Dual Energy Clinical Workshop, May 16-17, 2019
Katrin Seidel | 2019-03-18
Courtesy of University Hospital Frankfurt am Main, Frankfurt am Main, Germany
Course design and objectives
This two-day workshop introduces participants to the physics principles of Dual Energy CT and provides a supervised hands-on study of clinical data sets using syngo.via workstations, followed by interactive proof-reading and discussions (e.g. syngo.CT DE Brain Hemorrhage, syngo.CT DE Gout or syngo.CT DE Lung Analysis). The course format is a combination of lectures and hands-on for post-processing to enhance your clinical practice skills and to provide you with insight on current and future developments. Also included is a Computed Tomography and detector factory tour, to experience first-hand how our systems are manufactured.
- Details on physics principles of Dual Energy
- Introduction on data acquisition and image reconstruction
- Interactive evaluation of data sets showing how to optimize data reconstruction and clinical results
- Clinically proven tips and tricks on patient examination
Professor Ralf Bauer, MD
Professor Ralf Bauer is senior consultant of Radiology at the RNS Gemeinschaftspraxis GbR im Medicum II, Wiesbaden, Germany. He studied medicine at the Johann Wolfgang Goethe-University Medical School in Frankfurt, Germany and graduated his medical licensing exam with excellence (grade 1.0) in 2007. He is an expert in pulmonary, vascular/lung and cardiovascular imaging and does a lot of research on Dual Source Dual Energy applications and dose reduction strategies.
He is a member of several national and international radiological societies. He has been the author of more than 50 articles and has been invited as clinical speaker on CT imaging in various countries. Professor Ralf Bauer, MD, has conducted many hands-on workshops for dual energy as well as for cardiology.
The statements by Siemens Healthineers customers described herein are based on results that were achieved in the customer’s unique setting. Since there is no “typical” hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.