Patient-specific bone modeling and analysis: the role of integration and automation in clinical adoption

Predicting physiological hip joint loads with inverse bone remodeling using clinically available QCT images

BACKGROUND AND OBJECTIVE

Assessing joint-level loading conditions in vivo is challenging due to invasive measurement or complex computation. Inverse bone remodeling (IBR) offers a different approach by recovering the loading conditions directly from computed tomography (CT) images of the bone microstructure by finding the magnitudes to a set of load cases that load the bone optimally, i.e., maximally homogeneously. An efficient IBR method was recently proposed based on homogenized finite element (hFE) models. This study compared the hip joint load predictions of hFE-based IBR with clinically feasible CT scans to those obtained with the current gold standard, micro-FE-based IBR.

METHODS

A set of 20 proximal femora was scanned ex vivo, both with a clinical quantitative CT (QCT) scanner (0.3 mm resolution) and an Xtreme CT II (XCT2) scanner (0.03 mm resolution). Finite element (FE) models with decreasing complexity were automatically created from those images. Micro-FE (µFE) models based on XCT2 images served as a baseline. hFE models based on the QCT images were created as clinically feasible models. Further intermediate models were created to trace sources of errors. IBR was applied to predict the optimal scaling factors of twelve unit load cases distributed over the femoral head.

RESULTS

The predicted loads of the newly developed workflow for QCT images within IBR followed a trend seen previously with hFE models created from high-resolution images, such as XCT2. The peak load magnitudes of µFE and hFE-based IBR were well correlated (R²=76.8 %), and the overall distribution of the loads was similar. However, an additional peak load calibration was required to obtain quantitative agreement (CCC=82.8 %).

CONCLUSIONS

A thorough comparison of µFE-based IBR and hFE-based IBR using QCT data was performed for the first time. A clinically feasible workflow, including a peak calibration, is presented, allowing for fast prediction of physiological peak hip joint loads.

From MRI to FEM: an automated pipeline for biomechanical simulations of vertebrae and intervertebral discs

Introduction

Biomechanical simulations can enhance our understanding of spinal disorders. Applied to large cohorts, they can reveal complex mechanisms beyond conventional imaging. Therefore, automating the patient-specific modeling process is essential.

Methods

We developed an automated and robust pipeline that generates and simulates biofidelic vertebrae and intervertebral disc finite element method (FEM) models based on automated magnetic resonance imaging (MRI) segmentations. In a first step, anatomically-constrained smoothing approaches were implemented to ensure seamless contact surfaces between vertebrae and discs with shared nodes. Subsequently, surface meshes were filled isotropically with tetrahedral elements. Lastly, simulations were executed. The performance of our pipeline was evaluated using a set of 30 patients from an in-house dataset that comprised an overall of 637 vertebrae and 600 intervertebral discs. We rated mesh quality metrics and processing times.

Results

With an average number of 21 vertebrae and 20 IVDs per subject, the average processing time was 4.4 min for a vertebra and 31 s for an IVD. The average percentage of poor quality elements stayed below 2% in all generated FEM models, measured by their aspect ratio. Ten vertebra and seven IVD FE simulations failed to converge.

Discussion

The main goal of our work was to automate the modeling and FEM simulation of both patient-specific vertebrae and intervertebral discs with shared-node surfaces directly from MRI segmentations. The biofidelity, robustness and time-efficacy of our pipeline marks an important step towards investigating large patient cohorts for statistically relevant, biomechanical insight.

Age-related morphological changes of the pubic symphyseal surface: using three-dimensional statistical shape modeling

Computational analysis of the pubic symphyseal surface is widely used for accurate age estimation, offering quantitative precision through the detection of subtle morphological changes. However, these methods often lack insights into the underlying morphological changes across different age groups. To bridge this gap, the study utilizes statistical shape modeling (SSM), a versatile tool capable of describing diverse morphological variations within populations. This study aimed to elucidate the direction and extent of these morphological changes, identify the contributing factors, and pinpoint key variations crucial for distinguishing between age groups. Computed tomography (CT) scans of 252 subjects from the National Forensic Service of South Korea were employed, subjected to preprocessing and landmark-based alignment. Through this approach, the study visualized and validated significant age-related morphological changes and highlighted the shape variations essential for differentiating between consecutive age groups. This study holds significance in elucidating the intricate nature of age-related morphological changes in the pubic symphyseal surface. Thus, these findings can serve as valuable cornerstones for enhancing age-at-death estimation techniques in forensic anthropology.

Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions

Statistical shape modeling (SSM) is an essential tool for analyzing variations in anatomical morphology. In a typical SSM pipeline, 3D anatomical images, gone through segmentation and rigid registration, are represented using lower-dimensional shape features, on which statistical analysis can be performed. Various methods for constructing compact shape representations have been proposed, but they involve laborious and costly steps. We propose Image2SSM, a novel deep-learning-based approach for SSM that leverages image-segmentation pairs to learn a radial-basis-function (RBF)-based representation of shapes directly from images. This RBF-based shape representation offers a rich self-supervised signal for the network to estimate a continuous, yet compact representation of the underlying surface that can adapt to complex geometries in a data-driven manner. Image2SSM can characterize populations of biological structures of interest by constructing statistical landmark-based shape models of ensembles of anatomical shapes while requiring minimal parameter tuning and no user assistance. Once trained, Image2SSM can be used to infer low-dimensional shape representations from new unsegmented images, paving the way toward scalable approaches for SSM, especially when dealing with large cohorts. Experiments on synthetic and real datasets show the efficacy of the proposed method compared to the state-of-art correspondence-based method for SSM.

Construction of the Adjusted Scoliosis 3D Finite Element Model and Biomechanical Analysis under Gravity

OBJECTIVE

Adolescent idiopathic scoliosis (AIS) is a three-dimensional structural deformity of the spine caused by the disruption of the biomechanical balance of the spine. However, the current biomechanical modeling and analysis methods of scoliosis cannot really describe the real state of the spine. This study aims to propose a high-precision biomechanical modeling and analysis method that can reflect the spinal state under gravity and provide a theoretical basis for therapeutics.

METHODS

Combining CT and X-ray images of AIS patients, this study constructed an adjusted three-dimensional model and FE model of the spine corresponding to the patient's gravity position, including vertebral bodies, intervertebral discs, ribs, costal cartilage, ligaments, and facet cartilage. Then, the displacement and stress of the spine under gravity were analyzed.

RESULTS

A model of the T1-Sacrum with 1.7 million meshes was constructed. After adding the gravity condition, the maximum displacement point was at T1 of thoracic vertebra (20.4 mm). The analysis indicates that the stress on the lower surface of the vertebral body in thoracolumbar scoliosis tended to be locally concentrated, especially on the concave side of the primary curvature's vertebral body (the maximum stress on the lower surface of T9 is 32.33 MPa) and the convex side of the compensatory curvature's vertebral body (the maximum stress on the lower surface of L5 is 41.97 MPa).

CONCLUSION

This study provides a high-precision modeling and analysis method for scoliosis with full consideration of gravity. The reliability of the method was verified based on patient data. This model can be used to analyze the biomechanical characteristics of patients in the treatment plan design stage.

A combined active shape and mean appearance model for the reconstruction of segmental bone loss

This study investigates the novel combination of an active shape and mean appearance model to estimate missing bone geometry and density distribution from sparse inputs simulating segmental bone loss of the femoral diaphysis. An active shape Gaussian Process Morphable model was trained on healthy right femurs of South African males to model shape. The density distribution was approximated based on the mean appearance of computed tomography images from the training set. Estimations of diaphyseal resections were obtained by probabilistic fitting of the active shape model to sparse inputs consisting of proximal and distal femoral data on computed tomography images. The resulting shape estimates of the diaphyseal resections were then used to map the mean appearance model to the patients' missing bone geometry, constructing density estimations. In this way, resected bone surfaces were estimated with an average error of 2.24 (0.5) mm. Density distributions were approximated within 87 (0.7) % of the intensity of the original target images before the simulated segmental bone loss. These results fall within the acceptable tolerances required for surgical planning and reconstruction of long bone defects.

Validation of a Patient-Specific Musculoskeletal Model for Lumbar Load Estimation Generated by an Automated Pipeline From Whole Body CT

Background: Chronic back pain is a major health problem worldwide. Although its causes can be diverse, biomechanical factors leading to spinal degeneration are considered a central issue. Numerical biomechanical models can identify critical factors and, thus, help predict impending spinal degeneration. However, spinal biomechanics are subject to significant interindividual variations. Therefore, in order to achieve meaningful findings on potential pathologies, predictive models have to take into account individual characteristics. To make these highly individualized models suitable for systematic studies on spinal biomechanics and clinical practice, the automation of data processing and modeling itself is inevitable. The purpose of this study was to validate an automatically generated patient-specific musculoskeletal model of the spine simulating static loading tasks.

Methods: CT imaging data from two patients with non-degenerative spines were processed using an automated deep learning-based segmentation pipeline. In a semi-automated process with minimal user interaction, we generated patient-specific musculoskeletal models and simulated various static loading tasks. To validate the model, calculated vertebral loadings of the lumbar spine and muscle forces were compared with in vivo data from the literature. Finally, results from both models were compared to assess the potential of our process for interindividual analysis.

Results: Calculated vertebral loads and muscle activation overall stood in close correlation with data from the literature. Compression forces normalized to upright standing deviated by a maximum of 16% for flexion and 33% for lifting tasks. Interindividual comparison of compression, as well as lateral and anterior–posterior shear forces, could be linked plausibly to individual spinal alignment and bodyweight.

Conclusion: We developed a method to generate patient-specific musculoskeletal models of the lumbar spine. The models were able to calculate loads of the lumbar spine for static activities with respect to individual biomechanical properties, such as spinal alignment, bodyweight distribution, and ligament and muscle insertion points. The process is automated to a large extent, which makes it suitable for systematic investigation of spinal biomechanics in large datasets.

Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice

The dental practice has largely evolved in the last 50 years following a better understanding of the biomechanical behaviour of teeth and its supporting structures, as well as developments in the fields of imaging and biomaterials. However, many patients still encounter treatment failures; this is related to the complex nature of evaluating the biomechanical aspects of each clinical situation due to the numerous patient-specific parameters, such as occlusion and root anatomy. In parallel, the advent of cone beam computed tomography enabled researchers in the field of odontology as well as clinicians to gather and model patient data with sufficient accuracy using image processing and finite element technologies. These developments gave rise to a new precision medicine concept that proposes to individually assess anatomical and biomechanical characteristics and adapt treatment options accordingly. While this approach is already applied in maxillofacial surgery, its implementation in dentistry is still restricted. However, recent advancements in artificial intelligence make it possible to automate several parts of the laborious modelling task, bringing such user-assisted decision-support tools closer to both clinicians and researchers. Therefore, the present narrative review aimed to present and discuss the current literature investigating patient-specific modelling in dentistry, its state-of-the-art applications, and research perspectives.

Benchmarking off-the-shelf statistical shape modeling tools in clinical applications

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.

Model Construction for the Estimation of Healthy Bone Shape and Density Distribution

Statistical models are widely used within biomedical fields for automated segmentation and reconstruction of healthy geometry. In the absence of contralateral geometry, statistical models are a viable alternative for reconstructing healthy bone anatomy. Therefore, statistical models of shape and appearance were constructed from sample data based on the right femur of South African males, and their use in an automated segmentation and density estimation application was investigated. The models reproduced the shape and density distribution of the population with an average error of 1.3 mm and a 90% density fit. These results fall within the acceptable tolerance limits of reconstructive surgery and appear promising for practical use in implant design.Clinical Relevance- Constructing and validating statistical models and registration algorithms provides the groundwork for further investigation into automating the digital reconstruction of pathological bone for use in implant design.

Bone density optimized pedicle screw instrumentation improves screw pull-out force in lumbar vertebrae

Pedicle screw instrumentation is performed in the surgical treatment of a wide variety of spinal pathologies. A common postoperative complication associated with this procedure is screw loosening. It has been shown that patient-specific screw fixation can be automated to match standard clinical practice and that failure can be estimated preoperatively using computed tomography images. Hence, we set out to optimize three-dimensional preoperative planning to achieve more mechanically robust screw purchase allowing deviation from intuitive, standard screw parameters. Toward this purpose, we employed a genetic algorithm optimization to find optimal screw sizes and trajectories by maximizing the CT derived bone mechanical properties. The method was tested on cadaveric lumbar vertebrae (L1 to L5) of four human spines (2 female/2 male; age range 60-78 years). The main boundary conditions were the predefined, level-dependent areas of possible screw entry points, as well as the automatically located pedicle structures. Finite element analysis was used to compare the genetic algorithm output to standard clinical planning of screw positioning in terms of the simulated pull-out strength. The genetic algorithm optimization successfully found screw sizes and trajectories that maximize the sum of the Young's modulus within the screw's volume for all 40 pedicle screws included in this study. Overall, there was a 26% increase in simulated pull-out strength for optimized compared to traditional screw trajectories and sizes. Our results indicate that optimizing pedicle screw instrumentation in lumbar vertebrae based on bone quality measures improves screw purchase as compared to traditional instrumentation.

Finite element analysis of the lumbar spine in adolescent idiopathic scoliosis subjected to different loads

OBJECTIVE

To explore the biomechanical changes of the lumbar spine segment of idiopathic scoliosis under different loads by simulating six kinds of lumbar spine motions based on a three-dimensional finite element (FE) model. Methods According to the plain CT scan data of L1-L5 segment of an AIS patient, a three-dimensional FE model was established to simulate the biomechanics of lumbar scoliosis under different loads. The lumbar model was reconstructed using Mimics20.0, smoothed in Geomagic2013, assembled in Solidworks 2020, with FE analysis performed using Workbench19.0. Results The completed model had a total of 119029 C3D4 solid elements, 223805 nodes, including finely reconstructed tissue structures. In patients with AIS, the range of motion (ROM) is reduced under all loads. Under flexion loads, the vertebral concave stress distribution is greater; under extension lateral bending, and rotation load at the posterior side of the vertebral body, the stress is concentrated in the L3 vertebral arch. The buffering effect of intervertebral disc on the rotational load is the weakest. Different loads of AIS cause corresponding changes in the force and displacement of different positions of the vertebral body or intervertebral discs. Conclusions The change in physiological shape of the lumbar vertebrae limits the ROM of the lumbar vertebrae. The stress showed a trend of local concentration which located in the concave side of the scoliosis. The stress on the lumbar vertebrae comprising the greatest curvature is the most excessive. The stress in the intervertebral disc under the rotating load is greater than that under other kinds of loads, and the intervertebral disc is more likely to be injured because of the rotating load.

Mechano-driven regeneration predicts response variations in large animal model based on scaffold implantation site and individual mechano-sensitivity

It is well founded that the mechanical environment may regulate bone regeneration in orthopedic applications. The purpose of this study is to investigate the mechanical contributions of the scaffold and the host to bone regeneration, in terms of subject specificity, implantation site and sensitivity to the mechanical environment. Using a computational approach to model mechano-driven regeneration, bone ingrowth in porous titanium scaffolds was simulated in the distal femur and proximal tibia of three goats and compared to experimental results. The results showed that bone ingrowth shifted from a homogeneous distribution pattern, when scaffolds were in contact with trabecular bone (max local ingrowth 12.47%), to a localized bone ingrowth when scaffolds were implanted in a diaphyseal location (max local ingrowth 20.64%). The bone formation dynamics revealed an apposition rate of 0.37±0.28%/day in the first three weeks after implantation, followed by limited increase in bone ingrowth until the end of the experiment (12 weeks). According to in vivo data, we identified one animal whose sensitivity to mechanical stimulation was higher than the other two. Moreover, we found that the stimulus initiating bone formation was consistently higher in the femur than in the tibia for all the individuals. Overall, the dependence of the osteogenic response on the host biomechanics means that, from a mechanical perspective, the regenerative potential depends on both the scaffold and the host environment. Therefore, this work provides insights on how the mechanical conditions of both the recipient and the scaffold contribute to meet patient and location-specific characteristics.

On the limits of finite element models created from (micro)CT datasets and used in studies of bone-implant-related biomechanical problems

Patient-specific approach is gaining a wide popularity in computational simulations of biomechanical systems. Simulations (most often based on the finite element method) are to date routinely created using data from imaging devices such as computed tomography which makes the models seemingly very complex and sophisticated. However, using a computed tomography in finite element calculations does not necessarily enhance the quality or even credibility of the models as these depend on the quality of the input images. Low-resolution (medical-)CT datasets do not always offer detailed representation of trabecular bone in FE models and thus might lead to incorrect calculation of mechanical response to external loading. The effect of image resolution on mechanical simulations of bone-implant interaction has not been thoroughly studied yet. In this study, the effect of image resolution on the modeling procedure and resulting mechanical strains in bone was analyzed on the example of cranial implant. For this purpose, several finite element models of bone interacting with fixation-screws were generated using seven computed tomography datasets of a bone specimen but with different image resolutions (ranging from micro-CT resolution of 25 μm to medical-CT resolution of 1250 μm). The comparative analysis revealed that FE models created from images of low resolution (obtained from medical computed tomography) can produce biased results. There are two main reasons: 1. Medical computed tomography images do not allow generating models with complex trabecular architecture which leads to substituting of the intertrabecular pores with a fictitious mass; 2. Image gray value distribution can be distorted resulting in incorrect mechanical properties of the bone and thus in unrealistic or even completely fictitious mechanical strains. The biased results of calculated mechanical strains can lead to incorrect conclusion, especially when bone-implant interaction is investigated. The image resolution was observed not to significantly affect stresses in the fixation screw itself; however, selection of bone material representation might result in significantly different stresses in the screw.

Automated Pipeline to Generate Anatomically Accurate Patient-Specific Biomechanical Models of Healthy and Pathological FSUs

State-of-the-art preoperative biomechanical analysis for the planning of spinal surgery not only requires the generation of three-dimensional patient-specific models but also the accurate biomechanical representation of vertebral joints. The benefits offered by computational models suitable for such purposes are still outweighed by the time and effort required for their generation, thus compromising their applicability in a clinical environment. In this work, we aim to ease the integration of computerized methods into patient-specific planning of spinal surgery. We present the first pipeline combining deep learning and finite element methods that allows a completely automated model generation of functional spine units (FSUs) of the lumbar spine for patient-specific FE simulations (FEBio). The pipeline consists of three steps: (a) multiclass segmentation of cropped 3D CT images containing lumbar vertebrae using the DenseVNet network, (b) automatic landmark-based mesh fitting of statistical shape models onto 3D semantic segmented meshes of the vertebral models, and (c) automatic generation of patient-specific FE models of lumbar segments for the simulation of flexion-extension, lateral bending, and axial rotation movements. The automatic segmentation of FSUs was evaluated against the gold standard (manual segmentation) using 10-fold cross-validation. The obtained Dice coefficient was 93.7% on average, with a mean surface distance of 0.88 mm and a mean Hausdorff distance of 11.16 mm (N = 150). Automatic generation of finite element models to simulate the range of motion (ROM) was successfully performed for five healthy and five pathological FSUs. The results of the simulations were evaluated against the literature and showed comparable ROMs in both healthy and pathological cases, including the alteration of ROM typically observed in severely degenerated FSUs. The major intent of this work is to automate the creation of anatomically accurate patient-specific models by a single pipeline allowing functional modeling of spinal motion in healthy and pathological FSUs. Our approach reduces manual efforts to a minimum and the execution of the entire pipeline including simulations takes approximately 2 h. The automation, time-efficiency and robustness level of the pipeline represents a first step toward its clinical integration.

Validated Finite Element Models of Premolars: A Scoping Review

Finite element (FE) models are widely used to investigate the biomechanics of reconstructed premolars. However, parameter identification is a complex step because experimental validation cannot always be conducted. The aim of this study was to collect the experimentally validated FE models of premolars, extract their parameters, and discuss trends. A systematic review was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Records were identified in three electronic databases (MEDLINE [PubMed], Scopus, The Cochrane Library) by two independent reviewers. Twenty-seven parameters dealing with failure criteria, model construction, material laws, boundary conditions, and model validation were extracted from the included articles. From 1306 records, 214 were selected for eligibility and entirely read. Among them, 19 studies were included. A heterogeneity was observed for several parameters associated with failure criteria and model construction. Elasticity, linearity, and isotropy were more often chosen for dental and periodontal tissues with a Young's modulus mostly set at 18-18.6 GPa for dentine. Loading was mainly simulated by an axial force, and FE models were mostly validated by in vitro tests evaluating tooth strains, but different conditions about experiment type, sample size, and tooth status (intact or restored) were reported. In conclusion, material laws identified herein could be applied to future premolar FE models. However, further investigations such as sensitivity analysis are required for several parameters to clarify their indication.

Automated segmentation of cortical and trabecular bone to generate finite element models for femoral bone mechanics

Finite element (FE) models based on quantitative computed tomography (CT) images are better predictors of bone strength than conventional areal bone mineral density measurements. However, FE models require manual segmentation of the femur, which is not clinically applicable. This study developed a method for automated FE analyses from clinical CT images. Clinical in-vivo CT images of 13 elderly female subjects were collected to evaluate the method. Secondly, proximal cadaver femurs were harvested and imaged with clinical CT (N = 17). Of these femurs, 14 were imaged with µCT and three had earlier been tested experimentally in stance-loading, while collecting surface deformations with digital image correlation. Femurs were segmented from clinical CT images using an automated method, based on the segmentation tool Stradwin. The method automatically distinguishes trabecular and cortical bone, corrects partial volume effect and generates input for FE analysis. The manual and automatic segmentations agreed within about one voxel for in-vivo subjects (0.99 ± 0.23 mm) and cadaver femurs (0.21 ± 0.07 mm). The strains from the FE predictions closely matched with the experimentally measured strains (R² = 0.89). The method can automatically generate meshes suitable for FE analysis. The method may bring us one step closer to enable clinical usage of patient-specific FE analyses.

On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application

Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Recently, the increased availability of high-resolution in vivo images of anatomy has led to the development and distribution of open-source computational tools to model anatomical shapes and their variability within populations with unprecedented detail and statistical power. Nonetheless, there is little work on the evaluation and validation of such tools as related to clinical applications that rely on morphometric quantifications for treatment planning. To address this lack of validation, we systematically assess the outcome of widely used off-the-shelf SSM tools, namely ShapeWorks, SPHARM-PDM, and Deformetrica, in the context of designing closure devices for left atrium appendage (LAA) in atrial fibrillation (AF) patients to prevent stroke, where an incomplete LAA closure may be worse than no closure. This study is motivated by the potential role of SSM in the geometric design of closure devices, which could be informed by population-level statistics, and patient-specific device selection, which is driven by anatomical measurements that could be automated by relating patient-level anatomy to population-level morphometrics. Hence, understanding the consequences of different SSM tools for the final analysis is critical for the careful choice of the tool to be deployed in real clinical scenarios. Results demonstrate that estimated measurements from ShapeWorks model are more consistent compared to models from Deformetrica and SPHARM-PDM. Furthermore, ShapeWorks and Deformetrica shape models capture clinically relevant population-level variability compared to SPHARM-PDM models.

Parametric design of patient-specific fixation plates for distal femur fractures

To facilitate the creation, modification, and optimization of patient-specific plates for distal femur fractures, a novel approach was proposed for the rapid and convenient design of patient-specific plates for patients’ fractured femurs using feature parameterization. First, several femur parameter values were obtained for a specific patient and used to construct a restored surface model of the fractured femur. Next, combined with the particular femur anatomy and the fracture, a parameterized plate with a suitable shape was created automatically based on the parameter maps between the femur and plate. Finally, using finite-element analysis, the Von Mises stresses of the plate under human gait loads were calculated to evaluate the biomechanical performance of the plate, and the plate was optimized for specific patients by recursively adjusting the parameter values. Case results indicate that patient-specific plate models can be created rapidly based on the fractured femur modes of patients and can be optimized efficiently with high-level semantic parameters. Therefore, the proposed approach may be used as a basic tool for the design and modification of patient-specific plates for use in orthopedic operations.

Biomechanical advantages of robot-assisted pedicle screw fixation in posterior lumbar interbody fusion compared with freehand technique in a prospective randomized controlled trial-perspective for patient-specific finite element analysis

BACKGROUND CONTEXT

There have been conflicting results on the surgical outcome of lumbar fusion surgery using two different techniques: robot-assisted pedicle screw fixation and conventional freehand technique. In addition, there have been no studies about the biomechanical issues between both techniques.

PURPOSE

This study aimed to investigate the biomechanical properties in terms of stress at adjacent segments using robot-assisted pedicle screw insertion technique (robot-assisted, minimally invasive posterior lumbar interbody fusion, Rom-PLIF) and freehand technique (conventional, freehand, open approach, posterior lumbar interbody fusion, Cop-PLIF) for instrumented lumbar fusion surgery.

STUDY DESIGN

This is an additional post-hoc analysis for patient-specific finite element (FE) model.

PATIENT SAMPLE

The sample is composed of patients with degenerative lumbar disease.

OUTCOME MEASURES

Intradiscal pressure and facet contact force are the outcome measures.

METHODS

Patients were randomly assigned to undergo an instrumented PLIF procedure using a Rom-PLIF (37 patients) or a Cop-PLIF (41), respectively. Five patients in each group were selected using a simple random sampling method after operation, and 10 preoperative and postoperative lumbar spines were modeled from preoperative high-resolution computed tomography of 10 patients using the same method for a validated lumbar spine model. Under four pure moments of 7.5 Nm, the changes in intradiscal pressure and facet joint contact force at the proximal adjacent segment following fusion surgery were analyzed and compared with preoperative states.

RESULTS

The representativeness of random samples was verified. Both groups showed significant increases in postoperative intradiscal pressure at the proximal adjacent segment under four moments, compared with the preoperative state. The Cop-PLIF models demonstrated significantly higher percent increments of intradiscal pressure at proximal adjacent segments under extension, lateral bending, and torsion moments than the Rom-PLIF models (p=.032, p=.008, and p=.016, respectively). Furthermore, the percent increment of facet contact force was significantly higher in the Cop-PLIF models under extension and torsion moments than in the Rom-PLIF models (p=.016 under both extension and torsion moments).

CONCLUSIONS

The present study showed the clinical application of subject-specific FE analysis in the spine. Even though there was biomechanical superiority of the robot-assisted insertions in terms of alleviation of stress increments at adjacent segments after fusion, cautious interpretation is needed because of the small sample size.

Additively Manufactured Scaffolds for Bone Tissue Engineering and the Prediction of their Mechanical Behavior: A Review

Additive manufacturing (AM), nowadays commonly known as 3D printing, is a revolutionary materials processing technology, particularly suitable for the production of low-volume parts with high shape complexities and often with multiple functions. As such, it holds great promise for the fabrication of patient-specific implants. In recent years, remarkable progress has been made in implementing AM in the bio-fabrication field. This paper presents an overview on the state-of-the-art AM technology for bone tissue engineering (BTE) scaffolds, with a particular focus on the AM scaffolds made of metallic biomaterials. It starts with a brief description of architecture design strategies to meet the biological and mechanical property requirements of scaffolds. Then, it summarizes the working principles, advantages and limitations of each of AM methods suitable for creating porous structures and manufacturing scaffolds from powdered materials. It elaborates on the finite-element (FE) analysis applied to predict the mechanical behavior of AM scaffolds, as well as the effect of the architectural design of porous structure on its mechanical properties. The review ends up with the authors’ view on the current challenges and further research directions.

The Role of Fluid Dynamics in Distributing Ankle Stresses in Anatomic and Injured States

BACKGROUND

In 1976, Ramsey and Hamilton published a landmark cadaveric study demonstrating a dramatic 42% decrease in tibiotalar contact area with only 1 mm of lateral talar shift. An increase in maximum principal stress of at least 72% is predicted based on these findings though the delayed development of arthritis in minimally misaligned ankles does not appear to be commensurate with the results found in dry cadaveric models. We hypothesized that synovial fluid could be a previously unrecognized factor that contributes significantly to stress distribution in the tibiotalar joint in anatomic and injured states.

METHODS

As it is not possible to directly measure contact stresses with and without fluid in a cadaveric model, finite element analysis (FEA) was employed for this study. FEA is a modeling technique used to calculate stresses in complex geometric structures by dividing them into small, simple components called elements. Four test configurations were investigated using a finite element model (FEM): baseline ankle alignment, 1 mm laterally translated talus and fibula, and the previous 2 bone orientations with fluid added. The FEM selected for this study was the Global Human Body Models Consortium-owned GHBMC model, M50 version 4.2, a model of an average-sized male (distributed by Elemance, LLC, Winston-Salem, NC). The ankle was loaded at the proximal tibia with a distributed load equal to the GHBMC body weight, and the maximum principal stress was computed.

RESULTS

All numerical simulations were stable and completed with no errors. In the baseline anatomic configuration, the addition of fluid between the tibia, fibula, and talus reduced the maximum principal stress computed in the distal tibia at maximum load from 31.3 N/mm² to 11.5 N/mm². Following 1 mm lateral translation of the talus and fibula, there was a modest 30% increase in the maximum stress in fluid cases. Qualitatively, translation created less high stress locations on the tibial plafond when fluid was incorporated into the model.

CONCLUSIONS

The findings in this study demonstrate a meaningful role for synovial fluid in distributing stresses within the ankle that has not been considered in historical dry cadaveric studies. The increase in maximum stress predicted by simulation of an ankle with fluid was less than half that projected by cadaveric data, indicating a protective effect of fluid in the injured state. The trends demonstrated by these simulations suggest that bony alignment and fluid in the ankle joint change loading patterns on the tibia and should be accounted for in future experiments.

CLINICAL RELEVANCE

Synovial fluid may play a protective role in ankle injuries, thus delaying the onset of arthritis. Reactive joint effusions may also function to additionally redistribute stresses with higher volumes of viscous fluid.

Quantitative Computed Tomography (QCT) derived Bone Mineral Density (BMD) in finite element studies: a review of the literature

BACKGROUND

Finite element modeling of human bone provides a powerful tool to evaluate a wide variety of outcomes in a highly repeatable and parametric manner. These models are most often derived from computed tomography data, with mechanical properties related to bone mineral density (BMD) from the x-ray energy attenuation provided from this data. To increase accuracy, many researchers report the use of quantitative computed tomography (QCT), in which a calibration phantom is used during image acquisition to improve the estimation of BMD. Since model accuracy is dependent on the methods used in the calculation of BMD and density-mechanical property relationships, it is important to use relationships developed for the same anatomical location and using the same scanner settings, as these may impact model accuracy. The purpose of this literature review is to report the relationships used in the conversion of QCT equivalent density measures to ash, apparent, and/or tissue densities in recent finite element (FE) studies used in common density-modulus relationships. For studies reporting experimental validation, the validation metrics and results are presented.

RESULTS

Of the studies reviewed, 29% reported the use of a dipotassium phosphate (K2HPO4) phantom, 47% a hydroxyapatite (HA) phantom, 13% did not report phantom type, 7% reported use of both K2HPO4 and HA phantoms, and 4% alternate phantom types. Scanner type and/or settings were omitted or partially reported in 31% of studies. The majority of studies used densitometric and/or density-modulus relationships derived from different anatomical locations scanned in different scanners with different scanner settings. The methods used to derive various densitometric relationships are reported and recommendations are provided toward the standardization of reporting metrics.

CONCLUSIONS

This review assessed the current state of QCT-based FE modeling with use of clinical scanners. It was found that previously developed densitometric relationships vary by anatomical location, scanner type and settings. Reporting of all parameters used when referring to previously developed relationships, or in the development of new relationships, may increase the accuracy and repeatability of future FE models.

Development of an integrated CAD-FEA system for patient-specific design of spinal cages

Spinal cages are used to create a suitable mechanical environment for interbody fusion in cases of degenerative spinal instability. Due to individual variations in bone structures and pathological conditions, patient-specific cages can provide optimal biomechanical conditions for fusion, strengthening patient recovery. Finite element analysis (FEA) is a valuable tool in the biomechanical evaluation of patient-specific cage designs, but the time- and labor-intensive process of modeling limits its clinical application. In an effort to facilitate the design and analysis of patient-specific spinal cages, an integrated CAD-FEA system (CASCaDeS, comprehensive analytical spinal cage design system) was developed. This system produces a biomechanical-based patient-specific design of spinal cages and is capable of rapid implementation of finite element modeling. By comparison with commercial software, this system was validated and proven to be both accurate and efficient. CASCaDeS can be used to design patient-specific cages with a superior biomechanical performance to commercial spinal cages.

Numerical simulation of mechanically stimulated bone remodelling

The numerical simulation of bone remodelling provides a great opportunity to improve the choice of therapy in particular for complex bone defects. Despite this fact, its use in clinical practice is not yet expedient because of several unresolved problems. In this paper a new bone remodelling algorithm based on standard computer tomography datasets and finite element analysis is introduced.

Subject-specific musculoskeletal loading of the tibia: Computational load estimation

The systematic development of subject-specific computer models for the analysis of personalized treatments is currently a reality. In fact, many advances have recently been developed for creating virtual finite element-based models. These models accurately recreate subject-specific geometries and material properties from recent techniques based on quantitative image analysis. However, to determine the subject-specific forces, we need a full gait analysis, typically in combination with an inverse dynamics simulation study. In this work, we aim to determine the subject-specific forces from the computer tomography images used to evaluate bone density. In fact, we propose a methodology that combines these images with bone remodelling simulations and artificial neural networks. To test the capability of this novel technique, we quantify the personalized forces for five subject-specific tibias using our technique and a gait analysis. We compare both results, finding that similar vertical loads are estimated by both methods and that the dominant part of the load can be reliably computed. Therefore, we can conclude that the numerical-based technique proposed in this work has great potential for estimating the main forces that define the mechanical behaviour of subject-specific bone.

Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift

Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.