A computer implemented method for simulating microvascular networks to guide diagnosis or treatment of cardiovascular disease, the method comprising: receiving a patient-specific coronary model representing coronary arteries of a patient's anatomy; receiving a patient-specific model of a target tissue in which a blood flow may be estimated; receiving data related to one or more patient-specific perfusion characteristics of the target tissue, said data including 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; determining, using a processor, an association between coronary arteries of the patient-specific coronary model and respective perfusion regions in the tissue, dependent on the perfusion characteristics of the target tissue, either: - 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, or - by segmenting the target tissue into perfused regions using a shape model with a built in association with the arteries, wherein fitting of said shape model is driven using the perfusion characteristics of the tissue and using the patient-specific coronary model; generating a complete vascular tree model of the patient's coronary vasculature extending from the coronary arteries to the associated perfusion regions of the target tissue via a simulated microvascular tree network, by growing simulated microvascular trees using a constrained constructive optimization approach wherein tree growth is biased to avoid crossing changes in the perfusion characteristics of the target tissue; outputting the complete vascular tree model of the patient's coronary vasculature including the simulated microvascular tree network; and simulating tissue perfusion for the patient using the complete vascular tree model of the patient's coronary vasculature. receiving a patient-specific coronary model representing coronary arteries of a patient's anatomy; receiving a patient-specific model of a target tissue in which a blood flow may be estimated; receiving data related to one or more patient-specific perfusion characteristics of the target tissue, said data including 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; determining, using a processor, an association between coronary arteries of the patient-specific coronary model and respective perfusion regions in the tissue, dependent on the perfusion characteristics of the target tissue, either: - 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, or - by segmenting the target tissue into perfused regions using a shape model with a built in association with the arteries, wherein fitting of said shape model is driven using the perfusion characteristics of the tissue and using the patient-specific coronary model; - 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, or - by segmenting the target tissue into perfused regions using a shape model with a built in association with the arteries, wherein fitting of said shape model is driven using the perfusion characteristics of the tissue and using the patient-specific coronary model; generating a complete vascular tree model of the patient's coronary vasculature extending from the coronary arteries to the associated perfusion regions of the target tissue via a simulated microvascular tree network, by growing simulated microvascular trees using a constrained constructive optimization approach wherein tree growth is biased to avoid crossing changes in the perfusion characteristics of the target tissue; outputting the complete vascular tree model of the patient's coronary vasculature including the simulated microvascular tree network; and simulating tissue perfusion for the patient using the complete vascular tree model of the patient's coronary vasculature.