Traumatic brain injury (TBI) is a serious condition affecting multiple groups, from soldiers to athletes engaged in activities involving blunt force to the skull. Long-term neurological and other motor disorders are present in between 14% and 20% of veterans of the Iraq and Afghanistan wars,¹ while TBIs have been widely reported in relation to sports injuries. Although significant research has been carried out into the mechanisms of TBI, this has been limited by cadaver and other experimental samples. Numerical modelling using computational models from MRI data offers potential for flexible yet robust research into TBI and other forms of head impact.

Solutions to the challenge of creating computational models of the human head from scans have typically involved producing computer-aided design (CAD) models, with an idealised geometry used to produce a numerical model, or mesh, suitable for finite element analysis of impact. CAD methods, however, produce issues with complex anatomies and internal structures, leading to a large amount of manual fixing. The majority of models are also designed for specific applications.

For example, a head model has been created from 14 cross-sectional MRI slices and processed using C++ code before being converted into CAD coordinates and spline data,² while models have been produced by automotive companies to handle particular inputs like crash impact.³ These models have the limitation of being ‘fixed’ for their applications and not easily adapted for new inputs. The NRL-Simpleware model produced from MRI data is designed to solve this flexibility issue by creating finite element meshes from pre-segmented image data, rather than working with pre-defined meshes.

The model was developed by acquiring a whole head in vivo MRI scan of a 25-year old male volunteer at the Exeter MR Centre, UK. The T1-weighted scan produced coronal plane image slices with a resolution of 1.03516 mm x 1.03516 mm, with a slice-to-slice separation of 1.4001 mm. The DICOM image series was imported from the MRI scanner to Simpleware ScanIP software for image processing (Fig 1).

Regions of interest (ROIs) within the greyscale data were segmented and labelled, including brain, skull and muscles. A range of image processing tools were used to fully reconstruct the geometry of the head, skull and neck. Parts not available from the scan, such as facial and neck muscles, were imported into the software for conversion to image masks. This method also allows new objects, such as helmets, to be incorporated within the segmented anatomical data (Fig 2).

Multi-part meshes were generated from the segmented data, with options available for automatically converting ROIs into volumetric FE models using a multi-part Extended Volumetric Marching Cubes (EVOMAC) approach, and for multi-part surface decimation technique that allows control over the size of the model.⁴ The particular NRL-Simpleware model used for simulation of TBI was produced as an unstructured, all-tetrahedral mesh with 3.72 M volumetric elements.

The NRL-Simpleware model was set up for specific blunt impact (low-to-mid rate) and blast overpressure (mid-to-high rate) simulations in FE solver Abaqus/Explicit. Blunt impact simulations were validated against experimental data in the literature⁵ and blast overpressure simulations conducted using established thresholds.^{6, 7}

The blunt impact study involved post-processing of the simulation data to quantify injured brain volume as a function of event time based on injury threshold measures in the literature.⁸ Python scripting was used to calculate evolution of these volumes based on a pressure threshold of 173 kPa⁹ and a shear strain threshold of 5%,⁷ with good agreement found with the literature. For blast overpressure loading, a frontal loading with a Friedlander wave profile¹⁰ was used with a peak pressure of approximately 430 kPa (Fig 4). Blunt impact loading simulations using the NRL-Simpleware model that were validated against experimental data showed excellent agreement with benchmark results. Blast overpressure results, designed to reproduce conditions associated with military and explosive weaponry, also demonstrated good results for predicting brain injury as a function of both location and time.The NRL-Simpleware model demonstrates promise for both military and clinical research into TBI and other forms of head impact. The novel ability to easily generate new finite element meshes from the segmented image data means that the model can be tailored for specific applications, including electromagnetic simulation. Moreover, the head model is being used in sports research, for example in American football and for studying the effect of cricket ball impact, and is generating promising insights into the mechanics of head injuries and other scenarios that can be modelled from MRI and other image data.