Quantitative Validation of MRI-Based Motion Estimation for Brain Impact Biomechanics
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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.
KeywordsFinite strain Tagged MRI Brain deformation Impact
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.
- 7.Haacke EM, Brown RW, Thompson MR, Venkatesan R (1999) Magnetic resonance imaging: physical principles and sequence design. Wiley-Liss, New YorkGoogle Scholar
- 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.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
- 16.Venkateshan SP (2015) Mechanical measurements. Wiley, ChichesterGoogle Scholar
- 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