Systems and methods for processing electronic images to predict lesions

Abstract

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

1 - 29 . (canceled) 
     
     
         30 . A system for analysis of a vessel, the system comprising:
 at least one memory storing instructions; and   at least one processor operatively connected to the at least one memory and configured to execute the instructions to perform operations, including:
 receiving a plurality of images of a patient's vessel; 
 determining a location of a pathology in the vessel, in at least some of the plurality of images; 
 creating a signal describing a predetermined attribute at the location of the pathology, over time; 
 determining a value of a functional measurement for the pathology, based on the signal; and 
 displaying the value on a user interface device. 
   
     
     
         31 . The system of  claim 30 , wherein the operations further include determining the location of the pathology based on structural features of the vessel in at least one image from the plurality of images. 
     
     
         32 . The system of  claim 30 , wherein the operations further include:
 determining the location of the pathology in a first image from the plurality of images;   tracking the pathology in subsequent images from the plurality of images to determine the location of the pathology in the subsequent images; and   determining a value of the predetermined attribute at each location in each of the subsequent images, to create the signal.   
     
     
         33 . The system of  claim 32 , wherein the first image was generated via application of contrast agent to the patient. 
     
     
         34 . The system of  claim 30 , wherein the operations further include extracting a temporal feature from the signal and determining the value of the functional measurement based on the temporal feature. 
     
     
         35 . The system of  claim 34 , wherein the operations further include inputting the temporal feature to an estimator to determine the value of the functional measurement. 
     
     
         36 . The system of  claim 35 , wherein the operations further include inputting a structural feature of the pathology to the estimator to determine the value of the functional measurement. 
     
     
         37 . The system of  claim 35 , wherein the estimator includes a regressor. 
     
     
         38 . The system of  claim 34  wherein the temporal feature includes a calculation of a combination of attribute values determined from at least some of the plurality of images. 
     
     
         39 . The system of  claim 38  wherein the operations further include:
 assigning a weight to each attribute determined from the plurality of images, to obtain weighted attribute values; and 
 calculating a combination of the weighted attribute values to create the signal. 
 
     
     
         40 . The system of  claim 30 , wherein the predetermined attribute includes pixel intensity. 
     
     
         41 . A method for determining a Fractional Flow Reserve (FFR) value for a pathology in a vessel, the method comprising:
 extracting, from a location of the pathology in a plurality images of the vessel, values of a predetermined attribute;   calculating a temporal feature based on the values of the predetermined attribute;   inputting the temporal feature to an estimator; and   obtaining, from an output of the estimator, an FFR value for the pathology.   
     
     
         42 . The method of  claim 41 , further comprising:
 inputting a structural feature of the pathology to the estimator to obtain the FFR value based on the temporal feature and the structural feature.   
     
     
         43 . The method of  claim 41 , further comprising:
 tracking the location of the pathology throughout the plurality of images of the vessel; and   extracting a value of the predetermined attribute from the location of the pathology in at least some of the plurality of images,   wherein the temporal feature includes a calculation of the values of the predetermined attributes extracted from the plurality of images.   
     
     
         44 . The method of  claim 43 , wherein the temporal feature includes a calculation of weighted attributes. 
     
     
         45 . The method of  claim 44 , further comprising:
 assigning a weight to each of the predetermined attributes extracted from the plurality of images, based on a probability of pathology detection in each image of the plurality of images.   
     
     
         46 . The method of  claim 41  comprising:
 extracting a first value of the predetermined attribute from the location of the pathology in a first image; 
 extracting a second value of the predetermined attribute from the location of the pathology in a second image; and 
 combining the first and second values of attribute to obtain the FFR value for the pathology. 
 
     
     
         47 . The method of  claim 41 , further comprising:
 obtaining a first FFR value of the pathology in a first image of the vessel;   obtaining a second FFR value of the pathology in a second image of the vessel; and   combining the first and second values of FFR to obtain the FFR value for the pathology.   
     
     
         48 . The method of  claim 41 , further comprising:
 displaying the FFR value for the pathology on a user interface device.   
     
     
         49 . A non-transitory computer-readable medium comprising instructions for determining a Fractional Flow Reserve (FFR) value for a pathology in a vessel, in instructions executable by at least one processor to perform operations, including:
 extracting, from a location of the pathology in a plurality images of the vessel, values of a predetermined attribute;   calculating a temporal feature based on the values of the predetermined attribute;   inputting the temporal feature to an estimator; and   obtaining, from an output of the estimator, an FFR value for the pathology.