Abstract #2106

Automated Analysis of Dynamic Diffusion Abnormalities and Infarct Growth Following Ischemic Injury

Mihika Gangolli^1,2, Alexander Kharlamov³, David Y. Chung⁴, Fernando Boada⁵, Victor E. Yushmanov³, Dzung L. Pham^1,6, John A. Butman^1,7, and Stephen C. Jones^3,8

¹Center for Neuroscience and Regenerative Medicine, Bethesda, MD, United States, ²The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States, ³Allegheny-Singer Research Institute, Pittsburgh, PA, United States, ⁴Radiology, Harvard Medical School, Charlestown, MA, United States, ⁵Radiology, Stanford University School of Medicine, Palo Alto, CA, United States, ⁶Radiology and Radiological Sciences, Uniformed Services University, Bethesda, MD, United States, ⁷Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, United States, ⁸CerebroScope, Pittsburgh, PA, United States

Synopsis

Keywords: Stroke, Diffusion/other diffusion imaging techniques, Peri-infarct depolarization, dynamic diffusion MRIAn automated analysis pipeline was developed to detect and analyze the trajectories of dynamic diffusion abnormalities (DDAs) in a rat model of severe ischemic injury. DDAs were detected in both hemispheres of the brain and perilesional DDAs propagated along the infarct boundary. Infarct volume, quantified using apparent diffusion coefficient (ADC), increased during the recording period concomitantly with detected DDAs. Our approach could provide the means to explore the causal relationship between DDAs hypothesized to correspond with peri-infarct depolarizations and secondary expansion of infarcted tissue.

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