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Quantitative Validation of MRI-Based Motion Estimation for Brain Impact Biomechanics

  • Arnold D. GomezEmail author
  • Andrew K. Knutsen
  • Dzung L. Pham
  • Philip V. Bayly
  • Jerry L. Prince
Conference paper

Abstract

Head impact can cause traumatic brain injury (TBI) through axonal overstretch or subsequent inflammation and understanding the biomechanics of the impact event is useful for TBI prevention research. Tagged magnetic resonance imaging (MRI) acquired during a mild-acceleration impact has enabled measurement and visualization of brain deformation in vivo. However, measurements using MRI are subject to error, and having independent validation while imaging in vivo is very difficult. Thus, characterizing the accuracy of these measurements needs to be done in a separate experiment using a phantom where a gold standard is available. This study describes a method for error quantification using a calibration phantom compatible with MRI and high-speed video (the gold standard). During linear acceleration, the maximum shear strain (MSS) in the phantom ranged from 0 to 12%, which is similar to in vivo brain deformation at a similar acceleration. The mean displacement error against video was 0.3 ± 0.3 mm, and the MSS error was 1.4 ± 0.3%. To match resolutions, video data was filtered temporally using an averaging filter. Compared to the unfiltered results, resolution matching improved the agreement between MRI and video results by 15%. In conclusion, tagged MRI analysis compares well to video data provided that resolutions are matched—a finding that is also applicable when using MRI to validate simulations.

Keywords

Finite strain Tagged MRI Brain deformation Impact 

Notes

Acknowledgments

This research was funded by NIH Grants R01-NS055951 and R01-DC014717, and support by the U.S. Department of Defense in the Center for Neuroscience and Regenerative Medicine.

References

  1. 1.
    Langlois JA, Rutland-Brown W, Wald MM (2006) The epidemiology and impact of traumatic brain injury: a brief overview. J Head Trauma Rehabil 21:375–378CrossRefGoogle Scholar
  2. 2.
    Feng Y, Abney TM, Okamoto RJ, Pless RB, Genin GM, Bayly PV (2010) Relative brain displacement and deformation during constrained mild frontal head impact. J R Soc Interface 7:1677–1688CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Laksari K, Wu LC, Kurt M, Kuo C, Camarillo DC (2015) Resonance of human brain under head acceleration. J R Soc Interface 12:20150331CrossRefGoogle Scholar
  5. 5.
    Bayly PV, Clayton EH, Genin GM (2012) Quantitative imaging methods for the development and validation of brain biomechanics models. Annu Rev Biomed Eng 14:369–396CrossRefGoogle Scholar
  6. 6.
    Goriely A, Geers MGD, Holzapfel GA, Jayamohan J, Jérusalem A, Sivaloganathan S, Squier W, van Dommelen JAW, Waters S, Kuhl E (2015) Mechanics of the brain: perspectives, challenges, and opportunities. Biomech Model Mechanobiol 14:931–965CrossRefGoogle Scholar
  7. 7.
    Haacke EM, Brown RW, Thompson MR, Venkatesan R (1999) Magnetic resonance imaging: physical principles and sequence design. Wiley-Liss, New YorkGoogle Scholar
  8. 8.
    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
  9. 9.
    Fortune S, Jansen MA, Anderson T, Gray GA, Schneider JE, Hoskins PR, Marshall I (2012) Development and characterization of rodent cardiac phantoms: comparison with in vivo cardiac imaging. Magn Reson Imaging 30:1186–1191CrossRefGoogle Scholar
  10. 10.
    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
  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 Nature, Cham, pp 81–90CrossRefGoogle Scholar
  12. 12.
    Palchesko RN, Zhang L, Sun Y, Feinberg AW (2012) Development of polydimethylsiloxane substrates with tunable elastic modulus to study cell mechanobiology in muscle and nerve. PLoS One 7:e51499CrossRefGoogle Scholar
  13. 13.
    Bukhari F, Dailey MN (2013) Automatic radial distortion estimation from a single image. J Math Imaging Vision 45:31–45MathSciNetCrossRefGoogle Scholar
  14. 14.
    Willert CE, Gharib M (1991) Digital particle image velocimetry. Exp Fluids 10:181–193CrossRefGoogle Scholar
  15. 15.
    Kähler CJ, Scharnowski S, Cierpka C (2012) On the resolution limit of digital particle image velocimetry. Exp Fluids 52:1629–1639CrossRefGoogle Scholar
  16. 16.
    Venkateshan SP (2015) Mechanical measurements. Wiley, ChichesterGoogle Scholar
  17. 17.
    Wang X, Stone M, Prince JL, Gomez AD (2018) A novel filtering approach for 3D harmonic phase analysis of tagged MRI. In: Angelini ED, Landman BA (eds) Medical imaging 2018: image processing. SPIE, p 39Google Scholar
  18. 18.
    Spencer AJM (1985) Continuum mechanics. Dover Books, EssexzbMATHGoogle Scholar
  19. 19.
    Boyle JJ, Kume M, Wyczalkowski MA, Taber LA, Pless RB, Xia Y, Genin GM, Thomopoulos S (2014) Simple and accurate methods for quantifying deformation, disruption, and development in biological tissues. J R Soc Interface 11:20140685CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Arnold D. Gomez
    • 1
    Email author
  • Andrew K. Knutsen
    • 2
  • Dzung L. Pham
    • 2
  • Philip V. Bayly
    • 3
  • Jerry L. Prince
    • 1
  1. 1.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Center for Neuroscience and Regenerative Medicine, The Henry Jackson FoundationBethesdaUSA
  3. 3.Mechanical Engineering DepartmentWashington University in St. LouisSt. LouisUSA

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