title: Patient-Specific Biomechanical Modeling of Bone Strength Using Statistically-Derived Fabric Tensors.
abstract: Low trauma fractures are amongst the most frequently encountered problems in the clinical assessment and treatment of bones, with dramatic health consequences for individuals and high financial costs for health systems. Consequently, significant research efforts have been dedicated to the development of accurate computational models of bone biomechanics and strength. However, the estimation of the fabric tensors, which describe the microarchitecture of the bone, has proven to be challenging using in vivo imaging. On the other hand, existing research has shown that isotropic models do not produce accurate predictions of stress states within the bone, as the material properties of the trabecular bone are anisotropic. In this paper, we present the first biomechanical study that uses statistically-derived fabric tensors for the estimation of bone strength in order to obtain patient-specific results. We integrate a statistical predictive model of trabecular bone microarchitecture previously constructed from a sample of ex vivo micro-CT datasets within a biomechanical simulation workflow. We assess the accuracy and flexibility of the statistical approach by estimating fracture load for two different databases and bone sites, i.e., for the femur and the T12 vertebra. The results obtained demonstrate good agreement between the statistically-driven and micro-CT-based estimates, with concordance coefficients of 98.6 and 95.5% for the femur and vertebra datasets, respectively.
authors: Center for Computational Imaging & Simulation Technologies in Biomedicine, University of Sheffield, Sheffield, United Kingdom. karim.lekadir@upf.edu.|Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain. karim.lekadir@upf.edu.
authors: Karim
authors: K
authors: Lekadir
authors: Center for Computational Imaging & Simulation Technologies in Biomedicine, University of Sheffield, Sheffield, United Kingdom.|Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom.
authors: Christopher
authors: C
authors: Noble
authors: Orthopaedic Biomechanics, Biomedical Engineering Department, Eindhoven University, Eindhoven, Netherlands.
authors: Javad
authors: J
authors: Hazrati-Marangalou
authors: Center for Computational Imaging & Simulation Technologies in Biomedicine, University of Sheffield, Sheffield, United Kingdom.|Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
authors: Corné
authors: C
authors: Hoogendoorn
authors: Orthopaedic Biomechanics, Biomedical Engineering Department, Eindhoven University, Eindhoven, Netherlands.
authors: Bert
authors: B
authors: van Rietbergen
authors: Center for Computational Imaging & Simulation Technologies in Biomedicine, University of Sheffield, Sheffield, United Kingdom.|Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom.
authors: Zeike A
authors: ZA
authors: Taylor
authors: Center for Computational Imaging & Simulation Technologies in Biomedicine, University of Sheffield, Sheffield, United Kingdom.|Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom.
authors: Alejandro F
authors: AF
authors: Frangi
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001696:Biomechanical Phenomena; D005260:Female; D005269:Femur / Q000000981:diagnostic imaging / Q000378:metabolism / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D057285:Precision Medicine / Q000379:methods*; D013131:Spine / Q000000981:diagnostic imaging / Q000378:metabolism / Q000503:physiopathology; D055114:X-Ray Microtomography
keywords: Bone fracture; Bone microarchitecture; Finite element methods; Fracture load estimation; Statistical predictive models