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Showing papers by "Andrea Schenk published in 2020"


Journal ArticleDOI
TL;DR: It is imperative that rare anatomical variations are known preoperatively for the conduction of a safe surgical procedure, and increasing digitalization in surgical and perioperative preparation holds great potential for better planning and improved patient safety.
Abstract: Introduction: Buhler's anastomosis (or Buhler's arcade) is an embryonic relic and represents an arterio-arterial connection between the superior mesenteric artery and the celiac trunk. It can be found as a variety in 1-2% of patients. Case Presentation: We present a case of a patient with metatastatic squamous cell carcinoma of the lung. The patient was in stable disease for 4 years under palliative therapy (most recently second-line therapy with Nevolumab). In 2019, a locally advanced adenocarcinoma of the papilla vateri was diagnosed, additionally. The patient also underwent right hemicolectomy and patch plasty of the celiac trunk and superior mesenteric artery due to colonic ischemia and arteriosclerotic disease with 50-70% stenosis of the superior mesenteric artery several years ago. Due to a complex vascular prehistory, the standardized preoperative imaging was supplemented by two independent vascular reconstructions (a CT angiogram and a reconstruction based on the CT) for the planning of a pylorus-preserving pancreatic head resection and reconstruction according to Traverso-Longmire. In addition, a 3D print was produced. Both, the reconstruction based on the CT scan and the 3D print were created for off-label use as a part of a research project (VIVATOP: Versatile Immersive Virtual and Augmented Tangible OP). Discussion: In the standardized CT scan and in the clinical CT-angiography, there were no obvious surgically relevant anatomical variations. A Buhler anastomosis was detected in a digital, virtual and interactive 3D-reconstruction. In addition, in the 3D print of the abdominal site the anastomosis was seen as well. Intraoperatively, the presence of Buhler's anastomosis was confirmed. This information had a significant impact on the intraoperative approach. Retrospectively, the vessel variant could be surmised in the axial projection of the CT scan, if one knew what to look for. Conclusion: For the conduction of a safe surgical procedure, it is imperative that rare anatomical variations are known preoperatively. Increasing digitalization in surgical and perioperative preparation holds great potential for better planning and improved patient safety. Research and cooperation projects such as the VIVATOP project are instrumental for the development of new visualization techniques, which are able to enhance the understanding of complex anatomical relations.

7 citations


Book ChapterDOI
20 Oct 2020
TL;DR: Adapt volumetric illumination sampling is presented, a ray-casting-based direct volume rendering method that strongly reduces the amount of necessary illumination computations without introducing any noise and allows for interactive transfer function updates and clipping of the visualized data.
Abstract: Direct volume rendering is used to visualize data from sources such as tomographic imaging devices. The perception of certain structures depends very much on visual cues such as lighting and shadowing. According illumination techniques have been proposed for both surface rendering and volume rendering. However, in the case of direct volume rendering, some form of precomputation is typically required for real-time rendering. This however limits the application of the visualization. In this work we present adaptive volumetric illumination sampling, a ray-casting-based direct volume rendering method that strongly reduces the amount of necessary illumination computations without introducing any noise. By combining it with voxel cone tracing, realistic lighting including ambient occlusion and image-based lighting is facilitated in real-time. The method only requires minimal precomputation and allows for interactive transfer function updates and clipping of the visualized data.

2 citations