Medical Imaging Techniques for Characterizing Cerebral Angioarchitecture

There is a fundamental gap in how blood flow quantification can be accomplished in interventional digital subtraction angiography (DSA) to provide physicians with measurements to interpret the patient’s vascular health. Continued existence of this gap represents an important problem because, until it is filled, physicians can only rely on qualitative information of intensity changes to understand blood flow variations caused by the intervention treatment. The long-term goal is to transform DSA, the gold standard for qualitative blood flow assessment, into a fully automated quantitative measurement tool that can potentially aid clinical decision making in endovascular interventions. Current approaches focus on using signals from a single vessel to determine blood flow and have not been successful due to the slow frame rate. It is our central hypothesis that a radically different flow quantification approach, using all the information from the entire image set concurrently to overcome the slow DSA frame rate, will provide reliable blood flow measurement in all vessels. The immediate objective is to create the proposed whole-tree DSA blood flow measurement method and to test its feasibility and accuracy. Success here would justify a future clinical trial of DSA-based flow measurement. The results of our study will be used to determine whether our global approach can provide reliable flow quantification in a clinical situation. The approach is innovative, because it uses global information to reliably measurement the blood flow rate in DSA. The proposed research is significant, because it is expected to rapidly produce reliable flow measurements in all visible vessels to facilitate physician’s understanding of patient’s vascular status. The novel technique will gap the bridge between the large arteries and micro vessels, providing flow measurements in all visible vessels that can be used to study cerebral hemodynamics. Ultimately, such technique has the potential to alternate clinical decision making for better treatment prognosis to reduce healthcare cost in treating cerebral vascular diseases.