Construction of 3D human distal femoral surface models using a 3D statistical deformable model

Scan-Free and Fully Automatic Tracking of Native Knee Anatomy from Dynamic Stereo-Radiography with Statistical Shape and Intensity Models

Kinematic tracking of native anatomy from stereo-radiography provides a quantitative basis for evaluating human movement. Conventional tracking procedures require significant manual effort and call for acquisition and annotation of subject-specific volumetric medical images. The current work introduces a framework for fully automatic tracking of native knee anatomy from dynamic stereo-radiography which forgoes reliance on volumetric scans. The method consists of three computational steps. First, captured radiographs are annotated with segmentation maps and anatomic landmarks using a convolutional neural network. Next, a non-convex polynomial optimization problem formulated from annotated landmarks is solved to acquire preliminary anatomy and pose estimates. Finally, a global optimization routine is performed for concurrent refinement of anatomy and pose. An objective function is maximized which quantifies similarities between masked radiographs and digitally reconstructed radiographs produced from statistical shape and intensity models. The proposed framework was evaluated against manually tracked trials comprising dynamic activities, and additional frames capturing a static knee phantom. Experiments revealed anatomic surface errors routinely below 1.0 mm in both evaluation cohorts. Median absolute errors of individual bone pose estimates were below 1.0∘ or mm for 15 out of 18 degrees of freedom in both evaluation cohorts. Results indicate that accurate pose estimation of native anatomy from stereo-radiography may be performed with significantly reduced manual effort, and without reliance on volumetric scans.

Artificial Intelligence for 3D Reconstruction from 2D Panoramic X-rays to Assess Maxillary Impacted Canines

The objective of this study was to explore the feasibility of current 3D reconstruction in assessing the position of maxillary impacted canines from 2D panoramic X-rays. A dataset was created using pre-treatment CBCT data from a total of 123 patients, comprising 74 patients with impacted canines and 49 patients without impacted canines. From all 74 subjects, we generated a dataset containing paired 2D panoramic X-rays and pseudo-3D images. This pseudo-3D image contained information about the location of the impacted canine in the buccal/lingual, mesial/distal, and apical/coronal positions. These data were utilized to train a deep-learning reconstruction algorithm, a generative AI. The location of the crown of the maxillary impacted canine was determined based on the output of the algorithm. The reconstruction was evaluated using the structure similarity index measure (SSIM) as a metric to indicate the quality of the reconstruction. The prediction of the impacted canine's location was assessed in both the mesiodistal and buccolingual directions. The reconstruction algorithm predicts the position of the impacted canine in the buccal, middle, or lingual position with 41% accuracy, while the mesial and distal positions are predicted with 55% accuracy. The mean SSIM for the output is 0.71, with a range of 0.63 to 0.84. Our study represents the first application of AI reconstruction output for multidisciplinary care involving orthodontists, periodontists, and maxillofacial surgeons in diagnosing and treating maxillary impacted canines. Further development of deep-learning algorithms is necessary to enhance the robustness of dental reconstruction applications.

Integration of statistical shape modeling and alternating interpolation-based model tracking technique for measuring knee kinematics in vivo using clinical interleaved bi-plane fluoroscopy

Background

A 2D fluoroscopy/3D model-based registration with statistical shape modeling (SSM)-reconstructed subject-specific bone models will help reduce radiation exposure for 3D kinematic measurements of the knee using clinical alternating bi-plane fluoroscopy systems. The current study aimed to develop such an approach and evaluate in vivo its accuracy and identify the effects of the accuracy of SSM models on the kinematic measurements.

Methods

An alternating interpolation-based model tracking (AIMT) approach with SSM-reconstructed subject-specific bone models was used for measuring 3D knee kinematics from dynamic alternating bi-plane fluoroscopy images. A two-phase optimization scheme was used to reconstruct subject-specific knee models from a CT-based SSM database of 60 knees using one, two, or three pairs of fluoroscopy images. Using the CT-reconstructed model as a benchmark, the performance of the AIMT with SSM-reconstructed models in measuring bone and joint kinematics during dynamic activity was evaluated in terms of mean target registration errors (mmTRE) for registered bone poses and the mean absolute differences (MAD) for each motion component of the joint poses.

Results

The mmTRE of the femur and tibia for one image pair were significantly greater than those for two and three image pairs without significant differences between two and three image pairs. The MAD was 1.16 to 1.22° for rotations and 1.18 to 1.22 mm for translations using one image pair. The corresponding values for two and three image pairs were 0.75 to 0.89° and 0.75 to 0.79 mm; and 0.57 to 0.79° and 0.6 to 0.69 mm, respectively. The MAD values for one image pair were significantly greater than those for two and three image pairs without significant differences between two and three image pairs.

Conclusions

An AIMT approach with SSM-reconstructed models was developed, enabling the registration of interleaved fluoroscopy images and SSM-reconstructed models from more than one asynchronous fluoroscopy image pair. This new approach had sub-millimeter and sub-degree measurement accuracy when using more than one image pair, comparable to the accuracy of CT-based methods. This approach will be helpful for future kinematic measurements of the knee with reduced radiation exposure using 3D fluoroscopy with clinically alternating bi-plane fluoroscopy systems.

Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data

The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, object detection, and image segmentation models, to extract geometrical information from inputted DICOM files. Statistical shape models then used the information to create femur and tibia 3D surface model predictions which were ultimately used by computer aided design scripts to generate customised implant designs. The developed pipeline was trained and tested using CT scan images, along with segmented 3D models, obtained for 98 Korean Asian subjects. The performance of the pipeline was tested computationally by virtually fitting outputted implant designs with 'ground truth' 3D models for each test subject's bones. This demonstrated the pipeline was capable of repeatably producing highly accurate designs, and its performance was not impacted by subject sex, height, age, or knee side. In conclusion, a robust, accurate and automatic, CT-based total knee replacement customisation pipeline was shown to be feasible and could afford significant time and cost advantages over conventional methods. The pipeline framework could also be adapted to enable customisation of other medical implants.

Research Advances in the Application of AI for Preoperative Measurements in Total Knee Arthroplasty

Total knee arthroplasty (TKA) is widely used in clinical practice as an effective treatment for end-stage knee joint lesions. It can effectively correct joint deformities, relieve painful symptoms, and improve joint function. The reconstruction of lower extremity joint lines and soft tissue balance are important factors related to the durability of the implant; therefore, it is especially important to measure the joint lines and associated angles before TKA. In this article, we review the technological progress in the preoperative measurement of TKA.

The accuracy of statistical shape models in predicting bone shape: A systematic review

BACKGROUND

This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling.

METHODS

A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible.

RESULTS

2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error).

CONCLUSION

Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate.

A parametric head geometry model accounting for variation among adolescent and young adult populations

BACKGROUND AND OBJECTIVE

Modeling the size and shape of human skull and scalp is essential for head injury assessment, design of helmets and head-borne equipment, and many other safety applications. Finite element (FE) head models are important tools to assess injury risks and design personal protective equipment. However, current FE head models are mainly developed based on the midsize male, failing to account for the significant morphological variation that exists in the skull and brain. The objective of this study was to develop a statistical head geometry model that accounts for size and shape variations among the adolescent and young adult population.

METHODS

To represent subject-specific geometry using a homologous mesh, threshold-based segmentation of head CT scans of 101 subjects between 14 and 25 years of age was performed, followed by landmarking, mesh morphing, and projection. Skull and scalp statistical geometry models were then developed as functions of age, sex, stature, BMI, head length, head breadth, and tragion-to-top of head using generalized Procrustes analysis (GPA), principal component analysis (PCA) and multivariate regression analysis.

RESULTS

The statistical geometry models account for a high percentage of morphological variations in scalp geometry (R²=0.63), outer skull geometry (R²=0.66), inner skull geometry (R²=0.55), and skull thickness (error < 1 mm) CONCLUSIONS: Skull and scalp statistical geometry models accounts for size and shape variations among the adolescent and young adult population were developed as functions of subject covariates. These models may serve as the geometric basis to develop individualized head FE models for injury assessment and design of head-borne equipment.

Three-Dimensional Subject-Specific Knee Shape Reconstruction with Asynchronous Fluoroscopy Images Using Statistical Shape Modeling

Background and objectives: Statistical shape modeling (SSM) based on computerized tomography (CT) datasets has enabled reasonably accurate reconstructions of subject-specific 3D bone morphology from one or two synchronous radiographs for clinical applications. Increasing the number of radiographic images may increase the reconstruction accuracy, but errors related to the temporal and spatial asynchronization of clinical alternating bi-plane fluoroscopy may also increase. The current study aimed to develop a new approach for subject-specific 3D knee shape reconstruction from multiple asynchronous fluoroscopy images from 2, 4, and 6 X-ray detector views using a CT-based SSM model; and to determine the optimum number of planar images for best accuracy via computer simulations and in vivo experiments.

Methods: A CT-based SSM model of the knee was established from 60 training models in a healthy young Chinese male population. A new two-phase optimization approach for 3D subject-specific model reconstruction from multiple asynchronous clinical fluoroscopy images using the SSM was developed, and its performance was evaluated via computer simulation and in vivo experiments using one, two and three image pairs from an alternating bi-plane fluoroscope.

Results: The computer simulation showed that subject-specific 3D shape reconstruction using three image pairs had the best accuracy with RMSE of 0.52 ± 0.09 and 0.63 ± 0.085 mm for the femur and tibia, respectively. The corresponding values for the in vivo study were 0.64 ± 0.084 and 0.69 ± 0.069 mm, respectively, which was significantly better than those using one image pair (0.81 ± 0.126 and 0.83 ± 0.108 mm). No significant differences existed between using two and three image pairs.

Conclusion: A new two-phase optimization approach was developed for SSM-based 3D subject-specific knee model reconstructions using more than one asynchronous fluoroscopy image pair from widely available alternating bi-plane fluoroscopy systems in clinical settings. A CT-based SSM model of the knee was also developed for a healthy young Chinese male population. The new approach was found to have high mode reconstruction accuracy, and those for both two and three image pairs were much better than for a single image pair. Thus, two image pairs may be used when considering computational costs and radiation dosage. The new approach will be useful for generating patient-specific knee models for clinical applications using multiple asynchronous images from alternating bi-plane fluoroscopy widely available in clinical settings. The current SSM model will serve as a basis for further inclusion of training models with a wider range of sizes and morphological features for broader applications.

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.

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.

Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images

Computing 3D bone models using traditional Computed Tomography (CT) requires a high-radiation dose, cost and time. We present a fully automated, domain-agnostic method for estimating the 3D structure of a bone from a pair of 2D X-ray images. Our triplet loss-trained neural network extracts a 128-dimensional embedding of the 2D X-ray images. A classifier then finds the most closely matching 3D bone shape from a predefined set of shapes. Our predictions have an average root mean square (RMS) distance of 1.08 mm between the predicted and true shapes, making our approach more accurate than the average achieved by eight other examined 3D bone reconstruction approaches. Each embedding extracted from a 2D bone image is optimized to uniquely identify the 3D bone CT from which the 2D image originated and can serve as a kind of fingerprint of each bone; possible applications include faster, image content-based bone database searches for forensic purposes.

Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model

Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called "modes of variation" (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability. The SSM was built using 99 bony shapes (distal femur and proximal tibia surfaces obtained from segmented CT scans) of osteoarthritic patients. Hip-knee-ankle (HKA) angle, femoral varus-valgus (FVV) angle, internal-external femoral rotation (IER), tibial varus-valgus (TVV) angles, and tibial slope (TS) were available across the patient set. Discriminant analysis (DA) and logistic regression (LR) classifiers were adopted to underline specific MoVs accounting for knee instability. First, it was found that thirty-four MoVs were enough to describe 95% of the shape variability in the dataset. The most relevant MoVs were the one encoding the height of the femoral and tibial shafts (MoV #2) and the one representing variations of the axial section of the femoral shaft and its bending in the frontal plane (MoV #5). Second, using quadratic DA, the sensitivity results of the classification were very accurate, being all >0.85 (HKA: 0.96, FVV: 0.99, IER: 0.88, TVV: 1, TS: 0.87). The results of the LR classifier were mostly in agreement with DA, confirming statistical significance for MoV #2 (p = 0.02) in correspondence to IER and MoV #5 in correspondence to HKA (p = 0.0001), FVV (p = 0.001), and TS (p = 0.02). We can argue that the SSM successfully identified specific MoVs encoding ranges of alignment variability between distal femur and proximal tibia. This discloses the opportunity to use the SSM to predict potential misalignment in the knee for a new patient by processing the bone shapes, removing the need for measuring clinical landmarks as the rotation centers and mechanical axes.

Comparison between different screening strategies to determine the statistical shape model of the pelvises for implant design

BACKGROUND AND OBJECTIVES

The statistical shape model (SSM) of numerous bones has been used to determine the anatomical representative of the population- or race-specific design for periarticular implants. Whether to include size- and profile-mismatched bones in the SSM calculation is debatable. Therefore, the objective of this study was to characterize the screening strategies for the mismatched bones to improve the SSM calculation.

METHODS

The bone database used in this study consisted of 20 pelvises. A systematic four-staged SSM calculation was used to evaluate the accuracy of the predicted SSM shape among the four size- and profile-screening strategies. Additionally, the surface-smoothing effects on the SSM results were investigated. Two comparison indices were used in terms of profile difference and surface smoothness.

RESULTS

Significant variations in size and profile existed for the collected bones. By normalizing the aspect ratio of all bones, exclusion of the size-mismatched bones reduced the maximum and root mean square (RMS) error values of the profile difference by 18.9% and 17.5%, respectively. After further excluding the profile-improper bones, normalization reduced the RMS profile difference by 24.1% compared with the non-normalized strategy. Exclusion of the size-improper bones for non-normalized strategy would have reduced the RMS profile difference by 15.4%. After smoothness, the RMS profile difference of SSM was only 6.1% higher than that of the non-smoothness SSM.

CONCLUSIONS

The four-stage calculation showed that the most favorable strategy was to normalize bones to the same aspect ratio and exclude improperly shaped bones. The model permitted inclusion of the original characteristics of the bones and preserved their shapes and excluded only significantly improper bones. After SSM calculation, the smoothed process provided satisfaction in quality with a statistically insignificant loss in bone morphology for population- or race-specific designs of implants.

Pelvis and femur shape prediction using principal component analysis for body model on seat comfort assessment. Impact on the prediction of the used palpable anatomical landmarks as predictors

A personalized pelvis and femur shape is required to build a finite element buttock thigh model when experimentally investigating seating discomfort. The present study estimates the shape of pelvis and femur using a principal component analysis (PCA) based method with a limited number of palpable anatomical landmarks (ALs) as predictors. A leave-one-out experiment was designed using 38 pelvises and femurs from a same sample of adult specimens. As expected, prediction errors decrease with the number of ALs. Using the maximum number of easily palpable ALs (13 for pelvis and 4 for femur), average errors were 5.4 and 4.8 mm respectively for pelvis and femur. Better prediction was obtained when the shapes of pelvis and femur were predicted separately without merging the data of both bones. Results also show that the PCA based method is a good alternative to predict hip and lumbosacral joint centers with an average error of 5.0 and 9.2 mm respectively.

Pair-wise vs group-wise registration in statistical shape model construction: representation of physiological and pathological variability of bony surface morphology

Statistical shape models (SSM) of bony surfaces have been widely proposed in orthopedics, especially for anatomical bone modeling, joint kinematic analysis, staging of morphological abnormality, and pre- and intra-operative shape reconstruction. In the SSM computation, reference shape selection, shape registration and point correspondence computation are fundamental aspects determining the quality (generality, specificity and compactness) of the SSM. Such procedures can be made critical by the presence of large morphological dissimilarities within the surfaces, not only because of anthropometrical variability but also mainly due to pathological abnormalities. In this work, we proposed a SW pipeline for SSM construction based on pair-wise (PW) shape registration, which requires the a-priori selection of the reference shape, and on a custom iterative point correspondence algorithm. We addressed large morphological deformations in five different bony surface sets, namely proximal femur, distal femur, patella, proximal fibula and proximal tibia, extracted from a retrospective patient dataset. The technique was compared to a method from the literature, based on group-wise (GW) shape registration. As a main finding, the proposed technique provided generalization and specificity median errors, for all the five bony regions, lower than 2 mm. The comparative analysis provided basically similar results. Particularly, for the distal femur that was the shape affected by the largest pathological deformations, the differences in generalization, specificity and compactness were lower than 0.5 mm, 0.5 mm, and 1%, respectively. We can argue the proposed pipeline, along with the robust correspondence algorithm, is able to compute high-quality SSM of bony shapes, even affected by large morphological variability.

Review of 2-D/3-D Reconstruction Using Statistical Shape and Intensity Models and X-Ray Image Synthesis: Toward a Unified Framework

Patient-specific three-dimensional (3-D) bone models are useful for a number of clinical applications such as surgery planning, postoperative evaluation, as well as implant and prosthesis design. Two-dimensional-to-3-D (2-D/3-D) reconstruction, also known as model-to-modality or atlas-based 2-D/3-D registration, provides a means of obtaining a 3-D model of a patient's bones from their 2-D radiographs when 3-D imaging modalities are not available. The preferred approach for estimating both shape and density information (that would be present in a patient's computed tomography data) for 2-D/3-D reconstruction makes use of digitally reconstructed radiographs and deformable models in an iterative, non-rigid, intensity-based approach. Based on a large number of state-of-the-art 2-D/3-D bone reconstruction methods, a unified mathematical formulation of the problem is proposed in a common conceptual framework, using unambiguous terminology. In addition, shortcomings, recent adaptations, and persisting challenges are discussed along with insights for future research.

Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia

BACKGROUND

The quantitative morphological analysis of the trochlear region in the distal femur and the precise staging of the potential dysplastic condition constitute a key point for the use of personalized treatment options for the patella-femoral joint. In this paper, we integrated statistical shape models (SSM), able to represent the individual morphology of the trochlea by means of a set of parameters and stacked sparse autoencoder (SSPA) networks, which exploit the parameters to discriminate among different levels of abnormalities.

METHODS

Two datasets of distal femur reconstructions were obtained from CT scans, including pathologic and physiologic shapes. Both of them were processed to compute SSM of healthy and dysplastic trochlear regions. The parameters obtained by the 3D-3D reconstruction of a femur shape were fed into a trained SSPA classifier to automatically establish the membership to one of three clinical conditions, namely, healthy, mild dysplasia, and severe dysplasia of the trochlea. The validation was performed on a subset of the shapes not used in the construction of the SSM, by verifying the occurrence of a correct classification.

RESULTS

A major finding of the work is that SSM are able to represent anomalies of the trochlear geometry by means of specific eigenmodes of variation and to model the interplay between morphologic features related to dysplasia. Exploiting the patient-specific morphing parameters of SSM, computed by means of a 3D-3D reconstruction, SSPA is demonstrated to outperform traditional discriminant analysis in classifying healthy, mild, and severe trochlear dysplasia providing 99%, 97%, and 98% accuracy for each of the three classes, respectively (discriminant analysis accuracy: 85%, 89%, and 77%).

CONCLUSIONS

From a clinical point of view, this paper contributes to support the increasing role of SSM, integrated with deep learning techniques, in diagnostics and therapy definition as quantitative and advanced visualization tools.

A novel technology for 3D knee prosthesis planning and treatment evaluation using 2D X-ray radiographs: a clinical evaluation

PURPOSE

 To present a clinical validation of a novel technology called "3X" which allows for 3D prosthesis planning and treatment evaluation in total knee arthroplasty (TKA) using only 2D X-ray radiographs.

MATERIALS AND METHODS

 After local institution review board approvals, 3X was evaluated on 43 cases (23 for preoperative planning and 20 for postoperative treatment evaluation). All the patients underwent CT scans according to a standard protocol. The results measured on the CT data were regarded as the ground truth. Additionally, two X-ray images were acquired for each affected leg and were used by 3X technology to derive patient-specific measurements of the leg. In total, we compared seven parameters for planning TKA and five parameters for postoperative prosthesis alignment.

RESULTS

 Our experimental results demonstrated that the mean distances between the surface models reconstructed from 2D X-rays and the associated surface models obtained from 3D CT data were smaller than 1.5 mm. The average differences for all angular parameters were smaller than [Formula: see text]. In over 78% cases 3X technology derived the same femoral component size as the CT-based ground truth and this value went down to 70% when 3X technology was used to predict the size of tibial component.

CONCLUSION

 3X is a technology that allows for true 3D preoperative planning and postoperative treatment evaluation based on 2D X-ray radiographs.

Statistical shape modeling predicts patellar bone geometry to enable stereo-radiographic kinematic tracking

Complications in the patellofemoral (PF) joint of patients with total knee replacements include patellar subluxation and dislocation, and remain a cause for revision. Kinematic measurements to assess these complications and evaluate implant designs require the accuracy of dynamic stereo-radiographic systems with 3D-2D registration techniques. While tibiofemoral kinematics are typically derived by tracking metallic implants, PF kinematic measurements are difficult as the patellar implant is radiotransparent and a representation of the resected patella bone requires either pre-surgical imaging and precise implant placement or post-surgical imaging. Statistical shape models (SSMs), used to characterize anatomic variation, provide an alternative means to obtain the representation of the resected patella for use in kinematic tracking. Using a virtual platform of a stereo-radiographic system, the objectives of this study were to evaluate the ability of an SSM to predict subject-specific 3D implanted patellar geometries from simulated 2D image profiles, and to formulate an effective data collection methodology for PF kinematics by considering accuracy for a variety of patient pose scenarios. An SSM of the patella was developed for 50 subjects and a leave-one-out approach compared SSM-predicted and actual geometries; average 3D errors were 0.45±0.07mm (mean±standard deviation), which is comparable to the accuracy of traditional segmentation. Further, initial imaging of the patella in five unique stereo radiographic perspectives yielded the most accurate representation. The ability to predict the remaining patellar geometry of the implanted PF joint with radiographic images and SSM, instead of CT, can reduce radiation exposure and streamline in vivo kinematic evaluations.

2D/3D reconstruction of the distal femur using statistical shape models addressing personalized surgical instruments in knee arthroplasty: A feasibility analysis

BACKGROUND

Personalized surgical instruments (PSI) have gained success in the domain of total knee replacement, demonstrating clinical outcomes similar or even superior to both traditional and navigated surgeries. The key requirement for prototyping PSI is the availability of the digital bony surface. In this paper, we aim at verifying whether the 2D/3D reconstruction of the distal femur, based on statistical shape models (SSM), grants sufficient accuracy, especially in the condylar regions, to support a PSI technique.

METHODS

Computed tomographic knee datasets acquired on 100 patients with severe cartilage damage were retrospectively considered in this work. All the patients underwent total knee replacement using the PSI-based surgical technique. Eighty out of 100 reconstructed distal femur surfaces were used to build the statistical model. The remaining 20 surfaces were used for testing. The 2D/3D reconstruction process was based on digital reconstructed radiographies (DRRs) obtained with a simulated X-ray projection process. An iterative optimization procedure, based on an evolutionary algorithm, systematically morphed the statistical model to decrease the difference between the DRR, obtained by the original CT dataset, and the DRR obtained from the morphed surface.

RESULTS

Over the 80 variations, the first ten modes were found sufficient to reconstruct the distal femur surface with accuracy. Using three DRR, the maximum Hausdorff and RMS distance errors were lower than 1.50 and 0.75 mm, respectively. As expected, the reconstruction quality improved by increasing the number of DRRs. Statistical difference (P < 0.001) was found in the 2 vs 3, 2 vs 4 and 2 vs 5 DRR, thus proving that adding just a single displaced projection to the two traditional sagittal and coronal X-ray images improved significantly the reconstruction quality. The effect of the PSI contact area errors on the distal cut direction featured a maximum median error lower than 2° and 0.5° on the sagittal and frontal plane, respectively. Statistical difference was found (P < 0.0001) in the reconstruction accuracy when comparing SSM built using pathologic with respect to non-pathologic shapes (cadavers), meaning that, to improve the patient-specific reconstruction, the morphologic anomalies, specific to the pathology, must be embedded into the SSM.

CONCLUSIONS

We showed that the X-ray based reconstruction of the distal femur is reasonable also in presence of pathologic bony conditions, featuring accuracy results similar to earlier reports in the literature that reconstructed normal femurs. This finding discloses the chance of applying the proposed methodology to the reconstruction of bony surfaces used in the PSI surgical approach.

Computation of Femoral Canine Morphometric Parameters in Three-Dimensional Geometrical Models

OBJECTIVE

To define and validate a method for the measurement of 3-dimensional (3D) morphometric parameters in polygonal mesh models of canine femora.

STUDY DESIGN

Ex vivo/computerized model.

SAMPLE POPULATION

Sixteen femora from 8 medium to large-breed canine cadavers (mean body weight 28.3 kg, mean age 5.3 years).

METHODS

Femora were measured with a 3D scanner, obtaining 3D meshes. A computer-aided design-based (CAD) software tool was purposely developed, which allowed automatic calculation of morphometric parameters on a mesh model. Anatomic and mechanical lateral proximal femoral angles (aLPFA and mLPFA), anatomic and mechanical lateral distal femoral angles (aLDFA and mLDFA), femoral neck angle (FNA), femoral torsion angle (FTA), and femoral varus angle (FVA) were measured in 3D space. Angles were also measured onto projected planes and radiographic images.

RESULTS

Mean (SD) femoral angles (degrees) measured in 3D space were: aLPFA 115.2 (3.9), mLPFA 105.5 (4.2), aLDFA 88.6 (4.5), mLDFA 93.4 (3.9), FNA 129.6 (4.3), FTA 45 (4.5), and FVA -1.4 (4.5). Onto projection planes, aLPFA was 103.7 (5.9), mLPFA 98.4 (5.3), aLDFA 88.3 (5.5), mLDFA 93.6 (4.2), FNA 132.1 (3.5), FTA 19.1 (5.7), and FVA -1.7 (5.5). With radiographic imaging, aLPFA was 109.6 (5.9), mLPFA 105.3 (5.2), aLDFA 92.6 (3.8), mLDFA 96.9 (2.9), FNA 120.2 (8.0), FTA 30.2 (5.7), and FVA 2.6 (3.8).

CONCLUSION

The proposed method gives reliable and consistent information about 3D bone conformation. Results are obtained automatically and depend only on femur morphology, avoiding any operator-related bias. Angles in 3D space are different from those measured with standard radiographic methods, mainly due to the different definition of femoral axes.

Establishing a Customized Guide Plate for Osteotomy in Total Knee Arthroplasty Using Lower-extremity X-ray and Knee Computed Tomography Images

BACKGROUND

The conventional method cannot guarantee the precise osteotomies required for a perfect realignment and a better prognosis after total knee arthroplasty (TKA). This study investigated a customized guide plate for osteotomy placement in TKAs with the aid of the statistical shape model technique using weight-bearing lower-extremity X-rays and computed tomography (CT) images of the knee.

METHODS

From October 2014 to June 2015, 42 patients who underwent a TKA in Guizhou Provincial People's Hospital were divided into a guide plate group (GPG, 21 cases) and a traditional surgery group (TSG, 21 cases) using a random number table method. In the GPG group, a guide plate was designed and printed using preoperative three-dimensional measurements to plan and digitally simulate the operation. TSG cases were treated with the conventional method. Outcomes were obtained from the postoperative image examination and short-term follow-up.

RESULTS

Operative time was 49.0 ± 10.5 min for GPG, and 62.0 ± 9.7 min in TSG. The coronal femoral angle, coronal tibial angle, posterior tibial slope, and the angle between the posterior condylar osteotomy surface and the surgical transepicondylar axis were 89.2 ± 1.7°, 89.0 ± 1.1°, 6.6 ± 1.4°, and 0.9 ± 0.3° in GPG, and 86.7 ± 2.9°, 87.6 ± 2.1°, 8.9 ± 2.8°, and 1.7 ± 0.8° in TSG, respectively. The Hospital for Special Surgery scores 3 months after surgery were 83.7 ± 18.4 in GPG and 71.5 ± 15.2 in TSG. Statistically significant differences were found between GPG and TSG in all measurements.

CONCLUSIONS

A customized guide plate to create an accurate osteotomy in TKAs may be created using lower-extremity X-ray and knee CT images. This allows for shorter operative times and better postoperative alignment than the traditional surgery. Application of the digital guide plate may also result in better short-term outcomes.

Prediction of in vivo knee joint kinematics using a combined dual fluoroscopy imaging and statistical shape modeling technique

Using computed tomography (CT) or magnetic resonance (MR) images to construct 3D knee models has been widely used in biomedical engineering research. Statistical shape modeling (SSM) method is an alternative way to provide a fast, cost-efficient, and subject-specific knee modeling technique. This study was aimed to evaluate the feasibility of using a combined dual-fluoroscopic imaging system (DFIS) and SSM method to investigate in vivo knee kinematics. Three subjects were studied during a treadmill walking. The data were compared with the kinematics obtained using a CT-based modeling technique. Geometric root-mean-square (RMS) errors between the knee models constructed using the SSM and CT-based modeling techniques were 1.16 mm and 1.40 mm for the femur and tibia, respectively. For the kinematics of the knee during the treadmill gait, the SSM model can predict the knee kinematics with RMS errors within 3.3 deg for rotation and within 2.4 mm for translation throughout the stance phase of the gait cycle compared with those obtained using the CT-based knee models. The data indicated that the combined DFIS and SSM technique could be used for quick evaluation of knee joint kinematics.

Contribution of geometric design parameters to knee implant performance: Conflicting impact of conformity on kinematics and contact mechanics

BACKGROUND

Articular geometry of knee implant has a competing impact on kinematics and contact mechanics of total knee arthroplasty (TKA) such that geometry with lower contact pressure will impose more constraints on knee kinematics. The geometric parameters that may cause this competing effect have not been well understood. This study aimed to quantify the underlying relationships between implant geometry as input and its performance metrics as output.

METHODS

Parametric dimensions of a fixed-bearing cruciate retaining implant were randomized to generate a number of perturbed implant geometries. Performance metrics (i.e., maximum contact pressure, anterior-posterior range of motion [A-P ROM] and internal-external range of motion [I-E ROM]) of each randomized design were calculated using finite element analysis. The relative contributions of individual geometric variables to the performance metrics were then determined in terms of sensitivity indices (SI).

RESULTS

The femoral and tibial distal or posterior radii and femoral frontal radius are the key parameters. In the sagittal plane, distal curvature of the femoral and tibial influenced both contact pressure, i.e., SI=0.57; SI=0.65, and A-P ROM, i.e., SI=0.58; SI=0.6, respectively. However, posterior curvature of the femoral and tibial implants had a smaller impact on the contact pressure, i.e., SI=0.31; SI=0.23 and a higher impact on the I-E ROM, i.e., SI=0.72; SI=0.58. It is noteworthy that in the frontal plane, frontal radius of the femoral implant impacted both contact pressure (SI=0.38) and I-E ROM (SI=0.35).

CONCLUSION

Findings of this study highlighted how changes in the conformity of the femoral and tibial can impact the performance metrics.

Posterior stabilized versus cruciate retaining total knee arthroplasty designs: conformity affects the performance reliability of the design over the patient population

Commercially available fixed bearing knee prostheses are mainly divided into two groups: posterior stabilized (PS) versus cruciate retaining (CR). Despite the widespread comparative studies, the debate continues regarding the superiority of one type over the other. This study used a combined finite element (FE) simulation and principal component analysis (PCA) to evaluate "reliability" and "sensitivity" of two PS designs versus two CR designs over a patient population. Four fixed bearing implants were chosen: PFC (DePuy), PFC Sigma (DePuy), NexGen (Zimmer) and Genesis II (Smith & Nephew). Using PCA, a large probabilistic knee joint motion and loading database was generated based on the available experimental data from literature. The probabilistic knee joint data were applied to each implant in a FE simulation to calculate the potential envelopes of kinematics (i.e. anterior-posterior [AP] displacement and internal-external [IE] rotation) and contact mechanics. The performance envelopes were considered as an indicator of performance reliability. For each implant, PCA was used to highlight how much the implant performance was influenced by changes in each input parameter (sensitivity). Results showed that (1) conformity directly affected the reliability of the knee implant over a patient population such that lesser conformity designs (PS or CR), had higher kinematic variability and were more influenced by AP force and IE torque, (2) contact reliability did not differ noticeably among different designs and (3) CR or PS designs affected the relative rank of critical factors that influenced the reliability of each design. Such investigations enlighten the underlying biomechanics of various implant designs and can be utilized to estimate the potential performance of an implant design over a patient population.

Sensitivity analysis of human lower extremity joint moments due to changes in joint kinematics

Despite the widespread applications of human gait analysis, causal interactions between joint kinematics and joint moments have not been well documented. Typical gait studies are often limited to pure multi-body dynamics analysis of a few subjects which do not reveal the relative contributions of joint kinematics to joint moments. This study presented a computational approach to evaluate the sensitivity of joint moments due to variations of joint kinematics. A large data set of probabilistic joint kinematics and associated ground reaction forces were generated based on experimental data from literature. Multi-body dynamics analysis was then used to calculate joint moments with respect to the probabilistic gait cycles. Employing the principal component analysis (PCA), the relative contributions of individual joint kinematics to joint moments were computed in terms of sensitivity indices (SI). Results highlighted high sensitivity of (1) hip abduction moment due to changes in pelvis rotation (SI = 0.38) and hip abduction (SI = 0.4), (2) hip flexion moment due to changes in hip flexion (SI = 0.35) and knee flexion (SI = 0.26), (3) hip rotation moment due to changes in pelvis obliquity (SI = 0.28) and hip rotation (SI = 0.4), (4) knee adduction moment due to changes in pelvis rotation (SI = 0.35), hip abduction (SI = 0.32) and knee flexion (SI = 0.34), (5) knee flexion moment due to changes in pelvis rotation (SI = 0.29), hip flexion (SI = 0.28) and knee flexion (SI = 0.31), and (6) knee rotation moment due to changes in hip abduction (SI = 0.32), hip flexion and knee flexion (SI = 0.31). Highlighting the "cause-and-effect" relationships between joint kinematics and the resultant joint moments provides a fundamental understanding of human gait and can lead to design and optimization of current gait rehabilitation treatments.

Incorporating population-level variability in orthopedic biomechanical analysis: a review

Effectively addressing population-level variability within orthopedic analyses requires robust data sets that span the target population and can be greatly facilitated by statistical methods for incorporating such data into functional biomechanical models. Data sets continue to be disseminated that include not just anatomical information but also key mechanical data including tissue or joint stiffness, gait patterns, and other inputs relevant to analysis of joint function across a range of anatomies and physiologies. Statistical modeling can be used to establish correlations between a variety of structural and functional biometrics rooted in these data and to quantify how these correlations change from health to disease and, finally, to joint reconstruction or other clinical intervention. Principal component analysis provides a basis for effectively and efficiently integrating variability in anatomy, tissue properties, joint kinetics, and kinematics into mechanistic models of joint function. With such models, bioengineers are able to study the effects of variability on biomechanical performance, not just on a patient-specific basis but in a way that may be predictive of a larger patient population. The goal of this paper is to demonstrate the broad use of statistical modeling within orthopedics and to discuss ways to continue to leverage these techniques to improve biomechanical understanding of orthopedic systems across populations.

Deformation of articular cartilage during static loading of a knee joint--experimental and finite element analysis

Novel conical beam CT-scanners offer high resolution imaging of knee structures with i.a. contrast media, even under weight bearing. With this new technology, we aimed to determine cartilage strains and meniscal movement in a human knee at 0, 1, 5, and 30 min of standing and compare them to the subject-specific 3D finite element (FE) model. The FE model of the volunteer׳s knee, based on the geometry obtained from magnetic resonance images, was created to simulate the creep. The effects of collagen fibril network stiffness, nonfibrillar matrix modulus, permeability and fluid flow boundary conditions on the creep response in cartilage were investigated. In the experiment, 80% of the maximum strain in cartilage developed immediately, after which the cartilage continued to deform slowly until the 30 min time point. Cartilage strains and meniscus movement obtained from the FE model matched adequately with the experimentally measured values. Reducing the fibril network stiffness increased the mean strains substantially, while the creep rate was primarily influenced by an increase in the nonfibrillar matrix modulus. Changing the initial permeability and preventing fluid flow through noncontacting surfaces had a negligible effect on cartilage strains. The present results improve understanding of the mechanisms controlling articular cartilage strains and meniscal movements in a knee joint under physiological static loading. Ultimately a validated model could be used as a noninvasive diagnostic tool to locate cartilage areas at risk for degeneration.

Statistical shape and appearance models of bones

When applied to bones, statistical shape models (SSM) and statistical appearance models (SAM) respectively describe the mean shape and mean density distribution of bones within a certain population as well as the main modes of variations of shape and density distribution from their mean values. The availability of this quantitative information regarding the detailed anatomy of bones provides new opportunities for diagnosis, evaluation, and treatment of skeletal diseases. The potential of SSM and SAM has been recently recognized within the bone research community. For example, these models have been applied for studying the effects of bone shape on the etiology of osteoarthritis, improving the accuracy of clinical osteoporotic fracture prediction techniques, design of orthopedic implants, and surgery planning. This paper reviews the main concepts, methods, and applications of SSM and SAM as applied to bone.

Principal component analysis in construction of 3D human knee joint models using a statistical shape model method

The statistical shape model (SSM) method that uses 2D images of the knee joint to predict the three-dimensional (3D) joint surface model has been reported in the literature. In this study, we constructed a SSM database using 152 human computed tomography (CT) knee joint models, including the femur, tibia and patella and analysed the characteristics of each principal component of the SSM. The surface models of two in vivo knees were predicted using the SSM and their 2D bi-plane fluoroscopic images. The predicted models were compared to their CT joint models. The differences between the predicted 3D knee joint surfaces and the CT image-based surfaces were 0.30 ± 0.81 mm, 0.34 ± 0.79 mm and 0.36 ± 0.59 mm for the femur, tibia and patella, respectively (average ± standard deviation). The computational time for each bone of the knee joint was within 30 s using a personal computer. The analysis of this study indicated that the SSM method could be a useful tool to construct 3D surface models of the knee with sub-millimeter accuracy in real time. Thus, it may have a broad application in computer-assisted knee surgeries that require 3D surface models of the knee.

Evaluation of automated statistical shape model based knee kinematics from biplane fluoroscopy

State-of-the-art fluoroscopic knee kinematic analysis methods require the patient-specific bone shapes segmented from CT or MRI. Substituting the patient-specific bone shapes with personalizable models, such as statistical shape models (SSM), could eliminate the CT/MRI acquisitions, and thereby decrease costs and radiation dose (when eliminating CT). SSM based kinematics, however, have not yet been evaluated on clinically relevant joint motion parameters. Therefore, in this work the applicability of SSMs for computing knee kinematics from biplane fluoroscopic sequences was explored. Kinematic precision with an edge based automated bone tracking method using SSMs was evaluated on 6 cadaveric and 10 in-vivo fluoroscopic sequences. The SSMs of the femur and the tibia-fibula were created using 61 training datasets. Kinematic precision was determined for medial-lateral tibial shift, anterior-posterior tibial drawer, joint distraction-contraction, flexion, tibial rotation and adduction. The relationship between kinematic precision and bone shape accuracy was also investigated. The SSM based kinematics resulted in sub-millimeter (0.48-0.81mm) and approximately 1° (0.69-0.99°) median precision on the cadaveric knees compared to bone-marker-based kinematics. The precision on the in-vivo datasets was comparable to that of the cadaveric sequences when evaluated with a semi-automatic reference method. These results are promising, though further work is necessary to reach the accuracy of CT-based kinematics. We also demonstrated that a better shape reconstruction accuracy does not automatically imply a better kinematic precision. This result suggests that the ability of accurately fitting the edges in the fluoroscopic sequences has a larger role in determining the kinematic precision than that of the overall 3D shape accuracy.