A Novel Computational Biomechanics Framework to Model Vascular Mechanopropagation in Deep Bone Marrow.

The mechanical stimuli generated by body exercise can be transmitted from cortical bone into the deep bone marrow (mechanopropagation). Excitingly, a mechanosensitive perivascular stem cell niche has recently been identified within the bone marrow for osteogenesis and lymphopoiesis. Although it has been long known that they are maintained by exercise-induced mechanical stimulation, the mechanopropagation from compact bone to deep bone marrow vasculature remains elusive of this fundamental mechanobiology field. No experimental system is available yet to directly understand such exercise-induced mechanopropagation at the bone-vessel interface. To this end, taking advantage of the revolutionary in-vivo 3D deep bone imaging, we devised an integrated computational biomechanics framework to quantitatively evaluate the mechanopropagation capabilities for bone marrow arterioles, arteries, and sinusoids. As a highlight, we smoothly reconstructed the 3D geometries of blood vessels in the presence of vessel wall thickness and intravascular pulse pressure. By implementing the 5-parameter Mooney-Rivlin model that simulates the hyperelastic vessel properties, we performed finite element analysis to thoroughly investigate the mechanical effects of exercise-induced intravascular vibratory stretching on bone marrow vasculature. In addition, we examined the blood pressure and cortical bone bending effects on vascular mechanoproperties. For the first time, we numerically simulated movement-induced mechanopropagation from the hard cortical bone to the soft vasculature in the bone marrow. We conclude that arterioles and arteries are much more efficient in transducing mechanical force than sinusoids due to their stiffness. In the future, our in-silico approach could be extended to other clinical imaging modalities on subject/patient-specific vascular reconstruction and biomechanical analysis, providing large-scale phenotypic data for personalized mechanomedicine. This article is protected by copyright. All rights reserved.