Time Curve Analysis Techniques for Dynamic Contrast MRI Studies

Clinical magnetic resonance imaging of regional myocardial perfusion has recently become possible with the use of rapid acquisitions to track the kinetics of an intravenous injection of contrast. A great deal of processing is then needed to obtain clinical parameters. In particular, methods to automatically group alike regions for an increased signalto-noise ratio and improved parameter estimates are needed. This work explores two types of time curve analysis techniques for MRI perfusion imaging: factor analysis and clustering. Both methods are shown to work for extraction of the blood input function, with the clustering method appearing to be more robust. The availability of an accurate blood input function then enables more complex approaches to automatically fitting all of the relevant data to appropriate models. These more complex approaches are formulated here and tested in a preliminary fashion.