WO2017157828A1 - Systems and methods for measuring tissue distortions around fluid pockets - Google Patents

Abstract

A method for analyzing optical coherence tomography (OCT) data of an eye containing at least one pocket of fluid includes collecting two OCT scans at approximately the same transverse locations on the eye. The locations include a pocket of fluid at a depth in the eye. Portions of tissue above and below the pocket of fluid are identified in the two OCT scans. The phase of the OCT data is calculated in the portions of tissue above and below the pocket of fluid in each of the OCT scans. The difference in the phase of the OCT data in the portions of tissue above and below the pocket of fluid for each OCT scan is calculated. The phase change of the phase differences is calculated between the two OCT scans. Calculated phase change is displayed or stored or a further analysis thereof.

Description

SYSTEMS AND METHODS FOR MEASURING TISSUE DISTORTIONS AROUND FLUID POCKETS

PRIORITY

This application claims priority to US Provisional Application Serial No. 62/307,720 filed March 14, 2016, the contents of which are hereby incorporated by reference.

BACKGROUND

Optical coherence tomography (OCT) is a noninvasive, noncontact imaging modality that uses coherence gating to obtain high-resolution cross-sectional images of tissue

microstructure. Several implementations of OCT have been developed. In frequency domain OCT (FD-OCT), the interferometric signal between light from a reference and the back- scattered light from a sample point is recorded in the frequency domain typically either by using a dispersive spectrometer in the detection arm in the case of spectral-domain OCT (SD- OCT) or rapidly tuning a swept laser source in the case of swept-source OCT (SS-OCT). After a wavelength calibration, a one-dimensional Fourier transform is taken to obtain an A- line spatial distribution of the object's scattering profile.

Functional OCT can provide important clinical information that is not available in the typical intensity based structural OCT images. There have been several functional contrast enhancement methods including OCT Angiography, Doppler OCT, Phase-sensitive OCT, Polarization-Sensitive OCT, and Spectroscopic OCT. Integration of functional extensions can greatly enhance the capabilities of OCT for a range of applications in medicine.

The human retina is a flexible piece of tissue which can deform under normal biological forces. The ability to measure these elastic properties of the retina may provide insight into the underlying cellular structure and therefore the state or progression of disease or the efficacy of treatments. Such measurements have been investigated for in vivo anterior segment imaging using an air puff as an external force (Twa, Michael D., et al. (2014) "Spatial characterization of corneal biomcchanical properties with optical coherence eiastography after UV cross-linking." Biomedical optics express 5(5): 1419-1427), ex vivo retinal imaging using a laser pulse (M Ciller, I leike EL, et al. (2012) "Imaging thermal expansion and retinal tissue changes during photocoagulation by high speed

OCT." Biomedical optics express 3(5): 1025- 1046) in addition to fields outside of ophthalmology (Kennedy, Kelsey ML, et al. (2015) "Quantitative micro-eiastography:

imaging of tissue elasticity using compression optical coherence elastography." Scientific reports 5: 15538).

Common to the aforementioned techniques is the requirement of an external excitation source such as a focused puff of air, heating from a laser pulse, or physical contact with a probe. It is also possible to measure similar motions using intrinsic forces present in the tissue. This has been shown in the human retina as a result of the pulsatile flow of blood (O'Hara, Keith E., et ai. (2013) "Measuring pulse-induced natural relative motions within human ocular tissue in vivo using phase-sensitive optical coherence tomography." Journal of biomedical optics 18(12): 121506), (Spahr, Hendrik, et al. (201 5 ) "Imaging pulse wave propagation in human retinal vessels using full-field swept-source optical coherence tomography." Optics letters 40(20): 4771 -4774). Although using intrinsic forces makes quantitative electrography measurements challenging, other more relative measures (such as a binary decision of the presence or absence of motion) could prove useful as well. Finally, other distortions of the retina have been measured by dynamic thickness changes of the outer retina (Jonnai, Ravi S., et al. (2012 ) "Phase-sensitive imaging o the outer retina using optical coherence tomography and adaptive optics." Biomedical optics express 3(1): 104-124). These distortions are due to the regeneration of cone photoreceptor cells. Therefore, rather than providing insight to the elastic properties of the retina, these measurements describe cellular activity.

SUMMARY

The present application describes a method and algorithm for measuring and quantifying retinal distortions. A key aspect of the method is to analyze the intrinsic distortion of tissue above and below a pocket of fluid. The distortion of the tissue is measured as a relative thickness change of the pocket of fluid along the optical axis and utilizes the phase of the OCT signal.

Although our technique can measure motion resulting from pulsatile blood flow (as in the prior art), we believe the majority of the motion we measure in the present technique originates from tremors of the eye. One potential use of this technique is to measure the relative elasticity of the tissue. In one such case, neovascularization above the retina adds structure to the tissue, preventing it from moving while retaining a pocket of liquid beneath it. After anti-VEGF injections, the vessels reduce but the tissue remains. The tissue is less structurally sound leading to increased motion.

It should be noted that the language used in the specification has been principally selected for readability and instructional purposes and not to limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a generalized optical coherence tomography (OCT) system for imaging an eye. FIG. 2 is a flow chart of a generalized method for measuring and quantifying tissue distortions according to one aspect of the present application.

FIG. 3a and FIG. 3b show two different selections of tissue above and below a pocket of fluid in a B-scan. In particular, FIG. 3a shows a slab above the fluid as derived from a

segmentation of the inner limiting membrane (ILM) and a slab below the fluid as derived from a segmentation of the retinal pigment epithelium (RPE). FIG. 3b shows an arbitrary region above and below the fluid having fixed lateral extent.

FIGS. 4a-c shows different visualizations of the motion distortion analysis of the present invention. In particular, FIG. 4a shows a depth-projected phase from one region of an OCT scan. FIG. 4b shows the corresponding 'phase-stabilized' frame. FIG. 4c shows an en face view generated by averaging the phase change for motion analysis.

FIG. 5 is a block diagram of a general computer system that may perform the functions discussed in this disclosure according to one aspect of the present invention.

DETAILED DESCRIPTION

All patent and non-patent references cited within this specification are herein incorporated by reference in their entirety to the same extent as if the disclosure of each individual patent and non-patient reference was specifically and individually indicated to be incorporated by reference in its entirely.

A generalized FD-OCT system used to collect 3-D image data of the eye suitable for use with the present invention is illustrated in FIG. 1. An FD-OCT system 100 includes a light source, 101, typical sources including but not limited to, broadband light sources with short temporal coherence lengths for SD-OCT systems or swept laser sources for SS-OCT systems. Abeam of light from source 101 is routed, typically by optical fiber 105, to illuminate the sample 110, the sample in this case being tissues in the retina of the human eye. The light is scanned, typically with a scanner 107 between the output of the fiber and the sample, so that the beam of light (dashed line 108) is scanned laterally (in x and y) over the region of the sample to be imaged. Light scattered from the sample is collected, typically into the same fiber 105 used to route the light for illumination. Reference light derived from the same source 101 travels a separate path, in this case involving fiber 103 and retro-reflector 104 with an adjustable optical delay. Those skilled in the art recognize that a transmissive reference path can also be used and that the adjustable delay could be placed in the sample or reference arm of the interferometer. Collected sample light is combined with reference light, typically in a fiber coupler 102, to form light interference in a detector 120, said detector generating signals in response thereto. Although a single fiber port is shown going to the detector, those skilled in the art recognize that various designs of interferometers can be used for balanced or unbalanced detection of the interference signal. The output signal from the detector 120 is supplied to a processor 121 that converts the observed interference into depth information of the sample. The results can be stored in the processor 121 or other storage medium or displayed on display 122. The processing and storing functions may be localized within the OCT instrument or functions may be performed on an external processing unit to which the collected data is transferred. This unit could be dedicated to data processing or perform other tasks which are quite general and not dedicated to the OCT device. The processor 121 may contain for example a field-programmable gate array (FPGA), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a graphics processing unit (GPU), a system on chip (SoC) or a combination thereof, that performs some, or the entire data processing steps, prior to passing on to the host processor or in a parallelized fashion.

In Fourier Domain optical coherence tomography (FD-OCT), each measurement is the real- valued spectral interferogram (S(k, x, y)). The real-valued spectral data typically goes through several postprocessing steps including background subtraction, dispersion correction, etc. The Fourier transform of the processed interferogram, results in a complex valued OCT signal output A(z, x, y)=|A(z, x, y)|e^1<p(z' ^x' ^y). The absolute value of this complex OCT signal, |A(z, x, y)|, reveals the profile of scattering intensities at different path lengths, and therefore scattering as a function of depth (z-direction) in the sample. Similarly, the phase, φ(ζ, x, y) can also be extracted from the complex valued OCT signal. The profile of scattering as a function of depth is called an axial scan (A-scan). A set of A-scans measured at neighboring locations in the sample produces a cross-sectional image (tomogram or B-scan) of the sample. A collection of B-scans collected at different transverse locations on the sample makes up a data volume or cube. For a particular volume of data, the term fast axis refers to the scan direction along a single B-scan whereas slow axis refers to the axis along which multiple B-scans are collected. We use the term "cluster scan" herein to refer to a single unit or block of data generated by repeated acquisitions at the same location for the purposes of analyzing motion of interest within the sample. A cluster scan can consist of multiple A- scans or B-scans collected over time at approximately the same location(s) on the sample. Ideally the cluster scan would be collected using a system capable of tracking the sample during acquisition and reducing the incidence of undesirable motion artifacts to insure that the B-scans within a cluster scan are collected at the same set of transverse locations (see for example US Patent No. 8,857,988 the contents of which are hereby incorporated by reference). A variety of ways to create B-scans are known to those skilled in the art including but not limited to along the horizontal or x-direction, along the vertical or y-direction, along the diagonal of x and y, or in a circular or spiral pattern. The majority of the examples discussed herein refer to B-scans in the x-z dimensions but the invention would apply equally to any cross sectional image.

The sample and reference arms in the interferometer could consist of bulk-optics, fiber-optics or hybrid bulk-optic systems and could have different architectures such as Michelson, Mach- Zehnder or common-path based designs as would be known by those skilled in the art. Light beam as used herein should be interpreted as any carefully directed light path. In time- domain systems, the reference arm needs to have a tunable optical delay to generate interference. Balanced detection systems are typically used in TD-OCT and SS-OCT systems, while spectrometers are used at the detection port for SD-OCT systems. The invention described herein could be applied to any type of OCT system.

The general approach for measuring and quantifying tissue distortions around pocket fluids to be described in the present application is depicted in method 200 of FIG.2. To begin, at least two or more repeat OCT scans (be it A-scans, B-scans, or 3D data volumes) are

collected/received from approximately the same transverse (lateral, x) location or set of transverse locations on the sample (step 202). The timing between repeat scans should be considered to allow for intrinsic motion of the tissue. Depending on the frequency and amplitude of motion, timing differences on the order of 1 - 100 ms are preferable. If acquired too closely in time, no motion will be seen, and if acquired too far apart in time, phase wrapping may occur or information regarding the dynamics may be missed.

Traditional OCT processing is then performed to obtain a complex (amplitude and phase) signal, A_n(z,x) for each scan collected. In this application, n is the repetition number of the scan and, for simplicity, only the z (depth) and x (transverse) dimensions are denoted, but A is understood to be three-dimensional. Furthermore, the transverse variables z and x are understood to be discrete representing each location that data is acquired.

To ensure the repeated scans are aligned, bulk shifts in the axial and transverse locations may be necessary. This can be done using some or all of the acquired data and may utilize only the intensity or the full complex data. In addition, the data can be acquired using motion tracking as described in US Patent 8,857,988, the contents of which are hereby incorporated by reference.

In the next step of the method 200, a section of tissue above and below a pocket of fluid is isolated (step 204). The exact boundaries of the fluid pocket need not be identified, rather only a slab/region laying above and a slab/region laying below as is shown in FIGS. 3a and 3b. This can be accomplished, for instance, using traditional segmentation approaches as are well known in the prior art. In FIG. 3a, an OCT scan of retinal tissue (300) contains a pocket of fluid (302). Above the fluid, a slab is shown (304) as derived from the segmentation of the inner limiting membrane (ILM). Below the fluid, a slab is defined (306) as derived from a segmentation of the retinal pigment epithelium (RPE). Alternatively, as shown in FIG. 3b, one could simply choose an arbitrary region above (308) and below (310) the fluid having fixed lateral extent.

Once the slabs/regions are defined, the phase of the OCT data at different lateral locations in the tissue portions above and below the pocket of fluid is calculated (step 206). This can be done by projecting each slab along the z dimension to generate two phase values for each lateral location and scan repetition number, (p_n ^above(x) and (p_n ^below(x). It is noted that many techniques can be used for this projection. The simplest is a complex average along depth, but other methods involving first thresholding the data or normalizing each point by the amplitude, etc. can be imagined by one skilled in the art.

Then the phase differences between the phases above and below the pocket, Δφ_η(χ), are calculated such that Δφ„(χ) = (p„^below(x) - (p_n ^above(x) (step 208). We recognize that it is preferable to add and subtract phase values in the complex domain rather than just add or subtract the actual phase values to avoid phase wrapping errors (O'Hara, Keith E., et al. (2013) "Measuring pulse-induced natural relative motions within human ocular tissue in vivo using phase-sensitive optical coherence tomography." Journal of biomedical optics 18(12): 121506). For instance, exp(i(pi)*exp(-i(p₂) is often preferable over φι-φ₂ since the modulus 2π is more naturally built in. Furthermore, weighting each term by the amplitude of the signal can avoid using phase measurements from low SNR regions. For simplicity, though, we will use the following three calculations interchangeably and it is understood that moving between complex numbers and phase-only numbers can be performed naturally.

2) exp(i(pi)*exp(±icp₂)

3) |Ai |exp(i(pi)*|A₂|exp(±i(p₂)

Specifically, in the present application, by stating that phase differences are performed (such as φι-φ₂), it is not meant be restricted to only subtracting phase values, but also recognizing that the phases can be subtracted in complex form using exp(i(pi)*exp(±i(p₂) or

|Ai |exp(i(pi)*|A₂|exp(±i(p₂).

Finally, for at least two scans, i and j, taken at the same set of lateral locations on the sample separated in time, the phase change of the phase differences at each lateral location, Acpij(x) = Δφί(χ)- Acpj(x), can be calculated (step 210). The phase change can be stored or displayed or analyzed further (step 212). The phase change is directly proportional to the change in thickness between scan Ai(x) and Aj(x). Phase changes can be calculated between multiple pairs of scans in the set with the same time separations (e.g. between scan 1 & scan 2, scan 2 & scan 3, etc.) and then be combined such as averaging or curve fitting to improve the measurement. Alternatively, phase changes can be calculated and compared for scan pairs with different time separations (e.g. scan 1 and scan 2 vs. scan 1 & scan 4) to look at different time dynamics of the motion. The number of scans taken at the same location, N can be varied depending on the desired analysis of the dynamic motion. If only the amplitude of the motion is desired, N between 1 and 10 is likely sufficient. On the other hand, if advanced frequency analysis is desired, imaging each location for a longer period of time (N greater than 10 and even up to 500) may be necessary.

The change in thickness measured in microns is Δζ = o/(47m_eye)*(A(pij(x)) where λο is the central wavelength of light in free space (measured in microns) and n_eye is the refractive index of the eye. A cumulative sum (or other discrete integrals) can be computed to estimate the absolute change in thickness of the tissue over time. The change in thickness over time (or the incremental change in thickness) can be used for further analysis. One such example is the possibility of not observing dynamic changes in thickness before treatment then observing motion after treatment. This could provide insight into the effectiveness of the treatment or the health of the tissue. Similarly, an increase or decrease in measured motion could also be a useful metric. Many other metrics such as frequency of oscillations, power within a frequency band, standard deviation of motion over time or of differential motion over time could also equally be used by one skilled in the art.

It should also be noted the order of the steps here can easily be rearranged in any number of ways. For instance, one could first compare the phase changes between repeated OCT scans such that A(pij(z, x) = (pi(z, x)- (pj(z, x) and then project along depth using the slabs/regions to obtain A(pij^above(x) and A(pij^below(x) which then can be compared to obtain: Δς¾(χ) = A(pij^above(x) - A(pij^below(x). Depending on the type of axial projections chosen, this may or may not be the same as above.

For visualization, it may also be beneficial to use the depth-projected phase from one region (e.g. A(pij^below(x)) to 'phase-stabilize' the frame of data. This is shown in FIGS. 4a-c. In FIG. 4a, A(pi,i+i(z, x) is shown for a select i. Similar tissue and fluid pocket features as in FIGS. 3a and 3b can be seen. In FIG. 4b, a 'phase-stabilized' frame is shown. The frame is calculated using: A(pi,i+i(z, x)-A(pi,i+i^below(x) where A(pi,i+i^below(x) is calculated using the segmentation 306 defined in FIG.3a. As such, the RPE and choroid depths in the retina (404) show zero radians, meaning no motion. Above the fluid pocket (406), the retina shows some measured motion (as is seen by the whiter shade of grey). This means that, relative to the RPE, the retina on the left side moved up approximately 2.5 radians. This can be converted to physical units by multiplying the phase by λο/(4πη_βγβ). Furthermore, the tissue to the right of 406 shows, motion in the downward direction is measured (as is seen by the darker shade of grey).

In addition to individual frames, if a volume of data is acquired (e.g., a number clusters of B- scans at different spatial locations along a slow axis) using an OCT angiography technique

(Huang, Yanping, et al. (2014) "Swept-sourcc OCT angiography of the retinal vasculature using intensity differentiation-based optical microangiography algorithms." Ophthalmic Surgery, Lasers and Imaging Retina 45(5): 382-389), complete en face views of the motion can be viewed. One such example is shown in FIG. 4c. This en face view is generated by averaging the phase change, A(pi,i+i(z, x)-A(pi,i+i^below(x), along the z axis between the segmentation lines (304) shown in FIG. 3a. Other types of projections along the z axis are also possible. The outline of the cyst is shown (410), and an increase in motion within the cyst boundary can be seen (412).

The distortion of the retina due to a pocket of fluid is influenced by two main factors. The first factor is the pulsatile flow of blood. During the cardiac cycle, the retina naturally expands and contracts, especially near large blood vessels (Spahr, Hendrik, et al. (2015) "Imaging pulse wave propagation in human retinal vessels using full-field swept-source optical coherence tomography." Optics letters 40(20): 4771-4774). This motion, though, is relatively sparse in time. A second type of motion which can lead to distortions of the retina in the presence of fluid pockets is ocular tremors. These tremors occur continuously, at high frequency, and at low amplitudes. These are hypothesized to cause the fluid in a pocket to slosh and thus the tissue above it to deform at high frequencies. Since distortions due to tremors occur at all times, this type of motion is easier to measure and no timing with the heart beat or measuring over long periods of time is required.

Example Computer System

The processing unit 121 that has been discussed herein in reference to FIG. 1 can be implemented with a computer system configured to perform the functions that have been described herein for this unit. For instance, the processing unit 121 can be implemented with the computer system 500, as shown in FIG. 5. The computer system 500 may include one or more processors 502, one or more memories 504, a communication unit 508, an optional display 510, one or more input devices 512, and a data store 514. The display 510 is shown with dotted lines to indicate it is an optional component, which, in some instances, may not be a part of the computer system 500. In some embodiments, the display 510 discussed herein is the display 122 that has been discussed herein in reference to FIG. 1.

The components 502, 504, 508, 510, 512, and 514 are communicatively coupled via a communication or system bus 516. The bus 516 can include a conventional communication bus for transferring data between components of a computing device or between computing devices. It should be understood that the computing system 500 described herein is not limited to these components and may include various operating systems, sensors, video processing components, input/output ports, user interface devices (e.g., keyboards, pointing devices, displays, microphones, sound reproduction systems, and/or touch screens), additional processors, and other physical configurations.

The processor(s) 502 may execute various hardware and/or software logic, such as software instructions, by performing various input/output, logical, and/or mathematical operations. The processor(s) 502 may have various computing architectures to process data signals including, for example, a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, and/or architecture implementing a combination of instruction sets. The processor(s) 502 may be physical and/or virtual, and may include a single core or plurality of processing units and/or cores. In some embodiments, the processor(s) 502 may be capable of generating and providing electronic display signals to a display device, such as the display 510, supporting the display of images, capturing and transmitting images, performing complex tasks including various types of feature extraction and sampling, etc. In some embodiments, the processor(s) 502 may be coupled to the memory(ies) 504 via a data/communication bus to access data and instructions therefrom and store data therein. The bus 516 may couple the processor(s) 502 to the other components of the computer system 500, for example, the memory(ies) 504, the communication unit 508, or the data store 514.

The memory(ies) 504 may store instructions and/or data that may be executed by the processor(s) 502. In the depicted embodiment, the memory(ies) 504 stores a retinal distortion detection module 505 which may include software, code, logic, or routines for performing any and/or all of the techniques described herein. For instance, the retinal distortion detection module 505 may perform all or some of the operations depicted in FIG. 2. In some embodiments, the memory(ies) 504 may also be capable of storing other instructions and data including, for example, an operating system, hardware drivers, other software applications, databases, etc. The memory(ies) 504 are coupled to the bus 516 for communication with the processor(s) 502 and other components of the computer system 500. The memory(ies) 504 may include a non-transitory computer-usable (e.g., readable, writeable, etc.) medium, which can be any apparatus or device that can contain, store, communicate, propagate or transport instructions, data, computer programs, software, code, routines, etc. for processing by or in connection with the processor(s) 502. Anon-transitory computer-usable storage medium may include any and/or all computer-usable storage media. In some embodiments, the

memory(ies) 504 may include volatile memory, non- volatile memory, or both. For example, the memory(ies) 504 may include a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory, a hard disk drive, a floppy disk drive, a CD ROM device, a DVD ROM device, a DVD RAM device, a DVD RW device, a flash memory device, or any other mass storage device known for storing instructions on a more permanent basis.

The computer system for the processing unit 121 may include one or more computers or processing units at the same or different locations. When at different locations, the computers may be configured to communicate with one another through a wired and/or wireless network communication system, such as the communication unit 508. The communication unit 508 may include network interface devices (I/F) for wired and wireless connectivity. For example, the communication unit 508 may include a CAT-type interface, USB interface, or SD interface, transceivers for sending and receiving signals using Wi-Fi™; Bluetooth® , or cellular communications for wireless communication, etc. The communication unit 508 can link the processor(s) 502 to a computer network that may in turn be coupled to other processing systems.

The display 510 represents any device equipped to display electronic images and data as described herein. The display 510 may be any of a conventional display device, monitor or screen, such as an organic light-emitting diode (OLED) display, a liquid crystal display (LCD). In some embodiments, the display 510 is a touch-screen display capable of receiving input from one or more fingers of a user. For example, the device 510 may be a capacitive touch-screen display capable of detecting and interpreting multiple points of contact with the display surface.

The input device(s) 512 are any devices for inputting data on the computer system 500. In some embodiments, an input device is a touch-screen display capable of receiving input from one or more fingers of the user. The functionality of the input device(s) 512 and the display 510 may be integrated, and a user of the computer system 500 may interact with the system by contacting a surface of the display 510 using one or more fingers. In other embodiments, an input device is a separate peripheral device or combination of devices. For example, the input device(s) 512 may include a keyboard (e.g., a QWERTY keyboard) and a pointing device (e.g., a mouse or touchpad). The input device(s) 512 may also include a microphone, a web camera, or other similar audio or video capture devices.

The data store 514 can be an information source capable of storing and providing access to data. In the depicted embodiment, the data store 514 is coupled for communication with the components 502, 504, 508, 510, and 512 of the computer system 500 via the bus 516, and coupled, via the processor(s) 502, for communication with the data correction module 505 and the artifacts removal module 506. In some embodiments, the data correction module 505 and the artifacts removal module 506 are configured to manipulate, i.e., store, query, update, and/or delete, data stored in the data store 514 using programmatic operations.

In the above description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the specification. It should be apparent, however, that the subject matter of the present application can be practiced without these specific details. It should be understood that the reference in the specification to "one embodiment", "some embodiments", or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the description. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places in the specification are not necessarily all referring to the same embodiment(s).

Furthermore, the description can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

The foregoing description of the embodiments of the present subject matter has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the present embodiment of subject matter to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the present embodiment of subject matter be limited not by this detailed description, but rather by the claims of this application. As will be understood by those familiar with the art, the present subject matter may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Furthermore, it should be understood that the modules, routines, features, attributes, methodologies and other aspects of the present subject matter can be implemented using hardware, firmware, software, or any combination of the three.

Claims

Claims:

1. A method for analyzing optical coherence tomography (OCT) data of an eye, said eye containing at least one pocket of fluid, said method comprising:

collecting two OCT scans at approximately the same transverse locations on the eye, said locations including a pocket of fluid at a depth in the eye;

identifying portions of tissue above and below the pocket of fluid in the two OCT scans;

calculating the phase of the OCT data in the portions of tissue above (cpi^above) and below (cpi^below) the pocket of fluid in each of the scans;

calculating the difference in the phase of the OCT data in the portions of tissue above and below the pocket of fluid for each scan (Δφ; = cpi^above -cpi^below);

calculating the phase change of the phase differences between the two OCT scans (Δφ₂- Δφι); and

storing or displaying the calculated phase change or a further analysis thereof.

2. The method as recited in claim 1 , wherein the phase change ( Δφ₂- Δφι) is used to calculate the distortion of the retina in physical units due to the pocket of fluid.

3. The method as recited in claim 1, wherein calculated phase changes are calculated before and after an eye treatment to determine if relative changes in tissue elasticity or fluid viscosity occurred.

4. The method as recited in claim 1, further comprising collecting additional scans at the same transverse locations and repeating the identifying, phase calculating, phase difference calculating, and phase change calculating steps for the additional scans.

5. The method as recited in claim 4, wherein the number of additional scans is between 2 and 10.

6. The method as recited in claim 4, wherein the number of additional scans is greater than 10.

7. The method as recited in claim 4, wherein the phase changes are calculated for adjacent scan pairs.

8. The method as recited in claim 4, wherein the phase changes are calculated for non- adjacent scan pairs.

9. The method as recited in claim 4, wherein the phase changes for the different scan pairs are averaged.

10. The method as recited in claim 1, further comprising analyzing the two OCT scans using an OCT angiography processing algorithm.

11. The method as recited in claim 1 , wherein the tissue motion of interest is in response to tremors of the eye.

12. The method as recited in claim 1, wherein the phase, difference in phase, and phase change are calculated at different lateral locations in the portions of tissue above and below the pocket of fluid.

13. A computer program product comprising a non-transitory computer-readable medium storing a computer-readable program, wherein the computer readable program, when executed on a computing device, causes the computing device to:

receive two OCT scans at approximately the same transverse locations on the eye, said locations including a pocket of fluid at a depth in the eye;

identify portions of tissue above and below the pocket of fluid in the two OCT scans; calculate the phase of the OCT data in the portions of tissue above (cpi^above) and below (cpi^below) the pocket of fluid in each of the scans;

calculate the difference in the phase of the OCT data in the portions of tissue above and below the pocket of fluid for each scan (Δφ; = cpi^above -cpi^below);

calculate the phase change of the phase differences between the two OCT scans (Δφ₂- Δφι); and

store or display the calculated phase change.

14. The computer program product as recited in claim 13, wherein the phase change (Δφ₂- Δφι) is used to calculate the distortion of the retina in physical units due to the pocket of fluid.

15. The computer program product as recited in claim 13, wherein the computer readable program, when executed on the computing device, further causes the computing device to: receive additional scans at the same transverse locations;

repeat the identifying, phase calculating, phase difference calculating, and phase change calculating steps for the additional scans.

16. The computer program product as recited in claim 15, wherein the number of additional scans is between 2 and 10.

17. The computer program product as recited in claim 15, wherein the number of additional scans is greater than 10.

18. The computer program product as recited in claim 15, wherein the phase changes are calculated for adjacent scan pairs.

19. The computer program product as recited in claim 15, wherein the phase changes are calculated for non-adjacent scan pairs.

20. The computer program product as recited in claim 15, wherein the phase changes for the different scan pairs are averaged.