Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography.
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2016-08-19
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Minimally-invasive microsurgery has resulted in improved outcomes for patients. However, operating through a microscope limits depth perception and fixes the visual perspective, which result in a steep learning curve to achieve microsurgical proficiency. We introduce a surgical imaging system employing four-dimensional (live volumetric imaging through time) microscope-integrated optical coherence tomography (4D MIOCT) capable of imaging at up to 10 volumes per second to visualize human microsurgery. A custom stereoscopic heads-up display provides real-time interactive volumetric feedback to the surgeon. We report that 4D MIOCT enhanced suturing accuracy and control of instrument positioning in mock surgical trials involving 17 ophthalmic surgeons. Additionally, 4D MIOCT imaging was performed in 48 human eye surgeries and was demonstrated to successfully visualize the pathology of interest in concordance with preoperative diagnosis in 93% of retinal surgeries and the surgical site of interest in 100% of anterior segment surgeries. In vivo 4D MIOCT imaging revealed sub-surface pathologic structures and instrument-induced lesions that were invisible through the operating microscope during standard surgical maneuvers. In select cases, 4D MIOCT guidance was necessary to resolve such lesions and prevent post-operative complications. Our novel surgical visualization platform achieves surgeon-interactive 4D visualization of live surgery which could expand the surgeon's capabilities.
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Carrasco-Zevallos, OM, B Keller, C Viehland, L Shen, G Waterman, B Todorich, C Shieh, P Hahn, et al. (2016). Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography. Sci Rep, 6. p. 31689. 10.1038/srep31689 Retrieved from https://hdl.handle.net/10161/12788.
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Scholars@Duke
Christian Viehland
Sina Farsiu
I am the director of the Vision and Image Processing (VIP) Laboratory. Along with my colleagues, we investigate how to improve early diagnostic methods and find new imaging biomarkers of ocular and neurological diseases in adults (e.g. age-related macular degeneration, diabetic retinopathy, Glaucoma, Alzheimer) and children (e.g. retinopathy or prematurity). We also develop automatic artificial intelligence machine learning and deep learning algorithms to detect/segment/quantify anatomical/pathological structures seen on medical images.
On another front, we study efficient signal processing based methods to overcome the theoretical and practical limitations that constrain the achievable resolution of any imaging device. Our approach, which is based on adaptive extraction and robust fusion of relevant information from the expensive and sophisticated as well as simple and cheap sensors, has found wide applications in improving the quality of imaging systems such as ophthalmic SD-OCT, digital X-ray mammography, electronic and optical microscopes, and commercial digital camcorders. We are also interested in pursuing statistical signal processing based projects, including super-resolution, demosaicing, deblurring, denoising, motion estimation, compressive sensing/adaptive sampling, and sensor fusion.
Anthony Nanlin Kuo
Anthony Kuo, MD is an Associate Professor of Ophthalmology and Assistant Professor of Biomedical Engineering at Duke University. He is a clinician-scientist with an active clinical practice in cornea and refractive surgery and an active laboratory program developing and translating high resolution optical coherence tomography (OCT) technologies for ophthalmic use.
With collaborators at Duke, he is also involved in the development and translation of intra-surgical OCT technologies. His research has been sponsored by the National Institutes of Health, Research to Prevent Blindness, the U.S. Department of Defense, and the Coulter Foundation among others.
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