January 2019

NEWS & OPINION

Presentation spotlight
Ophthalmic imaging modalities: a lot of change in 2 years


by Stefanie Petrou Binder, MD, EyeWorld Contributing Writer












Treatment-naïve non-exudative CNV with dense microvascular network in an 88-year-old female patient
Source: David Huang, MD

 

Technological leaps that translate into better patient diagnostics

In the past 2 years, advancements in ophthalmic imaging technologies have made huge strides. According to David Huang, MD, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, just when you think you’ve hit the ceiling in technology, there is a new breakthrough that exceeds all previous expectations. He made this observation while speaking on “Landmark achievements in new imaging modalities 2016–2018” at the 2018 World Ophthalmology Congress.
Such is the case with optical coherence tomography angiography (OCTA), visible light OCT (vis-OCT), and the application of deep learning in ophthalmology, which represent three landmark technological changes that have developed with unimaginable speed and hold the potential of exciting clinical applicability.

OCTA

“Among the developments in the past 2 years, OCTA is by far the most important,” Dr. Huang said during his presentation. “Clinically, OCTA represents a paradigm change in angiography. It requires no dye injection, is faster (no waiting for dye transit), noninvasive, cheaper, and it can be used at every visit for screening and monitoring. OCTA will be used a lot more that fluorescein angiography ever was.”
OCTA is a noninvasive imaging modality that can visualize blood vessels down to the capillary level, creating high resolution 3-dimensional angiograms of the retinal and choroidal vasculature. It relies on consecutive scans taken at the same position, which create an image of circulatory motion. By subtracting adjacent frames, OCTA shows the flow signal based on intrinsic motion contrast. One important factor explaining why OCT angiography has gained in momentum is the increase in speed of OCT systems. “The OCT scan speed doubles every 2 years, according to Moore’s law,” he explained. “The development of high speed OCT systems and efficient algorithms allow a qualitative and quantitative assessment of the retina and choroid. Now, 4-D OCT detects motion and flow over time,” Dr. Huang said.
A number of developments make this technology even more interesting. Dr. Huang discovered that he could get a 4-fold increase in the signal-to-noise ratio of flow detection, with no increase in scan time, with the use of split-spectrum amplitude-decorrelation angiography (SSADA), which works by splitting the OCT signal into spectral fractions, giving additional flow information. Motion correction technology allows for accurate scans without eye motion interference. Also, projection-resolved OCTA images allow the clinician to distinguish between real vessels and projection artifacts.

OCTA in clinical trials

OCTA is a novel imaging modality, which means that clinical trials using this technology are needed to help understand the extent to which it can be applied for disease management. Studies so far have used OCTA and OCTA variations for glaucoma evaluation, identifying the vascular layers of the retina, identifying vascular changes in diabetes with and without retinopathy, in the monitoring of anti-angiogenic treatment in choroidal neovascularization (CNV), and more.
In open angle glaucoma, OCTA showed that decreased vessel density was highly correlated with the severity of visual field damage, more so than structural thinning. The study investigators maintained that OCTA was a promising technology in glaucoma management, potentially enhancing the understanding of the role of vasculature in the pathophysiology of the disease.1
Projection artifacts observed in the deep capillary plexuses are caused by the fluctuating shadows cast by red blood cells moving in the more superficial vessels. Projection artifacts in OCTA blur the retinal vascular plexus together and limit visualization of the individual plexuses. In a study that used a projection-resolved (PR) OCTA algorithm to suppress projection artifacts,2 capillary dropout in three retinal vascular plexuses were distinctly visualized in eyes with diabetic retinopathy (DR), greatly enhancing the detection and staging of DR.
High-speed swept-source OCTA was used to visualize changes in retinal microvasculature and the choriocapillaris in patients in all stages of DR, including vascular abnormalities like clustered capillaries, dilated capillary segments, tortuous capillaries, regions of capillary dropout, reduced capillary density, and more. OCTA shows potential for helping physicians understand DR pathogenesis, evaluating treatment responses, and for the earlier detection of vascular abnormalities in patients with diabetes.3
In another study that used OCTA to closely follow a case of exudative CNV over three cycles of anti-angiogenic treatment, OCTA could monitor the short-term blood flow changes in CNV in response to treatment. The authors explained that frequent OCTA reveals a previously unknown pattern of rapid shutdown and reappearance of CNV channels within treatment cycles. OCT angiographic changes precede fluid reaccumulation and could be useful as leading indicators of CNV activity to guide treatment timing.4

Visible light OCT

Visible light OCT (vis-OCT) is an emerging imaging modality based on visible light illumination, as opposed to near infrared light. The high axial resolution and contrast of vis-OCT can help quantify retinal layer thickness more accurately, which is useful in the assessment of glaucoma, Alzheimer’s, and Parkinson’s disease, according to a new report. The higher transverse resolution helps suppress projection artifact and measure capillary diameter, vessel wall thickness.5
More importantly, vis-OCT provides the spectroscopic contrast to measure retinal blood oxygen saturation. This may provide a useful early indicator of the pathophysiology of ophthalmic diseases. In one study, hemoglobin oxygen saturation was measured in the retinal circulation in healthy humans using vis-OCT. The measurements showed clear oxygenation differences between central retinal arteries and veins close to the optic nerve head. It appears that vis-OCT can achieve accurate retinal oximetry measurements in clinical settings.6 Using vis-OCT in rats, investigators demonstrated an automated spectroscopic retinal oximetry algorithm to measure the oxygen saturation within retinal arteries and veins. The algorithm was validated in vitro with flow phantoms and in vivo in rats. It also could measure oxygen extraction at different inhaled oxygen concentrations.7
“Visible light OCT allows us to view retinal function,” Dr. Huang explained. “It has higher resolution and contrast than standard OCTA and can measure oxygen metabolism, oxygen extraction, and the oxygen consumption rate. Human clinical imaging is possible with a safe power level. This represents a new frontier for study of retinal vascular diseases. Some limitations include expensive and noisy light sources, however, this is a hot research area with rapid improvement,” he said.

Deep learning

Deep learning is a recent advance in artificial intelligence machine learning technology. It can be useful for grading fundus images that are used to diagnose major eye diseases, such as DR, glaucoma, and AMD.
As the authors of one study explained, deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of annotated examples. When applying deep learning for automated detection of DR and DME from 128,175 retinal fundus photographs taken in diabetes patients, the algorithm proved to be highly sensitive and highly specific for detecting referable DR.8 An unrelated study that tested the diagnostic performance of a deep learning system similarly found high specificity and sensitivity for identifying DR and related eye diseases based on 494,661 retinal images.9 Further research is necessary to evaluate the feasibility of the algorithms in the clinical setting and whether the use of algorithms and deep learning lead to better care compared to current ophthalmologic assessment.
“Deep learning could have a wide impact on ophthalmology,” Dr. Huang said. “It is based on fundus photographs or OCT images that could be acquired by technicians using widely available devices. Machine grading can be cheaper and more consistent than human graders. This is a rapidly improving technology that could make telemedicine practical on a larger scale. Limitations include large datasets needed to train the algorithm and variation in image quality in the telemedicine setting. These advancements in diagnostics are moving us forward into a brighter future.”

References

1. Yarmohammadi A, et al. Relationship between optical coherence tomography angiography vessel density and severity of visual field loss in glaucoma. Ophthalmology. 2016;123:2498–2508.
2. Hwang TS, et al. Visualization of 3 distinct retinal plexuses by projection-resolved optical coherence tomography angiography in diabetic retinopathy. JAMA Ophthalmol. 2016;134:1411–1419.
3. Choi W, et al. Ultrahigh speed swept source optical coherence tomography angiography of retinal and choriocapillaris alterations in diabetic patients with and without retinopathy. Retina. 2017;37:11–21.
4. Huang D, et al. Optical coherence tomography angiography of time course of choroidal neovascularization in response to anti-angiogenic treatment. Retina. 2015;35:2260–4.
5. Pi S, et al. Angiographic and structural imaging using high axial resolution fiber-based visible-light OCT. Biomed Opt Express. 2017;8:4595–4608.
6. Chen S, et al. Retinal oximetry in humans using visible-light optical coherence tomography. Biomed Opt Express. 2017;8:1415–1429.
7. Pi S, et al. Automated spectroscopic retinal oximetry with visible-light optical coherence tomography. Biomed Opt Express. 2018;9:2056–2067.
8. Gulshan V, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–2410.
9. Ting DSW, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318:2211–2223.

Editors’ note: Dr. Huang has financial interests with Optovue (Fremont, California).

Contact information

David Huang
: huangd@ohsu.edu

Ophthalmic imaging modalities: a lot of change in 2 years Ophthalmic imaging modalities: a lot of change in 2 years
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