The Application of High-Performance Computing to Create and Analyze Simulations of Human Injury
Creators: name: McIver, Kevin G
Publisher: name: Purdue University Graduate School
Year: 2022
Subjects: Biomechanical engineering

description: Research in the field of human injury biomechanics with respect to athletes has indicated that head acceleration events (HAEs) suffered during participation in a contact sport can cause long-term neurological changes that present asymptomatically. This concept has been referred to as “mild” traumatic brain injury (mTBI). This mirrors results found in soldiers, where it is also now thought that traumatic brain injury, coupled with psychological trauma can lead to posttraumatic stress disorder (PTSD). Current consensus amongst the neurotrauma research community is that all HAEs matter, whether caused by blast, blunt force, or directed energy weapons. <br> Previous research has focused on the long-term changes that have been demonstrated and quantified, however very little research has been done to quantify the effects of a single insult to the brain. Several studies have had participants perform head motions while in a magnetic resonance imaging (MRI) scanner. Digital twins may be used to simulate the effects of an insult, be it blast, blunt force, or directed energy to an object. Finite element models of the human head and brain have a long history of development from the earliest models in the 1970s to today. Currently, numerous software packages allow for the regularization and comparison of MRI datasets. Some software packages offer additionally the ability to create subject specific finite element meshes interactively from a single MRI image. Previous research in the HIRRT Lab reduced the time to generate simulation geometry to approximately 48 hours to generate a patient specific finite element mesh. This represented a substantial reduction in the processing time for a single scan, which to the knowledge of the authors required on the time scale of weeks to process a single geometry including the skull robustly or required costly software licenses, and still required user interactive processes. The architecture and deployment of the HIRRT Lab Cluster, a high-performance computing system that is a cost-optimized research tool to enable rapid processing of scans to simulation geometry using batch processes on a Slurm cluster. There are software optimizations, operating system optimizations, and Linux kernel-level optimization (and selections) utilized that enable the hardware selected to perform optimally. <br> To the knowledge of the author, no single pipeline enables the automated generation of robust, patient specific finite element meshes from raw datasets fresh from an MRI. This package addresses those limitations with a design heavily tilted towards Linux cluster implementations. The author has created a pipeline of code designed to run on a Linux-based compute cluster that is capable of processing 1700 scans from raw T1-weighted MRI scans to a finite element mesh with regions of interest (ROIs) identified as element sets, and white matter fiber orientation determined from diffusion tensor imaging (DTI) scans in under 7 days using the current hardware available in the HIRRT Lab Cluster with appropriate software licensing. This represents a speed up of over 1200x compared to the original program overall at just mesh processing, and a speed up of 22x for a single scan being processed, with additional features and detail not captured by the original code. <br> Accurate representative models for subpopulations via their immutable traits (e.g. size, biological sex, ethnicity/ancestry, or age) can further reduce the number of simulations that are required to accurately assist in the improvement of finite element models that may be used to improve the design of personal protective equipment, create new techniques, or aid in the design of new vehicles capable of reducing the exposure of individuals to potentially traumatic damage. The use of subpopulation groupings rather than the simulation of each unique individual, even models consisting of bounding cases, such as the largest or smallest representative members of a subpopulation can reduce the amount of data that needs to be processed to generate useful design feedback for engineers. <br> Subject-specific models allow for greater variation in strain due to geometric differences between individuals brains and should be used where possible to describe a given individual’s strain history more accurately, which can be used to assess the formation of damage as indicated by biomarkers. To understand the long-term effects of blast overpressures on brain structure, function, and chemistry, and subsequently develop appropriate mitigation strategies, computational models of individual soldiers must be developed. These models must integrate blast physics and neuroimaging of actual tissue damage to the brain. There is a need to develop constitutive equations capable of being used in multi-scale models to relate various insults directly to damage in the brain. These equations should be linked to damage as indicated through various MRI scan types and used to robustly assess individuals over the course of their unique impact histories. Through the development of a digital twin in this manner, unique predictive medicine may be used to proactively identify those athletes and warfighters who may be at higher risk for long term detrimental effects from further exposure to HAEs.