title: Statistical estimation of femur micro-architecture using optimal shape and density predictors.
abstract: The personalization of trabecular micro-architecture has been recently shown to be important in patient-specific biomechanical models of the femur. However, high-resolution in vivo imaging of bone micro-architecture using existing modalities is still infeasible in practice due to the associated acquisition times, costs, and X-ray radiation exposure. In this study, we describe a statistical approach for the prediction of the femur micro-architecture based on the more easily extracted subject-specific bone shape and mineral density information. To this end, a training sample of ex vivo micro-CT images is used to learn the existing statistical relationships within the low and high resolution image data. More specifically, optimal bone shape and mineral density features are selected based on their predictive power and used within a partial least square regression model to estimate the unknown trabecular micro-architecture within the anatomical models of new subjects. The experimental results demonstrate the accuracy of the proposed approach, with average errors of 0.07 for both the degree of anisotropy and tensor norms.
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain. Electronic address: karim.lekadir@upf.edu.
authors.forename: Karim
authors.initials: K
authors.lastname: Lekadir
authors.affiliation: Orthopaedic Biomechanics, Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: Javad
authors.initials: J
authors.lastname: Hazrati-Marangalou
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Corné
authors.initials: C
authors.lastname: Hoogendoorn
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK.
authors.forename: Zeike
authors.initials: Z
authors.lastname: Taylor
authors.affiliation: Orthopaedic Biomechanics, Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: Bert
authors.initials: B
authors.lastname: van Rietbergen
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK.
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Frangi
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016880:Anisotropy; D001696:Biomechanical Phenomena / Q000502:physiology; D015519:Bone Density / Q000502:physiology*; D005260:Female; D005269:Femur / Q000033:anatomy & histology* / Q000000981:diagnostic imaging / Q000502:physiology*; D020342:Finite Element Analysis; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D015233:Models, Statistical; D012044:Regression Analysis; D055114:X-Ray Microtomography
keywords: Bone shape and density; Fabric tensors; Femur micro-architecture; Micro-CT; Statistical predictive modeling; Trabecular anisotropy