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

Abstract

This application provides a multi-terminal processing method and system for real-time sharing of emergency clinical data. Specifically, it utilizes a multimodal sensor on a modified cardiovascular catheter to detect intravascular conditions. This method employs a sensor integrated into the probe end of a catheter with a gradient refractive index nanocoating to acquire the transmittance distribution of the vascular wall and blood flow disturbance parameters. Based on the characteristics of this data, a computed tomography (CT) coding strategy related to vascular pathology is formulated. This strategy is translated into a rendering instruction set tailored to the characteristics of the terminal hardware and compressed into a data stream carrying vascular information using a video encoding protocol. Finally, the processed data is distributed to different devices, enabling visualization of vascular wall elasticity and allowing for optimization of the detection mode based on user feedback, generating control commands to further guide catheter operation. The technical solution provided in this application improves the accuracy and efficiency of multi-terminal data processing.

Description

Technical Field

[0001] This application relates 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 Technology

[0002] In emergency clinical settings, rapid diagnosis and treatment of cardiovascular diseases are crucial. This is especially true for emergencies such as acute coronary syndromes, where real-time monitoring of the vessel wall's condition, assessment of plaque rupture risk, and changes in hemodynamic parameters are paramount. This necessitates a method that can efficiently acquire and process detailed information about the vessel's internal structure and share this data instantly across multiple devices, enabling physicians to make rapid decisions.

[0003] Currently, the diagnosis of cardiovascular diseases mainly relies on traditional techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and angiography. In addition, there are some techniques that use catheters for localized detection, such as intravascular ultrasound or optical coherence tomography (OCT). However, these methods typically provide only limited information about the internal structure of blood vessels and cannot simultaneously achieve comprehensive monitoring of multiple parameters, including the distribution of light transmittance in the vessel wall and blood flow disturbances.

[0004] Several significant shortcomings exist in existing technologies: First, traditional imaging techniques cannot provide sufficient real-time data support in emergency situations, leading to delays in treatment. Second, while existing catheter technologies can enter blood vessels for more detailed examinations, they often lack the ability to integrate multiple sensors, failing to simultaneously collect multiple key parameters, including transmittance distribution and blood flow disturbance. Finally, in terms of data transmission and multi-terminal visualization, existing solutions have failed to effectively integrate video encoding and layered compression technologies, resulting in low data transmission efficiency and difficulties in achieving efficient data sharing and interaction between different terminal devices. These problems collectively limit the realization of rapid diagnosis and personalized treatment of cardiovascular diseases in emergency settings. Summary of the Invention

[0005] This application provides a multi-terminal processing method and system for real-time sharing of emergency clinical data, which solves the problems of poor accuracy and low efficiency in multi-terminal data processing in the prior art.

[0006] In a first aspect, embodiments of this application provide a multi-terminal processing method for real-time sharing of emergency clinical data, including:

[0007] The transmittance distribution parameters and blood flow disturbance parameters of the vascular inner wall are obtained by a multimodal sensor of a modified cardiovascular catheter. The multimodal sensor is integrated into the catheter probe end with a gradient refractive index nanocoating.

[0008] Based on the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, a computed tomography coding strategy linked to vascular pathological features is generated. The computed tomography coding strategy includes sampling frequency parameters driven by plaque rupture risk assessment and contrast agent injection parameters adapted to wall shear force fluctuations.

[0009] The computed tomography encoding strategy is input into the containerized microservice cluster to generate a set of rendering instructions related to the elastic modulus of blood vessels based on the terminal hardware architecture.

[0010] The rendering instruction set is subjected to layered compression using a video encoding protocol to generate a compressed data stream carrying blood vessel bifurcation markers. The layered compression integrates the polarization attenuation mode of the transmittance distribution parameters to generate a keyframe protection strategy, and adjusts the transmission priority of the dynamic parameter encoding blocks based on the wall shear force of the blood flow disturbance parameters.

[0011] The compressed data stream is distributed to a heterogeneous terminal cluster, and the visualization of the blood vessel wall elastic modulus is triggered on the mobile terminal. The key frame protection strategy is then invoked and associated with the wall shear force fluctuation.

[0012] The light transmission detection mode of the gradient refractive index nanocoating is reconstructed based on the terminal interaction data, and duct control commands are generated in conjunction with the plaque rupture risk assessment.

[0013] Optionally, the step of generating a computed tomography (CT) coding strategy linked to vascular pathology features based on the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, wherein the CT coding strategy includes sampling frequency parameters driven by plaque rupture risk assessment and contrast agent injection parameters adapted to wall shear force fluctuations, including:

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

[0015] Multimodal correlation analysis is performed between the plaque component characteristic parameters and the blood flow disturbance characteristic parameters to generate plaque rupture risk assessment parameters. The plaque rupture risk assessment parameters output quantitative indicators of the plaque surface through coupling effect.

[0016] Based on the plaque rupture risk assessment parameters, the sampling frequency parameters of the computed tomography scan are dynamically adjusted. The sampling frequency parameters achieve adaptive optimization of the computed tomography scan resolution by matching the spatial distribution differences of plaque rupture risk.

[0017] 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 vessel wall shear force and the contrast agent diffusion rate are derived, and the contrast agent injection parameters are generated.

[0018] The sampling frequency parameter is synchronized with the contrast agent injection parameter in time, and a computed tomography coding strategy linked to vascular pathology features is generated by controlling the phase difference of the contrast agent injection pulse.

[0019] Optionally, the step of performing multimodal correlation analysis between 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 a coupling effect, including:

[0020] Based on the correlation between the fibrous cap thickness distribution and the lipid core space ratio in the characteristic parameters of the plaque components, the stress concentration factor of the plaque structure is extracted through structural mechanics analysis.

[0021] Based on the spatial overlap between the shear force oscillation amplitude and the flow separation region in the blood flow disturbance characteristic parameters, and combined with the spatial location mapping relationship of the plaque structure stress concentration coefficient, a blood flow disturbance stress coupling factor is generated.

[0022] The gradient rate of change of the stress concentration factor of the plaque structure is convolved with the time-varying component of the blood flow disturbance stress coupling factor to output a dynamic stress offset that includes the cumulative effect of plaque deformation.

[0023] Based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameter of the calcified region in the patch composition characteristic parameters, the distribution difference of the stress gradient on the patch surface in the axial and radial directions is generated.

[0024] The distribution difference degree is nonlinearly superimposed with the eddy current energy attenuation rate in the blood flow disturbance characteristic parameters to generate a plaque rupture risk assessment parameter that includes stress-blood flow interaction weights. The plaque rupture risk assessment parameter is used as a quantitative index to characterize the rupture probability of each region on the plaque surface.

[0025] Optionally, the step of generating the distribution difference of the stress gradient on the patch surface 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 region in the patch composition characteristic parameters includes:

[0026] By analyzing the energy ratio between the dominant frequency component and the harmonic component of the dynamic stress offset, 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.

[0027] Based on the correlation between the distribution density of curvature abrupt change points and the gradient change rate in the edge sharpness parameter of the calcified region among the characteristic parameters of the plaque composition, the stress concentration factor at the calcified edge is analyzed to determine the stress enhancement ratio in the axial and radial directions.

[0028] The high-frequency stress spectral density distribution and the axial stress enhancement ratio of the calcification edge stress concentration factor are adaptively weighted and superimposed to generate an axial stress gradient component that includes the calcification edge stress amplification effect.

[0029] The low-frequency stress spectral density distribution and the radial stress enhancement ratio of the calcification edge stress concentration factor are directionally convolved and fused to generate a radial stress gradient component that includes stress diffusion attenuation characteristics.

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

[0031] Optionally, the step of performing layered compression on the rendering instruction set using a video encoding protocol to generate a compressed data stream carrying blood vessel bifurcation markers, wherein the layered compression integrates the polarization attenuation mode of the transmittance distribution parameters to generate a keyframe protection strategy, and adjusts the transmission priority of dynamic parameter encoding blocks based on the wall shear force of the blood flow disturbance parameters, includes:

[0032] The rendering instruction set is compressed in layers according to the geometric structure layer, dynamic parameter layer and texture detail layer using a video encoding protocol to generate an intermediate compressed frame containing geometric compression parameters, dynamic compression parameters and texture compression parameters.

[0033] The blood vessel bifurcation markers are spatiotemporally bound to the coded blocks of the intermediate compressed frame. A bifurcation marker index table is generated based on the geometric compression parameters. Topology check symbols are inserted into the boundaries of the coded blocks of the intermediate compressed frame to generate a compressed data stream.

[0034] A keyframe protection strategy is generated based on the polarization attenuation mode that integrates the transmittance distribution parameters in the layered compression, and the keyframe refresh interval is updated based on the polarization attenuation weight and the blood flow direction of the dynamic compression parameters.

[0035] Based on the wall shear force of the blood flow disturbance parameters, the shear force direction component of the dynamic parameter layer coding block is extracted. The motion vector of the coding block in the high shear force region is enhanced by 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 spatiotemporally binding the blood vessel bifurcation markers with the coded blocks of the intermediate compressed frame, and generating a bifurcation marker index table based on the geometric compression parameters, includes:

[0037] Based on the three-dimensional coordinates of the bifurcation point in the blood vessel bifurcation marker and the timestamp sequence of the intermediate compressed frame, a spatial position mapping relationship between the bifurcation point and the coded block of the intermediate compressed frame is generated.

[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, the spatiotemporal fit parameter between the intermediate compressed frame coding block and the bifurcation mark is obtained. Spatial weights are allocated to the intermediate compressed frame coding block according to the spatiotemporal fit parameter to generate a coding block binding set containing bifurcation point stability weights.

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

[0040] The curvature compression difference factor and the spatiotemporal fit parameter are sorted bidirectionally to generate a bifurcation marker 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 the containerized microservice cluster and generating a rendering instruction set related to the vascular elastic modulus based on the terminal hardware architecture includes:

[0042] The computed tomography (CT) scanning coding strategy is divided into multiple parallel scanning coding sub-strategies. Each scanning coding sub-strategy is injected into the containerized microservice cluster through a dynamic load balancing module, generating scanning coding sub-strategy identifiers.

[0043] Based on the scanning 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 containing the dynamic correlation between vascular wall deformation gradient and stress response.

[0044] The deformation gradient in the vascular elastic modulus sequence is mapped to multi-level rendering resolution parameters, and the stress response is mapped to matching texture sampling density parameters to form a dynamic rendering parameter set.

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

[0046] Secondly, embodiments of this application provide a multi-terminal processing system for real-time sharing of emergency clinical data, including:

[0047] The acquisition module acquires the transmittance distribution parameters and blood flow disturbance parameters of the vascular inner wall through a multimodal sensor of the modified cardiovascular catheter. The multimodal sensor is integrated into the catheter detection end with a gradient refractive index nanocoating.

[0048] The generation module generates a computed tomography (CT) coding strategy that is linked to vascular pathology features based on the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters. The CT coding strategy includes sampling frequency parameters driven by plaque rupture risk assessment and contrast agent injection parameters adapted to wall shear force fluctuations.

[0049] The input module inputs the computed tomography encoding strategy into the containerized microservice cluster and generates a rendering instruction set related to the vascular elastic modulus based on the terminal hardware architecture.

[0050] The compression module performs layered compression on the rendering instruction set through a video encoding protocol to generate a compressed data stream carrying blood vessel bifurcation markers. The layered compression integrates the polarization attenuation mode of the transmittance distribution parameters to generate a keyframe protection strategy, and adjusts the priority of the encoding blocks based on the wall shear force of the blood flow disturbance parameters.

[0051] The module is invoked to distribute the compressed data stream to a heterogeneous terminal cluster, triggering the visualization of the blood vessel wall elastic modulus on the mobile terminal, and invoking the key frame protection strategy to associate it with the wall shear force fluctuation.

[0052] The reconstruction module reconstructs the light transmission detection mode of the gradient refractive index nanocoating based on the terminal interaction data, and generates duct control commands that are linked to the plaque rupture risk assessment.

[0053] Thirdly, embodiments of this application provide 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 to be invoked and executed by the processing component to realize a multi-terminal processing method for real-time sharing of emergency clinical data as described in the first aspect above.

[0054] Fourthly, embodiments of this application provide 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 this embodiment, a multimodal sensor of a modified cardiovascular catheter is used to acquire the transmittance distribution parameters and blood flow disturbance parameters of the vascular wall. The multimodal sensor is integrated into the catheter probe end with a gradient refractive index nanocoating. Based on the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, a computed tomography (CT) coding strategy linked to vascular pathological features is generated. This CT coding strategy includes sampling frequency parameters driven by plaque rupture risk assessment and contrast agent injection parameters adapted to wall shear force fluctuations. The CT coding strategy is input into a containerized microservice cluster, and a vascular elastic modulus parameter is generated based on the terminal hardware architecture. The system generates a rendering instruction set; it performs layered compression on the rendering instruction set using a video encoding protocol to generate a compressed data stream carrying vascular bifurcation markers. The layered compression integrates the polarization attenuation mode of the transmittance distribution parameters to generate a keyframe protection strategy, and adjusts the transmission priority of dynamic parameter encoding blocks based on the wall shear force of the blood flow disturbance parameters. The compressed data stream is distributed to a heterogeneous terminal cluster, and the visualization of the vascular wall elastic modulus is triggered on the mobile terminal. The keyframe protection strategy is invoked and associated with the wall shear force fluctuation. The transmittance detection mode of the gradient refractive index nanocoating is reconstructed based on the terminal interaction data, and catheter control instructions linked to the plaque rupture risk assessment are generated.

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

[0057] This application utilizes a modified cardiovascular catheter and integrates multimodal sensors to acquire real-time parameters of vascular translucency distribution and blood flow disturbance, significantly enhancing the understanding of the internal state of blood vessels. This technology not only achieves precise capture of vascular pathological features but also dynamically adjusts computed tomography (CT) coding strategies based on this data, including sampling frequency and contrast agent injection volume driven by plaque rupture risk assessment, thereby improving diagnostic accuracy. Furthermore, by leveraging a containerized microservice cluster to generate rendering instruction sets optimized for different terminal hardware architectures and transmitting data through an efficient video coding protocol, the priority and integrity of critical information are ensured, enabling multi-terminal data sharing in emergency environments. This not only accelerates 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 perturbation components of the blood flow velocity field, more detailed plaque component characteristic parameters and blood flow perturbation characteristic parameters were generated. Multimodal correlation analysis of these two parameters allows for a more accurate assessment of the risk of plaque rupture, and the sampling frequency of computed tomography scans can be dynamically adjusted accordingly to achieve adaptive optimization of resolution. Simultaneously, the wall shear force fluctuation parameter derived from the blood flow perturbation characteristics is used to calculate the optimal contrast agent injection volume to match changes in vessel wall shear force. This method significantly improves the diagnostic accuracy of vascular pathology, especially in the rapid response capability for emergencies such as acute coronary syndrome, providing patients with safer and more effective treatment options.

[0059] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[0062] Figure 2 This invention provides a schematic diagram of the structure of a multi-terminal processing system for real-time sharing of emergency clinical data.

[0063] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0064] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0065] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0066] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] This solution aims to provide physicians with a more accurate and real-time diagnostic tool for cardiovascular diseases by integrating multiple sensors into the probe end of cardiovascular catheters and combining them with advanced image processing and data transmission technologies. The core of the research and development lies in creating a system capable of dynamically acquiring and analyzing information about the intravascular environment, including transmittance distribution parameters and blood flow disturbance parameters. Based on this information, it automatically generates optimized computed tomography (CT) coding strategies, ultimately achieving a visual representation and risk assessment of cardiovascular conditions.

[0068] Figure 1 This application provides a flowchart of a multi-terminal processing method for real-time sharing of emergency clinical data, as illustrated in the embodiments of this application. Figure 1 As shown, the method includes:

[0069] 101. The transmittance distribution parameters and blood flow disturbance parameters of the vascular inner wall are obtained by a multimodal sensor of a modified cardiovascular catheter, wherein the multimodal sensor is integrated into the catheter probe end with a gradient refractive index nanocoating;

[0070] In this step, a 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 as 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 special material coated on the surface of a conduit, which reduces light scattering and improves the detection accuracy of the sensor.

[0074] In this embodiment, firstly, during the design phase, it is necessary to consider how to effectively integrate the multimodal sensor onto the cardiovascular catheter and ensure that the sensor can operate stably. Secondly, a gradient refractive index nanocoating is used to enhance the sensor's sensitivity to changes in transmittance. Then, experimental verification is conducted to determine the optimal sensor configuration and coating thickness, ensuring that the catheter's operational flexibility is not affected while accurately capturing changes in the intravascular environment.

[0075] A patient was rushed to the hospital emergency department due to sudden chest pain. Doctors used a cardiovascular catheter designed specifically for emergency use, integrating multimodal sensors, for a rapid examination. The catheter's tip is coated with a gradient refractive index nanocoating to improve light transmittance and detection accuracy. Through this catheter, doctors were able to obtain real-time light transmittance distribution parameters and blood flow disturbance parameters within the patient's coronary arteries. The results showed a significant decrease in light transmittance and blood flow turbulence at the left anterior descending artery, suggesting possible plaque rupture or severe stenosis.

[0076] 102. Based on the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters, a computed tomography coding strategy linked to vascular pathological characteristics is generated. The computed tomography coding strategy includes sampling frequency parameters driven by plaque rupture risk assessment and contrast agent injection parameters adapted to wall shear force fluctuations.

[0077] In this step, the computed tomography coding strategy is an algorithm that guides the computed tomography process based on collected data, such as the spectral absorption characteristics of transmittance distribution parameters and the eddy current intensity of blood flow disturbance parameters.

[0078] The sampling frequency parameter determines the speed at which images are acquired under different risk levels, while the contrast agent injection parameter is adjusted according to the fluctuation of wall shear force to obtain clearer imaging results.

[0079] In this embodiment, firstly, a personalized computed tomography (CT) scan plan is developed based on the obtained data. Secondly, the sampling frequency and contrast agent dosage are adjusted for different types of pathological features (such as plaque rupture risk). Then, simulation software is used to test the effectiveness of the strategy under various conditions to ensure that diagnostic accuracy is improved without increasing the patient's radiation exposure or 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 condition, the system recommends using a higher sampling frequency to capture any subtle changes and adjusting the contrast agent injection volume according to wall shear force fluctuations to ensure optimal imaging while minimizing the burden on the patient. This strategy aims to quickly and accurately identify the location and severity of lesions for timely intervention.

[0081] 103. Input the computed tomography encoding strategy into the containerized microservice cluster, and generate a rendering instruction set related to the elastic modulus of blood vessels based on the terminal hardware architecture;

[0082] In this step, containerized microservice clusters are an architecture based on modern cloud computing technology that allows applications to be divided into independent small services, each running in its own process and communicating through lightweight mechanisms. For cardiovascular disease diagnosis applications, these microservices include modules such as data processing and image rendering.

[0083] The vascular elastic modulus-related rendering instruction set is a set of commands that guide the generation of high-quality images based on the elastic properties of a patient's blood vessel walls. These instructions ensure optimal display results across various hardware environments.

[0084] In this embodiment, firstly, the scanning encoding strategy is input into the containerized microservice cluster, and the system begins to adjust rendering parameters according to the patient's specific condition. 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 performance and image quality. Then, advanced algorithms are used to transform and optimize the raw data to generate the final visualization result. This process not only improves the image clarity 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. A containerized microservice cluster immediately went live, generating a high-quality 3D model of the blood vessels specific to the patient's condition. These images helped doctors quickly determine the exact location and severity of the lesion, providing crucial information for developing an emergency treatment plan.

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

[0087] In this step, video coding protocols are technical standards used to compress and transmit video data, effectively reducing data volume while maintaining sufficient image quality.

[0088] Layered compression is a special compression method that prioritizes the protection of important information (such as keyframes).

[0089] Compressed data streams carrying vascular bifurcation markers refer to data streams in which vascular branching points are specifically identified during the compression process, facilitating subsequent analysis.

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

[0091] In this embodiment, firstly, the system employs an efficient video coding protocol to perform layered compression on the rendering instruction set, prioritizing the protection of key frames containing crucial pathological information. Secondly, the transmission priority of dynamic parameter coding blocks is adjusted based on blood flow disturbance parameters to ensure that the most important information arrives at the receiving end first. Then, a series of tests verify whether the compressed data stream can be stably transmitted under various network conditions and maintain sufficient image quality.

[0092] During the emergency room procedure, an optimized video encoding protocol successfully compressed and transmitted high-quality vascular images acquired in the emergency room to the mobile devices of remote cardiologists. Despite less-than-ideal network conditions, images of critical areas remained clearly visible, enabling remote experts to provide immediate professional advice and support on-site treatment.

[0093] 105. Distribute the compressed data stream to a heterogeneous terminal cluster, trigger the visualization of the blood vessel wall elastic modulus on the mobile terminal, and call the key frame protection strategy to associate it 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 levels that can simultaneously receive and display the same information. In cardiovascular disease diagnosis, this includes high-definition displays in hospitals, handheld devices used by doctors, and smart devices used by patients' families.

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

[0096] In this embodiment, firstly, the compressed data stream is distributed to all associated terminal devices to ensure that each participant receives the latest patient status updates. Secondly, 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 to allow all terminal users to provide feedback to the system in order to further optimize the diagnosis and treatment process.

[0097] The compressed data stream was rapidly distributed to internal and external terminal devices within the hospital. High-definition displays in the emergency room immediately showed a 3D model of the patient's blood vessels, helping doctors quickly understand the condition. Simultaneously, remote cardiologists viewed the same images via mobile applications and participated in consultations. This instant sharing mechanism not only accelerated on-site treatment decisions but also ensured that the professional opinions of remote experts were promptly incorporated into the treatment plan.

[0098] 106. Reconstruct the light transmission detection mode of the gradient refractive index nanocoating based on the terminal interaction data, and generate duct control commands that are linked to the plaque rupture risk assessment.

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

[0100] The linkage between plaque rupture risk assessment and control means that the behavior of the catheter is automatically adjusted according to the currently identified risk level in order to better monitor high-risk areas.

[0101] In this embodiment, firstly, based on interactive feedback from various terminals, the system reassesses 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 detection parameters to capture more details. Then, the system continuously monitors changes in the patient's condition and takes timely and necessary interventions to ensure the effectiveness and safety of the treatment.

[0102] Upon detecting increased blood flow disturbance in the left anterior descending artery, the system automatically generated new catheter control commands, enabling more precise catheter positioning in the area and increasing the monitoring frequency at that site. Combining previous imaging data with predictions of potential changes, the system recommended preparing for interventional surgery in advance to prevent the condition from worsening. This dynamic adjustment mechanism significantly improved emergency response efficiency and patient survival rates.

[0103] In summary, steps 101 to 106 constructed a complete cardiovascular disease diagnostic solution, from data acquisition to visualization. This system can not only accurately capture minute changes within blood vessels but also intelligently generate examination strategies tailored to individual patients based on this information, significantly improving diagnostic accuracy and efficiency. Furthermore, through efficient data stream compression and transmission, it ensures high-quality image sharing and remote diagnosis and treatment even under resource-limited conditions, providing strong support for the early detection and treatment of cardiovascular diseases.

[0104] To further improve the accuracy and efficiency of cardiovascular disease diagnosis, especially for the rapid assessment of patients with acute coronary syndrome in emergency settings, combining transmittance distribution parameters and blood flow disturbance parameters can more accurately identify plaque components and their rupture risk within blood vessels, thereby enabling personalized, efficient, and safe treatment plans. In some embodiments, step 102 involves generating a computed tomography (CT) coding strategy linked to vascular pathology features based on the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters. This CT coding strategy includes sampling frequency parameters driven by plaque rupture risk assessment and contrast agent injection parameters adapted to wall shear force fluctuations, including:

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

[0106] In step 201, plaque composition characteristic parameters and blood flow disturbance characteristic parameters are generated based on 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 as it passes through different tissues, which helps quantify differences in plaque composition. The blood flow disturbance parameters describe the irregularities in blood flow, particularly the eddy current intensity, revealing changes in hemodynamics. The plaque composition characteristic parameters are obtained through analysis of spectral absorption characteristics, while the blood flow disturbance characteristic parameters are obtained by extracting the dynamic changes in the blood flow velocity field.

[0107] In this embodiment, firstly, after acquiring the transmittance distribution parameters, the system performs spectral decomposition to distinguish different plaque components. For example, components such as the lipid core and fibrous cap have different spectral absorption characteristics. Secondly, advanced algorithms are used to analyze this 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 changes in the blood flow velocity field in real time to capture any abnormal eddy current phenomena. Finally, by processing these dynamically changing data, 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. The plaque rupture risk assessment parameters output quantitative indicators of the plaque surface through coupling effect.

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

[0110] In this embodiment, firstly, the system integrates the data obtained in step 201 to establish a multi-dimensional data model, combining plaque composition characteristics with blood flow disturbance characteristics. Secondly, a machine learning algorithm is used to train the model, enabling it to identify which combinations of conditions are most likely to cause plaque rupture. Then, the effectiveness of the model is verified by simulating plaque behavior under different conditions, and parameter settings are adjusted to optimize prediction accuracy. Finally, a risk assessment parameter containing quantitative indicators of plaque surface is generated to provide doctors with intuitive risk warnings.

[0111] 203. Based on the plaque rupture risk assessment parameters, dynamically adjust the sampling frequency parameters of the computed tomography scan, wherein the sampling frequency parameters achieve adaptive optimization of the computed tomography scan resolution by matching the spatial distribution differences of plaque rupture risk;

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

[0113] In this embodiment, firstly, the system analyzes plaque rupture risk assessment parameters to determine the risk level of each region. Secondly, for high-risk regions, the system automatically increases the sampling frequency to ensure sufficient data is collected for detailed analysis. Then, for low-risk regions, the sampling frequency is reduced accordingly, ensuring both overall scan quality and improved efficiency. Finally, the entire process is controlled by an intelligent algorithm to ensure that each scan is optimized and adjusted based on the latest patient condition.

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

[0115] In step 204, based on the vortex energy accumulation characteristics in the blood flow disturbance feature parameters, wall shear force fluctuation parameters are derived to calculate the matching relationship between the instantaneous change rate of vessel wall shear force and the contrast agent diffusion rate, thus generating contrast agent injection parameters. The vortex energy accumulation characteristic refers to the local formation of vortices during blood flow due to vessel morphology or plaque presence. These vortices accumulate kinetic energy and affect the hemodynamic characteristics of the surrounding area. Wall shear force is an important indicator for measuring the force exerted by blood flow on the vessel wall, reflecting the frictional effect of blood flow on the vessel wall. By analyzing the vortex energy accumulation characteristics, the instantaneous change rate of vessel wall shear force can be derived, thereby predicting the diffusion rate and path of the contrast agent under different conditions, and thus formulating the optimal contrast agent injection strategy.

[0116] In this embodiment, firstly, the system collects and analyzes the vortex energy accumulation characteristics in the blood flow disturbance feature parameters to calculate the instantaneous rate of change of the vessel wall shear force. Secondly, based on these rates of change, the diffusion path and rate of the contrast agent within the vessel are predicted. Then, considering the patient's actual condition, the injection rate and dosage of the contrast agent are adjusted to ensure coverage of all critical areas. Finally, a series of preliminary experiments are conducted to verify the effectiveness of these parameters, ensuring that the final angiographic effect is optimal.

[0117] 205. Synchronize the sampling frequency parameter with the contrast agent injection parameter in time, and generate a computed tomography coding strategy that is linked to vascular pathological features by controlling the phase difference of the contrast agent injection pulse.

[0118] In step 205, the sampling frequency parameters and contrast agent injection parameters are synchronized in time. By controlling the phase difference of the contrast agent injection pulses, a computed tomography (CT) coding strategy linked to vascular pathology features is generated. Time synchronization refers to coordinating the order of different operations at specific points in time to ensure mutual cooperation and improve overall efficiency. Phase difference control of the contrast agent injection pulses refers to precisely adjusting the timing of the contrast agent injection to match the scanning equipment's cycle time, thereby acquiring the clearest image data at the optimal moment. This method not only improves image quality but also reduces unnecessary radiation exposure.

[0119] In this embodiment, firstly, the system integrates all parameters from steps 203 and 204 to formulate a detailed scanning plan. For example, it determines the sampling frequency and contrast agent injection volume for each region. Secondly, through precise timing control, it ensures that the scanning device is at its highest sampling frequency when the contrast agent reaches the target region. For example, this is achieved by accurately calculating the contrast agent diffusion time and scanning time. Then, software is used to simulate the scanning effects at different time points, adjusting the phase difference of the contrast agent injection pulses until an optimal solution is found. For example, the injection speed is adjusted to match the scanning rhythm. Finally, this complete coding strategy is applied to the actual scanning process to ensure that each operation maximizes the diagnostic effect.

[0120] Here is a specific example:

[0121] A patient was rushed to the hospital emergency department due to sudden chest pain. The medical team quickly used a cardiovascular catheter with integrated multimodal sensors to acquire transmittance distribution parameters and blood flow disturbance parameters. The system first analyzed 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 eddy energy accumulation characteristics, the system also optimized the contrast agent injection parameters to ensure optimal imaging results. For example, when high shear force was detected in a certain area, the contrast agent dose in that area was increased. Finally, through precise time synchronization, the system achieved high-quality tomographic scanning, helping doctors to formulate a timely treatment plan and saving the patient's life.

[0122] In summary, a highly intelligent cardiovascular disease diagnostic system was constructed through steps 201 to 205. This system can not only accurately identify the components of intravascular plaques and their rupture risk, but also dynamically adjust scanning parameters to ensure optimal imaging results. Furthermore, by optimizing the contrast agent injection strategy, unnecessary radiation exposure is effectively reduced, improving the safety and accuracy of diagnosis. In particular, by precisely controlling the phase difference and timing synchronization of the contrast agent injection pulses, the system ensures that the clearest images are acquired at the optimal time for each scan, greatly improving the efficiency and success rate of cardiovascular disease diagnosis in emergency settings and significantly improving patient prognosis.

[0123] To further improve the accuracy of cardiovascular disease diagnosis, especially for rapid assessment of patients with acute coronary syndrome, a cardiovascular tomography coding strategy based on multimodal data fusion is proposed. This strategy combines plaque component feature parameters with blood flow disturbance feature parameters, performs structural mechanics analysis and cross-modal convolution operations, and generates plaque rupture risk assessment parameters that include stress-blood flow interaction weights to quantify the rupture probability of each region. In some embodiments, step 202 involves performing multimodal correlation analysis on the plaque component feature parameters and the blood flow disturbance feature parameters to generate plaque rupture risk assessment parameters. These parameters, through coupling effects, output quantitative indicators of the plaque surface, including:

[0124] 301. Based on the correlation between the fibrous cap thickness distribution and the lipid core space ratio in the characteristic parameters of the plaque components, the stress concentration factor of the plaque structure is extracted through structural mechanics analysis;

[0125] In step 301, the fibrous cap thickness distribution and lipid core space ratio among the plaque component characteristic parameters are key indicators for evaluating plaque stability. The fibrous cap refers to a layer of tissue covering the plaque surface, and its thickness affects the plaque's ability to resist external stress; while the lipid core refers to the lipid-rich portion inside the plaque, and its size and location are crucial to the overall stability of the plaque. Structural mechanics analysis is used to extract the plaque structural stress concentration factor, which reflects the degree of stress concentration in specific regions within the plaque when subjected to external forces.

[0126] In this embodiment, firstly, high-resolution imaging technology is used to acquire detailed images of the plaque and its surrounding environment. Then, image processing algorithms are used to identify and quantify the thickness of the fibrous cap and the spatial proportion of the lipid core. Next, a finite element model is constructed based on this data to simulate plaque behavior under different stress conditions. Finally, the model output is analyzed to determine which regions may experience stress concentration, and the corresponding stress concentration coefficients are calculated.

[0127] 302. Based on the spatial overlap between the shear force oscillation amplitude and the flow separation region in the blood flow disturbance characteristic parameters, and combined with the spatial location mapping relationship of the plaque structure stress concentration coefficient, a blood flow disturbance stress coupling factor is generated.

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

[0129] In this embodiment, firstly, based on the results of step 301, the locations of stress concentration points in the plaque structure are determined. Secondly, computational fluid dynamics (CFD) techniques are used to simulate blood flow patterns, focusing particularly on shear force oscillation amplitudes and flow separation regions. Then, the fluid dynamics results are mapped onto 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 region, suggesting that this area may be more susceptible to damage.

[0130] 303. Perform 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 to output a dynamic stress offset that includes the cumulative effect of plaque deformation.

[0131] In step 303, the gradient rate of change of the plaque structure stress concentration factor refers to the rate of change of stress concentration at different locations within 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. Cross-modal convolution is a method that combines these two different types of data for analysis, thereby calculating the dynamic stress offset that includes the cumulative effect of plaque deformation.

[0132] In this embodiment, firstly, the time-dependent change of the blood flow disturbance stress coupling factor is determined based on the results of step 302. Next, a mathematical model is used to analyze the spatial gradient rate of change of the stress concentration factor in the plaque structure. Then, a cross-modal convolution algorithm is applied to process the two datasets to identify possible deformation patterns within the plaque. Finally, simulations and experiments are used to evaluate the impact of these deformations on plaque stability. For example, in the aforementioned case, the physician found that the stress concentration factor in a specific region of the plaque increased significantly over time; combined with blood flow disturbance characteristics, this predicted that the region might experience significant deformation in the future.

[0133] 304. Based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameter of the calcified region in the patch composition characteristic parameters, generate the distribution difference of the stress gradient on the patch surface in the axial and radial directions;

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

[0135] In this embodiment, firstly, high-resolution imaging technology is used to acquire detailed images of the plaque calcification region. Secondly, image processing technology is used to measure the sharpness of the calcification region's edges. Then, combined with dynamic stress offset, the differences in the axial and radial distribution of the stress gradient on the plaque surface are analyzed. Finally, this information is used to assess the risk of plaque fracture. For example, in the case described above, further imaging examination revealed that the edges of the plaque calcification region were relatively blurred, indicating that its structure was not as robust as expected, increasing the likelihood of fracture.

[0136] 305. The distribution difference degree is nonlinearly superimposed with the eddy current energy attenuation rate in the blood flow disturbance characteristic parameters to generate a plaque rupture risk assessment parameter containing stress-blood flow interaction weights. The plaque rupture risk assessment parameter is used as a quantitative index to characterize the rupture probability of each region on the plaque surface.

[0137] In step 305, the distribution variability refers to the magnitude of change in the stress gradient on the plaque surface in different directions. The eddy current energy decay rate is a parameter describing the rate at which eddy current energy in the blood flow decreases over time, reflecting the intensity of the force exerted by the blood flow on the plaque. Nonlinear superposition combines these two parameters to generate plaque rupture risk assessment parameters, which are used to quantify the probability of plaque rupture.

[0138] In this embodiment, firstly, based on the results of step 304, the distribution difference of the stress gradient on the plaque surface is determined. Secondly, eddy current behavior in blood flow is simulated, and its energy attenuation rate is calculated. Then, a nonlinear superposition method is used to integrate these two parameters to generate the final plaque rupture risk assessment parameters. Finally, personalized treatment recommendations are formulated based on these parameters. For example, in the above case, comprehensive analysis revealed a high risk of plaque rupture; therefore, 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 one emergency case, a patient was brought to the hospital with sudden chest pain. The medical team used a cardiovascular catheter with integrated sensors to acquire transmittance distribution and blood flow disturbance parameters, discovering significant plaque in the left anterior descending artery. The system analyzed plaque composition and blood flow disturbance characteristics, calculated the plaque structural stress concentration factor and blood flow disturbance stress coupling factor, and then generated dynamic stress offset. Based on this, the system identified high-risk rupture areas and optimized scanning parameters, helping doctors quickly develop a treatment plan and successfully saving the patient's life. This process demonstrates the significant application value of multimodal data analysis in acute cardiovascular events.

[0141] In summary, the analysis in steps 301-305 provides a comprehensive understanding of the structural and functional complexity of atherosclerotic plaques and enables a more accurate assessment of their rupture risk. This method not only considers the physical properties of the plaque itself but also delves into 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 incidence of acute cardiovascular events. This cross-modal correlation analysis method represents a cutting-edge direction in modern medical image processing and biomechanical research, demonstrating the enormous potential of multidisciplinary collaboration in solving complex medical problems.

[0142] To further improve the accuracy of plaque rupture risk prediction in cardiovascular disease diagnosis, a difference parameter containing axial and radial stress gradient components is generated by analyzing the high-frequency and low-frequency stress spectral density distributions and their relationship with the stress concentration factor at the calcification edge. This method can more accurately identify high-risk plaque regions, providing physicians with a more refined risk assessment tool, thereby enabling the development of effective treatment strategies. In some embodiments, step 303, based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameter of the calcified region in the plaque component characteristic parameters, generates the difference in the axial and radial distribution of the plaque surface stress gradient, including:

[0143] 401. By using the energy ratio relationship between the dominant frequency component and the harmonic component of the dynamic stress offset, 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.

[0144] In step 401, the dynamic stress offset reflects the changes in internal stress within the plaque under the influence of blood flow. The dominant frequency component and harmonic components are two important parameters describing these frequency variations, and their energy ratios reveal the main patterns of stress change. The high-frequency stress spectral density distribution represents rapid changes in the axial stress propagation direction, while the low-frequency stress spectral density distribution corresponds to slow changes in the radial stress diffusion direction. These data are used to determine the stress propagation characteristics in different directions.

[0145] In this embodiment, firstly, signal processing techniques are used to separate the dominant frequency component and harmonic components from the dynamic stress offset, and their energy proportions are calculated. Secondly, based on these energy proportions, high-frequency and low-frequency stress spectral density distribution maps are constructed. Then, through comparative analysis, the dominant frequency ranges of axial and radial stress propagation are determined. Finally, biomechanical principles are used to verify whether these stress distribution patterns conform to the expected physical behavior. For example, in a real-world case, the research team found that the dominant frequency component of a specific patch was concentrated in a higher frequency range, indicating that its axial stress propagation was relatively active.

[0146] 402. Based on the correlation between the distribution density of curvature abrupt change points and the gradient change rate in the edge sharpness parameter of the calcified region among the characteristic parameters of the plaque composition, analyze the stress concentration factor of the calcified edge in the axial and radial directions to increase the stress ratio.

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

[0148] In this embodiment, firstly, image processing techniques are used to measure the distribution density of curvature abrupt change points and the rate of gradient change at the edge of the calcified region. Secondly, based on these parameters, the stress distribution at the calcified edge is simulated using finite element analysis. Then, the stress concentration factor at the calcified edge is calculated based on the simulation results, showing the stress enhancement ratio in both the axial and radial directions. Finally, these ratios are compared with the theoretical model to ensure the reliability of the analysis results.

[0149] 403. Adaptively weight and superimpose the high-frequency stress spectral density distribution with the axial stress enhancement ratio of the calcification edge stress concentration factor to generate an axial stress gradient component that includes 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 stress concentration factor at the calcification edge, which amplifies the stress effect at the calcification edge, thereby more accurately capturing the changes in the axial stress gradient of the patch. Adaptive weighted superposition is an algorithm that automatically adjusts the weights according to different stress characteristics to ensure that the final axial stress gradient components reflect both the true stress distribution and highlight the stress changes in key areas.

[0151] In this embodiment, firstly, the high-frequency stress spectral density distribution map and the axial stress enhancement ratio of the stress concentration factor at the calcification edge are obtained. Secondly, an adaptive weighted superposition algorithm is applied to combine these two parameters according to certain rules. Then, through iterative optimization of the weight values, the final generated axial stress gradient components are made to both conform to the actual situation and effectively identify potential high-risk areas.

[0152] 404. The low-frequency stress spectral density distribution and the radial stress enhancement ratio of the calcification edge stress concentration factor are directionally convolved and fused to generate a radial stress gradient component containing stress diffusion attenuation characteristics.

[0153] In step 404, combining the low-frequency stress spectral density distribution with the radial stress enhancement ratio of the calcification edge stress concentration factor provides a better understanding of how radial stress diffuses and attenuates. Directional convolution fusion is a mathematical tool that can emphasize specific properties along the stress diffusion path, such as attenuation rate, while preserving the original stress distribution characteristics.

[0154] In this embodiment, firstly, low-frequency stress spectral density distribution data and the radial stress enhancement ratio of the stress concentration factor at the calcification edge are collected. Secondly, a directional convolution fusion algorithm is used to process these two sets of data to generate a radial stress gradient component that includes stress diffusion attenuation characteristics. Then, by analyzing the generated data, the main path of stress diffusion and the attenuation rate are determined. For example, in a specific case, after processing, researchers observed that the stress mainly diffuses along a specific direction and attenuates rapidly with increasing distance.

[0155] 405. Based on the peak position of the axial stress gradient component and the decay rate of the radial stress gradient component, a gradient difference parameter characterizing the difference between axial and radial stress distributions is output through a composite calculation of spatial position matching degree and energy decay 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 that together determine the stress distribution pattern on the patch surface. The combined calculation of spatial location matching degree and energy decay slope refers to a comprehensive analysis method that, by comparing the stress distribution characteristics in different directions, outputs a gradient difference parameter that can comprehensively reflect the stress state of the patch.

[0157] In this embodiment, firstly, the peak position of the axial stress gradient component and the attenuation rate of the radial stress gradient component are determined. Secondly, a composite calculation method combining spatial location matching degree and energy attenuation slope is used to combine the two parameters. Then, the effectiveness and accuracy of this composite calculation are verified through multiple experiments. For example, in practical applications, the research team successfully predicted the possible future rupture locations of plaques using this method and formulated preventive measures accordingly.

[0158] Here is a specific example:

[0159] In one emergency room case, a patient was brought to the hospital with sudden chest pain. The medical team used a cardiovascular catheter with integrated sensors to acquire transmittance distribution and blood flow disturbance parameters, revealing significant plaque in the left anterior descending artery. The system analyzed dynamic stress offset and edge sharpness parameters of the calcified area to generate axial and radial stress gradient components. The results showed high axial stress concentration and rapid radial stress diffusion in the area, indicating a high risk of rupture. Based on this data, the physician quickly developed a personalized treatment plan, successfully controlling the condition and demonstrating the crucial 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 steps 401 to 405, not only can the axial and radial distribution differences of plaque surface stress be precisely quantified, but also key areas that may lead to plaque rupture can be identified. This method significantly improves the accuracy and reliability of risk assessment for atherosclerotic plaque rupture, providing strong support for clinicians, helping to develop personalized treatment plans, and reducing the incidence of acute cardiovascular events. Furthermore, this method demonstrates the great potential of interdisciplinary collaboration, advancing medical imaging technology and biomechanical research.

[0161] To further improve the efficiency and quality of data transmission in cardiovascular disease diagnosis, especially for rapid assessment of patients with acute coronary syndrome in emergency settings, a layered compression method based on a video coding protocol is proposed. By layering and compressing the rendering instruction set into geometric structure, dynamic parameter, and texture detail layers, and combining this with a keyframe protection strategy generated from the polarization attenuation mode of vascular bifurcation markers and transmittance distribution parameters, high-quality image data can be transmitted efficiently and accurately under various network conditions. In some embodiments, step 104 involves performing layered compression on the rendering instruction set using a video coding protocol to generate a compressed data stream carrying vascular bifurcation markers. The layered compression integrates the polarization attenuation mode of the transmittance distribution parameters to generate a keyframe protection strategy, and adjusts the transmission priority of the dynamic parameter coding blocks based on the wall shear force of the blood flow disturbance parameters, including:

[0162] 501. The rendering instruction set is compressed in layers according to the geometric structure layer, dynamic parameter layer and texture detail layer by means of a video encoding protocol to generate an intermediate compressed frame containing geometric compression parameters, dynamic compression parameters and texture compression parameters.

[0163] In step 501, the system compresses the rendering instruction set in layers—geometric structure layer, dynamic parameter layer, and texture detail layer—using a video encoding protocol to generate an intermediate compressed frame containing geometric compression parameters, dynamic compression parameters, and texture compression parameters. The geometric structure layer mainly describes the basic shape information of the image, such as blood vessel paths and bifurcation point locations; the dynamic parameter layer contains information on the dynamic changes in 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 adapt to different levels of data requirements.

[0164] In this embodiment, firstly, the system identifies and separates the geometric structure information from the rendering instruction set, compressing it into geometric compression parameters to ensure the integrity of the basic shape information. Secondly, for the dynamic parameter layer, the system extracts data on dynamic changes in blood flow, such as velocity fields and shear forces, to form dynamic compression parameters. Then, for the texture detail layer, the system processes high-resolution image data to generate 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. Spatiotemporally bind the blood vessel bifurcation markers to the coding blocks of the intermediate compressed frame, generate a bifurcation marker index table based on the geometric compression parameters, insert topology check symbols at the boundaries of the coding blocks of the intermediate compressed frame, and generate a compressed data stream.

[0166] In step 502, the vessel bifurcation markers are spatiotemporally bound to the coded blocks of the intermediate compressed frame. A bifurcation marker index table is generated based on geometric compression parameters, and topology check symbols are inserted at the boundaries of the coded blocks to generate a compressed data stream. The bifurcation marker index table is used to quickly locate vessel bifurcation points, while the topology check symbols ensure that no critical information is lost during data transmission. This binding mechanism enhances the integrity and reliability of the data.

[0167] In this embodiment, firstly, the system matches the vascular bifurcation markers with the coded blocks of the intermediate compressed frame to ensure that each bifurcation point can be accurately located. Secondly, a bifurcation marker index table is generated based on geometric compression parameters, which records the position of each bifurcation point and its corresponding coded block number. Then, topology check symbols are inserted at the boundaries of the coded blocks; these symbols can verify the integrity of the data at the receiving end. Finally, the final compressed data stream is generated, ensuring stable transmission under any network conditions and enabling rapid parsing of important vascular structure information.

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

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

[0170] In this embodiment, firstly, the system analyzes the polarization attenuation mode of the transmittance distribution parameters to determine which areas require special protection. Secondly, based on the polarization attenuation weights, a keyframe protection strategy is generated to ensure that these areas do not lose information during compression. Then, according to the blood flow direction of the dynamic compression parameters, the keyframe refresh interval is adjusted so that the image can be updated more frequently in areas with significant blood flow changes, maintaining its up-to-date state. Finally, in this way, clear and accurate image data is ensured to be provided under all circumstances.

[0171] 504. Based on the wall shear force of the blood flow disturbance parameters, extract the shear force direction component of the dynamic parameter layer coding block, strengthen the motion vector of the coding block in the high shear force region 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 parameters. The motion vector of the coding block in the high shear force region is enhanced using a bifurcation marker index table, dynamically adjusting the transmission priority of the dynamic parameter layer coding block in the compressed data stream. This method can better capture dynamic changes in blood flow, especially in high-risk areas, ensuring timely transmission of critical information.

[0173] In this embodiment, firstly, the system extracts the directional component of the wall shear force from the blood flow disturbance parameters to identify high shear force regions. Secondly, using a bifurcation marker index table, the system finds the corresponding coding blocks for these high shear force regions and enhances their motion vectors to ensure these regions have 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 possible time. Finally, in this way, the system ensures that even under poor network conditions, the most important blood flow dynamic information can be obtained in a timely manner.

[0174] Here is a specific example:

[0175] A patient was brought 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, discovering significant plaque in the left anterior descending artery. The system first performed layered compression on the rendering instruction set, generating intermediate compressed frames with geometric compression parameters, dynamic compression parameters, and texture compression parameters. Next, it spatiotemporally bound vessel bifurcation markers to the coded blocks of the intermediate compressed frames, generating a bifurcation marker index table and inserting topology check symbols. Subsequently, the system generated a keyframe protection strategy based on polarization attenuation modes, adjusted the keyframe refresh interval, and optimized the transmission priority of the dynamic parameter layer coded blocks based on wall shear force. Ultimately, through efficient data transmission, doctors were able to quickly obtain high-quality imaging data, formulate effective treatment plans, and successfully control the patient's condition.

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

[0177] 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 emergency settings, a spatiotemporal binding-based method is proposed. By matching vessel bifurcation markers with the coded blocks of intermediate compressed frames and generating a bifurcation marker index table based on geometric compression parameters, high-quality image data can be transmitted efficiently and accurately under any network conditions. In some embodiments, step 502, which involves spatiotemporally binding vessel bifurcation markers with the coded blocks of the intermediate compressed frames and generating a bifurcation marker index table based on the geometric compression parameters, includes:

[0178] 601. Based on the three-dimensional coordinates of the bifurcation point in the blood vessel bifurcation marker and the timestamp sequence of the intermediate compressed frame, generate a spatial position mapping relationship between the bifurcation point and the coded block of the intermediate compressed frame;

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

[0180] In this embodiment, a dataset of vascular images of the patient is first acquired using a high-resolution imaging device. Then, the three-dimensional coordinates of the vascular bifurcation points are extracted using image processing algorithms, and the timestamp of each frame is recorded synchronously. Next, based on this temporal and spatial information, a database is constructed to store the spatial location mapping relationship between each bifurcation point and its corresponding intermediate compressed frame coded block. Finally, the effectiveness of this mapping relationship is verified by analyzing data from multiple patients, ensuring that it accurately reflects the actual dynamic changes of the vascular bifurcation points.

[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, the spatiotemporal fit parameter between the intermediate compressed frame coding block and the bifurcation mark is obtained. Spatial weight is allocated to the intermediate compressed frame coding block according to the spatiotemporal fit parameter to generate a coding block binding set containing the stability weight of the bifurcation point.

[0182] In step 602, the motion vector direction of the intermediate compressed frame coded blocks describes the direction of inter-frame content movement, while the blood flow direction at the bifurcation point refers to the path of blood flow through the blood vessel bifurcation. The spatiotemporal fit parameter measures the degree of consistency between these two parameters, reflecting the temporal and spatial matching between the coded blocks and the bifurcation points. Spatial weight allocation is a strategy that adjusts the importance of each coded block according to the spatiotemporal fit parameter, thereby generating a set of coded block bindings that includes bifurcation point stability weights. This step is particularly important for identifying unstable factors that may affect vascular health.

[0183] In this embodiment, the motion vector direction between each pair of adjacent intermediate compressed frames is first calculated, and the blood flow direction at each bifurcation point is determined. Secondly, based on this directional information, spatiotemporal fit parameters are calculated, and spatial weights are allocated to each coding block using these parameters. Then, through experimental verification, the weight allocation scheme is adjusted to optimize the results.

[0184] 603. Based on the compression ratio difference between the primary curvature and the secondary curvature in the coded block binding set, the curvature compression difference factor of the geometric compression parameters is obtained;

[0185] In step 603, the principal curvature and secondary curvature represent quantitative indicators of the primary and secondary tortuosity of the vascular surface, respectively. The compression ratio difference refers to the difference in the proportion of compression of these two curvatures during geometric compression. The curvature compression difference factor is a feature value extracted from this difference, reflecting the deformation characteristics of the vascular structure during compression. This factor is crucial for assessing the stability of vascular bifurcation points and predicting their future trends.

[0186] In this embodiment, the principal and secondary curvatures of the region near each bifurcation point are first measured and recorded. Then, a geometric compression algorithm is applied to process this data to calculate the compression ratio difference. Next, a curvature compression difference factor is calculated based on these differences and applied to subsequent analysis.

[0187] 604. The curvature compression difference factor and the spatiotemporal fit parameter are sorted bidirectionally 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 used to simultaneously consider the effects of the curvature compression difference factor and the spatiotemporal fit parameter, generating a comprehensive index table. The bifurcation point stability weights serve as the index keys, and the curvature compression difference factor as the correlation value. This index table facilitates the rapid search and analysis of the stability status of different bifurcation points. This step is particularly useful for developing personalized treatment plans.

[0189] In this embodiment, all data obtained in the previous steps are first integrated, including curvature compression difference factors and spatiotemporal fit parameters. Then, a bidirectional sorting algorithm is used to process this data to generate a final bifurcation marker index table. Finally, by comparing and analyzing the index tables of different patients, targeted medical recommendations are provided. For example, in a study of multiple patients with coronary artery disease, doctors developed more precise surgical plans based on the generated index table, significantly improving treatment outcomes.

[0190] Here is a specific example:

[0191] A patient was brought to the hospital emergency department due to sudden chest pain. The medical team used a cardiovascular catheter with integrated sensors to acquire transmittance distribution and blood flow disturbance parameters, discovering significant plaque in the left anterior descending artery. The system first generates intermediate compressed frames and establishes a spatial location mapping relationship based on the three-dimensional coordinates and timestamps of the bifurcation points. Next, it calculates spatiotemporal fit parameters and assigns spatial weights to the coded blocks, forming a coded block binding set. Subsequently, it generates a curvature compression difference factor based on the compression ratio difference between the primary and secondary curvatures and generates a bifurcation marker index table. Ultimately, doctors were able to quickly obtain high-quality imaging data, formulate an effective treatment plan, and successfully control the condition. This method demonstrates its important application value in acute cardiovascular events.

[0192] The overall technical benefits of steps 601 to 604 lie in not only improving the accuracy of identifying the location and stability of vascular bifurcation points, but also providing clinicians with a scientific basis for developing personalized treatment strategies, effectively reducing the risk of acute cardiovascular events. This method demonstrates the enormous potential of combining modern medical imaging processing technology with biomechanical theory.

[0193] 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 emergency settings, a method based on a containerized microservice cluster is proposed. By decomposing the computed tomography (CT) coding strategy into multiple parallel-executed sub-strategies and generating a rendering instruction set related to vascular elastic modulus based on the terminal hardware architecture, high-quality image data can be generated efficiently and accurately under any hardware conditions. In some embodiments, step 603, which involves inputting the CT coding strategy into the containerized microservice cluster and generating a rendering instruction set related to vascular elastic modulus based on the terminal hardware architecture, includes:

[0194] 701. The computed tomography (CT) scanning coding strategy is divided into multiple parallel scanning coding sub-strategies, and each scanning coding sub-strategy is injected into the containerized microservice cluster through a dynamic load balancing module to generate scanning coding sub-strategy identifiers.

[0195] In step 701, the system breaks down the computed tomography (CT) coding strategy into multiple parallel-executed sub-strategies. These sub-strategies are then injected into the containerized microservice cluster via a dynamic load balancing module, generating sub-strategy identifiers. Each sub-strategy is a subdivision of the original coding strategy, responsible for data processing in a specific area or function. The dynamic load balancing module optimizes resource allocation, ensuring that each sub-strategy executes under optimal conditions. The sub-strategy identifiers are used to track and manage the status and progress of each sub-strategy.

[0196] In this embodiment, firstly, the system analyzes the original computed tomography (CT) scanning coding strategy and decomposes it into multiple independently executable sub-strategies. These sub-strategies may involve different cardiovascular regions or different imaging parameters. Secondly, using a dynamic load balancing module, each sub-strategy is intelligently allocated to a suitable containerized microservice node for execution based on the current system resource status. Then, a unique identifier is generated for each sub-strategy to facilitate subsequent management and monitoring of its execution. Finally, this approach ensures that all sub-strategies can be executed efficiently in parallel, maximizing the utilization of system resources.

[0197] 702. Based on the scanning encoding sub-strategy identifier, traverse the number of computing units and parallel processing capabilities of the terminal hardware architecture to generate a vascular elastic modulus sequence containing the dynamic correlation between vascular wall deformation gradient and stress response.

[0198] In step 702, based on the scanning encoding sub-strategy identifier, the number of computing units and parallel processing capabilities of the terminal hardware architecture are traversed to generate a sequence of vascular elastic moduli containing the dynamic correlation between vascular wall deformation gradient and stress response. The number of computing units reflects the processing power 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 locations, while the stress response reflects the force exerted by blood flow on the vascular wall. This information together constitutes the vascular elastic modulus sequence, which is used to guide subsequent rendering operations.

[0199] In this embodiment, firstly, the system identifies the specific task to be processed based on the scanning encoding 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 vessel wall deformation gradient and stress response, forming a vessel elastic modulus sequence. For example, in high-stress regions, the deformation gradient may be larger, thus requiring more refined processing. Then, the system integrates these calculation results into a sequence to ensure that the elastic modulus of each region can be accurately represented. Finally, in this way, an elastic modulus sequence that comprehensively reflects the structural characteristics of the blood vessel is generated, providing basic data for subsequent rendering.

[0200] 703. Map the deformation gradient in the blood vessel elastic modulus sequence to multi-level rendering resolution parameters, and map the stress response to matching texture sampling density parameters to form a dynamic rendering parameter set;

[0201] In step 703, the deformation gradient in the vascular elastic modulus sequence is mapped to multi-level rendering resolution parameters, and the stress response is mapped to matching texture sampling density parameters, forming a dynamic rendering parameter set. The multi-level rendering resolution parameters determine the image resolution of different regions to accommodate different detail requirements. The texture sampling density parameters control the smoothness of the image surface, especially in areas with high stress response. The dynamic rendering parameter set integrates these two aspects of information to ensure that the generated image is both clear and realistic.

[0202] In this embodiment, firstly, 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 large deformation, a higher resolution is used to capture subtle changes. Secondly, the system also processes stress response information, converting it into matching texture sampling density parameters. This helps generate more detailed images in areas with high stress. Then, the system integrates these parameters into a dynamic rendering parameter set, ensuring that the rendering settings for each region match its characteristics. Finally, in this way, a complete set of dynamic rendering parameters is generated, providing a basis for subsequent memory allocation and shader instruction queues.

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

[0204] In step 704, the memory bandwidth of the terminal hardware architecture is allocated based on the dynamic rendering parameter set, generating a corresponding video memory allocation strategy and a bound shader instruction queue. These are then merged and output as a rendering instruction set associated with the blood vessel elastic modulus. Memory bandwidth determines the data transmission speed, while the video memory allocation strategy ensures that the graphics processing unit can efficiently utilize resources. The shader instruction queue contains specific rendering operation instructions, ensuring that each region can be rendered according to preset parameters. The final generated rendering instruction set is a complete set of instructions used to guide the actual rendering operations.

[0205] In this embodiment, firstly, the system assesses the memory bandwidth requirements of the terminal hardware architecture based on a dynamic rendering parameter set 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 region can be processed according to 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, high-quality image data can be generated efficiently and accurately under any hardware conditions, providing doctors with reliable diagnostic tools.

[0206] Here is a specific example:

[0207] A patient was brought 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, discovering significant plaque in the left anterior descending artery. The system first broke down the computed tomography (CT) coding strategy into multiple sub-strategies and distributed them to a containerized microservice cluster for execution via a dynamic load balancing module. Next, the system generated a sequence of vascular elastic moduli based on the number of computing units and parallel processing capabilities, then mapped deformation gradients and stress responses to rendering parameters, generating a dynamic rendering parameter set. Subsequently, the system generated a memory allocation strategy and shader instruction queue based on these parameters, ultimately outputting a high-quality rendering instruction set. Doctors were able to quickly obtain detailed imaging data, formulate an effective treatment plan, and successfully control the patient's condition.

[0208] In summary, steps 701 to 704 described above improve the efficiency and accuracy of data processing in cardiovascular disease diagnosis. This method not only ensures the efficient generation of high-quality image data under various hardware conditions but also significantly improves image quality and resolution by optimizing memory allocation and shader instruction queues. This innovative solution greatly improves the speed and accuracy of diagnosis in emergency settings, leading to better patient outcomes.

[0209] Figure 2 This application provides a schematic diagram of the structure of a multi-terminal processing system for real-time sharing of emergency clinical data, as shown in the embodiments of this application. Figure 2 As shown, the device includes:

[0210] The acquisition module 21 acquires the 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. The multimodal sensor is integrated into the catheter detection end with a gradient refractive index nanocoating.

[0211] The generation module 22 generates a computed tomography coding strategy linked to vascular pathology features based on the spectral absorption characteristics of the transmittance distribution parameters and the eddy current intensity of the blood flow disturbance parameters. The computed tomography coding strategy includes sampling frequency parameters driven by plaque rupture risk assessment and contrast agent injection parameters adapted to wall shear force fluctuations.

[0212] Input module 23 inputs the computed tomography encoding strategy into the containerized microservice cluster and generates a rendering instruction set related to the elastic modulus of blood vessels based on the terminal hardware architecture;

[0213] Compression module 24 performs layered compression on the rendering instruction set through a video encoding protocol to generate a compressed data stream carrying blood vessel bifurcation markers. The layered compression integrates the polarization attenuation mode of the transmittance distribution parameters to generate a key frame protection strategy, and adjusts the priority of the encoding blocks based on the wall shear force of the blood flow disturbance parameters.

[0214] Module 25 is invoked to distribute the compressed data stream to a heterogeneous terminal cluster, and the visualization of the elastic modulus of the blood vessel wall is triggered on the mobile terminal. The key frame protection strategy is invoked and associated with the wall shear force fluctuation.

[0215] The reconstruction module 26 reconstructs the light transmission detection mode of the gradient refractive index nanocoating based on the terminal interaction data, and generates duct control commands that are linked to the plaque rupture risk assessment.

[0216] Figure 2 The aforementioned multi-terminal processing system for real-time sharing of emergency clinical data can execute... Figure 1The implementation principle and technical effects of the multi-terminal processing method for real-time sharing of emergency clinical data described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit performs operations in the multi-terminal processing system for real-time sharing of emergency clinical data in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0217] In one possible design, Figure 2 The multi-terminal processing system for real-time sharing of emergency clinical data in the illustrated embodiment 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 invoked and executed by the processing component 32.

[0219] The processing component 32 is used for the above Figure 1 The embodiment describes 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-described method. Alternatively, the processing component may be implemented as 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-described method.

[0221] 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 storage, flash memory, magnetic disk, or optical disk.

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

[0223] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0224] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

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

[0226] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a multi-terminal processing method for real-time sharing of emergency clinical data.

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

[0228] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0229] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0230] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-terminal processing method for real-time sharing of emergency clinical data, characterized in that, The method comprises the following steps: Obtaining the light transmittance distribution parameters of the inner wall of the blood vessel and the blood flow disturbance parameters by modifying the multi-modal sensor of the cardiovascular catheter, the multi-modal sensor being integrated into the probe end of the catheter with a gradient refractive index nano coating; Generating a computer tomography encoding strategy linked to the pathological characteristics of the blood vessel according to the spectral absorption characteristics of the light transmittance distribution parameters and the vortex intensity of the blood flow disturbance parameters, the computer tomography encoding strategy including a sampling frequency parameter driven by a plaque rupture risk assessment and a contrast agent injection parameter adapted to the wall shear force fluctuation; Inputting the computer tomography encoding strategy into a containerized micro-service cluster to generate a rendering instruction set associated with the elastic modulus of the blood vessel based on the terminal hardware architecture; Performing hierarchical compression on the rendering instruction set through a video encoding protocol to generate a compressed data stream carrying the bifurcation markers of the blood vessel, the hierarchical compression integrating the polarization attenuation mode of the light transmittance distribution parameters 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 parameters; Distributing the compressed data stream to a heterogeneous terminal cluster to visualize the elastic modulus of the blood vessel wall on a mobile terminal and associate the key frame protection strategy with the wall shear force fluctuation; Reconstructing the light transmittance detection mode of the gradient refractive index nano coating according to the terminal interaction data to generate a catheter control instruction linked to the plaque rupture risk assessment; The process of generating a computer tomography encoding strategy linked to the pathological characteristics of the blood vessel according to the spectral absorption characteristics of the light transmittance distribution parameters and the vortex intensity of the blood flow disturbance parameters comprises: According to the spectral absorption characteristics of the light transmittance distribution parameters and the vortex intensity of the blood flow disturbance parameters, generating plaque component feature parameters by quantifying the spectral attenuation difference of the plaque components in the blood vessel wall, and generating blood flow disturbance feature parameters by extracting the dynamic disturbance components of the blood flow velocity field in the blood vessel lumen; performing multi-modal correlation analysis on the plaque component feature parameters and the blood flow disturbance feature parameters to generate plaque rupture risk assessment parameters, the plaque rupture risk assessment parameters outputting quantitative indicators of the plaque surface through coupling effect; The multi-modal correlation analysis of the plaque component feature parameters and the blood flow disturbance feature parameters to generate plaque rupture risk assessment parameters, and the output of quantitative indicators of the plaque surface through coupling effect of the plaque rupture risk assessment parameters, comprises: Based on the correlation between the fiber cap thickness distribution and the lipid core space ratio in the plaque component feature parameters, the structural stress concentration coefficient of the plaque structure is extracted through structural mechanics analysis; according to the spatial overlap degree of the shear shock amplitude and the flow separation area in the blood flow disturbance feature parameters, combined with the spatial position mapping relationship of the plaque structure stress concentration coefficient, the blood flow disturbance stress coupling factor is generated; the gradient change rate of the plaque structure stress concentration coefficient and the time-varying component of the blood flow disturbance stress coupling factor are cross-modal convolution operation, and the dynamic stress offset containing the cumulative effect of plaque deformation is output; based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameter of the calcified area in the plaque component feature parameters, the distribution difference of the plaque surface stress gradient in the axial and radial directions is generated; the distribution difference and the vortex energy attenuation rate in the blood flow disturbance feature parameters are nonlinearly superimposed to generate a plaque rupture risk assessment parameter containing stress-blood flow interaction weight, which is used to represent the quantitative index of the rupture probability of each region on the plaque surface.

2. The method of claim 1, wherein, According to the spectral absorption characteristics of the light transmittance distribution parameters and the vortex intensity of the blood flow disturbance parameters, a computer tomography encoding strategy linked with vascular pathological characteristics is generated, which contains a sampling frequency parameter driven by a plaque rupture risk assessment and a contrast agent injection parameter adapted to wall shear force fluctuations, including: According to the plaque rupture risk assessment parameter, the sampling frequency parameter of the computer tomography is dynamically adjusted, and the sampling frequency parameter is matched with the spatial distribution difference of the plaque rupture risk to realize adaptive optimization of the computer scanning resolution; Based on the vortex energy accumulation characteristics in the blood flow disturbance feature parameters, a wall shear force fluctuation parameter for calculating the matching relationship between the blood vessel wall shear force instantaneous change rate and the contrast agent diffusion rate is derived, and a contrast agent injection parameter is generated; The sampling frequency parameter and the contrast agent injection parameter are time-synchronized, and by controlling the phase difference of the contrast agent injection pulse, a computer tomography encoding strategy linked with vascular pathological characteristics is generated.

3. The method of claim 1, wherein, Based on the amplitude-frequency characteristics of the dynamic stress offset and the edge sharpness parameter of the calcified area in the plaque component feature parameters, the distribution difference of the plaque surface stress gradient in the axial and radial directions is generated, including: Through the energy ratio relationship between the main frequency component and the harmonic component of the dynamic stress offset, 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; According to the correlation between the curvature mutation point distribution density and the gradient change rate in the edge sharpness parameter of the calcified area in the plaque component feature parameters, the stress enhancement ratio of the calcified edge stress concentration factor in the axial and radial directions is analyzed; The high-frequency stress spectral density distribution and the axial stress enhancement ratio of the calcified edge stress concentration factor are adaptively weighted and superimposed to generate an axial stress gradient component containing the stress amplification effect of the calcified edge. The low-frequency stress spectrum density distribution is directionally convoluted with the radial stress enhancement ratio of the calcified 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 representing the difference between the axial and radial stress distributions is output by a composite operation of spatial position matching degree and energy attenuation slope.

4. The method of claim 1, wherein, The layered compression of the rendering instruction set is performed through a video encoding protocol to generate a compressed data stream carrying a blood vessel bifurcation marker, the layered compression integrates a polarization attenuation mode of the light transmittance distribution parameter to generate a key frame protection strategy, and adjusts the transmission priority of a dynamic parameter coding block based on the wall shear force of the blood flow disturbance parameter, including: The rendering instruction set is layered compressed according to the geometric structure layer, the dynamic parameter layer and the texture detail layer through the video encoding protocol to generate an intermediate compression frame containing geometric compression parameters, dynamic compression parameters and texture compression parameters; The blood vessel bifurcation marker is spatio-temporally bound with the coding block of the intermediate compression frame, a bifurcation marker index table is generated based on the geometric compression parameters, and a topological check symbol is inserted into the coding block boundary of the intermediate compression frame to generate a compressed data stream; According to the layered compression, a key frame protection strategy is generated based on the polarization attenuation mode of the light transmittance distribution parameter, and the key frame refresh interval is updated according to the polarization attenuation weight combination and the blood flow direction of the dynamic compression parameter; Based on the wall shear force of the blood flow disturbance parameter, the shear force direction component of the dynamic parameter layer coding block is extracted, the motion vector of the high shear force area coding block is strengthened through the bifurcation marker index table, and the transmission priority of the dynamic parameter layer coding block in the compressed data stream is dynamically adjusted.

5. The method of claim 4, wherein, The spatio-temporal binding of the blood vessel bifurcation marker with the coding block of the intermediate compression frame includes: According to the three-dimensional coordinates of the bifurcation point in the blood vessel bifurcation marker and the timestamp sequence of the intermediate compression frame, a spatial position mapping relationship between the bifurcation point and the intermediate compression frame coding block is generated; Based on the angle between the motion vector direction of the intermediate compression frame coding block and the blood flow direction of the bifurcation point, a spatio-temporal fitting degree parameter of the intermediate compression frame coding block and the bifurcation marker is obtained, the intermediate compression frame coding block is spatially weighted according to the spatio-temporal fitting degree parameter, and a coding block binding set containing the bifurcation point stability weight is generated; Based on the compression ratio difference value of the principal 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 spatio-temporal fitting degree parameter are bidirectionally sorted to generate a bifurcation marker index table with the bifurcation point stability weight as an index key and the curvature compression difference factor as an associated value.

6. The method of claim 1, wherein, The computer tomography encoding strategy is input into the containerized micro-service cluster, and a rendering instruction set associated with the blood vessel elastic modulus is generated based on the terminal hardware architecture, including: Splitting the computed tomography encoding strategy into multiple scan encoding sub-strategies executed in parallel, injecting each scan encoding sub-strategy into a containerized micro-service cluster through a dynamic load balancing module to generate a scan encoding sub-strategy identifier; According to the scan encoding sub-strategy identifier, traversing the number of computing units and parallel processing capabilities of the terminal hardware architecture to generate a sequence of vessel elastic modulus containing the dynamic correlation between the vessel wall deformation gradient and the stress response; Mapping the deformation gradient in the sequence of vessel elastic modulus to a multi-level rendering resolution parameter, and the stress response to a matching texture sampling density parameter to form a dynamic rendering parameter set; Based on the dynamic rendering parameter set, dividing the memory bandwidth of the terminal hardware architecture to generate a corresponding video memory allocation strategy and a bound shader instruction queue, and outputting the rendering instruction set associated with the vessel elastic modulus after fusion.

7. A multi-terminal processing system for emergency clinical data real-time sharing, used for executing the multi-terminal processing method for emergency clinical data real-time sharing according to any one of claims 1-6. Comprise: An acquisition module acquires the light transmittance distribution parameter of the blood vessel inner wall and the blood flow disturbance parameter through the multi-modal sensor of the modified cardiovascular catheter, and the multi-modal sensor is integrated into the catheter detection end of the gradient refractive index nano coating; A generation module generates a computed tomography encoding strategy linked with the blood vessel pathological characteristics according to the spectral absorption characteristics of the light transmittance distribution parameter and the vortex intensity of the blood flow disturbance parameter, and the computed tomography encoding strategy contains a sampling frequency parameter driven by a plaque rupture risk assessment and a contrast agent injection parameter adapted to wall shear force fluctuation; An input module inputs the computed tomography encoding strategy into a containerized micro-service cluster to generate a rendering instruction set associated with the vessel elastic modulus based on the terminal hardware architecture; A compression module performs hierarchical compression on the rendering instruction set through a video encoding protocol to generate a compressed data stream carrying a blood vessel bifurcation marker, and the hierarchical compression integrates a key frame protection strategy generated by the polarization attenuation mode of the light transmittance distribution parameter, and adjusts the encoding 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 to trigger the visualization of the vessel wall elastic modulus on a mobile terminal, and calls the key frame protection strategy associated with the wall shear force fluctuation; A reconstruction module reconstructs the light transmission detection mode of the gradient refractive index nano coating according to terminal interaction data to generate catheter control instructions linked with the plaque rupture risk assessment.

8. A computing device, comprising: Comprise 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 realize the multi-terminal processing method for real-time sharing of emergency clinical data according to any one of claims 1-6.

9. 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 according to any one of claims 1-6 is realized.

Images