By Peter Ehrhard, firstname.lastname@example.org
Traumatic brain injuries (TBIs) are an unfortunate but all too common occurrence during military training and deployment. Because mild TBIs often present no obvious signs of head trauma or facial lacerations, they are the most difficult to diagnose at the time of the injury, and patients often perceive the impact as mild or harmless. Brain injury is cumulative, so treating a patient within the “golden hour” — the first 60 minutes after injury — is crucial for improved long-term recovery.
Jie Huang, an assistant professor of electrical and computer engineering, is working to improve detection of TBIs by developing technology to autonomously collect and process data on trauma-inducing actions — in a reliable and “smart” manner for prompt identification. By embedding military helmets with sensors and other data-transmission technology, Huang hopes to help quickly and accurately diagnose and administer aid to mild TBI victims.
“Our aim is to develop a fundamental understanding of acute TBIs through large-scale data acquisition of blast lab impact events from pressure-sensor-equipped helmets processed through machine learning,” says Huang. “Military-related TBIs come primarily from repeated exposures to explosive blasts during planned training activities. Blast TBIs account for approximately 60% of all military-related TBIs, and of those, 80% are categorized as mild.”
With $2.25 million in funding from the Leonard Wood Institute, Huang and his research team are developing a smart-helmet prototype using a standard football helmet. The prototype will be equipped with fiber optic micro interferometer sensors.
The sensors will be activated by blunt-force impacts that range from 3 to 15, or mild to severe head injury, on the Glasgow Coma Scale (GCS). The GCS gives medical personnel a practical way to describe the level of consciousness in patients with acute brain injury by scoring verbal response, motor response and eye movement.
Once activated, the sensors send data wirelessly in real-time via the “smart” helmets, integrating machine learning based on a decision-making framework that can detect the severity of the impact level.
Once the helmet is developed, Huang will correlate laboratory testing data with field data to improve the overall configuration of the helmets.
“Our research project will use advanced optical fiber sensors, embedded in smart helmets, to instantly warn soldiers of the severity of a concussive event in the field so that treatment can be sought immediately,” says Huang. “Such a framework, with the ability to yield highly accurate predictions, will mitigate a soldier’s suffering and save time for medical personnel.”
Fatih Dogan, a professor of ceramic engineering, is expanding the idea of head protection by developing liquid body armor for use in helmets. His computational research combines materials processing and various mechanical test methods to look at energy absorption and energy redirection.
In addition to TBIs, Dogan’s work also relates to the equally worrying behind-armor blunt trauma (BABT). BABTs are the non-penetrating injuries caused by the distortion and warping of body armor designed to protect the body from explosive impacts. Bending of the surface of body armor could come from the impact of a bullet or other projectile.
“By studying the nanostructured composite fluids, we hope to better understand impact weakening and blast-wave mitigation,” says Dogan. “Multilayered viscoelastic materials — like polyurethane and rubber — could serve as ballistic and blast protection in relation to TBIs and BABT.”
While the initial focus of this TBI research is for military use, the work could one day be applied throughout the world of safety and prevention. Children could have better helmets when learning to ride a bicycle, football players could enjoy their pastime with a better sense of security and construction workers may be better equipped to prevent workplace accidents from becoming disasters.
Building models of protection
While some faculty are busy with TBI detection and protection, others have turned their focus on modeling injuries to better understand them and determine the best methods for healing.
Donald Wunsch, the Mary K. Finley Distinguished Professor of Electrical and Computer Engineering at S&T and director of the Applied Computational Intelligence Laboratory, is heading a team of researchers to analyze data from a TBI repository built by the National Institutes of Health and the Department of Defense.
The team, which includes Gayla Olbricht, associate professor of mathematics and statistics at S&T, and Tayo Obafemi-Ajayi, assistant teaching professor in the S&T Cooperative Engineering Program based at Missouri State University, will study data using neural networks that can “learn” on their own combined with statistics to allow for personalized medicine — medicine that is tailored for each individual patient and focuses on unique genetic profiles.
To better treat TBI sufferers, S&T researchers are also building better computer models to show the extent of injuries and help medical professionals determine the extent of damage to the brain. Hyoung K. Lee, associate professor of nuclear engineering at S&T, is developing a real-time computed tomography scan (CT scan) that would help to better design and test TBI prevention gear.
Instead of taking several images of a patient from different angles, Lee’s CT system would sweep X-ray beams around a patient. These individual ray sources need to be compact and fast in order to fit a large-enough number of projections for a successful head-and-neck CT, Lee says.
“The overall goal is to design a compact and fast X-ray tube that will be used for development of a stationary CT system,” says Lee. “Our approach is that a tungsten flat emitter, a drum-shaped anode, and an electron beam focusing and steering system will allow the size of an X-ray source to be reduced while still generating the required high-intensity X-ray beam.”
Lee and his team believe that the development of such a compact and fast X-ray will lead to the creation of a real-time CT scan — driving the field of imaging forward while allowing injuries like TBIs to be quickly diagnosed so treatment can begin.
Combining imaging and biomarkers
Jie Gao, associate professor of mechanical and aerospace engineering, hopes to develop spectroscopic imaging techniques that will allow researchers to understand the fundamental biochemical alterations and their underlying mechanisms in TBI processes, as well as the effects of N-acetylcysteine amide (NACA) treatment on the injuries.
Gao’s research team includes Xiaodong Yang, associate professor of mechanical and aerospace engineering, and Nuran Ercal, the Richard K. Vitek/Foundation for Chemical Research Endowed Chair in Biochemistry. Yang studies spectroscopy and imaging techniques, and Ercal studies animals and oxidative stress-related disorders.
Also combining chemistry with animal studies, Paul Nam, associate professor of chemistry, received a grant to develop antioxidant therapies and identify oxidative biomarkers to diagnose and treat both acute and chronic TBI.
Oxidative damage caused by free radicals, membrane lipid peroxidation in particular, are some of the most reliably validated secondary injuries from TBI, says Nam. Discovering antioxidants that inhibit this type of damage — and its neurotoxic consequences — could be used in therapeutic drugs to prevent and treat TBI, he says.
Nam’s research focuses on open-field blast settings that will generate shock waves simulating the ones that occur during battle. Similar to Gao’s work, Nam’s will examine the effects of NACA as a treatment and then conduct biomarker analysis and measure those markers.