For difficult cases in endoscopic sinus surgery, a careful planning of the intervention is necessary. Due to the reduced field of view during the intervention, the surgeons have less information about the surrounding structures in the working area compared to open surgery. Virtual endoscopy enables the visualization of the operating field and additional information, such as risk structures (e.g., optical nerve and skull base) and target structures to be removed (e.g., mucosal swelling). The Sinus Endoscopy system provides the functional range of a virtual endoscopic system with special focus on a realistic representation. Furthermore, by using direct volume rendering, we avoid time-consuming segmentation steps for the use of individual patient datasets. However, the image quality of the endoscopic view can be adjusted in a way that a standard computer with a modern standard graphics card achieves interactive frame rates with low CPU utilization. Thereby, characteristics of the endoscopic view are systematically used for the optimization of the volume rendering speed. The system design was based on a careful analysis of the endoscopic sinus surgery and the resulting needs for computer support. As a small standalone application it can be instantly used for surgical planning and patient education. First results of a clinical evaluation with ENT surgeons were employed to fine-tune the user interface, in particular to reduce the number of controls by using appropriate default values wherever possible. The system was used for preoperative planning in 102 cases, provides useful information for intervention planning (e.g., anatomic variations of the Rec. Frontalis), and closely resembles the intraoperative situation.
Surface models derived from medical image data often exhibit artefacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re-evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strongly and interfere with an interactive mesh generation procedure. In this paper, we present an approach to improve the handling of mesh smoothing filters. Based on a GPU mesh smoothing implementation of uniform and anisotropic filters, model quality is evaluated in real-time and provided to the user to support the mental optimization of input parameters. This is achieved by means of quality graphs and quality bars. Moreover, this framework is used to find appropriate smoothing parameters automatically and to provide data-specific parameter suggestions. These suggestions are employed to generate a preview gallery with different smoothing suggestions. The preview functionality is additionally used for the inspection of specific artefacts and their possible reduction with different parameter sets.Additional Supporting Information may be found in the online version of this article at the publisher's web site:Video S1: Real-time mesh smoothing.Video S2: Quality graphs and quality bars.Video S3: Parameter suggestions.
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