Statistical modelling of the whole human femur incorporating geometric and material properties

Bryan, Rebecca; Mohan, P. Surya; Hopkins, Andrew R.; Galloway, Francis; Taylor, Mark; Nair, Prasanth B.

doi:10.1016/j.medengphy.2009.10.008

Cited by 115 publications

(96 citation statements)

References 35 publications

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“…However, accurate reconstruction of bone morphology from MRI is still an issue as it requires at least 1.5-T scanners, which are not usually available in orthopaedic hospitals, and ad hoc acquisition protocols to increase the signal-to-noise ratio of the cortical bone [15]. On the other hand, the model reconstruction is not straightforward, but it should be noted that some recent studies on automated methodologies for bone surface reconstruction from CT scans [12,[56][57][58], and even from low-dose digital stereoradiography [59], showed that high accuracy and high speed were achievable.…”

Section: Discussionmentioning

confidence: 99%

Determination of the Whiteside line on femur surface models by fitting high-order polynomial functions to cross-section profiles of the intercondylar fossa

Cerveri¹,

Marchente²,

Manzotti³

et al. 2011

Computer Aided Surgery

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Innovative methods for morphological and functional analysis of bones have become a primary objective in the development of planning systems for total knee replacement (TKR). These methods involve the interactive identification of clinical landmarks (reference points, distances, angles, and functional axes of movement) and the determination of the optimal implant size and positioning. Among the functional axes used to estimate the correct alignment of the femoral component, the Whiteside line, namely, the anterior-posterior (AP) direction, is one of the most common. In this paper, we present a computational framework that allows automatic identification of the Whiteside line.The approach is based on geometric analysis of the saddle shape of the intercondylar fossa to extract the principal line in the AP direction. A plane parallel to the frontal plane is moved in the AP direction to obtain the 2D profiles of the intercondylar fossa. Each profile is fitted to a fifth-order polynomial curve and its maximum curvature point computed. The point set collected across all the profiles is then processed to compute the principal direction. The 2D profile-fitting and 3D line-fitting residual errors were analyzed to study the relationship between the intercondylar fossa aspect and the nominal saddle surface. The method was validated using femur specimens from elderly subjects reconstructed from CT scans. The repeatability of the method was evaluated across five different femur surface resolutions.For comparison, three expert orthopaedic surgeons identified, by virtual palpation, the Whiteside line on the same 3D femur models. The repeatability (median angular error) of the Whiteside lines computed by the automated method and by manual virtual palpation, was approximately 1.0 and 3.5 , respectively. The angular skew error between the two axes, measured on the axial plane, averaged approximately 4.00 (SD: 2.64 ) with no statistical difference. The automated method therefore proved more reproducible and was in agreement with the manual method. We conclude that operatorindependent methods, such the one presented in this paper, can be favorably introduced into orthopaedic surgical planning systems.

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Section: Discussionmentioning

confidence: 99%

Determination of the Whiteside line on femur surface models by fitting high-order polynomial functions to cross-section profiles of the intercondylar fossa

Cerveri¹,

Marchente²,

Manzotti³

et al. 2011

Computer Aided Surgery

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“…Early attempts to account for patient variability either manually modelled a small cohort of subjects (Radcliffe and Taylor, 2007;Perillo-Marcone et al, 2004;Lengsfeld et al, 2005) or scaled either the size (Viceconti et al, 2006) and/or the material properties (Viceconti et al, 2006;Wong et al, 2005) of a single femur. An alternative approach to account for patient variability is the use of active shape and active appearance models, which are statistical representations of the morphology and material properties of the bone segment (Taylor et al, 2013;Bryan et al, 2010) or joint . The advantage of active appearance models is that they can be used to generate 100s-1000s of synthetic instances based on a smaller training set of representative bones or joints.…”

Section: Design Of Computer Based Experimentsmentioning

confidence: 99%

Four decades of finite element analysis of orthopaedic devices: Where are we now and what are the opportunities?

Taylor

Prendergast

2015

Journal of Biomechanics

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140

106

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a b s t r a c tFinite element has been used for more than four decades to study and evaluate the mechanical behaviour total joint replacements. In Huiskes seminal paper "Failed innovation in total hip replacement: diagnosis and proposals for a cure", finite element modelling was one of the potential cures to avoid poorly performing designs reaching the market place. The size and sophistication of models has increased significantly since that paper and a range of techniques are available from predicting the initial mechanical environment through to advanced adaptive simulations including bone adaptation, tissue differentiation, damage accumulation and wear. However, are we any closer to FE becoming an effective screening tool for new devices? This review contains a critical analysis of currently available finite element modelling techniques including (i) development of the basic model, the application of appropriate material properties, loading and boundary conditions, (ii) describing the initial mechanical environment of the bone-implant system, (iii) capturing the time dependent behaviour in adaptive simulations, (iv) the design and implementation of computer based experiments and (v) determining suitable performance metrics.The development of the underlying tools and techniques appears to have plateaued and further advances appear to be limited either by a lack of data to populate the models or the need to better understand the fundamentals of the mechanical and biological processes. There has been progress in the design of computer based experiments. Historically, FE has been used in a similar way to in vitro tests, by running only a limited set of analyses, typically of a single bone segment or joint under idealised conditions. The power of finite element is the ability to run multiple simulations and explore the performance of a device under a variety of conditions. There has been increasing usage of design of experiments, probabilistic techniques and more recently population based modelling to account for patient and surgical variability. In order to have effective screening methods, we need to continue to develop these approaches to examine the behaviour and performance of total joint replacements and benchmark them for devices with known clinical performance.Finite element will increasingly be used in the design, development and pre-clinical testing of total joint replacements. However, simulations must include holistic, closely corroborated, multi-domain analyses which account for real world variability.

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“…Beyond shape, population models can include internal femur architecture [17,20], correlations to fracture and disease risk [14,21], mechanical behaviour [22] and anthropological and anthropometric data [5]. These models allow the prediction of femoral structure from correlated data, or vice versa.…”

Section: Population Models and The Musculoskeletal Atlas Projectmentioning

confidence: 99%

“…Although useful for the classification of normal or pathological gait, the weights obtained from a principal component analysis can also be used to synthetically generate feasible gait data across a larger population than what was originally sampled [27]. This can be useful for assessing the variability in biomechanical parameters across a population and has application to predict femur fracture risk [22] or assess joint replacement designs [27]. Population models might also be used to predict complex form -function relationships of the musculoskeletal system.…”

Section: Population Models and The Musculoskeletal Atlas Projectmentioning

confidence: 99%

Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling

Fernandez

Heidlauf

et al. 2016

Interface Focus.

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This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models.

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Statistical modelling of the whole human femur incorporating geometric and material properties

Cited by 115 publications

References 35 publications

Determination of the Whiteside line on femur surface models by fitting high-order polynomial functions to cross-section profiles of the intercondylar fossa

Determination of the Whiteside line on femur surface models by fitting high-order polynomial functions to cross-section profiles of the intercondylar fossa

Four decades of finite element analysis of orthopaedic devices: Where are we now and what are the opportunities?

Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling

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