Method of using lung airway carina locations to improve ENB registration

• US 10,709,352 B2
• Filed: 10/18/2016
• Issued: 07/14/2020
• Est. Priority Date: 10/27/2015
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: Costello, David M., Koyrakh, Lev A., Calcutt, Michael E.
• Primary Examiner(s): Brutus, Joel F
• Application Number:
  US15/296,141
• Publication Number:
  US 20170112411A1
• Time in Patent Office: 1,365 Days
• Field of Search: None
• CPC Class Codes: A61B 1/2676 Bronchoscopes
• CPC Class Codes: A61B 10/04 Endoscopic instruments
• CPC Class Codes: A61B 2017/00809 Lung operations
• CPC Class Codes: A61B 2034/105 Modelling of the patient, e.g. for ligaments or bones
• CPC Class Codes: A61B 2034/2051 Electromagnetic tracking systems
• CPC Class Codes: A61B 2090/363 Use of fiducial points
• CPC Class Codes: A61B 2090/365 augmented reality, i.e. correlating a live optical image with another image
• CPC Class Codes: A61B 2090/367 creating a 3D dataset from 2D images using position information
• CPC Class Codes: A61B 2090/3762 using computed tomography systems [CT]
• CPC Class Codes: A61B 2090/3925 ultrasonic
• 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 5/066 Superposing sensor position on an image of the patient, e.g. obtained by ultrasound or x-ray imaging
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/466 adapted to display 3D data
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Disclosed are systems, devices, and methods for registering a luminal network to a 3D model of the luminal network. An example method comprises generating a 3D model of a luminal network, identifying a target within the 3D model, determining locations of a plurality of carinas in the luminal network proximate the target, displaying guidance for navigating a location sensor within the luminal network, tracking the location of the location sensor, comparing the tracked locations of the location sensor and the portions of the 3D model representative of open space, displaying guidance for navigating the location sensor a predetermined distance into each lumen originating at the plurality of carinas proximate the target, tracking the location of the location sensor while the location sensor is navigated into each lumen, and updating the registration of the 3D model with the luminal network based on the tracked locations of the location sensor.

Claims

1. A method of registering a luminal network to a 3D model of the luminal network, the method comprising:

generating a 3D model of a luminal network based on images of the luminal network;

identifying a target within the 3D model of the luminal network;

determining locations of a plurality of carinas in the luminal network proximate the target;

displaying guidance for navigating a location sensor within the luminal network;

tracking a location of the location sensor while the location sensor is navigated within the luminal network;

registering the 3D model of the luminal network to the luminal network based on a tracked location of the location sensor within the luminal network;

displaying guidance for navigating the location sensor into each of a plurality of lumens originating at each of the plurality of carinas proximate the target;

tracking the location of the location sensor while the location sensor is navigated into each of the plurality of lumens;

locally registering the 3D model of the luminal network to the luminal network proximate the target based on the tracked location of the location sensor within each of the plurality of lumens; and

augmenting a registration of the 3D model of the luminal network to the luminal network with a local registration.

2. The method of claim 1, wherein the luminal network is an airway of a patient.

3. The method of claim 2, wherein the 3D model is a model of the airway of the patient.

4. The method of claim 1, wherein the carinas are used as fiducial markers for identifying the location of the target.

5. The method according to claim 1, further comprising:

using thin plate spline interpolation to non-rigidly register the 3D model of the luminal network to the luminal network based on the tracked location of the location sensor within the luminal network; and

augmenting the non-rigid registration of the 3D model of the luminal network to the luminal network with the local registration.

6. The method according to claim 1, further comprising using thin plate spline interpolation to locally register the 3D model of the luminal network to the luminal network proximate the target.

7. The method according to claim 1, further comprising:

determining if the local registration of the 3D model of the luminal network to the luminal network proximate the target is complete; and

displaying further guidance for navigating the location sensor into each of the plurality of lumens originating at each of the plurality of carinas proximate the target if it is determined that the local registration proximate the target is not complete.

8. The method according to claim 1, further comprising:

identifying at least one additional target within the 3D model of the luminal network;

determining if the local registration of the 3D model of the luminal network to the luminal network proximate the at least one additional target is complete; and

displaying further guidance for navigating the location sensor into each of a plurality of lumens originating at a plurality of carinas proximate the at least one additional target if it is determined that the local registration proximate the at least one additional target is not complete.

9. The method according to claim 1, further comprising:

storing, in a memory of a computing device, the registration of the 3D model of the luminal network to the luminal network; and

augmenting the stored registration of the 3D model of the luminal network to the luminal network with the local registration.

10. A system for registering a luminal network to a 3D model of the luminal network, the system comprising:

a location sensor capable of being navigated within a luminal network inside a patient'"'"'s body;

an electromagnetic field generator configured to detect a location of the location sensor as it is navigated within the luminal network; and

a computing device including a processor and a memory storing instructions which, when executed by the processor, cause the computing device to;

generate a 3D model of the luminal network based on images of the luminal network;

identify a target within the 3D model of the luminal network;

determine locations of a plurality of carinas in the luminal network proximate the target;

display guidance for navigating the location sensor within the luminal network;

track a location of the location sensor while the location sensor is navigated within the luminal network;

register the 3D model of the luminal network to the luminal network based on a tracked location of the location sensor within the luminal network;

display guidance for navigating the location sensor into each of a plurality of lumens originating at each of the plurality of carinas proximate the target;

track the location of the location sensor while the location sensor is navigated into each of the plurality of lumens;

locally register the 3D model of the luminal network to the luminal network proximate the target based on the tracked location of the location sensor within each of the plurality of lumens; and

augment a registration of the 3D model of the luminal network to the luminal network with a local registration.

11. The system of claim 10, wherein the luminal network is an airway of a patient.

12. The system of claim 11, wherein the 3D model is a model of the airway of the patient.

13. The system of claim 10, wherein the carinas are used as fiducial markers for identifying the location of the target.

14. A non-transitory computer-readable storing medium storing instructions which, when executed by a processor, cause a computing device to:

generate a 3D model of a luminal network based on images of the luminal network;

identify a target within the 3D model of the luminal network;

determine locations of a plurality of carinas in the luminal network proximate the target;

display guidance for navigating a location sensor within the luminal network;

track a location of the location sensor while the location sensor is navigated within the luminal network;

register the 3D model of the luminal network to the luminal network based on a tracked location of the location sensor within the luminal network;

display guidance for navigating the location sensor into each of a plurality of lumens originating at each of the plurality of carinas proximate the target;

track the location of the location sensor while the location sensor is navigated into each of the plurality of lumens; and

augment a registration of the 3D model of the luminal network to the luminal network with a local registration.

15. The non-transitory computer-readable storage medium of claim 14, wherein the luminal network is an airway of a patient.

16. The non-transitory computer-readable storage medium of claim 14, wherein the 3D model is a model of the airway of the patient.

17. The non-transitory computer-readable storage medium of claim 14, wherein the carinas are used as fiducial markers for identifying the location of the target.