A method, system, device and medium for accurate measurement of light absorption coefficient based on Monte Carlo model

Abstract

The application discloses a kind of based on Monte Carlo model's light absorption coefficient accurate measurement method, system, equipment and medium, wherein the method includes constructing multimodal microscopic imaging system;Obtain the experimental photoacoustic data of multimodal microscopic imaging system, and the actual absorption light energy density is obtained by solving photoacoustic wave equation using finite element method;Given a hypothetical absorption coefficient, simulated calculation is carried out using Monte Carlo model to obtain simulated absorption light energy density;The deviation between actual absorption light energy density and simulated absorption light energy density is minimized by iterative least square method optimization, and the absolute light absorption coefficient is obtained. A multimodal microscopic imaging system is constructed by a fluorescence imaging microscope and a photoacoustic microscope, which combines the accuracy of FEM in solving photoacoustic wave equation and the rigorous modeling of MC in photon migration in biological tissues, and realizes the comprehensive quantitative reconstruction of optical absorption characteristics.

Description

Technical Field

[0001] This invention relates to the field of biomedical imaging technology, specifically to a method, system, device, and medium for the precise measurement of optical absorption coefficient based on the Monte Carlo model, used for quantitative mapping of optical absorption coefficient in biological tissues and precise lesion tracking in disease models. Background Technology

[0002] Biomedical imaging is based on medical imaging technology and uses techniques such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and magnetic particle imaging (MPI) to achieve multi-scale observation of organisms from molecules to organs.

[0003] Existing biomedical imaging technologies have many limitations: MRI and CT technologies lack molecular specificity; PET (positron emission tomography) and fMRI (functional magnetic resonance imaging) technologies have low resolution. Optical microscopes have shallow imaging depth; Traditional photoacoustic microscopy techniques cannot directly quantify the absorption coefficient.

[0004] Although some studies have attempted to combine photoacoustic imaging with fluorescence imaging, there is still a lack of absolute quantitative ability for absorption coefficients, and most systems only provide structural or semi-quantitative information.

[0005] Therefore, it is evident that how to avoid the shortcomings of existing technologies in accurately quantifying the light absorption coefficient and thus failing to provide comprehensive and complete diagnostic evidence is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a method, system, device, and medium for accurate measurement of optical absorption coefficient based on the Monte Carlo model. By combining the accuracy of the finite element method in solving the photoacoustic wave equation with the rigidity of Monte Carlo modeling of photon migration in biological tissues, a comprehensive quantitative reconstruction of optical absorption characteristics can be achieved.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for accurately measuring the light absorption coefficient based on the Monte Carlo model, the key of which includes the following steps: Step 1: Construct a multimodal microscopic imaging system; Step 2: Obtain experimental photoacoustic data from the multimodal microscopic imaging system, and use the finite element method to solve the photoacoustic wave equation to obtain the actual absorbed light energy density. Step 3: Given an assumed absorption coefficient, use the Monte Carlo model to simulate and calculate the simulated absorbed light energy density; Step 4: Optimize using iterative least squares method to minimize the deviation between the actual absorbed light energy density and the simulated absorbed light energy density, and obtain the absolute light absorption coefficient.

[0008] Furthermore, the multimodal microscopic imaging system in step 1 includes a photoacoustic microscopic imaging section and a fluorescence microscopic imaging section, wherein the light signal input and output terminals of the fluorescence microscopic imaging section are directly facing the eyepiece of the photoacoustic microscopic imaging section, and the fluorescence microscopic imaging section scans the sample surface through a light-transmitting hole opened in the center of the ultrasonic transducer in the photoacoustic microscopic imaging section.

[0009] Furthermore, the fluorescence microscopy imaging section includes a fluorescence signal generation and receiving unit and a filter, a first convex lens, a vacuum, a second convex lens, an optical fiber, and a galvanometer scanner arranged sequentially in the optical path, wherein the filter is positioned close to the fluorescence signal generation and receiving unit, and the galvanometer scanner is positioned directly opposite the eyepiece of the photoacoustic microscopy imaging section.

[0010] Furthermore, the photoacoustic microscopy imaging section includes an eyepiece, an objective lens, and a sample placement mechanism arranged sequentially from top to bottom. The ultrasonic transducer is disposed within the sample placement mechanism and is located directly below the objective lens.

[0011] Furthermore, the sample placement mechanism includes an objective stage and a support. A lifting platform is provided on the objective stage. The support spans both sides of the lifting platform and is fixed at its bottom to the objective stage. A water tank is provided on the support, and the sensing end of the ultrasonic transducer is located in the water tank.

[0012] Furthermore, the formula for calculating the actual absorbed light energy density by solving the photoacoustic wave equation using the finite element method in step 2 is as follows:

[0013] in, Represents a pressure wave field. Indicates the speed of sound. Indicates the coefficient of thermal expansion. Indicates specific heat capacity. This represents the actual absorbed light energy density.

[0014] Furthermore, the formula for calculating the deviation between the actual absorbed light energy density and the simulated absorbed light energy density in step 4 is as follows:

[0015] in, This represents the actual absorbed light energy density. This represents the simulated absorbed light energy density, and , Represents luminous flux. This represents the light absorption coefficient.

[0016] In a second aspect, the present invention provides a precise measurement system for light absorption coefficient based on the Monte Carlo model, for implementing the measurement method described in the first aspect, comprising: The imaging system building module is used to construct multimodal microscopic imaging systems; The data processing module is used to acquire experimental photoacoustic data from the multimodal microscopic imaging system and to obtain the actual absorbed light energy density by solving the photoacoustic wave equation using the finite element method. It is also used to obtain the simulated absorbed light energy density by simulating and calculating using the Monte Carlo model, given an assumed absorption coefficient. It is also used to optimize by iterative least squares method to minimize the deviation between the actual absorbed light energy density and the simulated absorbed light energy density, and obtain the absolute light absorption coefficient.

[0017] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for accurate measurement of light absorption coefficient based on the Monte Carlo model as described in the first aspect.

[0018] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for accurately measuring the light absorption coefficient based on the Monte Carlo model as described in the first aspect.

[0019] The significant effects of this invention are: This invention first constructs a multimodal microscopic imaging system by integrating a fluorescence imaging microscope for molecular labeling and lesion localization with a photoacoustic microscope for depth-resolution imaging of vascular structure and function. Then, based on the experimental data of the multimodal microscopic imaging system, the absolute absorption coefficient is reconstructed from single-wavelength photoacoustic data using the MC-FEM algorithm. This combines the accuracy of FEM in solving the photoacoustic equation with the rigorous modeling of photon migration in biological tissues by MC, thus achieving a comprehensive and quantitative reconstruction of optical absorption characteristics. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method described in this invention; Figure 2 This is a schematic diagram of the structure of a multimodal microscopic imaging system; Figure 3This is a flowchart of optical absorption coefficient reconstruction; Figure 4 This is a schematic diagram of the simulation results of the vascular phantom; Figure 5 This is a schematic diagram of the experimental results of a simulated prosthesis. Figure 6 Bright-field images showing anatomical changes before infection and 1 hour / 12 hours after infection; Figure 7 This is a schematic diagram of the system structure; Figure 8 This is a schematic diagram of the structure of the electronic device. Detailed Implementation

[0021] The specific embodiments and working principles of the present invention will be further described in detail below with reference to the accompanying drawings.

[0022] Example 1: like Figure 1 As shown, this embodiment of the invention provides a method for accurately measuring the light absorption coefficient based on the Monte Carlo model. The specific steps are as follows: Step 1: Construct a multimodal microscopic imaging system; In this example, the multimodal microscopic imaging system is as follows: Figure 2 As shown, Figure 2 In this diagram, NDF represents a neutral density filter; CL1 and CL2 represent convex lenses; PH represents a pinhole; OF represents an optical fiber; M1 and M2 represent motors; GS represents a galvanometer scanner; EP represents an eyepiece; OB represents an objective lens; UT represents an ultrasonic transducer; LP represents a lifting platform; TF represents a thin film; WT represents a water tank; and OT represents an objective stage.

[0023] In this system, we improved upon an existing fluorescence microscope system (Eclipse Ni-U, Nikon Corporation). The system comprises a base, stage, objectives, eyepieces, and a CCD camera. An optical resolution photoacoustic microscopy imaging system is built upon this fluorescence system, using a pulsed laser with a wavelength of 532 nm, a pulse width of 1 nanosecond, and a repetition rate up to 5 kHz, focused into a 30 μm spot diameter to irradiate tissue. The beam first passes through an optical subsystem (NDF: neutral density filter; CL1 and CL2: convex lenses; PH: pinhole; OF: optical fiber), and is then coupled to a single-mode optical fiber. Next, the laser beam transmitted through the fiber is redirected by a galvanometer scanner (GS). After passing through the eyepiece (EP) and objective (OB) systems of the fluorescence microscope, the beam is finally focused onto the stage. Two-dimensional motors (M1 and M2) rotate the stage to scan the tissue. The ultrasonic transducer (UT) is carefully designed with a central aperture of 0.5 mm, a bandwidth of 80%, a working center frequency of 50 MHz, and a focal length of 10 mm. The sample is placed on the motorized lifting platform (LP), and the imaging area is firmly coupled to the thin film (TF) located at the bottom of the water tank (WT). The entire water tank system and objective stage (OT) can be scanned in xy under the coordinate control of motors M1 and M2. Currently, scanning a 9 mm × 9 mm area using this motorized scanning method takes 15-20 minutes. The photoacoustic signal is amplified by an amplifier (5073R, OLYMPUS, USA) and then collected on a computer to generate optically resolved photoacoustic microscopy (PAM) images. For fluorescence imaging, we need to place the CCD below the vibrating mirror (GS) and above the microscope objective, and remove the water tank (WT), ultrasonic probe (UT), and lifting platform (LP).

[0024] That is, the multimodal microscopic imaging system described in this example includes a photoacoustic microscopic imaging section and a fluorescence microscopic imaging section. The light signal input / output terminal of the fluorescence microscopic imaging section is directly opposite the eyepiece of the photoacoustic microscopic imaging section. The fluorescence microscopic imaging section scans the sample surface through a light-transmitting aperture located at the center of the ultrasonic transducer within the photoacoustic microscopic imaging section. Wherein: The fluorescence microscopy imaging section includes a fluorescence signal generation and receiving unit and a filter, a first convex lens, a vacuum, a second convex lens, an optical fiber, and a galvanometer scanner arranged sequentially in the optical path. The filter is positioned close to the fluorescence signal generation and receiving unit, and the galvanometer scanner is positioned directly opposite the eyepiece of the photoacoustic microscopy imaging section. The photoacoustic microscopy imaging section includes an eyepiece, an objective lens, and a sample placement mechanism arranged sequentially from top to bottom. The ultrasonic transducer is located within the sample placement mechanism and is positioned directly below the objective lens. The sample placement mechanism includes an objective stage and a support. A lifting platform is provided on the objective stage. The support spans both sides of the lifting platform and is fixed at its bottom to the objective stage. A water tank is provided on the support, and the sensing end of the ultrasonic transducer is located within the water tank.

[0025] Step 2: Obtain experimental photoacoustic data from the multimodal microscopic imaging system, and use the finite element method to solve the photoacoustic wave equation to obtain the actual absorbed light energy density. In this example, the scheme for reconstructing the optical absorption coefficient integrates two complementary computational methods: finite element modeling (FEM) for simulating acoustic wave propagation, and Monte Carlo (MC) optical transport simulation implemented using the MCML software package (available at https: / / omlc.org / software / mc / mcml / ). This dual-method framework combines the precision of FEM in solving the photoacoustic equations with the rigorous modeling of photon migration in biological tissues by MC, thus enabling a comprehensive and quantitative reconstruction of optical absorption properties. The FEM component accurately captures acoustic wave dynamics, while the MC simulation calculates the optical flux distribution, together forming a complete physical model of the photoacoustic microscopy process. Specifically: Reconstruction of actual absorbed light energy density: The forward problem is governed by the photoacoustic equation under three key assumptions: (1) acoustic homogeneity (constant sound velocity), (2) thermal constraint (negligible thermal diffusion during laser excitation), and (3) stress constraint (instantaneous pressure accumulation). The governing equation is expressed as follows: (1) in, Represents a pressure wave field. This indicates the speed of sound (1540 m / s in tissue). This represents the coefficient of thermal expansion (3.67 × 10⁻⁶). -4 K -1 )), This represents the specific heat capacity ((3.815 J / g·K)). It represents the absorbed light energy density, which is the light absorption coefficient. With luminous flux The product ( The primary objective of the initial reconstruction phase is to obtain the normalized absorbed light energy density from the photoacoustic data acquired in the experiment. Simultaneously, we performed Monte Carlo (MC) simulations to calculate and determine the absorbed light energy density within the tissue. This provides a key input for the subsequent quantitative reconstruction of the light absorption coefficient.

[0026] Step 3: Provide an initial guess of the assumed absorption coefficient. and step length The simulated absorbed light energy density was obtained by using the Monte Carlo model. ; Step 4: Optimize using iterative least squares method to minimize the deviation between the actual absorbed light energy density and the simulated absorbed light energy density, and obtain the absolute light absorption coefficient.

[0027] In this example, the formula for calculating the deviation between the actual absorbed light energy density and the simulated absorbed light energy density is as follows:

[0028] in, This represents the actual absorbed light energy density. This represents the simulated absorbed light energy density, and , Represents luminous flux. This represents the light absorption coefficient.

[0029] Calculate and compare the deviation between the actual absorbed light energy density and the simulated absorbed light energy density. If the deviation... If the value is small enough, stop iteratively solving; otherwise, proceed through... renew And repeat the iterative calculation according to the previous procedure ( j (Number of iterations), the iteration process is as follows: Figure 3 As shown.

[0030] Figure 4 The simulation results are shown in Figures (a)-(c), which illustrate the embedded background medium through three orthogonal cross-sections. =1cm -1 , =100cm -1 Constructing cross-sectional images of blood vessels in () =230.5cm -1 , =100cm -1 (Data from MCML software package): (a) xz plane when y=0, (b) yz plane when x=0, and (c) xy plane when z=0.1cm. Figure (d) shows the reconstruction absorption coefficient plot generated by our Monte Carlo-finite element hybrid method at 532nm wavelength (mean = 238.4cm). -1 Ten regions were sampled from the vascular structure under each condition and displayed on the xy plane. At this wavelength, the theoretical absorption coefficient of the blood vessel is 230.5 cm⁻¹. -1(Quantitative non-hemoglobin absorbers), and the reconstructed value deviates from the theoretical absorption coefficient by 3.4%.

[0031] In the phantom experiment, an absorber with a diameter of 1 mm was embedded in a solid cylindrical phantom with a diameter of 1 cm, composed of Intralipid (scattering agent) and Indian ink (absorbent), and then cured with 1-2% agar powder. The experimental results are as follows... Figure 5 As shown, where Figure 5 (a) A photograph of the Indian ink target (Δ=0.4 cm−1) embedded in the prosthesis. Figure 5 (b) is the reconstructed absorption coefficient (μ) distribution image. The absorption coefficient of the background phantom... It is 0.02 cm -1 Reduced scattering coefficient 1 cm -1 The absorption coefficient of the target body It is 0.4 cm -1 Reduced scattering coefficient 2 cm -1 ,like Figure 5 As shown in a. Figure 5 b shows the reconstructed absorption coefficient. The distribution has a mean of 0.395 cm. -1 This is different from the actual value of 0.4 cm. -1 The results showed a high degree of consistency, with a deviation of only 1.25%. These results preliminarily validate the accuracy and feasibility of this method in phantom experiments.

[0032] Subsequently, in vivo validation studies using the aforementioned multimodal microscopy system quantitatively monitored the progression of ear inflammation in mice through simultaneous photoacoustic microscopy and fluorescence imaging. Figure 6 As shown, Figure 6 (a) Bright-field images show anatomical changes before infection and 1 hour / 12 hours after infection. Figure 6 (b,c) Spatiotemporal dynamics of infection revealed by (b) photoacoustic microscopy (PAM, vascular structures) and (c) fluorescence imaging (molecular activity). Figure 6 (d) The superposition of PAM (red) and fluorescence (green) signals demonstrates the spatial correlation between vascular remodeling (PAM) and molecular inflammation (fluorescence). The white dashed lines indicate the inflammatory areas. Figure 6 (e) Box plots compared the optical absorption coefficients of normal and inflamed blood vessels (10 regions per condition from one mouse). Boxes represent the interquartile range (IQR) and median line, extended to 1.5 × IQR, with points representing individual measurements. Box plots compared measurements from 10 independent regions of interest (ROIs) per condition (200 × 200 µm per region).2 Spacing ≥1 mm. Spatial separation ensured statistical independence beyond the hemodynamically relevant length. Two-tailed t-test showed p < 0.001 (Cohen's d = 3.3). Bacteria expressing green fluorescent protein (GFP) (10⁸ CFU / mL, 50 μL) were injected into the mouse ear, and imaging was performed at baseline, 1 hour, and 12 hours post-injection. Figure 6 a-6c demonstrates the system's ability to capture dynamic pathophysiological changes: photoacoustic microscopy (PAM) shows rapid vascular aggregation at the site of infection within 1 hour (manifested as increased capillary density), while fluorescence imaging shows gradual spread of inflammation, with the affected area increasing from 0.3 ± 0.05 mm after 1 hour. 2 Increased to 1.5 ± 0.1 mm after 12 hours. 2 (Increased by 5.0 times). Reconstructed optical absorption coefficient at 532 nm wavelength. Quantitative analysis showed a statistically significant increase in infected blood vessels compared to normal blood vessels. Figure 6 The multimodal overlay plot in image d clearly demonstrates the spatial co-localization between vascular structures (PAM, red signal) and inflammatory markers (fluorescence, green signal), with the white dashed line representing the inflammatory boundary. Quantitative analysis shows the optical absorption coefficient of the infection site. Over time, the temperature increased from 248 cm one hour after infection. -1 (3.8% increase from baseline) increased to 258 cm 12 hours post-infection. -1 (Increased by 7.9%), while normal blood vessels (baseline) = 239 cm -1 ; Figure 6 e) remains unchanged. The measured baseline value is consistent with the theoretical value of 230.5 cm for the vessel at this wavelength predicted by MCML simulation. -1 (Bias 3.7%) Highly consistent. Statistical comparisons of the distribution between normal and inflamed vessels (10 regions from one mouse for each condition) showed significant differences. Figure 6 e). Box plots show that the boxes span the interquartile range (IQR) with a median line extending to 1.5 × IQR, and a single data point. The large effect size (Cohen's d = 3.3, two-tailed t-test p < 0.001) confirms a significant difference in optical absorption properties between normal and inflamed tissues.

[0033] Observed in inflammatory and tumor blood vessels The significant increase is primarily attributed to hemodynamic changes such as vasodilation and congestion. However, alterations in vessel wall composition, including potential microcalcifications detected by specialized pulse contrast-enhanced microvascular imaging (PAM), may also be a factor in chronic pathology and warrant further investigation using spectral imaging.

[0034] 7.9% of infected sites were observed. The increase (258 vs. 239 cm) -1 (p<0.001, Cohen's d=3.3) and a 5.5% increase in tumor vascular system (252 vs. 237 cm). -1 (p<0.001, Cohen's d=3.2), indicating that the system is sensitive to pathophysiological changes. Although these results are from only one animal in each model, they provide proof of concept for the method to resolve subtle hemodynamic changes.

[0035] Example 2: like Figure 7 As shown, this embodiment of the invention provides a precise measurement system for light absorption coefficient based on the Monte Carlo model, used to implement the measurement method described in Embodiment 1, including: The imaging system building module is used to construct multimodal microscopic imaging systems; The data processing module is used to acquire experimental photoacoustic data from the multimodal microscopic imaging system and to obtain the actual absorbed light energy density by solving the photoacoustic wave equation using the finite element method. It is also used to obtain the simulated absorbed light energy density by simulating and calculating using the Monte Carlo model, given an assumed absorption coefficient. It is also used to optimize by iterative least squares method to minimize the deviation between the actual absorbed light energy density and the simulated absorbed light energy density, and obtain the absolute light absorption coefficient.

[0036] Example 3: like Figure 8 As shown, this embodiment of the invention provides an electronic device, which may include a processor and a memory, wherein the processor and the memory communicate with each other via a communication bus. The processor can invoke logical instructions in the memory to execute the method described in Embodiment 1.

[0037] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0038] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the measurement method provided in Embodiment 1 above.

[0039] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0040] In summary, this invention first constructs a multimodal microscopic imaging system by integrating a fluorescence imaging microscope for molecular labeling and lesion localization with a photoacoustic microscope for depth-resolution imaging of vascular structure and function. Then, based on the experimental data of the multimodal microscopic imaging system, the absolute absorption coefficient is reconstructed from single-wavelength photoacoustic data using the MC-FEM algorithm. This combines the accuracy of FEM in solving the photoacoustic equation with the rigorous modeling of photon migration in biological tissues by MC, thus achieving a comprehensive and quantitative reconstruction of optical absorption characteristics.

[0041] The technical solution provided by this invention has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make several improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. A method for accurately measuring the light absorption coefficient based on the Monte Carlo model, characterized in that, Includes the following steps: Step 1: Construct a multimodal microscopic imaging system; Step 2: Obtain experimental photoacoustic data from the multimodal microscopic imaging system, and use the finite element method to solve the photoacoustic wave equation to obtain the actual absorbed light energy density. Step 3: Given an assumed absorption coefficient, use the Monte Carlo model to simulate and calculate the simulated absorbed light energy density; Step 4: Optimize using iterative least squares method to minimize the deviation between the actual absorbed light energy density and the simulated absorbed light energy density, and obtain the absolute light absorption coefficient.

2. The method for accurately measuring the light absorption coefficient based on the Monte Carlo model according to claim 1, characterized in that: The multimodal microscopy imaging system described in step 1 includes a photoacoustic microscopy imaging section and a fluorescence microscopy imaging section. The light signal input and output terminals of the fluorescence microscopy imaging section are directly facing the eyepiece of the photoacoustic microscopy imaging section. The fluorescence microscopy imaging section scans the sample surface through a light-transmitting hole opened in the center of the ultrasonic transducer within the photoacoustic microscopy imaging section.

3. The method for accurately measuring the light absorption coefficient based on the Monte Carlo model according to claim 2, characterized in that: The fluorescence microscopy imaging section includes a fluorescence signal generation and receiving unit, and a filter, a first convex lens, a vacuum, a second convex lens, an optical fiber, and a galvanometer scanner arranged sequentially in the optical path. The filter is positioned close to the fluorescence signal generation and receiving unit, and the galvanometer scanner is positioned directly opposite the eyepiece of the photoacoustic microscopy imaging section.

4. The method for accurately measuring the light absorption coefficient based on the Monte Carlo model according to claim 2, characterized in that: The photoacoustic microscopy imaging section includes an eyepiece, an objective lens, and a sample placement mechanism arranged sequentially from top to bottom. The ultrasonic transducer is disposed within the sample placement mechanism and is located directly below the objective lens.

5. The method for accurately measuring the light absorption coefficient based on the Monte Carlo model according to claim 4, characterized in that: The sample placement mechanism includes an objective stage and a support. A lifting platform is provided on the objective stage. The support spans both sides of the lifting platform and is fixed to the objective stage at its bottom. A water tank is provided on the support, and the sensing end of the ultrasonic transducer is located in the water tank.

6. The method for accurately measuring the light absorption coefficient based on the Monte Carlo model according to claim 1, characterized in that: The formula for calculating the actual absorbed light energy density by solving the photoacoustic wave equation using the finite element method in step 2 is as follows: in, Represents a pressure wave field. Indicates the speed of sound. Indicates the coefficient of thermal expansion. Indicates specific heat capacity. This represents the actual absorbed light energy density.

7. The method for accurately measuring the light absorption coefficient based on the Monte Carlo model according to claim 1, characterized in that: The formula for calculating the deviation between the actual absorbed light energy density and the simulated absorbed light energy density in step 4 is as follows: in, This represents the actual absorbed light energy density. This represents the simulated absorbed light energy density, and , Represents luminous flux. This represents the light absorption coefficient.

8. A precise measurement system for light absorption coefficient based on the Monte Carlo model, used to implement the measurement method as described in any one of claims 1-7, characterized in that, include: The imaging system building module is used to construct multimodal microscopic imaging systems; The data processing module is used to acquire experimental photoacoustic data from the multimodal microscopic imaging system and to obtain the actual absorbed light energy density by solving the photoacoustic wave equation using the finite element method. It is also used to obtain the simulated absorbed light energy density by simulating and calculating using the Monte Carlo model, given an assumed absorption coefficient. It is also used to optimize by iterative least squares method to minimize the deviation between the actual absorbed light energy density and the simulated absorbed light energy density, and obtain the absolute light absorption coefficient.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for accurately measuring the light absorption coefficient based on the Monte Carlo model as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method for accurately measuring the light absorption coefficient based on the Monte Carlo model as described in any one of claims 1 to 7.

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