A photoacoustic imaging method based on signal processing

Abstract

This invention relates to the field of photoacoustic imaging technology, and more particularly to a photoacoustic imaging method based on signal processing, comprising: acquiring imaging influence parameters of a target region to analyze the regional imaging characteristics of the target region; determining key laser emission parameters based on the regional imaging characteristics, and emitting a probe laser towards the target region based on the key laser emission parameters; periodically acquiring an initial photoacoustic signal of the target region in response to the target region receiving the probe laser; generating an initial photoacoustic image based on the initial photoacoustic signal within a target time period, and determining the image abrupt change characteristics corresponding to the initial photoacoustic image; and determining the imaging adjustment mode corresponding to the target region based on the image abrupt change characteristics. This invention can improve the image quality of photoacoustic imaging.

Description

Technical Field

[0001] This invention relates to the field of photoacoustic imaging technology, and more particularly to a photoacoustic imaging method based on signal processing. Background Technology

[0002] Photoacoustic imaging, as an emerging biomedical imaging technology, combines the high contrast of optical imaging with the high resolution of ultrasound imaging, providing structural and functional information about tissues. In photoacoustic imaging, it is often simplified by assuming that the incident light is uniformly distributed on and within the tissue surface and that its energy does not decrease with depth. However, in reality, most biological tissues are turbid media with high scattering and opacity. When laser light propagates within the tissue, it is both absorbed and scattered. The absorption and scattering of laser light, as well as the distribution of light flux, are non-uniform in different tissue structures. These factors reduce the accuracy of photoacoustic images, leading to unstable image quality. Therefore, improving the image quality of photoacoustic imaging is a technical problem that urgently needs to be solved by those skilled in the art.

[0003] Chinese patent application publication number CN119257543A discloses a photoacoustic imaging method and a photoacoustic imaging system. The method includes: acquiring ultrasound image data of the imaging target and determining the biological tissue type of the imaging target based on the characteristics of the ultrasound image data; determining the light flux distribution result corresponding to the biological tissue type, wherein the light flux distribution result is generated after laser irradiation of the simulated target tissue during the simulation imaging process of the simulated target tissue corresponding to the biological tissue type 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 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 for photoacoustic image data based on the light flux distribution results corresponding to the biological tissue type of the imaging target, which cannot adaptively adjust the imaging process and is difficult to adapt to complex biological tissue environments, resulting in poor image quality and low image accuracy of photoacoustic imaging. Summary of the Invention

[0005] To address this issue, the present invention provides a photoacoustic imaging method based on signal processing, which overcomes the problem in the prior art that the imaging process cannot be adaptively adjusted, resulting in poor image quality in photoacoustic imaging.

[0006] To achieve the above objectives, the present invention provides a photoacoustic imaging method based on signal processing, comprising:

[0007] Obtain imaging impact parameters of the target area to analyze the regional imaging characteristics of the target area;

[0008] Based on the imaging features of the region, key laser emission parameters are determined, and a probe laser is emitted toward the target region based on the key laser emission parameters;

[0009] In response to the target area receiving the detection laser, the initial photoacoustic signal of the target area is periodically acquired;

[0010] An initial photoacoustic image is generated based on the initial photoacoustic signal within the target time period, and the image abrupt change features corresponding to the initial photoacoustic image are determined.

[0011] Based on the image mutation features, the imaging adjustment method corresponding to the target region is determined, including a first imaging adjustment method and a second imaging adjustment method;

[0012] The first imaging adjustment method involves determining the comprehensive imaging index corresponding to the target area based on the imaging influence parameters, and adjusting the key laser emission parameters based on the comprehensive imaging index.

[0013] The second imaging adjustment method involves determining a feature compensation region based on the image abrupt change features to adjust the initial photoacoustic image.

[0014] Furthermore, the process of analyzing the regional imaging features of the target region includes:

[0015] Based on the imaging impact parameters of the target area, a regional imaging impact model corresponding to the target area is constructed.

[0016] Based on the regional imaging influence model, an imaging simulation is performed on the target region to determine the light flux distribution in the target region.

[0017] The regional imaging characteristics of the target area are determined based on the light flux distribution of the target area.

[0018] The imaging-affecting parameters include surface roughness, optical absorption coefficient, and optical scattering coefficient.

[0019] Furthermore, the process of determining key laser emission parameters based on the imaging features of the region includes:

[0020] Based on the regional imaging features, the regional interference category of the target region is determined, including the regional interference strong influence category and the regional interference weak influence category.

[0021] The determination method for the key laser emission parameters based on the regional interference category includes a first determination method and a second determination method;

[0022] Among them, for the category of strong regional interference, the determination method for the key laser emission parameters is the first determination method, which determines the regional imaging characterization value corresponding to the target region based on the regional imaging characteristics, and determines the key laser emission parameters based on the regional imaging characterization value and the initial laser reflection parameters.

[0023] For the category of weak regional interference, the determination method for the key laser emission parameters is the second determination method, in which the initial laser reflection parameters are determined as the key laser emission parameters.

[0024] Furthermore, the process of generating an initial photoacoustic image based on the initial photoacoustic signal within the target time period includes:

[0025] The target time period is divided into several time-domain segments, and the signal change characterization value corresponding to each time-domain segment is determined based on the initial photoacoustic signal in each time-domain segment.

[0026] The target time domain segment is determined based on the signal change characterization value corresponding to each of the aforementioned time domain segments;

[0027] The photoacoustic signal distribution characteristics of the target region are determined based on the initial photoacoustic signal within the target time domain sub-segment.

[0028] An initial photoacoustic image of the target region is generated based on the photoacoustic signal distribution characteristics of the target region.

[0029] Furthermore, the process of determining the image abrupt change features corresponding to the initial photoacoustic image includes:

[0030] The initial photoacoustic image is input into the image mutation analysis model to obtain the image mutation features corresponding to the initial photoacoustic image output by the image mutation analysis model. The image mutation features include general mutation features and abnormal mutation features.

[0031] Furthermore, the process of determining the imaging adjustment method corresponding to the target region based on the image mutation features includes,

[0032] If the image mutation feature is an abnormal mutation feature, then the imaging adjustment method corresponding to the target region is the first imaging adjustment method;

[0033] If the image mutation feature is a general mutation feature, then the imaging adjustment method corresponding to the target region is the second imaging adjustment method.

[0034] Furthermore, the process of determining the comprehensive imaging index corresponding to the target area based on the imaging influence parameters includes:

[0035] The region type of the target region is determined based on the imaging influence parameters;

[0036] Based on the region type, determine the imaging impact characterization value corresponding to the imaging impact parameter;

[0037] The comprehensive imaging index corresponding to the target area is determined based on the imaging influence characterization value.

[0038] Furthermore, the process of adjusting the key laser emission parameters based on the comprehensive imaging index includes:

[0039] The imaging adjustment coefficient is determined based on the comparison result between the comprehensive imaging index and the preset imaging index.

[0040] The target laser emission parameters are determined based on the imaging adjustment coefficient and the key laser emission parameters.

[0041] Furthermore, the process of determining the feature compensation region based on the image mutation features includes:

[0042] Based on the image mutation features, several mutation distribution feature points corresponding to the initial photoacoustic image are determined;

[0043] The feature compensation region is determined based on the positional relationship of each mutation distribution feature point.

[0044] Furthermore, the process of adjusting the initial photoacoustic image includes:

[0045] The regional mutation characterization value is determined based on the regional characteristics of the feature compensation region.

[0046] The image compensation coefficient is determined based on the comparison between the regional mutation characterization value and the preset mutation characterization value.

[0047] Image processing is performed on the feature compensation region based on the image compensation coefficient to adjust the initial photoacoustic image.

[0048] Compared with existing technologies, the advantages of this invention lie in its ability to analyze the regional imaging characteristics of the target area by acquiring imaging influence parameters, and to determine key laser emission parameters based on these characteristics. This allows the invention to adapt to the regional characteristics of the target area, generating clearer and more accurate photoacoustic signals and improving imaging accuracy. Targeted selection of key laser emission parameters for emitting probe lasers towards the target area enhances the intensity of the photoacoustic signal, making the detected signal more prominent and improving the signal-to-noise ratio, thereby increasing imaging accuracy. Periodically acquiring initial photoacoustic signals ensures their stability. Generating an initial photoacoustic image based on the initial photoacoustic signal within the target time period allows for preliminary identification of the photoacoustic information in the target area. Determining the imaging adjustment method corresponding to the target area based on the image abrupt change characteristics of the initial photoacoustic image enables adaptive adjustment of the imaging process, thereby improving the image quality of photoacoustic imaging.

[0049] Furthermore, this invention constructs a regional imaging influence model based on imaging influence parameters of the target region, which can accurately characterize the photoacoustic imaging influence mechanism of the target region, comprehensively analyze the influence of imaging influence parameters on photoacoustic imaging, and simulate the imaging of the target region through the regional imaging influence model. This can predict the light flux distribution in the target region, thereby determining the regional imaging characteristics of the target region. This helps to more accurately identify the structure and anomalies in the target region, thereby optimizing and adjusting the imaging process in a targeted manner, and further improving the image quality of photoacoustic imaging.

[0050] Furthermore, this invention determines the regional interference category of the target region based on regional imaging features, accurately identifies the interference characteristics of the target region, and comprehensively quantifies and evaluates the imaging influence of the target region. This allows for targeted adjustment of the laser emission parameters of the probe laser, thereby enhancing the quality of the photoacoustic signal and further improving the image quality and imaging accuracy of photoacoustic imaging.

[0051] Furthermore, by subdividing the target time period, the present invention can capture the rapid dynamic changes of photoacoustic signals, which helps to identify subtle features of the signals. By comparing the signal change characterization values ​​corresponding to each time domain segment, the target time domain segment is determined. Based on the initial photoacoustic signal within the target time domain segment, the photoacoustic signal distribution characteristics of the target area are determined. This can filter noise interference and focus photoacoustic signals with better imaging effects, thereby improving the image quality of photoacoustic imaging.

[0052] Furthermore, this invention determines the image mutation features corresponding to the initial photoacoustic image by inputting the initial photoacoustic image into an image mutation analysis model. The image mutation features reflect the abnormal changes in the initial photoacoustic image. General mutation features indicate that there may be abnormalities in some image regions in the initial photoacoustic image, while abnormal mutation features indicate that the initial photoacoustic image as a whole is abnormal. By determining these features through the image mutation analysis model, data processing efficiency and accuracy can be improved, thereby improving the image quality of photoacoustic imaging.

[0053] Furthermore, the present invention determines the region type of the target region through imaging influence parameters of the target region. By using the imaging influence characterization value corresponding to the imaging influence parameters, the degree of imaging influence of the imaging influence parameters on the target region can be quantitatively evaluated, thereby accurately assessing the imaging characteristics of the target region and adaptively adjusting the imaging process to improve the image quality of photoacoustic imaging.

[0054] Furthermore, this invention determines the imaging adjustment coefficient based on the comparison between the comprehensive imaging index and the preset imaging index, so as to adjust the key laser emission parameters. This enables precise location of imaging problems, quantitative determination of the adjustment coefficient, optimization of laser configuration, and improvement of photoacoustic signal quality, thereby improving the image quality and imaging stability of photoacoustic imaging.

[0055] Furthermore, this invention determines several mutation distribution feature points corresponding to the initial photoacoustic image through image mutation features, which can accurately identify key feature points in the initial photoacoustic image, quantify image features, and determine the areas that need to be compensated by analyzing the positional relationship of mutation distribution feature points, thereby optimizing the image in a targeted manner, effectively reducing distortion and errors in the image, and improving the image quality of photoacoustic imaging.

[0056] Furthermore, this invention determines the regional mutation characterization value based on the regional characteristics of the feature compensation region, which can quantify the regional characteristics of the feature compensation region and provide a basis for subsequent compensation. By determining the image compensation coefficient based on the comparison result between the regional mutation characterization value and the preset mutation characterization value, the feature compensation region can be accurately compensated, avoiding over-compensation or under-compensation. The image compensation coefficient is automatically adjusted according to the regional mutation characterization value of different feature compensation regions, making the image adjustment of photoacoustic imaging more adaptive. Targeted image processing of the feature compensation region can optimize the image details and improve the image quality of photoacoustic imaging. Attached Figure Description

[0057] Figure 1 This is a schematic flowchart of the photoacoustic imaging method based on signal processing according to an embodiment of the present invention;

[0058] Figure 2 This is a flowchart illustrating the process of analyzing the regional imaging features of the target area in an embodiment of the present invention;

[0059] Figure 3 A schematic diagram illustrating the process of determining key laser emission parameters in an embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram of the process for generating an initial photoacoustic image according to an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0062] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0063] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., 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 is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0064] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0065] Please see Figures 1-4 As shown, it is a schematic flowchart of the photoacoustic imaging method based on signal processing according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the process of analyzing the regional imaging features of the target area in an embodiment of the present invention; Figure 3 A schematic diagram illustrating the process of determining key laser emission parameters in an embodiment of the present invention; Figure 4 This is a schematic diagram of the 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, including:

[0066] Step S1: Obtain imaging influence parameters of the target area to analyze the regional imaging characteristics of the target area;

[0067] In practice, the target area can be a part of biological tissue, electronic product components, etc., without specific limitations, as long as photoacoustic imaging can be performed.

[0068] It is understood that imaging influence parameters include, but are not limited to, surface roughness, optical absorption coefficient, optical scattering coefficient, and refractive index. There are no specific limitations on the equipment and methods for obtaining imaging influence parameters. For example, the absorbance of the target area at different wavelengths can be measured using a spectrophotometer, and the optical absorption coefficient can be calculated based on the Lambert-Beer law by combining optical path and concentration information. The scattering intensity of light at different angles can be measured using a light scattering instrument, and the optical scattering coefficient can be calculated using Mie scattering theory or Rayleigh scattering theory. The refractive index can be calculated by measuring the phase change of light waves using a phase-shifting interferometer. The surface roughness can be calculated by measuring the micro-profile of the target area surface using the principle of optical interference through an optical surface profilometer.

[0069] Specifically, in step S1, analyzing the regional imaging features of the target area includes:

[0070] Step S11: Based on the imaging influence parameters of the target area, construct a regional imaging influence model corresponding to the target area;

[0071] Step S12: Based on the regional imaging influence model, perform imaging simulation on the target region to determine the light flux distribution of the target region;

[0072] Step S13: Determine the regional imaging features of the target region based on the light flux distribution of the target region.

[0073] In implementation, appropriate model types can be selected based on the characteristics of the target area and imaging requirements. These could include physics-based optical models (such as radiative transfer equation models) or empirical statistical models. Mathematical tools and software (such as MATLAB and COMSOL Multiphysics) are used to construct the framework of a regional imaging influence model, with imaging influence parameters as input variables and the model's output defined as the luminous flux distribution of the target area. Using the constructed regional imaging influence model, imaging simulations of the target area are performed. Numerical calculation methods (such as the finite element method and the finite difference method) are used to solve the model's equations, obtaining simulation results of light propagation, absorption, and scattering processes within the target area. Information on luminous flux distribution is extracted from the simulation results, the energy distribution of light within the target area is analyzed, and a luminous flux distribution image is constructed. Based on the luminous flux distribution image, regional imaging features of the target area can be extracted, including the peak position of luminous flux, intensity distribution, uniformity, and symmetry.

[0074] This invention constructs a regional imaging influence model based on imaging influence parameters of the target region, which can accurately characterize the photoacoustic imaging influence mechanism of the target region, comprehensively analyze the impact of imaging influence parameters on photoacoustic imaging, and simulate the imaging of the target region through the regional imaging influence model. It can predict the light flux distribution in the target region, thereby determining the regional imaging characteristics of the target region. This helps to more accurately identify the structure and anomalies in the target region, thereby optimizing and adjusting the imaging process in a targeted manner and further improving the image quality of photoacoustic imaging.

[0075] Step S2: Determine key laser emission parameters based on the regional imaging features, and emit a probe laser towards the target region based on the key laser emission parameters;

[0076] Specifically, in step S2, determining key laser emission parameters based on the region imaging features includes:

[0077] Step S21: Determine the regional interference category of the target region based on the regional imaging features, including a strong regional interference category and a weak regional interference category;

[0078] In implementation, the regional imaging features Y1, Y2, ..., Y... j , ..., Y m With preset imaging features E1, E2, ..., E j , ..., E m Determine the imaging alignment value CB, 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 the preset square root determination function, compare the imaging comparison value with the preset imaging comparison value, if the imaging comparison value is greater than the preset imaging comparison value, then the regional interference category of the target area is the weak regional interference category, if the imaging comparison value is less than or equal to the preset imaging comparison value, then the regional interference category of the target area is the strong regional interference category.

[0079] It is understandable that the implementers can set 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 implementers can also set preset imaging comparison values ​​based on the actual situation. Preferably, the preset imaging comparison values ​​are set to a range of 0.7 to 0.8.

[0080] Step S22: Determine the determination method of the key laser emission parameters based on the regional interference category, including a first determination method and a second determination method;

[0081] Among them, for the category of strong regional interference, the determination method for the key laser emission parameters is the first determination method, which determines the regional imaging characterization value corresponding to the target region based on the regional imaging characteristics, and determines the key laser emission parameters based on the regional imaging characterization value and the initial laser reflection parameters.

[0082] For the category of weak regional interference, the determination method for the key laser emission parameters is the second determination method, in which the initial laser reflection parameters are determined as the key laser emission parameters.

[0083] In practice, laser emission parameters include pulse wavelength, pulse energy, pulse width, and emission frequency.

[0084] It is understandable that if the regional interference category is the strong regional interference category, it indicates that the regional characteristics of the target area are relatively obvious and the imaging influence of the target area is relatively large. If the probe 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 parameters can be determined as the key laser emission parameters.

[0085] It is understandable that if the regional interference category is the weak influence category, it indicates that the regional characteristics of the target area are relatively conventional and the imaging influence of the target area is small. The probe laser can be emitted based on the initial laser reflection parameters. Therefore, the initial laser reflection parameters are determined as the key laser emission parameters.

[0086] It is understood that the specific structure of the photoacoustic imaging device is not limited. The photoacoustic imaging device may include a laser, which is used to emit laser to the target area based on key laser emission parameters, and a receiving mechanism, which is used to receive the photoacoustic signal returned by the target area under the excitation of the probe laser after receiving the probe laser in the target area.

[0087] This invention determines the regional interference category of the target region based on regional imaging features, accurately identifies the interference characteristics of the target region, and comprehensively quantifies the imaging influence of the target region. This allows for targeted adjustment of the laser emission parameters of the probe laser, thereby enhancing the quality of the photoacoustic signal and further improving the image quality and imaging accuracy of photoacoustic imaging.

[0088] Step S3: In response to the target area receiving the detection laser, the initial photoacoustic signal of the target area is periodically acquired;

[0089] Step S4: Generate an initial photoacoustic image based on the initial photoacoustic signal within the target time period, and determine the image abrupt change features corresponding to the initial photoacoustic image;

[0090] Specifically, in step S4, generating an initial photoacoustic image based on the initial photoacoustic signal within the target time period includes:

[0091] Step S41: Divide the target time period into several time-domain segments, and determine the signal change characterization value corresponding to each time-domain segment based on the initial photoacoustic signal in each time-domain segment.

[0092] Step S42: Determine the target time domain segment based on the signal change characterization value corresponding to each of the time domain segments;

[0093] Step S43: Determine the photoacoustic signal distribution characteristics of the target region based on the initial photoacoustic signal within the target time domain segment;

[0094] Step S44: Generate an initial photoacoustic image of the target region based on the photoacoustic signal distribution characteristics of the target region.

[0095] In practice, the number of target time periods can be determined based on the emission frequency of the probe laser. Preferably, 2 to 3 probe lasers are emitted in each time sub-segment, that is, 2 to 3 initial photoacoustic signals are acquired in each time sub-segment.

[0096] Understandably, signal preprocessing of the initial photoacoustic signal includes filtering and noise reduction, as well as signal normalization. Filtering and noise reduction 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 the range [0,1]. This is existing technology and will not be elaborated further. Based on the preprocessed initial photoacoustic signal, the signal characteristics of each initial photoacoustic signal are calculated, including the characteristic signal intensity (determined based on the maximum amplitude point of the preprocessed initial photoacoustic signal), the characteristic signal dominant frequency (the signal dominant frequency corresponds to the frequency component with the largest amplitude in the FFT result after performing a Fast Fourier Transform (FFT) on the preprocessed initial photoacoustic signal), and the characteristic signal rate of change (the rate of change of signal intensity between adjacent initial photoacoustic signals), etc.

[0097] It is understandable that the signal characteristics of the initial photoacoustic signal within any time domain sub-segment are 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 Let g represent the signal characteristics of the i-th initial photoacoustic signal within the time-domain sub-segment, where g = 1, 2, ..., h, and h is the number of signal characteristics. i,g Let g be the signal feature of the i-th initial photoacoustic signal within the time domain segment. Then, the signal change characterization value S corresponding to this time domain segment is S = (∑ h g=1 (∑) n i=1 (YJ i -avg(YJ)) 2 ) / n) / h),avg() is the preset average value determination function.

[0098] Understandably, the signal change characteristics of each time domain segment are sorted, and the time domain segment corresponding to the smallest signal change characteristic value is determined as the target time domain segment. The initial photoacoustic signal within the target time domain segment is averaged to obtain the key photoacoustic signal. The spatial distribution of the key photoacoustic signal can be reconstructed using a beamforming algorithm, and feature extraction is performed to obtain the photoacoustic signal distribution characteristics of the target area, including sound pressure distribution. An initial photoacoustic image of the target area is then generated based on an image reconstruction algorithm or model. The image reconstruction algorithm or model is not specifically limited; for example, back projection algorithms, iterative reconstruction algorithms, neural network models, etc.

[0099] This invention, by subdividing the target time period, can capture the rapid dynamic changes of photoacoustic signals, which helps to identify subtle features of the signals. By comparing the signal change characterization values ​​corresponding to each time domain segment, the target time domain segment is determined. Based on the initial photoacoustic signal within the target time domain segment, the photoacoustic signal distribution characteristics of the target area are determined. This can filter noise interference and focus photoacoustic signals with better imaging effects, thereby improving the image quality of photoacoustic imaging.

[0100] Specifically, in step S4, determining the image abrupt change features corresponding to the initial photoacoustic image includes:

[0101] The initial photoacoustic image is input into the image mutation analysis model to obtain the image mutation features corresponding to the initial photoacoustic image output by the image mutation analysis model. The image mutation features include general mutation features and abnormal mutation features.

[0102] In a specific embodiment, image mutation features can be marked based on photoacoustic images in historical data. Mutation features are those whose edge length, curvature, texture change gradient, etc., all exceed preset standards. Based on the degree to which mutation features exceed preset standards, mutation features are divided into general mutation features (less than 30% exceeding preset standards) and abnormal mutation features (more than 30% exceeding preset standards) to train neural network models or deep learning models and obtain image mutation analysis models. Practitioners can set preset standards based on actual conditions.

[0103] This invention determines the image mutation features corresponding to the initial photoacoustic image by inputting the initial photoacoustic image into an image mutation analysis model. The image mutation features reflect the abnormal changes in the initial photoacoustic image. General mutation features indicate that there may be abnormalities in some image regions in the initial photoacoustic image, while abnormal mutation features indicate that the initial photoacoustic image as a whole is abnormal. By determining these features through the image mutation analysis model, data processing efficiency and accuracy can be improved, thereby improving the image quality of photoacoustic imaging.

[0104] Step S5: Determine the imaging adjustment method corresponding to the target region based on the image mutation features, including a first imaging adjustment method and a second imaging adjustment method;

[0105] The first imaging adjustment method involves determining the comprehensive imaging index corresponding to the target area based on the imaging influence parameters, and adjusting the key laser emission parameters based on the comprehensive imaging index.

[0106] The second imaging adjustment method involves determining a feature compensation region based on the image abrupt change features to adjust the initial photoacoustic image.

[0107] Specifically, in step S5, determining the imaging adjustment method corresponding to the target region based on the image abrupt change features includes,

[0108] If the image mutation feature is an abnormal mutation feature, then the imaging adjustment method corresponding to the target region is the first imaging adjustment method;

[0109] If the image mutation feature is a general mutation feature, then the imaging adjustment method corresponding to the target region is the second imaging adjustment method.

[0110] Specifically, in step S5, determining the comprehensive imaging index corresponding to the target area based on the imaging influence parameters includes:

[0111] Step S511: Determine the region type of the target region based on the imaging influence parameters;

[0112] Step S512: Determine the imaging influence characterization value corresponding to the imaging influence parameter based on the region type of the target region;

[0113] Step S513: Determine the comprehensive imaging index corresponding to the target area based on the imaging influence characterization value.

[0114] In implementation, the target area can be divided into different region types based on different imaging influence parameters in historical data. Based on the average imaging influence parameter value corresponding to each region type, a corresponding regional imaging influence parameter is set for each region type. The imaging influence parameter of the target region is then compared with the corresponding regional imaging influence parameter to obtain the imaging influence characterization value. For example, the imaging influence parameters G1, G2, ..., G of the target region. a , ..., G b The region imaging influence parameters H1, H2, ..., H corresponding to the target region a H b The imaging influence characterization value CG = abs ((∑ b a=1 (G a -Ha ) / H a ) / b), where a=1,2,…,b; b is the number of imaging influence parameters, and abs() is a preset absolute value determination function.

[0115] Understandably, if the imaging influence characterization value is less than the preset influence characterization value, it indicates that the degree of influence of the imaging influence parameter on the target area is 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 degree of influence of the imaging influence parameter on the target area is not within a controllable range. If the imaging influence characterization value is used as the comprehensive imaging index for subsequent adjustments, the adjustment range will be too large, leading to uncontrollable risks in the imaging process, reducing the image quality of photoacoustic imaging, and reducing 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. Practitioners can determine the preset influence characterization value based on the actual situation or the average value 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 range of 0.6 to 0.7.

[0116] This invention determines the region type of the target region by using imaging influence parameters of the target region. By using the imaging influence characterization value corresponding to the imaging influence parameters, the degree of influence of the imaging influence parameters on the target region can be quantitatively evaluated, thereby accurately assessing the imaging characteristics of the target region and adaptively adjusting the imaging process to improve the image quality of photoacoustic imaging.

[0117] Specifically, in step S5, adjusting the key laser emission parameters based on the comprehensive imaging index includes:

[0118] Step S521: Determine the imaging adjustment coefficient based on the comparison result between the comprehensive imaging index and the preset imaging index;

[0119] Step S522: Determine the target laser emission parameters based on the imaging adjustment coefficient and the key laser emission parameters.

[0120] In practice, 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 by multiplying the imaging adjustment coefficient with the key laser emission parameter.

[0121] It is understandable that the implementers can determine the preset imaging index based on the minimum value of the comprehensive imaging index that has passed the qualification test in the actual situation or historical data. Preferably, the preset imaging index is set to a range of 0.4 to 0.6.

[0122] This invention determines the imaging adjustment coefficient based on the comparison between the comprehensive imaging index and the preset imaging index, so as to adjust the key laser emission parameters. This allows for precise location of imaging problems, quantitative determination of the adjustment coefficient, optimization of laser configuration, and improvement of photoacoustic signal quality, thereby enhancing the image quality and imaging stability of photoacoustic imaging.

[0123] Specifically, in step S5, determining the feature compensation region based on the image mutation features includes:

[0124] Step S531: Determine several mutation distribution feature points corresponding to the initial photoacoustic image based on the image mutation features;

[0125] Step S532: Determine the feature compensation region based on the positional relationship of each mutation distribution feature point.

[0126] In implementation, practitioners can set corresponding mutation standards for image mutation features. For general mutation features, the general mutation standard is set to exceed the preset standard by 20%. The target area is divided into grids, and it is determined whether the image mutation features corresponding to each grid area meet the general mutation standard. If they do (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 anomalous mutation features, the anomalous mutation standard is set to exceed the preset standard by 50%. The target area is divided into grids, and it is determined whether the image mutation features corresponding to each grid area meet the anomalous mutation standard. If they do (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 smallest closed area enclosed by all mutation distribution feature points is determined as the feature compensation area.

[0127] This invention identifies several mutation distribution feature points corresponding to the initial photoacoustic image by using image mutation features. It can accurately identify key feature points in the initial photoacoustic image, quantify image features, and determine the areas that need compensation by analyzing the positional relationship of mutation distribution feature points. This allows for targeted image optimization, effectively reducing image distortion and errors, and improving the image quality of photoacoustic imaging.

[0128] Specifically, in step S5, adjusting the initial photoacoustic image includes:

[0129] Step S541: Determine the regional mutation characterization value based on the regional characteristics of the feature compensation region;

[0130] Step S542: Determine the image compensation coefficient based on the comparison result between the regional mutation characterization value and the preset mutation characterization value;

[0131] Step S543: Perform image processing on the feature compensation region based on the image compensation coefficient to adjust the initial photoacoustic image.

[0132] In implementation, region features include the mean, variance, skewness, and kurtosis of pixel values ​​within the feature compensation region (mean represents the average gray value of the region, variance reflects the distribution range of gray values, and skewness and kurtosis describe the shape of the gray value distribution), and the gradient intensity of the edges of the feature compensation region (gradient intensity represents the prominence of the edges). The region features are compared with preset region features to determine the region abrupt change characterization values. For example, region features R1, R2, ..., R... p , ..., R q With preset region 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, where q is the number of regional features. In practice, the implementers can set preset regional features based on the photoacoustic images of photoacoustic imaging that have passed the qualification test in historical data.

[0133] It is understandable that the ratio of the regional mutation characterization value to the preset mutation characterization value is determined as the image compensation coefficient. 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, so as to adjust the initial photoacoustic image.

[0134] This invention determines the regional mutation characterization value based on the regional characteristics of the feature compensation region, which can quantify the regional characteristics of the feature compensation region 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 region can be accurately compensated, avoiding over-compensation or under-compensation. The image compensation coefficient is automatically adjusted according to the regional mutation characterization value of different feature compensation regions, making the image adjustment of photoacoustic imaging more adaptive. Targeted image processing of the feature compensation region can optimize the image details and improve the image quality of photoacoustic imaging.

[0135] This invention analyzes the regional imaging characteristics of a target area by acquiring imaging influence parameters and determines key laser emission parameters based on these characteristics. This allows for adaptation to the specific features of the target area, generating clearer and more accurate photoacoustic signals and improving imaging precision. Targeted selection of key laser emission parameters for emitting probe lasers towards the target area enhances the intensity of the photoacoustic signal, making the detected signal more prominent and improving the signal-to-noise ratio, thereby increasing imaging precision. Periodically acquiring initial photoacoustic signals ensures their stability. Generating an initial photoacoustic image based on the initial photoacoustic signal within the target time period allows for preliminary identification of the photoacoustic information in the target area. Determining the corresponding imaging adjustment method for the target area based on the image abrupt changes in the initial photoacoustic image enables adaptive adjustment of the imaging process, thus improving the image quality of photoacoustic imaging.

[0136] The technical solution of the present invention has been described above with reference to 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 can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A photoacoustic imaging method based on signal processing, characterized in that, include: Obtain imaging impact parameters of the target area to analyze the regional imaging characteristics of the target area; Based on the imaging features of the region, key laser emission parameters are determined, and a probe laser is emitted toward the target region based on the key laser emission parameters; In response to the target area receiving the detection laser, the initial photoacoustic signal of the target area is periodically acquired; An initial photoacoustic image is generated based on the initial photoacoustic signal within the target time period, and the image abrupt change features corresponding to the initial photoacoustic image are determined. Based on the image mutation features, the imaging adjustment method corresponding to the target region is determined, including a first imaging adjustment method and a second imaging adjustment method; Specifically, for the first imaging adjustment method, a comprehensive imaging index corresponding to the target area is determined based on the imaging influence parameters, and the key laser emission parameters are adjusted based on the comprehensive imaging index. For the second imaging adjustment method, a feature compensation region is determined based on the image mutation features in order 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 features of the target region includes: Based on the imaging impact parameters of the target area, a regional imaging impact model corresponding to the target area is constructed. Based on the regional imaging influence model, an imaging simulation is performed on the target region to determine the light flux distribution in the target region. The regional imaging characteristics of the target area are determined based on the light flux distribution of the target area. The imaging-affecting 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 features of the region includes: Based on the regional imaging features, the regional interference category of the target region is determined, including the regional interference strong influence category and the regional interference weak influence category. The determination method for the key laser emission parameters based on the regional interference category includes a first determination method and a second determination method; Among them, for the category of strong regional interference, the determination method for the key laser emission parameters is the first determination method, which determines the regional imaging characterization value corresponding to the target region based on the regional imaging characteristics, and determines the key laser emission parameters based on the regional imaging characterization value and the initial laser reflection parameters. For the category of weak regional interference, the determination method for the key laser emission parameters is the second determination method, in which the initial laser reflection parameters are determined as the key laser emission parameters.

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: The target time period is divided into several time-domain segments, and the signal change characterization value corresponding to each time-domain segment is determined based on the initial photoacoustic signal in each time-domain segment. The target time domain segment is determined based on the signal change characterization value corresponding to each of the aforementioned time domain segments; The photoacoustic signal distribution characteristics of the target region are determined based on the initial photoacoustic signal within the target time domain sub-segment. An initial photoacoustic image of the target region is generated based on the photoacoustic signal distribution characteristics of the target region.

5. The photoacoustic imaging method based on signal processing according to claim 4, characterized in that, The process of determining the image abrupt change features corresponding to the initial photoacoustic image includes: The initial photoacoustic image is input into the image mutation analysis model to obtain the image mutation features corresponding to the initial photoacoustic image output by the image mutation analysis model. 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 region based on the image mutation features includes: If the image mutation feature is an abnormal mutation feature, then the imaging adjustment method corresponding to the target region is the first imaging adjustment method; If the image mutation feature is a general mutation feature, then the imaging adjustment method corresponding to the target region is the second imaging adjustment method.

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 influence parameters includes: The region type of the target region is determined based on the imaging influence parameters; Based on the region type, determine the imaging impact characterization value corresponding to the imaging impact parameter; The comprehensive imaging index corresponding to the target area is determined based on the imaging influence 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: The imaging adjustment coefficient is determined based on the comparison result between the comprehensive imaging index and the preset imaging index. The 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 region based on the image mutation features includes: Based on the image mutation features, several mutation distribution feature points corresponding to the initial photoacoustic image are determined; The feature compensation region is determined based on the positional relationship of each mutation distribution feature point.

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: The regional mutation characterization value is determined based on the regional characteristics of the feature compensation region. The image compensation coefficient is determined based on the comparison between the regional mutation characterization value and the preset mutation characterization value. Image processing is performed on the feature compensation region based on the image compensation coefficient to adjust the initial photoacoustic image.

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