U.S. patent application number 15/080471 was filed with the patent office on 2016-09-29 for optical coherence tomography angiography methods.
This patent application is currently assigned to OREGON HEALTH & SCIENCE UNIVERSITY. The applicant listed for this patent is David Huang, Yali Jia. Invention is credited to David Huang, Yali Jia.
Application Number | 20160278627 15/080471 |
Document ID | / |
Family ID | 56976114 |
Filed Date | 2016-09-29 |
United States Patent
Application |
20160278627 |
Kind Code |
A1 |
Huang; David ; et
al. |
September 29, 2016 |
OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY METHODS
Abstract
Methods of applying OCT angiography are disclosed. In
particular, methods of detecting, visualizing and measuring the
extent of retinal neovascularization are disclosed. Further
disclosed are methods measuring retinal nonperfusion area and
choriocapillaris defect area.
Inventors: |
Huang; David; (Portland,
OR) ; Jia; Yali; (Portland, OR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Huang; David
Jia; Yali |
Portland
Portland |
OR
OR |
US
US |
|
|
Assignee: |
OREGON HEALTH & SCIENCE
UNIVERSITY
PORTLAND
OR
|
Family ID: |
56976114 |
Appl. No.: |
15/080471 |
Filed: |
March 24, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62138196 |
Mar 25, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 3/0041 20130101;
G06T 2207/30041 20130101; A61B 3/1233 20130101; A61B 3/0025
20130101; A61B 3/102 20130101; A61B 3/1241 20130101; A61B 3/14
20130101 |
International
Class: |
A61B 3/00 20060101
A61B003/00; A61B 3/12 20060101 A61B003/12; A61B 3/14 20060101
A61B003/14; A61B 3/10 20060101 A61B003/10 |
Goverment Interests
ACKNOWLEDGEMENT OF GOVERNMENT SUPPORT
[0001] This invention was made with the support of the United
States government under the terms of grant numbers R01 EY013516 and
R01 EY023285 awarded by the National Institutes of Health. The
United States government has certain rights to this invention.
Claims
1. A method of measuring a nonperfusion area, the method
comprising: receiving a set of cross sectional OCT angiograms;
generating an en face inner retina angiogram or an en face
choriocapillaris angiogram; identifying a set of pixels in the en
face inner retina angiogram or the en face choriocapillaris
angiogram wherein the pixels have decorrelation values less than a
cutoff point, thereby generating a nonperfusion map; identifying
nonperfusion areas with an area below a defined number of pixels;
summing the pixels in the nonperfusion areas, thereby measuring a
retinal nonperfusion area.
2. The method of claim 1 wherein the cutoff point is 2.33 standard
deviations below the mean decorrelation value in the en face inner
retina angiogram.
3. The method of claim 1 further comprising closing a hole in the
inner retina angiogram using morphological operations.
4. The method of claim 4 further comprising converting the area to
physical dimensions.
5. A method of indicating retinal neovascularization, the method
comprising: receiving a set of cross sectional OCT angiograms,
generating an en face inner retina angiogram and an en face
vitreous angiogram; generating a composite en face angiogram by
overlaying the en face inner retina angiogram and the en face
vitreous angiogram; wherein the vitreous layer is anterior to an
inner limiting membrane and the inner retina is between the inner
limiting membrane and an outer boundary of the outer plexiform
layer and wherein visualization of blood flow above the inner
limiting membrane is an indication of retinal
neovascularization.
6. A method of measuring a retinal neovascularization area and flow
index, the method comprising: receiving a set of cross sectional
OCT angiograms; generating an en face vitreous angiogram; applying
a filter to the en face vitreous angiogram; using pattern
recognition to identify retinal neovascularization; identifying a
set of pixels in the en face vitreous angiogram wherein the pixels
have decorrelation values greater a cutoff point converting the sum
of the set of pixels to physical dimensions, thereby measuring the
neovascularization area; dividing the neovascularization area by
the area of the en face vitreous angiogram, thereby determining the
RNV flow index.
7. A system for analyzing OCT angiography data comprising: an OCT
system configured to acquire an OCT image and OCT angiography
images of a sample; a logic subsystem; and a data holding subsystem
comprising machine-readable instructions stored thereon that are
executable by the logic subsystem to perform the steps of any of
claims 1-6.
Description
FIELD
[0002] Generally, the field involves methods of using optical
coherence tomography (OCT) in angiography. More specifically, the
field involves methods of processing OCT angiography images.
BACKGROUND
[0003] Retinal vascular diseases are a leading cause of blindness.
Optical coherence tomography (OCT) has become the standard imaging
modality in ophthalmology for evaluating fluid accumulation in
these diseases and guiding treatment. OCT provides cross-sectional
and three-dimensional (3D) imaging of the retina and optic nerve
head with micrometer-scale depth resolution. Structural OCT
enhances the clinician's ability to detect and monitor fluid
exudation associated with retinal vascular diseases. While
anatomical alterations that impact vision are readily visible,
structural OCT has a limited ability to image the retinal or
choroidal vasculatures. Furthermore, it is unable to directly
detect capillary dropout or pathologic new vessel growth
(neovascularization) that are the major vascular changes associated
with two of the leading causes of blindness, age-related macular
degeneration (AMD) and diabetic retinopathy. To visualize these
changes, traditional intravenous contrast dye-based angiography
techniques are currently used.
[0004] Fluorescein dye is primarily used to visualize the retinal
vasculature. A separate dye, indocyanine green (ICG), is necessary
to evaluate the choroidal vasculature. Both fluorescein angiography
(FA) and ICG angiography require intravenous injection, which is
time consuming, can cause nausea, vomiting, and, rarely,
anaphylaxis (Lopez-Saez M P et al, Ann Allergy Asthma Immunol 81,
428-430 (1998; incorporated by reference herein). Dye leakage or
staining provides information regarding vascular incompetence
(e.g., from abnormal capillary growth), but it also obscures the
image and blurs the boundaries of neovascularization, making
characterization of the shape and extent of such defects
unreliable. Additionally, conventional angiography is
two-dimensional (2D), which makes it difficult to distinguish
vascular abnormalities within different layers. Therefore, it is
desirable to develop a non-injection, dye-free method for 3D
visualization of ocular circulation.
[0005] In recent years, several OCT angiography methods have been
developed to detect changes in the OCT signal caused by flowing red
blood cells in blood vessels. Initially, Doppler OCT angiography
methods were investigated for the visualization and measurement of
blood flow (Wang R K et al, Opt Express 15, 4083-4097 (2007);
Grulkowski I et al, Opt Express 17, 23736-23754 (2009); Yu L and
Chen Z, J Biomed Opt 15, 016029 (2010); Makita S et al, Opt Express
19, 1271-1283 (2011); Zotter S et al Opt Express 19, 1217-1227
(2011); and Braaf B et al, Opt Express 20, 20516-20534 (2012)).
Because Doppler OCT is only sensitive to motion parallel to the OCT
probe beam, it is limited in its ability to image retinal and
choroidal circulations, which are predominantly perpendicular to
the OCT beam. More recent approaches, based on detecting variation
in the speckle pattern over time, are sensitive to both transverse
and axial flow. Several types of speckle-based techniques have been
described, including amplitude-based (Mariampillai A et al, Opt
Lett 33, 1530-1532 (2008); Motaghiannezam R and Fraser S, Biomed
Opt Express 3, 503-521 (2012); and Enfield J et al, Biomed Opt
Express 2, 1184-1193 (2011); all of which are incorporated by
reference herein), phase-based (Fingler J, Opt Express 17,
22190-22200 (2009); incorporated by reference herein), or a
combination of both amplitude+phase variance methods (Liu G et al,
Biomed Opt Express 3, 2669-2680 (2012); incorporated by reference
herein).
[0006] However, using these OCT imaging methods it remains
difficult to distinguish the vascular pathologies such as capillary
dropout and neovascularization with the retinal and choroidal
vasculature using en face projection views and cross sectional
views, and quantification of the area of these pathological
structures is problematic due to projection artifacts and image
noise. Thus, more clinically useful methods for extracting and
presenting information derived from structural OCT and OCT
angiography data is needed.
SUMMARY
[0007] Disclosed herein are systems and methods for OCT angiography
segmentation, visualization, and quantification to provide
comprehensive information that a clinician could use to assess and
manage a variety of ophthalmological pathologies.
[0008] Disclosed herein are methods of color coding blood flow in
an OCT angiogram. The method involves receiving a set of
cross-sectional angiograms, segmenting those cross sectional
angiograms into layers, and generating, for example, an en face
inner retina angiogram, an en face outer retinal angiogram, and a
choroid angiogram. Flow projection artifacts cast by more
superficial layers are removed from the en face outer retina
angiogram. Neovascularization is detected in the outer retina using
pattern recognition, image masks, and thresholding operations.
Colors are assigned to flow in the inner retina, choroid, and outer
retina.
[0009] Disclosed herein are methods of removing projection
artifacts in an OCT angiogram of the outer retina. These methods
involve receiving a set of cross-sectional OCT angiograms,
generating en face inner retina angiograms and en face outer retina
angiograms, applying a filter to the en face inner retina
angiogram, thereby creating a filtered en face inner retina
angiogram, generating a binary large inner retinal vessel mask from
the filtered en face inner retina angiogram, multiplying each
element from the mask matrix with its corresponding element in an
outer retina layer matrix generated from the en face outer retina
angiogram, and outputting a projection artifact free en face outer
retina angiogram. A similar methodology can also be applied to
remove projection artifacts in an OCT angiogram of a
choriocapillaris layer as well as an OCT angiogram of a choroid
layer.
[0010] Disclosed herein are methods of visualizing choroidal
neovascularization (CNV) using OCT. These methods involve receiving
a set of cross-sectional angiograms, segmenting the set of cross
sectional angiograms into layers, generating an en face inner
retina angiogram and an en face outer retina angiogram, and
removing flow projection artifacts from the en face outer angiogram
thereby producing a second en face outer retina angiogram. The
methods further involve generating a color coded composite en face
outer retina angiogram from the en face inner retina angiogram and
the en face outer retina angiogram. The CNV can then be classified
by type on the basis of outer retinal flow and position relative to
the retinal pigment epithelial (RPE).
[0011] Disclosed herein are methods of visualizing retinal
neovascularization (RNV) using OCT. These methods involve receiving
a set of cross-sectional angiograms, segmenting the set of cross
sectional angiograms into layers, generating an en face inner
retina angiogram and an en face vitreous angiogram. The methods
further involve generating a color coded composite en face
angiogram by overlaying the en face inner retina angiogram and the
en face vitreous angiogram.
[0012] Disclosed herein are methods of measuring the area of CNV
and flow index. These methods involve receiving a set of
cross-sectional angiograms, segmenting the set of cross sectional
angiograms into layers, generating an en face outer retina
angiogram, and removing a flow projection artifact from the en face
outer retina angiogram, thereby producing a second en face outer
retina angiogram, removing background noise using a filter,
identifying the CNV using vascular pattern recognition and
calculating the CNV area and flow index.
[0013] Disclosed herein are methods of measuring the area of RNV
and flow index. These methods involve receiving a set of
cross-sectional angiograms, segmenting the set of cross sectional
angiograms into layers, generating an en face vitreous angiogram,
removing background noise from the en face vitreous angiogram using
a filter, identifying the RNV using vascular pattern recognition,
and calculating the RNV area and flow index.
[0014] Disclosed herein are methods of measuring the nonperfusion
area of the inner retina. These methods involve receiving a set of
cross-sectional angiograms, segmenting the set of cross sectional
angiograms into layers, generating an en face inner retina
angiogram, thresholding the en face inner retina angiogram to
remove pixels that have decorrelation values greater than a cutoff
value, removing pixel clusters having an area below a specified
value, closing holes within the remaining pixel clusters, and
calculating the nonperfusion area. A similar methodology can be
used to measure a nonperfusion area or defect area in the
choriocapillaris.
[0015] It is an object of the invention to distinguish CNV from
surrounding outer retinal tissue, hemorrhage, RPE, BM, and non-flow
material under (pigment epithelial detachments (PED's.)
[0016] It is an object of the invention to provide quantitative
assessments of CNV area and flow index that are proportional to
average avascular density and flow velocity on the capillary
scale.
[0017] It is an object of the invention to use OCT angiography to
identify foveal avascular zone (FAZ) enlargement and irregularity,
capillary drop out, and microaneurysms and neovascularization,
particularly in patients with diabetic retinopathy, more accurately
than with fluorescein angiography.
[0018] It is an object of the invention to provide a comprehensive
OCT angiography system that includes scanning, flow detection,
segmentation, display, and quantification.
[0019] It is an object of the invention to provide a system that is
able to capture a large 6.times.6 mm view of the macula with
adequate resolution using a commercially available OCT system.
[0020] It is an object of the invention to provide a multicolor
display system showing multiple circulations in the same image
panel so that the location of pathologies can be located in
relation to the retinal vasculature with minimal interference from
flow projection artifacts.
[0021] It is an object of the invention to provide quantification
of RNV and quantification of the area of capillary dropout in the
retinal circulation and choriocapillaris.
[0022] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features of the disclosed subject matter,
nor is it intended to be used to limit the scope of the disclosed
subject matter. Furthermore, the disclosed subject matter is not
limited to implementations that solve any or all disadvantages
noted in any part of this disclosure.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0023] FIG. 1 is a flow chart of an example of a method of
generating an en face color coded composite angiogram.
[0024] FIG. 2 is a flow chart of an example of a method of
visualizing choroidal neovascularization.
[0025] FIG. 3 is a flow chart of an example of a method of
measuring choroidal neovascularization area and flow index
area.
[0026] FIG. 4 is a flow chart of an example of a method of removing
projection artifacts in an OCT angiogram of the outer retina.
[0027] FIG. 5 is a flow chart of an example of a method of removing
projection artifacts in an OCT angiogram of the
choriocapillaris.
[0028] FIG. 6 is a flow chart of an example of a method of removing
projection artifacts in an OCT angiogram of the choroid layer.
[0029] FIG. 7 is a flow chart of an example of a method of
measuring a retinal nonperfusion area.
[0030] FIG. 8 is a flow chart of an example of a method of
measuring a choriocapillaris defect area.
[0031] FIG. 9 is a flow chart of an example of a method of
measuring retinal neovascularization.
[0032] FIG. 10 is a flow chart of an example of a method of
measuring a retinal neovascularization area and flow index.
[0033] FIG. 11A is a cross sectional OCT reflectance image of a
healthy control subject.
[0034] FIG. 11B is a cross sectional OCT angiography image of a
healthy control subject. Arrows point to locations where flows in
inner retinal vessels are projected onto bright photoreceptor and
retinal pigment epithelium layers.
[0035] FIG. 11C is a cross sectional color OCT angiogram in a
healthy control subject. The internal limiting membrane (ILM) and
outer plexiform layer (OPL) and Bruch's membrane (BM) are
indicated. They are the boundaries separating inner retinal, outer
retinal and choroidal circulations.
[0036] FIG. 11D is an en face angiogram of the inner retina in a
healthy control subject. Dashed line indicates the cross sections
in Figures A-C above.
[0037] FIG. 11E is an en face angiogram of the outer retina in a
healthy control subject.
[0038] FIG. 11F is an en face angiogram of the choroid in a healthy
control subject.
[0039] En face angiograms of FIGS. 11D, 11E, and 11F were produced
by maximum flow projections within segmented layers and span a
3.times.3 mm area. A higher decorrelation value (scale to the right
of FIG. 11F) corresponds to higher blood flow density.
[0040] FIG. 12A is a color fundus photograph showing subretinal
hemorrhage in a first subject with age-related macular
degeneration. The red square outlines the area shown in the
angiograms of FIGS. 12B-12I below.
[0041] FIG. 12B is an early-phase fluorescein angiography image in
the subject.
[0042] FIG. 12C is an image of late-phase fluorescein angiography
in the subject.
[0043] FIG. 12D is an en face optical coherence tomography (OCT)
angiogram of the inner retina in the subject.
[0044] FIG. 12E is an en face OCT angiogram of the outer retina
showing the CNV in the subject. The yellow dashed lines indicate
the position of OCT cross-section shown in FIG. 12G. Yellow arrows
indicate the superior to inferior direction.
[0045] FIG. 12F is an en face angiogram of the choroid in the
subject showing patchy flow directly under the CNV (blue dotted
outline) and an adjacent area of reduced flow (green dotted
outline).
[0046] FIG. 12G is a cross-sectional color OCT angiogram in the
subject showing the CNV (yellow) was predominantly under the
retinal pigment epithelial (RPE). The blue arrow shows the location
of the subretinal fluid. The green arrow corresponds to the green
dashed outline in F showing a focal region of reduced choroidal
flow adjacent to the CNV.
[0047] FIG. 12H is a composite en face OCT angiogram in the subject
showing most subretinal fluid (dark blue) inferior to the CNV.
[0048] FIG. 12G is a retinal thickness deviation map showing
retinal thickening over the CNV in the subject. I=inferior;
S=superior.
[0049] FIG. 13A is a color fundus photograph showing subretinal
hemorrhage in a second subject with age related macular
degeneration. The red square outlines the area shown in the
angiograms of FIGS. 13B-13I below.
[0050] FIG. 13B is an early-phase fluorescein angiography image in
the subject.
[0051] FIG. 13C is an image of late-phase fluorescein angiography
in the subject.
[0052] FIG. 13D is an en face optical coherence tomography (OCT)
angiogram of the inner retina in the subject.
[0053] FIG. 13E is an en face OCT angiogram of the outer retina
showing the CNV in the subject. The yellow and green dashed lines
indicate the position of OCT cross-section shown in FIGS. 13G, 13H,
and 13I.
[0054] FIG. 13F is an en face angiogram of the choroid showing the
patchy reduced flow directly under the CNV (blue dotted outline)
and an adjacent area of reduced flow (green dotted outline) in the
subject.
[0055] FIG. 13G is a Vertical cross-sectional color OCT angiogram
showing the CNV (yellow) was predominantly above the retinal
pigment epithelial (RPE) in the subject. The green solid arrow
corresponds to the green dotted outline in FIG. 13F showing a focal
region of reduced choroidal flow inferonasal to the CNV. The green
hollow arrow points out a high choroidal flow signal superior to
the CNV.
[0056] FIG. 13H is a horizontal cross-sectional color OCT angiogram
showing the feeder vessel (yellow dotted circle) that corresponds
to the white arrows in FIGS. 13E and 13F. Also note cystic
intraretinal fluid above the CNV.
[0057] FIG. 13I is a horizontal cross-sectional OCT reflectance
image showing the feeder vessel seen as a flow void.
[0058] FIG. 13J is a composite en face OCT angiogram showing
subretinal fluid (dark blue) at the superonasal corner and
intraretinal fluid (light blue) over the CNV.
[0059] FIG. 13K is a retinal thickness deviation map showing
thickening over the CNV in the subject.
[0060] FIG. 14A is a color fundus photograph showing subretinal
hemorrhage, retinal pigment epithelium tear, and geographic atrophy
(blue dashed outline) in a third subject with age related macular
degeneration. The red square outlines indicate the area shown on
the angiograms in FIGS. 14B-14I below.
[0061] FIG. 14B is an early-phase fluorescein angiography image in
the subject.
[0062] FIG. 14C is an image of late-phase fluorescein angiography
in the subject.
[0063] FIG. 14D is an en face OCT angiogram of the inner retina in
the subject.
[0064] FIG. 14E is an en face angiogram of the outer retina showing
the CNV. The yellow dashed lines indicated the position of the OCT
cross-section shown in FIG. 14G.
[0065] FIG. 14F is an en face angiogram of the choroid showing
diffuse reduction of flow signal under the pigment epithelial
detachment and choriocapillaris defect in the area of geographic
atrophy (blue dashed outline).
[0066] FIG. 14G is a cross-sectional color OCT angiogram showing
the CNV both above and below the RPE. The subretinal hemorrhage was
over the CNV and overshadowed the CNV at its nasal age.
[0067] FIG. 14H is a composite en face OCT angiogram.
[0068] FIG. 14I is a retinal thickness deviation map showing
thinning over the CNV and thickening around it.
[0069] FIG. 15A is an image of the right eye of a subject with
diabetic retinopathy showing fluorescein angiography cropped to
6.times.6 mm with an ETDRS grid superimposed.
[0070] FIG. 15B is a 6.times.6 mm en face OCT retinal angiogram of
the subject showing FAZ enlargement temporally between the 300
(dotted) and 500 .mu.m diameter circles. The retinal flow signal,
detected between Bruch's membrane and the internal limiting
membrane is shown in magenta. Flow signal anterior to the ILM is
shown in yellow, displaying small tufts of neovascularization.
[0071] FIG. 15C is a magnified fluorescein angiography image of the
subject showing the FAZ enlargement temporally.
[0072] FIG. 16A is a 6.times.6 mm macular image of the left eye of
a patient with proliferative diabetic retinopathy--in particular,
an early frame fluorescein angiogram. Numerous microaneurysms are
seen throughout the macula as punctate areas of hyperfluoresence.
The green arrow points to an area of intraretinal microvascular
abnormality (IRMA). The red arrow points to a small area of
hyperfluoresence that leaked mildly in later frames.
[0073] FIG. 16B is an en face OCT angiogram showing flow signal
above the internal limiting membrane consistent with a tuft of
neovascularization. The area of IRMA was also identified by OCT
angiogram (green arrow). The largest microaneurysm on FA (blue
arrow) was not identifiable on the OCT angiogram.
[0074] FIG. 16C is a cross-sectional OCT angiogram at the level of
the neovascularization (yellow) shows it to be anterior to the ILM.
Retinal circulation is colored in magenta and choroidal circulation
(below Bruch's membrane is colored red.)
[0075] FIG. 17A is an OCT angiogram of a diabetic patient showing
areas of capillary dropout in the temporal macula with pruning of
the arterioles.
[0076] FIG. 17B is an image of fluorescent angiography of the same
diabetic patient as in 17A showing areas of capillary dropout in
the temporal macula with pruning of the arterioles. Diffuse leakage
obscures an area of capillary drop out otherwise seen in the OCT.
An arteriole with wall staining (blue arrow) in the FA is shown to
be a barely visible ghost vessel in OCT. Focal areas of leakage
near the fovea thought to be large microaneurysms in the FA were
shown to be neovascularization by OCT.
[0077] FIG. 18A is an en face OCT angiogram showing flow in the
abnormal vessels above the disc. The NVD was cropped outside of the
OCT scan volume nasal to the disc and is seen as shadows rather
than flow.
[0078] FIG. 18B shows that NVD is clearly seen using fluorescein
angiography. Clinically, the NVD appeared elevated above the
retinal surface.
[0079] FIGS. 19A-19F are images from OCT angiography (3.times.3 mm)
of a healthy human eye acquired using a 70 kHz spectral OCT system
with an 840 nm center wavelength.
[0080] FIG. 19A is a Cross-sectional composite OCT angiogram. Depth
layer segmentation lines are shown in green. ILM, internal limiting
membrane; OPL, outer plexiform layer; BM, Bruch's membrane. Flow
signals are color-coded by depth: purple, anterior to the OPL; red,
posterior to BM.
[0081] FIG. 19B is an en face OCT angiogram above the ILM shows the
normal, avascular vitreous layer.
[0082] FIG. 19C is an en face OCT angiogram between the ILM and OPL
that shows the normal retinal vasculature.
[0083] FIG. 19D is an en face OCT angiogram between the OPL and BM
showing the normal, avascular outer retina.
[0084] FIG. 19E is an en face OCT angiogram of the inner 10 .mu.m
of the choroid showing dense, relatively even flow throughout the
central macula (3.times.3 mm).
[0085] FIG. 19F is an en face OCT structural image with an inverse
gray scale shows the deeper choroid with medium- and large-sized
vessels.
[0086] FIGS. 20A-20D collectively are a quantification of inner
retinal blood flows in a normal control subject (top panels of
20A-20D) and in a subject with non-proliferative diabetic
neuropathy with macular edema (bottom panels of 20A-20D). OCT
angiography was acquired using a 70 kHz spectral OCT system with a
center wavelength of 840 nm. The white dashed circle represents the
normal foveal avascular zone (FAZ, 0.6 mm diameter white dashed
circle). The area between the white and blue dashed circles is the
parafoveal zone. The area between blue and green dashed circles is
the perifoveal zone. The normal subject had a FAZ of 0.30 mm.sup.2,
while the NPDR case showed an enlarged FAZ and scattered areas of
macular non perfusion totaling 7.07 mm.sup.2.
[0087] FIG. 20A (top panel) is a fundus photo of the normal control
subject.
[0088] FIG. 20A (bottom panel) is a fluorescein angiogram of a
subject with non-proliferative diabetic neuropathy.
[0089] FIG. 20B (top panel) is an en face 3.times.3 mm OCT
angiogram of normal control subject.
[0090] FIG. 20B (bottom panel) is an en face 3.times.3 mm OCT
angiogram of a subject with non-proliferative diabetic neuropathy.
Enlargement of the FAZ is present in the parafoveal region.
[0091] FIG. 20C (top panel) is a parafoveal and perifoveal retinal
flow index (vessel density) shown on am en face 6.times.6 mm OCT
angiogram in a normal control subject.
[0092] FIG. 20C (bottom panel) is a parafoveal and perifoveal
retinal flow index (vessel density) shown on am en face 6.times.6
mm OCT angiogram in a subject with non-proliferative diabetic
neuropathy.
[0093] FIG. 20D (top panel) is an angiogram showing nonperfusion
areas (blue) in a normal control subject.
[0094] FIG. 20D (bottom panel) is an angiogram showing nonperfusion
areas (blue) in a a subject with non-proliferative diabetic
neuropathy.
[0095] FIG. 21A-D collectively show a proliferative diabetic
retinopathy (PDR) case imaged using a 100 kHz swept-source OCT
system with a center wavelength of 1050 nm.
[0096] FIG. 21A is a confocal scanning laser ophthalmoscope (cSLO)
showing retinal neovascularization (NVD) at the optic disc and
attenuated retinal vessels.
[0097] FIG. 21B is an FA image showing NVD and peripapillary
capillary dropout. The green squares in A and B outline the
3.times.3 mm area shown on the OCT angiogram below.
[0098] FIG. 21C is an en face OCT angiography showing NVD and areas
of capillary dropout that correspond to FA (NV is shown in light
red gold; normal retinal vessels in purple). The area of NVD was
0.47 mm.sup.2. The vitreous flow index was 0.022.
[0099] FIG. 21D is a Cross-sectional composite OCT angiogram
showing NVD above the inner limiting membrane (red gold).
[0100] FIGS. 22A-22D collectively show a type I CNV case imaged by
a 100 kHz swept-source OCT system with a center wavelength of 1050
nm. The CNV is identified by OCT angiography (3.times.3 mm), but it
is ill-defined by fluorescein angiography (FA).
[0101] FIG. 22A is a fundus photograph. The black square outlines
the areas shown on the angiograms.
[0102] FIG. 22B is a late stage fluorescein angiograph showing an
occult CNV.
[0103] FIG. 22C is a composite en face color-coded OCT angiogram
with CNV flow highlighted in yellow. The CNV area was 0.96 mm.sup.2
and the outer retina flow index was 0.012. The yellow dashed line
indicates the position of the OCT cross-section.
[0104] FIG. 22D is a cross-sectional color OCT angiogram.
[0105] Both composite en face (C) and cross-sectional color OCT
angiograms (D) show inner retinal flow in purple, outer retinal
flow (CNV) in yellow, and choroidal flow in red. The CNV is
predominantly under the RPE.
[0106] FIGS. 23A-23F collectively show a geographic atrophy case
imaged by a 100 kHz swept-source OCT system with a center
wavelength of 1050 nm.
[0107] FIG. 23A is a fundus photograph showing the area of
geographic atrophy (GA) adjacent to the foveal center.
[0108] FIG. 23B is an autofluorescence image sharply outlines the
area of absent RPE and is surrounded by a halo of
hyperautofluorescence. The green squares in FIGS. 23A and 23B
outline the area shown in FIGS. 23C-F.
[0109] FIG. 23C is a drusen-RPE complex thickness map showing the
area of RPE thinning (the dark area nasally)
[0110] FIG. 23D is an en face OCT structural image on an inverse
gray scale of the deeper choroid reveals medium- and large-sized
vessels.
[0111] FIG. 23E is an en face OCT angiogram (3.times.3 mm) of the
choriocapillaris showing dramatically decreased, but not absent,
choriocapillaris flow in the area of GA.
[0112] FIG. 23F is a map of the same en face OCT angiogram of FIG.
23E. The light blue color represents the choriocapillaris
nonperfusion area (2.75 mm.sup.2).
[0113] FIG. 23G is a cross-sectional composite OCT angiogram
showing the absence of choriocapillaris flow in most of the area of
GA,
[0114] FIG. 23H is a cross-sectional composite OCT angiogram
showing that flow at the edge of the atrophy is spared (shown by
the green arrows in the magnified views).
[0115] FIGS. 24A-24E collectively show a choroideremia case imaged
by a 100 kHz swept-source OCT system with a center wavelength of
1050 nm. The large-field en face OCT angiograms
(.sup..about.3.times.8.5 mm) were produced by stitching together
three 3.times.3 mm scans.
[0116] FIG. 24A is an image resulting from OCT angiography of inner
retinal blood flow.
[0117] FIG. 24B is an image resulting from OCT angiography with
quantification of inner retinal blood flow demonstrating patchy
areas of nonperfusion (blue) in the extra-foveal macula. The total
nonperfusion area of the inner retina was 7.65 mm.sup.2.
[0118] FIG. 24C is an image resulting from OCT angiography of the
choroidal blood flow, including choriocapillaris and deep choroid.
It should be noted that OCT angiography is able to image deeper
choroidal vessels in this case due to the relative absence of the
overlying choriocapillaris and RPE.
[0119] FIG. 24D is an image resulting from OCT angiography of the
choroidal blood flow with quantification of the choriocapillaris
nonperfusion area (purple), which was 12.11 mm.sup.2 (47.5% of
image area).
[0120] FIG. 24E is an autofluorescence image outlined the area of
existing RPE (hyperautofluorescent area).
[0121] FIG. 25 is a schematic of a system for processing OCT
angiography data in accordance with the disclosure.
[0122] FIG. 26 is an example of a computing system in accordance
with the disclosure.
DETAILED DESCRIPTION
[0123] In the following detailed description, reference is made to
the accompanying drawings which form a part hereof, and in which
are shown by way of illustration embodiments that can be practiced.
It is to be understood that other embodiments can be utilized and
structural or logical changes can be made without departing from
the scope. Therefore, the following detailed description is not to
be taken in a limiting sense, and the scope of embodiments is
defined by the appended claims and their equivalents.
[0124] Various operations can be described as multiple discrete
operations in turn, in a manner that can be helpful in
understanding embodiments; however, the order of description should
not be construed to imply that these operations are order
dependent.
[0125] The description may use the terms "embodiment" or
"embodiments," which may each refer to one or more of the same or
different embodiments. Furthermore, the terms "comprising,"
"including," "having," and the like, as used with respect to
embodiments, are synonymous.
[0126] In various embodiments, structure and/or flow information of
a sample can be obtained using OCT (structure) and OCT angiography
(flow) imaging-based on the detection of spectral interference.
Such imaging can be two-dimensional (2-D) or three-dimensional
(3-D), depending on the application. Structural imaging can be of
an extended depth and scan-width range relative to prior art
methods, and flow imaging can be performed in real time. One or
both of structural imaging and flow imaging as disclosed herein can
be enlisted for producing 2-D or 3-D images.
[0127] Unless otherwise noted or explained, all technical and
scientific terms used herein are used according to conventional
usage and have the same meaning as commonly understood by one of
ordinary skill in the art which the disclosure belongs. Although
methods, systems, and apparatuses/materials similar or equivalent
to those described herein can be used in the practice or testing of
the present disclosure, suitable methods, systems, and
apparatuses/materials are described below.
[0128] All publications, patent applications, patents, and other
references mentioned herein are incorporated by reference in their
entirety. In case of conflict, the present specification, including
explanation of terms, will control. In addition, the methods,
systems, apparatuses, materials, and examples are illustrative only
and not intended to be limiting.
[0129] In order to facilitate review of the various embodiments of
the disclosure, the following explanation of specific terms is
provided:
[0130] A-scan: A reflectivity profile that contains information
about spatial dimensions and location of structures within an item
of interest. An A-scan is an axial scan directed along the optical
axis of the OCT device and penetrates the sample being imaged. The
A-scan encodes reflectivity information (for example, signal
intensity) as a function of depth.
[0131] B-scan: A cross-sectional tomograph that can be achieved by
laterally combining a series of axial depth scans (i.e., A-scans)
in the x-direction or y-direction. A B-scan encodes planar
cross-sectional information from the sample and is typically
presented as an image. Thus, a B-scan can be called a cross
sectional image.
[0132] C-scan: A cross-sectional tomograph that can be achieved by
combining a series of voxels at a given axial depth (i.e.,
z-direction) in a 3D OCT dataset. A C-scan encodes planar
cross-sectional information from the sample and is typically
presented as an image.
[0133] Dataset: As used herein, a dataset is an ordered-array
representation of stored data values that encodes relative spatial
location in row-column-depth (x-y-z axes) format. In the context of
OCT, as used herein, a dataset can be conceptualized as a three
dimensional array of voxels, each voxel having an associated value
(for example, an intensity value or a decorrelation value). An
A-scan corresponds to a set of collinear voxels along the depth
(z-axis) direction of the dataset; a B-scan is made up of set of
adjacent A-scans combined in the row or column (x- or y-axis)
directions. Such a B-scan can also be referred to as an image, and
its constituent voxels referred to as pixels. A C-scan is made up
of voxels at a specified depth (z-axis) in the data set; a B-scan
can also be referred to as an image, and its constituent voxels
referred to as pixels. A collection of adjacent B-scans or a
collection of adjacent C-scans can be combined form a 3D volumetric
set of voxel data referred to as a 3D image. In the system and
methods described herein, the dataset obtained by an OCT scanning
device is termed a "structural OCT" dataset whose values can, for
example, be complex numbers carrying intensity and phase
information. This structural OCT dataset can be used to calculate a
corresponding dataset termed an "OCT angiography" dataset of
decorrelation values reflecting flow within the imaged sample.
There is a one-to-one correspondence between the voxels of the
structural OCT dataset and the OCT angiography dataset. Thus,
values from the datasets can be "overlaid" to present composite
images of structure and flow (e.g., tissue microstructure and blood
flow) or otherwise combined or compared.
[0134] En Face angiogram: OCT angiography data can be presented as
a projection of the three dimensional dataset onto a single planar
image called an en face angiogram (Wallis J et al, Med Imaging IEEE
Trans 8, 297-230 (1989); Wang R K et al, 2007 supra; Jia Y et al,
2012 supra); incorporated by reference herein). Construction of
such an en face angiogram requires the specification of the upper
and lower depth extents that enclose the region of interest within
the retina to be projected onto the angiogram image. These upper
and lower depth extents can be specified as the boundaries between
different layers of the retina (e.g., the voxels between the inner
limiting membrane and outer plexiform layer can be used to generate
a 2D en face angiogram of the inner retina). Once generated, the en
face angiogram image may be used to quantify various features of
the retinal vasculature as described herein. This quantification
typically involves the setting of a threshold value to
differentiate, for example, the pixels that represent active
vasculature from static tissue within the angiogram. These 2D en
face angiograms can be interpreted in a manner similar to
traditional angiography techniques such as fluorescein angiography
(FA) or indocyanine green (ICG) angiography, and are thus
well-suited for clinical use.
System
[0135] A high-speed swept-source OCT system at 1050 nm wavelength
provides for deeper penetration compared with standard 830 nm OCT
and thus results in improved imaging below the RPE. An OCT system
configured to perform OCT angiography can further be used to detect
blood flow within retina as well as above and below it. For
example, an OCT angiography system using the spit-spectrum
amplitude decorrelation algorithm (SSADA) can be used to process
data from the OCT system. SSADA is based on detecting the
reflectance amplitude (or intensity) variation over time due to
flow in vascular volumes. Neither amplitude- nor intensity-based
OCT angiography requires accurate determination of background
tissue phase variation due to motion, and are therefore more robust
than Doppler or phase-based OCT angiography approaches (Tokayer J
et al, Biomed Opt Express 4, 1909-1924 (2013) and US Patent
Application Number 20120307014, both of which are incorporated by
reference herein). The SSADA algorithm improves on the standard
amplitude or intensity-based algorithms (Hendargo H C et al, Biomed
Opt Express 4, 803-821 (2013); Mariampillai A et al, Opt Lett 33,
1530-1532 (2008); Enfield J et al, Biomed Opt Express 2, 1184-1193
(2011); and Motaghiannezam R and Fraser S, Biomed Opt Express 3,
503-521 (2012); all of which are incorporated by reference herein)
by enhancing signal and suppressing noise through spectral
splitting of the OCT images (Ferris F L et al, Arch Ophthalmol 102,
1640-1642 (1984); incorporated by reference herein).
[0136] The system further involves methods for segmenting 3D
angiograms into separate vascular layers, for example, the inner
retina, the outer retina, and the choroid. Because the outer
retinal layer is normally devoid of blood flow, it is possible, for
instance, to provide a clean en face visualization of the any CNV
structure invading the outer retinal space using the system. The
method further involves optimizing the choice of color and
transparency used to display blood flow associated with specific
retinal layers, which grants the ability to highlight the
pathologic features such as neovascularization relative to the
other layers in a composite en face angiogram.
[0137] Other evidence of vascular pathology, such as subretinal
fluid and intraretinal cysts, can also be incorporated into the
composite view. This may be helpful to the clinician in the rapid
assessment of retinal vascular disease and its response to
treatment. Because both functional (blood flow) and structural
(fluid accumulations) information are taken from a single OCT scan,
they are naturally perfectly registered. This provides the
advantages of being simpler as well as potentially faster and more
robust than combining structural OCT with FA or ICG angiography
taken from separate instruments.
[0138] Disclosed herein are methods of using OCT angiography, to
identify ocular pathologies by the abnormal presence of flow in
layers that usually lack blood vessels or the absence of flow in
normally vascular layers. The depth-revolved nature of OCT
angiography, both in 2D and 3D implementations, allows separate
evaluation of abnormalities in retinal and choroidal circulations.
Because dye leakage and staining associated with FA and ICG
angiography do not occur in OCT angiography, the boundaries, and
therefore areas, of capillary dropout and neovascularization can be
measured using the disclosed methods. Thus, quantitative
information, such as vessel density, vessel area, and non-perfusion
area, can be obtained. The disclosed methods may be used with any
OCT system capable of providing structural and OCT angiography
data, but systems with suitably high scan speeds and depth may be
advantageous in clinical use. For instance, OCT angiography systems
using the SSADA algorithm (Jia Y et al, (2012) supra) can acquire
clinically useful data in a few seconds, a dramatic improvement
compared to several minutes for FA., Furthermore, the scan pattern
and SSADA processing can be implemented on spectral-domain or
swept-source OCT systems without any special hardware modification
provided that imaging speeds are sufficient, i.e. .gtoreq.70 kHz is
required to provide 6.times.6 mm angiograms with capillary details
within a reasonable scan time (.ltoreq.3 sec).
Applications
[0139] Computer based methods of using optical coherence tomography
(OCT) angiography are disclosed. One example of such a method 900
is outlined in FIG. 1 herein. The method in FIG. 1 is a computer
based method used to generate a color coded composite en face
angiogram and a color coded composite cross-sectional angiogram
showing the inner retina, outer retina and choroid. The subject's
eye is scanned using an OCT scan protocol 902. The data are
processed using, for example the SSADA algorithm 904 thereby
generating a set of cross-sectional OCT structural images and an en
face structural OCT image. Each cross-sectional OCT structural
image is segmented into a cross-sectional inner retina OCT
structural image, a cross-sectional outer retina OCT structural
image and a cross-sectional choroid OCT structural image. The
segmentation can be performed automatically by the software or by
the end user using a graphical user interface or other input
device. Blood flow is also detected, for example, using the SSADA
algorithm 904 thereby generating a set of cross-sectional OCT
angiograms. Following the segmentation of the corresponding
cross-sectional OCT structural image, each cross-sectional OCT
angiogram is then segmented 906 into a cross-sectional inner retina
angiogram, a first cross-sectional outer retina angiogram and a
cross-sectional choroid angiogram. An en face inner retina
angiogram visualizes retinal vasculature. The en face inner retina
angiogram is generated by projecting the cross-sectional inner
retina angiograms 908. A first en face outer retina angiogram is
generated by projecting the (first) cross-sectional outer retina
angiograms 912. The inner retina is defined as the area between the
inner limiting membrane (ILM) and the outer boundary of the outer
plexiform layer (OPL). The outer retina is defined as being between
the outer boundary of the outer plexiform layer and Bruch's
membrane (BM). Blood flow in the inner retina angiogram is assigned
a first color 910. Flow projection artifacts resulting from the
projections of inner retinal flow are removed from the en face
outer retina angiogram 914. A filter, such as a Gaussian filter or
a low pass filter is used to remove additional background noise
916. Choroidal neovascularization (CNV) is detected using pattern
recognition 918. Examples of pattern recognition approaches include
saliency based approaches that use features such as gradient,
direction, and brightness to recognize CNV. When the CNV is
detected, a second en face outer retina angiogram that shows the
CNV is produced 920. Blood flow in the outer retinal angiogram is
assigned a second color 922. An en face choroid angiogram is
generated by projecting the cross-sectional choroid angiograms 913.
Flow projection artifacts resulting from the projections of inner
retinal flow are removed from the en face choroid angiogram 915.
Blood flow in the choroid angiogram is assigned a third color 917.
A color coded composite en face angiogram with inner retina, outer
retina and choroid is generated 924 from overlaying the en face
inner retina angiogram, the second en face outer retina angiogram
and the en face choroid angiogram upon the en face structural OCT
image. The information in the second en face outer retina angiogram
is used to remove projection artifacts on the first cross-sectional
outer retina angiogram 926. A second cross-sectional outer retina
angiogram that shows the CNV is produced 928. Blood flow in the
inner retina, outer retina, choroid angiogram are assigned a first,
second, third color respectively 928. A color coded composite
cross-sectional angiogram with inner retina, outer retina and
choroid 930 is generated from overlaying the cross-sectional inner
retina angiogram, the second cross-sectional outer retina angiogram
and the cross-sectional choroid angiogram upon the corresponding
structural OCT cross-sectional image.
[0140] A second example is a method of using OCT to visualize
choroidal neovascularization (CNV) 1000 as outlined in FIG. 2
herein. In this method, a subject is scanned using an OCT scan
protocol such as SSADA 1002. The data is processed using, for
example, the SSADA algorithm 1004 thereby generating a set of
cross-sectional OCT structural images and an en face structural OCT
image. Each cross-sectional OCT structural image is segmented into
a cross-sectional inner retina OCT structural image and a
cross-sectional outer retina OCT structural image. The segmentation
can be performed automatically by the software or by the end user
using a graphical user interface or other input device as described
above. Blood flow is detected using, for example, the SSADA
algorithm 1004 thereby generating a set of cross-sectional OCT
angiograms as described above. Following the segmentation of the
corresponding cross-sectional OCT structural image, each
cross-sectional OCT angiogram is then segmented 1006 into a
cross-sectional inner retina angiogram and a first cross-sectional
outer retina angiogram. An en face inner retina angiogram is
generated by projecting the cross-sectional inner retina angiograms
1008 and a first en face outer retina angiogram is generated by
projecting the first cross-sectional outer retina angiograms 1010.
Flow projection artifacts are removed from the first en face outer
retina angiogram as described above 1012, thereby generating a
second en face outer retina angiogram. A composite en face
angiogram with inner and outer retina is generated 1014 from
overlaying the en face inner retina angiogram and the second en
face outer retina angiogram. The blood flow in the composite en
face angiogram can be color coded 1014. In the composite angiogram,
flow detected in the outer retina is an indication of CNV 1014. The
information in the second en face outer retina angiogram can be
used to remove projection artifacts in the first cross-sectional
outer retina angiogram 1016, thereby generating a second
cross-sectional outer retina angiogram. A composite cross-sectional
angiogram 1018 is then generated from overlaying the
cross-sectional inner retina angiogram and the second
cross-sectional outer retina angiogram on the corresponding
cross-sectional OCT structural image. The blood flow in the
composite cross-sectional angiogram can be color coded. The color
coded cross-sectional angiogram can then be used to classify the
CNV type 1020. CNV type can be classified based on the blood flow
relative to the retinal pigment epithelium (RPE). Type I CNV occurs
between the RPE and BM, Type II occurs above the RPE, and Type III
occurs in the inner retina. Combined types occur in any combination
thereof.
[0141] A third example is a method of using OCT to measure the area
of the choroidal neovascularization (CNV) and flow index 1100 as
outlined in FIG. 3 herein. An en face outer retina OCT angiogram is
generated as described above 1102. Flow projection artifacts are
removed as described above 1104. Background noise is removed using
filters as described above 1106. Vascular pattern recognition is
used to identify CNV as described above 1108 and the CNV area and
flow index are determined 1110. CNV area can be determined by
summing the number of pixels for which the decorrelation value is
above that of the background and converting the sum to physical
dimensions (for example, mm.sup.2). CNV flow index can be
determined by summing the decorrelation value of the pixels that
encompass the CNV and then dividing by the area of the en face
outer retina angiogram.
[0142] A fourth example is a method of removing projection
artifacts in an OCT angiogram of the outer retina 1200 as outlined
in FIG. 4 herein. This example involves generating an en face inner
retina angiogram 1202, applying a filter (such as a Gaussian or low
pass filter) to the en face inner retina angiogram. The filter
removes small internal retinal vessels from the image 1204.
Application of the filter results in a binary large inner retinal
vessel mask 1206. The example further involves generating an en
face outer retina angiogram 1208 and removing projection artifacts
in the en face outer retina OCT angiogram by multiplying each
element of the mask matrix with the corresponding element in the
outer retina layer matrix 1210. The result can be output as a
matrix presenting artifact-free en-face outer retina flow or an
artifact-free en-face outer retina flow angiogram 1212. The
information in the artifact-free en-face outer retina angiogram can
be used to remove projection artifacts in the cross-sectional outer
retina angiogram 1214, thereby generating an artifact-free
cross-sectional outer retina flow angiogram 1216.
[0143] A fifth example is a method of removing projection artifacts
from an OCT angiogram of a choriocapillaris layer 1300 as outlined
in FIG. 5 herein. This example involves generating an en face inner
retina angiogram 1302, applying a filter such as a Gaussian or low
pass filter 1304 to the en face inner retina angiogram. The filter
removes small inner retinal vessels. A binary large inner retinal
vessel mask is generated from the filtered inner retina angiogram
1306. The example further involves generating an en face
choriocapillaris angiogram 1308, removing projection artifacts in
the en face choriocapillaris angiogram by multiplying each element
of the mask matrix by the corresponding element in the
choriocapillaris layer matrix 1310. The result can be output as a
matrix presenting artifact-free en-face choriocapillaris flow or an
artifact-free en-face choriocapillaris flow angiogram 1312. The
information in the artifact-free en-face choriocapillaris angiogram
can be used to remove projection artifacts in the cross-sectional
choriocapillaris angiogram 1314, thereby generating an
artifact-free cross-sectional choriocapillaris angiogram 1316.
[0144] A sixth example is a method of removing projection artifacts
from an OCT angiogram of a choroid 1400 as outlined in FIG. 6
herein. This example involves generating an en face inner retina
angiogram 1402, applying a filter such as a Gaussian or low pass
filter 1404 to the en face inner retina angiogram. The filter
removes small inner retinal vessels. A binary large inner retinal
vessel mask is generated from the filtered inner retina angiogram
1406. The example further involves generating an en face choroid
OCT angiogram 1408, removing projection artifacts in the en face
choroid angiogram by multiplying each element of the mask matrix by
the corresponding element in the choroid matrix 1410. The result
can be output as a matrix presenting artifact-free en-face choroid
flow or an artifact-free en-face choroid flow angiogram 1412. The
information in the artifact-free en-face choroid angiogram can be
used to remove projection artifacts in the cross-sectional choroid
angiogram 1414, thereby generating an artifact-free cross-sectional
choroid angiogram 1416.
[0145] A seventh example is a method of measuring a retinal
nonperfusion area 1500 as outlined in FIG. 7 herein. The method
involves generating an en face inner retina angiogram as described
above 1502 and creating a nonperfusion map 1504. The nonperfusion
map can be created by identifying pixels with decorrelation values
lower than a set cutoff point. In one example, the set cutoff point
is 2.33 standard deviations below the mean decorrelation value
according to a normal distribution. Nonperfusion areas with an area
below a defined number of pixels are removed 1506. Holes within the
remaining clusters are closed using morphological operations 1508.
The retinal nonperfusion area is then calculated by summing the
pixels 1510. The area can also be converted to physical dimensions
such as mm.sup.2.
[0146] An eighth example is a method of measuring a
choriocapillaris defect area 1600 as outlined in FIG. 8 herein. The
method involves generating an en face choriocapillaris angiogram as
described above 1602, removing inner retinal flow projection
artifacts as described above 1604. Pixels that correspond to the
large vessels in the inner retinal mask are not considered), and
creating a nonperfusion map as described above 1606. Nonperfusion
areas that fall below a defined number of pixels are removed 1608.
Morphological operations close holes within the remaining clusters
1610. The choriocapillaris nonperfusion area is then calculated by
summing the pixels 1612. The area can also be converted to physical
dimensions such as mm.sup.2.
[0147] A ninth example is a method of visualizing retinal
neovascularization (RNV) using OCT 1700 as outlined in FIG. 9
herein. This example involves scanning a subject using an OCT scan
protocol such as SSADA scan protocol 1702. The data is processed
using, for example, the SSADA algorithm 1704 thereby generating a
set of cross-sectional OCT structure images and en face structural
OCT images. Each cross-sectional OCT structural image is segmented
into a cross-sectional inner retina OCT structural image and a
cross-sectional vitreous OCT structural image. The segmentation can
be performed automatically by the software or by the end user using
a graphical user interface or other input device as described
above. Blood flow is detected using, for example, the SSADA
algorithm 1704 thereby generating a set of cross-sectional OCT
angiograms. Following the segmentation of the corresponding
cross-sectional OCT structural image, each cross-sectional OCT
angiogram is then segmented 1706 into a cross-sectional inner
retina angiogram and a cross-sectional vitreous angiogram. The
vitreous layer is defined as anterior to the inner limiting
membrane (ILM) and the inner retina. The inner retinal layer is
defined as above 1706. An en face vitreous angiogram is generated
through by projecting the cross-sectional vitreous angiograms 1708,
and an en face inner retina angiogram is generated by projecting
the cross-sectional inner retina angiograms 1710. A composite en
face angiogram is produced from overlaying the en face inner retina
angiogram and vitreous angiogram on the structural OCT en face
image 1712. The blood flow in the composite en face angiogram can
be color coded 1712. In the composite angiogram, flow detected
above ILM is an indication of RNV 1712.
[0148] A tenth example is a method of measuring retinal
neovascularization (RNV) area and flow index 1800 as outlined in
FIG. 10 herein. The method involves generating an en face vitreous
angiogram 1802 as described above. The method further involves
removing the background noise using a Gaussian or low pass filter
as described above 1804, and using vascular pattern recognition as
described above to identify RNV 1806. When RNV has been identified,
the RNV area can be determined 1808 by summing the number of pixels
for which the decorrelation value is above that of the background.
The sum can be converted to physical dimensions such as mm.sup.2.
RNV flow index can be determined 1808 by summing the decorrelation
value of the pixels which comprise the RNV and then dividing by the
area of the en face vitreous angiogram.
EXAMPLES
[0149] The following examples are illustrative of disclosed
methods. In light of this disclosure, those of skill in the art
will recognize that variations of these examples and other examples
of the disclosed method would be possible without undue
experimentation.
Example 1
Quantitative Optical Coherence Tomography Angiography of Choroidal
Neovascularization in Age-Related Macular Degeneration
Summary
[0150] Purpose:
[0151] To detect and quantify choroidal neovascularization (CNV) in
patients with age-related macular degeneration (AMD) using optical
coherence tomography (OCT) angiography.
[0152] Design:
[0153] Observational, cross-sectional study.
[0154] Methods:
[0155] A total of 5 eyes with neovascular AMD and 5 normal
age-matched controls were scanned by a high-speed (100,000
A-scans/seconds) 1050-nm wavelength swept-source OCT. The macular
angiography scan covered a 3.times.3-mm area and comprised
200.times.200.times.8 A-scans acquired in 3.5 seconds. Flow was
detected using the split-spectrum amplitude decorrelation
angiography (SSADA) algorithm. Motion artifacts were removed by
3-dimensional (3D) orthogonal registration and merging of 4 scans.
The 3D angiography was segmented into 3 layers: inner retina (to
show retinal vasculature), outer retina (to identify CNV), and
choroid. En face maximum projection was used to obtain
2-dimensional angiograms from the 3 layers. The CNV area and flow
index were computed from the en face OCT angiogram of the outer
retinal layer. Flow (decorrelation) and structural data were
combined in composite color angiograms for both en face and
cross-sectional views.
[0156] Main Outcome Measures:
[0157] The CNV angiogram, CNV area, and CNV flow index.
[0158] Results:
[0159] En face OCT angiograms of CNV showed sizes and locations
that were confirmed by fluorescein angiography (FA). Optical
coherence tomography angiography provided more distinct vascular
network patterns that were less obscured by subretinal hemorrhage.
The en face angiograms also showed areas of reduced choroidal flow
adjacent to the CNV in all cases and significantly reduced retinal
flow in 1 case. Cross-sectional angiograms were used to visualize
CNV location relative to the retinal pigment epithelium and Bruch's
layer and classify type I and type II CNV. A feeder vessel could be
identified in 1 case. Higher flow indexes were associated with
larger CNV and type II CNV.
[0160] Conclusions:
[0161] Optical coherence tomography angiography provides
depth-resolved information and detailed images of CNV in
neovascular AMD. Quantitative information regarding CNV flow and
area can be obtained. Further studies are needed to assess the role
of quantitative OCT angiography in the evaluation and treatment of
neovascular AMD.
BACKGROUND
[0162] Age-related macular degeneration (AMD) is the leading cause
of blindness in older adults of European descent (Arch Ophthalmol
122, 477-485 (2004); incorporated by reference herein). Neovascular
AMD is an advanced form of macular degeneration that historically
has accounted for the majority of vision loss related to AMD
(Ferris F L et al 1984 supra). It is characterized by the presence
of choroidal neovascularization (CNV) which includes abnormal blood
vessels that originate from the choroid. The vessels grow through
Bruch's membrane (BM) and extend into the sub-retinal pigment
epithelial (RPE) or subretinal space. Choroidal neovascularization
can result in hemorrhage, fluid exudation, and fibrosis, resulting
in photoreceptor damage and vision loss (Ambati J et al,
Sun/Ophthalmol 48, 257-293 (2003); incorporated by reference
herein). To diagnose neovascular AMD and evaluate the efficacy of
treatment, determination of the presence and precise location of
the CNV lesion is essential.
[0163] Fluorescein angiography (FA) and indocyanine green
angiography (ICGA) are important diagnostic tools used to detect
and evaluate CNV in clinical practice. Leakage of dye in the later
frames of the angiogram is used to identify the presence of the
CNV. Both FA and ICGA require intravenous dye injection, which can
result in nausea and, rarely, anaphylaxis (Stanga P E et al, 2003
supra, Lopez-Saez M P et al, 1998 supra).
[0164] Optical coherence tomography (OCT) generates cross-sectional
images by measuring the echo time delay and magnitude of
backscattered light (Huang D et al, Science 254, 1178-1181 (1991);
incorporated by reference herein). Optical coherence tomography has
achieved micrometer-level axial resolution in cross-sectional
retinal imaging. The earliest retinal OCT imaging for studying
neovascular AMD was based on first-generation time-domain OCT
technology, which has limited speed and sensitivity (Hee M R et al,
Ophthalmology 103, 1260-1270 (1996); Do D V et al, Ophthalmology
119, 771-778 (2012); and Coscas F et al, Am J Ophthalmol 144,
592-599 (2007); all of which are incorporated by reference herein).
Spectral-domain OCT has greatly improved speed and sensitivity and
is able to detect small changes in the morphology of the retinal
layers and CNV activity in neovascular AMD (Framme C et al, Invest
Ophthalmol Vis Sci 51, 1671-1676 (2010) and Sayanagi K et al,
Ophthalmology 116, 947-955 (2009); both of which are incorporated
by reference herein). More recently, swept-source OCT has
demonstrated improved ranging depth by using a rapidly tuned laser
and a longer wavelength (1050-nm spectral range) allowing for
improved imaging beneath the RPE (de Bruin D M et al, Invest
Opthalmol Vis Sci 49, 4545-4552 (2008); incorporated by reference
herein). Therefore, this OCT modality may allow for better
visualization of the CNV beneath the RPE.
[0165] Structural OCT, using any technology, is only sensitive to
backscattering light intensity and cannot detect blood flow
information. Because of this limitation, structural OCT cannot
reliably discriminate vascular tissue from the surrounding tissues;
thus, the precise location and activity of the CNV cannot be
determined. Since 2007, several phase-based (e.g., Doppler shift
(Hong Y J et al, Opt Express 20, 2740-2760 (2012) and Grulkowski I
et al, Opt Express 17, 23736-23754 (2009), both of which are
incorporated by reference herein); Doppler variance (Liu G et al,
Opt Express 19, 3657-3666 (2011) and Wang L et al, Opt Commun 242,
345-350 (2004); both of which are incorporated by reference herein)
and phase-variance (Fingler J et al, Opt Express 2, 1504-1513
(2011) and Kim D Y et al, Biomed Opt Express 2, 1504-1513; both of
which are incorporated by reference herein)) and intensity-based
(e.g., speckle variance (Hendargo H C 2013 supra; Mariampillai A et
at Opt Lett 35, 1257-1259 (2010); incorporated by reference herein;
and Mariampillai A et al 2008 supra) and decorrelation (Enfield J
et al, 2011 supra and Johnathan E et al, J Biophotonics 4, 583-587
(2011); incorporated by reference herein). OCT angiography methods
have been described for 3-dimensional (3D) noninvasive vasculature
mapping at the microcirculation level. Miura et al 2011 supra and
Hong et al 2013 supra recently demonstrated Doppler optical
coherence angiography for imaging 3D views of ocular vascular
pathology in polypoidal choroidal vasculopathy and exudative
macular diseases, respectively.
[0166] Recently, the split-spectrum amplitude-decorrelation
angiography (SSADA) algorithm was developed to improve the
signal-to-noise ratio of flow detection (Jia Y et al, Opt Express
20, 4710-4725 (2012); incorporated by reference herein) This
technique enables OCT angiography within a practical image
acquisition time (in seconds) using a prototype that is only
slightly faster than the newest generation of commercial systems.
The first clinical study for demonstrating ocular vascular
disturbances in glaucoma was also recently performed (Jia Y et al,
Biomed Opt Express 3, 3127-3137 (2012); incorporated by reference
herein)
[0167] The use of OCT angiography using the SSADA algorithm to
investigate CNV associated with neovascular AMD is described
herein. A descriptive case series of neovascular AMD is presented
to describe the usefulness of OCT angiography for visualizing 3D
vascular architecture and quantifying the blood flow within
CNV.
Methods:
[0168] Patients were selected from clinical retina practices at the
Casey Eye Institute. Patients diagnosed with neovascular AMD
underwent a comprehensive eye examination and routine diagnostic
evaluation consisting of color fundus photography, FA, and OCT
(Spectralis; Heidelberg Engineering, Heidelberg, Germany). Patients
aged more than 50 years with the presence of drusen and
treatment-naive CNV confirmed by fluorescein dye leakage on
angiogram and the presence of 1 of the following on OCT: subretinal
fluid, intraretinal fluid, or sub-RPE fluid were included in the
study. Patients with subretinal hemorrhage of >50% of the CNV
lesion, visual acuity <20/200, and media opacity interfering
with OCT image quality, such as cataracts were excluded.
[0169] The OCT angiograms of normal subjects (aged 40-79 years)
from a separate study, the Functional and Structural Optical
Coherence Tomography for Glaucoma, were used as controls in this
study. Inclusion criteria from that study included vision
>20/40, no ocular surgery other than previous cataract surgery,
and no eye disease affecting vision. A total of 24 normal control
subjects' images were used for normative retinal thickness
measurements, and 5 control subjects (aged >60 years) underwent
OCT angiography processing.
Optical Coherence Technology Angiography:
[0170] The prototype high-speed swept-source OCT system was built
by the Laser Medicine and Medical Imaging Group at the
Massachusetts Institute of Technology and followed the
configuration published by Potsaid et al Opt Express 18,
20029-20048 (2010) which is incorporated by reference herein. The
device operated at an axial scan speed of 100 kHz using a
swept-source cavity laser operating at 1050 nm with a tuning range
of 100 nm. A resolution of 5.3 mm axially and 18 mm laterally at an
imaging depth of 2.9 mm in tissue was achieved. The ocular light
power exposure was 1.9 mW, which was within the American National
Standards Institute safety limit (American National Standard for
the Safe Use of Lasers, Laser Institute of America, Z136 (2007);
incorporated by reference herein).
[0171] A 3.times.3-mm scanning area centered on the fovea was
captured for blood flow measurements. In the fast transverse
scanning direction, 200 axial scans were sampled along a 3-mm
region to obtain a single B-scan. Eight consecutive B-scans (M-B
frames) were captured at a fixed position before proceeding to the
next sampling location. A total of 200 locations along a 3-mm
region in the slow transverse direction were sampled to form a 3D
data cube. With a B-scan frame rate of 455 frames per second, the
1600 Bscans in each scan were acquired in approximately 3.5
seconds. Four volumetric raster scans, including 2 horizontal
priority fast transverse (x-fast) scans and 2 vertical priority
fast transverse (yfast) scans, were obtained consecutively in 1
session.
[0172] The SSADA algorithm was used to distinguish blood flow from
static tissue as described in Jia et al, Opt Express 2012 supra. As
seen in real-time OCT structural images, the amplitude of the
signal returning from nonstatic tissue varies rapidly over time. By
calculating the decorrelation of signal amplitude from consecutive
B-scans, a contrast between static and nonstatic tissue is created
that allows for the visualization of blood flow. However,
decorrelation also can be generated by bulk (nonflow) eye motion.
The SSADA algorithm was developed to minimize bulk axial motion
noise due to orbital pulsation by splitting the spectrum and
thereby lengthening the axial resolution element. In addition, the
algorithm incorporated 3 steps to further remove motion artifacts
within each angiography scan. First, using outlier analysis, the
decorrelation frames with excessive median decorrelation values
(i.e., frames corrupted by saccadic and micro-saccadic eye
movements) were removed at each M-B scan position, and the
remaining individual frames were averaged to obtain the final
average decorrelation flow image. Second, if the number of
remaining individual frames is less than 3 for averaging, the
average decorrelation image at this location is replaced by the
spatial neighbors. Third, the median decorrelation (an estimate of
bulk motion effect) was calculated for each average decorrelation
frame and then subtracted from it. This sets the decorrelation
value for bulk tissue to approximately zero.
[0173] Physical flow phantom calibration experiments have been
described in previous references (Tokayer J et al, Biomed Opt
Express 4, 1909-1924 (2013) and Liu G et al, Biomed Opt Express 3,
2669-2680 (2013); both of which are incorporated by reference
herein). Decorrelation can be considered as a metric for measuring
fluctuation in the backscattered OCT signal amplitude (intensity)
that does not depend on the average signal level. To be specific,
the blood flow results in fluctuation in the amplitude of OCT
fringes (speckle) as red blood cells move within a particular
voxel. Thus, the 8 M-B frames contain fluctuating values of OCT
output intensities at any given voxel in the flow of blood, and
decorrelation is defined such that fluctuating intensities yield
high decorrelation values (approaching 1.0). Pixels in the M-B
frames that represent static tissue have constant intensities, and
thus yield small decorrelation values (approaching 0). The faster
the blood particles that move across the laser beam, the higher the
decorrelation of the received signals within a velocity range set
by the scan parameters. In other words, decorrelation is
approximately linear to flow velocity (the distance traveled by red
blood cells flowing across the light beam within a unit time).
However, beyond a saturation velocity that is defined by the time
interval between consecutive OCT M-B frames, the decorrelation
increases more slowly with velocity and eventually reaches an upper
boundary. This saturation velocity should be approximately 0.3 to
0.7 mm/second according to previous references describing physical
phantom experiments, accounting for a wavelength of 1050 nm and
inter-M-B frame interval of 2 milliseconds. The minimum velocity is
approximately 0.02 mm/second according to the phantom calibration.
This is determined by the threshold decorrelation value 0.09, which
is 2 standard deviations above the mean decorrelation value in the
noise region, the central foveal avascular zone in normal eyes.
[0174] Motion artifacts were further corrected by applying an image
registration algorithm that registered 4 orthogonal raster scanned
volumes (Kraus M F et al, Biomed Opt Express 3, 1182-1199 (2012);
incorporated by reference herein). Motion correction was first
performed on the structural OCT data. The motion correction
algorithm generated 3D displacement fields that map A-scans from
the input volumes into a common motion-corrected space. The same
displacement fields were applied to the decorrelation (flow) data
to produce motion-corrected flow data volumes. Flow data from 4
input volumes were weighted and merged, improving the
signal-to-noise ratio in the flow signal and reducing the flow
measurement variation due to local flow changes caused by the
cardiac cycle.
[0175] To enhance visualization, the 3D angiogram was separately
projected into en face views (FIG. 11) in 3 layers using an
automated algorithm (Tan 0 et al, Ophthalmology 116, 2305-2314
(2009) and Tan O et al, Ophthalmology 115, 949-956 (2008); both of
which are incorporated by reference herein. The inner retinal layer
was defined from the internal limiting membrane to the outer
boundary of the outer plexiform layer (OPL). Thus defined, the
inner retina should contain all of the normal retinal vasculature.
The outer retinal layer was defined from the outer OPL to the BM.
Because the outer retina is normally avascular, any flow in this
layer could be interpreted as CNV. The choroidal layer was defined
as below BM. All of these boundaries were identified through the
analysis of the reflectance and reflectance-gradient profiles in
depth. Clinician's interpretation and manual identification of BM
and the OPL was necessary when pathologies such as pigment
epithelial detachment (PED) and intraretinal fluid obscured the
outer retinal landmarks (AMD case 3). Separate en face images of
the inner retina, outer retina, and choroid were presented in a
sepia color scale. A composite view was developed, where each layer
was assigned a different color (FIG. 11C) to aid with visualization
as follows. The inner retina contained normal retinal circulation
and was coded purple. The outer retinal layer contained any
potential CNV and was coded yellow. The choroid layer was coded
red.
[0176] Structural OCT features were added to OCT angiography with
composite en face view and color coding demonstrating subretinal
fluid (dark blue) (FIG. 12G) and intraretinal cysts (light blue)
(FIG. 13) These two types of fluid are both detected using a
level-set segmentation method (Chan T F and Vese L A, IEEE Trans
Image Process 10, 266-277 (2001); incorporated by reference herein)
because the reflectance of cysts and subretinal fluid is
significantly lower than the surrounding tissue in the retina. On
the basis of the difference among their position, shape, and size,
these fluid regions can be classified as intraretinal or
subretinal. In addition, the variation in retinal thickness was
calculated, normalized by the normal retina thickness range, and
presented as a retinal thickness deviation map. For the purpose of
this pilot study, an estimate of normal retinal thickness with the
prototype OCT was obtained from 24 normal eyes from 24
subjects.
[0177] The cross-sectional angiogram (FIG. 11B) showed flow
projection artifacts on the photoreceptor inner segment/outer
segment boundary and RPE. The projection artifacts were due to
fluctuating shadows cast by flowing blood in large inner retinal
vessels that cause variation in the reflected signal in deeper
layers. The signal variation was detected as a decorrelation and
could not be differentiated from true flow on its own. However,
these artifacts were removed from under the flow pixels in the
inner retina. To remove flow projection artifacts from superficial
retinal blood vessels to the outer retina, a method was developed.
A binary large inner retinal vessel map was generated by applying a
30.times.30 pixel Gaussian filter. This filter removed small inner
retinal vessels and masked the outer retina flow map, thus enabling
the subtraction of large vessel projections. A binary outer retinal
flow map was then generated by applying a 10.times.10 pixel
Gaussian filter to remove remaining noise and mask the outer
retinal flow map again to obtain a clean map. After these artifacts
were removed by the mask subtraction operation, there were no
longer any flow artifacts in the normally avascular outer retina,
as shown in the cross-sectional color angiogram (FIG. 11C) and the
en face angiogram of the outer retina (FIG. 11E).
[0178] To quantify the blood flow within the CNV, the CNV area and
flow index were calculated from the 2-dimensional maximum
projection outer retina CNV angiogram. The CNV area was calculated
by multiplying the number of pixels (for which the decorrelation
value was above that of the background) and the pixel size. The CNV
flow index was the average decorrelation value in the CNV region,
given by, Equation 1 below:
.intg. A D V A .intg. A A ( V = 1 if vessel , V = 0 if not )
Equation 1 ##EQU00001##
where D is the decorrelation value acquired by SSADA. V is 1 when
the decorrelation value was above the background; otherwise, V is
0. Flow index is a dimensionless parameter between 0 and 1 that is
proportional to the density of blood vessels (fractional area
occupied by vessels) and the velocity of blood flow in the CNV
region.
Results:
[0179] The OCT angiograms of 5 neovascular AMD eyes were compared
with 5 normal eyes. The CNV area and flow index were calculated
from all neovascular AMD cases. None of the 5 normal cases had flow
detected in the outer retina, and CNV area and flow index were
zero. A representative normal control case and 3 of the neovascular
AMD cases are presented.
[0180] Normal Control Case:
[0181] A 69-year-old woman with no ocular disease served as a
control case. The inner retinal angiogram (FIG. 11D) showed the
normal retinal circulation with a small foveal avascular zone of
approximately 0.6 mm in diameter. The absence of any flow in the
outer retinal layer (FIG. 11E) allowed easy detection of CNV in the
cases to be shown later. The absence of blood flow in the outer
retina was noted in all 5 normal control participants. The flow in
the inner choroid\ was nearly confluent (FIGS. 11C, 11F) and masked
the vascular patterns in the outer choroid and sclera in the en
face angiogram (FIG. 11F). The signal voids in the larger vessels
in the outer choroid were due to the high flow velocity (FIGS.
11A-11C).
[0182] Age Related Macular Degeneration Case 1:
[0183] A 65-year-old woman noted vision loss in her right eye for 1
month. Visual acuity measured 20/100 in the right eye. Fundus
photography (FIG. 12A) showed drusen and a small subretinal
hemorrhage associated with a gray subretinal lesion just nasal to
the fovea. Fluorescein angiography (FIGS. 12B, 12C) revealed early
hyperfluorescence with late leakage consistent with classic
CNV.
[0184] Optical coherence tomography angiography showed a normal
retinal circulation (FIG. 12D, 12H). The outer retinal OCT
angiogram (FIGS. 12E, 12H) showed high flow in a CNV network in a
pattern strikingly similar to the early phase of FA. The
cross-sectional color OCT angiogram (FIG. 12G) showed the CNV to be
underneath the RPE and above the BM, indicating type I CNV. The
subretinal hemorrhage above the CNV (FIG. 12G) did not seem to
obscure the CNV on the FA or OCT angiograms (FIGS. 12E, 12H).
[0185] The en face OCT angiogram of the choroid (FIG. 12F) showed
loss of choriocapillaris revealing deeper, larger choroidal vessels
(compare with normal choroid in FIG. 11F). An area inferotemporal
to the CNV had particularly low flow in both the choriocapillaris
and the deeper choroid (FIG. 12F, green outline). This low flow
choroidal region had high OCT reflectance signal (FIG. 12G, green
arrow); therefore, the reduced flow was not caused by a shadow
artifact.
[0186] The composite en face OCT angiogram (FIG. 12H) showed that
the CNV was at the superonasal edge of the fovea avascular zone
(FAZ) and that subretinal fluid was accumulated next to the CNV.
The OCT retinal thickness map (FIG. 12I) showed retinal thickening
over the CNV that was primarily due to the inclusion of subretinal
hemorrhage (FIG. 12G) in the retinal thickness measurement and an
element of retinal edema inferior to the CNV. These en face OCT
views combined angiographic (CNV size, location, flow) and
structural information (fluid, edema) that would be useful for
clinical management.
[0187] Age Related Macular Degeneration Case 2:
[0188] A 76-year-old woman noticed vision loss in her left eye for
1 week. Visual acuity measured 20/30, and fundus examination (FIG.
13A) of the left eye revealed drusen and a gray/green lesion in the
temporal macula with associated subretinal hemorrhage. Early frames
of the FA revealed a hyperfluorescent vascular network I the
temporal macula with late leakage (FIGS. 13B, 13C).
[0189] The OCT angiography showed details of the CNV structure,
with a central feeder vessel from which radiated thick core vessels
ending in fine vascular fronds (FIGS. 13E, 13J). Both the FA and
OCT angiogram showed an identical CNV location, with slight
notching at the superonasal edge due to shadowing from the small
patch of subretinal hemorrhage. Cross-sectional OCT angiography
(FIGS. 13G, 13H) revealed that most of the CNV flow was above the
RPE, indicating a predominantly type II CNV. Because of the flow
projection artifact, there appeared to be flow in the RPE below the
CNV.
[0190] The en face OCT angiogram of the choroid (FIG. 13F) showed
patchy loss of choriocapillaris, which allowed visualization of
intermediate-to-large deeper choroidal vessels that were not
visible in the healthy control (FIG. 11F). There were focal regions
under and adjacent to the CNV where there was greatly reduced flow
in both the choriocapillaris and the deeper choroid (FIGS.
13F-13H). Although some of this might be explained by shadowing
under the CNV, the hypoperfused choroid adjacent to the CNV had
normal OCT reflectance (FIG. 13I), suggesting that the loss of
choroidal flow was real rather than an artifact.
[0191] The composite en face OCT angiogram showed that the CNV was
at the superotemporal edge of the FAZ. Subretinal fluid accumulated
superonasal to the CNV, and intraretinal cystic fluid accumulated
above the CNV. Retinal thickening shown on the relative thickness
map (FIG. 3K) correlated with the intraretinal fluid
accumulation.
[0192] Age-Related Macular Degeneration Case 3:
[0193] An 88-year-old woman noted vision loss in her right eye for
several months. Visual acuity in the right eye was 20/200. Fundus
photography (FIG. 14A) demonstrated chronic geographic atrophy in
the superior nasal macula with new subretinal hemorrhage and an
associated RPE tear temporal to the geographic atrophy. Fluorescein
angiography showed late leakage consistent with a CNV (FIGS. 14B,
14C). However, the location of the CNV was unclear because of
blocking from the subretinal hemorrhage. Hypofluorescence from the
scrolled RPE and hyperfluorescence-associated geographic atrophy
were evident.
[0194] The inner retinal angiogram in this case (FIG. 14D) showed
an apparent reduction in inner retinal blood flow that may have
indicated retinal atrophy. This patient had difficulty with
fixation, and slight motion artifacts (horizontal and vertical dark
lines) were evident despite the use of 3D registration
software.
[0195] The OCT angiography of the outer retina (FIGS. 14E-14H)
showed a distinct CNV adjacent to the subretinal hemorrhage. The
nasal edge of the CNV was blocked from view where the subretinal
hemorrhage was thicker and cast a shadow. The cross-sectional OCT
angiogram (FIG. 14G) revealed high CNV flow at the edge of the RPE
tear. Flow was detected both above and below the RPE, indicating a
combined type I and type II lesion. In addition to the CNV, there
was accumulation of a large amount of stationary (nonvascular)
material under the PED.
[0196] The en face choroidal angiogram (FIG. 14F) showed reduced
signal both under the PED and in the area of geographic atrophy.
The area under the PED showed low reflectance on OCT cross-section
(FIG. 14G) and no vascular pattern on the en face OCT angiogram
(FIG. 14F). This suggested that the reduced choroidal flow was a
shadow artifact associated with the PED and scrolled RPE (FIG.
14F). A similar area of blocked fluorescence was present on FA
(FIG. 14B, 14C). In contrast, the area of geographic atrophy showed
distinct large, deep choroidal vessels and loss of
choriocapillaris.
[0197] The composite en face OCT angiogram (FIG. 14H) showed the
CNV to be inferior to the FAZ and associated with a surrounding
accumulation of both intraretinal and subretinal fluids. There was
retinal thinning over the CNV (FIG. 14I), possibly due to focal
compression from the highly elevated CNV and RPE tear (FIG. 14G).
There was gross retinal thickening around the CNV (FIG. 14I)
associated with fluid accumulation (FIG. 14H). The heavy
accumulation of intraretinal fluid and reduced retinal blood flow
visualized on the en face composite OCT angiogram may explain the
poor visual acuity.
[0198] Evaluation of Choroidal Flow in Age-Related Macular
Degeneration:
[0199] The OCT angiography of the choroid showed reduced inner
choroidal flow in all 5 AMD cases compared with the control cases
that allowed visualization of larger and deeper choroidal vessels.
Conventional FA and structural OCT did not reveal geographic
atrophy or other abnormalities that accounted for the
choriocapillaris atrophy in most of these areas. In addition, focal
areas of decreased flow in both superficial and deeper choroidal
vessels were associated with CNV in all of the AMD cases, except
for case 3, in whom the presence of a focal choroidal flow defect
could not be determined because of shadowing by the scrolled RPE
(FIG. 14F).
[0200] Quantification of the Area and Flow Index of Choroidal
Neovascularization:
[0201] Quantitative measurements of CNV area and flow index are
summarized in Table 1. High flow index indicated active blood flow
within the CNV. Higher flow was detected with larger CNVs and those
that were type II compared with type I and combined CNVs.
TABLE-US-00001 TABLE 1 Summary of Choroidal Neovascularization
Types, Area, and Flow Index of 5 Scanned Age-related Macular
Degeneration Cases CNV CNV AMD CNV Area Flow Case No. Sex/Age Types
(mm2) Index (au) 1 Female/65 yrs I 0.29 0.127 2 Female/76 yrs II
2.18 0.146 3 Female/88 yrs Combined 0.13 0.13 4 Female/85 yrs II
0.89 0.148 5 Male/70 yrs Combined 0.05 0.12
Example 2
Optical Coherence Tomography Angiography Features of Diabetic
Retinopathy
Summary
[0202] Purpose:
[0203] To describe the optical coherence tomography (OCT)
angiography features of diabetic retinopathy.
[0204] Methods:
[0205] Using a 70 kHz OCT and the split-spectrum amplitude
decorrelation angiography (SSADA) algorithm, 6.times.6 mm
3-dimensional angiograms of the macula were obtained and compared
with fluorescein angiography (FA) for features catalogued by the
Early Treatment of Diabetic Retinopathy Study.
[0206] Results:
[0207] OCT angiography detected enlargement and distortion of the
foveal avascular zone, retinal capillary dropout, and pruning of
arteriolar branches. Areas of capillary loss obscured by
fluorescein leakage on FA were more clearly defined on OCT
angiography. Some areas of focal leakage on FA that were thought to
be microaneurysms were found to be small tufts of
neovascularization that extended above the inner limiting
membrane.
[0208] Conclusions:
[0209] OCT angiography does not show leakage, but can better
delineate areas of capillary dropout and detect early retinal
neovascularization. This new noninvasive angiography technology may
be useful for routine surveillance of proliferative and ischemic
changes in diabetic retinopathy.
BACKGROUND
[0210] Diabetic retinopathy is a microangiopathy that causes
capillary occlusion, vascular hyperpermeability, and
neovascularization in the retinal vasculature (Antonetti D et al, N
Engl J Med 366, 1227-1239 (2012); incorporated by reference
herein). Detailed clinical examination for grading disease severity
for risk of progression and vision loss is the standard of care but
ophthalmic angiography has played a critical role in understanding
and care of the disease. Early Treatment of Diabetic Retinopathy
Study (ETDRS) examined the fluorescein angiographic features of the
posterior pole of patients with non-proliferative diabetic
retinopathy and correlated the specific features with their risk of
disease progression (EDTRS Research Group, Ophthalmol 98, 834-840
(1991); EDTRS Research Group, Ophthalmol 98, 807-822 (1991);
incorporated by reference herein). Fluorescein angiography (FA) is
also used to identify retinal neovascularization (RNV) in
situations where clinical examination cannot detect RNV or
distinguish from other anomalous appearing vessels on the retinal
surface.
[0211] While angiography provides valuable additional information
compared to clinical examination or fundus photography, it is not
part of the routine diabetic eye examination. FA requires
venipuncture and intravenous injection of a dye that has a moderate
risk of nausea and a rare but well documented risk of anaphylaxis
and death (Bloome M A, Vis Res 20, 1083-1097 (1980); incorporated
by reference herein). Also, a standard protocol FA acquires images
over 10 minutes with repeated exposure to a very bright light
source, which can cause significant discomfort for patients.
[0212] Optical coherence tomography (OCT) angiography, an imaging
technique that uses decorrelation between resampled images to
detect flow to construct 2- and 3-dimensional images of blood flow
within the eye, offers an alternative angiographic technique
without some of the drawbacks of FA. In particular, the
split-spectrum amplitude decorrelation algorithm (SSADA) can be
used to detect flow signals for angiography efficiently (Jia et at
(2012) supra); incorporated by reference herein). Applying this
algorithm, an OCT angiogram in areas up to 6.times.6 mm area can be
acquired in 3.5 seconds without intravenous injection. This Example
describes features of diabetic retinopathy as seen on OCT
angiography.
Methods:
[0213] Patients were selected from the Retina Division of the Casey
Eye Institute for the diagnosis of proliferative diabetic
retinopathy, clear media, and the ability to fixate. They underwent
comprehensive ophthalmic examination and FA. Three dimensional (3D)
OCT angiography scans were acquired over 6.times.6 mm regions using
a commercially available 70 kHz OCT (RT-VUE XR, Optovue, Fremont,
Calif.) with a scan pattern of 5 repeated B-scans at 216 raster
positions and each B-scan consisting of 216 A-scans. Flow was
detected with the split-spectrum amplitude decorrelation
angiography (SSADA) algorithm (Jia et at (2012) supra; Jia Y et al,
(2014) supra); incorporated by reference herein) and motion
artifacts were removed by 3D orthogonal registration and merging of
2 scans. A retinal angiogram was created by projecting the flow
signal internal to the Bruch's membrane in en face orientation. The
signal above the internal limiting membrane (ILM) was further
segmented to isolate retinal neovascularization. Specific features
seen on OCT angiogram were then compared to FA features of the same
area. Images were examined for classic features of diabetic
retinopathy, such as microaneurysms (MAs) and RNV, as well as
gradable angiographic characteristics as described by the ETDRS
Report No. 11, including foveal avascular zone (FAZ) enlargement,
capillary dropout, and arteriolar abnormalities.
Results:
[0214] Four patients with proliferative diabetic retinopathy or
diabetic macular edema were imaged for the study. Their
characteristics are summarized in Table 1.
TABLE-US-00002 TABLE 1 Patient Characteristics Subject Age Gender
DM Type Imaged Eye Visual Acuity 1 47 M Type 2 OS 20/40 2 28 M Type
1 OS 20/30 + 2 3 53 F Type 1 OS 20/50 4 41 F Type 2 OD 20/20 OD,
right eye; OS, left Eye
Foveal Avascular Zone Size and Shape:
[0215] For all eyes imaged, the foveal avascular zone (FAZ) size
and shape were gradable according to the ETDRS grading criteria
using OCT angiography. The OCT angiogram disclosed the area of
perifoveal capillary loss that corresponded well to FA. FIG. 15
shows an OCT angiogram with superposed ETDRS grid that shows that
the size of the FAZ is between 300 microns radius (dotted circle)
and 500 micron (solid inner circle). At the same magnification, it
was easier to grade the OCT angiogram for FAZ characteristics than
the FA, as the capillaries were seen at a higher contrast on OCT
angiogram.
[0216] In one case, the foveal avascular zone was difficult to
grade on FA as the capillary details were obscured by leakage even
in early transit (FIG. 16). With the OCT angiography, the details
were not affected by leakage and the FAZ size and shape could be
easily graded.
Capillary Dropout and Arteriolar Characteristics:
[0217] Areas of capillary drop out beyond the FAZ were readily
identified with OCT angiogram in all eyes. FIG. 17 demonstrates
good correlation in the areas of capillary drop out between the OCT
angiogram and the FA. In this case, OCT angiography identified
additional areas of capillary drop out not seen on FA as early
diffuse fluorescein leakage made some areas of capillary dropout
indistinguishable from areas with intact capillaries in the FA. In
other cases, areas of capillary drop out that were obvious on OCT
angiogram were difficult to resolve with FA (Upper right hand
corner of FIG. 16 is an example.). In this series, OCT angiography
was more consistent in demonstrating presence or absence of retinal
capillaries than FA.
[0218] An area of intraretinal microvascular abnormality (IRMA),
characterized by dilated terminal vessels surrounded by an area of
capillary loss was identified with OCT angiography as well as FA.
The exact shape of the IRMA differed slightly between two images
(FIG. 16). Arteriolar narrowing and wall staining seen on FA was
seen as extreme attenuation of vessel caliber on OCT angiography
(FIG. 17).
Microaneurysms and Neovascularization:
[0219] OCT angiography with 6.times.6 mm field of view could not
identify microaneurysms seen on the FA. (FIG. 16). On the FA of one
patient, areas of focal hyperfluorescence with leakage in the
perifoveal area that were thought to be large microaneurysms were
determined to be small tufts of neovascularization on OCT
angiography. (FIG. 17) With the segmentation of the flow signal at
the level of the ILM and projecting the signal in the cross
sectional orientation, it was evident that these lesions were
vertical RNV protruding into the vitreous (FIGS. 15, 16, and
17).
[0220] While RNV close to the ILM were readily identified, flow
signal from the vessels that were highly elevated from the retinal
surface displayed as shadows rather than flow signals because the
most elevated portion of RNV outside the depth range of OCT imaging
(FIG. 18).
[0221] Features described in ETDRS that are inherently specific to
fluorescein angiography and unlikely to have correlates in OCT
angiography, such as retinal pigment epithelial defects, severity
of late fluorescein leakage and determining their source were not
evaluated for this study.
TABLE-US-00003 TABLE 2 Optical Coherence Tomography Angiographic
Findings Neovascularization and FAZ Size Capillary Other Vascular
Subject (.mu.) Outline of FAZ Drop Out Abnormalities 1 <300
Questionable Superotemporal and Isolated NVE temporally,
inferotemporal IRMA infratemporally 2 300-500 Less than half
Central, and extensive loss Perifoveal NVE and NVD throughout
temporal macula (not shown) Arteriolar pruning temporally
Arteriolar narrowing and staining superiorly 3 300-500 More than
half Central, superotemporal and Extensive NVD, NVE inferotemporal
inferiorly 4 300-500 More than half Central Extensive NVD, small
tufts of NVE along temporal arcades FAZ = foveal avascular zone,
NVE = neovascularization elsewhere, IRMA = intraretinal
microvascular abnormality, NVD = neovascularization at the disc
Example 3
Quantitative Optical Coherence Tomography Angiography of Vascular
Abnormalities in the Living Human Eye
Summary
[0222] Retinal vascular diseases are important causes of vision
loss. A detailed evaluation of the vascular abnormalities
facilitates diagnosis and treatment in these diseases. Optical
coherence tomography (OCT) angiography using the highly efficient
split-spectrum amplitude decorrelation angiography algorithm offers
an alternative to conventional dye-based retinal angiography. OCT
angiography has several advantages, including three-dimensional
visualization of retinal and choroidal circulations (including the
choriocapillaris) and avoidance of dye injection related
complications. Results from six illustrative cases are reported. In
diabetic retinopathy, OCT angiography can detect neovascularization
and quantify ischemia. In age-related macular degeneration,
choroidal neovascularization can be observed without the
obscuration of details caused by dye leakage in conventional
angiography. Choriocapillaris dysfunction can be detected in the
non-neovascular form of the disease, furthering the understanding
of pathogenesis. In choroideremia, its ability to show choroidal
and retinal vascular dysfunction separately may be valuable in
predicting progression and assessing treatment response. OCT
angiography shows promise as a non-invasive alternative to
dye-based angiography for highly detailed, in vivo,
three-dimensional, quantitative evaluation of retinal vascular
abnormalities.
[0223] The cases presented in the following Examples utilize an
amplitude-based OCT angiography method called split-spectrum
amplitude-decorrelation angiography (SSADA) (Jia Y et al, 2012
supra)). The SSADA algorithm detects motion in blood vessel lumen
by measuring the variation in reflected OCT signal amplitude
between consecutive cross-sectional scans. SSADA is a method of
processing an OCT signal to enhance flow detection and to reject
axial bulk motion noise. Compared to the full-spectrum amplitude
method, SSADA using four-fold spectral splits improved the
signal-to-noise ratio (SNR) by a factor of two, which is equivalent
to reducing the scan time by a factor of four (Jia Y et al, 2012
supra). More recent SSADA implementations use even more than a
four-fold split to further enhance the SNR of flow detection. This
highly efficient algorithm generates high quality angiograms of
both the retina and choroid. The angiograms have capillary-level
detail and can be obtained with currently available commercial OCT
systems.
[0224] Additionally described in the Examples below are techniques
designed to help clinicians rapidly interpret OCT angiograms and to
easily identify pathological vascular features. These techniques
include 1) separation of the 3D angiogram into individual vascular
beds via segmentation algorithms; 2) presentation of en face OCT
angiograms, analogous to traditional angiography; 3) creation of
cross-sectional structural OCT images with superimposed OCT
angiograms to help correlate anatomical alterations with vascular
abnormalities; and 4) quantification of neovascularization and
capillary dropout in both the retinal and choroidal
circulation.
Methods:
[0225] Human Subjects Imaging:
[0226] Study subjects were enrolled after informed consent in
accordance with an Institutional Review Board/Ethics Committee
approved protocol at Oregon Health & Science University and at
the Massachusetts Institute of Technology in compliance with the
Declaration of Helsinki. Healthy participants or patients with a
diagnosis of retinal disease (diabetic
retinopathy/AMD/choroideremia) were selected from the Retina
Division of the Casey Eye Institute for their clear media and
ability to fixate. In total, 15 healthy subjects, 14 diabetic
retinopathy, 26 AMD, and 3 choroideremia patients were enrolled. To
better demonstrate the potential clinical application of this novel
OCT methodology, six cases with characteristic pathological and
clinical features were selected for this article.
[0227] Color fundus and optic disc photographs were acquired with
Zeiss fundus cameras (FF3 for FIGS. 20A and 22A, and FF450 for FIG.
23; Carl Zeiss Meditec, Inc., Dublin, Calif., USA) and the Optos
200Tx confocal scanning laser ophthalmoscope (cSLO) (FIG. 21A;
Optos PLC, Dunfermline, Scotland). For fluorescein angiography, 10%
sodium fluorescein in water (500 mg/5 mL) was injected
intravenously using a 23- or 25-gauge needle, followed by a flush
of normal saline. Fluorescein angiography was performed using
either the Optos 200Tx cSLO (FIGS. 20A and 21B) or the Spectralis
HRA+OCT cSLO (FIG. 22B; Heidelberg Engineering, Heidelberg,
Germany). For both cSLO devices, a 488-nm wavelength laser excited
the fluorescein, and a barrier filter at 500 nm separated the
excitation and emission light. The fluorescein autofluorescence
image in FIGS. 23 and 24 was also acquired using the Spectralis
HRA+OCT cSLO. These procedures, imaging systems, and contrast dyes
are approved by the Food and Drug Administration.
[0228] OCT Systems:
[0229] Two OCT systems were utilized in this study. The first was a
custom-built swept-source OCT instrument (Jia Y et al,
Ophthalmology 121, 1435-1444 (2014A); Jia Y et al, Ophthalmology
121, 1322-1332 (2014); and Choi W et al, PLoS ONE 8, e81499 (2013);
all of which are incorporated by reference herein). The device
operated at an axial scan rate of 100 kHz using a swept-source
cavity laser operating at .sup..about.1050 nm with a sweep range of
100 nm. The instrument has a 5.3 .mu.m axial resolution and 18
.mu.m lateral resolution with an imaging range of 2.9 mm in tissue.
The ocular light exposure was 1.9 mW, which was within the American
National Standards Institute (ANSI) safety limit. (44) The second
OCT system was a commercial spectral domain OCT instrument
(RTVue-XR, Optovue, Inc., Fremont, Calif., USA). (36) The center
wavelength was .sup..about.840 nm with a full-width-half-maximum
bandwidth of 45 nm and an axial scan rate of 70 kHz.
[0230] OCT Imaging:
[0231] A 3.times.3- or 6.times.6-mm scanning area was used for OCT
angiography. In the fast transverse scanning direction, 200 axial
scans were sampled to obtain a single B-scan. Multiple repeated
B-scans (eight for the swept-source OCT and five for the spectral
OCT), were captured at a fixed position before proceeding to the
next sampling location. A total of 200 locations along a 3 or 6-mm
distance in the slow transverse direction were sampled to form a 3D
data cube. For the swept-source system, with a B-scan frame rate of
455 frames per second, the 1,600 B-scans in each scan were acquired
in .sup..about.3.5 seconds. Four volumetric raster scans, including
two horizontal priority fast transverse (x-fast) scans and two
vertical priority fast transverse (y-fast) scans, were obtained
consecutively in one session. For the spectral domain OCT system,
with a B-scan frame rate of 320 frames per second, the 1,000
B-scans in each scan were acquired in .sup..about.3.1 seconds. Two
volumetric raster scans including one x-fast scan and one y-fast
scan were obtained.
[0232] SSADA Processing:
[0233] The SSADA algorithm was used to distinguish blood flow from
static tissue as described in detail in a previous publication (Jia
Y et al 2012 supra). By calculating the decorrelation of the signal
amplitude from consecutive B-scans, contrast between static and
non-static tissue is created that enables visualization of blood
flow. Decorrelation is a mathematical function that quantifies
variation without being affected by the average signal strength, as
long as the signal is strong enough to predominate over optical
and/or electronic noise. Specifically, the algorithm splits the OCT
image into different spectral bands, thus increasing the number of
usable image frames. In the optimized SSADA technique, the OCT
signal is first split into 11 spectral bands to obtain 11 low axial
resolution images instead of a single image frame with high axial
resolution. Each new frame has a lower axial resolution that is
less susceptible to axial eye motion caused by retrobulbar
pulsation. This lower resolution also translates to a wider
coherence gate over which reflected signal from a moving particle
such as a blood cell can interfere with adjacent structures,
thereby increasing speckle contrast. In addition, each spectral
band contains a different speckle pattern and independent
information on flow. When amplitude decorrelation images from
multiple spectral bands are combined, the flow signal is increased.
By enhancing the flow signal and suppressing bulk motion noise,
SSADA improves the signal-to-noise ratio of flow detection by a
least a factor of two. Motion artifacts were further corrected and
the flow signal increased by applying an image registration
algorithm that registered orthogonal raster-scanned volumes (Kraus
M F et al, Biomed Opt Express 3, 1182-1199 (2012); incorporated by
reference herein).
[0234] OCT Angiogram Visualization and Quantification:
[0235] To enhance visualization, the 3D angiogram was separately
projected as en face views in five layers: vitreous, inner retina,
outer retina, choriocapillaris and deep choroid (see FIG. 19). The
separated en face OCT angiography images were presented in a sepia
color scale. A composite view was created, where each layer was
assigned a different color. The color-coded angiogram can be
superimposed on a gray-scale, cross-sectional, structural OCT image
to demonstrate blood flow and structural information
simultaneously. Flow projection artifacts are a common problem for
existing OCT angiography techniques. To better distinguish and
interpret the blood flow within different layers, a negative filter
was used to mask projection artifacts from the larger caliber
retinal vessels.
[0236] To quantify the blood flow within the regions of interest,
the flow index, vessel density and neovascularization area were
determined from the en face maximum projection angiogram. The flow
index was calculated as the average decorrelation value (which is
correlated with flow velocity) in the selected region, and the
vessel density was calculated as the percentage area occupied by
vessels and microvasculature in the selected region. For scans of
the macula, flow index and vessel density can be routinely
determined for the parafovea and/or perifovea. The parafovea is
defined to be an annular region with an inner diameter of 0.6 mm
and outer diameter of 2.5 mm centered on the FAZ. The perifovea is
defined to be the annular region extending from the edge of the
parafovea to an outer diameter of 5.5 mm. For a 3.times.3 mm scan,
only the parafovea values can be determined. An example from a
macular angiogram is shown in FIG. 20C. The vitreous or outer
retinal flow index can be used to indicate the RNV or CNV flow
within the scanned area (3.times.3 or 6.times.6 mm) of the vitreous
or outer retina. The retinal or choroidal neovascularization area
was the area occupied by vessels in the vitreous or outer retina.
To identify and quantify the capillary dropout of the inner retina
or choriocapillaris, the non-perfusion map was created by
identifying decorrelation values lower than a set cutoff point,
typically 2.33 standard deviations below the mean according to the
normal distribution. Morphologic operations were then used to
remove areas below a certain size to reduce noise. The remaining
areas were then summed and converted from pixels to metric
units.
Results:
Retinal and Choroidal Microcirculation in Normal Subjects:
[0237] The retina and choroid are two distinct vascular beds. The
3D nature of OCT angiography allows separate visualizations of
these two circulations from the same volumetric scan. In healthy
eyes, retinal circulation is located between the internal limiting
membrane (ILM) and the outer plexiform layer (OPL), while the
choroidal circulation is beneath Bruch's membrane. The vitreous
(anterior to the ILM) and outer retina (between OPL and Bruch's
membrane) are avascular in normal eyes. One way to present OCT
angiography is to use different colors, each representing different
vascular beds, superimposed on gray-scale, cross-sectional,
structural OCT images (FIG. 19A). Using this technique, both blood
flow and retinal structural information are presented on a single
image.
[0238] En face presentation of OCT angiography is comparable to the
traditional view of dye-based angiography and allows clinicians to
identify vascular patterns. Segmented en face OCT angiograms can be
displayed as individual retinal and choroidal circulations (FIG.
19C, 19E). In healthy eyes, the vitreous and outer retinal layers
are colored black (FIG. 19B, 19D), representing the absence of
flow.
[0239] In vivo imaging of the choroid is limited with current
imaging modalities (Mrejen S and Spade R F, Surv Ophthalmol 58,
387-429 (2013); incorporated by reference herein). High quality OCT
angiograms of the choriocapillaris can be obtained by segmenting a
thin slice (10 .mu.m) of the inner choroid below Bruch's membrane
(FIG. 19E). The larger and deeper choroidal vessels are more
difficult to visualize with SSADA-based OCT angiography. One reason
for this is that choriocapillaris flow causes a flow projection
artifact in the deeper layers. Selective removal of this artifact
is impossible given the near confluent nature of the
choriocapillaris. Second, high flow in the larger choroidal vessels
reduces the OCT signal due to interference fringe washout. However,
visualization of the larger, deeper choroidal vessels is possible
using an inverse reflectance display scale (FIG. 19F). This is
achieved by taking advantage of the OCT signal void produced by
interference fringe washout in regions of very high flow velocity
(Hendargo H C et al, Biomed Opt Express 2, 2175-2188 (2011);
incorporated by reference herein).
Retinal Neovascularization and Capillary Dropout in Diabetic
Retinopathy
[0240] Diabetic retinopathy is a microangiopathy characterized by
capillary occlusion, hyperpermeability, and neovascularization
(Frank R N, N Engl J Med 350, 48-58 (2004); incorporated by
reference herein). These pathophysiologic changes cause
proliferative diabetic retinopathy and macular edema, which are
responsible for most of the vision loss associated with this
disease (Antonetti D A et al, 2012 supra). Assessment of the
microvascular changes with FA has been validated as a way to
classify disease severity and predict progression (Ophthalmology
98, 807-822 (1991); incorporated by reference herein)
[0241] Like FA, OCT angiography can visualize areas of low
capillary perfusion or dropout. FIG. 20 compares en face retinal
OCT angiograms from a normal subject to those from a patient with
non-proliferative diabetic retinopathy (NPDR). The central 0.6-mm
circle encompasses the normal foveal avascular zone (FAZ), and
areas of capillary loss are represented by dark areas elsewhere in
the macula. No dark areas are present outside of the FAZ in the
normal subject (FIG. 20B, top panel, 20C top panel, 20D top panel),
whereas the patient with NPDR has enlargement of the normal FAZ and
extensive loss of the macular capillary bed (FIG. 20B bottom
panel). Nonperfusion areas on FA correspond to those on OCT
angiography (FIG. 20B bottom panel, 20C bottom panel).
[0242] SSADA also allows the quantitative evaluation of local
circulation by determining the flow index and vessel density in
areas of interest, such as the parafoveal and perifoveal areas that
correspond respectively to the 3-mm and 6-mm early treatment
diabetic retinopathy study (ETDRS) macular fields (as demonstrated
in FIGS. 20A-20D). Nonperfusion maps may be created (FIG. 20D) that
allows for the area of nonperfusion to be calculated and compared
between sequential angiograms.
[0243] The development of retinal neovascularization (RNV)
signifies progression to the proliferative phase of the disease.
Recognition of this change is important because it may guide
panretinal photocoagulation and other treatments to reduce the risk
of vision loss due to RNV (Ophthalmology 88, 583-600 (1981);
incorporated by reference herein). Because OCT angiograms can be
segmented by anatomic planes such as the ILM, it can be
particularly effective in distinguishing between intra-retinal
microvascular abnormalities, which occur in the same plane as the
retinal blood vessels, and early RNV, which develops anterior to
the retinal vessels and may extend into the vitreous cavity (FIG.
21A-21D). The extent and activity of RNV can also be quantified on
OCT angiography by vessel area and flow index. Thus, compared to
FA, OCT angiography has the advantages of 3D localization and
quantification.
Choroidal Neovascularization in Age-Related Macular
Degeneration
[0244] Choroidal neovascularization (CNV), the hallmark pathologic
feature of neovascular AMD, consists of abnormal blood vessels that
grow from the choriocapillaris and penetrate through Bruch's
membrane into the sub-retinal pigment epithelium (RPE) space and
subretinal space. Subsequent exudation and hemorrhage damage
retinal tissue, resulting in vision loss (Ambati J et al, 2003
supra) FA is the gold standard for CNV diagnosis; (Hee M R et al,
1996 supra) however it is limited by its 2D nature. In addition,
blocked fluorescence from the RPE or hemorrhage (if present)
reduces visibility of the CNV beneath the RPE, as well as
visualization of the choroid (Gass J, Stereoscopic atlas of macular
diseases: diagnosis and management, St. Louis, Mosby ed 24-26
(1997) OCT angiography has the capability to generate 3D angiograms
of the retina, choroid, and CNV that is otherwise obscured in FA by
RPE blockage or hemorrhage.
[0245] In an example of neovascular AMD (FIG. 22A), the late FA
image (FIG. 22B) shows a stippled hyperfluoresence leakage pattern,
indicating the presence of an occult CNV. The outer retina is
devoid of blood flow in healthy eyes, and the flow detected in this
area is associated with the presence of CNV. Color-coded composite
en face OCT angiography allows for 3D representation of retinal
flow, outer retinal flow, and choroidal flow on a single 2D image.
In this case, the CNV is highlighted in yellow, and the extent and
microvascular structure (FIG. 22C) is better defined compared to
traditional FA image (FIG. 22B). The cross-sectional structural OCT
image with a color-coded OCT angiogram overlay (FIG. 22D)
demonstrates the depth of the CNV, in this case beneath the RPE, as
well as the presence of fluid exudation and disruption of outer
retinal anatomy.
[0246] This case illustrates the capability of OCT angiography to
assess the morphology, extent, and depth of CNV in AMD. It has been
demonstrated that OCT angiography can be used to classify CNV as
type I (between the RPE and Bruch's membrane, FIG. 22D), type II
(above the RPE), type III (in the inner retina), or a combined type
(Jia et al, 2014A supra). OCT angiography can furthermore provide
quantitative data regarding CNV flow and area. These capabilities
may prove to be valuable in the assessment of disease severity and
monitoring of the effectiveness of treatment. It is likely that, in
many cases, CNV growth through Bruch's membrane occurs before the
onset of exudation and visual symptoms. While visual acuity at
presentation is strongly predictive of the post-treatment outcome,
(Ying G et al, Opththalmology 120, 122-129 (2013); incorporated by
reference herein) OCT angiography may enable the detection of CNV
prior to the development of symptoms or detectable changes with
structural OCT or FA. Since it is a safe, noninvasive, and rapid
imaging technique, at-risk patients may benefit from OCT
angiography screening.
Choriocapillaris Loss in Age-Related Macular Degeneration
[0247] The choriocapillaris has been implicated in the progression
of AMD. In advanced non-neovascular AMD, geographic atrophy (GA) is
associated with loss of photoreceptors, RPE, and the
choriocapillaris. Whether the RPE or choriocapillaris alterations
are the primary event in pathogenesis has been a matter of debate
(Bhutto I and Lutty G, Mol Asp Med 33, 295-317 (2012); incorporated
by reference herein). Histologic studies have described
choriocapillaris dysfunction in the intermediate stage of AMD,
prior to development of GA or choroidal neovascularization (McLeod
D S et al, Inv Ophthalmol Vis Sci 50, 4982-4991 (2009);
incorporated by reference herein). While efforts have been made to
assess the choriocapillaris in vivo, (Mullins R F et al, Inv
Ophthalmol Vis Sci 52, 1606-1612 (2011); incorporated by reference
herein) its small size, high density, and high permeability have
made it difficult for conventional imaging modalities, including FA
and ICG, to provide meaningful assessment. By segmenting a layer
extending 10 .mu.m posterior to Bruch's membrane, OCT angiography
provides qualitative and quantitative evaluation of the
choriocapillaris, which may be valuable in understanding its role
in AMD.
[0248] OCT angiography can elucidate the state of the
choriocapillaris in GA (FIGS. 23A-23H). In a patient with
perifoveal GA, the fundus photograph (FIG. 23A) and
autofluorescence image (FIG. 23B) show the affected region. The
drusen-RPE complex thickness map (the axial distance from the apex
of the drusen and the RPE layer to BM, (FIG. 23C) shows an area of
RPE loss corresponding to the clinically evident GA. The
choriocapillaris is absent in most of the area correlating to
clinical GA (FIG. 23E, 23F). In some areas near the border of the
GA with RPE loss, intact choriocapillaris flow is present (FIG.
23G, 23H). In this area of preserved choriocapillaris and slightly
beyond it, the outer nuclear layer is also preserved; however, in
most of the GA area, the outer nuclear layer, photoreceptors, and
RPE are absent.
Choriocapillaris loss in Choroideremia:
[0249] Pathology of the choriocapillaris has been implicated in
many disease processes apart from AMD, including those with
genetic, inflammatory, and infectious etiologies. Choroideremia is
an X-linked recessive chorioretinal dystrophy associated with
mutation of the CHM gene. This gene encodes the Rab escort protein
1 (REP-1) and is characterized by significant atrophy of the RPE
and choriocapillaris with photoreceptor loss (Coussa R G and
Traboulsi E I et al, Ophthalmic Genet 33, 57-65 (2012);
incorporated by reference herein). Effected patients typically
experience night blindness in their first or second decade,
followed by constriction of the peripheral visual fields until
central vision is lost. Patients with choroideremia often
demonstrate retinal vessels of normal caliber until late in the
disease, suggesting a relatively greater loss of choroidal blood
flow. Spectral-domain OCT studies of patients with choroideremia
show photoreceptor rosettes, suggesting loss of RPE prior to that
of photoreceptors. However, the temporal relationship of RPE loss
versus choriocapillaris loss is unknown.
[0250] OCT angiography with SSADA may aid in understanding the
disease pathogenesis and inform the debate on whether degeneration
occurs first in the choriocapillaris, RPE, or photoreceptors. En
face OCT angiography has the ability to map both retinal and
choroidal perfusion down to the capillary level. In the 3 subjects
with choroideremia, OCT angiography (FIG. 24A-24E) showed that the
choriocapillaris nonperfusion area was more extensive than the
retinal nonperfusion in all cases. The area of RPE loss was even
more extensive than choriocapillaris nonperfusion. These patterns
suggest that RPE loss might be the primary event, with subsequent
choroidal perfusion loss following more closely than retinal
perfusion loss. With the recent initiation of gene therapy trials,
(MacLaren R E et al, Lancet 383, 1129-1137 (2014); incorporated by
reference herein) the ability to quantify and map both choroidal
and retinal circulations may prove valuable in assessing disease
severity and response to treatment.
Example 4
Optical Coherence Tomography Angiography Image Processing
System
[0251] FIG. 25 schematically shows an example system 100 for OCT
image processing in accordance with various embodiments. System 100
comprises an OCT system 102 configured to acquire an OCT image
comprising OCT interferograms and one or more processors or
computing systems 104 that are configured to implement the various
processing routines described herein. OCT system 100 can comprise
an OCT system suitable for OCT angiography applications, e.g., a
swept source OCT system.
[0252] In various embodiments, an OCT system can be adapted to
allow an operator to perform various tasks. For example, an OCT
system can be adapted to allow an operator to configure and/or
launch various ones of the herein described methods. In some
embodiments, an OCT system can be adapted to generate, or cause to
be generated, reports of various information including, for
example, reports of the results of scans run on a sample.
[0253] In embodiments of OCT systems comprising a display device,
data and/or other information can be displayed for an operator. In
embodiments, a display device can be adapted to receive an input
(e.g., by a touch screen, actuation of an icon, manipulation of an
input device such as a joystick or knob, etc.) and the input can,
in some cases, be communicated (actively and/or passively) to one
or more processors. In various embodiments, data and/or information
can be displayed, and an operator can input information in response
thereto.
[0254] In some embodiments, the above described methods and
processes can be tied to a computing system, including one or more
computers. In particular, the methods and processes described
herein, e.g., the method depicted in FIGS. 1-4 described above, can
be implemented as a computer application, computer service,
computer API, computer library, and/or other computer program
product.
[0255] FIG. 26 schematically shows a non-limiting computing device
200 that can perform one or more of the above described methods and
processes. For example, computing device 200 can represent a
processor included in system 100 described above, and can be
operatively coupled to, in communication with, or included in an
OCT system or OCT image acquisition apparatus. Computing device 200
is shown in simplified form. It is to be understood that virtually
any computer architecture can be used without departing from the
scope of this disclosure. In different embodiments, computing
device 200 can take the form of a microcomputer, an integrated
computer circuit, printed circuit board (PCB), microchip, a
mainframe computer, server computer, desktop computer, laptop
computer, tablet computer, home entertainment computer, network
computing device, mobile computing device, mobile communication
device, gaming device, etc.
[0256] Computing device 200 includes a logic subsystem 202 and a
data-holding subsystem 204. Computing device 200 can optionally
include a display subsystem 206, a communication subsystem 208, an
imaging subsystem 210, and/or other components not shown in FIG.
26. Computing device 200 can also optionally include user input
devices such as manually actuated buttons, switches, keyboards,
mice, game controllers, cameras, microphones, and/or touch screens,
for example.
[0257] Logic subsystem 202 can include one or more physical devices
configured to execute one or more machine-readable instructions.
For example, the logic subsystem can be configured to execute one
or more instructions that are part of one or more applications,
services, programs, routines, libraries, objects, components, data
structures, or other logical constructs. Such instructions can be
implemented to perform a task, implement a data type, transform the
state of one or more devices, or otherwise arrive at a desired
result.
[0258] The logic subsystem can include one or more processors that
are configured to execute software instructions. For example, the
one or more processors can comprise physical circuitry programmed
to perform various acts described herein. Additionally or
alternatively, the logic subsystem can include one or more hardware
or firmware logic machines configured to execute hardware or
firmware instructions. Processors of the logic subsystem can be
single core or multicore, and the programs executed thereon can be
configured for parallel or distributed processing. The logic
subsystem can optionally include individual components that are
distributed throughout two or more devices, which can be remotely
located and/or configured for coordinated processing. One or more
aspects of the logic subsystem can be virtualized and executed by
remotely accessible networked computing devices configured in a
cloud computing configuration.
[0259] Data-holding subsystem 204 can include one or more physical,
non-transitory, devices configured to hold data and/or instructions
executable by the logic subsystem to implement the herein described
methods and processes. When such methods and processes are
implemented, the state of data-holding subsystem 204 can be
transformed (e.g., to hold different data).
[0260] Data-holding subsystem 204 can include removable media
and/or built-in devices. Data-holding subsystem 204 can include
optical memory devices (e.g., CD, DVD, HD-DVD, Blu-Ray Disc, etc.),
semiconductor memory devices (e.g., RAM, EPROM, EEPROM, etc.)
and/or magnetic memory devices (e.g., hard disk drive, floppy disk
drive, tape drive, MRAM, etc.), among others. Data-holding
subsystem 204 can include devices with one or more of the following
characteristics: volatile, nonvolatile, dynamic, static,
read/write, read-only, random access, sequential access, location
addressable, file addressable, and content addressable. In some
embodiments, logic subsystem 202 and data-holding subsystem 204 can
be integrated into one or more common devices, such as an
application specific integrated circuit or a system on a chip.
[0261] FIG. 26 also shows an aspect of the data-holding subsystem
in the form of removable computer-readable storage media 212, which
can be used to store and/or transfer data and/or instructions
executable to implement the herein described methods and processes.
Removable computer-readable storage media 212 can take the form of
CDs, DVDs, HD-DVDs, Blu-Ray Discs, EEPROMs, flash memory cards, USB
storage devices, and/or floppy disks, among others.
[0262] When included, display subsystem 206 can be used to present
a visual representation of data held by data-holding subsystem 204.
As the herein described methods and processes change the data held
by the data-holding subsystem, and thus transform the state of the
data-holding subsystem, the state of display subsystem 206 can
likewise be transformed to visually represent changes in the
underlying data. Display subsystem 206 can include one or more
display devices utilizing virtually any type of technology. Such
display devices can be combined with logic subsystem 202 and/or
data-holding subsystem 204 in a shared enclosure, or such display
devices can be peripheral display devices.
[0263] When included, communication subsystem 208 can be configured
to communicatively couple computing device 200 with one or more
other computing devices. Communication subsystem 208 can include
wired and/or wireless communication devices compatible with one or
more different communication protocols. As non-limiting examples,
the communication subsystem can be configured for communication via
a wireless telephone network, a wireless local area network, a
wired local area network, a wireless wide area network, a wired
wide area network, etc. In some embodiments, the communication
subsystem can allow computing device 200 to send and/or receive
messages to and/or from other devices via a network such as the
Internet.
[0264] When included, imaging subsystem 210 can be used acquire
and/or process any suitable image data from various sensors or
imaging devices in communication with computing device 200. For
example, imaging subsystem 210 can be configured to acquire OCT
image data, e.g., interferograms, as part of an OCT system, e.g.,
OCT system 102 described above. Imaging subsystem 210 can be
combined with logic subsystem 202 and/or data-holding subsystem 204
in a shared enclosure, or such imaging subsystems can comprise
periphery imaging devices. Data received from the imaging subsystem
can be held by data-holding subsystem 204 and/or removable
computer-readable storage media 212, for example.
[0265] It is to be understood that the configurations and/or
approaches described herein are exemplary in nature, and that these
specific embodiments or examples are not to be considered in a
limiting sense, because numerous variations are possible. The
specific routines or methods described herein can represent one or
more of any number of processing strategies. As such, various acts
illustrated can be performed in the sequence illustrated, in other
sequences, in parallel, or in some cases omitted. Likewise, the
order of the above-described processes can be changed.
[0266] The subject matter of the present disclosure includes all
novel and nonobvious combinations and subcombinations of the
various processes, systems and configurations, and other features,
functions, acts, and/or properties disclosed herein, as well as any
and all equivalents thereof.
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