Development of a statistical shape-function model of the implanted knee for real-time prediction of joint mechanics

Gibbons, Kalin; Clary, Chadd W.; Rullkoetter, Paul J.; Fitzpatrick, Clare K.

doi:10.1016/j.jbiomech.2019.03.010

Cited by 10 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some examples in the bio‐medical field include, organ segmentation 47 , 48 , 49 and extraction of morphology from medical images 50 , 51 , 52 Only a limited number of studies have tried to combine statistical shape modeling with computational models, and are mainly found for orthopedic applications. For example, statistical shape models have been used for real‐time prediction of knee joint mechanics, 53 predicting femur bone strength, 54 or creating parametric models to model cervical spine loading. 55 The study by Bredbenner et al 55 introduced uncertainty in geometry considering cervical spines that were

1

from the mean shape, and showed that shape variation influenced the computed axial, flexion‐extension, and lateral displacements.…”

Section: Discussionmentioning

confidence: 99%

The impact of shape uncertainty on aortic‐valve pressure‐drop computations

Hoeijmakers

Huberts

Rutten

et al. 2021

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Patient‐specific image‐based computational fluid dynamics (CFD) is widely adopted in the cardiovascular research community to study hemodynamics, and will become increasingly important for personalized medicine. However, segmentation of the flow domain is not exact and geometric uncertainty can be expected which propagates through the computational model, leading to uncertainty in model output. Seventy‐four aortic‐valves were segmented from computed tomography images at peak systole. Statistical shape modeling was used to obtain an approximate parameterization of the original segmentations. This parameterization was used to train a meta‐model that related the first five shape mode coefficients and flowrate to the CFD‐computed transvalvular pressure‐drop. Consequently, shape uncertainty in the order of 0.5 and 1.0 mm was emulated by introducing uncertainty in the shape mode coefficients. A global variance‐based sensitivity analysis was performed to quantify output uncertainty and to determine relative importance of the shape modes. The first shape mode captured the opening/closing behavior of the valve and uncertainty in this mode coefficient accounted for more than 90% of the output variance. However, sensitivity to shape uncertainty is patient‐specific, and the relative importance of the fourth shape mode coefficient tended to increase with increases in valvular area. These results show that geometric uncertainty in the order of image voxel size may lead to substantial uncertainty in CFD‐computed transvalvular pressure‐drops. Moreover, this illustrates that it is essential to assess the impact of geometric uncertainty on model output, and that this should be thoroughly quantified for applications that wish to use image‐based CFD models.

show abstract

1

from the mean shape, and showed that shape variation influenced the computed axial, flexion‐extension, and lateral displacements.…”

Section: Discussionmentioning

confidence: 99%

The impact of shape uncertainty on aortic‐valve pressure‐drop computations

Hoeijmakers

Huberts

Rutten

et al. 2021

Numer Methods Biomed Eng

View full text Add to dashboard Cite

show abstract

“…These studies used SSM and FEA to: investigate the relationship between patellofemoral shape and function 48 ; force-displacement behavior of proximal femurs 49 ; to investigate cervical spine loading, 50 or for real-time prediction of joint-mechanics. 51 Literature that combines SSM with FEA/CFD simulations is more scarce in the field of cardiovascular biomechanics. To our knowledge, only Khalafvand and colleagues used a SSM in combination with CFD simulations.…”

Section: Discussionmentioning

confidence: 99%

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Hoeijmakers

Waechter-Stehle

Weese

et al. 2020

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Background: Advances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired. Aim: The aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve. Methods: Principal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a metamodel. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop. Results: Even though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm 2. The proposed methodology was least accurate for aortic valve areas above 150 mm 2. Further reduction to a meta-model introduces an additional 3% error.

show abstract

“…A few studies have extended this technique to relate joint shapes with their function, resulting in statistical shape and function models. Examples include the relationship of knee anatomy with tibiofemoral and patellofemoral kinematics [28], total knee arthroplasty geometry and alignment with joint mechanics [29], and knee anatomy with knee laxity [30]. This paper aims to adapt shape-function statistical techniques to replace traditional calibration methods in the formulation of hip capsule models.…”

Section: Introductionmentioning

confidence: 99%

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

Anantha-Krishnan,

Myers,

Fitzpatrick

et al. 2023

Bioengineering

Self Cite

View full text Add to dashboard Cite

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

show abstract

Development of a statistical shape-function model of the implanted knee for real-time prediction of joint mechanics

Cited by 10 publications

References 56 publications

The impact of shape uncertainty on aortic‐valve pressure‐drop computations

The impact of shape uncertainty on aortic‐valve pressure‐drop computations

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

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

Contact Info

Product

Resources

About