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 Yeatman^4,7, 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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