Patient-specific Hip Fracture Strength Assessment with Microstructural MR Imaging-based Finite Element Modeling

Purpose: To describe a nonlinear finite element analysis method by using magnetic resonance (MR) images for the assessment of the mechanical competence of the hip and to demonstrate the reproducibility of the tool. Materials and Methods: This prospective study received institutional review board approval and fully complied with HIPAA regulations for patient data. Written informed consent was obtained from all subjects. A nonlinear finite element analysis method was developed to estimate mechanical parameters that relate to hip fracture resistance by using MR images. Twenty-three women (mean age +/- standard deviation, 61.7 years +/- 13.8) were recruited from a single osteoporosis center. To thoroughly assess the reproducibility of the finite element method, three separate analyses were performed: a test-retest reproducibility analysis, where each of the first 13 subjects underwent MR imaging on three separate occasions to determine longitudinal variability, and an intra-and interoperator reproducibility analysis, where a single examination was performed in each of the next 10 subjects and four operators independently performed the analysis two times in each of the subjects. Reproducibility of parameters that reflect fracture resistance was assessed by using the intraclass correlation coefficient and the coefficient of variation. Results: For test-retest reproducibility analysis and inter-and intraoperator analyses for proximal femur stiffness, yield strain, yield load, ultimate strain, ultimate load, resilience, and toughness in both stance and sideways-fall loading configurations each had an individual median coefficient of variation of less than 10%. Additionally, all measures had an intraclass correlation coefficient higher than 0.99. Conclusion: This experiment demonstrates that the finite element analysis model can consistently and reliably provide fracture risk information on correctly segmented bone images. (C) RSNA, 2016