Method and system for modeling lung movement

Abstract

A method of modeling lung movement includes acquiring images of a patient's lungs during the patient's breathing cycle, detecting an electromagnetic field with a plurality of sensors attached to the patient, determining location data from signals received from the plurality of sensors, and modeling the patient's lung movements based on the determined location data.

1 - 20 . (canceled) 
     
     
         21 . A method of modeling lung movement, comprising:
 acquiring an image of a patient's lungs during a fully inhaled phase of the patient's breathing cycle;   acquiring an image of a patient's lungs during a fully exhaled phase of the patient's breathing cycle;   generating an electromagnetic field;   detecting the electromagnetic field with a plurality of sensors attached to the patient;   determining location data from signals received from the plurality of sensors; and   modeling the patient's lung movements based on the determined location data.   
     
     
         22 . The method according to  claim 21 , wherein each sensor of the plurality of sensors is attached to the patient in proximity to one of a plurality of points of interest identified on at least one of the images of the patient's lungs. 
     
     
         23 . The method according to  claim 22 , further comprising identifying, on at least one of the images of the patient's lungs, a bifurcation in the patient's airways as one of the plurality of points of interest. 
     
     
         24 . The method according to  claim 22 , wherein modeling the patient's lung movements includes predicting locations of the plurality of points of interest at phases of the breathing cycle between said fully inhaled and fully exhaled phases of the patient's breathing cycle. 
     
     
         25 . The method according to  claim 22 , further comprising modeling movement of at least one of the plurality of points of interest during the patient's breathing cycle by identifying a phase in the breathing cycle between the fully inhaled phase and the fully exhaled phase at which the at least one point of interest was identified in a plurality of acquired images of the patient's lungs. 
     
     
         26 . The method according to  claim 25 , using the determined location data and application of the modeling to predict three-dimensional location data of at least one of the plurality of points of interest at phases of the patient's breathing cycle between the fully inhaled phase and the fully exhaled phase of the breathing cycle. 
     
     
         27 . The method according to  claim 22 , further comprising modeling movement of at least one of the plurality of points of interest during the patient's breathing cycle by calculating a polynomial transformation based on identification of a phase in the breathing cycle between the fully inhaled phase and the fully exhaled phase at which the at least one point of interest was identified in a plurality of acquired images of the patient's lungs. 
     
     
         28 . The method according to  claim 27 , using the determined location data and application of the calculated polynomial transformation to predict three-dimensional location data of at least one of the plurality of points of interest at phases of the patient's breathing cycle between the fully inhaled phase and the fully exhaled phase of the breathing cycle. 
     
     
         29 . The method according to  claim 21 , further comprising generating a skeletal tree model of the patient's lungs. 
     
     
         30 . A method of modeling movement of a patient's lungs, comprising:
 identifying at least one point of interest in a plurality of acquired images of the patient's lungs;   determining positions of sensors attached to the patient while the patient is breathing by detecting an electromagnetic field with the sensors;   modeling movement of the at least one point of interest by calculating a polynomial transformation based on identification of a phase in the breathing cycle between the fully inhaled phase and the fully exhaled phase at which the at least one point of interest was identified in a plurality of acquired images of the patient's lungs; and   predicting three-dimensional location data of the at least one identified point of interest at phases of the patient's breathing cycle between a fully inhaled phase and a fully exhaled phase of the breathing cycle based on the determined positions of the sensors and application of the calculated polynomial transformation.   
     
     
         31 . The method according to  claim 30 , wherein the plurality of acquired images of the patient's lungs include an image of the patient's lungs during the fully inhaled phase of the breathing cycle. 
     
     
         32 . The method according to  claim 30 , wherein the plurality of acquired images of the patient's lungs include an image of the patient's lungs during the fully exhaled phase of the breathing cycle. 
     
     
         33 . A method of modeling lung movement, comprising:
 identifying at least one point of interest in a plurality of acquired images of a patient's lungs;   identifying locations of a plurality of sensors attached to the patient by detecting an electromagnetic field with the plurality of sensors; and   using the identified locations of the plurality of sensors to predict locations of the identified at least one point of interest at phases of the breathing cycle between a fully inhaled phase of the patient's breathing cycle and a fully exhaled phase of the patient's breathing cycle.   
     
     
         34 . The method according to  claim 33 , further comprising identifying, on at least one image of the plurality of acquired images of the patient's lungs, a bifurcation in the patient's airways as the at least one point of interest. 
     
     
         35 . The method according to  claim 33 , wherein the at least one point of interest comprises a plurality of points of interest and each sensor of the plurality of sensors is attached to the patient in proximity to one of the plurality of points of interest. 
     
     
         36 . The method according to  claim 33 , further comprising modeling movement of the at least one point of interest during the patient's breathing cycle by identifying a phase in the breathing cycle between the fully inhaled phase and the fully exhaled phase at which the at least one point of interest was identified in a plurality of acquired images of the patient's lungs. 
     
     
         37 . The method according to  claim 36 , wherein using the identified locations of the plurality of sensors includes applying the modeling to predict three-dimensional location data of the at least one point of interest at phases of the patient's breathing cycle between the fully inhaled phase and the fully exhaled phase of the breathing cycle. 
     
     
         38 . The method according to  claim 33 , further comprising modeling movement of the at least one point of interest during the patient's breathing cycle by calculating a polynomial transformation based on identification of a phase in the breathing cycle between the fully inhaled phase and the fully exhaled phase at which the at least one point of interest was identified in a plurality of acquired images of the patient's lungs. 
     
     
         39 . The method according to  claim 38 , wherein using the identified locations of the plurality of sensors includes applying the calculated polynomial transformation to predict three-dimensional location data of the at least one point of interest at phases of the patient's breathing cycle between the fully inhaled phase and the fully exhaled phase of the breathing cycle. 
     
     
         40 . The method according to  claim 33 , wherein the plurality of acquired images of the patient's lungs include an image of the patient's lungs during the fully inhaled phase of the breathing cycle and an image of the patient's lungs during the fully exhaled phase of the breathing cycle.