Feature-based registration method

• US 9,117,258 B2
• Filed: 05/20/2013
• Issued: 08/25/2015
• Est. Priority Date: 06/03/2008
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: Averbuch, Dorian
• Primary Examiner(s): ROZANSKI, MICHAEL T
• Application Number:
  US13/897,983
• Publication Number:
  US 20130259349A1
• Time in Patent Office: 827 Days
• Field of Search: 600/407, 600/424, 600/427, 382/131, 382/173
• US Class Current: 1/1
• 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 image of a sensor, the method comprising:

inserting the sensor into a body lumen to acquire location data;

processing the location data to form a three-dimensional shape having clouds of voxels;

defining a plurality of parameters having predefined thresholds; and

comparing the three-dimensional shape having the clouds of voxels that meet the predefined thresholds of the plurality of parameters to a three-dimensional model to establish a feature-based registration.

2. The method of claim 1, further comprising assigning a value to each voxel of each of the clouds of voxels encountered by the sensor.

3. The method of claim 2, further comprising correlating the value of each voxel to a frequency with which each voxel of each of the clouds of voxels encounters the sensor.

4. The method of claim 3, further comprising adjusting a density of the clouds of voxels in accordance with the value of each voxel.

5. The method of claim 1, wherein the clouds of voxels have varying densities that match interior anatomical cavity features of the body volume.

6. The method of claim 1, wherein the processing step further includes de-cluttering, digitizing, and filtering the location data.

7. The method of claim 1, further comprising assigning voxels with higher densities a higher weight in registration than voxels with lower densities.

8. The method of claim 1, wherein defining parameters comprises:

defining a density range required for each voxel of the clouds of voxels;

defining a proximity from an already-designated voxel with the clouds of voxels that meet the predefined thresholds of the plurality of parameters; and

defining a parameter template including multiple parameters.

9. The method of claim 8, wherein defining the parameter template involves defining the predefined thresholds of the plurality of parameters as follows:

requiring the clouds of voxels to have a certain density corresponding to air;

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

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

10. The method of claim 1, wherein the comparing step further includes:

developing an initial guess;

using the initial guess to establish the feature-based registration; and

calculating a difference between the three-dimensional model and the three-dimensional shape including the clouds of voxels that meet the predefined thresholds of the plurality of parameters.

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

forming a computed tomography (CT) volume from image data, the CT volume corresponding to space within a tortuous channel, the CT volume defined as clouds of voxels representing values of a three-dimensional shape;

defining a plurality of parameters having predefined thresholds that the clouds of voxels are required to meet to be designated as candidate cavity voxels to be utilized in the three-dimensional shape; and

comparing the three-dimensional shape having the clouds of voxels that meet the predefined thresholds of the plurality of parameters to a three-dimensional model to establish a feature-based registration.

12. The method of claim 11, further comprising assigning a value to each voxel of each of the clouds of voxels encountered by the sensor inserted through the tortuous channel.

13. The method of claim 12, further comprising correlating the value of each voxel to a frequency with which each voxel of each of the clouds of voxels encounters the sensor.

14. The method of claim 13, further comprising adjusting a density of the clouds of voxels in accordance with the value of each voxel.

15. The method of claim 11, wherein the clouds of voxels have varying densities that match interior anatomical cavity features of the tortuous channel.

16. The method of claim 11, further including a processing step for de-cluttering, digitizing, and filtering the clouds of voxels encountered by the sensor.

17. The method of claim 16, further comprising assigning voxels with higher densities a higher weight in registration than voxels with lower densities.

18. The method of claim 11, further comprising creating a binary voxel model of the CT volume by assigning a value of zero to all voxels having densities higher than a threshold value set for the clouds of voxels that meet the predefined thresholds of the plurality of parameters.

19. The method of claim 18, further comprising assigning all other voxels a value of 1 and considering those voxels having a value of 1 as representing air.

20. The method of claim 19, wherein comparing the three-dimensional shape to the three dimensional model includes using a binary matching method.