Ricci-flow based conformal mapping of the proximal femur to identify exercise loading effects
Narra, N., Abe, S., Dimitrov, V., Nikander, R., Kouhia, R., Sievänen, H., & Hyttinen, J. (2018). Ricci-flow based conformal mapping of the proximal femur to identify exercise loading effects. Scientific Reports, 8, Article 4823. https://doi.org/10.1038/s41598-018-23248-y
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2018Discipline
Gerontologia ja kansanterveysGerontologian tutkimuskeskusHyvinvoinnin tutkimuksen yhteisöGerontology and Public HealthGerontology Research CenterSchool of WellbeingCopyright
© the Authors, 2018. This is an open access article distributed under the terms of the Creative Commons License.
The causal relationship between habitual loading and adaptive response in bone morphology is commonly explored by analysing the spatial distribution of mechanically relevant features. In this study, 3D distribution of features in the proximal femur of 91 female athletes (5 exercise loading groups representing habitual loading) is contrasted with 20 controls. A femur specific Ricci-flow based conformal mapping procedure was developed for establishing correspondence among the periosteal surfaces. The procedure leverages the invariance of the conformal mapping method to isometric shape differences to align surfaces in the 2D parametric domain, to produce dense correspondences across an isotopological set of surfaces. This is implemented through a multi-parametrisation approach to detect surface features and to overcome the issue of inconsistency in the anatomical extent present in the data. Subsequently, the group-wise distribution of two mechanically relevant features was studied – cortical thickness and surface principal strains (simulation results of a sideways fall). Statistical inferences over the surfaces were made by contrasting the athlete groups with the controls through statistical parametric mapping. With the aid of group-wise and composite-group maps, proximal femur regions affected by specific loading groups were identified with a high degree of spatial localisation.
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Except where otherwise noted, this item's license is described as © the Authors, 2018. This is an open access article distributed under the terms of the Creative Commons License.
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