DEEP FIELD: Seeing the Unseen in Image-guided Surgery

The future of surgery will be image-guided. But how will the surgeon navigate instruments beyond the camera's field of view without using harmful x-rays?

The potential of magnetic tracking to navigate surgical instruments without x-rays has long been appreciated but current technology lacks sufficient accuracy, speed, robustness and immunity to magnetic field distortion to change the clinical paradigm.

DEEP FIELD will break thorough the scientific frontier in surgical navigation to make magnetic tracking the new gold standard in surgical instrument navigation.

The results of DEEP FIELD will significantly reduce or eliminate the use of real-time radiation sources such as x-ray and CT in many procedures while also enabling more accurate surgery, advanced image fusion and significantly improved patient outcomes.

For this vision to become reality, DEEP FIELD will create ground-breaking magnetic field transmitter designs} with new magnetic field shaping and distortion rejection techniques (WP1) to provide faster (2.5X) and more accurate (8X) tracking than the current state of the art (NDI Aurora Tabletop transmitter).

The project will develop new and ambitiously complex magnetic sensors, including the first on-chip sensor suitable for system-in-package (SiP) fusion with other sensor types.

DEEP FIELD will also eliminate the wired connection to magnetic sensors which fail regularly in current clinical use (WP2).

DEEP FIELD will demonstrate entirely novel algorithms for surgical instrument tracking using sensor fusion and machine learning approaches (WP3) to compensate for magnetic field distortion, a major shortcoming of current technology.

An ambitious plan to integrate and test all three WPs in realistic pre-clinical settings (WP4) is included where DEEP FIELD designs will provide the scientific foundation for the future of intra-operative instrument tracking in cardiovascular navigation, endoscopy and robotic surgery without reliance on harmful radiation.