Magdalena Wysocki

Papers

Total Citations

H-Index

About

Magdalena Wysocki is pioneering the intersection of medical imaging and artificial intelligence, with a primary focus on advancing ultrasound technology through neural representation learning. Her most impactful work, "Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging" (2023, 16 citations), introduces a groundbreaking physics-enhanced implicit neural representation that learns tissue properties from overlapping ultrasound sweeps. By leveraging ray-tracing-based neural rendering for novel view synthesis, Wysocki has opened new possibilities for reconstructing and visualizing ultrasound data in ways previously unattainable with conventional imaging techniques. Her research addresses critical challenges in medical imaging, including improving image quality, reducing acquisition time, and enabling more accurate diagnostic capabilities. Wysocki’s contributions are particularly notable for bridging the gap between computer graphics and clinical ultrasound, demonstrating how neural radiance fields can be adapted to the unique physics of acoustic wave propagation. Her work has quickly garnered attention from both the computer vision and medical imaging communities, positioning her as an emerging leader in physics-informed deep learning for healthcare. Through Ultra-NeRF and related projects, Wysocki is helping to shape a future where AI-enhanced ultrasound becomes more accessible, interpretable, and clinically valuable.