Statistical modeling to characterize relationships between knee anatomy and kinematics

Smoger, Lowell M.; Fitzpatrick, Clare K.; Clary, Chadd W.; Cyr, Adam J.; Maletsky, Lorin P.; Rullkoetter, Paul J.; Laz, Peter J.

doi:10.1002/jor.22948

Cited by 51 publications

(67 citation statements)

References 51 publications

(123 reference statements)

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“…While internal/external rotation of the knee may be measured in vivo with conventional motion capture methods and incorporated into the model knee 10 , capturing activity-specific differences in knee translations is more challenging without techniques such as stereo radiography. However, it may be possible to use a principal component approach to leverage relationships between specimen-specific TF/PF kinematics and anatomy 32 . Given subject-specific geometries of femur and tibia, such a technique may allow inference of TF translations in anterior/posterior and superior/inferior directions for a given knee flexion task.…”

Section: Discussionmentioning

confidence: 99%

Dependence of Muscle Moment Arms on In Vivo Three-Dimensional Kinematics of the Knee

2016

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Quantification of muscle moment arms is important for clinical evaluation of muscle pathology and treatment, and for estimating muscle and joint forces in musculoskeletal models. Moment arms estimated with musculoskeletal models often assume a default motion of the knee derived from measurements of passive cadaveric flexion. However, knee kinematics are unique to each person and activity. The objective of this study was to estimate moment arms of the knee muscles with in vivo subject- and activity-specific kinematics from seven healthy subjects performing seated knee extension and single-leg lunge to show changes between subjects and activities. 3D knee motion was measured with a high-speed stereo-radiography system. Moment arms of ten muscles were estimated in OpenSim by replacing the default knee motion with in vivo measurements. Estimated inter-subject moment arm variability was similar to previously reported in vitro measurements. RMS deviations up to 9.0 mm (35.2% of peak value) were observed between moment arms estimated with subject-specific knee extension and passive cadaveric motion. The degrees of freedom that most impacted inter-activity differences were superior/inferior and anterior/posterior translations. Musculoskeletal simulations used to estimate in vivo muscle forces and joint loads may provide significantly different results when subject- and activity-specific kinematics are implemented.

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Section: Discussionmentioning

confidence: 99%

Dependence of Muscle Moment Arms on In Vivo Three-Dimensional Kinematics of the Knee

2016

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“…Researchers have applied SSM to a wide range of applications. Some studies focused on relating geometry to diseases, for instance the risk of developing osteoarthritis (Bredbenner et al, 2010) or geometry to kinematics (Smoger et al, 2015), creating thus a statistical shape–function model. Others considered the study of implant behavior or even optimization of implant designs across the population (see Figure 3) (Belenguer et al, 2006; Kozic et al, 2010).…”

Section: Uncertainty and Variability In Computational Models: Identifmentioning

confidence: 99%

“…Using this mesh-morphing approach, Fitzpatrick et al (2011a,b) analyzed the changes in knee kinematics and contact mechanics due to articular alignment. These studies were followed by Rao et al (2013) and Smoger et al (2015) who evaluated and related knee mechanics and kinematics to both shape and alignment variability (Figure 5). Malandrino et al (2015) used a similar approach to morph a generic mesh of lumbar vertebrae to subject-specific geometries and assess the interaction among biomechanical and biophysical processes and intervertebral disc condition.…”

Section: Uncertainty and Variability In Computational Models: Identifmentioning

confidence: 99%

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

Mangado

Piella

Noailly

et al. 2016

Front. Bioeng. Biotechnol.

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

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“…Statistical shape models (SSMs) have become an accepted tool to describe anatomical variation and have characterized the morphology of individual bones and whole joints (Bryan et al, 2010; Yang et al, 2008; Rao et al, 2013; Smoger et al, 2015). SSMs offer an alternative means to obtain the 3D subject-specific bone geometry from stereo-radiographic images, thus avoiding the need for segmentation.…”

Section: Introductionmentioning

confidence: 99%

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

Smoger

Shelburne

Cyr

et al. 2017

Journal of Biomechanics

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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±0.07 mm (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.

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Statistical modeling to characterize relationships between knee anatomy and kinematics

Cited by 51 publications

References 51 publications

Dependence of Muscle Moment Arms on In Vivo Three-Dimensional Kinematics of the Knee

Dependence of Muscle Moment Arms on In Vivo Three-Dimensional Kinematics of the Knee

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

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

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