Patient breathing modeling

• US 10,292,619 B2
• Filed: 07/09/2008
• Issued: 05/21/2019
• Est. Priority Date: 07/09/2007
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: Averbuch, Dorian
• Primary Examiner(s): Long, Scott
• Application Number:
  US12/170,385
• Publication Number:
  US 20090156951A1
• Time in Patent Office: 3,968 Days
• 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 fully inhaled phase of the breathing cycle;

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

generating at least one electromagnetic field;

identifying locations of a plurality of points of interest on said static images, wherein location sensors are attached to said patient proximate said points of interest;

detecting the at least one electromagnetic field with said location sensors;

determining location data from signals received from said location sensors; and

using said location data to model the lung movements by predicting mathematically locations of said plurality of points of interest at phases of the breathing cycle between said fully inhaled and fully exhaled phases of the breathing cycle.

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 between said fully inhaled and fully exhaled phases of the breathing cycle comprises:

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

averaging the measurements;

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

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.