System and method for vascular tree generation using patient-specific structural and functional data, and joint prior information

1. A computer implemented method for generating a vascular tree model, the method comprising:

receiving joint prior information associated with a patient-specific vascular model of a patient's anatomy and a patient-specific target tissue model, the joint prior information including geometrical, physiological, and/or topological priors electronically learned from intensity variation data in one or more patient-specific images of the patient's anatomy;

determining, using a processor, one or more associations between a vascular network of the patient-specific vascular model and one or more perfusion characteristics of a target tissue of the target tissue model using the joint prior information;

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;

calculating an estimation of supplied blood to the target tissue according to the generated vascular tree model; and

providing a diagnosis of cardiovascular disease according to the estimation of supplied blood to the target tissue.

2. The computer implemented method of claim 1 , 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.

3. The computer implemented method of claim 1 , 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.

4. The computer implemented method of claim 1 , 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.

5. The computer implemented method of claim 1 , 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.

6. The computer implemented method of claim 1 , 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.

7. The computer implemented method of claim 1 , 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 the nearest artery.

8. The computer implemented method of claim 7 , wherein the grouping is performed via watershed techniques, k-nearest neighbors, k-means, mean shift, and/or superpixels.

9. The computer implemented method of claim 1 , 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.

10. The computer implemented method of claim 1 , 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 the arteries.

11. The computer implemented method of claim 1 , wherein the output vascular tree model associates each of a plurality of locations in the target tissue to an artery.

12. 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:

receiving joint prior information associated with a patient-specific vascular model of a patient's anatomy and a patient-specific target tissue model, the joint prior information including geometrical, physiological, and/or topological priors electronically learned from intensity variation data in one or more patient-specific images of the patient's anatomy;

determining, using a processor, one or more associations between a vascular network of the patient-specific vascular model and one or more perfusion characteristics of a target tissue of the target tissue model using the joint prior information;

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;

calculating an estimation of supplied blood to the target tissue according to the generated vascular tree model; and

providing a diagnosis of cardiovascular disease according to the estimation of supplied blood to the target tissue.

13. The system of claim 12 , wherein data related to one or more perfusion characteristics include the 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.

14. The system of claim 12 , 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.

15. The system of claim 12 , 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.

16. The system of claim 12 , 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.

17. The system of claim 12 , 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 the nearest artery.

18. The system of claim 17 , wherein the grouping is performed via watershed techniques, k-nearest neighbors, k-means, mean shift, and/or superpixels.

19. The system of claim 12 , 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.

20. 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:

receiving joint prior information associated with a patient-specific vascular model of a patient's anatomy and a patient-specific target tissue model, the joint prior information including geometrical, physiological, and/or topological priors electronically learned from intensity variation data in one or more patient-specific images of the patient's anatomy;

determining, using a processor, one or more associations between a vascular network of the patient-specific vascular model and one or more perfusion characteristics of a target tissue of the target tissue model using the joint prior information;

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;

calculating an estimation of supplied blood to the target tissue according to the generated vascular tree model; and

providing a diagnosis of cardiovascular disease according to the estimation of supplied blood to the target tissue.