METHOD AND APPARATUS FOR PERFORMING MULTIDIMENSIONAL VELOCITY MEASUREMENTS USING AMPLITUDE AND PHASE IN OPTICAL INTERFEROMETRY

Abstract

According to an exemplary embodiment of the present disclosure, an apparatus can be provided which can include at least one first arrangement providing a radiation, and including a splitter structure separating the radiation into at least one first electro-magnetic radiation directed to a sample and at least one second electro-magnetic radiation directed to a reference. A frequency of the radiation provided by the first arrangement can be varied over time by the first arrangement. The apparatus can also include at least one detector second arrangement configured to detect a first interference and a second interference between at least one third radiation associated with the first radiation(s) and at least one fourth radiation associated with the second radiation(s), whereas the first and second interferences can be different from one another. The second arrangement(s) can include a computer that can be further configured to (i) obtain information associated with at least one relative phase between the first and second interferences, and (ii) determine an absolute phase of the first interference and/or the second interference based on the information.

Description

FIELD OF THE DISCLOSURE

Exemplary embodiments of the present disclosure relate generally to optical measurements of velocities, and more particularly to methods, systems and apparatus for providing multidimensional velocity measurements using amplitude and phase information in an optical interferometry.

BACKGROUND INFORMATION

Optical interferometric techniques to measure velocities can rely on the Doppler effect. The Doppler effect can describe a change in the frequency of light when it is reflected from a moving object. This permits the determination of the relative velocity of a sample, multiple samples or different parts of a sample with respect to the illuminating probe when it is shined with radiation that is analyzed in the frequency domain after reflection.

In some fields, it can be important to know a velocity map of a sample, for example when viscoelastic objects move with time. In these cases, the object can be considered as being composed of different parts that move independently, and therefore it can be valuable to make use of the Doppler effect in optical techniques that have higher imaging dimensionality, such as 2D and/or 3D imaging with a depth discrimination, so as to obtain a complete profile of the velocity distribution of the object of interest. In particular, Doppler Optical Coherence Tomography (D-OCT) is a technique known in the art for determining the velocity distribution as a function of depth in biological or other types of samples, with the potential of providing 2D and 3D velocity profiles when combined with multidimensional imaging or scanning systems. An optical system capable of performing D-OCT measurements consists of a phase-stable interferometer and data acquisition system that is able to detect changes in phase with time that come exclusively from the sample, without phase errors induced by fluctuations in the interferometer itself or by the acquisition process. D-OCT is very sensitive but has a limited range, being able to detect velocities as low as tens of micrometers per second up to millimeters per second. In the limited cases in which the sample of interest has a smooth velocity pattern and the spatial resolution of the Doppler phase map is sufficiently high, phase unwrapping algorithms can be used to extend the limits of the velocity range.

Currently, the fastest OCT imaging systems can be provided with a light source that sweeps the optical Fourier domain, known as Optical-Frequency Domain Imaging (OFDI) systems. The sources used on OFDI impose stringent requirements on the timing of the data acquisition, which may not be easy to satisfy in order to have a phase stable measurement, due to drift in the timing clocks of the high-frequency data acquisition systems used in OFDI. Some solutions for this problem have been proposed, but they usually add important complexity to the light source, such as an additional interferometer with its own data acquisition system. This has limited the mainstream implementation of D-OCT in swept-source systems, which has hindered the use of OFDI for the fast measurement of multidimensional velocity profiles in many fields.

D-OCT procedures and/or configuration are capable of establishing the sign of the direction of the movements, but can be directly sensitive only to movement in the line of sight (LOS). This has been another limitation for the use of D-OCT systems, because the angle that the D-OCT light beam makes with the moving object must be known in order to accurately determine the velocity. When the sample of interest has a map of velocities with varying orientation, such as those found in the most general flow of a liquid, it is not possible to know the angle of the velocities with respect to the light beam without a priori knowledge of the complete vectorial distribution of the velocities that the measurement intends to determine. Errors in the knowledge of this angle translate into errors in the measured velocities. This has been a very important drawback of the D-OCT method that makes it difficult to use in applications where an accurate description of the velocity profile is needed.

For example, one of these applications consists of determining the velocity profile in the blood flow inside blood vessels, and determining the total flow rate. Only when the flow is well-behaved unidirectional laminar flow, D-OCT is able to quantify the flow rate. Unfortunately, these conditions are rarely met in biological tissue. This inhibits, in principle, the use of D-OCT for characterizing blood flow inside blood vessels with branches and ramifications, as well as its use for quantifying the total flow rate in these conditions. For the reasons described above, it would be highly desirable to have a method that is able to determine velocity distributions without a prior knowledge of the velocity directional distribution.

There have been proposals for the use of Doppler analysis to determining a movement out of the LOS direction, in particular the use of the Doppler variance. However, Doppler variance is strongly linked with the signal-to-noise ratio (SNR) and the focusing optics, and depends on zero or a calibrated constant variance in the LOS direction, which makes it undesirable for use in real-world scenarios.

Another set of techniques use different quantities based on the speckle that forms when radiation is scattered from the sample in a coherent imaging system. In a coherent optical or acoustic system, speckle forms due to the interference between multiple signals scattered from different parts of the object under study inside the resolution volume, and speckle evolves as a stochastic process whose statistics are related to the movement of the scatterers. Speckle-based techniques do not rely on the phase information of the signal, only on the statistical fluctuations of speckle intensity. For example, it is possible to relate the variance of the speckle above a given threshold to the presence of moving scatterers in the sample, such as those occurring in a flowing liquid. However this technique is purely qualitative, as it is only capable of discriminating moving and static areas. Other speckle techniques are based on cross-correlation between speckle taken at different times, or on the time autocorrelation of speckle. Although in this case it is theoretically possible to quantify speed, so far there have been no known reliable systems for the measurement of speed distributions based on speckle statistics.

Dynamic light scattering (DLS) is a technique that analyzes statistics of complex OCT speckle. DLS can obtain quantitative information regarding the LOS and transverse motion of the scatterers by analyzing the complex speckle signal, but it relies on the phase of the OCT signal. Therefore, DLS requires a phase-stable OCT system to determine vectorial speed profiles. The use of the complex signal is at the core of the technique, which cannot be separated into distinct amplitude and phase analyses. It can be highly valuable to provide technique, system, method, apparatus and/or computer-accessible medium that can carry out the same measurements of DLS, depending only on the amplitude of the OCT signal.

Gradients in the LOS velocity can likely impact speckle statistics, and the effects can be severe when the velocity is mainly aligned. A known technique for addressing this problem relies on measuring the axial velocity using the phase information of the signal. However, this makes it difficult to make quantitative measurements when only the amplitude of the signal is available. It can be valuable to provide technique, system, method, apparatus and/or computer-accessible medium that, having access to only the amplitude signal, can determine the axial gradient distribution and to compensate for its effects on the decorrelation of the signal. Bringing the speckle correlation techniques to obtaining quantitative measurements would be useful for the widespread use of coherent systems for determining speed distributions, and could enable the use of OFDI systems for multidimensional velocity measurements.

Speckle techniques can be sensitive to speed in any direction, but no technique, system, method, apparatus and/or computer-accessible medium based on amplitude speckle statistics have been able to determine the axis in which the movement occurs. It would be valuable to develop a speckle technique that, although sensitive to velocity in any direction, is at the same time able to discriminate between flow in the LOS and out of the LOS. For example, in the viscous flow of a liquid, turbulence can appear at some regimes and the determination of the level of turbulence is highly valuable in some fields of study. Doppler would not be able to identify the areas with turbulent flow as it only identifies flow in the line of sight. Current speckle techniques could potentially be sensitive to the turbulent flow but cannot provide the directional information that is necessary to assess the total flow rate, which produces an overestimation of the total flow rate.

Apart from tracking the motion of a sample, in some areas, it can be of interest to track the motion of the probe that is being used to measure the sample interferometrically. In some fields, the motion of the probe cannot be completely controlled, with unexpected discontinuities during the scanning process. These discontinuities can appear in translating and rotating degrees of freedom, and without correction, can severely distort the images acquired. It would be valuable to provide technique, system, method, apparatus and/or computer-accessible medium that can use speckle amplitude statistics and/or phase to track the motion of the probe so as to compensate for image distortion.

Accordingly, there may be a need to address and/or overcome at least some of the issues of deficiencies described herein above.

Indeed, based on the above, it would be desirable to provide system, apparatus, methods and techniques that overcomes the limitations of the phase-based velocity measurements in high-speed interferometry systems, and address the line-of-sight problems of Doppler techniques, and overcome the lack of reliable quantitative and directionality information in intensity-based speckle speed measurements.

SUMMARY OF EXEMPLARY EMBODIMENTS

Exemplary embodiments according to an exemplary embodiment of the present disclosure can be provided to measure relative multidimensional velocity profiles between a part of the measuring object and the target sample objects, which overcome the limitations of the techniques known in the art described above.

In one exemplary embodiment, the first measuring object is configured as an interferometric imaging system that emits coherent acoustic or electromagnetic radiation, which can be scattered by the sample of interest. Furthermore, the measuring object can be configured to be sensitive to the amplitude and phase of the radiation reflected by the second object. The second object might be a sample that can possess a multidimensional distribution of vectorial velocities. A number of objects can be samples with a known or controlled velocity profile which also scatters the radiation emitted by the first object. In this exemplary embodiment, the first object can be further configured to determine the multidimensional distribution of vectorial velocities of the second, a number of objects from the collected scattered radiation, e.g., using the information contained in the amplitude, the phase, or both. In the exemplary embodiment of the present disclosure, the first object can utilize the phase information from the third or more objects with known or controlled velocity profiles in order to transform the raw phase of the scattered radiation from the second object into a profile of relative velocities with respect to the third or more objects, overcoming the necessity of phase-stable detection.

In a second exemplary embodiment of the present disclosure, the first measuring object can be configured as a coherent imaging system that is sensitive only to the amplitude of the radiation reflected by the second object. In this exemplary embodiment, the measuring object can be further configured to determine the multidimensional distribution of the vectorial velocities of the second, third or more objects from the collected scattered radiation.

According to still another exemplary embodiment of the present disclosure, the interferometric imaging system can be configured as and/or utilize an optical coherence tomography system (“OCT”) and/or an optical frequency-domain imaging (“OFDI”) system, that performs multidimensional interferometric coherent imaging of the second object. Furthermore the exemplary interferometric imaging system can be implemented in the time-domain and/or in the frequency-domain by means of a spectroscopic analyzer or a wavelength-swept source. This exemplary system can perform multidimensional imaging via optical or mechanical configurations to acquire one-, two- or three-dimensional imaging of the sample as a function of time. The exemplary system can also implement a variable-speed scanning in which time and spatial information is coded together in a given dimension set.

According to a further exemplary embodiment of the present disclosure, the exemplary system representing the first object does not need to include any configurations to guarantee phase-stable data acquisition in time. In this exemplary embodiment, the second object can be optically coupled to the exemplary system in order to collect the light or other electro-magnetic radiation reflected from the second object, and to produce interference between reflected light and the reference light, thereby generating interferometric signals that can be collected by a detector. The first exemplary system can also be configured to detect the scattered radiation of the third or more objects with known or controlled velocity profiles, which can be in the same or in a different optical path from the second object.

In this exemplary embodiment, the first exemplary system can be configured to perform Doppler analysis on the scattered radiation from the second, third or more objects, and can use the Doppler information from the third or more objects in order to determine the corrected velocity profile of the second object with respect to the first, third or more objects. In such exemplary embodiment, the first exemplary system can be further configured to discard the phase and to create a coherent multidimensional representation of the scattered radiation from the second object, and by a statistical analysis of the amplitude of this radiation it can determine the speed profiles of the second, third or more objects. According to still another exemplary embodiment of the present disclosure, the system can be configured not to discard the phase information, but to use the measured data from both the phase- and amplitude-based techniques to determine a vectorial velocity profile of the second object. In the case that the second object is composed of a flowing material, the system can be further configured to use the information in the vectorial velocity profile to detect the areas of turbulent flow.

In another exemplary embodiment of the present disclosure, the first object can be fitted to a medical catheter configured to deliver radiation that may be scattered by the second, third or more objects. In this exemplary embodiment, the exemplary system can be further fitted with a mechanism to perform a variable speed scanning of the second or more objects, in order to adapt the scanning to the phase analysis and/or to the amplitude statistical analysis of scattered radiation. In this exemplary embodiment, the exemplary imaging system can be a one-dimensional OCT or OFDI system that implements a scanning probe in one or more dimensions. As time and space are encoded in one or more of the scanning dimensions, the amplitude statistical analysis will link the second object velocity in one or more dimensions with the scanning speed of the probe. The exemplary system can be further configured to adapt the scanning speed in order to balance its contribution to the sample speed. Such exemplary system can be further configured to perform a set of measurements in which said scanning speed varies, which is then used to decouple the probe scanning speed from the second object velocity and to determine a vectorial velocity profile.

In certain exemplary embodiments of the present disclosure, an exemplary method can be provided for a measured data analysis on the phase of the detected radiation that can facilitate a velocity distribution in the axial direction, while data analysis on the amplitude of the detected radiation will provide scalar speed information. According to certain further exemplary embodiments of the present disclosure, still another exemplary method can be provided for the measured data analysis on the amplitude of the detected radiation that can facilitate a discrimination regarding the speeds in the axial and in the transversal directions. In still further exemplary embodiments of the present disclosure, yet another exemplary method can be provided for the analysis of the different types of velocity profiles obtained from the same or distinct raw data that can be combined to reconstruct a vectorial multidimensional velocity profile of the second object.

In another exemplary embodiment of the present disclosure, exemplary system, method and computer-accessible medium can be provided for determining and/or correcting for the influence of the physical dispersion mismatch in an interferometric system on the statistics of the amplitude of the interferometric signal. In still further exemplary embodiments of the present disclosure, yet another exemplary system, method and computer-accessible medium can be provided for determining the LOS-velocity gradients exclusively from the amplitude signal, e.g., to correct for inaccuracies produced by such gradients and provide a more accurate multidimensional velocity profile of the second object.

Further, according to another exemplary embodiment of the present disclosure (which can operate with all other exemplary embodiments), an apparatus can be provided which can include at least one first arrangement providing a radiation, and including a splitter structure separating the radiation into at least one first electro-magnetic radiation directed to a sample and at least one second electro-magnetic radiation directed to a reference. A frequency of the radiation provided by the first arrangement can be varied over time by the first arrangement. The apparatus can also include at least one detector second arrangement configured to detect a first interference and a second interference between at least one third radiation associated with the first radiation(s) and at least one fourth radiation associated with the second radiation(s), whereas the first and second interferences can be different from one another. The second arrangement(s) can include a computer that can be further configured to (i) obtain information associated with at least one relative phase between the first and second interferences, and (ii) determine an absolute phase of the first interference and/or the second interference based on the information.

For example, the computer can be further configured to determine at least one parameter for determining or correcting a motion within a structure associated with the sample based on the absolute phase. The structure can be or be part of a living body. The computer can be further configured to determine or correct the at least one parameter for correcting the motion between an imaging probe in which the second arrangement can be situated and the living body. The second arrangement can be further configured to detect a first interference and a second interference between at least one third radiation associated with the first radiation and at least one fourth radiation associated with the second radiation, where the first and second interferences can be are different from one another. The computer can be further configured to determine further information based on a difference between an amplitude of the first interference and an amplitude of the second interference. The computer can also be further configured to determine a velocity distribution of a structure or a liquid associated with the sample based on the absolute phase and the further information.

According to yet another exemplary embodiment of the present disclosure (which can be used with all other exemplary embodiments described herein), an apparatus can be provided which can include at least one first arrangement providing a radiation, and including a splitter structure separating the radiation into at least one first electro-magnetic radiation directed to a sample and at least one second electro-magnetic radiation directed to a reference. A frequency of the radiation provided by the first arrangement can be varied over time by the first arrangement. The apparatus can also include at least one detector second arrangement configured to configured to detect a first interference and a second interference between at least one third radiation associated with the first radiation and at least one fourth radiation associated with the second radiation, where the first and second interferences can be different from one another. The second arrangement can include a computer that is configured to determine information based on a difference between an amplitude of the first interference and an amplitude of the second interference.

In yet a further exemplary embodiment of the present disclosure (which can be used with all other exemplary embodiments described herein), an apparatus can be provided which can include at least one first arrangement providing a radiation, and including a splitter structure separating the radiation into at least one first electro-magnetic radiation directed to a sample and at least one second electro-magnetic radiation directed to a reference. The apparatus can also include at least one spectral separating second arrangement configured to (a) detect a first interference and a second interference between at least one third radiation associated with the first radiation and at least one fourth radiation associated with the second radiation, and (b) spectrally separate the first interference into a first separated interference and the second interference into a second separated interference. At least one third arrangement can be provided that can include a plurality of detectors which are configured to detect the first and second separated interferences, where the first and second separated interferences are different from one another. The third arrangement can includes a computer that is configured to (i) obtain information associated with at least one relative phase between the first and second separated interferences, and (ii) determine an absolute phase of the first interference and/or the second interference based on the information.

These and other objects, features and advantages of the present disclosure will become apparent upon reading the following detailed description of embodiments of the disclosure, and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objects, features and advantages of the present disclosure will become apparent from the following detailed description taken in conjunction with the accompanying figures showing illustrative embodiments of the present disclosure, in which:

FIG. 1 is a block diagram of a conventional coherent imaging system;

FIG. 2 is a block diagram of a conventional interferometric imaging system;

FIG. 3 is a block diagram of a conventional optical frequency-domain imaging system using a coherent single-frequency tuning source;

FIG. 4 is a block diagram of a conventional optical frequency-domain imaging system using a coherent single-frequency tuning source in accordance with an exemplary embodiment of the present disclosure;

FIG. 5 is an conventional optical coupling and scanning mechanism in the form of a medical catheter for measuring flow profiles of a cylindrical sample such as a flow velocity inside a blood vessel;

FIG. 6 is a flow diagram of a method for correcting the phase using a reference sample phase signal in accordance with in accordance with an exemplary embodiment of the present disclosure;

FIG. 7 is a flow diagram of a method for determining the speed profile of a sample using amplitude speckle statistics in accordance with an exemplary embodiment of the present disclosure;

FIG. 8 is a set of illustrations of a conventional OFDI intensity measurement of a flowing liquid;

FIGS. 9A-9C are exemplary illustrations of inverse correlation time profiles for a moving solid as measured using the conventional autocorrelation method;

FIGS. 9D-9F are exemplary illustrations of the exemplary inverse correlation time profiles for the moving solid as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system according to an exemplary embodiment of the present disclosure;

FIG. 10 is an illustration of an exemplary parametrization of speed as a function of τ⁻¹ and SNR based on the present exemplary material as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system according to an exemplary embodiment of the present disclosure;

FIG. 11 is a set of exemplary illustrations of speed profiles for a moving solid phantom as measured using the exemplary parametrization of speed as a function of τ⁻¹ and SNR based on the present exemplary material as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system according to an exemplary embodiment of the present disclosure;

FIGS. 12A-12C are exemplary illustrations of speed profiles for a flowing liquid as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system according to an exemplary embodiment of the present disclosure;

FIGS. 12D-12F are exemplary illustrations of the flow speed as measured by Doppler analysis using the exemplary embodiment of the phase-correction OFDI system according to an exemplary embodiment of the present disclosure;

FIGS. 13A-13C are exemplary illustrations of the conventional structural image from OFDI of the tube and the liquid;

FIGS. 13D-13F are exemplary illustrations of two-dimensional speed profiles for a flowing liquid as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system and method according to an exemplary embodiment of the present disclosure;

FIGS. 13G-13I are exemplary illustrations of two-dimensional flow speeds as measured by Doppler analysis using the exemplary embodiment of the phase-correction OFDI system and method according to an exemplary embodiment of the present disclosure;

FIGS. 14A-14C are exemplary illustrations of three-dimensional speed profiles for a flowing liquid at different planes in the tube longitudinal direction as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system and method according to an exemplary embodiment of the present disclosure;

FIGS. 14D-14F are exemplary illustrations of three-dimensional speed profiles for a flow velocity measured by Doppler analysis using the exemplary embodiment of the phase-correction OFDI system and method according to an exemplary embodiment of the present disclosure;

FIGS. 14G-14I are exemplary illustrations of conventional structural images from OFDI of the tube and the liquid;

FIG. 15 is a flow diagram of a method for determining a vectorial speed and a turbulence profile of the sample using at least two amplitude speckle statistics analysis with different scanning speeds in accordance with an exemplary embodiment of the present disclosure;

FIG. 16A is a flow diagram of a method for determining figures or areas of interest, such as axial and/or lateral speed profile, and/or an axial speed gradient of the sample using multiple amplitude speckle statistics analyses, e.g., with different k-space signal diversities in accordance with an exemplary embodiment of the present disclosure;

FIG. 16B is a set of exemplary illustrations of axial velocity gradient determination using the exemplary embodiment of the amplitude speckle statistics analysis using k-space signal diversity system and method according to the present invention;

FIG. 16C is a set of exemplary illustrations of axial and lateral speed determination using the exemplary embodiment of the amplitude speckle statistics analysis using k-space signal diversity system and method according to the present invention;

FIG. 17 is a flow diagram of a method for determining the vectorial speed profile of the sample using a combination of Doppler and amplitude speckle statistics analysis in accordance with an exemplary embodiment of the present disclosure;

FIG. 18 is a flow diagram of a method for determining the turbulence profile of the sample using a combination of Doppler and amplitude speckle statistics analysis in accordance with an exemplary embodiment of the present disclosure;

FIG. 19 is a flow diagram of a method for tracking the probe translational motion using Doppler analysis in accordance with an exemplary embodiment of the present disclosure;

FIG. 20 is a flow diagram of a method for tracking the probe rotational motion using amplitude speckle statistics analysis in accordance with an exemplary embodiment of the present disclosure;

FIG. 21 is a set of exemplary illustrations of translational probe motion tracking using the exemplary embodiment of the Doppler probe tracking system and method according to the present disclosure; and

FIG. 22 is a set of exemplary illustrations of rotational probe motion tracking and image correction using the exemplary embodiment of the amplitude speckle statistics analysis rotational probe tracking system and method according to the present disclosure.

Throughout the figures, the same reference numerals and characters, unless otherwise stated, are used to denote like features, elements, components or portions of the illustrated embodiments. Moreover, while the subject disclosure will now be described in detail with reference to the figures, it is done so in connection with the illustrative embodiments. It is intended that changes and modifications can be made to the described exemplary embodiments without departing from the true scope and spirit of the subject disclosure as defined by the appended claims.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Referring to FIG. 1, a conventional coherence imaging system can include a coherent source 110 which provides a temporally coherent electromagnetic or acoustic signal to a sample 120 and a reference sample 130. The radiation propagates through the radiation coupling 100a, which can consist of free-space components or of wave-guiding components. 100a can be fitted with means to perform multidimensional imaging, such as lenses or scanning systems. After radiation is scattered by 120 one more radiation coupling component 100c transmits the radiation to a coherent detector 140. Due to the coherent nature of the source and of the detection, when the sample 120 includes many subresolution scatterers, the image of sample 120 can likely be composed of speckle.

It is possible to analyze the statistical variations of the speckle amplitude in order to gain information on the movement of the sample. Such techniques usually rely on implementing 100a as a two-dimensional detector, and the analysis performed on the acquired amplitude of speckle as a function of time is known as laser speckle contrast imaging. These techniques usually provide qualitative information on whether a part of the object is moving or not, but determining actual speeds is not possible.

Referring now to FIG. 2, an exemplary interferometric imaging system can include a coherent source 210 which can provide an electromagnetic and/or acoustic signal to a multiport coupler 260a. This coupler 260a, in the case of the electromagnetic radiation, can be a beam splitter, as is generally known in the art. The radiation can propagate through a radiation coupling 200a, which can include free-space components or of wave-guiding components. After the radiation coupling 260a, the radiation can be separated in two couplings 200b and 200c. Radiation going into the coupling 200b is delivered to the sample 220. The coupling 200d can be fitted so as to perform multidimensional imaging, such as lenses or scanning systems. After radiation is scattered by 220, radiation is coupled back into the coupling 200b and transmitted through a multiport coupler 260a. Radiation is also delivered through 200c to the reference reflection 240, which can have means to change the effective optical path length as is known in the art. Coherent radiation reflected by the sample 220 and the reference 240 can be mixed by multiport coupler 260a and delivered by a coupler 200f into a coherent detector 250. Due to the coherent mixing of the signals, by appropriate measurement schemes as it is known in the art, it is possible to determine both the amplitude and phase of the radiation reflected from sample 220. In the case that sample 220 can include many subresolution scatterers, the amplitude and phase detected can present speckle.

It is possible to perform a Doppler analysis on the phase variations of the reflected radiation in order to gain information on the movement of the sample. Such techniques are generally only sensitive to the LOS movement of the sample, which inhibits the reconstruction of a complete velocity profile. As Doppler can be sensitive to the sign of the movement, movement that occurs in many directions in a determined region of the sample (such as that movement that appear in the turbulent flow of a liquid) may not be accurately determined, and such regions with rapid velocity direction changes cannot be identified. Furthermore, Doppler techniques have a defined limit given by the phase wrapping effect.

Referring now to FIG. 3, an exemplary OFDI imaging system is shown as a block diagram that can include a wavelength-swept coherent source 310 which can provide an electromagnetic signal to a multiport coupler 360a. This coupler 360a can be or include a beam splitter, as is generally known in the art. The radiation propagates through the radiation coupling 300a, which can consist of free-space components or of wave-guiding components. After the multiport coupler 360a, the radiation can be separated in two couplings 300b and 300c, which can comprise a sample and a reference arm, respectively. The radiation provided into a coupling 300b can be further split into couplers 300d and 300e. The coupler 300d can provide the radiation to a fiber Bragg grating that, upon going to an optical circulator 370, provides a clock signal at a detector 350b that can be further digitized in a data acquisition (DAQ) system 380. The portion of light/radiation that continues through a path 300e feeds the polarization and/or frequency shifter 390a for polarization diverse sensing and/or for removal of the depth degeneracy, as is well known in the art. Coupling 300f can feed a circulator 370 to provide radiation to the reference reflection 340 via a coupler 300g, and the coupler/path 300h delivers reflected light/radiation into a multiport coupler 360b.

Similarly, the sample arm is comprised of circulator 370 that collects light reflected from sample 320, which is delivered via 300k. Coupling 300j can guide the radiation into the polarization and/or frequency shifter 390b which are used as it is well known in the art. Coupling 300i can deliver the sample light/radiation into the multiport coupler 360b, which can mix light/radiation from the reference reflection, reference sample and sample, and balance detector assembly 350 consists of detectors and optionally multiport couplers to have polarization sensitive detection. 300k can be fitted with means to perform multidimensional imaging, such as lenses or scanning systems. The reference reflection 340 can have a way to change the effective optical path length as is known in the art. A signal from 350 can be digitized by the data acquisition board (DAQ) 380. Due to the coherent mixing of the signals, by appropriate measurement schemes, it is possible to determine both the amplitude and phase of the radiation reflected from the sample 320 as a function of depth. In the case that sample 320 consists of many subresolution scatterers, the amplitude and phase detected will present speckle.

It is possible to perform a Doppler analysis on the phase variations of the reflected radiation in order to gain information on the movement of the sample in a similar way to the exemplary embodiment shown in FIG. 2. The same or similar limitations can apply, e.g., the sensitivity to only LOS movement of the sample, the inability to accurately determine regions with movement in many directions (such as that movement that appear in the turbulent flow of a liquid) and the well-defined limit given by the phase wrapping effect.

FIG. 4 shows an exemplary OFDI imaging system according to an exemplary embodiment of the present disclosure with a similar configuration to the conventional system depicted in FIG. 3, configured to further subdivide the radiation in the light coupling 300k into couplings/paths 3001 and 300m due to a multiport coupler 360a.

For example, as shown in FIG. 4, the light coupling and scanning system 300m can have many implementations. For example, in the case of OFDI systems used in cardiovascular applications, the exemplary system 300m can be configured and/or provided in the form of a catheter that is well known in the art, as shown in FIG. 5. The sample 320, e.g., in cardiovascular applications, can be or include blood inside a vessel, the vessel wall itself, or a combination of both. An exemplary scanning system based on a catheter can perform one-dimensional measurements of the reflectance of the sample with depth (known as A-lines), and mechanical rotation allows for scanning of the transversal plane. At the same time the catheter can be “pulled-back” to provide sectioning along the longitudinal direction to gather three-dimensional data from the sample.

Exemplary Correction of the Absolute Phase Due to Phase Instabilities in Interferometer or Data Acquisition System

FIG. 6 shows a flow diagram of an exemplary embodiment of a method for correcting the absolute phase acquired from the sample using the exemplary embodiment of FIG. 4. An exemplary embodiment for this correction is as follows:

• • 1) Determine/calculate the axial complex-valued profile of each measured A-line a_km=DFT(S_km), where k denotes the depth index, m the time index and DFT a discrete Fourier transform function. The time index can optionally be coupled with a spatial scanning (step 500).
  • 2) Determine/calculate an absolute phase (φ_km=arg(a_km), where arg denotes the argument of the complex number a_km (step 510).
  • 3) Determine/calculate the correction for the phase difference Δ_km=φ_k,m+1−φ_km based on the reference sample(s) phase (330 in FIG. 4). Defining the phase correction δ_km, the corrected phase difference is {tilde over (Δ)}_km≡Δ_km+δ_km=φ_k,m+1−φ_km+δ_km. There are different types of phase corrections. There are cases in which an offset correction is necessary. For example when element 390a and/or 390b contains a frequency shifter, or when the scanning probe 300k is subject to unwanted vibrations. To correct an offset, the reference sample phase change Δ_k′,m at depth k′ is known and the corrected phase difference is {tilde over (Δ)}_km=Δ_k,m−Δ_k′,m so δ_km=Δ_k′m. When there is timing-induced phase jitter the correction is well known in the art: for instance, when a reference reflection at depth k′ is used, the correction has the form δ_km=k/k′Δ_k′m. When several phase artifacts are present they can be corrected accumulatively, and might need more than one reference reflection. For example, first correcting an offset correction due to a frequency shifter, secondly correcting a timing-induced jitter slope, and thirdly correcting an offset due to vibration of the scanning probe 300m (step 520).
  • 4) Determine/calculate a corrected absolute phase based on the correction for the phase differences by {tilde over (φ)}_km=φ_km+Σ_n=1^mδ_k,n+1. A phase offset as a function of depth for the first A-line m=1 will be present that cannot be compensated, which does not alter the results of the next step (step 530).
  • 5) Perform a Doppler frequency analysis using the corrected absolute phase. As it is well known in the art, this analysis can use Fourier analysis or autocorrelations such as Kasai autocorrelation. These methods only work on the absolute phase, not on the phase differences. These methods provide a robust way to calculate the Doppler mean frequency as well as other figures of interest such as Doppler frequency variance (step 540).
  • 6) Determine/calculate velocity profile from Doppler frequency analysis, where the Fourier analysis is performed at different regions of the sample image in order to extract a multidimensional velocity profile (step 550).

In the exemplary embodiment of the exemplary systems shown in FIGS. 4 and 5, it is possible to define two coordinate systems to which the velocity profiles can be referred. It should be understood that other exemplary embodiments can be used to determine multidimensional velocity profiles in accordance with an exemplary embodiment of the present disclosure. For example, the first coordinate system can define z as the light propagation direction, and its associated perpendicular xy plane. The second system can be defined with respect to the sample 320, which in case of the exemplary embodiment shown FIG. 5 has a cylindrical shape, however it is not limited in any way to this geometry. In this exemplary coordinate system, {tilde over (z)} is looking forward in the longitudinal direction of the catheter and the {tilde over (x)}{tilde over (y)} defines the plane of rotation of the beam. It is possible to define x and {tilde over (z)} as parallel, so an ideal laminar flow inside the tube flows along the x direction which is independent of the rotation angle θ of the beam, while the y direction depends on θ.

Noise-Tolerant Speckle-Amplitude Autocorrelation Technique

FIG. 7 shows a flow diagram of an exemplary embodiment of a method according to the present disclosure for calculating and/or otherwise determining a speed profile based on an analysis of the autocorrelation of the speckle amplitude acquired from the sample. The steps/procedures are explained in detail herein below.

In a coherent optical system where fully developed speckle is formed after reflection of light by moving particles, the speckle-decorrelation time is inversely proportional to the speed of the particles. In the case of OFDI, there is a speckle size in the axial direction that can be related to the axial resolution of the system (given by the bandwidth of the light source). At each depth, the speckle can evolve as a function of time so that its decorrelation time is inversely proportional to the particles reflecting light at that depth. FIG. 8 shows a set of illustrations providing examples of this relation, where the tomogram of a tube is shown that is filled with flowing scattering liquid (e.g., intralipid) at two different flow rates. Reference 800 in FIG. 8 indicates an M-mode tomogram of phantom fluid at 2.5 mL/min with low flow velocities and large speckle size, while reference 810 in FIG. 8 shows the same sample at 80 mL/min yielding high flow speeds and small speckle size. These tomograms are so called M-mode measurements, which consist of the measurement of A-lines as a function of time of the same position in the sample.

Thus, the horizontal axis shown in FIG. 8 corresponds to time, while the vertical axis to depth. The catheter is provided at the top, clearly identified by the strong reflections of the collimating ball lens and the protective sheath. The exemplary catheter can be placed near a center of the tube (e.g., 3.2 mm diameter), and the liquid-tube interface can correspond to the weak reflection at the bottom of the tomograms. It is easily seen that the speckle-decorrelation time (the speckle size in the horizontal direction) matches the expected relationship with particle speed. In particular, the horizontal speckle size gets bigger near the interfaces due to the no-slip condition of the flow.

For example, the speckle correlation time can be inversely proportional to the modulus of the velocity (the speed) of the particles

$\tau \propto \frac{1}{\overrightarrow{v}}\equiv \frac{1}{v}.$

The way of calculating the autocorrelation for speckle statistics can include, e.g., the use of the Pearson correlation function, either between A-lines at two consecutive times, or at a single or a group of depths as a function of time. This determination can produce an estimation of the decorrelation time that is highly influenced by noise, especially in the case of using the cross-correlation coefficient. Instead, according to an exemplary embodiment of the present disclosure, it is possible to utilize a modified correlation function tailored for the determination of speckle size, and analyze the speckle size at each depth as a function of time. The exemplary normalized correlation function can be defined as

$\begin{array}{cc}{g}^{\left(2\right)}\left(\Delta t\right)=\frac{<\sum I\left(t\right)I\left(t+\Delta t\right)>}{\sum <I\left(t\right)><I\left(t+\Delta t\right)>}.& \left(2\right)\end{array}$

where < > denotes an ensemble average, I is the intensity, and the summation is along the discrete time dimension. In a system with unity speckle contrast and in absence of noise, this definition of the autocorrelation function has a maximum value of 2, totally decorrelated signals have a value of 1, and anti-correlated signals have values below 1. The ensemble average allows taking into account multiple correlation windows in the calculation, reducing the statistical fluctuations on speckle size and the effect of noise.

After certain testing, with the use of the above-defined autocorrelation function on the scattered radiation amplitude, as opposed to the traditional approach on the scattered radiation intensity, decorrelation profiles can be produced that can be significantly more homogeneous. This can be linked to the effect that the square has on outliers in the statistical fluctuations of speckle intensity. When this square is avoided, the outliers likely have a smaller weight on the autocorrelation function which produces more homogeneous results. A side effect of this can be that the autocorrelation no longer reaches a value of 2 for speckle with perfect contrast.

As noise is the most important factor in the accuracy of speckle decorrelation times, it is possible to define another exemplary autocorrelation by performing the following exemplary transformations: for example, it is possible to remove the value at Δt=0, optionally apply a small Gaussian smoothing filter (FWHM=3 px), renormalize the autocorrelation by defining the value at Δt=1 pX as one, and define the median of the value in a certain range of the autocorrelation as zero

$\begin{array}{cc}{g}^{\left(2\right)}\left(\Delta t\right)\equiv \frac{{\tilde{g}}_{\mathrm{trad}}^{\left(2\right)}\left(\Delta t\ge 1\mathrm{px}\right)-\mathrm{median}\left\{{\tilde{g}}_{\mathrm{trad}}^{\left(2\right)}\left(\Delta t>w\right)\right\}}{{\tilde{g}}_{\mathrm{trad}}^{\left(2\right)}\left(\Delta t=1\mathrm{px}\right)-\mathrm{median}\left\{{\tilde{g}}_{\mathrm{trad}}^{\left(2\right)}\left(\Delta t>w\right)\right\}},& \left(3\right)\end{array}$

where {tilde over (g)}≡g*e^{−ln2Δt2/(FWHM/2)2} is the Gaussian filtered version, and w is defined based on the autocorrelation properties. This corresponds to step 600 in the exemplary method shown in FIG. 7. For example, by defining such exemplary autocorrelation, most if not all functions can have similar contrast and cover the range from 1 to approximately 0. The contrast C is defined before the final normalization as

C=g _trad ⁽²⁾(Δt=1px)−median {g_trad⁽²⁾(Δt>w)}.  (4)

C can be a good indicator of presence of flow, similar to the speckle variance technique. This corresponds to step 610 in the exemplary method of FIG. 7.

It may be preferable to estimate the noise floor of the exemplary system. This can be possible, e.g., if the system is fitted with a mechanism to block the sample arm of the interferometer while taking a measurement of scattered radiation. Then, a tomogram reconstruction on this data can be performed, and an average over all depths can provide an estimation for the noise floor. Further, most if not all scattered radiation measurements can be converted from intensity into SNR by subtracting the estimated noise floor value, corresponding to step 620 in the exemplary method shown in FIG. 7.

In this exemplary embodiment, the decorrelation time for each region of the sample can be determined by calculating the time it takes the autocorrelation function calculated in step 600 to reach the value 0.5. This corresponds to step 630 in the exemplary method shown in FIG. 7.

FIGS. 9A-9C show exemplary illustrations of complete inverse autocorrelation profiles for a solid material moving at a known and stable speed. The profiles were measured using the conventional autocorrelation method. The complete inverse autocorrelation profiles shown in FIGS. 9D-9F correspond to the same set of speeds and are measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system according to an exemplary embodiment of the present disclosure. FIGS. 9A-9F illustrate a significant improvement in the homogeneity of the profile using the exemplary embodiments of the present disclosure.

With respect to determining the speckle speed from the speckle decorrelation time (e.g., step 640 in the exemplary method shown in FIG. 7), it is possible to consider a movement in the x direction. In this exemplary case, the total apparent mean speed can be, e.g.:

{tilde over (v)}=√{square root over (K_BM²+v_x²)}+K_BM,  (5)

where K_BM denotes an offset given by stochastic movement, such as that produced by Brownian motion of the scatterers. The speckle-decorrelation time τ is inversely proportional to this speed. For example, zero speed may provide an infinite decorrelation time. However, because the autocorrelation can be calculated using a window of finite width, even at zero velocity, there may be a finite decorrelation time (equal to the window size). If necessary, an offset can be added to account for this effect to the K_BM constant outside the radical to define a new offset k_c. Finally,

τ⁻¹=k√{square root over (K_BM²+v_x²)}+k_c≡√{square root over (K_BM²+k²v_x²)}+k_c,  (6)

where the k proportionality constant has been absorbed into the new Brownian motion contribution constant.

Considering the exemplary embodiment shown in FIG. 5 in which the scanning mechanism is a rotating catheter, the rotation of the catheter with angular velocity ω_R can con produce decorrelation that is linked to motion in the y direction. However, there will not be a pure dependence on the tangential velocity v_y=ω_Rz because as the catheter rotates, the light beam is not only displaced an amount v_ydt but light is entering the material at a different angle θ(t+dt)=θ(t)+ω_Rdt. As the speckle pattern is also dependent on incident angle, the combination of these exemplary movements can produce a speckle pattern whose size is mostly independent of z. It is possible to consider this exemplary decorrelation as arising directly from the angular velocity ω_R via another proportionality constant k_R, which can have some z dependence. It is possible to see why the dependence is with the angular velocity of the catheter: if the whole sample were made to rotate at exactly the same speed, the decorrelation due to the rotation of the catheter would be exactly canceled. Therefore, in a rotating catheter system with sample movement only in x, the speckle-decorrelation time can be described by, e.g.:

τ⁻¹=√{square root over (k_BM²+k_R²ω_R²+k²v_x²)}+k_c,  (7)

and the sample speed |v_x| can be found using, e.g.:

$\begin{array}{cc}v_x=\sqrt{\frac{{\left({\tau }^{-1}-k_c\right)}^2-k_{\mathrm{BM}}^2-k_R^2w_R^2}{k^2}},& \left(8\right)\end{array}$

where it is made explicit that it is not possible to determine the sign of the flow velocity. This corresponds to step 640 in the exemplary method of FIG. 7 when sample motion happens only in the x direction.

Considering, e.g., the corrected decorrelation time τ_c⁻¹≡τ⁻¹−k_c, the equation above indicates that at high flow speeds, the relation between τ_c⁻¹ and speed is linear

$\begin{array}{cc}\underset{v_x\to \infty }{\lim }{\tau }_c^{-1}=kv_x,& \left(9\right)\end{array}$

while it can be non-linear at low flow speeds with an offset at zero flow speed given by Brownian motion and catheter rotation

$\begin{array}{cc}\underset{v_x\to \infty }{\lim }{\tau }_c^{-1}=\sqrt{k_{\mathrm{BM}}^{2}+k_R^2w_R^2}.& \left(10\right)\end{array}$

This indicates that speckle-decorrelation flow measurements can be better suited for quantifying high flow speeds than Doppler, while the opposite can be the case when the rotational speed of the catheter is significant.

In the general case of sample motion in any direction, the proportionality constants can be different if the voxel that corresponds to the point spread function (PSF) is asymmetric. This can be usually the case, unless some post-processing on the OFDI data is performed. This exemplary case is described by, e.g.:

τ⁻¹=√{square root over (k_BM²+k_R²ω_R²+k_pp²v_x²+k_pp²v_y²+k_pl²v_z²)}+k_c,  (11)

where k_pp is the proportionality constant for flow perpendicular to the beam, k_pl the constant for flow parallel to the beam, v_y is the tangential speed of the flow in the y direction, and k_R²{tilde over (ω)}_R² is a term that represents the decorrelation due to the angular scanning, and can be dependent on depth and tangential speed. To understand the effect of the two constants that depend on the sample velocity, it is possible to assume for simplicity no motion in the y direction. Therefore

$\begin{array}{cc}{\hat{v}}_{\mathrm{xz}}\equiv \sqrt{\frac{k_{\mathrm{pp}}}{k_{\mathrm{pl}}}v_x^2+\frac{k_{\mathrm{pl}}}{k_{\mathrm{pp}}}v_z^2}=\sqrt{\frac{{\left({\tau }^{-1}-k_c\right)}^2-k_{\mathrm{BM}}^2-k_R^2w_R^2}{k^2}},& \left(12\right)\end{array}$

where it is clear that |{circumflex over (v)}_xz|≠|v_xz|≡√{square root over (v_x²+v_z²)} due to the different constants. In order to avoid this it is necessary to rescale the tomogram in the axial direction to make the axial and transversal PSFs of the system equal. If the tomogram is rescaled to have a symmetric PSF and define now k≡k_pp=k_pl in the case of no flow in the y direction, e.g.:

$\begin{array}{cc}\begin{array}{c}v_{\mathrm{xz}}=\sqrt{v_x^2+v_z^2}\\ =\sqrt{\frac{{\left({\tau }^{-1}-k_c\right)}^2-k_{\mathrm{BM}}^2-k_R^2w_R^2}{k^2}}.\end{array}& \left(13\right)\end{array}$

The case of flow in the y direction can be more complex because of the coupling with the rotational contribution. However, many exemplary methods for determining the total speed can be utilize, such as, e.g., measuring at different rotational speeds solving the equations for v².

Further exemplary embodiments can be provided with a usage of a signal-to-noise (SNR) ration calibration method. The rationale for the parametrization can be the following: when measuring a solid sample with a static catheter k_BM=ω_R=0, so τ⁻¹ as a function of speed should be a linear function, although when reaching τ_max⁻¹ the slope should increase to indicate that higher speeds will all produce values close to the maximum inverse correlation time given by the time resolution of the system. The exemplary illustrations in FIG. 9 were used as calibration data to parametrize sample speed as a function of inverse correlation time and SNR. The best parametrization for this behavior was a fourth order polynomial where the linear term dominates except for correlation times near the limit of the system. The exemplary behavior of the SNR can be mostly linear for >25 dB SNR and higher-order for lower SNR.

Therefore, the speed was parametrized as a fourth order polynomial of the two variables τ⁻¹ and SNR and a fit of this exemplary model to the data can be performed by minimizing the mean absolute value error, instead of the mean quadratic error, to minimize the influence of outliers. An exemplary parametrization based on the present exemplary material as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system according to an exemplary embodiment of the present disclosure is shown in an exemplary illustration of FIG. 10. FIG. 11 shows a set of an exemplary illustrations of speed profiles for a moving solid phantom as measured using the exemplary parametrization of speed as a function of τ⁻¹ and SNR based on the present exemplary material as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system according to an exemplary embodiment of the present disclosure. The solid phantom was translated by using a motorized linear stage at a fixed speed, corresponding to 80 mm/s (illustration 1100), 100 mm/s (illustration 1110), 200 mm/s (illustration 1120) and 300 mm/s (illustration 1130). The measured speed by our technique matches extremely well the expected values.

Another exemplary calibration relates the newly defined inverse correlation time with the velocity of a given material. As a matter of illustration, the scattering liquid used in the exemplary configuration can be intralipid and the results from this fitting procedure are:

k _BM ²=734.9

k ²=0.177

k _c=16.1  (14)

Examples of Multidimensional Speed Profiles

The system and methods described above allows for the accurate measurement of speeds from speckle-amplitude statistics as described in the present disclosure. FIGS. 12A-12F shows illustrations of measurements taken of Intralipid flow through a tube at different flow rates when the catheter is not rotating. In particular, FIGS. 12A-12C provide exemplary illustrations of speed profiles for a flowing liquid as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system according to an exemplary embodiment of the present disclosure. Further, FIGS. 12D-12F show exemplary illustrations of the flow speed as measured by Doppler analysis using the exemplary embodiment of the phase-correction OFDI system according to an exemplary embodiment of the present disclosure.

FIG. 13 shows a set of illustrations of exemplary two-dimensional speed profile measurements when the catheter is rotating where the contribution from rotation has been calibrated as

k _BM ² +k _R ²ω_R=7310

k ²=0.156

k _c=−43.0  (15)

In particular, FIGS. 13A-13C show exemplary illustrations of the conventional structural image from OFDI of the tube and the liquid. FIGS. 13D-13F show exemplary illustrations of two-dimensional speed profiles for a flowing liquid as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system and method according to an exemplary embodiment of the present disclosure. Further, FIGS. 13G-13I show exemplary illustrations of two-dimensional flow speeds as measured by Doppler analysis using the exemplary embodiment of the phase-correction OFDI system and method according to an exemplary embodiment of the present disclosure

The system according to an exemplary embodiment of the present disclosure can be further configured to provide an additional scanning mechanism in 300m in FIG. 5. For example, a pullback mechanism can provide longitudinal scanning of the sample 320. If the measurements described above can be carried out while the pullback system is in operation, it is possible to obtain three-dimensional speed profiles of the sample. FIGS. 14A-14I provide a set of illustrations of three-dimensional speed profile measurements when the catheter is rotating using the above-described exemplary embodiment of the systems and methods according to the present disclosure. In particular, FIGS. 14A-14C show exemplary illustrations of three-dimensional speed profiles for a flowing liquid at different planes in the tube longitudinal direction as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system and method according to an exemplary embodiment of the present disclosure. In addition, FIGS. 14D-14F show exemplary illustrations of three-dimensional speed profiles for a flow velocity measured by Doppler analysis using the exemplary embodiment of the phase-correction OFDI system and method according to an exemplary embodiment of the present disclosure. Further, FIGS. 14G-14I show exemplary illustrations of conventional structural images from OFDI of the tube and the liquid.

Multidimensional Vectorial Speed and Velocity Profiles

For example, the scanning speed can contribute to the decorrelation time. The equivalent in a tabletop scanning system can correspond to a translational speed, which can have a simpler relationship with decorrelation. In this exemplary case, the angle of incidence does not change, although there can be a scanning translational speed in the xy plane. If the scanning speeds are much lower than sample speeds, this correction can be small and can be ignored. If they are, or are made comparable it is possible to make use of different scanning speeds to determine the individual components of the velocity vector in the transversal plane. For example, by scanning in the y direction with velocity v_s we have

$\begin{array}{cc}\begin{array}{c}\frac{{\left({\tau }^{-1}-k_c\right)}^2-k_{\mathrm{BM}}^2-k^2v_s^2}{k^2}=v_x^2+v_y^2+v_z^2\pm v_sv_y\\ =v^2\pm v_sv_y.\end{array}& \left(16\right)\end{array}$

By measuring with different scanning speeds in x and Y, several of these equations can be obtained and solved for v², v_x, v_y and v_z.

Following the reasoning of Eq. (16), an exemplary embodiment of a method according to the present disclosure as shown in FIG. 15 can be provided as follows:

• • 1. Perform a minimum of two measurements with different scanning speeds in the direction in which the velocity component v_a that is to be determined. In the case of a translational scanning system this corresponds to at least two scanning speeds v_s (step 1500).
  • 2. Analyze the speckle decorrelation according to exemplary embodiment of FIG. 7. The resulting decorrelation time is described by (τ⁻¹−k_c)²=k_BM²+k²v²±k²v_sv_a+k²v_s² (step 1510).
  • 3. Because all constants and v_s are known, from these two or more equations determine v_a (step 1520).
  • 4. Repeat steps 1, 2 and 3 for all directions of interest (step 1530).
  • 5. If at least two directions of interest are measured, the equations can be solved for v², v_x and v_y, and |v_z| can be extracted from |v_z|=v²−√{square root over (v_x²+v_y²)} (step 1540).

In a further exemplary embodiment, it is possible to extract two- and three-component vectorial velocity profiles of the sample, depending on the scanning/imaging implementation. In the case of a mechanically scanning system in which time and the transversal coordinates are coupled a two-component vectorial profile can be determined, where the two components correspond to the longitudinal speed and the transversal speed. Eq. (11) can be written as

τ⁻¹=√{square root over (k_BM²+k_R²ω_R²+k_pp²v_pp²+k_pl²v_pl²)}+k_c,  (17)

where the transversal speed is v_pp²=v_x²+v_y² and the longitudinal speed is v_pl²=v_z². k_pl can be synthetically modified by different methods after measuring, because its value depends on the axial resolution of the tomogram. For different axial resolutions, its value can be calibrated. One possibility includes splitting the raw spectrum and reconstructing different realizations of the tomogram with a reduced bandwidth, which results in a tomogram with reduced longitudinal resolution. In general, it is possible to consider such exemplary process as signal filtering in k-space, and the use of several filters in k-space can be considered as k-space filtering diversity. Multiple possibilities of k-space filtering are possible, which can provide numerous diversities, such as, e.g., axial resolution diversity, group velocity dispersion diversity, quartic dispersion diversity, etc. For axial and lateral flow discrimination, we will focus on axial resolution diversity. This exemplary step/procedure does not require a phase stability, as all A-lines can be inherently phase-stable with respect to points inside the same A-line. Exemplary different realizations can be used in the calculation of the autocorrelation function (step 600 in FIG. 7) to improve the SNR. As each realization will be described by an Eq. (17) with a different k_pl, a system of equations can be created and solved for v_pl² and v_pp². In general, modifications to the k-space signal can produce a different k_pl, which can then be used to provide an exemplary system, method and/or computer-accessible medium which can be configured and/or programmed to utilize equations, and solve for axial and lateral speeds.

Following the reasoning of Eq. (17), an exemplary embodiment of a method according to the present disclosure as shown in FIG. 16A, can be provided as follows:

• • 6. Perform at least one measurement to analyze the speckle-amplitude decorrelation time (step 1600′).
  • 7. Analyze the speckle decorrelation according to exemplary embodiment of FIG. 7. The resulting decorrelation time is described by τ⁻¹=√{square root over (k_BM²+k_R²ω_R²+k_pp²v_pp²+k_pl²v_pl²)}+k_c, (step 1610′).
  • 8. Create or provide a different realization of the tomogram by, e.g., modifying the signal in k-space. For example, reduce the bandwidth in order to have smaller k_pl constant for this realization. The speckle decorrelation of this realization can be analyzed (continuation of step 1610′).
  • 9. Provide an exemplary system, method and/or computer-accessible medium which can be configured and/or programmed to utilize equations from the result for performing each analysis, and considering that the different k_pl constants are known via a calibration, solve for v_pl² and v_pp² (step 1620′).

There can be other figures of interest that can be determined in a similar manner, e.g., by k-space filtering. In general, interferometric systems can have varying degrees of dispersion mismatch between the reference and sample arms. Furthermore, it is possible to add or subtract the dispersion mismatch synthetically after measuring. As a particular exemplary embodiment, consider the case of so-called group velocity dispersion (GVD). A variation in GVD influences the decorrelation produced by axial velocity gradients, which can then be used to determine them. Consider that the system has a GVD given by a quadratic dispersion of amplitude 2πγ. This can be produced either by, e.g., physical GVD between the two arms of the interferometer, and/or synthetically in post processing (e.g., k-space filtering producing GVD diversity). In presence of GVD, the complex amplitude spread function (ASF) of the system is

$\begin{array}{cc}\mathrm{ASF}\left(k\right)=\sqrt{\frac{w_z}{{\hat{w}}_z}}\exp \left(-2{\mathrm{k}}_0z\right)\exp \left(-z^2/{\hat{w}}_z\right)\exp \left(-\mathrm{\pi \gamma }z^2/{\hat{w}}_z\right),& \left(18\right)\end{array}$

where z is the axial direction, k₀ the central wave number of the spectrum, w_z the diffraction-limited axial 1/e size of the point spread function (PSF), and ŵ_z the actual 1/e size of the PSF due to the quadratic dispersion.

Assuming an axial speed profile inside the ASF with a linear gradient of the form v_z(z)=v_z0+zv_z1/w_z. After some approximations, the autocorrelation function becomes

$\begin{array}{cc}{g}^{\left(2\right)}\left(\tau \right)=1+{g^{\left(1\right)}\left(\tau \right)}^2\approx 1+\exp \left(-8n^2k_o^2D\tau \right)\exp \left(-v_z^2{\tau }^2/w_z^2\right)\exp \left(-2v_{\mathrm{xy}}^2{\tau }^2/w_{\mathrm{xy}}^2\right)\times \times \exp \left(-\frac{1}{2}\ln \left(\frac{25}{4}+{\pi }^2{\gamma }^2\frac{9}{4}\right)v_{z1}\tau /w_z\right)\exp \left(-2\mathrm{\pi \gamma }k_0v_{z0}v_{z1}{\tau }^2/w_z^2\right)\exp \left(-k_0^2v_{z1}^2\left(1+{\pi }^2{\gamma }^2\right){\tau }^2\right),& \left(19\right)\end{array}$

where xy are the transverse directions, n is the refraction index, D the diffusion constant of the scatterers, and w_xy the lateral 1/e size of the PSF. By defining the speckle decorrelation time τ_c as the time when the second-order autocorrelation function reaches the threshold 1+g_c, the following is provided:

$\begin{array}{cc}{\tau }_c^{-1}=K_{\mathrm{BM}}+{\mathrm{GVD}}_1\left(\gamma \right)+\sqrt{\begin{array}{c}{\left[K_{\mathrm{BM}}+{\mathrm{GVD}}_1\left(\gamma \right)\right]}^2+{{\mathrm{GVD}}_2\left(\gamma \right)}^2+\\ {\hat{g}}_cv_z^2/w_z^2+2{\hat{g}}_cv_{\mathrm{xy}}^2/w_{\mathrm{xy}}^2+k_0^2v_{z1}^2\end{array}},& \left(20\right)\end{array}$

where the diffusion constant is K_BM=4ĝ_cn²k_o²D, the group velocity dispersion contributions are

${\mathrm{GVD}}_1\left(\gamma \right)=\frac{{\hat{g}}_c}{2}\ln \left(\frac{25}{4}+{\pi }^2{\gamma }^2\frac{9}{4}\right)\frac{v_{z1}}{w_z}$

and GVD₂(γ)=2πγk₀v_z0v_z1/w_z²+k₀²v_z1²π²γ². We identify the axial velocity gradient contribution to decorrelation as the last term in Eq. (20). It is easy to see that by varying γ synthetically, we can find the decorrelation contribution from v_z1 by producing different values of the decorrelation time due to GVD₁(γ) and GVD₂(γ). The data can be assembled in an exemplary system, method and/or computer-accessible medium which can be configured and/or programmed to utilize equations to find the axial velocity gradient contribution to decorrelation. Further, e.g., when physical dispersion mismatch is present, its effects persist even when compensated synthetically. This can be found by performing an exemplary calibration when the axial velocity gradient is known, which produces the GVD₁(γ) and GVD₂(γ) baseline contributions from the physical dispersion mismatch.

Following the reasoning of Eq. (20), an exemplary embodiment of a method according to the present disclosure as shown in FIG. 16A can be provided as follows:

• • 10. Perform at least one measurement to analyze the speckle-amplitude decorrelation time (step 1600).
  • 11. Analyze the speckle decorrelation according to exemplary embodiment of FIG. 7. The resulting decorrelation time is described by

${\tau }_c^{-1}=K_{\mathrm{BM}}+{\mathrm{GVD}}_1\left(\gamma \right)+\sqrt{\begin{array}{c}{\left[K_{\mathrm{BM}}+{\mathrm{GVD}}_1\left(\gamma \right)\right]}^2+{{\mathrm{GVD}}_2\left(\gamma \right)}^2+\\ {\hat{g}}_cv_z^2/w_z^2+2{\hat{g}}_cv_{\mathrm{xy}}^2/w_{\mathrm{xy}}^2+k_0^2v_{z1}^2\end{array}},\mathrm{with}$ ${\mathrm{GVD}}_1\left(\gamma \right)=\frac{{\hat{g}}_c}{2}\ln \left(\frac{25}{4}+{\pi }^2{\gamma }^2\frac{9}{4}\right)\frac{v_{z1}}{w_z}$

and GVD₂(γ)=2πγk₀v_z0v_z1/w_z²+k₀²v_z1²π²γ² (step 1610).

• • 12. Create a different realization of the tomogram by filtering the signal in k-space. For example, add GVD by adding to the signal a quadratic phase for a new value of γ. Analyze the speckle decorrelation of this realization (step 1610).
  • 13. Provide an exemplary system, method and/or computer-accessible medium which can be configured and/or programmed to utilize equations from the result for each analysis, and considering that the GVD₁ and GVD₂ contributions are different for each γ, solve for k₀²v_z1². If there is a physical dispersion baseline contribution, this would add GVD₁ and GVD₂ contributions that can be found in a calibration (step 1620).

In the case of an imaging system (in which the tomograms of transversal one- or two-dimensional regions are taken simultaneously, for example using a camera in which each pixel corresponds to different transversal positions of the sample), the A-lines taken simultaneously can be phase-coherent. For this reason, it is possible to synthetically alter the transversal resolution in one of the two transverse dimensions (for example, by calculating the convolution of the two- or three-dimensional complex-valued tomogram with a kernel in one of the transverse directions), and generate a number of realizations of the tomogram which can be described by an equation similar to Eq. (17) where the proportionality constant that changes is the k_pp in the transversal direction where the filter is applied. It is possible to determine a three-dimensional profile if the above-explained technique is performed first, so the values for v_pl² and v_pp² are already known. Then, using the exemplary technique for transversal manipulation of the resolution, an exemplary embodiment of the system of equations can be provided and can be used solve for v_x² or v_y² (depending on which one has the filter applied) and given that v_pp=v_x²+v_y² is known, solve for the remaining transversal component.

Following the reasoning above, a method according to another exemplary embodiment of the present disclosure shown in FIG. 16A can be provided as follows:

• • 1. Perform at least one measurement to analyze the speckle-amplitude decorrelation time (step 1600′).
  • 2. Synthetically modify the signal in k-space, such as reducing the bandwidth to reduce the resolution in the direction of interest (e.g. axial) and perform a decorrelation time analysis. Prior to the decorrelation time analysis, optionally perform a correction for axial velocity gradient effects using GVD as explained above. In the case of a rotational-scanning system in which the axial resolution is varied each reconstruction is described by τ⁻²−k_BM²−k_R²ω_R²−k_c=k_pp²v_pp²+k_pl²v_pl², where only k_pl depends on the axial resolution (step 1610′).
  • 3. Use the system of equations generated in the previous step (step 1610) with an exemplary system, method and/or computer-accessible medium which can be configured and/or programmed to solve for the square of the velocity component of interest (e.g. v_pp² and v_pl² in the case of axial resolution change) (step 1620).

In another exemplary embodiment, it is possible to extract two-component vectorial velocity profiles of the sample, where the two components correspond to the longitudinal speed and the transversal speed using amplitude-based and phase-based velocity measurements. Speckle amplitude-based analysis provides information about the total speed of the sample v, while phase-based Doppler analysis provides v_z=v_pl, which can then be used to determine the transversal speed profile v_pp=√{square root over (v²−v_pl²)}.

Following the reasoning above, a method according to an exemplary embodiment of the present disclosure shown in FIG. 17 can be provided as follows:

• • 1. Perform at least one measurement to analyze the speckle-amplitude decorrelation time and phase-based Doppler (step 1700).
  • 2. Provide an exemplary system, method and/or computer-accessible medium which can be configured and/or programmed to utilize equations using the results from both techniques, and solve for v_pp² and v_pl² (step 1710).

Examples of Axial Velocity Gradient Correction and Axial and Lateral Speed Profiles

The exemplary systems, methods, techniques and computer-accessible medium described herein above facilitates an accurate measurement of speeds from speckle-amplitude statistics in presence of axial velocity gradients, as well as the determination of the axial and lateral components as described in the present disclosure. FIG. 16C shows a set of illustrations of measurements taken of Intralipid flow through a tube at different flow rates to correct for axial velocity gradient errors in speckle decorrelation speed measurements as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system and method according to the present disclosure.

For example, image 1631 of FIG. 16B are exemplary illustrations of axial velocity gradient determination using the exemplary embodiment of the k-space GVD diversity analysis for a flowing liquid as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system and method according to the present disclosure. Indeed, the exemplary image 1630 is the expected parabolic speed profile, the exemplary image 1631 is the uncorrected speckle flow speed profile, the exemplary image 1632 is the relation between data in images 1630 and 1631, the exemplary image 1633 is the expected GVD_1 due to axial velocity gradient, 1634 is the GVD_1 as determined by the methods according to the exemplary embodiments shown in FIG. 16A (1600, 1610, 1620), the exemplary image 1636 is the expected GVD_2 due to axial velocity gradient, the exemplary image 1637 is the GVD_2 as determined by the methods according to the exemplary embodiments of FIG. 16A (1600, 1610, 1620), the exemplary image 1638 is the corrected speckle flow speed profile, and the exemplary image 1635 is the relation between data in the images 1630 and 1638.

FIG. 16C shows a set of illustrations of exemplary speed profile measurements of axial, lateral and total flow speed, in which the axial gradient velocity correction was used for more accurate results, e.g., of the k-space bandwidth diversity analysis for a flowing liquid as measured by the exemplary embodiment of the speckle-amplitude statistics OFDI system and method according to the present disclosure. In particular, the exemplary set of images 1650 shows on the left column thereof the speckle determined flow, on the right column the parabolic expected flow, the top row—the axial flow, in the center row—the lateral flow, and at the bottom row—the total flow speed, e.g., as determined by the method according to the exemplary embodiments of Figures (see exemplary procedures/steps 1600, 1610, 1620). The exemplary set of images 1655 indicate average profiles and expected profiles shown in the exemplary images of 1650. The exemplary image 1660 shows the corresponding flow angle as a function of depth as determined for 3 different angles in the region of interest using the exemplary embodiment of lateral and axial flow determination according to an exemplary embodiment of the present disclosure.

Exemplary Turbulence Determination in Flow Speed Profiles

There can be different approaches for determining turbulence when the speed profile corresponds to a flowing material. For example, the exemplary method shown as a flow diagram in FIG. 18 can be used for this exemplary purpose. The exemplary steps/procedures shown in FIG. 18 are as follows:

• • 1. Determine the Doppler velocity profile in the LOS, step 1800.
  • 2. Determine the total speed profile using speckle decorrelation, step 1810.
  • 3. Laminar flow inside a cavity usually exists only when the direction of the flow is homogeneous, therefore the absolute value of the Doppler velocity profile is proportional to the actual speed profile. Subtracting the Doppler profile from the speckle profile will highlight areas where the two profiles disagree, which directly mark the areas with flow with different directions, step 1820.
  • 4. Areas with large discrepancies are marked as areas with turbulent flow to avoid false positives due to statistical fluctuations of the speckle speed profile, step 1830

For example, by implementing a method for determining a vectorial velocity profile based on speckle decorrelation (such as the exemplary method shown in FIG. 16), no assumptions on the direction of the flow inside a cavity are needed in order to detect turbulent flow. The vectorial profile can provide a directional information, and analysis of this information (such as a velocity direction variance profile) can provide direct information on turbulence.

Exemplary Tracking of Motion of Probe

For example, by measuring the relative motion of the probe and sample, instead of determining the velocity profile of the sample, if the sample corresponds to a monolithic structure, this information can be used to track the motion of the probe during measurements. In the exemplary embodiment of the system shown in FIG. 5, although the blood flow inside the vessel carries information of interest, the vessel wall is a monolithic landmark that can be used to track the movement of the catheter while it is performing measurements. This can be important when performing a pullback scan (scanning in the longitudinal direction of the vessel), as unwanted motion of the tissue can lead to structural imaging and flow imaging artifacts. The exemplary embodiment of the method according to the present disclosure can be used to track the motion of the probe without the need of additional radiation apart from the configuration and/or technique used for imaging. It is also possible to track not only displacements, but also the rotational speed of the catheter as it scans the tissue. Irregular rotational scanning, known in the literature as non-uniform rotation distortion (NURD), is a prevalent problem in catheter-based imaging. The exemplary embodiment of the method according to the present disclosure can track the rotational speed of the catheter to compensate for such distortions.

Another exemplary embodiment of a method according the present disclosure is shown in FIG. 19, and provided as follows:

• • 1. Determine/calculate a Doppler velocity profile of a reference surface. The surface should ideally encompass all or most azimuthal angles, such as the vessel in FIG. 5. It can be readily recognized that many reference surfaces are possible depending on the application, such as the wall of the airways in pulmonary applications, or the wall of the esophagus in gastrointestinal applications (step 1900).
  • 2. Determine/calculate the transversal velocity of the probe by analyzing the motion of the center of gravity of the reference surface. This is accomplished by a proper model of the reference surface (e.g. an ellipsoid in the case of a blood vessel), and implementation of computer vision algorithms to track the wall (step 1910).
  • 3. Subtract the transversal contribution to the Doppler signal from the previous step so that the residual Doppler signal corresponds to pure longitudinal motion. As the Doppler signal has contribution from both longitudinal and transversal motion (due to the angle θ in FIG. 5), the transverse contribution has to be determined/calculated from the data from step 1910 and then subtracted from the experimental Doppler data. This provides a Doppler signal that comes exclusively from longitudinal motion of the catheter (step 1920).

Using the information from the longitudinal motion of the catheter and the transversal motion derived from image analysis, e.g., the motion of the catheter in three-dimensional space can be obtained. This can be used, for instance, to correct image artifacts from inhomogeneous pullback speeds, unwanted vessel motion, patient motion, among other applications.

Another exemplary embodiment of a method according to another exemplary embodiment of the present disclosure is shown in FIG. 20, and provided as follows:

• • 1. Determine/calculate the transverse speed of the probe by analyzing the speckle decorrelation of a reference surface. This surface should ideally encompass all or most azimuthal angles, such as the vessel in FIG. 5. It can be readily recognized that many reference surfaces are possible depending on the application, such as the wall of the airways in pulmonary applications, or the wall of the esophagus in gastrointestinal applications (step 2000).
  • 2. Convert transverse speed into rotational speed by taking into account the distance of the surface to the center of rotation. Use transverse speed to calculate angular position as a function of time (step 2010).
  • 3. Optionally correct for missing or excess speed due to inaccuracies in the speed estimation (step 2020).
  • 4. Optionally correct the image data taking into account the determined angular position of the probe as a function of time (step 2030).

Examples of Longitudinal and Rotational Probe Motion Tracking

The exemplary systems, methods and computer-accessible medium described herein can facilitate a performance of accurate tracking of the measuring probe. FIG. 21 shows a set of illustrations of images and graphs of measurements taken of a probe undergoing stable motion except for a single perturbation during scanning. For example, the exemplary image 2100 is an exemplary frame of reference surface (wall) tracking; the exemplary graph 2110 indicates a transverse position of the probe in time from the reference tracking surface tracking; the exemplary graph 2120—estimated transverse speed of the probe from the results in the graph 2110; the exemplary image 2130—estimated Doppler shift from the transverse speed in the graph 2120; the exemplary image 2140—experimental Doppler shift at the reference surface as a function of frame; the exemplary graph 2150—residual longitudinal velocity of the probe after subtraction from the graph 2140 of the estimated Doppler shift in the image 2130. The longitudinal velocity of the probe as determined by the exemplary embodiment shows the expected behavior.

FIG. 22 shows a set of illustrations of exemplary rotational probe speed tracking and compensation of image deformation, according to the present disclosure. For example, a set of images/graph 2200 provides an exemplary analysis of the transverse speed of the probe from speckle decorrelation, translation into rotational speed and correction of a deformed image due to NURD. The exemplary images 2210 indicate a projection of a group of 400 images, before and after NURD detection and correction, as explained in the various exemplary embodiments of the methods according to the present disclosure. Although only a few exemplary embodiments of the present disclosure have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel and non-obvious teachings and advantages of the present disclosure. Accordingly, all such modifications are intended to be included within the scope of the present disclosure as defined in the following claims.

Indeed, the arrangements, systems and methods according to the exemplary embodiments of the present disclosure can be used with and/or implement any OCT system, OFDI system, SD-OCT system or other imaging systems, and for example with those described in International Patent Application PCT/US2004/029148, filed Sep. 8, 2004 which published as International Patent Publication No. WO 2005/047813 on May 26, 2005, U.S. patent application Ser. No. 11/266,779, filed Nov. 2, 2005 which published as U.S. Patent Publication No. 2006/0093276 on May 4, 2006, and U.S. patent application Ser. No. 10/501,276, filed Jul. 9, 2004 which published as U.S. Patent Publication No. 2005/0018201 on Jan. 27, 2005, and U.S. Patent Publication No. 2002/0122246, published on May 9, 2002, the disclosures of which are incorporated by reference herein in their entireties. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures which, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. In addition, all publications and references referred to above can be incorporated herein by reference in their entireties. It should be understood that the exemplary procedures described herein can be stored on any computer accessible medium, including a hard drive, RAM, ROM, removable disks, CD-ROM, memory sticks, etc., and executed by a processing arrangement and/or computing arrangement which can be and/or include a hardware processors, microprocessor, mini, macro, mainframe, etc., including a plurality and/or combination thereof. In addition, certain terms used in the present disclosure, including the specification, drawings and claims thereof, can be used synonymously in certain instances, including, but not limited to, e.g., data and information. It should be understood that, while these words, and/or other words that can be synonymous to one another, can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it can be explicitly being incorporated herein in its entirety. All publications referred to herein are hereby incorporated herein by reference.

Claims

1. An apparatus comprising: at least one first arrangement providing a radiation, and including a splitter which separates the radiation into at least one first electro-magnetic radiation directed to a sample and at least one second electro-magnetic radiation directed to a reference, wherein a frequency of the radiation provided by the at least one first arrangement is controlled thereby to vary over time; andat least one detector second arrangement configured to detect a first interference and a second interference between at least one third radiation associated with the at least one first radiation and at least one fourth radiation associated with the at least one second radiation, wherein the first and second interferences are different from one another,wherein the at least one second arrangement includes a computer that is configured to:i. obtain information associated with at least one relative phase between the first and second interferences, andii. determine an absolute phase of at least one of the first interference or the second interference based on the information.

2. The apparatus according to claim 1, wherein the computer is further configured to determine at least one parameter for determining or correcting a motion within a structure associated with the sample based on the absolute phase.

3. The apparatus according to claim 2, wherein the structure includes a living body.

4. The apparatus according to claim 3, wherein the computer is further configured to determine or correct the at least one parameter for correcting at least one of the motion or an orientation between an imaging probe in which the at least one second arrangement is situated and the living body.

5. The apparatus according to claim 1, wherein the second arrangement is further configured to detect a first interference and a second interference between at least one third radiation associated with the at least one first radiation and at least one fourth radiation associated with the at least one second radiation, wherein the first and second interferences are different from one another, and wherein the computer is further configured to determine further information based on a difference between an amplitude of the first interference and an amplitude of the second interference.

6. The apparatus according to claim 5, wherein the computer is further configured to determine a velocity distribution of a structure or a liquid associated with the sample based on the absolute phase and the further information.

7. The apparatus according to claim 1, wherein the computer is further configured to determine at least one parameter for determining or correcting a motion within a structure associated with the sample using a filter.

8. The apparatus according to claim 7, wherein the computer generates further measurements regarding the sample based on the filtered motion.

9. An apparatus comprising: at least one first arrangement providing a radiation, and including a splitter structure which separates the radiation into at least one first electro-magnetic radiation directed to a sample and at least one second electro-magnetic radiation directed to a reference, wherein a frequency of the radiation provided by the at least one first arrangement is controlled thereby to vary over time; andat least one detector second arrangement configured to detect a first interference and a second interference between at least one third radiation associated with the at least one first radiation and at least one fourth radiation associated with the at least one second radiation, wherein the first and second interferences are different from one another,wherein the at least one second arrangement includes a computer that is configured to determine information based on a difference between an amplitude of the first interference and an amplitude of the second interference.

10. The apparatus according to claim 9, wherein the computer is further configured to determine at least one parameter for determining or correcting a motion within a structure associated with the sample based on the absolute phase or using a filter.

11. The apparatus according to claim 10, wherein the structure includes a living body.

12. The apparatus according to claim 11, wherein the computer is further configured to determine or correct the at least one parameter for correcting at least one of the motion or an orientation between an imaging probe in which the at least one second arrangement is situated and the living body.

13. The apparatus according to claim 9, wherein the determined information corresponds to a plurality of components of a velocity profile of a structure or a liquid associated with the sample.

14. The apparatus according to claim 10, wherein the computer generates further measurements regarding the sample based on the filtered motion.

15. An apparatus comprising: at least one first arrangement providing a radiation, and including a splitter structure that separates the radiation into at least one first electro-magnetic radiation directed to a sample and at least one second electro-magnetic radiation directed to a reference; andat least one spectral separating second arrangement which is configured to (a) receive a first interference and a second interference between at least one third radiation associated with the at least one first radiation and at least one fourth radiation associated with the at least one second radiation, and (b) spectrally separate the first interference into a first separated interference and the second interference into a second separated interference;at least one third arrangement including a plurality of detectors which are configured to detect the first and second separated interferences, wherein the first and second separated interferences are different from one another,wherein the at least one third arrangement includes a computer that is configured to:i. obtain information associated with at least one relative phase between the first and second separated interferences, andii. determine an absolute phase of at least one of the first interference or the second interference based on the information.

16. The apparatus according to claim 15, wherein the computer is further configured to determine at least one parameter for determining or correcting a motion within a structure associated with the sample based on the absolute phase.

17. The apparatus according to claim 16, wherein the structure includes a living body.

18. The apparatus according to claim 17, wherein the computer is further configured to determine or correct the at least one parameter for correcting the motion between an imaging probe in which the at least one second arrangement is situated and the living body.

19. The apparatus according to claim 15, wherein the computer is further configured to determine further information based on a difference between an amplitude of the first interference and an amplitude of the second interference.

20. The apparatus according to claim 19, wherein the computer is further configured to determine a velocity distribution of a structure or a liquid associated with the sample based on the absolute phase and the further information.

21. The apparatus according to claim 15, wherein the computer is further configured to determine at least one parameter for determining or correcting a motion within a structure associated with the sample using a filter.