Abstract #3470

Quantifying voxel-wise differences between diffusion propagators across multiple MAP-MRI datasets

Alexandru V. Avram¹, Adam S. Bernstein^2,3, M. Okan Irfanoglu¹, Craig C. Weinkauf⁴, Amber Simmons², Martin Cota⁵, Neville Gai⁶, Neekita Jikaria⁵, Anita Moses⁵, L. Christine Turtzo⁷, Lawrence Latour⁷, Dzung L. Pham⁵, John A. Butman⁶, and Peter J. Basser²

¹National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, United States, ²National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States, ³Biomedical Engineering, University of Arizona, Tucson, AZ, United States, ⁴Department of Surgery, University of Arizona, Tucson, AZ, United States, ⁵Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, MD, United States, ⁶Clinical Center, National Institutes of Health, Bethesda, MD, United States, ⁷National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States

We describe a technique for voxel-wise analysis across multiple mean apparent propagator (MAP) MRI datasets warped using a diffeomorphic tensor-based registration algorithm. By measuring propagators from co-registered MAP datasets with the same MAP basis functions we can directly quantify voxel-wise differences using angular dissimilarity metrics. We show examples from a cohort of healthy volunteers, and from a longitudinal clinical dataset of a patient undergoing carotid endarterectomy. This approach could provide improved sensitivity in the detection and characterization of subtle microstructural tissue changes in cross-sectional group and longitudinal single-subject clinical studies.

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