Recent advances in CT technology have made possible the acquisition of high resolution images of the heart in which important anatomical features such as the coronary vessels are visible. The state of the art in MR Imaging techniques, on the other hand, typically provides lower resolution but can facilitate the differentiation of viable (healthy) cardiac tissue from infarcted (injured) tissue. The registration of CT and MR images of the same heart permit the association of anatomy with function. As an example, given an infarcted region, the coronary vessels responsible for irrigating that region may be isolated and targeted for treatment (e.g., coronary bypass). We demonstrate a software package, PROTEUS, which is capable of registering CT and MR images. Since the manifestation of cardiac tissue in the two modalities differs so greatly we rely on features to perform the matching. Specifically, we employ a model-based approach that relies on segmented contours delineating the inner and outer borders of the left ventricle (LV). Given these contours, our technique is completely automatic.

Virtual reality environments provide highly interactive, natural control of the visualization process, significantly enhancing the scientific value of the data produced by medical imaging systems. Due to the computational and real-time display update requirements of virtual reality interfaces, the complexity of organ and tissue surfaces which can be displayed is limited. In this paper, we present two new algorithms for the production of anatomic surface models containing a pre-specified number of polygons from patient- or subject-specific volumetric image data sets. The advantage of these algorithms is that they efficiently tile complex surfaces with a specified number of polygons selected to optimize the trade-off between surface detail and real-time display rates. Surface detail is preserved by extracting key shape features from the segmented objects, which adaptively constrains the model tilers. To illustrate the utility of these models, we present an overview of their application in computed endoscopy and surgery planning as developed in the Virtual Reality Assisted Surgery Program (VRASP) in the Biomedical Imaging Resource.