Flowometry in optical coherence tomography for analyte level estimation

Description

BACKGROUND

The present application relates to methods and devices for performing measurements with optical coherence tomographic techniques, and in some instances particularly for detecting analytes using optical coherence tomography.

Optical coherence tomography (herein “OCT”) is an optical imaging technique that has shown great promise for use in many applications such as glucose monitoring. In this sensor modality, the high depth resolution of this technique is able to measure, with a high degree of accuracy, the scattering properties of a subject's tissue (e.g., the skin). These scattering properties can be sensitive to the changes in analyte levels in a subject's tissue. e.g., blood glucose levels in a subject. Thus, changes in the OCT signal can be correlated with changes in the analyte levels and thus serve as a prospective predictor of changes in that subject's analyte levels.

While OCT-based devices and techniques have shown promise in detecting analytes such as glucose, improvements are still needed. For instance, improvements in the speed and ease with which such techniques can be applied could help commercial acceptability of such devices. Accordingly, a need persists to provide improved OCT devices and techniques, which can provide accelerated estimates of analyte levels in a subject's tissue in an easy to use and reliable manner.

SUMMARY

Optical coherence tomography (herein “OCT”) based analyte monitoring systems are disclosed. In one aspect, techniques are disclosed that can identify fluid flow in vivo (e.g., blood flow), which can act as a metric for gauging the extent of blood perfusion in tissue. For instance, if OCT is to be used to estimate the level of an analyte (e.g., glucose) in tissue, a measure of the extent of blood flow can potentially indicate the presence of an analyte correlating region, which would be suitable for analyte level estimation with OCT. Another aspect is related to systems and methods for scanning multiple regions. An optical beam is moved across the surface of the tissue in two distinct manners. The first can be a coarse scan, moving the beam to provide distinct scanning positions on the skin. The second can be a fine scan where the beam is applied for more detailed analysis.

Some aspects of the present invention are directed to techniques which can detect fluid flow in vivo (e.g., blood flow) using optical coherence tomography (herein “OCT”). Such techniques can utilize the speckle from an OCT scan and/or the fringe-frequency modulation to identify flow. Detection of flow by OCT can be used in a variety of applications such as heart monitoring or imaging of fluid flow in a non-invasive manner. In some embodiments, OCT is applied to detect blood flow, which can act as a metric for gauging the extent of blood perfusion in tissue. For instance, if OCT is to be used to estimate the level of an analyte (e.g., glucose) in tissue, a measure of the extent of blood flow can potentially indicate the presence of an analyte correlating region (herein “ACR”) which would be suitable for analyte level estimation with OCT. While not necessarily being bound by any particular theory, if a particular analyte is typically associated with blood (e.g., related to the presence of arterioles and venules), the identification of blood perfused tissue can act as a marker of where detailed OCT measurements and/or analysis of measurements should be performed to obtain estimates of analyte levels. Accordingly, some embodiments are directed to methods and device for analyte level estimation using OCT, where OCT scanning can identify tissue regions having blood flow that are suitable for analyte estimation.

For example, exemplary embodiments are drawn toward systems and methods of determining blood analyte levels in a tissue sample using optical coherence tomography (OCT). In an exemplary method, a plurality of regions in the tissue sample are scanned using OCT to provide corresponding intensity data measurements. Each tissue region can be raster scanned to provide the intensity data. The tissue regions can optionally be scanned over multiple depths. The intensity measurements can include signals from backscattered light and/or can exhibit speckle. The intensity data measurements, and/or speckle, can be analyzed to determine whether blood flow is present in the corresponding region. Blood analyte levels can be obtained from one or more of the regions having blood flow present as analyzed from the OCT measurements.

Scanning of a tissue sample can be performed in a variety of manners. In some instances, multiple regions are scanned over multiple moments in time to provide the intensity measurements. Comparison of intensity data, which can exhibit speckle, taken over the multiple time moments can be used to determine whether blood flow is present. In some instances, intensity data measurements are compared over multiple tissue regions to determine whether blood flow is present.

Other aspects of the invention are directed to systems for determining at least one blood characteristic in a tissue sample. The system can comprise an OCT apparatus configured to obtain intensity data measurements from a plurality of regions in the tissue sample. Such systems can be consistent with any of the OCT systems disclosed in the present application and/or those within the knowledge of one skilled in the art. The system can further comprise a processor configured to analyze the intensity data measurements from the OCT apparatus to determine whether blood blow is present in at least one region. In some instances, the system can be configured to provide different intensity data measurements over a plurality of regions and a plurality of depths. The system can also be configured to provide raster scans of a plurality of regions. Systems that provide scanning over different regions can utilize a beam scanner, which can be appropriately configured to achieve the desired scanning modality. In some embodiments, the OCT apparatus can be configured to provide different intensity data measurements over a plurality of moments in time.

In related embodiments, the processor can be configured to convert the intensity data measurements into analyte levels (e.g., blood glucose levels). The processor can be configured to analyze speckle in the intensity data measurements and optionally compare those measurements to determine whether blood flow is present. In other embodiments, the processor can analyze fringe-modulated data, such as high frequency components and/or amplitude modulation. The processor can also comprise a signal filter for converting at least a portion of the intensity data measurement into a reduced intensity signal, which can indicate the presence of blood flow through the correlation with fringe frequency modulation. The signal filter can comprise a narrow and/or wide band filter to convert at least a portion of the intensity data measurements into an attenuated output, with the processor optionally configured to compare the reduced intensity signal and the attenuated output to determine the presence of blood flow.

In some exemplary embodiments, a method is utilized to determine the presence of fluid flow in tissue using OCT. These embodiments can optionally be combined with other steps to determine blood analyte levels, as described herein. A tissue sample can be scanned over each of one or more tissue regions, for example at various depths, to obtain corresponding fringe-modulated data, which can be analyzed to determine the presence of blood flow. The fringe-modulated data to be analyzed can include determining a peak fringe frequency shift, or analyzing the amplitude modulation in the data, to determine whether blood flow is present. In the latter case, selected high frequency components of the intensity data, which can be related to speckle, can be analyzed to determine whether blood flow is present. In some instances, the intensity data measurements having fringe-modulated data can be passed through a filter, wherein the filter is configured to produce a filtered signal indicating fringe frequency modulation indicative of blood flow's presence. For example, the intensity data measurements can be passed through a narrow band filter, which can be configured to produce a reduced intensity signal output from an intensity data measurement exhibiting blood flow. Optionally, the intensity data measurements can also be passed through a wide band filter, which can be configured to produce an attenuated output exhibiting at least one fringe-modulated feature from an intensity data measurement exhibiting blood flow. Subsequently, the reduced intensity output and the attenuated output can be compared to determine whether blood flow is present. Digital signal processing, analog signal processing, or a combination of the two techniques can be utilized to analyze the intensity data measurements in any fashion.

Other aspects of the present invention are directed to methods, devices, and/or systems that can scan multiple tissue sites. Tissue sites can each be scanned using an optical coupler, which can be attached to a single location on a subject's skin; each tissue site can also, or alternatively, be associated with a separate potential analyte correlating region (herein “ACR”) or be separated by a distance at least as large as some distance associated with an ACR. An assessment of the quality of each tissue site to provide acceptable analyte levels (i.e., that the site is a ACR) can be made, followed by analyzing OCT data at one or more of the identified ACRS to provide an estimate of an analyte level in the scanned tissue. Other aspects of devices and methods that embody some or all of these features are included in the present application.

For instance, some embodiments are drawn toward OCT-based analyte monitoring systems in which the optical interrogation beam is moved across the surface of the tissue in two distinct ways. The first is a coarse tune of the beam position, moving the beam over a large distance in order to provide positions on the skin that are spatially distinct from each other. The second is a fine scan where the beam is applied over one or more smaller areas that are selected by a controller for more detailed analysis.

Coarse position tuning over a large area of tissue can be useful when the tissue is inhomogeneous so that regions of little or no analytic value can be ignored or discounted, and/or regions of high analytic content can be identified for future analysis. For example, hairs with their associated structures (i.e., sebaceous glands, erector muscles, etc.) can be avoided. These structures are large and, due to the depth at which their blood vessels lie, have little to no associated perfusion in the dermis. Thus, coarse scanning over a large spatial area allows the sensor to avoid such structures. Another example is an arteriole and/or venule plexus, a highly perfused region of tissue, and a desirable scanning location for the sensor. These structures are more sparsely distributed and thus coarse scanning over a large area is desirable to ensure that such a plexus can be located. In both cases, the location of these structures cannot easily be discerned by the naked eye and thus the sensor can also accommodate for sub-optimal sensor placement by the user.

The fine or raster portion of the scan can provide a smaller region of tissue from which data is obtained to correlate to glucose changes. The fine scan takes advantage of the high spatial resolution of the system and allows regions of the tissue with a high density of analyte correlating regions (ACRS) to be monitored, improving the system response to glucose.

Some exemplary embodiments are drawn toward optical coherence tomography (OCT) systems for determining an analyte level in tissue. The systems, which can optionally be a non-imaging system unlike many other OCT systems, can include a light source for generating skin-penetrating radiation suitable for OCT measurements and a detector for receiving reflected light from the skin-penetrating radiation. An interferometer can be coupled to the system to form a combined signal from the reflected light from the skin and a reference beam, the combined signal being received by the detector. A phase shifter can be included and configured to shift the phase of the reference beam, which can provide tissue depth scanning.

A beam scanner can be included for directing a beam of radiation from the source to a plurality of sites to provide a measure of analyte levels (e.g., glucose levels) in the tissue at one or more of the sites. Each site can be associated with a different analyte correlating region. For instance, each site can include a non-overlapping area (e.g., each site being a unique spatial area) relative to all other sites. The sites can form a selected pattern and/or substantially cover a designated larger tissue area. The beam scanner can scan within a tissue site in a variety of manners. For example, the scanner can scan the entire area of at least one of the plurality of sites and/or scan in a manner to provide an aggregated OCT measurement that has reduced speckle content. Scanning within a tissue area can be conducted to provide OCT data sufficient to provide an analyte level estimation and/or sufficient to provide a measure of validity of a tissue site as an ACR.

In some embodiments, a beam scanner, which can be part of an OCT apparatus for detecting analyte levels in scanned tissue, can be configured to adjust movement of a beam on at least two-different length scales. One of the length scales can be a site-scale where the beam moves over a distance large enough such that the beam scanner probes a plurality of tissue sites. Another length scale can be a measurement-scale wherein the beam moves over a distance smaller than the site-scale. OCT measurements can be taken over the measurement-scale such that the data obtained can provide analyte measurement correlation.

Beam scanners can include one or more optical elements (e.g., mirror or rotatable prism) configured to move a beam of radiation. For instance a rotatable prism can be used to move the beam to a variety of tissue sites, while a moveable mirror can provide scanning within a tissue site. Other types of devices, such as a flexure, can also be used to control optical element movement.

Also, a controller can be included for selecting sites from the plurality of sites for monitoring. The controller can be integrated into a single unit with a beam scanner, or be a separate device coupled to the beam scanner. The controller can be configured to associate OCT measurements with each of the plurality of sites, and can subsequently provide an aggregated measure of the analyte level using OCT measurements from a plurality of sites. OCT measurements can correspond with a measure of an analyte level in the scanned tissue. In some instances, the controller can be configured to select one or more sites based on whether a site is validated for analyte level measurement (e.g., whether the site is validated based upon a tissue hydration level). OCT measurements from one or more validated site scan be used to calibrate an OCT system.

Some embodiments are directed toward a structure for providing a coupling between an optic and a subject during OCT measurements. One or more optical elements can be optically couplable to an OCT system to guide reflected light. A patch for aligning the optical element with skin of the subject can be included. The patch can include a rigid body coupled to the optical element. The rigid body can be configured to stabilize optical path lengths between the optical element and the skin. A plurality of pliable extensions can be coupled to the rigid body. Each pliable extension can extend away from the rigid body, and can be configured to hinder movement of the rigid body relative to the skin. In some instances, a patch for providing a coupling between an optic and a subject during OCT measurements can have an optical element and a moisture-removing structure configured to contact skin of the subject. The moisture-removing structure can be configured to transport moisture away from the skin of the subject to hinder moisture build up at an interface between the optical element and the skin. Moisture removing structures can include a moisture-absorbent material, and/or a perforated hydrophilic material.

Other embodiments are directed to methods for performing OCT measurements to determine an analyte level in tissue, such as a blood glucose level. Such methods can be implemented without the need to form and/or process an image. An OCT system can be aligned to a subject through an optical coupler. Then a beam can be scanned over a first tissue site using the attached optical coupler to collect a first set of OCT measurements. Such scanning can combine light reflected from scanning a designated tissue site with a reference beam to produce an interference signal, which can be processed to aid determination of the analyte level in the tissue. The beam can then be scanned over one or more other sites to provide corresponding OCT measurements. Each tissue site can be spatially distinct from the one or more of the other tissue sites. The tissue sites can also form a selected pattern. Scanning within a tissue site can include scanning the beam over the entire spatial area of the tissue site and/or scanning to provide data to obtain an analyte level which can reduce the effect of speckle. A plurality of depths can also be scanned.

Specific sites can be selected from the scanned sites for monitoring analyte levels, with the corresponding OCT measurements being processed to measure the analyte level in the tissue. The selection of sites can include designating whether one or more sites is associated with a validated site. This can be determine, for example, by determining whether corresponding OCT measurements indicate a selected level of tissue hydration. If a two or more sites are validated and corresponded with a measure of an analyte level, the plurality of levels can be aggregated into an aggregated measure of analyte levels in the tissue, which can be used to calibrate an OCT system. Alternatively, data from validated sites can be used to calibrate an OCT system without aggregating the data.

BRIEF DESCRIPTION OF THE DRAWINGS

The present application will be more fully understood from the following detailed description taken in conjunction with the accompanying drawings (not necessarily drawn to scale), in which:

FIG. 1 is a schematic diagram of an OCT system, consistent with an embodiment of the present invention;

FIG. 2 is a schematic diagram of a beam scanner implemented to scan tissue, according to one embodiment of the present invention;

FIG. 3A is a schematic diagram of a number of tissue sites to be scanned oriented around a circular path, according to one embodiment of the present invention;

FIG. 3B is a schematic diagram of a number of tissue sites to be scanned oriented in a gridded pattern to substantially cover a tissue area, according to another embodiment of the present invention;

FIG. 3C is a schematic diagram of the tissue site distribution around a circular pattern overlaid with varying analyte correlating regions, according to some embodiments of the present invention;

FIG. 4 is a cross-sectional perspective view of a beam scanner with a scanning optic, according to one embodiment of the present invention;

FIG. 5 is a cross-sectional perspective view of a beam scanner with a two scanning optics, according to another embodiment of the present invention;

FIG. 6A is a schematic diagram corresponding to locations scanned within a tissue site to substantially scan the tissue site, according to one embodiment of the present invention;

FIG. 6B is a schematic diagram corresponding to locations scanned within a tissue site to scan a plurality of locations in the tissue site, according to another embodiment of the present invention;

FIG. 7A is a side view of a skin contacting device, consistent with an embodiment of the present invention;

FIG. 7B is a top view of the skin contacting device of FIG. 7A;

FIG. 8 is a schematic of a flow diagram for detecting blood analyte levels in tissue, according to an embodiment of the present invention;

FIG. 9 is a flow diagram of a method for processing OCT measurements, according to an embodiment of the present invention;

FIG. 10 is a flow diagram of a method for determining whether a site is valid, consistent with one embodiment of the present invention;

FIG. 11 is a flow diagram showing a variety of techniques for aggregating OCT data measurements, according to one embodiment of the present invention;

FIG. 12 is a schematic flow diagram of a multi-filtering method for detecting fluid flow, according to an embodiment of the present invention;

FIG. 13 provides graphs of a difference signal from OCT scanning at varying tissue depths as a function of time, and a corresponding graph of measurements of blood glucose values as a function of time, according to some embodiments of the present invention;

FIG. 14 provides a graphs showing overlapping traces of a perfusion signal and a glucose signal derived from the data shown in FIG. 13; and

FIG. 15 provides a graph of pulsatile blood flow from OCT blood flow measurements, according to an embodiment of the present invention.

DETAILED DESCRIPTION

Aspects of the present application that are consistent with some embodiments described herein can improve the use of OCT to determine analyte levels. The type of analytes that can be identified can include any analyte capable of being detected with OCT. In many instances, these analytes are water-based analytes and/or analytes that are present in blood perfused tissue. Examples include oxygen, hemoglobin, glucose, water, components of blood, and other analytes present in tissues or other samples. In many embodiments herein, glucose is an analyte of interest. An analyte correlating region (ACR) is a region of a subject's tissue in which OCT can be applied to provide an accurate estimate of an analyte's level in the tissue. Accordingly, using OCT to provide measurements substantially within a ACR can yield data allowing an accurate estimate of the analyte level.

For a selected analyte, regions of the tissue in which an OCT signal is sensitive to changes in the analyte level are not typically uniformly distributed, either as a function of depth or laterally across the tissue. For example, analytes that are present in blood perfused tissue (e.g., glucose) can be non-uniformly distributed due to the non-uniform distribution of blood vessels in the tissue. In particular, the potentially high resolution of OCT, both depth and lateral, enables the location of ACRS, for example glucose correlating regions (herein “GCRs”), to be identified. However, since the resolution of an individual OCT measurement is typically much higher than the scale of many ACRS, it can be difficult to insure that these regions will exist at a given tissue location which is scanned using conventional techniques. Thus, for example, an OCT glucose monitor's performance in the past has been highly dependent on the location of the skin upon which the sensor is placed.

Though OCT can be applied using a brute force technique, analyzing a large tissue region in depth and extent followed by extensive data analysis to isolate portions of the data that do correspond with good ACRS, such a methodology is costly in terms of time and effort expended to produce accurate analyte level readings. As well, the equipment and data processing commensurate with such effort can be a financial burden. Accordingly, if one can utilize an OCT technique to quickly identify tissue regions with substantial blood flow, more extensive OCT measurements can potentially be performed, and data analyzed, therewith to yield faster and more accurate results. In addition, the identification of ACRS can be beneficial by reducing the effort needed to utilize OCT to accurately identify an analyte level. Furthermore, if ACRS can be identified more readily, OCT measurements can be less dependent upon the placement of the sensor vis-à-vis the patient's skin location. Some embodiments of the present invention can potentially address some of these problems. By utilizing embodiments disclosed herein, such as techniques that provide multiple tissue site OCT sensing and/or assessing whether the spots are ACRS, (e.g., by blood flow detection) an OCT technique can focus data gathering or analysis on the ACR regions to collect an analyte level without performing unneeded data collection and/or analysis on regions which do not correlate analyte levels acceptably.

Other potential advantages can also be accrued. For instance, sensor placement by the user can be less critical. Since the ACRS within the tissue are not visible to the unaided eye, it is not possible for the user to place the sensor on a region of tissue that has these structures. Further, there are structures within tissue that do not correlate with analyte changes, such as sebaceous glands associated with hairs in skin when attempting to sense glucose. In this case again, sebaceous glands are not visible to the user, thus a sensor with the ability to monitor multiple tissue sites over a wide spatial extent provides less sensitivity to sensor placement.

In another instance, monitoring multiple tissue sites potentially provides multiple signals that can be used to improve the sensitivity, stability, and signal-to-noise levels of an OCT system relative to the analyte changes in the subject.

Also, diversity of tissue sites can provide stability to the OCT system's analyte correlation by minimizing the effects of physiologic variances in a subject during monitoring. An example of this would be changes in microcirculation on the subject from pharmacologically or temperature induced changes. In this case, tissue sites where the blood perfusion changes dramatically (up or down) can identified and weighted by the sites where the perfusion remained more constant generating a more stable output of the sensor.

Systems and Devices for OCT Scanning

Some embodiments are directed to systems for performing OCT to determine the presence of blood flow in a scanned tissue region. Some additional embodiments are directed to systems for performing OCT to determine an analyte level in tissue. Other embodiments combine such detection with other features to perform analyte level estimation with an OCT system. Such embodiments can utilize an OCT system to provide data intensity measurements that are analyzed by a processor. Exemplary OCT systems can include a number of components such as a light source for generating skin-penetrating radiation suitable for OCT measurements, interferometers, and/or a detector for receiving reflecting light from the skin-penetrating radiation. The system can also include a beam scanner, which can be used to direct a beam of radiation from the source to one or more tissue sites. Beam scanners can include devices that operate in accord with the disclosure of the present application and/or operate in a conventional manner. Data collected from one or more of the sites can be processed by a processor to provide a measure of the presence of blood flow or the measure of analyte levels at the respective sites using any combination of the methodologies disclosed herein and/or known to those skilled in the art. The term “processor” is used generally to include any actual number of devices configured to perform the designated processes. Accordingly, a processor can be embodied as any combination of a programmable unit, a microprocessor, an embedded preprogrammed processor, a stand-alone computing unit, or other devices and configurations as understood by those skilled in the art. The system can also include a controller, which can be used to designate a tissue site associated with blood flow or can be used to select one or more of the multiple sites for further processing (e.g., extracting analyte data therefrom).

As utilized herein, a tissue site can be a selected tissue region (e.g., an area or volume of the tissue) which can be probed using OCT. In many instances, a tissue site has a geometrical size larger than the resolution of the OCT system. Accordingly, in some instances, the tissue site is at least large enough to allow an OCT system to perform scanning within the site, which can potentially lead to data of an analyte level within the site.

Some exemplary embodiments of OCT systems can be described with reference to the schematic diagram of an OCT system shown in FIG. 1. A light source 110 is used to generate skin-penetrating radiation 115 suitable for OCT measurements. A interferometer is employed to perform the measurement. For example, the interferometer can include a beam splitter 140 which can direct a measurement light beam 141 to an optical element 160 and a reference light beam 142 to a phase shifter 130 (e.g., a mirror). The sample beam 141 is directed by the optical element 160 to a tissue sample 120 (e.g., a skin portion of a subject). Light 121 is reflected from the tissue 120 and directed to the interferometer. The reference beam 142 is reflected by the phase shifter 130 back to the interferometer, where the phase shifter can be positioned to change the phase of the reflected light. The interferometer can combine the light from the phase shifter and tissue sample, and direct the light to a detector 150. The constructively interfered light will provide a signal to the detector 150. Such constructively interfered light thus interrogates a depth of the tissue 120 selected by the distance traveled by the sample beam 142 to the phase shifter 130. As used herein, tissue depth is typically the shortest distance from a location in the tissue to a tangent plane on the tissue surface.

In some embodiments where fringe frequency modulation is utilized, the phase shifter 130 is moved at a constant velocity V, which results in a fringe frequency proportional to the ratio of V to the wavelength of the radiation. When the sample beam strikes a moving target, this fringe frequency is modulated. Accordingly, some embodiments herein utilize the modulation of the fringe frequency to detect blood flow.

The general use of OCT systems to provide estimates of analyte levels in tissues is described in a number of references. For example, U.S. Pat. No. 7,254,429 provides descriptions of methods, devices, and systems which can be used to perform OCT measurements in tissue; U.S. Patent Application Publication No. U.S. 2006/0264719 A1 provides techniques for calibrating an OCT system for performing analyte measurements; and U.S. Patent Application No. U.S. 2006/0276696 A1 provides techniques for performing analyte measurements and calibration that include the use of multiple wavelengths of radiation. The teachings of each of these references is included herein in their entirety. Accordingly, OCT systems consistent with embodiments herein can utilize any one or combination of features of OCT systems described in the references herein. For example, in some embodiments, the light source is a low coherence source, and/or the system can be specifically configured for detecting glucose (e.g., blood glucose). OCT systems can be configured as non-imaging systems, which can optionally allow scanning of a tissue site such as to reduce the effect of speckle. In another example, the light source of an OCT system can utilize one or more wavelengths to enable glucose reading detection. Other aspects of OCT systems that can be utilized with embodiments herein can include features understood by those skilled in the art.

Beam Scanners and Controllers

Some embodiments herein include a beam scanner which can be configured to direct beams of radiation to one or more tissue sites for an OCT system. Such beam directing can be achieved in a variety of manners. For instance, as shown in FIG. 1, a beam scanner 170 can be coupled to one or more optical elements 160 to direct the beam to a particular site. A beam scanner, however, can also be coupled to any combination of optics and/or other light directing devices to provide the scanning. For example, the beam splitter, optionally in conjunction with one or more other optical elements, can also be coupled to the beam scanner such that the scanner can direct scanning of light impinging on a sample region. A controller 175 can also be included. The controller, which can be coupled to the beam scanner, can be used to facilitate operation of the beam scanner in a variety of manners (e.g., identify and/or direct which tissue sites should be utilized to provide an analyte measurement). A controller can be an integral portion of a beam scanner (e.g., within the inner workings of a single device) or can be embodied separately (e.g., as a processor of a microcomputer which is in electrical communication with a beam scanner). It should be understood that a beam scanner coupled with a controller can be part of an OCT system, or alternatively constitute an embodiment by itself.

The imaging optics of a beam scanner can be configured to have a variety of characteristics. First the optics can be configured to reduce variations in focal depth during the scanning at widely differing spatial positions. Also, the optical train of a beam scanner can be configured to limit unwanted spurious reflections that can corrupt the interferometer signal such as from optical surfaces at critical positions in the optical path. The numerical aperture of the optics can be selected to reduce the returned scattering signal while minimizing the confocal effect through the depth of interest in the skin. Aberrations due to far off axis scanning through the optic can also be desirably reduced.

Beam scanners can be configured to scan one or more tissue sites where the distinction between sites and the spatial extent of a particular tissue site can also be varied. For instance, each site can be chosen to have a unique region relative to every other site (i.e., none of the sites overlap). The sites can substantially cover a tissue section to be investigated, or can be distributed within a tissue section with space therebetween. The sites can form a selected pattern or can be randomly distributed therein. In some embodiments, the sites can be spaced farther apart than some characteristic length scale of an ACR. Such embodiments can allow an OCT system to quickly assess a tissue sample to locate ACRS where further measurements can be taken and/or analysis of particular measurements is to be performed to reduce the effort of data gathering and analysis. The characteristic length scale of an ACR can depend upon the analyte to be estimated and/or the type of tissue to be scanned. For example, with glucose correlating regions (GCRs), the characteristic length scale can be on a length associated with the distribution of blood vessels in a dermal region.

The spatial extent of a tissue site can also selected in a variety of geometries. For instance, different tissue sites can be distributed over a certain area with all the sites being substantially at the same depth. In other situations, the tissue sites can span a variety of depths and/or spatial extents, encompassing various volumes.

With regard to the size of a tissue site, as mentioned earlier, each tissue site can be a tissue region large enough that an OCT system can probe a variety of locations in the tissue site to determine an analyte level in the region (e.g., the region can be large enough so that analyte correlation in the region is possible). As well, the tissue site can be a region large enough to encompass an ACR, or to perform some other functionality associated with analyte detection (e.g., large enough to allow scanning therein to reduce speckle in an OCT system in which an analyte level is being estimated as described in U.S. Pat. No. 7,254,429). The tissue site can also be limited in size such that scanning in the site can be relatively rapid vis-à-vis scanning the entire area covered by a plurality of sites. The tissue size can also be limited such that such that the effects of structures that do not change with an analyte (such as a hair on glucose sensing) on one tissue site does not influence the signal received in another tissue site. The relative sizes each of the tissue sites can also vary, or be substantially uniform.

As well, the beam scanning can be performed using various data acquisition techniques to provide OCT data that can be used for a variety of purposes. For example, the scanning within a tissue sample can be sufficient to provide data allowing an analyte level measurement within the tissue sample. In another example, the scanning can provide data that allow validation of a site, as described herein, to determine whether a tissue site is an ACR which can provide good analyte level estimation. For instance, validation of a site can depend upon detecting the presence of blood flow. The latter example, can potentially lead to a more rapid data collection methodology where sites can initially be scanned for validation—and the validated sites are rescanned to provide more detailed OCT data for analyte level measurements within each validated site. Other varieties of scanning within a tissue site are also contemplated.

In some embodiments, beam scanners for use with OCT systems can adjust movement of a beam over multiple length scales, e.g., at least two different length scales. For instance, the beam scanner can move a beam over a site-scale, where the beam is moved over a distance large enough that the beam scanner probes a plurality of tissue sites within the distance of a coarse scale (e.g., site scale). Accordingly, the beam can interrogate a multiplicity of tissue sites (e.g., non-overlapping sites, and/or sites separated by a distance larger than a characteristic length of an ACR). The beam scanner can also be configured to move the beam over a measurement scale. Typically, the measurement scale is smaller than the coarse scale. OCT measurements can be taken within the measurement scale to provide an analyte level estimate in the tissue (e.g., the measurement scale has a size sufficient to allow analyte measurement correlation). It is understood that scanning over a coarse scale can incorporate any of the features regarding different tissue sites (e.g., the site-scale scans over tissue sites that do overlap one another), while scanning over the measurement scale can incorporate any of the features described herein regarding scanning within a tissue site (e.g., scanning in a tissue site to reduce speckle from a composite signal). It is understood that scanning can also be performed over additional length scales as well.

Some illustrations of features previously described with respect to beam scanners are described with reference to FIGS. 2 and 3A-3C. FIG. 2 provides an illustration of some features of particular embodiments of the present invention which utilize a beam scanner. The scanner 210 can comprise a body of a device with one or more optics 261, 262. The optics 261, 262 can be configured to direct light from an interferometer 240 to an imaging optic 263, which further directs the light to tissue 250. By configuring the optics 261, 262 appropriately, a beam that impinges upon tissue can be manipulated to provide multispot scanning. For example, one optic 261 can position the beam over multiple tissue sites, while another optic 262 can provide scanning within a tissue site, which can provide data for analyte measurements. Alternatively, a single optic can provide both the coarse and fine scanning functions (e.g., a galvanic reflecting mirror driven for large scale beam displacements in the coarse tuning mode and fine dithering in the analytic mode).

FIGS. 3A-3C provide some exemplary orientations for tissue sites which can be scanned with an OCT device. As shown in FIG. 3A, a beam scanner can be configured to distribute tissue sites to be scanned around a circular path. The beam scanner can thus move the beam around a diameter 13 to distribute tissue sites therearound. As well, the beam scanner can move an OCT beam within a length scale 23 to provide OCT measurements which can be correlated to an ACR potentially. FIG. 3B presents another pattern of tissue sites that can be scanned with an exemplary beam scanner. The tissue section 310 can be gridded into a set of adjacent sites 311 that substantially cover the tissue section 310. Accordingly, a beam scanner can be configured to move the beam such that tissue sites are distributed over a length scale 14, while each tissue site is interrogated on a smaller length scale 24. It is understood that for any tissue site pattern, the number of tissue sites to be distributed, the shape of the pattern on which tissue sites are distributed, and the scanning within the tissue site can all be varied. For example, the tissue site need not be square shaped, and the entire tissue site need not be interrogated to obtain data for OCT scanning.

FIG. 3C schematically depicts a tissue site 325, 326, 327, 328 distribution pattern to be interrogated by a beam scanner overlaid with the analyte correlating regions 330, 331, 332 of a tissue sample 320. As depicted, some tissue sites 325, 326, 328 intersection portions of ACRS 330, 332, 331, while one tissue site 327 does not interrogate an ACR. Accordingly, by identifying the tissue sites that intersect ACRS, a more targeted analysis and gathering of OCT data can be performed to obtain analyte levels in the tissue 320.

FIGS. 4 and 5 depict embodiments of beam scanners that have been reduced to practice. FIG. 4 depicts a beam scanner 400 which utilizes two discrete optical elements. One optical element can locate tissue sites by utilizing a rotating wedge prism 410, which can displace the tissue sites around a circle, as depicted in FIGS. 3A and 3C. The other optical element can provide the scanning within a tissue site (e.g., raster scanning). This element can be embodied as a moveable mirror, for example a mirror coupled to a flexure. The flexure can be attached to electromagnetic coils to affect its positioning of the mirror, which allows scanning within the tissue site.

FIG. 5 depicts another embodiment of a beam scanner, which utilizes a single optical element for beam steering. The optical element is coupled to a flexure, where the flexure is configured to move the optic (e.g., a mirror) with a greater deflection angle, relative to the flexure used in the device shown in FIG. 4. In this particular flexure, all positions of within a selected spatial extent are scannable, akin to what is shown in FIG. 3B. Accordingly, a greater variation in designation of tissue sites, and scanning within tissue sites, can be achieved with this design. It is understood that other beam scanners can utilize any number of optical elements and/or devices to manipulate the optical elements, in a manner consistent with the functionality of the described devices, including using such elements within the knowledge of one skilled in the art.

With regard to scanning within a tissue site, beam scanners can be configured to perform such scanning using various methodologies. The scanning can be sufficient to substantially cover an entirety of a tissue site as shown in FIG. 6A, or can scan a variety of locations in the tissue site that are spaced apart. For instance, the scanning can be such that enough locations are scanned in a manner to allow reduction of speckle when the OCT measurements are combined to estimate an analyte level. In some embodiments, a raster scan is performed within the tissue site. The raster scanning can be performed in any designated pattern, with as many or few locations actually probed as desired; one example is shown in FIG. 6B. For example, the raster scanning can be of an area, a line, or a volume of locations. In many instances, the raster scanning is done at a plurality of depths of tissue.

As discussed earlier, a controller can be incorporated with an OCT system and/or a beam scanner to aid in the scanner's operation. For example, a controller can be used to associate each of a plurality of tissue sites with particular OCT measurements taken therefrom. The OCT measurements can be used to estimate selected analyte levels therefrom, or can be used to “validate” the site for estimating selected analyte levels (e.g., using the data to determine whether the site covers a sufficient amount of an ACR to extract analyte level data therefrom). In some embodiments, the controller can select the tissue sites (e.g., the validated tissue sites) that will be interrogated in further detail, for instance taking further OCT measurements to determine analyte levels from data taken therefrom. Alternatively, if data for determining analyte levels has been extracted from a set of tissue sites previously, the controller can act to determine which tissue site(s) should be examined to determine analyte levels.

Validation of a tissue site for OCT analyte measurement can be performed using any number of techniques including those known to one skilled in the art for analyzing OCT data. Some embodiments utilize OCT measures of tissue hydration as a technique for validating a tissue site. When an analyte is associated with the level of hydration in a tissue sample (e.g., the amount of blood perfused tissue), one measure of whether the site encompasses an ACR is a measure of the hydration level in the tissue. As discussed in U.S. Pat. No. 7,254,429 and U.S. Patent Application Publication No. U.S. 2006/0276696 A1, a section of tissue can be scanned at two wavelengths, one wavelength that has a low absorption coefficient for water/blood components and a high scattering coefficient for such components (e.g., 1310 nm), and one wavelength that has a high absorption coefficient for the water/blood components (e.g., 1450 nm). By comparing the intensity of backscattered light over a tissue sample at each of these components, one can establish a measure of hydration in the tissue sample. For example, the difference in the average intensity level of OCT backscattering of a tissue site between two wavelengths can be calculated and serve as a validation metric. When the average difference exceeds some threshold value, the site can be designated “valid,” while a measure below the threshold value can be designated “not valid.” Since the average intensity value depends upon the spatially distributed backscattering intensity, validation of a tissue site can also depend upon the extent to which an ACR overlaps the interrogated tissue site. Referring back to FIG. 3C, a tissue site 327 which does not intersect an ACR will have a low difference value. Tissue sites 325, 326, 328 that intersect an ACR will provide a difference value that depends not only on the analyte level in the ACR but the relative overlap of the ACR with the tissue site. Accordingly, one tissue site 326 can exhibit a higher difference value that other tissue sites 325, 328 that intersect ACRS even if all the ACRS have the same analyte levels therein. Variations on such techniques are also included within the scope of the present application. For example, validation of a tissue site can be performed by examining OCT data taken at two different wavelengths where the wavelengths are selected to provide a contrasting signal indicative of the presence of a particular analyte, for example oxygenated hemoglobin vis-à-vis deoxygenated hemoglobin as discussed in U.S. Patent Application Publication No. U.S. 2005/0059868 A1. It is also understood that combinations of validation techniques can be implemented to provide tissue site validation.

Some embodiments are directed to beam scanners and/or controllers that are configured to validate and/or select a site based upon an OCT measurement indicating the presence of blood flow. The measure of blood flow can be identified in a variety of ways, as described within the present application. Accordingly, embodiments of the invention are directed to devices (e.g., OCT systems, beam scanner, and/or controllers) that are configured to detect fluid flow (e.g., blood flow) using any combination of the techniques discussed herein. Such blood flow can be associated with an analyte, and therefore the validated tissue site with blood flow can be subjected to further OCT measurements and/or data analysis to extract analyte levels.

As previously mentioned, validated tissue site(s) can be interrogated and/or data analyzed therefrom to associate a corresponding analyte level therewith. In some instances, a controller can aggregate the analyte levels from two or more validated sites to provide an aggregated measure of the analyte level in the tissue. Such aggregation can be an average value, a median value, or other aggregated value as understood by one skilled in the art; some further aspects of how aggregation can occur, and thus be implemented in a controller, are discussed herein. Alternatively, or additionally, the analyte levels corresponding with one or more validated sites can be used to calibrate an OCT system such that future OCT measurements utilize the calibration. Such calibration can utilize any of the techniques described in U.S. Patent Application Publication Nos. U.S. 2006/0264719 A1 and U.S. 2006/0276696 A1.

Optic/Skin Contacting Devices

Some embodiments of the present invention are directed to a structure for coupling an optic to a subject, which is configured for use with an OCT measurement. Some exemplary embodiments include the structure itself, an OCT system with such a structure, or a patch. Embodiments can include one or more features as described herein. Such coupling structures can be configured to reduce mechanical distortion of the skin at a measuring site. The distortion includes two types. First, the local distortion of the skin due to the protrusion of the optic surface into the skin. Reducing this effect can hinder longer term stretching and/or the need to accommodate a continuous change in the baseline signal due to the stretching. Such changes, referred to as settling, are not correlated with analyte changes and represent the viscoelastic response of the skin to the pressure of the optic. At the same time, however, protrusion of the optic can be desirable to insure stable continuous contact of the measuring surface with the skin under all conditions of force on the sensor or assembly. The second category of distortion is due to forces on the sensor such as sensor weight, inertia during movement, or brushing the sensor against an object. Also, a sensor cable can transmit some force to the assembly in the form of a torque or a pull, causing motion that deforms the skin macroscopically over the disposable attachment region. Examples are a twisting of the skin that may rearrange the analyte correlating structures in depth, or a pull that due to the varying modulus of the features in the skin causes a change to the baseline interferometer signal.

Some exemplary embodiments are directed to a structure for coupling an optic to a subject that include one or more optical elements that can be coupled to an OCT system to guide reflect light from skin. One particular embodiment is shown in FIGS. 7A and 7B. An optic 740 can include a protruding portion 745 for contacting the skin of a subject. A patch can be used to align the optical element on the subject's skin. The patch can include a rigid body 720 coupled to the optical element 740 to stabilize the optical path lengths between the element and the skin. The optical element 740 can optionally be removably couplable with the rigid element. Accordingly, in some embodiments, the patch section can be configured as a disposable element, with the optic being reusable. The patch can further include a plurality of pliable extensions 710 coupled to the rigid body 720. Each extension can be configured to extend away from the rigid body, and configured to hinder movement of the rigid body relative to the skin. Extensions can be constructed with any suitable material having a flexible quality; non-limiting examples include polymer-based materials that can optionally have elastic qualities. Adhesive 730 can be applied to couple the structure to the skin. It is understood that the precise configuration of such a structure as shown in FIGS. 7A and 7B does not limit the scope of such structures.

Other embodiments are directed to a optic-skin interface that can help remove moisture from the interface. If moisture from the skin is allowed to build up at the interface, the optical interface to the skin can be distorted and the characteristic shape of the baseline scan can change. Accordingly, one exemplary embodiment is directed to a patch for OCT measurements that includes an optical element and a moisture removing structure 750 as shown in FIG. 7A; it is understood that the moisture removing structure need not be incorporated with all the exact features depicted in FIG. 7A. The moisture removing structure can be configured to transport moisture away from the skin to hinder moisture build up at the interface between the optical element ad the skin. The moisture removing structure can utilize any number of moisture-absorbent materials and/or hydrophilic materials. For example, a hydrophilic foam can be used which allows moisture to be removed by capillary action from the skin and transported to an exterior surface of the foam for evaporation to the external environment. A moisture removing structure can also be embodied as a perforated structure around the optic, which can allow moisture to evaporate directly through the perforations.

It is understood that other features for coupling OCT optics to a subject can also be incorporated within the scope of the present invention. Some of the these features are described in U.S. Patent Application Publication No. U.S. 2007/0219437 A1.

Methods of Estimating Analyte Levels Using OCT

Some embodiments are directed to methods of determining or estimating analyte levels in a tissue using OCT measurements. These methods can be practiced using any number of devices including any appropriate combination of the devices revealed in the present application. However, it is understood that such methods can also be implemented using other devices, including those known to one skilled in the art, albeit specifically configured to practice the embodied method. Accordingly, these methods are not limited by the specific devices, and their delineated operation, revealed elsewhere in the present application. Likewise, the methods described herein, and portions thereof, can be implemented on devices described in the present application. Accordingly, some devices, such as a beam scanner and/or controller, can be configured to carry out portions or the entirety of a method consistent with some embodiments of the present application.

Some embodiments of methods for estimating analyte levels in tissue are consistent with the flow diagram depicted in FIG. 8. One or more tissue sites can be scanned using OCT 810. Scanning can occur over a two-dimensional region (e.g., at a specified depth), or can be scanning over a volumetric region (e.g., scanning a designated area at a multiplicity of depths). A tissue site can be defined as discussed with respect to beam scanners herein. For example, the tissue site can be spatially distinct from any other (e.g., not overlapping), and/or can be spaced apart from one another. Next, the data collected from the scanning can be analyzed to determine whether blood flow is present in the corresponding tissue site 820. The data can correspond with backscattered light from the OCT measurements, which can potentially carry information about the presence of blood flow. Finally, a measure of a blood analyte level can be obtained from one or more of the tissue sites that exhibits blood flow from the analyzed data 830. Measurement can include the steps of obtaining OCT data from the sites where blood flow is present to obtain data sufficient to estimate an analyte level, and/or analyzing pre-obtained OCT data in a manner to extract analyte levels.

Some embodiments of methods for estimating analyte levels in tissue are consistent with the flow diagram depicted in FIG. 9. An OCT system can be aligned using an optical coupler to a subject 910. For instance, a coupling can be attached to a subject through which light is transmitted and reflected to obtain OCT measurements.

A plurality of tissue sites (e.g., at least two) can be scanned using the optical coupler 920. Scanning can occur over a two-dimensional region (e.g., at a specified depth,) or can be scanning over a volumetric region (e.g., scanning a designated area at a multiplicity of depths). The tissue sites can be defined as discussed with respect to beam scanners herein. For example, the tissue sites can be spatially distinct from one another (e.g., not overlapping), or can be spaced apart using some characteristic length scale, as discussed earlier. As discussed earlier, any combination of the OCT measurement techniques discussed by the references disclosed herein, or as utilized by embodiments including a beam scanner as discussed herein, can be utilized to perform the scanning step. In one example, the entirety or portions of a tissue can be scanned as depicted in the selected patterns of FIGS. 3A and 3B, or any other selected pattern. As well, within a tissue site the entirety or a portion of the tissue site can be scanned. In another example, scanning can be performed within a tissue site to provide an OCT measure of an analyte level that is reduced in speckle content and/or is oriented toward a non-imaging technique. Scanning of a tissue site can be performed to render a variety of data types as well. For example, the scanning can provide data that can be converted to an estimate of analyte levels, and/or the scanning can provide data that allows validation of a tissue site as described in the present application.

One or more of the scanned tissue sites can be selected 930 for further processing. Such selection can be performed either after scanning of all tissue sites is completed, or at any time after a particular tissue site has been scanned. In some embodiments, a survey of tissue sites can be initially scanned to determine which will be selected for OCT analyte estimation processing.

Alternatively, or in addition, sites can be investigated in an on-going manner (e.g., after a selected number of interrogations for analyte level estimation on a cyclic basis) to continue to validate that the tissue site is appropriate for analyte level estimation. Such on-going selection of tissue sites can act to monitor the potential perturbations and/or physiologic drift that can occur in tissue to alter the location of an ACR. Use of an on-going site selection monitoring technique can result in an adaptive technique that helps maintain the accuracy of an analyte level estimator. In some embodiments, the selection is performed by validating the tissue site using any of the variations discussed within the present application (e.g., using a measure of tissue hydration level, or using a multiple wavelength measuring technique that highlights the presence or absence of a selected analyte).

OCT measurements at the selected tissue sites can be processed to estimate analyte levels 940. For example, processing can include using data at validated sites to calibrate an OCT sensor. Such calibration can be as previously mentioned in the present application. In another example, when more than one site has been selected (e.g., validated) analyte levels corresponding with each of the validated sites can be aggregated to provide an aggregated measure of the analyte level in a tissue. Aggregation can also include aggregating all the data associated with selected tissue sites at once without determining an individual analyte level estimate for each tissue site. Aggregation can be performed in a variety of manners. Some non-limiting examples include: simple or trimmed mean/median of all signals, weighted averaging based on signal properties, weighted averaging based on input from the site selection algorithm, or some kind of cross-correlation/convolution of the signals.

FIG. 10 provides one example of a method for performing site selection. Scans (e.g., depth scans) are performed for a number of tissue sites, with depths being probed by a raster scan 1010. Next, site metrics are applied 1020 to determine if a site is acceptable. Such site metrics can include site validation determination using OCT scans at different wavelengths of light to characterize tissue. If a site is acceptable 1030, 1040, its OCT measurements can be used to identify an analyte level, such as by using its measurements in an aggregation algorithm with other acceptable site data. If the site is not validated 1030, 1050 it can be considered unacceptable for measuring analyte levels. It is understood that site that are designated unacceptable may be acceptable at later times, for example when implementing an on-going validation site methodology.

Aggregation can take place at several points in the data process as exemplified in the flow diagram of FIG. 11: First, depth scans can be collected 1110 from raster scanning a number of tissue sites. In one instance, the individual depth scans can be validated using a site selection algorithm 1120, 1121, 1122 with the selected sites being aggregate to form a base scan 1130. An analyte converting technique (e.g., glucose conversion technique 1140) can be applied to the aggregated base scan to form an analyte level measure, which can subsequently be used to calibrate an OCT system 1170. In another instance, the individual depth scans can be averaged to form corresponding base scans 1150. The base scans can each be subjected to a site selection algorithm 1121 to determine which are valid. The valid scans can then be further analyzed to report analyte levels for calibration of an OCT system. The analyte levels for corresponding to each valid site can be aggregated 1180, or the OCT data of the valid sites can first be combined into a single scan 1160 before the analyte level is determined for the base scan.

Fluid flow detection using OCT can be achieved using a variety of methodologies. One methodology involves detecting changes in a speckle pattern indicative of the presence of fluid flow. In many instances, each raw OCT scan can exhibit a high degree of speckle in the signal due to the micro-roughness of the skin and the coherence of the optical source. Some aspects of this effect are described in U.S. Pat. No. 7,254,429. This speckle “noise” on the underlying depth intensity signal is a function of the optical coherence length, wavelength, distance from the focal point, size and shape of the scattering centers, and optical properties of the scatterers and the surrounding media. In speckle flowometry, the particles in the flowing fluid (e.g., red blood cells in blood) can cause the speckle pattern in the region of the flow to change in time more rapidly relative to the change in the speckle of the surrounding tissue. Tissue sites with a high degree of speckle temporal variations are sites where blood flow exists. Thus, when an blood analyte (e.g., blood glucose) is sought to be identified, the presence of blood flow can correspond with the presence of an ACR.

Accordingly, some embodiments are directed to techniques that utilize OCT intensity data measurements that exhibit speckle. The speckle in these measurements can be analyzed to determine whether blood flow is present, for example by comparing two or more different intensity data measurements. Comparison of intensity data measurements for speckle differences can occur in a number of ways. In one instance, two or more scans at one location at a particular tissue depth are repeated at different moments in time (e.g., over a time period of less than about a few seconds). Comparison of the corresponding intensity data sets can indicate the presence of fluid flow if the changes in speckle are substantial. If this is also performed at varying depths, providing intensity data sets of a set of locations at different times and depths, comparison of the intensity sets can also indicate where blood flow is more of less prevalent depending upon the relative temporal changes in the speckle pattern as different times (e.g., more change corresponding with more flow). It is understood that more than one location could potentially be scanned, with the trade-off of requiring more time to complete which can complicate a temporal analysis.

In another instance, a set of scans (e.g., raster scans) is performed at a variety of locations (e.g., linear raster scans) at varying depths. The depths that are more similar to one another in speckle pattern can be indicative of less blood perfused tissue, while depths that differ in speckle pattern can be more indicative of the presence of blood perfused tissue. This technique can potentially require less data than the formerly described technique, but with a trade off of signal to noise ratio.

Another methodology for detecting fluid flow using OCT relies on techniques associated with Doppler OCT, or more particularly in examining the frequency modulation in an OCT fringe signal due to the interaction with fluid flow. As mentioned earlier, when the phase shifter in a reference arm of an OCT system moves at a constant velocity, a corresponding fringe frequency is established. In fringe frequency modulation, moving particles in a flowing fluid can cause a shift in the observed frequency of the interferogram in the region of the blood flow. Accordingly, this technique utilizes some principles like what is exploited in ultrasonic imaging of blood flow. Previous work in Doppler flowometry has adopted this technique for OCT, and relies on analyzing the shift in the peak frequency. Examples of such work include Zvyagin, et al., “Real-time detection technique for Doppler optical coherence tomography,” Optics Letters, Vol. 25, No. 22, Nov. 15, 2000, pp. 1645-47; Davé et al., “Doppler-angle measurement in highly scattering media,” Optics Letters, Vol. 25, No. 20, Oct. 15, 2000, pp. 1523-25; Zhao et al., “Doppler standard deviation imaging for clinical monitoring of in vivo human skin blood flow,” Optics Letters, Vol. 25, No. 18, Sep. 15, 2000, pp. 1358-60; Zhao et al., “Real-time phase-resolved functional optical coherence tomography by use of optical Hilbert transformation,” Optics Letters, Vol. 27, No. 2, Jan. 15, 2002, pp. 98-100; Barton et al., “Flow measurement without phase information in optical coherence tomography images,” Optics Express, Vol. 13, No. 14, Jul. 11, 2005, pp. 5234-39. Doppler flowometry can be very sensitive to orientation of the flow relative to the incoming beam from the OCT sensor which provides a challenge in extracting quantitative blood flow measurements.

Accordingly, some embodiments are directed to techniques for detecting fluid flow (e.g., blood flow) by scanning one or more tissue sites to collect fringe modulated data and analyzing the fringe modulated data for the presence of blood flow in the tissue site. Such measurements can be made at varying depths, which can allow the fringe frequency to be determined.

Determination of the presence of fluid flow can be achieved in a variety of manners. A shift in fringe frequency components (e.g., shift in the peak fringe frequency) relative to what is expected from a moving phase shifter can indicate the presence of fluid flow. Using other techniques known to those skilled in the art, further data can be extracted including directionality of the scatterers and a velocity profile. Such detailed information, however, can require substantial data processing. Accordingly, some embodiments utilize a digital signal processing to extract the shift in a peak frequency. For example, when a phase shifter is configured to provide a 600 kHz baseline signal, oversampling by a factor of 5 or more will typically utilize a 3 Msps system with at least 12 bits of data conversion resolution.

Some embodiments are directed to techniques that examine the amplitude modulation of an OCT signal (e.g., vis-à-vis a tissue sample with no fluid flow) to determine if fluid flow is present. Amplitude modulation is distinct from looking at the shift in the peak fringe frequency, and can include such observables as the examining the intensity changes at selected frequencies, the frequency broadening, and/or a shape change in a fringe-frequency envelope. For instance, the backscattered OCT signal can be examined for a change in total intensity of the signal (e.g., over a given bandwidth), and/or a measure of the broadening of the fringe-frequency envelope relative to the initial OCT signal. As an OCT beam interacts with moving particles, not only does the main frequency shift due to the movement but the breadth of the frequency components can also grow. Without necessarily being bound by any particular theory, it is believed that speckle plays a role in frequency broadening. Since speckle has a tendency to create high frequency components in the data, such components are more likely to be shifted by interaction with scatterers. Accordingly, detection of the amplitude modulation of the envelope of frequencies can be an indicator of the presence of fluid flow. For example, digital signal processing can be applied to a backscattered signal to create a measure of the amplitude modulation (e.g., looking at a measure of the shape change in the envelope relative to what is expected for tissue without fluid flow or relative to an initial signal).

In some embodiments, examination of the amplitude modulation can also allow the use of less data intensive techniques for signal processing relative to known techniques that follow the Doppler shift in a peak frequency. If properties such as directionality and relative velocity are not to be determined, simpler signal processing techniques (e.g., analog signal processing) can be utilized, though more advanced data processing is not precluded. For instance, the integrated power of an interferogram can be examined to determine the presence of fluid flow. Thus, detection of the envelop shape of a return signal, relative to an expected shape, can be enough to determine the presence of fluid flow. Thus even though directionality information is lost, an indication of fluid flow is more easily identified with less data processing. It is understood, however, that techniques susceptible to use with analog signal processing can also substitute digital signal processing, consistent with some embodiments of the present invention.

For instance, in some embodiments, a backscattered wide band signal from an OCT system is filtered to determine the presence of fluid flow. The filtered signal of a backscattered wide band signal can be used, in some embodiments, to produce a filtered signal indicating fringe frequency modulation indicative of blood flow's presence. Such a system can utilize either analog or digital signal filtering. One filter applied to the backscattered signal can be a narrow band filter, which can be centered at the base fringe frequency set by the phase shifter. The width of the filter frequencies can be chosen such that the filtered backscattered signal is indicative of the presences of fluid flow. For example, the filtered backscattered signal from tissue with little to no fluid flow has substantially higher intensity than the filtered backscattered signal from tissue with fluid flow present. Accordingly, in some situations, the bandwidth of the narrow band filter can act to reduce the intensity of a backscattered OCT signal by eliminating higher and lower frequency components that shift out of the bandwidth due to the presence of fluid flow.

Another filter can also be applied to the raw backscattered OCT signal, where this filter is a wide band filter, relative to the narrow band filter, which can also be centered at the base fringe frequency set by the phase shifter. The wide band filter can be configured with a bandwidth such that a portion of backscattered OCT signal, when scattering is from tissue having fluid flow present, is still in the filtered signal (e.g., at least one fringe-modulated feature). Accordingly, comparing the intensity data from the wideband filter output and the narrow band filter output can be indicative of the presence of fluid flow. In particular, the use of the wide band filter can act to allow adjustments due to tissue movement or other changes not due to detecting fluid flow, thus decreasing the possibility of false positives.

It is understood that digital or analog filters for amplitude modulation examination can have a number of other configurations that differ from what has just been described. For example, a filter need not be symmetric about a centered frequency, and/or the filter need not be centered at the frequency of the initial fringe-frequency. Indeed, the filter(s) can be chosen in any suitable manner to accentuate identification of amplitude modulation to aid fluid flow determination.

One exemplary embodiment of the use of a multifiltered backscattered OCT signal system for fluid flow detection is shown in FIG. 12. A backscattered signal can be received from reflected light from the tissue of a subject interrogated using an OCT system. The OCT system can utilize a base phase fringe signal having a frequency of around 600 kHz centered. The wide band output of an optical receiver 1210 that accepts the reflected light can be routed through at least two filter channels. The narrowband channel contains a narrow band pass filter 1222, centered on the original fringe rate of the interferometer. The filtered signal is then integrated 1232 and passed through a data acquisition board (DAQ) 1242 to be further processed by a computer 1270 (e.g., for obtaining glucose level estimations). The narrow filter signal can be used to compute structural changes induced by glucose variations. As a side effect, this filter 1222 will reduce the contribution of scattering by blood and blood related components as Doppler induced shift from the blood flow will move the signal outside of the filter band pass.

The wideband channel of FIG. 12 passes the backscattered OCT signal through a wide band pass filter 1221 which is also centered at the original fringe rate of the interferometer. The wide band filtered signal is also integrated 1231 and then digitized by the DAQ 1241. The wideband channel contains higher or lower additional frequency components of the signal. Due to the Doppler shift and spectral broadening generated by the passing blood cells, the wide band signal will increase relative to the narrow band one whenever flowing blood is encountered. Differences between these narrow band and wide band signals, as determined by a comparitor 1250 for example, can then be used as indicators of the depth and magnitude of blood flow. An indication of the blood perfusion in the scanned tissue 1260 can then be provided. While the directional information is lost with this technique, the simplicity of implementation, and the natural conversion speed of analog processing make such an embodiment potentially useful.

As mentioned earlier, the use of amplitude modulation in an OCT signal to detect fluid flow can also utilize analysis of the speckle in the backscattered OCT signal. For instance, the multi filter approach described above can also be applied to the speckle analysis. Since a narrow band filter can be configured to reduce the intensity of the observed speckle due to fringe frequency shifting, the variance of the speckle can also be lower. Thus, the speckle variance of the resulting OCT signals from the two filters can be compared to identify a difference in the speckle variance in space, in time, or both.

OCT identification of blood flow in a tissue can be used in a number of ways. As alluded to earlier, blood flow identification can provide a measure of blood perfusion in tissue, which can identify whether a tissue section exhibits characteristics for analyte level estimation (e.g., identifying that the tissue has an ACR at a particular set of locations such as at particular depths or lateral extents). The blood flow identification can also serve as an indicator of heart rate by choosing an blood flow location and monitoring the signal's sinusoidal behavior, correlating the oscillatory behavior with heart rate. In some embodiments, during an analyte monitoring session, these blood flow identification techniques can also provide a timely indication of significant sensor disturbance (i.e., the loss of a signal indicating the presence of blood flow), which otherwise may render the analyte measurement inaccurate.

Example Embodiments

The following example is provided to illustrate some embodiments of the invention. The example is not intended to limit the scope of any particular embodiment(s) utilized.

An OCT system was implemented to carry out the method diagrammed in FIG. 12 using commercially available equipment.

A test is carried out at a specific tissue site. An initial OCT scan of the tissue at varying depths is performed. The data over each depth is averaged to give an averaged intensity data at that point. Subsequently, OCT scans of the same tissue site are performed at varying moments in time, each of which is designated by a scan number in FIG. 13. The averaged value of the OCT scan versus depth for each time was subtracted from the initial OCT averaged scan value versus depth. The differences are plotted in the pixel plot shown in the top portion of FIG. 13, where a measure of the difference is provided by the shading of the pixel. The lower portion of FIG. 13 provides a corresponding time graph of measured glucose values in the blood stream of the subject whose tissue was scanned. Accordingly, the comparison of the two plots shows that the OCT method can identify regions of tissue that are susceptible to change due to glucose and the presence of blood flow.

FIG. 14 aggregates the pixel data of FIG. 13 by summing all the difference data at a given depth location over all times. That summation is plotted as a function of depth to provide an indicator of the glucose signal, which is the thick line shown in FIG. 14. The data is normalized on the maximum summation in the data set. The thin line corresponds to a perfusion signal measure obtained using the signal processing exemplified in FIG. 12. The backscattered data from a scanned tissue sample at a given depth was sent through a wide band filter and a narrow band filter. An integrator aggregated the data. The differences between the filter outputs at varying depths was plotted as shown by the thin line of FIG. 14. Again, the data is normalized on the maximum difference value. The correspondence between the lines as a function of time shows that a measure of tissue perfusion can act to identify glucose distortion regions, and can provide a glucose independent site metric.

FIG. 15 is a trace showing the pulsatile blood flow indicative of heart rate that is derived from the OCT data. In particular, the perfusion signal of a well-perfused tissue site is aggregated and followed as a function of time. Since the blood flow rate changes through a cycle of a heart beat, the perfusion signal provides a corresponding change, which is shown in FIG. 14. Accordingly, the heart rate of a subject can be followed.

It is understood that a number of variations on the methods described herein are possible, including variations within the knowledge of one skilled in the art. Indeed, numerous other steps can be added, or particular step omitted (e.g., omitting the aligning step; or simply performing the site selection step on existing OCT data). Such variations are within the scope of the present invention.

Additional Embodiments

While the present invention has been described in terms of specific methods, structures, and devices it is understood that variations and modifications will occur to those skilled in the art upon consideration of the present invention. For example, the methods and compositions discussed herein can be utilized beyond the preparation of metallic surfaces for implants in some embodiments. As well, the features illustrated or described in connection with one embodiment can be combined with the features of other embodiments. Such modifications and variations are intended to be included within the scope of the present invention. Those skilled in the art will appreciate, or be able to ascertain using no more than routine experimentation, further features and advantages of the invention based on the above-described embodiments. Accordingly, the invention is not to be limited by what has been explicitly shown and described.

All publications and references are herein expressly incorporated by reference in their entirety. The terms “a” and “an” can be used interchangeably, and are equivalent to the phrase “one or more” as utilized in the present application. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

Claims

1. A method comprising: receiving, from an optical coherence tomography apparatus, an intensity data measurement for a region of tissue;passing the intensity data measurement through a first frequency band filter having a first bandwidth to produce a first output;passing the intensity data measurement through a second frequency band filter having a second bandwidth that is wider than the first bandwidth to produce a second output;determining, by a processor, an indication of fluid flow in the region of tissue by comparing the first output and the second output; andin response to determining a positive indication of fluid flow in the region of tissue, determining, by the processor, a blood analyte measurement based on at least some of the intensity data measurement.
2. The method of claim 1, wherein both the first bandwidth and the second bandwidth are centered at a same base frequency.
3. The method of Claim 1, wherein the blood analyte measurement comprises a measurement of at least one of: blood glucose, blood hemoglobin, or blood oxygen.
4. The method of claim 1, wherein determining the indication of fluid flow in the region of tissue comprises: analyzing fringe frequency data in the first output and the second output.
5. The method of claim 4, wherein analyzing fringe frequency data comprises: analyzing frequency modulation in the fringe frequency data in the first output and the second output.
6. The method of claim 4, wherein analyzing fringe frequency data comprises: analyzing amplitude modulation in the fringe frequency data in the first output and the second output.
7. The method of claim 6, wherein analyzing fringe frequency data further comprises: comparing speckle intensity in the first output and the second output.
8. The method of claim 1, wherein determining the indication of fluid flow in the region of tissue comprises: comparing speckle intensity in the first output and the second output.
9. The method of claim 2, wherein the intensity data measurement comprises a backscattered wide band signal.
10. A method comprising: receiving, from an optical coherence tomography apparatus, an intensity data measurement for a region of tissue;passing the intensity data measurement through a first filter to produce a first output;passing the intensity data measurement through a second filter to produce a second output;analyzing, by a processor, fringe frequency data of the first output and the second output to determine an indication of fluid flow in the region of tissue; andin response to determining a positive indication of fluid flow in the region of tissue, analyzing, by the processor, the intensity data measurement to determine a blood analyte measurement.
11. The method of claim 10 further comprising: analyzing a plurality of intensity data measurements for a same region of tissue to determine a blood analyte measurement.
12. The method of claim 11, wherein the plurality of intensity data measurements are obtained at a plurality of depths in the same region of tissue.
13. The method of claim 11, wherein the plurality of intensity data measurements are obtained at a plurality of times for the same region of tissue.
14. The method of claim 10, wherein the blood analyte measurement comprises a measurement of at least one of: blood glucose, blood hemoglobin, or blood oxygen.
15. The method of claim 10, wherein analyzing fringe frequency data comprises: analyzing frequency modulation in the fringe frequency data in the first output and the second output.
16. The method of claim 10, wherein analyzing fringe frequency data comprises: analyzing amplitude modulation in the fringe frequency data in the first output and the second output.
17. The method of claim 16, wherein analyzing fringe frequency data further comprises: comparing speckle intensity in the first output and the second output.
18. The method of claim 17, wherein determining the indication of fluid flow in the region of tissue comprises: comparing speckle intensity in the first output and the second output.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent application Ser. No. 14/063,977, filed Oct. 25, 2013, titled “Flowometry in Optical Coherence Tomography for Analyte Level Estimation,” which is a continuation of U.S. patent application Ser. No. 12/397,593, filed Mar. 4, 2009, titled “Flowometry in Optical Coherence Tomography for Analyte Level Estimation,” which claims the benefit of U.S. Provisional Application No. 61/068,058, filed Mar. 4, 2008, titled “Flowometry in Optical Coherence Tomography for Analyte Level Estimation,” and U.S. Provisional Application No. 61/033,584, filed Mar. 4, 2008, titled “Multispot Monitoring for Use in Optical Coherence Tomography.” The present application is also related to U.S. patent application Ser. No. 12/397,577, filed Mar. 4, 2009, titled “Multispot Monitoring for Use in Optical Coherence Tomography.” The entirety of each of the above-mentioned applications is hereby incorporated by reference herein for all purposes.

US Referenced Citations (994)

Number	Name	Date	Kind
3826905	Valkama et al.	Jul 1974	A
3958560	March	May 1976	A
4014321	March	Mar 1977	A
4476875	Nilsson et al.	Oct 1984	A
4590948	Nilsson	May 1986	A
4606351	Lubbers	Aug 1986	A
4655225	Dahne et al.	Apr 1987	A
4704029	Van Heuvelen	Nov 1987	A
4731363	Hamilton et al.	Mar 1988	A
4743604	Alig et al.	May 1988	A
4746211	Ruth et al.	May 1988	A
4750830	Lee	Jun 1988	A
4834111	Khanna et al.	May 1989	A
4871755	Alig et al.	Oct 1989	A
4873989	Einzig	Oct 1989	A
4882492	Schlager	Nov 1989	A
4883953	Koashi et al.	Nov 1989	A
4890621	Hakky et al.	Jan 1990	A
4901728	Hutchinson	Feb 1990	A
4948248	Lehman	Aug 1990	A
4960128	Gordon et al.	Oct 1990	A
4964408	Hink et al.	Oct 1990	A
4979509	Hakky	Dec 1990	A
4989978	Groner	Feb 1991	A
5025785	Weiss	Jun 1991	A
5028787	Rosenthal et al.	Jul 1991	A
5041187	Hink et al.	Aug 1991	A
5054487	Clarke	Oct 1991	A
5069213	Polczynski	Dec 1991	A
5070874	Barnes et al.	Dec 1991	A
5101814	Palti	Apr 1992	A
5112124	Harjunmaa et al.	May 1992	A
5115133	Knudson	May 1992	A
5163438	Gordon et al.	Nov 1992	A
5168325	Yoder-Short	Dec 1992	A
5178153	Einzig	Jan 1993	A
5209231	Cote et al.	May 1993	A
5222495	Clarke et al.	Jun 1993	A
5222496	Clarke et al.	Jun 1993	A
5243983	Tarr et al.	Sep 1993	A
5267152	Yang et al.	Nov 1993	A
5277181	Mendelson et al.	Jan 1994	A
5278627	Aoyagi et al.	Jan 1994	A
5313941	Braig et al.	May 1994	A
5319355	Russek	Jun 1994	A
5321501	Swanson et al.	Jun 1994	A
5337744	Branigan	Aug 1994	A
5341805	Stavridi et al.	Aug 1994	A
5348003	Caro	Sep 1994	A
5349953	McCarthy et al.	Sep 1994	A
5361758	Hall et al.	Nov 1994	A
D353195	Savage et al.	Dec 1994	S
D353196	Savage et al.	Dec 1994	S
5370114	Wong et al.	Dec 1994	A
5372136	Steuer et al.	Dec 1994	A
5376336	Lubbers et al.	Dec 1994	A
5377676	Vari et al.	Jan 1995	A
5379238	Stark	Jan 1995	A
5383452	Buchert	Jan 1995	A
5398681	Kupershmidt	Mar 1995	A
D359546	Savage et al.	Jun 1995	S
5431170	Mathews	Jul 1995	A
5433197	Stark	Jul 1995	A
5435309	Thomas et al.	Jul 1995	A
D361840	Savage et al.	Aug 1995	S
D362063	Savage et al.	Sep 1995	S
5448992	Kupershmidt	Sep 1995	A
5452716	Clift	Sep 1995	A
5452717	Branigan et al.	Sep 1995	A
D363120	Savage et al.	Oct 1995	S
5456252	Vari et al.	Oct 1995	A
5457535	Schmidtke et al.	Oct 1995	A
5459570	Swanson et al.	Oct 1995	A
5479934	Imran	Jan 1996	A
5482036	Diab et al.	Jan 1996	A
5490505	Diab et al.	Feb 1996	A
5492118	Gratton et al.	Feb 1996	A
5494043	O'Sullivan et al.	Feb 1996	A
5501226	Petersen et al.	Mar 1996	A
5533511	Kaspari et al.	Jul 1996	A
5534851	Russek	Jul 1996	A
5535743	Backhaus et al.	Jul 1996	A
5549114	Petersen et al.	Aug 1996	A
5551422	Simonsen et al.	Sep 1996	A
5553616	Ham et al.	Sep 1996	A
5561275	Savage et al.	Oct 1996	A
5562002	Lalin	Oct 1996	A
5582168	Samuels et al.	Dec 1996	A
5582171	Chornenky et al.	Dec 1996	A
5590649	Caro et al.	Jan 1997	A
5602924	Durand et al.	Feb 1997	A
5632272	Diab et al.	May 1997	A
5638816	Kiani-Azarbayjany et al.	Jun 1997	A
5638818	Diab et al.	Jun 1997	A
5645440	Tobler et al.	Jul 1997	A
5685299	Diab et al.	Nov 1997	A
5710630	Essenpreis et al.	Jan 1998	A
D393830	Tobler et al.	Apr 1998	S
5743262	Lepper, Jr. et al.	Apr 1998	A
5758644	Diab et al.	Jun 1998	A
5760910	Lepper, Jr. et al.	Jun 1998	A
5769785	Diab et al.	Jun 1998	A
5782757	Diab et al.	Jul 1998	A
5785659	Caro et al.	Jul 1998	A
5791347	Flaherty et al.	Aug 1998	A
5795295	Hellmuth et al.	Aug 1998	A
5810734	Caro et al.	Sep 1998	A
5823950	Diab et al.	Oct 1998	A
5830131	Caro et al.	Nov 1998	A
5833618	Caro et al.	Nov 1998	A
5860919	Kiani-Azarbayjany et al.	Jan 1999	A
5890929	Mills et al.	Apr 1999	A
5904654	Wohltmann et al.	May 1999	A
5919134	Diab	Jul 1999	A
5934925	Tobler et al.	Aug 1999	A
5940182	Lepper, Jr. et al.	Aug 1999	A
5995855	Kiani et al.	Nov 1999	A
5997343	Mills et al.	Dec 1999	A
6002952	Diab et al.	Dec 1999	A
6011986	Diab et al.	Jan 2000	A
6027452	Flaherty et al.	Feb 2000	A
6036642	Diab et al.	Mar 2000	A
6036919	Thym et al.	Mar 2000	A
6045509	Caro et al.	Apr 2000	A
6067462	Diab et al.	May 2000	A
6081735	Diab et al.	Jun 2000	A
6088607	Diab et al.	Jul 2000	A
6110522	Lepper, Jr. et al.	Aug 2000	A
6124597	Shehada	Sep 2000	A
6128521	Marro et al.	Oct 2000	A
6129675	Jay	Oct 2000	A
6144868	Parker	Nov 2000	A
6147108	Hauptman	Nov 2000	A
6151516	Kiani-Azarbayjany et al.	Nov 2000	A
6152754	Gerhardt et al.	Nov 2000	A
6157850	Diab et al.	Dec 2000	A
6165005	Mills et al.	Dec 2000	A
6173197	Boggett et al.	Jan 2001	B1
6184521	Coffin, IV et al.	Feb 2001	B1
6206830	Diab et al.	Mar 2001	B1
6229856	Diab et al.	May 2001	B1
6232609	Snyder et al.	May 2001	B1
6236872	Diab et al.	May 2001	B1
6241683	Macklem et al.	Jun 2001	B1
6253097	Aronow et al.	Jun 2001	B1
6256523	Diab et al.	Jul 2001	B1
6263222	Diab et al.	Jul 2001	B1
6278522	Lepper, Jr. et al.	Aug 2001	B1
6280213	Tobler et al.	Aug 2001	B1
6285896	Tobler et al.	Sep 2001	B1
6294062	Bruck, Jr. et al.	Sep 2001	B1
6301493	Marro et al.	Oct 2001	B1
6308089	von der Ruhr et al.	Oct 2001	B1
6317627	Ennen et al.	Nov 2001	B1
6321100	Parker	Nov 2001	B1
6325761	Jay	Dec 2001	B1
6334065	Al-Ali et al.	Dec 2001	B1
6343224	Parker	Jan 2002	B1
6349228	Kiani et al.	Feb 2002	B1
6360114	Diab et al.	Mar 2002	B1
6368283	Xu et al.	Apr 2002	B1
6371921	Caro et al.	Apr 2002	B1
6377829	Al-Ali	Apr 2002	B1
6388240	Schulz et al.	May 2002	B2
6397091	Diab et al.	May 2002	B2
6425863	Werner et al.	Jul 2002	B1
6430437	Marro	Aug 2002	B1
6430525	Weber et al.	Aug 2002	B1
6443881	Finger	Sep 2002	B1
6463311	Diab	Oct 2002	B1
6470199	Kopotic et al.	Oct 2002	B1
6501975	Diab et al.	Dec 2002	B2
6505059	Kollias et al.	Jan 2003	B1
6515273	Al-Ali	Feb 2003	B2
6519487	Parker	Feb 2003	B1
6525386	Mills et al.	Feb 2003	B1
6526300	Kiani et al.	Feb 2003	B1
6541216	Wilsey et al.	Apr 2003	B1
6541756	Schulz et al.	Apr 2003	B2
6542764	Al-Ali et al.	Apr 2003	B1
6544212	Galley et al.	Apr 2003	B2
6556853	Cabib et al.	Apr 2003	B1
6572566	Effenhauser	Jun 2003	B2
6580086	Schulz et al.	Jun 2003	B1
6584336	Ali et al.	Jun 2003	B1
6595316	Cybulski et al.	Jul 2003	B2
6597932	Tian et al.	Jul 2003	B2
6597933	Kiani et al.	Jul 2003	B2
6606511	Ali et al.	Aug 2003	B1
6632181	Flaherty et al.	Oct 2003	B2
6639668	Trepagnier	Oct 2003	B1
6640116	Diab	Oct 2003	B2
6643530	Diab et al.	Nov 2003	B2
6650917	Diab et al.	Nov 2003	B2
6654624	Diab et al.	Nov 2003	B2
6658276	Kiani et al.	Dec 2003	B2
6661161	Lanzo et al.	Dec 2003	B1
6671531	Al-Ali et al.	Dec 2003	B2
6678543	Diab et al.	Jan 2004	B2
6684090	Ali et al.	Jan 2004	B2
6684091	Parker	Jan 2004	B2
6697656	Al-Ali	Feb 2004	B1
6697657	Shehada et al.	Feb 2004	B1
6697658	Al-Ali	Feb 2004	B2
RE38476	Diab et al.	Mar 2004	E
6699194	Diab et al.	Mar 2004	B1
6707554	Miltner et al.	Mar 2004	B1
6714804	Al-Ali et al.	Mar 2004	B2
RE38492	Diab et al.	Apr 2004	E
6721582	Trepagnier et al.	Apr 2004	B2
6721585	Parker	Apr 2004	B1
6725073	Motamedi et al.	Apr 2004	B1
6725075	Al-Ali	Apr 2004	B2
6728560	Kollias et al.	Apr 2004	B2
6735459	Parker	May 2004	B2
6745060	Diab et al.	Jun 2004	B2
6760607	Al-Ali	Jul 2004	B2
6770028	Ali et al.	Aug 2004	B1
6771994	Kiani et al.	Aug 2004	B2
6780651	Douglas et al.	Aug 2004	B2
6788965	Ruchti et al.	Sep 2004	B2
6792300	Diab et al.	Sep 2004	B1
6813511	Diab et al.	Nov 2004	B2
6816741	Diab	Nov 2004	B2
6822564	Al-Ali	Nov 2004	B2
6826419	Diab et al.	Nov 2004	B2
6830711	Mills et al.	Dec 2004	B2
6836337	Cornsweet	Dec 2004	B2
6837337	Thomas et al.	Jan 2005	B2
6847449	Bashkansky et al.	Jan 2005	B2
6850787	Weber et al.	Feb 2005	B2
6850788	Al-Ali	Feb 2005	B2
6852083	Caro et al.	Feb 2005	B2
6861639	Al-Ali	Mar 2005	B2
6898452	Al-Ali et al.	May 2005	B2
6920345	Al-Ali et al.	Jul 2005	B2
6931268	Kiani-Azarbayjany et al.	Aug 2005	B1
6934570	Kiani et al.	Aug 2005	B2
6939305	Flaherty et al.	Sep 2005	B2
6943348	Coffin, IV	Sep 2005	B1
6950687	Al-Ali	Sep 2005	B2
6961598	Diab	Nov 2005	B2
6970792	Diab	Nov 2005	B1
6979812	Al-Ali	Dec 2005	B2
6985764	Mason et al.	Jan 2006	B2
6990364	Ruchti et al.	Jan 2006	B2
6993371	Kiani et al.	Jan 2006	B2
6996427	Ali et al.	Feb 2006	B2
6999904	Weber et al.	Feb 2006	B2
7003338	Weber et al.	Feb 2006	B2
7003339	Diab et al.	Feb 2006	B2
7015451	Dalke et al.	Mar 2006	B2
7020506	Fine et al.	Mar 2006	B2
7024233	Ali et al.	Apr 2006	B2
7027849	Al-Ali	Apr 2006	B2
7030749	Al-Ali	Apr 2006	B2
7039449	Al-Ali	May 2006	B2
7041060	Flaherty et al.	May 2006	B2
7044918	Diab	May 2006	B2
7048687	Reuss et al.	May 2006	B1
7067893	Mills et al.	Jun 2006	B2
7096052	Mason et al.	Aug 2006	B2
7096054	Abdul-Hafiz et al.	Aug 2006	B2
7132641	Schulz et al.	Nov 2006	B2
7142901	Kiani et al.	Nov 2006	B2
7149561	Diab	Dec 2006	B2
7186966	Al-Ali	Mar 2007	B2
7190261	Al-Ali	Mar 2007	B2
7190464	Alphonse	Mar 2007	B2
7215984	Diab	May 2007	B2
7215986	Diab	May 2007	B2
7221971	Diab	May 2007	B2
7225006	Al-Ali et al.	May 2007	B2
7225007	Al-Ali	May 2007	B2
RE39672	Shehada et al.	Jun 2007	E
7239905	Kiani-Azarbayjany et al.	Jul 2007	B2
7245953	Parker	Jul 2007	B1
7254429	Schurman et al.	Aug 2007	B2
7254431	Al-Ali	Aug 2007	B2
7254433	Diab et al.	Aug 2007	B2
7254434	Schulz et al.	Aug 2007	B2
7272425	Al-Ali	Sep 2007	B2
7274955	Kiani et al.	Sep 2007	B2
D554263	Al-Ali	Oct 2007	S
7280858	Al-Ali et al.	Oct 2007	B2
7280860	Ikeda et al.	Oct 2007	B2
7289835	Mansfield et al.	Oct 2007	B2
7292883	De Felice et al.	Nov 2007	B2
7295866	Al-Ali	Nov 2007	B2
7307734	Dogariu	Dec 2007	B2
7328053	Diab et al.	Feb 2008	B1
7332784	Mills et al.	Feb 2008	B2
7340287	Mason et al.	Mar 2008	B2
7341559	Schulz et al.	Mar 2008	B2
7343186	Lamego et al.	Mar 2008	B2
D566282	Al-Ali et al.	Apr 2008	S
7353055	Hogan	Apr 2008	B2
7355512	Al-Ali	Apr 2008	B1
7356365	Schurman	Apr 2008	B2
7371981	Abdul-Hafiz	May 2008	B2
7373193	Al-Ali et al.	May 2008	B2
7373194	Weber et al.	May 2008	B2
7376453	Diab et al.	May 2008	B1
7377794	Al Ali et al.	May 2008	B2
7377899	Weber et al.	May 2008	B2
7383070	Diab et al.	Jun 2008	B2
7415297	Al-Ali et al.	Aug 2008	B2
7428432	Ali et al.	Sep 2008	B2
7438683	Al-Ali et al.	Oct 2008	B2
7440787	Diab	Oct 2008	B2
7454240	Diab et al.	Nov 2008	B2
7467002	Weber et al.	Dec 2008	B2
7469157	Diab et al.	Dec 2008	B2
7471969	Diab et al.	Dec 2008	B2
7471971	Diab et al.	Dec 2008	B2
7483729	Al-Ali et al.	Jan 2009	B2
7483730	Diab et al.	Jan 2009	B2
7489958	Diab et al.	Feb 2009	B2
7496391	Diab et al.	Feb 2009	B2
7496393	Diab et al.	Feb 2009	B2
D587657	Al-Ali et al.	Mar 2009	S
7499741	Diab et al.	Mar 2009	B2
7499835	Weber et al.	Mar 2009	B2
7500950	Al-Ali et al.	Mar 2009	B2
7509154	Diab et al.	Mar 2009	B2
7509494	Al-Ali	Mar 2009	B2
7510849	Schurman et al.	Mar 2009	B2
7526328	Diab et al.	Apr 2009	B2
7530942	Diab	May 2009	B1
7530949	Al Ali et al.	May 2009	B2
7530955	Diab et al.	May 2009	B2
7557929	Fang-Yen et al.	Jul 2009	B2
7563110	Al-Ali et al.	Jul 2009	B2
7596398	Al-Ali et al.	Sep 2009	B2
7618375	Flaherty	Nov 2009	B2
D606659	Kiani et al.	Dec 2009	S
7647083	Al-Ali et al.	Jan 2010	B2
D609193	Al-Ali et al.	Feb 2010	S
D614305	Al-Ali et al.	Apr 2010	S
RE41317	Parker	May 2010	E
7729733	Al-Ali et al.	Jun 2010	B2
7734320	Al-Ali	Jun 2010	B2
7761127	Al-Ali et al.	Jul 2010	B2
7761128	Al-Ali et al.	Jul 2010	B2
7764982	Dalke et al.	Jul 2010	B2
D621516	Kiani et al.	Aug 2010	S
7791155	Diab	Sep 2010	B2
7801581	Diab	Sep 2010	B2
7822452	Schurman et al.	Oct 2010	B2
RE41912	Parker	Nov 2010	E
7844313	Kiani et al.	Nov 2010	B2
7844314	Al-Ali	Nov 2010	B2
7844315	Al-Ali	Nov 2010	B2
7865222	Weber et al.	Jan 2011	B2
7873497	Weber et al.	Jan 2011	B2
7880606	Al-Ali	Feb 2011	B2
7880626	Al-Ali et al.	Feb 2011	B2
7884945	Srinivasan et al.	Feb 2011	B2
7891355	Al-Ali et al.	Feb 2011	B2
7894868	Al-Ali et al.	Feb 2011	B2
7899507	Al-Ali et al.	Mar 2011	B2
7899518	Trepagnier et al.	Mar 2011	B2
7904132	Weber et al.	Mar 2011	B2
7909772	Popov et al.	Mar 2011	B2
7910875	Al-Ali	Mar 2011	B2
7919713	Al-Ali et al.	Apr 2011	B2
7937128	Al-Ali	May 2011	B2
7937129	Mason et al.	May 2011	B2
7937130	Diab et al.	May 2011	B2
7941199	Kiani	May 2011	B2
7951086	Flaherty et al.	May 2011	B2
7957780	Lamego et al.	Jun 2011	B2
7962188	Kiani et al.	Jun 2011	B2
7962190	Diab et al.	Jun 2011	B1
7976472	Kiani	Jul 2011	B2
7988637	Diab	Aug 2011	B2
7990382	Kiani	Aug 2011	B2
7991446	Ali et al.	Aug 2011	B2
8000761	Al-Ali	Aug 2011	B2
8008088	Bellott et al.	Aug 2011	B2
RE42753	Kiani-Azarbayjany et al.	Sep 2011	E
8019400	Diab et al.	Sep 2011	B2
8028701	Al-Ali et al.	Oct 2011	B2
8029765	Bellott et al.	Oct 2011	B2
8036727	Schurman et al.	Oct 2011	B2
8036728	Diab et al.	Oct 2011	B2
8046040	Ali et al.	Oct 2011	B2
8046041	Diab et al.	Oct 2011	B2
8046042	Diab et al.	Oct 2011	B2
8048040	Kiani	Nov 2011	B2
8050728	Al-Ali et al.	Nov 2011	B2
RE43169	Parker	Feb 2012	E
8118620	Al-Ali et al.	Feb 2012	B2
8126528	Diab et al.	Feb 2012	B2
8128572	Diab et al.	Mar 2012	B2
8130105	Al-Ali et al.	Mar 2012	B2
8145287	Diab et al.	Mar 2012	B2
8150487	Diab et al.	Apr 2012	B2
8175672	Parker	May 2012	B2
8180420	Diab et al.	May 2012	B2
8182443	Kiani	May 2012	B1
8185180	Diab et al.	May 2012	B2
8190223	Al-Ali et al.	May 2012	B2
8190227	Diab et al.	May 2012	B2
8203438	Kiani et al.	Jun 2012	B2
8203704	Merritt et al.	Jun 2012	B2
8204566	Schurman et al.	Jun 2012	B2
8219168	Lowery	Jul 2012	B2
8219172	Schurman et al.	Jul 2012	B2
8224411	Al-Ali et al.	Jul 2012	B2
8228181	Al-Ali	Jul 2012	B2
8229533	Diab et al.	Jul 2012	B2
8233955	Al-Ali et al.	Jul 2012	B2
8244325	Al-Ali et al.	Aug 2012	B2
8255026	Al-Ali	Aug 2012	B1
8255027	Al-Ali et al.	Aug 2012	B2
8255028	Al-Ali et al.	Aug 2012	B2
8260577	Weber et al.	Sep 2012	B2
8265723	McHale et al.	Sep 2012	B1
8274360	Sampath et al.	Sep 2012	B2
8280473	Al-Ali	Oct 2012	B2
8301217	Al-Ali et al.	Oct 2012	B2
8306596	Schurman et al.	Nov 2012	B2
8310336	Muhsin et al.	Nov 2012	B2
8315683	Al-Ali et al.	Nov 2012	B2
RE43860	Parker	Dec 2012	E
8337403	Al-Ali et al.	Dec 2012	B2
8346330	Lamego	Jan 2013	B2
8353842	Al-Ali et al.	Jan 2013	B2
8355766	MacNeish, III et al.	Jan 2013	B2
8359080	Diab et al.	Jan 2013	B2
8364223	Al-Ali et al.	Jan 2013	B2
8364226	Diab et al.	Jan 2013	B2
8374665	Lamego	Feb 2013	B2
8385995	Al-Ali et al.	Feb 2013	B2
8385996	Smith et al.	Feb 2013	B2
8388353	Kiani et al.	Mar 2013	B2
8399822	Al-Ali	Mar 2013	B2
8401602	Kiani	Mar 2013	B2
8405608	Al-Ali et al.	Mar 2013	B2
8414499	Al-Ali et al.	Apr 2013	B2
8418524	Al-Ali	Apr 2013	B2
8423106	Lamego et al.	Apr 2013	B2
8428967	Olsen et al.	Apr 2013	B2
8430817	Al-Ali et al.	Apr 2013	B1
8437825	Dalvi et al.	May 2013	B2
8455290	Siskavich	Jun 2013	B2
8457703	Al-Ali	Jun 2013	B2
8457707	Kiani	Jun 2013	B2
8463349	Diab et al.	Jun 2013	B2
8466286	Bellot et al.	Jun 2013	B2
8471713	Poeze et al.	Jun 2013	B2
8473020	Kiani et al.	Jun 2013	B2
8483787	Al-Ali et al.	Jul 2013	B2
8489364	Weber et al.	Jul 2013	B2
8498684	Weber et al.	Jul 2013	B2
8504128	Blank et al.	Aug 2013	B2
8509867	Workman et al.	Aug 2013	B2
8515509	Bruinsma et al.	Aug 2013	B2
8523781	Al-Ali	Sep 2013	B2
8529301	Al-Ali et al.	Sep 2013	B2
8532727	Ali et al.	Sep 2013	B2
8532728	Diab et al.	Sep 2013	B2
D692145	Al-Ali et al.	Oct 2013	S
8547209	Kiani et al.	Oct 2013	B2
8548548	Al-Ali et al.	Oct 2013	B2
8548549	Schurman et al.	Oct 2013	B2
8548550	Al-Ali	Oct 2013	B2
8560032	Al-Ali et al.	Oct 2013	B2
8560034	Diab et al.	Oct 2013	B1
8570167	Al-Ali	Oct 2013	B2
8570503	Vo et al.	Oct 2013	B2
8571617	Reichgott et al.	Oct 2013	B2
8571618	Lamego et al.	Oct 2013	B1
8571619	Al-Ali et al.	Oct 2013	B2
8584345	Al-Ali et al.	Oct 2013	B2
8577431	Lamego et al.	Nov 2013	B2
8581732	Al-Ali et al.	Nov 2013	B2
8588880	Abdul-Hafiz et al.	Nov 2013	B2
8600467	Al-Ali et al.	Dec 2013	B2
8606342	Diab	Dec 2013	B2
8626255	Al-Ali et al.	Jan 2014	B2
8630691	Lamego et al.	Jan 2014	B2
8634889	Al-Ali et al.	Jan 2014	B2
8641631	Sierra et al.	Feb 2014	B2
8652060	Al-Ali	Feb 2014	B2
8663107	Kiani	Mar 2014	B2
8666468	Al-Ali	Mar 2014	B1
8667967	Al- Ali et al.	Mar 2014	B2
8670811	O'Reilly	Mar 2014	B2
8670814	Diab et al.	Mar 2014	B2
8676286	Weber et al.	Mar 2014	B2
8682407	Al-Ali	Mar 2014	B2
RE44823	Parker	Apr 2014	E
RE44875	Kiani et al.	Apr 2014	E
8690799	Telfort et al.	Apr 2014	B2
8700112	Kiani	Apr 2014	B2
8702627	Telfort et al.	Apr 2014	B2
8706179	Parker	Apr 2014	B2
8712494	MacNeish, III et al.	Apr 2014	B1
8715206	Telfort et al.	May 2014	B2
8718735	Lamego et al.	May 2014	B2
8718737	Diab et al.	May 2014	B2
8718738	Blank et al.	May 2014	B2
8720249	Al-Ali	May 2014	B2
8721541	Al-Ali et al.	May 2014	B2
8721542	Al-Ali et al.	May 2014	B2
8723677	Kiani	May 2014	B1
8740792	Kiani et al.	Jun 2014	B1
8754776	Poeze et al.	Jun 2014	B2
8755535	Telfort et al.	Jun 2014	B2
8755856	Diab et al.	Jun 2014	B2
8755872	Marinow	Jun 2014	B1
8761850	Lamego	Jun 2014	B2
8764671	Kiani	Jul 2014	B2
8768423	Shakespeare et al.	Jul 2014	B2
8771204	Telfort et al.	Jul 2014	B2
8777634	Kiani et al.	Jul 2014	B2
8781543	Diab et al.	Jul 2014	B2
8781544	Al-Ali et al.	Jul 2014	B2
8781549	Al-Ali et al.	Jul 2014	B2
8788003	Schurman et al.	Jul 2014	B2
8790268	Al-Ali	Jul 2014	B2
8801613	Al-Ali et al.	Aug 2014	B2
8821397	Al-Ali et al.	Sep 2014	B2
8821415	Al-Ali et al.	Sep 2014	B2
8830449	Lamego et al.	Sep 2014	B1
8831700	Schurman et al.	Sep 2014	B2
8840549	Al-Ali et al.	Sep 2014	B2
8847740	Kiani et al.	Sep 2014	B2
8849365	Smith et al.	Sep 2014	B2
8852094	Al-Ali et al.	Oct 2014	B2
8852994	Wojtczuk et al.	Oct 2014	B2
8868147	Stippick et al.	Oct 2014	B2
8868150	Al-Ali et al.	Oct 2014	B2
8870792	Al-Ali et al.	Oct 2014	B2
8886271	Kiani et al.	Nov 2014	B2
8888539	Al-Ali et al.	Nov 2014	B2
8888708	Diab et al.	Nov 2014	B2
8892180	Weber et al.	Nov 2014	B2
8897847	Al-Ali	Nov 2014	B2
8909310	Lamego et al.	Dec 2014	B2
8911377	Al-Ali	Dec 2014	B2
8912909	Al-Ali et al.	Dec 2014	B2
8920317	Al-Ali et al.	Dec 2014	B2
8921699	Al-Ali et al.	Dec 2014	B2
8922382	Al-Ali et al.	Dec 2014	B2
8929964	Al-Ali et al.	Jan 2015	B2
8942777	Diab et al.	Jan 2015	B2
8948834	Diab et al.	Feb 2015	B2
8948835	Diab	Feb 2015	B2
8965471	Lamego	Feb 2015	B2
8983564	Al-Ali	Mar 2015	B2
8989831	Al-Ali et al.	Mar 2015	B2
8996085	Kiani et al.	Mar 2015	B2
8998809	Kiani	Apr 2015	B2
9028429	Telfort et al.	May 2015	B2
9037207	Al-Ali et al.	May 2015	B2
9060721	Reichgott et al.	Jun 2015	B2
9066666	Kiani	Jun 2015	B2
9066680	Al-Ali et al.	Jun 2015	B1
9072474	Al-Ali et al.	Jul 2015	B2
9078560	Schurman et al.	Jul 2015	B2
9084569	Weber et al.	Jul 2015	B2
9095316	Welch et al.	Aug 2015	B2
9106038	Telfort et al.	Aug 2015	B2
9107625	Telfort et al.	Aug 2015	B2
9107626	Al-Ali et al.	Aug 2015	B2
9113831	Al-Ali	Aug 2015	B2
9113832	Al-Ali	Aug 2015	B2
9119595	Lamego	Sep 2015	B2
9131881	Diab et al.	Sep 2015	B2
9131882	Al-Ali et al.	Sep 2015	B2
9131883	Al-Ali	Sep 2015	B2
9131917	Telfort et al.	Sep 2015	B2
9138180	Coverston et al.	Sep 2015	B1
9138182	Al-Ali et al.	Sep 2015	B2
9138192	Weber et al.	Sep 2015	B2
9142117	Muhsin et al.	Sep 2015	B2
9153112	Kiani et al.	Oct 2015	B1
9153121	Kiani et al.	Oct 2015	B2
9161696	Al-Ali et al.	Oct 2015	B2
9161713	Al-Ali et al.	Oct 2015	B2
9167995	Lamego et al.	Oct 2015	B2
9176141	Al-Ali et al.	Nov 2015	B2
9186102	Bruinsma et al.	Nov 2015	B2
9192312	Al-Ali	Nov 2015	B2
9192329	Al-Ali	Nov 2015	B2
9192351	Telfort et al.	Nov 2015	B1
9195385	Al-Ali et al.	Nov 2015	B2
9211072	Kiani	Dec 2015	B2
9211095	Al-Ali	Dec 2015	B1
9218454	Kiani et al.	Dec 2015	B2
9226696	Kiani	Jan 2016	B2
9241662	Al-Ali et al.	Jan 2016	B2
9245668	Vo et al.	Jan 2016	B1
9259185	Abdul-Hafiz et al.	Feb 2016	B2
9267572	Barker et al.	Feb 2016	B2
9277880	Poeze et al.	Mar 2016	B2
9289167	Diab et al.	Mar 2016	B2
9295421	Kiani et al.	Mar 2016	B2
9307928	Al-Ali et al.	Apr 2016	B1
9323894	Kiani	Apr 2016	B2
D755392	Hwang et al.	May 2016	S
9326712	Kiani	May 2016	B1
9333316	Kiani	May 2016	B2
9339220	Lamego et al.	May 2016	B2
9341565	Lamego et al.	May 2016	B2
9351673	Diab et al.	May 2016	B2
9351675	Al-Ali et al.	May 2016	B2
9364181	Kiani et al.	Jun 2016	B2
9368671	Wojtczuk et al.	Jun 2016	B2
9370325	Al-Ali et al.	Jun 2016	B2
9370326	McHale et al.	Jun 2016	B2
9370335	Al-ali et al.	Jun 2016	B2
9375185	Ali et al.	Jun 2016	B2
9386953	Al-Ali	Jul 2016	B2
9386961	Al-Ali et al.	Jul 2016	B2
9392945	Al-Ali et al.	Jul 2016	B2
9397448	Al-Ali et al.	Jul 2016	B2
9408542	Kinast et al.	Aug 2016	B1
9436645	Al-Ali et al.	Sep 2016	B2
9445759	Lamego et al.	Sep 2016	B1
9466919	Kiani et al.	Oct 2016	B2
9474474	Lamego et al.	Oct 2016	B2
9480422	Al-Ali	Nov 2016	B2
9480435	Olsen	Nov 2016	B2
9492110	Al-Ali et al.	Nov 2016	B2
9510779	Poeze et al.	Dec 2016	B2
9517024	Kiani et al.	Dec 2016	B2
9532722	Lamego et al.	Jan 2017	B2
9538949	Al-Ali et al.	Jan 2017	B2
9538980	Telfort et al.	Jan 2017	B2
9549696	Lamego et al.	Jan 2017	B2
9554737	Schurman et al.	Jan 2017	B2
9560996	Kiani	Feb 2017	B2
9560998	Al-Ali et al.	Feb 2017	B2
9566019	Al-Ali et al.	Feb 2017	B2
9579039	Jansen et al.	Feb 2017	B2
9591975	Dalvi et al.	Mar 2017	B2
9622692	Lamego et al.	Apr 2017	B2
9622693	Diab	Apr 2017	B2
D788312	Al-Ali et al.	May 2017	S
9636055	Al-Ali et al.	May 2017	B2
9636056	Al-Ali	May 2017	B2
9649054	Lamego et al.	May 2017	B2
9662052	Al-Ali et al.	May 2017	B2
9668679	Schurman et al.	Jun 2017	B2
9668680	Bruinsma et al.	Jun 2017	B2
9668703	Al-Ali	Jun 2017	B2
9675286	Diab	Jun 2017	B2
9687160	Kiani	Jun 2017	B2
9693719	Al-Ali et al.	Jul 2017	B2
9693737	Al-Ali	Jul 2017	B2
9697928	Al-Ali et al.	Jul 2017	B2
9717425	Kiani et al.	Aug 2017	B2
9717458	Lamego et al.	Aug 2017	B2
9724016	Al-Ali et al.	Aug 2017	B1
9724024	Al-Ali	Aug 2017	B2
9724025	Kiani et al.	Aug 2017	B1
9730640	Diab et al.	Aug 2017	B2
9743887	Al-Ali et al.	Aug 2017	B2
9749232	Sampath et al.	Aug 2017	B2
9750442	Olsen	Sep 2017	B2
9750443	Smith et al.	Sep 2017	B2
9750461	Telfort	Sep 2017	B1
9775545	Al-Ali et al.	Oct 2017	B2
9775546	Diab et al.	Oct 2017	B2
9775570	Al-Ali	Oct 2017	B2
9778079	Al-Ali et al.	Oct 2017	B1
9782077	Lamego et al.	Oct 2017	B2
9782110	Kiani	Oct 2017	B2
9787568	Lamego et al.	Oct 2017	B2
9788735	Al-Ali	Oct 2017	B2
9788768	Al-Ali et al.	Oct 2017	B2
9795300	Al-Ali	Oct 2017	B2
9795310	Al-Ali	Oct 2017	B2
9795358	Telfort et al.	Oct 2017	B2
9795739	Al-Ali et al.	Oct 2017	B2
9801556	Kiani	Oct 2017	B2
9801588	Weber et al.	Oct 2017	B2
9808188	Perea et al.	Nov 2017	B1
9814418	Weber et al.	Nov 2017	B2
9820691	Kiani	Nov 2017	B2
9833152	Kiani et al.	Dec 2017	B2
9833180	Shakespeare et al.	Dec 2017	B2
9839379	Al-Ali et al.	Dec 2017	B2
9839381	Weber et al.	Dec 2017	B1
9847002	Kiani et al.	Dec 2017	B2
9847749	Kiani et al.	Dec 2017	B2
9848800	Lee et al.	Dec 2017	B1
9848806	Al-Ali et al.	Dec 2017	B2
9848807	Lamego	Dec 2017	B2
9861298	Eckerbom et al.	Jan 2018	B2
9861304	Al-Ali et al.	Jan 2018	B2
9861305	Weber et al.	Jan 2018	B1
9867578	Al-Ali et al.	Jan 2018	B2
9872623	Al-Ali	Jan 2018	B2
9876320	Coverston et al.	Jan 2018	B2
9877650	Muhsin et al.	Jan 2018	B2
9877686	Al-Ali et al.	Jan 2018	B2
9891079	Dalvi	Feb 2018	B2
9895107	Al-Ali et al.	Feb 2018	B2
9913617	Al-Ali et al.	Mar 2018	B2
9924893	Schurman et al.	Mar 2018	B2
9924897	Abdul-Hafiz	Mar 2018	B1
9936917	Poeze et al.	Apr 2018	B2
9943269	Muhsin et al.	Apr 2018	B2
9949676	Al-Ali	Apr 2018	B2
9955937	Telfort	May 2018	B2
9965946	Al-Ali	May 2018	B2
9980667	Kiani et al.	May 2018	B2
D820865	Muhsin et al.	Jun 2018	S
9986919	Lamego et al.	Jun 2018	B2
9986952	Dalvi et al.	Jun 2018	B2
9989560	Poeze et al.	Jun 2018	B2
9993207	Al-Ali et al.	Jun 2018	B2
10007758	Al-Ali et al.	Jun 2018	B2
D822215	Al-Ali et al.	Jul 2018	S
D822216	Barker et al.	Jul 2018	S
10010276	Al-Ali et al.	Jul 2018	B2
10032002	Kiani et al.	Jul 2018	B2
10039482	Al-Ali et al.	Aug 2018	B2
10052037	Kinast et al.	Aug 2018	B2
10058275	Al-Ali et al.	Aug 2018	B2
10064562	Al-Ali	Sep 2018	B2
10130291	Schurman et al.	Nov 2018	B2
20020016533	Marchitto et al.	Feb 2002	A1
20040162471	Ikeda et al.	Aug 2004	A1
20040167382	Gardner et al.	Aug 2004	A1
20040260158	Hogan	Dec 2004	A1
20050043597	Xie	Feb 2005	A1
20050070771	Rule et al.	Mar 2005	A1
20050101846	Fine et al.	May 2005	A1
20050105097	Fang-Yen et al.	May 2005	A1
20050226912	Lowery	Oct 2005	A1
20050254061	Alphonse	Nov 2005	A1
20060063988	Schurman et al.	Mar 2006	A1
20060161054	Reuss et al.	Jul 2006	A1
20060187462	Srinivasan	Aug 2006	A1
20060264719	Schurman et al.	Nov 2006	A1
20060276696	Schurman et al.	Dec 2006	A1
20070249917	Ballerstadt et al.	Oct 2007	A1
20070282478	Al-Ali et al.	Dec 2007	A1
20090247924	Lamego et al.	Oct 2009	A1
20090247984	Lamego et al.	Oct 2009	A1
20090275813	Davis	Nov 2009	A1
20090275844	Al-Ali	Nov 2009	A1
20090299157	Telfort et al.	Dec 2009	A1
20100004518	Vo et al.	Jan 2010	A1
20100030040	Poeze et al.	Feb 2010	A1
20100261979	Kiani	Oct 2010	A1
20110001605	Kiani et al.	Jan 2011	A1
20110082711	Poeze et al.	Apr 2011	A1
20110105854	Kiani et al.	May 2011	A1
20110125060	Telfort et al.	May 2011	A1
20110208015	Welch et al.	Aug 2011	A1
20110209915	Telfort et al.	Sep 2011	A1
20110213212	Al-Ali	Sep 2011	A1
20110230733	Al-Ali	Sep 2011	A1
20110237911	Lamego et al.	Sep 2011	A1
20120059267	Lamego et al.	Mar 2012	A1
20120116175	Al-Ali et al.	May 2012	A1
20120165629	Merritt et al.	Jun 2012	A1
20120179006	Jansen et al.	Jul 2012	A1
20120209082	Al-Ali	Aug 2012	A1
20120209084	Olsen et al.	Aug 2012	A1
20120227739	Kiani	Sep 2012	A1
20120265039	Kiani	Oct 2012	A1
20120283524	Kiani et al.	Nov 2012	A1
20120286955	Welch et al.	Nov 2012	A1
20120296178	Lamego et al.	Nov 2012	A1
20120319816	Al-Ali	Dec 2012	A1
20120330112	Lamego et al.	Dec 2012	A1
20130023775	Lamego et al.	Jan 2013	A1
20130045685	Kiani	Feb 2013	A1
20130046204	Lamego et al.	Feb 2013	A1
20130041591	Lamego	Mar 2013	A1
20130060108	Schurman et al.	Mar 2013	A1
20130060147	Welch et al.	Mar 2013	A1
20130096405	Garfio	Apr 2013	A1
20130096936	Sampath et al.	Apr 2013	A1
20130109935	Al-Ali et al.	May 2013	A1
20130162433	Muhsin et al.	Jun 2013	A1
20130190581	Al-Ali et al.	Jul 2013	A1
20130197328	Diab et al.	Aug 2013	A1
20130211214	Olsen	Aug 2013	A1
20130243021	Siskavich	Sep 2013	A1
20130253334	Al-Ali et al.	Sep 2013	A1
20130274571	Diab et al.	Oct 2013	A1
20130296672	O'Neil et al.	Nov 2013	A1
20130296713	Al-Ali et al.	Nov 2013	A1
20130317370	Dalvi et al.	Nov 2013	A1
20130324808	Al-Ali et al.	Dec 2013	A1
20130331660	Al-Ali et al.	Dec 2013	A1
20130331670	Kiani	Dec 2013	A1
20130338461	Lamego et al.	Dec 2013	A1
20140012100	Al-Ali et al.	Jan 2014	A1
20140025306	Weber et al.	Jan 2014	A1
20140034353	Al-Ali et al.	Feb 2014	A1
20140051952	Reichgott et al.	Feb 2014	A1
20140051953	Lamego et al.	Feb 2014	A1
20140051954	Al-Ali et al.	Feb 2014	A1
20140058230	Abdul-Hafiz et al.	Feb 2014	A1
20140066783	Kiani et al.	Mar 2014	A1
20140077956	Sampath et al.	Mar 2014	A1
20140081100	Muhsin et al.	Mar 2014	A1
20140081175	Telfort	Mar 2014	A1
20140094667	Schurman et al.	Apr 2014	A1
20140100434	Diab et al.	Apr 2014	A1
20140114199	Lamego et al.	Apr 2014	A1
20140120564	Workman et al.	May 2014	A1
20140121482	Merritt et al.	May 2014	A1
20140121483	Kiani	May 2014	A1
20140125495	Al-Ali	May 2014	A1
20140127137	Bellott et al.	May 2014	A1
20140128696	Al-Ali	May 2014	A1
20140128699	Al-Ali et al.	May 2014	A1
20140129702	Lamego et al.	May 2014	A1
20140135588	Al-Ali et al.	May 2014	A1
20140142401	Al-Ali et al.	May 2014	A1
20140142402	Al-Ali et al.	May 2014	A1
20140163344	Al-Ali	Jun 2014	A1
20140163402	Lamego et al.	Jun 2014	A1
20140166076	Kiani et al.	Jun 2014	A1
20140171763	Diab	Jun 2014	A1
20140180038	Kiani	Jun 2014	A1
20140180154	Sierra et al.	Jun 2014	A1
20140180160	Brown et al.	Jun 2014	A1
20140187973	Brown et al.	Jul 2014	A1
20140194709	Al-Ali et al.	Jul 2014	A1
20140194711	Al-Ali	Jul 2014	A1
20140194766	Al-Ali et al.	Jul 2014	A1
20140200420	Al-Ali	Jul 2014	A1
20140200422	Weber et al.	Jul 2014	A1
20140206963	Al-Ali	Jul 2014	A1
20140213864	Abdul-Hafiz et al.	Jul 2014	A1
20140243627	Diab et al.	Aug 2014	A1
20140266790	Al-Ali et al.	Sep 2014	A1
20140275808	Poeze et al.	Sep 2014	A1
20140275835	Lamego et al.	Sep 2014	A1
20140275871	Lamego et al.	Sep 2014	A1
20140275872	Merritt et al.	Sep 2014	A1
20140275881	Lamego et al.	Sep 2014	A1
20140288400	Diab et al.	Sep 2014	A1
20140296664	Bruinsma et al.	Oct 2014	A1
20140303520	Telfort et al.	Oct 2014	A1
20140309506	Lamego et al.	Oct 2014	A1
20140309559	Telfort et al.	Oct 2014	A1
20140316217	Purdon et al.	Oct 2014	A1
20140316218	Purdon et al.	Oct 2014	A1
20140316228	Blank et al.	Oct 2014	A1
20140323825	Al-Ali et al.	Oct 2014	A1
20140323897	Brown et al.	Oct 2014	A1
20140323898	Purdon et al.	Oct 2014	A1
20140330092	Al-Ali et al.	Nov 2014	A1
20140330098	Merritt et al.	Nov 2014	A1
20140330099	Al-Ali et al.	Nov 2014	A1
20140333440	Kiani	Nov 2014	A1
20140336481	Shakespeare et al.	Nov 2014	A1
20140343436	Kiani	Nov 2014	A1
20140357966	Al-Ali et al.	Dec 2014	A1
20150005600	Blank et al.	Jan 2015	A1
20150011907	Purdon et al.	Jan 2015	A1
20150018650	Al-Ali et al.	Jan 2015	A1
20150032029	Al-Ali et al.	Jan 2015	A1
20150038859	Dalvi et al.	Feb 2015	A1
20150080754	Purdon et al.	Mar 2015	A1
20150087936	Al-Ali et al.	Mar 2015	A1
20150094546	Al-Ali	Apr 2015	A1
20150099950	Al-Ali et al.	Apr 2015	A1
20150101844	Al-Ali et al.	Apr 2015	A1
20150106121	Muhsin et al.	Apr 2015	A1
20150112151	Muhsin et al.	Apr 2015	A1
20150165312	Kiani	Jun 2015	A1
20150196249	Brown et al.	Jul 2015	A1
20150216459	Al-Ali et al.	Aug 2015	A1
20150238722	Al-Ali	Aug 2015	A1
20150245773	Lamego et al.	Sep 2015	A1
20150245794	Al-Ali	Sep 2015	A1
20150257689	Al-Ali et al.	Sep 2015	A1
20150272514	Kiani et al.	Oct 2015	A1
20150351697	Weber et al.	Dec 2015	A1
20150359429	Al-Ali et al.	Dec 2015	A1
20150366507	Blank	Dec 2015	A1
20160029932	Al-Ali	Feb 2016	A1
20160066824	Al-Ali et al.	Mar 2016	A1
20160081552	Wojtczuk et al.	Mar 2016	A1
20160095543	Telfort et al.	Apr 2016	A1
20160095548	Al-Ali et al.	Apr 2016	A1
20160103598	Al-Ali et al.	Apr 2016	A1
20160166182	Al-Ali et al.	Jun 2016	A1
20160166183	Poeze et al.	Jun 2016	A1
20160196388	Lamego	Jul 2016	A1
20160197436	Barker et al.	Jul 2016	A1
20160213281	Eckerbom et al.	Jul 2016	A1
20160228043	O'Neil et al.	Aug 2016	A1
20160233632	Scruggs et al.	Aug 2016	A1
20160234944	Schmidt et al.	Aug 2016	A1
20160270735	Diab et al.	Sep 2016	A1
20160283665	Sampath et al.	Sep 2016	A1
20160287090	Al-Ali et al.	Oct 2016	A1
20160287786	Kiani	Oct 2016	A1
20160296169	McHale et al.	Oct 2016	A1
20160310052	Al-Ali et al.	Oct 2016	A1
20160314260	Kiani	Oct 2016	A1
20160324488	Olsen	Nov 2016	A1
20160327984	Al-Ali et al.	Nov 2016	A1
20160331332	Al-Ali	Nov 2016	A1
20160367173	Dalvi et al.	Dec 2016	A1
20170000394	Al-Ali et al.	Jan 2017	A1
20170007134	Al-Ali et al.	Jan 2017	A1
20170007198	Al-Ali et al.	Jan 2017	A1
20170014083	Diab et al.	Jan 2017	A1
20170014084	Al-Ali et al.	Jan 2017	A1
20170042488	Muhsin	Feb 2017	A1
20170055851	Al-Ali	Mar 2017	A1
20170055882	Al-Ali et al.	Mar 2017	A1
20170055887	Al-Ali	Mar 2017	A1
20170055896	Al-Ali et al.	Mar 2017	A1
20170079594	Telfort et al.	Mar 2017	A1
20170086723	Al-Ali et al.	Mar 2017	A1
20170143281	Olsen	May 2017	A1
20170147774	Kiani	May 2017	A1
20170156620	Al-Ali et al.	Jun 2017	A1
20170173632	Al-Ali	Jun 2017	A1
20170187146	Kiani et al.	Jun 2017	A1
20170188919	Al-Ali et al.	Jul 2017	A1
20170196464	Jansen et al.	Jul 2017	A1
20170196470	Lamego et al.	Jul 2017	A1
20170224262	Al-Ali	Aug 2017	A1
20170228516	Sampath et al.	Aug 2017	A1
20170245790	Al-Ali et al.	Aug 2017	A1
20170251974	Shreim et al.	Sep 2017	A1
20170251975	Shreim et al.	Sep 2017	A1
20170258403	Abdul-Hafiz et al.	Sep 2017	A1
20170311851	Schurman et al.	Nov 2017	A1
20170311891	Kiani et al.	Nov 2017	A1
20170325728	Al-Ali et al.	Nov 2017	A1
20170332976	Al-Ali et al.	Nov 2017	A1
20170340293	Al-Ali et al.	Nov 2017	A1
20170360310	Kiani et al.	Dec 2017	A1
20170367632	Al-Ali et al.	Dec 2017	A1
20180008146	Al-Ali et al.	Jan 2018	A1
20180014752	Al-Ali et al.	Jan 2018	A1
20180028124	Al-Ali et al.	Feb 2018	A1
20180055385	Al-Ali	Mar 2018	A1
20180055390	Kiani et al.	Mar 2018	A1
20180055430	Diab et al.	Mar 2018	A1
20180064381	Shakespeare et al.	Mar 2018	A1
20180069776	Lamego et al.	Mar 2018	A1
20180070867	Smith et al.	Mar 2018	A1
20180082767	Al-Ali et al.	Mar 2018	A1
20180085068	Telfort	Mar 2018	A1
20180087937	Al-Ali et al.	Mar 2018	A1
20180103874	Lee et al.	Apr 2018	A1
20180103905	Kiani	Apr 2018	A1
20180110478	Al-Ali	Apr 2018	A1
20180116575	Perea et al.	May 2018	A1
20180125368	Lamego et al.	May 2018	A1
20180125430	Al-Ali et al.	May 2018	A1
20180125445	Telfort et al.	May 2018	A1
20180130325	Kiani et al.	May 2018	A1
20180132769	Weber et al.	May 2018	A1
20180132770	Lamego	May 2018	A1
20180146901	Al-Ali et al.	May 2018	A1
20180146902	Kiani et al.	May 2018	A1
20180153442	Eckerbom et al.	Jun 2018	A1
20180153446	Kiani	Jun 2018	A1
20180153447	Al-Ali et al.	Jun 2018	A1
20180153448	Weber et al.	Jun 2018	A1
20180161499	Al-Ali et al.	Jun 2018	A1
20180168491	Al-Ali et al.	Jun 2018	A1
20180174679	Sampath et al.	Jun 2018	A1
20180174680	Sampath et al.	Jun 2018	A1
20180182484	Sampath et al.	Jun 2018	A1
20180184917	Kiani	Jul 2018	A1
20180192953	Shreim et al.	Jul 2018	A1
20180192955	Al-Ali et al.	Jul 2018	A1
20180199871	Pauley et al.	Jul 2018	A1
20180206795	Al-Ali	Jul 2018	A1
20180206815	Telfort	Jul 2018	A1
20180213583	Al-Ali	Jul 2018	A1
20180214031	Kiani et al.	Aug 2018	A1
20180214090	Al-Ali et al.	Aug 2018	A1
20180218792	Muhsin et al.	Aug 2018	A1
20180225960	Al-Ali et al.	Aug 2018	A1
20180238718	Dalvi	Aug 2018	A1
20180242853	Al-Ali	Aug 2018	A1
20180242921	Muhsin et al.	Aug 2018	A1
20180242926	Muhsin et al.	Aug 2018	A1
20180247353	Al-Ali et al.	Aug 2018	A1
20180247712	Muhsin et al.	Aug 2018	A1
20190029574	Schurman et al.	Jan 2019	A1

Foreign Referenced Citations (23)

Number	Date	Country
0282234	Sep 1988	EP
0160768	May 1989	EP
0127947	Aug 1990	EP
0280986	Jul 1992	EP
0317121	Feb 1994	EP
0536187	Sep 1994	EP
0589191	Mar 1997	EP
0603658	Feb 1999	EP
0631137	Mar 2002	EP
0670143	May 2003	EP
WO 8806726	Sep 1988	WO
WO 8910087	Nov 1989	WO
WO 9118548	Dec 1991	WO
WO 9210131	Jun 1992	WO
WO 9217765	Oct 1992	WO
WO 9300855	Jan 1993	WO
WO 9307801	Apr 1993	WO
WO 9309421	May 1993	WO
WO 9316629	Sep 1993	WO
WO 9404070	Mar 1994	WO
WO 9413193	Jun 1994	WO
WO 9532416	Nov 1995	WO
WO 0265090	Aug 2002	WO

Non-Patent Literature Citations (80)

Entry
US 8,845,543 B2, 09/2014, Diab et al. (withdrawn)
Arnold, V.W. et al., “Fourier Transformation Infrared Spectrometry—A New (Old) Method of Detection in Forensic Chemistry and Criminal Investigation,” Beitr Gerichtl Med. 47:123-47 (1989).
Arnold. M.A. et al., “Determination of Physiological Levels of Glucose in an Aqueous Matrix with Digitally Filtered Fourier Transform Near-Infrared Spectra,” Anal. Chem. 64(14):1457-64 (1990).
Bruulsema, J.T. et al., “Correlation Between Blood Glucose Concentration in Diabetics and Noninvasively Measured Tissue Optical Scattering Coefficient,” Opt. Lett. 22(3):190-93 (1997).
Burritt, M.F., “Current Analytical Approaches to Measuring Blood Analytes,” Clin. Chem. 36(8 pt.2):1562-66 (1990).
Chira, I.S. et al., “Light Scattering by Blood Components After Supplying Glucose,” Biomed. Tech. 35(5): 102-06 (1990).
Christison, G.B. et al., “Laser Photoacoustic Determination of Physiological Glucose Concentrations in Human Whole Blood,” Med. Biol. Eng. Comput. 31(3):284-90 (1993).
Cote, G.L. et al., “Noninvasive Optical Polarimetric Glucose Sensing Using a True Phase Measurement Technique,” IEEE Trans. Biomed. Enq, 39(7):752-56 (1992).
CRC Press, “Handbook of Chemistry and Physics” 64th ed. pp. D-223, D-224, and D. 235.
Drezek, R. et al., “Light Scattering From Cell: Finite Difference Time-Domain Simulations and Goniometric Measurements,” Appl. Opt. 38(16):3651-61 (1999).
Duck, F. A., Physical Properties of Tissue, (Academic London 1990).
Dyer, D.G. et al., “Accumulation of Maillard Reaction Products in Skin Collagen in Diabetes and Aging,” J. Clin. Invest. 91:2463-69 (1993).
Esenaliev, R.O, et al., “Noninvasive Monitoring of Glucose Concentration with Optical Coherence Tomography,” Optics Lett. 26(13):992-94 (2001).
Faber, D.J. et al., “Light Absorption of (oxy-)Hemoglobin Assessed by Spectroscopic Optical Coherence Tomography,” Optics Lett. 28(16):1436-38 (2003).
Fercher, A. et al., “In Vivo Optical Coherence Tomography,” Amer, J. Opthalmol, 116(1):113-14 (1993).
Flock, S.T. et al., “Total Attentuation Coefficients and Scattering Phase Functions of Tissues and Phantom Materials at 633 nm,” Med. Phvs. 14(5):835-41 (1987).
Fogt, E.J., “Continuous Ex Vivo and In Vivo Monitoring with Chemical Sensors,” Clin. Chem. 36(8 pt.2):1573-80 (1990).
Frank, K.H. et al., “Measurements of Angular Distributions of Rayleigh and Mie Scattering Events in Biological Models,” Phvs. Med. Biol. 34(8):1901-16 (1989).
Gabriely.I. et al., “Transcutaneous Glucose Measurement Using Near-Infrared Spectroscopy During Hypoglycemia,” Diabetes Care 22(12):2026-32 (1999).
Galanzha, E.I. et al., “Skin Backreflectance and Microvascular System Functioning at the Action of Osmotic Agents,” J, Phvs. D. Appl. Phvs. 36:1739-46 (2003).
Gilbert, J.W. et al., “A Cerebrospinal Fluid Glucose Biosensor for Diabetes Mellitus,” ASAIO J. 38(2):82-87 (1992).
Goetz, M.J. et al., “Application of a Multivariate Technique to Raman Spectra for Quantification of Body Chemicals,” IEEE Trans, Biomed. Eng, 42:728-31 (1995).
Goodman, J.W., Some Fundamental Properties of Speckle, J. Optical Soc. of America 66(11):1145-50 (1976).
Gough, D.A., “The Composition and Optical Rotary Dispersion of Bovine Aqueous Humor,” Diabetes Care 5(3):266-70 (1982).
Gunby, P., “Laser-Implant Contact Lens Could be Glucose Monitor,” JAMA 243(4):317 (1980).
Guyton, A.C., Textbook of medical physiology, (W.B. Saunders Company 1992).
Huang, D, et al., “Optical Coherence Tomograph,” Science 254:1178-81 (1991).
Huang, Y.L. et al., “On-Line Determination of Glucose Concentration Throughout Animal Cell Cultures Based on Chemiluminescent Detection of Hydrogen Peroxide Coupled with Flow-Injection Analysis,” J. Biotechnol. 18(1-2):161-72 (1991).
International Search Report from PCT/US05/26744, dated Oct. 25, 2006.
International Search Report, from corresponding PCT/US06/13775, dated Sep. 13, 2006.
International Search Report, from corresponding PCT/US06/21535, dated Feb. 21, 2008.
Kaiser, N., “Laser Absorption Spectroscopy with an ATR Prism—Noninvasive in Vivo Determination of Glucose,” Horm. Metab. Res. Suppl. 8:30-33 (1979).
Kajiwara, K. et al., “Spectroscopic Quantitative Analysis of Blood Glucose by Fourier Transform Infrared Spectroscopy with an Attenuated Total Reflection Prism,” Med, Prog. Technol. 18(3):181-89 (1992).
Khalil, O.S. “Spectroscopic and Clinical Aspects of Noninvasive Glucose Measurements,” Clin. Chern. 45(2):165-77 (1999).
Kholodnykh, A.I. et al., “Precision of Measurement of Tissue Optical Properties with Optical Coherence Tomography,” Appl. Optics 42(16):3027-37 (2003).
King, T.W. et al., “Multispectral Polarimetric Glucose Detection Using a Single Pockels Cell,” Optical Engineering 33(8):2746-53 (1994).
Kohl, M. et al., “Influence of Glucose Concentration on Light Scattering in Tissue-Simulating Phantoms,” Optics Letters 19(24):2170-72 (1994).
Kohl, M. et al., “The Influence of Glucose Concentration Upon the Transport of Light in Tissue-Simulating Phantoms,” Phvs. Med. Biol. 40:1267-87(1995).
Kruse-Jarres, J.D., “Physicochemical Determinations of Glucose in Vivo,” J. Clin. Chem. Clin. Biochem. 26(4):201-08 (1988).
Larin, K.V. et al., “Noninvasive Blood Glucose Monitoring With Optical CoherenceTomography,” Diabetes Care 25(12):2263-67 (2002).
Larin, K.V. et al., “Phase-Sensitive Optical Low-Coherence Reflectometry for the Detection of Analyte Concentrations,” Appl. Optics 43(17):3408-14 (2004).
Larin, K.V. et al., “Specificity of Noninvasive Blood Glucose Sensing Using Optical Coherence Tomography Technique: A Pilot Study,” Physics in Med. & Biol. 48:1371-90 (2003).
Larin, K.V. et al., “Optoacoustic Signal Profiles for Monitoring Glucose Concentration in Turbid Media,” SPIE Proc. 3726:576-83 (1988).
Lide, D.R., CRC Handbook of Chemistry and Physics, 79th ed. (CRC Press, Boca Raton, Florida, 1998).
Mackenzie, H.A. et al., “Advances in Photoacoustic Noninvasive Glucose Testing,” Clin. Chem. 45(9):1587-95 (1999).
Maier, J.S. et al., “Possible Correlation Between Blood Glucose Concentration and the Reduced Scattering Coefficient of Tissues in the Near Infrared,” Optics Lett. 19(24):2062-64 (1994).
March, W. et al., “Optical Monitor of Glucose,” Trans. Am. Soc. Artlf. Intern. Organs 25:28-31 (1979).
March, W.F. et al., “Noninvasive Glucose Monitoring of the Aqueous Humor of the Eye: Part II. Animal Studies and the Scleral Lens,” Diabetes Care 5(3):259-65 (1982).
Mendelson, Y. et al., “Blood Glucose Measurement by Multiple Attenuated Total Reflection and Infrared Absorption Spectroscopy,” IEEE Trans. Biomed. Eng. 37(5):458-65 (1990).
Moreno-Bondi, M,C. et al., “Oxygen Optrode for Use in a Fiber-Optic Glucose Biosensor,” Anal. Chem. 62(21):2377-80 (1990).
Muller, A., “In Vivo Measurement of Glucose Concentration with Lasers,” Harm. Metab. Res. Suppl. 8:33-35 (1979).
Narayanaswamy, R., “Current Developments in Optical Biochemical Sensors,” Biosens. Bioelectron. 6(6):467-75 (1991).
Pan, S. et al., “Near-Infrared Spectroscopic Measurement of Physiological Glucose Levels in Variable Matrices of Protein and Triglycerides,” Anal, Chem. 68:1124-35 (1996).
Peterson, J.I. et al., “A Miniature Fiberoptic pH Sensor Potentially Suitable for Glucose Measurements,” Diabetes Care 5(3):272-74 (1982).
Quan, K.M. et al., “Glucose Determination by a Pulsed Photoacoustic Technique—An Experimental Study Using a Gelatin-Based Tissue Phantom,” Phys. Med. Biol. 38(12): 1911-22 (1993).
Rabinovitch, B. et al., “Noninvasive Glucose Monitoring of the Aqueous Humor of the Eye: Part I. Measurement of Very Small Optical Rotations,” Diabetes Care 5(3):254-58 (1982).
Robinson, M.R. et al., “Noninvasive Glucose Monitoring in Diabetic Patients: A Preliminary Evaluation,” Clin. Chem. 38(9):1618-22 (1992).
Robinson, R.J, et al., “Glucose-Sensitive Membrane and Infrared Absorption Spectroscopy for Potential Use as an Implantable Glucose Sensor,” ASAIO J. 38(3):M458-62 (1992).
Rusch, T.L. et al., “Signal Processing Methods for Pulse Oximetry,” Comput. Biol. Med. 26(2):143-59 (1996).
Ruth, Bernhard, et al. “Noncontact determination of skin blood flow using the laser speckle method: Application to patients with peripheral arterial occlusive disease (PAOD) and to type-1 diabetics.” 1993. Wiley-Liss. Lasers in Surgery and Medicine. 13: 179-188.
Schmitt, J.M. et al., “Measurement of Optical Properties of Biological Tissues by Low-Coherence Reflectrometrv,” Appl. Optics 32(30):6032-42 (1993).
Schmitt, J.M. et al., “Speckle in Optical Coherence Tomography,” J. Biomed. Optics 4(1 ):95-105 (1999).
Schmitt, J.M. et al., “Optical Coherence Tomography (OCT): A Review,” IEEE J. Selected Topics in Quantum Electronics 5(4):1205-15 (1999).
Sevick, E.M. et al., “Near-Infrared Optical Imaging of Tissue Phantoms with Measurement in the Change of Optical Path Lengths,” Adv. Exp. Med. Biol. 345:815-23 (1994).
Sodickson, L.A. et al., “Kromoscopic Analysis: A Possible Alternative to Spectroscopic Analysis for Noninvasive Measurement of Analytes in Vivo,” Clin. Chern. 40(9):1838-44 (1994).
Star, W.M. et al., “Light Dosimetry: Status and Prospects,” J. Photochem. Photobiol. 1(2):149-67 (1987).
Stoddart, S. et al., “Pulse Oximetry: What it is and How to Use it, ” J. Neonatal Nursing 10:12-14 (1997).
Takai, N. et al., “Studies of the Development of Optical Fiber Sensors for Biochemical Analysis,” Artif. Organs 15(2):86-89 (1991).
Tunchin, V.V. et al., “Light Propagation in Tissues with Controlled Optical Properties,” J. Biomed. Opt. 2(4):401-17 (1997).
Wang, L. et al., “Speckle Reduction in Laser Projection Systems by Diffractive Optical Elements,” Appl. Optics 37(10):1770-75 (1998).
Weast, R.C., et al, CRC Handbook of Chemistry and Physics, 70th ed., (CRC Cleveland, Ohio, 1989).
Welch, A.J. et al., Practical Models for Light Distribution in Laser-Irradiated Tissue, Lasers Surq. Med. 6(6):488-93 (1987).
Wicksted, J.P. et al., “Monitoring of Aqueous Humor Metabolites Using Raman Spectroscopy,” SPIE Proc. 2135:264-74 (1994).
Zeller, H. et al., Blood Glucose Measurement by Infrared Spectroscopy, J. Artif. Organs 12(2):129-35 (1989).
Zhao, Yonghua, et al. “Doppler standard deviation imaging for clinical monitoring of in vitro human skin blood flow.” Optics Letters. Sep. 15, 2000. vol. 25, No. 18 pp. 1358-1360.
U.S. Appl. No. 14/737,242, filed Jun. 11, 2015 including its ongoing prosecution history, including without limitation Office Actions, Amendments, Remarks, and any other potentially relevant documents, GLT Acquisition Corp.
2014/0094667, U.S. Appl. No. 14/036,786, filed Sep. 25, 2013 including its ongoing prosecution history, including without limitation Office Actions, Amendments, Remarks, and any other potentially relevant documents, Apr. 3, 2014, GLT Acquisition Corp.
2014/0336481, U.S. Appl. No. 14/280,294, filed May 16, 2014 including its ongoing prosecution history, including without limitation Office Actions, Amendments, Remarks, and any other potentially relevant documents, Nov. 13, 2014, GLT Acquisition Corp.
U.S. Appl. No. 15/605,460, Method for Data Reduction and Calibration of an OCT-Based Physiological Monitor, filed May 25, 2017.
U.S. Appl. No. 15/799,689, Multispot Monitoring for Use in Optical Coherence Tomography, filed Oct. 31, 2017.

Related Publications (1)

	Number	Date	Country
	20160058347 A1	Mar 2016	US

Provisional Applications (2)

	Number	Date	Country
	61068058	Mar 2008	US
	61033584	Mar 2008	US

Continuations (2)

	Number	Date	Country
Parent	14063977	Oct 2013	US
Child	14711610		US
Parent	12397593	Mar 2009	US
Child	14063977		US

Flowometry in optical coherence tomography for analyte level estimation

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

Agents

CPC

Field of Search

CPC

International Classifications

Disclaimer

Term Extension

Abstract