Statistical estimation of femur micro-architecture using optimal shape and density predictors

Karim Lekadir, Javad Hazrati Marangalou, Corné Hoogendoorn, Zeike A. Taylor, Bert van Rietbergen, Alejandro F. Frangi

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)598-603
JournalJournal of biomechanics
Volume48
Issue number4
DOIs
Publication statusPublished - 2015

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Bone Density
Femur
Bone
X-Rays
Anatomic Models
Anisotropy
Minerals
Least-Squares Analysis
Image resolution
Costs and Cost Analysis
Bone and Bones
Tensors
Imaging techniques
Radiation
X rays
Costs
Radiation Exposure

Keywords

  • METIS-320115
  • IR-102785

Cite this

Lekadir, Karim ; Hazrati Marangalou, Javad ; Hoogendoorn, Corné ; Taylor, Zeike A. ; van Rietbergen, Bert ; Frangi, Alejandro F. / Statistical estimation of femur micro-architecture using optimal shape and density predictors. In: Journal of biomechanics. 2015 ; Vol. 48, No. 4. pp. 598-603.
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Statistical estimation of femur micro-architecture using optimal shape and density predictors. / Lekadir, Karim; Hazrati Marangalou, Javad; Hoogendoorn, Corné; Taylor, Zeike A.; van Rietbergen, Bert; Frangi, Alejandro F.

In: Journal of biomechanics, Vol. 48, No. 4, 2015, p. 598-603.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Statistical estimation of femur micro-architecture using optimal shape and density predictors

AU - Lekadir, Karim

AU - Hazrati Marangalou, Javad

AU - Hoogendoorn, Corné

AU - Taylor, Zeike A.

AU - van Rietbergen, Bert

AU - Frangi, Alejandro F.

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AB - 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.

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