Patent Publication Number: US-2023154004-A1

Title: Retinal vascular stress test for diagnosis of vision-impairing diseases

Description:
PRIORITY CLAIM 
     This application claims the benefit of and priority to U.S. Provisional Application Number 62/971,910, filed on Feb. 7, 2020, which is hereby incorporated by reference in its entirety for all purposes. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to ophthalmological tools and techniques generally and more specifically to tools and techniques to measure and analyze the retina. 
     BACKGROUND 
     In the field of ophthalmology, various techniques can be used to diagnose ocular conditions, such as glaucoma. Glaucoma and other ocular conditions inflict many individuals. Early detection of various conditions can be important to minimizing negative effects of the condition and providing an opportunity for early treatment. In some cases, intraocular pressure can be an important indicator and/or therapeutic intervention for various ocular conditions, such as glaucoma. 
     SUMMARY 
     The term embodiment and like terms are intended to refer broadly to all of the subject matter of this disclosure and the claims below. Statements containing these terms should be understood not to limit the subject matter described herein or to limit the meaning or scope of the claims below. Embodiments of the present disclosure covered herein are defined by the claims below, not this summary. This summary is a high-level overview of various aspects of the disclosure and introduces some of the concepts that are further described in the Detailed Description section below. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this disclosure, any or all drawings and each claim. 
     Embodiments of the present disclosure include a method comprising: capturing imaging data of a retina of an eye, where the imaging data includes first topographical data and second topographical data, where the second topographical data is captured subsequent the first topographical data, and where the imaging data is associated with an imaged volume of the eye; identifying vasculature and nerve fibers using the imaging data, where identifying vasculature and nerve fibers includes comparing the first topographical data to the second topographical data; generating a connectivity model of vasculature of the retina using the identified vasculature and nerve fibers; calculating deformation data of at least a portion of the imaged volume of the eye, where calculating deformation data includes using at least one of the imaging data and additional imaging data associated with the imaged volume of the eye; generating a conformal sector based on trajectory data for the nerve fibers; obtaining visual field data related to function of the nerve fibers; obtaining thickness data for a retina nerve fiber layer comprising the nerve fibers; and colocalizing the connectivity model, the deformation data, the conformal sector, the visual filed data, and the thickness data for the retina nerve fiber layer. 
     In some cases, the method further comprises capturing additional imaging data of the retina of the eye, where an intraocular pressure of the eye when capturing the additional imaging data is different than when capturing the imaging data, and where calculating deformation data includes using the imaging data and the additional imaging data. 
     In some cases, calculating deformation data includes comparing the first topographical data and the second topographical data. 
     In some cases, the method further comprises generating a topographical map of retinal vasculature and nerve fibers using the identified vasculature and nerve fibers and the imaging data, where generating the connectivity model includes using the topographical map of the retinal vasculature and the nerve fibers. 
     In some cases, the method further comprises: generating a deformation map using the deformation data; generating a visual field map using the visual field data; and generating a retina nerve fiber layer thickness map using the thickness data for the retina nerve fiber layer, where colocalizing comprises mapping and registering the connectivity model, the deformation map, the conformal sector, the visual field map, and the retina nerve fiber layer thickness map. 
     In some cases, the method further comprises generating a strain, connectivity, and functional using the colocalized connectivity model, deformation map, conformal sector, visual field map, and retina nerve fiber layer thickness map. 
     In some cases, the method further comprises predicting retinal blood perfusion and/or nerve function using the colocalized connectivity model, deformation map, conformal sector, visual field map, and retina nerve fiber layer thickness map. 
     In some cases, the method further comprises determining a diagnosis, where determining a diagnosis comprises using the colocalized connectivity model, deformation map, conformal sector, visual field map, and retina nerve fiber layer thickness map. 
     Embodiments of the present disclosure include a system, comprising: one or more data processors; and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: capturing imaging data of a retina of an eye, where the imaging data includes first topographical data and second topographical data, where the second topographical data is captured subsequent the first topographical data, and where the imaging data is associated with an imaged volume of the eye; identifying vasculature and nerve fibers using the imaging data, where identifying vasculature and nerve fibers includes comparing the first topographical data to the second topographical data; generating a connectivity model of vasculature of the retina using the identified vasculature and nerve fibers; calculating deformation data of at least a portion of the imaged volume of the eye, where calculating deformation data includes using at least one of the imaging data and additional imaging data associated with the imaged volume of the eye; generating a conformal sector based on trajectory data for the nerve fibers; obtaining visual field data related to function of the nerve fibers; obtaining thickness data for a retina nerve fiber layer comprising the nerve fibers; and colocalizing the connectivity model, the deformation data, the conformal sector, the visual filed data, and the thickness data for the retina nerve fiber layer. 
     In some cases, the operations further comprise capturing additional imaging data of the retina of the eye, where an intraocular pressure of the eye when capturing the additional imaging data is different than when capturing the imaging data, and where calculating deformation data includes using the imaging data and the additional imaging data. 
     In some cases, calculating deformation data includes comparing the first topographical data and the second topographical data. 
     In some cases, the operations further comprise generating a topographical map of retinal vasculature and nerve fibers using the identified vasculature and nerve fibers and the imaging data, where generating the connectivity model includes using the topographical map of the retinal vasculature and the nerve fibers. 
     In some cases, the operations further comprise: generating a deformation map using the deformation data; generating a visual field map using the visual field data; and generating a retina nerve fiber layer thickness map using the thickness data for the retina nerve fiber layer, where colocalizing comprises mapping and registering the connectivity model, the deformation map, the conformal sector, the visual field map, and the retina nerve fiber layer thickness map. 
     In some cases, the operations further comprise generating a strain, connectivity, and functional using the colocalized connectivity model, deformation map, conformal sector, visual field map, and retina nerve fiber layer thickness map. 
     In some cases, the operations further comprise predicting retinal blood perfusion and/or nerve function using the colocalized connectivity model, deformation map, conformal sector, visual field map, and retina nerve fiber layer thickness map. 
     In some cases, the operations further comprise determining a diagnosis, where determining a diagnosis comprises using the colocalized connectivity model, deformation map, conformal sector, visual field map, and retina nerve fiber layer thickness map. 
     Embodiments of the present disclosure include a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform operations including: capturing imaging data of a retina of an eye, where the imaging data includes first topographical data and second topographical data, where the second topographical data is captured subsequent the first topographical data, and where the imaging data is associated with an imaged volume of the eye; identifying vasculature and nerve fibers using the imaging data, where identifying vasculature and nerve fibers includes comparing the first topographical data to the second topographical data; generating a connectivity model of vasculature of the retina using the identified vasculature and nerve fibers; calculating deformation data of at least a portion of the imaged volume of the eye, where calculating deformation data includes using at least one of the imaging data and additional imaging data associated with the imaged volume of the eye; generating a conformal sector based on trajectory data for the nerve fibers; obtaining visual field data related to function of the nerve fibers; obtaining thickness data for a retina nerve fiber layer comprising the nerve fibers; and colocalizing the connectivity model, the deformation data, the conformal sector, the visual filed data, and the thickness data for the retina nerve fiber layer. 
     In some cases, the operations further comprise: generating a deformation map using the deformation data; generating a visual field map using the visual field data; and generating a retina nerve fiber layer thickness map using the thickness data for the retina nerve fiber layer, where colocalizing comprises mapping and registering the connectivity model, the deformation map, the conformal sector, the visual field map, and the retina nerve fiber layer thickness map. 
     In some cases, the operations further comprise generating a strain, connectivity, and functional using the colocalized connectivity model, deformation map, conformal sector, visual field map, and retina nerve fiber layer thickness map. 
     In some cases, the operations further comprise predicting retinal blood perfusion and/or nerve function using the colocalized connectivity model, deformation map, conformal sector, visual field map, and retina nerve fiber layer thickness map. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The specification makes reference to the following appended figures, in which use of like reference numerals in different figures is intended to illustrate like or analogous components. 
         FIG.  1    is a schematic diagram depicting a retinal measurement and analysis system according to certain aspects of the present disclosure. 
         FIG.  2    is a schematic diagram of an eye as measured and modeled according to certain aspects of the present disclosure. 
         FIG.  3    is a set of schematic diagrams depicting a topographical map of the retina of an eye and a connectivity model generated from the topographical map according to certain aspects of the present disclosure. 
         FIG.  4    is a set of schematic diagrams depicting a topographical map of the retina of an eye and a deformation map generated from the topographical map according to certain aspects of the present disclosure. 
         FIG.  5    is a flowchart depicting a process for generating a connectivity model and deformation map for an eye, according to certain aspects of the present disclosure. 
         FIG.  6    is a flowchart depicting a process for using various models and maps according to certain aspects of the present disclosure. 
         FIG.  7    is a set of two-dimensional images representing data obtained or generated according to certain aspects of the present disclosure. 
         FIG.  8    is a set of two-dimensional images representing data obtained or generated according to certain aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Retinal diseases such as glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR) are leading causes of permanent blindness. There are a number of parameters and/or biomarkers that may be used for detection of early onset of retina disease and to monitor their progression. For example, elevated intraocular pressure (IOP) can be indicative of glaucoma and other ocular conditions, retinal blood perfusion is typically impaired in glaucoma, AMD, and RD, pathological neovascularization is a biomarker for AMD, dropout in capillary perfusion is commonly observed in glaucoma, and increased blood flow resistance is commonly observed in DR. Lowering IOP is a proven therapeutic intervention for these retinal diseases. IOP is also related to blood perfusion of the retina. The amount of blood perfusion in the retina is primarily dependent upon the difference between systemic and intraocular pressures. Increases in intraocular pressure without a change to systemic pressure correlates to a decrease in blood perfusion, as well as an alteration of the ocular tissue morphology. However, many of these parameters and/or biomarkers are considered or observed clinically in isolation without consideration of the relationship between each of these parameters and/or biomarkers and the overall impact on the structure and function of the eye. 
     In order to overcome these problem and limitations, various aspects and features of the present disclosure relate to tools and techniques for visualizing and quantizing the relationship between morphological changes to an eye due to IOP changes and nerve fiber function and blood perfusion changes in the retina. For example, IOP changes can cause mechanical deformation of the eye, which results in morphological changes to the eye including stretching the retina around the optic nerve head (ONH) and stretching blood vessels within the retina. The stretching of the retina around the ONH can lead to nerve fiber functional impairment around the ONH, and can ultimately lead to nerve death and loss of visual information. The stretching of blood vessels within the retina can make the vessels weepy or less elastic, which impacts blood perfusion and the ability of the vessels to deliver adequate blood supply to the retina, and can ultimately lead to nerve death and loss of visual information. 
     The visualizing and quantizing techniques may combine structural data (morphology, density, and trajectory of the retina nerve fiber layer (RNFL), functional data (visual field data), vasculature data (retinal blood perfusion), and biomechanical data (mechanical strain) to measure the amount of mechanical deformation in the retina (e.g., around the ONH) and use the measured amount of mechanical deformation as a biomarker to enhance detection of early onset of retina disease such as glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR) and monitor their progression. 
     In some embodiments, the visualizing and quantizing techniques include manipulating the IOP value of the eye by temporarily applying a mechanical pressure to the eyeball (“stress test”), and obtaining (i) optical coherence tomography-angiography (OCT-A) volumetric scans (or a single wild-field scan) of the macula and the ONH at different intraocular pressure values (at least two intraocular pressure values), (ii) retinal blood perfusion data from the OCT-A volumetric scans (or a single wild-field scan) of the macula and the ONH at different IOP values (at least two intraocular pressure values), and (iii) volumetric displacement and strain field data by means of a template-matching approach based on digital volume correlation changes in the retina morphology caused by the changes in the IOP. The visualizing and quantizing techniques may further include obtaining: (i) visual field data from visual field analyzers such as a Humphrey or Zeiss field analyzer, (ii) morphology and density data of the RNFL from the OCT-A volumetric scans (or a single wild-field scan) of various retinal layers at different IOP values (at least two IOP values), and (iii) estimated trajectory data of the retinal fibers within the RNFL that carry visual information from the retinal ganglion cells to the brain. 
     The visualizing and quantizing techniques further include mapping and registering, by a communal reference system, all the acquired scans and data together into a two-dimensional model of the morphology and vasculature of the eye based on changes observed due to the IOP “stress test”. As used herein, when an action is “based on” something, this means the action is based at least in part on at least a part of the something. The mapping and registering may include colocalizing structural morphology data from the OCT-A volumetric scans (or a single wild-field scan) of the macula and the ONH, retinal blood perfusion data from the OCT-A volumetric scans (or a single wild-field scan) of the macula and the ONH, mechanical deformation or strain data (volumetric displacement and strain field data), the visual field data from visual field analyzers, the morphology and density data of the RNFL from the OCT-A volumetric scans (or a single wild-field scan) of various retinal layers, and RNFL trajectory described mathematically as a fibration of a two-dimensional domain (e.g., a partition of a certain region into smooth curves). 
     The two-dimensional model of the structural data, functional data, vasculature data, and biomechanical data may be used to measure the amount of mechanical deformation in the retina (e.g., around the ONH) of the eye and predict changes in nerve fiber strain and blood perfusion in various areas of the retina due to IOP-induced mechanical deformations. The amount of mechanical deformation and/or predicted response in retina nerve fiber strain and blood perfusion due to IOP-induced mechanical deformations can be used as a biomarker and to monitor and predict progression of retina degenerative diseases. 
     In optical coherence tomography, coherent light is used to capture three-dimensional imaging data through interferometry (e.g., analysis of interference between split coherent light reflected from a sample and a reference mirror). Optical coherence tomography can be used to generate three-dimensional imaging data of an imaged volume. Generally, optical coherence tomography can be used to capture B-scans of a retina, or cross-sectional slice images of a retina. By combining multiple, displaced B-scans, a three-dimensional topographical map of the retina can be generated. The topographical map generated using OCT is limited to displaying information about the shape and structure of the imaged tissue. 
     Optical coherence tomography angiography (OCT-A) is a form of OCT that expands on the capabilities of OCT. OCT-A involves taking multiple, optionally sequential, measurements of individual volumes (e.g., voxels) of the imaged volume to not only create a topographical map as done in traditional OCT, but to also attempt to identify whether that voxel can be characterized as vasculature or non-vasculature structure. By comparing the measurements taken (e.g., intensity) of a single voxel at different times, a determination can be made. If the measurements of the voxel change over time (e.g., change the same or more than a threshold amount), an inference can be made that the voxel represents vasculature, since the changes are likely due to blood movement. If the measurements of the voxel show little or no change over time (e.g., change less than a threshold amount), an inference can be made that the voxel represents non-vasculature structure (e.g., collagen), since measurements of such structures are not likely to change over time. OCT-A data can represent a collection of binary voxels, with each voxel either having a value of 0 or 1, representing time-invariant voxels (e.g., not vasculature) or time-varying voxels (e.g., vasculature), respectively. This data indicating whether or not each voxel of a volume is vasculature can be referred to as an angiography map and is used to generate en-face images or OCT angiograms. The OCT angiograms can then be scrolled outward from the internal limiting membrane (ILM) to the choroid to visualize the individual vascular plexus and segment the inner retina, outer retina, choriocapillaris, or other area of interest. 
     As described in further detail herein, certain aspects of the present disclosure make use of OCT angiograms (OCTA scans) to obtain both structural and functional (i.e. blood flow) information of the retina. For example, as disclosed herein, OCTA scans of the macula and ONH may be acquired at different IOP values and the OCTA scans may be stitched together with scans and data acquired from other sources including visual field data obtained from a visual field analyzer. 
     Certain aspects of the present disclosure expand upon the capabilities of OCT-A. The three-dimensional topographical maps generated by OCT-A are inherently noisy and only provide a topographical view that identifies which regions are vasculature and which are not. Certain aspects of the present disclosure involve computing a connectivity model from the three-dimensional topographical map generated by OCT-A. Computing the connectivity model can include generating a three-dimensional model of the vasculature capable of modeling fluid flow through the vasculature. 
     In some cases, computing a connectivity model can include performing a skeletonization of the three-dimensional topographical maps, such as using the OCT-A data. In some cases, denoising of the three-dimensional topographical map can be performed prior to skeletonization. The skeletonization can include analyzing regions of the three-dimensional topographical map, including groups of adjacent voxels indicative of vasculature, to generate a “skeleton” associated with the three-dimensional shape of the vasculature of the measured area. Any suitable technique can be used. In some cases, a thinning technique can successively erode away the outermost voxels of a group of adjacent voxels, while simultaneously preserving the end points, to approximate connected segments (e.g., lines or curves), which can correlate with the vasculature. In some cases, a distance transform technique can be used to generate a skeleton correlated with the vasculature. In some cases, a Voronoi-skeleton technique can be used to generate a skeleton correlated with the vasculature. 
     In some cases, generating the connectivity model can include converting a skeletonization of the three-dimensional topographical maps into a three-dimensional graph representative of the vessel tree of the vasculature. The skeleton generated through skeletonization can include various nodes (e.g., nodal points) indicative of a branching of the vasculature. A three-dimensional graph can be generated using the nodal points and optionally establishing connections between these points. In some cases, conversion to a three-dimensional graph can permit the efficient exploitation of computational algorithms to characterize morphological parameters of the vessel tree of the vasculature. For example, once the nodes of the graph, which correlate to branching points in the vasculature, are identified, it can be easy to compute distance between two nodal points, which can optionally include passing through intermediate nodal points (e.g., the distance between point A and point C may include a the distances between points A and B as well as B and C). The three-dimensional graph can also be used to easily calculate the shortest path from one nodal point to another, which can be useful for blood-flow predictions. Further, the three-dimensional graph can be used to easily determine the number of connections (e.g., branches) across nodal points, which can be used to estimate possible alternative paths for blood flow. Any of these calculations or determinations can be especially useful when combined with deformation information as described herein. While described as a three-dimensional graph, the information can be stored and processed in any suitable manner, which may or may not include a visualization. 
     In some cases, techniques other than skeletonization can be used to generate a connectivity model. 
     In some cases, data from an OCT-A system can be collected and used to estimate deformation intensity information (e.g., mechanical deformation or strain data) for the retina as the IOP changes over time. Mechanical deformation or strain data, which is not normally used by an OCT-A system, can be obtained by measuring the amount of deformation occurring at a voxel and/or a collection of voxels between two different intraocular pressures. For example, in some cases, deformation data is obtained by measuring the deformation of a collection of voxels in a defined region that are identified as vasculature (e.g., based on an angiography map or a connectivity model). The mechanical deformation or strain data can be stored in the form of a three-dimensional map, or deformation map (e.g., strain map). The mechanical deformation or strain data can be stored as values for a particular change in IOP (e.g., a 10 mmHg change). Deformation data can relate to mechanical strain. 
     In some cases, computing the connectivity model can include using the deformation map. For example, the deformation map can be used to validate whether a voxel is correctly identified as vasculature or non-vasculature structure. Further, the deformation map can be used to help predict and/or validate fluid connections between identified vasculature. 
     In some cases, the same raw data from an OCT machine can be stored and/or used to generate both a connectivity model (e.g., three-dimensional vasculature model) and a deformation map (e.g., three-dimensional map of deformation intensity information). 
     Certain aspects of the present disclosure involve obtaining visual filed data (functional eye data) from a visual field test. For example, during an eye exam, a visual field testing may be performed on one eye at a time, with the opposite eye completely covered to avoid errors. In all testing, the patient must look straight ahead at all times in order accurately map the peripheral visual field. Most modern visual field testing devices also continuously monitor fixation, or the ability of the patient to maintain a consistent straight ahead gaze. 
     In some cases, the visual field test is a administered as a confrontation visual field test, a Amsler grid, a static automated perimetry (e.g., Humphrey Field Analyzer), a kinetic perimetry, or a frequency doubling perimetry. The raw data from the visual field test can be stored and/or used to generate a visual field map (e.g., two-dimensional map of the visual field). 
     Certain aspects of the present disclosure involve computing a RNFL map from the three-dimensional topographical map generated by OCT-A. Computing the RNFL map can include generating a three-dimensional model of the morphology and density of RNFL. 
     In some cases, the RNFL thickness map shows RNFL thickness along a color scale from areas of thinner RNFL in one color to areas of very thick RNFL in another color. Average RNFL thickness indicates a patient’s overall RNFL health. The mean value for RNFL thickness in the general population is 92.9 +/- 9.4 microns. Typically, a normal, nonglaucomatous eye has an RNFL thickness of 80 microns or greater. An eye with an average RNFL thickness of 70 to 79 may be suspicious for glaucoma. An average thickness of 60 to 69 is seen in less than 5% of the normal population and implies glaucoma. 
     Certain aspects of the present disclosure involve estimating the trajectory of retinal fibers of the RNFL using a family of curves that closely models the observed geometry of the nerve fiber layer. In some cases, the data from estimating the trajectory of retinal fibers can be stored and/or used to generate a nerve fiber trajectory map (e.g., two-dimensional map of nerve trajectory information). 
     In some case, custom conformal (angle preserving geometry) sectors are developed spanning from the ONH to the macula based on the size and shape defined by estimated trajectory of retinal fibers of the RNFL and density of the nerve fibers from the RNFL thickness map colocalized with functional information from the visual field map. Statistical analysis of variance for the spatial (within the same eye or across fellow eyes) and/or temporal (longitudinally in follow-up scans) distribution of the developed conformal sectors would detect and monitor abnormal trends in the association of quantified parameters. 
     Once the connectivity model, deformation map, RNFL thickness map, visual field map, and nerve fiber trajectory map are generated, they can be colocalized to identify regions of the connectivity model that undergo various amounts of strain during changes in IOP. Thus, for a particular change in IOP, the colocalized connectivity model, deformation map, RNFL thickness map, visual field map, and nerve fiber trajectory map can be used to estimate how nerve strain or blood perfusion in the retina will change with respect to the change in IOP. For example, an increase in IOP (e.g., of 10 mmHg) may result in various amounts of deformation of various blood vessels and loss of nerve function across the connectivity model, as identifiable by the colocalized deformation map, RNFL thickness map, visual field map, and nerve fiber trajectory map, which can then be used by the connectivity model to predict blood flow, a change in blood flow through the now-deformed vasculature of the connectivity model, nerve strain, or a loss of nerve function now visible through the colocalized RNFL thickness map, visual field map, and nerve fiber trajectory map, and thus predict retinal blood perfusion and/or a change in retinal blood perfusion and nerve strain and/or a change in nerve function. Generally, measurements can be taken, models can be generated, and predictions can be made for regions of the retina at or near the optic nerve head and macula, although that need not be the case. 
     Unlike the topographical maps generated by traditional OCT-A, the colocalization of the connectivity model, deformation map, RNFL thickness map, visual field map, and nerve fiber traj ectory map can be used to predict how changes in IOP will affect nerve function and blood perfusion in the retina. 
     In some cases, measurement techniques other than OCT can be used as appropriate, such as Doppler interferometry or ultrasonic techniques, as long as a connectivity model can be generated and deformation information be obtained (e.g., a deformation map be generated). 
     These illustrative examples are given to introduce the reader to the general subject matter discussed here and are not intended to limit the scope of the disclosed concepts. The following sections describe various additional features and examples with reference to the drawings in which like numerals indicate like elements, and directional descriptions are used to describe the illustrative embodiments but, like the illustrative embodiments, should not be used to limit the present disclosure. The elements included in the illustrations herein may not be drawn to scale. 
       FIG.  1    is a schematic diagram depicting a retinal measurement and analysis system  100  according to certain aspects of the present disclosure. The measurement and analysis system  100  can include a processing system  104  coupled to an optical coherence tomography system  102  (e.g., an optical coherence tomography instrument and optionally additional hardware for operating the optical coherence tomography instrument) and one or more additional analyzers  105  (e.g., a visual field analyzer). In some cases, the processing system  104 , optical coherence tomography system  102 , and one or more additional analyzers  105  can be located in a single housing or located in separate housings, including being located near one another or remotely (e.g., in another city, country, or continent). 
     The optical coherence tomography system  102  can use coherent light  108  to capture measurements from an eye  106 , or more specifically from a retina of an eye  106 . The optical coherence tomography system  102  can provide raw or processed data to the processing system  104 . Processed data can include raw data that has been filtered, denoised, amplified, or otherwise processed, which can also include generation of topographical maps. 
     The one or more additional analyzers  105  can capture measurements (e.g., peripheral view data) from an eye  106 , or more specifically from a retina of an eye  106 . The one or more additional analyzers  105  can provide raw or processed data to the processing system  104 . Processed data can include raw data that has been filtered, denoised, amplified, or otherwise processed, which can also include generation of topographical maps 
     The processing system  104  can use the measurements captured from the optical coherence tomography system  102  to generate a connectivity model and a deformation map for the imaged portion of the eye  106 , as disclosed herein. The processing system  104  can use the measurements captured from the one or more additional analyzers  105  to generate a visual field map. In some cases, the processing system  104  can use the measurements from the optical coherence tomography system  102  and/or the one or more additional analyzers  105  to a generate RNFL thickness map and nerve fiber trajectory map. 
     In some cases, the processing system  104  includes a communal reference system  110  for colocalizing (e.g., mapping and registering) the connectivity model, deformation map, the visual field map, RNFL thickness map, and nerve fiber trajectory map into a strain, connectivity, and functional model. In some cases, the processing system  104  can use the strain, connectivity, and functional model to generate predictions of retinal nerve function and blood perfusion (e.g., ONH blood perfusion) based on a particular change in IOP. 
       FIG.  2    is a schematic diagram of an eye  206  as measured and modeled according to certain aspects of the present disclosure. The eye  206  can include a retina  210  containing a retinal vasculature  212  and nerve fibers  213 . The vasculature  212  can contain numerous blood vessels. The retina  210  can include an optic nerve head  214  where the optic nerve  216  couples to the nerve fibers  213  of the eye  206 . During optical coherence tomography or other imaging techniques, an imaged volume  218  is imaged and processed. The vasculature  212  and nerve fibers  213  within the imaged volume  218  can be captured, measured, and modeled as described herein. In some cases, the imaged volume  218  can include the optic nerve head  214  and/or macula  220 , however that need not be the case. The imaged volume  218  can take on any suitable shape or size, as available to the measuring instrument (e.g., OCT system). 
       FIG.  3    is a set of schematic diagrams depicting a topographical map  320  of the retina of an eye  306  and a connectivity model  326  generated from the topographical map  320  according to certain aspects of the present disclosure. The topographical map  320  can be generated using optical coherence tomography techniques, such as using the optical coherence tomography system  102  of  FIG.  1   . Eye  306  can be similar to eye  206  of  FIG.  2   . 
     The topographical map  320  can be generated for a particular imaged volume  318  by taking measurements of each voxel  320 ,  322  of the imaged volume  318 . As depicted in  FIG.  3   , the voxels  320 ,  322  are shown in exaggerated size for illustrative purposes. The topographical map  320  can be simply topographical in nature, or can also include vasculature information. As described herein, by taking multiple measurements of each voxel  320 ,  322  over time, each voxel  320 ,  322  can be characterized as being either vasculature, nerve fiber, or not. As depicted in  FIG.  3   , voxel  320  is schematically depicted as having constant measurements over time (depicted by constant, diagonal lines), and thus can be characterized as not being vasculature and/or nerve fiber. However, voxel  322  is schematically depicted as having changing measurements over time (depicted by changing diagonal lines), and thus can be characterized as being vasculature and/or nerve fiber. 
     As described in further detail herein, analysis of the topographical map  320  and vasculature and nerve information can be used to generate a connectivity model  326  of the vasculature and nerve fibers within the imaged volume  318  of the eye  306 . In some cases, multiple topographical maps  320  taken at different intraocular pressures can be compared and used to generate and/or improve the connectivity model  326 . 
     While described with reference to a topographical map  320 , the connectivity model  326  can be generated procedurally without generation of an intermediate graphical representation (e.g., visual topographical map) of the imaged volume  318 . 
       FIG.  4    is a set of schematic diagrams depicting a topographical map  420  of the retina of an eye  406  and a deformation map  428  generated from the topographical map  420  according to certain aspects of the present disclosure. The topographical map  420  can be generated using optical coherence tomography techniques, such as using the optical coherence tomography system  102  of  FIG.  1   . Eye  406  can be similar to eye  206  of  FIG.  2   . 
     The topographical map  420  can be generated for a particular imaged volume  418  by taking measurements of each voxel  420 ,  422  of the imaged volume  418 . As depicted in  FIG.  4   , the voxels  420 ,  422  are shown in exaggerated size for illustrative purposes. The topographical map  420  can be simply topographical in nature, or can also include vasculature and nerve information. As described herein, by taking multiple measurements of each voxel  420 ,  422  over time, each voxel  420 ,  422  can be characterized as being either vasculature, nerve fiber, or not. As depicted in  FIG.  4   , voxel  420  is schematically depicted as having constant measurements over time (depicted by constant, diagonal lines), and thus can be characterized as not being vasculature and/or nerve fiber. However, voxel  422  is schematically depicted as having changing measurements over time (depicted by changing diagonal lines), and thus can be characterized as being vasculature and/or nerve fiber. 
     As described in further detail herein, analysis of the topographical map  420  and vasculature and nerve information changes between at least two different intraocular pressures can be used to generate a deformation map  428  of the vasculature and nerve fibers within the imaged volume  418  of the eye  406 . By detecting voxels or regions (e.g., collections of adjacent voxels) that are representative of deforming tissue, changes in those voxels or regions due to changes in intraocular pressure can be used to obtain deformation measurements. Collected deformation measurements for a particular imaged volume  418  can be represented as a deformation map  428 , whether graphically or numerically. The deformation map  428  is schematically depicted in  FIG.  4    to show regions of higher strain with bolder lines and regions of lower strain with thinner lines. While described with reference to a topographical map  420 , the deformation map  428  can be generated procedurally without generation of an intermediate graphical representation (e.g., visual topographical map) of the imaged volume  418 . 
       FIG.  5    is a flowchart depicting a process  500  for generating a connectivity model and deformation map for an eye, according to certain aspects of the present disclosure. At block  502 , imaging data of an eye can be captured. The imaging data can be captured using any suitable technique, such as using an OCT system (e.g., OCT system  102  of  FIG.  1   ). 
     Capturing imaging data at block  502  can include capturing first topographical data at block  504  and capturing second topographical data at block  506 . The first topographical data can include topographical measurement(s) for an imaged volume of the eye. The second topographical data can include topographical measurement(s) for the same imaged volume of the eye, but taken at a different time. In some cases, the second topographical data is taken at a different intraocular pressure than the first topographical data. In some cases, the intraocular pressure at the time each of the first and second topographical data is captured can be stored (e.g., as metadata) along with the imaging data captured at block  502 . In some cases, imaging data can include topographical data and/or any other related metadata. Capturing imaging data of the eye at block  502  can include capturing numerous iterations of topographical data (e.g., beyond just first and second topographical data) at numerous different times, all of which can include the imaged volume of the eye. In some cases, capturing imaging data of the eye at block  502 , including capturing at least first and second topographical data of the imaged volume at blocks  504  and  506 , can be considered a single OCT-A scan. 
     Capturing first topographical data at block  504  and capturing second topographical data at block  506  can each refer to capture of a single voxel of information, in which case blocks  504  and  506  can be repeated as necessary for each voxel. In such a case, the coherent light of an OCT scanner being used to capture the data may capture first and second topographical data at one or more voxels before being scanned to focus on a different region of the eye, at which point first and second topographical data capture can be repeated for that different region. In some cases, however, capturing first topographical data at block  504  and capturing second topographical data at block  506  can each refer to capturing topographical data of an entire imaged volume. In such a case, the coherent light of an OCT scanner being used to capture the data may scan over the eye to capture the entire imaged volume and store the data as the first topographical data before repeating the process at a later time to capture and store the second topographical data. 
     In some cases, capturing first topographical data at block  504  and capturing second topographical data at block  506  can occur in a single session or in a single scan. A single session or a single scan can each include measurements taken within a period of time spanning no more than 1 millisecond, 3 milliseconds, 5 milliseconds, 7 milliseconds, 10 milliseconds, tens of milliseconds, 100 milliseconds, hundreds of milliseconds, 1 second, 3 seconds, 5 seconds, 7 seconds, 10 seconds, tens of seconds, hundreds of seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, or 10 minutes, although in some cases a single session or scan may take longer. For example, first and second topographical data can be captured within 10 milliseconds of one another. In some cases, a single session can include one scan or multiple scans. In some cases, a single session can include measurements taken between the time a patient’s eye is positioned to be measured by the measurement device (e.g., OCT system) and the time the patient’s eye is removed from that position. For example, a patient’s head may be placed on a rest or guide such that the patient’s eye is in position to be measured by an OCT system, in which case the single session may begin when the patient’s head is placed on the rest or guide and may end when the patient’s head is next removed from the rest or guide. In such cases, multiple OCT or OCT-A scans may occur in the single session. In some cases, first and second topographical data may be captured across multiple sessions. As used herein, historical data can refer to data collected and/or generated at a time prior to the current session (e.g., data from a prior session). 
     At block  508 , vasculature and nerve morphology is identified using the imaging data. As described herein, comparison of topographical data (e.g., intensity measurements) between two or more time periods can be used to identify if a particular voxel is or is not vasculature. At optional block  510 , a topographical map of the retinal vasculature and nerve fibers can be generated. Generation of the topographical map of the retinal vasculature and nerve fibers can include generating a three-dimensional topographical map based on the imaging data and can include an indication of regions (e.g., collections of voxels) that have been identified as vasculature and nerve fibers at block  508 . In some cases, it can be preferable to identify vasculature and nerve fibers at block  508  using imaging data captured (e.g., at block 502) at relatively low intraocular pressures, since more of the vasculature and nerve fibers would likely be represented in the topographical data and not potentially obscured by deformations associated with higher pressures. 
     At block  512 , a connectivity model of the retinal vasculature and nerve fibers can be generated. Generating the connectivity model can include using the imaging data, including the voxels or other regions identified as vasculature or nerve fibers at block  508 . In some cases, generating the connectivity model can include using the topographical map of the retinal vasculature or nerve fibers generated at block  510 . The connectivity model can be generated to establish a three-dimensional, connected vasculature and nerve fiber layer. Generating the connectivity model at block  512  can be generating the connectivity model using data from an OCT-A scan, as described herein. 
     In some cases, an existing connectivity model can be improved at block  512  via further iterations of block  502 , block  508 , and optionally block  510 . For example, an existing connectivity model for a particular eye may be improved upon by capturing additional imaging data of the eye in an additional iteration of block  502 , which may include re-using the first or second topographical data as the first topographical data in the additional iteration of block  502 , or may include capturing all new topographical data; identifying vasculature and nerve fibers using that additional imaging data at block  508 ; optionally generating a topographical map of the retinal vasculature and nerve fibers based on that additional imaging data at block 510; and updating the existing connectivity model of the retinal vasculature and nerve fibers at bock  512 . 
     At block  514 , deformation data is calculated using imaging data. Deformation data can include information and/or representations of how the topography of the imaged volume changes with respect to a variable, such as intraocular pressure. Calculating deformation data at block  514  can include receiving imaging data including first and second topographical data (e.g., first and second topographical measurements) and variable data related to the first and second topographical data (e.g., intraocular pressure data for the first topographical measurement(s) and intraocular pressure data for the second topographical measurement(s), or merely an amount of change of intraocular pressure data between the first and second topographical measurements). The imaging data and variable data can be collected during a single OCT-A scan. However, in some cases, calculating deformation data at block  514  can include comparing imaging data collected at block  502  (e.g., first and/or second topographical data) with additional imaging data collected at optional block  518  (e.g., additional topographical data), which can include comparing variable information for the image data and additional image data, such as intraocular pressure and additional intraocular pressure, respectively. As used with respect to blocks  518  and  514 , the term “additional” can refer to prior or subsequent, such as prior imaging data or subsequent imaging data. In some cases, capturing additional imaging data at block  518  can include capturing additional imaging data during an OCT or OCT-A scan performed subsequent to the OCT-A scan performed at block  502 . In some cases, capturing the additional imaging data at block  518  can include capturing the additional imaging data of the imaged volume of the eye at a higher intraocular pressure than the imaging data captured at block  502 . However, in some cases, the intraocular pressure when the additional imaging data is captured at block  518  is lower than when the imaging data is captured at block  502 . Calculating deformation data at block  514  can be performed on a voxel-by-voxel basis, on a regional basis, or for an entire imaged volume. 
     In some cases, deformation data can be calculated at block  514  using additional imaging data captured at block  518  and without using the imaging data captured at block  502 . For example, the connectivity model can be generated using a first OCT-A scan and the deformation data can be calculated using a second OCT or OCT-A scan and optionally additional OCT or OCT-A scans. In such cases, the imaged volume associated with the imaging data captured at block  502  can overlap with the imaged volume associated with the additional imaging data captured at block  518 . The overlapping region can be used for colocalization, as described herein. In some cases, the deformation data can be calculated at block  514  using the vasculature data even when the vasculature data is not provided as a connectivity map or connectivity model. For example, the deformation data can be calculated at block  514  using the raw vasculature data as a binary Mask, without necessarily connecting the raw vasculature voxels. 
     At block  516 , a deformation map can be generated using the deformation data. The deformation map can be a model, dataset, and/or three-dimensional representation of the calculated strain of the topography of the imaged volume. In some cases, calculation of deformation data and/or generation of the deformation map can be constrained to only those regions identified as vasculature at block  508 . While described as a deformation map, in some cases, the deformation map can include any suitable deformation-related information, such as stress or force, strain, or temperature. 
     In some cases, the deformation map can be used to improve the connectivity model of the retinal vasculature and nerve fibers. For example, if the deformation map shows regions of deformations that are characteristic of being or not being vasculature or nerve fibers, and if the connectivity model shows that region as being opposite of what the deformation map is indicating, the connectivity model may be updated accordingly. In some cases, initial generation of the connectivity model at block  512  can include using a generated deformation map as well as any vasculature or nerve information and/or topographical map data. Likewise, in some cases, the deformation map can be improved and/or initially generated using the connectivity model. For example, calculation of deformation data at block  514  and/or generation of the deformation map at block  516  may be constrained to only those regions of the imaged volume that are vasculature and/or nerve fibers in the connectivity model. 
       FIG.  6    is a flowchart depicting a process  600  for using a connectivity model, deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map according to certain aspects of the present disclosure. At block  602 , a connectivity model of retinal vasculature for an eye is accessed. Accessing the connectivity model can include accessing a stored model or generating a model, such as described with reference to  FIG.  5   . At block  604 , a deformation map for the eye is accessed. Accessing the deformation map can include accessing a stored deformation map or generating a deformation map, such as described with reference to  FIG.  5   . At block  606 , a visual field map for the eye is accessed. Accessing the visual field map can include accessing a stored visual field map or generating a visual field map using visual field test data. At block  608 , a RNFL thickness map for the eye is accessed. Accessing the RNFL thickness map can include accessing a stored RNFL thickness map or generating a RNFL thickness map using OCTA nerve layer data. At block  610 , a nerve fiber trajectory map for the eye is accessed. Accessing the nerve fiber trajectory map can include accessing a stored nerve fiber trajectory map or generating a nerve fiber trajectory map using OCTA nerve layer data. 
     At block  612 , the connectivity model, deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map are colocalized. Colocalization can include aligning the connectivity model, deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map. Colocalization can include applying deformation data from the deformation map, visual field data from the visual field map, RNFL thickness data from the RNFL thickness map, a nerve fiber trajectory data from the nerve fiber trajectory map to the connectivity model. As a result of colocalization, changes in certain variables, such as intraocular pressure and nerve function, can be accurately modeled based not only on the connectivity model, but also on the deformation data for the blood vessels modeled in the connectivity model, visual field data, RNFL thickness data, and nerve fiber trajectory data for nerves modeled in the connectivity model. 
     At optional block  608 , a strain, connectivity, and functional model of the retinal vasculature and nerve fibers can be generated using the connectivity model, the deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map (e.g., via colocalization of the connectivity model, deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map at block  606 ). The strain, connectivity, and functional model can be a three-dimensional model that models not only the connectivity between blood vessels and nerve fibers in the imaged volume, but also models the structural characteristics (e.g., nerve fiber trajectory and thickness, strain, stress/strain relationships, deformations, or other characteristics of the deformation map) and functional characteristics (e.g., nerve function from the visual field map) for the modeled blood vessels and nerve fibers. 
     At optional block  610 , retinal blood perfusion and/or nerve function can be predicted. Predicting retinal blood perfusion and/or nerve function can include using the connectivity model, deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map from block  612  or using the generated strain, connectivity, and functional model of block  614 . Predicting retinal blood perfusion and/or nerve function can include predicting the blood perfusion and/or nerve function for a particular region of the imaged volume and/or predicting blood perfusion and/or nerve function for more than the imaged volume, using the imaged volume as an indicator or guide for predicting non-imaged regions. Predicting retinal blood perfusion and/or nerve function can include supplying different conditions to the model(s) (e.g., changes in intraocular pressure, changes in systemic pressure, changes in heart rate, or other changes), to predict how the blood vessels and/or nerve function would react to the different conditions and to estimate a quantification of retinal blood perfusion and/or nerve function given the supplied conditions. 
     At optional block  620 , a diagnosis can be made. Making the diagnosis can include using the connectivity model, deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map from block  612 , using the generated strain, connectivity, and functional model of block  614 , and/or using the predicted retinal blood perfusion and/or nerve function of block  616 . Making the diagnosis can include either predicting future behavior or function of the retinal vasculature and/or nerve fibers (e.g., future blood perfusion, nerve function, or other behavior) using the model(s) or validating current behavior using historical data. For example, with respect to predicting future behavior, current imaging data can be used to generate the connectivity model, deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map, which can be used as described with respect to  FIG.  6    to predict various conditions, such as decreased retinal blood perfusion or impaired nerve function, under expected future circumstances. Based on the predicted condition (e.g., decreased retinal blood perfusion, impaired nerve function, loss of visual information), a diagnosis may be made. As an example with respect to validating current behavior using historical data, historical data (e.g., from a previous doctor visit), can be used to generate the connectivity model, deformation map, visual field map, RNFL thickness map, and nerve fiber trajectory map. During a current visit, certain conditions can be determined (e.g., current systemic pressure, current intraocular pressure, current heart rate, or other conditions) and applied to the model(s) to achieve expected results. The expected results can be compared to current measurements (e.g., current blood perfusion, current imaging data, current visual field data, current nerve fiber trajectory, model(s) generated using current imaging data, and/or deformation maps generated using current imaging data) to determine if the current results are unexpected or indicative of a diagnosable condition. 
       FIG.  7    is a set of two-dimensional images representing data obtained or generated according to certain aspects of the present disclosure. The images depicted in  FIG.  7    are two-dimensional, although the data used to generate the images may include three-dimensional data. Two-dimensional images can be generated from the three-dimensional data by viewing a slice of the three-dimensional data or viewing a surface of the three-dimensional data. The images  702 ,  704 ,  706 ,  708 ,  710  depict different representations of the same region of a retina, specifically a region near the optic nerve head. 
     Image  702  is a topographical map of the vasculature of a retina around an optic nerve head taken at an intraocular pressure of 10 mmHg. The regions of the image in white have been identified as vasculature, whereas regions in black have been identified as non-vasculature structure. Image  706  is a topographical map of the vasculature of a retina around an optic nerve head taken at an intraocular pressure of 20 mmHg. The regions of the image in white have been identified as vasculature, whereas regions in black have been identified as non-vasculature structure. 
     Images  704  and  708  are deformation maps of the retina around the optic nerve head generated based on the data from images  702  and  706 . The deformation maps of images  704  and  708  depict deformation data for a change in intraocular pressure of 10 mmHg. Lighter regions (e.g., including an area extending towards approximately 11 o’clock from near the center of the optic nerve head, as well as a region towards the bottom-left of the image) are representative of higher strain, whereas darker regions are representative of lower strain. 
     Image  710  is a two-dimensional representation of a connectivity model of the vasculature of the retina around the optic nerve head. The connectivity model can be generated using data related to one or more of images  702 ,  704 , and  706 . As described with reference to  FIG.  6   , colocalization of the connectivity model and the deformation map can be visualized by comparing the same regions of images  708  and  710 . For example the region of the connectivity model that is also indicated as having higher strain in image  708  may deform more under high intraocular pressure, and therefore perfusion around that area when intraocular pressure increase may decrease. The connectivity model can facilitate determining how deformations in those vessels may affect other, connected vessels. 
       FIG.  8    is a set of two-dimensional images representing data obtained or generated according to certain aspects of the present disclosure. The images depicted in  FIG.  8    are two-dimensional, although the data used to generate the images may include three-dimensional data. Two-dimensional images can be generated from the three-dimensional data by viewing a slice of the three-dimensional data or viewing a surface of the three-dimensional data. The images  802  and  804  depict different representations of the same region of a retina, specifically a region near the optic nerve head. 
     Image  802  is a colocalization of various maps to illustrate structure and function of the vasculature and nerve fibers of a retina around an ONH. The left plot area labeled (A) is generated from a RNFL thickness map of the ONH and macula mapped and registered in a communal reference system. The left and right plot area labeled (B) is a custom conformal sector connection between ONH and macula data with a trajectory defined by the retinal fibers and generated from a nerve fiber trajectory map of the ONH and macula mapped and registered in a communal reference system. The left plot area labeled (C) is generated from a visual filed map of the retina mapped and registered in a communal reference system to retina morphology obtained from the connectivity model. The right plot area labeled (D) is generated from the connectivity model showing blood perfusion mapped and registered in a communal reference system. 
     Image  804  a colocalization of various maps to illustrate structure and function of the vasculature and nerve fibers of a retina around an ONH. First note the wedge defect (E) in the RNFL thickness map of the ONH is highlighted by a conformal sector (F) mapped and registered in a communal reference system. Secondly note the darker region (G) in the connectivity model showing blood perfusion mapped and registered in a communal reference system. If observed separately, the RNFL thinning in the ONH (darker shaded area) and the lower perfusion (darker shaded area) in the macula look unrelated. The conformal sector (F) shows instead that the two regions share the same retinal nerve fibers and are thus tightly related. Accordingly, it may be predicted that this glaucoma subject will later develop further loss of RNFL thickness and associated nerve function in that area. 
     Individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function. Various processes described herein, such as those described with respect to  FIGS.  5  and  6   , may be stored as instructions on a non-transitory machine-readable storage medium for operation by a computing device, such as processing system  104  of  FIG.  1   . Further, any models or other data, such as those generated with respect to  FIGS.  5  and  6   , may be stored on a non-transitory machine-readable storage medium. 
     The term “machine-readable storage medium” or “computer-readable storage medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A machine-readable medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc. 
     In the foregoing description, for the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described. It should also be appreciated that the methods described above may be performed by hardware components or may be embodied in sequences of machine-executable instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods. These machine-executable instructions may be stored on one or more machine readable mediums, such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions. Alternatively, the methods may be performed by a combination of hardware and software. 
     Where components are described as being configured to perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof. 
     Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a machine-readable medium. A processor(s) may perform the necessary tasks. 
     Systems depicted in some of the figures may be provided in various configurations. In some embodiments, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system. 
     The foregoing description of the embodiments, including illustrated embodiments, has been presented only for the purpose of illustration and description and is not intended to be exhaustive or limiting to the precise forms disclosed. Numerous modifications, adaptations, and uses thereof will be apparent to those skilled in the art.