Abstract
Clinical systems for optical coherence tomography (OCT) are used routinely to diagnose and monitor patients with a range of ocular diseases. They are large tabletop instruments operated by trained staff, and require mechanical stabilization of the head of the patient for positioning and motion reduction. Here we report the development and performance of a robot-mounted OCT scanner for the autonomous contactless imaging, at safe distances, of the eyes of freestanding individuals without the need for operator intervention or head stabilization. The scanner uses robotic positioning to align itself with the eye to be imaged, as well as optical active scanning to locate the pupil and to attenuate physiological eye motion. We show that the scanner enables the acquisition of OCT volumetric datasets, comparable in quality to those of clinical tabletop systems, that resolve key anatomic structures relevant for the management of common eye conditions. Robotic OCT scanners may enable the diagnosis and monitoring of patients with eye conditions in non-specialist clinics.
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Data availability
The main data supporting the results in this work are available within the paper and its Supplementary Information. The raw data acquired during the study are available from the corresponding author on reasonable request, subject to approval from the Duke University Medical Center Institutional Review Board.
References
Huang, D. et al. Optical coherence tomography. Science 254, 1178–1181 (1991).
Izatt, J. A. et al. Micrometer-scale resolution imaging of the anterior eye in vivo with optical coherence tomography. JAMA Ophthalmol. 112, 1584–1589 (1994).
Hee, M. R. et al. Optical coherence tomography of the human retina. JAMA Ophthalmol. 113, 325–332 (1995).
Puliafito, C. A. et al. Imaging of macular diseases with optical coherence tomography. Ophthalmology 102, 217–229 (1995).
Virgili, G. et al. Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy. Cochrane Database Syst. Rev. 1, CD008081 (2015).
Miguel, A. I. M., Silva, A. B. & Azevedo, L. F. Diagnostic performance of optical coherence tomography angiography in glaucoma: a systematic review and meta-analysis. Br. J. Ophthalmol. 103, 1677–1684 (2019).
Carrasco-Zevallos, O. M. et al. Review of intraoperative optical coherence tomography: technology and applications. Biomed. Opt. Express 8, 1607–1637 (2017).
Ehlers, J. P. et al. The DISCOVER study 3-year results: feasibility and usefulness of microscope-integrated intraoperative OCT during ophthalmic surgery. Ophthalmology 125, 1014–1027 (2018).
American Academy of Ophthalmology-Preferred Practice Patterns Retina/Vitreous Panel, Hoskins Center for Quality Eye Care. Age-Related Macular Degeneration PPP (American Academy of Ophthalmology, 2015).
Schmidt-Erfurth, U., Klimscha, S., Waldstein, S. & Bogunović, H. A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration. Eye 31, 26–44 (2017).
American Academy of Ophthalmology-Preferred Practice Patterns Retina/Vitreous Panel, Hoskins Center for Quality Eye Care. Diabetic Retinopathy PPP (American Academy of Ophthalmology, 2017).
Schuman, J. S., Puliafito, C. A., Fujimoto, J. G. & Duker, J. S. Optical Coherence Tomography of Ocular Diseases 3rd edn (Slack, 2012).
American Academy of Ophthalmology Cornea/External Disease Preferred Practice Patterns Panel, Hoskins Center for Quality Eye Care. Corneal Edema and Opacification PPP (American Academy of Ophthalmology, 2013).
Radhakrishnan, S. et al. Real-time optical coherence tomography of the anterior segment at 1310 nm. Arch. Ophthalmol. 119, 1179–1185 (2001).
Jung, W. et al. Handheld optical coherence tomography scanner for primary care diagnostics. IEEE Trans. Biomed. Eng. 58, 741–744 (2011).
Lu, C. D. et al. Handheld ultrahigh speed swept source optical coherence tomography instrument using a MEMS scanning mirror. Biomed. Opt. Express 5, 293–311 (2013).
Nankivil, D. et al. Handheld, rapidly switchable, anterior/posterior segment swept source optical coherence tomography probe. Biomed. Opt. Express 6, 4516–4528 (2015).
LaRocca, F. et al. In vivo cellular-resolution retinal imaging in infants and children using an ultracompact handheld probe. Nat. Photon. 10, 580–584 (2016).
Yang, J., Liu, L., Campbell, J. P., Huang, D. & Liu, G. Handheld optical coherence tomography angiography. Biomed. Opt. Express 8, 2287–2300 (2017).
Viehland, C. et al. Ergonomic handheld OCT angiography probe optimized for pediatric and supine imaging. Biomed. Opt. Express 10, 2623–2638 (2019).
Song, S. et al. Development of a clinical prototype of a miniature hand-held optical coherence tomography probe for prematurity and pediatric ophthalmic imaging. Biomed. Opt. Express 10, 2383–2398 (2019).
Spaide, R. F., Fujimoto, J. G. & Waheed, N. K. Image artifacts in optical coherence angiography. Retina 35, 2163–2180 (2015).
Ricco, S., Chen, M., Ishikawa, H., Wollstein, G. & Schuman, J. Correcting motion artifacts in retinal spectral domain optical coherence tomography via image registration. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Yang, G. Z. et al.) 100–107 (Springer, 2009).
Kraus, M. F. et al. Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns. Biomed. Opt. Express 3, 1182–1199 (2012).
Lu, W. et al. Deep learning-based automated classification of multi-categorical abnormalities from optical coherence tomography images. Transl. Vis. Sci. Technol. 7, 41 (2018).
Loo, J., Fang, L., Cunefare, D., Jaffe, G. J. & Farsiu, S. Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2. Biomed. Opt. Express 9, 2681–2698 (2018).
dos Santos, V. A. et al. CorneaNet: Fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning. Biomed. Opt. Express 10, 622–641 (2019).
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
Asaoka, R. et al. Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images. Am. J. Ophthalmol. 198, 136 – 145 (2019).
Bruce, B. B. et al. Nonmydriatic ocular fundus photography in the emergency department. N. Engl. J. Med. 364, 387–389 (2011).
Bruce, B. B. et al. Feasibility of nonmydriatic ocular fundus photography in the emergency department: phase I of the FOTO-ED study. Acad. Emerg. Med. 18, 928–933 (2011).
Drexler, W. et al. Optical coherence tomography today: speed, contrast, and multimodality. J. Biomed. Opt. 19, 071412 (2014).
Draelos, M. et al. Automatic optical coherence tomography imaging of stationary and moving eyes with a robotically-aligned scanner. In 2019 International Conference on Robotics and Automation 8897–8903 (IEEE, 2019).
Van Buskirk, E. M. The anatomy of the limbus. Eye 3, 101–108 (1989).
MacLachlan, C. & Howland, H. C. Normal values and standard deviations for pupil diameter and interpupillary distance in subjects aged 1 month to 19 years. Ophthalmic Physiol. Opt. 22, 175–182 (2002).
Schröder, S., Chashchina, E., Janunts, E., Cayless, A. & Langenbucher, A. Reproducibility and normal values of static pupil diameters. Eur. J. Ophthalmol. 28, 150–156 (2018).
Herndon, L. W. et al. Central corneal thickness in normal, glaucomatous, and ocular hypertensive eyes. Arch. Ophthalmol. 115, 1137–1141 (1997).
La Rosa, F. A., Gross, R. L. & Orengo-Nania, S. Central corneal thickness of Caucasians and African Americans in glaucomatous and nonglaucomatous populations. Arch. Ophthalmol. 119, 23–27 (2001).
Yap, T. E., Archer, T. J., Gobbe, M. & Reinstein, D. Z. Comparison of central corneal thickness between fourier-domain OCT, very high-frequency digital ultrasound, and Scheimpflug imaging systems. J. Refract. Surg. 32, 110–116 (2016).
Buehl, W., Stojanac, D., Sacu, S., Drexler, W. & Findl, O. Comparison of three methods of measuring corneal thickness and anterior chamber depth. Am. J. Ophthalmol. 141, 7–12 (2006).
Lam, A. K. C., Chan, R. & Pang, P. C. K. The repeatability and accuracy of axial length and anterior chamber depth measurements from the IOLMaster. Ophthalmic Physiol. Opt. 21, 477–483 (2001).
Fotedar, R. et al. Distribution of axial length and ocular biometry measured using partial coherence laser interferometry (IOL Master) in an older white population. Ophthalmology 117, 417–423 (2010).
Sánchez-Tocino, H., Alvarez-Vidal, A., Maldonado, M. J., Moreno-Montañés, J. & García-Layana, A. Retinal thickness study with optical coherence tomography in patients with diabetes. Invest. Ophthalmol. Vis. Sci. 43, 1588–1594 (2002).
Goebel, W. & Kretzchmar-Gross, T. Retinal thickness in diabetic retinopathy: a study using optical coherence tomography (OCT). Retina 22, 759–767 (2002).
Alamouti, B. & Funk, J. Retinal thickness decreases with age: an OCT study. Br. J. Ophthalmol. 87, 899–901 (2003).
Ko, F. et al. Association of retinal nerve fiber layer thinning with current and future cognitive decline: a study using optical coherence tomography. JAMA Neurol. 75, 1198–1205 (2018).
Mutlu, U. et al. Association of retinal neurodegeneration on optical coherence tomography with dementia: a population-based study. JAMA Neurol. 75, 1256–1263 (2018).
Qian, R. et al. Characterization of long working distance optical coherence tomography for imaging of pediatric retinal pathology. Transl. Vis. Sci. Technol. 6, 12 (2017).
Pircher, M., Baumann, B., Götzinger, E., Sattmann, H. & Hitzenberger, C. K. Simultaneous SLO/OCT imaging of the human retina with axial eye motion correction. Opt. Express 15, 16922–16932 (2007).
Mecê, P., Scholler, J., Groux, K. & Boccara, C. High-resolution in-vivo human retinal imaging using full-field OCT with optical stabilization of axial motion. Biomed. Opt. Express 11, 492–504 (2020).
Wojtkowski, M., Leitgeb, R., Kowalczyk, A., Bajraszewski, T. & Fercher, A. F. In vivo human retinal imaging by Fourier domain optical coherence tomography. J. Biomed. Opt. 7, 457–463 (2002).
Viehland, C. et al. Enhanced volumetric visualization for real time 4D intraoperative ophthalmic swept-source OCT. Biomed. Opt. Express 7, 1815–1829 (2016).
Carrasco-Zevallos, O. et al. Pupil tracking optical coherence tomography for precise control of pupil entry position. Biomed. Opt. Express 6, 3405–3419 (2015).
Escudero-Sanz, I. & Navarro, R. Off-axis aberrations of a wide-angle schematic eye model. J. Opt. Soc. Am. A 16, 1881–1891 (1999).
Bradski, G. The OpenCV Library. Dr Dobb’s J. Softw. Tools 25, 120–125 (2000).
Zadeh, A., Lim, Y. C., Baltrušaitis, T. & Morency, L. Convolutional experts constrained local model for 3D facial landmark detection. In 2017 IEEE International Conference on Computer Vision Workshops 2519–2528 (IEEE, 2017).
Atchison, D. A. Optical models for human myopic eyes. Vis. Res. 46, 2236 – 2250 (2006).
Tan, C. S. & Sadda, S. R. The role of central reading centers–current practices and future directions. Indian J. Ophthalmol. 63, 404–405 (2015).
Acknowledgements
This work was partially supported by the Duke Coulter Translational Partnership and by National Eye Institute grants F30-EY027280, R01-EY029302 and U01-EY028079.
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Contributions
M.D. and P.O. designed and assembled the scan heads and robotic system. R.M. designed and assembled the reference arm. R.Q., C.V. and R.M. optimized the OCT system. M.D., C.V. and R.M. determined optical safety exposure limits. A.N.K. obtained bioethics approval and consented participants. M.D. and P.O. performed the experiments. M.D. analysed the data and drafted the manuscript. P.O., R.Q., R.M., K.H., A.N.K. and J.A.I. revised the manuscript. K.H., A.N.K. and J.A.I. supervised the project.
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Competing interests
J.A.I. is an inventor on OCT-related patents filed by Duke University and licensed by Leica Microsystems, Carl Zeiss Meditec and St Jude Medical. J.A.I. has additional financial interests (including royalty and milestone payments) in these companies. R.M. and A.N.K. are inventors on OCT-related patents filed by Duke University and licensed by Leica Microsystems. M.D., P.O., R.M., A.N.K. and J.A.I. are inventors on provisional patent application US62/798,052 filed by Duke University that concerns the robotic OCT system presented in this manuscript. R.Q., C.V. and K.H. declare no competing interests.
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Supplementary figures.
Supplementary Video 1
Robotic arm following the participant to keep the eye centred within the working range for optical active tracking.
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Draelos, M., Ortiz, P., Qian, R. et al. Contactless optical coherence tomography of the eyes of freestanding individuals with a robotic scanner. Nat Biomed Eng 5, 726–736 (2021). https://doi.org/10.1038/s41551-021-00753-6
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DOI: https://doi.org/10.1038/s41551-021-00753-6
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