VISUALIZING BRAIN NETWORK CONNECTIVITY

• US 20130113816A1
• Filed: 07/02/2012
• Published: 05/09/2013
• Est. Priority Date: 11/04/2011
• Status: Active Grant

• Current Assignee: Siemens Healthcare GMBH (Siemens AG)
• Original Assignee: Siemens Corp. (Siemens AG)
• Inventors: Sudarsky, Sandra, Nadar, Mariappan S., Geiger, Bernhard, Lefebvre, Alban, Sun, Shanhui
• Granted Patent: US 9,519,981 B2
• Time in Patent Office: Days
• US Class Current: 345/589
• CPC Class Codes: A61B 5/0042 for the brain
• CPC Class Codes: A61B 5/4064 Evaluating the brain A61B5/031, A61B5/369, A61B5/14553 take precedence
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs

Abstract

A method for visualizing brain connectivity includes receiving image data including molecular diffusion of brain tissue, constructing a tree data structure from the image data, wherein the tree data structure comprises a plurality of network nodes, wherein each network node is connected to a root of the tree data structure, rendering a ring of a radial layout depicting the tree data structure, wherein a plurality of vertices may be traversed from the top to the bottom, duplicating at least one control point for spline edges sharing a common ancestor, and bundling spline edges by applying a global strength parameter β.

Claims

1. A method for visualizing brain network connectivity, comprising:

receiving image data corresponding to a brain of interest;

constructing a tree data structure from the image data, wherein the tree data structure comprises a plurality of network nodes, wherein each network node is connected to a root of the tree data structure;

rendering a ring of a radial layout depicting the tree data structure, wherein a plurality of vertices may be traversed from the top to the bottom;

duplicating at least one control point for spline edges sharing a common ancestor;

bundling spline edges by applying a global strength parameter β

; and

displaying a visualization of the brain network connectivity in the radial layout including bundled spline edges.

2. The method of claim 1, wherein the tree data structure comprises a plurality of layers including a top layer having a first vertex corresponding to an entirety of the image data, a bottom layer comprising second vertices corresponding to the plurality of network nodes, and a plurality of intermediate layers comprising third vertices corresponding to a clustered region.

3. The method of claim 2, wherein a segment size of the ring is proportional to a number of vertices at the bottom layer in a corresponding tree branch.

4. The method of claim 1, further comprising applying a mono color for connection spline edges.

5. The method of claim 1, further comprising determining a color value for each spline edge by a linear interpolation using a length of the corresponding spline edge.

6. The method of claim 1, further comprising a blending of the image data.

7. The method of claim 1, wherein constructing the tree data structure from the image data comprises determining a structural connectivity of the brain of interest, wherein the tree structure depicts the structural connectivity.

8. The method of claim 7, wherein constructing the tree data structure from the image data comprises determining a functional connectivity of the brain of interest, wherein the tree structure depicts the functional connectivity.

9. The method of claim 1, wherein the image data is obtained by one of molecular diffusion, resting-state functional magnetic resonance imaging, magnetoencephalography and electroencephalography.

10. A computer program product embodying instructions executable by a processor to perform a method for visualizing brain connectivity, comprising:

receiving image data including molecular diffusion of a brain of interest;

constructing a tree data structure from the image data, wherein the tree data structure comprises a plurality of network nodes, wherein each network node is connected to a root of the tree data structure;

rendering a ring of a radial layout depicting the tree data structure, wherein a plurality of vertices may be traversed from the top to the bottom;

duplicating at least one control point for spline edges sharing a common ancestor; and

bundling spline edges by applying a global strength parameter β

.

11. The computer program product of claim 10, wherein the tree data structure comprises a plurality of layers including a top layer having a first vertex corresponding to an entirety of the image data, a bottom layer comprising second vertices corresponding to the plurality of network nodes, and a plurality of intermediate layers comprising third vertices corresponding to a clustered region.

12. The computer program product of claim 11, wherein a segment size of the ring is proportional to a number of vertices at the bottom layer in a corresponding tree branch.

13. The computer program product of claim 10, further comprising applying a mono color for connection spline edges.

14. The computer program product of claim 10, further comprising determining a color value for each spline edge by a linear interpolation using a length of the corresponding spline edge.

15. The computer program product of claim 10, further comprising a blending of the image data.

16. The computer program product of claim 10, wherein constructing the tree data structure from the image data comprises determining a structural connectivity of the brain of interest, wherein the tree structure depicts the structural connectivity.

17. The computer program product of claim 10, wherein constructing the tree data structure from the image data comprises determining a functional connectivity of the brain of interest, wherein the tree structure depicts the functional connectivity.

18. The computer program product of claim 10, wherein the image data is obtained by one of molecular diffusion, resting-state functional magnetic resonance imaging, magnetoencephalography and electroencephalography.

19. A method for visualizing brain network connectivity, comprising:

receiving image data including molecular diffusion of a brain of interest;

constructing one of a structural and a functional tree data structure from the image data, wherein a tree data structure comprises a plurality of network nodes, wherein each network node is connected to a root of the tree data structure;

displaying the tree data structure as a ring of a radial layout including the spine edges;

duplicating at least one control point for a plurality of spline edges sharing a common ancestor;

bundling the spline edges into a plurality of bundles according to the molecular diffusion of the brain of interest; and

displaying a visualization of brain network connectivity in the radial layout including bundled spline edges.

20. The method of claim 19, wherein the tree data structure comprises a plurality of layers including a top layer having a first vertex corresponding to an entirety of the image data, a bottom layer comprising second vertices corresponding to the plurality of network nodes, and a plurality of intermediate layers comprising third vertices corresponding to a clustered region.

21. The method of claim 20, wherein a segment size of the ring is proportional to a number of vertices at the bottom layer in a corresponding tree branch.

22. The method of claim 19, further comprising a blending of a background of the image data.