Small animal in-vivo multi-parameter dynamic monitoring method based on photoacoustic imaging

Abstract

The invention discloses a small animal in-vivo multi-parameter dynamic monitoring method based on photoacoustic imaging, and relates to the technical field of biomedical imaging, and the method comprises the steps: obtaining a photoacoustic signal generated by a small animal under the excitation of pulse laser output by an optical excitation module through an acoustic detection module; reconstructing a photoacoustic image of the photoacoustic signal through a filtering back projection algorithm; calculating the similarity between adjacent images in the photoacoustic image, identifying the image of which the similarity is lower than a set frame threshold value as a cross section slice polluted by respiratory motion artifacts and removing the cross section slice, and replacing the removed image frame by using the interpolation of an adjacent artifact-free image so as to obtain an updated photoacoustic image; selecting a region of interest in the photoacoustic image, and carrying out oxygenation kinetics monitoring, pharmacokinetics monitoring, perfusion kinetics monitoring or nanoparticle in-vivo kinetics monitoring; according to the monitoring method, motion artifacts caused by breathing of the small animals are effectively inhibited, and in-vivo multi-parameter dynamics monitoring of the small animals is achieved.

Description

Technical Field

[0001] This invention relates to the field of biomedical imaging technology, and in particular to a method for in vivo multi-parameter dynamic monitoring of small animals based on photoacoustic imaging. Background Technology

[0002] Monitoring in vivo multi-parameter kinetics in small animals, covering oxygenation kinetics, pharmacokinetics, perfusion kinetics, and nanoparticle in vivo kinetics, is crucial for revealing physiological and pathological processes in small animals.

[0003] Traditional biomedical imaging techniques for monitoring suffer from inherent contradictions in spatial resolution, temporal resolution, imaging depth, and contrast. For example, while X-ray computed tomography (CT) offers high resolution, it lacks sensitivity to functional information and is accompanied by ionizing radiation; magnetic resonance imaging (MRI) and positron emission tomography (PET) provide deep tissue and functional imaging capabilities, but their spatiotemporal resolution is low and they rely on exogenous reagents or ionizing radiation; ultrasound (US) imaging can achieve real-time imaging, but it lacks optical contrast; optical imaging, while possessing subcellular resolution and molecular sensitivity, has limited penetration depth. In contrast, photoacoustic imaging (PA) combines the advantages of rich optical contrast and high ultrasound resolution, making it an ideal tool for dynamic monitoring of living organisms, with penetration depths reaching the centimeter level. Among various PA technologies, photoacoustic computed tomography (PACT) can image tissue components, vascular function, and oxygenation status without the need for contrast agents. The two-dimensional PACT monitoring method based on ring array transducers has advantages such as compactness, low cost and dense planar angle sampling. It can achieve high-fidelity image reconstruction without relying on complex geometric structures or sparse sampling strategies, thereby realizing in vivo dynamic monitoring.

[0004] Currently, typical methods for in vivo multi-parameter dynamic monitoring of small animals using two-dimensional photoacoustic computed tomography (PACT) with ring transducer arrays include single-pulse panoramic photoacoustic computed tomography (SIP-PACT), transmission-reflection photoacoustic ultrasound computed tomography (TROPUS), and video-rate full-ring ultrasound and photoacoustic computed tomography (VF-USPACT). While SIP-PACT can achieve imaging and monitoring of anatomical structures, functional states, cellular activities, and molecular levels in small animals, its soft tissue contrast performance is poor. TROPUS combines photoacoustic computed tomography and transmission-reflection ultrasound to simultaneously present optical absorption, acoustic reflectivity, sound velocity (SOS), and acoustic attenuation information, achieving sub-millimeter-level anatomical structure imaging, but it is still limited to anatomical structure visualization and fails to achieve functional specificity and precise molecular-level imaging. VF-USPACT achieves dual-contrast imaging effects of optical absorption and ultrasound reflectivity and has real-time video transmission capabilities, but it is also limited to anatomical structure visualization and lacks functional and molecular-specific analysis. In addition, these methods generally employ nonlinear image post-processing techniques such as vascular filtering, contrast-limited adaptive histogram equalization, and nonlocal mean filtering for enhancement, but these methods increase the complexity of post-processing and may introduce artifacts.

[0005] In summary, these technical limitations hinder the realization of high-precision multi-parameter imaging of multi-scale dynamic physiological and pathological processes in vivo, while also highlighting the urgent need for in vivo multi-parameter dynamic monitoring methods for small animals. Summary of the Invention

[0006] Based on the technical problems existing in the background technology, this invention proposes a method for monitoring the in vivo multi-parameter dynamics of small animals based on photoacoustic imaging, which effectively suppresses motion artifacts caused by the respiration of small animals and realizes the monitoring of the in vivo multi-parameter dynamics of small animals.

[0007] The present invention proposes a method for in vivo multi-parameter dynamic monitoring of small animals based on photoacoustic imaging, comprising: The acoustic detection module acquires the photoacoustic signal generated by the small animal under the pulsed laser excitation output by the optical excitation module; Photoacoustic images are reconstructed from photoacoustic signals using a filtered back-projection algorithm; The similarity between adjacent images in the photoacoustic image is calculated. Images with similarity below a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and can be removed. The removed image frames are replaced by interpolation of adjacent artifact-free images to obtain the updated photoacoustic image. Regions of interest in photoacoustic images are selected for monitoring oxygenation kinetics, pharmacokinetics, perfusion kinetics, or in vivo kinetics of nanoparticles.

[0008] Furthermore, the acoustic detection module is fixed at a set height by the motion scanning module to obtain the photoacoustic signal of a selected section of the small animal, and reconstructs a two-dimensional photoacoustic image frame sequence from the photoacoustic signal using a filtered back projection algorithm. Furthermore, the acoustic detection module is controlled by the motion scanning module to move up and down on the outside of the small animal to obtain photoacoustic signals from different sections of the small animal, and a three-dimensional photoacoustic image is reconstructed from the photoacoustic signals using a filtering back projection algorithm.

[0009] Furthermore, after reconstructing the two-dimensional photoacoustic image frame sequence from the photoacoustic signal using the filtered back projection algorithm, the similarity value between adjacent frames in the two-dimensional photoacoustic image is calculated. Image frames with similarity values ​​lower than a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and can be removed. The removed image frames are replaced by interpolation of adjacent artifact-free images to obtain the updated two-dimensional photoacoustic image.

[0010] Furthermore, after reconstructing the three-dimensional photoacoustic image from the photoacoustic signal using the filtered back projection algorithm, the similarity value of each adjacent two-dimensional image slice in the three-dimensional photoacoustic image is calculated. Two-dimensional image slices with similarity values ​​lower than a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and are removed. The removed image frames are replaced by interpolation of adjacent artifact-free images to obtain the updated three-dimensional photoacoustic image.

[0011] Furthermore, the removed image frames are replaced by interpolation of adjacent artifact-free images, wherein linear interpolation is used.

[0012] Furthermore, the removed image frames are replaced by interpolation of adjacent artifact-free images, wherein three-dimensional spline interpolation is used for the replacement.

[0013] Furthermore, when monitoring oxygenation kinetics, hypoxic and eutrophic states of small animals are first established, and these states are alternated. During this process, photoacoustic imaging of small animals is performed using in vivo multi-parameter kinetic monitoring methods to obtain updated photoacoustic images, which are then used to monitor the changes in oxygenation kinetics throughout the small animal under different oxygenation states.

[0014] Furthermore, when conducting pharmacokinetic monitoring, after injecting the drug into small animals, photoacoustic imaging of the small animals is performed using in vivo multi-parameter kinetic monitoring methods to quantify, analyze, and compare the metabolic processes of the regions of interest within the small animals. When perfusion dynamics are monitored, after drugs are injected into small animals and perfusion occurs, photoacoustic imaging is performed on the small animals using in vivo multi-parameter dynamics monitoring methods to obtain updated photoacoustic images, which are then used to quantitatively analyze and compare the perfusion dynamics of regions of interest within the small animals.

[0015] Furthermore, when performing in vivo kinetics analysis of nanoparticles, after injecting a solution containing suspended nanoparticles into a small animal, photoacoustic imaging of the small animal is performed using a multi-parameter in vivo kinetics monitoring method to obtain updated photoacoustic images. These images are then used to detect, locate, and track nanoparticles in the image time series, thereby obtaining the trajectory of nanoparticles in blood vessels and quantifying the flow velocity of nanoparticles in blood vessels.

[0016] The advantages of the in vivo multi-parameter dynamic monitoring method for small animals based on photoacoustic imaging provided by this invention are as follows: by using photoacoustic imaging technology, combined with linear interpolation and smoothing filtering technology, motion artifacts caused by small animal respiration are effectively suppressed. At the same time, noise is reduced while retaining dynamic characteristics. Nonlinear processing is used only to correct respiratory motion artifacts in the three-dimensional volume photoacoustic imaging process to reduce dependence on image post-processing, thereby making up for the shortcomings of existing technologies in monitoring rapid, spatially heterogeneous and hierarchical physiological and pathological dynamic changes. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram of the detection equipment corresponding to the multi-parameter dynamics monitoring method of Example 1; Figure 3 This is a schematic diagram of the correction process for respiratory motion artifacts in three-dimensional volumetric photoacoustic imaging based on three-dimensional spline interpolation in Example 1; Figure 4 This is a schematic diagram of the monitoring results of the whole-body oxygenation dynamics of mice in Example 1. (a) is a three-dimensional photoacoustic image of five typical oxygenation states of mice under continuous alternation of hypoxic and hyperoxic environments, and (b) is a graph of the photoacoustic signals of the mouse trunk and intestines changing over time. Figure 5 This is a schematic diagram of monitoring oxygenation kinetics at the renal-splenic junction in mice. (a) shows two-dimensional photoacoustic images of five typical oxygenation states in a cross-section of the renal-splenic junction under continuous alternation of hypoxic and hyperoxic environments. (b) is a graph showing the change in photoacoustic signal intensity over time in different blood vessel regions of the cross-section at the renal-splenic junction. (c1) shows... Figure 5 (b) shows a magnified image of the photoacoustic signal intensity at the renal-splenic junction, revealing obvious respiratory motion artifacts. (c2) shows the motion artifact-corrected curve obtained by identifying artifact-contaminated frames through inter-frame SSIM and performing linear interpolation replacement. (c3) shows the... Figure 5 (c2) is the curve obtained after smoothing by the Savitzky-Golay filter, and (d) is the curve obtained after smoothing by the Savitzky-Golay filter. Figure 5 The curve obtained after the data in (b) were processed by linear interpolation to remove respiratory artifacts and smoothing filtering; Figure 6 A schematic diagram showing the three-dimensional volumetric photoacoustic imaging results of small animals in a baseline state (oxygen-rich) and a severely hypoxic state when photoacoustic imaging is performed using lasers of different wavelengths. Figure 7 This is a schematic diagram of the detection equipment corresponding to the multi-parameter dynamics monitoring method in Example 2; Figure 8 This is a schematic diagram of ICG pharmacokinetics monitoring in small animals. (a) shows three-dimensional volumetric photoacoustic images of the small animals at five key time points (baseline state, i.e., before injection, 10 minutes, 60 minutes, 150 minutes, and 280 minutes after injection); (b) shows... Figure 8 (a) shows a schematic diagram of four different cross sections (S1, S2, S3, S4) of the three-dimensional volumetric photoacoustic images at five time points, and (c) shows the normalized photoacoustic intensity curves of four typical metabolic organs (liver, duodenum, small intestine, large intestine) as a function of time after injection. Figure 9 This is a schematic diagram of the monitoring of ICG perfusion dynamics in mice. (a) shows two-dimensional photoacoustic images of a liver cross-section at 0 seconds (baseline), 64 seconds, 80 seconds, and 300 seconds; (b) shows the photoacoustic intensity changes over time in different blood vessels or tissue regions of the liver; and (c1) shows… Figure 9 (b) is an enlarged view of the artifacts caused by respiratory movements, and (c2) is a magnified view of the artifacts caused by respiratory movements. Figure 9 The curve obtained after linear interpolation of (c1), and (c3) is the curve obtained after linear interpolation of (c1). Figure 9 The curve obtained by smoothing (c2) using a filter; Figure 10 This is a schematic diagram illustrating the monitoring of ICG nanoparticle dynamics in mice. (a) is a photoacoustic image of a liver cross-section before injection; (b) is a photoacoustic image of a liver cross-section 14 seconds after injection of ICG formulation B (nanoparticles); and (c) is... Figure 10 (b) is a schematic diagram of a series of consecutive frames within the brown dashed box, with each frame spaced 0.1 seconds apart; Among them, 1-optical excitation module, 2-small animal, 3-acoustic detection module, 4-motion scanning module, 5-reconstruction processing module, 6-water tank, 7-fiber optic bundle, 8-gas mixer, and 9-injector. Detailed Implementation

[0018] The technical solution of the present invention will now be described in detail through specific embodiments. Many specific details are set forth in the following description to provide a thorough understanding of the invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] like Figures 1 to 10 As shown, the in vivo multi-parameter dynamics monitoring method for small animals based on photoacoustic imaging proposed in this invention includes: Step 1: Obtain the photoacoustic signal generated by the small animal under the pulsed laser excitation output by the optical excitation module through the acoustic detection module; Step 2: Reconstruct the photoacoustic image from the photoacoustic signal using a filtered back-projection algorithm; Step 3: Calculate the similarity between adjacent images in the photoacoustic image. Images with similarity below a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and can be removed. The removed image frames are replaced by interpolation of adjacent artifact-free images to obtain the updated photoacoustic image. Step 4: Select the region of interest in the photoacoustic image and monitor oxygenation kinetics, pharmacokinetics, perfusion kinetics, or in vivo kinetics of nanoparticles.

[0020] This embodiment is used to achieve precise monitoring of in vivo multi-parameter kinetics in live small animals. These multi-parameter kinetics include oxygenation kinetics, pharmacokinetics, perfusion kinetics, and in vivo nanoparticle kinetics. Different small animals were used for different kinetic studies.

[0021] This method employs photoacoustic imaging technology, combined with linear interpolation and smoothing filtering techniques, to effectively suppress motion artifacts caused by small animal respiration. At the same time, it reduces noise while preserving dynamic features. Nonlinear processing is used only to correct respiratory motion artifacts during three-dimensional volumetric photoacoustic imaging to reduce dependence on image post-processing, thereby making up for the shortcomings of existing technologies in monitoring rapid, spatially heterogeneous, and hierarchical physiological and pathological dynamic changes.

[0022] In one embodiment, the motion scanning module controls the fixed or moving state of the acoustic detection module to perform two-dimensional planar photoacoustic imaging or three-dimensional volumetric photoacoustic imaging of the small animal, specifically: First, the acoustic detection module is placed on the periphery of the small animal, and the motion scanning module controls the fixed or moving state of the acoustic detection module.

[0023] The optical excitation module is used to generate pulsed lasers, which excite small animals to produce ultrasound through the photoacoustic effect. This ultrasound is also known as photoacoustic signal.

[0024] Two-dimensional planar photoacoustic imaging: The acoustic detection module is fixed at a set height by the motion scanning module to obtain the photoacoustic signal of the small animal in the selected section, and the two-dimensional photoacoustic image frame sequence is reconstructed from the photoacoustic signal by the filtering back projection algorithm.

[0025] Three-dimensional volumetric photoacoustic imaging: The acoustic detection module moves up and down on the outside of the small animal through the motion scanning module to obtain photoacoustic signals from different sections of the small animal, and then reconstructs a three-dimensional photoacoustic image from the photoacoustic signals through a filtering back projection algorithm.

[0026] In one embodiment, for two-dimensional planar photoacoustic imaging, in order to suppress respiratory motion artifacts in small animals, the similarity (e.g., structural similarity, SSIM) value between adjacent frames in the two-dimensional photoacoustic image is calculated. Image frames with similarity values ​​lower than a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and can be removed. The removed image frames are replaced by interpolation (e.g., linear interpolation) of adjacent artifact-free images. The interpolation result is further denoised by a Savitzky-Golay filter. The filter parameters, such as window width and polynomial order, are set to suppress noise in the signal as much as possible while preserving signal features to the greatest extent (i.e., to preserve the fluctuation features of photoacoustic image intensity caused by fluctuations in blood oxygen saturation due to pulsation in the waveform), so as to balance the noise suppression effect and the signal feature preservation effect, thereby obtaining an updated two-dimensional photoacoustic image.

[0027] In one embodiment, for three-dimensional volumetric photoacoustic imaging, in order to suppress respiratory motion artifacts in small animals, the similarity (e.g., structural similarity SSIM) value of each adjacent two-dimensional image slice in the three-dimensional photoacoustic image is calculated. Two-dimensional image slices with similarity values ​​lower than a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and are removed. The removed image frames are replaced with interpolation (e.g., three-dimensional spline interpolation) of adjacent artifact-free images to obtain an updated three-dimensional photoacoustic image.

[0028] In this embodiment, the Structural Similarity Index (SSIM) can be used to identify cross-sectional slices contaminated by respiratory motion artifacts in both two-dimensional planar photoacoustic imaging and three-dimensional volumetric photoacoustic imaging. SSIM is an indicator of perceived image quality that comprehensively considers image brightness, contrast, and structural information, rather than just pixel-level differences (such as mean square error, MSE). This allows SSIM to more effectively detect image structural changes caused by respiratory motion, such as blurred organ boundaries or texture distortion. Compared to simple thresholding methods (such as those based on image intensity changes), SSIM reduces the risk of false detections because it focuses on structural consistency rather than global brightness variations.

[0029] In photoacoustic imaging, breathing motion often causes non-rigid deformation between image frames. SSIM can sensitively capture these changes, thereby accurately identifying contaminated frames.

[0030] This embodiment processes two-dimensional planar photoacoustic imaging sequences using linear interpolation. Specifically, once a cross-sectional slice contaminated by respiratory motion artifacts is identified, it is replaced with the linear interpolation of adjacent artifact-free images. This is a simple and fast method. Linear interpolation is computationally lightweight, suitable for real-time or high frame rate processing, especially when motion artifacts are intermittent, as it can better recover the overall trend of the slice. For two-dimensional imaging, linear interpolation is usually sufficient because changes between adjacent frames are typically gradual and do not introduce obvious unnatural traces or flaws. In contrast, more complex interpolation methods (such as spline interpolation) may overfit noise or increase computational burden in two dimensions, while linear interpolation performs well in balancing speed and accuracy.

[0031] This embodiment optimizes noise suppression and feature preservation in two-dimensional photoacoustic imaging sequences using a Savitzky-Golay filter. The Savitzky-Golay filter is a smoothing filter based on local polynomial fitting, effectively removing high-frequency noise while preserving signal features (such as peaks and edges). In photoacoustic imaging, this helps maintain important physiological dynamics information (such as blood perfusion delay) during denoising. The filter parameters (such as window size and polynomial order) are carefully selected to balance noise suppression with signal feature preservation. This differs from traditional low-pass filters (such as Gaussian filters), which may over-smooth the signal, leading to loss of detailed features.

[0032] In three-dimensional volumetric photoacoustic imaging, this embodiment replaces the discarded image frames with three-dimensional spline interpolation of adjacent artifact-free images. The advantages of using three-dimensional spline interpolation are 1) to 3). 1) It can maintain the continuity and smoothness of three-dimensional spatial data; 3D spline interpolation (usually cubic splines) is a high-order interpolation method. It estimates the value of an unknown point by fitting a smooth polynomial curve (a surface in 3D) between adjacent data points. This not only guarantees the continuity of the interpolated point values, but also the continuity of the first and second derivatives (i.e., slope and curvature).

[0033] Importance in 3D volumetric photoacoustic imaging: Biological organs and tissues have continuous and smooth anatomical structures in 3D space (such as tortuous blood vessels and organ surfaces). 3D spline interpolation can most realistically restore this natural spatial continuity, making the reconstructed 3D volumetric data smooth and natural, without harsh "steps" or "slices" artifacts.

[0034] 2) High-precision fitting, effectively preserving anatomical details; Because it considers broader neighborhood information (not just the two nearest points) and approximates the true physical spatial distribution through polynomial fitting, its interpolation results are mathematically more accurate.

[0035] This is crucial for subsequent 3D visualization (such as volume rendering), quantitative analysis (such as calculating organ volume and vascular morphology), and dynamic monitoring (such as tracking changes in blood oxygenation). High-precision interpolation ensures the accuracy of these analyses and avoids introducing erroneous trends due to interpolation errors. 3) Particularly suitable for handling non-uniform or complex structures; In three-dimensional space, changes in tissue structure may not be linear. For example, an organ may undergo complex changes in shape or curvature between two adjacent slices. Linear interpolation simplifies this to a straight line connection, while spline interpolation can capture and simulate such curved transitions, thus more faithfully reconstructing complex three-dimensional anatomical morphology.

[0036] In addition, the choice of three-dimensional spline interpolation for three-dimensional imaging in this embodiment is based on the comprehensive consideration that other interpolation methods have obvious shortcomings in the context of three-dimensional imaging and cannot meet the requirements of high-quality three-dimensional reconstruction. For example, (1) nearest neighbor interpolation will make the updated photoacoustic image appear as a "mosaic" or "staircase", which will seriously destroy the spatial continuity and smoothness of the data. At the same time, it completely ignores any gradual information between adjacent slices. For photoacoustic imaging that requires precise geometric shape and signal intensity, this is unacceptable. That is, although nearest neighbor interpolation is fast, it will seriously reduce the image quality and is not suitable for scientific analysis and medical imaging. (2) Linear interpolation can only guarantee the continuity of numerical values, but cannot guarantee the continuity of derivatives. Although the result is a continuous transition in the slice direction (Z-axis), the transition is not smooth enough. It may cause "patchwork" or "angular" phenomena when rendering three-dimensional isosurfaces or reconstructing curved surfaces. For nonlinear morphological changes such as the curvature of organ contours, linear interpolation will produce deviations, making the reconstructed structure appear stiff and unnatural. In other words, linear interpolation is a good approach for two-dimensional scenes, but in three-dimensional imaging where spatial continuity is more critical, its accuracy and smoothness are insufficient to provide the best reconstruction results.

[0037] Therefore, in this embodiment, three-dimensional photoacoustic imaging sacrifices some computational cost by choosing three-dimensional spline interpolation in order to achieve the highest reconstruction accuracy and spatial smoothness. This is determined by the nature of three-dimensional data and the high requirements of subsequent advanced analyses (such as dynamic monitoring and three-dimensional morphological analysis). It ensures that the final photoacoustic image can realistically and reliably reflect the internal anatomical structure and functional information of the small animal, providing a high-quality data foundation for scientific research and medical applications.

[0038] By comparing the motion artifact removal method set in this embodiment with other image processing procedures, the following conclusions can be drawn: Existing image registration or motion correction algorithms (such as rigid or non-rigid registration) attempt to align frames by distorting the image, but respiratory motion is often non-rigid, involving complex deformations of multiple organs. Registration may introduce secondary artifacts or errors, especially when the motion amplitude is large, leading to a decrease in registration accuracy. Furthermore, registration is computationally intensive, which is detrimental to high frame rate real-time imaging. The motion artifact removal method proposed in this embodiment avoids directly deforming the image; instead, it uses detection and replacement, making it more reliable.

[0039] Existing direct filtering or frequency domain processing: If low-pass filters (such as Butterworth filters) or frequency domain filters (such as Fourier transforms) are directly applied to remove motion-related frequency components, useful physiological signals may be removed because respiratory motion frequencies may overlap with signals of interest (such as hemodynamics). The Savitzky-Golay filter set in this embodiment is a time-domain smoothing filter that focuses more on preserving local features.

[0040] Existing machine learning-based methods, while deep learning can be used for motion artifact detection and repair, require large amounts of labeled data to train the model, and the model's generalization ability may be limited. In photoacoustic imaging, data variability is high (e.g., different animals, different physiological states), making it difficult to train a reliable model. The motion artifact removal method described in this embodiment is based on traditional image processing, requires no training, and is easier to implement and debug.

[0041] In summary, the motion artifact removal method described in this embodiment achieves a good balance between accuracy, computational efficiency, and practicality, and is particularly suitable for suppressing respiratory motion artifacts in photoacoustic imaging.

[0042] It should be noted that oxygenation kinetics can be monitored based on two-dimensional planar photoacoustic imaging or three-dimensional volumetric photoacoustic imaging. Two-dimensional planar photoacoustic imaging and three-dimensional volumetric photoacoustic imaging can be used to monitor oxygenation kinetics, pharmacokinetics, perfusion kinetics or in vivo kinetics of nanoparticles. When performing in vivo kinetic monitoring, the detection is based on the updated photoacoustic image.

[0043] In one embodiment, when monitoring oxygenation kinetics, hypoxic and eutrophic states are established for the small animal, with these states alternating. During this process, photoacoustic imaging is performed on the small animal using a multi-parameter dynamics monitoring method to obtain updated photoacoustic images. These images are then used to monitor the changes in oxygenation kinetics throughout the small animal under different oxygenation states. Specifically, the updated photoacoustic images are acquired, and regions of interest (such as organs or tissues) are selected from these images. The average photoacoustic signal intensity or the relative change in photoacoustic signal intensity of the regions of interest is extracted. These extracted quantities are used as features of blood oxygen saturation. Time-varying curves of the feature quantities in different regions are plotted, and the changing patterns of the feature quantities, such as relative delay, period, and sequence, are analyzed. The regions of interest are then quantitatively analyzed and compared.

[0044] It should be noted that small animals can establish hypoxic and oxygen-enriched states through respiration under natural conditions, which can be used to monitor oxygenation kinetics. Alternatively, they can establish hypoxic and oxygen-enriched states by inhaling a preset gas combination through a gas mixer. Furthermore, oxygenation kinetics of small animals can be monitored during pharmacokinetics, perfusion kinetics, or in vivo nanoparticle kinetics studies. This allows for the acquisition of oxygenation kinetic monitoring results for small animals under different conditions.

[0045] Pharmacokinetic monitoring: When a drug is metabolized in a small animal, changes in photoacoustic signal intensity can be monitored for pharmacokinetic effects. First, the drug is injected into the small animal. Then, photoacoustic imaging is performed using in vivo multi-parameter kinetic monitoring methods to quantify and compare the metabolic processes in the region of interest (ROI) within the animal. Specifically, a solution containing the drug is infused into the small animal, and in vivo multi-parameter kinetic monitoring is performed. Regions of interest (such as organs or tissues) are selected from the photoacoustic images. The metabolic process is analyzed based on the regions with strong photoacoustic signal intensity representing the drug in the images. The average photoacoustic signal intensity or change in the ROI at different times is extracted. These quantities are used as characteristic indicators of drug concentration. Curves showing the changes in characteristic quantities at different times and in different regions, as well as fitted curves, are plotted to analyze the patterns of change in characteristic quantities, such as their order of occurrence, thus quantifying the metabolic process in the ROI.

[0046] Monitoring of perfusion kinetics: Since perfusion occurs when a drug is injected into a small animal, monitoring perfusion kinetics can be performed if the drug alters the photoacoustic signal intensity. Specifically, the drug-containing solution is first infused into the small animal until perfusion occurs. Then, a multi-parameter kinetic monitoring method is executed. A region of interest (such as an organ or tissue) is selected from the photoacoustic image. The average photoacoustic signal intensity or change in the region of interest is extracted. Time-varying curves of characteristic quantities in different regions are plotted, and the patterns of change in these characteristic quantities, such as relative delay, period, and sequence, are analyzed to achieve quantitative analysis and comparison of the perfusion dynamics of the region of interest.

[0047] Monitoring of nanoparticle dynamics in vivo: If nanoparticles alter the intensity of photoacoustic signals, their dynamics in vivo can be monitored. First, a solution containing suspended nanoparticles is infused into a small animal. Then, a multi-parameter dynamics monitoring method is performed. A region of interest (such as an organ or tissue) is selected from the photoacoustic image. Images of the region of interest are extracted, and the centroids of the bright spots (i.e., the positions of the nanoparticles) in each frame are extracted. The positions of the nanoparticles in the frame sequence are connected to form a curve, yielding the trajectory of the nanoparticle drug movement in vivo. This allows for the quantification of parameters such as the flow rate of the nanoparticles.

[0048] It should be noted that photoacoustic imaging of small animals injected with two different ICG formulations demonstrated the excellent effect of the in vivo multi-parameter kinetic monitoring method in this embodiment on the monitoring of pharmacokinetics, perfusion kinetics, and in vivo kinetics of nanoparticles in small animals.

[0049] The multi-parameter dynamics monitoring method of this embodiment is implemented by a monitoring device, which includes an optical excitation module 1, a small animal 2, an acoustic detection module 3, a motion scanning module 4, a reconstruction processing module 5, a water tank 6, an optical fiber bundle 7, a gas mixer 8, a syringe 9, ICG preparation A, ICG preparation B, etc.

[0050] In this system, the optical excitation module 1 can be a pulsed laser used to excite the small animal 2 to generate photoacoustic signals. The acoustic detection module 3 can receive the photoacoustic signals and digitize them. The reconstruction processing module 5 can be a computer used to reconstruct the photoacoustic signals into photoacoustic images and perform data analysis. The motion scanning module 4 can be an electrically driven translation stage that can move vertically, enabling the method to acquire photoacoustic images of the small animal 2 at fixed and different cross-sections, achieving two-dimensional planar photoacoustic imaging and three-dimensional volumetric photoacoustic imaging.

[0051] The water tank 6 holds water to ensure acoustic coupling between the small animal 2 and the acoustic detection module 3. The water tank 6 contains a heating device and a water pump to maintain a uniform water temperature of approximately 35 degrees Celsius. The fiber optic bundle 7 can be wrapped around the imaging plane of the small animal 2 to achieve uniform laser illumination. The gas mixer 8 can mix oxygen and nitrogen in any proportion for the small animal to inhale, establishing hypoxic or oxygen-rich states. The syringe 9 is used to inject ICG preparation A or ICG preparation B.

[0052] The small animals used were female BALB / c-nu / nu mice, 8-12 weeks old, weighing 20-25 grams. ICG formulation A was prepared by dissolving 20 mg of ICG powder in 0.4 mL of DMSO and agitating with an ultrasonic vibrator until a solution free of visible particles was obtained. This solution was then diluted with physiological saline to 1 mg / mL, resulting in a clear, green ICG solution. ICG formulation A was used for pharmacokinetic and perfusion kinetic monitoring in small animals.

[0053] ICG formulation B is prepared by dissolving 20 mg of ICG powder directly in 20 mL of physiological saline, resulting in a turbid ICG solution containing suspended nanoparticles at a concentration of 1 mg / mL. ICG formulation B is used for monitoring the in vivo dynamics of nanoparticles in small animals.

[0054] Example 1 This embodiment adopts Figure 3 The method shown is used to perform in vivo multi-parameter kinetics in small animals, mainly referring to the monitoring of oxygenation kinetics.

[0055] Taking mice as an example, the specific process of the in vivo multi-parameter dynamics monitoring method is as follows (a1) to (a6): (a1) After anesthetizing the mouse, fix it in a water tank 6 filled with water, and connect its head above the water surface to the output end of the gas mixer 8 through a breathing mask.

[0056] (a2) A gas mixer was used to control small animals to inhale pure oxygen through a breathing mask for 150 seconds to establish an oxygen-rich environment in mice.

[0057] (a3) A gas mixer was used to control small animals to inhale a mixture of 10% oxygen and 90% nitrogen through a breathing mask for 90 seconds to establish an anaerobic environment in mice.

[0058] (a4) During three-dimensional volumetric photoacoustic imaging, the optical excitation module 1 and the acoustic detection module 3 are vertically moved by the motion scanning module 4, and photoacoustic imaging of two-dimensional sections is performed every approximately 200-250 micrometers. The reconstruction processing module 5 uses layer-by-layer reconstruction technology to generate a three-dimensional dataset by stitching together photoacoustic images from different sections. The total scanning time for completing the overall volumetric imaging is typically 10 to 20 seconds, and the resolution of vertical imaging is approximately 1.1 millimeters.

[0059] In the process of performing three-dimensional volumetric photoacoustic imaging, the acoustic detection module 3 is first controlled by the motion scanning module 4 to reach the set position, and the photoacoustic signal of the current cross-section is collected by the acoustic detection module 3. Then, the acoustic detection module 3 is adjusted to the next set position by the motion scanning module 4, and the photoacoustic signal of the corresponding cross-section is collected again by the acoustic detection module 3. Finally, the photoacoustic signals of different cross-sections are stitched together and reconstructed into a three-dimensional photoacoustic image by using layer-by-layer reconstruction technology.

[0060] (a5) To remove breathing artifacts from the 3D photoacoustic image, the SSIM value of each adjacent 2D image slice is calculated, and slices below a threshold are identified as cross-sectional slices contaminated by breathing motion artifacts. These contaminated slices are removed and replaced with 3D spline interpolation from adjacent artifact-free images. The effect of removing breathing artifacts can be seen in [the following section / document / reference]. Figure 3 .

[0061] (a6) Alternating between hyperoxia and hypoxia environments, continuous three-dimensional volumetric photoacoustic imaging of the entire mouse body was performed during this process to monitor changes in oxygenation kinetics under different oxygenation states. The monitoring results of whole-body oxygenation kinetics are shown in [link to documentation]. Figure 4 .

[0062] To observe changes in oxygenation kinetics at a higher frame rate, two-dimensional planar photoacoustic imaging was performed, with the imaging plane positioned at the renal-splenic junction, specifically as follows: (b1) to (b4): (b1) First, oxygen-rich and hypoxic environments are established in mice using steps (a1) to (a3), followed by two-dimensional planar photoacoustic imaging: the pulsed laser wavelength of the optical excitation module 1 is set to 1064 nm, and the pulsed laser is transmitted to the mouse surface through the fiber bundle 7. Simultaneously, the photoacoustic signal is collected using the acoustic detection module 3. The reconstruction processing module 5 uses a GPU-accelerated filtered back projection algorithm to reconstruct the photoacoustic signal data into a two-dimensional photoacoustic image. The imaging frame rate is 10 Hz, and the resolution within the imaging plane is approximately 122 micrometers. (b2) To remove breathing motion artifacts from two-dimensional planar photoacoustic images, the SSIM value between each image frame is calculated, and image frames below the threshold are identified as being contaminated by breathing motion artifacts. Subsequently, two-dimensional linear interpolation of adjacent artifact-free images is used for replacement. The result after interpolation replacement is further denoised by a Savitzky-Golay filter, and the filter parameters are carefully selected to balance noise suppression and signal feature preservation.

[0063] (b3) Alternating between hyperoxia and hypoxia environments, the oxygenation kinetics of the renal-splenic junction in mice were monitored under different oxygenation states. The monitoring results of the oxygenation kinetics at the renal-splenic junction are shown in [link to relevant documentation]. Figure 5 .

[0064] (b4) Using lasers of different wavelengths (700nm, 797nm, 1064nm), under alternating hyperoxia and hypoxia conditions, the changes in oxygenation kinetics of mice at baseline (oxygen-rich) and under severe hypoxia were monitored. The monitoring results of multi-wavelength oxygenation kinetics are shown in [link to documentation]. Figure 6 .

[0065] For the final results of this embodiment, please refer to [link / reference]. Figures 3 to 6 : Figure 3 This paper demonstrates the correction process for respiratory motion artifacts in volumetric photoacoustic imaging based on 3D interpolation. During volumetric scanning of the mouse trunk, periodic respiratory motion generates artifacts along the height direction (indicated by arrows) in the reconstructed photoacoustic image. By calculating the SSIM value of each adjacent 2D image slice, slices below a threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts. These contaminated slices are removed and replaced with 3D spline interpolation from adjacent artifact-free images. This method effectively suppresses periodic respiratory motion artifacts and significantly improves the overall image consistency.

[0066] Figure 4 The results of monitoring the whole-body oxygenation kinetics of small animals were presented. Figure 4 (a) presents three-dimensional photoacoustic images of mice under continuous alternation between hypoxic and oxygen-rich environments, showing five typical oxygenation states (baseline state, moderate hypoxia, severe hypoxia, partial reoxygenation, and complete reoxygenation). Figure 4 In (a), depth information is color-coded. It can be seen that, due to the lower optical absorptivity of deoxygenated hemoglobin at 1064 nm wavelength compared to oxyhemoglobin, the photoacoustic signal in the mouse trunk decreases with increasing hypoxia and recovers after reoxygenation. During hypoxia, intestinal signals are enhanced because reduced hemoglobin absorption increases the ability of photons to penetrate intestinal tissue, resulting in more photons being absorbed by the intestinal contents. During partial reoxygenation, the abdominal aorta shows discontinuities caused by pulsation (marked by dashed boxes), revealing regional oxygenation heterogeneity. Figure 4 (b) shows the time curves of relative changes in photoacoustic signals in the mouse trunk and intestines. It can be seen that it takes about 2 minutes for the small animal to transition from the baseline state to a state of severe hypoxia, while it only takes about 30 seconds to transition from severe hypoxia to a state of complete reoxygenation. This reflects the gradual nature of oxygen consumption under hypoxic conditions and the rapid response of oxygen supply under hyperoxia conditions.

[0067] Figure 5 The study demonstrated the monitoring of oxygenation kinetics at the kidney-spleen junction in small animals. Figure 5 (a) presents two-dimensional photoacoustic images of five typical oxygenation states (baseline state T1, moderate hypoxia T2, severe hypoxia T3, partial reoxygenation T4, and complete reoxygenation T5) in cross-sections of the kidney-spleen junction of mice under continuous alternation of hypoxia and hyperoxygenation, as well as magnified views within the green box. Figure 5 (b) shows the time curves of the relative changes in photoacoustic signal intensity in different blood vessel regions of this section, where the photoacoustic signal intensity exhibits obvious respiratory motion artifacts. (See enlarged image below.) Figure 5 (c1).

[0068] Figure 5 (c1) identifies artifact-contaminated frames through inter-frame SSIM and performs linear interpolation replacement, thus obtaining the result after motion artifact correction, i.e. Figure 5 (c2). After being smoothed by the Savitzky-Golay filter, (c2) forms... Figure 5 (c3) effectively reduces noise while preserving the basic oxygenation dynamics. Observation Figure 5 As shown in (c3), under hypoxic conditions, respiration periodically produces oxygenated blood clots, resulting in periodic peaks in the photoacoustic signal intensity. Based on the phase difference of the periodic peaks in different blood vessels and organs, the perfusion time of blood from the abdominal aorta to the renal artery is calculated to be approximately 0.3 seconds, while the time required to pass through the capillaries and enter the renal vein is approximately 1.3 seconds longer.

[0069] Will Figure 5 The data in (b) were processed by respiratory artifact linear interpolation removal and smoothing filtering to obtain Figure 5 (d). In Figure 5In (d), the green arrow indicates a transient overshoot in the abdominal aortic oxygen saturation during reoxygenation, suggesting that oxygen-rich blood is preferentially delivered to the proximal vascular system before being redistributed to distal tissues. The gray arrow indicates a slower response of the spleen to changes in oxygenation, which is related to its lower metabolic demand, blood reserve capacity, and slower perfusion rate. Under hypoxic conditions, the photoacoustic signal exhibits a decreasing gradient from the abdominal aorta through the renal artery to the renal vein, reflecting a gradual change along the circulatory pathway. During reoxygenation, slight overshoot occurs in both renal vessels, due to the preferential redistribution of oxygen to the kidneys, temporarily increasing local blood volume.

[0070] Figure 6 This study presents the three-dimensional volumetric photoacoustic imaging results of small animals under baseline (oxygen-rich) and severely hypoxic conditions using photoacoustic imaging at different laser wavelengths (700 nm, 797 nm, 1064 nm). At 700 nm, the photoacoustic signal intensity is higher in the severely hypoxic state than in the oxygen-rich state because oxyhemoglobin has lower light absorption than deoxyhemoglobin. At 797 nm, the light absorption of oxyhemoglobin and deoxyhemoglobin is comparable, resulting in similar photoacoustic signal intensities in the severely hypoxic and oxygen-rich states. However, at 1064 nm, the light absorption of oxyhemoglobin is higher than that of deoxyhemoglobin, leading to a lower photoacoustic signal intensity in the severely hypoxic state compared to the oxygen-rich state. This result further validates the close correlation between photoacoustic signal intensity and the hemoglobin absorption spectrum.

[0071] In summary, this method fully records the dynamic changes in oxygenation kinetics at the systemic and vascular scales under hypoxic experimental conditions, reveals organ-specific differences in hypoxic responses, clarifies the differences in recovery priorities among different tissues, and captures the wavelength dependence of oxygenation kinetics.

[0072] Example 2 This embodiment adopts Figure 7 The detection equipment shown performs in vivo multi-parameter kinetics on the small animals to be imaged, mainly referring to the monitoring of pharmacokinetics, perfusion kinetics, and in vivo kinetics of nanoparticles.

[0073] The following explanation uses a mouse as an example. The specific process of this embodiment is as follows (c1) to (c6): (c1) After anesthetizing the small animal, fix it in a water tank 6 filled with water. Inject 100 μL of ICG preparation A through a needle inserted into the tail vein of the mouse underwater using a syringe 9.

[0074] (c2) The pulsed laser wavelength output by optical excitation module 1 is set to 785nm. Three-dimensional volumetric photoacoustic imaging is performed before injection (baseline state), 1 minute after injection, and every 5 minutes starting from 5 minutes later, for a total monitoring time of 300 minutes. To remove respiratory artifacts from the three-dimensional photoacoustic images, the SSIM value of each adjacent two-dimensional image slice is calculated, and slices below the threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts. These contaminated slices are removed and replaced with three-dimensional spline interpolation from adjacent artifact-free images, ultimately obtaining... Figure 8 The results of monitoring ICG drug metabolism kinetics are shown in the image.

[0075] (c3) Take another small animal, anesthetize it, and fix it in a water tank 6 filled with water. Inject 100 μL of ICG preparation A through a needle inserted into the tail vein of the mouse underwater using a syringe 9.

[0076] (c4) The wavelength of the pulsed laser output from optical excitation module 1 is set to 785 nm. To observe changes in perfusion dynamics at a higher frame rate, two-dimensional planar photoacoustic imaging is continuously performed from approximately 1 minute before injection to 3 minutes after injection, with the imaging plane fixed at the liver section. Linear interpolation and smoothing filtering are used to suppress respiratory motion artifacts, resulting in... Figure 9 The monitoring results of ICG perfusion dynamics are shown in the figure.

[0077] (c5) Take another small animal, anesthetize it, and fix it in a water tank 6 filled with water. Inject 100 μL of ICG preparation B through a needle inserted into the tail vein of the mouse underwater using a syringe 9.

[0078] (c6) The pulsed laser wavelength output by optical excitation module 1 is set to 785 nm. To observe the changes in in vivo dynamics of nanoparticles at a higher frame rate, two-dimensional planar photoacoustic imaging is continuously performed before and after injection, with the imaging plane fixed at a section of the liver. Linear interpolation and smoothing filtering are used to suppress respiratory motion artifacts, resulting in... Figure 10 The results of monitoring the in-situ dynamics of ICG nanoparticles are shown in the image.

[0079] The results of Example 2 can be found in [link to example]. Figures 8 to 10 : Figure 8 The study demonstrated the monitoring of ICG pharmacokinetics in small animals. Figure 8 (a) presents three-dimensional volumetric photoacoustic images of small animals at five key moments (baseline state, i.e., before injection, 10 minutes, 60 minutes, 150 minutes, and 280 minutes after injection), with the images color-coded using depth information. Figure 8 (b) shows Figure 8 Four different sections (S1, S2, S3, S4) of the three-dimensional photoacoustic images at five time points in (a). Figure 8(c) details the changes in normalized photoacoustic intensity over time in four typical metabolic organ regions (liver, duodenum, small intestine, and large intestine). The scatter plots represent actual measurements, and the solid lines represent polynomial fitted values. Through analysis... Figure 8 It was observed that after injection of ICG preparation A, the photoacoustic signal in the liver of the small animals gradually increased, indicating that the liver began to take up ICG, and the peak of ICG uptake in the liver occurred within 5-10 minutes. Subsequently, ICG signals appeared in the duodenum between approximately 5 and 140 minutes; ICG signals appeared in the small intestine at approximately 70 minutes; and ICG signals appeared in the large intestine at approximately 200 minutes. This series of changes fully reflects the four typical drug metabolism stages in small animals: hepatic uptake, duodenal drainage, small intestinal clearance, and large intestinal clearance.

[0080] Figure 9 The monitoring of ICG perfusion kinetics in small animals was demonstrated. ICG injection began at 57 seconds and was completed at 63 seconds. Figure 9 (a) presents two-dimensional photoacoustic images of the liver cross section at 0 seconds (baseline), 64 seconds, 80 seconds and 300 seconds. Figure 9 (b) records the time curves of relative changes in photoacoustic intensity in different blood vessels or tissue regions of the liver. Artifacts caused by respiratory movements can be seen in the figure. Figure 9 (c1) is Figure 9 (b) is an enlarged view. Figure 9 (c1) was processed by linear interpolation, which effectively suppressed respiratory motion artifacts, resulting in Figure 9 (c2); (c2) is further smoothed using a filter to obtain Figure 9 (c3). From Figure 9 Analysis of (a) shows that after ICG injection was completed at 63 seconds, the photoacoustic image at 64 seconds showed that ICG was first perfused into the inferior vena cava; the image at 80 seconds showed that the liver began to take up ICG; by 300 seconds, the image showed that the liver’s uptake of ICG increased significantly, and the gradual accumulation of ICG in the peripheral liver enhanced light absorption, limiting photons from penetrating to the central region of the liver, thereby weakening the photoacoustic signal in that region. Figure 9 (c3) further revealed that after tail vein injection, ICG was first perfused into the inferior vena cava, then into the abdominal aorta after about 1.7 seconds, into the hepatic artery after about 2.1 seconds, and into the central liver after about 5 seconds, and then rapidly diffused into the entire liver parenchyma.

[0081] Figure 10 The study demonstrated the monitoring of ICG nanoparticle dynamics in small animals. Figure 10 (a) is a photoacoustic image of a cross-section of the liver before injection. Figure 10(b) is a photoacoustic image of the liver cross section 14 seconds after injection of ICG formulation B (nanoparticles). ICG particles were detected in the inferior vena cava, liver and subcutaneous tissue (green arrows). Figure 10 (c) shows Figure 10 (b) shows a series of consecutive frames of the region of interest (brown dashed box), with each frame spaced 0.1 seconds apart. To demonstrate the molecular particle tracking effect, the position of the nanoparticles in each frame is marked with a red circle, the initial position is marked with a brown cross, and the particle path formed by connecting the frames is marked with a brown line. Figure 10 (c) represents the transport process of an ICG particle in the subcutaneous microcirculation. The depicted trajectory shows the dynamic movement of the particle from the subcutaneous tissue to the dermis and back to the subcutaneous tissue. The estimated flow velocity of the particle is 1.1 mm / s.

[0082] In summary, from the perspectives of pharmacokinetics, perfusion kinetics, and in vivo nanoparticle kinetics, this method can monitor ICG clearance in real time during circulatory system, hepatic uptake, and gastrointestinal excretion, providing a non-invasive observation window for assessing liver function, drug metabolism, and systemic pharmacokinetics. Furthermore, by utilizing ICG particle tracking technology in subcutaneous capillaries, local hemodynamic flux and probe delivery can be quantified, thus opening up new research avenues for assessing microvascular permeability, hemodynamic changes, and the dynamics of targeted drug delivery.

[0083] This embodiment effectively overcomes the shortcomings of technologies such as CT, MRI, PET, and ultrasound (US), achieving precise capture of multi-parameter information including functional activity, metabolic processes, and molecular dynamics. Through linear interpolation and smoothing filtering techniques, respiratory motion artifacts in two-dimensional planar photoacoustic imaging are effectively suppressed, with nonlinear processing applied only to suppress three-dimensional respiratory motion artifacts, thus significantly reducing reliance on image post-processing. This method can deeply analyze oxygenation kinetic responses in small animal organs, blood vessels, and spectral dimensions, accurately depicting the metabolic and perfusion kinetics of drugs in small animals, and monitoring flowing nanoparticles in vivo at the molecular level. This method successfully addresses the shortcomings of existing technologies in monitoring rapid, spatially heterogeneous, and hierarchical physiological and pathological dynamic changes, fully demonstrating its unique advantages in capturing multi-parameter dynamic physiological changes in vivo, enabling its further application in long-term monitoring of cross-scale physiological and pathological conditions and comprehensive evaluation of treatment and diagnosis.

[0084] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for in vivo multi-parameter dynamic monitoring of small animals based on photoacoustic imaging, characterized in that, include: The acoustic detection module acquires the photoacoustic signal generated by the small animal under the pulsed laser excitation output by the optical excitation module; Photoacoustic images are reconstructed from photoacoustic signals using a filtered back-projection algorithm; The similarity between adjacent images in the photoacoustic image is calculated. Images with similarity below a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and can be removed. The removed image frames are replaced by interpolation of adjacent artifact-free images to obtain the updated photoacoustic image. Regions of interest in photoacoustic images are selected for monitoring oxygenation kinetics, pharmacokinetics, perfusion kinetics, or in vivo kinetics of nanoparticles.

2. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 1, characterized in that, The acoustic detection module is fixed at a set height by the motion scanning module to obtain the photoacoustic signal of a selected section of the small animal, and reconstructs a two-dimensional photoacoustic image frame sequence from the photoacoustic signal using a filtering back projection algorithm.

3. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 1, characterized in that, The acoustic detection module moves up and down on the outside of the small animal under the control of the motion scanning module to obtain photoacoustic signals from different sections of the small animal, and reconstructs a three-dimensional photoacoustic image from the photoacoustic signals using a filtering back projection algorithm.

4. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 2, characterized in that, After reconstructing the two-dimensional photoacoustic image frame sequence from the photoacoustic signal using the filtered back projection algorithm, the similarity value between adjacent frames in the two-dimensional photoacoustic image is calculated. Image frames with similarity values ​​lower than a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and can be removed. The removed image frames are replaced by interpolation of adjacent artifact-free images to obtain the updated two-dimensional photoacoustic image.

5. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 3, characterized in that, After reconstructing the three-dimensional photoacoustic image from the photoacoustic signal using the filtered back projection algorithm, the similarity value of each adjacent two-dimensional image slice in the three-dimensional photoacoustic image is calculated. Two-dimensional image slices with similarity values ​​lower than a set frame threshold are identified as cross-sectional slices contaminated by respiratory motion artifacts and are removed. The removed image frames are replaced by interpolation of adjacent artifact-free images to obtain the updated three-dimensional photoacoustic image.

6. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 4, characterized in that, The removed image frames are replaced by interpolation of adjacent artifact-free images, wherein linear interpolation is used.

7. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 5, characterized in that, The removed image frames are replaced by interpolation of adjacent artifact-free images, wherein three-dimensional spline interpolation is used for replacement.

8. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 1, characterized in that, When monitoring oxygenation kinetics, hypoxic and euoxygenated states are established for small animals, and these states are alternated. During this process, photoacoustic imaging is performed on the small animals using in vivo multi-parameter kinetic monitoring methods to obtain updated photoacoustic images, which are then used to monitor the changes in oxygenation kinetics throughout the small animals under different oxygenation states.

9. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 1, characterized in that, When conducting pharmacokinetic monitoring, after injecting drugs into small animals, photoacoustic imaging is performed on the small animals using in vivo multi-parameter kinetic monitoring methods to quantify, analyze, and compare the metabolic processes in the regions of interest within the small animals. When perfusion dynamics are monitored, after drugs are injected into small animals and perfusion occurs, photoacoustic imaging is performed on the small animals using in vivo multi-parameter dynamics monitoring methods to obtain updated photoacoustic images, which are then used to quantitatively analyze and compare the perfusion dynamics of regions of interest within the small animals.

10. The method for in vivo multi-parameter dynamic monitoring of small animals according to claim 1, characterized in that, When performing in vivo kinetics analysis on nanoparticles, a solution containing suspended nanoparticles is injected into a small animal. The animal is then subjected to photoacoustic imaging using a multi-parameter in vivo kinetics monitoring method. This results in updated photoacoustic images, which are used to detect, locate, and track nanoparticles in the image time series. This allows for the determination of the nanoparticle trajectory in the blood vessels and the quantification of the nanoparticle flow velocity in the blood vessels.

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