Directional statistics visualization tool
View Patent ↗A computer-implemented method for visualization of diffusion tensor images includes providing a diffusion tensor image input and providing a volume of interest within the diffusion tensor image input. The method includes determining a plurality of direction-based classifications of the volume of interest, wherein classes are defined by a set of parameters, An optimal solution is then selected within the classifications by using a criterion defined as a ratio of inertia indicators. To represent the chosen classification, a cone graph is determined for each of the directional classes to be displayed or stored, each cone pair being the geometrical interpretation of the class parameters. The method further includes determining a spherical scatterplot of the volume of interest augmented with a cone graph for visualization of at least one of the directional classes, and displaying and/or storing the scatterplot.
1. A computer-implemented method for visualization of diffusion tensor images comprising:
providing a diffusion tensor image input produced by a magnetic resonance imaging device, the diffusion tensor image input corresponding to a brain;
providing a volume of interest within the diffusion tensor image input;
determining a plurality of directional classes of the volume of interest, wherein the directional classes are defined by a set of parameters;
determining an optimal number of the directional classes using a criterion defined as a ratio of inertia indicators;
determining a spherical scatterplot of the volume of interest augmented with a cone graph for visualization of at least one of the directional classes; and
displaying the scatterplot.
2. The computer-implemented method of claim 1 , wherein the volume of interest is a set of voxels selected by a user.
3. The computer-implemented method of claim 2 , further comprising determining a principal diffusion direction for each voxel.
4. The computer-implemented method of claim 3 , further comprising plotting each voxel and its principal diffusion direction on the spherical scatterplot indicating a direction.
5. The computer-implemented method of claim 3 , further comprising plotting each voxel and its principal diffusion direction on the spherical scatterplot indicating a weight according to a fractional anisotropy of a respective voxel.
6. The computer-implemented method of claim 3 , wherein each voxel and its principal diffusion direction are plotted on the spherical scatterplot, the method further comprising:
indicating a direction by color; and
indicating a fractional anisotropy by opacity.
7. The computer-implemented method of claim 1 , further wherein each cone graph is a geometrical interpretation of the set of parameters defining a respective directional class.
8. A non-transitory computer readable medium embodying an application program executable by a processor to perform a method for visualization of diffusion tensor images, the method comprising:
providing a diffusion tensor image input;
providing a volume of interest within the diffusion tensor image input;
determining a plurality of directional classes of the volume of interest, wherein the directional classes are defined by a set of parameters;
determining an optimal number of the directional classes using a criterion defined as a ratio of inertia indicators; and
determining a spherical scatterplot of the volume of interest augmented with a cone graph for visualization of at least one of the directional classes.
9. The method of claim 8 , wherein the volume of interest is a set of voxels selected by a user.
10. The method of claim 9 , further comprising determining a principal diffusion direction for each voxel.
11. The method of claim 10 , further comprising plotting each voxel and its principal diffusion direction on the spherical scatterplot indicating a direction.
12. The method of claim 10 , further comprising plotting each voxel and its principal diffusion direction on the spherical scatterplot indicating a weight according to a fractional anisotropy of a respective voxel.
13. The method of claim 10 , wherein each voxel and its principal diffusion direction are plotted on the spherical scatterplot, the method further comprising:
indicating a direction by color; and
indicating a fractional anisotropy by opacity.
14. The method of claim 8 , wherein each cone graph is a geometrical interpretation of the set of parameters defining a respective directional class.