Subject-specific Finite Element Research Articles

Mechanobiological cues to bone cells during early metastasis drive later osteolysis: a computational mechanoregulation framework prediction

Bone cells contribute to tumour metastasis by producing biochemical factors that stimulate tumour cell homing and proliferation, but also by resorbing bone matrix (osteolysis) that releases further stimulatory factors for tumour growth in a vicious cycle. Changes in the local mechanical environment of bone tissue occur during early metastasis, which might activate mechanobiological responses by resident bone cells (osteocytes) to activate resorption (osteoclasts) and thereby contribute to tumour invasion. The objective of this study is to investigate whether bone osteolysis is driven by early changes in the bone mechanical environment during metastasis by (a) implementing subject-specific FE models of metastatic femora to predict the mechanical environment within bone tissue during early metastasis (3-weeks after tumour inoculation) and then (b) applying mechanoregulation theory to predict bone tissue remodelling as a function of the evolving mechanical environment within bone tissue during breast cancer-bone metastasis. We implemented a global resorption rate derived from an experimental model, but the mechanoregulation algorithm predicted localised bone loss in the greater trochanter region, the same region where osteolysis was prevalent after three weeks of metastasis development in the animal model. Moreover, the mechanical environment evolved in a similar manner to that reported in separate subject-specific finite element models of these same animals by 6 weeks. Thus, we propose that early changes in the physical environment of bone tissue during metastasis may elicit mechanobiological cues for bone cells and activate later osteolytic bone destruction.

A Deep Recurrent Neural Network for Predicting Subject-specific Facial Soft Tissue Interaction

Predicting biological soft tissue interaction is of great interest for developing computer-aided decision systems. This study aims to develop and evaluate a novel deep-learning approach based on the recurrent neural network for predicting facial soft tissue impact with a rubber ball. A computational workflow was established including a subject-specific finite element model of the facial soft tissue under interaction with the rubber ball. A series of simulations under different ball velocities was performed to build the learning database. We implemented a long-short-term memory (LSTM) model and then evaluated its performance using root mean square error (RMSE) and regression coefficient metrics. The obtained results showed a RMSE of 3.13 mm and a Pearson correlation coefficient of 0.98 for soft tissue displacement prediction. A RMSE of 0.001 MPa and a Pearson correlation coefficient of 0.94 was also obtained for soft tissue von Mises stress prediction. The present study showed the robustness and accuracy of the recurrent neural network for predicting complex soft tissue interaction behaviours. Our findings open new avenues for deploying novel deep learning workflow for human-facial softtissue interaction. As perspective, this workflow will be integrated into our interactive facial analysis and rehabilitation system.

Comparison of Left Ventricular Function Derived from Subject-Specific Inverse Finite Element Modeling Based on 3D ECHO and Magnetic Resonance Images.

Three-dimensional echocardiography (3D ECHO) and magnetic resonance (MR) imaging are frequently used in patients and animals to evaluate heart functions. Inverse finite element (FE) modeling is increasingly applied to MR images to quantify left ventricular (LV) function and estimate myocardial contractility and other cardiac biomarkers. It remains unclear, however, as to whether myocardial contractility derived from the inverse FE model based on 3D ECHO images is comparable to that derived from MR images. To address this issue, we developed a subject-specific inverse FE model based on 3D ECHO and MR images acquired from seven healthy swine models to investigate if there are differences in myocardial contractility and LV geometrical features derived using these two imaging modalities. We showed that end-systolic and end-diastolic volumes derived from 3D ECHO images are comparable to those derived from MR images (R2=0.805 and 0.969, respectively). As a result, ejection fraction from 3D ECHO and MR images are linearly correlated (R2=0.977) with the limit of agreement (LOA) ranging from -17.95% to 45.89%. Using an inverse FE modeling to fit pressure and volume waveforms in subject-specific LV geometry reconstructed from 3D ECHO and MR images, we found that myocardial contractility derived from these two imaging modalities are linearly correlated with an R2 value of 0.989, a gradient of 0.895, and LOA ranging from -6.11% to 36.66%. This finding supports using 3D ECHO images in image-based inverse FE modeling to estimate myocardial contractility.

Biomechanical analysis of various internal fracture fixation devices used for treating femoral neck fractures: A comparative finite element analysis

IntroductionSeveral internal fixation devices are available for treating Pauwels type I, II and III femoral neck fractures. The present study compared various fixation implants for all Pauwels fracture types using a CT-based subject-specific finite element model of the femur and determined the most effective implant for each fracture type. Materials and MethodsThe analysis included four different configurations of cannulated screw models, Femoral Neck System, Dynamic Hip Screw and Dynamic Condylar Screw (with and without anti-rotational screw). Ti-alloy was considered as the implant material. Heterogeneous bone material property was assigned based on CT grey value. Frictional contact was assumed in the contact interfaces. Peak loading corresponding to normal walking and stair-climbing were considered. Equivalent strain in bone, equivalent stress in the implants, femoral head deformation and rotation, micromotion in the contact interfaces, and strain-shielding in bone were evaluated for each implanted model. ResultsStresses generated in the implants were within the yield limit of the implant material. In Pauwels I and II, the micromotion predicted at the contact regions in all the implanted models was within 100 µm, which is suitable for bone integration. However, in Pauwels III fracture, most of the implanted models other than DHS with AR-screw model exhibited micromotion of more than 150 µm in the contact regions, which is expected to inhibit bone growth. ConclusionsThe DHS with AR-screw implanted model was identified as the most effective in treating Pauwels I and III fractures. However, for Pauwels type II, DCS with an AR-screw implant was deemed superior to the other configurations.

Exploring vertebral bone density changes in a trunk with adolescent idiopathic scoliosis: a mechanobiological modeling investigation of intact and unilaterally paralyzed muscles

This study aimed to elucidate the vertebral bone density variations associated with adolescent idiopathic scoliosis (AIS), specifically examining the impact of unilateral muscle paralysis using an integrated approach combining Frost’s Mechanostat theory, a three-dimensional subject-specific finite element model and a musculoskeletal model of the L2 vertebra. The findings revealed a spectrum of bone density values ranging from 0.29 to 0.31 g/cm3, along with vertebral micro-strain levels spanning from 300 to 2200, consistent with existing literature. Furthermore, the ratio of maximum von Mises stress between the concave and convex side in the AIS model with intact muscles was approximately 1.08, which decreased by 4% due following unilateral paralysis of longissimus thoracis pars thoracic muscle. Overall, this investigation contributes to a deeper understanding of AIS biomechanics and lays the groundwork for future research endeavors aimed at optimizing clinical management approaches for individuals with this condition.

ESMAC Best Paper Award 2023: Increased knee flexion in participants with cerebral palsy results in altered stresses at the distal femoral growth plate compared to a typically developing cohort

IntroductionFemoral deformities are highly prevalent in children with cerebral palsy (CP) and can have a severe impact on patients’ gait abilities. While the mechanical stress regime within the distal femoral growth plate remains underexplored, understanding it is crucial given bone's adaptive response to mechanical stimuli. We quantified stresses at the distal femoral growth plate to deepen our understanding of the relationship between healthy and pathological gait patterns, internal loading, and femoral growth patterns. MethodsThis study included three-dimensional motion capture data and magnetic resonance images of 13 typically developing children and twelve participants with cerebral palsy. Employing a multi-scale mechanobiological approach, integrating musculoskeletal simulations and subject-specific finite element analysis, we investigated the orientation of the distal femoral growth plate and the stresses within it. Limbs of participants with CP were grouped depending on their knee flexion kinematics during stance phase as this potentially changes the stresses induced by knee and patellofemoral joint contact forces. ResultsDespite similar growth plate orientation across groups, significant differences were observed in the shape and distribution of growth values. Higher growth rates were noted in the anterior compartment in CP limbs with high knee flexion while CP limbs with normal knee flexion showed high similarity to the group of healthy participants. DiscussionResults indicate that the knee flexion angle during the stance phase is of high relevance for typical bone growth at the distal femur. The evaluated growth rates reveal plausible results, as long-term promoted growth in the anterior compartment leads to anterior bending of the femur which was confirmed for the group with high knee flexion through analyses of the femoral geometry. The framework for these multi-scale simulations has been made accessible on GitHub, empowering peers to conduct similar mechanobiological studies. Advancing our understanding of femoral bone development could ultimately support clinical decision-making.

Validation and Estimation of Obesity-Induced Intervertebral Disc Degeneration through Subject-Specific Finite Element Modelling of Functional Spinal Units.

(1) Background: Intervertebral disc degeneration has been linked to obesity; its potential mechanical effects on the intervertebral disc remain unknown. This study aimed to develop and validate a patient-specific model of L3-L4 vertebrae and then use the model to estimate the impact of increasing body weight on disc degeneration. (2) Methods: A three-dimensional model of the functional spinal unit of L3-L4 vertebrae and its components were developed and validated. Validation was achieved by comparing the range of motions (RoM) and intradiscal pressures with the previous literature. Subsequently, the validated model was loaded according to the body mass index and estimated stress, deformation, and RoM to assess disc degeneration. (3) Results: During validation, L3-L4 RoM and intradiscal pressures: flexion 5.17° and 1.04 MPa, extension 1.54° and 0.22 MPa, lateral bending 3.36° and 0.54 MPa, axial rotation 1.14° and 0.52 MPa, respectively. When investigating the impact of weight on disc degeneration, escalating from normal weight to obesity reveals an increased RoM, by 3.44% during flexion, 22.7% during extension, 29.71% during lateral bending, and 33.2% during axial rotation, respectively. Also, stress and disc deformation elevated with increasing weight across all RoM. (4) Conclusions: The predicted mechanical responses of the developed model closely matched the validation dataset. The validated model predicts disc degeneration under increased weight and could lay the foundation for future recommendations aimed at identifying predictors of lower back pain due to disc degeneration.

Load transfer in bone after partial, multi-compartmental, and total knee arthroplasty.

Introduction: Arthroplasty-associated bone loss remains a clinical problem: stiff metallic implants disrupt load transfer to bone and, hence, its remodeling stimulus. The aim of this research was to analyze how load transfer to bone is affected by different forms of knee arthroplasty: isolated partial knee arthroplasty (PKA), compartmental arthroplasty [combined partial knee arthroplasty (CPKA), two or more PKAs in the same knee], and total knee arthroplasty (TKA). Methods: An experimentally validated subject-specific finite element model was analyzed native and with medial unicondylar, lateral unicondylar, patellofemoral, bi-unicondylar, medial bicompartmental, lateral bicompartmental, tricompartmental, and total knee arthroplasty. Three load cases were simulated for each: gait, stair ascent, and sit-to-stand. Strain shielding and overstraining were calculated from the differences between the native and implanted states. Results: For gait, the TKA femoral component led to mean strain shielding (30%) more than three times higher than that of PKA (4%-7%) and CPKA (5%-8%). Overstraining was predicted in the proximal tibia (TKA 21%; PKA/CPKA 0%-6%). The variance in the distribution for TKA was an order of magnitude greater than for PKA/CPKA, indicating less physiological load transfer. Only the TKA-implanted femur was sensitive to the load case: for stair ascent and gait, almost the entire distal femur was strain-shielded, whereas during sit-to-stand, the posterior femoral condyles were overstrained. Discussion: TKA requires more bone resection than PKA and CPKA. These finite element analyses suggest that a longer-term benefit for bone is probable as partial and multi-compartmental knee procedures lead to more natural load transfer compared to TKA. High-flexion activity following TKA may be protective of posterior condyle bone resorption, which may help explain why bone loss affects some patients more than others. The male and female bone models used for this research are provided open access to facilitate future research elsewhere.

Custom orthotic design by integrating 3D scanning and subject-specific FE modelling workflow

The finite element (FE) foot model can help estimate pathomechanics and improve the customized foot orthoses design. However, the procedure of developing FE models can be time-consuming and costly. This study aimed to develop a subject-specific scaled foot modelling workflow for the foot orthoses design based on the scanned foot surface data. Six participants (twelve feet) were collected for the foot finite element modelling. The subject-specific surface-based finite element model (SFEM) was established by incorporating the scanned foot surface and scaled foot bone geometries. The geometric deviations between the scaled and the scanned foot surfaces were calculated. The SFEM model was adopted to predict barefoot and foot-orthosis interface pressures. The averaged distances between the scaled and scanned foot surfaces were 0.23 ± 0.09 mm. There was no significant difference for the hallux, medial forefoot, middle forefoot, midfoot, medial hindfoot, and lateral hindfoot, except for the lateral forefoot region (p = 0.045). The SFEM model evaluated slightly higher foot-orthoses interface pressure values than measured, with a maximum deviation of 7.1%. These results indicated that the SFEM technique could predict the barefoot and foot-orthoses interface pressure, which has the potential to expedite the process of orthotic design and optimization.Graphical abstract

Subject-specific finite element head models for skull fracture evaluation-a new tool in forensic pathology.

Post-mortem computed tomography (PMCT) enables the creation of subject-specific 3D head models suitable for quantitative analysis such as finite element analysis (FEA). FEA of proposed traumatic events is an objective and repeatable numerical method for assessing whether an event could cause a skull fracture such as seen at autopsy. FEA of blunt force skull fracture in adults with subject-specific 3D models in forensic pathology remains uninvestigated. This study aimed to assess the feasibility of FEA for skull fracture analysis in routine forensic pathology. Five cases with blunt force skull fracture and sufficient information on the kinematics of the traumatic event to enable numerical reconstruction were chosen. Subject-specific finite element (FE) head models were constructed by mesh morphing based on PMCT 3D models and A Detailed and Personalizable Head Model with Axons for Injury Prediction (ADAPT) FE model. Morphing was successful in maintaining subject-specific 3D geometry and quality of the FE mesh in all cases. In three cases, the simulated fracture patterns were comparable in location and pattern to the fractures seen at autopsy/PMCT. In one case, the simulated fracture was in the parietal bone whereas the fracture seen at autopsy/PMCT was in the occipital bone. In another case, the simulated fracture was a spider-web fracture in the frontal bone, whereas a much smaller fracture was seen at autopsy/PMCT; however, the fracture in the early time steps of the simulation was comparable to autopsy/PMCT. FEA might be feasible in forensic pathology in cases with a single blunt force impact and well-described event circumstances.

Accuracy and precision of internal displacement and strain measurements in long human bones using HR-pQCT and digital volume correlation

ABSTRACT Digital volume correlation (DVC) is a technique for measuring 3D, internal displacements and strains in loaded structures. One application of DVC is the study of internal bone mechanics and the validation of subject-specific finite element (FE) models. While micro-computed tomography (µCT) is a common choice for DVC studies of small samples of bone, long human bones such as the tibia may exceed the spatial limitations of conventional µCT. High resolution peripheral quantitative CT (HR-pQCT) scanners, with their large bore and open-ended design, may be a viable option for long-bone DVC. However, HR-pQCT is afflicted by stitching artefacts, caused by the stacking of scan blocks to form the large scan volume, resulting in erroneous steps in DVC displacements and bands of increased strain error. This study proposed a modified HR-pQCT scanning protocol and stitching methodology to mitigate the effects of stitching artefacts on DVC measurements of displacement and strain. With the application of the proposed scanning/stitching methodology, displacements greater than 11.7µm (0.29 voxels) and strains greater than 1633µε could be repeatedly measured by DVC. These results show that HR-pQCT combined with DVC is suitable for measuring internal bone displacements in long human bones with sub-voxel precision and strains greater than 1633µε.

Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage

Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subject-specific models include these factors, resulting in enhanced predictive precision, but are laborious and time intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm using a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. For comparison, a manual FE model was developed through manual segmentation (i.e., the de-facto standard approach). Both FE models were subjected to gait loading and the predicted mechanical response was compared. The semi-automated segmentation achieved a Dice similarity coefficient (DSC) of over 98% for both the femur and tibia. Hausdorff distance (mm) between the semi-automated and manual segmentation was 1.4 mm. The mechanical results (max principal stress and strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. We have made our semi-automated models publicly accessible to support and facilitate biomechanical modeling and medical image segmentation efforts (https://data.mendeley.com/datasets/k5hdc9cz7w/1).

Rapid patient-specific FEM meshes from 3D smart-phone based scans

Objective. The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images. Approach. The method was evaluated on an iPhone® that utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes. The approach was quantitatively validated via mannequin and volunteer tests using an infrared tracker as the gold standard, and qualitatively assessed in a series of tidal-breathing EIT images recorded from 9 subjects. Main results. The subject-specific meshes can be generated in as little as 6.3 min, which requires on average 3.4 min of user interaction. The mannequin tests yielded high levels of precision and accuracy at 3.2 ± 0.4 mm and 4.0 ± 0.3 mm root mean square error (RMSE), respectively. Errors on volunteers were only slightly larger (5.2 ± 2.1 mm RMSE precision and 7.7 ± 2.9 mm RMSE accuracy), illustrating the practical RMSE of the method. Significance. Easy-to-generate, subject-specific meshes could be utilized in the thoracic EIT community, potentially reducing geometric-based artifacts and improving the clinical utility of EIT.

Instantaneous Generation of Subject-Specific Finite Element Models of the Hip Capsule.

Subject-specific hip capsule models could offer insights into impingement and dislocation risk when coupled with computer-aided surgery, but model calibration is time-consuming using traditional techniques. This study developed a framework for instantaneously generating subject-specific finite element (FE) capsule representations from regression models trained with a probabilistic approach. A validated FE model of the implanted hip capsule was evaluated probabilistically to generate a training dataset relating capsule geometry and material properties to hip laxity. Multivariate regression models were trained using 90% of trials to predict capsule properties based on hip laxity and attachment site information. The regression models were validated using the remaining 10% of the training set by comparing differences in hip laxity between the original trials and the regression-derived capsules. Root mean square errors (RMSEs) in laxity predictions ranged from 1.8° to 2.3°, depending on the type of laxity used in the training set. The RMSE, when predicting the laxity measured from five cadaveric specimens with total hip arthroplasty, was 4.5°. Model generation time was reduced from days to milliseconds. The results demonstrated the potential of regression-based training to instantaneously generate subject-specific FE models and have implications for integrating subject-specific capsule models into surgical planning software.

Analysis of the pattern of microstructural changes in the brain after mTBI with diffusion tensor imaging and subject-specific FE models

Traumatic brain injury (TBI) is a major public health challenge. Up to 90 % of TBIs are on the mild spectrum of TBI (mTBI), where diagnosis is a major challenge. Majority of studies in this field have been conducted on human subjects, which inherently suffer from the lack of appropriate control group, selection bias, and individual differences in patients. To overcome these limitations, animal studies have been used as an alternative approach to provide deeper insights into the underlying mechanism related to the injury. Therefore our aim is to investigate various quantitative imaging biomarkers acquired from T1-W and diffusion tensor imaging (DTI) sequences to provide more information about the microstructural changes in the brain after mTBI. We then use this to generate subject-specific finite element models of the brain and examine how the changes in the brain material properties reflected in MR images affects strain distribution patterns on a subsequent head hit. Our study revealed a decrease in FA and an increase in diffusivity indices (MD, AD, RD) in the white matter tracts of the brain. This finding may represent the axonal damage, demyelination and gliosis after mild TBI, which have been shown in other animal and human studies. Moreover, our FE analysis showed that microstructural changes in the brain after mTBI might have weakened the structural integrity of the brain as the subsequent head hit led to wider and more severe brain deformations. SignificanceAnimal models have been used to investigate biomechanical and pathophysiological aspects of mild traumatic brain injuries in the past. Still, most of them used small animals such as rats and mice. These models provided valuable insight into the pathophysiology of mTBI, but their findings have limitations due to their inherent differences to human brains. We have developed a large animal model of mTBI with sheep brains by combining advanced MRI and finite element analysis as they mimic the human brain better. To the best of our knowledge, this study is the first mTBI neuroimaging study conducted on large animal brains to investigate the diffusional changes in the white matter tracts after mTBI. Our FE analysis revealed that such microstructural changes resulted in tissue softening as the extent of brain deformation increased on a subsequent head hit, indicating increased brain vulnerability after head impacts.

The long head of the biceps tendon undergoes multiaxial deformation during shoulder motion

The long head biceps tendon (LHBT) is presumed a common source of shoulder joint pain and injury. Despite common LHBT pathologies, diagnosis and preferred treatment remain frequently debated. This Short Communication reports the development of a subject-specific finite element model of the shoulder joint based on one subject’s 3D reconstructed anatomy and 3D in vivo kinematics recorded from bone-fixed electromagnetic sensors. The primary purpose of this study was to use the developed finite element model to investigate the LHBT mechanical environment during a typical shoulder motion of arm raising. Furthermore, this study aimed to assess the viability of material models derived from uniaxial tensile tests for accurate simulation of in vivo motion. The findings of our simulations indicate that the LHBT undergoes complex multidimensional deformations. As such, uniaxial material properties reported in the existing body of literature are not sufficient to simulate accurately the in vivo mechanical behavior of the LHBT. Further experimental tests on cadaveric specimens, such as biaxial tension and combinations of tension and torsion, are needed to describe fully the mechanical behavior of the LHBT and investigate its mechanisms of injury.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Impact of tricuspid annuloplasty device shape and size on valve mechanics—a computational study

BackgroundTricuspid valve disease significantly affects 1.6 million Americans. The gold standard treatment for tricuspid disease is the implantation of annuloplasty devices. These ring-like devices come in various shapes and sizes. Choices for both shape and size are most often made by surgical intuition rather than scientific rationale. MethodsTo understand the impact of shape and size on valve mechanics and to provide a rational basis for their selection, we used a subject-specific finite element model to conduct a virtual case study. That is, we implanted 4 different annuloplasty devices of 6 different sizes in our virtual patient. After each virtual surgery, we computed the coaptation area, leaflet end-systolic angles, leaflet stress, and chordal forces. ResultsWe found that contoured devices are better at normalizing end-systolic angles, whereas the one flat device, the Edwards Classic, maximized the coaptation area and minimized leaflet stress and chordal forces. We further found that reducing device size led to increased coaptation area but also negatively impacted end-systolic angles, stress, and chordal forces. ConclusionsBased on our analyses of the coaptation area, leaflet motion, leaflet stress, and chordal forces, we found that device shape and size have a significant impact on valve mechanics. Thereby, our study also demonstrates the value of simulation tools and device tests in “virtual patients.” Expanding our study to many more valves may, in the future, allow for universal recommendations.

Validation and evaluation of subject-specific finite element models of the pediatric knee

Finite element (FE) models have been widely used to investigate knee joint biomechanics. Most of these models have been developed to study adult knees, neglecting pediatric populations. In this study, an atlas-based approach was employed to develop subject-specific FE models of the knee for eight typically developing pediatric individuals. Initially, validation simulations were performed at four passive tibiofemoral joint (TFJ) flexion angles, and the resulting TFJ and patellofemoral joint (PFJ) kinematics were compared to corresponding patient-matched measurements derived from magnetic resonance imaging (MRI). A neuromusculoskeletal-(NMSK)-FE pipeline was then used to simulate knee biomechanics during stance phase of walking gait for each participant to evaluate model simulation of a common motor task. Validation simulations demonstrated minimal error and strong correlations between FE-predicted and MRI-measured TFJ and PFJ kinematics (ensemble average of root mean square errors < 5 mm for translations and < 4.1° for rotations). The FE-predicted kinematics were strongly correlated with published reports (ensemble average of Pearson's correlation coefficients (ρ) > 0.9 for translations and ρ > 0.8 for rotations), except for TFJ mediolateral translation and abduction/adduction rotation. For walking gait, NMSK-FE model-predicted knee kinematics, contact areas, and contact pressures were consistent with experimental reports from literature. The strong agreement between model predictions and experimental reports underscores the capability of sequentially linked NMSK-FE models to accurately predict pediatric knee kinematics, as well as complex contact pressure distributions across the TFJ articulations. These models hold promise as effective tools for parametric analyses, population-based clinical studies, and enhancing our understanding of various pediatric knee injury mechanisms. They also support intervention design and prediction of surgical outcomes in pediatric populations.

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images.

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93-0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.