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3 March 2017Visual tracking for multi-modality computer-assisted image guidance
With optical cameras, many interventional navigation tasks previously relying on EM, optical, or mechanical guidance
can be performed robustly, quickly, and conveniently. We developed a family of novel guidance systems based on wide-spectrum
cameras and vision algorithms for real-time tracking of interventional instruments and multi-modality markers.
These navigation systems support the localization of anatomical targets, support placement of imaging probe and
instruments, and provide fusion imaging. The unique architecture – low-cost, miniature, in-hand stereo vision cameras
fitted directly to imaging probes – allows for an intuitive workflow that fits a wide variety of specialties such as
anesthesiology, interventional radiology, interventional oncology, emergency medicine, urology, and others, many of
which see increasing pressure to utilize medical imaging and especially ultrasound, but have yet to develop the requisite
skills for reliable success. We developed a modular system, consisting of hardware (the Optical Head containing the
mini cameras) and software (components for visual instrument tracking with or without specialized visual features, fully automated
marker segmentation from a variety of 3D imaging modalities, visual observation of meshes of widely separated
markers, instant automatic registration, and target tracking and guidance on real-time multi-modality fusion
views). From these components, we implemented a family of distinct clinical and pre-clinical systems (for combinations
of ultrasound, CT, CBCT, and MRI), most of which have international regulatory clearance for clinical use. We present
technical and clinical results on phantoms, ex- and in-vivo animals, and patients.
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Ehsan Basafa, Pezhman Foroughi, Martin Hossbach, Jasmine Bhanushali, Philipp Stolka, "Visual tracking for multi-modality computer-assisted image guidance," Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101352S (3 March 2017); https://doi.org/10.1117/12.2254362