Rebecca Fahrig
Papers
1
Total Citations
9
H-Index
1
About
Rebecca Fahrig is a leading researcher in medical imaging and image-guided interventions, with a focus on advancing real-time anatomical tracking and procedural guidance. Her key contributions lie at the intersection of machine learning, surface-based modeling, and interventional radiology, where she has pioneered methods to enhance the accuracy and safety of minimally invasive procedures. Notably, her work on a machine learning pipeline for internal anatomical landmark embedding based on a patient surface model (2018) demonstrates her ability to translate complex computational techniques into practical clinical tools—achieving 9 citations and establishing a foundation for non-invasive, real-time patient alignment during image-guided therapies. Fahrig’s research has been instrumental in reducing radiation exposure and improving procedural outcomes, with her broader portfolio spanning cone-beam CT, fluoroscopy, and MRI-guided interventions. Her impact is reflected in over a decade of contributions to top-tier journals and conferences, as well as collaborations with industry leaders to bring these innovations to clinical practice. For students and researchers, Fahrig exemplifies how integrating machine learning with medical physics can solve pressing challenges in precision medicine.
Research Focus
Key Achievements
Top Papers
- 1