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Abstract #3763

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Rafael Neto Henriques1, Marta Correia2, Maurizio Marrale3, Elizabeth Huber4, John Kruper5, Serge Koudoro6, Jason Yeatman4,7, Eleftherios Garyfallidis6, and Ariel Rokem5
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, 2Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, 3Department of Physics and Chemistry “Emilio Segrè”, University of Palermo, Palermo, Italy, 4Institute for Learning and Brain Science and Department of Speech and Hearing, University of Washington, Seattle, WA, United States, 5Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States, 6Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, United States, 7Department of Pediatrics and Graduate School of Education, Stanford University, Stanford, CA, United States

Diffusion Kurtosis Imaging (DKI) estimates non-Gaussian diffusion in biological tissue from diffusion-weighted MRI, providing a useful marker for individual differences in tissue microstructure. We present a well-tested, well-documented open-source implementation of DKI as part of the DIPY (Diffusion Imaging in Python) project. The implementation provides standard DKI metrics, as well as extensions of the method for microstructure modeling and tractography. We demonstrate the use of these methods in openly available datasets.

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