Method, system, equipment and medium for tracking dynamic nerve imaging of in-vivo microvessels

Abstract

The invention relates to a living body capillary dynamic nerve imaging tracking method, system and device and a medium, and the method comprises the steps: collecting the continuous capillary image data of a living body tissue; performing blood vessel segmentation processing on the continuous capillary image data, and identifying a capillary network skeleton for motion analysis; based on the capillary network skeleton, calculating a motion vector of the segmented continuous capillary image data, and obtaining a motion track of red blood cells in the capillary; according to the motion trail, haemodynamic parameters including the flow velocity, the direction and the blood oxygen level are generated; synchronously acquiring local neural activity data of the continuous capillary image data under the same space-time coordinate system; and according to the haemodynamic parameters and the local neural activity data, generating a tracking result of the in-vivo microvascular haemodynamics and neural activity, so as to achieve the purpose of performing synchronous tracking and dynamic correlation analysis on the microvascular haemodynamics and neural activity in the in-vivo environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomedical imaging and neural engineering, and particularly relates to a kind of in vivo microvessel dynamic neural imaging tracking method, system, equipment and medium. BACKGROUND

[0002] In the field of neuroscience and medical research, it is crucial to reveal the dynamic correlation between brain neural activity and local microcirculation. Traditional research methods usually rely on functional magnetic resonance imaging and other technologies, which have limited spatial resolution and cannot clearly observe the fine blood flow dynamics at the capillary level (diameter <100 microns). Although optical microscopic imaging techniques such as OCTA and two-photon microscopy provide the possibility of observing microvessels, in in vivo applications, physiological movements of biological tissues (such as breathing and heartbeat) can introduce severe motion artifacts, greatly interfering with the clear identification of microvessel structures and the accurate tracking of red blood cell movement. In addition, existing technologies are mostly limited to single blood flow or neural signal acquisition, making it difficult to accurately correlate capillary blood flow velocity, direction, and blood oxygen levels with transient neural firing or calcium activity in a strict spatiotemporal synchronization framework, resulting in bias and ambiguity in the analysis of neural vascular coupling mechanisms. SUMMARY

[0003] The main purpose of the present application is to provide an in vivo microvessel dynamic neural imaging tracking method, system, equipment and medium to achieve the purpose of synchronously tracking and dynamically correlating microvessel hemodynamics and neural activity in in vivo environment.

[0004] To achieve the above purpose, the present application provides an in vivo microvessel dynamic neural imaging tracking method, comprising the following steps: acquiring continuous microvessel image data of in vivo tissue; performing blood vessel segmentation processing on the continuous microvessel image data to identify a microvessel network skeleton for motion analysis; based on the microvessel network skeleton, calculating the motion vector of the segmented continuous microvessel image data to obtain the motion trajectory of red blood cells in the microvessel; generating hemodynamic parameters including flow velocity, direction, and blood oxygen level based on the motion trajectory; synchronously acquiring local neural activity data in the same spatiotemporal coordinate system as the continuous microvessel image data; generating tracking results of in vivo microvessel hemodynamics and neural activity based on hemodynamic parameters and local neural activity data.

[0005] Further, the step of acquiring continuous microvessel image data of in vivo tissue comprises: performing three-dimensional scanning on the in vivo tissue region; Adjusting the scanning focal plane dynamically according to the real-time obtained tissue surface topography data, compensating the axial movement caused by physiological activities; Obtaining continuous microvessel image data with motion artifacts suppressed.

[0006] Further, the step of performing blood vessel segmentation processing on the continuous microvessel image data and identifying the microvessel network skeleton for motion analysis includes: Inputting the continuous microvessel image data into a pre-trained deep learning blood vessel segmentation model, which is trained by a data set containing motion artifact enhanced data and has motion robustness; Outputting a pixel-level blood vessel and background segmentation binary image from the deep learning blood vessel segmentation model; Performing morphological skeleton extraction processing on the blood vessel and background segmentation binary image to obtain a microvessel network skeleton for representing the microvessel topological structure.

[0007] Further, based on the microvessel network skeleton, the step of calculating the motion vector of the segmented continuous microvessel image data to obtain the motion trajectory of red blood cells in the microvessel includes: Selecting a continuous image frame sequence in the blood vessel spatial region defined by the microvessel network skeleton; Calculating the spatial displacement of corresponding image blocks between adjacent frames in the blood vessel spatial region to generate an initial motion vector field; Performing spatiotemporal filtering based on motion consistency on the initial motion vector field to eliminate abnormal vectors caused by noise; Obtaining a motion trajectory describing the continuous motion path of red blood cells.

[0008] Further, the step of generating blood flow hemodynamic parameters including flow velocity, direction and blood oxygen level based on the motion trajectory includes: Based on the displacement of red blood cells in the motion trajectory and the corresponding time interval, calculating the flow velocity of red blood cells in the microvessel; According to the vector direction of the motion trajectory, determining the blood flow direction; At the same time, based on the multispectral optical signal extracted from the continuous microvessel image data, combined with the effective optical path length inferred from the motion trajectory, calculating the blood oxygen saturation of blood; Integrating the red blood cell flow velocity, blood flow direction and blood oxygen saturation to generate blood flow hemodynamic parameters.

[0009] Further, the step of synchronously collecting local neural activity data in the same spatiotemporal coordinate system of the continuous microvessel image data includes: Electrophysiological recording electrodes or calcium ion fluorescence imaging systems co-located with optical imaging systems are used to acquire neuronal action potential signals or calcium transient signals. Mark the action potential signal of each neuron with the timestamp of the corresponding microvascular image data for each frame; The neuronal action potential signal and the microvascular image data are mapped to the same spatial coordinate system to obtain local neural activity data.

[0010] Further, the steps for generating in vivo microvascular hemodynamic and neural activity tracking results based on hemodynamic parameters and local neural activity data include: Align the time series of the hemodynamic parameters with the time series of the local neural activity data; Calculate the cross-correlation function between the changes in the hemodynamic parameters and neural activity events under multiple preset time lags; Based on the peak value of the cross-correlation function, tracking results characterizing the neurovascular relationship are generated.

[0011] The present invention also provides a live microvascular dynamic neural imaging tracking system, comprising: An optical imaging unit is used to acquire continuous microvascular image data of living tissues; The image processing unit is used to perform vessel segmentation processing on the continuous microvascular image data and identify the microvascular network skeleton for motion analysis. The motion analysis unit is used to calculate the motion vector of the segmented continuous microvascular image data based on the microvascular network skeleton, and to obtain the motion trajectory of red blood cells in the microvessels. The parameter calculation unit is used to generate hemodynamic parameters including flow velocity, direction and blood oxygen level based on the motion trajectory. The signal acquisition unit is used to synchronously acquire local neural activity data in the same spatiotemporal coordinate system of the continuous microvascular image data; The results generation unit is used to generate tracking results of in vivo microvascular hemodynamics and neural activity based on hemodynamic parameters and local neural activity data.

[0012] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described in vivo microvascular dynamic neural imaging tracking method.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described in vivo microvascular dynamic neural imaging tracking method.

[0014] The present invention provides a method, system, device, and medium for dynamic neural imaging tracking of living microvessels, which has the following beneficial effects: The present invention fundamentally suppresses in vivo motion artifacts through dynamic focal plane compensation and optimized scanning protocols, obtaining stable, high-contrast microvessel images, laying a reliable data foundation for subsequent analysis; it utilizes a motion-robust deep learning model for vessel segmentation, combined with morphological skeletonization processing, to accurately extract the topological structure of complex microvessel networks, overcoming the problem of inaccurate segmentation under noise interference in traditional methods; by constraining motion vector calculation to a precise vascular spatial region and employing motion consistency filtering, the accuracy of erythrocyte motion trajectory tracking is improved, effectively eliminating interference from background noise and abnormal motion; furthermore, through hardware synchronization and spatiotemporal registration technology, precise alignment of hemodynamic parameters and neural activity data is achieved, enabling accurate and synchronous tracking of the dynamic interaction between in vivo microvessel hemodynamics and neural activity. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for dynamic neural imaging and tracking of living microvessels in one embodiment of the present invention. Figure 2 This is a structural block diagram of a live microvascular dynamic neural imaging and tracking system according to an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Reference Figure 1 The diagram below illustrates the process of a live microvascular dynamic neural imaging tracking method proposed in this invention, which includes the following steps: S11, Acquire continuous microvascular image data of living tissue; S12, Perform vessel segmentation processing on the continuous microvascular image data to identify the microvascular network skeleton for motion analysis; S13, Based on the microvascular network skeleton, calculate the motion vector of the segmented continuous microvascular image data to obtain the movement trajectory of red blood cells in the microvessels; S14, Based on the motion trajectory, generate hemodynamic parameters including flow velocity, direction and blood oxygen level; S21, synchronously acquire local neural activity data in the same spatiotemporal coordinate system of the continuous microvascular image data; S31 generates tracking results of in vivo microvascular hemodynamics and neural activity based on hemodynamic parameters and local neural activity data.

[0019] In one embodiment, for step S11, The steps for acquiring continuous microvascular image data of living tissue include: Perform three-dimensional scanning of living tissue areas; The scanning focal plane is dynamically adjusted based on real-time acquired tissue surface morphology data to compensate for axial movement caused by physiological activities; Obtain continuous microvascular image data with motion artifacts suppressed.

[0020] In practice, high-speed optical coherence tomography (OCT) or two-photon microscopy systems are used to perform three-dimensional scanning of living tissue regions. During the scanning process, living tissue inevitably undergoes axial motion due to physiological activities such as respiration and heartbeat. If these movements are not compensated for, they will lead to severe motion artifacts such as image blurring and vascular structure distortion, affecting the subsequent accurate identification and blood flow analysis of microvessels (diameter <100μm). This embodiment acquires tissue surface morphology data in real time (e.g., through low coherence interferometry or confocal plane detection technology), dynamically controls the scanning mirror or objective lens of the imaging system, and adjusts the depth position of the scanning focal plane in real time to ensure that it always tracks the true position of the tissue surface, thereby effectively compensating for the axial motion caused by physiological activities. Through an active compensation mechanism, combined with high-speed scanning protocols and optimized illumination strategies (such as dark-field illumination at specific angles or multispectral illumination), the contrast between microvessels and surrounding background tissue is improved, obtaining high-quality, high-contrast continuous microvessel image data with effectively suppressed motion artifacts.

[0021] In one embodiment, for step S12, The steps of performing vessel segmentation processing on the continuous microvascular image data and identifying the microvascular network skeleton for motion analysis include: The continuous microvascular image data is input into a pre-trained deep learning vascular segmentation model, which is trained on a dataset containing motion artifact enhancement data and has motion robustness. The deep learning blood vessel segmentation model outputs pixel-level binary images of blood vessel and background segmentation. The binary image of the blood vessel and background segmentation is subjected to morphological skeletonization extraction to obtain a microvascular network skeleton for characterizing the microvascular topology.

[0022] In practical implementation, traditional image segmentation methods are ineffective because in vivo image data inevitably contains residual motion artifacts and has limited contrast between microvessels and the background. This embodiment inputs the continuous microvessel image data into a pre-trained deep learning blood vessel segmentation model. The training dataset for this model is not ideal static images, but rather microvessel images containing various motion artifacts simulated through data augmentation techniques. This allows the model to be motion robust when facing real, noisy, and jittery live images, accurately distinguishing microvessel pixels from background tissue pixels. After model inference, pixel-level binary images of blood vessel and background segmentation are output, with the blood vessel regions precisely labeled. Morphological skeletonization extraction is performed on the binary images of blood vessel and background segmentation to extract the blood vessel centerline structure for guiding motion analysis. Through iterative erosion operations, the blood vessel regions are refined to a single pixel width, while topological principles are used to maintain their original connectivity and branching structure, resulting in a microvessel network skeleton that clearly represents the complex topological structure of microvessels.

[0023] In one embodiment, for step S13, Based on the microvascular network skeleton, the steps of calculating the motion vectors of segmented continuous microvascular image data and obtaining the motion trajectory of red blood cells within the microvessels include: Select a continuous sequence of image frames within the vascular spatial region defined by the microvascular network skeleton; Calculate the spatial displacement of corresponding image blocks between adjacent frames within the blood vessel spatial region to generate an initial motion vector field; The initial motion vector field is subjected to spatiotemporal filtering based on motion consistency to remove abnormal vectors caused by noise; Obtain the trajectory describing the coherent movement path of red blood cells.

[0024] In practical implementation, the microvascular network skeleton is a set of vessel centerlines with a single pixel width obtained after morphological refinement. It completely preserves the topological connections and branching structure of the original microvascular network, but does not contain spatial width information. To define an analysis region with actual width that can be calculated, this embodiment uses the microvascular network skeleton as the center guide and applies a region growing method based on Euclidean distance transformation to allocate a spatial range for each centerline. Specifically, based on the statistical information of the actual diameter of the vessels in the segmented binary image obtained in step S12, a distance threshold is set, and the distance from each background pixel in the image to the nearest skeleton point is calculated. All pixels with a distance less than the threshold are included in the vessel spatial region, thereby generating a tubular analysis region mask with the skeleton as the center line and a width equivalent to the original vessel. This restricts subsequent motion vector calculations to only be performed inside the actual vessel, fundamentally eliminating the interference of background noise from external tissues, while ensuring the integrity of the analysis and not missing blood flow signals near the vessel wall. After selecting a continuous sequence of image frames within the defined vascular spatial region, a block matching algorithm is used to calculate the spatial displacement of corresponding image blocks between adjacent frames, generating an initial motion vector field.

[0025] A spatiotemporal filtering method based on motion consistency is introduced to eliminate anomalous vectors in the initial vector field caused by noise in the living environment: in the spatial dimension, the continuity of blood flow direction within the vascular structure is used to remove anomalous vectors that differ too much from the mainstream direction; in the temporal dimension, the inertial characteristics of red blood cell movement are used to remove transient vectors that change irrationally between consecutive frames, thereby obtaining the motion trajectory describing the coherent motion path of red blood cells in microvessels.

[0026] In one embodiment, for step S14, The step of generating hemodynamic parameters including flow velocity, direction, and blood oxygen level based on the motion trajectory includes: Based on the displacement of red blood cells in the motion trajectory and the corresponding time interval, the flow velocity of red blood cells in the microvessels is calculated. The direction of blood flow is determined based on the vector direction of the motion trajectory; Simultaneously, based on the multispectral optical signals extracted from the continuous microvascular image data, combined with the effective optical path length inferred from the motion trajectory, the blood oxygen saturation is calculated. Integrating red blood cell flow velocity, blood flow direction, and blood oxygen saturation, hemodynamic parameters are generated.

[0027] In practical implementation, when calculating the flow velocity of red blood cells in microvessels, based on the motion trajectory obtained in step S13, the displacement Δd of red blood cells between consecutive frames and the corresponding acquisition time interval Δt are extracted. According to the physical definition, the flow velocity v = Δd / Δt. By taking the median of the calculation results of multiple consecutive displacement points on a motion trajectory, the representative flow velocity of that trajectory is obtained. The blood flow direction is directly obtained from the vector direction of the motion trajectory. By statistically analyzing the main patterns of all motion trajectory directions within a specific vascular region, the overall blood flow direction of that vascular segment is determined.

[0028] Traditional methods for calculating blood oxygen saturation use a fixed optical path length, which introduces significant errors in tortuous microvessels. This embodiment is based on light intensity signals I at at least two specific wavelengths (e.g., the isoabsorption point wavelength and its oxygenation-sensitive wavelength) extracted from continuous microvessel image data. λ Calculate optical density OD λ = -ln(I λ / I0 λ ), where I0 λ Using the reference light intensity, the dynamic effective optical path length L of the red blood cell movement in the capillary is inferred by using the red blood cell trajectory obtained in step S13. eff The L eff The OD value is determined by both the curvature of the trajectory and the three-dimensional structural information of the blood vessel, rather than by a fixed value. λ With L eff Substituting into the extended Lambert-Beer law corrected for the dynamic path length factor η:

[0029] Where ε HbO2 and ε Hb and represent the molar extinction coefficients of oxyhemoglobin and deoxyhemoglobin at the corresponding wavelengths, respectively, and are known parameters. L in the formula... eff The η factor is canceled out in the ratio calculation, and finally, by solving the equation for blood oxygen saturation SO2, a more accurate local blood oxygen saturation that matches the specific movement path of red blood cells is calculated. The red blood cell velocity, blood flow direction, and blood oxygen saturation of each analysis unit are integrated to generate a co-localized set of hemodynamic parameters.

[0030] In one embodiment, for step S21, The steps of synchronously acquiring local neural activity data in the same spatiotemporal coordinate system of the continuous microvascular image data include: Electrophysiological recording electrodes or calcium ion fluorescence imaging systems co-located with optical imaging systems are used to acquire neuronal action potential signals or calcium transient signals. Mark the action potential signal of each neuron with the timestamp of the corresponding microvascular image data for each frame; The neuronal action potential signal and the microvascular image data are mapped to the same spatial coordinate system to obtain local neural activity data.

[0031] In practical implementation, an integrated hardware system is employed: a miniature electrophysiological recording electrode array co-located mechanically with a high-speed optical imaging system (such as OCTA or two-photon microscopy) directly acquires action potential signals generated by neuronal firing; or, through the same optical path, while exciting microvascular images, the fluorescence of gene-encoded calcium indicators (such as GCaMP) is excited at different wavelengths, thereby acquiring calcium transient signals reflecting neuronal activity. To address the subtle but crucial time delay between different modal data, a unified hardware master clock is used to stamp each acquired neuronal action potential peak or calcium transient signal start point, as well as the exposure start time of each frame of microvascular image data, with a high-resolution timestamp with microsecond-level precision, achieving precise alignment in the time dimension. For spatial registration, a unified spatial coordinate system is established by implanting tiny fluorescent markers clearly visible in both imaging modes within the imaging field of view, or by using unique vascular branch points as natural markers; using these markers, a rigid transformation matrix is ​​calculated to map the spatial coordinates of the electrophysiological electrode recording sites or calcium signal active regions to the pixel coordinate system of the microvascular image data. Through the spatiotemporal synchronization mechanism of this embodiment, the resulting local neural activity data not only strictly corresponds to hemodynamic changes in time, but also precisely co-locates with the microvascular network that generates these blood flows in space.

[0032] In one embodiment, for step S31, The steps for generating in vivo microvascular hemodynamic and neural activity tracking results based on hemodynamic parameters and local neural activity data include: Align the time series of the hemodynamic parameters with the time series of the local neural activity data; Calculate the cross-correlation function between the changes in the hemodynamic parameters and neural activity events under multiple preset time lags; Based on the peak value of the cross-correlation function, tracking results characterizing the neurovascular relationship are generated.

[0033] In specific implementation, the high-frequency time series of hemodynamic parameters (such as flow velocity and blood oxygen) generated in step S14 are precisely aligned with the time series of local neural activity data (such as action potential sequences or calcium signal fluorescence intensity) obtained in step S21 using the high-precision timestamps embedded in previous steps, forming a synchronous multimodal data stream. Since there is a variable time lag between neural activity and microvascular response, and the response dynamics of different parameters (such as flow velocity and blood oxygen) are different, a dynamic cross-correlation analysis method based on a sliding time window is introduced to quantify this dynamic relationship. For each hemodynamic parameter P(t) (t being a time series), its cross-correlation function CCF(τ) with the neural activity event sequence N(t) at multiple preset time lags τ is calculated. The cross-correlation function is calculated using a standardized formula to eliminate the influence of signal amplitude differences. The formula for calculating the cross-correlation function is: CCF(τ) = [ Σ t ( (P(t)-μ P ) * (Ñ(t+τ)-μ Ñ ) ) ] / [ (n-1) * σ P * σ Ñ ] Where Ñ(t) is the continuous representation of the sequence of neural activity events (e.g., converting discrete action potential peaks into continuous signals through Gaussian kernel convolution), μ P and μ Ñ These are the mean values ​​of P(t) and Ñ(t) within the time window, respectively, and σ P and σ Ñ These are their standard deviations, and n is the number of sampling points within that time window. In [-τ] max , τ max The CCF(τ) is calculated within the hysteresis range of τ, and its global peak CCF is automatically identified. peak and the corresponding lag time τ peak CCF peak The absolute value of quantifies the coupling strength, while τ peak This reveals the dominant time delay in hemodynamic response induced by neural activity. Finally, a comprehensive tracking result is generated, presented in the form of data overlay or structured report, which includes at least: a spatial mapping map, showing the local blood flow direction, mean flow velocity, blood oxygen saturation, and neurovascular coupling strength at each analysis point on the microvascular network framework using vector or color coding; and a time evolution curve, showing the dynamic interaction between hemodynamic parameters and neural activity at key locations over time, and marking the calculated key lag times.

[0034] Reference Figure 2Here is a structural block diagram of a live microvascular dynamic neural imaging tracking system according to an embodiment of the present invention, comprising: An optical imaging unit is used to acquire continuous microvascular image data of living tissues; The image processing unit is used to perform vessel segmentation processing on the continuous microvascular image data and identify the microvascular network skeleton for motion analysis. The motion analysis unit is used to calculate the motion vector of the segmented continuous microvascular image data based on the microvascular network skeleton, and to obtain the motion trajectory of red blood cells in the microvessels. The parameter calculation unit is used to generate hemodynamic parameters including flow velocity, direction and blood oxygen level based on the motion trajectory. The signal acquisition unit is used to synchronously acquire local neural activity data in the same spatiotemporal coordinate system of the continuous microvascular image data; The results generation unit is used to generate tracking results of in vivo microvascular hemodynamics and neural activity based on hemodynamic parameters and local neural activity data.

[0035] For the specific implementation of each unit in the above device example, please refer to the method embodiments described above, and will not be repeated here.

[0036] Reference Figure 3 This invention also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0037] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0038] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0039] In summary, this invention acquires continuous microvascular image data from living tissue; performs vascular segmentation on the continuous microvascular image data to identify the microvascular network skeleton for motion analysis; calculates the motion vectors of the segmented continuous microvascular image data based on the microvascular network skeleton to obtain the motion trajectory of red blood cells within the microvessels; generates hemodynamic parameters including flow velocity, direction, and blood oxygen level based on the motion trajectory; simultaneously acquires local neural activity data of the continuous microvascular image data in the same spatiotemporal coordinate system; and generates tracking results of in vivo microvascular hemodynamics and neural activity based on the hemodynamic parameters and local neural activity data, thereby achieving the purpose of synchronous tracking and dynamic correlation analysis of microvascular-level hemodynamics and neural activity in a living environment.

[0040] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0041] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0042] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for dynamic neural imaging and tracking of microvessels in vivo, characterized in that, The method comprises the following steps: Collecting continuous microvessel image data of living tissue; Performing blood vessel segmentation processing on the continuous microvessel image data, and identifying a microvessel network skeleton for motion analysis; Based on the microvessel network skeleton, calculating a motion vector of the segmented continuous microvessel image data, and obtaining a motion trajectory of red blood cells in the microvessel; Generating hemodynamic parameters including flow velocity, direction and blood oxygen level according to the motion trajectory; Synchronously collecting local neural activity data in the same space-time coordinate system as the continuous microvessel image data; Generating tracking results of microvessel hemodynamics and neural activity of living tissue according to the hemodynamic parameters and the local neural activity data.

2. The method of claim 1, wherein the method further comprises: The step of collecting continuous microvessel image data of living tissue comprises: Performing three-dimensional scanning on the region of living tissue; Adjusting the scanning focal plane dynamically according to the real-time obtained tissue surface topography data, and compensating the axial motion caused by physiological activity; Obtaining continuous microvessel image data with motion artifacts suppressed.

3. The method of claim 1, wherein the method further comprises: The step of performing blood vessel segmentation processing on the continuous microvessel image data, and identifying a microvessel network skeleton for motion analysis comprises: Inputting the continuous microvessel image data into a pre-trained deep learning blood vessel segmentation model, wherein the deep learning blood vessel segmentation model is trained by a data set containing motion artifact enhanced data, and has motion robustness; Outputting a pixel-level blood vessel and background segmentation binary image from the deep learning blood vessel segmentation model; Performing morphological skeleton extraction processing on the blood vessel and background segmentation binary image, and obtaining a microvessel network skeleton for representing the topological structure of microvessels.

4. The method of claim 1, wherein the method further comprises: The step of calculating a motion vector of the segmented continuous microvessel image data based on the microvessel network skeleton, and obtaining a motion trajectory of red blood cells in the microvessel comprises: Selecting a continuous image frame sequence in a blood vessel space region defined by the microvessel network skeleton; Calculating the spatial displacement of corresponding image blocks between adjacent frames in the blood vessel space region, and generating an initial motion vector field; Performing spatio-temporal filtering based on motion consistency on the initial motion vector field, and eliminating abnormal vectors caused by noise; Obtaining a motion trajectory describing the continuous motion path of red blood cells.

5. The method of claim 1, wherein the method further comprises: The step of generating hemodynamic parameters including flow velocity, direction and blood oxygen level according to the motion trajectory comprises: Calculating the flow velocity of red blood cells in the microvessel based on the displacement of red blood cells in the motion trajectory and the corresponding time interval; Determining the blood flow direction according to the vector direction of the motion trajectory; Meanwhile, calculating the blood oxygen saturation of blood based on the multispectral optical signals extracted from the continuous microvessel image data and the effective optical path length inferred from the motion trajectory; Integrating the flow velocity of red blood cells, the blood flow direction and the blood oxygen saturation, and generating hemodynamic parameters.

6. The method of claim 1, wherein the method further comprises: The step of synchronously collecting local neural activity data in the same space-time coordinate system as the continuous microvessel image data comprises: Collecting neuron action potential signals or calcium transient signals by using electrophysiological recording electrodes or calcium ion fluorescence imaging systems which are co-located with the optical imaging system; timestamp each neuron action potential signal and corresponding each frame of microvascular image data; map the neuron action potential signal and the microvascular image data to the same spatial coordinate system to obtain local neural activity data.

7. The method of claim 1, wherein the method further comprises: The step of generating the tracking result of the microvascular hemodynamics and neural activity of the living body according to the hemodynamic parameter and the local neural activity data comprises: aligning the time sequence of the hemodynamic parameter with the time sequence of the local neural activity data; calculating the cross-correlation function of the change of the hemodynamic parameter and the neural activity event at a plurality of preset time lags; generating the tracking result representing the neural vascular relationship according to the peak value of the cross-correlation function.

8. A living microvessel dynamic neuroimaging tracking system, characterized in that, comprise: an optical imaging unit for collecting continuous microvascular image data of a living tissue; an image processing unit for performing blood vessel segmentation processing on the continuous microvascular image data to identify a microvascular network skeleton for motion analysis; a motion analysis unit for calculating a motion vector of the segmented continuous microvascular image data based on the microvascular network skeleton to obtain a motion trajectory of a red blood cell in a microvessel; a parameter calculation unit for generating hemodynamic parameters including flow velocity, direction and blood oxygen level according to the motion trajectory; a signal acquisition unit for synchronously acquiring local neural activity data in the same space-time coordinate system of the continuous microvascular image data; a result generation unit for generating a tracking result of the microvascular hemodynamics and neural activity of the living body according to the hemodynamic parameter and the local neural activity data. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8. The processor executes the computer program to realize the steps of the living microvascular dynamic neural imaging tracking method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the living microvascular dynamic neural imaging tracking method in any one of claims 1 to 7.

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