Abstract WMP109: Characterizing Brain Microstructural Changes in Childhood Arterial Ischemic Stroke Using Multi-shell Diffusion Magnetic Resonance Imaging

Introduction: New imaging markers employing sophisticated models of tissue microstructure may lead to better understanding of neurobiological substrates in childhood arterial ischemic stroke. Methods: Four children with acute unilateral MCA territory ischemic stroke were retrospectively included [two males, age range 2 -16 years]. Parameters of two complementary diffusion modeling techniques: Neurite Orientation Dispersion and Density Imaging (NODDI), and Spherical Mean Technique (SMT) were estimated using two-shell diffusion data acquired at presentation ( b- values = 1000 and 3000 s/mm 2 ). For comparison, diffusion tensor imaging (DTI) parameters were also estimated. Parameter estimates were derived from the acute infarct-affected gray matter, the reconstructed pyramidal tract and the contra-hemispheric homologs. Contrast-to-noise ratios (CNRs) were calculated for each pair of homologous brain regions. Results: In both the acute infarct-affected gray matter, and pyramidal tracts, the multi-shell parameter estimates demonstrated increased intra-cellular volume fraction (F icvf and INVF in figure), predominant reduced extra-cellular microscopic diffusivity (ENMMD and ENTMD in figure), and increased orientation dispersion of the infarct-affected axonal fibers (ODI in figure). These findings were consistent with respective acute stroke histopathological features of cytotoxic edema, narrowed extracellular spaces, and axonal swelling. The CNRs were associated with stroke severity and motor function outcome at follow-up. Conclusion: The parameter estimates derived from multi-shell diffusion data produces robust infarct contrast, and provides histopathologically plausible imaging biomarkers representing brain microstructural changes in acute childhood stroke. Routine clinical introduction of this imaging technique requires further elucidations of its prognostic values in predicting stroke severity and/or motor outcomes.