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Biplanar radiography displays promising results in the production of subject-specific (S.specific) biomechanical models. However, the focus has predominantly centred on methodological investigations in gait analysis. Exploring the influence of such models on the analysis of high range of motion tasks linked to hip pathologies is warranted. The aim of this study is to investigate the effect of S.Specific modelling techniques on the reliability of deep squats kinematics in comparison to generic modelling. 8 able-bodied male participants attended 5 motion capture sessions conducted by 3 observers and performed 5 deep squats in each. Prior to each session a biplanar scan was acquired with the reflective-markers attached. Inverse kinematics of pelvis and thigh segments were calculated based on S.specific and Generic model definition. Agreement between the two models femoropelvic orientation in standing was assessed with Bland-Altman plots and paired t- tests. Inter-trial, inter-session, inter-observer variability and observer/trial difference and ratio were calculated for squat kinematic data derived from the two modelling approaches. Compared to the Generic model, the S.Specific model produced a calibration trial that is significantly offset into more posterior pelvis tilt (–2.8±2.7), hip extension (–2.2±3.8), hip abduction (–1.2±3.6) and external rotation (–13.8±11.4). The S.specific model produced significantly different squat kinematics in the sagittal plane of the pelvis (entire squat cycle) and hip (between 40 % and 60 % of the squat cycle). Variability analysis indicated that the error magnitude between the two models was comparable (difference<2°). The S.specific model exhibited a lower variability in the observer/trial ratio in the sagittal pelvis and hip, the frontal hip, but showed a higher variability in the transverse hip. S.specific modelling appears to introduce a calibration offset that primarily translates into an effect in the sagittal plane kinematics. However, the clinical added value of S.specific modelling in terms of reducing experimental sources of kinematic variability was limited. • Biplanar radiography was used to produce subject-specific hip and pelvis model. • S.specific model kinematics were compared to generic model kinematics. • S.specific modelling significantly offsets the calibration of the standing trial. • This offset influenced the kinematics of the Sagittal pelvis and hip kinematics. • This offset had minimum impact on the variability of deep squat kinematics. [ABSTRACT FROM AUTHOR]

Background: Mandibular reconstruction patients often suffer abnormalities in the mandibular kinematics. In silico simulations, such as musculoskeletal modelling, can be used to predict post‐operative mandibular kinematics. It is important to validate the mandibular musculoskeletal model and analyse the factors influencing its accuracy. Objectives: To investigate the jaw opening‐closing movements after mandibular reconstruction, as predicted by the subject‐specific musculoskeletal model, and the factors influencing its accuracy. Methods: Ten mandibular reconstruction patients were enrolled in this study. Cone‐beam computed tomography images, mandibular movements, and surface electromyogram signals were recorded preoperatively. A subject‐specific mandibular musculoskeletal model was established to predict surgical outcomes using patient‐averaged muscle parameter changes as model inputs. Jaw bone geometry was replaced by surgical planning results, and the muscle insertion sites were registered based on the non‐rigid iterative closest point method. The predicted jaw kinematic data were validated based on 6‐month post‐operative measurements. Correlations between the prediction accuracy and patient characteristics (age, pathology and surgical scope) were further analysed. Results: The root mean square error (RMSE) for lower incisor displacement was 31.4%, and the error for peak magnitude of jaw opening was 4.9 mm. Age, post‐operative infection and radiotherapy influenced the prediction accuracy. The amount of masseter detachment showed little correlation with jaw opening. Conclusion: The mandibular musculoskeletal model successfully predicted short‐range jaw opening functions after mandibular reconstruction. It provides a novel surgical planning method to predict the risk of developing trismus. [ABSTRACT FROM AUTHOR]

As a solution to restore knee function and reduce pain, the demand for Total Knee Arthroplasty (TKA) has dramatically increased in recent decades. The high rates of dissatisfaction and revision makes it crucially important to understand the relationships between surgical factors and post-surgery knee performance. Tibial implant alignment in the sagittal plane (i.e., posterior tibia slope, PTS) is thought to play a key role in quadriceps muscle forces and contact conditions of the joint, but the underlying mechanisms and potential consequences are poorly understood. To address this biomechanical challenge, we developed a subject-specific musculoskeletal model based on the bone anatomy and precise implantation data provided within the CAMS-Knee datasets. Using the novel COMAK algorithm that concurrently optimizes joint kinematics, together with contact mechanics, and muscle and ligament forces, enabled highly accurate estimations of the knee joint biomechanics (RMSE

Subject-specific finite element models of knee joint contact mechanics are used in assessment of interventions and disease states. Cartilage thickness distribution is one factor influencing the distribution of pressure. Precision of cartilage geometry capture varies between imaging protocols. This work evaluated the cartilage thickness distribution precision needed for contact mechanics prediction in models of the tibiofemoral joint by comparing model outputs to experimental measurements for three cadaveric specimens. Models with location-specific cartilage thickness were compared to those with a uniform thickness, for a fixed relative orientation of the femur and tibia and with tibial freedom of movement. Under constrained conditions, the advantage of including location-specific cartilage thickness was clear. Models with location-specific thickness predicted the proportion of force through each condyle with an average error of 5% (compared to 27% with uniform thickness) and predicted the experimental contact area with an error of 21 mm 2 (compared to 98 mm 2 with uniform thickness). With tibial freedom, the advantage of location-specific cartilage thickness not clear. The attempt to allow three degrees of relative freedom at the tibiofemoral joint resulted in a high degree of experimental and computational uncertainty. It is therefore recommended that researchers avoid this level of freedom. This work provides some evidence that highly constrained conditions make tibiofemoral contact mechanics predictions more sensitive to cartilage thickness and should perhaps be avoided in studies where the means to generate subject-specific cartilage thickness are not available., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

• CFD multiscale model can be used together to improve the subject specific response of a determined modelling approach. • CFD models can be integrated into a clinically workflow to assess non invasively the severity of aortic coarctation in a heterogeneous cohort of patients starting from retrospective imaging data. • Good agreement between CFD models and clinical derived measurements in case of aortic coarctation assessment. We present a multi-scale CFD-based study conducted in a cohort of 11 patients with coarctation of the aorta (CoA). The study explores the potential for implementation of a workflow using non-invasive routinely collected medical imaging data and clinical measurements to provide a more detailed insight into local aortic haemodynamics in order to support clinical decision making. Our approach is multi-scale, using a reduced-order model (1D/0D) and an optimization process for the personalization of patient-specific boundary conditions and aortic vessel wall parameters from non-invasive measurements, to inform a more complex model (3D/0D) representing 3D aortic patient-specific anatomy. The reliability of the modelling approach is investigated by comparing 3D/0D model pressure drop estimation with measured peak gradients recorded during diagnostic cardiac catheterization and 2D PC-MRI flow rate measurements in the descending aorta. The current study demonstrated that the proposed approach requires low levels of user interaction, making it suitable for the clinical setting. The agreement between computed blood pressure drop and catheter measurements is 10 ± 8 mmHg at the coarctation site. The comparison between CFD derived and catheter measured pressure gradients indicated that the model has to be improved, suggesting the use of time varying pressure waveforms to further optimize the tuning process and modelling assumptions. [ABSTRACT FROM AUTHOR]

Introduction Musculoskeletal models are important tools for studying movement patterns, tissue loading, and neuromechanics. Personalising bone anatomy within models improves analysis accuracy. Few studies have focused on personalising foot bone anatomy, potentially incorrectly estimating the foot’s contribution to locomotion. Statistical shape models have been created for a subset of foot-ankle bones, but have not been validated. This study aimed to develop and validate statistical shape models of the functional segments in the foot: first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus; then, to assess reconstruction accuracy of these shape models using sparse anatomical data. Methods Magnetic resonance images of 24 individuals feet (age = 28 ± 6 years, 52% female, height = 1.73 ± 0.8 m, mass = 66.6 ± 13.8 kg) were manually segmented to generate three-dimensional point clouds. Point clouds were registered and analysed using principal component analysis. For each bone segment, a statistical shape model and principal components were created, describing population shape variation. Statistical shape models were validated by assessing reconstruction accuracy in a leave-one-out cross validation. Statistical shape models were created by excluding a participant’s bone segment and used to reconstruct that same excluded bone using full segmentations and sparse anatomical data (i.e. three discrete points on each segment), for all combinations in the dataset. Tali were not reconstructed using sparse anatomical data due to a lack of externally accessible landmarks. Reconstruction accuracy was assessed using Jaccard index, root mean square error (mm), and Hausdorff distance (mm). Results Reconstructions generated using full segmentations had mean Jaccard indices between 0.77 ± 0.04 and 0.89 ± 0.02, mean root mean square errors between 0.88 ± 0.19 and 1.17 ± 0.18 mm, and mean Hausdorff distances between 2.99 ± 0.98 mm and 6.63 ± 3.68 mm. Reconstructions generated using sparse anatomical data had mean Jaccard indices between 0.67 ± 0.06 and 0.83 ± 0.05, mean root mean square error between 1.21 ± 0.54 mm and 1.66 ± 0.41 mm, and mean Hausdorff distances between 3.21 ± 0.94 mm and 7.19 ± 3.54 mm. Jaccard index was higher (P < 0.01) and root mean square error was lower (P < 0.01) in reconstructions from full segmentations compared to sparse anatomical data. Hausdorff distance was lower (P < 0.01) for midfoot and calcaneus reconstructions using full segmentations compared to sparse anatomical data. Conclusion For the first time, statistical shape models of the primary functional segments of the foot were developed and validated. Foot segments can be reconstructed with minimal error using full segmentations and sparse anatomical landmarks. In future, larger training datasets could increase statistical shape model robustness, extending use to paediatric or pathological populations.

Introduction: Musculoskeletal models are important tools for studying movement patterns, tissue loading, and neuromechanics. Personalising bone anatomy within models improves analysis accuracy. Few studies have focused on personalising foot bone anatomy, potentially incorrectly estimating the foot's contribution to locomotion. Statistical shape models have been created for a subset of foot-ankle bones, but have not been validated. This study aimed to develop and validate statistical shape models of the functional segments in the foot: first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus; then, to assess reconstruction accuracy of these shape models using sparse anatomical data. Methods: Magnetic resonance images of 24 individuals feet (age = 28 ± 6 years, 52% female, height = 1.73 ± 0.8 m, mass = 66.6 ± 13.8 kg) were manually segmented to generate three-dimensional point clouds. Point clouds were registered and analysed using principal component analysis. For each bone segment, a statistical shape model and principal components were created, describing population shape variation. Statistical shape models were validated by assessing reconstruction accuracy in a leave-one-out cross validation. Statistical shape models were created by excluding a participant's bone segment and used to reconstruct that same excluded bone using full segmentations and sparse anatomical data (i.e. three discrete points on each segment), for all combinations in the dataset. Tali were not reconstructed using sparse anatomical data due to a lack of externally accessible landmarks. Reconstruction accuracy was assessed using Jaccard index, root mean square error (mm), and Hausdorff distance (mm). Results: Reconstructions generated using full segmentations had mean Jaccard indices between 0.77 ± 0.04 and 0.89 ± 0.02, mean root mean square errors between 0.88 ± 0.19 and 1.17 ± 0.18 mm, and mean Hausdorff distances between 2.99 ± 0.98 mm and 6.63 ± 3.68 mm. Reconstructions generated using sparse anatomical data had mean Jaccard indices between 0.67 ± 0.06 and 0.83 ± 0.05, mean root mean square error between 1.21 ± 0.54 mm and 1.66 ± 0.41 mm, and mean Hausdorff distances between 3.21 ± 0.94 mm and 7.19 ± 3.54 mm. Jaccard index was higher (P < 0.01) and root mean square error was lower (P < 0.01) in reconstructions from full segmentations compared to sparse anatomical data. Hausdorff distance was lower (P < 0.01) for midfoot and calcaneus reconstructions using full segmentations compared to sparse anatomical data. Conclusion: For the first time, statistical shape models of the primary functional segments of the foot were developed and validated. Foot segments can be reconstructed with minimal error using full segmentations and sparse anatomical landmarks. In future, larger training datasets could increase statistical shape model robustness, extending use to paediatric or pathological populations. [ABSTRACT FROM AUTHOR]

Epileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a generalizable method for patient-specific seizure prediction by evaluating the spatio-temporal correlation in the features obtained from multichannel EEG signals. Spectral band power, statistical moment and Hjorth parameters are used to reveal the frequency and time domain features of the EEG signals. The features are given as input to a convolutional neural network (CNN) by transforming into a sequence of multi-color images according to the topology of the EEG channels. The multi-frame 3D CNN model is proposed to evaluate the temporal and spatial correlation in training data collectively. The proposed 3D CNN model achieves a sensitivity of 85.7%, a false prediction rate of 0.096/h, and a proportion of time-in-warning of 10.5%, in the tests performed with 16 patients from the CHB-MIT scalp EEG dataset. The results show that the superiority of the proposed method to a Poisson based random predictor was statistically significant for 93.7% of the patients, at significance level of 0.05. Our experiments with various timing constraints show that epileptic stage lengths are an important factor affecting seizure performance. We present a subject-specific seizure prediction method that is robust for unbalanced data and can be generalized to any scalp EEG dataset without the need for subject-specific engineering. [ABSTRACT FROM AUTHOR]

• Mechanical and geometric properties important for orthopaedic reconstruction. • Statistical shape models allow reconstruction of healthy state. • Reconstructions geometrically comparable to contralateral side. • Differences in stress and strain in similar range as contralateral side. Accurate models of bone shapes are essential for orthopaedic reconstructions. The commonly used methods of using the contralateral side requires an intact bone and anatomical symmetry. Recent studies have shown that statistical shape and appearance models (SSAMs) as an alternative can predict accurate geometric models, but the accuracy of the mechanical property prediction is typically not addressed. This study compares stress and strain differences under identical loading conditions for reconstructions from partial anatomies. SSAMs representing shape and grey values were created using 40 female cadaveric X-ray computed tomography scans. Finite element models were created for shape reconstructions from partial bone of various lengths with boundary conditions obtained from musculoskeletal simulations. Commonly used anatomical measures, measures of the surface deviations and maximal stresses and strains were used to compare the reconstruction accuracy to the contralateral side. Surface errors were smaller compared to the contralateral side for reconstructions with 90% of the bone and slightly bigger when less bone was available. Anatomical measures were comparable. The contralateral side showed slightly smaller relative errors for strains of up to 6% on average. This study has shown that SSAM reconstructions using partial bone geometry are a possible alternative to the contralateral side. [ABSTRACT FROM AUTHOR]

This paper presents a lumped-parameter model that can reproduce blood volume response to fluid infusion. The model represents the fluid shift between the intravascular and interstitial compartments as the output of a hypothetical feedback controller that regulates the ratio between the volume changes in the intravascular and interstitial fluid at a target value (called target volume ratio). The model is characterized by only three parameters: the target volume ratio, feedback gain (specifying the speed of fluid shift), and initial blood volume. This model can obviate the need to incorporate complex mechanisms involved in the fluid shift in reproducing blood volume response to fluid infusion. The ability of the model to reproduce real-world blood volume response to fluid infusion was evaluated by fitting it to a series of data reported in the literature. The model reproduced the data accurately with average error and root-mean-squared error (RMSE) of 0.6 % and 9.5 % across crystalloid and colloid fluids when normalized by the underlying responses. Further, the parameters derived for the model showed physiologically plausible behaviors. It was concluded that this simple model may accurately reproduce a variety of blood volume responses to fluid infusion throughout different physiological states by fitting three parameters to a given dataset. This offers a tool that can quantify the fluid shift in a dataset given the measured fractional blood volumes.

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank – up to 26 mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods. [ABSTRACT FROM AUTHOR]

Capturing patient- or condition-specific intervertebral disc (IVD) properties in finite element models is outmost important in order to explore how biomechanical and biophysical processes may interact in spine diseases. However, disc degenerative changes are often modelled through equations similar to those employed for healthy organs, which might not be valid. As for the simulated effects of degenerative changes, they likely depend on specific disc geometries. Accordingly, we explored the ability of continuum tissue models to simulate disc degenerative changes. We further used the results in order to assess the interplay between these simulated changes and particular IVD morphologies, in relation to disc cell nutrition, a potentially important actor in disc tissue regulation. A protocol to derive patient-specific computational models from clinical images was applied to different spine specimens. In vitro IVD creep tests were used to optimize poro-hyperelastic input material parameters in these models, in function of the IVD degeneration grade. The use of condition-specific tissue model parameters in the specimen-specific geometrical models was validated against independent kinematic measurements in vitro. Then, models were coupled to a transport-cell viability model in order to assess the respective effects of tissue degeneration and disc geometry on cell viability. While classic disc poromechanical models failed in representing known degenerative changes, additional simulation of tissue damage allowed model validation and gave degeneration-dependent material properties related to osmotic pressure and water loss, and to increased fibrosis. Surprisingly, nutrition-induced cell death was independent of the grade-dependent material properties, but was favoured by increased diffusion distances in large IVDs. Our results suggest that in situ geometrical screening of IVD morphology might help to anticipate particular mechanisms of disc degeneration.

• Statistical shape models (SSM) of bones have been developed for adults, though these have limited applicability to paediatric populations. • SSM of paediatric lower limb bones were built and were capable of accurately reconstructing bone geometric models from anatomical landmarks. • The SSM can be used to reconstruct subject-specific bone geometry from readily accessible anatomical landmarks, avoiding the need for medical imaging. • The built paediatric SSM can reconstruct paediatric lower limb bones more accurately than adult-based SSM. Accurate representation of bone shape is important for subject-specific musculoskeletal models as it may influence modelling of joint kinematics, kinetics, and muscle dynamics. Statistical shape modelling is a method to estimate bone shape from minimal information, such as anatomical landmarks, and to avoid the time and cost associated with reconstructing bone shapes from comprehensive medical imaging. Statistical shape models (SSM) of lower limb bones have been developed and validated for adult populations but are not applicable to paediatric populations. This study aimed to develop SSM for paediatric lower limb bones and evaluate their reconstruction accuracy using sparse anatomical landmarks. We created three-dimensional models of 56 femurs, 29 pelves, 56 tibias, 56 fibulas, and 56 patellae through segmentation of magnetic resonance images taken from 29 typically developing children (15 females; 13 ± 3.5 years). The SSM for femur, pelvis, tibia, fibula, patella, haunch (i.e., combined femur and pelvis), and shank (i.e., combined tibia and fibula) were generated from manual segmentation of comprehensive magnetic resonance images to describe the shape variance of the cohort. We implemented a leave-one-out cross-validation method wherein SSM were used to reconstruct novel bones (i.e., those not included in SSM generation) using full- (i.e., full segmentation) and sparse- (i.e., anatomical landmarks) input, and then compared these reconstructions against bones segmented from magnetic resonance imaging. Reconstruction performance was evaluated using root mean squared errors (RMSE, mm), Jaccard index (0-1), Dice similarity coefficient (DSC) (0-1), and Hausdorff distance (mm). All results reported in this abstract are mean ± standard deviation. Femurs, pelves, tibias, fibulas, and patellae reconstructed via SSM using full-input had RMSE between 0.89 ± 0.10 mm (patella) and 1.98 ± 0.38 mm (pelvis), Jaccard indices between 0.77 ± 0.03 (pelvis) and 0.90 ± 0.02 (tibia), DSC between 0.87 ± 0.02 (pelvis) and 0.95 ± 0.01 (tibia), and Hausdorff distances between 2.45 ± 0.57 mm (patella) and 9.01 ± 2.36 mm (pelvis). Reconstruction using sparse-input had RMSE ranging from 1.33 ± 0.61 mm (patella) to 3.60 ± 1.05 mm (pelvis), Jaccard indices ranging from 0.59 ± 0.10 (pelvis) to 0.83 ± 0.03 (tibia), DSC ranging from 0.74 ± 0.08 (pelvis) to 0.90 ± 0.02 (tibia), and Hausdorff distances ranging from 3.21 ± 1.19 mm (patella) to 12.85 ± 3.24 mm (pelvis). The SSM of paediatric lower limb bones showed reconstruction accuracy consistent with previously developed SSM and outperformed adult-based SSM when used to reconstruct paediatric bones. [ABSTRACT FROM AUTHOR]

Abstract: We assessed and compared sensitivities of dynamic simulations to musculotendon (MT) parameters for gait and dynamometer experiments. Our aim with this comparison was to investigate whether dynamometer experiments could provide information about MT-parameters that are important to reliably study MT-function during gait. This would mean that dynamometer experiments could be used to estimate these parameters. Muscle contribution to the joint torque (MT-torque) rather than relative MT-force primarily affects the resulting gait pattern and torque measured by the dynamometer. In contrast to recent studies, therefore, we assessed the sensitivity of the MT-torque, rather than the sensitivity of the relative MT-force. Based on sensitivity of the MT-torque to a parameter perturbation, MT-parameters of the knee flexors and extensors were classified in three categories: low, medium, and high. For gait, classification was based on the average sensitivity during a gait cycle. For isometric and isokinetic dynamometer experiments, classification was based on the highest sensitivity found in the experiments. The calculated muscle contributions to the knee torque during gait and dynamometer experiments had a high sensitivity to only a limited number of MT-parameters of the knee flexors and extensors, suggesting that not all MT-parameters need to be estimated. In general, the highest sensitivity was found for tendon slack length. However, for some muscles the sensitivity to the optimal fibre length or the maximal isometric muscle force was also high or medium. The classification of the individual MT-parameters for gait and dynamometer experiments was largely similar. We therefore conclude that dynamometer experiments provide information about MT-parameters important to reliably study MT-function during gait, so that subject-specific estimates of MT-parameters could be made based on dynamometer experiments. [Copyright &y& Elsevier]

Abstract: Morphometrical and postural features of the cervical spine are supposed to significantly influence its biomechanical behaviour. However, the effects of these geometrical parameters are quite difficult to evaluate. An original numerical method is proposed in order to automatically generate parametric and subject-specific meshes of the lower cervical spine. Sixteen finite element models have been built from cadaver specimens using low dose biplanar X-rays. All the generated meshes fulfilled the quality criteria. A preliminary evaluation was performed on the C5–C6 functional units using a database of previous experimental tests. The principal and coupled motions were simulated. The responses of the numerical models were within the experimental standard deviation corridors in most cases. Rotation–moment relationships were then compared to assess the influence of geometry on the mechanical response. Geometry was found to play a significant role in the motion patterns. [Copyright &y& Elsevier]

Despite evidence of the biomechanical role of cortical bone, current state of the art finite element models of the proximal femur built from clinical CT data lack a subject-specific representation of the bone cortex. Our main research hypothesis is that the subject-specific modelling of cortical bone layer from CT images, through a deconvolution procedure known as Cortical Bone Mapping (CBM, validated for cortical thickness and density estimates) can improve the accuracy of CT-based FE models of the proximal femur, currently limited by partial volume artefacts. Our secondary hypothesis is that a careful choice of cortical-specific density-elasticity relationship may improve model accuracy. We therefore: (i) implemented a procedure to include subject-specific CBM estimates of both cortical thickness and density in CT-based FE models. (ii) defined alternative models that included CBM estimates and featured a cortical-specific or an independently optimised density-elasticity relationship. (iii) tested our hypotheses in terms of elastic strain estimates and failure load and location prediction, by comparing with a published cohort of 14 femurs, where strain and strength in stance and fall loading configuration were experimentally measured, and estimated through reference FE models that did not explicitly model the cortical compartment. Our findings support the main hypothesis: an explicit modelling of the proximal femur cortical bone layer including CBM estimates of cortical bone thickness and density increased the FE strains prediction, mostly by reducing peak errors (average error reduced by 30%, maximum error and 95th percentile of error distribution halved) and especially when focusing on the femoral neck locations (all error metrics at least halved). We instead rejected the secondary hypothesis: changes in cortical density-elasticity relationship could not improve validation performances. From these improved baseline strain estimates, further work is needed to achieve accurate strength predictions, as models incorporating cortical thickness and density produced worse estimates of failure load and equivalent estimates of failure location when compared to reference models. In summary, we recommend including local estimates of cortical thickness and density in FE models to estimate bone strains in physiological conditions, and especially when designing exercise studies to promote bone strength. • New femur FE models including Cortical Bone Mapping (CBM), i.e. local estimates of cortical thickness&density from CT data • CBM models improved bone strain estimates in the elastic range • Thus, CBM models can be used to estimate bone strains in exercise • Cortical-specific (experimental or optimised) density-elasticity law could not improve accuracy of CBM models • CBM models do not accurately estimate strength yet, but provide accurate strain data to develop biofidelic failure criteria [ABSTRACT FROM AUTHOR]

Biomechanical models of the oropharynx are beneficial to treatment planning of speech impediments by providing valuable insight into the speech function such as motor control. In this paper, we develop a subject-specific model of the oropharynx and investigate its utility in speech production. Our approach adapts a generic tongue-jaw-hyoid model (Stavness et al. 2011) to fit and track dynamic volumetric MRI data of a normal speaker, subsequently coupled to a source-filter based acoustic synthesizer. We demonstrate our model's ability to track tongue tissue motion, simulate plausible muscle activation patterns, as well as generate acoustic results that have comparable spectral features to the associated recorded audio. Finally, we propose a method to adjust the spatial resolution of our subject-specific tongue model to match the fidelity level of our MRI data and speech synthesizer. Our findings suggest that a higher resolution tongue model - using similar muscle fibre definition - does not show a significant improvement in acoustic performance, for our speech utterance and at this level of fidelity; however we believe that our approach enables further refinements of the muscle fibres suitable for studying longer speech sequences and finer muscle innervation using higher resolution dynamic data.