System and method for processing electronic images for vascular tree generation

Abstract

Systems and methods are disclosed for simulating microvascular networks from a vascular tree model to simulate tissue perfusion under various physiological conditions to guide diagnosis or treatment for cardiovascular disease. One method includes: receiving a patient-specific vascular model of a patient's anatomy, including a vascular network; receiving a patient-specific target tissue model in which a blood supply may be estimated; receiving joint prior information associated with the vascular model and the target tissue model; receiving data related to one or more perfusion characteristics of the target tissue; determining one or more associations between the vascular network of the patient-specific vascular model and one or more perfusion characteristics of the target tissue using the joint prior information; and outputting a vascular tree model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network and the perfusion characteristics.

1 - 20 . (canceled) 
     
     
         21 . A computer implemented method for generating a vascular tree model, the method comprising:
 determining, using a processor, one or more associations between a vascular network of a patient-specific vascular model and one or more perfusion characteristics of a target tissue of a patient-specific target tissue model using intensity variation data in one or more patient-specific images of a patient's anatomy;   generating, using a processor, a vascular tree model including a microvascular model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue; and   providing a diagnosis of cardiovascular disease based on the generated vascular tree model.   
     
     
         22 . The computer implemented method of  claim 21 , wherein data related to one or more perfusion characteristics include data obtained from CT perfusion scans, PET perfusion scans, SPECT perfusion scans, MR perfusion scans, data pertaining to stress echo information, correlation data, intensity variation data, vessel size, vessel shape, vessel tortuosity, vessel length, vessel thickness, or a combination thereof. 
     
     
         23 . The computer implemented method of  claim 21 , wherein anatomical characteristics of the vascular model include, one or more of:
 location and geometry of large vessel outlets;   location and geometry of arteries, arterioles, and capillaries; and   location and degree of stenosis.   
     
     
         24 . The computer implemented method of  claim 21 , wherein, the patient-specific vascular model of a patient's anatomy, the target tissue model, or a combination thereof, is obtained via segmentation of the one or more images, including but not limited to CTA, PET, SPECT, or MR imaging techniques. 
     
     
         25 . The computer implemented method of  claim 21 , wherein the patient-specific vascular model of a patient anatomy and the patient-specific target tissue model includes, one or more of:
 a coronary vascular model and a myocardium;   a cerebral vascular model and a brain;   a peripheral vascular model and a muscle;   a hepatic vascular model and a liver;   a renal vascular model and a kidney;   a visceral vascular model and a bowel; or   any target organ and vascular model with vessels supplying blood to the target organ.   
     
     
         26 . The computer implemented method of  claim 21 , wherein the association between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue is determined by associating a location on the target tissue with an artery, arteriole, or capillary located closest with a Euclidean distance weighted by changes in the perfusion characteristics such that variations of the perfusion characteristics may be considered in the distance computation. 
     
     
         27 . The computer implemented method of  claim 21 , wherein the association between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue is determined by grouping the perfusion characteristics and associating each group with a nearest artery. 
     
     
         28 . The computer implemented method of  claim 27 , wherein the grouping is performed via watershed techniques, k-nearest neighbors, k-means, mean shift, and/or superpixels. 
     
     
         29 . The computer implemented method of  claim 21 , wherein the association between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue is determined by generating a vascular tree model, the association subsequently being used to generate another vascular tree model. 
     
     
         30 . The computer implemented method of  claim 21 , wherein the association of the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue is determined by segmenting the target tissue into perfused regions using a shape model with a built in association with arteries of the vascular network. 
     
     
         31 . The computer implemented method of  claim 21 , wherein the generated vascular tree model associates each of a plurality of locations in the target tissue to an artery. 
     
     
         32 . A system for generating a vascular tree model, the system comprising:
 a data storage device storing instructions for   generating a vascular tree model; and   a processor configured to execute the instructions to perform a method including the steps of:
 determining, using a processor, one or more associations between a vascular network of a patient-specific vascular model and one or more perfusion characteristics of a target tissue of a patient-specific target tissue model using intensity variation data in one or more patient-specific images of a patient's anatomy; 
 generating, using a processor, a vascular tree model including a microvascular model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue; and 
 providing a diagnosis of cardiovascular disease based on the generated vascular tree model. 
   
     
     
         33 . The system of  claim 32 , wherein data related to one or more perfusion characteristics include a measured or estimated perfusion attenuation map or territory, data obtained from CT perfusion scans, PET perfusion scans, SPECT perfusion scans, MR perfusion scans, data pertaining to stress echo information, correlation data, intensity variation data, vessel size, vessel shape, vessel tortuosity, vessel length, vessel thickness, or a combination thereof. 
     
     
         34 . The system of  claim 32 , wherein anatomical characteristics of the vascular model include, one or more of:
 location and geometry of large vessel outlets;   location and geometry of arteries, arterioles, and capillaries; and   location and degree of stenosis.   
     
     
         35 . The system of  claim 32 , wherein the patient-specific vascular model of a patient anatomy and the patient-specific target tissue model includes, one or more of:
 a coronary vascular model and a myocardium;   a cerebral vascular model and a brain;   a peripheral vascular model and a muscle;   a hepatic vascular model and a liver;   a renal vascular model and a kidney;   a visceral vascular model and a bowel; or   any target organ and vascular model with vessels supplying blood to the target organ.   
     
     
         36 . The system of  claim 32 , wherein the association between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue is determined by associating a location on the target tissue with an artery, arteriole, or capillary located closest with a Euclidean distance weighted by changes in the perfusion characteristics such that variations of the perfusion characteristics may be considered in a Euclidean distance computation. 
     
     
         37 . The system of  claim 32 , wherein the association between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue is determined by grouping the perfusion characteristics and associating each group with a nearest artery. 
     
     
         38 . The system of  claim 37 , wherein the grouping is performed via watershed techniques, k-nearest neighbors, k-means, mean shift, and/or superpixels. 
     
     
         39 . The system of  claim 32 , wherein the association between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue is determined by generating a vascular tree model, the association subsequently being used to generate another vascular tree model. 
     
     
         40 . A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for performing a method for generating a vascular tree model, the method comprising:
 determining, using a processor, one or more associations between a vascular network of a patient-specific vascular model and one or more perfusion characteristics of a target tissue of a patient-specific target tissue model using intensity variation data in one or more patient-specific images of a patient's anatomy;   generating, using a processor, a vascular tree model including a microvascular model that extends to perfusion regions in the target tissue, using the determined associations between the vascular network of the patient-specific vascular model and the perfusion characteristics of the target tissue; and   providing a diagnosis of cardiovascular disease based on the generated vascular tree model.