BMBF joint project on MR Imaging and Deep Learning at the University of Würzburg in Cooperation with University Hospital Würzburg and University of Erlangen
| Date: | 02/13/2020, 10:05 AM |
| Organizer: | Lehrstuhl für Mathematik IX (Wissenschaftliches Rechnen) |
We are proud to announce the funding of “Intelligent MR Diagnosis of the Liver by Linking Model and Data-driven Processes (iDeLIVER)“ by BMBF. The project examines the use and further development of machine learning methods for MR image reconstruction and for the classification of liver lesions.
Based on a comparison model and data-driven image reconstruction methods, these are to be systematically linked in order to enable high acceleration without sacrificing diagnostic value. In addition to the design of suitable networks, research should also be carried out to determine whether metadata (e.g. age of the patient) can be incorporated into the reconstruction. Furthermore, suitable classification algorithms on an image basis are to be developed and the potential of direct classification on the raw data is to be explored. In the long term, intelligent MR diagnostics can significantly increase the efficiency of use of MR hardware, guarantee better patient care and set new impulses in medical technology.
The University of Würzburg receives about 260,000 EUR of funding in this project.
The following research and application partners are involved in this project:
Prof. Dr. Bernadette Hahn (project coordinator), Universität Stuttgart, Lehrstuhl OIP (Optimierung und Inverse Probleme)
FB Mathematik, IMNG
Prof. Dr. Alfio Borzì, Lehrstuhl für Wissenschaftliches Rechnen, JMU Würzburg
Prof. Dr. Andreas Maier, Pattern Recognition Lab, Computer Science, FAU Erlangen-Nürnberg
Prof. Dr. Herbert Köstler and Priv.-Doz. Dr. Tobias Wech, Experimental Radiology, Universitätsklinikum Würzburg
Prof. Dr. Thorsten Bley, Department of Diagnostic and Interventional Radiology, Universitätsklinikum Würzburg
Dr. Moritz Berger, Siemens Healthcare GmbH, Erlangen
Prof. Dr. Karsten König and Dr. Andreas Schindele, JenLab GmbH, Berlin
