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This script shows how to compute the Diffusion Kurtosis (DKI) Coefficients from a given Diffusion-Weighted MRI dataset. The script implements several fitting methods with various constraints from the article by A. Barmpoutis and J. Zhuo "Diffusion Kurtosis Imaging: Robust Estimation from DW-MRI using Homogeneous Polynomials", In the Proceedings of ISBI, 2011, pp. 262-265.
A Matlab library for Diffusion Weighted MRI (DW-MRI) Processing, Diffusion Tensor (DTI) Estimation, Diffusion Kurtosis (DKI) Estimation, Higher-order Diffusion Tensor Analysis, Tensor ODF estimation, Visualization and more.
This script shows how to compute a Diffusion Tensor (DTI) from a given DW-MRI (Diffusion-Weighted MRI) dataset. The method guarantees that the estimated tensor is positive-definite or at least positive semi-definite. The implementation is based on...
This function plots a 2D field of 3D even-order tensors as spherical functions. The 3D tensors must be given as a list of the unique tensor coefficients. The field can contain either a single tensor, or a row of tensors or a 2D field of tensors.
The plotDTI function plots a single tensor, a row of tensors, or a 2D field of tensors as Gaussian ellipsoids. The tensors must be given as 3x3 symmetric positive definite matrices.
This method computes an accurate continuous approximation of the DW-MRI signal using adaptive kernels (A. Barmpoutis et al. "Adaptive kernels for multi-fiber reconstruction", In the Proceedings of IPMI, 2009). The approximation is based...
This script shows how to compute a Higher-order Diffusion Tensor from a given Diffusion-Weighted MRI dataset. The method guarantees that the estimated tensor is positive-definite or at least positive semi-definite. This implementation is based on...
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