Citation

Sarkar S, Majumder S, Roychowdhury A. Crit. Rev. Biomed. Eng. 2018; 46(4): 289-310.

Affiliation

Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur, India.

Copyright

(Copyright © 2018, Begell House)

DOI

10.1615/CritRevBiomedEng.2018027249

PMID

30806247

Abstract

Diffuse axonal injury (DAI) is the most common pathological feature of brain injury which accounts for half of the traumatic lesions in the United States. Although direct shear strain measures indicate DAI, it is localized and varies greatly in the brain. It has limitations when correlated with the possibility and severity of DAI in different brain regions along different planes for variable factors. Rather, a statistical strain measure such as peak shear strain possibility (PSSP) is proposed as a head injury criteria. In this study, computer tomography (CT) was used to derive a finite element model of the skull-brain complex including viscoelastic behavior for brain material. It was simulated under blunt impact for variable factors such as five impact directions, four impact velocities, and four head sizes. Nodal shear strain measures were obtained for seven brain regions along three planes. Proposed PSSPs for different shear strain level (10%90%) were calculated. Considering 30% shear strain as the critical level, PSSPs were 0.49 for corpus callosum and 0.71 for the brain stem along the sagittal plane, and 0.63 for frontal impact. Among eighty simulation cases (240 strain measures), the corpus callosum and brain stem have the highest possibility (30%) of DAI. Frontal impact is the most dangerous direction, followed by side-back and back impact. For all impact directions, the highest PSSP along the sagittal plane indicates the predominance of rotational motion of the brain for causing DAI. Head size variation has the least effect on DAI possibility. At higher impact velocities, DAI possibility increases nonlinearly. Hence, these proposed criteria are expected to predict DAI under variable factors.

Language: en