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About

Johanna Sprenger is a leading researcher in the intersection of medical imaging, robotics, and machine learning, with a primary focus on advancing image-guided needle interventions and motion compensation. Her work centers on using Optical Coherence Tomography (OCT) and volumetric ultrasound to enable precise, markerless tissue tracking and navigation. A key contribution is her development of a deep learning approach using complex OCT data and CNNs to detect tissue ruptures during needle insertion—a critical challenge that can misguide physicians. This work, published in 2021, has already garnered 14 citations, highlighting its impact on improving needle placement accuracy. Sprenger has also pioneered automated robotic surface scanning with OCT, achieving high-resolution depth imaging for tissue characterization. Her research extends to synthesizing strategies for needle steering in phantoms and systematic analysis of volumetric ultrasound for 4D motion tracking, with applications in radiation therapy. Notably, she has demonstrated in-vivo markerless motion detection from volumetric OCT data, eliminating the need for optical markers in robot-assisted surgery. Through these contributions, Sprenger is shaping the future of minimally invasive procedures, making them safer and more effective.