Evaluation device, evaluation method, and program

Abstract

Provided is a method for evaluating biological tissue, the method making it possible to quantitatively evaluate dynamics of the biological tissue. The evaluation method according to the present embodiment comprises acquiring an optical coherence tomography (OCT) signal that indicates the state of biological tissue serving as a specimen, acquiring a signal value based on the OCT signal for each observation point in the specimen, and calculating a change-over-time characteristic value that indicates a characteristic of the signal value that changes over time during a prescribed period. The present embodiment makes it possible to also implement an evaluation device or a program.

Description

[Technical field]

[0001] The present invention relates to an evaluation device, method, and program, and more particularly to a technique for visualizing and quantitatively evaluating the state of a sample such as a biological tissue by processing a measurement signal measured by, for example, an optical coherence tomography (OCT). This application claims priority to Japanese Patent Application No. 2019-207348, filed in Japan on November 15, 2019, and Japanese Patent Application No. 2020-070309, filed in Japan on April 9, 2020, the contents of which are incorporated herein by reference. [Background technology]

[0002] In recent years, a technology called "OCT microscopy" that uses OCT to image ex vivo samples has been researched. However, OCT microscopy is generally a morphological imaging technology and cannot image tissue functions such as metabolism. In response to this, a signal analysis method called "dynamic OCT" has been proposed (Non-Patent Document 1). However, this method lacks quantitativeness, and it is difficult to correctly evaluate the degree of biological activity using this method. In addition, this method is compatible with a special type of OCT called full-field OCT (FF-OCT), and it is difficult to implement it in the widely used scanning OCT. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Apelian et al., “Dynamic Full Field Optical Coherence Tomography: Subcellular Metabolic Contrast Revealed in Tissues by Interferometric Signals Temporal Analysis.” Biomedical Optics Express 7, No.4, p.1511-1524, March 24, 2016. Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention has been made in consideration of the above circumstances, and one of its objectives is to provide an evaluation method that enables quantitative evaluation of the dynamic characteristics of a sample, such as the dynamics of biological tissue and intracellular activity. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention employs the following means.

[0006] (1) One aspect of the present invention provides a method for measuring a biological tissue quality using a measuring unit that acquires an optical coherence tomography (OCT) signal indicating a state of a biological tissue sample and acquires a signal value based on the OCT signal for each observation point on the sample, and an evaluation unit that calculates a time variation characteristic value indicating a time variation characteristic of the signal value within a predetermined period of time. The evaluation unit calculates a correlation coefficient between the signal value and a signal value obtained by time-shifting the signal value by a time shift amount τ for each time shift amount τ, and calculates, as the time variation characteristic value, a decay rate of the correlation coefficient associated with an increase in the time shift amount τ, using the correlation coefficient when the time shift amount τ is not zero, without using the correlation coefficient when the time shift amount τ is zero. Evaluation equipment.

[0010] (2)In another aspect of the present invention, the evaluation unit may calculate, as a variance, a sum of squares of deviations between the signal intensity of the OCT signal at each frame time within the specified period and an average value of the signal intensity, calculate, as a covariance, a sum of products of a deviation between the signal intensity of the OCT signal at each frame time within the specified period and the average value of the signal intensity, and a deviation between the signal intensity of the OCT signal at a shift time shifted from the frame time by a time shift amount τ and the average value of the time-shifted signal intensity, divide the covariance by the variance to calculate the correlation coefficient for each of the time shift amounts τ, perform a regression analysis using a predetermined decay function using the correlation coefficient for each of the time shift amounts τ, and calculate a parameter of the decay function that approximates the correlation coefficient as the decay rate for each observation point.

[0012] (3) In another embodiment of the present invention, A measuring unit that acquires an optical coherence tomography (OCT) signal indicating a state of a biological tissue to be sampled, and acquires a signal value based on the OCT signal for each observation point on the sample, and an evaluation unit that calculates a time variation characteristic value indicating a time variation characteristic of the signal value within a predetermined period of time, The measurement unit may determine a polarization characteristic value based on the polarization characteristics at an observation point within the sample based on a first measurement signal having a first polarization state among first interference components resulting from interference between a first incident component that is incident on the sample in a first polarization state and a component that is reflected or scattered from the sample, a second measurement signal having a second polarization state for the first interference component, a third measurement signal having a first polarization state among second interference components resulting from interference between the second incident component that is incident on the sample in a second polarization state and a component that is reflected or scattered from the sample, and a fourth measurement signal having a second polarization state for the second interference component, and the evaluation unit may determine the time-varying characteristic value indicating a time-varying characteristic of the polarization characteristic value.

[0013] (4) In another aspect of the present invention, the measurement unit may determine a Jones matrix for each observation point based on the first measurement signal, the second measurement signal, the third measurement signal, and the fourth measurement signal, determine a cumulative Jones matrix at the observation point from the Jones matrix at the observation point within the sample and the Jones matrix at a surface of the sample, and determine a cumulative phase lag index value, which is a phase difference between eigenvalues ​​of the cumulative Jones matrix, as the polarization characteristic value.

[0014] (5) In another aspect of the present invention, the measurement unit may determine a Jones matrix for each observation point based on the first measurement signal, the second measurement signal, the third measurement signal, and the fourth measurement signal, determine a local Jones matrix between the first observation point and the second observation point from the Jones matrix at the first observation point in the sample and the Jones matrix at the second observation point in the sample, and determine the polarization characteristic value based on a local phase delay, which is a phase difference between eigenvalues ​​of the local Jones matrices.

[0015] (6) In another aspect of the present invention, the measurement unit may determine the birefringence by dividing the local phase delay by a wave number of light incident on the sample and a thickness between the first observation point and the second observation point.

[0016] (7) In another aspect of the present invention, the evaluation section may calculate the time-varying characteristic value based on a variance or a standard deviation of the polarization characteristic value.

[0017] (8) In another aspect of the present invention, the evaluation section may calculate the time-varying characteristic value based on a variance or a standard deviation of a logarithmic value of the polarization characteristic value.

[0018] (9) In another aspect of the present invention, the evaluation section may calculate the dynamic contrast by dividing the standard deviation of the polarization characteristic values ​​by the average value of the birefringence.

[0019] (10) In another aspect of the present invention, the measurement unit may convert, as the polarization characteristic value, a first Jones vector based on the first measurement signal and the second measurement signal, and a second Jones vector based on the third measurement signal and the fourth measurement signal, into a first Stokes vector and a second Stokes vector, respectively, and the evaluation unit may determine, as the time-varying characteristic value, a time polarization uniformity based on the time-averaged value of the first Stokes vector and the time-averaged value of the second Stokes vector.

[0020] (11) In another aspect of the present invention, the measurement unit may determine the temporal polarization uniformity based on a time-averaged value of a corrected first Stokes vector obtained by subtracting a noise component from the first Stokes vector, and a time-averaged value of a corrected second Stokes vector obtained by subtracting a noise component from the second Stokes vector.

[0021] (12) In another aspect of the present invention, the measurement unit may determine a Jones matrix for each observation point based on the first measurement signal, the second measurement signal, the third measurement signal, and the fourth measurement signal as the polarization characteristic value, and the evaluation unit may calculate a von Neumann entropy of the Jones matrix as the time-varying characteristic value.

[0022] (13) In another aspect of the present invention, the evaluation unit may calculate an entropy of a noise component from the time polarization uniformity of a first Stokes vector and the time polarization uniformity of a second Stokes vector converted from a first Jones vector based on the first measurement signal and the second measurement signal, and a second Jones vector based on the third measurement signal and the fourth measurement signal, respectively, and correct the von Neumann entropy based on the entropy of the noise component.

[0023] (14) In another aspect of the invention, the first polarization state may be horizontally polarized and the second polarization state may be vertically polarized, the first measurement signal may be a first horizontally polarized spectral interference signal, the second measurement signal may be a second horizontally polarized spectral interference signal, the third measurement signal may be a first vertically polarized spectral interference signal, and the fourth measurement signal may be a second vertically polarized spectral interference signal.

[0024] (15) In another aspect of the present invention, the evaluation unit may calculate the time variation characteristic value for each observation period interval that is longer than the predetermined period.

[0025] (16) Another aspect of the present invention may further include an output processing unit that determines an evaluation value indicating an activity state of the sample based on the time-varying characteristic value.

[0026] (17) Another aspect of the present invention may include an image processing unit that generates image data having, as a signal value, an output value for the time-varying characteristic value for each observation point using a function that gives an output value that changes monotonically in response to a change in an input value.

[0027] (18) Another aspect of the present invention is a method for an evaluation device, comprising: a measurement step of acquiring an optical coherence tomography (OCT) signal indicating a state of a biological tissue to be used as a sample, and acquiring a signal value based on the OCT signal for each observation point on the sample; and an evaluation step of calculating a time variation characteristic value indicating a time variation characteristic of the signal value within a predetermined period of time. In the measuring step, a polarization characteristic value based on the polarization characteristics at an observation point in the sample is determined based on a first measurement signal having a first polarization state among first interference components resulting from interference between a first incident component that is incident on the sample in a first polarization state and a component that is reflected or scattered from the sample, a second measurement signal having a second polarization state for the first interference component, a third measurement signal having a first polarization state among second interference components resulting from interference between a second incident component that is incident on the sample in a second polarization state and a component that is reflected or scattered from the sample, and a fourth measurement signal having a second polarization state for the second interference component, and in the evaluating step, the time variation characteristic value indicating the time variation characteristic of the polarization characteristic value is determined. This is an evaluation method.

[0028] (19) Another aspect of the present invention is a program for causing a computer of an evaluation device to execute a measurement procedure for acquiring an optical coherence tomography (OCT) signal indicating a state of a biological tissue to be used as a sample, and acquiring a signal value based on the OCT signal for each observation point on the sample, and an evaluation procedure for calculating a time variation characteristic value indicating a time variation characteristic of the signal value within a predetermined period. a measurement step of determining a polarization characteristic value based on polarization characteristics at an observation point in the sample based on a first measurement signal having a first polarization state among first interference components resulting from interference between a first incident component that is incident on the sample in a first polarization state and a component that is reflected or scattered from the sample, a second measurement signal having a second polarization state for the first interference component, a third measurement signal having a first polarization state among second interference components resulting from interference between a second incident component that is incident on the sample in a second polarization state and a component that is reflected or scattered from the sample, and a fourth measurement signal having a second polarization state for the second interference component, and a step of evaluating the polarization characteristic value based on polarization characteristics at an observation point in the sample based on the time variation characteristic value It is. Effect of the Invention

[0029] According to this embodiment, minute changes (fluctuations) that have been overlooked in the past can be detected, and quantitative evaluation of the dynamics of biological tissue can be realized. [Brief description of the drawings]

[0030] [Figure 1] FIG. 1 is a diagram illustrating a biological tissue evaluation method according to a first embodiment. [Diagram 2]1 shows images obtained by applying the methods of Comparative Example 1 and Examples 1 and 2 to a portion of biological tissue. [Figure 3A] FIG. 13 is a diagram showing speckle variance (time dispersion of speckle signal intensity) corresponding to the image of Example 1. [Figure 3B] FIG. 13 shows the decay rate of OCT correlation corresponding to the images of Example 2. [Figure 3C] FIG. 13 is a diagram showing the attenuation coefficient of the OCT signal intensity when the method of the third embodiment is applied. [Figure 4A] FIG. 2 is a diagram showing the viability and death state of biological tissue to which the method of Example 1 is applied. [Figure 4B] FIG. 11 is a diagram showing the viability of biological tissue to which the method of Example 2 is applied. [Diagram 5] 13 shows images obtained by applying the methods of Comparative Example 2 and Examples 4 and 5 to a portion of biological tissue. [Figure 6A] FIG. 13 is a diagram showing speckle variance corresponding to an image of Example 4. [Figure 6B] FIG. 13 shows the decay rate of OCT correlation corresponding to the images of Example 5. [Figure 6C] FIG. 13 is a diagram showing the attenuation coefficient of the OCT signal intensity when the method of the sixth embodiment is applied. [Figure 7A] FIG. 13 is a diagram showing the viability of biological tissue to which the method of Example 4 is applied. [Figure 7B] FIG. 13 is a diagram showing the viability of biological tissue to which the method of Example 5 is applied. [Figure 8] 1 is a configuration diagram showing an example of an OCT system according to a first embodiment. [Figure 9] FIG. 11 is a configuration diagram illustrating an example of an OCT system according to a second embodiment. [Figure 10] FIG. 11 is a block diagram showing an example of the configuration of a measurement signal processing device according to a second embodiment. [Figure 11] 10 is a flowchart showing an example of OCT signal processing according to the second embodiment. [Figure 12] FIG. 11 is an explanatory diagram of an observation period according to the second embodiment. [Figure 13]FIG. 1 is a diagram showing an example of a spatial distribution of dispersion of birefringence. [Figure 14] FIG. 13 is a diagram showing another example of the spatial distribution of dispersion of birefringence. [Figure 15] FIG. 1 is a diagram showing an example of the spatial distribution of the dispersion of the average local birefringence. [Figure 16] FIG. 1 is a diagram showing an example of the correlation between the dispersion of birefringence and the average local birefringence. [Figure 17] FIG. 1 is a diagram showing an example of the correlation between the dispersion of birefringence and the dispersion of logarithmic intensity. [Figure 18] 13 is a flowchart illustrating an example of a calculation process of a TPU according to the second embodiment. [Figure 19] FIG. 1 shows an example of spatial distribution of TPU. [Figure 20] FIG. 13 is a diagram showing an example of a time change in dispersion of birefringence. [Figure 21] FIG. 13 is a diagram showing another example of time change of TPU. [Figure 22] FIG. 13 is a diagram showing an example of a time change in dynamic contrast of birefringence. [Diagram 23] FIG. 13 is a diagram showing an example of a change in log intensity variance over time. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] The evaluation method according to the embodiment of the present invention will be described in detail below with reference to the drawings. Note that the drawings used in the following description may show characteristic parts in an enlarged scale for the sake of convenience in order to make the characteristics easier to understand, and the dimensional ratios of each component may not necessarily be the same as in reality. In addition, the materials, dimensions, etc. exemplified in the following description are merely examples, and the present invention is not limited thereto, and may be appropriately modified and implemented within the scope of the present invention.

[0032] <First embodiment> Fig. 1 is a diagram for explaining a biological tissue evaluation method according to a first embodiment of the present invention. In the biological tissue evaluation method of this embodiment, an OCT system 1 uses a biological tissue evaluation device 20 including an imaging means (imaging unit) 10 for performing OCT (optical coherence tomography) imaging of a sample Sm multiple times within a predetermined time, a measurement means (measurement unit) 22 for measuring the time change in OCT signal intensity obtained from each OCT image, and an evaluation means (evaluation unit) 24 for quantitatively evaluating the activity of the biological tissue as the sample Sm based on the time change, to visualize and quantitatively evaluate minute fluctuations in the biological tissue according to the following procedure (see Fig. 8).

[0033] First, the imaging means 10 performs OCT (optical coherence tomography) imaging (measurement) on the same site at the same position of the biological tissue multiple times (several to several hundred times = several to several hundred frames) within a predetermined period (for example, 1 to 15 minutes, typically within 3 minutes), preferably 10 frames or more, more preferably 15 frames or more. The predetermined period (observation period) for one OCT imaging is shorter than the observation period interval between adjacent observation periods, and may be a period that can ensure the accuracy required for evaluation of the dynamic characteristics of the sample that appear in the OCT signal obtained by the OCT imaging at that time. More specifically, the observation period may be determined between the frame interval so that the frequency components of the dynamic characteristics of the sample are included in the frequency band from the lowest frequency corresponding to the observation period to the highest frequency corresponding to the frame interval. In addition, the observation period interval may be a period that can ensure the accuracy required for evaluation of the overall change tendency of the activity state of the biological tissue to be used as the sample. For example, the observation interval may be set to a time that is sufficiently shorter than the time required for a series of processes from when the sampled biological tissue is separated from the mother body to when it undergoes cell death (apoptosis), or a series of processes from when a cause occurs in the sampled biological tissue in the mother body to when it undergoes cell death (necrosis), etc. From the viewpoint of knowing the characteristics of the entire sample, it is preferable to perform the same OCT imaging on other parts as well.

[0034] Next, the measuring means 22 and the evaluation means 24 quantitatively evaluate the activity of the biological tissue based on the time change in OCT signal intensity obtained from each OCT image. Specific methods of quantitative evaluation include, for example, calculation of speckle variance (SV) and OCT correlation decay speed (OCDS), and further evaluation of the calculation results.

[0035] Speckle variance indicates the dispersion (fluctuation) of OCT signal intensity over a short period of time, and can be calculated using the following formula (1).

[0036]

number

[0037] In the above formula (1), x and z respectively indicate the position within the surface of the biological tissue and the depth direction from the surface, and I(x,z,t i ) indicates the OCT signal intensity at each position and time, displayed on a linear or logarithmic scale, etc. indicates the average OCT signal intensity, and N indicates the number of frames of the OCT signal within a given period. x, z correspond to the positions of the observation points corresponding to the individual pixels forming the OCT image.

[0038] More specifically, first, the measurement means 22 measures the time t 1 ,t 2 , t N For each observation point (x, z), the OCT signal intensity I(x, z, t 1 ), I(x,z,t 2 ), I(x,z,t N ) is measured. Next, the evaluation means 24 measures the average OCT signal intensity for N times. Next, the evaluation means 24 calculates the OCT signal intensity for each time and the average That is, the evaluation means 24 squares the difference between [I(x,z,t 1 )― ] 2 、[I(x,z,t 2 )― ] 2 、···[I(x,z,t N )― ] 2 Finally, the evaluation means 24 calculates the speckle variance σ by dividing the sum by N. 2 You can get (x,z).

[0039] The decay rate of the OCT correlation indicates the rate at which the correlation coefficient between adjacent times (t, t+τ) decreases as the time shift amount τ increases. The correlation coefficient ρ(x,z,τ) can be calculated using the following equation (2).

[0040]

number

[0041] In the above formula (2), x and z respectively indicate the position within the surface of the biological tissue and the depth direction from the surface, and σ cov 2 (x,z,τ), σ I 2 (x,z) respectively indicate the covariance between the OCT signal intensity I(x,z,t) and I(x,z,t+τ), and the speckle variance (dispersion). The OCT signal intensity I(x,z,t+τ) is the signal value obtained by time shifting the OCT signal intensity I(x,z,t) by a time shift amount τ. In other words, the correlation coefficient ρ(x,z,τ) corresponds to the auto-correlation function of the signal value I(x,z,t) of the OCT signal at the observation point (x,z).

[0042] More specifically, the evaluation means 24 evaluates the covariance σ cov 2 (x,z,τ) = [I(x,z,t)― ]×[I(x,z,t+τ)― ]. Next, the evaluation means 24 calculates the covariance σ cov 2 (x, z, τ) is the speckle variance σ calculated using the above formula (1). 2 By dividing by (x,z), a correlation coefficient ρ(x,z,τ) can be obtained. Then, the evaluation means 24 applies a predetermined function of the time shift amount τ by performing regression analysis on the obtained correlation coefficient ρ(x,z,τ), and can obtain a parameter of the function as the decay rate of the OCT correlation so that the function value of the function is more approximate to the correlation coefficient (x,z,τ). The predetermined function may be a function, for example, an exponential function, in which the function value given to the time shift amount decays as the time shift amount increases. When an exponential function is used, the base, which is the parameter, is obtained as an index of the decay rate. As a method of regression analysis, linear analysis may be used, but is not limited thereto, and nonlinear analysis may also be used.

[0043] As described above, the biological tissue evaluation method according to the present embodiment quantitatively evaluates the activity of biological tissue by acquiring images of a specific part of the biological tissue many times in a short period of time and calculating speckle variance, the decay rate of OCT correlation, etc. This makes it possible to detect minute changes (fluctuations) that have been overlooked in the past, and to realize quantitative evaluation of the dynamics of biological tissue. EXAMPLES

[0044] The effects of this embodiment will be made clearer by the following examples. Note that this embodiment is not limited to the following examples, and can be appropriately modified and implemented without departing from the spirit and scope of the present invention.

[0045] Comparative Example 1 Conventional OCT imaging was performed every four hours on tumor aggregate samples, which were made by culturing cells derived from human cancer tissue into spherical shapes.

[0046] Example 1 For example, 100 high-speed OCT images were taken at 13 ms intervals every 2 hours for a tumor aggregate sample similar to that of Comparative Example 1. The observation interval was set to 2 hours because it is sufficient to capture the change tendency of the activity state during the period from when the tumor aggregate tissue sample was removed from the living body until death (typically about 1 to 3 days). In addition, the observation period was set to 100 times at 13 ms intervals, that is, 1.3 seconds, because this observation period is sufficiently shorter than the observation interval and is sufficient to grasp the time change of optical properties corresponding to the movement of cells constituting the tissue or the activity within the cells (for example, several Hz to 20 Hz). Next, the speckle variance was calculated based on the time change of the OCT signal intensity obtained from the OCT image of each time.

[0047] Example 2 High-speed OCT imaging was performed in the same manner as in Example 1 on a tumor aggregate sample similar to that in Comparative Example 1. Next, the decay rate of the OCT correlation was calculated based on the time change in OCT signal intensity obtained from each OCT image.

[0048] Example 3 The same high-speed OCT imaging as in Example 1 was performed on a tumor aggregate sample as in Comparative Example 1. Next, the attenuation coefficient (AC) of the OCT signal intensity in the depth direction was calculated based on the OCT signal intensity obtained from the OCT image of each time.

[0049] FIG. 2 shows images of tumor aggregate samples obtained by applying the methods of Comparative Example 1, Examples 1, and 2. The images in the upper, middle, and lower rows correspond to Comparative Example 1, Examples 1, and 2, respectively. The values ​​0 hr, 8 hr, and 28 hr indicate the time elapsed from the time when the tumor aggregate samples were cut out from the human body and culture was started. In the image of Comparative Example 1, no change over time is observed in the state of the tumor aggregate samples. In contrast, in the images of Examples 1 and 2, there is a dark area in the center at the early stage and a bright area around it, but it can be seen that the bright area becomes darker over time. It is considered that the dark area represents a dead state and the bright area represents a living state, so the live or dead state of the tumor aggregate samples can be confirmed from the changes in these images.

[0050] 3A and 3B are diagrams showing the time-dependent changes in the speckle variance and the decay rate of OCT correlation calculated for each observation period from the samples shown in the images of Examples 1 and 2 in FIG. 2, respectively. FIG. 3C is a diagram showing the decay coefficient of OCT signal intensity obtained by applying the method of Example 3. The horizontal axis of each of FIG. 3A, FIG. 3B, and FIG. 3C indicates the elapsed time since the tumor aggregate sample was cut out, and the vertical axis indicates the average value of the speckle variance, the decay rate of OCT correlation, and the decay coefficient of OCT signal intensity in the entire tumor aggregate sample, respectively. In each diagram, a tendency for each average value to decrease with time is observed. From a comparison of these three diagrams, it is considered that the slope of the decay rate of OCT correlation is large, and therefore it is most suitable for quantitative evaluation of tumor samples. The magnitude of the slope for the speckle variance is the next largest, and is significantly larger than the slope for the decay coefficient of OCT signal intensity proposed in the past. Note that AU indicates an arbitrary unit.

[0051] In addition, the first of the multiple OCT images does not correctly reflect the decay trend of the correlation coefficient with subsequent samples, and results in a significant difference from the estimated value estimated from the decay trend. Therefore, in quantitative evaluation, it is preferable to exclude the correlation coefficient when the time shift amount τ is zero, and calculate the decay rate of the OCT correlation using the correlation coefficient for a non-zero time shift amount τ other than zero.

[0052] 4A and 4B are diagrams showing the viability of biological tissues to which the methods of Examples 1 and 2 were applied, respectively. The horizontal axis of both Figures 4A and 4B indicates the elapsed time, and the vertical axis indicates the content rate of live or dead cells. The boundary between viability and death was set at 3.0 in Example 1 (SV) and 5×10 in Example 2 (OCDS). -4 ms -1 In both graphs, there is a tendency for the number of live cells to decrease and the number of dead cells to increase over time.

[0053] Comparative Example 2 Conventional OCT imaging was performed on mouse liver samples excised as biopsy specimens at hourly intervals.

[0054] Example 4 High-speed OCT imaging was performed 100 times at 10 ms intervals every hour on the same liver sample as in Comparative Example 2. Next, speckle variance was calculated based on the time change in OCT signal intensity obtained from each OCT image.

[0055] Example 5 The same high-speed OCT imaging as in Example 4 was performed on the same liver sample as in Comparative Example 2. Next, the decay rate of the OCT correlation was calculated based on the time change in OCT signal intensity obtained from each OCT image.

[0056] Example 6 The same high-speed OCT imaging as in Example 4 was performed on the same liver sample as in Comparative Example 2. Next, the attenuation coefficient (AC) of the OCT signal intensity in the depth direction was calculated based on the OCT signal intensity obtained from each OCT image.

[0057] FIG. 5 shows images of liver samples obtained by applying the methods of Comparative Example 2, Examples 4, and 5. The images in the upper, middle, and lower rows correspond to Comparative Example 2, Examples 4, and 5, respectively. The values ​​0hr, 8hr, and 16hr indicate the time elapsed since the liver samples were cut out from the mother mice. In the image of Comparative Example 2, no change in the state of the liver samples over time is observed. In contrast, in the images of Examples 4 and 5, there are dark areas on the lower side and bright areas on the upper side at the early stage, but it can be seen that the bright areas become darker over time. It is considered that the dark areas represent a dead state and the bright areas represent a living state, so the live or dead state of the liver samples can be confirmed from the changes in these images.

[0058] 6A and 6B are diagrams showing the decay rate of speckle variance and OCT correlation calculated for each observation period from the samples shown in the images of Examples 4 and 5, respectively. FIG. 6C is a diagram showing the decay coefficient of OCT signal intensity obtained by applying the method of Example 6. The horizontal axis of each of FIG. 6A, FIG. 6B, and FIG. 6C indicates the elapsed time after the liver sample is cut out, and the vertical axis indicates the average value of the speckle variance, the decay rate of OCT correlation, and the decay coefficient in the entire liver sample, respectively. In each diagram, a two-stage tendency of decrease with different slopes is observed for each average value over time. From a comparison of these three diagrams, it is considered that the slope of the decay rate of OCT correlation is large, and therefore it is most suitable for quantitative evaluation of tumor samples. The magnitude of the slope for speckle variance is the next largest, and is significantly larger than the slope for the attenuation coefficient of OCT signal intensity proposed in the past.

[0059] In addition, the first of the multiple OCT images does not correctly reflect the decay trend of the correlation coefficient with subsequent samples and results in a significant difference from the estimated value estimated from the decay trend. Therefore, in quantitative evaluation, it is preferable to calculate the decay rate of the OCT correlation excluding cases where τ is zero.

[0060] 7A and 7B are diagrams showing the viability of biological tissues when the methods of Examples 4 and 5 were applied, respectively. The horizontal axis of both Figures 7A and 7B indicates the elapsed time, and the vertical axis indicates the content rate of live or dead cells. The boundary between viability and death is set to 3.0 in Example 4 (SV) and 5×10 in Example 5 (OCDS). -4 ms -1 In both graphs, there is a tendency that the number of live cells decreases and the number of dead cells increases over time. In this way, according to this embodiment, time-varying characteristic values ​​such as speckle variance (SV) and the decay rate of OCT correlation (OCDS) are calculated in each observation period, and the dynamics of cell life and death can be observed by observing these values ​​for a longer period of time.

[0061] <Second embodiment> Next, a second embodiment of the present invention will be described. The above-mentioned analysis method called dynamic OCT makes it possible to express the local activity of endogenous scattering factors that constitute biological tissues such as cells. However, the method described in Non-Patent Document 1 has poor quantification of the expressed local activity, so it is sometimes difficult to correctly evaluate the activity of the living body. On the other hand, in the application aspects of diabetes research, circulatory system research, and the like, the application of dynamics imaging is expected as a method for visualizing specific tissues such as microvessels, lymphatic vessels, and cardiac muscle with specificity. In order to quantitatively evaluate the activity of biological tissue, it is also possible to perform OCT measurements multiple times at the same position of the biological tissue and analyze the time change in OCT signal intensity obtained by each measurement. However, since every minute movement in the tissue is reflected in the time change in the OCT signal, there are cases where the activity of a specific tissue cannot be quantitatively evaluated by simply analyzing the time change in signal intensity. This embodiment has been proposed in consideration of this point.

[0062] FIG. 9 is a block diagram showing an example of an OCT system 1 according to this embodiment. The OCT system 1 constitutes a PS-OCT. The PS-OCT includes an optical system for irradiating a sample Sm with incident light having a known polarization state and acquiring interference light caused by interference between the reflected light from the sample Sm and a reference light. The OCT system 1 also includes a measurement signal processing device 200 that analyzes the change characteristics of the polarization state in the sample Sm from the polarization state of the interference light acquired by the optical system. The measurement signal processing device 200 functions as an evaluation device that analyzes the state of a biological tissue as the sample Sm using an OCT signal. The measurement signal processing device 200 generates an image that visualizes the analyzed change characteristics.

[0063] The object to be observed as the sample Sm is mainly a part of a living body such as a human or an animal. More specifically, it may be any of the fundus, blood vessels, teeth, subcutaneous tissue, etc. This allows the state inside the sample Sm to be measured or observed non-invasively. Therefore, it is expected to be applied to the diagnosis of in-vivo tissues, such as microvessels in the fundus, lymphatic vessels, cardiac muscle, etc.

[0064] The OCT system 1 illustrated in FIG. 9 is an observation system that applies swept-wavelength OCT (SS-OCT) for obtaining a spectral interference signal by sweeping the wavelength of light generated by a light source 102. In the OCT system 1, the light emitted from the light source 102 is split and input to a probe arm (described later) and a reference arm 130. The OCT system 1 separates the light split into a horizontally polarized component and a vertically polarized component, irradiates the sample Sm to be measured with the light including the polarized components having different optical path lengths while scanning (B-scanning), and obtains the reflected light (object light) reflected from the sample Sm. The OCT system 1 obtains the interference light by causing the reference light split into the reference arm 130 to interfere with the reflected light, which is a component obtained by reflection, scattering, or both from the sample Sm. In this application, the direction in which light is irradiated to the sample Sm is defined as the depth direction. Obtaining a measurement signal by scanning an observation point in the depth direction of the sample Sm is called an A-scan. In SS-OCT, an A-scan is achieved by using a wavelength swept light source, and a B-scan is a scan in a direction perpendicular to the depth direction of the sample Sm.

[0065] The OCT system 1 includes a light source 102, a coupler 104, a polarization delay unit 110, a circulator 120, a probe 128, a reference arm 130, a polarization separation and detection unit 150, a photodetector 190, and a measurement signal processing device 200. The light source 102, the coupler 104, the polarization delay unit 110, the circulator 120, the probe 128, the polarization separation and detection unit 150, and the photodetector 190 are components that constitute an optical system. The components of the optical system are connected to each other using optical fibers as optical paths.

[0066] The light source 102 is a wavelength swept source that generates light having a wavelength that periodically sweeps within a predetermined wavelength width (for example, 40 to 120 nm). The light source 102 has a near-infrared wavelength (for example, 1000 to 1400 nm) such as an SLD (Superluminescent Diode). The light generated by the light source 102 is incident on the coupler 104.

[0067] The coupler 104 separates the light incident from the light source 102 into two systems, a probe arm and a reference arm 130, at a predetermined intensity ratio. The ratio of the light intensity to the probe arm and the light intensity to the reference arm 130 is, for example, 90%:10%. The light supplied to the probe arm is supplied to a polarization diversity detection unit (PDDU) 150. The probe arm is a path in which a fiber collimator 106, a polarization controller 108, a polarization delay unit (PDU) 110, a circulator 120, a fiber collimator 122, a polarization controller 124, an objective lens 126, and a probe 128 are connected in this order. The probe arm is also called a sample arm or a measurement arm. The light supplied to the probe arm is incident on the polarization delay unit 110 via the fiber collimator 106 and the polarization controller 108. The light of the other system is incident on the PPDU 150 via the reference arm 130. The polarization controller 108 amplifies the intensity of the incident light to a predetermined sufficient intensity, and emits the amplified light.

[0068] The PDU 110 includes a linear polarizer 112, a polarization beam splitter (PBS) 114, and two right angle prisms (RAP) 116 and 118. The PDU 110 separates the incident light into a horizontally polarized component and a vertically polarized component having two mutually orthogonal polarization states, and supplies the light obtained by combining the separated components to a circulator 120.

[0069] The linear polarizer 112 converts the polarization state of the light incident from the coupler 104 into linear polarization and outputs the converted light to the PBS 114. The polarization angle of the linear polarizer 112 is set to 45° to equalize the horizontal polarization component and the vertical polarization component of the output light. The PBS 114 has a reflective layer whose surface is arranged in a direction with an incidence angle of 45°, and the vertical polarization component of the incident light incident from the linear polarizer 112 to the reflective layer is transmitted as transmitted light, and the horizontal polarization component is reflected from the surface of the reflective layer as reflected light. The reflected light containing the horizontal polarization component and the transmitted light containing the vertical polarization component from the PBS 114 are respectively incident on the RAPs 116 and 118. On the other hand, the reflective layer of the PBS 114 combines the transmitted light that has passed through the incident light containing the horizontal polarization component that is incident on the reflective layer from the RAP 116 with the reflected light of the incident light containing the vertical polarization component that is incident from the PBS 118, and outputs the combined light to the circulator 120.

[0070] The RAPs 116 and 118 each have a cross section parallel to the optical path that forms a right-angled isosceles triangle, and are disposed so that the base of the right-angled isosceles triangle is perpendicular to the optical path from the PBS 114. The light incident from the PBS 114 passes through a side surface parallel to the base of the right-angled isosceles triangle, is reflected by a side surface parallel to one of the two sides facing the base, and the reflected light is reflected by a side surface parallel to the other side, returns to the side surface parallel to the base, passes through the side surface, and is incident on the PBS 114. The position of the RAP 118 is adjusted in advance so that the optical path length between the PBS 114 and the RAP 116 and the optical path length between the PBS 114 and the RAP 118 are significantly different. As a result, the horizontally polarized component and the vertically polarized component incident on the sample Sm from the PBS 114 are superimposed with a predetermined phase difference between them and are output.

[0071] The circulator 120 outputs the light incident from the PDU 110 to the objective lens 126 via the fiber collimator 122 and the polarization controller 124. The objective lens 126 collects the light incident thereon and irradiates it on the sample Sm via the probe 128. The light reflected or scattered from the sample Sm, or obtained by both, passes through the probe 128 and is converted into parallel light by the objective lens 126, returns to the circulator 120 via the polarization controller 124 and the fiber collimator 122 as a measurement beam, and is incident on the PPDU 150.

[0072] The reference arm 130 is a path in which a fiber collimator 132, a fiber Bragg grating (FBG) 134, a fiber collimator 136, a delay line 138, a fiber collimator 140, and a polarization controller 142 are connected in that order.

[0073] The FBG 134 reflects a specific wavelength component of the incident light as reflected light, and transmits the remaining components, which are then incident as transmitted light via the fiber collimator 136 to the delay line 138. The reflected light from the FBG 134 returns to the coupler 104 via the fiber collimator 132, and is incident from the coupler 104 to the photodetector 190. The band of the components reflected from the FBG 134 is sufficiently narrower than the band of the light generated by the light source 102. The photodetector 190 detects the intensity of the reflected light from the FBG 136, and outputs an intensity signal indicating the detected intensity as a trigger signal to the measurement signal processing device 200. The trigger signal is used as a trigger for the A-scan. The wavelength of the light generated by the light source 102 changes periodically within a range of a predetermined wavelength width, and the photodetector 190 detects the timing at which the wavelength becomes the predetermined wavelength, and the A-scan is reset by the optical system control unit 212 at that timing. For example, the lower limit of the wavelength width is preset in the photodetector 190 as the wavelength to be detected. This is because in SS-OCT, the depth of the observation target point is determined by the wavelength of the probe light.

[0074] The delay line 138 delays the incident light from the FBG 134, and outputs the delayed light to the PPDU 150 via the fiber collimator 140 and the polarization controller 142. The delay line 138 varies the amount of delay for the incident light, and adjusts the amount of delay so that the optical path length of the probe arm and the optical path length of the reference arm 130 are equal. The polarization controller 142 adjusts the intensity of the incident light to a predetermined intensity, and outputs the light with the adjusted intensity.

[0075] The PPDU 150 includes a linear polarizer 152, a non-polarization beam splitter (NPBS) 154, two PBSs 156 and 158, four photodetectors 162, 164, 166 and 168, and two balanced polarization detectors (BPDs) 170 and 172.

[0076] The linear polarizer 152 converts the polarization state of the light incident from the reference arm 130 into linearly polarized light, and outputs the converted light to the NPBS 154. The polarization angle of the linear polarizer 152 is set to 45°. The NPBS 154 combines the incident light incident from the reference arm 130 via the linear polarizer 152 and the incident light from the probe arm. The NPBS 154 has a reflective layer arranged in a direction such that the surface of the incident light from the reference arm 130 and the incident light from the probe arm are oriented such that the incident angle is 45°. The reflective layer combines the transmitted light obtained by transmitting the incident light from the reference arm 130 and the reflected light obtained by reflecting the incident light from the probe arm, and outputs the interference light obtained by combining to the PBS 158. The reflective layer combines the reflected light obtained by transmitting the incident light from the reference arm 130 and the transmitted light obtained by transmitting the incident light from the probe arm, and outputs the interference light obtained by combining to the PBS 156.

[0077] The PBS 156 separates the interference light incident from the NPBS 154 into a horizontally polarized component and a vertically polarized component, and outputs the separated horizontally polarized component and vertically polarized component to the optical receivers 162 and 166, respectively. The optical receivers 162 and 166 receive the horizontally polarized component and vertically polarized component incident from the PBS 156, and guide them as a first horizontally polarized component and a first vertically polarized component to the BPDs 170 and 172, respectively. The first horizontally polarized component and the first vertically polarized component correspond to the horizontally polarized component and vertically polarized component of the interference light based on the horizontally polarized component incident on the sample Sm, respectively.

[0078] The PBS 158 separates the light incident from the NPBS 154 into a horizontally polarized component and a vertically polarized component, and outputs the separated horizontally polarized component and vertically polarized component to the optical receivers 164 and 168, respectively. The optical receivers 164 and 168 receive the horizontally polarized component and the vertically polarized component incident from the PBS 158, and guide them as a second horizontally polarized component and a second vertically polarized component to the BPDs 170 and 172, respectively. The second horizontally polarized component and the second vertically polarized component correspond to the horizontally polarized component and the vertically polarized component of the interference light based on the vertically polarized component incident on the sample Sm, respectively.

[0079] The BPD 170 detects the first horizontally polarized component and the second horizontally polarized component guided from the optical receivers 162 and 166, respectively, and converts them into a first horizontally polarized spectral interference signal and a second horizontally polarized spectral interference signal, which are analog electrical signals indicating the intensities of the detected first horizontally polarized component and second horizontally polarized component. The BPD 170 outputs the generated first horizontally polarized spectral interference signal and second horizontally polarized spectral interference signal to the measurement signal processing device 200 via a low pass filter (LPF: Low Pass Filter) 182 and a high pass filter (HPF: High Pass Filter) 186.

[0080] The BPD 172 detects the first vertically polarized signal and the second vertically polarized component guided from the photoreceivers 164 and 168, respectively, and converts them into a first vertically polarized spectral interference signal and a second vertically polarized spectral interference signal, which are analog electrical signals indicating the intensities of the detected first vertically polarized component and the second vertically polarized component. The BPD 172 outputs the generated first vertically polarized spectral interference signal and the second vertically polarized spectral interference signal to the measurement signal processing device 200 via the LPF 184 and the HPF 188. The first horizontally polarized spectral interference signal, the second horizontally polarized spectral interference signal, the first vertically polarized spectral interference signal, and the second vertically polarized spectral interference signal are each used to generate one frame of an OCT image, making it possible to realize Jones Matrix-OCT (JM-OCT).

[0081] The BPDs 170 and 172 sample the signal values ​​of a predetermined number of samples (e.g., 400 to 2000 samples) for each A-scan, at a predetermined sampling frequency, to define the wavelength width of the light emitted by the light source 102. The LPFs 182 and 184 and the HPFs 186 and 188 are, for example, Chebyshev filters. The cutoff frequency of the LPFs 182 and 184 is, for example, 62 MHz. The cutoff frequency of the HPFs 186 and 188 is, for example, 1 MHz.

[0082] (Measurement signal processing device) Next, a configuration example of the measurement signal processing device 200 according to the present embodiment will be described. FIG. 10 is a block diagram showing a configuration example of the measurement signal processing device 200 according to the present embodiment. The measurement signal processing device 200 analyzes the polarization characteristics at the observation points in the sample from the first horizontal polarization spectrum interference signal, the second horizontal polarization spectrum interference signal, the first vertical polarization spectrum interference signal, and the second vertical polarization spectrum interference signal input from the PPDU 150, analyzes the time change characteristics of the polarization characteristic value indicating the analyzed polarization characteristics, and determines the time variation characteristic value indicating the analyzed time variation characteristic. The measurement signal processing device 200 may convert the converted signal value into a signal value indicating a color or gradation according to the time variation characteristic value determined for each observation point, generate image data for each pixel corresponding to the observation point, and output the generated image data.

[0083] The measurement signal processing device 200 includes a control unit 210, a storage unit 230, an input / output unit 240, a display unit 250, and an operation unit 260. A part or all of the functions of the control unit 210 are realized as a computer including a processor such as a CPU (Central Processing Unit). The processor reads a program stored in advance in the storage unit 230, and performs processing instructed by commands written in the read program to fulfill its functions. In the present application, performing processing instructed by commands written in the program may be referred to as executing a program, running a program, etc. A part or all of the control unit 210 is not limited to general-purpose hardware such as a processor, and may be configured to include dedicated hardware such as an LSI (Large Scale Integration) or an ASIC (Application Specific Integrated Circuit). The control unit 210 includes an optical system control unit 212, a measurement signal acquisition unit 214, an ellipsometry unit 216, a fluctuation characteristic analysis unit 218, an image processing unit 220, and an output processing unit 222. The measurement signal acquisition unit 214 and the ellipsometry unit 216 according to this embodiment function as a measurement unit that acquires an OCT signal and acquires a signal value based on the acquired OCT signal for each observation point of the sample. The fluctuation characteristic analysis unit 218 functions as an evaluation unit that calculates a time fluctuation characteristic value that indicates the time fluctuation characteristic of the acquired signal value for each predetermined period.

[0084] The optical system control unit 212 drives a driving mechanism that changes the position of the probe, and scans the observation point of the sample Sm (B-scan). The optical system control unit 212 scans the observation point of the sample Sm in a predetermined direction (for example, the x direction on the front surface perpendicular to the depth direction of the sample Sm) that intersects with the depth direction of the sample Sm (hereinafter, z direction). For example, when a trigger signal input from the photodetector 190 becomes equal to or greater than a predetermined intensity, the optical system control unit 212 outputs a control signal indicating movement of the position of the probe 128 in that direction to the driving mechanism. This movement distance corresponds to a preset observation point interval in the x direction. Also, at this time, the optical system control unit 212 returns the position of the probe 128 to the reference point each time the number of observation points in the x direction from the reference point reaches a predetermined number (number of lines). Thereafter, the measurement signal acquisition unit 214 acquires the measurement signal of the next frame. By repeating the measurement signal, the measurement signal is accumulated for each frame as time passes. In the example shown in FIG. 12, the x direction, z direction, and time t are shown to the right, below, and above right, respectively, and each frame is shown as an individual rectangle.

[0085] The first horizontal polarization spectral interference signal, the second horizontal polarization spectral interference signal, the first horizontal polarization spectral interference signal, and the second vertical polarization spectral interference signal are input as measurement signals from the PPDU 150 via the input / output unit 240 to the measurement signal acquisition unit 214. The measurement signal acquisition unit 214 performs Fourier transform on the first horizontal polarization spectral interference signal, the second horizontal polarization spectral interference signal, the first vertical polarization spectral interference signal, and the second vertical polarization spectral interference signal, respectively, and calculates the first horizontal polarization OCT signal, the second horizontal polarization OCT signal, the first vertical polarization OCT signal, and the second vertical polarization OCT signal indicating the respective complex amplitudes for each observation point. The measurement signal acquisition unit 214 outputs the calculated first horizontal polarization OCT signal, the second horizontal polarization OCT signal, the first vertical polarization OCT signal, and the second vertical polarization OCT signal to the polarization analysis unit 216.

[0086] The polarization analysis unit 216 calculates a polarization characteristic value indicating the polarization characteristics at the observation point for each observation point in the sample Sm based on the first horizontally polarized OCT signal, the second horizontally polarized OCT signal, the first vertically polarized OCT signal, and the second vertically polarized OCT signal input from the measurement signal acquisition unit 214. The polarization analysis unit 216 constructs a Jones matrix indicating the polarization characteristics of the light wave related to the measurement, and calculates a predetermined polarization characteristic value as an index value indicating the polarization characteristics from the constructed Jones matrix.

[0087] The Jones matrix is ​​a matrix with two rows and two columns that indicates a change in the polarization state. The first and second columns of the Jones matrix are respectively arranged with the first and second Jones vectors. The first and second Jones vectors are acquired using incident light having mutually orthogonal polarization components. That is, the first Jones vector is a two-dimensional vector including as elements the complex amplitudes indicated by the first horizontally polarized OCT signal and the first vertically polarized OCT signal. The second Jones vector is a two-dimensional vector including as elements the complex amplitudes indicated by the second horizontally polarized OCT signal and the second vertically polarized OCT signal. Each column of the Jones matrix corresponds to a polarization state related to the incidence, and each row corresponds to a detected polarization state. The polarization analysis unit 216 outputs the calculated polarization characteristic value for each observation point to the fluctuation characteristic analysis unit 218. In this application, the Jones matrix directly constructed from the OCT signal or its frequency domain conversion coefficient at this stage may be called a measured Jones matrix.

[0088] The fluctuation characteristic analysis unit 218 calculates a predetermined time-varying characteristic value as an index value indicating a time-varying characteristic during an observation period, which is a predetermined period set in advance, for the polarization characteristic value input from the polarization analysis unit 216. The observation period is typically about 150-600 frames when the frame rate is 60 frames / second, for example. The fluctuation characteristic analysis unit 218 outputs the calculated time-varying characteristic value to the image processing unit 220. Examples of the polarization characteristic value and the time-varying characteristic value will be described later.

[0089] The image processing unit 220 converts the time variation characteristic value input from the variation characteristic analysis unit 218 into a pixel value within a predetermined value range that can be expressed by the bit depth for each pixel using a predetermined function. For example, the image processing unit 220 calculates a pixel value by multiplying a function value of a sigmoid function for the time variation characteristic value by a predetermined multiple and adding a predetermined offset value to the multiplied value. The function for converting the time variation characteristic value into a pixel value is not limited to the sigmoid function, and any function, such as a linear function or a logarithmic function, can be used as long as the function value obtained for the input value monotonically increases or decreases with an increase in the input value. The image processing unit 220 generates output image data indicating the pixel value converted for each observation point, and outputs the generated output image data to the display unit 250. The image processing unit 220 may store the output image data in the storage unit 230 in response to a control signal input from the output processing unit 222 .

[0090] The output processing unit 222 controls the generation or output of output image data showing a display image based on an operation signal input from the operation unit 260. For example, the operation signal indicates, as parameters, whether or not the display image is to be displayed or stored, the observation target area, and the like. When the control unit 210 has the ability to calculate a plurality of types of time-varying characteristic values, the type of the time-varying characteristic values ​​to be calculated or displayed may be indicated by the operation signal. The output processing unit 222 may display, on the display unit, a setting screen for guiding parameters that can be set by operation, parameter settings, and parameters that can be set, thereby forming a user interface related to the display of the image.

[0091] For example, when an operation signal indicating whether or not a display image needs to be displayed is input, output processing unit 222 outputs a control signal indicating whether or not the display is needed to image processing unit 220. When a control signal indicating a need for display is input from output processing unit 222, image processing unit 220 outputs output image data to the display unit, and when a control signal indicating a need for display is input from output processing unit 222, image processing unit 220 does not output output image data to the display unit.

[0092] When an operation signal indicating the observation target region is input, the output processing unit 222 outputs a control signal indicating the range of x coordinates or y coordinates within the observation target region to the measurement signal acquiring unit 214. The measurement signal acquiring unit 214 executes a B-scan within the range indicated by the control signal input from the output processing unit 222. The observation target region is determined, for example, by the range of the surface of the sample Sm as a parameter.

[0093] The storage unit 230 stores the above programs, as well as various data used in the processes executed by the control unit 210 and various data acquired by the control unit 210. The storage unit 230 includes, for example, a non-volatile (non-transient) storage medium such as a ROM (Read Only Memory), a flash memory, or a HDD (Hard Disk Drive). The storage unit 230 includes, for example, a volatile storage medium such as a RAM (Random Access Memory) or a register.

[0094] The input / output unit 240 is connected to other devices wirelessly or via a wire to input and output various data. The input / output unit 240 includes, for example, an input / output interface. The input / output unit 240 is connected to, for example, the polarization separation detection unit 150 and the photodetector 190. The display unit 250 displays an image based on the output image data input from the control unit 210. The display unit 250 may include, for example, a liquid crystal display, an organic electroluminescence display, or the like.

[0095] The operation unit 260 may be configured to include members that receive a user's operation and generate an operation signal according to the received operation, such as a button, a knob, a dial, a mouse, or a joystick. The operation unit 260 outputs the acquired operation signal to the control unit 210. The operation unit 260 may be an input interface that receives an operation signal wirelessly or wired from another device (for example, a portable device such as a remote controller).

[0096] (OCT signal processing) Next, an example of OCT signal processing according to this embodiment will be described below. Fig. 11 is a flowchart showing an example of OCT signal processing according to this embodiment. (Step S102) The measurement signal acquisition unit 214 acquires a first horizontal polarization spectral interference signal, a second horizontal polarization spectral interference signal, a first vertical polarization spectral interference signal, and a second vertical polarization spectral interference signal from the optical system, and calculates a first horizontal polarization OCT signal, a second horizontal polarization OCT signal, a first vertical polarization OCT signal, and a second vertical polarization OCT signal from each of the acquired signals. (Step S104) The polarization analysis unit 216 constructs a Jones matrix for each observation point based on the first horizontally polarized OCT signal, the second horizontally polarized OCT signal, the first vertically polarized OCT signal, and the second vertically polarized OCT signal acquired by the measurement signal acquisition unit 214. The polarization analysis unit 216 calculates a predetermined polarization characteristic value from the constructed Jones matrix.

[0097] (Step S106) The fluctuation characteristic analysis unit 218 calculates a predetermined time fluctuation characteristic value from the changed characteristic value calculated by the polarization analysis unit 216 during a preset observation period. (Step S108) Image processing unit 220 converts the time variation characteristic values ​​for each observation point calculated by variation characteristic analysis unit 218 into pixel values ​​of the pixels corresponding to each observation point. (Step S110) Image processing unit 220 outputs output image data indicating the converted pixel values ​​to display unit 250 (image display). Thus, display unit 250 visualizes the distribution in the observation target area of ​​time-varying characteristic values ​​indicating the polarization characteristics in the observation target area.

[0098] (Examples of polarization characteristics and time-varying characteristics) Next, examples of the polarization characteristic value and the time variation characteristic value will be described. The polarization analysis unit 216 calculates, for example, phase retardation as the polarization characteristic value. The phase retardation is the phase difference between an ordinary ray and an extraordinary ray caused by birefringence. In other words, the ordinary ray and the extraordinary ray have polarization directions orthogonal to the optical axis of the sample, and transmit through the sample at different speeds. Of the phase retardation, the phase retardation accumulated from the surface to a target observation point within the sample is called cumulative phase retardation (CPR). The polarization analysis unit 216 calculates the inverse matrix J of the Jones matrix at a position on the sample surface corresponding to the observation point at depth z as shown in equation (3). m0 -1 The Jones matrix J at the observation point at depth z is mz Cumulative Jones Matrix J at the observation point is calculated by multiplying cz Calculate the cumulative Jones matrix J cz indicates the change in polarization state from the sample surface to the observation point at depth z.

[0099]

number

[0100] The ellipsometer 216 calculates the cumulative Jones matrix J cz The two eigenvalues ​​λ obtained by performing eigenvalue decomposition on c1 , λ c2 Phase difference arg(λ c1 λ c2 * ) can be calculated as the CPR. Here, arg(...) indicates the argument of the complex number..., and * denotes the complex conjugate of the complex number ~.

[0101] As another example of the polarization characteristic value, the polarization analyzer 216 may calculate a local phase retardation (LPR), which is a polarization phase retardation in a local depth region. More specifically, the polarization analyzer 216 calculates a polarization phase retardation in a local depth region at a depth z 1 The inverse matrix J of the cumulative Jones matrix at the observation point cz1 -1 to depth z 2 The cumulative Jones matrix at the observation point is J cz2 The depth z obtained by multiplying 1 , z 2 The local Jones matrix J between the observation points l12 Calculate the local Jones matrix J l12 is the depth z 1 From the observation point at depth z 2 The change in polarization state from the observation point to the observation point is shown.

[0102]

number

[0103] Depth z 1 From the observation point at depth z 2 The thickness Δz 12 may be, for example, the thickness of the tissue to be observed, which is instructed by an operation signal from the operation unit 260, or may be the distance between the observation points in the z direction. l12 The two eigenvalues ​​λ of l1 , λ l2 Phase difference arg(λ l1 λ l2 * ) can be calculated as the LPR. 1 Inverse matrix J of the measurement Jones matrix at the observation point mz1 -1 to depth z 2 Measurement Jones matrix J at the observation point mz2 Multiply by depth z 1 , z 2 The local Jones matrix J between the observation points l12 may be calculated.

[0104] As another example of the polarization characteristic value, the polarization analyzer 216 calculates LPR as 2k 0 Δz 12 Divide by depth z 1 , z 2 The birefringence between the observation points is b 12 However, k 0 is the center of the incident light Wave number Shows.

[0105]

number

[0106] The fluctuation characteristic analysis unit 218 calculates the variance of the polarization characteristic value as an example of the time fluctuation characteristic value within a predetermined observation period. The calculated time fluctuation characteristic value indicates the magnitude of the time fluctuation of the polarization state indicated by the polarization characteristic value. In equation (6), σ l 2 (x,z) denotes the variance of LPR. N, φ(x,z,t i ), <φ(x,z)> respectively indicate the number of frames in a given observation period, LPR, and the time average value of LPR. (x,z) indicates the coordinates of the observation point in the xz plane. t i indicates the i-th sample time.

[0107]

number

[0108] Although formula (6) illustrates the variance of the LPR, the fluctuation characteristic analysis unit 218 may calculate the variance of the CPR or the variance of the birefringence instead of the LPR. The fluctuation characteristic analysis unit 218 may calculate the square root of these variances as the standard deviation.

[0109] The fluctuation characteristic analysis unit 218 may calculate the variance or standard deviation of the logarithmic values ​​of the polarization characteristic values ​​as another example of the time fluctuation characteristic value. In the process of calculating the variance or standard deviation, the logarithmic value of the time average value is subtracted from the logarithmic value of the polarization characteristic value at each time, thereby eliminating the constant potentially multiplied by the polarization characteristic value. Therefore, it can be applied to the evaluation of substantial time fluctuation characteristics. Furthermore, by taking the logarithmic value, it can be applied to the comparison between phenomena with significantly different scales. In equation (7), logσ l 2 (x,z) denotes the variance of the logarithm of the LPR.

[0110]

number

[0111] Although the formula (7) uses the variance of the logarithm of the LPR as an example, the fluctuation characteristic analysis unit 218 may calculate the variance of the logarithm of the CPR or the variance of the logarithm of the birefringence instead of the variance of the logarithm of the LPR. The fluctuation characteristic analysis unit 218 may calculate the square root of these variances as the standard deviation.

[0112] The fluctuation characteristic analysis unit 218 may calculate, for example, the dynamic contrast of the polarization characteristic value as the time fluctuation characteristic value within a predetermined observation period. The dynamic contrast of the polarization characteristic value corresponds to a value normalized by dividing the standard deviation of the polarization characteristic value by the time average value. In formula (8), φ d indicates the dynamic contrast of LPR.

[0113]

number

[0114] Although equation (8) uses the dynamic contrast of LPR as an example, the fluctuation characteristic analysis unit 218 may calculate the dynamic contrast of CPR or birefringence instead of LPR. In the process of calculating the dynamic contrast, the standard deviation is normalized by the time average value, so that the dynamic contrast can be applied to the evaluation of the time fluctuation characteristic more substantially than the standard deviation.

[0115] The fluctuation characteristic analysis unit 218 may calculate temporal polarization uniformity (TPU) as another example of the time fluctuation characteristic value. A method of calculating TPU will be described with reference to FIG. 18. First, the polarization analysis unit 216 calculates the measured Jones matrix J mz The first Jones vector J 1 , the second Jones vector J 2 and the first Stokes vector S 1 , the second Stokes vector S 2 (FIG. 18, step S122). As shown in equation (9), the first Jones vector J 1 , the second Jones vector J 2 are the Jones matrices J mz It is composed of the elements of the first column and the elements of the second column.

[0116]

number

[0117] First Stokes vector S 1 , the second Stokes vector S 2 are the first Jones vectors J as shown in equation (10). 1 , the second Jones vector J 2 are four-dimensional vectors that represent the polarization state shown by the first Jones vector J 1 , the second Jones vector J 2 In the following description, the first Stokes vector S 1 , the second Stokes vector S 2 Each of the four element values ​​s 10 ~s 13 , s 20 ~s 23 The 0th to 3rd Stokes parameters s 10 ~s 13 , s 20 ~s 23 It is called.

[0118]

number

[0119] As shown in equation (10), the 0th Stokes parameter s 10 , s 20 is the power of the horizontal component |g 1H | 2 , |g 2H | 2 and the power of the vertical component |g 1V | 2 , |g 2V | 2 The first Stokes parameter is the power of the horizontal component |g 1H | 2 , |g 2H | 2 The power of the vertical component |g 1V | 2 , |g 2V | 2 The second Stokes parameter is the product of the complex conjugates of the horizontal and vertical components g 1H g 1V * , g 2H g 2V * That is, the horizontal component g 1H , g 2H and the vertical component g 1V , g 2V The product of the intensities |g 1H ||g 1V |, |g 2H ||g 2V |, the phase difference between the horizontal component and the vertical component δ 1 , δ 2 Cosine value of cosδ 1 , cos δ 2 The third Stokes parameter corresponds to the complex conjugate product of the horizontal and vertical components g 1H g 1V * , g 2H g 2V * That is, the horizontal component g 1H , g 2H and the vertical component g 1V , g 2V The product of the intensities |g 1H ||g 1V |, |g 2H ||g 2V |, the phase difference between the horizontal component and the vertical component δ 1 , δ 2 Sine value of sinδ 1 , sin δ 2 It is equivalent to the value obtained by multiplying by 2.

[0120] The fluctuation characteristic analysis unit 218 calculates the first Stokes vector S 1 , the second Stokes vector S 2 The time average value during the observation period for each 1 >, 2 > is calculated, and the sum of the 0th to 3rd Stokes parameters, which are the element values, is the 0th time average value. 10 +s 20 >, 1st hour average value 11 +s 21 >, second hourly average value 12 +s 22 >, 3rd hour average value 13 +s 23 > (FIG. 18, step S124).

[0121] Then, the fluctuation characteristic analysis unit 218 determines the value obtained by dividing the square root of the sum of squares for the first time average value, the second time average value, and the third time average value by the zeroth time average value as TPU, as shown in formula (11) (FIG. 18, step S126). The smaller the time fluctuation of the polarization state, the smaller the TPU value. Therefore, it is expected that the more active the biological tissue sample is, the smaller the TPU will be. In contrast, the above-mentioned time fluctuation characteristic value for CPR, LPR, or birefringence will be larger as the time fluctuation of the polarization state becomes more significant.

[0122]

number

[0123] The measured Jones matrix contains noise components as shown in equation (12). 1H ,n 1V ,n 2H ,n 2V are the signal components E 1H ,E 1V ,E 2H ,E​​​​​​ 2V The noise component added to the

[0124]

number

[0125] The polarization analyzer 216 calculates the time average value of the first Stokes vector as shown in equation (13). 1 >, the time average value of the second Stokes vector <s>Here, the fluctuation characteristic analysis unit 218 can compensate for the noise component by subtracting the time average power of the element value of the noise component from the time average power of the element value contained in each of them. 1 >, the time average value of the second Stokes vector 2 Instead of the above, the time average value of the first Stokes vector for the signal component after the noise component is corrected is 1 '>, the time-averaged value of the second Stokes vector 2 '> can be substituted to calculate the TPU with the noise components removed.

[0126]

number

[0127] Alternatively, the fluctuation characteristic analysis unit 218 may calculate, for example, the von Neumann entropy of a Jones matrix determined at an observation point as a time fluctuation characteristic value within a predetermined observation period. The von Neumann entropy is defined by equation (14). In equation (14), λ i denotes the eigenvalues ​​of the Hermitian matrix T (described later), and each eigenvalue λ i is normalized by the sum of these eigenvalues ​​to obtain the normalized eigenvalue λ i ' is obtained.

[0128]

number

[0129]

number

[0130] As shown in equation (15), the expected value of LPR E(R) is the LPR of the optical axis corresponding to each eigenvalue R i ​​​​It is known that the weighted average is the normalized eigenvalue λ of each optical axis. i '. Therefore, the normalized eigenvalue λ i The von Neumann entropy of the Jones matrix defined using ' can also be considered as a type of time-varying characteristic value related to the polarization state. The von Neumann entropy of the Jones matrix is ​​described in detail in the following document, but in this embodiment, the randomness due to the time variation of the Jones matrix is ​​evaluated as the polarization characteristic value.

[0131] Masahiro Yamanari et al.: "Estimation of Jones matrix, birefringence and entropy using Cloude-Pottier decomposition in polarization-sensitive optical coherence tomography," Biomedical Optics EXPRESS Vol. 7, No.9, p.3551-3572, published 1 Sep. 2016 Masahiro Yamanari et al.: "Estimation of Jones matrix, birefringence and entropy using Cloude-Pottier decomposition in polarization-sensitive optical coherence tomography: erratum," Biomedical Optics EXPRESS Vol. 7, No.11, p.4636-4637, published 1 Nov. 2016

[0132] The polarization analysis unit 216 and the fluctuation characteristic analysis unit 218 can calculate the von Neumann entropy H in the following procedure. First, the polarization analysis unit 216 calculates the measured Jones matrix J mz The four-dimensional vector [g 1H g 2H g 1V g 2V ] T Let κ be the target vector. L This target vector κ L is the measurement Jones matrix J mz It shows substantially the same value as

[0133] The polarization analyzer 216 calculates the Hermitian conjugate κ of the target vector. L + Then, the target vector κ L Multiplying by , we get a 4-by-4 square matrix κ L κ L + The fluctuation characteristic analysis unit 218 calculates a square matrix κ L κ L + Time average value of <κ L κ L + > is defined as the matrix T. The matrix T is a 4-row, 4-column positive semidefinite Hermitian matrix.

[0134]

number

[0135] Then, the fluctuation characteristic analysis unit 218 diagonalizes the matrix T and calculates the four eigenvalues ​​λ i (i=1 to 4) is calculated, where i is the eigenvalue λ i The fluctuation characteristic analysis unit 218 calculates the individual eigenvalues ​​λ i The sum of the four eigenvalues ​​Σ j=1 4 λ j Divide by the normalized eigenvalue λ i The fluctuation characteristic analysis unit 218 calculates the normalized eigenvalue λ i ' and its logarithm log(λ i The von Neumann entropy H can be calculated by inverting the sum of the products of x and y '. The von Neumann entropy is a value between 0 and 1. For a completely random set of Jones matrices, the von Neumann entropy H is 1. Note that the base of the logarithm in equation (14) is 4.

[0136] The fluctuation characteristic analysis unit 218 calculates the von Neumann entropy H m From the von Neumann entropy of the noise component, H n The von Neumann entropy of the signal component H s may be determined.

[0137]

number

[0138] The fluctuation characteristic analysis unit 218 calculates the von Neumann entropy H n (E 1 ,E 2 ) into the Jones matrix J mz The first Jones vector E 1 , the second Jones vector E 2 The first noise component n 1 The first noise component entropy H(E 1 ) and the second noise component n 2 The second noise component entropy H(E 2 ) where the first noise component n 1 and the second noise component n 2 are assumed to be independent of each other.

[0139]

number

[0140] The fluctuation characteristic analysis unit 218 calculates the i-th noise component entropy H(E i ) as the j-th eigenvalue ζ of the i-th noise component j (i) and its logarithm log(ζ j (i) ) can be calculated by inverting the sign of the sum.

[0141]

number

[0142] However, as shown in equation (20), the fluctuation characteristic analysis unit 218 calculates the first eigenvalue ζ by adding or subtracting P(i), which is the TPU of the i-th Jones vector, to or from 1 and dividing the result by 2. 1 (i) , the second eigenvalue ζ 2 (i) It can be calculated as follows:

[0143]

number

[0144] The fluctuation characteristic analysis unit 218 calculates the TPU of the ith Jones vector, P (i) can be calculated by dividing the square root of the sum of the squares of the time average values ​​of the first to third Stokes parameters of the first and second Stokes vectors after correction with the above noise components by the time average value of the zeroth Stokes parameter.

[0145]

number

[0146] (Example of calculation of time-varying characteristic values) Next, a calculation example of the time-varying characteristic value will be described. Fig. 13 is a diagram showing an example of spatial distribution of birefringence variance. In Fig. 13, the birefringence variance for each observation point in biological tissue is shown by shading. The observation points are distributed in the xz plane. The brighter the area, the larger the birefringence variance, and the darker the area, the smaller the birefringence variance. The upper black area in Fig. 13 indicates the outside of the tissue. Overall, there is a tendency for the birefringence variance to be larger inside the tissue than on the surface.

[0147] FIG. 14 is a diagram showing another example of the spatial distribution of the birefringence variance. FIG. 14 shows the birefringence variance for each observation point in the biological tissue in shades of gray. FIG. 15 shows the mean local birefringence for each observation point. In the example shown in FIG. 15, the same biological tissue as in FIG. 14 is observed. However, in the example shown in FIG. 14, a biological tissue different from that in FIG. 13 is observed. In both FIG. 14 and FIG. 15, the area brighter than the surroundings indicates the distribution range of the biological tissue. In both cases, the birefringence or the mean local birefringence tends to be relatively high in the lower part of the drawing, and the birefringence or the mean local birefringence tends to be relatively high in the left part of the drawing. FIG. 16 is a diagram showing an example of the correlation between the birefringence variance and the mean local birefringence. The vertical axis and the horizontal axis of FIG. 16 show the birefringence variance and the mean local birefringence, respectively. FIG. 16 shows that there is a significant correlation between the local birefringence and the birefringence variance. The correlation coefficient was 0.776. This confirms that there is a tendency for the birefringence variance to be greater in areas with higher local birefringence. Figure 17 is a diagram showing an example of the correlation between the birefringence variance and the logarithmic intensity variance. The vertical and horizontal axes of Figure 17 show the birefringence variance and the logarithmic intensity variance, respectively. The logarithmic intensity is the logarithmic value of the signal intensity for each observation point. Figure 17 shows that there is no significant correlation between the local birefringence and the birefringence variance. The correlation coefficient was 0.280. This confirms that the polarization state cannot be fully expressed by logarithmic intensity alone.

[0148] FIG. 19 is a diagram showing an example of the spatial distribution of TPU. FIG. 19 shows the TPU for each observation point in biological tissue in shades of gray. In the example shown in FIG. 19, the same biological tissue as in FIG. 13 is observed. The brighter the area, the larger the TPU, and the darker the area, the smaller the TPU. Overall, the TPU tends to be smaller inside the tissue than on the surface. This tendency is opposite to the tendency of the dispersion of birefringence. This is because the larger the TPU, the smaller the change in the polarization state over time, whereas the larger the dispersion of birefringence, the larger the change in the polarization state over time.

[0149] Fig. 20 is a diagram showing an example of the time change of the dispersion of birefringence. Fig. 21 is a diagram showing another example of the time change of the TPU. Fig. 22 is a diagram showing an example of the time change of the dynamic contrast of birefringence. Fig. 23 is a diagram showing an example of the time change of the logarithmic intensity dispersion. Figs. 20, 21, 22, and 23 respectively show the dispersion of birefringence, TPU, birefringence, and logarithmic intensity dispersion for each hour at a certain observation point in a common biological tissue. The activity state of the biological tissue decreases over time.

[0150] FIG. 20 shows that the dispersion of birefringence significantly decreases with time from time 0 to 44 hours, whereas the birefringence is almost constant from 45 hours to 60 hours. FIG. 20 shows that the dispersion of birefringence significantly decreases with time from time 0 to 44 hours. The correlation coefficient was -0.9486. In contrast, the birefringence is almost constant from 45 hours to 60 hours. The correlation coefficient was -0.1711. FIG. 21 shows that the TPU significantly increases with time from time 0 to 42 hours. The correlation coefficient was 0.9413. In contrast, the TPU is almost constant from 43 hours to 60 hours. The correlation coefficient was 0.0735. FIG. 22 shows that the dynamic contrast of birefringence significantly decreases with time. The correlation coefficient was -0.905. 20 shows that the logarithmic intensity variance decreases from time 0 to 3 hours, increases from 4 hours to 19 hours, decreases from 20 hours to 42 hours, and becomes almost constant from 42 hours to 60 hours. These examples show that this embodiment makes it possible to measure the time-varying characteristic value of the polarization characteristic value for each observation point in the sample, and to evaluate the activity state of biological tissue based on the measured time-varying characteristic value.

[0151] As described above, the measurement signal processing device 200 according to the above-described embodiment generates a first measurement signal (e.g., a first horizontally polarized spectral interference signal) having a first polarization state (e.g., horizontal polarization) among first interference components resulting from interference between a first incident component (e.g., a horizontally polarized component) that is incident on a sample in a first polarization state and a component that is reflected or scattered from the sample, a second measurement signal (e.g., a second horizontally polarized spectral interference signal) having a second polarization state (e.g., vertical polarization) relative to the first interference component ... third measurement signal (e.g., a second horizontally polarized spectral interference signal) having a second polarization state (e.g., vertical polarization) relative to the first interference component, and a fourth measurement signal (e.g., a second horizontally polarized spectral interference signal) having a second polarization state (e.g., vertical polarization) relative to the first interference component. The polarization analyzer 216 determines a polarization characteristic value based on the polarization characteristics at an observation point in the sample based on a third measurement signal (e.g., a first vertically polarized spectral interference signal) having a first polarization state among the first interference components resulting from interference between the first incident component and the component reflected or scattered from the sample with respect to the incident component (e.g., a vertically polarized component), and a fourth measurement signal (e.g., a second vertically polarized spectral interference signal) having a second polarization state with respect to the second interference component, and a fluctuation characteristic analyzer 218 determines a time fluctuation characteristic value indicating the time fluctuation characteristic of the polarization characteristic value. With this configuration, the time fluctuation characteristic value of the polarization characteristic value is determined for each observation point in the sample. Since the time fluctuation of the polarization characteristic value has a significant correlation with the activity of the tissue, the activity of the tissue can be quantitatively evaluated based on the distribution of the time fluctuation characteristic value determined for each observation point.

[0152] The polarization analysis unit 216 may also determine a Jones matrix for each observation point based on the first, second, third, and fourth measurement signals, determine a cumulative Jones matrix at the observation point from the Jones matrix at the observation point inside the sample and the Jones matrix at the surface of the sample, and determine CPR, which is the phase difference between eigenvalues ​​of the cumulative Jones matrix, as the polarization characteristic value. With this configuration, the time-varying characteristic value of CPR, which corresponds to the phase difference between the polarization components between the surface of the sample and the observation point, can be used to evaluate the activity of the tissue.

[0153] The polarization analysis unit 216 may also determine a Jones matrix for each observation point based on the first, second, third, and fourth measurement signals, determine a local Jones matrix between the first and second observation points from the Jones matrix at the first observation point in the sample and the Jones matrix at the second observation point in the sample, and determine an LPR that is a phase difference between two eigenvalues ​​of the local Jones matrix. With this configuration, the time-varying characteristic value of the LPR that corresponds to the phase difference between the polarization components occurring in the section between the first and second observation points can be used to evaluate the activity of the tissue. Therefore, the activity of the tissue can be evaluated for each minute region.

[0154] The polarization analyzer 216 may also determine the birefringence by dividing the local phase delay by the wave number of the light incident on the sample and the thickness of the first observation point and the second observation point. This allows the activity of the tissue to be evaluated for each minute region, and the use of the birefringence makes it easy to compare the birefringence with other tissues or existing substances to be observed.

[0155] The fluctuation characteristic analysis unit 218 may calculate a time fluctuation characteristic value based on the variance or standard deviation of the polarization characteristic value, thereby quantitatively evaluating the magnitude of the time fluctuation of the polarization characteristic value.

[0156] The fluctuation characteristic analysis unit 218 may calculate a time fluctuation characteristic value based on the variance or standard deviation of the logarithmic value of the polarization characteristic value. In the process of calculating the variance or standard deviation, the constant potentially multiplied by the polarization characteristic value is eliminated, so that the substantial time fluctuation of the polarization characteristic value is evaluated. In addition, taking the logarithmic value makes it easier to compare the time fluctuation characteristic value of other tissues or existing substances to be observed that have different scales.

[0157] The fluctuation characteristic analysis unit 218 may calculate the dynamic contrast by dividing the standard deviation of the polarization characteristic values ​​by the average value of the birefringence. By dividing by the average value of the polarization characteristic values, the standard deviation of the polarization characteristic values ​​is normalized, so that the substantial time fluctuation of the polarization characteristic values ​​can be evaluated without a change in scale.

[0158] The polarization analysis unit 216 may convert the first Jones vector based on the first measurement signal and the second measurement signal, and the second Jones vector based on the third measurement signal and the fourth measurement signal, into a first Stokes vector and a second Stokes vector, respectively, as the polarization characteristic value, and the fluctuation characteristic analysis unit 218 may determine a TPU based on the time average value of the first Stokes vector and the time average value of the second Stokes vector as the time fluctuation characteristic value. According to this configuration, the uniformity of the polarization state at the observation point over time is quantified by using the TPU. The TPU tends to be larger as the activity of the tissue is lower. Therefore, the inactive state of the tissue can be quantitatively evaluated by the distribution of the TPU determined for each observation point.

[0159] The fluctuation characteristic analysis unit 218 may determine the TPU based on the time average value of the first Stokes vector after correction obtained by subtracting the noise component from the first Stokes vector, and the time average value of the second Stokes vector after correction obtained by subtracting the noise component from the second Stokes vector. According to this configuration, the noise components are removed from the first Stokes vector and the second Stokes vector, and the signal components remain. Therefore, the effect of noise on the TPU can be suppressed, and the activity of the tissue can be accurately evaluated.

[0160] The polarization analysis unit 216 may determine a Jones matrix for each observation point from the first measurement signal, the second measurement signal, the third measurement signal, and the fourth measurement signal as the polarization characteristic value, and the fluctuation characteristic analysis unit 218 may calculate the von Neumann entropy of the Jones matrix as the time fluctuation characteristic value. With this configuration, the randomness of the Jones matrix indicating the polarization state at the observation point is quantified. Therefore, the activity of the tissue can be quantitatively evaluated based on the distribution of the von Neumann entropy determined for each observation point.

[0161] The fluctuation characteristic analysis unit 218 may calculate the entropy of the noise component from the time polarization uniformity of the first Stokes vector and the time polarization uniformity of the second Stokes vector, which are respectively converted from the first Jones vector based on the first measurement signal and the second measurement signal, and the second Jones vector based on the third measurement signal and the fourth measurement signal, and correct the von Neumann entropy based on the entropy of the noise component. According to this configuration, the contribution of the entropy of the noise component is compensated for from the von Neumann entropy, and the von Neumann entropy of the signal component is obtained. Therefore, the influence of noise on the von Neumann entropy can be suppressed, and the activity of the tissue can be accurately evaluated.

[0162] The measurement signal processing device 200 may include an image processing unit 220 that generates image data having, as a signal value, an output value for a time-varying characteristic value for each observation point using a function that gives an output value that changes monotonically with respect to a change in an input value. According to this configuration, an image having a distribution of brightness or color tone corresponding to the time-varying characteristic value for each observation point is obtained. The measurement signal processing device 200 may include an output processing unit 222 that determines an evaluation value indicating the activity state of the biological tissue to be sampled based on the time-varying characteristic value. For example, the evaluation value may be a real value that is larger as the activity level increases. For example, a function indicating the relationship between the evaluation value and the time-varying characteristic value and its parameters are set in advance in the output processing unit 222. The output processing unit 222 may store the evaluation value in the storage unit 230 or may output it to another device. The image processing unit 220 may convert the evaluation value calculated by the output processing unit 222 for each observation point into a signal value for each pixel as described above, and generate image data having the converted signal value. Therefore, a user can easily evaluate the activity of the tissue in the observation area by contacting the obtained image.

[0163] Although the embodiment of the present invention has been described in detail above with reference to the drawings, the specific configuration is not limited to the above, and various design changes, etc. are possible within the scope that does not deviate from the gist of the present invention.

[0164] For example, in the above description, the evaluation device 20 and the measurement signal processing device 200 are each a part of the OCT system 1, but the present invention is not limited to this. The evaluation device 20 and the measurement signal processing device 200 may each be a single device independent of the OCT system 1 and not equipped with an optical system. In this case, the optical system control unit 212 may be omitted from the control unit 210 of the measurement signal processing device 200. Similarly, the evaluation device 20 may be omitted from a control means for controlling the optical system. The evaluation device 20 and the measurement signal acquisition unit 214 are not limited to optical systems, and may acquire detection signals and measurement signals wirelessly or wired, for example, via a network, from other devices such as a data storage device and a PC. The measurement signal processing device 200 may include any of the input / output unit 240, the display unit 250, and the operation unit 260 as described above, or may omit some or all of them. The evaluation device 20 may also include any of the functional configurations corresponding to the input / output unit 240, the display unit 250, and the operation unit 260, or may omit some or all of them. In addition, in the control unit 210 of the measurement signal processing device 200, one or both of the image processing unit 220 and the output processing unit 222 may be omitted. When the image processing unit 220 is omitted, the control unit 210 may output the generated data indicating the time-varying characteristic value to other devices such as a data storage device, a PC, or another image processing device wirelessly or wired, for example, via a network. In addition, the evaluation device 20 may also have functional configurations corresponding to one or both of the image processing unit 220 and the output processing unit 222, or a part or all of them may be omitted. The device to which the output is to be sent may have a function similar to that of the image processing unit 220, that is, a function of generating output image data based on data input from the evaluation device 20 or the measurement signal processing device 200 and displaying an image based on the generated output image data. In the evaluation device 20, functional configurations corresponding to one or both of the image processing unit 220 and the output processing unit 222 of the measurement signal processing device 200 may be omitted. In addition, a part or the whole of the evaluation device 20 or the measurement signal processing device 200 in the above-mentioned embodiment may be realized as an integrated circuit such as an LSI (Large Scale Integration). Each functional block of the evaluation device 20 or the measurement signal processing device 200 may be individually processed, or a part or the whole may be integrated and processed. The integrated circuit method is not limited to LSI, and may be realized by a dedicated circuit or a general-purpose processor. Furthermore, when an integrated circuit technology that replaces LSI appears due to the progress of semiconductor technology, an integrated circuit based on that technology may be used. Although the preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments and their modifications. Addition, omission, substitution, and other modifications of the configuration are possible without departing from the spirit of the present invention. Furthermore, the present invention is not limited by the foregoing description, but only by the scope of the appended claims. [Industrial Applicability]

[0165] The above-described embodiments are extremely useful, for example, in quality control of cultured tissues for regenerative medicine, organoids, animal experiments, efficacy evaluation when measuring efficacy of cultured tissues, and therapeutic effect evaluation in gene therapy of fundus photoreceptor cells. [Explanation of symbols]

[0166] 1...OCT system, 10...imaging means, 20...evaluation device, 22...measurement means, 24...evaluation means, 102...light source, 110...polarization delay unit, 128...probe, 130...reference arm, 150...polarization separation detection unit, 200...measurement signal processing device, 210...control unit, 212...optical system control unit, 214...measurement signal acquisition unit, 216...polarization analysis unit, 218...variation characteristic analysis unit, 220...image processing unit, 222...output processing unit, 230...storage unit, 240...input / output unit, 250...display unit, 260...operation unit< / s> ​

Claims

1. Acquire an optical coherence tomography (OCT) signal that indicates the state of the biological tissue sample; a measurement unit that acquires a signal value based on the OCT signal for each observation point on the sample; an evaluation unit that calculates a time variation characteristic value that indicates a time variation characteristic of the signal value within a predetermined period of time, The evaluation unit is Calculating a correlation coefficient between the signal value and a signal value obtained by time-shifting the signal value by a time shift amount τ for each of the time shift amounts τ; The correlation coefficient in which the time shift amount τ is zero is not used, and the correlation coefficient in which the time shift amount τ is not zero is used, A decay rate of the correlation coefficient associated with an increase in the time shift amount τ is calculated as the time variation characteristic value. Evaluation equipment.

2. The evaluation unit is Calculating a sum of squares of deviations between the signal intensity of the OCT signal at each frame time within the predetermined period and an average value of the signal intensity as a variance; Calculate a sum of the products of the deviation between the signal intensity of the OCT signal at each frame time within the predetermined period and the average value of the signal intensity, and the deviation between the signal intensity of the OCT signal at a shift time shifted from the frame time by a time shift amount τ and the average value of the time-shifted signal intensity, as a covariance; The correlation coefficient is calculated by dividing the covariance by the variance for each of the time shift amounts τ; A regression analysis is performed using a predetermined decay function by using the correlation coefficient for each time shift amount τ, and a parameter of the decay function that approximates the correlation coefficient is calculated as the decay rate for each observation point. The evaluation device according to claim 1 .

3. Acquire an optical coherence tomography (OCT) signal that indicates the state of the biological tissue sample; a measurement unit that acquires a signal value based on the OCT signal for each observation point on the sample; an evaluation unit that calculates a time variation characteristic value that indicates a time variation characteristic of the signal value within a predetermined period of time, The measurement unit is a polarization characteristic value based on the polarization characteristics at an observation point within the sample is determined based on: a first measurement signal having a first polarization state among first interference components resulting from interference between a first incident component that is incident on the sample in a first polarization state and a component that is reflected or scattered from the sample, the first incident component; a second measurement signal having a second polarization state for the first interference component; a third measurement signal having a first polarization state among second interference components resulting from interference between a second incident component that is incident on the sample in a second polarization state and a component that is reflected or scattered from the sample, the second incident component; and a fourth measurement signal having a second polarization state for the second interference component; The evaluation unit is A time-varying characteristic value indicating a time-varying characteristic of the polarization characteristic value is determined. Evaluation equipment.

4. the measurement unit determines a Jones matrix for each observation point based on the first measurement signal, the second measurement signal, the third measurement signal, and the fourth measurement signal, and determines a cumulative Jones matrix at the observation point from the Jones matrix at the observation point within the sample and the Jones matrix at a surface of the sample; As the polarization characteristic value, a cumulative phase lag index value which is a phase difference between eigenvalues ​​of the cumulative Jones matrix is ​​defined. The evaluation device according to claim 3 .

5. the measurement unit determines a Jones matrix for each observation point based on the first measurement signal, the second measurement signal, the third measurement signal, and the fourth measurement signal, and determines a local Jones matrix between the first observation point and the second observation point from the Jones matrix at the first observation point in the sample and the Jones matrix at the second observation point in the sample; determining the polarization characteristic value based on a local phase retardation, the local phase retardation being the phase difference between eigenvalues ​​of the local Jones matrix; The evaluation device according to claim 3 .

6. The measurement unit is The birefringence is determined by dividing the local phase retardation by the wave number of the incident light on the sample and the thickness between the first observation point and the second observation point. The evaluation device according to claim 5 .

7. The evaluation unit calculates the time-varying characteristic value based on a variance or a standard deviation of the polarization characteristic value. The evaluation device according to claim 3 .

8. The evaluation unit calculates the time-varying characteristic value based on a variance or a standard deviation of a logarithmic value of the polarization characteristic value. The evaluation device according to claim 3 .

9. The evaluation unit calculates a dynamic contrast by dividing the standard deviation of the polarization characteristic values ​​by an average value of the birefringence. The evaluation device according to claim 3 .

10. the measurement unit converts, as the polarization characteristic value, a first Jones vector based on the first measurement signal and the second measurement signal, and a second Jones vector based on the third measurement signal and the fourth measurement signal, into a first Stokes vector and a second Stokes vector, respectively; The evaluation unit determines a time polarization uniformity as the time-varying characteristic value based on a time average value of the first Stokes vector and a time average value of the second Stokes vector. The evaluation device according to claim 3 .

11. The measurement unit determines a temporal polarization uniformity based on a time average value of a corrected first Stokes vector obtained by subtracting a noise component from the first Stokes vector and a time average value of a corrected second Stokes vector obtained by subtracting a noise component from the second Stokes vector. The evaluation device according to claim 10.

12. the measurement unit determines a Jones matrix for each observation point based on the first measurement signal, the second measurement signal, the third measurement signal, and the fourth measurement signal as the polarization characteristic value; The evaluation unit calculates the von Neumann entropy of the Jones matrix as the time-varying characteristic value. The evaluation device according to claim 3 .

13. The evaluation unit is calculating an entropy of a noise component from a time polarization uniformity of a first Stokes vector and a time polarization uniformity of a second Stokes vector, which are respectively converted from a first Jones vector based on the first measurement signal and the second measurement signal and a second Jones vector based on the third measurement signal and the fourth measurement signal; The von Neumann entropy is corrected based on the entropy of the noise component. The evaluation device according to claim 12.

14. the first polarization state is horizontal polarization and the second polarization state is vertical polarization; the first measurement signal is a first horizontal polarization spectral interferometry signal; the second measurement signal is a second horizontal polarization spectral interferometry signal; the third measurement signal is a first vertically polarized spectral interference signal; The fourth measurement signal is a second vertically polarized spectral interference signal. The evaluation device according to any one of claims 3 to 13.

15. The evaluation unit is The time-varying characteristic value is calculated for each observation period interval that is longer than the predetermined period. The evaluation device according to any one of claims 1 to 14.

16. an output processing unit that determines an evaluation value indicating an activity state of the sample based on the time-varying characteristic value; The evaluation device according to any one of claims 1 to 15.

17. an image processing unit that generates image data having an output value for the time-varying characteristic value for each of the observation points as a signal value by using a function that gives an output value that changes monotonically with respect to a change in an input value; The evaluation device according to any one of claims 1 to 16.

18. A method in an evaluation device, comprising: Acquire an optical coherence tomography (OCT) signal that indicates the state of the biological tissue sample; a measuring step of acquiring a signal value based on the OCT signal for each observation point on the sample; and an evaluation step of calculating a time variation characteristic value indicating a time variation characteristic of the signal value within a predetermined period of time, In the measuring step, a polarization characteristic value based on the polarization characteristics at an observation point within the sample is determined based on: a first measurement signal having a first polarization state among first interference components resulting from interference between a first incident component that is incident on the sample in a first polarization state and a component that is reflected or scattered from the sample, the first incident component; a second measurement signal having a second polarization state for the first interference component; a third measurement signal having a first polarization state among second interference components resulting from interference between a second incident component that is incident on the sample in a second polarization state and a component that is reflected or scattered from the sample, the second incident component; and a fourth measurement signal having a second polarization state for the second interference component; In the evaluation step, A time-varying characteristic value indicating a time-varying characteristic of the polarization characteristic value is determined. Evaluation method.

19. The computer of the evaluation device Acquire an optical coherence tomography (OCT) signal that indicates the state of the biological tissue sample; a measurement step of acquiring a signal value based on the OCT signal for each observation point on the sample; An evaluation procedure for calculating a time-varying characteristic value indicating a time-varying characteristic of the signal value within a predetermined period of time, In the measurement procedure, a polarization characteristic value based on the polarization characteristics at an observation point within the sample is determined based on: a first measurement signal having a first polarization state among first interference components resulting from interference between a first incident component that is incident on the sample in a first polarization state and a component that is reflected or scattered from the sample, the first incident component; a second measurement signal having a second polarization state for the first interference component; a third measurement signal having a first polarization state among second interference components resulting from interference between a second incident component that is incident on the sample in a second polarization state and a component that is reflected or scattered from the sample, the second incident component; and a fourth measurement signal having a second polarization state for the second interference component; In the evaluation procedure, A time-varying characteristic value indicating a time-varying characteristic of the polarization characteristic value is determined. program.

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