PATIENT BREATHING MODELING

• US 20090156951A1
• Filed: 07/09/2008
• Published: 06/18/2009
• Est. Priority Date: 07/09/2007
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: superDimension, Ltd. (Medtronic Plc)
• Inventors: Averbuch, Dorian
• Granted Patent: US 10,292,619 B2
• Time in Patent Office: Days
• US Class Current: 600/534
• CPC Class Codes: A61B 1/267 for the respiratory tract, e.g. laryngoscopes, bronchoscopes
• CPC Class Codes: A61B 2034/105 Modelling of the patient, e.g. for ligaments or bones
• CPC Class Codes: A61B 5/06 Devices, other than using radiation, for detecting or locating foreign bodies for removing same A61B17/50 ; determining position of probes within or on the body of the patient
• 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 6/541 involving acquisition triggered by a physiological signal

Abstract

A method of accounting for the movement of the lungs during an endoscopic procedure such that a previously acquired image may be dynamically registered with a display showing the position of a locatable guide. After an initial image set is acquired, an area of interest is identified and the movement thereof due to breathing is mathematically modeled. Patient movement sensors are then used to provide information on the patient'"'"'s breathing patterns. This information is entered into the mathematical model in order to update the registration between the image set and a display showing the position of the locatable guide.

Claims

1. A method of modeling lung movements due to breathing in a patient comprising:

acquiring at least one static image of a patient'"'"'s lungs during a predetermined phase of the breathing cycle;

identifying locations of a plurality of points of interest on said at least one static image;

predicting mathematically locations of said plurality of points of interest at phases of the breathing cycle other than said predetermined phase.

2. The method of claim 1 wherein said acquiring at least one static image of a patient'"'"'s lungs during a predetermined phase of the breathing cycle comprises acquiring a static image at a phase in the breathing cycle that is between inhalation and exhalation.

3. The method of claim 1 wherein said acquiring at least one static image of a patient'"'"'s lungs during a predetermined phase of the breathing cycle comprises acquiring a static image at a phase in the breathing cycle that is between exhalation and inhalation.

4. The method of claim 1 wherein said acquiring at least one static image of a patient'"'"'s lungs during a predetermined phase of the breathing cycle comprises acquiring a static image at a phase in the breathing cycle that is between inhalation and exhalation and acquiring a second image in at a phase in the breathing cycle that is between exhalation and inhalation.

5. The method of claim 1 wherein identifying locations of a plurality of points of interest on said at least one static image comprises identifying, on said at least one static image, a plurality of bifurcations in the airways.

6. The method of claim 1 wherein predicting, mathematically, locations of said plurality of points of interest at phases of the breathing cycle other than said predetermined phase comprises developing a mathematical algorithm that models the lung movement of the patient.

7. The method of claim 1 wherein predicting, mathematically, locations of said plurality of points of interest at phases of the breathing cycle other than said predetermined phase comprises calculating a polynomial transformation.

8. The method of claim 1 wherein predicting, mathematically, locations of said plurality of points of interest at phases of the breathing cycle other than said predetermined phase comprises:

measuring movement due to breathing for at a plurality of points in a plurality of patients;

averaging the results of said plurality of points in said plurality of patients;

creating a 3-dimensional vector field representative of average movements in the lungs;

applying a vector corresponding to an area of interest in the lung to said at least one image in order to predict a location of said area of interest at a given point in the breathing cycle.

9. The method of claim 1 wherein predicting, mathematically, locations of said plurality of points of interest at phases of the breathing cycle other than said predetermined phase comprises creating a skeletal tree of the lungs and modeling the skeletal motion of the lungs.

10. A method of registering at least one previously-acquired image to a real-time representation of a probe location, comprising:

attaching at least one patient sensor to the patient;

monitoring locations of said at least one patient sensor as the patient inhales and exhales;

determining a point in time said patient is in a breathing cycle based on said locations of said at least one patient sensor;

selecting one of said at least one previously-acquired images taken closest to said point in time said patient is in said breathing cycle;

superimposing said selected one of said at least one previously-acquired images over said real-time representation of said probe location.

11. A method of representing a real-time location of a probe on a previously-acquired image, comprising:

measuring a breathing cycle of a patient;

monitoring a real-time location of a probe inside the patient;

manipulating said real-time data such that said probe appears in a correct location on one of at least one previously-acquired images.

12. The method of claim 11 wherein manipulating said real-time data such that said probe appears in a correct location of said real-time location of said image comprises:

monitoring locations of said at least one patient sensor as the patient inhales and exhales;

determining a point in a breathing cycle said patient is based on said locations of said at least one patient sensor;

selecting one of said at least one previously-acquired images taken closest to said point said patient is in said breathing cycle;

superimposing said selected one of said at least one previously-acquired images over said real-time representation of said probe location.

13. The method of claim 11 wherein manipulating said real-time data such that said probe appears in a correct location of said real-time location of said image comprises:

monitoring locations of said at least one patient sensor as the patient inhales and exhales;

determining a point in a breathing cycle said patient is based on said locations of said at least one patient sensor;

selecting one of said at least one previously-acquired images;

superimposing said selected one of said at least one previously-acquired images over said real-time representation of said probe location;

warping said previously-acquired image according to said monitored locations of said patient sensors such that said real-time representation of said probe location appears in an accurate position on said previously-acquired image.

14. A method of navigating a probe through airways of the lungs of a patient comprising:

measuring patient characteristics;

imaging said patient to acquire one or more images;

identifying an area of interest in the lungs of said patient;

collecting data regarding positions of key points on the patient'"'"'s body while the patient is breathing;

developing a mathematical model of movement due to breathing using said data;

inserting a probe into said patient;

displaying at least one of said acquired images;

displaying a real-time indication of a position of said probe on said at least one of said acquired images;

using said mathematical model to adjust relative positions of said real-time indication and said at least one acquired images such that said real-time image is accurately portrayed on said at least one acquired image.

15. The method of claim 14 wherein measuring patient characteristics comprises taking measurements selected from the set consisting of lung size, lung shape, lung health, altitude, patient age, diaphragm size, and diaphragm health.

16. The method of claim 14 wherein collecting data regarding positions of key points on the patient'"'"'s body while the patient is breathing comprises collecting data from sensors placed on the patient.

17. The method of claim 14 wherein developing a mathematical model of movement due to breathing using said data comprises creating a skeletal tree of the lungs and modeling the skeletal motion of the lungs.

18. The method of claim 14 wherein developing a mathematical model of movement due to breathing using said data comprises:

measuring movement due to breathing for at a plurality of points in a plurality of patients;

averaging the results of said plurality of points in said plurality of patients;

creating a 3-dimensional vector field representative of average movements in the lungs;

applying a vector corresponding to an area of interest in the lung to said at least one image in order to predict a location of said area of interest at a given point in the breathing cycle.

19. The method of claim 14 wherein developing a mathematical model of movement due to breathing using said data comprises developing a mathematical algorithm that models the lung movement of the patient.

20. The method of claim 14 wherein developing a mathematical model of movement due to breathing using said data comprises calculating a polynomial transformation.

21. The method of claim 19 wherein developing a mathematical algorithm that models the lung movement of the patient comprises collecting data on a number of subjects in a study and relating movement of given locations in said subjects to points in the respective breathing cycles of said subjects in which the positions were acquired.