Apparatus, systems, methods and computer-accessible medium for spectral analysis of optical coherence tomography images

Abstract

According to an exemplary embodiment of the present disclosure, apparatus and method can be provided for generating information for at least one structure. For example, using at least one first arrangement, it is possible to receive at least one first radiation from the at least one structure and at least one second radiation from a reference, and interfere the first and second radiations to generate at least one third radiation. Further, with at least one second arrangement, it is possible to generate spectroscopic data as a function of the at least one third radiation, and reduce at least one scattering effect in the spectroscopic data to generate the information. In addition or as an alternative, according to a further exemplary embodiment of the present disclosure, it is possible to classify a type of the structure based on the spectroscopic data to generate the information.

Claims

1. An apparatus for generating information for at least one structure, comprising: at least one interferometer first arrangement which is configured to receive at least one single-spatial mode first radiation from the at least one structure and at least one second radiation from a reference, and combine only one of the first radiation with the at least one second radiation to generate a single third radiation; at least one computer second arrangement which is configured to (i) generate spectroscopic data as a function of the single third radiation; and (ii) reduce at least one scattering effect in the spectroscopic data using only the single third radiation to generate the information.

2. The apparatus according to claim 1, wherein the at least one second arrangement is further configured to reduce an intensity variation of the of the spectroscopic data.

3. The apparatus according to claim 1, wherein the at least one second arrangement is further configured to smooth the spectroscopic data based a polynomial fit to the spectroscopic data.

4. The apparatus according to claim 3, wherein the at least one structure is an anatomical structure.

5. The apparatus according to claim 4, wherein the anatomical structure is an artery wall.

6. The apparatus according to claim 1, wherein the at least one second arrangement is further configured to classify a type of the structure based on the spectroscopic data.

7. A method for generating information for at least one structure, comprising: receiving at least one single-spatial mode first radiation from the at least one structure and at least one second radiation from a reference; causing an interference of only one of the first radiation with the at least one second radiation to generate a single third radiation; generating spectroscopic data as a function of the single third radiation; and with a computing arrangement, causing a reduction at least one scattering effect in the spectroscopic data using only the single third radiation to generate the information.

8. An apparatus for generating information for at least one structure, comprising: at least one interferometer first arrangement which is configured to receive at least one first radiation from the at least one structure and at least one second radiation from a reference, and interfere the first and second radiations to generate at least one third radiation; and at least one computer second arrangement which is configured to (i) generate spectroscopic data as a function of only the at least one third radiation, and (ii) classify a type of the structure based on the spectroscopic data using a statistical pattern recognition procedure to generate the information.

9. The apparatus according to claim 8, wherein the classification includes a generation of a probability that the structure is of a particular type.

10. The apparatus according to claim 9, wherein the probability is provided for a depth-resolved two-dimensional map.

11. The arrangement according to claim 8, wherein the at least one structure is an anatomical structure.

12. The apparatus according to claim 11, wherein the anatomical structure is an artery wall.

13. The apparatus according to claim 8, wherein the type is a type of tissue or a type of plaque.

14. The apparatus according to claim 8, wherein the plaque is an atherosclerotic plaque.

15. The apparatus of claim 8, wherein the statistical pattern recognition procedure provides more than two possible outcomes.

16. A method for generating information for at least one structure, comprising: receiving at least one first radiation from the at least one structure and at least one second radiation from a reference; causing an interference of the first and second radiations to generate a single third radiation; generating spectroscopic data as a function of only the single third radiation; and with a computer arrangement, classifying a type of the structure based on the spectroscopic data using a statistical pattern recognition procedure using the third radiation to generate the information.

17. An apparatus for generating information for at least one first structure and at least one second structure, comprising: at least one interferometer first arrangement which is configured to receive at least one first radiation from the at least one first structure, at least one second radiation from the at least one second structure and at least one third radiation from a reference, and interfere the first, second and third radiations to generate a single fourth radiation; and at least one computer second arrangement which is configured to (i) determine first data regarding the at least one first structure and the second data regarding the at least one second structure, (ii) generate statistical pattern recognition data from spectra as a function of the first and second data using the fourth radiation, and (iii) compare the statistical pattern recognition data with the predetermined data to determine the information.

18. The apparatus according to claim 17, wherein the at least one structure is an anatomical structure.

19. The apparatus according to claim 18, wherein the anatomical structure is an artery wall.

20. The apparatus according to claim 17, wherein the type is a type of tissue or a type of plaque.

21. A method for generating information for at least one first structure and at least one second structure, comprising: receiving at least one first radiation from the at least one first structure, at least one second radiation from the at least one second structure and at least one third radiation from a reference; causing an interference of the first, second and third radiations to generate a single fourth radiation; determining first data regarding the at least one first structure and the second data regarding the at least one second structure; generating statistical pattern recognition data from spectra using the fourth radiation as a function of the first and second data; and with a computer arrangement, comparing the statistical pattern recognition data with predetermined data to determine the information.

22. An apparatus for generating information for at least one structure, comprising: at least one interferometer first arrangement which is configured to receive at least one single-spatial mode first radiation from the at least one structure and at least one second radiation from a reference, and combine only one of the first radiation with the at least one second radiations to generate a single third radiation; and at least one computer second arrangement which is configured to generate spectroscopic data as a function of the single third radiation, wherein the spectroscopic data represents, at least partially, an interference between the first and second radiations, and wherein the at least one second arrangement is further configured to reduce at least one scattering effect in the spectroscopic data to generate the information based only on the single third radiation.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) 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:

(2) FIG. 1 is a flow diagram of an exemplary embodiment of a method associated with determining if a tissue component such as lipid or necrotic core is present within a material or tissue imaged with optical coherence tomography (OCT) or OFDI in accordance with the present disclosure;

(3) FIG. 2 is a block diagram of an exemplary embodiment of a device associated with controlling an apparatus to determine whether there is a component present in the tissue within a region of interest in accordance with the present disclosure;

(4) FIG. 3 is a flow diagram of an exemplary embodiment of a method which can be used for determining the presence of a tissue component within OCT/OFDI images as a way to guide treatment in accordance with the present disclosure;

(5) FIG. 4 is a flow diagram of an exemplary embodiment of the method applied to Fourier Domain OCT (e.g., OFDI, SSOCT, SDOCT) signals for the identification of areas containing a component, such as lipid rich areas, within OCT images in accordance with the present disclosure;

(6) FIG. 5 are exemplary graphs and images of obtaining depth resolved spectrums in accordance with the present disclosure;

(7) FIG. 6 are exemplary graphs and images of calculating attenuation spectrum in accordance with the present disclosure;

(8) FIG. 7 are exemplary graphs and images of the effect of preprocessing of spectra on the attenuation spectra in accordance with the present disclosure;

(9) FIG. 8 are exemplary visualizations of depth resolved SOFDI classification, probability of the presence of a component such as lipid or necrotic core, or parameters with OFDI image in accordance with the present disclosure;

(10) FIG. 9 are exemplary visualizations of parameters or probability classification using a chemogram in accordance with the present disclosure;

(11) FIG. 10 is a flow diagram of an exemplary embodiment of a method for the calibration of the wavelength (or wavenumber) axis for spectroscopic OCT derived spectra in accordance with the present disclosure.

(12) FIG. 11 is a set of exemplary visualizations of depth resolved parametric maps in accordance with an exemplary embodiment of the present disclosure;

(13) FIG. 12 is a set of exemplary visualizations of pullback parametric or probability maps in accordance with an exemplary embodiment of the present disclosure;

(14) FIG. 13 is a graph of attenuation basis spectra used within the classification model in accordance with an exemplary embodiment of the present disclosure;

(15) FIG. 14 is a flow diagram of an exemplary embodiment of a method for classification of multiple components using spectroscopic OCT in accordance with the present disclosure.

(16) FIG. 15 is a set of exemplary visualizations of depth resolved classification maps in accordance with an exemplary embodiment of the present disclosure;

(17) 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

(18) FIG. 1 illustrates a flow diagram of an exemplary embodiment of a method 100 associated with determining if a component is present within a material or tissue imaged with optical coherence tomography (OCT) or OFDI. In one exemplary embodiment, this component can be lipid. In other exemplary embodiments, the component can include cholesterol, cholesterol ester, smooth muscle cells, calcification, fibrin, thrombus, hemoglobin, macrophages, necrotic core, or the like. Method 100 can, at procedure 110, provide for acquiring an OCT signal from an area within the sample. The sample can be tissue, for example, coronary arteries. The acquired OCT/OFDI signal at procedure 120 can be an inteferogram (e.g., a Time Domain OCT configuration) or spectral inteferogram (e.g., a Fourier Domain OCT configuration). The OCT/OFDI signal can be further processed to produce an axial scan by computing (e.g., using a computer arrangement which can include or be connected to a physical storage arrangement) the envelope of the inteferogram (Time Domain OCT) or Fourier Transform (Fourier Domain OCT).

(19) Exemplary time frequency analysis can be used to obtain depth resolved spectra at procedure 130. Exemplary time frequency analysis methods include, but are not limited to, the Short Time Fourier Transform, the Wigner-Ville distribution, Cohens classes, and the Dual Window approach (e.g., multiplying the results of two Short Time Fourier Transform spectrograms obtained with varying window sizes). Depth resolved spectra are used to determine if the component such as lipid is present, at procedure 140. Example parameters that can be computed from the preprocessed attenuation spectrum include but are not limited to mean central frequency, skewness, dispersion, and kurtosis. Exemplary parameters can also be extracted by fitting the attenuation spectra to basis spectra. Basis spectra can be obtained through principal component analysis or pure chemical components that affect the NIR spectrum within the wavelength range of interest (e.g. cholesterol, cholesterol esters, collagen, elastin, water) and ex vivo animal tissues that are rich in structural proteins such as collagen and elastin. For example, using multivariate analysis techniques (e.g. discriminant analysis, logistic regression, multivariate regression), the composition of depth resolved spectra to key chemical components (e.g. water, cholesterol, cholesteryl oleate, collagen) or key plaque components (lipid, fibrous, calcium, adventitial fat) can be used to derive probability maps of each component. Attenuation coefficient, backscattering coefficient, and intensity can be additional parametric inputs into the multivariate analysis. Parametric or probability values are displayed as images, at procedure 150, either alone or overlaid on OCT/OFDI images.

(20) FIG. 2 illustrates a block diagram of an exemplary embodiment of a device 200 associated with controlling an apparatus to determine whether there is tissue component, compound or chemical present within a region of interest, according to the present disclosure. The exemplary device 200 can include a detector 210 to acquire OCT signal 230 from an area within the coronary artery. The exemplary device 200 can also include a development logic 220 (e.g., stored on a storage arrangement and accessible by a computing arrangement) which configures the computing arrangement to control an apparatus to determine whether the tissue component, compound or chemical is present in a region of interest, based in part on parameters or model fits obtained from depth resolved spectra computed from an OCT signal 230.

(21) The detector 210 can acquire the OCT signal 230. The detector 210 can be a time domain OCT system, or Fourier Domain OCT systems (swept source OCT/OFDI or spectral domain OCT), with our without polarization diversity detection, with or without polarization sensitive detection. In one exemplary embodiment, the exemplary device 200 can include an OFDI system with a swept wavelength light source, centered at about 1300 nm with approximately 120 nm bandwidth, with polarization diversity detection. Alternatively, the exemplary device 200 can include a swept wavelength light source or broadband light source covering the wavelength range of about 1200 to 1350 nm. In another exemplary embodiment, the swept source can cover a range from within the range of about 1000-1400 nm. In yet another embodiment, the swept source can additionally include the range of about 1500-1800 nm.

(22) It should be understood that other parameters and/or models can be implemented, as understood by those having ordinary skill in the art.

(23) FIG. 3 illustrates a flow diagram of an exemplary embodiment of a method 300 which can be used for determining the presence of a tissue component within OCT/OFDI images as a way to guide treatment in accordance with the present disclosure. This exemplary method 300, can include acquiring an OCT/OFDI signal at procedure 320, computing depth resolved spectra using time frequency analysis. Parametric thresholds or modeling fitting can be used to determine if tissue component, compound or chemical such as lipid is present, 340. Probability or parametric values can then be visualized, at procedure 350. If done in real time, comprehensive visualization of tissue component, compound or chemical such as lipid within the material (e.g. coronary artery) can guide treatment option decision-making, at procedure 360 (e.g. stent placement, length of stent to be used, stent versus drug treatment).

(24) It should be understood that other thresholds and/or models can be implemented, as understood by those having ordinary skill in the art.

(25) FIG. 4 illustrates a flow diagram of an exemplary embodiment of the method applied to Fourier Domain OCT (OFDI, SSOCT, SDOCT) signals for the identification of lipid or other chemical containing areas within OCT images in accordance with the present disclosure. This exemplary method is not limited to analysis of OFDI images of coronary arteries, but can be applied to any OCT dataset where there is absorption contrast within the OCT light source bandwidth. In a preferred embodiment, this method applies to OFDI (SS-OCT) data—this exemplary method can be termed “SOFDI”. Exemplary procedures 410 and 420 can be beneficial to Fourier Domain OCT systems, where the recorded signal is a spectral inteferogram. Fourier transform of the spectral inteferogram in wavenumber space, at procedure 420, can result in the axial line, 430. Time frequency analysis at procedure 430 results in the procedure 440 (described in FIG. 1, as procedure 130).

(26) Within SOFDI processing of tissue, there can be (according to exemplary embodiments of the present disclosure) possibly limited ways to perform incoherent compounding to increase SNR and reduce variability with the spectra that is analyzed. In prior art, SOCT studies 50-200 spectra are averaged before computation of spectra parameters are made. Due to the heterogeneity of tissue, e.g., coronary arteries, averaging 50 spectra may not be feasible. Preprocessing, at procedure 460, of the spectra can be used to reduce variability and remove inherent intensity based differences. Preprocessing methods can include, but are not limited to, standard normal variate transformation (SNV), filtering/smoothing, first derivative with filtering, second derivative with smoothing, or multiplicative scatter correction. Attenuation spectra, at procedure 470, can be computed, e.g., by a processing/computing arrangement, using Beer's law. Post processing procedure 480 of the attenuation spectrum 470 can include calculating parameters describing the shape of the attenuation spectrum or fitting the attenuation spectra to models to compute probability of lipid present. Parameters or probability measures are then visualized. Exemplary procedures 430-480 can apply to all OCT signals (e.g., signals from time domain and Fourier domain OCT systems).

(27) It should be understood that other models can be implemented, as understood by those having ordinary skill in the art.

(28) FIG. 5 are exemplary graphs and images of obtaining depth resolved spectrums in accordance with the present disclosure. For example, the OFDI system that can be used within these examples has approximately 1320 nm center wavelength and about 120 nm bandwidth. 510 is a spectral inteferogram. Exemplary graph 510 is Fourier Transformed to obtain a depth resolved axial scan 520. Time frequency analysis can then be used to obtain depth resolved spectra. Time frequency analysis graph 520 can generate an exemplary depth resolved spectra image 530. The time frequency analysis method/procedure used in this example can be a dual windowed—short time Fourier transform method. Hanning windows of two different sizes can be used to optimize time/frequency resolutions. The depth resolved spectra can be transformed from wavenumber space to wavelength space, S(λ,z), for ease of interpretation.

(29) FIG. 6 shows exemplary graphs and images of calculating attenuation spectrum. Exemplary images can include an area with calcium 610 and an area with lipid 650. Regions of interest (e.g., 620, 660) can be used to compute attenuation spectrum. The first preference is to identify pixels that encompass tissue with sufficient SNR in which the algorithm will compute metrics. Region of interest identification can be computed manually or automatically. One such exemplary automated method is to identify the tissue area as having intensity above a threshold. The noise floor of the image can be computed, and threshold intensity can be computed as pixel intensity greater than a percentage of the noise floor. Time frequency analysis can be computed on the axial line, (630, 670) as shown in FIG. 5. Depth resolved spectra can be computed for each axial line. Within this example, 8 adjacent spectra were averaged. Attenuation spectra (640, 680) can be obtained using, e.g., Beer's Law, −log10[S(λ,z.sub.2)/S(λ,z.sub.1)]. For example, z.sub.1 can be the top surface, and z.sub.2 can be a fixed distance below the surface.

(30) FIG. 7 shows exemplary graphs and images of the effect of preprocessing of spectra on the attenuation spectra according to the present disclosure. The exemplary embodiments encompass an image 710 with normal tissue 720 and a lipid pool 730. Plotted in graph 740 are mean attenuation spectra +/−95% confidence interval within the regions 720 and 730 without preprocessing. Exemplary graphs 750, 760, 770 are exemplary mean attenuation spectra +/−95% confidence interval within the regions 720 and 730 with preprocessing, to reduce variability and high frequency oscillations within spectra. Exemplary graphs 750 show the effect of the Savitzky-Golay filter, where in this exemplary embodiment, a 4.sup.th order polynomial was used, smoothing out the spectra and reducing high frequency modulations. Exemplary graphs 760 show the effect of the standard normal variate transformation, where the variability within the spectra is reduce, while preserving the spectral shape. exemplary graphs and images of 770 show a combined effect of the standard normal variate transformation and Savitzky-Golay 4.sup.th order polynomial filter, reducing variability, maintaining spectral shape, and reducing high frequency oscillations.

(31) FIG. 8 show exemplary visualizations of depth resolved SOFDI classification, probability of lipid, or parameters with OFDI image according to the present disclosure. Attenuation spectra can be computed for a fixed length and iterated through the axial line for the entire length of the region of interest, as described above. Examples shown in this figure are OFDI images with lipid pool, exemplary image 810, and calcium, exemplary images 840. 820 and 850 are parametric images of the σ.sub.s=second moment divided by the zero-th moment of the attenuation spectra, preprocessed using the standard normal variate transformation. The hot color map is used, where yellow indicates high σ.sub.s, and red indicates low σ.sub.s. Exemplary images 830 and 860 are images with the parametric image overlayed on top of the OFDI image. Lipid rich regions have a high σ.sub.s. Similar maps can be generated for each parameter or probability computed with the algorithm.

(32) FIG. 9 illustrate exemplary visualizations of parameters or probability classification using a chemogram in accordance with the present disclosure. An exemplary chemogram method of visualizing the spectral parameters can be used to easily identify areas of interest (e.g. high lipid content). One parametric or probability value is assigned to an axial line, and displayed using the hot color map. Examples shown in FIG. 9 include OFDI images with a lipid pool image 910, and a calcium image 920. Mean values from the depth resolved SOFDI parametric or probability image (as shown in FIG. 8) can be used to generate the chemogram. The chemogram can be displayed on the bottom of the OFDI image. In this example, a hot color map was used, where a lighter shade corresponds to high σ.sub.s (as shown and described in relation to FIG. 8) and a darker shade corresponds to low σ.sub.s.

(33) FIG. 10 shows a flow diagram of an exemplary embodiment of a method 1000 for the calibration of the wavelength (or wavenumber) axis for spectroscopic OCT derived spectra. Calibration of the wavelength axis is important for interpretation of attenuation spectra according to the present disclosure. Calibration of the wavelength axis can include comparison of SOFDI derived spectra with known and measured spectra. The signal inputs to the signal comparison process 1030 are the SOFDI derived spectra 1010 and the reference or measured (gold standard) spectra 1020. Spectra 1010 can include spectra obtained from images from the experimental data set (e.g. intracoronary image) or spectra obtained through a calibration experiment (e.g. imaging a mirror or imaging a phantom). According to one exemplary embodiment, comparison of the depth resolved spectra 1010 can be used which is measured from a highly reflective surface within the image (e.g. sheath, guide wire) compared to measured spectrum 1020 of the OCT/OFDI light source. An exemplary signal comparison method to determine the wavelength axis shift can include, but is not limited to, a cross correlation between spectra 1010 and 1020. The wavelength shift can be determined by the detecting the wavelength shift in which the cross correlation of spectra 1010 and the time shifted version of spectra 1020 is a maximum. Numeral 1040 is the wavelength axis shift, determined by the exemplary procedure 1030. In combination with the wavelength bandwidth determined by the point spread function of the exemplary system, this exemplary calibration procedure can facilitate a self referencing of the wavelength range, regardless of the system used.

(34) According to another exemplary embodiment for the signal comparison process 1030, it is possible to compare reference water absorption spectra 1020 to the water absorption spectrum 1010 obtained by Beer's law (e.g., measured using time frequency analysis of experimental or calibration images, −log10[S(λ,z.sub.2)/S(λ,z.sub.1)]). The absorption spectrum from time frequency analysis of an image can be measured between the reflections from the sheath (e.g., at depth z1) to the reflection from the tissue surface (e.g., at depth z2). An exemplary cross correlation can be used from signal comparison procedure 1030 to determine a wavelength shift 1040. The wavelength axis of subsequent spectra 1050 computed using time frequency analysis can be shifted by the shift 1040, to produce a new signal 1070 with the correct wavelength (wavenumber) axis. In yet another exemplary embodiment, the calibration can be performed by measuring the absorption spectra of a medium between the probe and the tissue surface, such as, e.g., radiocontrast media, saline, dextran, dextrose or other media.

(35) FIG. 11 shows a set of exemplary visualizations of depth resolved SOFDI classification, probability of lipid, or parameters with OFDI image according to an exemplary embodiment of the present disclosure. For example, an attenuation spectra can be determined or computed for a fixed length and iterated through the axial line for the entire length of the region of interest, as described above. Exemplary visualizations shown in FIG. 11 include OFDI images with lipid pool, exemplary image 1110, and intimal thickening, fibrous plaque, exemplary images 1130. 1140 and 1150 are parametric images of the optical attenuation coefficient multiplied by the slope of the attenuation spectra overlaid on top of the OFDI image using a Hue Saturation Value (HSV) convention. The attenuation spectra can be preprocessed using the standard normal variate transformation (SVN). The hot gray scale map can be used used, where light gray indicates high attenuation coefficient and decreasing attenuation slope.sub.s. Lipid rich regions, 1120, have a high spectral and optical attenuation. Similar maps can be generated for each parameter or probability computed with the exemplary procedure according to the exemplary embodiment of the present disclosure.

(36) FIG. 12 show a set of exemplary visualizations of pullback SOFDI classification, probability of lipid, or parameters within OFDI image according to an exemplary embodiment of the present disclosure. An automated volumetric reconstruction 1210 of individual depth resolved probability or parametric images is shown therein, as indicated in FIG. 11 and/or FIG. 8. An exemplary visualization of a chemometric pullback 1220 is also provided in FIG. 12, where each line in the image is obtained as described in FIG. 9. The chemometric visualization highlights superficial lipid or components.

(37) FIG. 13 show an exemplary graph of exemplary basis attenuation spectra. The basis spectra 1300 shown therein can be obtained using a principal component analysis from OFDI images of human coronary arteries obtained using an exemplary OFDI system with a 145 nm bandwidth. The first principal component 1310, the second principal component 1320, and the third principal component 1330 are also shown in FIG. 13. Basis spectra can also be derived by obtaining the absorption, scattering, and/or attenuation spectra of isolated components (i.e. lipid, cholesterol, collagen, calcium).

(38) FIG. 14 shows a flow diagram for obtaining probability maps of multiple components according to another exemplary embodiment of the present disclosure. For example, the input to the model can be the recorded spectral inteferogram S(k). The processed axial scan in procedure 1430 can be processed using time frequency analysis in procedure 1435 to obtain depth resolved spectra in procedure 1440. It should be understood that other analysis and/or procedures can be implemented, as understood by those having ordinary skill in the art. An exemplary optical model, such as the signal scattering model (see 1450, 1460), can be used to obtain attenuation and backscattering coefficients for each pixel (μ.sub.t(z′), μ.sub.b(z′)) and each wavelength μ.sub.t(z′,λ). The attenuation spectra, μ.sub.t(z′,λ), can be fit to basis spectra 1480 using least squares or non-negative least squares analysis, as provided in block 1470. Fitting coefficients for each basis spectra, μ.sub.t(z′), and μ.sub.b(z′) are inputs into a prediction model, as provided in block 1490. Prediction models can include multinomial logistic regression, discriminant analysis, decision trees. The output of block 1495 can include classifications, for example: lipid, calcium, fibrous, adventitial fat, collagen, cholesterol, macrophage, etc, and the probability associated with that classification. It should be understood that other models can be implemented, as understood by those having ordinary skill in the art.

(39) FIG. 15 show examples of exemplary probability maps of multiple components or chemicals according to an exemplary embodiment of the present disclosure. Examples shown in FIG. 15 used discriminant analysis for the prediction model. Exemplary results for the discrimant analysis model output can include a probability of key components or chemicals. This example of FIG. 15 shows exemplary probability and classification maps of lipid (light scale), calcium (dark scale), adventitial fat (medium scale), and fibrous tissue (bright scale). The intensity of the scale denotes the classification and the value denotes the probability.

(40) It should be understood that other models can be implemented for the exemplary embodiments of FIGS. 14 and 15, as understood by those having ordinary skill in the art.

(41) Implementing the exemplary embodiments of the methods discussed herein can increase the contrast of OCT and OFDI intracoronary images, thus reducing the time and increasing the accuracy of interpreted images. Enhanced contrast and identification of areas with lipid can facilitate a rapid comprehensive visualization, and enable the guidance of local therapy methods or assessment of appropriate treatment options. This additional exemplary information on tissue component, compound or chemical that is obtained by the disclosed method can be computed using a processing apparatus and displayed in real time in two or three dimensions to guide the diagnostic and/or therapeutic procedure.

(42) The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. 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 methods which, although not explicitly shown or described herein, embody the principles of the disclosure and are thus within the spirit and scope of the present disclosure. Further, the exemplary embodiments described herein can operate together with one another and interchangeably therewith. In addition, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly being incorporated herein in its entirety. All publications referenced herein above are incorporated herein by reference in their entireties.