Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain.
Ahmed Alshareef, Andrew K Knutsen, Curtis L Johnson, Aaron Carass, Kshitiz Upadhyay, Philip V Bayly, Dzung L Pham, Jerry L Prince, K T Ramesh
Author Information
Ahmed Alshareef: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, 3400 N Charles St., MD 21218, United States.
Andrew K Knutsen: Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, 6720A Rockledge Dr, Bethesda, MD 20814, United States.
Curtis L Johnson: Department of Biomedical Engineering, University of Delaware, Newark, 210 South College Ave., DE 19716, United States.
Aaron Carass: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, 3400 N Charles St., MD 21218, United States.
Kshitiz Upadhyay: Department of Mechanical Engineering, Johns Hopkins University, 3400 N Charles St., Baltimore, MD 21218, United States.
Philip V Bayly: Mechanical Engineering and Materials Science, Washington University in St. Louis, 1 Brookings Drive, St. Louis, MO 63130, United States.
Dzung L Pham: Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, 6720A Rockledge Dr, Bethesda, MD 20814, United States.
Jerry L Prince: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, 3400 N Charles St., MD 21218, United States.
K T Ramesh: Department of Mechanical Engineering, Johns Hopkins University, 3400 N Charles St., Baltimore, MD 21218, United States.
Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95 percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions.
