Development of subject-specific and statistical shape models of the knee using an efficient segmentation and mesh-morphing approach

Swin UNETR Segmentation with Automated Geometry Filtering for Biomechanical Modeling of Knee Joint Cartilage

PURPOSE

Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development.

METHODS

This study aimed to facilitate subject-specific knee joint FE modeling by integrating an automated cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided initial segmentation of knee cartilage, followed by automated geometry filtering to refine surface roughness and continuity. In addition to the standard metrics of image segmentation performance, such as Dice similarity coefficient (DSC) and Hausdorff distance, the method's effectiveness was also assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual and automated segmentation methods, were developed to compare the predicted stress and strain responses during gait.

RESULTS

The automated segmentation achieved high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual segmentation. Mechanical results including maximum principal stress and strain, fluid pressure, fibril strain, and contact area showed no significant differences between the manual and automated FE models.

CONCLUSION

These findings demonstrate the effectiveness of the proposed automated segmentation method in creating accurate knee joint FE models. The automated models developed in this study have been made publicly accessible to support biomechanical modeling and medical image segmentation studies ( https://data.mendeley.com/datasets/dc832g7j5m/1 ).

A statistical shape and density model can accurately predict bone morphology and regional femoral bone mineral density variation in children

Finite element analysis (FEA) is a widely used tool to predict bone biomechanics in orthopaedics for prevention, treatment, and implant design. Subject-specific FEA models are more accurate than generic adult-scaled models, especially for a paediatric population, due to significant differences in bone geometry and bone mineral density. However, creating these models can be time-consuming, costly and requires medical imaging. To address these limitations, population-based models have been successful in characterizing bone shape and density variation in adults. However, children are not small adults and need their own population-based model to generate accurate and accessible musculoskeletal geometry and bone mineral density in a paediatric population. Therefore, this study aimed to create a biomechanical research tool to predict the personalized shape and density of the paediatric femur using a statistical shape and density model for a population of children aged from 4 to 18 years old. Femur morphology and bone mineral density were extracted from 330 CT scans of children. Variations in shape and density were captured using Principal Component Analysis (PCA). Principal components were correlated to demographic and linear bone measurements to create a predictive statistical shape-density model, which was used to predict femoral shape and density. A leave-one-out analysis showed that the shape-density model can predict the femur geometry with a root mean square error (RMSE) of 1.78 ± 0.46 mm and the bone mineral density with a normalized RMSE ranging from 8.9 % to 13.5 % across various femoral regions. These results underscore the model's potential to reflect real-world physiological variations in the paediatric femur. This statistical shape and density model has the potential for clinical application in rapidly generating personalized computational models using partial or no medical imaging data.

Machine learning and statistical shape modelling for real-time prediction of stent deployment in realistic anatomies

BACKGROUND AND OBJECTIVE

The rise in minimally invasive procedures has created a demand for efficient and reliable planning software to predict intra- and post-operative outcomes. Surrogate modelling has shown promise, but challenges remain, particularly in cardiovascular applications, due to the complexity of parametrising anatomical structures and the need for large training datasets. This study aims to apply statistical shape modelling and machine learning for predicting stent deployment in real time using patient-specific models. ► METHODS:: We built a statistical shape model starting from an open-source clinical dataset, which we then used to generate new synthetic cases. Finite element simulations of stent deployment were performed on these cases using an in-house software. A surrogate model was then trained to map the statistical features of the synthetic models to the corresponding stent configurations, evaluating sensitivity to dataset size. ► RESULTS:: Even with the smallest dataset (400 samples), the average prediction error in position among the tested cases never exceeded 8.6%, with a median one within the testing dataset of 1.6%. As the number of training samples increased (4900), we achieved a median position error lower than 0.1 mm (0.97%) and a maximum position error of 0.5 mm (4.8%). Notably, the largest errors occur in the radial direction of the stent, while the deployed length is accurately predicted in all the cases. ► CONCLUSIONS:: The consistent success in performance strongly suggests that surrogate modelling represents a clinically valuable tool for accurately computing stent deployment outcomes in real time, even within complex anatomical scenarios.

Impact of Surgical Alignment, Bone Properties, Anterior-Posterior Translation, and Implant Design Factors on Fixation in Cementless Unicompartmental Knee Arthroplasty

Micromotion exceeding 150 μm at the implant-bone interface may prevent bone formation and limit fixation after cementless knee arthroplasty. Understanding the critical parameters impacting micromotion is required for optimal implant design and clinical performance. However, few studies have focused on unicompartmental knee arthroplasty (UKA). This study assessed the impacts of alignment, surgical, and design factors on implant-bone micromotions for a novel cementless UKA design during a series of simulated daily activities. Three finite element models that were validated for predicting micromotion of cementless total knee arthroplasty (TKA) were loaded with design-specific kinematics/loading to simulate gait (GT), deep knee bending (DKB), and stair descent (SD). The implant-bone micromotion and the porous surface area ideal for bone ingrowth were estimated and compared to quantify the impact of each factor. Overall, the peak tray-bone micromotions were consistently found at the lateral aspect of the tibial baseplate and were consistently higher than the femoral micromotions. The femoral micromotion was insensitive to almost all the factors studied, and the porous area favorable for bone ingrowth was no less than 93%. For a medial uni, implanting the tray 1 mm medially or the femoral component 1 mm laterally reduced the tibial micromotion by 19.3% and 26.3%, respectively. Differences in tray-bone micromotion due to bone moduli were up to 59.8%. A 5 mm more posterior femoral translation increased the tray-bone micromotion by 35.8%. The presence of the tray keel prevented the spread of the micromotion and increased the overall porous surface area, but also increased peak micromotion. The tray peg and the femoral anterior peg had little impact on the micromotion of their respective implants. In conclusion, centralizing the load transfer to minimize tibial tray applied moment and optimizing the fixation features to minimize micromotion are consistent themes for improving cementless fixation in UKA. Perturbation of femoral-bone alignment may be preferred as it would not create under/overhang on the tibia.

Statistical shape analysis and computational modeling reveal novel relationships between tibiofemoral bony geometry and knee mechanics in young, female athletes

Young female athletes participating in sports requiring rapid changes of direction are at heightened risk of suffering traumatic knee injury, especially noncontact rupture of the anterior cruciate ligament (ACL). Clinical studies have revealed that geometric features of the tibiofemoral joint are associated with increased risk of suffering noncontact ACL injury. However, the relationship between three-dimensional (3D) tibiofemoral geometry and knee mechanics in young female athletes is not well understood. We developed a statistically augmented computational modeling workflow to determine relationships between 3D geometry of the knee and tibiofemoral kinematics and ACL force in response to an applied loading sequence of compression, valgus, and anterior force, which is known to load the ACL. This workflow included 3D characterization of tibiofemoral bony geometry via principal component analysis and multibody dynamics models incorporating subject-specific knee geometries. A combination of geometric features of both the tibia and the femur that spanned all three anatomical planes was related to increased ACL force and to increased kinematic coupling (i.e., anterior, medial, and distal tibial translations and internal tibial rotation) in response to the applied loads. In contrast, a uniplanar measure of tibiofemoral geometry that is associated with ACL injury risk, sagittal plane slope of the lateral tibial plateau subchondral bone, was not related to ACL force. Thus, our workflow may aid in developing mechanics-based ACL injury screening tools for young, active females based on a unique combination of bony geometric features that are related to increased ACL loading.

Automated 2D and 3D finite element overclosure adjustment and mesh morphing using generalized regression neural networks

Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment. These algorithms were implemented, and test cases were used to validate them against existing algorithms to demonstrate improved performance. The resulting algorithms demonstrate improvements to existing techniques based on Radial Basis Function (RBF) networks by converting to GRNN-based implementations. Implementations in MATLAB of these algorithms and the source code are publicly available at the following locations: https://github.com/thor-andreassen/femors; https://simtk.org/projects/femors-rbf; https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing.

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 ).

Preoperative Virtual Reduction Planning Algorithm of Fractured Pelvis Based on Adaptive Templates

OBJECTIVE

The minimally invasive treatment of pelvic fractures is one of the most challenging trauma orthopedics surgeries, where preoperative planning is crucial for the performance and outcome of the surgery. However, planning the ideal position of fragments currently relies heavily on the experience of the surgeon.

METHODS

A pelvic fracture virtual reduction algorithm for target position is provided based on statistical shape models (SSM). First, according to sexual dimorphism, pelvic SSM based on point cloud curvature down-sampling are constructed as adaptive templates. Then, an optimization algorithm is designed to iteratively adjust the target pose of the fragments and the adaptive matching of the templates. Finally, the feasibility of the method is verified by simulating fractures and clinical data.

RESULTS

The pelvis has complex shape characteristics, which can be analyzed by SSM to clearly understand the pattern of change. Experiments showed that the SSM-based pelvic fracture reduction method had translation and rotation errors of 2.20±1.09 mm and 3.16±1.26° in simulated cases, and 2.78±0.95 mm and 3.10±0.53° in clinical cases, which has higher accuracy than methods based on mean shape models, and wider applicability than methods based on pelvic symmetry.

CONCLUSION

The pelvic digital model created by SSM has good generalization properties, and the SSM-based virtual reduction algorithm can effectively reconstruct the target position of the fractured pelvis in preoperative planning.

SIGNIFICANCE

The proposed reduction method has the characteristics of high precision and wide application range, which provides a powerful tool for the surgeon's virtual preoperative planning.

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects

In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds.

Statistical shape modeling-based algorithm for replacing missing beats in blood pressure signals

In clinical environments, such as the Intensive Care Unit (ICU), continuous and uninterrupted monitoring of vital signs is critical for the early detection of patient deterioration and prompt intervention. Since data collected in these settings are often corrupted by noise, artifacts, or recording gaps, it is important to estimate missing data for a more accurate signal assessment.In this study, we propose an automatic algorithm for reconstructing of arterial blood pressure signal waveforms. The methodological core of the algorithm is based on the idea of statistical shape modeling, which basically estimates the shape variation of beat waveforms in order to reconstruct them in noisy segments. The waveform reconstruction is achieved by combining the average beat template from a 90-second segment of clean signal preceding the gap with the main shape variations of the estimated waveform.The algorithm was validated using arterial blood pressure recordings from 9 subjects admitted in the ICU and collected in the MIMIC-III Waveform Database, each lasting 1 hour and sampled at 125 Hz. For each record, ten fictitious gaps were created, and the reconstructed segments were compared to the original signals with the metrics proposed within the PhysioNet / Computing in Cardiology Challenge 2010. Results demonstrate the excellent performance of the proposed algorithm, with overall averages of both Q1 and Q2 metrics greater than 0.85.

Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction

Objective.Monte-Carlo simulation studies have been essential for advancing various developments in single photon emission computed tomography (SPECT) imaging, such as system design and accurate image reconstruction. Among the simulation software available, Geant4 application for tomographic emission (GATE) is one of the most used simulation toolkits in nuclear medicine, which allows building systems and attenuation phantom geometries based on the combination of idealized volumes. However, these idealized volumes are inadequate for modeling free-form shape components of such geometries. Recent GATE versions alleviate these major limitations by allowing users to import triangulated surface meshes.Approach.In this study, we describe our mesh-based simulations of a next-generation multi-pinhole SPECT system dedicated to clinical brain imaging, called AdaptiSPECT-C. To simulate realistic imaging data, we incorporated in our simulation the XCAT phantom, which provides an advanced anatomical description of the human body. An additional challenge with the AdaptiSPECT-C geometry is that the default voxelized XCAT attenuation phantom was not usable in our simulation due to intersection of objects of dissimilar materials caused by overlap of the air containing regions of the XCAT beyond the surface of the phantom and the components of the imaging system.Main results.We validated our mesh-based modeling against the one constructed by idealized volumes for a simplified single vertex configuration of AdaptiSPECT-C through simulated projection data of123I-activity distributions. We resolved the overlap conflict by creating and incorporating a mesh-based attenuation phantom following a volume hierarchy. We then evaluated our reconstructions with attenuation and scatter correction for projections obtained from simulation consisting of mesh-based modeling of the system and the attenuation phantom for brain imaging. Our approach demonstrated similar performance as the reference scheme simulated in air for uniform and clinical-like123I-IMP brain perfusion source distributions.Significance.This work enables the simulation of complex SPECT acquisitions and reconstructions for emulating realistic imaging data close to those of actual patients.

Statistical modeling: Assessing the anatomic variability of knee joint space width

Population-based knee joint space width (JSW) assessments are promising for the prevention and early diagnosis of osteoarthritis. This study aimed to establish the statistical shape and alignment model (SSAM) of knee joints for assessing anatomic variation in knee JSW in the healthy Chinese male population. CT scans of asymptomatic knee joints of healthy male participants (n = 107) were collected for manual segmentation to create mesh samples. The as-scanned positional error was reduced by a standard processing flow of deformable mesh registration. Principal component analysis (PCA) was performed to create a tibiofemoral SSAM that was trained on all mesh samples. The anatomic variability of the JSW in the healthy Chinese male population was then assessed using the SSAM with regression analysis and 3D analysis by color-coded mapping. Almost all PCA modes had a linear influence on the anatomic variation of the medial and lateral JSW. The JSW variability within the SSAM was mainly explained by mode 1 (45.1 % of variation), demonstrating that this mode had the greatest influence on JSW variation. 3D assessment of the JSW showed that the minimum medial JSW varied from 2.76 to 3.23 mm, and its site shifted a short distance on the medial tibial plateau. The root-mean-square fitting and generalization errors of the SSAM were below 1 mm. This study will benefit the design and optimization of prosthetic devices, and may be applicable to the prevention and early diagnosis of osteoarthritis.

Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation

To improve the quality of magnetic resonance (MR) image edge segmentation, some researchers applied additional edge labels to train the network to extract edge information and aggregate it with region information. They have made significant progress. However, due to the intrinsic locality of convolution operations, the convolution neural network-based region and edge aggregation has limitations in modeling long-range information. To solve this problem, we proposed a novel transformer-based multilevel region and edge aggregation network for MR image segmentation. To the best of our knowledge, this is the first literature on transformer-based region and edge aggregation. We first extract multilevel region and edge features using a dual-branch module. Then, the region and edge features at different levels are inferred and aggregated through multiple transformer-based inference modules to form multilevel complementary features. Finally, the attention feature selection module aggregates these complementary features with the corresponding level region and edge features to decode the region and edge features. We evaluated our method on a public MR dataset: Medical image computation and computer-assisted intervention atrial segmentation challenge (ASC). Meanwhile, the private MR dataset considered infrapatellar fat pad (IPFP). Our method achieved a dice score of 93.2% for ASC and 91.9% for IPFP. Compared with other 2D segmentation methods, our method improved a dice score by 0.6% for ASC and 3.0% for IPFP.

Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee

Osteoarthritis of the knee is increasingly prevalent as our population ages, representing an increasing financial burden, and severely impacting quality of life. The invasiveness of in vivo procedures and the high cost of cadaveric studies has left computational tools uniquely suited to study knee biomechanics. Developments in deep learning have great potential for efficiently generating large-scale datasets to enable researchers to perform population-sized investigations, but the time and effort associated with producing robust hexahedral meshes has been a limiting factor in expanding finite element studies to encompass a population. Here we developed a fully automated pipeline capable of taking magnetic resonance knee images and producing a working finite element simulation. We trained an encoder-decoder convolutional neural network to perform semantic image segmentation on the Imorphics dataset provided through the Osteoarthritis Initiative. The Imorphics dataset contained 176 image sequences with varying levels of cartilage degradation. Starting from an open-source swept-extrusion meshing algorithm, we further developed this algorithm until it could produce high quality meshes for every sequence and we applied a template-mapping procedure to automatically place soft-tissue attachment points. The meshing algorithm produced simulation-ready meshes for all 176 sequences, regardless of the use of provided (manually reconstructed) or predicted (automatically generated) segmentation labels. The average time to mesh all bones and cartilage tissues was less than 2 min per knee on an AMD Ryzen 5600X processor, using a parallel pool of three workers for bone meshing, followed by a pool of four workers meshing the four cartilage tissues. Of the 176 sequences with provided segmentation labels, 86% of the resulting meshes completed a simulated flexion-extension activity. We used a reserved testing dataset of 28 sequences unseen during network training to produce simulations derived from predicted labels. We compared tibiofemoral contact mechanics between manual and automated reconstructions for the 24 pairs of successful finite element simulations from this set, resulting in mean root-mean-squared differences under 20% of their respective min-max norms. In combination with further advancements in deep learning, this framework represents a feasible pipeline to produce population sized finite element studies of the natural knee from subject-specific models.

Correlation between knee anatomy and joint laxity using principal component analysis

Knee articular geometry and surface morphology greatly affect knee joint mechanics. Intra-subject variations in bone morphology and the passive range of motion have been well documented in the literature; however, the relationship between these two characteristics is not well understood. The objective of this study was to describe the correlation between knee joint anatomical features and passive range of motion using a statistical model. A principal component model was developed using femoral and tibial articular geometry, knee joint initial stance position, and the passive laxity envelope obtained from 27 cadaveric knees. The results from the principal component analysis showed high correlation between the anatomical features and the tibiofemoral passive envelope; an increase in the average femoral condyle radii, an increase in slope of the tibial spine, and a higher tibial plateau concavity correlated with a decrease in varus-valgus and internal-external range of motion. Understanding the correlation between anatomical features and tibiofemoral laxity could aid in the development of orthopedic implant designs by quantifying the effect of perturbing specific anatomical features on knee laxity and identifying specific implant femoral and tibial articular geometry necessary to obtain a targeted passive range of motion.

Personalised statistical modelling of soft tissue structures in the ankle

BACKGROUND AND OBJECTIVE

Revealing the complexity behind subject-specific ankle joint mechanics requires simultaneous analysis of three-dimensional bony and soft-tissue structures. 3D musculoskeletal models have become pivotal in orthopedic treatment planning and biomechanical research. Since manual segmentation of these models is time-consuming and subject to manual errors, (semi-) automatic methods could improve the accuracy and enlarge the sample size of personalised 'in silico' biomechanical experiments and computer-assisted treatment planning. Therefore, our aim was to automatically predict ligament paths, cartilage topography and thickness in the ankle joint based on statistical shape modelling.

METHODS

A personalised cartilage and ligamentous prediction algorithm was established using geometric morphometrics, based on an 'in-house' generated lower limb skeletal model (N = 542), tibiotalar cartilage (N = 60) and ankle ligament segmentations (N = 10). For cartilage, a population-averaged thickness map was determined by use of partial least-squares regression. Ligaments were wrapped around bony contours based on iterative shortest path calculation. Accuracy of ligament path and cartilage thickness prediction was quantified using leave-one-out experiments. The novel personalised thickness prediction was compared with a constant cartilage thickness of 1.50 mm by use of a paired sample T-test.

RESULTS

Mean distance error of cartilage and ligament prediction was 0.12 mm (SD 0.04 mm) and 0.54 mm (SD 0.05 mm), respectively. No significant differences were found between the personalised thickness cartilage and segmented cartilage of the tibia (p = 0.73, CI [-1.60 .10^-17, 1.13 .10^-17]) and talus (p = 0.95, CI[ -1.35 .10^-17, 1.28 .10^-17]). For the constant thickness cartilage, a statistically significant difference was found in 89% and 92% of the tibial (p < 0.001, CI [0.51, 0.58]) and talar (p < 0.001, CI [0.33, 0.40]) cartilage area.

CONCLUSIONS

In this study, we described a personalised prediction algorithm of cartilage and ligaments in the ankle joint. We were able to predict cartilage and main ankle ligaments with submillimeter accuracy. The proposed method has a high potential for generating large (virtual) sample sizes in biomechanical research and mitigates technological advances in computer-assisted orthopaedic surgery.

Morphological variation in paediatric lower limb bones

Available methods for generating paediatric musculoskeletal geometry are to scale generic adult geometry, which is widely accessible but can be inaccurate, or to obtain geometry from medical imaging, which is accurate but time-consuming and costly. A population-based shape model is required to generate accurate and accessible musculoskeletal geometry in a paediatric population. The pelvis, femur, and tibia/fibula were segmented from 333 CT scans of children aged 4–18 years. Bone morphology variation was captured using principal component analysis (PCA). Subsequently, a shape model was developed to predict bone geometry from demographic and linear bone measurements and validated using a leave one out analysis. The shape model was compared to linear scaling of adult and paediatric bone geometry. The PCA captured growth-related changes in bone geometry. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91 ± 0.99 mm in the pelvis, 2.01 ± 0.62 mm in the femur, and 1.85 ± 0.54 mm in the tibia/fibula. Linear scaling of an adult mesh produced RMSE of 4.79 ± 1.39 mm in the pelvis, 4.38 ± 0.72 mm in the femur, and 4.39 ± 0.86 mm in the tibia/fibula. We have developed a method for capturing and predicting lower limb bone shape variation in a paediatric population more accurately than linear scaling without using medical imaging.

Tibiofemoral bony morphology features associated with ACL injury and sex utilizing three-dimensional statistical shape modeling

Statistical shape modeling was employed to assess three-dimensional (3D) bony morphology between distal femurs and proximal tibiae of anterior cruciate ligament (ACL) injured knees, the contralateral uninjured knees of ACL injured subjects, and knees with no history of injury. Surface models were created by segmenting bone from bilateral computed-tomography scans of 20 subjects of their ACL injured knees and non-injured contralateral knees, and 20 knees of control subjects with no history of a knee injury. Correspondence particles were placed on each surface, and a principal component analysis determined modes of variation in the positions of the correspondence particles describing anatomical variation. ANOVAs assessed the statistical differences of 3D bony morphological features with main effects of injury state and sex. ACL injured knees were determined to have a more lateral femoral mechanical axis and a greater angle between the long axis and condylar axis of the femur. A smaller anterior-posterior dimension of the lateral tibial plateau was also associated with ACL injured knees. Results of this study demonstrate that there are more bony morphological features predisposing individuals for ACL injury than previously established. These bony morphological parameters may cause greater internal and valgus torques increasing stresses in the ACL. No differences were determined between the ACL injured knees and their uninjured contralateral knees demonstrating that knees of ACL injured individuals are at similar risk for injury. Further understanding of the effect of bony morphology on the risk for ACL injury could improve individualized ACL injury treatment and prevention.

Artificial intelligence-based automatic assessment of lower limb torsion on MRI

Abnormal torsion of the lower limbs may adversely affect joint health. This study developed and validated a deep learning-based method for automatic measurement of femoral and tibial torsion on MRI. Axial T2-weighted sequences acquired of the hips, knees, and ankles of 93 patients (mean age, 13 ± 5 years; 52 males) were included and allocated to training (n = 60), validation (n = 9), and test sets (n = 24). A U-net convolutional neural network was trained to segment both femur and tibia, identify osseous anatomic landmarks, define pertinent reference lines, and quantify femoral and tibial torsion. Manual measurements by two radiologists provided the reference standard. Inter-reader comparisons were performed using repeated-measures ANOVA, Pearson’s r, and the intraclass correlation coefficient (ICC). Mean Sørensen-Dice coefficients for segmentation accuracy ranged between 0.89 and 0.93 and erroneous segmentations were scarce. Ranges of torsion as measured by both readers and the algorithm on the same axial image were 15.8°–18.0° (femur) and 33.9°–35.2° (tibia). Correlation coefficients (ranges, .968 ≤ r ≤ .984 [femur]; .867 ≤ r ≤ .904 [tibia]) and ICCs (ranges, .963 ≤ ICC ≤ .974 [femur]; .867 ≤ ICC ≤ .894 [tibia]) indicated excellent inter-reader agreement. Algorithm-based analysis was faster than manual analysis (7 vs 207 vs 230 s, p < .001). In conclusion, fully automatic measurement of torsional alignment is accurate, reliable, and sufficiently fast for clinical workflows.

A Method to Compare Heterogeneous Types of Bone and Cartilage Meshes

Accurately capturing the bone and cartilage morphology and generating a mesh remains a critical step in the workflow of computational knee joint modeling. Currently, there is no standardized method to compare meshes of different element types and nodal densities, making comparisons across research teams a significant challenge. The aim of this paper is to describe a method to quantify differences in knee joint bone and cartilages meshes, independent of bone and cartilage mesh topology. Bone mesh-to-mesh distances, subchondral bone boundaries, and cartilage thicknesses from meshes of any type of mesh are obtained using a series of steps involving registration, resampling, and radial basis function fitting after which the comparisons are performed. Subchondral bone boundaries and cartilage thicknesses are calculated and visualized in a common frame of reference for comparison. The established method is applied to models developed by five modeling teams. Our approach to obtain bone mesh-to-mesh distances decreased the divergence seen in selecting a reference mesh (i.e., comparing mesh A-to-B versus mesh B-to-A). In general, the bone morphology was similar across teams. The cartilage thicknesses for all models were calculated and the mean absolute cartilage thickness difference was presented, the articulating areas had the best agreement across teams. The teams showed disagreement on the subchondral bone boundaries. The method presented in this paper allows for objective comparisons of bone and cartilage geometry that is agnostic to mesh type and nodal density.

3D Geometric Shape Reconstruction for Revision TKA and UKA Knees Using Gaussian Process Regression

Revision knee surgery is complicated by distortion of previous components and removal of additional bone, potentially causing misalignment and inappropriate selection of implants. In this study, we reconstructed the native femoral and tibial surface shapes in simulated total/unicompartmental knee arthroplasty (TKA/UKA) for 20 femurs and 20 tibias using a statistical inference method based on Gaussian Process regression. Compared to the true geometry, the average absolute errors (mean absolute distances) in the prediction of resected femur bones in TKA, medial UKA, and lateral UKA were 1.0 ± 0.3 mm, 1.0 ± 0.3 mm, and 0.8 ± 0.2 mm, respectively, while those in the prediction of tibia resections in the corresponding surgeries were 1.0 ± 0.4 mm, 0.8 ± 0.2 mm, and 0.7 ± 0.2 mm, respectively. Furthermore, it was found that the prediction accuracy depends on the size and gender of the resected bone. For example, the prediction accuracy for UKA cuts was significantly better than that for TKA cuts (p < 0.05). The female and male cuts were often overfit and underfit, respectively. The data indicated that this reconstruction approach can be a viable option for planning of revision surgeries, especially when contralateral anatomy is pathological or cannot be available.

Discrete-MultiResUNet: Segmentation and feature extraction model for knee MR images

Deep learning has shown outstanding efficiency in medical image segmentation. Segmentation of knee tissues is an important task for early diagnosis of rheumatoid arthritis (RA) with selecting variant features. Automated segmentation and feature extraction of knee tissues are desirable for faster and reliable analysis of large datasets and further diagnosis. In this paper a novel architecture called as Discrete-MultiResUNet, which is a combination of discrete wavelet transform (DWT) with MultiResUNet architecture is applied for feature extraction and segmentation, respectively. This hybrid architecture captures more prominent features from the knee magnetic resonance image efficiently with segmenting vital knee tissues. The hybrid model is evaluated on the knee MR dataset demonstrating outperforming performance compared with baseline models. The model achieves excellent segmentation performance accuracy of 96.77% with a dice coefficient of 98%.

Prediction of interfragmentary movement in fracture fixation constructs using a combination of finite element modeling and rigid body assumptions

The amount of interfragmentary movement has been identified as a crucial factor for successful fracture healing. The aim of our study was to combine finite element analysis with a rigid body assumption to efficiently predict interfragmentary movement in fixed tibial fractures. The interfragmentary movement in a transverse tibial shaft fracture (AO/OTA type 42-A3) fixed with a locked plating construct was simulated using finite element analysis. In order to assess the contribution of the components on the resulting interfragmentary movement, the tibia, screws and embedding was either simulated deformable or as rigid body. The rigid and the deformable model accurately predicted the interfragmentary movement (R² = 0.99). The axial movement ranged between 0.1 mm and 1.3 mm and shear movements were between 0.2 mm and 0.5 mm. Differences between the two models were smaller than 73 μm (axial) and 46 μm (shear). The rigid body assumption reduced computation time and memory usage by up to 61% and 97%, respectively.

A comparison of Cartesian-only vs. Cartesian-spherical hybrid coordinates for statistical shape modeling in the lumbar spine

OBJECTIVE

The purpose of this study was to compare two methods for quantifying differences in geometric shapes of human lumbar vertebra using statistical shape modeling (SSM).

METHODS

A novel 3D implementation of a previously published 2D, nonlinear SSM was implemented and compared to a commonly used, Cartesian method of SSM. The nonlinear method, or Hybrid SSM, and Cartesian SSM were applied to lumbar vertebra shapes from a cohort of 18 full lumbar triangle meshes derived from CT scans. The comparison included traditional metrics for cumulative variance, generality, and specificity and results from application-based biomechanics using finite element simulation.

RESULTS

The Hybrid SSM has less compactness - likely due to the increased number of mathematical constraints in the SSM formulation. Similar results were found between methods for specificity and generality. Compared to the previously validated, manually-segmented FE model, both SSM methods produced similar and agreeable results.

CONCLUSION

Visual, statistical, and biomechanical findings did not convincingly support the superiority of the Hybrid SSM over the simpler Cartesian SSM.

SIGNIFICANCE

This work suggests that, of the two methods compared, the Cartesian SSM is adequate to capture the variations in shape of the posterior spinal structures for biomechanical modeling applications.

Reporting checklist for verification and validation of finite element analysis in orthopedic and trauma biomechanics

Finite element analysis (FEA) has become a fundamental tool for biomechanical investigations in the last decades. Despite several existing initiatives and guidelines for reporting on research methods and results, there are still numerous issues that arise when using computational models in biomechanical investigations. According to our knowledge, these problems and controversies lie mainly in the verification and validation (V&V) process as well as in the set-up and evaluation of FEA. This work aims to introduce a checklist including a report form defining recommendations for FEA in the field of Orthopedic and Trauma (O&T) biomechanics. Therefore, a checklist was elaborated which summarizes and explains the crucial methodologies for the V&V process. In addition, a report form has been developed which contains the most important steps for reporting future FEA. An example of the report form is shown, and a template is provided, which can be used as a uniform basis for future documentation. The future application of the presented report form will show whether serious errors in biomechanical investigations using FEA can be minimized by this checklist. Finally, the credibility of the FEA in the clinical area and the scientific exchange in the community regarding reproducibility and exchangeability can be improved.

Regularized multi-structural shape modeling of the knee complex based on deep functional maps

The incorporation of a-priori knowledge on the shape of anatomical structures and their variation through Statistical Shape Models (SSMs) has shown to be very effective in guiding highly uncertain image segmentation problems. In this paper, we construct multiple-structure SSMs of purely geometric nature, that describe the relationship between adjacent anatomical components through Canonical Correlation Analysis. Shape inference is then conducted based on a regularization term on the shape likelihood providing more reliable structure representations. A fundamental prerequisite for performing statistical shape analysis on a set of objects is the identification of corresponding points on their associated surfaces. We address the correspondence problem using the recently proposed Functional Maps framework, which is a generalization of point-to-point correspondence to manifolds. Additionally, we show that, by incorporating techniques from the deep learning theory into this framework, we can further enhance the ability of SSMs to better capture the shape variation in a given dataset. The efficiency of our approach is illustrated through the creation of 3D models of the human knee complex in two application scenarios: incomplete or noisy shape reconstruction and missing structure estimation.

In vivo kinematical validated knee model for preclinical testing of total knee replacement

BACKGROUND AND OBJECTIVE

A computational knee model facilitates efficient component design evaluations and preclinical testing under various dynamic loadings. However, the development of a highly mimicked dynamic whole knee model with specified ligament constraints that provides high predictive accuracy with in-vivo experiments remains a challenge.

METHODS

In the present study, a musculoskeletal integrated force-driven explicit finite-element knee model with tibiofemoral and patellofemoral joints constrained with detailed soft tissue was developed. A proportional-integral-derivative controller was concurrently added to the knee model to track the boundary conditions. The actuations of the quadriceps and hamstrings were predicted via a subject-specific musculoskeletal model and matched with electromyography results.

RESULTS

Compared to in-vivo fluoroscopic results in a gait cycle, the predicted results of the kinematics of the tibiofemoral joint exhibited an agreement in terms of tendency and magnitude (anterior-posterior translation: RMSE = 1.1 mm, r² = 0.87; inferior-superior translation: RMSE = 0.83 mm, r² = 0.84; medial-lateral translation: RMSE = 0.82 mm, r² = 0.05; flexion-extension rotation: RMSE = 0.23°, r² = 1; internal-external rotation: RMSE = 1.85°, r² = 0.65; varus-valgus rotation: RMSE = 1.39°, r² = 0.08). Contact mechanics, including the contact area, pressure, and stress, were synchronously simulated on the tibiofemoral and patellofemoral joints.

CONCLUSIONS

The study provides a calibrated knee model and a kinematical validation approach that can be widely used in preclinical testing and knee prosthesis design.

Influence of pelvic shape on strain patterns: A computational analysis using finite element mesh morphing techniques

The pelvis functions to transmit upper body loads to the lower limbs and is critical in human locomotion. Semi-automated, finite element (FE) morphing techniques eliminate the need for segmentation and have shown to accelerate the generation of multiple specimen-specific pelvic FE models to enable the study of pelvic mechanical behaviour. The purpose of this research was to produce simulated human pelvic FE models representing android, gynecoid, anthropoid and platypelloid morphologies and to isolate differences in strain patterns due to anatomic shape under physiologic loading. Using five initially generated specimen-specific FE models, each specimen-specific FE model was reconfigured into three different morphologies using FE mesh morphing techniques. Significantly different strains were found comparing the gynecoid (classical female pelvis') to the android ('true male pelvis') models (p = 0.040), with strains twice as high in the superior pubic rami. No significant differences were seen in comparing overall strains between the other pelvic shapes (p = 0.61-0.126). The highest strain regions in all models were found in the supra-acetabular regions, with high strains also found in the regions of the superior pubic rami, the greater sciatic notch and sacral regions about the L5 vertebrae. Quantifying the contributions of shape to strain in the pelvis may increase the understanding of sex and patient-specific differences in fracture risk and motivate the consideration of treatment strategies that account for anatomic pelvic differences.

Image-based biomechanical models of the musculoskeletal system

Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described.

Separating positional noise from neutral alignment in multicomponent statistical shape models

Given sufficient training samples, statistical shape models can provide detailed population representations for use in anthropological and computational genetic studies, injury biomechanics, musculoskeletal disease models or implant design optimization. While the technique has become extremely popular for the description of isolated anatomical structures, it suffers from positional interference when applied to coupled or articulated input data. In the present manuscript we describe and validate a novel approach to extract positional noise from such coupled data. The technique was first validated and then implemented in a multicomponent model of the lower limb. The impact of noise on the model itself as well as on the description of sexual dimorphism was evaluated. The novelty of our methodology lies in the fact that no rigid transformations are calculated or imposed on the data by means of idealized joint definitions and by extension the models obtained from them.

Clinical Contrast-Enhanced Computed Tomography With Semi-Automatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint

Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis (OA). Currently, the joint geometries utilized in modeling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semi-automatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical computed tomography (CT) with contrast enhancement and then segmented with semi-automatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic (FRPVE) properties were generated and the mechanical responses of articular cartilage were computed during physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semi-automatic and manual segmentations were <1 MPa, <0.72% and <0.40%, respectively. Furthermore, contact areas, contact forces, average pore pressures, and average maximum principal strains were not statistically different between the models (p >0.05). This semi-automatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semi-automatic segmentations. Thus, the presented CT imaging-based segmentation method represents a novel tool for application in FE modeling in the clinic when a physician needs to evaluate knee joint function.

Multiobjective Design Optimization of a Biconcave Mobile-Bearing Lumbar Total Artificial Disk Considering Spinal Kinematics, Facet Joint Loading, and Metal-on-Polyethylene Contact Mechanics

Total disk arthroplasty (TDA) using an artificial disk (AD) is an attractive surgical technique for the treatment of spinal disorders, since it can maintain or restore spinal motion (unlike interbody fusion). However, adverse surgical outcomes of contemporary lumbar TDAs have been reported. We previously proposed a new mobile-bearing AD design concept featuring a biconcave ultrahigh-molecular-weight polyethylene (UHMWPE) mobile core. The objective of this study was to develop an artificial neural network (NN) based multiobjective optimization framework to refine the biconcave-core AD design considering multiple TDA performance metrics, simultaneously. We hypothesized that there is a tradeoff relationship between the performance metrics in terms of range of motion (ROM), facet joint force (FJF), and polyethylene contact pressure (PCP). By searching the resulting three-dimensional (3D) Pareto frontier after multiobjective optimization, it was found that there was a "best-tradeoff" AD design, which could balance all the three metrics, without excessively sacrificing each metric. However, for each single-objective optimum AD design, only one metric was optimal, and distinct sacrifices were observed in the other two metrics. For a commercially available biconvex-core AD design, the metrics were even worse than the poorest outcomes of the single-objective optimum AD designs. Therefore, multiobjective design optimization could be useful for achieving native lumbar segment biomechanics and minimal PCPs, as well as for improving the existing lumbar motion-preserving surgical treatments.

A novel approach for optimal graft positioning and tensioning in anterior cruciate ligament reconstructive surgery based on the finite element modeling technique

BACKGROUND

In ACL-reconstructed patients the postoperative knee biomechanics may differ from the intact knee biomechanical behavior which can alter knee kinematics and kinetics, and as a result lead to the progression of knee osteoarthritis. The aim of this study was to demonstrate the potential of finite element models to define the optimal choices in surgical parameters in terms of optimal graft positioning in combination with graft type in order to restore the kinematic and kinetic behavior of the knee as best as possible.

METHODS

A workflow was proposed based on cadaveric experiments in order to restore the injured knee to a near normal physiological condition. Femoral and tibial graft insertion sites and graft fixation tension were optimized to obtain similar intact knee laxity, for three common single-bundle and one double-bundle reconstructions. To verify the success of the surgery with the variables calculated using the proposed workflow, a full walking cycle was simulated with the intact, ACL-ruptured, optimal ACL-reconstructed and non-optimal reconstructed knees.

RESULTS

Our results suggested that for patellar tendon and hamstring tendon grafts, anatomical positioning (fixation force: 40 N), and for quadriceps tendon graft, isometric positioning (fixation tension: 85 N) could recover the intact joint kinematics and kinetics. Also for double-bundle reconstruction, with the numerically calculated optimal insertion sites, both bundles needed 50-N fixation force.

CONCLUSIONS

With optimal graft positioning parameters, following the proposed workflow in this study, any of the single-bundle graft types and surgical techniques (single vs. double-bundle) may be used to acceptably recover the intact knee joint biomechanical behavior.

Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development

PURPOSE

Our clinical understanding of the relationship between 3D bone morphology and knee osteoarthritis, as well as our ability to investigate potential causative factors of osteoarthritis, has been hampered by the time-intensive nature of manually segmenting bone from MR images. Thus, we aim to develop and validate a fully automated deep learning framework for segmenting the patella and distal femur cortex, in both adults and actively growing adolescents.

METHODS

Data from 93 subjects, obtained from on institutional review board-approved protocol, formed the study database. 3D sagittal gradient recalled echo and gradient recalled echo with fat saturation images and manual models of the outer cortex were available for 86 femurs and 90 patellae. A deep-learning-based 2D holistically nested network (HNN) architecture was developed to automatically segment the patella and distal femur using both single (sagittal, uniplanar) and 3 cardinal plane (triplanar) methodologies. Errors in the surface-to-surface distances and the Dice coefficient were the primary measures used to quantitatively evaluate segmentation accuracy using a 9-fold cross-validation.

RESULTS

Average absolute errors for segmenting both the patella and femur were 0.33 mm. The Dice coefficients were 97% and 94% for the femur and patella. The uniplanar, relative to the triplanar, methodology produced slightly superior segmentation. Neither the presence of active growth plates nor pathology influenced segmentation accuracy.

CONCLUSION

The proposed HNN with multi-feature architecture provides a fully automatic technique capable of delineating the often indistinct interfaces between the bone and other joint structures with an accuracy better than nearly all other techniques presented previously, even when active growth plates are present.

In vivo prediction of temporomandibular joint disc thickness and position changes for different jaw positions

Temporomandibular joint disorders (TMD) are common dysfunctions of the masticatory region and are often linked to dislocation or changes of the temporomandibular joint (TMJ) disc. Magnetic resonance imaging (MRI) is the gold standard for TMJ imaging but standard clinical sequences do not deliver a sufficient resolution and contrast for the creation of detailed meshes of the TMJ disc. Additionally, bony structures cannot be captured appropriately using standard MRI sequences due to their low signal intensity. The objective of this study was to enable researchers to create high resolution representations of all structures of the TMJ and consequently investigate morphological as well as positional changes of the masticatory system. To create meshes of the bony structures, a single computed tomography (CT) scan was acquired. In addition, a high-resolution MRI sequence was produced, which is used to collect the thickness and position change of the disc for various static postures using bite blocks. Changes in thickness of the TMJ disc as well as disc translation were measured. The newly developed workflow successfully allows researchers to create high resolution models of all structures of the TMJ for various static positions, enabling the investigation of TMJ disc translation and deformation. Discs were thinnest in the lateral part and moved mainly anteriorly and slightly medially. The procedure offers the most comprehensive picture of disc positioning and thickness changes reported to date. The presented data can be used for the development of a biomechanical computer model of TMJ anatomy and to investigate dynamic and static loads on the components of the system, which could be useful for the prediction of TMD onset.

The interaction of muscle moment arm, knee laxity, and torque in a multi-scale musculoskeletal model of the lower limb

INTRODUCTION

Musculoskeletal modeling allows insight into the interaction of muscle force and knee joint kinematics that cannot be measured in the laboratory. However, musculoskeletal models of the lower extremity commonly use simplified representations of the knee that may limit analyses of the interaction between muscle forces and joint kinematics. The goal of this research was to demonstrate how muscle forces alter knee kinematics and consequently muscle moment arms and joint torque in a musculoskeletal model of the lower limb that includes a deformable representation of the knee.

METHODS

Two musculoskeletal models of the lower limb including specimen-specific articular geometries and ligament deformability at the knee were built in a finite element framework and calibrated to match mean isometric torque data collected from 12 healthy subjects. Muscle moment arms were compared between simulations of passive knee flexion and maximum isometric knee extension and flexion. In addition, isometric torque results were compared with predictions using simplified knee models in which the deformability of the knee was removed and the kinematics at the joint were prescribed for all degrees of freedom.

RESULTS

Peak isometric torque estimated with a deformable knee representation occurred between 45° and 60° in extension, and 45° in flexion. The maximum isometric flexion torques generated by the models with deformable ligaments were 14.6% and 17.9% larger than those generated by the models with prescribed kinematics; by contrast, the maximum isometric extension torques generated by the models were similar. The change in hamstrings moment arms during isometric flexion was greater than that of the quadriceps during isometric extension (a mean RMS difference of 9.8 mm compared to 2.9 mm, respectively).

DISCUSSION

The large changes in the moment arms of the hamstrings, when activated in a model with deformable ligaments, resulted in changes to flexion torque. When simulating human motion, the inclusion of a deformable joint in a multi-scale musculoskeletal finite element model of the lower limb may preserve the realistic interaction of muscle force with knee kinematics and torque.

Morphing the feature-based multi-blocks of normative/healthy vertebral geometries to scoliosis vertebral geometries: development of personalized finite element models

Personalized Finite Element (FE) models and hexahedral elements are preferred for biomechanical investigations. Feature-based multi-block methods are used to develop anatomically accurate personalized FE models with hexahedral mesh. It is tedious to manually construct multi-blocks for large number of geometries on an individual basis to develop personalized FE models. Mesh-morphing method mitigates the aforementioned tediousness in meshing personalized geometries every time, but leads to element warping and loss of geometrical data. Such issues increase in magnitude when normative spine FE model is morphed to scoliosis-affected spinal geometry. The only way to bypass the issue of hex-mesh distortion or loss of geometry as a result of morphing is to rely on manually constructing the multi-blocks for scoliosis-affected spine geometry of each individual, which is time intensive. A method to semi-automate the construction of multi-blocks on the geometry of scoliosis vertebrae from the existing multi-blocks of normative vertebrae is demonstrated in this paper. High-quality hexahedral elements were generated on the scoliosis vertebrae from the morphed multi-blocks of normative vertebrae. Time taken was 3 months to construct the multi-blocks for normative spine and less than a day for scoliosis. Efforts taken to construct multi-blocks on personalized scoliosis spinal geometries are significantly reduced by morphing existing multi-blocks.

The potential of statistical shape modelling for geometric morphometric analysis of human teeth in archaeological research

This paper introduces statistical shape modelling (SSM) for use in osteoarchaeology research. SSM is a full field, multi-material analytical technique, and is presented as a supplementary geometric morphometric (GM) tool. Lower mandibular canines from two archaeological populations and one modern population were sampled, digitised using micro-CT, aligned, registered to a baseline and statistically modelled using principal component analysis (PCA). Sample material properties were incorporated as a binary enamel/dentin parameter. Results were assessed qualitatively and quantitatively using anatomical landmarks. Finally, the technique’s application was demonstrated for inter-sample comparison through analysis of the principal component (PC) weights. It was found that SSM could provide high detail qualitative and quantitative insight with respect to archaeological inter- and intra-sample variability. This technique has value for archaeological, biomechanical and forensic applications including identification, finite element analysis (FEA) and reconstruction from partial datasets.

An Approach to Developing Customized Total Knee Replacement Implants

Total knee replacement (TKR) has been performed for patients with end-stage knee joint arthritis to relieve pain and gain functions. Most knee replacement patients can gain satisfactory knee functions; however, the range of motion of the implanted knee is variable. There are many designs of TKR implants; it has been suggested by some researchers that customized implants could offer a better option for patients. Currently, the 3-dimensional knee model of a patient can be created from magnetic resonance imaging (MRI) or computed tomography (CT) data using image processing techniques. The knee models can be used for patient-specific implant design, biomechanical analysis, and creating bone cutting guide blocks. Researchers have developed patient-specific musculoskeletal lower limb model with total knee replacement, and the models can be used to predict muscle forces, joint forces on knee condyles, and wear of tibial polyethylene insert. These available techniques make it feasible to create customized implants for individual patients. Methods and a workflow of creating a customized total knee replacement implant for improving TKR kinematics and functions are discussed and presented in this paper.

Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint

Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.

3D knee segmentation based on three MRI sequences from different planes

In clinical practice, knee MRI sequences with 3.5~5 mm slice distance in sagittal, coronal, and axial planes are often requested for the knee examination since its acquisition is faster than high-resolution MRI sequence in a single plane, thereby reducing the probability of motion artifact. In order to take advantage of the three sequences from different planes, a 3D segmentation method based on the combination of three knee models obtained from the three sequences is proposed in this paper. In the method, the sub-segmentation is respectively performed with sagittal, coronal, and axial MRI sequence in the image coordinate system. With each sequence, an initial knee model is hierarchically deformed, and then the three deformed models are mapped to reference coordinate system defined by the DICOM standard and combined to obtain a patient-specific model. The experimental results verified that the three sub-segmentation results can complement each other, and their integration can compensate for the insufficiency of boundary information caused by 3.5~5 mm gap between consecutive slices. Therefore, the obtained patient-specific model is substantially more accurate than each sub-segmentation results.

Combined measurement and modeling of specimen-specific knee mechanics for healthy and ACL-deficient conditions

Quantifying the mechanical environment at the knee is crucial for developing successful rehabilitation and surgical protocols. Computational models have been developed to complement in vitro studies, but are typically created to represent healthy conditions, and may not be useful in modeling pathology and repair. Thus, the objective of this study was to create finite element (FE) models of the natural knee, including specimen-specific tibiofemoral (TF) and patellofemoral (PF) soft tissue structures, and to evaluate joint mechanics in intact and ACL-deficient conditions. Simulated gait in a whole joint knee simulator was performed on two cadaveric specimens in an intact state and subsequently repeated following ACL resection. Simulated gait was performed using motor-actuated quadriceps, and loads at the hip and ankle. Specimen-specific FE models of these experiments were developed in both intact and ACL-deficient states. Model simulations compared kinematics and loading of the experimental TF and PF joints, with average RMS differences [max] of 3.0° [8.2°] and 2.1° [8.4°] in rotations, and 1.7 [3.0] and 2.5 [5.1] mm in translations, for intact and ACL-deficient states, respectively. The timing of peak quadriceps force during stance and swing phase of gait was accurately replicated within 2° of knee flexion and with an average error of 16.7% across specimens and pathology. Ligament recruitment patterns were unique in each specimen; recruitment variability was likely influenced by variations in ligament attachment locations. ACL resections demonstrated contrasting joint mechanics in the two specimens with altered knee motion shown in one specimen (up to 5mm anterior tibial translation) while increased TF joint loading was shown in the other (up to 400N).

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

Dynamic Simulation of the Effects of Graft Fixation Errors During Medial Patellofemoral Ligament Reconstruction

Background:

Medial patellofemoral ligament (MPFL) reconstruction is performed to prevent recurrent instability, but errors in femoral fixation can elevate graft tension.

Hypothesis:

Errors related to femoral fixation will overconstrain the patella and increase medial patellofemoral pressures.

Study Design:

Controlled laboratory study.

Methods:

Five knees with patellar instability were represented with computational models. Kinematics during knee extension were characterized from computational reconstruction of motion performed within a dynamic computed tomography (CT) scanner. Multibody dynamic simulation of knee extension, with discrete element analysis used to quantify contact pressures, was performed for the preoperative condition and after MPFL reconstruction. A standard femoral attachment and graft resting length were set for each knee. The resting length was decreased by 2 mm, and the femoral attachment was shifted 5 mm posteriorly. The simulated errors were also combined. Root-mean-square errors were quantified for the comparison of preoperative patellar lateral shift and tilt between computationally reconstructed motion and dynamic simulation. Simulation output was compared between the preoperative and MPFL reconstruction conditions with repeated-measures Friedman tests and Dunnett comparisons against a control, which was the standard MPFL condition, with statistical significance set at P < .05.

Results:

Root-mean-square errors for simulated patellar tilt and shift were 5.8° and 3.3 mm, respectively. Patellar lateral tracking for the preoperative condition was significantly larger near full extension compared with the standard MPFL reconstruction (mean differences of 8 mm and 13° for shift and tilt, respectively, at 0°), and lateral tracking was significantly smaller for a posterior femoral attachment (mean differences of 3 mm and 4° for shift and tilt, respectively, at 0°). The maximum medial pressure was also larger for the short graft with a posterior femoral attachment than for standard MPFL reconstruction, with a significant increase in the mean value of 1.6 MPa at 30°.

Conclusion:

MPFL reconstruction reduces lateral tracking, but nonanatomic femoral fixation and overtensioning the graft overcorrect patellar tracking and increase pressure applied to medial patellar cartilage.

Clinical Relevance:

Errors in femoral fixation and graft tensioning can lead to postoperative loss of flexion and overloading of medial cartilage.

Validation of predicted patellofemoral mechanics in a finite element model of the healthy and cruciate-deficient knee

Healthy patellofemoral (PF) joint mechanics are critical to optimal function of the knee joint. Patellar maltracking may lead to large joint reaction loads and high stresses on the articular cartilage, increasing the risk of cartilage wear and the onset of osteoarthritis. While the mechanical sources of PF joint dysfunction are not well understood, links have been established between PF tracking and abnormal kinematics of the tibiofemoral (TF) joint, specifically following cruciate ligament injury and repair. The objective of this study was to create a validated finite element (FE) representation of the PF joint in order to predict PF kinematics and quadriceps force across healthy and pathological specimens. Measurements from a series of dynamic in-vitro cadaveric experiments were used to develop finite element models of the knee for three specimens. Specimens were loaded under intact, ACL-resected and both ACL and PCL-resected conditions. Finite element models of each specimen were constructed and calibrated to the outputs of the intact knee condition, and subsequently used to predict PF kinematics, contact mechanics, quadriceps force, patellar tendon moment arm and patellar tendon angle of the cruciate resected conditions. Model results for the intact and cruciate resected trials successfully matched experimental kinematics (avg. RMSE 4.0°, 3.1mm) and peak quadriceps forces (avg. difference 5.6%). Cruciate resections demonstrated either increased patellar tendon loads or increased joint reaction forces. The current study advances the standard for evaluation of PF mechanics through direct validation of cruciate-resected conditions including specimen-specific representations of PF anatomy.