An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models
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Annals of Biomedical Engineering (Ó 2020) https://doi.org/10.1007/s10439-020-02584-z
Original Article
An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models J. SEBASTIAN GIUDICE ,1 AHMED ALSHAREEF,1 TAOTAO WU,1 CHRISTINA A. GANCAYCO,2 KRISTEN A. REYNIER,1 NICHOLAS J. TUSTISON,3 T. JASON DRUZGAL,3 and MATTHEW B. PANZER 1,4 1 Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, 4040 Lewis and Clark Dr., Charlottesville, VA 229011, USA; 2Advanced Research Computing, University of Virginia, Charlottesville, VA, USA; 3Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA; and 4Brain Injury and Sports Concussion Center, University of Virginia, Charlottesville, VA, USA
(Received 27 April 2020; accepted 22 July 2020) Associate Editor Joel D. Stitzel oversaw the review of this article.
Abstract—Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subjectspecific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subjectspecific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis. Keywords—Magnetic resonance imaging (MRI), Traumatic brain injury (TBI), Personalized medicine, Computational mechanics.
Address correspondence to Matthew B. Panzer, Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, 4040 Lewis and Clark Dr., Charlottesville, VA 229011, USA. Electronic mail: panzer@virginia. edu
INTRODUCTION Traumatic brain injuries (TBI) are a significant and costly public health issue. Recent epidemiological studies have estimated that TBI account for approximately one third of all injury-related deaths in the United States.10 However, despite major scientific pushes to reduce their societal cost, the incidence of TBIrelated injuries and deaths continues to rise.16 Scientists and clinicians have st
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