Photoacoustic imaging method based on signal processing

Abstract

The invention relates to the technical field of photoacoustic imaging, in particular to a photoacoustic imaging method based on signal processing, which comprises the following steps: acquiring imaging influence parameters of a target area to analyze area imaging characteristics of the target area; key laser emission parameters are determined based on the regional imaging characteristics, and detection laser is emitted to the target region based on the key laser emission parameters; after receiving the detection laser in response to the target area, periodically acquiring an initial photoacoustic signal of the target area; generating an initial photoacoustic image based on the initial photoacoustic signal in the target time period, and determining an image mutation feature corresponding to the initial photoacoustic image; and determining an imaging adjustment mode corresponding to the target area based on the image mutation features. According to the invention, the image quality of photoacoustic imaging can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photoacoustic imaging, and in particular to a photoacoustic imaging method based on signal processing. Background Art

[0002] Photoacoustic imaging, as an emerging biomedical imaging technology, combines the high contrast of optical imaging with the high resolution of ultrasound imaging, and can provide structural and functional information within tissues. In photoacoustic imaging, the problem is often simplified by assuming that the incident light is evenly distributed on the surface and inside the tissue and that the energy does not attenuate with depth. However, in reality, most biological tissues are turbid media with high scattering and opaque characteristics. When the laser is transmitted within the tissue, it is both absorbed and scattered. The absorption and scattering of the laser by tissue structures of different components, as well as the distribution of the light flux, are all uneven. These factors can reduce the accuracy of the photoacoustic image, resulting in unstable quality of the generated photoacoustic image. 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 application publication number CN119257543A discloses a photoacoustic imaging method and a photoacoustic imaging system, which include: acquiring ultrasonic image data of an imaging target, and determining the biological tissue type of the imaging target based on characteristics of the ultrasonic image data; determining a light flux distribution result corresponding to the biological tissue type, the light flux distribution result being generated after laser irradiation of a simulated target tissue corresponding to the biological tissue type of the imaging target during simulated imaging, and being used to characterize the light flux distribution of the biological tissue at different imaging depths relative to the body surface; acquiring photoacoustic image data of the imaging target; determining compensation parameters for the photoacoustic image data based on the light flux distribution result; compensating the photoacoustic image data based on the compensation parameters to obtain compensated photoacoustic image data; and generating a photoacoustic image based on the compensated photoacoustic image data.

[0004] The existing technology has the following problems: it only compensates the photoacoustic image data according to the light flux distribution results corresponding to the biological tissue type of the imaging target, cannot adaptively adjust the imaging process, and has difficulty adapting to the complex biological tissue environment, resulting in poor image quality and low image accuracy of photoacoustic imaging. Summary of the Invention

[0005] To this end, the present invention provides a photoacoustic imaging method based on signal processing to overcome the problem in the prior art that the imaging process cannot be adaptively adjusted, resulting in relatively poor image quality of photoacoustic imaging.

[0006] To achieve the above objectives, the present invention provides a photoacoustic imaging method based on signal processing, comprising: Obtaining imaging influencing parameters of the target area to analyze regional imaging characteristics of the target area; Determining key laser emission parameters based on the imaging characteristics of the region, and emitting a detection laser to the target region based on the key laser emission parameters; In response to the target area receiving the detection laser, periodically acquiring an initial photoacoustic signal of the target area; generating an initial photoacoustic image based on an initial photoacoustic signal within a target time period, and determining an image mutation feature corresponding to the initial photoacoustic image; Determining an imaging adjustment mode corresponding to the target area based on the image mutation feature, including a first imaging adjustment mode and a second imaging adjustment mode; The first imaging adjustment method is to determine a comprehensive imaging index corresponding to the target area based on the imaging influencing parameter, and adjust the key laser emission parameters based on the comprehensive imaging index; The second imaging adjustment method is to determine a feature compensation area based on the image mutation feature to adjust the initial photoacoustic image.

[0007] Furthermore, the process of analyzing the regional imaging characteristics of the target area includes: constructing a regional imaging impact model corresponding to the target area based on the imaging impact parameters of the target area; Performing imaging simulation on the target area based on the regional imaging influence model to determine the luminous flux distribution of the target area; Determining regional imaging characteristics of the target area based on the light flux distribution of the target area; The imaging influencing parameters include surface roughness, optical absorption coefficient and optical scattering coefficient.

[0008] Furthermore, the process of determining key laser emission parameters based on the regional imaging characteristics includes: Determining a regional interference category of the target area based on the regional imaging features, including a regional interference strong impact category and a regional interference weak impact category; Determining a method for determining the key laser emission parameters based on the regional interference category, including a first determination method and a second determination method; For the strong regional interference impact category, the key laser emission parameter is determined in the first determination method, which determines the regional imaging characterization value corresponding to the target area based on the regional imaging feature, and determines the key laser emission parameter based on the regional imaging characterization value and the initial laser reflection parameter; For the regional interference weak impact category, the key laser emission parameter is determined in the second determination method, and the initial laser reflection parameter is determined as the key laser emission parameter.

[0009] Further, the process of generating an initial photoacoustic image based on the initial photoacoustic signals in the target time period comprises: dividing the target time period into a plurality of time domain sub-periods, determining a signal change representation value corresponding to each time domain sub-period based on the initial photoacoustic signals in the time domain sub-period; determining a target time domain sub-period based on the signal change representation values corresponding to each time domain sub-period; determining a photoacoustic signal distribution feature of a target region based on the initial photoacoustic signals in the target time domain sub-period; generating an initial photoacoustic image of the target region based on the photoacoustic signal distribution feature of the target region.

[0010] Further, the process of determining the image mutation feature corresponding to the initial photoacoustic image comprises: inputting the initial photoacoustic image into an image mutation analysis model to obtain an image mutation feature corresponding to the initial photoacoustic image output by the image mutation analysis model, wherein the image mutation feature includes a general mutation feature and an abnormal mutation feature.

[0011] Further, the process of determining the imaging adjustment mode corresponding to the target region based on the image mutation feature comprises, if the image mutation feature is an abnormal mutation feature, the imaging adjustment mode corresponding to the target region is a first imaging adjustment mode; if the image mutation feature is a general mutation feature, the imaging adjustment mode corresponding to the target region is a second imaging adjustment mode.

[0012] Further, the process of determining the comprehensive imaging index corresponding to the target region based on the imaging influence parameter comprises: determining a region type of the target region based on the imaging influence parameter; determining an imaging influence representation value corresponding to the imaging influence parameter based on the region type; determining a comprehensive imaging index corresponding to the target region based on the imaging influence representation value.

[0013] Further, the process of adjusting the key laser emission parameter based on the comprehensive imaging index comprises: determining an imaging adjustment coefficient based on the comparison result of the comprehensive imaging index and a preset imaging index; determining a target laser emission parameter based on the imaging adjustment coefficient and the key laser emission parameter.

[0014] Further, the process of determining a feature compensation region based on the image mutation feature comprises: determining a plurality of mutation distribution feature points corresponding to the initial photoacoustic image based on the image mutation feature; A feature compensation area is determined based on the positional relationship of each of the mutation distribution feature points.

[0015] Furthermore, the process of adjusting the initial photoacoustic image includes: Determining a regional mutation characterization value based on the regional characteristics of the feature compensation region; Determining an image compensation coefficient based on a comparison result of the regional mutation characterization value and a preset mutation characterization value; Image processing is performed on the characteristic compensation region based on the image compensation coefficient to adjust the initial photoacoustic image.

[0016] Compared with the prior art, the beneficial effect of the present invention is that the present invention analyzes the regional imaging characteristics of the target area by acquiring the imaging influencing parameters of the target area, and determines the key laser emission parameters based on the regional imaging characteristics, which can adapt to the regional characteristics of the target area, produce clearer and more accurate photoacoustic signals, and improve imaging accuracy. Targeted selection of key laser emission parameters for emitting detection lasers to the target area can enhance the intensity of the photoacoustic signal, make the detected signal more obvious, improve the signal-to-noise ratio, and thus improve imaging accuracy. By periodically acquiring the initial photoacoustic signal, the stability of the photoacoustic signal is ensured. By generating the initial photoacoustic image based on the initial photoacoustic signal within the target time period, the photoacoustic information of the target area can be preliminarily identified, and the imaging adjustment method corresponding to the target area can be determined based on the image mutation characteristics corresponding to the initial photoacoustic image. The imaging process can be adaptively adjusted, thereby improving the image quality of the photoacoustic imaging.

[0017] Furthermore, the present invention constructs a regional imaging influence model based on the imaging influence parameters of the target area, which can accurately characterize the photoacoustic imaging influence mechanism of the target area, comprehensively analyze the influence of the imaging influence parameters on photoacoustic imaging, and simulate the imaging of the target area through the regional imaging influence model. It can predict the light flux distribution in the target area, thereby determining the regional imaging characteristics of the target area, which helps to more accurately identify the structure and abnormal conditions of the target area, thereby optimizing and adjusting the imaging process in a targeted manner, and further improving the image quality of photoacoustic imaging.

[0018] Furthermore, the present invention determines the regional interference category of the target area based on the regional imaging characteristics, accurately identifies the interference characteristics of the target area, and comprehensively and quantitatively evaluates the imaging impact of the target area, thereby specifically adjusting the laser emission parameters of the detection laser, thereby enhancing the quality of the photoacoustic signal and further improving the image quality and imaging accuracy of photoacoustic imaging.

[0019] Furthermore, the present invention can capture the rapid dynamic changes of the photoacoustic signal by subdividing the target time period, which helps to identify the subtle characteristics of the signal. The target time domain subsegment is determined by comparing the signal change characterization values ​​corresponding to each time domain subsegment. The photoacoustic signal distribution characteristics of the target area are determined based on the initial photoacoustic signal in the target time domain subsegment. This can filter out noise interference and focus on the photoacoustic signal with better imaging effect, thereby improving the image quality of photoacoustic imaging.

[0020] Furthermore, the present invention inputs the initial photoacoustic image into an image mutation analysis model to determine the image mutation features corresponding to the initial photoacoustic image. The image mutation features reflect abnormal changes in the initial photoacoustic image. General mutation features indicate that there may be abnormalities in some image regions of the initial photoacoustic image, and abnormal mutation features indicate that there are abnormalities in the initial photoacoustic image as a whole. Determination by the image mutation analysis model can improve data processing efficiency and accuracy, thereby improving the image quality of photoacoustic imaging.

[0021] Furthermore, the present invention determines the area type of the target area through the imaging influence parameters of the target area, and uses the imaging influence characterization values ​​corresponding to the imaging influence parameters to quantitatively evaluate the degree of imaging influence of the target area, thereby accurately evaluating the imaging characteristics of the target area, adaptively adjusting the imaging process, and improving the image quality of photoacoustic imaging.

[0022] Furthermore, the present invention determines an imaging adjustment coefficient based on the comparison result between the comprehensive imaging index and the preset imaging index to adjust key laser emission parameters. This can accurately locate imaging problems, quantitatively determine the adjustment coefficient, optimize the laser configuration, and improve the quality of the photoacoustic signal, thereby improving the image quality and imaging stability of photoacoustic imaging.

[0023] Furthermore, the present invention determines several mutation distribution feature points corresponding to the initial photoacoustic image through image mutation characteristics, can accurately identify key feature points in the initial photoacoustic image, quantify image features, and by analyzing the positional relationship of the mutation distribution feature points, can determine the area that needs to be compensated, thereby optimizing the image in a targeted manner, effectively reducing distortion and errors in the image, and improving the image quality of photoacoustic imaging.

[0024] Furthermore, the present invention determines the regional mutation characterization value based on the regional characteristics of the feature compensation area, can quantify the regional characteristics of the feature compensation area, and provide a basis for subsequent compensation. By determining the image compensation coefficient based on the comparison result of the regional mutation characterization value and the preset mutation characterization value, the feature compensation area can be accurately compensated to avoid over-compensation or under-compensation. The image compensation coefficient is automatically adjusted according to the regional mutation characterization value of different feature compensation areas, making the image adjustment of photoacoustic imaging more adaptable. Targeted image processing is performed on the feature compensation area, which can optimize the details of the image and improve the image quality of photoacoustic imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Schematic diagram of the process of a photoacoustic imaging method based on signal processing according to an embodiment of the present invention; Figure 2 A schematic diagram of a process for analyzing regional imaging features of a target area according to an embodiment of the present invention; Figure 3 A schematic diagram of a process for determining key laser emission parameters according to an embodiment of the present invention; Figure 4 A schematic diagram of a process for generating an initial photoacoustic image according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0027] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0028] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the 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. Therefore, it cannot be understood as a limitation on the present invention.

[0029] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0030] See also Figure 1-Figure 4 , which is a flow chart of a photoacoustic imaging method based on signal processing according to an embodiment of the present invention; Figure 2 A schematic diagram of a process for analyzing regional imaging features of a target area according to an embodiment of the present invention; Figure 3 A schematic diagram of a process for determining key laser emission parameters according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a process for generating an initial photoacoustic image according to an embodiment of the present invention. The present invention provides a photoacoustic imaging method based on signal processing, comprising: Step S1, obtaining imaging influencing parameters of the target area to analyze regional imaging characteristics of the target area; In practice, the target area may be a part of a biological tissue, an electronic product component, or the like, and there is no specific limitation thereto, as long as photoacoustic imaging can be performed.

[0031] It is understandable that the imaging influencing parameters include but are not limited to surface roughness, optical absorption coefficient, optical scattering coefficient, refractive index, etc., and there is no specific limitation on the equipment and method for obtaining the imaging influencing parameters. For example, the absorbance of the target area at different wavelengths can be measured based on a spectrophotometer, and the optical absorption coefficient can be calculated according to the Lambert-Beer law in combination with the optical path and concentration information. The scattering intensity of light at different angles can be measured based on a light scattering meter, and the optical scattering coefficient can be calculated using the Mie scattering theory or the Rayleigh scattering theory. The refractive index can be calculated by measuring the phase change of the light wave based on a phase shift interferometer. The microscopic profile of the surface of the target area can be measured using the principle of optical interference using an optical surface profiler to calculate the surface roughness.

[0032] Specifically, in step S1, analyzing the regional imaging features of the target area includes: Step S11, constructing a regional imaging influence model corresponding to the target area based on the imaging influence parameters of the target area; Step S12, performing imaging simulation on the target area based on the regional imaging influence model to determine the light flux distribution of the target area; Step S13: determining the regional imaging characteristics of the target area based on the light flux distribution of the target area.

[0033] In implementation, according to the characteristics of the target region and the imaging requirements, a suitable model type can be selected, such as a physical-based optical model (such as a radiation transfer equation model) or an empirical-based statistical model, a framework of the regional imaging influence model is constructed by using mathematical tools and software (such as MATLAB, COMSOL Multiphysics, etc.), the imaging influence parameters are used as input variables of the model, and the output of the model is defined as the luminous flux distribution of the target region. The regional imaging influence model constructed is used for imaging simulation of the target region, the equation of the model is solved by using a numerical calculation method (such as a finite element method, a finite difference method, etc.), the simulation results of the propagation, absorption, scattering and other processes of light in the target region are obtained, the information of the luminous flux distribution is extracted from the simulation results, the energy distribution of light in the target region is analyzed, and a luminous flux distribution image is constructed. The regional imaging features of the target region can be extracted based on the luminous flux distribution image, including the peak position, intensity distribution, uniformity, symmetry and the like of the luminous flux.

[0034] The regional imaging influence model is constructed based on the imaging influence parameters of the target region, the photoacoustic imaging influence mechanism of the target region can be accurately represented, the influence of the imaging influence parameters on photoacoustic imaging is comprehensively analyzed, the target region is imaged by using the regional imaging influence model, the luminous flux distribution in the target region can be predicted, the regional imaging features of the target region are determined, the structure and abnormal conditions of the target region can be more accurately identified, and the imaging process can be optimized and adjusted accordingly, thereby further improving the image quality of photoacoustic imaging.

[0035] In step S2, the key laser emission parameters are determined based on the regional imaging features, and the detection laser is emitted to the target region based on the key laser emission parameters. Specifically, in step S2, the key laser emission parameters are determined based on the regional imaging features, including: In step S21, the regional interference category of the target region is determined based on the regional imaging features, including a regional interference strong influence category and a regional interference weak influence category. In implementation, the regional imaging features Y1, Y2, …, Y j , …, Y m are compared with the preset imaging features E1, E2, …, E j , …, E m , an imaging comparison value CB is determined, CB=(∑ m j=1 Y j ×E j ) / (sqrt(∑ m j=1 (Y j ) 2 )×sqrt(∑m j=1 (E j ) 2 )), j = 1, 2, ..., m, m is the number of imaging features, sqrt() is a preset square root determination function, the imaging comparison value is compared with the preset imaging comparison value, if the imaging comparison value is greater than the preset imaging comparison value, the regional interference category of the target area is the regional interference weak impact category, if the imaging comparison value is less than or equal to the preset imaging comparison value, the regional interference category of the target area is the regional interference strong impact category.

[0036] It can be understood that the actual implementer can set the preset imaging features based on the actual situation or the average value of the imaging features of the target area that has passed the qualification test in historical data. The actual implementer can set the preset imaging comparison value based on the actual situation. Preferably, the preset imaging comparison value is set in the range of 0.7 to 0.8.

[0037] Step S22, determining a method for determining the key laser emission parameters based on the regional interference category, including a first determination method and a second determination method; For the strong regional interference impact category, the key laser emission parameter is determined in the first determination method, which determines the regional imaging characterization value corresponding to the target area based on the regional imaging feature, and determines the key laser emission parameter based on the regional imaging characterization value and the initial laser reflection parameter; For the regional interference weak impact category, the key laser emission parameter is determined in the second determination method, and the initial laser reflection parameter is determined as the key laser emission parameter.

[0038] In implementation, laser emission parameters include pulse wavelength, pulse energy, pulse width, emission frequency, etc.

[0039] It can be understood that if the regional interference category is the regional interference strong influence category, it indicates that the regional characteristics of the target area are more obvious and the imaging influence of the target area is greater. If the detection laser is emitted with the initial laser reflection parameters, the signal quality of the photoacoustic signal will be reduced, thereby reducing the image quality. Therefore, the regional imaging characterization value corresponding to the target area is determined based on the regional imaging characteristics, and the key laser emission parameters are determined based on the regional imaging characterization value and the initial laser reflection parameters. In the specific implementation process, the ratio of the preset imaging comparison value to the imaging comparison value can be determined as the regional imaging characterization value, and the product of the regional imaging characterization value and the initial laser reflection parameter can be determined as the key laser emission parameter.

[0040] It can be understood that if the regional interference category is the regional interference weak influence category, it indicates that the regional characteristics of the target region are relatively conventional, and the imaging influence degree of the target region is smaller, and the initial laser reflection parameter can be determined as the key laser emission parameter based on the initial laser reflection parameter.

[0041] It can be understood that the specific structure of the photoacoustic imaging device is not limited, and the photoacoustic imaging device can include a laser for emitting laser to the target region based on the key laser emission parameter, and a receiving mechanism for receiving the photoacoustic signal returned by the target region under the excitation of the detection laser after the target region receives the detection laser.

[0042] The application can enhance the quality of the photoacoustic signal and further improve the image quality and imaging accuracy of the photoacoustic imaging by determining the regional interference category of the target region based on the regional imaging characteristics, accurately identifying the interference characteristics of the target region, comprehensively quantitatively evaluating the imaging influence degree of the target region, and adjusting the laser emission parameter of the detection laser.

[0043] Step S3, periodically acquiring the initial photoacoustic signal of the target region in response to the target region receiving the detection laser; Step S4, generating an initial photoacoustic image based on the initial photoacoustic signal in a target time period, and determining the image mutation characteristics corresponding to the initial photoacoustic image; Specifically, in the step S4, the initial photoacoustic image is generated based on the initial photoacoustic signal in the target time period, comprising: Step S41, dividing the target time period into a plurality of time domain subsegments, and determining the signal change representation value corresponding to each time domain subsegment based on the initial photoacoustic signal in each time domain subsegment; Step S42, determining a target time domain subsegment based on the signal change representation value corresponding to each time domain subsegment; Step S43, determining the photoacoustic signal distribution characteristics of the target region based on the initial photoacoustic signal in the target time domain subsegment; Step S44, generating an initial photoacoustic image of the target region based on the photoacoustic signal distribution characteristics of the target region.

[0044] In the implementation, the number of divisions of the target time period can be determined based on the emission frequency of the detection laser, and preferably, the detection laser is emitted 2-3 times in each time domain subsegment, that is, 2-3 initial photoacoustic signals are acquired in each time domain subsegment.

[0045] It is understandable that the initial photoacoustic signal is subjected to signal preprocessing, including filtering and denoising, and signal normalization. Filtering and denoising can remove high-frequency noise and low-frequency interference through bandpass filtering, such as Butterworth filters and Chebyshev filters, and normalize the signal amplitude to between [0, 1]. This is a prior art and will not be described in detail. The signal characteristics of each initial photoacoustic signal are calculated based on the initial photoacoustic signal after signal preprocessing, including characteristic signal intensity (determined based on the maximum amplitude point of the initial photoacoustic signal after signal preprocessing), characteristic signal main frequency (the initial photoacoustic signal after signal preprocessing is subjected to fast Fourier transform (FFT), and the signal main frequency corresponds to the frequency component with the largest amplitude in the FFT result), characteristic signal change rate (the signal intensity change rate of adjacent initial photoacoustic signals), etc.

[0046] It can be understood that the signal feature YJ=(YJ1, YJ2, ..., YJ i ,…,YJ n ), YJ i =(YJ i,1 , YJ i,2 ,…,YJ i,g ,…,YJ i,h ), where i = 1, 2, ..., n, n is the number of initial photoacoustic signals, YJ i is the signal feature of the i-th initial photoacoustic signal in the time domain subsegment, g = 1, 2, ..., h, h is the number of signal features, YJ i,g is the gth signal feature of the initial photoacoustic signal in the time domain sub-segment i, then the signal change representation value S corresponding to the time domain sub-segment is (∑ h g=1 ((∑ n i=1 (YJ i -avg(YJ)) 2 ) / n) / h), avg() is the preset average value determination function.

[0047] It is understood that the signal change representation values ​​of each time domain subsegment are sorted, and the time domain subsegment corresponding to the minimum signal change representation value is determined as the target time domain subsegment. The initial photoacoustic signal in the target time domain subsegment is averaged to obtain a key photoacoustic signal. The spatial distribution of the key photoacoustic signal can be reconstructed according to a beamforming algorithm, and feature extraction can be performed to obtain the photoacoustic signal distribution characteristics of the target area, including the sound pressure distribution. An initial photoacoustic image of the target area is generated according to an image reconstruction algorithm or model, which is not specifically limited, for example, a back-projection algorithm, an iterative reconstruction algorithm, a neural network model, etc.

[0048] By subdividing the target time period, the present invention can capture the rapid dynamic changes of the photoacoustic signal, help identify the subtle characteristics of the signal, determine the target time domain subsegment by comparing the signal change characterization values ​​corresponding to each time domain subsegment, and determine the photoacoustic signal distribution characteristics of the target area based on the initial photoacoustic signal in the target time domain subsegment. It can filter out noise interference and focus on the photoacoustic signal with better imaging effect, thereby improving the image quality of photoacoustic imaging.

[0049] Specifically, in step S4, determining the image mutation feature corresponding to the initial photoacoustic image includes: The initial photoacoustic image is input into an image mutation analysis model to obtain image mutation features corresponding to the initial photoacoustic image output by the image mutation analysis model, wherein the image mutation features include general mutation features and abnormal mutation features.

[0050] In a specific embodiment, image mutation feature marking can be performed based on the photoacoustic images in historical data. The mutation features are features whose edge length, curvature, texture change gradient, etc. exceed the preset standards. Based on the degree to which the mutation features exceed the preset standards, the mutation features are divided into general mutation features (exceeding the preset standards by less than 30%) and abnormal mutation features (exceeding the preset standards by more than 30%), so as to train a neural network model or a deep learning model to obtain an image mutation analysis model. Actual implementers can set preset standards based on actual conditions.

[0051] The present invention inputs an initial photoacoustic image into an image mutation analysis model to determine image mutation features corresponding to the initial photoacoustic image. The image mutation features reflect abnormal changes in the initial photoacoustic image. General mutation features indicate that some image regions in the initial photoacoustic image may be abnormal, while abnormal mutation features indicate that the initial photoacoustic image as a whole is abnormal. Determination by the image mutation analysis model can improve data processing efficiency and accuracy, thereby improving the image quality of photoacoustic imaging.

[0052] Step S5, determining an imaging adjustment mode corresponding to the target area based on the image mutation feature, including a first imaging adjustment mode and a second imaging adjustment mode; The first imaging adjustment method is to determine a comprehensive imaging index corresponding to the target area based on the imaging influencing parameter, and adjust the key laser emission parameters based on the comprehensive imaging index; The second imaging adjustment method is to determine a feature compensation area based on the image mutation feature to adjust the initial photoacoustic image.

[0053] Specifically, in step S5, the imaging adjustment method corresponding to the target area is determined based on the image mutation feature, including: If the image mutation feature is an abnormal mutation feature, the imaging adjustment mode corresponding to the target area is the first imaging adjustment mode; If the image mutation feature is a general mutation feature, the imaging adjustment mode corresponding to the target area is the second imaging adjustment mode.

[0054] Specifically, in step S5, determining the comprehensive imaging index corresponding to the target area based on the imaging influencing parameters includes: Step S511, determining the region type of the target region based on the imaging influencing parameters; Step S512, determining an imaging impact representation value corresponding to the imaging impact parameter based on the area type of the target area; Step S513: determining a comprehensive imaging index corresponding to the target area based on the imaging impact representation value.

[0055] In implementation, the target area can be divided into different area types based on different imaging influence parameters in the historical data, and corresponding regional imaging influence parameters are set for each area type based on the mean value of the imaging influence parameters corresponding to each area type, and the imaging influence parameters of the target area are compared with the regional imaging influence parameters corresponding to the target area to obtain the imaging influence representation value, for example, the imaging influence parameters G1, G2, ..., G a ,…,G b , the regional imaging influence parameters H1, H2, ..., H corresponding to the target area a ,…,H b , imaging impact characterization value CG=abs((∑ b a=1 (G a -H a ) / H a ) / b), where a=1, 2, ..., b; b is the number of imaging influencing parameters, and abs() is a preset absolute value determination function.

[0056] It is understandable that if the imaging influence characterization value is less than the preset influence characterization value, it indicates that the imaging influence parameter has an influence degree on the imaging of the target area within a controllable range. In this case, the imaging influence characterization value is determined as the comprehensive imaging index corresponding to the target area. If the imaging influence characterization value is greater than or equal to the preset influence characterization value, it indicates that the imaging influence parameter has an influence degree on the imaging of the target area that is not within a controllable range. If the imaging influence characterization value is used as the comprehensive imaging index for subsequent adjustment, the adjustment range will be too large, resulting in uncontrollable risks in the imaging process, reducing the image quality of the photoacoustic imaging, and reducing the imaging efficiency. Therefore, the preset influence characterization value is determined as the comprehensive imaging index corresponding to the target area to facilitate subsequent fine-tuning. The actual implementation personnel can determine the preset influence characterization value based on the actual situation or the average of the imaging influence characterization values ​​that have passed the qualification test in historical data. Preferably, the preset influence characterization value is set to a value range of 0.6 to 0.7.

[0057] The present invention determines the region type of the target region through the imaging influence parameters of the target region, and uses the imaging influence characterization values ​​corresponding to the imaging influence parameters to quantitatively evaluate the degree of imaging influence of the imaging influence parameters on the target region, thereby accurately evaluating the imaging characteristics of the target region, adaptively adjusting the imaging process, and improving the image quality of photoacoustic imaging.

[0058] Specifically, in step S5, adjusting the key laser emission parameters based on the comprehensive imaging index includes: Step S521, determining an imaging adjustment coefficient based on a comparison result between the comprehensive imaging index and a preset imaging index; Step S522 : determining target laser emission parameters based on the imaging adjustment coefficient and the key laser emission parameters.

[0059] In implementation, the ratio of the comprehensive imaging index to the preset imaging index is determined as the imaging adjustment coefficient, and the target laser emission parameter is determined according to the product of the imaging adjustment coefficient and the key laser emission parameter.

[0060] It is understandable that the actual implementer may determine the preset imaging index based on the actual situation or the minimum value of the comprehensive imaging index that has passed the qualification test in historical data. Preferably, the preset imaging index value range is set to 0.4 to 0.6.

[0061] The present invention determines an imaging adjustment coefficient based on the comparison result between the comprehensive imaging index and the preset imaging index to adjust key laser emission parameters. This can accurately locate imaging problems, quantitatively determine the adjustment coefficient, optimize the laser configuration, and improve the quality of the photoacoustic signal, thereby improving the image quality and imaging stability of photoacoustic imaging.

[0062] Specifically, in step S5, determining a feature compensation area based on the image mutation feature includes: Step S531, determining a plurality of mutation distribution feature points corresponding to the initial photoacoustic image based on the image mutation feature; Step S532: determining a feature compensation area based on the positional relationship of each of the sudden change distribution feature points.

[0063] During implementation, the actual implementer can set corresponding mutation standards for image mutation features. For general mutation features, the general mutation standard is set to 20% exceeding the preset standard. The target area is divided into grids, and the image mutation feature corresponding to each grid area is judged to meet the general mutation standard. If it does (exceeding the preset standard by 20% or more), the center point of the corresponding grid area is determined as the mutation distribution feature point. For abnormal mutation features, the abnormal mutation standard is set to 50% exceeding the preset standard. The target area is divided into grids, and the image mutation feature corresponding to each grid area is judged to meet the abnormal mutation standard. If it does (exceeding the preset standard by 50% or more), the center point of the corresponding grid area is determined as the mutation distribution feature point. The minimum enclosed area enclosed by each mutation distribution feature point is determined as the feature compensation area.

[0064] The present invention determines several mutation distribution feature points corresponding to the initial photoacoustic image through image mutation characteristics, can accurately identify key feature points in the initial photoacoustic image, quantify image features, and by analyzing the positional relationship of the mutation distribution feature points, can determine the area that needs to be compensated, thereby performing targeted image optimization processing, effectively reducing distortion and errors in the image, and improving the image quality of photoacoustic imaging.

[0065] Specifically, in step S5, adjusting the initial photoacoustic image includes: Step S541, determining a regional mutation characterization value based on the regional characteristics of the feature compensation area; Step S542, determining an image compensation coefficient based on a comparison result of the regional mutation characterization value and a preset mutation characterization value; Step S543 : performing image processing on the characteristic compensation region based on the image compensation coefficient to adjust the initial photoacoustic image.

[0066] In implementation, regional features include the mean, variance, skewness, and kurtosis of the pixel values ​​within the feature compensation region (the mean represents the average grayscale value of the region, the variance reflects the distribution range of the grayscale value, and the skewness and kurtosis describe the shape of the grayscale value distribution), and the gradient strength of the edge of the feature compensation region (the gradient strength represents the degree of edge visibility). The regional features are compared with the preset regional features to determine the regional mutation characterization value. For example, the regional features R1, R2, ..., Rp ,…,R q With the preset regional features T1, T2, ..., T p ,…,T q Determine the regional mutation characterization value TB, TB=(∑ q p=1 R p ×T p ) / (sqrt(∑ q p=1 (R p ) 2 )×sqrt(∑ q p=1 (T p ) 2 )), p=1, 2, …, q, q is the number of regional features, and actual implementers can set preset regional features based on the photoacoustic images of photoacoustic imaging that have passed the qualification test in historical data.

[0067] It can be understood that the ratio of the regional mutation characterization value to the preset mutation characterization value is determined as the image compensation coefficient, and the pixel values ​​in the feature compensation area are adjusted based on the image compensation coefficient. Preferably, the adjusted pixel values ​​at each position in the feature compensation area are determined based on the product of the image compensation coefficient and the pixel values ​​at each position in the feature compensation area to adjust the initial photoacoustic image.

[0068] The present invention determines a regional mutation characterization value based on the regional characteristics of the feature compensation area, can quantify the regional characteristics of the feature compensation area, and provide a basis for subsequent compensation. By determining an image compensation coefficient based on the comparison result of the regional mutation characterization value and a preset mutation characterization value, the feature compensation area can be accurately compensated to avoid overcompensation or undercompensation. The image compensation coefficient is automatically adjusted according to the regional mutation characterization values ​​of different feature compensation areas, making the image adjustment of photoacoustic imaging more adaptable. Targeted image processing is performed on the feature compensation area, which can optimize the details of the image and improve the image quality of the photoacoustic imaging.

[0069] The present invention analyzes the regional imaging characteristics of the target area by acquiring the imaging influencing parameters of the target area, and determines the key laser emission parameters based on the regional imaging characteristics. It can adapt to the regional characteristics of the target area, produce clearer and more accurate photoacoustic signals, and improve imaging accuracy. Targeted selection of the key laser emission parameters for emitting a detection laser to the target area can enhance the intensity of the photoacoustic signal, make the detected signal more obvious, improve the signal-to-noise ratio, and thus improve imaging accuracy. By periodically acquiring the initial photoacoustic signal, the stability of the photoacoustic signal is ensured. By generating an initial photoacoustic image based on the initial photoacoustic signal within the target time period, the photoacoustic information of the target area can be preliminarily identified. The imaging adjustment method corresponding to the target area is determined based on the image mutation characteristics corresponding to the initial photoacoustic image. The imaging process can be adaptively adjusted, thereby improving the image quality of the photoacoustic imaging.

[0070] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A photoacoustic imaging method based on signal processing, characterized in that: include: Obtaining imaging influencing parameters of the target area to analyze regional imaging characteristics of the target area; Determining key laser emission parameters based on the imaging characteristics of the region, and emitting a detection laser to the target region based on the key laser emission parameters; In response to the target area receiving the detection laser, periodically acquiring an initial photoacoustic signal of the target area; generating an initial photoacoustic image based on an initial photoacoustic signal within a target time period, and determining an image mutation feature corresponding to the initial photoacoustic image; Determining an imaging adjustment mode corresponding to the target area based on the image mutation feature, including a first imaging adjustment mode and a second imaging adjustment mode; Wherein, for the first imaging adjustment mode, a comprehensive imaging index corresponding to the target area is determined based on the imaging influencing parameter, and the key laser emission parameter is adjusted based on the comprehensive imaging index; For the second imaging adjustment mode, a feature compensation region is determined based on the image mutation feature to adjust the initial photoacoustic image.

2. The photoacoustic imaging method based on signal processing according to claim 1, characterized in that: The process of analyzing the regional imaging characteristics of the target area includes: constructing a regional imaging impact model corresponding to the target area based on the imaging impact parameters of the target area; Performing imaging simulation on the target area based on the regional imaging influence model to determine the luminous flux distribution of the target area; Determining regional imaging characteristics of the target area based on the light flux distribution of the target area; The imaging influencing parameters include surface roughness, optical absorption coefficient and optical scattering coefficient.

3. The photoacoustic imaging method based on signal processing according to claim 2, characterized in that: The process of determining key laser emission parameters based on the imaging characteristics of the region includes: Determining a regional interference category of the target area based on the regional imaging features, including a regional interference strong impact category and a regional interference weak impact category; Determining a method for determining the key laser emission parameters based on the regional interference category, including a first determination method and a second determination method; For the strong regional interference impact category, the key laser emission parameter is determined in the first determination method, which determines the regional imaging characterization value corresponding to the target area based on the regional imaging feature, and determines the key laser emission parameter based on the regional imaging characterization value and the initial laser reflection parameter; For the regional interference weak impact category, the key laser emission parameter is determined in the second determination method, and the initial laser reflection parameter is determined as the key laser emission parameter.

4. The photoacoustic imaging method based on signal processing according to claim 3, characterized in that: The process of generating an initial photoacoustic image based on the initial photoacoustic signal within the target time period includes: Dividing the target time period into a plurality of time domain sub-segments, and determining a signal change representation value corresponding to each time domain sub-segment based on an initial photoacoustic signal in each time domain sub-segment; Determine a target time domain subsegment based on the signal change representation value corresponding to each of the time domain subsegments; determining a photoacoustic signal distribution feature of a target area based on the initial photoacoustic signal in the target time domain subsegment; An initial photoacoustic image of the target area is generated based on the photoacoustic signal distribution characteristics of the target area.

5. The photoacoustic imaging method based on signal processing according to claim 4, characterized in that: The process of determining the image mutation feature corresponding to the initial photoacoustic image includes: The initial photoacoustic image is input into an image mutation analysis model to obtain image mutation features corresponding to the initial photoacoustic image output by the image mutation analysis model, wherein the image mutation features include general mutation features and abnormal mutation features.

6. The photoacoustic imaging method based on signal processing according to claim 5, characterized in that: The process of determining the imaging adjustment method corresponding to the target area based on the image mutation characteristics includes: If the image mutation feature is an abnormal mutation feature, the imaging adjustment mode corresponding to the target area is the first imaging adjustment mode; If the image mutation feature is a general mutation feature, the imaging adjustment mode corresponding to the target area is the second imaging adjustment mode.

7. The photoacoustic imaging method based on signal processing according to claim 6, characterized in that: The process of determining the comprehensive imaging index corresponding to the target area based on the imaging influencing parameters includes: determining a region type of the target region based on the imaging influencing parameter; Determining an imaging impact characterization value corresponding to the imaging impact parameter based on the region type; A comprehensive imaging index corresponding to the target area is determined based on the imaging impact characterization value.

8. The photoacoustic imaging method based on signal processing according to claim 7, characterized in that: The process of adjusting the key laser emission parameters based on the comprehensive imaging index includes: Determining an imaging adjustment coefficient based on a comparison result of the comprehensive imaging index and a preset imaging index; Target laser emission parameters are determined based on the imaging adjustment coefficient and the key laser emission parameters.

9. The photoacoustic imaging method based on signal processing according to claim 8, characterized in that: The process of determining the feature compensation area based on the image mutation feature includes: Determining a plurality of mutation distribution feature points corresponding to the initial photoacoustic image based on the image mutation feature; A feature compensation area is determined based on the positional relationship of each of the mutation distribution feature points.

10. The photoacoustic imaging method based on signal processing according to claim 9, characterized in that: The process of adjusting the initial photoacoustic image includes: Determining a regional mutation characterization value based on the regional characteristics of the feature compensation region; Determining an image compensation coefficient based on a comparison result of the regional mutation characterization value and a preset mutation characterization value; Image processing is performed on the characteristic compensation region based on the image compensation coefficient to adjust the initial photoacoustic image.

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