A Photoacoustic Imaging Method and System Based on Data Analysis

Abstract

The present invention relates to the field of photoacoustic imaging technology, and particularly to a photoacoustic imaging method and system based on data analysis. The method includes: determining the imaging state of the initial photoacoustic image according to the image anomaly coefficient and the variation value of the detection domain, and determining the analysis method according to the imaging state; in the abnormal analysis detection, determining the first optimization method according to the noise modulation coefficient; in the tissue influence analysis, determining the second optimization method according to the distortion influence coefficient and the regional dislocation coefficient, and the second optimization method is to determine the adjustment method according to the blood flow influence index, or to determine the compensation method according to the tissue difference coefficient and the deviation uniformity; the adjustment method is to adjust the detection angle range according to the blood flow influence index, or to adjust the laser repetition frequency according to the distortion influence coefficient, and the compensation method is characteristic frequency compensation or correlation frequency compensation. The present invention can improve the image quality of photoacoustic imaging.

Description

Technical Field

[0001] The present invention relates to the technical field of photoacoustic imaging, and particularly to a photoacoustic imaging method and system based on data analysis. Background Art

[0002] As an emerging biomedical imaging technology, photoacoustic imaging combines the high contrast of optical imaging and the deep penetration of ultrasonic imaging, and can provide structural and functional information inside tissues. However, there are problems such as artifacts, blurring, and insufficient resolution in photoacoustic imaging, resulting in unstable quality of the generated photoacoustic images. Therefore, how to improve the image quality of photoacoustic imaging is a technical problem that needs to be solved urgently by those skilled in the art.

[0003] Chinese Patent Publication No. CN119257543A discloses a photoacoustic imaging method and a photoacoustic imaging system. The method includes: acquiring ultrasonic image data of an imaging target, and determining the type of biological tissue of the imaging target based on the characteristics of the ultrasonic image data; determining a light flux distribution result corresponding to the type of biological tissue, where the light flux distribution result is generated after laser irradiates a simulated target tissue during a simulation imaging process of a simulated target tissue corresponding to the type of biological tissue of the imaging target, and is used to characterize the light flux distribution of the biological tissue relative to the body surface at different imaging depths; acquiring photoacoustic image data of the imaging target; determining a compensation parameter for the photoacoustic image data based on the light flux distribution result; compensating the photoacoustic image data based on the compensation parameter to obtain compensated photoacoustic image data; and generating a photoacoustic image according to the compensated photoacoustic image data. It can be seen that the above technical solution has the following problems: only compensating the photoacoustic image data through the light flux distribution, it is impossible to adaptively adjust the imaging parameters according to the image state, and it is difficult to adapt to complex biological tissue environments and dynamic physiological processes, resulting in poor image quality of photoacoustic imaging. Summary of the Invention

[0004] Therefore, the present invention provides a photoacoustic imaging method and system based on data analysis to overcome the problems in the prior art that it is impossible to adaptively adjust the imaging parameters according to the image state, and it is difficult to adapt to complex biological tissue environments and dynamic physiological processes, resulting in poor image quality of photoacoustic imaging.

[0005] To achieve the above object, the present invention provides a photoacoustic imaging method based on data analysis, including:

[0006] Determining the imaging state of the initial photoacoustic image according to the image anomaly coefficient and the detection domain variation value, and determining the analysis method according to the imaging state, where the analysis method is anomaly detection analysis or tissue influence analysis;

[0007] In the detection anomaly analysis, a first optimization method is determined according to the noise modulation coefficient. The first optimization method is to adjust the laser wavelength according to the noise modulation coefficient, or to perform detection adjustment optimization;

[0008] In the tissue influence analysis, a second optimization method is determined according to the distortion influence coefficient and the regional dislocation coefficient. The second optimization method is to determine the adjustment method according to the blood flow influence index, or to determine the compensation method according to the tissue difference coefficient and the deviation uniformity;

[0009] The adjustment method is to adjust the detection angle range according to the blood flow influence index, or to adjust the laser repetition frequency according to the distortion influence coefficient. The compensation method is characteristic frequency compensation or relevant frequency compensation.

[0010] Furthermore, if the imaging state of the initial photoacoustic image is that the image anomaly coefficient is greater than or equal to the preset image anomaly coefficient and the detection domain variation value is less than the preset detection domain variation value, the analysis method is detection anomaly analysis.

[0011] Furthermore, if the imaging state of the initial photoacoustic image is that the image anomaly coefficient is less than the preset image anomaly coefficient or the detection domain variation value is greater than or equal to the preset detection domain variation value, the analysis method is tissue influence analysis.

[0012] Furthermore, if the noise modulation coefficient is greater than or equal to the preset noise modulation coefficient, the first optimization method is to perform a decreasing adjustment of the laser wavelength according to the noise modulation coefficient;

[0013] The decreasing value of the laser wavelength has a positive correlation with the noise modulation coefficient.

[0014] Furthermore, if the noise modulation coefficient is less than the preset noise modulation coefficient, the first optimization method is detection adjustment optimization;

[0015] In the detection adjustment optimization, a detection adjustment interval is determined based on the detection anomaly coefficient, and a partitioning method is determined based on the interval comparison coefficient to obtain several partition intervals, and the detection angle range of each partition interval is adjusted according to the interval influence coefficient.

[0016] Furthermore, determining the partitioning method based on the interval comparison coefficient includes:

[0017] If the interval comparison coefficient is greater than or equal to the preset interval comparison coefficient, the partitioning method is to perform associated partitioning according to the point position correlation degree;

[0018] If the interval comparison coefficient is less than the preset interval comparison coefficient, the partitioning method is to perform uniform partitioning according to the anomaly coefficient.

[0019] Furthermore, the detection angle range of each partition interval is increased according to the interval influence coefficient;

[0020] The increase value of the detection angle range corresponding to a single divided interval is positively correlated with the interval influence coefficient corresponding to this divided interval.

[0021] Further, if the distortion influence coefficient is less than the preset distortion influence coefficient and the regional dislocation coefficient is less than the preset regional dislocation coefficient, the second optimization method is to determine the compensation method according to the tissue difference coefficient and the deviation uniformity;

[0022] If the tissue difference coefficient is greater than or equal to the preset tissue difference coefficient or the deviation uniformity is greater than or equal to the preset deviation uniformity, the compensation method is related frequency compensation;

[0023] If the tissue difference coefficient is less than the preset tissue difference coefficient and the deviation uniformity is less than the preset deviation uniformity, the compensation method is characteristic frequency compensation.

[0024] Further, if the distortion influence coefficient is greater than or equal to the preset distortion influence coefficient or the regional dislocation coefficient is greater than or equal to the preset regional dislocation coefficient, the second optimization method is to determine the adjustment method according to the blood flow influence index;

[0025] If the blood flow influence index is greater than or equal to the preset blood flow influence index, the adjustment method is to increase the detection angle range according to the blood flow influence index;

[0026] If the blood flow influence index is less than the preset blood flow influence index, the adjustment method is to increase the laser repetition frequency according to the distortion influence coefficient.

[0027] The present invention also provides a photoacoustic imaging system based on data analysis, including:

[0028] A state analysis unit, used to determine the imaging state of the initial photoacoustic image according to the image anomaly coefficient and the detection domain variation value, and determine the analysis method according to the imaging state. The analysis method is detection anomaly analysis or tissue influence analysis;

[0029] A first optimization unit, connected to the state analysis unit, used to determine the first optimization method according to the noise modulation coefficient in the detection anomaly analysis. The first optimization method is to adjust the laser wavelength according to the noise modulation coefficient, or, detection adjustment optimization;

[0030] A second optimization unit, connected to the state analysis unit, used to determine the second optimization method according to the distortion influence coefficient and the regional dislocation coefficient in the tissue influence analysis. The second optimization method is to determine the adjustment method according to the blood flow influence index, or, determine the compensation method according to the tissue difference coefficient and the deviation uniformity;

[0031] An adjustment unit, which is connected to the second optimization unit, is used to determine the adjustment method according to the blood flow influence index, that is, to adjust the detection angle range according to the blood flow influence index, or to adjust the laser repetition frequency according to the distortion influence coefficient;

[0032] A compensation unit, which is connected to the second optimization unit, is used to determine the compensation method as characteristic frequency compensation or correlation frequency compensation according to the tissue difference coefficient and the deviation uniformity.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows. In the technical solution of the present invention, the imaging state of the initial photoacoustic image is determined according to the image anomaly coefficient and the detection domain variation value. The anomaly degree of the initial photoacoustic image is effectively reflected by the image anomaly coefficient and the detection domain variation value. Furthermore, different analysis methods are adaptively selected according to the imaging state, so that the selection of the analysis method is more in line with the actual application scenario, the abnormal problems in imaging can be accurately identified and processed, the artifacts and noise in the image can be significantly reduced, the clarity and resolution of the image can be improved, the uncertainty in the imaging process can be reduced, and thus the image quality of photoacoustic imaging is improved.

[0034] Furthermore, in the present invention, the abnormal situation of the noise is effectively reflected by the noise modulation coefficient. Furthermore, different first optimization methods are adaptively selected according to the noise modulation coefficient, so that the selected first optimization method can improve the signal collection efficiency and quality, thereby enhancing the contrast of the image. By adjusting the wavelength according to the noise modulation coefficient, the most suitable wavelength for the current imaging target can be selected, thereby improving the adaptability of imaging, reducing signal noise and artifacts, and thus improving the image quality.

[0035] Furthermore, in the present invention, by analyzing the detection anomaly coefficient, the abnormal time point in the imaging process can be identified, and the detection adjustment interval can be determined, so that the abnormal area in the imaging can be accurately located, unnecessary adjustments can be reduced. The partitioning method is determined based on the interval comparison coefficient, and the detection parameters can be adjusted more precisely through associated partitioning, improving the imaging quality. Through the uniform partitioning method, the detection resources can be more evenly distributed, improving the imaging efficiency. By analyzing the interval influence coefficient of each divided interval, the detection angle range can be adjusted specifically, thereby optimizing the detection parameters and reducing the influence of noise on imaging.

[0036] Further, in the present invention, the second optimization method is determined according to the distortion influence coefficient and the regional dislocation coefficient. The distortion influence coefficient and the regional dislocation coefficient can effectively reflect the distortion and regional dislocation problems in the imaging process. Then, different second optimization methods are adaptively selected according to the distortion influence coefficient and the regional dislocation coefficient. When the distortion or regional dislocation problem is relatively serious, the blood flow influence index can quantify the influence of blood flow on the imaging quality. By adjusting the detection angle range or the laser repetition frequency, the imaging parameters can be optimized, and the negative influence of blood flow on the imaging quality can be reduced. When the distortion and regional dislocation problems are not serious, the influence of tissue characteristics on imaging is further analyzed. The tissue difference coefficient and the deviation uniformity can reflect the inhomogeneity of tissues and the characteristic uniformity of abnormal regions. Through relevant frequency compensation or characteristic frequency compensation, the imaging quality can be optimized, and the artifacts and noise in the image can be significantly reduced, and the clarity and resolution of the image can be improved.

[0037] Further, in the present invention, the tissue difference coefficient and the deviation uniformity can effectively reflect the significant tissue characteristic differences or the signal distribution uniformity degree in the imaging region. Then, different compensation methods are adaptively selected according to the tissue difference coefficient and the deviation uniformity. The relevant frequency compensation can effectively improve the frequency characteristics of the signal by adjusting the frequencies with higher frequency coupling degrees, and reduce the image quality problems caused by frequency distortion. The characteristic frequency compensation focuses on optimizing the signals of the characteristic frequencies, further improving the clarity and resolution of the image, and then significantly improving the quality and efficiency of photoacoustic imaging.

[0038] Further, in the present invention, the adjustment method is determined according to the blood flow influence index. The blood flow influence index can effectively reflect the influence degree of blood flow on imaging. When the blood flow influence index is relatively high, it indicates that the influence of blood flow on the imaging quality is significant. By increasing the detection angle range, the artifacts and noise caused by blood flow can be reduced. When the blood flow influence index is relatively small, by increasing the laser repetition frequency, the resolution and sensitivity of imaging can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a schematic diagram of the photoacoustic imaging method based on data analysis of the present invention;

[0040] Figure 2 is a flowchart of determining the first optimization method according to the noise modulation coefficient of the present invention;

[0041] Figure 3 is a flowchart of determining the second optimization method according to the distortion influence coefficient and the regional dislocation coefficient of the present invention;

[0042] Figure 4 is a unit connection diagram of the photoacoustic imaging system based on data analysis of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In order to make the objectives and advantages of the present invention more clearly understood, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0045] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0046] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] Please refer to Figures 1 to 3 As shown, the present invention provides a photoacoustic imaging method based on data analysis, including:

[0048] Determine the imaging state of the initial photoacoustic image according to the image anomaly coefficient and the variation value of the detection domain, and determine the analysis method according to the imaging state. The analysis method is anomaly detection analysis or tissue influence analysis;

[0049] In the anomaly detection analysis, determine the first optimization method according to the noise modulation coefficient. The first optimization method is to adjust the laser wavelength according to the noise modulation coefficient, or, detection adjustment optimization;

[0050] In the tissue influence analysis, determine the second optimization method according to the distortion influence coefficient and the regional dislocation coefficient. The second optimization method is to determine the adjustment method according to the blood flow influence index, or, determine the compensation method according to the tissue difference coefficient and the deviation uniformity;

[0051] The adjustment method is to adjust the detection angle range according to the blood flow influence index, or, adjust the laser repetition frequency according to the distortion influence coefficient. The compensation method is characteristic frequency compensation or related frequency compensation.

[0052] The application scenario of the present invention is to optimize the subsequent imaging process through the initial photoacoustic image to generate a photoacoustic image. In the present invention, a number of historical records are correspondingly set. Any historical record records at least the sub-image anomaly coefficient, image anomaly coefficient, detection domain variation value, sub-noise modulation coefficient, detection anomaly coefficient, and interval comparison coefficient, etc. in the historical process of generating a photoacoustic image at least once. And each historical record corresponds to a qualified mark, and the qualified mark records whether the process of generating the photoacoustic image meets the user's requirements. The qualified mark can be manually recorded. It can be understood that the user can determine whether the process of generating the photoacoustic image meets the requirements according to the self-set index. The self-set index can be, but is not limited to, the artifact area, which will not be elaborated here. Among them, the artifact area is the area of the artifact in the generated photoacoustic image.

[0053] The initial photoacoustic image is the photoacoustic image generated by irradiating the target tissue with pulsed laser for the first time;

[0054] The photoacoustic imaging steps include: irradiating the target tissue with pulsed laser, detecting the ultrasonic signal propagating from the tissue surface through an ultrasonic transducer, and using a reconstruction algorithm to invert the light absorption distribution inside the tissue according to the received ultrasonic signal to generate a photoacoustic image. The reconstruction algorithm includes, but is not limited to, the back-projection algorithm and the iterative reconstruction algorithm. The target tissue includes, but is not limited to, tumor tissue, cardiovascular tissue, nervous system tissue, lymphatic system tissue, and skin system tissue. This is easy for those skilled in the art to understand and will not be elaborated here.

[0055] Specifically, if the imaging state of the initial photoacoustic image is that the image anomaly coefficient is greater than or equal to the preset image anomaly coefficient and the detection domain variation value is less than the preset detection domain variation value, the analysis method is detection anomaly analysis.

[0056] Among them, the imaging state includes the first imaging state and the second imaging state. The first imaging state is that the image anomaly coefficient is greater than or equal to the preset image anomaly coefficient and the detection domain variation value is less than the preset detection domain variation value. The second imaging state is that the image anomaly coefficient is less than the preset image anomaly coefficient or the detection domain variation value is greater than or equal to the preset detection domain variation value;

[0057] Image anomaly coefficient = |sub-image anomaly coefficient corresponding to the initial photoacoustic image - average value of sub-image anomaly coefficients corresponding to the photoacoustic images generated by historical records that can meet the user's requirements|, sub-image anomaly coefficient = standard deviation of pixel values corresponding to each pixel point in the initial photoacoustic image / average value of pixel values corresponding to each pixel point in the initial photoacoustic image;

[0058] After irradiating the tissue with pulsed laser, the time period during which the tissue generates a photoacoustic signal and is received by the detector is recorded as the full interval. The full interval is equally divided into a parts, and the value of a is positively correlated with the duration of the full interval. The latest moment among each equal division point and the reference time point is recorded as the time point, and the optical power corresponding to a single time point is measured by using an optical power meter or an energy meter;

[0059] Among them, a is the smallest integer greater than or equal to a1, and a1 = 0.1 × the duration of the full interval, and the unit of the duration of the full interval is s;

[0060] The variation value of the detection domain = (the optical flux corresponding to the earliest time point in the full interval - the optical flux corresponding to the latest time point in the full interval) / the standard deviation of the optical fluxes corresponding to each time point in the full interval;

[0061] The values of the preset image anomaly coefficient and the preset detection domain variation value can be determined by the user according to the actual application scenario. The smaller the value of the preset image anomaly coefficient and the larger the value of the preset detection domain variation value, the greater the user's need for detecting anomaly analysis. Provide a set of values for the preset image anomaly coefficient and the preset detection domain variation value, detect the historical records of the user's detection anomaly analysis, and record the average value of the image anomaly coefficients corresponding to the historical records that can meet the user's needs as the preset image anomaly coefficient, and record the average value of the detection domain variation values corresponding to the historical records that can meet the user's needs as the preset detection domain variation value.

[0062] Specifically, if the imaging state of the initial photoacoustic image is that the image anomaly coefficient is less than the preset image anomaly coefficient or the detection domain variation value is greater than or equal to the preset detection domain variation value, the analysis method is tissue impact analysis.

[0063] Specifically, if the noise modulation coefficient is greater than or equal to the preset noise modulation coefficient, the first optimization method is to adjust the laser wavelength to decrease according to the noise modulation coefficient;

[0064] The decrease value of the laser wavelength is positively correlated with the noise modulation coefficient.

[0065] Among them, the noise modulation coefficient is the maximum value of the sub-noise modulation coefficients corresponding to the initial photoacoustic image and the photoacoustic images generated from the historical records that can meet the user's needs;

[0066] The calculation formula for the sub-noise modulation coefficient Z corresponding to a single photoacoustic image generated from a historical record that can meet the user's needs and the initial photoacoustic image is:

[0067] ;

[0068] Among them, I(α,β) and K(α,β) are the pixel values of a single photoacoustic image and the initial photoacoustic image generated from historical records that can meet user requirements at position (α,β), m and ε are the number of rows and columns of pixels included in the image respectively;

[0069] The decrease value of the laser wavelength = (K / preset noise modulation coefficient) × noise modulation coefficient, where K is a proportionality coefficient and K = 0.9nm;

[0070] For the value of the preset noise modulation coefficient, the user can determine it according to the actual application scenario. The smaller the value of the preset noise modulation coefficient, the greater the user's need to adjust the laser wavelength according to the noise modulation coefficient. Provide a value of the preset noise modulation coefficient, detect the historical records of the user's adjustment of the laser wavelength according to the noise modulation coefficient, and record the average value of the noise modulation coefficients corresponding to the historical records that can meet the user's requirements as the preset noise modulation coefficient;

[0071] The laser wavelength is the wavelength of the pulsed laser used.

[0072] Specifically, if the noise modulation coefficient is less than the preset noise modulation coefficient, the first optimization method is detection adjustment optimization;

[0073] In detection adjustment optimization, based on the detection anomaly coefficient, determine the detection adjustment interval, and based on the interval comparison coefficient, determine the partitioning method to obtain several partition intervals, and adjust the detection angle range of each partition interval according to the interval influence coefficient.

[0074] Among them, when determining the detection adjustment interval based on the detection anomaly coefficient, mark the time points with the detection anomaly coefficient greater than the preset detection anomaly coefficient as abnormal time points, and mark the smallest time period that can include each abnormal time point as the detection adjustment interval;

[0075] The confirmation method of the detection anomaly coefficient is that for a single time point, the detection anomaly coefficient corresponding to this time point = |the light flux corresponding to this time point - the average value of the light fluxes corresponding to each time point in the full interval|,

[0076] For the value of the preset detection anomaly coefficient, the user can determine it according to the actual application scenario. The greater the user's need to improve the imaging quality, the smaller the value of the preset detection anomaly coefficient. Provide a value of the preset detection anomaly coefficient, and record the minimum value of the detection anomaly coefficients corresponding to each abnormal time point in the historical records that can meet the user's requirements as the preset detection anomaly coefficient.

[0077] Specifically, determining the partitioning method based on the interval comparison coefficient includes:

[0078] If the interval comparison coefficient is greater than or equal to the preset interval comparison coefficient, the partitioning method is to perform associated partitioning according to the point position correlation degree;

[0079] If the interval comparison coefficient is less than the preset interval comparison coefficient, the partitioning method is to perform uniform partitioning according to the anomaly coefficient.

[0080] Among them, the interval comparison coefficient is the standard deviation of the light fluxes corresponding to each abnormal time point in the detection adjustment interval;

[0081] For the value of the preset interval comparison coefficient, the user can determine it according to the actual application scenario. The greater the user's demand for improving the imaging quality, the smaller the value of the preset interval comparison coefficient. Provide a value of the preset interval comparison coefficient, detect the historical records of the user's uniform partitioning according to the anomaly coefficient, and record the average value of the interval comparison coefficients corresponding to the historical records that can meet the user's needs as the preset interval comparison coefficient;

[0082] Record each time point in the detection adjustment interval as a range point;

[0083] When performing associated partitioning according to the point position correlation degree, the point position correlation degree between any two range points in a single partition interval is greater than or equal to the preset point position correlation degree, and there is a neighboring time point for any range point in a single partition interval. The point position correlation degree between any edge time point corresponding to a single partition interval and any range point in this partition interval is less than the preset point position correlation degree;

[0084] For a single range point, record this range point as the target range point. The neighboring time point corresponding to the target range point is the range point that is adjacent to the target range point and in the same partition interval as the target range point. The edge time point corresponding to a single partition interval is the time point that is adjacent to the range points in this partition interval and not in this partition interval;

[0085] When performing uniform partitioning according to the anomaly coefficient, divide the detection adjustment interval into several partition intervals with the same time length. The number of partition intervals has a negative correlation with the anomaly coefficient, and the anomaly coefficient is the time length of the detection adjustment interval;

[0086] The number of partition intervals is the smallest integer greater than or equal to M, where M = (the number of abnormal time points included in the detection adjustment interval / the anomaly coefficient) × the total number of range points included in the detection adjustment interval × 0.5;

[0087] For any two time points, the point correlation degree = 1 - (the absolute value of the difference between the abnormal reference values corresponding to the two time points / the larger value of the abnormal reference values corresponding to the two time points). For a single time point, denote this time point as the target time point, detect the historical time points with the same detection abnormal coefficient as the target time point, and denote the average value of the sub-abnormal reference values corresponding to each historical time point as the abnormal reference value corresponding to the target time point;

[0088] Denote the time points in the detection adjustment interval corresponding to each initial photoacoustic image in the historical records that can meet the user's requirements as historical time points;

[0089] The confirmation method of the sub-abnormal reference value is as follows: for a single historical time point, denote this historical time point as the target historical time point, detect the initial photoacoustic image corresponding to the target historical time point, and the sub-abnormal reference value corresponding to the target historical time point = the image abnormal coefficient of the initial photoacoustic image corresponding to the target historical time point × [the order of the target historical time point in the detection adjustment interval corresponding to the target historical time point in ascending order / (the total number of historical time points in the detection adjustment interval corresponding to the target historical time point + 1)];

[0090] The value of the preset point correlation degree can be determined by the user according to the actual application scenario. The greater the user's demand for improving the imaging accuracy, the greater the value of the preset point correlation degree. Provide a value of the preset point correlation degree, detect the historical records in which the user makes correlation partitions according to the point correlation degree, and denote the average value of the reference point correlation degrees corresponding to each division interval in the historical records that can meet the user's requirements as the preset point correlation degree. The reference point correlation degree is the point correlation degree corresponding to any two time points in a single division interval of the historical records that can meet the user's requirements.

[0091] Specifically, increase and adjust the detection angle range for each division interval according to the interval influence coefficient;

[0092] The increase value of the detection angle range corresponding to a single division interval is positively correlated with the interval influence coefficient corresponding to this division interval.

[0093] Among them, the confirmation method of the interval influence coefficient is as follows: for a single division interval, denote this interval as the target division interval, and the interval influence coefficient corresponding to the target division interval = [|the first standard deviation - the second standard deviation| / the larger value of the first standard deviation and the second standard deviation] - [the number of frequencies in the target power spectrum that are the same as those in the reference power spectrum / (the number of frequencies appearing in the reference power spectrum + 1)];

[0094] Denote the standard deviation of the power corresponding to each frequency in the target power spectrum as the first standard deviation, and denote the standard deviation of the power corresponding to each frequency in the reference power spectrum as the second standard deviation,

[0095] Denote the power spectrum corresponding to the target division interval as the target power spectrum, denote the time period during which the ultrasonic signal propagated from the tissue surface is detected by the ultrasonic transducer as the full interval, and denote the power spectrum corresponding to the time period in the full interval except the detection adjustment interval as the reference power spectrum;

[0096] Convert the ultrasonic signal of a single interval into a frequency-domain signal through the Fourier transform method, so as to obtain the power spectrum of the signal. The power spectrum reflects the power distribution of the signal at different frequencies;

[0097] The detection angle range corresponding to a single division interval is the angle interval at which the ultrasonic transducer can receive the photoacoustic signal;

[0098] The increase value of the detection angle range corresponding to a single division interval The calculation formula is:

[0099] ;

[0100] Where C is the interval influence coefficient corresponding to this division interval, s1 is the first coefficient, s2 is the second coefficient, s1 = 1.353, s2 = 2;

[0101] When adjusting the detection angle range to increase, the increase amount extends the detection angle range from both sides of the initial detection angle range based on the initial detection angle range and the increase amounts on both sides are the same. The initial detection angle range is 45°.

[0102] Specifically, if the distortion influence coefficient is less than the preset distortion influence coefficient and the regional dislocation coefficient is less than the preset regional dislocation coefficient, the second optimization method is to determine the compensation method according to the tissue difference coefficient and the deviation uniformity;

[0103] If the tissue difference coefficient is greater than or equal to the preset tissue difference coefficient or the deviation uniformity is greater than or equal to the preset deviation uniformity, the compensation method is related frequency compensation;

[0104] If the tissue difference coefficient is less than the preset tissue difference coefficient and the deviation uniformity is less than the preset deviation uniformity, the compensation method is characteristic frequency compensation.

[0105] Among them, the distortion influence coefficient = |the image reference value corresponding to the initial photoacoustic image - the average value of the image reference values corresponding to each photoacoustic image generated by the historical record that can meet the user's needs|;

[0106] The image reference value corresponding to a single photoacoustic image = the standard deviation of the power corresponding to each frequency in the power spectrum of the full interval corresponding to this photoacoustic image / the average value of the power corresponding to each frequency in the power spectrum of the full interval corresponding to this photoacoustic image;

[0107] The regional dislocation coefficient is the average value of the distance reference values corresponding to each abnormal region. For a single abnormal region, the distance reference value corresponding to this abnormal region is the average value of the shortest distances from this abnormal region to other abnormal regions;

[0108] The initial photoacoustic image is divided into several rectangular regions with the same and equal areas. The number of rectangular regions is positively correlated with the area of the initial photoacoustic image;

[0109] The number of rectangular regions = (the area of the initial photoacoustic image / S) × K, where S is the correction coefficient, S = 1 square centimeter, the unit of the area of the initial photoacoustic image is square centimeter, and K is the weight coefficient, K = 10;

[0110] The rectangular regions with characteristic coefficients greater than the preset characteristic coefficient are recorded as abnormal regions;

[0111] The characteristic coefficient of a single rectangular region = |the average value of the pixel values corresponding to each pixel point in this rectangular region - the average value of the pixel values corresponding to each pixel point in the initial photoacoustic image|;

[0112] For the value of the preset characteristic coefficient, the user can determine it according to the actual application scenario. The greater the user's accuracy requirement for improving the photoacoustic imaging quality, the smaller the value of the preset characteristic coefficient. Provide a value of the preset characteristic coefficient, and record the average value of the characteristic coefficients corresponding to each abnormal region in the historical records that can meet the user's needs as the preset characteristic coefficient;

[0113] For the values of the preset distortion influence coefficient and the preset regional dislocation coefficient, the user can determine them according to the actual application scenario. The greater the values of the preset distortion influence coefficient and the preset regional dislocation coefficient, the greater the user's need to determine the compensation method according to the tissue difference coefficient and the deviation uniformity. Provide values of the preset distortion influence coefficient and the preset regional dislocation coefficient, detect the historical records of the user determining the compensation method according to the tissue difference coefficient and the deviation uniformity, and record the average value of the distortion influence coefficients corresponding to the historical records that can meet the user's needs as the preset distortion influence coefficient, and record the average value of the regional dislocation coefficients corresponding to the historical records that can meet the user's needs as the preset regional dislocation coefficient;

[0114] The tissue difference coefficient is the average value of the sub - tissue difference degrees corresponding to each rectangular region. The confirmation method of the sub - tissue difference degree corresponding to a single rectangular region is as follows: for a single rectangular region, the connected domains that appear in this rectangular region are recorded as reference connected domains, and the sub - tissue difference degree corresponding to this rectangular region = 1 / (the average value of the region emergence coefficients corresponding to each reference connected domain + 1). The region emergence coefficient corresponding to a single reference connected domain is the number of rectangular regions in which this reference connected domain appears;

[0115] Each connected region contains a number of pixel points. The pixel values corresponding to the pixel points in a single connected region are all the same, and the pixel values of the adjacent pixel points corresponding to the connected region are all different from the pixel value corresponding to the connected region. The adjacent pixel points corresponding to a single connected region are the pixel points adjacent to the connected region;

[0116] Deviation uniformity = 1 / standard deviation of the characteristic coefficients corresponding to each abnormal region;

[0117] For the values of the preset tissue difference coefficient and the preset deviation uniformity, the user can determine them according to the actual application scenario. The smaller the values of the preset tissue difference coefficient and the preset deviation uniformity, the greater the user's need for relevant frequency compensation. Provide a set of values for the preset tissue difference coefficient and the preset deviation uniformity, detect the historical records of the user's relevant frequency compensation, and record the average value of the tissue difference coefficients corresponding to the historical records that can meet the user's needs as the preset tissue difference coefficient, and record the average value of the deviation uniformities corresponding to the historical records that can meet the user's needs as the preset deviation uniformity;

[0118] Each time period obtained by equally dividing the entire interval into a parts is recorded as a sub-interval;

[0119] Record the power spectrum corresponding to the entire interval as the full power spectrum, and record the power spectra corresponding to each sub-interval as sub-power spectra;

[0120] In relevant frequency compensation, record the frequencies in the full power spectrum with the pre-compensation coefficient greater than the preset pre-compensation coefficient as pre-compensation frequencies. For a single frequency, record this frequency as the target frequency. The pre-compensation coefficient corresponding to the target frequency = |the compensation reference value corresponding to the target frequency in the initial photoacoustic image - the average value of the compensation reference values corresponding to the photoacoustic images generated in each historical record that can meet the user's needs|. The compensation reference value corresponding to the target frequency in a single photoacoustic image is the standard deviation of the power corresponding to the power spectra of each sub-interval corresponding to the target frequency in this photoacoustic image;

[0121] Record the other frequencies except the pre-compensation frequencies as frequencies to be selected, and perform compensation for the relevant frequencies corresponding to each pre-compensation frequency and each pre-compensation frequency. For a single pre-compensation frequency, record this pre-compensation frequency as the target pre-compensation frequency. The relevant frequency corresponding to the target pre-compensation frequency is the frequency to be selected whose frequency coupling degree with the target pre-compensation frequency is greater than the preset frequency coupling degree;

[0122] For any two frequencies, the calculation formula for the frequency coupling degree r is:

[0123] ;

[0124] where n is the number of sub-intervals corresponding to the initial photoacoustic image; and They are the powers corresponding to the power spectra in the i-th sub-interval at two frequencies respectively. is the average value of the powers corresponding to the power spectra in each sub-interval at the corresponding frequencies. is the average value of the powers corresponding to the power spectra in each sub-interval at the corresponding frequencies, where i = 1, 2, 3, ……, n.

[0125] For the values of the preset pre-compensation coefficient and the preset frequency coupling degree, the user can determine them according to the actual application scenario. The greater the user's demand for improving the photoacoustic imaging accuracy, the smaller the value of the preset pre-compensation coefficient and the larger the value of the preset frequency coupling degree. Provide a set of values for the preset pre-compensation coefficient and the preset frequency coupling degree. Denote the average value of the pre-compensation coefficients corresponding to each pre-compensation frequency in the historical records that can meet the user's needs as the preset pre-compensation coefficient, and the preset frequency coupling degree is 0.8.

[0126] When compensating for a single frequency, denote this frequency as the target frequency, and adjust the power corresponding to the target frequency in the full power spectrum according to the frequency comparison deviation value.

[0127] If the frequency comparison deviation value corresponding to the target frequency is greater than or equal to the preset frequency comparison deviation value, then reduce the power corresponding to the target frequency in the full power spectrum according to the compensation coefficient.

[0128] If the frequency comparison deviation value corresponding to the target frequency is less than the preset frequency comparison deviation value, then increase the power corresponding to the target frequency in the full power spectrum according to the compensation coefficient.

[0129] Both the increase value or decrease value of the power corresponding to the target frequency and the compensation coefficient corresponding to the target frequency are in a positive correlation relationship; the compensation coefficient corresponding to the target frequency = |the pre-compensation coefficient corresponding to the target frequency × (the frequency comparison deviation value corresponding to the target frequency + 1)|.

[0130] The increase value or decrease value of the power corresponding to the target frequency = the compensation coefficient corresponding to the target frequency × k1 × the power corresponding to the target frequency in the full power spectrum, where K1 = 0.4.

[0131] The frequency comparison deviation value = (the power corresponding to the target frequency in the full power spectrum corresponding to the initial photoacoustic image / the frequency mean value corresponding to the initial photoacoustic image) - (the average value of the powers corresponding to the power spectra in the photoacoustic images generated from each historical record that can meet the user's needs / the average value of the frequency mean values of each full power spectrum in the photoacoustic images generated from each historical record that can meet the user's needs).

[0132] The value of the preset frequency comparison deviation value can be determined by the user according to the actual application scenario. The larger the value of the preset frequency comparison deviation value, the greater the user's demand for increasing the power corresponding to the frequency. A value of the preset frequency comparison deviation value is provided, and the preset frequency comparison deviation value is 0;

[0133] The frequency mean value is the average value of the powers corresponding to the frequencies in a single full spectrum;

[0134] In characteristic frequency compensation: The sub-intervals with the sub-interval turbulence coefficient greater than or equal to the preset sub-interval turbulence coefficient are recorded as characteristic sub-intervals, the sub-intervals with the sub-interval turbulence coefficient less than the preset sub-interval turbulence coefficient are recorded as non-characteristic sub-intervals, and the frequencies in the characteristic sub-intervals are recorded as characteristic frequencies;

[0135] When compensating for a single characteristic frequency, the characteristic frequency is recorded as the target characteristic frequency, and the power corresponding to the target characteristic frequency in the full power spectrum is adjusted according to the characteristic amplitude;

[0136] If the characteristic amplitude corresponding to the target characteristic frequency is greater than or equal to the preset characteristic amplitude, the power corresponding to the target characteristic frequency in the full power spectrum is adjusted to decrease according to the characteristic compensation degree;

[0137] If the characteristic amplitude corresponding to the target characteristic frequency is less than the preset characteristic amplitude, the power corresponding to the target characteristic frequency in the full power spectrum is adjusted to increase according to the characteristic compensation degree;

[0138] It can be understood that by adjusting the corresponding frequencies in the full power spectrum, the image quality can be improved; after adjusting the power corresponding to the frequencies in the full power spectrum, the inverse Fourier transform is performed on the adjusted spectrum to obtain the optimized ultrasonic signal. Using the reconstruction algorithm, the light absorption distribution inside the tissue is inversely calculated according to the received ultrasonic signal to generate a photoacoustic image, which is easy for those skilled in the art to understand and will not be elaborated here;

[0139] Characteristic compensation degree = |average value of the sub-interval turbulence coefficients of each sub-interval in the first interval corresponding to the target characteristic frequency × (characteristic amplitude corresponding to the target characteristic frequency + 1)|;

[0140] Both the increase value or decrease value of the power corresponding to the target characteristic frequency and the characteristic compensation degree corresponding to the target characteristic frequency are in a positive correlation relationship;

[0141] Increase value or decrease value of the power corresponding to the target characteristic frequency = (characteristic compensation degree corresponding to the target characteristic frequency / power corresponding to the target characteristic frequency) × k2 × power corresponding to the target characteristic frequency in the full power spectrum, k2 = 0.3;

[0142] The sub-interval turbulence coefficient is the average value of the powers corresponding to the frequencies in the power spectrum corresponding to the sub-interval;

[0143] The value of the preset sub - interval turbulence coefficient can be determined by the user according to the actual application scenario. The larger the value of the preset sub - interval turbulence coefficient, the greater the user's demand for increasing the power corresponding to the frequency. A value of the preset sub - interval turbulence coefficient is provided, and the preset sub - interval turbulence coefficient is 0;

[0144] The characteristic amplitude corresponding to the target characteristic frequency = the average value of the power corresponding to the target characteristic frequency in the power spectra corresponding to each first interval - the average value of the power corresponding to the target characteristic frequency in the power spectra corresponding to each second interval; The characteristic sub - intervals where the target characteristic frequency appears are denoted as the first intervals, and the non - characteristic sub - intervals where the target characteristic frequency appears are denoted as the second intervals.

[0145] Specifically, if the distortion influence coefficient is greater than or equal to the preset distortion influence coefficient or the regional dislocation coefficient is greater than or equal to the preset regional dislocation coefficient, the second optimization method is to determine the adjustment method according to the blood flow influence index;

[0146] If the blood flow influence index is greater than or equal to the preset blood flow influence index, the adjustment method is to increase the detection angle range according to the blood flow influence index;

[0147] If the blood flow influence index is less than the preset blood flow influence index, the adjustment method is to increase the laser repetition frequency according to the distortion influence coefficient.

[0148] Among them, the blood flow influence index is the maximum value of the broadening reference values corresponding to each sub - interval of the initial photoacoustic image. The broadening reference value corresponding to a single sub - interval is the number of frequencies appearing in the power spectrum corresponding to that sub - interval. It can be understood that the turbulence or uneven velocity distribution of the blood flow will cause changes in the broadening reference value;

[0149] The value of the preset blood flow influence index can be determined by the user according to the actual application scenario. The smaller the value of the preset blood flow influence index, the greater the user's demand for increasing the detection angle according to the blood flow influence index. A value of the preset blood flow influence index is provided. The historical records of the user increasing the detection angle according to the blood flow influence index are detected, and the average value of the blood flow influence indices corresponding to each historical record that can meet the user's needs is denoted as the pre - blood flow influence index;

[0150] Increase the detection angle range of the entire interval according to the blood flow influence index. The increase value of the detection angle range is positively correlated with the blood flow influence index. When increasing the detection angle range, the increase amount expands the detection angle range from both sides based on the initial detection angle range and the increase amounts on both sides are the same. The initial detection angle range is 45°;

[0151] The increased value of the detection angle range = (blood flow influence index / preset blood flow influence index) × initial detection angle range;

[0152] Increase the laser repetition frequency according to the distortion influence coefficient. The increased value of the laser repetition frequency has a positive correlation with the distortion influence coefficient;

[0153] The increased value of the laser repetition frequency = initial laser repetition frequency × ln (distortion influence coefficient + 1), and the initial laser repetition frequency is 2 pulses / s;

[0154] The laser repetition frequency is the number of laser pulses output per unit time.

[0155] Please refer to Figure 4 as shown. It is the unit connection diagram of the photoacoustic imaging system based on data analysis of the present invention. The present invention also provides a photoacoustic imaging system based on data analysis, including:

[0156] A state analysis unit for determining the imaging state of the initial photoacoustic image according to the image anomaly coefficient and the detection domain variation value, and determining the analysis method according to the imaging state. The analysis method is detection anomaly analysis or tissue influence analysis;

[0157] A first optimization unit connected to the state analysis unit for determining a first optimization method according to the noise modulation coefficient in the detection anomaly analysis. The first optimization method is to adjust the laser wavelength according to the noise modulation coefficient, or detection adjustment optimization;

[0158] A second optimization unit connected to the state analysis unit for determining a second optimization method according to the distortion influence coefficient and the regional dislocation coefficient in the tissue influence analysis. The second optimization method is to determine the adjustment method according to the blood flow influence index, or determine the compensation method according to the tissue difference coefficient and the deviation uniformity;

[0159] An adjustment unit connected to the second optimization unit for determining the adjustment method according to the blood flow influence index as adjusting the detection angle range according to the blood flow influence index, or adjusting the laser repetition frequency according to the distortion influence coefficient;

[0160] A compensation unit connected to the second optimization unit for determining the compensation method as characteristic frequency compensation or related frequency compensation according to the tissue difference coefficient and the deviation uniformity.

[0161] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, those skilled in the art can easily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0162] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A photoacoustic imaging method based on data analysis, characterized in that Including: Determine the imaging state of the initial photoacoustic image according to the image anomaly coefficient and the variation value of the detection domain, and determine the analysis method according to the imaging state. The analysis method is anomaly detection analysis or tissue influence analysis; In the anomaly detection analysis, determine the first optimization method according to the noise modulation coefficient. The first optimization method is to adjust the laser wavelength according to the noise modulation coefficient, or, detection adjustment optimization; In the tissue influence analysis, determine the second optimization method according to the distortion influence coefficient and the regional dislocation coefficient. The second optimization method is to determine the adjustment method according to the blood flow influence index, or, determine the compensation method according to the tissue difference coefficient and the deviation uniformity; The adjustment method is to adjust the detection angle range according to the blood flow influence index, or, adjust the laser repetition frequency according to the distortion influence coefficient. The compensation method is characteristic frequency compensation or correlation frequency compensation; Image anomaly coefficient = |Sub-image anomaly coefficient corresponding to the initial photoacoustic image - Average value of sub-image anomaly coefficients corresponding to the photoacoustic images generated by the historical records that can meet the user's needs|, Sub-image anomaly coefficient = Standard deviation of the pixel values corresponding to each pixel point in the initial photoacoustic image / Average value of the pixel values corresponding to each pixel point in the initial photoacoustic image; The noise modulation coefficient is the maximum value of the sub-noise modulation coefficients corresponding to the initial photoacoustic image and the photoacoustic images generated by the historical records that can meet the user's needs; The calculation formula for the sub-noise modulation coefficient Z corresponding to a single photoacoustic image generated by the historical records that can meet the user's needs and the initial photoacoustic image is: ； Where, I(α,β) and K(α,β) are the pixel values of a single photoacoustic image and the initial photoacoustic image generated by the historical records that can meet the user's needs at the position (α,β) respectively, m and ε are the number of rows and columns of the pixel points included in the image; Distortion influence coefficient = |Image reference value corresponding to the initial photoacoustic image - Average value of the image reference values corresponding to the photoacoustic images generated by the historical records that can meet the user's needs|; The image reference value corresponding to a single photoacoustic image = Standard deviation of the powers corresponding to each frequency in the power spectrum of the entire interval corresponding to this photoacoustic image / Average value of the powers corresponding to each frequency in the power spectrum of the entire interval corresponding to this photoacoustic image; The blood flow influence index is the maximum value of the broadening reference values corresponding to each sub-interval of the initial photoacoustic image. The broadening reference value corresponding to a single sub-interval is the number of frequencies that appear in the power spectrum corresponding to this sub-interval; The tissue difference coefficient is the average value of the sub-tissue difference degrees corresponding to each rectangular area. The confirmation method for the sub-tissue difference degree corresponding to a single rectangular area is: for a single rectangular area, record the connected domain that appears in this rectangular area as the reference connected domain. The sub-tissue difference degree corresponding to this rectangular area = 1 / (Average value of the area emergence coefficients corresponding to each reference connected domain + 1). The area emergence coefficient corresponding to a single reference connected domain is the number of rectangular areas where this reference connected domain appears; Deviation uniformity = 1 / Standard deviation of the characteristic coefficients corresponding to each abnormal area; Detection domain variation value = (Optical flux corresponding to the earliest time point in the full interval - Optical flux corresponding to the latest time point in the full interval) / Standard deviation of the optical fluxes corresponding to each time point in the full interval; The regional dislocation coefficient is the average value of the distance reference values corresponding to each abnormal region. For a single abnormal region, the distance reference value corresponding to this abnormal region is the average value of the shortest distances from this abnormal region to other abnormal regions; Characteristic frequency compensation is to compensate the frequencies of the sub-intervals whose sub-interval turbulence coefficients are greater than or equal to the preset sub-interval turbulence coefficient; Correlation frequency compensation is to compensate the frequencies in the power spectrum of the full interval through a pre-compensation coefficient; The sub-interval turbulence coefficient is the average value of the powers corresponding to each frequency in the power spectrum corresponding to this sub-interval.

2. The photoacoustic imaging method based on data analysis according to claim 1, wherein If the imaging state of the initial photoacoustic image is that the image anomaly coefficient is greater than or equal to the preset image anomaly coefficient and the detection domain variation value is less than the preset detection domain variation value, the analysis method is detection anomaly analysis.

3. The photoacoustic imaging method based on data analysis according to claim 2, wherein If the imaging state of the initial photoacoustic image is that the image anomaly coefficient is less than the preset image anomaly coefficient or the detection domain variation value is greater than or equal to the preset detection domain variation value, the analysis method is tissue influence analysis.

4. The photoacoustic imaging method based on data analysis according to claim 2, characterized in that, If the noise modulation coefficient is greater than or equal to the preset noise modulation coefficient, the first optimization method is to perform a decreasing adjustment on the laser wavelength according to the noise modulation coefficient; The decreasing value of the laser wavelength and the noise modulation coefficient have a positive correlation.

5. The photoacoustic imaging method based on data analysis according to claim 4, wherein If the noise modulation coefficient is less than the preset noise modulation coefficient, the first optimization method is detection adjustment optimization; In detection adjustment optimization, based on the detection anomaly coefficient, determine the detection adjustment interval, and based on the interval comparison coefficient, determine the partitioning method to obtain several partition intervals, and adjust the detection angle range of each partition interval according to the interval influence coefficient.

6. The photoacoustic imaging method based on data analysis according to claim 5, wherein Determining the partitioning method based on the interval comparison coefficient includes: If the interval comparison coefficient is greater than or equal to the preset interval comparison coefficient, the partitioning method is to perform associated partitioning according to the point position correlation degree; If the interval comparison coefficient is less than the preset interval comparison coefficient, the partitioning method is to perform uniform partitioning according to the anomaly coefficient.

7. The photoacoustic imaging method based on data analysis according to claim 5, characterized in that Increase and adjust the detection angle range of each partition interval according to the interval influence coefficient; The increasing value of the detection angle range corresponding to a single partition interval and the interval influence coefficient corresponding to this partition interval have a positive correlation.

8. The photoacoustic imaging method based on data analysis according to claim 3, wherein If the distortion influence coefficient is less than the preset distortion influence coefficient and the regional dislocation coefficient is less than the preset regional dislocation coefficient, the second optimization method is to determine the compensation method according to the tissue difference coefficient and the deviation uniformity; If the tissue difference coefficient is greater than or equal to the preset tissue difference coefficient or the deviation uniformity is greater than or equal to the preset deviation uniformity, the compensation method is correlation frequency compensation; If the tissue difference coefficient is less than the preset tissue difference coefficient and the deviation uniformity is less than the preset deviation uniformity, the compensation method is characteristic frequency compensation.

9. The photoacoustic imaging method based on data analysis according to claim 8, characterized in that If the distortion influence coefficient is greater than or equal to the preset distortion influence coefficient or the regional dislocation coefficient is greater than or equal to the preset regional dislocation coefficient, the second optimization method is to determine the adjustment method according to the blood flow influence index; If the blood flow influence index is greater than or equal to the preset blood flow influence index, the adjustment method is to perform an increasing adjustment on the detection angle range according to the blood flow influence index; If the blood flow influence index is less than the preset blood flow influence index, the adjustment method is to increase the adjustment for the laser repetition frequency according to the distortion influence coefficient.

10. An imaging system applying the photoacoustic imaging method based on data analysis according to any one of claims 1 to 9, characterized in that, Including: A state analysis unit, which is used to determine the imaging state of the initial photoacoustic image according to the image anomaly coefficient and the detection domain variation value, and determine the analysis method according to the imaging state. The analysis method is anomaly detection analysis or tissue influence analysis; A first optimization unit, which is connected to the state analysis unit, and is used to determine the first optimization method according to the noise modulation coefficient in the anomaly detection analysis. The first optimization method is to adjust the laser wavelength according to the noise modulation coefficient, or detection adjustment optimization; A second optimization unit, which is connected to the state analysis unit, and is used to determine the second optimization method according to the distortion influence coefficient and the regional dislocation coefficient in the tissue influence analysis. The second optimization method is to determine the adjustment method according to the blood flow influence index, or determine the compensation method according to the tissue difference coefficient and the deviation uniformity; An adjustment unit, which is connected to the second optimization unit, and is used to determine the adjustment method according to the blood flow influence index as adjusting the detection angle range according to the blood flow influence index, or adjusting the laser repetition frequency according to the distortion influence coefficient; A compensation unit, which is connected to the second optimization unit, and is used to determine the compensation method as characteristic frequency compensation or related frequency compensation according to the tissue difference coefficient and the deviation uniformity.

Images