Advisors

Medical Image Registration

Medical Image Registration is an essential component in many clinical applications. In general, registration denotes the process of data alignment. Among others, medical applications include data fusion from different modalities, the generation of anatomic alatlases, and the analysis of spatiotemporal pathology progression.

Research at the LME: In close collaboration with leading clinical and industrial partners worldwide, we develop novel techniques for rigid and non-rigid registration, as well as innovative applications and efficient clinical workflows. The interdisciplinary research provides the basis to develop applications close to medical practice.

We focus on: Multi-modal Fusion The growing number of imaging modalities demands for methods to combine complementary information and making it easily accessible to the physician. We develop algorithms to align multiple datasets into a common reference system (see figure). Electrophysiology Procedures are minimally invasive interventions that focus on the treatment of heart arrhythmias. Our goal is to provide navigational support for such procedures by incorporating all information available. We focus on motion compensation, the integration of external signals, procedure planning & automatic catheter localization. Image-guided Therapy requires techniques for accurate patient setup and motion compensation. Using real-time 3-D range imaging and efficient data enhancement on the GPU, we monitor the external patient surface during treatment delivery and develop methods for automatic patient positioning and the management of respiratory motion. 3-D Endoscopy Knowledge about the local surface geometry during minimally invasive procedures holds great benefits compared to conventional 2-D endoscopy. Our work focuses on improving both efficiency and quality of minimally invasive procedures based on augmented reality and collision detection techniques.

MR data of the brain of an epileptic patient, registered and overlaid with molecular SPECT data.

Contact:

joachim.hornegger@informatik.uni-erlangen.de