Evaluation device, evaluation method, and program

Abstract

To evaluate a sample having the fast dynamics and slow dynamics by image acquisition with a limited resource.SOLUTION: An evaluation device comprises: an optical coherence tomography (OCT) signal acquisition unit which acquires signal values at plural different times, the signal value being based on an OCT signal indicating the state of a biological tissue to be a sample; and a calculation unit which calculates a time variation characteristic value indicating a time variation characteristic in a prescribed period of the signal value. The calculation unit calculates the time variation characteristic value on the basis of the signal value acquired at a first time interval and the signal value acquired at a second time interval different from the first time interval.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an evaluation device, an evaluation method, and a program. [Background technology]

[0002] In recent years, research has been conducted on a technology called "OCT microscopy," which uses optical coherence tomography (OCT) to image in vitro / ex vivo samples. However, OCT microscopy is generally a morphological imaging technique and cannot image tissue functions such as metabolism. In response to this, a signal analysis method called "dynamic OCT (DOCT)" has been proposed (see, for example, Patent Document 1). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] El-Sadek et al., “Optical coherence tomography-based tissue dynamics imaging for longitudinal and drug response evaluation of tumor spheroids.” Biomedical Optics Express 11, 6231 (2020). Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional DOCT technology has enabled label-free visualization of intracellular activities such as metabolism and intracellular motility. However, when using DOCT to visualize and observe fast dynamics of intracellular activities, such as cell death (e.g., necrosis and apoptosis) in three dimensions, extremely long measurement times (several minutes) or ultrafast OCT systems are required. Therefore, it is not practical to visualize and observe fast dynamics of intracellular activities using conventional technology.

[0005] The present invention has been made in view of the above circumstances, and aims to provide a technique that makes it possible to evaluate samples having both fast and slow dynamics by acquiring images using limited resources. [Means for solving the problem]

[0006] An evaluation device according to one aspect of the present invention includes an OCT signal acquisition unit that acquires signal values based on OCT (Optical Coherence Tomography) signals indicating the state of a biological tissue to be used as a sample at a plurality of different times, and a calculation unit that calculates a time-varying characteristic value that indicates a time-varying characteristic of the signal values within a predetermined period of time, wherein the calculation unit calculates the time-varying characteristic value based on the signal values acquired at a first time interval and the signal values acquired at a second time interval different from the first time interval. The calculation unit calculates a time variance of the logarithm of the signal strength of the signal value as the time-varying characteristic value. do.

[0007] In addition, in an evaluation device according to one aspect of the present invention, the first time interval is shorter than the second time interval, and the calculation unit calculates the time-varying characteristic value based on a second signal value group, which is a plurality of signal values acquired at the second time interval, when the first signal value group is a first signal value group consisting of a plurality of signal values acquired at the first time interval.

[0008] Furthermore, in an evaluation device according to one aspect of the present invention, the calculation unit calculates the time-varying characteristic value based on the signal values acquired at a third time interval that is longer than the first time interval and the second time interval, and the calculation unit calculates the time-varying characteristic value based on a third signal value group that is a plurality of the signal values acquired multiple times consecutively at the third time interval.

[0009] In the evaluation device according to an aspect of the present invention, the calculation unit calculates the variance of the signal values as the time-varying characteristic value.

[0010] In addition, in an evaluation device according to one aspect of the present invention, the calculation unit calculates the time-varying characteristic value using a pixel kernel consisting of multiple pixels on the same image among OCT images of the sample and multiple pixels that differ in the time direction among multiple OCT images captured over time.

[0011] In the evaluation device according to one aspect of the present invention, the calculation unit calculates the time-varying characteristic value in a predetermined time domain, and calculates a correspondence relationship between the time domain and the time-varying characteristic value.

[0013] In addition, in an evaluation device according to one aspect of the present invention, the calculation unit calculates the time variance of the logarithm of the signal strength of the signal value in a plurality of different time domains, and calculates an approximation formula based on the correspondence between the time domains and the time-varying characteristic value.

[0014] In addition, in the evaluation device according to one aspect of the present invention, the calculation unit calculates the time variation characteristics based on the calculated time variance of the logarithms of the signal strengths of the signal values in the multiple time domains.

[0015] Moreover, the evaluation device according to one aspect of the present invention further includes a presentation unit that presents an approximate curve corresponding to the calculated approximate expression.

[0016] Moreover, the evaluation device according to one aspect of the present invention further includes an image processing unit that visualizes the behavior of the sample based on the constants of the approximation formula.

[0017] In addition, in the evaluation device according to one aspect of the present invention, the image processing unit divides the approximation curve corresponding to the approximation formula into a plurality of different regions in the time direction, and colors the OCT image of the sample according to the area of each divided region.

[0018] An evaluation method according to one aspect of the present invention includes an OCT signal acquisition step of acquiring signal values based on OCT (Optical Coherence Tomography) signals indicating the state of a biological tissue sample at a plurality of different times, and a calculation step of calculating a time-varying characteristic value indicating a time-varying characteristic of the signal values within a predetermined period of time, wherein the calculation step calculates the time-varying characteristic value based on the signal values acquired at a first time interval and the signal values acquired at a second time interval different from the first time interval. The calculation step calculates a time variance of the logarithm of the signal intensity of the signal value as the time-varying characteristic value. do.

[0019] A program according to one aspect of the present invention causes a computer to execute a signal acquisition step of acquiring signal values based on optical coherence tomography (OCT) signals indicating a state of a biological tissue to be sampled at a plurality of different times, and a calculation step of calculating a time-varying characteristic value indicating a time-varying characteristic of the signal values within a predetermined period, the calculation step including calculating the time-varying characteristic value based on the signal values acquired at a first time interval and the signal values acquired at a second time interval different from the first time interval. The calculation step calculates a time variance of the logarithm of the signal intensity of the signal value as the time-varying characteristic value. do.

[0020] An evaluation device according to one embodiment of the present invention is an evaluation device comprising: an OCT signal acquisition unit that acquires signal values based on OCT (Optical Coherence Tomography) signals indicating the state of a biological tissue sample at a plurality of different times; and a calculation unit that calculates a time variation characteristic value that indicates the time variation characteristic of the signal value within a predetermined period of time, wherein the calculation unit calculates the time variation characteristic value as the time variance of the logarithm of the signal intensity of the signal value in a predetermined time domain, and calculates the correspondence between the time domain and the time variation characteristic value. [Effects of the Invention]

[0021] According to the present invention, samples with fast and slow dynamics can be evaluated using image acquisition with limited resources. [Brief explanation of the drawings]

[0022] [Figure 1] 1A and 1B are diagrams for explaining a biological tissue evaluation method according to an embodiment. [Figure 2] FIG. 1 is a functional configuration diagram showing an example of the functional configuration of an OCT system according to an embodiment. [Figure 3] FIG. 2 is a functional configuration diagram illustrating an example of a functional configuration of an evaluation device according to an embodiment. [Figure 4] 10 is a flowchart illustrating an example of OCT signal processing according to the embodiment. [Figure 5] FIG. 10 is a diagram for explaining the correspondence between the Maximum Time Window and the LIV according to the embodiment. [Figure 6] 10A and 10B are diagrams for explaining an example of a case where coloring is performed according to the correspondence relationship between the maximum time window and the LIV according to the embodiment. [Figure 7] 10 is a timing chart for explaining a scanning protocol according to an embodiment. [Figure 8] 1 is a table for comparing four scanning protocols according to an embodiment. [Figure 9]FIG. 10 is a diagram for comparing the results when tumor spheroids are evaluated using an evaluation method according to an embodiment. [Figure 10] FIG. 10 is a diagram for comparing the results of evaluating fibrotic kidneys using the evaluation method according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, an evaluation method according to an embodiment of the present invention will be described in detail with reference to the drawings. Note that the drawings used in the following description may show characteristic portions enlarged for the sake of clarity, and the dimensional ratios of each component element may not necessarily be the same as in reality. Furthermore, the materials, dimensions, etc. exemplified in the following description are merely examples, and the present invention is not limited thereto. Appropriate modifications may be made within the scope of the present invention.

[0024] [OCT System Overview] 1 is a diagram for explaining a biological tissue evaluation method according to an embodiment. First, an overview of the biological tissue evaluation method according to an embodiment will be described with reference to the drawing. In the following description, a sample Sm will be described as an example of a living tissue to be observed by the OCT system 1.

[0025] First, the OCT system according to the embodiment performs OCT (optical coherence tomography) imaging (measurement) multiple times on the same location in biological tissue. Here, an OCT image obtained by one OCT imaging is referred to as an imaging frame (or simply frame). The OCT system obtains multiple OCT images by performing OCT imaging multiple times over time.

[0026] From the viewpoint of observing the characteristics of the entire sample, it is preferable to perform the same OCT imaging on multiple different parts.

[0027] Next, the OCT system quantitatively evaluates the activity of biological tissue based on the time variation of OCT signal intensity obtained from multiple OCT images taken of the same area at the same position in the biological tissue.The OCT system quantitatively evaluates the activity of biological tissue by visualizing the minute fluctuations that biological tissue possesses.

[0028] 2 is a functional configuration diagram showing an example of the functional configuration of an OCT system according to an embodiment, which will be described with reference to the same figure. The OCT system 1 includes an imaging means (imaging unit) 10 and an evaluation device 20. The OCT system 1 evaluates a sample Sm.

[0029] The imaging means 10 performs OCT imaging (measurement) on the sample Sm. Specifically, the imaging means 10 performs OCT imaging multiple times (several to several hundred times = several to several hundred frames), preferably 10 frames or more, more preferably 15 frames or more, within a predetermined period (for example, 1 to 15 minutes, typically within 3 minutes). The specified period (observation period) for one OCT imaging session should be shorter than the observation period interval between adjacent observation periods, and should be long enough to ensure the accuracy required for evaluating the dynamic properties of the sample as reflected in the OCT signal obtained by the OCT imaging at that time.

[0030] More specifically, the observation period may be determined relative to the frame interval so that the frequency components of the dynamic characteristics of the sample are included in a frequency band ranging from the lowest frequency corresponding to the observation period to the highest frequency corresponding to the frame interval. Furthermore, the observation period interval may be determined to be a period that ensures the accuracy required for evaluating the overall change in the activity state of the sample biological tissue. For example, the observation period interval may be determined to be sufficiently shorter than the time required for the series of processes from when the sample biological tissue is separated from the mother to cell death (apoptosis), or the series of processes from when a cause occurs in the sample biological tissue in the mother to cell death (necrosis).

[0031] The evaluation device 20 includes a measurement means (measurement unit) 22 and an evaluation means (evaluation unit) 24. The measurement means 22 measures the time change in OCT signal intensity obtained from multiple OCT images taken of the same part of the biological tissue at the same position in the biological tissue. The evaluation means 24 quantitatively evaluates the activity of the biological tissue serving as the sample Sm based on the time change.

[0032] Next, the measurement means 22 and the evaluation means 24 quantitatively evaluate the activity of biological tissue based on the time variation of OCT signal intensity obtained from the multiple OCT images. Specific methods of quantitative evaluation include, for example, calculating speckle variance (SV) and OCT correlation decay speed (OCDS), and then evaluating the calculation results.

[0033] [Functional configuration of evaluation device] 3 is a functional configuration diagram showing an example of the functional configuration of the evaluation device according to the embodiment. An example of the functional configuration of the evaluation device 20 will be described with reference to the same figure. The evaluation device 20 includes a measurement unit 22 and an evaluation unit 24. The measurement unit 22 includes an OCT signal acquisition unit 221. The evaluation unit 24 includes a calculation unit 241, a presentation unit 242, and an image processing unit 243.

[0034] The OCT signal acquisition unit 221 acquires a signal value based on an optical coherence tomography (OCT) signal. The OCT signal indicates the state of a biological tissue serving as a sample Sm. The OCT signal acquisition unit 221 acquires signal values at a plurality of different times.

[0035] The calculation unit 241 calculates a time-varying characteristic value of the signal value acquired by the OCT signal acquisition unit 221. The time-varying characteristic indicates the time-varying characteristic within a predetermined period. Specifically, the calculation unit 241 may analyze the polarization characteristic at an observation point in the sample, analyze the time-varying polarization characteristic of the polarization characteristic value indicating the analyzed polarization characteristic, and calculate the time-varying characteristic value indicating the analyzed time-varying characteristic.

[0036] The calculation unit 241 calculates the variance of the signal values as the time-varying characteristic value. The variance of the signal values may be, for example, the variance of the polarization characteristic value. The variance of the signal value may also be the variance of the phase delay, which may be the variance of the local phase retardation (LPR), which is the polarization phase delay in a local depth region, or the variance of the cumulative phase retardation (CPR), which is the phase delay accumulated from the surface to the observation point of interest within the sample. The dispersion of the signal values may also be the dispersion of the logarithmic values of the polarization characteristic values, phase delays, and the like.

[0037] The image processing unit 243 converts the time-varying characteristic values calculated by the calculation unit 241 into pixel values of pixels corresponding to individual observation points, and creates a display image.

[0038] The presentation unit 242 presents the display image created by the image processing unit 243. For example, the presentation unit 242 displays the display image on a display unit (not shown).

[0039] [OCT signal processing] 4 is a flowchart showing an example of OCT signal processing according to this embodiment, which will be described with reference to the same figure. (Step S102) The OCT signal acquisition unit 221 acquires OCT signals from the imaging means 10. More specifically, the OCT signal acquisition unit 221 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. The OCT signal acquisition unit 221 also 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.

[0040] (Step S104) The calculation unit 241 calculates a polarization characteristic value based on the acquired OCT signals. More specifically, the calculation unit 241 constructs a Jones matrix for each observation point based on the acquired first horizontally polarized OCT signal, second horizontally polarized OCT signal, first vertically polarized OCT signal, and second vertically polarized OCT signal. The calculation unit 241 calculates a predetermined polarization characteristic value from the constructed Jones matrix.

[0041] (Step S106) The calculation unit 241 calculates the time-varying characteristic. Specifically, the calculation unit 241 calculates a predetermined time-varying characteristic value from the polarization characteristic value during a preset observation period. (Step S108) Image processing unit 243 converts the calculated time variation characteristic value for each observation point into a pixel value for the pixel corresponding to each observation point. (Step S110) The presentation unit 242 presents the output image data based on the converted pixel values. That is, the presentation unit 242 visualizes the distribution in the observation target area of time-varying characteristic values that indicate the polarization characteristics in the observation target area.

[0042] [OCT signal analysis method] This section describes an analytical method for visualizing a dynamics image of a sample by analyzing OCT signals acquired over time. First, the analytical method according to this embodiment is classified into two analytical methods: logarithmic intensity variance (LIV) and time decorrelation analysis. Time decorrelation analysis is further classified into three types: correlation damping factor (CDF), early OCT correlation decay speed (OCDSe), and late OCDS (OCDSl). In other words, the analytical method according to this embodiment is classified into four types: LIV, CDF, OCDSe, and OCDsl.

[0043] [CDF(Correlation Damping Factor)] First, let us explain about CDF. In CDF, OCDS is visualized by fitting the time-delay autocorrelation (ρ) to an approximate formula.

[0044] The calculation unit 241 fits the time delay autocorrelation (ρ) using, for example, the following equation (1).

[0045]

number

[0046] where ρ(τ) in Equation (1) is the autocorrelation value of the delay time τ, and a, c, and b are parameters for fitting. In particular, the coefficient b is used to compare the dynamics of the tissue.

[0047] In this embodiment, the autocorrelation of the delay time τ is calculated from 32 frames using, for example, a 3 × 3 [px (pixels)] kernel in the depth and lateral directions. The depth direction is the time direction of multiple OCT images captured over time. The lateral direction is the direction within the same OCT image of the sample. By using a kernel that extends in the depth and lateral directions, more pixels are involved in the calculation of the correlation coefficient. This improves the accuracy of the calculation. That is, the calculation unit 241 calculates the time-varying characteristic value using a pixel kernel consisting of multiple pixels on the same image among OCT images of a sample and multiple pixels that differ in the time direction among multiple OCT images captured over time. The kernel according to this embodiment may be 1×1 [px], which does not have any extent in the depth direction or the horizontal direction.

[0048] [LIV(Logarithmic Intensity Variance)] Next, we will explain LIV. LIV is measured by comparing the magnitude of signal fluctuations. Specifically, LIV is defined as the time variance of the logarithm of the OCT signal intensity. Because the time variance of the logarithm of the OCT signal intensity is calculated logarithmically, LIV is not affected by the signal intensity, but only by the time-varying component of the signal. Specifically, when calculating the LIV, the calculation unit 241 calculates the time variance of the logarithm of the signal intensity of the signal value as the time variation characteristic value. In other words, the LIV is the variance of the time fluctuation of the OCT signal within a predetermined time domain.

[0049] Here, if the time domain for calculating LIV (hereinafter referred to as MTW: Maximum Time Window) is small, only fast sample movements are observed, but as the MTW is increased, slower sample movements are also observed. In other words, as the MTW is increased, slower movements gradually become observable. Therefore, LIV becomes sensitive to different sample movement speed domains depending on the MTW setting.

[0050] Next, we will explain a method for observing the tissue activity of a biological tissue sample based on the correspondence between MTW and LIV.

[0051] 5 is a diagram for explaining the correspondence relationship between the Maximum Time Window and the LIV according to the embodiment. A plurality of points shown in the diagram are the LIVs calculated by the calculation unit 241. The calculation unit 241 calculates the time variance (LIV) of the logarithm of the signal intensity of the signal value in a predetermined time domain (MTW) as the time variation characteristic value, and calculates the correspondence relationship between the time domain and the time variation characteristic value. The calculation unit 241 calculates the time variance (LIV) of the logarithm of the signal intensity of the signal value in a plurality of different time domains (MTW), and calculates an approximation formula based on the correspondence relationship between the time domain and the time variation characteristic value. The presentation unit 242 presents an approximation curve according to the calculated approximation formula. The approximation curve W1 is expressed, for example, by the following formula (2).

[0052]

number

[0053] where a and b are parameters for fitting, and m is the MTW. Alternatively, the approximation curve may be expressed by a power series of MTW or the like.

[0054] The approximation curve shown in equation (2) is merely an example, and may be expressed by, for example, equation (3) below.

[0055]

number

[0056] Equation (3) is a saturation function, and m is MTW. After the calculation unit 241 calculates an approximate curve based on Equation (3), the image processing unit 243 constructs an image using b or 1 / b and a as pixel values, thereby obtaining a more useful dynamics image.

[0057] The image processing unit 243 visualizes the behavior of the sample based on the calculated constants of the approximate expression. Specifically, the image processing unit 243 divides the approximate curve W1 into multiple regions that differ in the time direction, and colors the OCT image of the sample in accordance with the area of each divided region. 5, the OCT image is colored with R (red) when the MTW is 0 to 100 [ms (milliseconds)], G (green) when the MTW is 100 to 360 [ms], and B (blue) when the MTW is 360 to 483 [ms]. The range colored red is referred to as range A1, the range colored green is referred to as range A2, and the range colored blue is referred to as range A3.

[0058] When a fast-moving sample is used, the approximation curve rises quickly, so the area in range A1 becomes larger, i.e., the red color becomes deeper. On the other hand, for a slow-moving sample, the approximation curve rises slowly, so the area in range A1 becomes smaller and the components in range A3 become relatively more abundant, i.e., the green becomes darker. In this way, the image processing unit 243 applies color based on the approximation curve, thereby making it possible to visualize the speed of the movement of the tissue (rather than the magnitude of the movement of the tissue).

[0059] 6 is a diagram for explaining an example of a case where an OCT image is colored according to the correspondence relationship between the Maximum Time Window and the LIV according to the embodiment. With reference to the same figure, an example of a case where an OCT image is colored based on the approximation curve in FIG. 5 will be explained. FIG. 6(A) is an example of a case where range A1 is colored red. FIG. 6(B) is an example of a case where range A2 is colored green. FIG. 6(C) is an example of a case where range A1 is colored blue. FIG. 6(D) is an example of a case where FIG. 6(A) and FIG. 6(C) are combined. As shown in FIG. 6(D), the areas indicated in colors close to red are areas where the sample moves quickly, and the areas indicated in colors close to blue are areas where the sample moves slowly.

[0060] Note that, from one data set, multiple sub-data sets with the same MTW may be extracted and the LIV may be calculated. The LIV of a specific MTW may be calculated by performing statistical calculations (e.g., maximum, minimum, peak, average, etc.) on multiple LIVs. That is, the calculation unit 241 may calculate the time variation characteristics based on the time variance (LIV) of the logarithm of the signal intensity of the signal values in the calculated multiple time domains (MTW).

[0061] [OCDSe and OCDsl] Both OCDSe and OCDSl are defined as the slope of the decorrelation curve over a specific time delay range. The slope of the decorrelation curve is obtained by linear regression of the decorrelation curve. OCDSe is obtained over a range of, for example, 12.8 ms to 460.8 ms and is sensitive to fast fluctuations in the OCT signal. On the other hand, OCDSl is obtained over a range of 345.6 ms to 1011.2 ms and is sensitive to slow fluctuations in the OCT signal.

[0062] [Scanning Protocol] 7 is a timing chart for explaining scanning protocols according to an embodiment. The scanning protocols according to an embodiment will be explained with reference to the same figure. The figure shows four scanning protocols: CRS (Continuous Repeating Scan), ETS (Equi-time-spacing Sampling), BSA (Burst Sparse Acquisition), and Double BSA (Double Burst Sparse Acquisition). These scanning protocols can be used in any of the above-mentioned LIV, CDF, OCDSe and OCD1 techniques.

[0063] 7, the sampling timing is shown with the sampling points as vertical lines and the horizontal axis as time. Each scanning protocol acquires 32 frames of the same region at the same position in the biological tissue.

[0064] FIG. 7(A) shows a timing chart of the CRS. The CRS performs continuous and repeated scans at the same period (or approximately the same period). In the example shown in the figure, scans are performed every time t11. As a result, a period of time t15 is required to acquire 32 frames. In other words, time t15 is expressed as time t11 × 31. As a specific example, time t11 may be 12.8 [ms]. In this case, time t15 is 12.8 [ms] × 31 [frame interval] = 397 [ms]. The width of the same period (or approximately the same period) is a width that is set as the same period by a program, and includes a configuration in which the period differs depending on hardware.

[0065] FIG. 7(B) shows a timing chart of ETS. ETS is similar to CRS in that it performs continuous, repeated scans at the same period (or approximately the same period). On the other hand, ETS differs from CRS in that the time interval between frames is longer than that of CRS. That is, ETS allows observation over a long period of time, making it possible to observe slow movements of biological tissue. On the other hand, ETS cannot observe fast movements of biological tissue because the scan interval is long.

[0066] In the example shown in FIG. 7(B), scanning is performed every time t21. As a result, a period of time t25 is required to acquire 32 frames. In other words, time t25 is expressed as time t21 × 31. As a specific example, time t21 may be 204.8 [ms]. In this case, time t25 is 204.8 [ms] × 31 [frame interval] = 6.35 [s (seconds)]. ETS can be implemented as a repeated raster scan, i.e., 32 raster scans at intervals of 204.8 ms.

[0067] FIG. 7(C) shows a timing chart for BSA. BSA differs from CRS and ETS in that it does not perform scanning at a single cycle. BSA first performs scanning a predetermined number of times at intervals of time t31. This predetermined number of scans is also referred to as burst scanning. Time t31 is also referred to as the first time interval. BSA further repeats burst scanning at intervals of time t32. Time t32 is also referred to as the second time interval. As a result of performing scanning at these cycles, a period of time t35 is required to acquire 32 frames.

[0068] When the evaluation device 20 performs a scan using BSA, the calculation unit 241 calculates a time-varying characteristic value based on a signal value acquired at a first time interval and a signal value acquired at a second time interval different from the first time interval.

[0069] Here, the first time interval (time t31) is shorter than the second time interval (time t32). The calculation unit 241 performs a burst scan at the first time interval, and calculates the time variation characteristics based on the signal values obtained by repeating the burst scan multiple times. The plurality of signal values acquired by one burst scan is also referred to as a first signal value set SG1. The signal values acquired by repeating multiple burst scans is also referred to as a second signal value set SG2. That is, the calculation unit 241 calculates a time-varying characteristic value based on the first signal value set SG1, which is a plurality of signal values acquired multiple times consecutively in a first time interval, and the second signal value set SG2, which is a plurality of signal values acquired in a second time interval.

[0070] In the example shown in Figure 7(C), the number of scans per burst scan is four, and the total number of burst scans is eight. Specifically, time t31 may be 12.8 [ms], and time t32 may be 409.6 [ms]. Therefore, time t35 is 409.6 [ms] x 7 [burst interval] + 12.8 [ms] x 3 [frame interval] = 2.9 [s]. By repeating burst scans at predetermined intervals, BSA can decorrelate both short and long delay times. When implemented as a raster scan, BSA repeats raster scans eight times at intervals of 409.6 ms, and acquires four consecutive frames in each raster scan.

[0071] 7D shows a timing chart of Double BSA. Double BSA is similar to BSA in that it performs burst scans, but differs from BSA in that it performs first and second burst scans.

[0072] Double BSA first performs a first burst scan every time t41. Time t41 is also referred to as the first time interval. The multiple signal values obtained by the first burst scan are also referred to as a first signal value group SG1. Thereafter, the first burst scan is repeated at intervals of time t42. The repetition of the first burst scan at intervals of time t42 is also referred to as the second burst scan. Time t42 is also referred to as the second time interval. The multiple signal values obtained by the second burst scan are also referred to as the second signal value group SG2. Thereafter, the second burst scan is repeated at intervals of time t43. Time t43 is also referred to as the third time interval. The signal values obtained by repeating the second burst scan multiple times are also referred to as the third signal value set SG3. As a result, a period of time t45 is required to acquire 32 frames.

[0073] That is, when the evaluation device 20 performs a scan using Double BSA, the calculation unit 241 calculates the time-varying characteristic value based on signal values acquired at a third time interval that is longer than the first time interval and the second time interval. The calculation unit 241 also calculates the time-varying characteristic value based on a third signal value set SG3, which is a plurality of signal values acquired multiple times consecutively from the second signal value set SG2 at the third time interval.

[0074] In the example shown in Figure 7(D), the number of scans per first burst scan is four, the number of first burst scans per second burst scan is three, and the number of second burst scans is three. Specifically, time t41 may be 12.8 ms, time t42 may be 102.4 ms, and time t43 may be 409.6 ms. Therefore, time t45 is 409.6 ms x 2 (second burst interval) + 102.4 ms x 2 (first burst interval) + 12.8 ms x 3 (frame interval) = 1.1 s. By performing a first burst scan and a second burst scan, Double BSA allows for more detailed observation of biological tissues compared to BSA.

[0075] The BSA and Double BSA scanning protocols have been described as examples of acquiring signal values based on two or more periods. However, these protocols are merely examples of this embodiment. For example, a Triple BSA may be configured to acquire a third signal value group SG3 (not shown), which is a plurality of signal values acquired by Double BSA, at a fourth time interval (not shown).

[0076] Furthermore, in the above-described embodiment, an example of acquiring signal values based on two or more periods has been described, but according to this embodiment, it is not necessary to base the signal values on any period. That is, instead of observing the fast behavior of the sample at the same period (or approximately the same period), a configuration having a fast time interval and a slow time interval may be adopted by acquiring signal values randomly.

[0077] Note that periodically acquiring signal values has the advantage that it is easy to apply the technique of moving the galvanometer. In other words, by setting aside a period during which signal values are not acquired periodically, other locations can be measured during the unused time. Therefore, periodic observation is more preferable than random observation.

[0078] 8 is a table for comparing four scanning protocols according to the embodiment. The four scanning protocols, CRS, ETS, BSA, and Double BSA, described above, will be compared with each other with reference to the same figure. The figure shows the number of frames (Frames / position), shortest interval, total time window, and possible volume scan for each of the four acquisition protocols.

[0079] The CRS has a frame count of "32", a minimum time interval of "12.8 [ms]", and a total time frame of "397 [ms]". The ETS has a frame count of "32", a minimum time interval of "204.8 [ms]", and a total time frame of "6.35 [s]". The BSA has a frame count of "32", a minimum time interval of "12.8 [ms]", and a total time frame of "2.9 [s]". The double BSA has a frame count of "36", a minimum time interval of "12.8 [ms]", and a total time frame of "1.1 [s]".

[0080] CRS has only a short "minimum interval" and ETS has only a long "total time." That is, CRS allows fast dynamics to be observed but slow dynamics cannot be observed. Conversely, ETS allows slow dynamics to be observed but fast dynamics cannot be observed. In contrast, BSA has both a short "minimum interval" and a long "total time," meaning that both fast and slow dynamics can be observed with BSA. In addition, Double BSA allows for more detailed observations than BSA because the "total time" is shorter.

[0081] [Proof of Concept] Next, we will compare the results of scanning using CRS, ETS, and BSA with reference to Figures 9 and 10. We will also compare examples of coloring using LIV, CDF, OCDSe, and OCD1 with reference to the same figures.

[0082] The results shown in Figures 9 and 10 were generated by generating simulated CRS, ETS, and BSA sequences from 512 consecutively acquired OCT frames at a frame interval of 12.8 ms. The data were captured using an OCT microscope equipped with a 1.3 μm wavelength light source. The scan rate was 50 kHz, and the lateral and axial resolutions were 19 μm and 14 μm, respectively, within the tissue.

[0083] 9 is a diagram for comparing the results of evaluating tumor spheroids using the evaluation method according to the embodiment. The figure shows an example of a displayed image of a tumor spheroid of human breast adenocarcinoma (MCF7 cell line) visualized by LIV, CDF, OCDSe, and OCD1 based on data scanned using the CRS, ETS, and BSA scanning protocols. The diameter of the tumor shown in the figure is 200 μm to 300 μm. Figures 9(a),(b),(c),(d) show an example using CRS, Figures 9(e),(f),(g),(h) show an example using ETS, Figures 9(i),(j),(k),(l) show an example using BSA, Figures 9(a),(e),(i) show an example using LIV, Figures 9(b),(f),(j) show an example using CDF, Figures 9(c),(g),(k) show an example using OCDSe, and Figures 9(d),(h),(l) show an example using OCDSI. Each figure shows the speed of tissue movement by varying the shade of gray.

[0084] In CRS, the entire tissue is displayed in approximately one color, and it is not possible to observe differences in dynamics between the outside and inside of the tissue. ETS allows for the observation of slow movements. It also allows for the observation of differences between the outside and inside of tissue, with slower movements occurring on the outside compared to the inside. As with ETS, BSA also shows differences between the outside and inside of tissue, with slower movement occurring on the outside compared to the inside. In particular, when OCDSe (fast dynamics) is observed with BSA, it is clear that fast movement also occurs inside the tissue.

[0085] According to LIV and OCDSl, slow movements can be contrasted. In particular, ETS provides the highest contrast and CRS provides the lowest contrast according to LIV. Because ETS has a small MTW, it cannot capture the entire cycle of tissue movement when the dynamics within the tissue are slow, with a period of several seconds, such as several tens to 1 Hz. Specifically, the MTW of ETS is approximately 1 / 16 of that of CRS.

[0086] Although the MTW of BSA is about half that of ETS, ETS and BSA show comparable contrast in the CDF.

[0087] CDF provides higher contrast than other methods such as LIV, OCDSe, and OCDSi, and allows clear observation of differences in dynamics between the outside and inside of tissue.

[0088] 10 is a diagram for comparing the results of evaluating fibrotic kidneys using the evaluation method according to the embodiment. The figure shows ex vivo fibrotic kidneys from a one-year-old mouse, a model of fibrosis three weeks after the onset of the disease. Figures 10(a), (b), (c), and (d) show examples using CRS, Figures 10(e), (f), (g), and (h) show examples using ETS, and Figures 10(i), (j), (k), and (l) show examples using BSA. Figures 10(a), (e), and (i) show examples using LIV, Figures 10(b), (f), and (j) show examples using CDF, Figures 10(c), (g), and (k) show examples using OCDSe, and Figures 10(d), (h), and (l) show examples using OCDSI.

[0089] In the example shown in Figure 10, the same tendency as that described with reference to Figure 9 can be observed. That is, with CRS, the entire tissue is displayed in approximately one color, and it is not possible to observe differences in dynamics between the outside and inside of the tissue. With ETS, slow movements can be observed. Furthermore, differences can be observed between the outside and inside of the tissue, with slower movements occurring on the outside compared to the inside. With BSA, as with ETS, differences can be observed between the outside and inside of the tissue, with slower movements occurring on the outside compared to the inside. In particular, when OCDSe (fast dynamics) is observed with BSA, it can be seen that fast movements also occur inside the tissue.

[0090] The difference between the example shown in Figure 9 and the example shown in Figure 10 is that only BSA clearly shows two layers in OCDSe. That is, BSA allows for clear observation of the difference in dynamics between the outer and inner tissues. That is, the surface of the kidney shows slow dynamics, while the subsurface layer shows fast dynamics.

[0091] [Summary of the first embodiment] According to the embodiment described above, the evaluation device 20 includes the OCT signal acquisition unit 221 to acquire signal values based on OCT signals at multiple different times, and the calculation unit 241 to calculate a time variation characteristic value that indicates the time variation characteristic of the signal values within a predetermined period. The calculation unit 241 also calculates the time variation characteristic value based on the signal values acquired at the first time interval and the second time interval. That is, according to the scanning protocol of this embodiment, by including fast time intervals and slow time intervals, both fast and slow dynamics can be observed. That is, according to this embodiment, even if a sample has fast and slow dynamics, both fast and slow dynamics can be evaluated with limited resources while maintaining hardware resources.

[0092] Furthermore, according to the above-described embodiment, the calculation unit 241 acquires a first signal value set SG1 at a first time interval and acquires a second signal value set SG2 at a second time interval. That is, the calculation unit 241 calculates a time-varying characteristic value based on signal values scanned by BSA. Therefore, according to this embodiment, it is possible to capture fast dynamics at the first time interval and slower dynamics at the second time interval.

[0093] Furthermore, according to the above-described embodiment, the calculation unit 241 acquires the third signal value set SG3 by successively scanning the second signal value set SG2 multiple times at a third time interval. That is, the calculation unit 241 calculates the time-varying characteristic value based on the signal values scanned by Double BSA. In other words, the calculation unit 241 calculates the time-varying characteristic value based on the signal values scanned at three different periods. Therefore, it is possible to prevent the omission of dynamics that may be missed by BSA. Therefore, according to this embodiment, it is possible to observe the dynamics of a sample more accurately than BSA.

[0094] Furthermore, according to the above-described embodiment, the calculation unit 241 of the evaluation device 20 calculates the variance of the signal value as the time-varying characteristic value. Therefore, according to this embodiment, the evaluation device 20 can easily observe the dynamics of the sample.

[0095] Furthermore, according to the above-described embodiment, the calculation unit 241 calculates the time variance of the logarithm of the signal intensity of the signal value as the time-varying characteristic value. That is, the calculation unit 241 calculates the LIV. Therefore, according to this embodiment, the evaluation device 20 can easily visualize fast and slow movements of the sample.

[0096] Furthermore, according to the above-described embodiment, the calculation unit 241 calculates the time variance (LIV) of the logarithm of the signal intensity in a predetermined time domain, and calculates the correspondence between the time domain and the LIV. Therefore, according to this embodiment, the speed of the movement of the sample can be observed.

[0097] Furthermore, according to the above-described embodiment, the evaluation device 20 calculates the time-varying characteristic value using a pixel kernel consisting of multiple pixels on the same image among OCT images of the sample and multiple pixels that differ in the time direction among multiple OCT images captured over time. Therefore, according to this embodiment, the speed of movement of the sample can be observed based on more pixel information. Therefore, according to this embodiment, the speed of movement of the sample can be observed with high accuracy.

[0098] Furthermore, according to the above-described embodiment, the calculation unit 241 of the evaluation device 20 calculates the time variance of the logarithm of the signal intensity (LIV) of the signal values at a plurality of different MTWs, and calculates an approximation formula based on the correspondence between the MTWs and the LIVs. Therefore, according to this embodiment, the speed of the movement of the sample can be observed from the calculated approximation curve.

[0099] Furthermore, according to the above-described embodiment, the calculation unit 241 of the evaluation device 20 calculates the time variation characteristics based on the calculated LIVs for the multiple MTWs. In other words, the calculation unit 241 extracts and calculates the LIV based on multiple sub-datasets from one data set. Therefore, according to this embodiment, the accuracy of the LIV calculation can be improved.

[0100] Furthermore, according to the above-described embodiment, the evaluation device 20 includes the presentation unit 242, which presents an approximation curve according to the calculated approximation formula. That is, the presentation unit 242 displays a curve for each pixel, with MTW on the horizontal axis and LIV on the vertical axis, as the sample characteristics. Therefore, according to this embodiment, the sample characteristics can be displayed by displaying the curve itself.

[0101] Furthermore, according to the above-described embodiment, the evaluation device 20 includes the image processing unit 243, thereby visualizing the behavior of the sample based on the constants of the approximation formula. In other words, the image processing unit 243 obtains parameters by quantifying the calculated curve, and recreates an image based on the parameters. Therefore, according to this embodiment, fast and slow movements of the sample can be visualized with greater accuracy.

[0102] Furthermore, according to the above-described embodiment, the evaluation device 20 includes the image processing unit 243, which divides the approximation curve into multiple regions that differ in the time direction and colors the OCT image of the sample in accordance with the area of each divided region. Here, the area of a divided region can be considered as the energy of the signal moving at that speed. Therefore, according to this embodiment, the energy of the movement of the sample can be visualized.

[0103] All or part of the functions of the evaluation device 20 described above may be recorded as a program on a computer-readable recording medium, and the program may be executed by a computer system. The computer system includes hardware such as an OS and peripheral devices. Examples of computer-readable recording media include portable media such as flexible disks, magneto-optical disks, ROMs (Read Only Memory), and CD-ROMs, storage devices such as hard disks built into computer systems, and volatile memory (Random Access Memory: RAM) provided in servers on networks such as the Internet. Volatile memory is an example of a recording medium that retains a program for a certain period of time.

[0104] Furthermore, the above-described program may be transmitted to another computer system via a transmission medium, for example, a network such as the Internet, or a communication line such as a telephone line.

[0105] The program may be a program that realizes all or part of the above-described functions. Note that the program that realizes part of the above-described functions may be a so-called differential program, which is a program that can realize the above-described functions in combination with a program pre-recorded in the computer system.

[0106] The above describes an embodiment of the present invention with reference to the drawings, but the specific configuration is not limited to the above-described embodiment, and design changes and the like are also included within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0107] 1...OCT system, 10...imaging means, 20...evaluation device, 22...measuring means, 24...evaluation means, 221...OCT signal acquisition unit, 241...calculation unit, 242...presentation unit, 243...image processing unit, Sm...sample

Claims

1. an OCT signal acquisition unit that acquires signal values based on an optical coherence tomography (OCT) signal indicating a state of a biological tissue sample at a plurality of different times; a calculation unit that calculates a time variation characteristic value that indicates a time variation characteristic of the signal value within a predetermined period, the calculation unit calculates the time-varying characteristic value based on the signal value acquired at a first time interval and the signal value acquired at a second time interval different from the first time interval; The calculation unit calculates a time variance of the logarithm of the signal strength of the signal value as the time-varying characteristic value. Evaluation equipment.

2. the first time interval is shorter than the second time interval; The calculation unit calculates the time-varying characteristic value based on a first signal value group, which is a plurality of the signal values acquired consecutively a plurality of times during the first time interval, and a second signal value group, which is a plurality of the signal values acquired during the second time interval. The evaluation device according to claim 1 .

3. the calculation unit calculates the time-varying characteristic value based on the signal values acquired at a third time interval that is longer than the first time interval and the second time interval; The calculation unit calculates the time-varying characteristic value based on a third signal value group, which is a plurality of signal values acquired consecutively a plurality of times during the third time interval. The evaluation device according to claim 2 .

4. The calculation unit calculates the variance of the signal value as the time-varying characteristic value. The evaluation device according to any one of claims 1 to 3.

5. The calculation unit calculates the time-varying characteristic value using a pixel kernel configured from a plurality of pixels on the same image among OCT images obtained by capturing an image of the sample and a plurality of pixels that differ in the time direction among a plurality of OCT images captured over time. The evaluation device according to any one of claims 1 to 4.

6. The calculation unit calculates the time-varying characteristic value in a predetermined time domain and calculates a correspondence relationship between the time domain and the time-varying characteristic value. The evaluation device according to any one of claims 1 to 5.

7. The calculation unit calculates a time variance of a logarithm of a signal strength of the signal value in a plurality of different time domains, and calculates an approximation formula based on a correspondence relationship between the time domains and the time-varying characteristic value. The evaluation device according to any one of claims 1 to 6.

8. The calculation unit calculates the time variation characteristic based on the time variance of the logarithm of the signal strength of the signal value in the calculated plurality of time domains. The evaluation device according to claim 7 .

9. The presenting unit presents an approximate curve corresponding to the calculated approximate expression. The evaluation device according to claim 7 or 8.

10. an image processing unit that visualizes the behavior of the sample based on the constants of the approximation formula; The evaluation device according to any one of claims 7 to 9.

11. The image processing unit divides the approximation curve according to the approximation formula into a plurality of different regions in the time direction, and colors the OCT image of the sample according to the area of each divided region. The evaluation device according to claim 10.

12. an OCT signal acquisition step of acquiring signal values based on an optical coherence tomography (OCT) signal indicating the state of a biological tissue sample at a plurality of different times; a calculation step of calculating a time variation characteristic value indicating a time variation characteristic of the signal value within a predetermined period, the calculating step calculates the time-varying characteristic value based on the signal value acquired at a first time interval and the signal value acquired at a second time interval different from the first time interval; The calculation step calculates a time variance of the logarithm of the signal intensity of the signal value as the time-varying characteristic value. Evaluation method.

13. On the computer, an OCT signal acquisition step of acquiring signal values based on an optical coherence tomography (OCT) signal indicating a state of a biological tissue as a sample at a plurality of different times; a calculation step of calculating a time variation characteristic value indicating a time variation characteristic of the signal value within a predetermined period, the calculating step calculates the time-varying characteristic value based on the signal value acquired at a first time interval and the signal value acquired at a second time interval different from the first time interval; The calculation step calculates a time variance of the logarithm of the signal intensity of the signal value as the time-varying characteristic value. program.

14. an OCT signal acquisition unit that acquires signal values based on an optical coherence tomography (OCT) signal indicating a state of a biological tissue sample at a plurality of different times; a calculation unit that calculates a time variation characteristic value that indicates a time variation characteristic of the signal value within a predetermined period, The calculation unit calculates a time variance of a logarithm of a signal strength of the signal value in a predetermined time domain as the time variation characteristic value, and calculates a correspondence relationship between the time domain and the time variation characteristic value. Evaluation equipment.

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