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dc.contributor.authorNarra, Nathaniel
dc.contributor.authorAbe, Shinya
dc.contributor.authorDimitrov, Vassil
dc.contributor.authorNikander, Riku
dc.contributor.authorKouhia, Reijo
dc.contributor.authorSievänen, Harri
dc.contributor.authorHyttinen, Jari
dc.date.accessioned2018-03-26T08:56:57Z
dc.date.available2018-03-26T08:56:57Z
dc.date.issued2018
dc.identifier.citationNarra, 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. <i>Scientific Reports</i>, <i>8</i>, Article 4823. <a href="https://doi.org/10.1038/s41598-018-23248-y" target="_blank">https://doi.org/10.1038/s41598-018-23248-y</a>
dc.identifier.otherCONVID_27964759
dc.identifier.otherTUTKAID_77149
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57418
dc.description.abstractThe 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.
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.ispartofseriesScientific Reports
dc.subject.otherbiomekaniikka
dc.subject.otherluu
dc.subject.otherreisiluu
dc.subject.otherfyysinen rasitus
dc.subject.otherdifferentiaaligeometria
dc.subject.otherbiomechanics
dc.subject.otherbone
dc.subject.otherfemur
dc.subject.otherphysical stress
dc.subject.otherdifferential geometry
dc.titleRicci-flow based conformal mapping of the proximal femur to identify exercise loading effects
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201803221816
dc.contributor.laitosLiikuntatieteellinen tiedekuntafi
dc.contributor.laitosFaculty of Sport and Health Sciencesen
dc.contributor.oppiaineGerontologia ja kansanterveysfi
dc.contributor.oppiaineGerontology and Public Healthen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-03-22T16:15:08Z
dc.description.reviewstatuspeerReviewed
dc.relation.issn2045-2322
dc.relation.numberinseries0
dc.relation.volume8
dc.type.versionpublishedVersion
dc.rights.copyright© the Authors, 2018. This is an open access article distributed under the terms of the Creative Commons License.
dc.rights.accesslevelopenAccessfi
dc.subject.ysobiomekaniikka
dc.subject.ysoluu
dc.subject.ysoreisiluu
dc.subject.ysofyysinen rasitus
dc.subject.ysodifferentiaaligeometria
jyx.subject.urihttp://www.yso.fi/onto/yso/p20292
jyx.subject.urihttp://www.yso.fi/onto/yso/p24244
jyx.subject.urihttp://www.yso.fi/onto/yso/p13756
jyx.subject.urihttp://www.yso.fi/onto/yso/p23889
jyx.subject.urihttp://www.yso.fi/onto/yso/p16682
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1038/s41598-018-23248-y


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© the Authors, 2018. This is an open access article distributed under the terms of the Creative Commons License.
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.