An OCT-based attenuation imaging method

Abstract

The present invention provides an OCT-based attenuation imaging method, which limits the calculation of OAC within a local area of the same type of medium, avoiding the influence on the OAC result caused by the differences in BsAR of different media. When calculating the OAC of a complex sample with multiple layers of media using the traditional DR method, deviations will occur. This is mainly manifested in that when there is an ultra-high or ultra-low signal in the deep layer of the medium, it will affect the OAC of the shallow layer of the medium. If there is an ultra-high signal in the deep layer, the OAC result of the shallow layer will be on the low side; on the contrary, if there is an ultra-low signal in the deep layer, the OAC result of the shallow layer will be on the high side. The method of the present invention solves the problem of estimating the remaining light intensity based on local data by limiting the calculation of OAC within a local area containing only the same type of medium. Thus, the calculation of OAC is not interfered by other layers of media, and the calculation result is more accurate.

Description

Technical Field

[0001] The present invention belongs to the field of optical imaging, and particularly relates to an attenuation imaging method based on OCT. Background Art

[0002] Optical Coherence Tomography (OCT) technology is an imaging technology that can obtain the three-dimensional structure of a sample with ultra-high resolution. The attenuation imaging technology based on OCT is a functional extension of OCT. It resolves different sample types or sample property information by calculating the Optical Attenuation Coefficient (OAC) from the OCT signal. OAC is an index reflecting the attenuation rate of light in the sample. It is only related to the optical properties of the sample and is not affected by interference factors such as light intensity and incident angle. Therefore, converting the OCT image into an OAC image is an effective method for detecting the composition or traits of the sample. In addition, the perception of sample traits by OAC is more sensitive than the OCT signal.

[0003] There are mainly two types of algorithms for calculating OAC from the OCT signal: exponential fitting and depth-resolved (DR) algorithms. Among them, exponential fitting requires the sample to be uniform within a certain depth range, and the obtained result represents the average OAC of this section of the uniform sample. The DR algorithm can convert each pixel of the OCT signal into the corresponding OAC, greatly improving the resolution of the attenuation information. The disadvantage is that the DR algorithm will generate a serious error that increases with depth at the signal tail. This algorithm also needs to meet two harsh assumptions: (1) Almost all light is completely attenuated within the imaging depth range; (2) The backscattered light reflected back to the OCT system must be a fixed proportion of the attenuated light. In recent years, with the proposal of improved algorithms such as depth-resolved confocal method, optimized depth-resolved estimation method, and depth-resolved unbiased estimation method, most of the problems of the DR algorithm have been solved. However, there is still a key problem remaining, that is, if the backscattered light reflected back to the OCT system from the media at different depths of the sample is not a fixed proportion of the attenuated light, the DR algorithm and a series of improved algorithms based on DR will lose the accuracy of OAC calculation.

[0004] The traditional DR algorithm is shown in formula (1),

[0005]

[0006] In the formula, μ[z] represents the OAC of the sample pixel point at depth z, I[z] represents the OCT signal intensity at this point, and the denominator part represents the accumulation of all OCT signals below depth z.

[0007] If there is an obvious low backscattering region in the deep part of the sample, a large amount of low signals will be superimposed on the denominator of formula (1), making the current OAC calculation result on the high side. That is to say, the abnormality of the deep OCT signal will have a great impact on the calculation of the shallow OAC.

[0008] For example Figure 3 As shown, there is an obvious low-signal medium (PVC pipe) on one side under the homogeneous medium, while there is none on the other side. Then even if the shallow media on both sides of the image are exactly the same (which should have the same OAC), the obtained results are quite different. This is because on the side with the low-signal medium, a large amount of low signals are superimposed on the denominator of the DR algorithm calculation formula, making the current OAC result on the high side. Therefore, a high-brightness artifact appears. Similarly, if there is an ultra-high signal in the deep layer, the obtained current OAC result will be on the low side.

[0009] The essential reason for this problem is that the DR method needs to estimate the remaining light intensity by accumulating all the remaining signals (i.e., the denominator part of formula (1)). This method can only hold on the basis of the assumption that "the backscattered light is a fixed proportion of the attenuated light". The attenuation coefficient is equal to the absorption coefficient plus the scattering coefficient, and the scattering is divided into forward scattering and backscattering. The ratio of the backscattering coefficient to the attenuation coefficient is called the backscattering attenuation ratio (BsAR). Obviously, different media have different BsARs. The backscattered light is not a fixed proportion of the attenuated light. Using the method of accumulating the remaining signals to estimate the remaining light intensity is inaccurate. Due to the existence of this problem, when the DR method calculates the OAC of a complex sample with multiple layers of media, serious deviations will occur. The abnormality of the deep OCT signal will have a great impact on the calculation of the shallow OAC. Summary of the Invention

[0010] Based on the above problems, the present invention proposes an OCT-based attenuation imaging method. The main principle of this method is to limit the calculation of the OAC within a local area with the same type of medium and solve the problem of estimating the remaining light intensity based on local data. Avoid the influence on the OAC result caused by different backscattering attenuation ratios BsARs of different media.

[0011] An OCT-based attenuation imaging method includes the following steps:

[0012] Step 1: Obtain a three-dimensional image of the original OCT, and the three-dimensional image includes M B-scan (x-z direction) images;

[0013] Step 2: Determine the area to be processed, where the upper edge depth of the area to be processed is A and the lower edge depth is D;

[0014] Step 3: Select a section of uniform thin layer medium (B-C) of the same type and density from the area to be processed, where the upper edge depth of the uniform thin layer medium is B and the lower edge depth is C. The thin layer medium can be located at any position in the local area (A-D). A and B can overlap, and C and D can overlap.

[0015] Step 4: Calculate the light attenuation coefficient in the area to be processed;

[0016]

[0017] Where z is the image depth expressed in pixels, μ L [z] represents the light attenuation coefficient at depth z (unit: mm -1 ), I[z] is the OCT signal intensity at depth z (the pixel value at depth z in the OCT image), I[B] and I[C] represent the OCT signal intensities at depths B and C, respectively, and Δ represents the thickness of a single pixel in the OCT image (i.e., the actual size in the depth direction, in mm).

[0018] Step 5: After calculating the OAC of a local area of a B-scan image, add μ L Perform mean processing and convert the two-dimensional OAC image into a row of OAC projection data. After all M B-scans are calculated, combine the M one-dimensional projection data to form a two-dimensional projection image. The two-dimensional OAC projection image can be used to detect the type, density and other indicators of the sample.

[0019] Beneficial effects of the present invention

[0020] The present invention proposes an attenuation imaging method based on OCT, which can solve the problem of OAC calculation deviation in traditional methods. Traditional DR methods will have deviations when calculating the OAC of complex samples with multi-layer media. It is mainly manifested in that when there are ultra-high or ultra-low signals in the deep medium, it will affect the OAC of the shallow medium. If there is an ultra-high signal in the deep layer, the shallow OAC result will be low; on the contrary, if there is an ultra-low signal in the deep layer, the shallow OAC result will be high. The method of the present invention limits the OAC calculation to a local area that only contains the same type of media, and solves the problem of residual light intensity estimation based on local data. Thereby, the calculation of OAC is not interfered by other layers of media, and the calculation result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of the OCT-based attenuation imaging method of the present invention.

[0022] Figure 2Schematic diagram for solving the optical attenuation coefficient in the present invention; on the left is the OCT image simulating the sample to be measured. A - D represent media of the same type, and B - C represent uniform thin - layer media with the same type and density.

[0023] Figure 3 Calculation result diagram of OAC for artificial samples by the method of the present invention; among them, (a) is a series of OCT B - scan images of artificial samples, (b) is the high - signal artifact display diagram, (c) is the display diagram of high - signal artifacts in the average projection image, (d) is the local OAC image at the position of the white square in (a) obtained by the traditional method, (e) is the OAC curve diagram, (f) is the schematic diagram for selecting uniform thin - layer media with the same type and density, (g) is the OAC of all media from layer A to C obtained by the method in this article, (h) is the OAC curve extracted from the solid - line position, (i) is for Figure 3 (g) The OAC average projection image obtained by performing average projection in the depth direction. Detailed implementation mode

[0024] The invention will be further described below in conjunction with the attached drawings and specific implementation examples.

[0025] Step 1: Obtain the three - dimensional image of the original OCT. The three - dimensional image contains M B - scan (x - z direction) images. Convert the original data into the corresponding format (such as matlab,.mat format) according to the data - processing software used. The original data can be collected by the laboratory system itself or obtained from a commercial OCT system. The original data consists of M B - scans, as Figure 1 shown. Each B - scan is a cross - sectional view.

[0026] Step 2: Determine the area to be processed. Use image segmentation or edge - detection methods to determine the area to be processed, such as Figure 2 the areas A - D in. Among them, the upper - edge depth of the area to be processed is A, and the lower - edge depth is D. The calculation of OAC is only performed within A to D.

[0027] Step 3: Select a section of uniform thin - layer media (B - C) with the same type and density from the area to be processed. Among them, the upper - edge depth of the uniform thin - layer media is B, and the lower - edge depth is C. This thin - layer media can be located at any position in the local area (A - D). A and B can coincide, and C and D can coincide.

[0028] Step 4: Calculate the optical attenuation coefficient within the area to be processed;

[0029]

[0030] In the formula, z is the image depth represented by the number of pixels, μ L[z] represents the light attenuation coefficient at depth z (unit: mm -1 ), I[z] is the OCT signal intensity at depth z (the pixel value at depth z in the OCT image), I[B] and I[C] respectively represent the OCT signal intensities at depths B and C, and Δ represents the thickness of a single pixel in the OCT image (i.e., the actual size in the depth direction, unit: mm).

[0031] When the depth z is greater than or equal to A and less than or equal to C, the upper part of formula (2) is used to calculate OAC. If A coincides with B, the calculation method remains unchanged; when the depth z is greater than C and less than or equal to D, the lower part of formula (2) is used to calculate OAC; if C coincides with D, it means that point C is the lower edge of this local area, then the lower part of formula (2) does not need to be used, and only the upper part of formula (2) is used.

[0032] Step 5: Average projection. After calculating the OAC of each local area of a B-scan image (such as Figure 3 (g)), the μ of each column of the image is averaged along the depth direction L to convert the two-dimensional OAC image into a one-dimensional OAC projection data. After all M B-scans are calculated, the M one-dimensional projection data are combined together to form a two-dimensional projection image. As shown in Figure 3 (i). The two-dimensional OAC projection image can be used to detect indicators such as the type and density of the sample.

[0033] Figure 3 The artificial sample in is composed of a TiO2 solution with a concentration of 0.05 w% and a transparent polyvinyl chloride tube (PVC) filled with air. Figure 3 (a) shows a series of OCT B-scan images of the artificial sample, a total of M. A B-scan is taken one by one using a for loop for calculation. Next, the area to be processed is selected, as shown by the white square. Since the refractive index difference between air and the tube wall is large, after light passes through the hollow PVC tube, only a small amount of light energy can be reflected back to the detector. This results in a large area of low signal under the PVC tube. The media within the white square have the same concentration, and the OACs on the left and right sides should be the same. However, due to the presence of the low signal under the PVC tube, obvious differences appear on both sides when calculated using the traditional DR method. The OAC above the PVC tube is severely overestimated, generating a clear high-signal artifact. As shown by the arrow in Figure 3 (b). This artifact is more obvious in the average projection image( Figure 3 (c)). Figure 3 (d) is Figure 3 the local OAC image (obtained by the traditional DR method) at the position of the white square in Figure 3(e). It is found that the OAC above the PVC tube is much larger than the OAC on the other side. The OAC of the medium in the white box is calculated using the method of the present invention ( Figure 3 (f)). Select a thin layer of uniform medium (B~C) below the medium, and use formula (2) to calculate the OAC of all the media in layers A~C ( Figure 3 (g)). The OAC curve extracted from the solid line position is as follows Figure 3 (h) is shown. Figure 3 (g) Perform average projection along the depth direction, and after all B-scans are processed, we get Figure 3 (i) The average projection image of OAC is shown, and the highlight artifacts are removed.

[0034] The specific derivation process of formula (2) in step 3 above is as follows:

[0035] First, use formula (1) to calculate Figure 2 OAC at point C:

[0036]

[0037]

[0038] From formula (4), we can see that the accumulation of all signals below point C can be expressed as I[C] / μ[C]. The denominator of formula (1) is can be decomposed into Plus Therefore, formula (1) can be rewritten as:

[0039]

[0040] From formula (5), we can know that as long as we know μ[C], we can estimate the remaining light intensity using only the data before point C, and thus accurately calculate μ[z]. At this time, μ[z] is not affected by other layers of media. So how do we get μ[C]? Here we need to use a small section of uniform thin layer of media. Figure 2 As shown in B to C. Using formula (5) to calculate the OAC of point B, we get:

[0041]

[0042]

[0043] Since homogeneous media have the same OAC, then:

[0044]

[0045]

[0046] In this way, μ[C] is obtained. Substituting formula (9) into formula (5), the first half of formula (2) can be obtained. Next, the OAC calculation formula from point C to point D is derived. When z > C, the denominator of formula (1) can be expressed as subtracting then:

[0047]

[0048] Substituting formula (9) into formula (10), the second half of formula (2) can be obtained.

Claims

1. An OCT-based attenuation imaging method, characterized in that, Including: Step 1: Obtain the three-dimensional image of the original OCT, where the three-dimensional image includes M B-scan images; Step 2: Determine the area to be processed, where the upper edge depth of the area to be processed is A and the lower edge depth is D; Step 3: Select a uniform thin-layer medium with the same type and density from the area to be processed, where the upper edge depth is B and the lower edge depth is C; Step 4: Calculate the light attenuation coefficient within the area to be processed; Step 5: After calculating the OAC of the local area of each B-scan image, perform an averaging process on the light attenuation coefficient values of each column of the image along the depth direction, convert the two-dimensional OAC image into a one-dimensional OAC projection data, and combine the one-dimensional projection data of M B-scan images to form a two-dimensional projection image; The specific description of Step 4 is as follows: where z is the image depth expressed in the number of pixels, μ L [z] represents the light attenuation coefficient at a depth of z, I[z] is the OCT signal intensity at a depth of z, I[B] and I[C] respectively represent the OCT signal intensities at depths of B and C, and Δ represents the thickness of a single pixel in the OCT image; When the depth z is greater than or equal to A and less than or equal to C, use the upper part of the formula to calculate the OAC; if A coincides with B, the calculation method remains unchanged; when the depth z is greater than C and less than or equal to D, use the lower part of the formula to calculate the OAC; if C coincides with D, it means that point C is the lower edge of this local area, and then the lower part of the formula does not need to be used, and only the upper part of the formula is used.

2. The OCT-based attenuation imaging method according to claim 1, wherein, In Step 2, an image segmentation or edge detection method is used to determine the area to be processed.

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