2017 | OriginalPaper | Buchkapitel
Real-Time-Capable GPU-Framework for Depth-Aware Rigid 2D/3D Registration
verfasst von : Matthias Utzschneider, Jian Wang, Roman Schaffert, Anja Borsdorf, Andreas Maier
Erschienen in: Bildverarbeitung für die Medizin 2017
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
2D/3D image fusion is used for a variety of interventional procedures. Overlays of 2D images with perspective-correctly rendered 3D images provide the physicians additional information during the interventions. In this work, a real-time capable 2D/3D registration framework is presented. An adapted parallelization using GPU is investigated for the depth-aware registration algorithm. The GPU hardware architecture is specially taken into account by optimizing memory access patterns and exploiting CUDA-texture memory. The real-time capability is achieved with a median runtime of one 2D/3D registration iteration of 86.1ms with an median accuracy of up to 1.15 mm.