Advertisement

Atlas of Acceleration-Induced Brain Deformation from Measurements In Vivo

  • Arnold D. GomezEmail author
  • Andrew Knutsen
  • Deva Chan
  • Yuan-Chiao Lu
  • Dzung L. Pham
  • Philip Bayly
  • Jerry L. Prince
Conference paper
  • 348 Downloads

Abstract

In traumatic brain injury (TBI), rapid head acceleration resulting from a blow or fall results in detrimental brain tissue deformation. These types of injuries are frequent and can have devastating effects. Understanding the relationship between acceleration and deformation is a challenging and essential step towards designing effective preventive strategies. This study describes patterns of acceleration-induced brain deformation in a group of human volunteers (n = 7). Unlike previous research, the analysis herein involved spatiotemporal analysis of 3D kinematics. In each subject, tagged magnetic resonance imaging (MRI) was acquired during a mild acceleration event, and displacements were extracted using a mechanically regularized motion estimation algorithm. This technique involved registering an anatomical template (a finite-element mesh) to all of the subjects allowing translation of scalar strain projections back to the template to be averaged. Our results show that, in individuals, weighting acceleration measurements by the subject’s brain volume improves the correlation between acceleration magnitude and deformation (R2 of 0.66 in the weighted comparison, compared to 0.34). In individuals, and the group, brain deformation peaked after the peak acceleration, and near the interface between the brain and the skull. However, some deformation was also observed near medial brain structures, which supports the idea that the falx plays a role in transferring mechanical power to the middle of the brain.

Keywords

Magnetic resonance Finite strain Brain Atlas TBI Statistical analysis 

Notes

Acknowledgments

This research was funded by NIH Grant R01-NS055951, supplement PA12-149, and support by the Center for Neuroscience and Regenerative Medicine.

References

  1. 1.
    Shaw NA (2002) The neurophysiology of concussion. Prog Neurobiol 67:281–344CrossRefGoogle Scholar
  2. 2.
    Siedler DG, Chuah MI, Kirkcaldie MTK, Vickers JC, King AE (2014) Diffuse axonal injury in brain trauma: insights from alterations in neurofilaments. Front Cell Neurosci 8:429CrossRefGoogle Scholar
  3. 3.
    Dickie DA, Shenkin SD, Anblagan D, Lee J, Blesa Cabez M, Rodriguez D, Boardman JP, Waldman A, Job DE, Wardlaw JM (2017) Whole brain magnetic resonance image atlases: a systematic review of existing atlases and caveats for use in population imaging. Front Neuroinform 11:1CrossRefGoogle Scholar
  4. 4.
    Ibrahim E-SH (2011) Myocardial tagging by cardiovascular magnetic resonance: evolution of techniques-pulse sequences, analysis algorithms, and applications. J Cardiovasc Magn Reson 13:36CrossRefGoogle Scholar
  5. 5.
    Abney TM, Feng Y, Pless R, Okamoto RJ, Genin GM, Bayly PV (2011) Principal component analysis of dynamic relative displacement fields estimated from MR images. PLoS One 6:e22063CrossRefGoogle Scholar
  6. 6.
    Laksari K, Wu LC, Kurt M, Kuo C, Camarillo DC (2015) Resonance of human brain under head acceleration. J R Soc Interface 12:331CrossRefGoogle Scholar
  7. 7.
    Peng H, Orlichenko A, Dawe RJ, Agam G, Zhang S, Arfanakis K (2009) Development of a human brain diffusion tensor template. NeuroImage 46:967–980CrossRefGoogle Scholar
  8. 8.
    Bai W, Shi W, de Marvao A, Dawes TJW, O’Regan DP, Cook SA, Rueckert D (2015) A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med Image Anal 26:133–145CrossRefGoogle Scholar
  9. 9.
    Subsol G, Roberts N, Doran M, Thirion JP, Whitehouse GH (1997) Automatic analysis of cerebral atrophy. Magn Reson Imaging 15:917–927CrossRefGoogle Scholar
  10. 10.
    Knutsen AK, Magrath E, McEntee JE, Xing F, Prince JL, Bayly PV, Butman JA, Pham DL (2014) Improved measurement of brain deformation during mild head acceleration using a novel tagged MRI sequence. J Biomech 47:3475–3481CrossRefGoogle Scholar
  11. 11.
    Gomez AD, Xing F, Chan D, Pham D, Prince J (2017) Motion estimation with finite-element biomechanical models and tracking constraints from tagged MRI. In: Wittek A, Joldes G, Nielsen PMF, Doyle BJ, Miller K (eds) Computational biomechanics for medicine. Springer, Cham, pp 81–90CrossRefGoogle Scholar
  12. 12.
    Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans AC (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17: 463–468CrossRefGoogle Scholar
  13. 13.
    Roy S, Carass A, Prince JL, Pham DL (2014) Mach Learn Med Imaging 8679:248–255CrossRefGoogle Scholar
  14. 14.
    Kroon D-J (2011) Segmentation of the mandibular canal in cone-beam CT data. Doctoral Dissertation, University of Twente, Enschede, Netherlands. isbn:978-90-365-3280-8Google Scholar
  15. 15.
    Tobon-Gomez C, De Craene M, McLeod K, Tautz L, Shi W, Hennemuth A, Prakosa A, Wang H, Carr-White G, Kapetanakis S, Lutz A, Rasche V, Schaeffter T, Butakoff C, Friman O, Mansi T, Sermesant M, Zhuang X, Ourselin S, Peitgen HO, Pennec X, Razavi R, Rueckert D, Frangi AF, Rhode KS (2013) Benchmarking framework for myocardial tracking and deformation algorithms: an open access database. Med Image Anal 17:632–648CrossRefGoogle Scholar
  16. 16.
    Vadakkumpadan F, Arevalo H, Ceritoglu C, Miller M, Trayanova N (2012) Image-based estimation of ventricular fiber orientations for personalized modeling of cardiac electrophysiology. IEEE Trans Med Imaging 31:1051–1060CrossRefGoogle Scholar
  17. 17.
    Spencer AJM (1985) Continuum mechanics. Dover, New YorkzbMATHGoogle Scholar
  18. 18.
    Zhang L, Yang KH, King AI (2004) A proposed injury threshold for mild traumatic brain injury. J Biomech Eng 126:226–236CrossRefGoogle Scholar
  19. 19.
    Alexander DC, Pierpaoli C, Basser PJ, Gee JC (2001) Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans Med Imaging 20:1131–1139CrossRefGoogle Scholar
  20. 20.
    Kumaresan S, Radhakrishnan S (1996) Importance of partitioning membranes of the brain and the influence of the neck in head injury modelling. Med Biol Eng Comput 34:27–32CrossRefGoogle Scholar
  21. 21.
    Monea AG, Verpoest I, Vander Sloten J, Van der Perre G, Goffin J, Depreitere B (2012) Assessment of relative brain-skull motion in quasistatic circumstances by MR imaging. J Neurotrauma 29:2305–2317CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Arnold D. Gomez
    • 1
    Email author
  • Andrew Knutsen
    • 2
  • Deva Chan
    • 2
  • Yuan-Chiao Lu
    • 2
  • Dzung L. Pham
    • 2
  • Philip Bayly
    • 3
  • Jerry L. Prince
    • 1
  1. 1.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Center for Neuroscience and Regenerative MedicineThe Henry Jackson FoundationBethesdaUSA
  3. 3.Mechanical Engineering DepartmentWashington University in St. LouisSt. LouisUSA

Personalised recommendations