CN116616738A - Intensity OCTA imaging method and device combining local signal to noise ratio - Google Patents
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Abstract
The application discloses an intensity OCTA imaging method and device combining local signal to noise ratio, wherein the imaging method comprises the following steps: step one, extracting a target area by utilizing a structure image without spectrum division; axially dividing N B-scan spectrums at the same transverse position into M spectrums, obtaining M.N structural images after Fourier, and calculating the average value of the background intensity squares and the average value of the local intensity squares of the images; step three, calculating a local inverse signal-to-noise ratio ISNR and a local decorrelation value D, mapping the ISNR and the local decorrelation value D to an ID space, and removing static voxels according to a classification line; and step four, carrying out mean value projection to obtain a front microvascular image. The thought of the frequency division spectrum is adopted, the calculated average decorrelation value of each frequency division spectrum is processed, the calculated decorrelation value is classified in a dynamic and static mode by combining the local signal to noise ratio, a low threshold is set for a region with low local signal to noise ratio, and a high threshold is set for a region with high local signal to noise ratio, so that static voxel noise is removed, and the quality of an environment map is improved.
Description
Technical Field
The application relates to an intensity OCTA imaging method and device combining local signal to noise ratio, and belongs to the technical field of OCTA imaging.
Background
Optical Coherence Tomography (OCTA) is a functional imaging technique based on Optical Coherence Tomography (OCT). When the scattering element within the resolution cell is stationary, the speckle is also stationary, such as static tissue; the moving scattering elements such as red blood cells in the blood vessel cause the speckle of its imaging element to change over time, and the OCTA uses this dynamic scattering property of OCT to extract blood flow from the tissue. Based on the information utilized, the OCTAs can be classified into intensity-based OCTAs, phase-based OCTAs, and composite information-based OCTAs.
In intensity OCTA, a differential or decorrelation method is typically employed to calculate the difference between the signals. The difference method is as follows:
1) Power value difference method (PID)
And calculating the square of the adjacent B-Scan intensity difference, wherein the calculation formula is as follows:
PID i (x,z)＝I i (x,z)- i+1 (x,z)) 2
wherein I is i (, z) represents the intensity value of the ith B-scan at the lateral position x, depth position z
2) Speckle variance method (SVOCT)
N times of scanning are carried out on the same position, and inter-frame speckle variance signals are calculated
Wherein N represents the number of repetitions of B-scan, I mean Mean intensity of N times B-scan, I i (, z) is consistent with the above.
Correlation method:
the correlation method is different according to the difference of the calculation formulas of the adopted correlation coefficients, and two kinds of correlation coefficients, namely Pearson and Berger, are commonly used in the field of OCTA.
1) Pearson decorrelation
2) Berger decorrelation
Wherein P X Q defines the window size to be averaged, A n And A n+1 Is the gray value of a pair of adjacent frame images, A n (, z) represents the intensity value of the nth time B-scan at the lateral position x, the depth position z.
In addition, the frequency division spectrum amplitude decorrelation contrast method (split spectrum amplitude-decorrelation angiography, SSADA) is a classical correlation mapping type OCTA method, adopts a Berger decorrelation form, and proposes that an OCT interference spectrum is divided into a plurality of narrower wave bands, B-scan decorrelation among the wave bands is calculated respectively, and then an average value is taken, so that the limit of sensitivity of OCT to axial motion is overcome, axial motion artifacts are reduced, and the quality of an enface image is improved.
Technical defects:
a. the difference method is simple and has high operation speed, but from the viewpoint of maximum likelihood estimation, the difference method only considers the absolute magnitude of the difference value, is insensitive to small difference, and can not distinguish effective signals with insufficient pixel gray level difference values.
b. The correlation method considers the gray value of the pixel to be compared, and is thus sensitive to a small difference. However, data with low signal-to-noise ratio is greatly affected by noise, and a static voxel with a decorrelation value of 0 will obtain a higher decorrelation value, resulting in a low quality of the generated frontal microvascular image (end map).
The human eye back-scatters light much less than the skin, so the signal-to-noise ratio of the collected data is low for skin. For the skin, the correlation method is sensitive to differences, which is an advantage in that blood flow signals that are not detected by the differential method can be detected. However, for fundus data with low signal-to-noise ratio, the effect of the correlation method cannot be exerted, noise caused by static tissues is large, and the quality of the image is poor.
Disclosure of Invention
The application aims to solve the technical problems that for the OCTA imaging technology, the correlation mapping method is sensitive to small differences and has better effect than a difference method, but for a low signal-to-noise ratio area, the correlation method is greatly influenced by noise and has poorer effect than the difference method.
In order to solve the technical problems, the technical scheme of the application provides an intensity OCTA imaging method combining local signal to noise ratio, which is characterized by applying dynamic and static classification thresholds with different sizes to different local signal to noise ratio areas through frequency spectrum division processing, and specifically comprises the following steps:
step one, extracting a target area by utilizing a structure image without spectrum division;
axially dividing N B-scan spectrums at the same transverse position into M spectrums, obtaining M.N structural images after Fourier, and calculating the average value of the background intensity squares and the average value of the local intensity squares of the images;
step three, calculating a local inverse signal-to-noise ratio ISNR and a local decorrelation value D, mapping the ISNR and the local decorrelation value D to an ID space, and removing static voxels according to a classification line;
and step four, carrying out mean value projection to obtain a front microvascular image.
Preferably, the third step includes,
step 3.1 spectral calculation of amplitude decorrelation: dividing each interference spectrum of the A-scan into M sub-spectrums by using a band-pass filter, adopting a Hamming window as a window, and then carrying out Fourier transform;
performing decorrelation calculation on B-scan images from each band, and calculating a decorrelation value D by adopting Berger coefficients;
step 3.2, dynamic and static classification: in the process of calculating the decorrelation value D, the denominator of the decorrelation value between every two frequency spectrums is used as the zero-order autocorrelation value of the central voxel, and the local inverse signal-to-noise ratio iSNR is calculated by using the zero-order autocorrelation value;
and simulating the relation between the decorrelation value D of the static voxels and the inverse signal-to-noise ratio iSNR to obtain a progressive curve distribution result, and extracting a right boundary line in the scattered point distribution diagram as a classification line of the dynamic and static voxels.
Preferably, the decorrelation value D has a calculation formula:
wherein A is m,n (x+, z+) represents the amplitude at the mth spectral, nth B-scan lateral position x+p, depth position z+q, and the size of the spatiotemporal kernel is defined as N x P x Q.
Preferably, the local inverse signal-to-noise ratio iSNR calculation formula is:
s 2 variance E [ n (m, t) n for Gaussian white noise per two partial spectrums * (m,t)]I.e. the mean value of the square of the pixel intensity in the background area, the calculation formula is as follows:
the application also provides an intensity OCTA imaging device combining local signal to noise ratio, which comprises a processor and a memory, wherein the memory is used for storing application program codes for executing the steps of the imaging method and is controlled by the processor to execute; the processor is configured to execute the application code stored in the memory to implement the imaging method described above.
The method has the advantages that the thought of the frequency division spectrum is adopted, the calculated average decorrelation value of each frequency division spectrum is processed, the calculated decorrelation value is classified in a dynamic and static mode by combining the local signal to noise ratio, a low threshold value is set for a region with low local signal to noise ratio, and a high threshold value is set for a region with high local signal to noise ratio, so that static voxel noise is removed, the sensitivity to axial motion is reduced, and the quality of an edge map is improved.
Drawings
FIG. 1 is a schematic diagram of spectral calculation amplitude in an embodiment of the present application;
FIG. 2 is a graph showing the comparison of different space-time kernel size progression curves in an embodiment of the present application;
FIG. 3 is a schematic flow chart of an intensity OCTA imaging method combining local signal to noise ratios in an embodiment of the application;
FIG. 4 is a schematic diagram showing a simulation experiment curve of the imaging method applied to flowing milk according to the embodiment of the application;
fig. 5 is a schematic diagram of contrast between an imaging method and a separation spectrum amplitude decorrelation vessel imaging SSADA method according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
Modifications of the embodiments which do not creatively contribute to the application may be made by those skilled in the art after reading the present specification, but are protected by patent laws within the scope of the claims of the present application. This embodiment is described.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to facilitate understanding of the technical solution proposed by the present application, several elements that may be introduced in the description of the present application are first introduced herein. It should be understood that the following description is only for convenience in understanding these elements, and is not necessarily intended to cover all possible scenarios for an understanding of the embodiments of the present application.
In OCTA technology, A-scan refers to the depth reflection profile reflected from the sample in the depth direction, one time of A-scan obtains an interference spectrum signal, and an axial depth image of the sample can be obtained through Fourier transform; b-scan refers to a two-dimensional image composed of a plurality of adjacent consecutive A-scans at different locations, i.e., one B-scan is composed of a plurality of A-scans.
The embodiment of the application provides an OCTA imaging method combining local signal-to-noise ratio and intensity optical coherence tomography, which is executed by electronic equipment, and compared with a traditional decorrelation method, the difference between signals is calculated.
Referring to fig. 3, the method specifically includes:
step one, extracting a target area by utilizing a structure image without spectrum division;
axially dividing N B-scan spectrums at the same transverse position into M spectrums, obtaining M.N structural images after Fourier, and calculating the average value of the background intensity squares and the average value of the local intensity squares of the images;
step three, calculating a local inverse signal-to-noise ratio ISNR and a local decorrelation value D, mapping the ISNR and the local decorrelation value D to an ID space, and removing static voxels according to a classification line;
the embodiment of the application provides an OCTA imaging method, and the improvement compared with the prior art mainly focuses on a step three, wherein the step three specifically comprises,
step 3.1 spectral calculation of amplitude decorrelation:
dividing each interference spectrum of the A-scan into M sub-spectrums by using a band-pass filter, wherein a Hamming window is adopted as a window, and the sizes of the window along the transverse direction and the depth are P, Q respectively; then carrying out Fourier transform; see fig. 1;
performing decorrelation calculation on B-scan images from each band, and calculating a decorrelation value D by using Berger coefficients, wherein the calculation formula is as follows:
wherein A is m,n (x+, z+) represents the amplitude at the mth spectrum, the nth B-scan lateral position x+p, and the depth position z+q, the size of the spatiotemporal kernel being defined as N x P x Q;
step 3.2
Dynamic and static classification:
from the perspective of probability theory, in the process of calculating the decorrelation value D, the denominator of the decorrelation value between every two frequency spectrums is used as the zero-order autocorrelation value of the central voxel, and the zero-order autocorrelation value is used for calculating the local inverse signal-to-noise ratio iSNR, and the calculation formula is as follows:
wherein A is n (x+p, z+q) represents the amplitude at the n-th B-scan lateral position x+p, depth position z+q in the single spectrum; s is(s) 2 Variance E [ n (m, t) n for Gaussian white noise per two partial spectrums * (m,t)]I.e. the mean value of the square of the pixel intensity in the background area, the calculation formula is as follows:
wherein Num is background Representing the total number of pixels in the background region, (, z) represents (, z) as the pixel points in the background region.
And simulating the relation between the decorrelation value D of the static voxels and the inverse signal-to-noise ratio iSNR to obtain a progressive curve distribution result, wherein the distribution variance is only related to the size of the space-time core, and the distribution is gradually concentrated along with the increase of the space-time core. The right boundary line in the scatter distribution diagram is extracted as the classification line of the moving and static voxels, see fig. 2.
And step four, carrying out mean value projection to obtain a front microvascular image.
The intensity OCTA imaging method combining the local signal to noise ratio provided by the embodiment of the application has better quality of the obtained source image, and the reasons are as follows:
1. the idea of frequency division spectrum superposition is adopted, so that the blood flow signal is enhanced while the axial motion noise is weakened.
2. The relation between the amplitude signal decorrelation value and the inverse signal-to-noise ratio is concerned, a demarcation curve is fitted, and most of static voxels are removed.
By adopting the intensity OCTA imaging method combining local signal to noise ratio, which is provided by the embodiment of the application, a simulation experiment is carried out by taking flowing milk as a dynamic voxel, taking paper and a background as static voxels, and referring to fig. 4, wherein blue dispersion points are static voxels, red dispersion points are dynamic voxels, the ordinate is inverse signal to noise ratio ISNR, the abscissa is decorrelation value D, the distribution of the dynamic voxels in an ID space is different, and the static voxels also approximately meet the curve distribution.
Compared with the traditional SSADA method, the retina imaging is carried out by adopting the intensity OCTA imaging method combining the local signal to noise ratio, which is provided by the embodiment of the application, and referring to fig. 5, the left side of fig. 5 is an imaging example of the SSADA method, and the right side is an imaging example of the imaging method of the embodiment of the application, so that the definition is greatly improved.
Based on the intensity OCTA imaging method combining local signal to noise ratio and the application example provided by the embodiment of the application, the following can be found:
the correlation mapping method has the advantages that the sensitivity to small differences is better than that of the difference method, but the correlation mapping method is greatly influenced by noise in a low signal-to-noise ratio area, and the effect is often worse than that of the difference method; the application firstly adopts the thought of frequency division spectrum, processes the calculated average decorrelation value of each frequency division spectrum, combines the local signal-to-noise ratio to carry out dynamic and static classification on the calculated decorrelation value, sets a low threshold for the area with low local signal-to-noise ratio, and sets a high threshold for the area with high local signal-to-noise ratio, thereby removing static voxel noise, reducing the sensitivity to axial motion and improving the quality of the source image.
The embodiment of the application also provides an intensity OCTA imaging device combining local signal to noise ratio, which comprises: a processor and a memory. Wherein the processor is coupled to the memory. The processor may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various exemplary logic, methods described in connection with this disclosure. A processor may also be a combination that performs computing functions, e.g., including one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The memory may be, but is not limited to, ROM or other type of static storage device, RAM or other type of dynamic storage device, which can store static information and instructions, EEPROM, CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disc, etc.), magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory is used for storing application program codes for executing the imaging method steps provided in the embodiment of the application, and the execution is controlled by the processor. The processor is configured to execute the application program code stored in the memory to implement the content of the imaging method in the embodiment of the present application.
Claims (5)
1. An intensity OCTA imaging method incorporating local signal to noise ratio comprising the steps of:
step one, extracting a target area by utilizing a structure image without spectrum division;
axially dividing N B-scan spectrums at the same transverse position into M spectrums, obtaining M.N structural images after Fourier, and calculating the average value of the background intensity squares and the average value of the local intensity squares of the images;
step three, calculating a local inverse signal-to-noise ratio ISNR and a local decorrelation value D, mapping the ISNR and the local decorrelation value D to an ID space, and removing static voxels according to a classification line;
and step four, carrying out mean value projection to obtain a front microvascular image.
2. A method of intensity OCTA imaging in combination with a local signal-to-noise ratio as recited in claim 1, wherein said step three comprises,
step 3.1 spectral calculation of amplitude decorrelation: dividing each interference spectrum of the A-scan into M sub-spectrums by using a band-pass filter, adopting a Hamming window as a window, and then carrying out Fourier transform;
performing decorrelation calculation on B-scan images from each band, and calculating a decorrelation value D by adopting Berger coefficients;
step 3.2, dynamic and static classification: in the process of calculating the decorrelation value D, the denominator of the decorrelation value between every two frequency spectrums is used as the zero-order autocorrelation value of the central voxel, and the local inverse signal-to-noise ratio iSNR is calculated by using the zero-order autocorrelation value;
and simulating the relation between the decorrelation value D of the static voxels and the inverse signal-to-noise ratio iSNR to obtain a progressive curve distribution result, and extracting a right boundary line in the scattered point distribution diagram as a classification line of the dynamic and static voxels.
3. The method of intensity OCTA imaging in combination with local signal to noise ratio as claimed in claim 2, wherein the decorrelation value D is calculated as:
wherein A is m,n (x+, z+) represents the amplitude at the mth spectral, nth B-scan lateral position x+p, depth position z+q,the size of the spatiotemporal kernel is defined as n×p×q.
4. The method of intensity osta imaging in combination with local signal to noise ratio as defined in claim 2, wherein said local inverse signal to noise ratio iSNR calculation formula is:
s 2 variance E [ n (m, t) n for Gaussian white noise per two partial spectrums * (m,t)]I.e. the mean value of the square of the pixel intensity in the background area, the calculation formula is as follows:
5. an intensity OCTA imaging device incorporating local signal to noise ratio, comprising a processor and a memory for storing application code for performing the imaging method steps of any of claims 1 to 4 and controlled by the processor for execution; a processor for executing application code stored in a memory to implement the imaging method of any of claims 1-4.
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