70, 0.76)], inter-trochanter [0.77, (0.75, 0.82)] and sub-trochanter [0.76, (0.74, 0.81)] were higher than that by femoral neck bone mineral density [0.71, (0.65, 0.78)]. INTERPRETATION: The DXA-beam model is a simple and yet effective mechanical model. It had promising performance in discrimination of fracture cases from controls.