By sealing the fistula, a successful endoscopic esophageal intubation ends the severe respiratory contamination and the inability to swallow, improving the quality of life and survival period. After the procedure, it is the malignant tumor and not the fistula that determines the future of the patient.
These tumors seem to be specific forms of esophageal cancers. For a better quality of life and longer survival time for these patients, there should be earlier diagnosis and endoscopic intubation as the best palliative treatment should be performed.
Stent implantation improves the quality of life and gives an opportunity for adjuvant oncological therapy. Evaluation of morphologic anomalies is of considerable importance for achieving success in treatment through implantation.
A new potential application of [(99m)Tc]-MAA was developed and presents a simple and very effective means to quantitatively characterize liver cold spot lesions resulting from Kupffer cell dysfunctions as a consequence of tumor burden.
Most of the industrial parts are designed as trimmed NURBS. For their efficient rendering multiresolution models are needed. To create such models without artifacts at the trimming curves, one needs to sew parts together along the common boundaries. Due to the problem of determining the geometric places in 3D space along the trimming curves where sewing should be done, current approaches need to have a priori neighbourhood information of the patches and this way they do not provide an automatic solution to create large connected models just from a set of surfaces. In this paper we describe a method, which automatically determines common boundaries of trimmed NURBS surfaces and sews along them. Such a method provides us a non-manifold or manifold structure, which can be handled using standard multi-resolution techniques. Several examples of industrial data demonstrate the efficiency and applicability of our new method. The introduced techniques will also be included into the OpenSG scenegraph API [8] as the basic tool for NURBS rendering.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.