Blood perfusion monitoring methods, monitoring equipment, monitoring systems and media

Abstract

This application provides a method, device, system, and medium for monitoring blood perfusion. The method includes: acquiring a speckle image sequence of the tissue region to be monitored based on a laser emitted to it; acquiring ambient light information corresponding to the speckle image sequence and motion information between adjacent speckle images in the speckle image sequence; performing anti-interference processing on the speckle image sequence based on the ambient light information and the motion information between adjacent speckle images to obtain a corrected speckle image sequence; calculating the spatiotemporal joint speckle contrast of the corrected speckle image sequence to obtain a spatiotemporal joint speckle contrast; determining the blood perfusion parameters of the tissue region to be monitored based on the spatiotemporal joint speckle contrast using a pre-established mapping model between speckle contrast and blood perfusion parameters; and outputting a warning message when the blood perfusion parameters meet preset warning conditions. This method aims to improve the accuracy of blood perfusion monitoring.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a blood perfusion monitoring method, monitoring equipment, monitoring system and medium. Background Technology

[0002] Blood perfusion status reflects the microcirculatory blood supply to tissues and is an important reference for assessing burn wound healing, diagnosing retinopathy, observing cerebral blood flow, and evaluating postoperative tissue recovery. By obtaining information on local blood flow velocity, perfusion volume, and spatial distribution, medical staff can understand whether there is insufficient blood supply, local circulatory disorders, or abnormal recovery in the tissue.

[0003] Current methods for monitoring blood perfusion mainly include laser Doppler flow detection, thermal imaging, and ultrasound Doppler imaging. Laser Doppler flow detection typically obtains blood flow-related information by detecting frequency changes between incident light and moving red blood cells. This method is often used for single-point or small-area detection, and the results are easily affected by the measurement location, probe contact pressure, and changes in local tissue condition, making it difficult to consistently reflect differences in blood flow. Thermal imaging typically acquires infrared radiation distribution on the human body surface and indirectly determines local blood flow status based on temperature changes. However, body surface temperature is affected not only by blood flow but also by ambient temperature, skin humidity, tissue thickness, and heat dissipation conditions. Ultrasound Doppler imaging typically uses ultrasound echo frequency shift information to obtain blood flow data. However, the detection process usually relies on probe contact with the tissue surface; probe angle, pressure applied, and movement of the examined area can all affect the stability of the echo signal, thus affecting the accuracy of the blood flow data.

[0004] It is evident that in the existing blood perfusion monitoring process, the detection signal is easily affected by factors such as measurement position, contact state, external environment, and movement of the examined site, resulting in low accuracy of blood perfusion monitoring. Summary of the Invention

[0005] This application provides a blood perfusion monitoring method, monitoring equipment, monitoring system, and medium to improve the accuracy of blood perfusion monitoring.

[0006] In a first aspect, embodiments of this application provide a method for monitoring blood perfusion, including:

[0007] Based on the laser emitted to the tissue region to be monitored, a speckle image sequence of the tissue region to be monitored is acquired, as well as ambient light information corresponding to the speckle image sequence and / or motion information between adjacent frames of speckle images in the speckle image sequence is acquired;

[0008] Based on the ambient light information and / or the motion information between adjacent speckle images, the speckle image sequence is subjected to anti-interference processing to obtain a corrected speckle image sequence;

[0009] Spatiotemporal joint speckle contrast is calculated by performing spatiotemporal joint speckle contrast on the corrected speckle image sequence;

[0010] Based on the spatiotemporal speckle contrast, the blood perfusion parameters of the tissue region to be monitored are determined through a pre-established mapping model between speckle contrast and blood perfusion parameters. When the blood perfusion parameters meet the preset warning conditions, a warning message is output.

[0011] In one possible implementation, the anti-interference processing includes ambient light background correction and / or motion compensation;

[0012] Accordingly, based on the ambient light information and the motion information between adjacent speckle images, the speckle image sequence is subjected to anti-interference processing to obtain a corrected speckle image sequence, including:

[0013] Based on the ambient light information, the speckle image sequence is subjected to ambient light background correction to obtain a background-corrected speckle image sequence;

[0014] Based on the motion information between adjacent speckle images, motion compensation is performed on the background-corrected speckle image sequence to obtain a corrected speckle image sequence.

[0015] In one possible implementation, the step of performing spatiotemporal joint speckle contrast calculation on the corrected speckle image sequence to obtain the spatiotemporal joint speckle contrast includes:

[0016] Based on a preset window size, multiple calculation windows are determined on each frame of the corrected speckle image in the corrected speckle image sequence.

[0017] Based on each calculation window in each frame of corrected speckle image, calculate the mean gray value and standard deviation of the gray value of the pixels within the calculation window;

[0018] Based on the grayscale standard deviation and the grayscale mean, determine the spatial speckle contrast corresponding to each calculation window;

[0019] Based on the acquisition timing of the corrected speckle image sequence, the spatial speckle contrast of the calculation window at the same monitoring location in multiple consecutive frames of corrected speckle images is obtained.

[0020] The spatial speckle contrast at the same monitoring location is weighted and smoothed to obtain the spatiotemporal joint speckle contrast at the same monitoring location.

[0021] Based on the spatiotemporal joint speckle contrast corresponding to each monitoring location, the spatiotemporal joint speckle contrast distribution of the tissue region to be monitored is determined.

[0022] In one possible implementation, the step of performing ambient light background correction on the speckle image sequence based on the ambient light information to obtain a background-corrected speckle image sequence includes:

[0023] The speckle image sequence is processed by grayscale conversion and normalization of each frame of speckle image to obtain a normalized speckle image sequence.

[0024] Identify abnormal pixels in the normalized speckle image sequence whose gray values ​​meet preset abnormal conditions, and correct the abnormal pixels according to the gray values ​​of the neighboring pixels of the abnormal pixels to obtain a preprocessed speckle image sequence.

[0025] Based on the ambient light information, determine the ambient light background intensity corresponding to the preprocessed speckle image sequence;

[0026] An adaptive segmentation threshold is determined based on the ambient light background intensity and the grayscale distribution of each frame of speckle image in the preprocessed speckle image sequence.

[0027] Based on the adaptive segmentation threshold, the ambient light background region and the effective speckle region are determined from the preprocessed speckle image sequence;

[0028] Based on the grayscale information corresponding to the ambient light background region, background noise suppression processing is performed on the effective speckle region to obtain the background-corrected speckle image sequence.

[0029] In one possible implementation, the step of performing motion compensation on the background-corrected speckle image sequence based on the motion information between the adjacent speckle image frames to obtain a corrected speckle image sequence includes:

[0030] Based on the acquisition timing of the background-corrected speckle image sequence, two adjacent speckle images in the background-corrected speckle image sequence are selected sequentially as the reference frame and the frame to be compensated, respectively.

[0031] Optical flow analysis is performed on the reference frame and the frame to be compensated to obtain the pixel displacement information of the frame to be compensated relative to the reference frame;

[0032] Based on the pixel displacement information, identify the tissue motion region in the frame to be compensated;

[0033] Based on the pixel displacement information corresponding to the tissue motion region, inter-frame displacement compensation is performed on the pixels within the tissue motion region to obtain the compensated speckle image.

[0034] Based on the acquisition timing, the compensated speckle images are arranged to obtain the corrected speckle image sequence.

[0035] In one possible implementation, the blood perfusion parameters include at least one of blood flow velocity, blood perfusion volume, and blood flow distribution density;

[0036] Accordingly, based on the spatiotemporal joint speckle contrast, the blood perfusion parameters of the tissue region to be monitored are determined through a pre-established mapping model between speckle contrast and blood perfusion parameters, including:

[0037] Obtain a pre-established mapping model between speckle contrast and blood perfusion parameters;

[0038] The spatiotemporal speckle contrast corresponding to each monitoring location is input into the mapping model to determine at least one of the blood flow velocity, blood flow perfusion, and blood flow distribution density corresponding to each monitoring location.

[0039] In one possible implementation, the step of outputting warning information when the blood perfusion parameters meet preset warning conditions includes:

[0040] Obtain multiple preset anomaly threshold ranges corresponding to the current monitoring scenario;

[0041] The blood perfusion parameters corresponding to each monitoring area in the tissue region to be monitored are compared with the preset abnormal threshold range;

[0042] When the blood perfusion parameters in the monitoring area meet any of the preset abnormal threshold ranges, it is determined that the monitoring area meets the preset early warning conditions, and the early warning level corresponding to the abnormal threshold range is obtained;

[0043] The monitoring area that meets the preset warning conditions is determined as the warning area, and audible and visual alarm information and mobile terminal push information corresponding to the warning area and the warning level are generated.

[0044] The system outputs the audible and visual alarm information and sends the push notification from the mobile terminal to the mobile device.

[0045] Secondly, embodiments of this application provide a blood perfusion monitoring device, comprising:

[0046] The information acquisition module is used to acquire a speckle image sequence of the tissue region to be monitored based on the laser emitted to the tissue region to be monitored, and to acquire ambient light information corresponding to the speckle image sequence and / or acquire motion information between adjacent frames of speckle images in the speckle image sequence;

[0047] An image correction module is used to perform anti-interference processing on the speckle image sequence based on the ambient light information and / or the motion information between adjacent frame speckle images to obtain a corrected speckle image sequence;

[0048] The joint analysis module is used to perform spatiotemporal joint speckle contrast calculation on the corrected speckle image sequence to obtain the spatiotemporal joint speckle contrast.

[0049] The result feedback module is used to determine the blood perfusion parameters of the tissue region to be monitored based on the spatiotemporal joint speckle contrast and through a pre-established mapping model between speckle contrast and blood perfusion parameters, and to output warning information when the blood perfusion parameters meet the preset warning conditions.

[0050] Thirdly, embodiments of this application provide a blood perfusion monitoring device, including: at least one processor and a memory;

[0051] The memory stores computer-executed instructions;

[0052] The at least one processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0053] Fourthly, embodiments of this application provide a blood perfusion monitoring system, including: an illumination device, an image acquisition device, an ambient light acquisition device, and a blood perfusion monitoring device;

[0054] The illumination device is used to emit laser light onto the area of ​​the tissue to be monitored;

[0055] The image acquisition device is used to continuously acquire speckle images of the tissue area to be monitored based on laser light emitted to the tissue area to be monitored;

[0056] The ambient light acquisition device is used to acquire ambient light data related to the speckle image;

[0057] The blood perfusion monitoring device is connected to the image acquisition device and the ambient light acquisition device, respectively, and is used to receive the speckle image sequence and the ambient light information, and to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0058] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0059] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0060] This application provides a blood perfusion monitoring method, monitoring device, monitoring system, and medium. By acquiring a speckle image sequence based on laser light emitted to the monitored tissue region, blood flow-related speckle changes in the monitored tissue region can be collected as continuous image data, providing an image data foundation for subsequent determination of blood perfusion parameters. By acquiring ambient light information and / or motion information between adjacent speckle image frames corresponding to the speckle image sequence, interference data affecting the quality of the speckle image sequence can be obtained. This allows subsequent processing to no longer rely solely on the original image itself, but can incorporate the acquisition environment and / or inter-frame variations to correct the image data. By performing anti-interference processing on the speckle image sequence based on ambient light information and / or motion information between adjacent speckle image frames, the influence of ambient light changes and / or tissue movement on the speckle image sequence can be reduced, making the obtained corrected speckle image sequence more stably reflect the speckle changes of the monitored tissue region itself. This corrected speckle image sequence serves as the data foundation for subsequent calculations, reducing interference from non-blood flow factors in the blood perfusion parameter determination process and improving the reliability of the monitoring results. By performing spatiotemporal joint speckle contrast calculation on a sequence of corrected speckle images, and utilizing the spatial distribution characteristics and continuous temporal variation characteristics of the speckle images, a spatiotemporal joint speckle contrast that can more fully characterize the blood flow changes in the monitored tissue region is obtained. Compared to analysis based solely on a single frame image or single-dimensional changes, this processing improves the stability and representativeness of the speckle contrast results. Through a pre-established mapping model between speckle contrast and blood perfusion parameters, the spatiotemporal joint speckle contrast is converted into blood perfusion parameters for the monitored tissue region. When the blood perfusion parameters meet preset warning conditions, a warning message is output, ensuring timely alerts for abnormal blood perfusion states. In summary, this application addresses the technical problem of low monitoring accuracy in existing blood perfusion monitoring due to environmental interference, tissue motion, and insufficient image analysis stability. Attached Figure Description

[0061] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0062] Figure 1 A schematic flowchart of the blood perfusion monitoring method provided in the embodiments of this application;

[0063] Figure 2 This is a schematic flowchart of a method for anti-interference processing of speckle image sequences provided in an embodiment of this application;

[0064] Figure 3 This is a schematic diagram of the blood perfusion monitoring system provided in the embodiments of this application;

[0065] Figure 4 This is a schematic diagram of the blood perfusion monitoring device provided in the embodiments of this application;

[0066] Figure 5 This is a schematic diagram of the blood perfusion monitoring device provided in the embodiments of this application.

[0067] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0068] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0069] In current blood perfusion monitoring, the results are easily affected by factors such as measurement location, contact state, external lighting, and tissue movement, resulting in the inclusion of variations in the acquired signal that are unrelated to the actual blood perfusion state. If the blood flow state is directly calculated based on such interfered data, the obtained blood perfusion results are likely to deviate from the actual tissue blood flow state.

[0070] Based on this, the inventive concept of this application is not simply to acquire speckle images, nor is it simply to perform conventional calculations on the images. Instead, it first acquires a speckle image sequence based on a laser emitted to the tissue region to be monitored, and further acquires ambient light information and / or motion information between adjacent speckle images that can characterize the source of interference. In this way, the speckle image sequence provides the basic image data required for blood perfusion analysis, while the ambient light information and / or motion information provide the basis for the interference that affects the accuracy of the basic image data. On this basis, according to the ambient light information and / or the motion information between adjacent speckle images, the speckle image sequence is subjected to anti-interference processing to obtain a corrected speckle image sequence. Before calculating blood perfusion parameters, the influence of non-blood flow factors caused by environmental changes or tissue movement is reduced, so that the image data on which the subsequent calculations depend is closer to the true speckle change state of the tissue region to be monitored.

[0071] Subsequently, spatiotemporal speckle contrast was calculated on the corrected speckle image sequence to obtain the spatiotemporal speckle contrast ratio. This calculation considers not only the speckle distribution at different spatial locations within the image but also the temporal variations between consecutive frames, enabling the obtained spatiotemporal speckle contrast ratio to more stably characterize blood flow-related changes in the monitored tissue region. Finally, using a pre-established mapping model between speckle contrast and blood perfusion parameters, the spatiotemporal speckle contrast ratio was converted into blood perfusion parameters, and a warning message was output when the blood perfusion parameters met preset warning conditions. This ensures that the determination of blood perfusion parameters is based on image data that has undergone anti-interference processing and spatiotemporal joint analysis, improving the accuracy of blood perfusion monitoring and enabling timely alerts for abnormal states.

[0072] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0073] Figure 1 This is a schematic flowchart of the blood perfusion monitoring method provided in the embodiments of this application, as shown below. Figure 1 As shown, this blood perfusion monitoring method includes:

[0074] S11, based on the laser emitted to the tissue region to be monitored, acquire a speckle image sequence of the tissue region to be monitored, and acquire ambient light information corresponding to the speckle image sequence and / or acquire motion information between adjacent frames of speckle images in the speckle image sequence.

[0075] In this embodiment, the tissue region to be monitored refers to the tissue region for which blood perfusion status monitoring is required. After the laser is emitted to the tissue region to be monitored, the laser interacts with the scattering medium within the tissue region, forming a speckle image on the imaging surface corresponding to the tissue region. The speckle image sequence refers to multiple frames of speckle images obtained according to the acquisition sequence. The speckle image sequence is used to characterize the speckle changes in the tissue region to be monitored over a continuous time period, providing basic data for subsequent anti-interference processing and determination of blood perfusion parameters. Based on the speckle signal formed by the laser emitted to the tissue region to be monitored, multiple frames of speckle images corresponding to the tissue region to be monitored are obtained, and a speckle image sequence is formed according to the acquisition time of each frame of speckle image. By acquiring the speckle image sequence, the image change information of the tissue region to be monitored in the time dimension can be preserved, enabling subsequent processing to determine the blood perfusion status of the tissue region to be monitored based on continuous images rather than single frames.

[0076] Ambient light information refers to the external illumination status information corresponding to the acquisition process of the speckle image sequence. It reflects the changes in background illumination during the acquisition of the speckle image sequence. Acquiring the ambient light information corresponding to the speckle image sequence provides a basis for subsequent processing of the external illumination influence in the speckle image sequence, reducing the impact of external illumination changes on speckle image quality and subsequent calculation results. Motion information between adjacent speckle image frames refers to the image change information between two adjacent speckle image frames in the speckle image sequence. It can characterize the relative changes in the monitored tissue region between adjacent acquisition times. Acquiring the motion information between adjacent speckle image frames in the speckle image sequence provides a basis for subsequent processing of motion influence in the speckle image sequence, reducing the impact of minute movements in the monitored tissue region on the subsequent determination of blood perfusion parameters.

[0077] This embodiment acquires a speckle image sequence based on laser light emitted to the area of ​​the tissue to be monitored, and obtains ambient light information corresponding to the speckle image sequence and / or motion information between adjacent frames of speckle images in the speckle image sequence. This allows subsequent processing to simultaneously or separately obtain the basic image data and interference data. Therefore, it provides a data foundation for anti-interference processing of speckle image sequences, spatiotemporal joint speckle contrast calculation, and determination of blood perfusion parameters, improving the reliability of blood perfusion monitoring results.

[0078] S12, based on ambient light information and / or motion information between adjacent speckle images, perform anti-interference processing on the speckle image sequence to obtain a corrected speckle image sequence.

[0079] In this embodiment, anti-interference processing refers to correcting or suppressing the speckle image sequence based on interference-related information generated during the acquisition process, thereby reducing the impact of non-blood flow factors on the speckle image sequence. The corrected speckle image sequence refers to the speckle image sequence obtained after anti-interference processing, which, compared to the unprocessed speckle image sequence, more stably reflects speckle changes related to blood perfusion status in the monitored tissue region.

[0080] In one implementation, ambient light information can be used to perform anti-interference processing on the speckle image sequence. Ambient light information can characterize the external lighting conditions during the acquisition of the speckle image sequence. By using ambient light information as the processing basis, the influence of external lighting changes on the image intensity distribution in the speckle image sequence can be reduced, making the obtained corrected speckle image sequence more suitable for subsequent speckle contrast calculation, thus weakening the influence of external lighting changes.

[0081] In another implementation, the speckle image sequence can be processed to resist interference based on the motion information between adjacent speckle image frames. The motion information between adjacent speckle image frames can characterize the image changes of the speckle image sequence between adjacent acquisition times. By using the motion information between adjacent speckle image frames as the processing basis, the impact of minute movements in the monitored tissue area on the speckle image sequence can be reduced, making the corrected speckle image sequence more stable in the time dimension.

[0082] In another implementation, anti-interference processing of the speckle image sequence can be performed simultaneously based on ambient light information and motion information between adjacent speckle images. By considering both external lighting conditions and changes in adjacent frames, the anti-interference processing can take into account both lighting and motion interference, reducing variations in the speckle image sequence that are unrelated to blood perfusion status, resulting in a corrected speckle image sequence for subsequent calculations. In summary, by performing anti-interference processing based on ambient light information and / or motion information between adjacent speckle images, subsequent spatiotemporal joint speckle contrast calculations can be performed based on image data with minimal interference. This reduces the impact of external lighting changes and / or tissue motion on the determination of blood perfusion parameters, improving the accuracy of blood perfusion monitoring.

[0083] S13, perform spatiotemporal joint speckle contrast calculation on the corrected speckle image sequence to obtain the spatiotemporal joint speckle contrast.

[0084] In this embodiment, spatiotemporal speckle contrast calculation refers to the joint analysis of speckle variations in the corrected speckle image sequence in both spatial and temporal dimensions to obtain contrast information that characterizes the blood perfusion status of the monitored tissue region. After anti-interference processing, the external interference in the corrected speckle image sequence is weakened; therefore, using the corrected speckle image sequence as the calculation object is beneficial for improving the stability and accuracy of spatiotemporal speckle contrast. The spatiotemporal speckle intensity variations at different locations in the corrected speckle image sequence are analyzed, and combined with the changes in the acquisition time sequence of the corrected speckle image sequence, the spatiotemporal speckle contrast corresponding to the monitored tissue region is determined. This spatiotemporal speckle contrast not only reflects the speckle differences at different locations within the monitored tissue region at the same time, but also reflects the speckle variation trend at the same location during continuous acquisition, so that the calculation results simultaneously include spatial distribution information and temporal variation information.

[0085] Spatiotemporal joint speckle contrast is used to characterize the dispersion and dynamic changes of speckle intensity in a corrected speckle image sequence. Since tissue blood perfusion status affects the changes in speckle images, calculating the spatiotemporal joint speckle contrast of the corrected speckle image sequence yields results related to the blood perfusion status of the monitored tissue region, providing data for subsequent determination of blood perfusion parameters based on a mapping model. By calculating the spatiotemporal joint speckle contrast of the corrected speckle image sequence, the determination of subsequent blood perfusion parameters no longer relies solely on image information from a single moment or region, but can comprehensively utilize the spatial differences and temporal variations within the corrected speckle image sequence. This improves the stability of the characterization of the blood perfusion status of the monitored tissue region and reduces the impact of local image fluctuations on the monitoring results.

[0086] S14. Based on the spatiotemporal speckle contrast, the blood perfusion parameters of the tissue area to be monitored are determined through a pre-established mapping model between speckle contrast and blood perfusion parameters. When the blood perfusion parameters meet the preset warning conditions, a warning message is output.

[0087] In this embodiment, the mapping model between speckle contrast and blood perfusion parameters refers to a pre-established data processing model used to characterize the correspondence between speckle contrast and blood perfusion status. This mapping model can convert the spatiotemporal speckle contrast into blood perfusion parameters of the tissue region to be monitored based on a preset correspondence. Blood perfusion parameters are parameters used to characterize the blood perfusion status of the tissue region to be monitored; they reflect changes in blood flow in the monitored tissue region and serve as a basis for subsequent judgment of whether blood flow abnormalities exist in the monitored tissue region. The spatiotemporal speckle contrast is processed to correspond with the pre-established mapping model between speckle contrast and blood perfusion parameters to determine the blood perfusion parameters of the tissue region to be monitored. Since the spatiotemporal speckle contrast has been obtained based on a corrected speckle image sequence, these blood perfusion parameters can reflect the blood perfusion status of the tissue region to be monitored while reducing the influence of environmental and / or motion interference.

[0088] Preset warning conditions refer to pre-defined criteria used to determine whether blood perfusion parameters correspond to abnormal states. After determining the blood perfusion parameters of the tissue area to be monitored, these parameters can be compared with the preset warning conditions. When the blood perfusion parameters meet the preset warning conditions, it indicates that the blood perfusion status of the monitored tissue area has reached a state requiring alert, and a warning message is output. Warning messages are used to indicate abnormal blood perfusion status or information requiring attention, enabling relevant personnel to promptly understand changes in blood perfusion in the monitored tissue area. Outputting warning messages when blood perfusion parameters meet the preset warning conditions allows blood perfusion monitoring results to be further transformed from image analysis into parameter results usable for anomaly assessment. This improves the accuracy of blood perfusion parameters and enables timely alerts for abnormal blood perfusion states in the monitored tissue area.

[0089] In one implementation, blood perfusion parameters of the tissue region to be monitored are determined based on speckle image sequences, enabling the monitoring of blood flow status at minute local locations within that region. For example, this can achieve a spatial resolution of 0.1 mm, allowing for more refined characterization of blood perfusion changes in minute areas such as skin wounds and the retina. By determining the spatiotemporal combined speckle contrast at each monitoring location and mapping it to obtain blood perfusion parameters, a distribution of blood perfusion parameters at different locations within the monitored tissue region can be established, enabling the monitoring results to reflect the spatial distribution differences within the monitored tissue region.

[0090] Spatiotemporal speckle contrast calculation and blood perfusion parameter determination can be performed in real-time. For example, the data processing time for a single frame speckle image can be less than 20 milliseconds, and the system response time from abnormal blood perfusion parameters to early warning output can be less than 1 second. By processing speckle image sequences in real time and outputting early warning information when preset early warning conditions are met, the time between image acquisition and abnormality notification can be shortened, ensuring timely notification when abnormal blood perfusion occurs in the monitored tissue area. Furthermore, through ambient light background correction, motion compensation, and spatiotemporal speckle contrast calculation, the impact of ambient light changes, tissue motion, and single-frame image fluctuations on blood perfusion parameters can be reduced, making the blood perfusion parameters closer to the actual blood perfusion state of the monitored tissue area. For example, the monitoring error can be controlled within the range of less than 3%. By reducing errors caused by non-blood flow factors, the accuracy and stability of blood perfusion parameters can be improved.

[0091] In one embodiment, the anti-interference processing includes ambient light background correction and / or motion compensation. Ambient light background correction reduces the impact of external illumination changes corresponding to ambient light information on the speckle image sequence, while motion compensation reduces the impact of relative motion between adjacent speckle image frames on the speckle image sequence. By performing anti-interference processing on the speckle image sequence, the image data subsequently used for spatiotemporal joint speckle contrast calculation can be made more stable, improving the accuracy of blood perfusion parameter determination.

[0092] Furthermore, Figure 2 This is a schematic flowchart illustrating a method for anti-interference processing of speckle image sequences provided in an embodiment of this application. Based on the above embodiments, as follows... Figure 2 As shown, based on ambient light information and motion information between adjacent speckle images, anti-interference processing is performed on the speckle image sequence to obtain a corrected speckle image sequence, including:

[0093] S21, Based on the ambient light information, perform ambient light background correction on the speckle image sequence to obtain the background-corrected speckle image sequence;

[0094] S22, based on the motion information between adjacent speckle images, perform motion compensation on the background-corrected speckle image sequence to obtain the corrected speckle image sequence.

[0095] Specifically, ambient light information can characterize the external illumination state during the acquisition of speckle image sequences. By using ambient light information as a correction basis, the influence of external illumination changes on speckle image sequences can be weakened, making the background-corrected speckle image sequences better reflect the speckle changes generated by the monitored tissue region. After obtaining the background-corrected speckle image sequence, motion compensation is performed on the background-corrected speckle image sequence based on the motion information between adjacent speckle image frames. The motion information between adjacent speckle image frames can reflect the relative changes in image content during continuous acquisition. By processing the background-corrected speckle image sequence based on this motion information, the influence of minute movements in the monitored tissue region on the speckle image sequence can be reduced, making the corrected speckle image sequence have better continuity and consistency in the time dimension. By incorporating ambient light background correction and / or motion compensation into anti-interference processing, ambient light interference and / or motion interference in the speckle image sequence are reduced. The corrected speckle image sequence can serve as input data for subsequent spatiotemporal joint speckle contrast calculation, reducing the transmission of interference factors to the blood perfusion parameter determination process and improving the accuracy and stability of blood perfusion monitoring results.

[0096] In one embodiment, spatiotemporal joint speckle contrast calculation is performed on the corrected speckle image sequence to obtain the spatiotemporal joint speckle contrast, including:

[0097] S131, According to the preset window size, determine multiple calculation windows on each frame of the corrected speckle image in the corrected speckle image sequence;

[0098] S132, based on each calculation window in each frame of corrected speckle image, calculate the mean gray value and standard deviation of the gray value of the pixels in the calculation window;

[0099] S133, determine the spatial speckle contrast corresponding to each calculation window based on the grayscale standard deviation and grayscale mean;

[0100] S134, Based on the acquisition timing of the corrected speckle image sequence, obtain the spatial speckle contrast of the calculation window at the same monitoring location in multiple consecutive frames of corrected speckle images;

[0101] S135, perform weighted smoothing on the spatial speckle contrast at the same monitoring location to obtain the spatiotemporal joint speckle contrast at the same monitoring location;

[0102] S136, Based on the spatiotemporal joint speckle contrast corresponding to each monitoring location, determine the spatiotemporal joint speckle contrast distribution of the tissue region to be monitored.

[0103] In this embodiment, a preset window size is used to define the image range for local speckle analysis within the corrected speckle image sequence. The corrected speckle images in the sequence are arranged according to their acquisition time sequence, with each frame corresponding to the speckle distribution state of the tissue region to be monitored at a given acquisition time. Based on the preset window size, multiple calculation windows are determined on each frame of the corrected speckle image, with each calculation window corresponding to a local region within the tissue region to be monitored. The calculation windows can slide across the corrected speckle image to cover different monitoring locations within the tissue region. This allows subsequent calculations to reflect the differences in speckle changes at different locations within the monitored tissue region, meeting the needs of analyzing blood perfusion status in small areas.

[0104] After determining multiple calculation windows, for each calculation window in each frame of corrected speckle image, the mean gray value and standard deviation of the pixel gray values ​​within the calculation window are calculated. The mean gray value is used to characterize the average level of speckle intensity within the calculation window, and the standard deviation gray value is used to characterize the dispersion of speckle intensity within the calculation window. Since blood flow motion causes changes in the intensity distribution in the speckle image, the mean gray value and standard deviation gray value can serve as basic statistical quantities to characterize local speckle variations. By calculating the mean gray value and standard deviation gray value separately within each calculation window, a quantitative description of the local speckle intensity distribution at each monitoring location is achieved.

[0105] The spatial speckle contrast for each calculation window is determined based on the gray-level standard deviation and gray-level mean. Spatial speckle contrast characterizes the fluctuation of speckle intensity in local areas of a single-frame corrected speckle image. In one specific implementation, the spatial speckle contrast can be determined based on the ratio between the gray-level standard deviation and the gray-level mean; that is, the degree of change in the gray-level standard deviation relative to the gray-level mean is used as the basis for calculating the spatial speckle contrast. In this way, each calculation window in each frame of the corrected speckle image can obtain its corresponding spatial speckle contrast, forming a local contrast distribution of the monitored tissue region in the spatial dimension. This achieves spatial characterization of local blood flow-related speckle changes in the monitored tissue region.

[0106] After obtaining the spatial speckle contrast corresponding to each calculation window, the spatial speckle contrast of the calculation window at the same monitoring location is obtained in multiple consecutive frames of corrected speckle images. The same monitoring location refers to the calculation location corresponding to the same local location within the monitored tissue region in multiple consecutive frames of corrected speckle images. Since the corrected speckle image sequence is arranged continuously according to the acquisition time sequence, the spatial speckle contrasts corresponding to the same monitoring location in multiple consecutive frames of corrected speckle images can be composed of a time series to characterize the speckle changes at that same monitoring location during continuous acquisition. This allows for further analysis of the spatial speckle contrast in the time dimension, avoiding the influence of local fluctuations caused by relying solely on single-frame image results.

[0107] A weighted smoothing process is applied to the spatial speckle contrast at the same monitoring location. Weighted smoothing refers to fusing the spatial speckle contrast at the same monitoring location in multiple consecutive frames of corrected speckle images according to preset weights, so that speckle variations from multiple frames collectively contribute to the contrast determination at the same monitoring location. The preset weights can be set according to the acquisition sequence of the corrected speckle image sequence; for example, spatial speckle contrast closer to the current acquisition time can have a higher weight, while spatial speckle contrast farther from the current acquisition time can have a lower weight. This reduces the impact of single-frame noise or instantaneous fluctuations on the speckle contrast results. It achieves joint analysis of spatial and temporal dimensions, improving the stability of the speckle contrast results.

[0108] Based on the spatiotemporal joint speckle contrast at each monitoring location, the spatiotemporal joint speckle contrast distribution of the monitored tissue region is determined. This distribution characterizes the changes in spatiotemporal joint speckle contrast at different monitoring locations within the monitored tissue region, and can be represented as a parameter matrix, distribution data, or a visualized image. Since the spatiotemporal joint speckle contrast at each monitoring location is jointly determined by the local spatial speckle contrast and the temporal smoothing results across multiple frames, this distribution can simultaneously reflect both local spatial differences and continuous temporal changes within the monitored tissue region. This provides a more stable data foundation for subsequently determining blood perfusion parameters based on the spatiotemporal joint speckle contrast.

[0109] In one embodiment, step S21 is provided as an implementation method. Based on the above embodiment, ambient light background correction is performed on the speckle image sequence according to ambient light information to obtain a background-corrected speckle image sequence, including:

[0110] S211, perform grayscale and normalization processing on each frame of the speckle image sequence to obtain a normalized speckle image sequence.

[0111] Specifically, grayscale processing converts each frame of speckle image into a grayscale image that can characterize pixel intensity changes, ensuring that subsequent processing is based on uniform grayscale data. Normalization processing adjusts the pixel grayscale values ​​of each frame of speckle image to a preset grayscale range, for example, converting the grayscale values ​​of different frames to the same dimension or numerical range to reduce overall grayscale differences caused by variations in acquisition conditions. Through grayscale and normalization processing, the speckle image sequence maintains consistency in format and numerical range, providing a stable data foundation for subsequent abnormal pixel correction and ambient light background correction.

[0112] S212, identify abnormal pixels in the normalized speckle image sequence whose gray values ​​meet the preset abnormal conditions, and correct the abnormal pixels according to the gray values ​​of the neighboring pixels to obtain the preprocessed speckle image sequence.

[0113] Specifically, abnormal pixels refer to pixels in a normalized speckle image sequence whose grayscale performance does not conform to normal imaging patterns (i.e., bad pixels). Preset abnormality conditions can include a pixel grayscale value greater than a preset upper grayscale limit, a pixel grayscale value less than a preset lower grayscale limit, or a difference between the pixel grayscale value and the average grayscale value of its neighboring pixels exceeding a preset difference. When correcting abnormal pixels, the original grayscale value of the abnormal pixel can be replaced with the average, median, or interpolation result of its surrounding pixels, ensuring that the corrected pixel grayscale value matches the grayscale variation trend of the surrounding area. Through abnormal pixel correction, local abnormal pixels generated during the acquisition process are processed, reducing the impact of abnormal pixels on subsequent determination of ambient light background intensity and adaptive segmentation threshold determination.

[0114] S213, Based on the ambient light information, determine the ambient light background intensity corresponding to the preprocessed speckle image sequence.

[0115] Specifically, ambient light information can be the external illumination intensity information acquired synchronously during the acquisition of the speckle image sequence. The ambient light background intensity is used to characterize the background components in the preprocessed speckle image sequence caused by external ambient light. Since there is a correspondence between ambient light information and the speckle image sequence, the ambient light information can be mapped to the preprocessed speckle image sequence based on the acquisition time or acquisition interval of each frame of speckle image to obtain the ambient light background intensity used for subsequent correction. Determining the ambient light background intensity through ambient light information allows ambient light background correction to be performed based on the actual illumination conditions at the time of acquisition, improving the targeting of background correction.

[0116] S214. Determine the adaptive segmentation threshold based on the ambient light background intensity and the grayscale distribution of each frame of speckle image in the preprocessed speckle image sequence.

[0117] Specifically, grayscale distribution is used to characterize the distribution of different grayscale values ​​in each frame of the speckle image, while ambient light background intensity is used to characterize the degree of influence of background illumination on image grayscale under the current acquisition conditions. The adaptive segmentation threshold is a dynamically determined segmentation threshold based on ambient light background intensity and grayscale distribution. Unlike a fixed threshold, it can be adjusted according to changes in ambient light and image grayscale distribution. For example, when the ambient light background intensity is high, the adaptive segmentation threshold can be adjusted accordingly to avoid the ambient light background being misjudged as a valid speckle signal; when the ambient light background intensity is low, the adaptive segmentation threshold can be determined based on the image's own grayscale distribution to retain more valid speckle information. By determining the adaptive segmentation threshold, the distinction between background and valid regions under varying ambient light conditions is achieved.

[0118] In one specific embodiment, the grayscale distribution of all pixels in each frame of the speckle image is statistically analyzed to form a corresponding grayscale histogram. The grayscale histogram is then corrected for background shift based on the ambient light background intensity to reduce the impact of ambient light variations on the grayscale distribution. Subsequently, by combining the concentrated areas, peak positions, and grayscale change trends of the grayscale values ​​in the corrected grayscale histogram, a segmentation boundary is determined to distinguish between the ambient light background region and the effective speckle region. For example, the combination of the grayscale mean and grayscale standard deviation can be used as the threshold adjustment basis. By superimposing a preset proportion of the grayscale standard deviation on the grayscale mean, a corresponding adaptive segmentation threshold is obtained. Since the ambient light background intensity and the grayscale distribution of the speckle image may change at different acquisition times, the determined adaptive segmentation threshold can be dynamically adjusted according to the grayscale characteristics of the current speckle image, making the segmentation result between the ambient light background region and the effective speckle region more accurate. By combining the ambient light background intensity and the grayscale distribution of the speckle image to jointly determine the adaptive segmentation threshold, adaptive separation of speckle image regions under different lighting conditions is achieved, improving the accuracy of subsequent background noise suppression processing.

[0119] S215, based on an adaptive segmentation threshold, determines the ambient light background region and the effective speckle region from the preprocessed speckle image sequence.

[0120] Specifically, the ambient light background region refers to the image region in the preprocessed speckle image sequence that is mainly affected by the ambient light background, while the effective speckle region refers to the image region in the preprocessed speckle image sequence that mainly contains the effective speckle signal formed by the laser emitted to the tissue region to be monitored. By dividing the preprocessed speckle image sequence into regions using an adaptive segmentation threshold, the ambient light background components and the effective speckle signal can be distinguished, allowing subsequent background noise suppression processing to target the effective speckle region. By determining the ambient light background region and the effective speckle region, changes in the ambient light background are avoided from being directly introduced into the subsequent speckle contrast calculation process.

[0121] S216, Based on the grayscale information corresponding to the ambient light background area, background noise suppression processing is performed on the effective speckle area to obtain a speckle image sequence after background correction.

[0122] Specifically, the grayscale information corresponding to the ambient light background region can be used to estimate the ambient light background noise level in the preprocessed speckle image sequence. Background noise suppression processing can subtract, compensate, or weaken the background components in the effective speckle region based on this grayscale information, making the image grayscale in the effective speckle region closer to the grayscale distribution formed by the laser speckle signal. Through background noise suppression processing, the ambient light background noise is corrected while preserving the speckle variation information in the effective speckle region.

[0123] In summary, the speckle image sequence undergoes sequential processing including grayscale conversion, normalization, abnormal pixel correction, ambient light background intensity determination, adaptive segmentation threshold determination, partitioning of the ambient light background region and the effective speckle region, and background noise suppression, ultimately yielding a background-corrected speckle image sequence. This background-corrected speckle image sequence reduces the impact of ambient light variations and local abnormal pixels on the image grayscale distribution, providing a more stable image data foundation for subsequent motion compensation, spatiotemporal joint speckle contrast calculation, and blood perfusion parameter determination, thereby improving the accuracy of blood perfusion monitoring results.

[0124] In one embodiment, step S22 is provided as an implementation method. Based on the above embodiment, motion compensation is performed on the background-corrected speckle image sequence according to the motion information between adjacent frame speckle images to obtain a corrected speckle image sequence, including:

[0125] S221, based on the acquisition timing of the background-corrected speckle image sequence, select two adjacent speckle images from the background-corrected speckle image sequence in sequence as the reference frame and the frame to be compensated, respectively.

[0126] Specifically, the acquisition sequence refers to the order in which the speckle images in each frame are acquired. Based on the acquisition sequence of the background-corrected speckle image sequence, two adjacent speckle images are selected sequentially from the sequence, serving as the reference frame and the frame to be compensated, respectively. This allows motion compensation processing to be performed frame-by-frame according to the continuous relationship of image acquisition, avoiding excessive differences between images across time points that could affect the determination of motion information. The reference frame is used as the reference image for displacement correction in the frame to be compensated, and the frame to be compensated is used for subsequent compensation processing based on its displacement relative to the reference frame.

[0127] S222, perform optical flow analysis on the reference frame and the frame to be compensated to obtain the pixel displacement information of the frame to be compensated relative to the reference frame.

[0128] Specifically, optical flow analysis refers to a processing method that estimates pixel position changes in an image based on the changes in pixel grayscale or image texture between adjacent frames. Pixel displacement information is used to characterize the displacement of pixels in the frame to be compensated relative to their corresponding pixels in the reference frame, and this can include the displacement direction and magnitude. Pixel-level comparisons are performed on two adjacent speckle images to calculate the gradient changes in pixel displacement; by minimizing the displacement energy function, the direction and magnitude of pixel displacement are estimated to form a motion vector field; based on the motion vector field, pixels with significant displacement changes in the tissue motion region are identified and marked as the areas to be compensated. Because the monitored tissue region may be affected by respiration, limb micro-movements, or minor movements of the tissue surface during acquisition, the frame to be compensated may have a local positional shift relative to the reference frame. Obtaining pixel displacement information through optical flow analysis provides a basis for subsequent identification of tissue motion regions and inter-frame displacement compensation.

[0129] S223, based on pixel displacement information, identify the tissue motion region in the frame to be compensated.

[0130] Specifically, based on the magnitude, direction, or continuity of displacement corresponding to different pixels in the pixel displacement information, regions in the frame to be compensated that exhibit tissue motion are identified. Regions with small displacement changes or no obvious positional changes can be considered less affected by tissue motion; regions with displacement changes meeting preset judgment conditions can be identified as tissue motion regions. Identifying tissue motion regions through pixel displacement information enables the localization of motion artifacts, avoiding indiscriminate compensation of the entire frame and thus affecting speckle information in areas without obvious motion.

[0131] S224, based on the pixel displacement information corresponding to the tissue motion region, perform inter-frame displacement compensation on the pixels within the tissue motion region to obtain the compensated speckle image.

[0132] Specifically, inter-frame displacement compensation refers to correcting the position or pixel value of pixels within the tissue motion region based on the pixel displacement information of the frame to be compensated relative to the reference frame, so that the tissue motion region in the frame to be compensated is spatially consistent with or nearly consistent with the corresponding region in the reference frame. This process can reduce speckle image misalignment caused by breathing, limb micro-movements, or minor tissue surface movements, and reduce the impact of motion artifacts on subsequent spatiotemporal joint speckle contrast calculations. By performing inter-frame displacement compensation on pixels within the tissue motion region, the correction of moving pixels is achieved, enabling the compensated speckle image to more stably reflect the speckle changes in the monitored tissue region.

[0133] S225, according to the acquisition time sequence, the compensated speckle images are arranged to obtain the corrected speckle image sequence.

[0134] Specifically, the compensated speckle images are reverted to their original temporal positions within the background-corrected speckle image sequence according to their corresponding acquisition times, ensuring that the resulting corrected speckle image sequence maintains temporal continuity consistent with the original acquisition process. The corrected speckle image sequence is an image sequence that has undergone background correction and motion compensation. It reduces the impact of ambient light background and tissue motion on the speckle images while retaining speckle change information related to the blood perfusion status of the monitored tissue area. By arranging the compensated speckle images according to the acquisition sequence, the temporal continuity of the corrected speckle image sequence is guaranteed, ensuring its stability for subsequent spatiotemporal joint speckle contrast calculation.

[0135] In summary, the background-corrected speckle image sequence undergoes sequential processing including adjacent frame selection, optical flow analysis, pixel displacement information determination, tissue motion region identification, inter-frame displacement compensation, and temporal arrangement to obtain the corrected speckle image sequence. This reduces signal distortion caused by tissue movements such as respiration and limb micro-movements, making subsequent spatiotemporal joint speckle contrast calculations based on the corrected speckle image sequence more stable and improving the accuracy of blood perfusion parameter determination.

[0136] In one embodiment, the blood perfusion parameters include at least one of blood flow velocity, blood perfusion volume, and blood flow distribution density. Specifically, the blood perfusion parameters characterize the blood perfusion status of the monitored tissue region; blood flow velocity characterizes the speed of blood flow movement within the monitored tissue region; blood perfusion volume characterizes the degree of blood supply per unit area or unit time; and blood flow distribution density characterizes the spatial density of blood flow distribution within the monitored tissue region. By setting the blood perfusion parameters to at least one of the above parameters, the monitoring results can not only reflect the changes in the speckle image but also be further transformed into parameter results for evaluating the tissue blood flow status.

[0137] Furthermore, based on the spatiotemporal joint speckle contrast, the blood perfusion parameters of the tissue region to be monitored are determined through a pre-established mapping model between speckle contrast and blood perfusion parameters, including:

[0138] S1411, Obtain the pre-established mapping model between speckle contrast and blood perfusion parameters;

[0139] S1412, input the spatiotemporal speckle contrast corresponding to each monitoring location into the mapping model to determine at least one of the blood flow velocity, blood flow perfusion, and blood flow distribution density corresponding to each monitoring location.

[0140] In this embodiment, a pre-established mapping model between speckle contrast and blood perfusion parameters is used to characterize the correspondence between spatiotemporal joint speckle contrast and blood perfusion parameters. This mapping model can be pre-established based on the relationship between changes in speckle contrast and changes in blood perfusion state, enabling the spatiotemporal joint speckle contrast to be converted into at least one of the corresponding blood flow velocity, blood perfusion volume, and blood flow distribution density. This mapping model can exist in the form of a correspondence, a function, a parameter relationship, or a data table, as long as the corresponding blood perfusion parameters can be determined based on the input spatiotemporal joint speckle contrast.

[0141] After obtaining the spatiotemporal speckle contrast at each monitoring location within the monitored tissue region, the spatiotemporal speckle contrast at each monitoring location is input into a mapping model to determine at least one of the following: blood flow velocity, blood perfusion volume, and blood flow distribution density at each monitoring location. Each monitoring location can correspond to a local location within the monitored tissue region, and the spatiotemporal speckle contrast reflects the degree of speckle change at that local location after joint processing in both spatial and temporal dimensions. Based on the spatiotemporal speckle contrast, the mapping model outputs the blood perfusion parameters corresponding to that monitoring location, ensuring that each monitoring location receives a corresponding blood flow state characterization result.

[0142] In one specific implementation, if the tissue region to be monitored includes multiple monitoring locations, the spatiotemporal speckle contrast corresponding to each monitoring location can be input into the mapping model to obtain at least one of blood flow velocity, blood perfusion volume, and blood flow distribution density corresponding to each monitoring location. The blood perfusion parameter distribution of the tissue region to be monitored is then formed from the blood perfusion parameters corresponding to each monitoring location. This blood perfusion parameter distribution can reflect the differences in blood perfusion at different locations within the tissue region to be monitored, and is suitable for monitoring local blood flow changes in small areas such as wounds and the retina. By determining parameters at the monitoring location level, the conversion from the spatiotemporal speckle contrast distribution to the blood perfusion parameter distribution is achieved.

[0143] This processing procedure corresponds to an improved speckle contrast analysis algorithm, also known as the Corrected Speckle Contrast Analysis algorithm. Its core lies in obtaining the spatiotemporal joint speckle contrast based on the anti-interference processed speckle image results, and outputting at least one of blood flow velocity, blood flow perfusion volume, and blood flow distribution density through a mapping model between speckle contrast and blood perfusion parameters. In this way, image change information in the speckle image sequence is converted into parameter information that can characterize the blood perfusion state, improving the usability and accuracy of blood perfusion monitoring results.

[0144] This embodiment acquires a pre-established mapping model between speckle contrast and blood perfusion parameters, and inputs the spatiotemporal joint speckle contrast corresponding to each monitoring location into the mapping model to determine at least one of the blood flow velocity, blood perfusion volume, and blood flow distribution density at each monitoring location, thus achieving a quantitative conversion from speckle contrast results to blood perfusion parameters. Since the spatiotemporal joint speckle contrast input to the mapping model has undergone anti-interference processing and spatiotemporal joint calculation, its corresponding blood perfusion parameters can reduce the impact of ambient light changes, tissue motion, and single-frame fluctuations on the monitoring results, improving the accuracy and stability of blood perfusion parameter determination for the monitored tissue area.

[0145] In one embodiment, when the blood perfusion parameters meet preset warning conditions, a warning message is output, including:

[0146] S1421, obtain multiple preset abnormal threshold ranges corresponding to the current monitoring scenario.

[0147] Specifically, the current monitoring scenario refers to the clinical or testing context in which the blood perfusion monitoring method is currently applied. Under different current monitoring scenarios, the normal and abnormal blood perfusion states of the monitored tissue area may differ. For example, the current monitoring scenario could be wound healing monitoring, retinal blood flow monitoring, or cerebral blood flow status monitoring. Obtaining multiple preset abnormal threshold ranges corresponding to the current monitoring scenario is to ensure that the judgment of abnormalities in blood perfusion parameters can be adapted to different tissue areas and different medical application needs. The preset abnormal threshold range is a pre-set parameter range used to characterize abnormalities in blood perfusion status, which can be determined based on at least one of the blood flow velocity, blood perfusion volume, and blood flow distribution density corresponding to the current monitoring scenario.

[0148] S1422, compare the blood perfusion parameters corresponding to each monitoring area in the tissue region to be monitored with the preset abnormal threshold range.

[0149] Specifically, the monitoring area is a local region within the tissue region to be monitored, which may correspond to one or more monitoring locations. Since the blood perfusion status may differ between different local areas within the monitored tissue region, comparisons made on a regional basis can determine the specific location of areas within the monitored tissue region where blood perfusion abnormalities occur. This achieves regionalized abnormality assessment of the monitored tissue region.

[0150] S1423, when the blood perfusion parameters in the monitoring area meet any preset abnormal threshold range, determine that the monitoring area meets the preset early warning conditions, and obtain the early warning level corresponding to the abnormal threshold range.

[0151] Specifically, the preset warning conditions are used to determine whether a warning needs to be triggered in the monitored area, and the warning level is used to characterize the degree of abnormal blood perfusion. Multiple preset abnormal threshold ranges can correspond to different warning levels respectively. For example, a mild abnormal range can correspond to a lower warning level, and a severe abnormal range can correspond to a higher warning level. By matching the blood perfusion parameters with multiple preset abnormal threshold ranges, it is possible to determine the corresponding warning level while determining whether to give a warning, so that the subsequent output warning information can reflect the degree of abnormality.

[0152] S1424, determine the monitored area that meets the preset warning conditions as the warning area, and generate an audible and visual alarm message and a mobile terminal push message corresponding to the warning area and the warning level.

[0153] Specifically, the audible and visual alarm message can be used to trigger a sound prompt and a light prompt, enabling on-site medical staff to promptly learn that there is an abnormality in the tissue area to be monitored. The mobile terminal push message can include the warning area, the warning level, and the corresponding blood perfusion parameter information, and is used to send a prompt to the mobile terminal, enabling medical staff to view the warning content on the mobile terminal. This step realizes the linkage between on-site prompts and remote prompts by generating the audible and visual alarm message and the mobile terminal push message simultaneously.

[0154] S1425, output the audible and visual alarm message, and send the mobile terminal push message to the mobile terminal.

[0155] Specifically, the mobile terminal can be a mobile device used by medical staff, such as a mobile phone or a tablet device. By outputting the audible and visual alarm message, it is possible to promptly prompt abnormal blood perfusion at the monitoring site; by sending the mobile terminal push message to the mobile terminal, medical staff can promptly obtain the abnormal information corresponding to the warning area and the warning level, facilitating subsequent viewing and intervention. This step improves the timeliness of abnormal blood perfusion prompts through the coordinated output of the audible and visual alarm message and the mobile terminal push message. Through the above processing, the blood perfusion monitoring result can not only reflect the blood flow state of the tissue area to be monitored, but also promptly prompt the corresponding area and the degree of abnormality when an abnormality occurs, improving the clinical practicability of blood perfusion monitoring.

[0156] In a specific implementation, after determining the warning area, the warning area can be marked in the display image corresponding to the tissue area to be monitored. The display image can be a speckle image, a blood perfusion parameter distribution map, or a visualization image corresponding to the tissue area to be monitored. By marking the warning area in the display image, the warning information can not only include an abnormal prompt, but also indicate the specific location where abnormal blood perfusion occurs in the tissue area to be monitored. This processing method is used in conjunction with the audible and visual alarm message and the mobile terminal push message, enabling medical staff to quickly locate the abnormal area after receiving the warning.

[0157] When generating push notifications for mobile devices, the notifications can include the warning area, warning level, corresponding blood perfusion parameters, and the location of the warning area within the monitored tissue. After receiving the push notification, the mobile terminal can display the image markers corresponding to the warning area, enabling medical personnel to determine the extent and severity of the blood perfusion abnormality based on the spatial location of the warning area. This achieves a combination of abnormality alerts and abnormality localization.

[0158] In one specific implementation, the blood perfusion monitoring method continuously acquires speckle image sequences at a preset acquisition frequency and continuously updates the blood perfusion parameters of the monitored tissue area based on the continuously acquired speckle image sequences. The continuous monitoring period can be set according to clinical monitoring needs, for example, it can support 24-hour continuous monitoring. By continuously acquiring speckle image sequences and continuously updating blood perfusion parameters, the blood perfusion status of the monitored tissue area can be dynamically observed over a long period of time, which is suitable for wound healing process, postoperative tissue perfusion status, and other scenarios that require continuous observation.

[0159] During continuous monitoring, speckle image sequences, blood perfusion parameters, early warning information, audible and visual alarm information, and mobile push notifications can be stored. The speckle image sequences can be used to review the image status of the monitored tissue area at different time points; blood perfusion parameters can be used to analyze the blood flow change trend in the monitored tissue area; and early warning information, audible and visual alarm information, and mobile push notifications can be used to record the time, location, and severity of abnormalities. Storing this data forms a complete monitoring data record, facilitating subsequent analysis and traceability.

[0160] In another specific implementation, a monitoring timeline can be established based on the generation time of speckle image sequences, blood perfusion parameters, and early warning information. The monitoring timeline is used to sequentially associate speckle image sequences, blood perfusion parameters, and early warning information corresponding to different time points. Through the monitoring timeline, historical monitoring data of the monitored tissue area can be queried based on a specified time point or time interval, enabling medical staff to trace the changes in blood perfusion in the monitored tissue area over a continuous monitoring period. Through continuous monitoring, data storage, and the establishment of the monitoring timeline, long-term preservation and retrospective querying of blood perfusion monitoring data are achieved. Therefore, the blood perfusion monitoring results can not only be used for anomaly detection at the current moment but also for analyzing the trend of blood perfusion changes in the monitored tissue area over a period of time, improving the usability of monitoring data in disease observation and treatment plan adjustment.

[0161] Furthermore, in one specific implementation, a blood perfusion monitoring report is generated based on stored speckle image sequences, blood perfusion parameters, early warning information, and a monitoring timeline. The blood perfusion monitoring report may include the monitoring time of the monitored tissue area, changes in blood perfusion parameters, early warning area, early warning level, early warning occurrence time, and corresponding image labeling results. By integrating image data, parameter data, and early warning records into the blood perfusion monitoring report, complete monitoring data can be provided for disease analysis and treatment plan adjustments.

[0162] Blood perfusion monitoring reports can be generated as Portable Document Format (PDF) files. This format facilitates the saving, transmission, printing, and archiving of the reports. The reports can be used by healthcare professionals to view changes in blood perfusion in the monitored tissue area over a continuous monitoring period and to assess subsequent treatment measures in conjunction with warning areas and warning levels.

[0163] Figure 3 This is a schematic diagram of the blood perfusion monitoring system provided in an embodiment of this application. Figure 3 As shown, the blood perfusion monitoring system 30 includes: a light source 301, an image acquisition device 302, an ambient light acquisition device 303, and a blood perfusion monitoring device 304.

[0164] Illumination device 301 is used to emit laser light onto the tissue area to be monitored. By emitting low-power laser light, speckle signals for imaging are formed in the tissue area under the action of the laser. Specifically, the laser emitted by illumination device 301 can be a near-infrared low-power laser, such as a laser with a wavelength of 850 nanometers and a power of no more than 5 milliwatts. By using a near-infrared low-power laser, a stable speckle signal can be generated in the tissue area to be monitored while meeting medical safety irradiation requirements. Illumination device 301 can uniformly irradiate the tissue area to be monitored with laser light, so that speckle light that can be acquired by image acquisition device 302 can be generated at different local locations in the tissue area, providing an image basis for subsequent determination of blood perfusion parameters corresponding to different monitoring locations.

[0165] Image acquisition device 302 is used to continuously acquire speckle images of the tissue area to be monitored based on laser light emitted onto the tissue area. The image acquisition device 302 can be spaced apart from the tissue area. Because the image acquisition device 302 can continuously acquire speckle images without contact with the tissue area, this blood perfusion monitoring system is suitable for monitoring scenarios where contact is not advisable, such as wounds and eye tissues, reducing the risk of contamination, infection, or secondary injury caused by contact measurements. In one specific implementation, the image acquisition device 302 can employ a metal-oxide-semiconductor (CMOS) camera. The CMOS camera can have an image resolution of 2048×2048 and continuously acquire speckle images of the tissue area to be monitored at a frame rate of 50 frames per second. With high image resolution and a high frame rate, the speckle changes of the tissue area to be monitored over a continuous time period can be obtained, allowing the speckle images to form a speckle image sequence according to the acquisition time sequence.

[0166] The ambient light acquisition device 303 is used to acquire ambient light data for speckle images. Ambient light data characterizes the external illumination state around the monitored tissue area during speckle image acquisition, such as ambient light intensity. The ambient light acquisition device 303 can work synchronously with the image acquisition device 302, ensuring that the ambient light data corresponds to the speckle image in terms of acquisition time. After receiving the ambient light data, the blood perfusion monitoring device 304 can use this data as ambient light information for subsequent anti-interference processing of the speckle image sequence. By setting up the ambient light acquisition device 303, the blood perfusion monitoring system can simultaneously obtain the external illumination state during image acquisition, providing a corresponding acquisition basis for subsequent ambient light background correction.

[0167] The blood perfusion monitoring device 304 is connected to both the image acquisition device 302 and the ambient light acquisition device 303 to receive speckle image sequences and ambient light information, and to execute the blood perfusion monitoring method. Specifically, the blood perfusion monitoring device 304 may include a processor and a memory. The memory stores computer execution instructions for executing the blood perfusion monitoring method. When the processor executes these instructions, it can perform anti-interference processing, spatiotemporal joint speckle contrast calculation, blood perfusion parameter determination, and early warning information output based on the speckle image sequence and ambient light information. By inputting the data sets from the image acquisition device 302 and the ambient light acquisition device 303 into the blood perfusion monitoring device 304, a data closed loop is achieved between the speckle image data, ambient light data, and subsequent blood perfusion parameter calculations.

[0168] In one specific implementation, the blood perfusion monitoring device 304 can also communicate with an audible and visual alert device and a mobile terminal. When the blood perfusion monitoring device 304 determines that the blood perfusion parameters meet preset warning conditions, it can generate an audible and visual alarm message and output it to the audible and visual alert device, while simultaneously generating a mobile push notification message and sending it to the mobile terminal. The mobile terminal can be a mobile phone or tablet device used by medical personnel. Through the coordination of the audible and visual alarm message and the mobile push notification message, the blood perfusion monitoring system can provide timely on-site and remote alerts when abnormal blood perfusion occurs in the monitored tissue area.

[0169] In one specific implementation, the blood perfusion monitoring device 304 can also store the received speckle image sequence, ambient light information, determined blood perfusion parameters, and output warning information, and form a monitoring timeline based on the storage time. Through the monitoring timeline, speckle images, blood perfusion parameters, and warning information of the monitored tissue area at different time points or time intervals can be retrieved retrospectively. The blood perfusion monitoring device 304 can also generate a blood perfusion monitoring report based on the stored data. The blood perfusion monitoring report can include changes in blood perfusion parameters of the monitored tissue area, warning areas, warning levels, and warning occurrence times. This monitoring report can be generated as a portable document file for saving, transmitting, printing, and archiving.

[0170] Through the aforementioned blood perfusion monitoring system, the illumination device 301, image acquisition device 302, ambient light acquisition device 303, and blood perfusion monitoring device 304 respectively undertake the functions of laser irradiation, speckle image acquisition, ambient light data acquisition, and blood perfusion parameter processing. This can reduce the influence of ambient light changes and tissue movement on speckle image sequences, enabling the determination of blood perfusion parameters to be based on continuous speckle images and corresponding environmental information, thereby improving the accuracy of blood perfusion monitoring results and supporting continuous monitoring, abnormality alerts, and data backtracking of the monitored tissue area.

[0171] Figure 4 This is a schematic diagram of the blood perfusion monitoring device provided in the embodiments of this application, as shown below. Figure 4 As shown, the blood perfusion monitoring device 40 provided in this embodiment includes:

[0172] The information acquisition module 401 is used to acquire a speckle image sequence of the tissue region to be monitored based on the laser emitted to the tissue region to be monitored, as well as acquire ambient light information corresponding to the speckle image sequence and / or acquire motion information between adjacent frames of speckle images in the speckle image sequence;

[0173] The image correction module 402 is used to perform anti-interference processing on the speckle image sequence based on ambient light information and / or motion information between adjacent frame speckle images to obtain a corrected speckle image sequence;

[0174] The joint analysis module 403 is used to perform spatiotemporal joint speckle contrast calculation on the corrected speckle image sequence to obtain the spatiotemporal joint speckle contrast.

[0175] The result feedback module 404 is used to determine the blood perfusion parameters of the tissue area to be monitored based on the spatiotemporal speckle contrast and a pre-established mapping model between speckle contrast and blood perfusion parameters, and to output warning information when the blood perfusion parameters meet the preset warning conditions.

[0176] The blood perfusion monitoring device 40 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0177] Figure 5 This is a schematic diagram of the blood perfusion monitoring device provided in an embodiment of this application. Figure 5 As shown, the blood perfusion monitoring device provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the blood perfusion monitoring device further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0178] In the specific implementation process, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to execute the above-described blood perfusion monitoring method.

[0179] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0180] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0181] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0182] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0183] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0184] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0185] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0186] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0187] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0190] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this 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 of the various embodiments of this 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.

[0191] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0192] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for monitoring blood perfusion, characterized in that, include: Based on the laser emitted to the tissue region to be monitored, a speckle image sequence of the tissue region to be monitored is acquired, as well as ambient light information corresponding to the speckle image sequence and / or motion information between adjacent frames of speckle images in the speckle image sequence is acquired; Based on the ambient light information and / or the motion information between adjacent speckle images, the speckle image sequence is subjected to anti-interference processing to obtain a corrected speckle image sequence; the anti-interference processing includes ambient light background correction and / or motion compensation. Based on a preset sliding time window, the spatiotemporal joint speckle contrast is calculated on the corrected speckle image sequence to obtain the spatiotemporal joint speckle contrast. Based on the spatiotemporal speckle contrast, the blood perfusion parameters of the tissue region to be monitored are determined through a pre-established mapping model between speckle contrast and blood perfusion parameters. When the blood perfusion parameters meet the preset warning conditions, a warning message is output. The mapping model includes the correspondence between the spatiotemporal speckle contrast and the blood perfusion parameters.

2. The blood perfusion monitoring method according to claim 1, characterized in that, The step of performing anti-interference processing on the speckle image sequence based on the ambient light information and the motion information between adjacent speckle images to obtain a corrected speckle image sequence includes: Based on the ambient light information, the speckle image sequence is subjected to ambient light background correction to obtain a background-corrected speckle image sequence; Based on the motion information between adjacent speckle images, motion compensation is performed on the background-corrected speckle image sequence to obtain a corrected speckle image sequence.

3. The blood perfusion monitoring method according to claim 1, characterized in that, The step of performing spatiotemporal joint speckle contrast calculation on the corrected speckle image sequence to obtain the spatiotemporal joint speckle contrast includes: Based on a preset window size, multiple calculation windows are determined on each frame of the corrected speckle image in the corrected speckle image sequence. Based on each calculation window in each frame of corrected speckle image, calculate the mean gray value and standard deviation of the gray value of the pixels within the calculation window; Based on the grayscale standard deviation and the grayscale mean, determine the spatial speckle contrast corresponding to each calculation window; Based on the acquisition timing of the corrected speckle image sequence, the spatial speckle contrast of the calculation window at the same monitoring location in multiple consecutive frames of corrected speckle images is obtained. The spatial speckle contrast at the same monitoring location is weighted and smoothed to obtain the spatiotemporal joint speckle contrast at the same monitoring location. Based on the spatiotemporal joint speckle contrast corresponding to each monitoring location, the spatiotemporal joint speckle contrast distribution of the tissue region to be monitored is determined.

4. The blood perfusion monitoring method according to claim 2, characterized in that, The step of performing ambient light background correction on the speckle image sequence based on the ambient light information to obtain a background-corrected speckle image sequence includes: The speckle image sequence is processed by grayscale conversion and normalization of each frame of speckle image to obtain a normalized speckle image sequence. Identify abnormal pixels in the normalized speckle image sequence whose gray values ​​meet preset abnormal conditions, and correct the abnormal pixels according to the gray values ​​of the neighboring pixels of the abnormal pixels to obtain a preprocessed speckle image sequence. Based on the ambient light information, determine the ambient light background intensity corresponding to the preprocessed speckle image sequence; An adaptive segmentation threshold is determined based on the ambient light background intensity and the grayscale distribution of each frame of speckle image in the preprocessed speckle image sequence. Based on the adaptive segmentation threshold, the ambient light background region and the effective speckle region are determined from the preprocessed speckle image sequence; Based on the grayscale information corresponding to the ambient light background region, background noise suppression processing is performed on the effective speckle region to obtain the background-corrected speckle image sequence.

5. The blood perfusion monitoring method according to claim 2, characterized in that, The step of performing motion compensation on the background-corrected speckle image sequence based on the motion information between the adjacent speckle images to obtain the corrected speckle image sequence includes: Based on the acquisition timing of the background-corrected speckle image sequence, two adjacent speckle images in the background-corrected speckle image sequence are selected sequentially as the reference frame and the frame to be compensated, respectively. Optical flow analysis is performed on the reference frame and the frame to be compensated to obtain the pixel displacement information of the frame to be compensated relative to the reference frame; Based on the pixel displacement information, identify the tissue motion region in the frame to be compensated; Based on the pixel displacement information corresponding to the tissue motion region, inter-frame displacement compensation is performed on the pixels within the tissue motion region to obtain the compensated speckle image. Based on the acquisition timing, the compensated speckle images are arranged to obtain the corrected speckle image sequence.

6. The blood perfusion monitoring method according to claim 1, characterized in that, The blood perfusion parameters include at least one of blood flow velocity, blood perfusion volume, and blood flow distribution density; Accordingly, based on the spatiotemporal joint speckle contrast, the blood perfusion parameters of the tissue region to be monitored are determined through a pre-established mapping model between speckle contrast and blood perfusion parameters, including: Obtain a pre-established mapping model between speckle contrast and blood perfusion parameters; The spatiotemporal speckle contrast corresponding to each monitoring location is input into the mapping model to determine at least one of the blood flow velocity, blood flow perfusion, and blood flow distribution density corresponding to each monitoring location.

7. The blood perfusion monitoring method according to claim 1, characterized in that, When the blood perfusion parameters meet the preset warning conditions, a warning message is output, including: Obtain multiple preset anomaly threshold ranges corresponding to the current monitoring scenario; The blood perfusion parameters corresponding to each monitoring area in the tissue region to be monitored are compared with the preset abnormal threshold range; When the blood perfusion parameters in the monitoring area meet any of the preset abnormal threshold ranges, it is determined that the monitoring area meets the preset early warning conditions, and the early warning level corresponding to the abnormal threshold range is obtained; The monitoring area that meets the preset warning conditions is determined as the warning area, and audible and visual alarm information and mobile terminal push information corresponding to the warning area and the warning level are generated. The system outputs the audible and visual alarm information and sends the push notification from the mobile terminal to the mobile device.

8. A blood perfusion monitoring device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the blood perfusion monitoring method as described in any one of claims 1 to 7.

9. A blood perfusion monitoring system, characterized in that, include: Illumination equipment, image acquisition equipment, ambient light acquisition equipment, and the blood perfusion monitoring equipment as described in claim 8; The illumination device is used to emit laser light onto the area of ​​the tissue to be monitored; The image acquisition device is used to continuously acquire speckle images of the tissue area to be monitored based on laser light emitted to the tissue area to be monitored; The ambient light acquisition device is used to acquire ambient light data related to the speckle image; The blood perfusion monitoring device is connected to the image acquisition device and the ambient light acquisition device, respectively, and is used to receive the speckle image sequence and the ambient light information, and to perform the blood perfusion monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

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