Highly Automated Analysis of 4-D Cardiovascular MR Data

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DESCRIPTION (provided by applicant): Magnetic resonance (MR) imaging plays an increasingly important role in the diagnosis and management of congenital heart disease. Often, cardiovascular MR data are analyzed qualitatively. Enhanced computing power and quantitative image analysis should provide rapid, comprehensive and reproducible assessment of 4-dimensional MR data sets. Starting with development of a general-purpose cardiac image segmentation method, this proposal focuses on two groups of subjects - postoperative tetralogy of Fallot patients and patients with connective tissue disorders. These patients require accurate, serial assessment of right ventricular function and aortic dimensions, respectively. In this proposal, an image analysis methodology based on Active Appearance Models (AAM) will be applied to both tasks. During training, the AAM is built automatically from manually analyzed image examples. In the analysis stage, the AAM allows fully automated segmentation of image data using its learned knowledge of allowed shapes and appearances of objects of interest - the ventricles and the thoracic aorta. Hypotheses driving this proposal are that a) active appearance model-based segmentation can provide automated, reproducible assessment of cardiovascular MR images and increase the information content of these studies by analyzing data in four dimensions (3-D + time), eliminating operator variability and labor-intensive border tracing, and that b) complete 4-D data sets of ventricular and aortic surface morphology and motion will provide novel quantitative indices of disease status. We propose to: I) Develop and validate an active appearance model (AAM) based method for 3-D and 4-D (3-D + time) segmentation of the left and right ventricles and the thoracic aorta from volumetric MR images. 2) Use the 4-D AAM segmentation approach to develop and validate a patient-specific method for highly automated and reproducible serial analysis of the right and left ventricles and the thoracic aorta. 3) Develop a set of novel quantitative indices of ventricular and aortic morphology and function and validate the reproducibility of these measurements in postoperative tetralogy of Fallot patients and connective tissue disorder pa tients. The relationship between disease status, standard measures of ventricular function and aortic size, and novel quantitative indices will be assessed.