Visualization Tools for Peripheral CT-Angiography

BACKGROUND: Peripheral arterial occlusive disease (PAOD) is a significant health problem in the industrialized world. Latest technology multi-slice computed tomography angiography (CTA) has the potential to non-invasively image the entire lower extremity arterial tree. Image segmentation and visualization techniques are a prerequisite for effective interpretation CTA. However, due to the large dataset volumes and due to the specific abnormalities found in PAOD, currently available image post-processing methods are unsuited for visualizing the lower extremity vascular territory. AIMS: We intend to develop new, time efficient methods for visualizing PAOD, imaged with CTA. These methods may allow CTA to become a clinically applicable imaging modality for peripheral vascular disease in the future. METHODS: We propose an interdisciplinary approach, with one clinical-radiological site and two technical development sites in the field of computer graphics. We expect to complete our research within a two year study period. During the initial phase of our research we will develop new algorithms for (semi)automated removal of bones from the datasets and for automated vessel centerline detection. These algorithms will enable us to apply well known visualization techniques such as Maximum Intensity Projection (MIP) and Curved Planar Reformation (CPR) to the new vascular territory of the lower extremities. During the remainder of the project we will explore and develop completely new segmentation and visualization techniques. Segmentation topics will encompass vessel enhancement techniques using multiscale-filtering, and a new model-based vessel identification approach, which utilizes a-priori knowledge of the vessel tree spatial arrangement to identify vascular structures in the datasets. We will develop new visualization techniques for displaying the vessel lumen only ("luminogram"), and use non-photorealistic visualization techniques. A third focus of this project is dedicated to optimal handling of large datasets - such a those from peripheral CTA. With the successful completion of this project, we hope to lay the ground for non-invasive CTA to ultimately replace invasive and costly intra-arterial catheter angiography as a pre-therapeutic imaging modality in patients with PAOD