Computational optical imaging of the human retina
The optics of the human eye have strong aberrations, which makes it very difficult to acquire high-resolution images of the living human retina. We have developed a technique called computational adaptive optics (CAO) which utilizes the complex field measured with OCT to computationally correct for optical aberrations. We have demonstrated this technique in the living human eye for imaging of very small retinal structures, such as the photorecptor cells. This technique could supplement, or potentially replace, expensive and complicated hardware adaptive optics components.
|Shemonski ND, Adie SG, South FA, Liu Y-Z, Carney PS, Boppart SA, "Computational high-resolution optical imaging of the living human retina," Nature Photonics, 9:440-443, 2015.||PubMed Abstract|
Handheld imaging of the human eye
Optical coherence tomography (OCT) has long been used as a diagnostic tool in the field of ophthalmology. The ability to observe microstructural changes in the tissues of the eye has proved very effective in diagnosing ocular disease. However, this technology has yet to be introduced into the primary care office, where indications of disease are first encountered. We have developed a portable, handheld imaging probe for use in the primary care setting and evaluated its tissue site accessibility, ability to observe diseased tissue, and screening capabilities in in vivo human patients, particularly for pathologies related to the eye.
LEFT: (A) Schematic of the portable OCT system and handheld imaging scanner. M: mirror, FL: focusing lens, DC: dispersion compensator, FM: flip mirror, NDF: neutral density filter, CM: collimator, PC: polarization controller, FC: fiber coupler, DG: diffraction grating, LSC: line scanning camera, BBS: broadband source. (B) Anatomy of the handheld OCT scanner.
RIGHT: Handheld imaging scanner functionality. (A, B) Photographs of handheld scanner with and without ear speculum tip, respectively. (C) Scanner attachments for retina, anterior segment/skin, oral mucosa, and ear, from left to right in the photo. (D, E) Photographs of the handheld scanner used for eye (D) and ear (E) imaging. LCD screen shows color video image and OCT cross-section simultaneously.
Corneal and retinal imaging
Examples of common pathologies imaged with the OCT handheld scanner.
ABOVE: (A, B) shows the cornea of a patient who has undergone LASIK corrective surgery. The doubleheaded arrows show the direction of the scan that occurred in the X–Y (transverse) plane across the tissue, and each pair of images represents
the two orthogonal (X–Z and Y–Z) planes.
BELOW: OCT images of the macular region of the retina in patients with various stages of diabetic retinopathy. (A, B) Normal retina from a control patient. (C, D) Early-stage diabetic retinopathy, showing disturbance of the layers near the choroid. (E, F) Diabetic retinopathy with accompanying macular edema. Swelling and thickening of the retina is evident. (G, H) Advanced diabetic retinopathy. Cysts, or pockets of fluid are found throughout the thickness of the retina. The double-headed arrows show the direction of the scan that occurred in the X–Y (transverse) plane across the tissue, and each pair of images represents the two orthogonal (X–Z and Y–Z) planes.
|Shelton R, Jung W, Sayegh SI, McCormick DT, Kim J, Boppart SA, "Optical coherence tomography for advanced screening in the primary care office," Journal of Biophotonics, 7:525-533, 2014.||PubMed Abstract|
Quantitative analysis of retinal images
Quantitative parameters can be extracted from the retinal images to be used as disease indicators. One such metric is the ratio of thicknesses between the many retinal cell layers. The is a robust metric that will not be affected by overall retinal thickness variation between patients. A ratiometric analysis of retinal layer thicknesses reveals a statistically different set of thickness ratio values for diabetic patients, as well as multiple sclerosis patients.
A ratiometric analysis on layer thicknesses in the retina of diabetic and control patients reveals a statistically different set of thickness ratio values for diabetic patients. These results are encouraging for future efforts to diagnose or screen for diabetes at an early stage, before symptoms or gross retinal abnormalities occur. Previous studies have investigated layer thicknesses in the retina to draw conclusions about the presence of diabetes; however, an analysis of the ratios of these layer thicknesses is a more robust measure. Variations in overall retinal thickness between patients will not affect the accuracy of ratio measurements.
LEFT: Representative OCT image of the retina of a diabetic (DM+) patient with manual layer segmentation.
RIGHT: Statistical analysis of 12 retinal layer thickness ratios. Red squares: control subjects (DM-), blue circles: diabetic subjects (DM+).
|Shelton R, Tailb J, Shemonski N, Sayegh SI, Boppart SA, "Subretinal layer thickness ratio changes for early detection of diabetes," Association for Research in Vision and Ophthalmology (ARVO) Annual Meeting, Seattle, WA, May 5-9, 2013.||Link|
Ratiometric analysis was also performed on multiple slecrosis (MS) patients. The ratiometric analysis identified differences in several retinal layer thickness ratios in the cohort of MS subjects without a history of optic neuritis (ON) compared to healthy control (HC) subjects, while there was no difference in the standard metric of retinal nerve fiber layer thickness (RNFL). The difference in such ratios between HC subjects and those with mild MS-disability, without a difference in RNFL , further suggests the possibility of using layer ratiometric analysis for detecting early retinal changes in MS. Ratiometric analysis may be useful and potentially more sensitive for detecting disease changes in MS.
|Bhaduri B, Nolan RM, Shelton RL, Pilutti LA, Motl RW, Boppart SA. Ratiometric analysis of in vivo retinal layer thicknesses in multiple sclerosis . J Biomedcial Optics, 21:095001 2016.||PubMed Abstract|
We also quantitatively measured the blood vessel diameter (BVD) and blood vessel number (BVN) for the retinas of MS patients. It was found that MS eyes had a lower total BVD and BVN than control eyes. The effect was more pronounced with increased MS disability, and persisted in multivariate models adjusting for retinal nerve fiber layer (RNFL) thickness and optic neuritis (ON) history.
|Bhaduri B, Nolan RM, Shelton RL, Pilutti LA, Motl RW, Pula JH, Boppart SA. Detection of retinal blood vessel volume loss in multiple sclerosis with optical coherence tomography . Biomedical Optics Express, 7:2321-2330 2016.||PubMed Abstract|