Virtual endoscopy: Development and evaluation using the Visible Human Datasets

Virtual endoscopy (VE) is a new method of diagnosis using computer processing of 3D image datasets (such as CT or MRI scans) to provide simulated visualizations of patient specific organs similar or equivalent to those produced by standard endoscopic procedures. Conventional endoscopy is invasive and often uncomfortable for patients. It sometimes has serious side effects such as perforation, infection and hemorrhage. VE visualization avoids these risks and can minimize difficulties and decrease morbidity when used before actual endoscopic procedures. In addition, there are many body regions not compatible with real endoscopy that can be explored with VE. Eventually, VE may replace many forms of real endoscopy. There remains a critical need to refine and validate VE visualizations for routine clinical use. We have used the Visible Human Dataset from the National Library of Medicine to develop and test these procedures and to evaluate their use in a variety of clinical applications. We have developed specific clinical protocols to compare virtual endoscopy with real endoscopy. We have developed informative and dynamic on-screen navigation guides to help the surgeon or physician interactively determine body orientation and precise anatomical localization while performing the VE procedures. Additionally, the adjunctive value of full 3D imaging (e.g. looking 'outside' of the normal field of view) during the VE exam is being evaluated. Quantitative analyses of local geometric and densitometric properties obtained from the virtual procedures ('virtual biopsy') are being developed and compared with other direct measures. Preliminary results suggest that these virtual procedures can provide accurate, reproducible and clinically useful visualizations and measurements. These studies will help drive improvements in and lend credibility to VE procedures and simulations as routine clinical tools. VE holds significant promise for optimizing endoscopic diagnostic procedures, minimizing patient risk and morbidity, and reducing health care costs. (C) 2000 Elsevier Science Ltd.