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.
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References
Langlois JA, Rutland-Brown W, Wald MM (2006) The epidemiology and impact of traumatic brain injury: a brief overview. J Head Trauma Rehabil 21:375–378
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 7:1677–1688
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:e22063
Laksari K, Wu LC, Kurt M, Kuo C, Camarillo DC (2015) Resonance of human brain under head acceleration. J R Soc Interface 12:20150331
Bayly PV, Clayton EH, Genin GM (2012) Quantitative imaging methods for the development and validation of brain biomechanics models. Annu Rev Biomed Eng 14:369–396
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 14:931–965
Haacke EM, Brown RW, Thompson MR, Venkatesan R (1999) Magnetic resonance imaging: physical principles and sequence design. Wiley-Liss, New York
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 47:3475–3481
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 30:1186–1191
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 17:632–648
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 81–90
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:e51499
Bukhari F, Dailey MN (2013) Automatic radial distortion estimation from a single image. J Math Imaging Vision 45:31–45
Willert CE, Gharib M (1991) Digital particle image velocimetry. Exp Fluids 10:181–193
Kähler CJ, Scharnowski S, Cierpka C (2012) On the resolution limit of digital particle image velocimetry. Exp Fluids 52:1629–1639
Venkateshan SP (2015) Mechanical measurements. Wiley, Chichester
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 39
Spencer AJM (1985) Continuum mechanics. Dover Books, Essex
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 11:20140685
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.
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Gomez, A.D., Knutsen, A.K., Pham, D.L., Bayly, P.V., Prince, J.L. (2020). Quantitative Validation of MRI-Based Motion Estimation for Brain Impact Biomechanics. In: Nash, M., Nielsen, P., Wittek, A., Miller, K., Joldes, G. (eds) Computational Biomechanics for Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-15923-8_5
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DOI: https://doi.org/10.1007/978-3-030-15923-8_5
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