Multi-terminal processing method and system for real-time sharing of emergency clinical data

Abstract

The embodiment of the invention provides a multi-terminal processing method and system for real-time sharing of emergency clinical data. A method for detecting intravascular conditions by modifying a multi-modal sensor on a cardiovascular catheter. According to the method, a sensor integrated at a detection end of a catheter with a gradient refractive index nano coating is utilized to obtain light transmittance distribution and blood flow disturbance parameters of the inner wall of a blood vessel. According to the characteristics of the data, a computed tomography coding strategy related to vascular pathology is formulated, the strategy is converted into a rendering instruction set for terminal hardware characteristics, and the rendering instruction set is compressed into a data stream carrying vascular information through a video coding protocol. And finally, distributing the processed data to different devices, realizing the elasticity visualization of the blood vessel wall, optimizing a detection mode according to user feedback, and generating a control instruction to further guide the catheter operation. According to the technical scheme provided by the embodiment of the invention, the accuracy and efficiency of data multi-terminal processing can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of multi-terminal data processing, and in particular, to a multi-terminal processing method and system for real-time sharing of emergency clinical data. Background Art

[0002] In the emergency clinical environment, rapid diagnosis and treatment of cardiovascular diseases are crucial. Especially for emergency situations such as acute coronary syndrome, real-time monitoring of the state of the inner wall of the blood vessels, assessment of the risk of plaque rupture, and changes in hemodynamic parameters are particularly important. This requires a method that can efficiently obtain and process detailed information inside the blood vessels, and can instantly share this data through multiple terminal devices so that doctors can make decisions quickly.

[0003] At present, the diagnosis of cardiovascular diseases mainly relies on traditional computed tomography, magnetic resonance imaging, and angiography. In addition, there are some technologies that use catheters for local detection, such as intravascular ultrasound or optical coherence tomography. However, these methods usually can only provide limited information about the internal structure of the blood vessels, and it is difficult to simultaneously achieve comprehensive monitoring of multiple parameters such as the transmittance distribution of the inner wall of the blood vessel and blood flow disturbance.

[0004] There are several obvious deficiencies in existing technologies: first, traditional imaging technology cannot provide sufficient real-time data support in emergency situations, resulting in delayed treatment; second, although existing catheter technology can enter the blood vessels for more detailed inspections, it often lacks the ability to integrate multiple sensors and cannot simultaneously collect multiple key parameters including transmittance distribution and blood flow disturbances; finally, in terms of data transmission and multi-terminal visualization, existing solutions fail to effectively integrate video encoding and layered compression technology, resulting in low data transmission efficiency and difficulty in achieving efficient data sharing and interaction between different terminal devices. These problems together limit the rapid diagnosis and personalized treatment of cardiovascular diseases in emergency settings. Summary of the invention

[0005] The embodiments of the present application provide a multi-terminal processing method and system for real-time sharing of emergency clinical data, so as to solve the problems of poor accuracy and low efficiency of multi-terminal data processing in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a multi-terminal processing method for real-time sharing of emergency clinical data, including:

[0007] The light transmittance distribution parameters and blood flow disturbance parameters of the inner wall of the blood vessel are obtained by using a multimodal sensor of the modified cardiovascular catheter, wherein the multimodal sensor is integrated into the catheter detection end of the gradient refractive index nanocoating;

[0008] Generate a computed tomography encoding strategy linked to vascular pathology characteristics according to the spectral absorption characteristics of the transmittance distribution parameter and the eddy current intensity of the blood flow disturbance parameter, wherein the computed tomography encoding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuation;

[0009] Inputting the computed tomography encoding strategy into a containerized microservice cluster, and generating a rendering instruction set associated with the vascular elastic modulus based on the terminal hardware architecture;

[0010] Performing hierarchical compression on the rendering instruction set through a video coding protocol to generate a compressed data stream carrying a blood vessel bifurcation marker, wherein the hierarchical compression integrates the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusts the transmission priority of the dynamic parameter coding block based on the wall shear force of the blood flow disturbance parameter;

[0011] Distributing the compressed data stream to a heterogeneous terminal cluster, triggering visualization of the elastic modulus of the blood vessel wall on a mobile terminal, and invoking the key frame protection strategy to associate with the wall shear force fluctuation;

[0012] The light transmission detection mode of the gradient refractive index nanocoating is reconstructed according to the terminal interaction data, and a catheter control instruction linked to the plaque rupture risk assessment is generated.

[0013] Optionally, the computer tomography coding strategy linked to vascular pathology characteristics is generated according to the spectral absorption characteristics of the transmittance distribution parameter and the eddy current intensity of the blood flow disturbance parameter, and the computer tomography coding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuation, including:

[0014] According to the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, by quantifying the spectral attenuation differences of the plaque components in the blood vessel wall, plaque component characteristic parameters are generated, and at the same time, the dynamic disturbance component of the blood flow velocity field in the blood vessel cavity is extracted to generate blood flow disturbance characteristic parameters;

[0015] Performing multimodal correlation analysis on the plaque component characteristic parameters and the blood flow disturbance characteristic parameters to generate plaque rupture risk assessment parameters, wherein the plaque rupture risk assessment parameters output quantitative indicators of the plaque surface through coupling effect;

[0016] Dynamically adjusting a sampling frequency parameter of a computer tomography scan according to the plaque rupture risk assessment parameter, wherein the sampling frequency parameter achieves adaptive optimization of computer scan resolution by matching the spatial distribution difference of the plaque rupture risk;

[0017] Based on the vortex energy accumulation characteristics in the blood flow disturbance characteristic parameters, deriving a wall shear force fluctuation parameter for calculating the matching relationship between the instantaneous change rate of the vascular wall shear force and the contrast agent diffusion rate, and generating a contrast agent injection parameter;

[0018] The sampling frequency parameter is synchronized with the contrast agent injection parameter in time sequence, and a computer tomography encoding strategy linked to the vascular pathological characteristics is generated by controlling the phase difference of the contrast agent injection pulse.

[0019] Optionally, the plaque component characteristic parameter and the blood flow disturbance characteristic parameter are subjected to multimodal correlation analysis to generate a plaque rupture risk assessment parameter, and the plaque rupture risk assessment parameter outputs a quantitative index of the plaque surface through a coupling effect, including:

[0020] Based on the correlation between the distribution of fiber cap thickness and the proportion of lipid core space in the characteristic parameters of plaque components, the stress concentration coefficient of the plaque structure is extracted through structural mechanics analysis;

[0021] Generate a blood flow disturbance stress coupling factor according to the spatial overlap of the shear force oscillation amplitude and the flow separation area in the blood flow disturbance characteristic parameters and the spatial position mapping relationship of the plaque structure stress concentration coefficient;

[0022] Performing a cross-modal convolution operation on the gradient change rate of the stress concentration coefficient of the plaque structure and the time-varying component of the blood flow disturbance stress coupling factor, and outputting a dynamic stress offset including the cumulative effect of plaque deformation;

[0023] Based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameters of the calcified area in the plaque component characteristic parameters, the distribution difference of the stress gradient on the plaque surface in the axial and radial directions is generated;

[0024] The distribution difference is nonlinearly superimposed with the eddy current energy attenuation rate in the blood flow disturbance characteristic parameter to generate a plaque rupture risk assessment parameter containing a stress-blood flow interaction weight. The plaque rupture risk assessment parameter is used to characterize a quantitative indicator of the rupture probability of each area on the plaque surface.

[0025] Optionally, the generating of the distribution difference of the stress gradient on the surface of the plaque in the axial and radial directions based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameter of the calcified area in the plaque component characteristic parameter includes:

[0026] The high-frequency stress spectral density distribution in the axial stress propagation direction and the low-frequency stress spectral density distribution in the radial stress diffusion direction are extracted through the energy proportion relationship between the main frequency component and the harmonic component of the dynamic stress offset;

[0027] According to the correlation between the distribution density of curvature mutation points in the edge sharpness parameter of the calcified area in the plaque component characteristic parameter and the gradient change rate, the stress enhancement ratio of the calcified edge stress concentration factor in the axial and radial directions is analyzed;

[0028] Adaptively weighted superposition the high-frequency stress spectrum density distribution and the axial stress enhancement ratio of the calcification edge stress concentration factor to generate an axial stress gradient component including the calcification edge stress amplification effect;

[0029] Directionally convolve and fuse the low-frequency stress spectrum density distribution with the radial stress enhancement ratio of the calcification edge stress concentration factor to generate a radial stress gradient component containing stress diffusion attenuation characteristics;

[0030] Based on the peak position of the axial stress gradient component and the attenuation rate of the radial stress gradient component, a gradient difference parameter characterizing the difference between the axial and radial stress distributions is output through a composite calculation of spatial position matching and energy attenuation slope.

[0031] Optionally, performing hierarchical compression on the rendering instruction set through a video coding protocol to generate a compressed data stream carrying a blood vessel bifurcation mark, the hierarchical compression integrating the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusting the transmission priority of the dynamic parameter coding block based on the wall shear force of the blood flow disturbance parameter, includes:

[0032] The rendering instruction set is hierarchically compressed according to the geometry layer, the dynamic parameter layer and the texture detail layer through a video coding protocol to generate an intermediate compressed frame including the geometry compression parameters, the dynamic compression parameters and the texture compression parameters;

[0033] Binding the blood vessel bifurcation mark with the coding block of the intermediate compressed frame in time and space, generating a bifurcation mark index table based on the geometric compression parameter, inserting a topology check code element into the coding block boundary of the intermediate compressed frame, and generating a compressed data stream;

[0034] Generate a key frame protection strategy according to the polarization attenuation mode of the layered compression integrated transmittance distribution parameter, and update the key frame refresh interval according to the polarization attenuation weight combination and the blood flow direction of the dynamic compression parameter;

[0035] The shear force direction component of the dynamic parameter layer coding block is extracted based on the wall shear force of the blood flow disturbance parameter, the motion vector of the high shear force area coding block is enhanced through the bifurcation mark index table, and the transmission priority of the dynamic parameter layer coding block in the compressed data stream is dynamically adjusted.

[0036] Optionally, the step of temporally and spatially binding the blood vessel bifurcation mark with the coding block of the intermediate compressed frame and generating a bifurcation mark index table based on the geometric compression parameter includes:

[0037] Generate a spatial position mapping relationship between the bifurcation point and the coding block of the intermediate compressed frame according to the three-dimensional coordinates of the bifurcation point in the blood vessel bifurcation mark and the timestamp sequence of the intermediate compressed frame;

[0038] Based on the angle between the motion vector direction of the intermediate compressed frame coding block and the blood flow direction of the bifurcation point, a spatiotemporal fit parameter between the intermediate compressed frame coding block and the bifurcation mark is obtained, and spatial weights are allocated to the intermediate compressed frame coding block according to the spatiotemporal fit parameter to generate a coding block binding set including a bifurcation point stability weight;

[0039] Based on the compression ratio difference between the primary curvature and the secondary curvature in the coding block binding set, a curvature compression difference factor of the geometric compression parameter is obtained;

[0040] The curvature compression difference factor and the space-time fit parameter are bidirectionally sorted to generate a bifurcation mark index table with the bifurcation point stability weight as the index key and the curvature compression difference factor as the associated value.

[0041] Optionally, the step of inputting the computed tomography encoding strategy into a containerized microservice cluster and generating a rendering instruction set associated with the vascular elastic modulus based on a terminal hardware architecture includes:

[0042] Splitting the computed tomography scanning coding strategy into multiple scanning coding sub-strategies executed in parallel, injecting each scanning coding sub-strategy into the containerized microservice cluster through a dynamic load balancing module, and generating a scanning coding sub-strategy identifier;

[0043] traversing the number of computing units and parallel processing capabilities of the terminal hardware architecture according to the scan coding sub-strategy identifier to generate a vascular elastic modulus sequence including a dynamic association between a vascular wall deformation gradient and a stress response;

[0044] Mapping the deformation gradient in the vascular elastic modulus sequence into a multi-level rendering resolution parameter, and mapping the stress response into a matching texture sampling density parameter, to form a dynamic rendering parameter set;

[0045] The memory bandwidth of the terminal hardware architecture is divided based on the dynamic rendering parameter set, a corresponding video memory allocation strategy and a bound shader instruction queue are generated, and a rendering instruction set associated with the vascular elastic modulus is output after fusion.

[0046] In a second aspect, the embodiment of the present application provides a multi-terminal processing system for real-time sharing of emergency clinical data, including:

[0047] An acquisition module, which acquires the transmittance distribution parameters of the inner wall of the blood vessel and the blood flow disturbance parameters through a multimodal sensor of a modified cardiovascular catheter, wherein the multimodal sensor is integrated into the detection end of the catheter with a gradient refractive index nanocoating;

[0048] A generation module, which generates a computer tomography coding strategy linked to vascular pathology characteristics according to the spectral absorption characteristics of the transmittance distribution parameter and the eddy current intensity of the blood flow disturbance parameter, wherein the computer tomography coding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuation;

[0049] An input module, inputting the computed tomography encoding strategy into a containerized microservice cluster, and generating a rendering instruction set associated with a vascular elastic modulus based on a terminal hardware architecture;

[0050] A compression module, performing hierarchical compression on the rendering instruction set through a video encoding protocol to generate a compressed data stream carrying a blood vessel bifurcation marker, wherein the hierarchical compression integrates the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusts the coding block priority based on the wall shear force of the blood flow disturbance parameter;

[0051] A calling module distributes the compressed data stream to a heterogeneous terminal cluster, triggers the visualization of the elastic modulus of the blood vessel wall on the mobile terminal, and calls the key frame protection strategy to associate with the wall shear force fluctuation;

[0052] The reconstruction module reconstructs the light transmission detection mode of the gradient refractive index nanocoating according to the terminal interaction data, and generates a catheter control instruction linked with the plaque rupture risk assessment.

[0053] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-terminal processing method for real-time sharing of emergency clinical data as described in the first aspect above.

[0054] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a multi-terminal processing method for real-time sharing of emergency clinical data as described in the first aspect.

[0055] In an embodiment of the present application, a multimodal sensor of a modified cardiovascular catheter is used to obtain transmittance distribution parameters and blood flow disturbance parameters of the inner wall of the blood vessel, and the multimodal sensor is integrated in the detection end of the catheter with a gradient refractive index nanocoating; a computed tomography coding strategy linked to vascular pathological characteristics is generated according to the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, and the computed tomography coding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuations; the computed tomography coding strategy is input into a containerized microservice cluster, and a vascular elastic modulus correlation is generated based on the terminal hardware architecture. The method comprises the steps of: performing hierarchical compression on the rendering instruction set through a video coding protocol to generate a compressed data stream carrying a vascular bifurcation marker, wherein the hierarchical compression integrates the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusting the transmission priority of the dynamic parameter coding block based on the wall shear force of the blood flow disturbance parameter; distributing the compressed data stream to a heterogeneous terminal cluster, triggering the visualization of the elastic modulus of the vascular wall on a mobile terminal, and calling the key frame protection strategy to associate with the wall shear force fluctuation; reconstructing the light transmittance detection mode of the gradient refractive index nanocoating according to the terminal interaction data, and generating a catheter control instruction linked to the plaque rupture risk assessment.

[0056] The technical solution of this application has the following beneficial effects:

[0057] This application uses a modified cardiovascular catheter and integrates a multimodal sensor to obtain the vascular wall transmittance distribution parameters and blood flow disturbance parameters in real time, greatly improving the depth of understanding of the internal state of the blood vessels. This technology not only achieves accurate capture of vascular pathological characteristics, but also can dynamically adjust the computed tomography encoding strategy based on these data, including the sampling frequency and contrast agent injection volume driven by plaque rupture risk assessment, thereby improving diagnostic accuracy. In addition, a containerized microservice cluster is used to generate rendering instruction sets optimized for different terminal hardware architectures, and data is transmitted through an efficient video encoding protocol, ensuring the priority and integrity of key information, making multi-terminal data sharing in emergency environments possible. This not only speeds up the medical decision-making process, but also improves the effectiveness and personalization of treatment.

[0058] Furthermore, by quantifying the spectral attenuation differences of plaque components and analyzing the dynamic disturbance components of the blood flow velocity field, more detailed plaque component characteristic parameters and blood flow disturbance characteristic parameters were generated. Multimodal correlation analysis of these two parameters can more accurately assess the risk of plaque rupture, and dynamically adjust the sampling frequency of computed tomography to achieve adaptive optimization of resolution. At the same time, the wall shear force fluctuation parameters derived from the blood flow disturbance characteristics are used to calculate the optimal contrast agent injection amount to match the changes in vascular wall shear force. This method significantly improves the diagnostic accuracy of vascular pathological conditions, especially the ability to respond quickly to emergency situations such as acute coronary syndrome, providing patients with safer and more effective treatment options.

[0059] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A flowchart of a multi-terminal processing method for real-time sharing of emergency clinical data provided by the present application is shown;

[0062] Figure 2 A schematic diagram of the structure of a multi-terminal processing system for real-time sharing of emergency clinical data provided by the present application is shown;

[0063] Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0065] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0066] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0067] This solution aims to provide doctors with more accurate and real-time cardiovascular disease diagnostic tools by integrating multiple sensors at the detection end of cardiovascular catheters and combining advanced image processing and data transmission technologies. The core of the research and development is to develop a system that can dynamically acquire and analyze intravascular environmental information, including transmittance distribution parameters, blood flow disturbance parameters, etc., and automatically generate optimized computed tomography encoding strategies based on this information, ultimately achieving visual display of cardiovascular conditions and risk assessment.

[0068] Figure 1 A flowchart of a multi-terminal processing method for real-time sharing of emergency clinical data is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0069] 101. Obtaining the transmittance distribution parameters and blood flow disturbance parameters of the inner wall of the blood vessel by means of a multimodal sensor of a modified cardiovascular catheter, wherein the multimodal sensor is integrated in the catheter detection end of the gradient refractive index nanocoating;

[0070] In this step, the multimodal sensor is a device that integrates multiple detection methods such as optics and acoustics to simultaneously acquire different types of biophysical signals.

[0071] The transmittance distribution parameter refers to the attenuation of light when it passes through the blood vessel wall, reflecting the structural characteristics of the blood vessel wall.

[0072] Blood flow disturbance parameters describe the irregularities in blood flow, such as eddy current intensity, and are key indicators for assessing cardiovascular health.

[0073] Gradient refractive index nanocoating refers to a layer of special material coated on the surface of the catheter, which reduces light scattering and improves sensor detection accuracy.

[0074] In the embodiments of the present application, firstly, it is necessary to consider how to effectively integrate the multimodal sensor into the cardiovascular catheter during the design phase and ensure that the sensor can work stably. Secondly, a gradient refractive index nanocoating is used to enhance the sensor's sensitivity to changes in transmittance. Then, experimental verification is performed to determine the optimal sensor configuration and coating thickness to ensure that the operational flexibility of the catheter is not affected and that changes in the internal environment of the blood vessel can be accurately captured.

[0075] A patient was rushed to the emergency department of a hospital due to sudden chest pain. The doctor used a cardiovascular catheter designed for emergency use and integrated with a multimodal sensor for a quick examination. The front end of this catheter is coated with a gradient refractive index nanocoating to improve light transmittance and detection accuracy. Through this catheter, doctors can obtain the light transmittance distribution parameters and blood flow disturbance parameters inside the patient's heart and blood vessels in real time. The results showed that there was a significant decrease in light transmittance and blood flow disturbance in the left anterior descending artery, indicating the presence of plaque rupture or severe stenosis.

[0076] 102. Generate a computed tomography coding strategy linked to vascular pathology characteristics according to the spectral absorption characteristics of the transmittance distribution parameter and the eddy current intensity of the blood flow disturbance parameter, wherein the computed tomography coding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuation;

[0077] In this step, the computed tomography encoding strategy is a set of algorithms that guide the way the computed tomography is performed based on the collected data (such as the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters).

[0078] The sampling frequency parameter determines the speed of acquiring images at different risk levels, while the contrast agent injection parameters are adjusted according to the wall shear force fluctuations to obtain clearer imaging effects.

[0079] In the examples of the present application, first, a personalized computed tomography plan is formulated based on the data obtained. Secondly, the sampling frequency and the amount of contrast agent are adjusted according to different types of pathological features (such as the risk of plaque rupture). Then, simulation software is used to test the effectiveness of the strategy under various conditions to ensure that the diagnostic accuracy can be improved without increasing the patient's radiation exposure or risk of side effects.

[0080] Based on the preliminary data obtained in step 101, the system automatically generates a computed tomography coding strategy specifically for acute cardiovascular events. Considering the patient's current critical state, the system recommends using a higher sampling frequency to capture any subtle changes and adjusting the amount of contrast agent injection according to the wall shear force fluctuations to ensure optimal imaging while reducing the burden on the patient. This strategy is designed to quickly and accurately identify the location of the lesion and its severity so that timely measures can be taken.

[0081] 103. Input the computed tomography encoding strategy into the containerized microservice cluster, and generate a rendering instruction set associated with the vascular elastic modulus based on the terminal hardware architecture;

[0082] In this step, the containerized microservice cluster is an architecture based on modern cloud computing technology that allows applications to be split into independent small services, each of which runs in its own process and communicates through lightweight mechanisms. For the application scenario of cardiovascular disease diagnosis, these microservices include modules such as data processing and image rendering.

[0083] The rendering instruction set associated with the vascular elastic modulus is a set of commands used to instruct how to generate high-quality images based on the elastic properties of the patient's vascular wall. These instructions ensure that the best display effect can be obtained under different hardware environments.

[0084] In the embodiment of the present application, first, the scan encoding strategy is input into the containerized microservice cluster, and the system begins to adjust the rendering parameters according to the patient's specific situation. Secondly, considering the different needs of the emergency department and remote consultation, the system dynamically allocates computing resources to meet the requirements of real-time and image quality. Then, the raw data is converted and optimized using advanced algorithms to generate the final visualization results. This process not only improves the clarity of the image, but also ensures the accuracy and timeliness of information transmission.

[0085] In the emergency room, the medical team quickly input the patient's latest examination data into the system. The containerized microservice cluster was immediately started to generate a high-quality 3D vascular model tailored to the patient's specific situation. These images helped doctors quickly determine the specific location and severity of the lesion, providing a key basis for formulating emergency treatment plans.

[0086] 104. Performing hierarchical compression on the rendering instruction set through a video coding protocol to generate a compressed data stream carrying a blood vessel bifurcation mark, wherein the hierarchical compression integrates the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusts the transmission priority of the dynamic parameter coding block based on the wall shear force of the blood flow disturbance parameter;

[0087] In this step, the video coding protocol is a technical standard for compressing and transmitting video data, which can effectively reduce the amount of data while maintaining sufficient image quality.

[0088] Layered compression is a special compression method that gives priority to protecting important information (such as key frames).

[0089] The compressed data stream carrying the vascular bifurcation mark refers to a data stream that specifically marks the vascular branch point during the compression process to facilitate subsequent analysis.

[0090] The polarization attenuation pattern is a method to describe the intensity change of light when it passes through a medium, while the wall shear force is a physical quantity that measures the force exerted by blood flow on the blood vessel wall.

[0091] In the embodiment of the present application, first, the system uses an efficient video coding protocol to perform hierarchical compression on the rendering instruction set, focusing on protecting key frames containing key pathological information. Secondly, the transmission priority of the dynamic parameter coding block is adjusted according to the blood flow disturbance parameters to ensure that the most important information reaches the receiving end first. Then, a series of tests are performed to verify whether the compressed data stream can be stably transmitted under various network conditions and maintain sufficient image quality.

[0092] During the emergency treatment process, the optimized video encoding protocol successfully compressed the high-quality vascular images obtained in the emergency room and sent them to the remote cardiologist's mobile device. Although the network conditions were not ideal, the images of key areas were still clearly visible, allowing the remote expert to provide professional advice in real time to support on-site treatment.

[0093] 105. Distribute the compressed data stream to a heterogeneous terminal cluster, trigger visualization of the elastic modulus of the blood vessel wall on the mobile terminal, and call the key frame protection strategy to associate with the wall shear force fluctuation;

[0094] In this step, a heterogeneous terminal cluster refers to a collection of devices of different types and performance that can simultaneously receive and display the same information. In cardiovascular disease diagnosis, this includes high-definition monitors in hospitals, doctors' handheld devices, and smart devices used by patients' families.

[0095] Wall shear force fluctuation correlation means adjusting the display content or operation mode according to the change of the force exerted by blood flow on the blood vessel wall.

[0096] In the embodiment of the present application, first, the compressed data stream is distributed to all associated terminal devices to ensure that each participant can get the latest patient status update. Secondly, the display settings are adjusted according to the characteristics of different terminals, such as simplifying the interface on mobile devices and displaying detailed images on large screens. Then, a feedback loop is established so that all end users can provide feedback to the system to further optimize the diagnosis and treatment process.

[0097] The compressed data stream is quickly distributed to the hospital's internal and external terminal devices. The high-definition display in the emergency room immediately displays the patient's vascular 3D model, helping doctors quickly understand the condition. At the same time, remote cardiology experts view the same imaging data through mobile applications to participate in the consultation. This instant sharing mechanism not only speeds up on-site treatment decisions, but also allows the professional opinions of remote experts to be incorporated into the treatment plan in a timely manner.

[0098] 106. Reconstruct the light transmittance detection mode of the gradient refractive index nanocoating according to the terminal interaction data, and generate a catheter control instruction linked to the plaque rupture risk assessment.

[0099] In this step, the catheter control instructions are a series of signals used to guide the operation of the cardiovascular catheter, which can automatically adjust the position and detection mode of the catheter according to the real-time collected data.

[0100] Plaque rupture risk assessment linkage means automatically adjusting the behavior of the catheter based on the currently identified risk level to better monitor high-risk areas.

[0101] In the embodiment of the present application, first, based on the interactive feedback from various terminals, the system re-evaluates the patient's condition and generates new catheter control instructions accordingly. Secondly, these instructions guide the catheter to more accurately locate the lesion site, while adjusting the detection parameters to capture more details. Then, the patient's condition changes are continuously monitored, and necessary intervention measures are taken in a timely manner to ensure the effectiveness and safety of the treatment.

[0102] After discovering that the blood flow disturbance in the left anterior descending artery had intensified, the system automatically generated new catheter control instructions, allowing the catheter to be more accurately positioned in the area and increasing the monitoring frequency of the area. Combining the previous imaging data to predict the possible trend of changes, the system recommended preparing for interventional surgery in advance to prevent the condition from worsening. This dynamic adjustment mechanism significantly improves the efficiency of emergency treatment and the patient's chance of survival.

[0103] In summary, steps 101 to 106 construct a complete cardiovascular disease diagnosis solution from data acquisition to visualization. This system can not only accurately capture the subtle changes inside the blood vessels, but also intelligently generate examination strategies suitable for individual patients based on this information, greatly improving the accuracy and efficiency of diagnosis. In addition, through the efficient compression and transmission of data streams, high-quality image sharing and remote diagnosis and treatment are guaranteed even in limited resources, providing strong support for the early detection and treatment of cardiovascular diseases.

[0104] In order to further improve the accuracy and efficiency of cardiovascular disease diagnosis, especially for the rapid assessment of patients with acute coronary syndrome in an emergency environment, by combining the transmittance distribution parameters and the blood flow disturbance parameters, the plaque components in the blood vessels and their rupture risks can be more accurately identified, thereby achieving a personalized, efficient and safe diagnosis and treatment plan. In some embodiments, the step 102 generates a computed tomography encoding strategy linked to vascular pathological characteristics based on the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, and the computed tomography encoding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuations, including:

[0105] 201. According to the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, by quantifying the spectral attenuation difference of the plaque components in the blood vessel wall, the plaque component characteristic parameters are generated, and at the same time, the dynamic disturbance component of the blood flow velocity field in the blood vessel cavity is extracted to generate the blood flow disturbance characteristic parameters;

[0106] In step 201, plaque component characteristic parameters and blood flow disturbance characteristic parameters are generated according to the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters. The transmittance distribution parameters reflect the attenuation of light when passing through different tissues, which helps to quantify the differences in plaque components. The blood flow disturbance parameters describe the irregularities in blood flow, especially the eddy current intensity, which can reveal changes in hemodynamics. The plaque component characteristic parameters are obtained by analyzing the spectral absorption characteristics, while the blood flow disturbance characteristic parameters are obtained by extracting the dynamic changes of the blood flow velocity field.

[0107] In the embodiment of the present application, first, after obtaining the transmittance distribution parameters, the system will perform spectral decomposition on them to distinguish different plaque components. For example, components such as lipid core and fibrous cap have different spectral absorption characteristics. Secondly, advanced algorithms are used to analyze these spectral information, calculate the specific proportion of each component, and form plaque component characteristic parameters. Then, for blood flow disturbance parameters, the system needs to monitor the changes in the blood flow velocity field in real time to capture any abnormal eddy current phenomena. Finally, by processing these dynamically changing data, the blood flow disturbance characteristic parameters are extracted to provide basic data support for subsequent risk assessment.

[0108] 202. Perform multimodal correlation analysis on the plaque component characteristic parameters and the blood flow disturbance characteristic parameters to generate plaque rupture risk assessment parameters, wherein the plaque rupture risk assessment parameters output quantitative indicators of the plaque surface through coupling effect;

[0109] In step 202, a multimodal correlation analysis is performed on the plaque component characteristic parameters and the blood flow disturbance characteristic parameters to generate a plaque rupture risk assessment parameter. The plaque rupture risk assessment parameter not only considers the stability of the internal components of the plaque, but also combines the influence of external blood flow conditions, such as shear force and eddy current intensity, to comprehensively assess the possibility of plaque rupture. The quantitative indicators of the plaque surface are output through the coupling effect, which means that not only the physical properties of the plaque itself but also the influence of the surrounding environment on it should be considered.

[0110] In the embodiment of the present application, first, the system starts to integrate the data obtained from step 201, establishes a multi-dimensional data model, and combines the characteristics of plaque composition with the characteristics of blood flow disturbance. Secondly, the model is trained using a machine learning algorithm so that it can identify which combination of conditions the plaque is most likely to rupture. Then, by simulating the behavior of plaques under different conditions, the effectiveness of the model is verified, and the parameter settings are adjusted to optimize the prediction accuracy. Finally, a risk assessment parameter containing quantitative indicators of the plaque surface is generated to provide doctors with intuitive risk prompts.

[0111] 203. Dynamically adjust a sampling frequency parameter of a computer tomography scan according to the plaque rupture risk assessment parameter, wherein the sampling frequency parameter realizes adaptive optimization of computer scan resolution by matching the spatial distribution difference of the plaque rupture risk;

[0112] In step 203, the sampling frequency parameters of the computed tomography are dynamically adjusted according to the plaque rupture risk assessment parameters. The sampling frequency parameters achieve adaptive optimization of the computer scan resolution by matching the spatial distribution differences of the plaque rupture risk. This means that the sampling frequency is increased in high-risk areas to ensure image clarity and detail presentation, while the sampling frequency can be appropriately reduced in low-risk areas to save resources.

[0113] In the embodiment of the present application, first, the system analyzes the plaque rupture risk assessment parameters to determine the risk level of each area. Secondly, for high-risk areas, the system automatically increases the sampling frequency of the area to ensure that enough data is collected for detailed analysis. Then, for low-risk areas, the sampling frequency is reduced accordingly, which not only ensures the overall scan quality but also improves efficiency. Finally, the entire process is controlled by an intelligent algorithm to ensure that each scan can be optimized and adjusted according to the latest patient status.

[0114] 204. Based on the vortex energy accumulation characteristics in the blood flow disturbance characteristic parameters, derive a wall shear force fluctuation parameter for calculating the matching relationship between the instantaneous change rate of the vascular wall shear force and the contrast agent diffusion rate, and generate a contrast agent injection parameter;

[0115] In step 204, based on the vortex energy accumulation characteristics in the blood flow disturbance characteristic parameters, the wall shear force fluctuation parameters used to calculate the matching relationship between the instantaneous change rate of the vascular wall shear force and the diffusion rate of the contrast agent are derived to generate contrast agent injection parameters. The vortex energy accumulation characteristics refer to the local formation of vortices due to the presence of vascular morphology or plaques during blood flow. These vortices will accumulate a certain amount of kinetic energy and affect the hemodynamic characteristics of the surrounding area. Wall shear force is an important indicator to measure the force of blood flow on the vascular wall, reflecting the friction effect of blood flow on the vascular wall. By analyzing the vortex energy accumulation characteristics, the instantaneous change rate of the vascular wall shear force can be derived, and then the diffusion rate and path of the contrast agent under different conditions can be predicted, so as to formulate the optimal contrast agent injection strategy.

[0116] In the embodiment of the present application, first, the system collects and analyzes the vortex energy accumulation characteristics in the blood flow disturbance characteristic parameters, and calculates the instantaneous rate of change of the shear force on the vascular wall. Secondly, based on these rates of change, the diffusion path and rate of the contrast agent in the blood vessel are predicted. Then, based on the actual situation of the patient, the injection speed and dosage of the contrast agent are adjusted to ensure that it can cover all key areas. Finally, the effectiveness of these parameters is verified through a series of preliminary experiments to ensure that the final angiography effect is optimal.

[0117] 205. Perform time-series synchronization on the sampling frequency parameter and the contrast agent injection parameter, and generate a computer tomography encoding strategy linked to vascular pathological characteristics by controlling the phase difference of contrast agent injection pulses.

[0118] In step 205, the sampling frequency parameters are time-synchronized with the contrast agent injection parameters, and a computed tomography encoding strategy linked to the vascular pathology characteristics is generated by controlling the phase difference of the contrast agent injection pulse. Time synchronization refers to coordinating the order of occurrence of different operations at a specific time point to ensure that the operations can cooperate with each other and improve overall efficiency. The phase difference control of the contrast agent injection pulse refers to accurately adjusting the time point of contrast agent injection to match it with the working cycle of the scanning equipment, so as to obtain the clearest image data at the best time. This method not only improves image quality, but also reduces unnecessary radiation exposure.

[0119] In an embodiment of the present application, first, the system integrates all parameters from steps 203 and 204 to develop a detailed scanning plan. For example, the sampling frequency and contrast agent injection amount of each area are determined. Secondly, through precise time control, ensure that the scanning device is in the highest sampling frequency state when the contrast agent reaches the target area. For example, by accurately calculating the diffusion time and scanning time of the contrast agent. Then, use software to simulate the scanning effects at different time points, and adjust the phase difference of the contrast agent injection pulse until the optimal solution is found. For example, adjust the injection speed to match the scanning rhythm. Finally, apply this complete coding strategy to the actual scanning process to ensure that each operation can maximize the diagnostic effect.

[0120] Here is a specific example:

[0121] A patient was rushed to the emergency department of a hospital due to sudden chest pain. The medical team quickly used a cardiovascular catheter with an integrated multimodal sensor to obtain transmittance distribution parameters and blood flow disturbance parameters. The system first analyzed the spectral absorption characteristics and eddy current intensity to generate plaque component characteristic parameters and blood flow disturbance characteristic parameters. Next, through multimodal correlation analysis, the system generated plaque rupture risk assessment parameters to guide the dynamic adjustment of the scanning sampling frequency. Based on the vortex energy accumulation characteristics, the system also optimized the injection parameters of the contrast agent to ensure the best imaging effect. For example, when a higher shear force was detected in a certain area, the contrast agent dose in that area was increased. Finally, through precise timing synchronization, the system achieved high-quality tomography, helping doctors to formulate treatment plans in a timely manner and save patients' lives.

[0122] In summary, a highly intelligent cardiovascular disease diagnosis system is constructed through the above steps 201 to 205. This system can not only accurately identify the composition of intravascular plaques and their rupture risks, but also dynamically adjust scanning parameters to ensure the best imaging effect. In addition, by optimizing the contrast agent injection strategy, unnecessary radiation exposure is effectively reduced, and the safety and accuracy of diagnosis are improved. In particular, by precisely controlling the phase difference and timing synchronization of the contrast agent injection pulse, it is ensured that each scan can obtain the clearest imaging data at the best time, greatly improving the diagnostic efficiency and success rate of cardiovascular diseases in emergency environments, and significantly improving the prognosis of patients.

[0123] In order to further improve the accuracy of cardiovascular disease diagnosis, especially for the rapid assessment of patients with acute coronary syndrome, a cardiovascular tomography encoding strategy based on multimodal data fusion is proposed. By combining plaque component characteristic parameters and blood flow disturbance characteristic parameters, structural mechanics analysis and cross-modal convolution operations are performed to generate plaque rupture risk assessment parameters containing stress-blood flow interaction weights to quantify the rupture probability of each area. In some embodiments, the plaque component characteristic parameters and the blood flow disturbance characteristic parameters are subjected to multimodal correlation analysis in step 202 to generate plaque rupture risk assessment parameters. The plaque rupture risk assessment parameters output quantitative indicators of the plaque surface through coupling effects, including:

[0124] 301. Based on the correlation between the distribution of fiber cap thickness and the proportion of lipid core space in the characteristic parameters of plaque components, extract the stress concentration coefficient of the plaque structure through structural mechanics analysis;

[0125] In step 301, the fibrous cap thickness distribution and lipid core space ratio in the plaque component characteristic parameters are key indicators for evaluating plaque stability. The fibrous cap refers to a layer of tissue covering the surface of the plaque, and its thickness affects the ability of the plaque to resist external stress; while the lipid core refers to the lipid-rich part inside the plaque, and its size and position are crucial to the overall stability of the plaque. Structural mechanics analysis is used to extract the plaque structure stress concentration coefficient, which reflects the degree of stress concentration generated when a specific area inside the plaque is subjected to external force.

[0126] In the examples of the present application, first, high-resolution imaging technology is used to obtain detailed images of the plaque and its surroundings. Then, the thickness of the fibrous cap and the spatial proportion of the lipid core are identified and quantified by an image processing algorithm. Next, a finite element model is constructed based on these data to simulate the behavior of the plaque under different stress conditions. Finally, the model output is analyzed to determine which areas may experience stress concentration and calculate the corresponding stress concentration coefficient.

[0127] 302. Generate a blood flow disturbance stress coupling factor according to the spatial overlap of the shear force oscillation amplitude and the flow separation area in the blood flow disturbance characteristic parameter and the spatial position mapping relationship of the plaque structure stress concentration coefficient;

[0128] In step 302, the shear force oscillation amplitude describes the change in friction force applied to the blood vessel wall during blood flow, while the flow separation area refers to the vortex area formed after blood flows through irregularly shaped objects (such as plaques). These two factors act together on the surface of the plaque and affect its stability. The blood flow disturbance stress coupling factor combines the above factors with the stress concentration coefficient of the plaque structure to measure the interaction strength between the two.

[0129] In the embodiment of the present application, first, based on the result of step 301, the location of the stress concentration point of the plaque structure is determined. Secondly, the blood flow pattern is simulated using computational fluid dynamics technology, especially focusing on the shear force oscillation amplitude and the flow separation area. Then, the fluid dynamics results are mapped to the plaque structure, and the blood flow disturbance stress coupling factor at each stress concentration point is calculated. For example, in the aforementioned case, further fluid dynamics analysis revealed significant blood flow disturbances near the plaque, especially in the stress concentration area, indicating that this part may be more vulnerable to damage.

[0130] 303. Perform a cross-modal convolution operation on the gradient change rate of the plaque structure stress concentration coefficient and the time-varying component of the blood flow disturbance stress coupling factor, and output a dynamic stress offset including the cumulative effect of plaque deformation;

[0131] In step 303, the gradient change rate of the stress concentration coefficient of the plaque structure refers to the rate of change of stress concentration at different locations inside the plaque, which helps to understand the possibility of plaque deformation. The time-varying component of the blood flow disturbance stress coupling factor reflects how the influence of hemodynamic factors on the plaque changes over time. The cross-modal convolution operation is a method of combining and analyzing these two different types of data to calculate the dynamic stress offset that includes the cumulative effect of plaque deformation.

[0132] In an embodiment of the present application, first, the time-dependent change of the blood flow disturbance stress coupling factor is determined according to the result of step 302. Next, a mathematical model is used to analyze the gradient change rate of the stress concentration coefficient of the plaque structure in space. Then, the cross-modal convolution algorithm is applied to process the above two data sets to identify the possible deformation patterns within the plaque. Finally, the impact of these deformations on plaque stability is evaluated through simulation and experimental verification. For example, in the aforementioned case, the doctor found that the stress concentration coefficient of the plaque in a specific area increased significantly over time. Combined with the blood flow disturbance characteristics, it is predicted that the area may undergo larger deformations in the future.

[0133] 304. Generate the distribution difference of the stress gradient on the surface of the plaque in the axial and radial directions based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameter of the calcified area in the plaque component characteristic parameter;

[0134] In step 304, the dynamic stress offset describes the change of stress distribution inside the plaque over time. The edge sharpness parameter of the calcified area is an indicator for evaluating the degree of plaque hardening, reflecting the clarity of the boundary of the calcified area of ​​the plaque. The distribution difference of the stress gradient on the plaque surface in the axial and radial directions is used to quantify the change of stress on the plaque surface in different directions.

[0135] In the embodiments of the present application, first, a detailed image of the calcified area of ​​the plaque is obtained using high-resolution imaging technology. Secondly, the sharpness of the edge of the calcified area is measured by image processing technology. Then, combined with the dynamic stress offset, the distribution difference of the stress gradient on the surface of the plaque in the axial and radial directions is analyzed. Finally, this information is used to assess the risk of plaque rupture. For example, in the above case, further imaging examinations revealed that the edge of the calcified area of ​​the plaque was blurred, indicating that its structure was not as strong as expected, increasing the possibility of rupture.

[0136] 305. Nonlinearly superimpose the distribution difference with the eddy current energy attenuation rate in the blood flow disturbance characteristic parameter to generate a plaque rupture risk assessment parameter including a stress-blood flow interaction weight, wherein the plaque rupture risk assessment parameter is used to characterize a quantitative index of the rupture probability of each area on the plaque surface.

[0137] In step 305, the distribution difference refers to the variation of the stress gradient on the plaque surface in different directions. The eddy energy decay rate is a parameter that describes the speed at which the eddy energy in the blood flow decreases over time, reflecting the strength of the force exerted by the blood flow on the plaque. Nonlinear superposition combines these two parameters to generate a plaque rupture risk assessment parameter to characterize the quantitative value of the probability of plaque rupture.

[0138] In the embodiment of the present application, first, based on the result of step 304, the distribution difference of the stress gradient on the surface of the plaque is determined. Secondly, the eddy behavior in the blood flow is simulated, and its energy attenuation rate is calculated. Then, the nonlinear superposition method is used to integrate these two parameters to generate the final plaque rupture risk assessment parameter. Finally, a personalized treatment recommendation is formulated based on this parameter. For example, in the above case, a comprehensive analysis found that the plaque had a high risk of rupture, so it was decided to arrange closer medical monitoring for the patient and consider interventional treatment to reduce the risk.

[0139] Here is a specific example:

[0140] In an emergency case, a patient was sent to the hospital due to sudden chest pain. The medical team used a cardiovascular catheter with integrated sensors to obtain transmittance distribution and blood flow disturbance parameters, and found significant plaques in the left anterior descending artery. The system analyzed the composition of the plaque and the characteristics of blood flow disturbance, calculated the stress concentration coefficient of the plaque structure and the blood flow disturbance stress coupling factor, and then generated a dynamic stress offset. Based on this, the system identified areas with high rupture risk and optimized scanning parameters, helping doctors to quickly develop treatment plans and successfully save the patient's life. This process demonstrates the important application value of multimodal data analysis in acute cardiovascular events.

[0141] In summary, through the analysis of steps 301-305, we can fully understand the complexity of atherosclerotic plaques at the structural and functional levels, and make a more accurate assessment of their rupture risk. This method not only takes into account the physical properties of the plaque itself, but also deeply explores the impact of hemodynamic factors on plaque stability. The resulting plaque rupture risk assessment parameters provide clinicians with a powerful tool to help develop personalized treatment strategies and reduce the occurrence of acute cardiovascular events. This cross-modal correlation analysis method represents the frontier direction of modern medical image processing and biomechanics research, and demonstrates the great potential of multidisciplinary collaboration to solve complex medical problems.

[0142] In order to further improve the accurate prediction of plaque rupture risk in cardiovascular disease diagnosis, by analyzing the high-frequency and low-frequency stress spectrum density distribution and its relationship with the calcification edge stress concentration factor, a difference parameter containing axial and radial stress gradient components is generated. This method can more accurately identify high-risk plaque areas and provide doctors with more sophisticated risk assessment tools to formulate effective treatment strategies. In some embodiments, the distribution difference of the stress gradient on the plaque surface in the axial and radial directions is generated based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameters of the calcified area in the plaque component characteristic parameters in step 303, including:

[0143] 401. Extracting the high-frequency stress spectrum density distribution in the axial stress propagation direction and the low-frequency stress spectrum density distribution in the radial stress diffusion direction according to the energy proportion relationship between the main frequency component and the harmonic component of the dynamic stress offset;

[0144] In step 401, the dynamic stress offset reflects the change in internal stress of the plaque when it is affected by blood flow. The main frequency component and the harmonic component are two important parameters that describe the frequency characteristics of this change, and the energy ratio relationship reveals the main mode of stress change. The high-frequency stress spectrum density distribution represents the rapid change in the axial stress propagation direction, while the low-frequency stress spectrum density distribution corresponds to the slow change in the radial stress diffusion direction. These data are used to determine the propagation characteristics of stress in different directions.

[0145] In the embodiments of the present application, first, the signal processing technology is used to separate the main frequency component and the harmonic component from the dynamic stress offset, and their energy proportions are calculated. Secondly, based on these energy proportions, high-frequency and low-frequency stress spectrum density distribution diagrams are constructed. Then, through comparative analysis, the dominant frequency range of axial and radial stress propagation is determined. Finally, combined with the principles of biomechanics, it is verified whether these stress distribution patterns conform to the expected physical behavior. For example, in an actual case, the research team found that the main frequency components of a particular plaque were concentrated in the higher frequency band, indicating that its axial stress propagation was more active.

[0146] 402. Analyze the stress enhancement ratio of the axial and radial directions of the stress concentration factor of the calcification edge according to the correlation between the curvature mutation point distribution density and the gradient change rate in the edge sharpness parameter of the calcification area in the plaque component characteristic parameter;

[0147] In step 402, the curvature mutation point distribution density and gradient change rate in the edge sharpness parameters of the calcified region are key indicators for evaluating the boundary clarity and hardness of the calcified region. The calcified edge stress concentration factor refers to the degree of local stress concentration caused by the presence of the calcified region, and the stress enhancement ratio in the axial and radial directions reflects the impact of stress concentration on plaque stability.

[0148] In the embodiment of the present application, first, the distribution density of the curvature mutation points and the gradient change rate at the edge of the calcified area are measured by image processing technology. Secondly, based on these parameters, the finite element analysis method is used to simulate the distribution of stress at the edge of the calcification. Then, the stress enhancement ratio of the stress concentration factor at the edge of the calcification in the axial and radial directions is calculated based on the simulation results. Finally, these ratios are compared with the theoretical model to ensure the reliability of the analysis results.

[0149] 403. Adaptively weighted superposition the high-frequency stress spectrum density distribution and the axial stress enhancement ratio of the calcification edge stress concentration factor to generate an axial stress gradient component including the calcification edge stress amplification effect;

[0150] In step 403, the high-frequency stress spectral density distribution is combined with the axial stress enhancement ratio of the calcification edge stress concentration factor to amplify the stress effect of the calcification edge, thereby more accurately capturing the changes in the axial stress gradient of the plaque. Adaptive weighted superposition is an algorithm that automatically adjusts weights according to different stress characteristics to ensure that the final axial stress gradient component can reflect the true stress distribution and highlight the stress changes in key areas.

[0151] In the embodiment of the present application, first, the high-frequency stress spectrum density distribution map and the axial stress enhancement ratio of the calcification edge stress concentration factor are obtained. Secondly, the adaptive weighted superposition algorithm is applied to combine these two parameters according to certain rules. Then, the weight value is optimized through repeated iterations, so that the axial stress gradient component finally generated not only conforms to the actual situation, but also can effectively identify potential high-risk areas.

[0152] 404. Directionally convolve and fuse the low-frequency stress spectrum density distribution with the radial stress enhancement ratio of the calcification edge stress concentration factor to generate a radial stress gradient component including stress diffusion attenuation characteristics;

[0153] In step 404, the low-frequency stress spectral density distribution is combined with the radial stress enhancement ratio of the calcification edge stress concentration factor to better understand how radial stress diffuses and decays. Directional convolution fusion is a mathematical tool that can emphasize special properties on the stress diffusion path, such as decay rate, while maintaining the original stress distribution characteristics.

[0154] In the embodiments of the present application, first, the low-frequency stress spectral density distribution data and the radial stress enhancement ratio of the calcification edge stress concentration factor are collected. Secondly, the two sets of data are processed using a directional convolution fusion algorithm to generate a radial stress gradient component containing stress diffusion attenuation characteristics. Then, by analyzing the generated data, the main path and attenuation rate of stress diffusion are determined. For example, in a specific case, after processing, the researchers observed that the stress mainly diffused along a specific direction and decayed rapidly with increasing distance.

[0155] 405. Based on the peak position of the axial stress gradient component and the attenuation rate of the radial stress gradient component, a gradient difference parameter characterizing the difference between the axial and radial stress distributions is output through a composite calculation of spatial position matching and energy attenuation slope.

[0156] In step 405, the peak position of the axial stress gradient component and the decay rate of the radial stress gradient component are two important characteristic parameters, which together determine the distribution pattern of the plaque surface stress. The composite operation of spatial position matching and energy decay slope refers to a comprehensive analysis method, which outputs a gradient difference parameter that can fully reflect the stress state of the plaque by comparing the stress distribution characteristics in different directions.

[0157] In the embodiments of the present application, first, the peak position of the axial stress gradient component and the attenuation rate of the radial stress gradient component are determined. Secondly, the above two parameters are combined by using a composite calculation method of spatial position matching and energy attenuation slope. Then, the effectiveness and accuracy of the composite calculation are verified through multiple experiments. For example, in actual applications, the research team used this method to successfully predict the possible rupture location of the plaque in the future and formulated preventive measures accordingly.

[0158] Here is a specific example:

[0159] In an emergency case, a patient was sent to the hospital with sudden chest pain. The medical team used a cardiovascular catheter with integrated sensors to obtain transmittance distribution and blood flow disturbance parameters, and found significant plaque in the left anterior descending artery. The system analyzed the dynamic stress offset and edge sharpness parameters of the calcified area to generate axial and radial stress gradient components. The results showed that there was a high axial stress concentration and rapid radial stress diffusion in this area, indicating a high risk of rupture. Based on these data, the doctor quickly developed a personalized treatment plan and successfully controlled the condition, demonstrating the key role of multimodal data analysis in acute cardiovascular events. This method not only improves diagnostic accuracy, but also provides patients with more effective treatment options.

[0160] By implementing the method of steps 401 to 405, not only can the distribution difference of the stress on the plaque surface in the axial and radial directions be accurately quantified, but also the key areas that may cause plaque rupture can be identified. This method greatly improves the accuracy and reliability of the risk assessment of atherosclerotic plaque rupture, provides strong support for clinicians, helps to formulate personalized treatment plans, and reduces the occurrence of acute cardiovascular events. In addition, this method demonstrates the great potential of interdisciplinary cooperation and promotes the progress of medical imaging technology and biomechanics research.

[0161] In order to further improve the efficiency and quality of data transmission in the diagnosis of cardiovascular diseases, especially for the rapid assessment of patients with acute coronary syndrome in an emergency environment, a hierarchical compression method based on a video coding protocol is proposed. By compressing the rendering instruction set in layers according to the geometric structure layer, dynamic parameter layer and texture detail layer, and combining the polarization attenuation mode of the vascular bifurcation mark and the transmittance distribution parameter to generate a key frame protection strategy, it is ensured that high-quality image data can be transmitted efficiently and accurately under various network conditions. In some embodiments, the rendering instruction set described in step 104 is hierarchically compressed by the video coding protocol to generate a compressed data stream carrying the vascular bifurcation mark, the hierarchical compression integrates the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusts the transmission priority of the dynamic parameter coding block based on the wall shear force of the blood flow disturbance parameter, including:

[0162] 501. Compress the rendering instruction set in layers according to the geometry layer, the dynamic parameter layer and the texture detail layer through a video coding protocol to generate an intermediate compressed frame including the geometry compression parameters, the dynamic compression parameters and the texture compression parameters;

[0163] In step 501, the system compresses the rendering instruction set in layers according to the geometry layer, dynamic parameter layer and texture detail layer through the video coding protocol to generate an intermediate compressed frame containing geometry compression parameters, dynamic compression parameters and texture compression parameters. The geometry layer mainly describes the basic shape information of the image, such as the blood vessel path and the location of the bifurcation point; the dynamic parameter layer contains information on the dynamic changes of blood flow; and the texture detail layer provides finer image details, such as tissue surface features. These layers are compressed into different parameter sets to meet different levels of data requirements.

[0164] In the embodiment of the present application, first, the system identifies and separates the geometric structure information in the rendering instruction set, compresses it into geometric compression parameters, and ensures the integrity of the basic shape information. Secondly, for the dynamic parameter layer, the system extracts data on the dynamic changes of blood flow, such as velocity field and shear force, to form dynamic compression parameters. Then, for the texture detail layer, the system processes high-resolution image data and generates texture compression parameters to retain as much detail information as possible. Finally, all these parameters are integrated into an intermediate compressed frame as the basis for subsequent processing.

[0165] 502. Bind the blood vessel bifurcation mark with the coding block of the intermediate compressed frame in time and space, generate a bifurcation mark index table based on the geometric compression parameter, insert a topology check code element into the coding block boundary of the intermediate compressed frame, and generate a compressed data stream;

[0166] In step 502, the vascular bifurcation mark is spatiotemporally bound to the coding block of the intermediate compression frame, a bifurcation mark index table is generated based on the geometric compression parameters, and topological check code elements are inserted into the coding block boundaries to generate a compressed data stream. The bifurcation mark index table is used to quickly locate the vascular bifurcation point, and the topological check code elements ensure that key information is not lost during data transmission. This binding mechanism enhances the integrity and reliability of the data.

[0167] In the embodiment of the present application, first, the system matches the vascular bifurcation mark with the coding block of the intermediate compression frame to ensure that each bifurcation point can be accurately located. Secondly, a bifurcation mark index table is generated based on the geometric compression parameters, which records the location of each bifurcation point and its corresponding coding block number. Then, topology check code elements are inserted at the boundaries of the coding blocks, which can verify the integrity of the data at the receiving end. Finally, the final compressed data stream is generated to ensure stable transmission under any network conditions and to quickly parse out important vascular structure information.

[0168] 503. Generate a key frame protection strategy according to the polarization attenuation mode of the layered compression integrated transmittance distribution parameter, and update a key frame refresh interval according to the polarization attenuation weight combination and the blood flow direction of the dynamic compression parameter;

[0169] In step 503, a key frame protection strategy is generated based on the polarization attenuation pattern of the layered compression integrated transmittance distribution parameter, and the key frame refresh interval is updated based on the polarization attenuation weight combined with the blood flow direction of the dynamic compression parameter. The polarization attenuation pattern reflects the intensity change of light when it passes through different media, which helps to identify important areas. The key frame protection strategy ensures that the most critical information is always protected and updated first during data transmission.

[0170] In the embodiment of the present application, first, the system analyzes the polarization attenuation pattern of the transmittance distribution parameters to determine which areas need special protection. Secondly, based on the polarization attenuation weight, a key frame protection strategy is generated to ensure that these areas do not lose information during the compression process. Then, according to the blood flow direction of the dynamic compression parameter, the key frame refresh interval is adjusted so that the image can be updated more frequently in areas with large blood flow changes to keep the latest status. Finally, in this way, clear and accurate image data can be provided in any situation.

[0171] 504. Extract the shear force direction component of the dynamic parameter layer coding block based on the wall shear force of the blood flow disturbance parameter, strengthen the motion vector of the high shear force area coding block through the bifurcation mark index table, and dynamically adjust the transmission priority of the dynamic parameter layer coding block in the compressed data stream.

[0172] In step 504, the shear force direction component of the dynamic parameter layer coding block is extracted based on the wall shear force of the blood flow disturbance parameter, and the motion vector of the high shear force area coding block is strengthened through the bifurcation mark index table, and the transmission priority of the dynamic parameter layer coding block in the compressed data stream is dynamically adjusted. This method can better capture the dynamic changes of blood flow, especially in high-risk areas, and ensure that key information is transmitted in a timely manner.

[0173] In the embodiment of the present application, first, the system extracts the directional component of the wall shear force from the blood flow disturbance parameters and identifies the high shear force area. Secondly, the bifurcation mark index table is used to find the coding blocks corresponding to these high shear force areas, and their motion vectors are enhanced to ensure that these areas have a higher priority in the compressed data stream. Then, the transmission order of these coding blocks is dynamically adjusted so that they can reach the receiving end in the shortest time. Finally, in this way, it is ensured that the most important blood flow dynamic information can be obtained in a timely manner even under poor network conditions.

[0174] Here is a specific example:

[0175] A patient was sent to the hospital emergency department due to sudden chest pain. The medical team quickly used a cardiovascular catheter with integrated sensors to obtain transmittance distribution and blood flow disturbance parameters, and found significant plaques in the left anterior descending artery. The system first hierarchically compresses the rendering instruction set to generate intermediate compressed frames of geometric compression parameters, dynamic compression parameters, and texture compression parameters. Next, the vascular bifurcation mark is spatiotemporally bound to the coding block of the intermediate compression frame, a bifurcation mark index table is generated, and a topology check code element is inserted. Subsequently, the system generates a key frame protection strategy based on the polarization attenuation mode, adjusts the key frame refresh interval, and optimizes the transmission priority of the dynamic parameter layer coding block based on the wall shear force. Ultimately, through efficient data transmission, doctors can quickly obtain high-quality imaging data, formulate effective treatment plans, and successfully control the disease.

[0176] In summary, the efficiency and accuracy of data transmission in cardiovascular disease diagnosis are improved through the above steps 501 to 504. This method not only ensures the stable transmission of high-quality image data under various network conditions, but also significantly improves the diagnosis speed and accuracy in emergency environments by optimizing key frame protection strategies and dynamically adjusting transmission priorities. This innovative solution greatly improves the diagnosis and treatment of acute cardiovascular events and brings better prognosis to patients.

[0177] In order to further improve the accuracy and efficiency of data transmission in cardiovascular disease diagnosis, especially for the rapid assessment of patients with acute coronary syndrome in an emergency environment, a method based on spatiotemporal binding is proposed. By matching the vascular bifurcation mark with the coding block of the intermediate compressed frame and generating a bifurcation mark index table based on the geometric compression parameters, it is ensured that high-quality image data can be transmitted efficiently and accurately under any network conditions. In some embodiments, the spatiotemporal binding of the vascular bifurcation mark with the coding block of the intermediate compressed frame and the generation of the bifurcation mark index table based on the geometric compression parameters in step 502 include:

[0178] 601. Generate a spatial position mapping relationship between a bifurcation point and an intermediate compressed frame coding block according to the three-dimensional coordinates of the bifurcation point in the blood vessel bifurcation mark and the timestamp sequence of the intermediate compressed frame;

[0179] In step 601, the vascular bifurcation mark refers to the location of the vascular branch identified in the medical image, and its three-dimensional coordinates are accurate spatial positioning information obtained through high-resolution imaging technology. The timestamp sequence of the intermediate compressed frames records the distribution of these image frames in time. The spatial position mapping relationship refers to the establishment of a correspondence between the bifurcation point and the intermediate compressed frame coding block based on the above information, which is crucial for understanding the dynamic changes of blood flow. This mapping relationship helps to track the changes of specific bifurcation points over time.

[0180] In the embodiment of the present application, a patient's vascular image data set is first obtained using a high-resolution imaging device. Then, the three-dimensional coordinates of the vascular bifurcation point are extracted through an image processing algorithm, and the timestamp of each frame is synchronously recorded. Then, based on these time and space information, a database is constructed to store the spatial position mapping relationship between each bifurcation point and its corresponding intermediate compressed frame coding block. Finally, the validity of the mapping relationship is verified by analyzing the data of multiple patients to ensure that it can accurately reflect the actual dynamic changes of the vascular bifurcation point.

[0181] 602. Based on the angle between the motion vector direction of the intermediate compressed frame coding block and the blood flow direction of the bifurcation point, obtain a spatiotemporal fit parameter between the intermediate compressed frame coding block and the bifurcation mark, and perform spatial weight allocation on the intermediate compressed frame coding block according to the spatiotemporal fit parameter to generate a coding block binding set including a bifurcation point stability weight;

[0182] In step 602, the motion vector direction of the intermediate compressed frame coding block describes the direction of content movement between frames, while the blood flow direction at the bifurcation point refers to the flow path of blood through the bifurcation of the blood vessel. The spatiotemporal fit parameter is used to measure the degree of consistency between the two, reflecting the degree of match between the coding block and the bifurcation point in time and space. Spatial weight allocation is a strategy that adjusts the importance of each coding block according to the spatiotemporal fit parameter, thereby generating a set of coding block bindings containing bifurcation point stability weights. This step is particularly important for identifying unstable factors that may affect vascular health.

[0183] In the embodiment of the present application, the direction of the motion vector between each pair of adjacent intermediate compressed frames is first calculated, and the blood flow direction of each bifurcation point is determined. Secondly, based on this direction information, the spatiotemporal fit parameter is calculated, and this is used as a basis for spatial weight allocation of each coding block. Then, through experimental verification, the weight allocation scheme is adjusted to optimize the results.

[0184] 603. Obtain a curvature compression difference factor of the geometric compression parameter based on a compression ratio difference between a primary curvature and a secondary curvature in the coding block binding set;

[0185] In step 603, the major curvature and the minor curvature represent quantitative indicators of the major and minor curvatures of the vascular surface, respectively. The compression ratio difference refers to the difference in the ratio of the two curvatures to be compressed during the geometric compression process. The curvature compression difference factor is a characteristic value extracted from this difference, reflecting the deformation characteristics of the vascular structure during the compression process. This factor is critical for evaluating the stability of the vascular bifurcation point and predicting its future change trend.

[0186] In the present application, the major curvature and minor curvature of the area near each bifurcation point are first measured and recorded. Then, the geometric compression algorithm is applied to process these data to calculate the compression ratio difference. Next, the curvature compression difference factor is calculated based on these differences and applied to subsequent analysis.

[0187] 604. Bidirectionally sort the curvature compression difference factor and the space-time fit parameter to generate a bifurcation mark index table with the bifurcation point stability weight as the index key and the curvature compression difference factor as the associated value.

[0188] In step 604, bidirectional sorting is a method for simultaneously considering the influence of curvature compression difference factor and spatiotemporal fit parameter to generate a comprehensive index table. The bifurcation point stability weight is used as the index key and the curvature compression difference factor is used as the associated value. Such an index table can help quickly find and analyze the stability status of different bifurcation points. This step is particularly useful for formulating personalized treatment plans.

[0189] In the embodiment of the present application, all the data obtained in the previous steps are first integrated, including the curvature compression difference factor and the spatiotemporal fit parameter. Then, these data are processed using a bidirectional sorting algorithm to generate a final bifurcation marker index table. Finally, by comparing and analyzing the index tables of different patients, targeted medical advice is provided. For example, in a study of multiple patients with coronary artery disease, doctors developed a more accurate surgical plan based on the generated index table, which significantly improved the treatment effect.

[0190] Here is a specific example:

[0191] A patient was sent to the hospital emergency department due to sudden chest pain. The medical team used a cardiovascular catheter with integrated sensors to obtain transmittance distribution and blood flow disturbance parameters, and found significant plaques in the left anterior descending artery. The system first generates an intermediate compressed frame and establishes a spatial position mapping relationship based on the three-dimensional coordinates and timestamp of the bifurcation point. Next, the spatiotemporal fit parameters are calculated and the coding blocks are spatially weighted to form a set of coding block bindings. Subsequently, the curvature compression difference factor is generated based on the compression ratio difference between the primary curvature and the secondary curvature, and a bifurcation mark index table is generated. Ultimately, doctors were able to quickly obtain high-quality imaging data, develop effective treatment plans, and successfully control the disease. This method demonstrates its important application value in acute cardiovascular events.

[0192] The complete technical effect of the scheme of steps 601 to 604 is that it not only improves the recognition accuracy of the location and stability of the vascular bifurcation point, but also provides a scientific basis for clinicians to formulate personalized treatment strategies and effectively reduce the risk of acute cardiovascular events. This method demonstrates the great potential of combining modern medical image processing technology with biomechanical theory.

[0193] In order to further improve the efficiency and quality of data processing in cardiovascular disease diagnosis, especially for the rapid assessment of patients with acute coronary syndrome in an emergency environment, a method based on a containerized microservice cluster is proposed. By splitting the computed tomography encoding strategy into multiple sub-strategies executed in parallel, and generating a rendering instruction set associated with the vascular elastic modulus according to the terminal hardware architecture, it is ensured that high-quality image data can be generated efficiently and accurately under any hardware conditions. In some embodiments, the step 603 inputs the computed tomography encoding strategy into a containerized microservice cluster, and generates a rendering instruction set associated with the vascular elastic modulus based on the terminal hardware architecture, including:

[0194] 701. Split the computed tomography scanning coding strategy into multiple scanning coding sub-strategies executed in parallel, inject each scanning coding sub-strategy into the containerized microservice cluster through a dynamic load balancing module, and generate a scanning coding sub-strategy identifier;

[0195] In step 701, the system splits the computed tomography coding strategy into multiple scanning coding sub-strategies executed in parallel, and injects each sub-strategy into the containerized microservice cluster through the dynamic load balancing module to generate a scanning coding sub-strategy identifier. The scanning coding sub-strategy is a subdivision of the original coding strategy, and each sub-strategy is responsible for data processing in a specific area or function. The dynamic load balancing module is used to optimize resource allocation to ensure that each sub-strategy can be executed under optimal conditions. The scanning coding sub-strategy identifier is used to track and manage the status and progress of each sub-strategy.

[0196] In an embodiment of the present application, first, the system analyzes the original computed tomography encoding strategy and decomposes it into multiple independently executable sub-strategies. These sub-strategies may involve different cardiovascular areas or different imaging parameters. Secondly, using the dynamic load balancing module, each sub-strategy is intelligently assigned to the appropriate containerized microservice node to run according to the resource status of the current system. Then, a unique identifier is generated for each sub-strategy to facilitate subsequent management and monitoring of its execution. Finally, in this way, it is ensured that all sub-strategies can be executed efficiently in parallel to maximize the use of system resources.

[0197] 702. According to the scan coding sub-strategy identifier, the number of computing units and parallel processing capabilities of the terminal hardware architecture are traversed to generate a vascular elastic modulus sequence including a dynamic association between a vascular wall deformation gradient and a stress response;

[0198] In step 702, the number of computing units and parallel processing capabilities of the terminal hardware architecture are traversed according to the scan coding sub-strategy identifier to generate a vascular elastic modulus sequence containing a dynamic association between the vascular wall deformation gradient and the stress response. The number of computing units reflects the processing capability of the terminal device, while the parallel processing capability determines the number of tasks that can be processed simultaneously. The vascular wall deformation gradient describes the degree of change of the vascular wall at different positions, and the stress response reflects the force of blood flow on the vascular wall. These information together constitute the vascular elastic modulus sequence, which is used to guide subsequent rendering operations.

[0199] In an embodiment of the present application, first, the system identifies the specific task to be processed based on the scanned coding sub-strategy identifier, and obtains relevant information about the terminal hardware architecture, such as the number of computing units and parallel processing capabilities. Secondly, based on this information, the system calculates the dynamic correlation between the deformation gradient of the vascular wall and the stress response to form a vascular elastic modulus sequence. For example, in high stress areas, the deformation gradient may be larger, so more sophisticated processing is required. The system then integrates these calculation results into a sequence to ensure that the elastic modulus of each area can be accurately represented. Finally, in this way, an elastic modulus sequence that fully reflects the characteristics of the vascular structure is generated, providing basic data for subsequent rendering.

[0200] 703. Mapping the deformation gradient in the vascular elastic modulus sequence into a multi-level rendering resolution parameter, and mapping the stress response into a matching texture sampling density parameter, to form a dynamic rendering parameter set;

[0201] In step 703, the deformation gradient in the vascular elastic modulus sequence is mapped to a multi-level rendering resolution parameter, and the stress response is mapped to a matching texture sampling density parameter to form a dynamic rendering parameter set. The multi-level rendering resolution parameter determines the image resolution of different regions to meet different detail requirements. The texture sampling density parameter controls the fineness of the image surface, especially in areas with higher stress responses. The dynamic rendering parameter set combines these two aspects of information to ensure that the generated image is both clear and realistic.

[0202] In an embodiment of the present application, first, the system extracts deformation gradient information from the vascular elastic modulus sequence and maps it to corresponding multi-level rendering resolution parameters. For example, in areas with larger deformations, higher resolutions are used to capture subtle changes. Secondly, the system also processes stress response information and converts it into matching texture sampling density parameters. This helps to generate more delicate images in areas with higher stress. The system then integrates these parameters into a dynamic rendering parameter set to ensure that the rendering settings of each area match its characteristics. Finally, in this way, a complete set of dynamic rendering parameters is generated to provide a basis for subsequent video memory allocation and shader instruction queues.

[0203] 704. Divide the memory bandwidth of the terminal hardware architecture based on the dynamic rendering parameter set, generate a corresponding video memory allocation strategy and a bound shader instruction queue, and output a rendering instruction set associated with the vascular elastic modulus after fusion.

[0204] In step 704, the memory bandwidth of the terminal hardware architecture is divided based on the dynamic rendering parameter set, and the corresponding video memory allocation strategy and the bound shader instruction queue are generated, and the rendering instruction set associated with the vascular elastic modulus is output after fusion. The memory bandwidth determines the speed of data transmission, and the video memory allocation strategy ensures that the graphics processing unit can use resources efficiently. The shader instruction queue contains specific rendering operation instructions to ensure that each area can be rendered according to the preset parameters. The final generated rendering instruction set is a complete set of instructions for guiding the actual rendering operation.

[0205] In an embodiment of the present application, first, the system evaluates the memory bandwidth requirements of the terminal hardware architecture based on a set of dynamic rendering parameters, and generates a video memory allocation strategy accordingly. For example, more video memory is allocated in high-resolution areas to ensure image quality. Secondly, the system generates a bound shader instruction queue to ensure that each area can be processed according to the preset rendering parameters. Then, the system merges the video memory allocation strategy with the shader instruction queue to generate a complete set of rendering instructions. Finally, in this way, it is ensured that high-quality image data can be generated efficiently and accurately under any hardware conditions, providing doctors with a reliable diagnostic tool.

[0206] Here is a specific example:

[0207] A patient was sent to the hospital emergency department due to sudden chest pain. The medical team quickly used a cardiovascular catheter with integrated sensors to obtain transmittance distribution and blood flow disturbance parameters, and found significant plaques in the left anterior descending artery. The system first split the computed tomography encoding strategy into multiple sub-strategies and distributed them to the containerized microservice cluster for execution through a dynamic load balancing module. Next, the system generates a vascular elastic modulus sequence based on the number of computing units and parallel processing capabilities, and then maps the deformation gradient and stress response to rendering parameters to generate a dynamic rendering parameter set. Subsequently, the system generates a video memory allocation strategy and a shader instruction queue based on these parameters, and finally outputs a high-quality rendering instruction set. Doctors were able to quickly obtain detailed imaging data, develop effective treatment plans, and successfully control the disease.

[0208] In summary, the efficiency and accuracy of data processing in cardiovascular disease diagnosis are improved through the above steps 701 to 704. This method not only ensures that high-quality image data can be efficiently generated under various hardware conditions, but also significantly improves image quality and resolution by optimizing video memory allocation and shader instruction queues. This innovative solution greatly improves the speed and accuracy of diagnosis in emergency environments, bringing better prognosis to patients.

[0209] Figure 2 A schematic diagram of a multi-terminal processing system for real-time sharing of emergency clinical data is provided for the present application embodiment. Figure 2 As shown, the device comprises:

[0210] An acquisition module 21 acquires the light transmittance distribution parameters and blood flow disturbance parameters of the inner wall of the blood vessel through a multimodal sensor of the modified cardiovascular catheter, wherein the multimodal sensor is integrated into the catheter detection end of the gradient refractive index nanocoating;

[0211] A generating module 22 generates a computed tomography coding strategy linked to vascular pathology characteristics according to the spectral absorption characteristics of the transmittance distribution parameter and the eddy current intensity of the blood flow disturbance parameter, wherein the computed tomography coding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuation;

[0212] An input module 23 inputs the computed tomography encoding strategy into the containerized microservice cluster, and generates a rendering instruction set associated with the vascular elastic modulus based on the terminal hardware architecture;

[0213] A compression module 24 performs hierarchical compression on the rendering instruction set through a video coding protocol to generate a compressed data stream carrying a blood vessel bifurcation marker, wherein the hierarchical compression integrates the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusts the coding block priority based on the wall shear force of the blood flow disturbance parameter;

[0214] Calling module 25, distributing the compressed data stream to a heterogeneous terminal cluster, triggering the visualization of the elastic modulus of the blood vessel wall on the mobile terminal, and calling the key frame protection strategy to associate with the wall shear force fluctuation;

[0215] The reconstruction module 26 reconstructs the light transmission detection mode of the gradient refractive index nano-coating according to the terminal interaction data, and generates a catheter control instruction linked to the plaque rupture risk assessment.

[0216] Figure 2 The multi-terminal processing system for real-time sharing of emergency clinical data can be executed Figure 1The implementation principle and technical effect of the multi-terminal processing method for real-time sharing of emergency clinical data described in the embodiment shown are not repeated here. The specific way in which each module and unit performs operations in the multi-terminal processing system for real-time sharing of emergency clinical data in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0217] In one possible design, Figure 2 The multi-terminal processing system for real-time sharing of emergency clinical data in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0218] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0219] The processing component 32 is used for the above Figure 1 The embodiment provides a multi-terminal processing method for real-time sharing of emergency clinical data.

[0220] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0221] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component 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 memory, flash memory, magnetic disk or optical disk.

[0222] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0223] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0224] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0225] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0226] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a multi-terminal processing method for real-time sharing of emergency clinical data.

[0227] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0228] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0229] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0230] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-terminal processing method for real-time sharing of emergency clinical data, characterized in that: include: The light transmittance distribution parameters and blood flow disturbance parameters of the inner wall of the blood vessel are obtained by using a multimodal sensor of the modified cardiovascular catheter, wherein the multimodal sensor is integrated into the catheter detection end of the gradient refractive index nanocoating; Generate a computed tomography encoding strategy linked to vascular pathology characteristics according to the spectral absorption characteristics of the transmittance distribution parameter and the eddy current intensity of the blood flow disturbance parameter, wherein the computed tomography encoding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuation; Inputting the computed tomography encoding strategy into a containerized microservice cluster, and generating a rendering instruction set associated with the vascular elastic modulus based on the terminal hardware architecture; Performing hierarchical compression on the rendering instruction set through a video coding protocol to generate a compressed data stream carrying a blood vessel bifurcation marker, wherein the hierarchical compression integrates the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusts the transmission priority of the dynamic parameter coding block based on the wall shear force of the blood flow disturbance parameter; Distributing the compressed data stream to a heterogeneous terminal cluster, triggering visualization of the elastic modulus of the blood vessel wall on a mobile terminal, and invoking the key frame protection strategy to associate with the wall shear force fluctuation; The light transmission detection mode of the gradient refractive index nanocoating is reconstructed according to the terminal interaction data, and a catheter control instruction linked to the plaque rupture risk assessment is generated.

2. The method according to claim 1, characterized in that The method generates a computed tomography coding strategy linked to vascular pathology characteristics according to the spectral absorption characteristics of the transmittance distribution parameter and the eddy current intensity of the blood flow disturbance parameter, wherein the computed tomography coding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuation, including: According to the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, by quantifying the spectral attenuation differences of the plaque components in the blood vessel wall, plaque component characteristic parameters are generated, and at the same time, the dynamic disturbance component of the blood flow velocity field in the blood vessel cavity is extracted to generate blood flow disturbance characteristic parameters; Performing multimodal correlation analysis on the plaque component characteristic parameters and the blood flow disturbance characteristic parameters to generate plaque rupture risk assessment parameters, wherein the plaque rupture risk assessment parameters output quantitative indicators of the plaque surface through coupling effect; Dynamically adjusting a sampling frequency parameter of a computer tomography scan according to the plaque rupture risk assessment parameter, wherein the sampling frequency parameter achieves adaptive optimization of computer scan resolution by matching the spatial distribution difference of the plaque rupture risk; Based on the vortex energy accumulation characteristics in the blood flow disturbance characteristic parameters, deriving a wall shear force fluctuation parameter for calculating the matching relationship between the instantaneous change rate of the vascular wall shear force and the contrast agent diffusion rate, and generating a contrast agent injection parameter; The sampling frequency parameter is synchronized with the contrast agent injection parameter in time sequence, and a computer tomography encoding strategy linked to the vascular pathological characteristics is generated by controlling the phase difference of the contrast agent injection pulse.

3. The method according to claim 2, characterized in that The plaque component characteristic parameters and the blood flow disturbance characteristic parameters are subjected to multimodal correlation analysis to generate plaque rupture risk assessment parameters, and the plaque rupture risk assessment parameters are output as quantitative indicators of the plaque surface through coupling effects, including: Based on the correlation between the distribution of fiber cap thickness and the proportion of lipid core space in the characteristic parameters of plaque components, the stress concentration coefficient of the plaque structure is extracted through structural mechanics analysis; Generate a blood flow disturbance stress coupling factor according to the spatial overlap of the shear force oscillation amplitude and the flow separation area in the blood flow disturbance characteristic parameters and the spatial position mapping relationship of the plaque structure stress concentration coefficient; Performing a cross-modal convolution operation on the gradient change rate of the stress concentration coefficient of the plaque structure and the time-varying component of the blood flow disturbance stress coupling factor, and outputting a dynamic stress offset including the cumulative effect of plaque deformation; Based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameters of the calcified area in the plaque component characteristic parameters, the distribution difference of the stress gradient on the plaque surface in the axial and radial directions is generated; The distribution difference is nonlinearly superimposed with the eddy current energy attenuation rate in the blood flow disturbance characteristic parameter to generate a plaque rupture risk assessment parameter containing a stress-blood flow interaction weight. The plaque rupture risk assessment parameter is used to characterize a quantitative indicator of the rupture probability of each area on the plaque surface.

4. The method according to claim 3, characterized in that The generating of the distribution difference of the stress gradient on the surface of the plaque in the axial and radial directions based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameter of the calcified area in the plaque component characteristic parameter comprises: The high-frequency stress spectral density distribution in the axial stress propagation direction and the low-frequency stress spectral density distribution in the radial stress diffusion direction are extracted through the energy proportion relationship between the main frequency component and the harmonic component of the dynamic stress offset; According to the correlation between the distribution density of curvature mutation points in the edge sharpness parameter of the calcified area in the plaque component characteristic parameter and the gradient change rate, the stress enhancement ratio of the calcified edge stress concentration factor in the axial and radial directions is analyzed; Adaptively weighted superposition the high-frequency stress spectrum density distribution and the axial stress enhancement ratio of the calcification edge stress concentration factor to generate an axial stress gradient component including the calcification edge stress amplification effect; Directionally convolve and fuse the low-frequency stress spectrum density distribution with the radial stress enhancement ratio of the calcification edge stress concentration factor to generate a radial stress gradient component containing stress diffusion attenuation characteristics; Based on the peak position of the axial stress gradient component and the attenuation rate of the radial stress gradient component, a gradient difference parameter characterizing the difference between the axial and radial stress distributions is output through a composite calculation of spatial position matching and energy attenuation slope.

5. The method according to claim 1, characterized in that: The step of performing hierarchical compression on the rendering instruction set through a video coding protocol to generate a compressed data stream carrying a blood vessel bifurcation mark, the hierarchical compression integrating the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusting the transmission priority of the dynamic parameter coding block based on the wall shear force of the blood flow disturbance parameter, includes: The rendering instruction set is hierarchically compressed according to the geometry layer, the dynamic parameter layer and the texture detail layer through a video coding protocol to generate an intermediate compressed frame including the geometry compression parameters, the dynamic compression parameters and the texture compression parameters; Binding the blood vessel bifurcation mark with the coding block of the intermediate compressed frame in time and space, generating a bifurcation mark index table based on the geometric compression parameter, inserting a topology check code element into the coding block boundary of the intermediate compressed frame, and generating a compressed data stream; Generate a key frame protection strategy according to the polarization attenuation mode of the layered compression integrated transmittance distribution parameter, and update the key frame refresh interval according to the polarization attenuation weight combination and the blood flow direction of the dynamic compression parameter; The shear force direction component of the dynamic parameter layer coding block is extracted based on the wall shear force of the blood flow disturbance parameter, the motion vector of the high shear force area coding block is enhanced through the bifurcation mark index table, and the transmission priority of the dynamic parameter layer coding block in the compressed data stream is dynamically adjusted.

6. The method according to claim 5, characterized in that The step of temporally and spatially binding the blood vessel bifurcation mark with the coding block of the intermediate compressed frame and generating a bifurcation mark index table based on the geometric compression parameter includes: Generate a spatial position mapping relationship between the bifurcation point and the coding block of the intermediate compressed frame according to the three-dimensional coordinates of the bifurcation point in the blood vessel bifurcation mark and the timestamp sequence of the intermediate compressed frame; Based on the angle between the motion vector direction of the intermediate compressed frame coding block and the blood flow direction of the bifurcation point, a spatiotemporal fit parameter between the intermediate compressed frame coding block and the bifurcation mark is obtained, and spatial weights are allocated to the intermediate compressed frame coding block according to the spatiotemporal fit parameter to generate a coding block binding set including a bifurcation point stability weight; Based on the compression ratio difference between the primary curvature and the secondary curvature in the coding block binding set, a curvature compression difference factor of the geometric compression parameter is obtained; The curvature compression difference factor and the space-time fit parameter are bidirectionally sorted to generate a bifurcation mark index table with the bifurcation point stability weight as the index key and the curvature compression difference factor as the associated value.

7. The method according to claim 1, characterized in that The step of inputting the computed tomography encoding strategy into a containerized microservice cluster and generating a rendering instruction set associated with the vascular elastic modulus based on a terminal hardware architecture includes: Splitting the computed tomography scanning coding strategy into multiple scanning coding sub-strategies executed in parallel, injecting each scanning coding sub-strategy into the containerized microservice cluster through a dynamic load balancing module, and generating a scanning coding sub-strategy identifier; traversing the number of computing units and parallel processing capabilities of the terminal hardware architecture according to the scan coding sub-strategy identifier to generate a vascular elastic modulus sequence including a dynamic association between a vascular wall deformation gradient and a stress response; Mapping the deformation gradient in the vascular elastic modulus sequence into a multi-level rendering resolution parameter, and mapping the stress response into a matching texture sampling density parameter, to form a dynamic rendering parameter set; The memory bandwidth of the terminal hardware architecture is divided based on the dynamic rendering parameter set, a corresponding video memory allocation strategy and a bound shader instruction queue are generated, and a rendering instruction set associated with the vascular elastic modulus is output after fusion.

8. A multi-terminal processing method for real-time sharing of emergency clinical data, characterized in that: include: An acquisition module, which acquires the transmittance distribution parameters of the inner wall of the blood vessel and the blood flow disturbance parameters through a multimodal sensor of a modified cardiovascular catheter, wherein the multimodal sensor is integrated into the detection end of the catheter with a gradient refractive index nanocoating; A generation module, which generates a computer tomography coding strategy linked to vascular pathology characteristics according to the spectral absorption characteristics of the transmittance distribution parameter and the eddy current intensity of the blood flow disturbance parameter, wherein the computer tomography coding strategy includes a sampling frequency parameter driven by plaque rupture risk assessment and a contrast agent injection parameter adaptive to wall shear force fluctuation; An input module, inputting the computed tomography encoding strategy into a containerized microservice cluster, and generating a rendering instruction set associated with the vascular elastic modulus based on a terminal hardware architecture; A compression module, performing hierarchical compression on the rendering instruction set through a video encoding protocol to generate a compressed data stream carrying a blood vessel bifurcation marker, wherein the hierarchical compression integrates the polarization attenuation mode of the transmittance distribution parameter to generate a key frame protection strategy, and adjusts the coding block priority based on the wall shear force of the blood flow disturbance parameter; A calling module distributes the compressed data stream to a heterogeneous terminal cluster, triggers the visualization of the elastic modulus of the blood vessel wall on the mobile terminal, and calls the key frame protection strategy to associate with the wall shear force fluctuation; A reconstruction module reconstructs the light transmission detection mode of the gradient refractive index nanocoating according to the terminal interaction data, and generates a catheter control instruction linked to the plaque rupture risk assessment.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-terminal processing method for real-time sharing of emergency clinical data as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a multi-terminal processing method for real-time sharing of emergency clinical data as described in any one of claims 1 to 7 is implemented.

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