Meet Us @ RSNA 2024

The RSNA Annual Meeting is the flagship event of the Radiological Society of North America (RSNA). It is one of the largest and most prestigious conferences in the field of radiology and medical imaging. It is a great opportunity to get to know the latest advances in imaging technology, participate in educational sessions, and engage in scientific presentations and discussions.

Industry Exhibition

Location: South Hall, Level 3, Site 2609

Our experts look forward to presenting our latest developments:

SAFIR: A Comprehensive Software Platform for Interventional Radiology

SAFIR is a software platform that enhances interventional radiology by integrating advanced image processing into a user-friendly tool. Key features include pre-procedural planning, real-time support during interventions, and efficient post-procedural verification. Validated by leading clinics, SAFIR's reliable algorithms are widely used in clinical practice.

gammaSTAR: Hardware-agnostic MRI Pulse Sequences

gammaSTAR advances MRI pulse sequences with hardware-agnostic compatibility, enhancing imaging consistency and streamlining workflows. Key features include a state-of-the-art sequence library of dynamic sequences and an intuitive user interface for researchers and clinics.

At this year's RSNA, we showcase a hands-on demonstrator of our hardware-agnostic MRI framework gammaSTAR, featuring an intuitive UI for pulse sequence development that directly transforms results into interactive 3D models of MRI scanners for an immersive simulation experience.

Transforming 3D Visualization with AVIS

AVIS is a 3D visualization algorithm for realistic images using Volume Rendering, ideal for AR/VR. It's efficient, performs well on older hardware, and integrates AI for highlighting structures. A study shows it helps surgeons answer clinical questions faster without losing accuracy.

At RSNA 2024 we demonstrate how AVIS & Deep Learning segmentation can help to plan e.g. a visceral surgery. It visualizes CT abdomen datasets with AVIS as well as Deep Learning-based segmentation masks of key structures like liver, vessels and pancreas.

Streamlined Image Segmentation: Uncertainty-Driven Training Loop

Our uncertainty-driven training loop enables efficient model improvement with minimal annotations. By iteratively refining uncertain areas, users guide the model to higher accuracy, achieving precise segmentation with reduced annotation effort.

Scientific Program (Poster)

Kai Geissler

Multi-Institutional Evaluation of Background Parenchymal Enhancement Classification Using Deep Learning

(Authors: Kai Geissler, Hans Meine, Robert Grimm, Heinrich von Busch, Hendrik Laue, Susanne Diekmann, Markus Wenzel)

Poster: S3A-SPBR-2

Session: Scientific Poster Sessions | Imaging Informatics Sunday Poster Discussions I | S3A-SPBR

Sunday, December 1, 2024, 11:45 AM – 12:15 PM CST

Location: Learning Center