Smart Interactive Vessel Visualization in Radiology

Abstract

Cardiovascular diseases occur with increasing frequency in our society. Their diagnosis often requires tailored visualization techniques, e.g., to examine the blood flow channel in case of luminal narrowing. Curved Planar Reformation (CPR) addresses this field by creating longitudinal sections along the centerline of blood vessels. With the possibility to rotate around an axis, the entire vessel can be assessed for possible vascular abnormalities (e.g., calcifications on the vessel wall, stenoses, and occlusions).In this thesis, we present a visualization technique, called Centerline Reformation (CR), that offers the possibility to investigate the interior of any blood vessel, regardless of its spatial orientation. Starting from the projected vessel centerlines, the lumen of any vessel is generated by employing wavefront propagation in image space. The vessel lumen can be optionally delineated by halos, to enhance spatial relationships when examining a dense vasculature. We present our method in a focus+context setup, by rendering a different kind of visualization around the lumen. We explain how to resolve correct visibility of multiple overlapping vessels in image space. Additionally, our visualization method allows the examination of a complex vasculature by means of interactive vessel filtering and subsequent visual querying.We propose an improved version of the Centerline Reformation (CR) technique, by generating a completely three-dimensional reformation of vascular structures using ray casting. We call this process Curved Surface Reformation (CSR). In this method, the cut surface is smoothly extended into the surrounding tissue of the blood vessels. Moreover, automatically generated cutaways reveal as much of the vessel lumen as possible, while still retaining correct visibility. This technique offers unrestricted navigation within the inspected vasculature and allows diagnosis of any tubular structure, regardless of its spatial orientation.The growing amount of data requires increasing knowledge from a user in order to select the appropriate visualization method for their analysis. In this thesis, we present an approach that externalizes the knowledge of domain experts in a human readable form and employs an inference system to provide only suitable visualization techniques for clinical diagnosis, namely Smart Super Views. We discuss the visual representation of such automatically suggested visualizations by encoding the respective relevance into shape and size of their view. By providing a smart spatial arrangement and integration, the image becomes the menu itself. Such a system offers a guided medical diagnosis by domain experts.After presenting the approach in a general setting, we describe an application scenario for diagnostic vascular visualization techniques. Since vascular structures usually consist of many vessels, we describe an anatomical layout for the investigation of the peripheral vasculature of the human lower extremities. By aggregating the volumetric information around the vessel centerlines in a circular fashion, we provide only a single static image for the assessment of the vessels. We call this method Curvicircular Feature Aggregation (CFA). In addition, we describe a stability analysis on the local deviations of the centerlines of vessels to determine potentially imprecise definitions. By conveying this information in the visualization, a fast visual analysis of the centerline stability is feasible.