Research Goals

Our lab primarily focuses on translational research, with the goal of making basic and clinical discoveries so that clinical patient care can be improved. Main areas of current research include:

    Longitudinal and cross-sectional analysis of glaucoma diagnosis and progression detection

    Glaucoma is a slowly progressing disease that can eventually lead to blindness, therefore it is important to understand how the disease behaves in different individuals and over time. Glaucoma is diagnosed based on structural changes in the optic nerve head and retinal structures with associated functional vision changes. We evaluate ocular structural and functional measurements from healthy eyes, eyes suspected of having glaucoma, and a wide range of glaucomatous eyes to better understand the relationship between structural and functional testing and disease progression in glaucoma monitoring. Advanced imaging technologies allow more detailed assessment and comparison of structures related to the glaucomatous disease process such as the ganglion cells in the retina and the lamina cribrosa deep within the optic nerve head. We are developing new tools and use advanced imaging to determine more precise ways to detect and predict glaucoma and glaucoma progression.


    Recent Publications:

    1. Wang B, Lucy KA, Schuman JS, Sigal IA, Bilonick RA, Kagemann L, Kostanyan T, Lu C, Liu J, Grulkowski I, Fujimoto JG, Ishikawa H, Wollstein G. Decreased Lamina Cribrosa Beam Thickness and Pore Diameter Relative to Distance From the Central Retinal Vessel Trunk. Invest Ophthalmol Vis Sci 2016; 57: 3088-92.[pubmed]

    PURPOSE: To investigate how the lamina cribrosa (LC) microstructure changes with distance from the central retinal vessel trunk (CRVT), and to determine how this change differs in glaucoma.

    METHODS: One hundred nineteen eyes (40 healthy, 29 glaucoma suspect, and 50 glaucoma) of 105 subjects were imaged using swept-source optical coherence tomography (OCT). The CRVT was manually delineated at the level of the anterior LC surface. A line was fit to the distribution of LC microstructural parameters and distance from CRVT to measure the gradient (change in LC microstructure per distance from the CRVT) and intercept (LC microstructure near the CRVT). A linear mixed-effects model was used to determine the effect of diagnosis on the gradient and intercept of the LC microstructure with distance from the CRVT. A Kolmogorov-Smirnov test was applied to determine the difference in distribution between the diagnostic categories.

    RESULTS: The percent of visible LC in all scans was 26 ± 7%. Beam thickness and pore diameter decreased with distance from the CRVT. Glaucoma eyes had a larger decrease in beam thickness (-1.132 ± 0.503 μm, P = 0.028) and pore diameter (-0.913 ± 0.259 μm, P = 0.001) compared with healthy controls per 100 μm from the CRVT. Glaucoma eyes showed increased variability in both beam thickness and pore diameter relative to the distance from the CRVT compared with healthy eyes (P < 0.05).

    CONCLUSIONS: These findings results demonstrate the importance of considering the anatomical location of CRVT in the assessment of the LC, as there is a relationship between the distance from the CRVT and the LC microstructure, which differs between healthy and glaucoma eyes.


    (A) En-face slice of the LC with the CRVT manually delineated (red). (B) Automated segmentation showing the beams (green) as well as the pores (red) on a single C-mode slice from the 3D stack. (C) Schematic diagram demonstrating the quantification of pore (black) by local thickness defined in 3D (green). (D) Schematic diagram demonstrating the method of determining how LC pore diameter (green dashed lines) varied with distance from the CRVT (blue lines). (E) A linear fit (red line) of the association between pore diameter and distance from CRVT for a single eye.



    Boxplot of (A, C) intercept and (B, D) gradient for beam thickness and pore diameter versus distance from the CRVT, respectively, among the clinical diagnostic groups (H, healthy; GS, glaucoma suspect; GL, glaucomatous eyes). Gradient denotes the micron change in lamina cibrosa microstructure (beam thickness or pore diameter) per 100-μm distance from the CRVT.



    2. Lucy KA, Wang B, Schuman JS, Bilonick RA, Ling Y, Kagemann L, Sigal IA, Grulkowski I, Lui JJ, Fujimoto JG, Ishikawa H, Wollstein G. Thick Prelaminar Tissue Decreases Lamina Cribrosa Visibility. Invest Ophthalmol Vis Sci 2017; 58: 1751-7.[pubmed]

    PURPOSE: Evaluation of the effect of prelaminar tissue thickness on visualization of the lamina cribrosa (LC) using optical coherence tomography (OCT).

    METHODS: The optic nerve head (ONH) region was scanned using OCT. The quality of visible LC microstructure was assessed subjectively using a grading system and objectively by analyzing the signal intensity of each scan's superpixel components. Manual delineations were made separately and in 3-dimensions quantifying prelaminar tissue thickness, analyzable regions of LC microstructure, and regions with a visible anterior LC (ALC) boundary. A linear mixed effect model quantified the association between tissue thickness and LC visualization.

    RESULTS: A total of 17 healthy, 27 glaucoma suspect, and 47 glaucomatous eyes were included. Scans with thicker average prelaminar tissue measurements received worse grading scores (P = 0.007), and superpixels with low signal intensity were associated significantly with regions beneath thick prelaminar tissue (P < 0.05). The average prelaminar tissue thickness in regions of scans where the LC was analyzable (214 μm) was significantly thinner than in regions where the LC was not analyzable (569 μm; P < 0.001). Healthy eyes had significantly thicker average prelaminar tissue measurements than glaucoma or glaucoma suspect eyes (both P < 0.001), and glaucoma suspect eyes had significantly thicker average prelaminar tissue measurements than glaucoma eyes (P = 0.008). Significantly more of the ALC boundary was visible in glaucoma eyes (63% of ONH) than in healthy eyes (41%; P = 0.005).

    CONCLUSIONS: Thick prelaminar tissue was associated with impaired visualization of the LC. Healthy subjects generally had thicker prelaminar tissue, which potentially could create a selection bias against healthy eyes when comparing LC structures.


    Swept source-OCT scan of the ONH of a glaucomatous eye. (B) Software view of the delineation of the ILM/anterior prelaminar surface (yellow line) and ALC surface (blue line). The prelaminar tissue thickness was quantified by (C) measuring the average distance between the anterior prelaminar surface (yellow dashed line) and the ALC (blue dashed line) in the region enclosed by the Bruch's membrane opening (black dashed lines). For the measurement of regions in which the ALC was visible, regions such as the area highlighted in pink were not included.



    The average prelaminar tissue thickness of each diagnostic group for (A) the entire ONH region and (B) the regions that featured analyzable en face LC. A significant difference in total average prelaminar thickness (A) was seen between glaucaoma (GL) and healthy (H) eyes and glaucoma and glaucoma suspect (GS) eyes (both P < 0.001). The average prelaminar tissue thickness in regions where the LC was analyzable was significantly thinner in glaucoma eyes than glaucoma suspect and healthy eyes (P = 0.008 and P < 0.001, respectively), and glaucoma suspect eyes had significantly thinner prelaminar tissue than healthy eyes (P < 0.001).



    3. Kostanyan T, Sung KR, Schuman JS, Ling Y, Lucy KA, Bilonick RA, Ishikawa H, Kagemann L, Lee JY, Wollstein G. Glaucoma Structural and Functional Progression in American and Korean Cohorts. Ophthalmology 2016; 123: 783-8.[pubmed]

    PURPOSE: To compare the rate of glaucoma structural and functional progression in American and Korean cohorts.

    DESIGN: Retrospective longitudinal study.

    PARTICIPANTS: Three hundred thirteen eyes from 189 glaucoma and glaucoma suspects, followed up for an average of 38 months.

    METHODS: All subjects were examined semiannually with visual field (VF) testing and spectral-domain optical coherence tomography. All subjects had 5 or more reliable visits.

    MAIN OUTCOME MEASUREMENTS: The rates of change of retinal nerve fiber layer (RNFL) thickness, cup-to-disc (C/D) ratios, and VF mean deviation (MD) were compared between the cohorts. Variables affecting the rate of change for each parameter were determined, including ethnicity, refraction, baseline age and disease severity, disease subtype (high- vs. normal-tension glaucoma), clinical diagnosis (glaucoma vs. glaucoma suspect), and the interactions between variables.

    RESULTS: The Korean cohort predominantly demonstrated normal-tension glaucoma, whereas the American cohort predominantly demonstrated high-tension glaucoma. Cohorts had similar VF parameters at baseline, but the Korean eyes had significantly thicker mean RNFL and larger cups. Korean glaucoma eyes showed a faster thinning of mean RNFL (mean, -0.71 μm/year vs. -0.24 μm/year; P < 0.01). There were no detectable differences in the rate of change between the glaucoma cohorts for C/D ratios and VF MD and for all parameters in glaucoma suspect eyes. Different combinations of the tested variables significantly impacted the rate of change.

    CONCLUSIONS: Ethnicity, baseline disease severity, disease subtype, and clinical diagnosis should be considered when comparing glaucoma progression studies.


    "Graph showing individual rate of change in retinal nerve fiber layer (RNFL) thickness accounting for baseline measurements. Red = American participants; blue = Korean participants.



    4. Nadler Z, Wang B, Schuman JS, Ferguson RD, Patel A, Hammer DX, Bilonick RA, Ishikawa H, Kagemann L, Sigal IA, Wollstein G. In Vivo Three-Dimensional Characterization of the Healthy Human Lamina Cribrosa with Adaptive Optics Spectral-Domain Optical Coherence Tomography. Inves Ophthalmol Vis Sci 2014; 55: 6459-66.[pubmed]

    PURPOSE: To characterize the in vivo three-dimensional (3D) lamina cribrosa (LC) microarchitecture of healthy eyes using adaptive optics spectral-domain optical coherence tomography (AO-SDOCT).

    METHODS: A multimodal retinal imaging system with a light source centered at 1050 nm and AO confocal scanning laser ophthalmoscopy was used in this study. One randomly selected eye from 18 healthy subjects was scanned in a 6° × 6° window centered on the LC. Subjects also underwent scanning with Cirrus HD-OCT. Lamina cribrosa microarchitecture was semiautomatically segmented and quantified for connective tissue volume fraction (CTVF), beam thickness, pore diameter, pore area, and pore aspect ratio. The LC was assessed in central and peripheral regions of equal areas and quadrants and with depth. A linear mixed effects model weighted by the fraction of visible LC was used to compare LC structure between regions.

    RESULTS: The nasal quadrant was excluded due to poor visualization. The central sector showed greater CTVF and thicker beams as compared to the periphery (P < 0.01). Both superior and inferior quadrants showed greater CTVF, pore diameter, and pore mean area than the temporal quadrant (P < 0.05). Depth analysis showed that the anterior and posterior aspects of the LC contained smaller pores with greater density and thinner beams as compared to the middle third (P < 0.05). The anterior third also showed a greater CTVF than the middle third (P < 0.05).

    CONCLUSIONS: In vivo analysis of healthy eyes using AO-SDOCT showed significant, albeit small, regional variation in LC microarchitecture by quadrant, radially, and with depth, which should be considered in further studies of the LC.



    Delineation with depth permits 3D visualization from the anterior (A), side (B), and posterior (C) surfaces, which are divided into three regions for depth analysis.



    Quantification of LC pores and beam structure performed in cross section (A) and magnified (B, C). Pores were segmented using automated technique, with the boundary between beam and pore shown with the solid green line (C). Parameters such as pore mean area and aspect ratio (ratio of long yellow arrow to short blue arrow) were calculated as the average of all pores observed. Beam thickness (green arrow and dotted lines) and pore diameter were calculated voxel-wise using method of expanding spheres and then averaged (C).




    Interplay between intraocular and intracranial pressures on the optic nerve head

    Although the mechanisms by which elevated intraocular pressure (IOP) leads to vision loss in glaucoma are not completely understood, it is widely thought that pressure induced deformations of the neural tissues within the lamina cribrosa in the optic nerve head (ONH) play an important role. In recent years, evidence suggests that the risk of neural tissue damage in glaucoma may also depend on intracranial pressure (ICP). We utilize an in vivo nonhuman primate model, optical coherence tomography (OCT), and computational image analysis tools to measure pressure-induced three-dimensional deformations by IOP and ICP on the ONH structures. We are aiming to identify eye-specific markers that could indicate eyes with increased sensitivity to pressure based on to their structural features, and better understand the biomechanical response of the eye to pressure modulation. This project is in collaboration with the Laboratories of Ocular Biomechanics and Visual Neuroscience at the University of Pittsburgh.

    Recent Publications:


    1. Wang B, Tran H, Smith MA, Kostanyan T, Schmitt SE, Bilonick RA, Jan NJ, Kagemann L, Tyler-Kabara EC, Ishikawa H, Schuman, JS, Sigal IA, Wollstein G. In-vivo effects of intraocular and intracranial pressures on the lamina cribrosa microstructure. PLoS One 2017; 12: e0188302.[pubmed]

    Abstract: There is increasing clinical evidence that the eye is not only affected by intraocular pressure (IOP), but also by intracranial pressure (ICP). Both pressures meet at the optic nerve head of the eye, specifically the lamina cribrosa (LC). The LC is a collagenous meshwork through which all retinal ganglion cell axons pass on their way to the brain. Distortion of the LC causes a biological cascade leading to neuropathy and impaired vision in situations such as glaucoma and idiopathic intracranial hypertension. While the effect of IOP on the LC has been studied extensively, the coupled effects of IOP and ICP on the LC remain poorly understood. We investigated in-vivo the effects of IOP and ICP, controlled via cannulation of the eye and lateral ventricle in the brain, on the LC microstructure of anesthetized rhesus monkeys eyes using the Bioptigen spectral-domain optical coherence tomography (OCT) device (Research Triangle, NC). The animals were imaged with their head upright and the rest of their body lying prone on a surgical table. The LC was imaged at a variety of IOP/ICP combinations, and microstructural parameters, such as the thickness of the LC collagenous beams and diameter of the pores were analyzed. LC microstructure was confirmed by histology. We determined that LC microstructure deformed in response to both IOP and ICP changes, with significant interaction between the two. These findings emphasize the importance of considering both IOP and ICP when assessing optic nerve health.


    (A) Diagram of the experimental setup. Intraocular pressure (IOP) and intracranial pressure (ICP) were controlled using a gravity-based perfusion system. OCT imaging of the lamina cribrosa (LC) (red box) was performed after altering IOP and/or ICP. (B) A sagittal slice of the OCT volume. White dotted line denotes the plane of the (C) enface view of the ONH. (D) At every given ICP, IOP was altered and the ONH was imaged after allowing the tissue to stabilize for 5 minutes at every IOP condition. After completing all IOP conditions, a new ICP was set and the IOP conditions repeated.



    Image analysis procedure.(A) Images were adjusted for isotropic dimensions, (B) and rotated to match the angle of Bruch membrane opening (BMO). (C) Images were translated in the axial direction to match the axial height of the BMO. (D) The microstructures were aligned manually via 3D rotation and translation. (E) Visible LC was denoted and a common overlapping region (white color region) was used for analysis.




    Glaucoma and quality of life

    The health of a person’s vision affects their learning, communication, and interaction with their environment. Our research goals are to evaluate how the interaction between these factors influences a person’s capabilities for independence. The effect of visual impairment on a person’s capabilities is a silent burden of disability. We are exploring how visual impairment influences not only the quality of life but also the ability to accomplish tasks meaningful to their life experiences. Our goal is to implement our findings clinically in order to detect disability and manage the progression toward disability as early as possible in order to improve quality of life and independence.


    Recent Publications:


    1. Livengood HM, Baker N. The role of occupational therapy in vision rehabilitation of individuals with glaucoma. Disabil Rehabil 2015; 37:1202-8.[pubmed]

    PURPOSE: Specific to individuals with glaucoma: (1) provide an overview of the role of occupational therapists (OTs) as part of the vision rehabilitation team, (2) outline evaluation and intervention approaches provided by OTs, and (3) summarize the evidence to support those intervention approaches.

    METHODS: Literature on vision rehabilitation and the typical practice patterns of OTs working with individuals with glaucoma are reviewed and the occupational therapy process is applied to evaluation and intervention approaches. The evidence which supports intervention approaches for individuals with glaucoma is presented.

    RESULTS: The strength of the evidence to support common intervention approaches employed by OTs is weak or inconclusive; many studies lack quality methodological rigor. Moderate evidence supports patient education programs and strong evidence supports problem-solving and self-management strategies; this evidence is based on a limited number of studies.

    CONCLUSION: The prevalence of eye diseases is increasing; knowledge of how visual impairment affects disability will inform resource allocation and development of rehabilitation programs that address the unique needs of individuals with glaucoma. Rehabilitation specialists are key members of the healthcare team aligned to proactively recognize and develop comprehensive rehabilitation programs to maximize individuals' function, quality of life and independence in everyday living.

    IMPLICATIONS FOR REHABILITATION: Glaucoma is one of the four major eye diseases that may result in visual impairment leading to disability. Research supports intervention approaches and vision rehabilitation techniques used by occupational therapists to optimize the health and well-being of individuals with glaucoma. Rehabilitation specialists are key members of the healthcare team who need to be alert to subtle behaviors that may be indicative of visual impairment versus attributed to other client factors.




    Image processing and other basic studies on ocular imaging

    Prior to performing any analysis using ocular images appropriate processing must be applied to the images, such as noise reduction and segmentation analysis. The performance quality of this processing step directly affects the outcome of qualitative or quantitative analysis. We actively develop image processing algorithms and techniques to improve both research and clinical analysis on images acquired using a number of different ocular imaging devices. This effort has resulted in many papers and US patents. In the past decade, one of our major research focuses in this basic computational field was OCT signal normalization. Currently many commercial OCT scanners are available, however, there has been no standard established, therefore image appearance and clinical measurements cannot be directly compared. This hinders not only multi-center research projects but also daily clinical practice in situations when referred patients have been tested using different OCT devices. For such patients, a new set of baseline measurements needs to be established, wasting any existing longitudinal observations. We developed a solution for this problem that applies several image processing techniques in a sequential manner. The signal normalization technique provides fully normalized OCT images and measurements that can be directly compared regardless the OCT devices used for image acquisition.


    Recent Publications:


    1. Chen CL, Ishikawa H, Wollstein G, Bilonick RA, Kagemann L, Schuman JS. Signal Normalization Reduces Image Appearance Disparity Among Multiple Optical Coherence Tomography Devices. Transl Vis Sci Technol. 2017 Feb 28;6(1):13.[pubmed]

    PURPOSE: To assess the effect of the previously reported optical coherence tomography (OCT) signal normalization method on reducing the discrepancies in image appearance among spectral-domain OCT (SD-OCT) devices.

    METHODS: Healthy eyes and eyes with various retinal pathologies were scanned at the macular region using similar volumetric scan patterns with at least two out of three SD-OCT devices at the same visit (Cirrus HD-OCT, Zeiss, Dublin, CA; RTVue, Optovue, Fremont, CA; and Spectralis, Heidelberg Engineering, Heidelberg, Germany). All the images were processed with the signal normalization. A set of images formed a questionnaire with 24 pairs of cross-sectional images from each eye with any combination of the three SD-OCT devices either both pre- or postsignal normalization. Observers were asked to evaluate the similarity of the two displayed images based on the image appearance. The effects on reducing the differences in image appearance before and after processing were analyzed.

    RESULTS: Twenty-nine researchers familiar with OCT images participated in the survey. Image similarity was significantly improved after signal normalization for all three combinations (P ≤ 0.009) as Cirrus and RTVue combination became the most similar pair, followed by Cirrus and Spectralis, and RTVue and Spectralis.

    CONCLUSIONS: The signal normalization successfully minimized the disparities in the image appearance among multiple SD-OCT devices, allowing clinical interpretation and comparison of OCT images regardless of the device differences.

    TRANSLATIONAL RELEVANCE: The signal normalization would enable direct OCT images comparisons without concerning about device differences and broaden OCT usage by enabling long-term follow-ups and data sharing.


    Histogram of subjective evaluation of similarity. A substantial improvement in similarity is noticeable after the signal normalization.



    2. Chen CL, Ishikawa H, Wollstein G, Bilonick RA, Kagemann L, Schuman JS. Virtual Averaging Making Nonframe-Averaged Optical Coherence Tomography Images Comparable to Frame-Averaged Images. Transl Vis Sci Technol. 2016 Jan 11;5(1):1.[pubmed]

    PURPOSE: Developing a novel image enhancement method so that nonframe-averaged optical coherence tomography (OCT) images become comparable to active eye-tracking frame-averaged OCT images.

    METHODS: Twenty-one eyes of 21 healthy volunteers were scanned with noneye-tracking nonframe-averaged OCT device and active eye-tracking frame-averaged OCT device. Virtual averaging was applied to nonframe-averaged images with voxel resampling and adding amplitude deviation with 15-time repetitions. Signal-to-noise (SNR), contrast-to-noise ratios (CNR), and the distance between the end of visible nasal retinal nerve fiber layer (RNFL) and the foveola were assessed to evaluate the image enhancement effect and retinal layer visibility. Retinal thicknesses before and after processing were also measured.

    RESULTS: All virtual-averaged nonframe-averaged images showed notable improvement and clear resemblance to active eye-tracking frame-averaged images. Signal-to-noise and CNR were significantly improved (SNR: 30.5 vs. 47.6 dB, CNR: 4.4 vs. 6.4 dB, original versus processed, P < 0.0001, paired t-test). The distance between the end of visible nasal RNFL and the foveola was significantly different before (681.4 vs. 446.5 μm, Cirrus versus Spectralis, P < 0.0001) but not after processing (442.9 vs. 446.5 μm, P = 0.76). Sectoral macular total retinal and circumpapillary RNFL thicknesses showed systematic differences between Cirrus and Spectralis that became not significant after processing.

    CONCLUSION: The virtual averaging method successfully improved nontracking nonframe-averaged OCT image quality and made the images comparable to active eye-tracking frame-averaged OCT images.

    TRANSLATIONAL RELEVANCE: Virtual averaging may enable detailed retinal structure studies on images acquired using a mixture of nonframe-averaged and frame-averaged OCT devices without concerning about systematic differences in both qualitative and quantitative aspects.



    Processing flow of virtual averaging. (1) For each sampling voxel (the center square in Step 1, with thick black border), one neighboring voxel out of nine (including the center) on the same z-position was randomly selected according to a 2D Gaussian distribution. (2) A random Gaussian deviation was added to the selected voxel value. (3) Repeating previous two steps 15 times, and the new voxel value was calculated by averaging all 15 values replacing the original voxel value.



    3. Chen CL, Ishikawa H, Wollstein G, Bilonick RA, Sigal IA, Kagemann L, Schuman JS. Histogram Matching Extends Acceptable Signal Strength Range on Optical Coherence Tomography Images. Inves Ophthalmol Vis Sci 2015; 56: 3810-9.[pubmed]

    PURPOSE: We minimized the influence of image quality variability, as measured by signal strength (SS), on optical coherence tomography (OCT) thickness measurements using the histogram matching (HM) method.

    METHODS: We scanned 12 eyes from 12 healthy subjects with the Cirrus HD-OCT device to obtain a series of OCT images with a wide range of SS (maximal range, 1-10) at the same visit. For each eye, the histogram of an image with the highest SS (best image quality) was set as the reference. We applied HM to the images with lower SS by shaping the input histogram into the reference histogram. Retinal nerve fiber layer (RNFL) thickness was automatically measured before and after HM processing (defined as original and HM measurements), and compared to the device output (device measurements). Nonlinear mixed effects models were used to analyze the relationship between RNFL thickness and SS. In addition, the lowest tolerable SSs, which gave the RNFL thickness within the variability margin of manufacturer recommended SS range (6-10), were determined for device, original, and HM measurements.

    RESULTS: The HM measurements showed less variability across a wide range of image quality than the original and device measurements (slope = 1.17 vs. 4.89 and 1.72 μm/SS, respectively). The lowest tolerable SS was successfully reduced to 4.5 after HM processing.

    CONCLUSIONS: The HM method successfully extended the acceptable SS range on OCT images. This would qualify more OCT images with low SS for clinical assessment, broadening the OCT application to a wider range of subjects.



    Flow chart of the histogram matching (HM) method. (1) Circular B-scan was resampled along the 3.4-mm diameter circle (the red circle on the en face image, left column) centered to the optic nerve head for each image. After speckle noise reduction, the circular scans were partitioned into two halves: inner and outer retina. (2) Histograms of inner and outer retina from the image with highest signal strength were set as the reference histograms. (3) The HM was applied to inner and outer retina respectively, and finally combined together to generate the histogram matched image (final output). The ranges of vertical and horizontal axes were adjusted in the figure for better visualization of the histogram and the effect of the processing. After HM, the processed histograms almost overlapped with the reference histograms, which are presented as the gray shadow in the histograms in (3).



    4. Ishikawa H, Chen CL, Wollstein G, Grimm JL, Ling Y, Bilonick RA, Sigal IA, Kagemann L, Schuman JS. High dynamic range imaging concept-based signal enhancement method reduced the optical coherence tomography measurement variability. Invest Ophthalmol Vis Sci. 2013 Jan 30;54(1):836-41.[pubmed]

    PURPOSE: To develop and test a novel signal enhancement method for optical coherence tomography (OCT) images based on the high dynamic range (HDR) imaging concept.

    METHODS: Three virtual channels, which represent low, medium, and high signal components, were produced for each OCT signal dataset. The dynamic range of each signal component was normalized to the full gray scale range. Finally, the three components were recombined into one image using various weights. Fourteen eyes of 14 healthy volunteers were scanned multiple times using time-domain (TD)-OCT before and while preventing blinking in order to produce a wide variety of signal strength (SS) images on the same eye scanned on the same day. For each eye, a pair of scans with the highest and lowest SS with successful retinal nerve fiber layer (RNFL) segmentation was selected to test the signal enhancement effect. In addition, spectral-domain (SD)-OCT images with poor signal qualities were also processed.

    RESULTS: Mean SS of good and poor quality scans were 9.0 ± 1.1 and 4.4 ± 0.9, respectively. TD-OCT RNFL thickness showed significant differences between good and poor quality scans on the same eye (mean difference 11.9 ± 6.0 μm, P < 0.0001, paired t-test), while there was no significant difference after signal enhancement (1.7 ± 6.2 μm, P = 0.33). However, HDR had weaker RNFL compensation effect on images with SS less than or equal to 4, while it maintained good compensation effect on images with SS greater than 4. Successful signal enhancement was also confirmed subjectively on SD-OCT images.

    CONCLUSION: The HDR imaging successfully restored OCT signal and image quality and reduced RNFL thickness differences due to variable signal level to the level within the expected measurement variability. This technique can be applied to both TD- and SD-OCT images.



    SD-OCT images before and after HDR imaging. Top row: visibility of the retinal layers became clearer across the image, especially the area within the red bar on top. Signal levels also became more homogeneous with HDR imaging. Bottom row: RNFL segmentation failed on original image but succeeded after HDR imaging (red arrow).

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