Artery-vein separation via MRA--an image processing approach

Analysis of vasculature for liver surgical planning

For liver surgical planning, the structure and morphology of the hepatic vessels and their relationship to tumors are of major interest. To achieve a fast and robust assistance with optimal quantitative and visual information, we present methods for a geometrical and structural analysis of vessel systems. Starting from the raw image data a sequence of image processing steps has to be carried out until a three-dimensional representation of the relevant anatomic and pathologic structures is generated. Based on computed tomography (CT) scans, the following steps are performed. 1) The volume data is preprocessed and the vessels are segmented. 2) The skeleton of the vessels is determined and transformed into a graph enabling a geometrical and structural shape analysis. Using this information the different intrahepatic vessel systems are identified automatically. 3) Based on the structural analysis of the branches of the portal vein, their vascular territories are approximated with different methods. These methods are compared and validated anatomically by means of corrosion casts of human livers. 4) Vessels are visualized with graphics primitives fitted to the skeleton to provide smooth visualizations without aliasing artifacts. The image analysis techniques have been evaluated in the clinical environment and have been used in more than 170 cases so far to plan interventions and transplantations.

Volume rendering of segmented image objects

This paper describes a new method of combining ray-casting with segmentation. Volume rendering is performed at interactive rates on personal computers, and visualizations include both "superficial" ray-casting through a shell at each object's surface and "deep" ray-casting through the confines of each object. A feature of the approach is the option to smoothly and interactively dilate segmentation boundaries along all axes. This ability, when combined with selective "turning off" of extraneous image objects, can help clinicians detect and evaluate segmentation errors that may affect surgical planning. We describe both a method optimized for displaying tubular objects and a more general method applicable to objects of arbitrary geometry. In both cases, select three-dimensional points are projected onto a modified z buffer that records additional information about the projected objects. A subsequent step selectively volume renders only through the object volumes indicated by the z buffer. We describe how our approach differs from other reported methods for combining segmentation with ray-casting, and illustrate how our method can be useful in helping to detect segmentation errors.