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Abstract #0603

Neurite Beading Model of Acute Stroke from Tensor-Valued Diffusion Encoding Predicts Diffusion Time Effects with Oscillating Gradients

Mi Zhou1, Robert Stobbe1,2, Matthew Budde3, and Christian Beaulieu1,2
1Biomedical Engineering, University of Alberta, Edmonton, AB, Canada, 2Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada, 3Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States

Synopsis

Keywords: Simulation/Validation, Simulations

Motivation: Can two different diffusion experiments (tensor-valued encoding and oscillating-gradient-spin-echo) support the role of axon beading for diffusion restriction in acute ischemic stroke?

Goal(s): To evaluate whether tensor-valued diffusion encoding yields an axon beading model that predicts experimental ischemic changes of diffusivity measured with OGSE.

Approach: Tensor-valued and OGSE/PGSE diffusion MRI were measured in the same acute stroke patients. Monte Carlo simulations were used to assess the links between these two independent measurements.

Results: The tensor-valued derived beading model predictions were in general agreement with independent experiments of less diffusivity reduction with OGSE than PGSE within stroke lesions.

Impact: Novel diffusion MRI sequences such as tensor-valued encoding and oscillating-gradient-spin-echo are complementary methods that point to the same microstructural basis (i.e. beading and elevated intra-cellular volume fraction) for the clinically useful diffusion reduction in acute ischemic stroke.

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Keywords