title: Development and implementation of a coupled computational muscle force optimization bone shape adaptation modeling method.
abstract: Improved methods to analyze and compare the muscle-based influences that drive bone strength adaptation can aid in the understanding of the wide array of experimental observations about the effectiveness of various mechanical countermeasures to losses in bone strength that result from age, disuse, and reduced gravity environments. The coupling of gradient-based and gradientless numerical optimization routines with finite element methods in this work results in a modeling technique that determines the individual magnitudes of the muscle forces acting in a multisegment musculoskeletal system and predicts the improvement in the stress state uniformity and, therefore, strength, of a targeted bone through simulated local cortical material accretion and resorption. With a performance-based stopping criteria, no experimentally based or system-based parameters, and designed to include the direct and indirect effects of muscles attached to the targeted bone as well as to its neighbors, shape and strength alterations resulting from a wide range of boundary conditions can be consistently quantified. As demonstrated in a representative parametric study, the developed technique effectively provides a clearer foundation for the study of the relationships between muscle forces and the induced changes in bone strength. Its use can lead to the better control of such adaptive phenomena.
authors: Department of Mechanical and Industrial Engineering, Newark College of Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, U.S.A.
authors: C S
authors: CS
authors: Florio
mesh_terms: D001842:Bone and Bones / Q000502:physiology*; D019295:Computational Biology / Q000379:methods; D055595:Mechanical Phenomena; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000502:physiology*; D013314:Stress, Mechanical
keywords: cortical bone adaptation; gradient projection optimization; gradientless shape optimization; muscle force magnitude; parametric studies