Cardiovascular diseases are one of the major causes of human morbidity and mortality, calling for novel and more efficient methods of treatment. Non-invasive diagnostic procedures are therefore of particular importance in managing the diseases. Among the different diagnostic modalities available today, magnetic resonance imaging (MRI) stands out as a potential single non-invasive tool for a comprehensive cardiac examination, allowing the assessment of vessel morphology and the quantification of blood flow through larger vessels. Despite its great potential, MRI exhibits limitations in particular when information with very high spatial and/or temporal resolution is needed. It has also been shown that inter-individual differences in vessel geometry have considerable impact on the flow regime. Accordingly, the applicability of generalized models and experimental setups for studying human blood flow is limited. Due to these restrictions it cannot be expected, that MR imaging can directly provide all data necessary for diagnosis and interventional planning. It has to be combined with advanced image analysis and simulation tools, which allow to extract and to reliably extrapolate data from the MR measurements, leading to a dense and detailed spatio- temporal description of the vascular flow. The objective of this project is therefore to create the fundaments of a comprehensive computational framework unifying appropriate MR imaging methods, image analysis, model building and visualization algorithms, and simulation techniques based on computational fluid dynamics (CFD), providing a highly efficient tool for the clinicians to select the best therapy for cardiovascular malfunctions.
Partners:Indian Institute of Technology, Kanpur, India