LCSR/ERC Seminar: Bernhard Fuerst and Jie (Jack) Zhang
Robotics and Multi-Modal Imaging in Computer Assisted Interventions
Providing the desired and correct image information to the surgeon during the intervention is of crucial importance to reduce the task load and duration of the surgery, while increasing the accuracy and patient outcome. Our approach is to automate simple task (e.g. robotic ultrasound), provide novel imaging techniques (e.g. da Vinci SPECT) and fuse information from different images. Therefore, this talk will be focussing on our work on imaging techniques applicable during medical interventions, and the registration of images from different or the same imaging modalities.
Bernhard Fuerst is a research engineer at the Engineering Research Center at Johns Hopkins University. He received his Bachelor’s degree in Biomedical Computer Science at the University for Medical Technology in Austria in 2009 while researching on improving life sciences through semantic search techniques. His Master’s degree in Biomedical Computing was awarded by the Technical University in Munich, Germany in 2011. During his studies he joined Siemens Corporate Research in Princeton to research biomechanical simulations for compensation of respiratory motion, and Georgetown University to investigate techniques for meta-optimization using particle swarm optimizers. Since joining The Johns Hopkins University in 2013, he worked on establishing Dr. Nassir Navab’s research group to focus on robotic ultrasound, minimally invasive nuclear imaging, and intraoperative imaging technologies.
Jie (Jack) Zhang
A Low power Pixel-Wise Coded Exposure CMOS imager for insect based sensor node
There are growing interests on converting insects such as beetles or dragonflies into the carriers for low power image sensors for simultaneous localization and mapping tasks (SLAM). These small biological entities have excellent maneuverability and can enter extreme areas not accessible to human. Due to physical constraints, CMOS Video Cameras mounted on the insects must be small and low power. It also needs to provide videos with high spatial (high pixel counts, good SNR) and temporal resolution (high frame-rate, low motion blur) when the scene is changing under different lighting conditions.
To address these tradeoffs simultaneously, we present a CMOS image sensor with Compressed Sensing based Pixel Wise Coded Exposure imaging method. This architecture can provide up to 20x more frame rate with both high spatial and temporal resolution compared to a traditional image sensor with the same readout speed. The power consumption of the imager is 41uW while providing 100fps video.
Jie (Jack) Zhang received the B.Sc. degree in Electrical Engineering, in 2010, from The Johns Hopkins University, Baltimore, MD, where he is currently pursuing the Ph.D. degree in Electrical Engineering. He has been an International Scholar with the Ultra Low Power – Biomedical Circuit group at IMEC, Belgium, from October 2011 to July 2012. The focus of his research is image sensor design, compressive sensing, analog and mixed Signal Circuits for biomedical applications.