Computed tomographic (CT) angiography is a well-known tool for detection of intracranial aneurysms and the planning of therapeutic intervention. Despite a wealth of existing studies and an increase in image quality due to use of multisection CT and increasingly sophisticated postprocessing tools such as direct volume rendering, CT angiography has still not replaced digital subtraction angiography as the standard of reference for detection of intracranial aneurysms. One reason may be that CT angiography is still not a uniformly standardized method, particularly with regard to image postprocessing. Several methods for two- and three-dimensional visualization can be used: multiplanar reformation, maximum intensity projection, shaded surface display, and direct volume rendering. Pitfalls of CT angiography include lack of visibility of small arteries, difficulty differentiating the infundibular dilatation at the origin of an artery from an aneurysm, the kissing vessel artifact, demonstration of venous structures that can simulate aneurysms, inability to identify thrombosis and calcification on three-dimensional images, and beam hardening artifacts produced by aneurysm clips. Finally, an algorithm for the safe and useful application of CT angiography in patients with subarachnoid hemorrhage has been developed, which takes into account the varying quality of equipment and software at different imaging centers.

Computed tomography (CT) angiography is a well-known imaging technique commonly applied to both the detection and therapy planning of intracranial aneurysms. For this purpose, current studies predominantly focus on three-dimensional (3D) representations of CT angiographic volumes obtained with varying visualization approaches on different computers. Interactive manipulation performed by users individually is an important prerequisite for data analysis. However, this leads to inconsistent and barely reproducible 3D visualization results. Furthermore, the quality of any 3D representation depends on the applied visualization strategy (eg, maximum-intensity projection, shaded-surface display, direct volume rendering). To overcome these limitations, the authors present a novel method for standardized visualization of CT angiographic volumes, consisting of three steps: (a) transfer of the image data to a remote high-end graphics workstation, (b) automatic 3D visualization with high-resolution direct volume rendering, and (c) consecutive video generation performed according to a standardized protocol. The recorded video sequences are transferred for evaluation to a local desktop computer. In the experimental setup, high-quality videos based on 3D visualizations were produced in less than 60 minutes per patient. Although aneurysms above the skull base are usually visualized with excellent quality, the analysis of aneurysms at the skull base is still difficult.

CT-angiography is a well established medical imaging technique for the detection, evaluation and therapy planning of intracranial aneurysms. Different 3D visualization algorithms such as maximum intensity projection, shaded surface display and direct volume rendering support the analysis of the resulting volumes. Despite the available flexibility, this general approach leads to almost unreproducible and patient specific results. They depend completely on the applied algorithm and the parameter setting chosen in a wide range. Therefore, the results are inapplicable for inter-patient or inter-study comparisons. As a solution to this problem, we suggest to make the visualization fully independent of any user interaction. In consequence the main focus of the presented work lies on standardization and automation which guarantees comparable 3D representations for the analysis of intracranial aneurysms. For this purpose, we introduce a web-based system providing digital video sequences based on automatically performed hardware accelerated direct volume rendering. Any preprocessing such as the setting of transfer functions and the placement of clip planes is performed according to a predefined protocol. In addition to an overview using the whole volume, every dataset is divided into four subvolumes supporting a detailed inspection of the vessels and their branches. Overall, the value of the system is demonstrated with several clinical examples.