UNIFIED APPROACH TO QUANTIFY & VISUALIZE CARDIAC IMAGERY

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Cardiac imaging has emerged as an important tool for assessing the diagnosis and prognosis of patients with coronary artery disease. The extent and severity of coronary arterial obstruction (provided by coronary arteriography), along with the degree of myocardial hypoperfusion (provided by perfusion tomography) and the extent of myocardial dysfunction (provided for instance with the measurement of myocardial thickening by magnetic resonance imaging (MRI) together, have long been recognized to be directly related to morbidity and mortality in patients with coronary artery disease. Yet, in clinical practice these imaging studies are most often viewed independently of each other and conventionally displayed in only 2 dimensions, severely limiting the clinicians ability to synthesize the true extent of the abnormalities. Accurate assessment of the extent and severity of coronary artery disease requires the multidimensional (equal to or greater than 3) integration of anatomic and physiologic information obtained independently from these cardiac imaging modalities. However, this integration has traditionally been subjective, time consuming, lacking standardization and difficult to conceptualize multidimensionally. The long term objectives of the proposed research in this competing continuation application are to overcome these limitations by continuing to develop and validate computer-based methods to multidimensionally quantify, unify and visualize the coronary arterial tree and the distributions of myocardial perfusion and thickening. During this next funding period, collaborators from Emory University and the Georgia Institute of Technology propose to continue to focus their efforts in fully completing the development and validation of each of the following methodologies: 1) complete automation of multidimensional reconstruction of the coronary vasculature from arbitrary, non-orthogonal biplane angiographic projections using non-parallel geometry, 2) multidimensional sampling and rendering of myocardial perfusion distributions, 3) multidimensional count based quantification of the amplitude and phase of the onset of myocardial thickening from multigated perfusion tomographic studies, and 4) complete automation of the unification of the multidimensional coronary vasculature with myocardial distributions. In addition, the unification concept will be extended to include myocardial thickening distributions from MRI. The extensive progress thus far achieved in these aims during the initial funding period is strong evidence to support further research, development, and clinical implementation of this multidimensional unified approach.