Research Papers

J Biomech Eng. 2016;138(9):091001-091001-11. doi:10.1115/1.4033964.

Advancements in image-based computational modeling are producing increasingly more realistic representations of vasculature and hemodynamics, but so far have not compensated for cardiac motion when imposing inflow boundary conditions. The effect of cardiac motion on aortic flow is important when assessing sequelae in this region including coarctation of the aorta (CoA) or regurgitant fraction. The objective of this investigation was to develop a method to assess and correct for the influence of cardiac motion on blood flow measurements through the aortic valve (AoV) and to determine its impact on patient-specific local hemodynamics quantified by computational fluid dynamics (CFD). A motion-compensated inflow waveform was imposed into the CFD model of a patient with repaired CoA that accounted for the distance traveled by the basal plane during the cardiac cycle. Time-averaged wall shear stress (TAWSS) and turbulent kinetic energy (TKE) values were compared with CFD results of the same patient using the original waveform. Cardiac motion resulted in underestimation of flow during systole and overestimation during diastole. Influences of inflow waveforms on TAWSS were greatest along the outer wall of the ascending aorta (AscAo) (∼30 dyn/cm2). Differences in TAWSS were more pronounced than those from the model creation or mesh dependence aspects of CFD. TKE was slightly higher for the motion-compensated waveform throughout the aortic arch. These results suggest that accounting for cardiac motion when quantifying blood flow through the AoV can lead to different conclusions for hemodynamic indices, which may be important if these results are ultimately used to predict patient outcomes.

Commentary by Dr. Valentin Fuster
J Biomech Eng. 2016;138(9):091002-091002-12. doi:10.1115/1.4034060.

Computational walking simulations could facilitate the development of improved treatments for clinical conditions affecting walking ability. Since an effective treatment is likely to change a patient's foot-ground contact pattern and timing, such simulations should ideally utilize deformable foot-ground contact models tailored to the patient's foot anatomy and footwear. However, no study has reported a deformable modeling approach that can reproduce all six ground reaction quantities (expressed as three reaction force components, two center of pressure (CoP) coordinates, and a free reaction moment) for an individual subject during walking. This study proposes such an approach for use in predictive optimizations of walking. To minimize complexity, we modeled each foot as two rigid segments—a hindfoot (HF) segment and a forefoot (FF) segment—connected by a pin joint representing the toes flexion–extension axis. Ground reaction forces (GRFs) and moments acting on each segment were generated by a grid of linear springs with nonlinear damping and Coulomb friction spread across the bottom of each segment. The stiffness and damping of each spring and common friction parameter values for all springs were calibrated for both feet simultaneously via a novel three-stage optimization process that used motion capture and ground reaction data collected from a single walking trial. The sequential three-stage process involved matching (1) the vertical force component, (2) all three force components, and finally (3) all six ground reaction quantities. The calibrated model was tested using four additional walking trials excluded from calibration. With only small changes in input kinematics, the calibrated model reproduced all six ground reaction quantities closely (root mean square (RMS) errors less than 13 N for all three forces, 25 mm for anterior–posterior (AP) CoP, 8 mm for medial–lateral (ML) CoP, and 2 N·m for the free moment) for both feet in all walking trials. The largest errors in AP CoP occurred at the beginning and end of stance phase when the vertical ground reaction force (vGRF) was small. Subject-specific deformable foot-ground contact models created using this approach should enable changes in foot-ground contact pattern to be predicted accurately by gait optimization studies, which may lead to improvements in personalized rehabilitation medicine.

Commentary by Dr. Valentin Fuster
J Biomech Eng. 2016;138(9):091003-091003-7. doi:10.1115/1.4034172.

Quantitative computed tomography-based finite-element analysis (QCT/FEA) has become increasingly popular in an attempt to understand and possibly reduce vertebral fracture risk. It is known that scanning acquisition settings affect Hounsfield units (HU) of the CT voxels. Material properties assignments in QCT/FEA, relating HU to Young's modulus, are performed by applying empirical equations. The purpose of this study was to evaluate the effect of QCT scanning protocols on predicted stiffness values from finite-element models. One fresh frozen cadaveric torso and a QCT calibration phantom were scanned six times varying voltage and current and reconstructed to obtain a total of 12 sets of images. Five vertebrae from the torso were experimentally tested to obtain stiffness values. QCT/FEA models of the five vertebrae were developed for the 12 image data resulting in a total of 60 models. Predicted stiffness was compared to the experimental values. The highest percent difference in stiffness was approximately 480% (80 kVp, 110 mAs, U70), while the lowest outcome was ∼1% (80 kVp, 110 mAs, U30). There was a clear distinction between reconstruction kernels in predicted outcomes, whereas voltage did not present a clear influence on results. The potential of QCT/FEA as an improvement to conventional fracture risk prediction tools is well established. However, it is important to establish research protocols that can lead to results that can be translated to the clinical setting.

Commentary by Dr. Valentin Fuster
J Biomech Eng. 2016;138(9):091004-091004-10. doi:10.1115/1.4034171.

The rostral-caudally aligned fiber-reinforced structure of spinal cord white matter (WM) gives rise to transverse isotropy in the material. Stress and strain patterns generated in the spinal cord parenchyma following spinal cord injury (SCI) are multidirectional and dependent on the mechanism of the injury. Our objective was to develop a WM constitutive model that captures the material transverse isotropy under dynamic loading. The WM mechanical behavior was extracted from the published tensile and compressive experiments. Combinations of isotropic and fiber-reinforcing models were examined in a conditional quasi-linear viscoelastic (QLV) formulation to capture the WM mechanical behavior. The effect of WM transverse isotropy on SCI model outcomes was evaluated by simulating a nonhuman primate (NHP) contusion injury experiment. A second-order reduced polynomial hyperelastic energy potential conditionally combined with a quadratic reinforcing function in a four-term Prony series QLV model best captured the WM mechanical behavior (0.89 < R2 < 0.99). WM isotropic and transversely isotropic material models combined with discrete modeling of the pia mater resulted in peak impact forces that matched the experimental outcomes. The transversely isotropic WM with discrete pia mater resulted in maximum principal strain (MPS) distributions which effectively captured the combination of ipsilateral peripheral WM sparing, ipsilateral injury and contralateral sparing, and the rostral/caudal spread of damage observed in in vivo injuries. The results suggest that the WM transverse isotropy could have an important role in correlating tissue damage with mechanical measures and explaining the directional sensitivity of the spinal cord to injury.

Commentary by Dr. Valentin Fuster
J Biomech Eng. 2016;138(9):091005-091005-13. doi:10.1115/1.4034170.

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

Commentary by Dr. Valentin Fuster
J Biomech Eng. 2016;138(9):091006-091006-9. doi:10.1115/1.4034263.

Measurements of joint kinematics are essential to understand the pathomechanics of ankle disease and the effects of treatment. Traditional motion capture techniques do not provide measurements of independent tibiotalar and subtalar joint motion. In this study, high-speed dual fluoroscopy images of ten asymptomatic adults were acquired during treadmill walking at 0.5 m/s and 1.0 m/s and a single-leg, balanced heel-rise. Three-dimensional (3D) CT models of each bone and dual fluoroscopy images were used to quantify in vivo kinematics for the tibiotalar and subtalar joints. Dynamic tibiotalar and subtalar mean joint angles often exhibited opposing trends during captured stance. During both speeds of walking, the tibiotalar joint had significantly greater dorsi/plantarflexion (D/P) angular ROM than the subtalar joint while the subtalar joint demonstrated greater inversion/eversion (In/Ev) and internal/external rotation (IR/ER) than the tibiotalar joint. During balanced heel-rise, only D/P and In/Ev were significantly different between the tibiotalar and subtalar joints. Translational ROM in the anterior/posterior (AP) direction was significantly greater in the subtalar than the tibiotalar joint during walking at 0.5 m/s. Overall, our results support the long-held belief that the tibiotalar joint is primarily responsible for D/P, while the subtalar joint facilitates In/Ev and IR/ER. However, the subtalar joint provided considerable D/P rotation, and the tibiotalar joint rotated about all three axes, which, along with translational motion, suggests that each joint undergoes complex, 3D motion.

Topics: Kinematics , Rotation , Bone
Commentary by Dr. Valentin Fuster

Technical Brief

J Biomech Eng. 2016;138(9):094501-094501-9. doi:10.1115/1.4034005.

We present processing methods and visualization techniques for accurately characterizing and interpreting kinematical data of flexion–extension motion of the knee joint based on helical axes. We make use of the Lie group of rigid body motions and particularly its Lie algebra for a natural representation of motion sequences. This allows to analyze and compute the finite helical axis (FHA) and instantaneous helical axis (IHA) in a unified way without redundant degrees of freedom or singularities. A polynomial fitting based on Legendre polynomials within the Lie algebra is applied to provide a smooth description of a given discrete knee motion sequence which is essential for obtaining stable instantaneous helical axes for further analysis. Moreover, this allows for an efficient overall similarity comparison across several motion sequences in order to differentiate among several cases. Our approach combines a specifically designed patient-specific three-dimensional visualization basing on the processed helical axes information and incorporating computed tomography (CT) scans for an intuitive interpretation of the axes and their geometrical relation with respect to the knee joint anatomy. In addition, in the context of the study of diseases affecting the musculoskeletal articulation, we propose to integrate the above tools into a multiscale framework for exploring related data sets distributed across multiple spatial scales. We demonstrate the utility of our methods, exemplarily processing a collection of motion sequences acquired from experimental data involving several surgery techniques. Our approach enables an accurate analysis, visualization and comparison of knee joint articulation, contributing to the evaluation and diagnosis in medical applications.

Commentary by Dr. Valentin Fuster
J Biomech Eng. 2016;138(9):094502-094502-8. doi:10.1115/1.4034254.

The effects of formalin fixation on bone material properties remain debatable. In this study, we collected 36 fresh-frozen cuboid-shaped cortical specimens from five male bovine femurs and immersed half of the specimens into 4% formalin fixation liquid for 30 days. We then conducted three-point bending tests and used both beam theory method and an optimization method combined with specimen-specific finite element (FE) models to identify material parameters. Through the optimization FE method, the formalin-fixed bones showed a significantly lower Young's modulus (−12%) compared to the fresh-frozen specimens, while no difference was observed using the beam theory method. Meanwhile, both the optimization FE and beam theory methods revealed higher effective failure strains for formalin-fixed bones compared to fresh-frozen ones (52% higher through the optimization FE method and 84% higher through the beam theory method). Hence, we conclude that the formalin fixation has a significant effect on bovine cortical bones at small, elastic, as well as large, plastic deformations.

Commentary by Dr. Valentin Fuster


J Biomech Eng. 2016;138(9):095501-095501-2. doi:10.1115/1.4034217.

The biological response of living arteries to mechanical forces is an important component of the atherosclerotic process and is responsible, at least in part, for the well-recognized spatial variation in atherosusceptibility in man. Experiments to elucidate this response often generate maps of force and response variables over the arterial surface, from which the force–response relationship is sought. Rowland et al. discussed several statistical approaches to the spatial autocorrelation that confounds the analysis of such maps and applied them to maps of hemodynamic stress and vascular response obtained by averaging these variables in multiple animals. Here, we point out an alternative approach, in which discrete surface regions are defined by the hemodynamic stress levels they experience, and the stress and response in each animal are treated separately. This approach, applied properly, is insensitive to autocorrelation and less sensitive to the effect of confounding hemodynamic variables. The analysis suggests an inverse relation between permeability and shear that differs from that in Rowland et al. Possible sources of this difference are suggested.

Commentary by Dr. Valentin Fuster

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