Feature-based registration method

• US 9,659,374 B2
• Filed: 07/24/2015
• Issued: 05/23/2017
• 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:
  US14/808,454
• Publication Number:
  US 20150332462A1
• Time in Patent Office: 669 Days
• 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 body volume with a real-time location of a sensor, the method comprising:

acquiring a plurality of CT scans;

assembling the plurality of CT scans into a three-dimensional model;

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

establishing a data stream between the sensor and a system processor;

processing the location data to form a three-dimensional shape to generate a cloud of voxels, wherein a density of a voxel generated from the location data is weighted as a function of a speed of the sensor through the body lumen; and

comparing the three-dimensional shape to the three-dimensional model to register the three-dimensional shape to the three-dimensional model such that the three-dimensional shape is within boundaries of the three-dimensional model.

2. The method of claim 1, further comprising defining a plurality of parameters having predefined thresholds.

3. The method of claim 2, wherein defining parameters includes defining a density range required for each voxel of the cloud of voxels of the three-dimensional shape.

4. The method of claim 3, wherein defining parameters includes defining a proximity from an already-designated voxel with the cloud of voxels that meet the predefined thresholds of the plurality of parameters.

5. The method of claim 3, wherein defining parameters includes defining a parameter template including multiple parameters.

6. The method of claim 5, wherein defining the parameter template requires the cloud of voxels to have a certain density corresponding to air.

7. The method of claim 6, wherein defining the parameter template requires the cloud of voxels to be located adjacent another voxel having the certain density corresponding to air.

8. The method of claim 7, wherein defining the parameter template requires the cloud of voxels to be adjacent to voxels having densities corresponding to blood vessels.

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

10. The method of claim 9, further comprising correlating a value of each voxel to a frequency with which each voxel of each of the cloud of voxels encounters the sensor.

11. The method of claim 10, further comprising adjusting a density of the cloud of voxels in accordance with the value of each voxel.

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

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

14. The method of claim 1, wherein the comparing step further includes developing an initial guess.

15. The method of claim 14, wherein the comparing step further includes using the initial guess to establish a feature-based registration.

16. The method of claim 15, wherein the comparing step further includes calculating a difference between the three-dimensional model and the three-dimensional shape.

17. The method of claim 1, wherein comparing the three-dimensional shape to the three-dimensional model includes using a binary matching method.

18. The method of claim 1, wherein a density of a voxel is inversely proportional to an advancement speed of the sensor passing at a location of the body lumen, which corresponds to the voxel.