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Abstract #3139

Explicit Formula for Diffusion Orientation Distribution Function Using a Kurtosis Approximation

Jens H. Jensen1, 2, Ali Tabesh1, 2, Joseph A. Helpern1, 2

1Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, United States; 2Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, United States

The diffusion orientation distribution function (dODF) can be used to estimate the directions of axonal fiber bundles and may be combined with white matter fiber tractography algorithms. Here a new analytical representation for the dODF is presented that includes the leading non-Gaussian corrections through the diffusional kurtosis tensor. By using numerical simulations, it is shown that including the non-Gaussian corrections both improves the accuracy of the predicted fiber bundle directions and allows for the direct quantification of intra-voxel fiber crossings. This explicit formula for the dODF may be of utility when diffusional kurtosis imaging data is available.

Keywords

accuracies accuracy accurate acquisition admixture affects allows although analytical anatomical angular application applying approaches approximation approximations audience axons biomedical black brain bundle bundles calculation chosen coincide combined commonly compartment compartments components consider consisted constant corrections crossing crossings curves dataset defined density depends describe diagonal diffusing diffusion diffusional diffusivity displacement distribution dominant equal error errors estimation exact explicit extent extra facilitate fiber fibers formula fraction fractions full function generalizes gives grant improve improved improves included includes incorporate incorporating incorporation increasing indicate individual integration interpreted intersecting intersection intra intrinsic investigate kurtosis larger least long make mapping measured medical model models normalization numerical obtainable orientation orientations part particularly parts power practical predicted predicting prescriptions previously principal probability proposed quantification quantify radiological reduce regard relatively representation required resolve restricted science sensitivity significantly simple simplifies simulations slope south sponsors suggest sums target tends tensor together type undetectable unit upon utility utilizes various vector volume water white