Former graduate student Hassan Bagher-Ebadian publishes in PLoS ONE

Hassan Bagher-Ebadian obtained his PhD in Biomedical Sciences with a specialization in Medical Physics in 2010. He studies stroke, and in particular the recovery of brain function following a stroke. His most recent paper is an attempt at “Predicting Final Extent of Ischemic Infarction Using Artificial Neural Network Analysis of Multi-Parametric MRI in Patients with Stroke” (PLoS ONE, 6:e22626, 2011). After a stroke, a doctor would like to know how severe the damage to the brain is, so the treatment will be appropriate. Typically, a magnetic resonance image of the brain is taken three months after the stroke to access the extent of the infarct. But by three months, it is often too late for treatment. What would be ideal is a way to determine immediately how extensive the ultimate damage will be.

This is exactly what Bagher-Ebadian is trying to develop. He uses computational method called an artificial neural network to predict the properties of the magnetic resonance image after 3-months from a series of MRIs taken immediately after the stroke. He concludes that “even this small pilot study demonstrates a robust and sensitive predictor of infarct outcome, and because it can be produced almost as quickly as the image sets emerge from the MRI scanner, this approach shows great potential as a tool that may eventually influence clinical practice.”

Other OU authors on this study are Distinguished Professor Michael Chopp, of the Department of Physics, and Adjunct Associate Professor James Ewing. Chopp and Ewing have their laboratory in the Department of Neurology at Henry Ford Hospital. The research was supported by Ewing’s grant from the National Institutes of Health.

For those who do not have subscriptions or easy access to biomedical journals, note that this article was published in PLoS (Public Library of Science) ONE, a leading open access journal that anyone can access over the internet, with no need for a subscription.