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Radiation force-based elasticity imaging is currently being investigated as a possible diagnostic modality for a number of clinical tasks, including liver fibrosis staging and the characterization of cardiovascular tissue. In this study, we evaluate the relationship between peak displacement magnitude and image quality and propose using a Bayesian estimator to overcome the challenge of obtaining viable data in low displacement signal environments. Displacement data quality were quantified for two common radiation force-based applications, acoustic radiation force impulse imaging, which measures the displacement within the region of excitation, and shear wave elasticity imaging, which measures displacements outside the region of excitation. Performance as a function of peak displacement magnitude for acoustic radiation force impulse imaging was assessed in simulations and lesion phantoms by quantifying signal-to-noise ratio (SNR) and contrast-to-noise ratio for varying peak displacement magnitudes. Overall performance for shear wave elasticity imaging was assessed in ex vivo chicken breast samples by measuring the displacement SNR as a function of distance from the excitation source. The results show that for any given displacement magnitude level, the Bayesian estimator can increase the SNR by approximately 9 dB over normalized cross-correlation and the contrast-to-noise ratio by a factor of two. We conclude from the results that a Bayesian estimator may be useful for increasing data quality in SNR-limited imaging environments.
Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.
Radiation-force-based elasticity imaging describes a group of techniques that use acoustic radiation force (ARF) to displace tissue to obtain qualitative or quantitative measurements of tissue properties. Because ARF-induced displacements are on the order of micrometers, tracking these displacements in vivo can be challenging. Previously, it has been shown that Bayesian-based estimation can overcome some of the limitations of a traditional displacement estimator such as normalized cross-correlation (NCC). In this work, we describe a Bayesian framework that combines a generalized Gaussian-Markov random field (GGMRF) prior with an automated method for selecting the prior's width. We then evaluate its performance in the context of tracking the micrometer-order displacements encountered in an ARF-based method such as ARF impulse (ARFI) imaging. The results show that bias, variance, and mean-square error (MSE) performance vary with prior shape and width, and that an almost one order-of-magnitude reduction in MSE can be achieved by the estimator at the automatically selected prior width. Lesion simulations show that the proposed estimator has a higher contrast-to-noise ratio but lower contrast than NCC, median-filtered NCC, and the previous Bayesian estimator, with a non-Gaussian prior shape having better lesion-edge resolution than a Gaussian prior. In vivo results from a cardiac, radio-frequency ablation ARFI imaging dataset show quantitative improvements in lesion contrast-to-noise ratio over NCC as well as the previous Bayesian estimator.
Bone grafts used to repair weight-bearing tibial plateau fractures often experience cyclic loading, and there is a need for bone graft substitutes that prevent failure of fixation and subsequent morbidity. However, the specific mechanical properties required for resorbable grafts to optimize structural compatibility with native bone have yet to be established. While quasi-static tests are utilized to assess weight-bearing ability, compressive strength alone is a poor indicator of in vivo performance. In the present study, we investigated the effects of interfacial bonding on material properties under conditions that re-capitulate the cyclic loading associated with weight-bearing fractures. Dynamic compressive fatigue properties of polyurethane (PUR) composites made with either unmodified (U-) or polycaprolactone surface-modified (PCL-) 45S5 bioactive glass (BG) particles were compared to a commercially available calcium sulfate and phosphate-based (CaS/P) bone cement at physiologically relevant stresses (5-30 MPa). Fatigue resistance of PCL-BG/polymer composite was superior to that of the U-BG/polymer composite and the CaS/P cement at higher stress levels for each of the fatigue failure criteria, related to modulus, creep, and maximum displacement, and was comparable to human trabecular bone. Steady state creep and damage accumulation occurred during the fatigue life of the PCL-BG/polymer and CaS/P cement, whereas creep of U-BG/polymer primarily occurred at a low number of loading cycles. From crack propagation testing, fracture toughness or resistance to crack growth was significantly higher for the PCL-BG composite than for the other materials. Finally, the fatigue and fracture toughness properties were intermediate between those of trabecular and cortical bone. These findings highlight the potential of PCL-BG/polyurethane composites as weight-bearing bone grafts.
Published by Elsevier Ltd.
Water that is bound to bone's matrix is implied as a predictor of fracture resistance; however, bound water is an elusive variable to be measured nondestructively. To date, the only nondestructive method used for studying bone hydration status is magnetic resonance variants (NMR or MRI). For the first time, bone hydration status was studied by short-wave infrared (SWIR) Raman spectroscopy to investigate associations of mineral-bound and collagen-bound water compartments with mechanical properties. Thirty cortical bone samples were used for quantitative Raman-based water analysis, gravimetric analysis, porosity measurement, and biomechanical testing. A sequential dehydration protocol was developed to replace unbound (heat drying) and bound (ethanol treatment) water in bone. Raman spectra were collected serially to track the OH-stretch band during dehydration. Four previously identified peaks were investigated: I3220/I2949, I3325/I2949 and I3453/I2949 reflect status of organic-matrix related water (mostly collagen-related water) compartments and collagen portion of bone while I3584/I2949 reflects status of mineral-related water compartments and mineral portion of bone. These spectroscopic biomarkers were correlated with elastic and post-yield mechanical properties of bone. Collagen-water related biomarkers (I3220/I2949 and I3325/I2949) correlated significantly and positively with toughness (R(2)=0.81 and R(2)=0.79; p<0.001) and post-yield toughness (R(2)=0.65 and R(2)=0.73; p<0.001). Mineral-water related biomarker correlated significantly and negatively with elastic modulus (R(2)=0.78; p<0.001) and positively with strength (R(2)=0.46; p<0.001). While MR-based techniques have been useful in measuring unbound and bound water, this is the first study which probed bound-water compartments differentially for collagen and mineral-bound water. For the first time, we showed an evidence for contributions of different bound-water compartments to mechanical properties of wet bone and the reported correlations of Raman-based water measurements to mechanical properties underline the necessity for enabling approaches to assess these new biomarkers noninvasively in vivo to improve the current diagnosis of those who may be at risk of bone fracture due to aging and diseases.
Published by Elsevier Inc.
Biomaterial substrates composed of semi-aligned electrospun fibers are attractive supports for the regeneration of connective tissues because the fibers are durable under cyclic tensile loads and can guide cell adhesion, orientation, and gene expression. Previous studies on supported electrospun substrates have shown that both fiber diameter and mechanical deformation can independently influence cell morphology and gene expression. However, no studies have examined the effect of mechanical deformation and fiber diameter on unsupported meshes. Semi-aligned large (1.75 μm) and small (0.60 μm) diameter fiber meshes were prepared from degradable elastomeric poly(esterurethane urea) (PEUUR) meshes and characterized by tensile testing and scanning electron microscopy (SEM). Next, unsupported meshes were aligned between custom grips (with the stretch axis oriented parallel to axis of fiber alignment), seeded with C3H10T1/2 cells, and subjected to a static load (50 mN, adjusted daily), a cyclic load (4% strain at 0.25 Hz for 30 min, followed by a static tensile loading of 50 mN, daily), or no load. After 3 days of mechanical stimulation, confocal imaging was used to characterize cell shape, while measurements of deoxyribonucleic acid (DNA) content and messenger ribonucleic acid (mRNA) expression were used to characterize cell retention on unsupported meshes and expression of the connective tissue phenotype. Mechanical testing confirmed that these materials deform elastically to at least 10%. Cells adhered to unsupported meshes under all conditions and aligned with the direction of fiber orientation. Application of static and cyclic loads increased cell alignment. Cell density and mRNA expression of connective tissue proteins were not statistically different between experimental groups. However, on large diameter fiber meshes, static loading slightly elevated tenomodulin expression relative to the no load group, and tenascin-C and tenomodulin expression relative to the cyclic load group. These results demonstrate the feasibility of maintaining cell adhesion and alignment on semi-aligned fibrous elastomeric substrates under different mechanical conditions. The study confirms that cell morphology is sensitive to the mechanical environment and suggests that expression of select connective tissue genes may be enhanced on large diameter fiber meshes under static tensile loads.
An evidence gap exists in fully understanding and reliably modeling the variations in elastic anisotropy that are observed at the millimeter scale in human cortical bone. The porosity (pore volume fraction) is known to account for a large part, but not all, of the elasticity variations. This effect may be modeled by a two-phase micromechanical model consisting of a homogeneous matrix pervaded by cylindrical pores. Although this model has been widely used, it lacks experimental validation. The aim of the present work is to revisit experimental data (elastic coefficients, porosity) previously obtained from 21 cortical bone specimens from the femoral mid-diaphysis of 10 donors and test the validity of the model by proposing a detailed discussion of its hypotheses. This includes investigating to what extent the experimental uncertainties, pore network modeling, and matrix elastic properties influence the model's predictions. The results support the validity of the two-phase model of cortical bone which assumes that the essential source of variations of elastic properties at the millimeter-scale is the volume fraction of vascular porosity. We propose that the bulk of the remaining discrepancies between predicted stiffness coefficients and experimental data (RMSE between 6% and 9%) is in part due to experimental errors and part due to small variations of the extravascular matrix properties. More significantly, although most of the models that have been proposed for cortical bone were based on several homogenization steps and a large number of variable parameters, we show that a model with a single parameter, namely the volume fraction of vascular porosity, is a suitable representation for cortical bone. The results could provide a guide to build specimen-specific cortical bone models. This will be of interest to analyze the structure-function relationship in bone and to design bone-mimicking materials.
Copyright © 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Extracellular matrix (ECM) strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell-ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model) and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned). We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions.
The most common mechanical measure of the heart integrates ventricular strain between end-diastole and end-systole in order to provide a measure of contraction. Here an approach is described for estimating a correlate to local passive mechanical properties. Passive strain is measured by estimating ventricular strain during atrial systole. During atrial systole the atria contract causing passive stretching in the ventricles from increased volume. This modification to traditional cardiac strain is here termed atrial kick induced strain (AKIS) imaging. AKIS imaging was evaluated in a canine ablation model of chronic infarct and a canine true chronic infarct model. AKIS images of ablation lesions were compared against acoustic radiation force impulse (ARFI) images and tissue blanching, and true chronic infarct AKIS images were compared against delayed enhanced-contrast magnetic resonance. AKIS images were made with 2-D and 3-D ultrasound data. In both studies, AKIS images and the comparison images show good qualitative agreement and good contrast and contrast-to-noise ratio.
Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.
A 2-D matrix ultrasound array is used to monitor acoustic radiation force impulse (ARFI) induced shear wave propagation in 3-D in excised canine muscle. From a single acquisition, both the shear wave phase and group velocity can be calculated to estimate the shear wave speed (SWS) along and across the fibers, as well as the fiber orientation in 3-D. The true fiber orientation found using the 3-D radon transform on B-mode volumes of the muscle was used to verify the fiber direction estimated from shear wave data. For the simplified imaging case when the ARFI push can be oriented perpendicular to the fibers, the error in estimating the fiber orientation using phase and group velocity measurements was 3.5 ± 2.6° and 3.4 ± 1.4° (mean ± standard deviation), respectively, over six acquisitions in different muscle samples. For the more general case when the push is oblique to the fibers, the angle between the push and the fibers is found using the dominant orientation of the shear wave displacement magnitude. In 30 acquisitions on six different muscle samples with oblique push angles up to 40°, the error in the estimated fiber orientation using phase and group velocity measurements was 5.4 ± 2.9° and 5.3 ± 3.2°, respectively, after estimating and accounting for the additional unknown push angle. Either the phase or group velocity measurements can be used to estimate fiber orientation and SWS along and across the fibers. Although it is possible to perform these measurements when the push is not perpendicular to the fibers, highly oblique push angles induce lower shear wave amplitudes which can cause inaccurate SWS measurements.
The elastic properties of bone tissue determine the biomechanical behavior of bone at the organ level. It is now widely accepted that the nanoscale structure of bone plays an important role to determine the elastic properties at the tissue level. Hence, in addition to the mineral density, the structure and organization of the mineral nanoparticles and of the collagen microfibrils appear as potential key factors governing the elasticity. Many studies exist on the role of the organization of collagen microfibril and mineral nanocrystals in strongly remodeled bone. However, there is no direct experimental proof to support the theoretical calculations. Here, we provide such evidence through a novel approach combining several high resolution imaging techniques: scanning acoustic microscopy, quantitative scanning small-Angle X-ray scattering imaging and synchrotron radiation computed microtomography. We find that the periodic modulations of elasticity across osteonal bone are essentially determined by the orientation of the mineral nanoparticles and to a lesser extent only by the particle size and density. Based on the strong correlation between the orientation of the mineral nanoparticles and the collagen molecules, we conclude that the microfibril orientation is the main determinant of the observed undulations of microelastic properties in regions of constant mineralization in osteonal lamellar bone. This multimodal approach could be applied to a much broader range of fibrous biological materials for the purpose of biomimetic technologies.