Feature-based registration method

• US 8,473,032 B2
• Filed: 06/02/2009
• Issued: 06/25/2013
• Est. Priority Date: 06/03/2008
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: superDimension, Ltd. (Medtronic Plc)
• Inventors: Averbuch, Dorian
• Primary Examiner(s): ROZANSKI, MICHAEL T
• Application Number:
  US12/476,976
• Publication Number:
  US 20100030064A1
• Time in Patent Office: 1,484 Days
• Field of Search: 600/407, 600/424, 600/427, 600/160, 382/131, 382/154, 382/173
• US Class Current: 600/424
• CPC Class Codes: A61B 2090/364 Correlation of different images or relation of image positions in respect to the body
• CPC Class Codes: A61B 2090/3762 using computed tomography systems [CT]
• CPC Class Codes: A61B 34/20 Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/12 Arrangements for detecting or locating foreign bodies
• CPC Class Codes: A61B 6/5229 combining image data of a patient, e.g. combining a functional image with an anatomical image
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06T 15/08 Volume rendering
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30021 Catheter; Guide wire
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/0014 using an image reference approach
• CPC Class Codes: G06T 7/30 Determination of transform parameters for the alignment of images, i.e. image registration
• CPC Class Codes: G06T 7/337 involving reference images or patches
• CPC Class Codes: G06T 7/344 involving models

Abstract

Methods for registering a three-dimensional model of a body volume to a real-time indication of a sensor position that involve analyzing scanned and sensed voxels and using parameters or thresholds to identify said voxels as being either tissue or intraluminal fluid. Those voxels identified as fluid are then used to construct a real-time sensed three-dimensional model of the lumen which is then compared to a similarly constructed, but previously scanned model to establish and update registration.

Claims

1. A method for registering a three-dimensional model of a body volume to a real-time indication of a sensor position, the method comprising:

collecting reference data on the body volume;

creating the three-dimensional model of said body volume from the reference data;

inserting a sensor into a body lumen and recording location data from said sensor including a real-time sensor position;

processing said location data recorded by the inserted sensor to form a three-dimensional shape corresponding to space within said lumen, the three-dimensional shape including a plurality of cavity voxels;

assigning a value to each cavity voxel of the plurality of cavity voxels encountered by the sensor, the value of each cavity voxel corresponding to a frequency with which each cavity voxel encounters the sensor;

adjusting a density of the plurality of cavity voxels in accordance with the value of each cavity voxel;

creating clouds of cavity voxels having varying densities that match interior anatomical cavity features;

defining a plurality of parameters having predefined thresholds;

determining which of the cavity voxels of the plurality of cavity voxels identified by the location data satisfy the predefined thresholds of the plurality of parameters;

selecting the cavity voxels satisfying the predefined thresholds of the plurality of parameters; and

comparing said three-dimensional shape including only cavity voxels satisfying the predefined thresholds of the plurality of parameters to said three dimensional model to establish a feature-based registration.

2. The method of claim 1 wherein collecting reference data comprises acquiring a plurality of CT scans.

3. The method of claim 1 wherein creating the three-dimensional model of said body volume involves assembling a plurality of CT scans into a three-dimensional CT model.

4. The method of claim 1 wherein inserting the sensor into the body lumen and recording location data from said sensor comprises placing a catheter having said sensor at its distal tip into the body lumen, and recording position data from said sensor while moving said sensor within at least said body lumen.

5. The method of claim 1 wherein processing said location data comprises de-cluttering said location data.

6. The method of claim 5 wherein processing said location data further comprises digitizing said location data.

7. The method of claim 6 wherein processing said location data further comprises filtering said location data.

8. The method of claim 1 wherein cavity voxels with higher densities are given higher weight in registration than cavity voxels with lower densities.

9. The method of claim 1 wherein assigning values to cavity voxels of said location data involves assigning values based on tissue value Hounsfield numbers.

10. The method of claim 1 wherein defining parameters comprises defining a density range required for each cavity voxel of the plurality of cavity voxels.

11. The method of claim 1 wherein defining parameters comprises defining a proximity from an already-designated cavity voxel with cavity voxels satisfying the predefined thresholds of the plurality of parameters.

12. The method of claim 1 wherein defining parameters comprises defining a parameter template including multiple parameters.

13. The method of claim 12 wherein defining a parameter template involves defining the predefined thresholds of the plurality of parameters as follows:

requiring the cavity voxels to have a certain density corresponding to air;

requiring the cavity voxels to be located adjacent another cavity voxel having said certain density corresponding to air; and

requiring the cavity voxels to be adjacent to cavity voxels having densities corresponding to blood vessels.

14. The method of claim 1 wherein the comparing step further includes the steps of:

developing an initial guess;

using said initial guess to establish said feature-based registration;

calculating a difference between said three-dimensional model and said three-dimensional shape including only cavity voxels satisfying the predefined thresholds of the plurality of parameters;

finding a closest cavity voxel in said three-dimensional model that matches parameters based on a cavity voxel visited by said sensor, each time said sensor encounters a new cavity voxel; and

updating said feature-based registration as said sensor encounters new cavity voxels.

15. The method of claim 14 wherein said comparing step is iterative.

16. The method of claim 14 wherein said comparing step is continuous.

17. A method for registering a three-dimensional model of a lung volume to a real-time indication of a sensor position, the method comprising:

collecting reference data on the lung volume;

creating the three-dimensional model of said lung volume;

inserting a sensor into an airway and recording location data from said sensor including a real-time sensor location;

processing said location data;

setting a threshold value for sensing volume voxels of the location data;

forming a three-dimensional shape corresponding to space within said airway, the three-dimensional shape including a plurality of cavity voxels;

assigning a value to each cavity voxel of the plurality of cavity voxels encountered by the sensor, the value of each cavity voxel corresponding to a frequency with which each cavity voxel encounters the sensor;

adjusting a density of the plurality of cavity voxels in accordance with the value of each cavity voxel;

creating clouds of cavity voxels having varying densities that match interior anatomical cavity features;

defining a plurality of parameters having predefined thresholds;

determining which of the cavity voxels of the plurality of cavity voxels identified by the location data satisfy the predefined thresholds of the plurality of parameters;

selecting the cavity voxels satisfying the predefined thresholds of the plurality of parameters; and

comparing said three-dimensional shape including only cavity voxels satisfying the predefined thresholds of the plurality of parameters to said three dimensional model to establish a feature-based registration.

18. The method of claim 17 wherein collecting reference data comprises acquiring a plurality of CT scans.

19. The method of claim 17 wherein creating the three-dimensional model of said lung volume involves assembling a plurality of CT scans into a three-dimensional CT model.

20. The method of claim 19 wherein assembling the plurality of CT scans into a three-dimensional model involves segmenting voxels of the CT scan representing internal lung air.

21. The method of claim 17 wherein inserting the sensor into the airway and recording location data from said sensor comprises placing a catheter having said sensor at its distal tip into the airway, and recording position data from said sensor while moving said sensor within at least said airway.

22. The method of claim 17 wherein processing said location data comprises de-cluttering said location data.

23. The method of claim 22 wherein processing said location data further comprises digitizing said location data.

24. The method of claim 23 wherein processing said location data further comprises filtering said location data.

25. The method of claim 17 wherein cavity voxels with higher densities are given higher weight in registration than cavity voxels with lower densities.

26. The method of claim 17 wherein assigning values to cavity voxels of said location data involves assigning values based on tissue value Hounsfield numbers.

27. The method of claim 17 wherein forming a three-dimensional shape corresponding to space within said airway comprises forming a binary voxel space.

28. The method of claim 27 wherein creating a three-dimensional model of said lung volume comprises creating a binary voxel model of said lung volume.

29. The method of claim 28 wherein forming a binary voxel space comprises assigning a value of zero to all voxels having densities higher than said threshold value.

30. The method of claim 29 further comprising assigning all other voxels a value of 1 and considering those voxels having a value of 1 as representing air.

31. The method of claim 30 wherein comparing said three-dimensional shape to said three dimensional model comprises:

using a binary matching method to compare said three-dimensional shape including only cavity voxels satisfying the predefined thresholds of the plurality of parameters to said three dimensional model to establish the feature-based registration.

32. The method of claim 31 wherein using the binary matching method involves using a subtraction method.

33. The method of claim 32 wherein using the subtraction method involves superimposing a segment of the reference data over a corresponding segment of the location data.