TRACKING AND REPRESENTATION OF MULTI-DIMENSIONAL ORGANS

• US 20110044521A1
• Filed: 06/23/2010
• Published: 02/24/2011
• Est. Priority Date: 06/26/2009
• Status: Active Grant

• Current Assignee: Regents of The University of Minnesota (University of Minnesota)
• Original Assignee: Regents of The University of Minnesota (University of Minnesota)
• Inventors: Kinney, Timothy, Tewfik, Ahmed H., Wang, Dan
• Granted Patent: US 10,026,016 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: A61B 1/000095 for image enhancement
• CPC Class Codes: G06F 18/213 Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30084 Kidney; Renal
• CPC Class Codes: G06T 7/251 involving models
• CPC Class Codes: G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking
• CPC Class Codes: G06V 10/754 involving a deformation of the sample pattern or of the reference pattern; Elastic matching
• CPC Class Codes: G06V 10/7715 Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
• CPC Class Codes: G06V 2201/031 of internal organs
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing

Abstract

A method includes receiving data corresponding to a sequence of deformations of a surface of an object. The method also includes generating spherical harmonics expressions based on the data. The method includes identifying a subspace of the spherical harmonics expression corresponding to the surface.

Claims

1. A method comprising:

receiving data corresponding to a sequence of deformations of a surface of an object;

generating spherical harmonics expressions based on the data; and

identify a subspace of the spherical harmonics expression corresponding to the surface.

2. The method of claim 1 wherein receiving data includes receiving at least one of model data, ultrasound data, computerized tomography data, and magnetic resonance imaging data.

3. The method of claim 1 wherein identifying the subspace includes identifying an orthogonal subspace.

4. The method of claim 1 wherein identifying the subspace includes removing selected vectors from the subspace.

5. The method of claim 1 further including applying a selection criteria based on principal angles and vectors of pairs of subspaces.

6. The method of claim 1 wherein identifying the subspace includes using an orthogonal least squares sparse representation algorithm.

7. The method of claim 1 wherein identifying the subspace includes using subspace pursuit and clustering.

8. The method of claim 1 wherein identifying the subspace includes constructing a dictionary by performing Orthogonal Subspace Pursuit (OSP).

9. The method of claim 1 wherein the object includes an organ.

10. The method of claim 1 wherein generating spherical harmonics expressions includes calculating a matrix having coefficients corresponding to the deformation.

11. A method comprising:

receiving data corresponding to a sequence of deformations of a deformable object;

transforming the data to an expression in spherical coordinates; and

using the expression in spherical coordinates to perform a decomposition.

12. The method of claim 11 wherein receiving data includes receiving data from an in vivo organ.

13. The method of claim 11 wherein transforming the data to an expression in spherical coordinates includes calculating a matrix having coefficients corresponding to the deformation.

14. The method of claim 11 wherein using the expression in spherical coordinates to perform a decomposition includes identify a correlation between deformations in the sequence.

15. A system comprising:

an imaging module configured to receive image data corresponding to a pre-operative image of an organ, the organ having a deformable surface and the image data including at least one of ultrasound image data, computerized tomography data, or magnetic resonance data;

a memory configured to store training data corresponding to the organ, the training data including spherical harmonic representations of a plurality of deformations of the organ;

a sampling unit configured to receive sample measurements of an exterior surface of the organ, the sample measurements corresponding to a subset of the exterior surface;

a processor configured to receive the image data and receive the sample measurements and generate a reconstructed image of the organ based on a spherical harmonics transformation and based on the training data; and

an output device configured to display the reconstructed image.

16. The system of claim 15 wherein the processor is configured to generate the reconstructed image having an interior surface.

17. The system of claim 15 wherein the sampling unit includes at least one of an endoscope and a laparoscope.

18. The system of claim 15 wherein the processor is configured to perform a decomposition including a subspace pursuit and clustering.

19. The system of claim 15 wherein the reconstructed image includes the exterior surface and wherein the subset of the surface is approximately 10% of the exterior surface.

20. The system of claim 15 wherein the processor is configured to generate real time data corresponding to the reconstructed image.

21. A system comprising:

a processor configured to receive pre-operative image data for an object;

a sampling unit coupled to the processor and configured to provide sample data corresponding to a view of the object; and

a memory coupled to the processor and configured to execute a spherical harmonic transform and configured to identify a subspace corresponding to the object.

22. The system of claim 21 further including an output device coupled to the processor, the output device including at least one of a printer and a display.