Abstract

The overall objectives of this study are to develop and enhance technology to obtain in vivo data on acceleration-induced brain deformation, tissue mechanical properties, and brain-skull interaction. Computer models of head-brain biomechanics offer enormous potential for developing strategies to prevent or reduce traumatic brain injury (TBI), and for understanding the neurological sequelae of different mechanical insults. However existing models remain controversial because computer predictions have yet to be validated in vivo. The primary barrier to validation is the difficulty of direct measurement of brain deformation. The proposed project responds to PA-04-006 Neurotechnology Research, Development, and Enhancement by developing technology to improve our understanding of a major neurological health problem: TBI. The project is need-driven: the technological advances will provide much-needed data on brain biomechanics for the development and validation of reliable computer models of brain injury.
Two Specific Aims are proposed:
Aim 1 : Obtain quantitative measurements of tissue deformation (strain) induced by head acceleration, using MR tagging in human and animal studies. Tagged MR images will be obtained during controlled accelerations of the in vivo human and porcine brains. Brain deformation (strain), as well as relative motion between the brain and skull, will be measured by tracking intersections of tag lines. Studies will also be performed in the in situ and isolated porcine brain for comparison to in vivo measurements.
Aim 2 : Obtain improved estimates of brain tissue mechanical parameters from MR elastography (MRE), combined with diffusion tensor imaging (DTI) of tissue anisotropy. The dynamic stiffness of brain tissue can be estimated non-invasively, in vivo, from MR measurement of elastic wave propagation. Mechanical parameters may be anisotropic. Anisotropy data obtained via DTI will be combined with MR elastography data to enable robust estimation of direction-dependent properties. Technology transfer: Experiments and data processing procedures will be designed to maximize accessibility and usefulness to the finite element (FE) modeling research community. The project involves a multi- disciplinary research team from Washington University and the University of Pennsylvania with expertise in solid mechanics, biomechanics, MR imaging, MR instrumentation, and FE modeling of TBI.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS055951-04
Application #
7878611
Study Section
Special Emphasis Panel (ZRG1-BDCN-K (10))
Program Officer
Ludwig, Kip A
Project Start
2007-07-01
Project End
2012-06-30
Budget Start
2010-07-01
Budget End
2012-06-30
Support Year
4
Fiscal Year
2010
Total Cost
$332,238
Indirect Cost

  Institution

Name
Washington University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130

  Publications

Wang, Xiaokai; Stone, Maureen L; Prince, Jerry L et al. (2018) A Novel Filtering Approach for 3D Harmonic Phase Analysis of Tagged MRI. Proc SPIE Int Soc Opt Eng 10574:
Gomez, Arnold David; Knutsen, Andrew; Xing, Fangxu et al. (2018) 3D Measurements of Acceleration-Induced Brain Deformation via Harmonic Phase Analysis and Finite-Element Models. IEEE Trans Biomed Eng :
Ganpule, S; Daphalapurkar, N P; Cetingul, M Pirtini et al. (2018) Effect of bulk modulus on deformation of the brain under rotational accelerations. Shock Waves 28:127-139
Gomez, Arnold D; Elsaid, Nahla; Stone, Maureen L et al. (2018) Laplace-based modeling of fiber orientation in the tongue. Biomech Model Mechanobiol 17:1119-1130
Guertler, Charlotte A; Okamoto, Ruth J; Schmidt, John L et al. (2018) Mechanical properties of porcine brain tissue in vivo and ex vivo estimated by MR elastography. J Biomech 69:10-18
Bayly, P V; Garbow, J R (2018) Pre-clinical MR elastography: Principles, techniques, and applications. J Magn Reson 291:73-83
Badachhape, Andrew A; Okamoto, Ruth J; Johnson, Curtis L et al. (2018) Relationships between scalp, brain, and skull motion estimated using magnetic resonance elastography. J Biomech 73:40-49
Schmidt, J L; Tweten, D J; Badachhape, A A et al. (2018) Measurement of anisotropic mechanical properties in porcine brain white matter ex vivo using magnetic resonance elastography. J Mech Behav Biomed Mater 79:30-37
Chan, Deva D; Knutsen, Andrew K; Lu, Yuan-Chiao et al. (2018) Statistical Characterization of Human Brain Deformation During Mild Angular Acceleration Measured In Vivo by Tagged Magnetic Resonance Imaging. J Biomech Eng 140:
Tolpadi, Aniket A; Stone, Maureen L; Carass, Aaron et al. (2018) Inverse Biomechanical Modeling of the Tongue via Machine Learning and Synthetic Training Data. Proc SPIE Int Soc Opt Eng 10576:

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