Building 10, Room 10N109, 10 Center Drive, Bethesda, MD 20892-1860
The goal of our research is to understand the onset and progression of retinal diseases at the cellular level, using advanced optical imaging techniques such as adaptive optics.
Adaptive optics is a technology for measuring and correcting the optical imperfections utilized in astronomy, microscopy, and vision science. When combined with a state-of-the-art ophthalmic imaging platform, highly detailed images of the cells in the human retina can be acquired.
The approach is to visualize healthy and diseased cells directly inside patients’ eyes to determine the sequence and timing of all the cumulative microscopic changes that give rise to clinically-significant disease phenotypes. Our research spans the development, implementation, and application of advanced optical instrumentation, as well as the acquisition, processing, and analysis of rich imaging datasets. We are particularly interested in studying the outer retina, consisting of photoreceptor neurons, retinal pigment epithelial cells, and choriocapillaris blood vessels. This multi-layered complex is not only critical for the phenomenon of vision, but also, is a useful system for modeling the in vivo interactions of neurons, epithelial cells, and vasculature within the central nervous system, in health, aging, and disease.
This unit is part of the Ophthalmic Genetics and Visual Function Branch.
Z. Liu, J. Tam, O. Saeedi, D.X. Hammer, “Trans-retinal cellular imaging with multimodal adaptive optics,” Biomedical Optics Express 9(9):4246-4262, 2018
B. Gu, X. Wang, M.D. Twa, J. Tam, C.A. Girkin, and Y. Zhang, “Noninvasive in vivo characterization of erythrocyte motion in human retinal capillaries using high-speed adaptive optics near confocal imaging,” Biomedical Optics Express 9(8):3653-3677, 2018
J. Liu, H. Jung, J. Tam, “Computer-aided detection of pattern changes in longitudinal adaptive optics images of the retinal pigment epithelium,” 15th International Symposium on Biomedical Imaging (ISBI): 34-38, 2018
J. Liu, H. Jung, J. Tam, “Accurate Correspondence of Cone Photoreceptor Neurons in the Human Eye Using Graph Matching Applied to Longitudinal Adaptive Optics Images,” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2017, Lecture Notes in Computer Science, Vol 10434, Springer, 2017
T. Liu, H. Jung, J. Liu, M. Droettboom, and J. Tam, “Noninvasive near infrared autofluorescence imaging of retinal pigment epithelial cells in the human retina using adaptive optics,” Biomedical Optics Express 8(10):4348-4360, 2017
J. Liu, H. Jung, A. Dubra, and J. Tam, “Automated Photoreceptor Cell Identification on Non-Confocal Adaptive Optics Images Using Multi-Scale Circular Voting,” Investigative Ophthalmology and Visual Science 58(11):4477-4489, 2017
W. Ma, Y. Zhang, C. Gao, R. Fariss, J. Tam, W. Wong, “Monocyte infiltration reestablishes retinal myeloid cell homeostasis following retinal pigment epithelial cell injury,” Scientific Reports 7:8433, 2017
J. Tam, J. Liu, A. Dubra, R. Fariss, “In vivo imaging of the human retinal pigment epithelial mosaic using adaptive optics enhanced indocyanine green ophthalmoscopy,” Investigative Ophthalmology and Visual Science 57(10):4376-4384, 2016
J. Tam and D. Merino, “STORM in comparison with STED and other imaging methods,” Journal of Neurochemistry 135(4): 643-658, 2015
J. Tam, “Adaptive Optics and its use in inflammatory eye disease,” Book Chapter in Multimodal Imaging in Uveitis, Springer International Publishing, 2018
J. Tam, M. Droettboom, J. Liu, and H. Jung, “Noninvasive infrared autofluorescence imaging of intrinsic fluorophores in the human retina at cellular-level resolution using adaptive optics,” Optics in the Life Sciences 2017, Optical Society of America (OSA) Technical Digest JTu5A.1, 2017
J. Liu, A. Dubra, J. Tam, “Computer-Aided Detection of Human Cone Photoreceptor Inner Segments Using Multi-scale Circular Voting,” SPIE Medical Imaging 2016: Computer-Aided Diagnosis, Vol 9785, 97851A, 2016
J. Liu, A. Dubra, J. Tam, “A Fully Automatic Framework for Cell Segmentation on Non-confocal Adaptive Optics Images,” SPIE Medical Imaging 2016: Computer-Aided Diagnosis, Vol 9785, 97852J, 2016
|Johnny Tam, Ph.D.
|Jianfei Liu||Adaptive Optics Engineeremail@example.com|
|Nancy Aguilera||Adaptive Optics Imaging Technicianfirstname.lastname@example.org|
|Tao Liu||Postdoctoral Fellowemail@example.com|
|Rongwen Lu||Postdoctoral Fellowfirstname.lastname@example.org|
|Christine Shen||Student IRTAemail@example.com|