Abstract #1812

Characterizing diffusion weighted images using Clustering Analysis of Spherical Harmonics (CASH)

Manish Amin¹, Guita Banan¹, Matthew Hey², Luis Colon-Perez³, Haiqing Huang⁴, Mingzhou Ding⁴, Catherine Price⁵, and Thomas Mareci^1,6

¹Physics, University of Florida, Gainesville, FL, United States, ²Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States, ³Psychiatry, University of Florida, Gainesville, FL, United States, ⁴Biomedical Engineering, University of Florida, Gainesville, FL, United States, ⁵Clinical and Health Psychology, University of Florida, Gainesville, FL, United States, ⁶Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, United States

Diffusion weighted imaging has become an important tool for understanding how pathology affects brain structure. However, the standard method of diffusion tensor imaging (DTI) is inadequate in complex fiber regions. Other more complex diffusion models calculate the diffusion displacement probability function (DPF) 1, but current methods to extract the information from the DPF are limited. To this end, we introduce a data-driven method combining spherical harmonic representations of the DPF with the clustering analysis of spherical harmonic (CASH) coefficients, to provide an enhanced diffusion data characterization that includes information about the number of unique fiber orientations present in each voxel.

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