Renal sympathetic nerve nuclide evaluation system and method for RDN

Abstract

The invention relates to the technical field of biomedical engineering, and discloses a renal sympathetic nerve nuclide evaluation system and method for RDN, and the system comprises a nuclide image collection module, a data analysis module, a nuclide kinetic parameter analysis module, a neural activity quantification module and a dynamic evaluation and decision module, and is used for collecting a nuclide image data set; identifying a kidney area in the nuclide image data set, and analyzing the radioactivity counting rate of the kidney area; analyzing an uptake index and a retention index of the radionuclide marker based on the radioactivity counting rate; analyzing a sympathetic nerve activity quantitative index of the kidney region according to the uptake index, the retention index and the electrocardio physiological data; and determining a dynamic threshold value of the sympathetic nerve activity quantitative index based on the activity expression level index of the target object, and performing dynamic analysis on the sympathetic nerve activity quantitative index according to the dynamic threshold value to obtain an evaluation index. According to the invention, the accuracy of kidney sympathetic nerve nuclide evaluation can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomedical engineering, and particularly relates to a renal sympathetic nerve nuclide evaluation system and method for RDN. BACKGROUND

[0002] In medical clinics, the sympathetic nerve activity in the kidney region needs to be accurately evaluated to provide a basis for developing a treatment plan for atrial fibrillation (such as a renal denervation procedure). At present, the radionuclide tracing technology is an effective means for evaluating the autonomic nerve function of the heart and the kidney, which reflects the nerve activity through the uptake and storage characteristics of a specific radionuclide marker in nerve endings.

[0003] The existing nuclide image acquisition and analysis equipment adopts single-angle scanning or fixed-time-point acquisition when acquiring a nuclide image, which is easy to cause incomplete coverage of the kidney region by the ray data, low spatial resolution of the reconstructed nuclide distribution image, and difficulty in accurately capturing the radioactive distribution difference of the fine region of the kidney. In addition, when evaluating the sympathetic nerve activity, the electrocardiophysiological data of the target object is not associated, which leads to a lack of accuracy of the activity quantization result and inability to provide accurate reference for clinical treatment decisions. SUMMARY

[0004] The present application provides a renal sympathetic nerve nuclide evaluation system and method for RDN, which mainly aims to solve the problem of low accuracy in evaluating the renal sympathetic nerve nuclide.

[0005] To achieve the above-mentioned purpose, the renal sympathetic nerve nuclide evaluation system for RDN provided by the present application comprises: A nuclide image acquisition module is configured to acquire a nuclide image dataset of a preset radionuclide marker in the kidney region of a target object by using a radioactive detection device. A data analysis module is in signal connection with the nuclide image acquisition module and is configured to identify the target region and the background region of the kidney in the nuclide image dataset and analyze the first and second radioactive count rates of the target region and the background region, respectively. A nuclide kinetic parameter analysis module is configured to analyze the uptake index and the retention index of the radionuclide marker by using a kinetic model built in an imaging device based on the first and second radioactive count rates. A nerve activity quantization module is configured to analyze the sympathetic nerve activity quantization index of the kidney region according to the uptake index, the retention index, and the pre-received electrocardiophysiological data of the target object. A dynamic evaluation and decision module is configured to determine a dynamic threshold of the sympathetic nerve activity quantification index based on an activity expression level index of the target object, and dynamically analyze the sympathetic nerve activity quantification index according to the dynamic threshold to obtain an evaluation index of the renal sympathetic nerve nuclide, and output the evaluation index to a display interface of the imaging device.

[0006] To solve the above problems, the application further provides a renal sympathetic nerve nuclide evaluation method for RDN, which comprises the following steps: A nuclide image data set of a preset radionuclide marker in a kidney region of a target object is collected by a radioactive detection device; A target region and a background region of the kidney in the nuclide image data set are identified, and a first radioactivity count rate and a second radioactivity count rate of the target region and the background region are analyzed respectively; Based on the first radioactivity count rate and the second radioactivity count rate, an uptake index and a retention index of the radionuclide marker are analyzed by a kinetic model built in the imaging device; A sympathetic nerve activity quantification index of the kidney region is analyzed according to the uptake index, the retention index and pre-received electrocardiophysiological data of the target object; A dynamic threshold of the sympathetic nerve activity quantification index is determined based on an activity expression level index of the target object, and the sympathetic nerve activity quantification index is dynamically analyzed according to the dynamic threshold to obtain an evaluation index of the renal sympathetic nerve nuclide, and the evaluation index is output to a display interface of the imaging device.

[0007] The embodiments of the application can obtain high-quality nuclide image data sets by multi-angle scanning, three-dimensional reconstruction, noise suppression and spatial registration processing, and provide a reliable basis for subsequent analysis; the embodiments of the application can solve the defects of subjective region identification and large deviation of count rate by standardized region identification and targeted count rate processing, and ensure accurate radioactivity count rate; the embodiments of the application can solve the problems of simple device model and low parameter calculation precision by constructing a dual-chamber model and optimizing parameters, and accurately calculate the uptake and retention indexes; the embodiments of the application can make the activity quantification index more in line with the actual situation by combining with electrocardiophysiological data weighting correction; the embodiments of the application can solve the defects of fixed threshold and disconnection between evaluation and decision by formulating a dynamic threshold based on individual protein expression level, correcting the activity deviation degree and generating a clinical suggestion according to the actual situation; and the embodiments of the application can realize accurate and personalized evaluation of renal sympathetic nerve activity as a whole, provide reliable clinical basis for atrial fibrillation treatment, and promote the standardized application of autonomic nerve function evaluation in atrial fibrillation treatment. Therefore, the renal sympathetic nerve nuclide evaluation system and method for RDN provided by the application can solve the problem of low accuracy in renal sympathetic nerve nuclide evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1A functional module diagram of a renal sympathetic nerve nuclease evaluation system for RDN provided by an embodiment of the present application; Figure 2 A flowchart of a running method of a renal sympathetic nerve nuclease evaluation system for RDN provided by an embodiment of the present application.

[0009] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0010] It should be understood that the specific embodiments described herein merely exemplify the present application and do not limit the present application.

[0011] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of the present application.

[0012] The terms used in the embodiments of the present application are merely for the purpose of describing the specific embodiments, and are not intended to limit the present application. The singular forms “a”, “an” and “the” used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. “Plural” generally includes at least two.

[0013] Depending on the context, the word “if” as used herein can be interpreted as meaning “when” or “while” or “in response to determining” or “in response to detecting”. Similarly, depending on the context, the phrase “if determined” or “if detecting (a stated condition or event)” can be interpreted as meaning “when determined” or “in response to determining” or “when detecting (a stated condition or event)” or “in response to detecting (a stated condition or event)”.

[0014] In addition, the step sequence in each of the following method embodiments is only an example, and is not strictly limited.

[0015] In fact, the server device deployed by the renal sympathetic nerve nuclide evaluation system for RDN can be composed of one or more devices. The renal sympathetic nerve nuclide evaluation system for RDN can be implemented as a business instance, a virtual machine, or a hardware device. For example, the renal sympathetic nerve nuclide evaluation system for RDN can be implemented as a business instance deployed on one or more devices in a cloud node. In short, the renal sympathetic nerve nuclide evaluation system for RDN can be understood as a software deployed on a cloud node, which provides a renal sympathetic nerve nuclide evaluation system for RDN for each user terminal. Alternatively, the renal sympathetic nerve nuclide evaluation system for RDN can also be implemented as a virtual machine deployed on one or more devices in a cloud node. The virtual machine has application software installed for managing each user terminal. Alternatively, the renal sympathetic nerve nuclide evaluation system for RDN can also be implemented as a server composed of a plurality of hardware devices of the same or different types, and one or more hardware devices are provided to provide a renal sympathetic nerve nuclide evaluation system for RDN for each user terminal.

[0016] In an implementation form, the renal sympathetic nerve nuclide evaluation system for RDN and the user terminal are mutually adaptive. That is, the renal sympathetic nerve nuclide evaluation system for RDN is an application installed on a cloud service platform, and the user terminal is a client that establishes a communication connection with the application; or the renal sympathetic nerve nuclide evaluation system for RDN is implemented as a website, and the user terminal is implemented as a web page; or the renal sympathetic nerve nuclide evaluation system for RDN is implemented as a cloud service platform, and the user terminal is implemented as an applet in an instant messaging application.

[0017] Referring to Figure 1 FIG. 1 is a functional module diagram of a renal sympathetic nerve nuclide evaluation system for RDN according to an embodiment of the present application.

[0018] The renal sympathetic nerve nuclide evaluation system 100 for RDN according to the present application can be deployed in a cloud server, and in an implementation form, can be implemented as one or more server devices, or can be installed as an application on a cloud (such as a server of a mobile service operator, a server cluster, etc.), or can be developed as a website. According to the functions implemented, the renal sympathetic nerve nuclide evaluation system 100 for RDN can include a nuclide image acquisition module 101, a data analysis module 102, a nuclide kinetics parameter analysis module 103, a neural activity quantification module 104, and a dynamic evaluation and decision module 105. The modules according to the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by a device processor and can complete a fixed function, and are stored in the memory of the device.

[0019] In the embodiment of the present application, each of the above modules in the renal sympathetic nerve nuclide evaluation system for RDN can be independently implemented and called by other modules. The calling here can be understood as that a module can be connected to multiple modules of another type and provide corresponding services for the connected multiple modules. For example, the sharing evaluation module can call the same information collection module to obtain the information collected by the information collection module. Based on the above characteristics, the renal sympathetic nerve nuclide evaluation system for RDN provided in the embodiment of the present application can adjust the application scope of the renal sympathetic nerve nuclide evaluation system architecture for RDN by increasing modules and directly calling without modifying program codes, realize cluster horizontal expansion, and achieve the purpose of quickly and flexibly expanding the renal sympathetic nerve nuclide evaluation system for RDN. In actual application, the above modules can be arranged in the same device or different devices, or can be arranged in a virtual device, such as a service instance in a cloud server.

[0020] The following will be described in combination with specific embodiments, respectively for each component and specific workflow of the renal sympathetic nerve nuclide evaluation system for RDN: The nuclide image collection module 101 is configured to collect a nuclide image data set of a preset radionuclide marker in the kidney region of a target object by a radioactive detection device.

[0021] In the embodiment of the present application, the nuclide image data set is a structured data set containing information such as radionuclide distribution and intensity of the kidney region after collection, reconstruction and processing, covering multi-angle ray data, three-dimensional nuclide distribution images and processed standardized image data.

[0022] In the embodiment of the present application, the nuclide image collection module 101 is configured to, when collecting the nuclide image data set of the preset radionuclide marker in the kidney region of the target object by the radioactive detection device, specifically: control the radioactive detection device in the imaging device to perform multi-angle scanning on the radionuclide marker in the kidney region of the target object within a preset time window to obtain multi-angle ray data; reconstruct a three-dimensional nuclide distribution image of the kidney region based on the multi-angle ray data; perform noise suppression and spatial registration processing on the three-dimensional nuclide distribution image to obtain a nuclide image data set of the kidney region.

[0023] In detail, the radioactive detection device is a device capable of sensing the rays (such as gamma rays) emitted by radionuclides and converting the ray signals into measurable electrical signals, such as sodium iodide scintillation detectors, etc., which can accurately capture the ray information of the target region. The radionuclide marker is a pre-selected radionuclide labeled compound that can be specifically taken up and stored by the renal sympathetic nerve endings, such as norepinephrine analog 123I-metanephrine (123I-MIBG), which can simulate the metabolic process of norepinephrine in sympathetic nerve endings, and its distribution in the kidney region can directly reflect sympathetic nerve activity. The target object can be an experimental atrial fibrillation animal model. According to the metabolic characteristics of the selected radionuclide marker (such as 123I-MIBG), a preset time window is determined, for example, after intravenous injection of 123I-MIBG into the target object, wait for 30 minutes to ensure that the marker has reached the kidney with the blood circulation and started to be taken up by the sympathetic nerve endings, and then start the preset time window (lasting for 90 minutes); fix the target object on the examination bed of the imaging device, adjust the position of the device so that the radioactive detection device (such as sodium iodide scintillation detector) is distributed in a ring shape around the kidney region, set the scanning angle interval to 20°, a total of 18 scanning angles, and set the scanning time of each angle to 5 minutes to ensure that each angle can collect ray signals with sufficient intensity; control the detection device to start scanning in turn according to the preset angles, convert the ray signals received at each angle into electrical signals, and store them as multi-angle ray data after analog-to-digital conversion, which ensures comprehensive coverage and sufficient signal of the ray data through multi-angle and long time window acquisition.

[0024] Specifically, the ray data of each angle is preprocessed using a filtered back-projection algorithm to remove low-energy noise and scattered ray interference, and the preprocessed multi-angle ray data is input into the reconstruction unit of the imaging device; the reconstruction unit generates a three-dimensional image by superimposing two-dimensional ray data of different angles through back-projection calculation according to the ray projection principle, and sets the spatial resolution of the reconstructed image to 1mm x 1mm x 1mm to ensure that the nuclide distribution of the kidney cortex, medulla and other fine structures can be clearly presented. Using a Gaussian filter algorithm for noise suppression processing, a filter kernel size of 3x3x3 is set for convolution operation on the three-dimensional nuclide distribution image to smooth the image noise while preserving the edge details of the nuclide distribution; during spatial registration processing, the kidney CT anatomical image of the target object is first obtained, the matching feature points of the three-dimensional nuclide distribution image and the CT image are queried through an image feature extraction algorithm (such as extracting the key points and gray scale features of the kidney outline), and then the affine transformation algorithm is used to adjust the spatial position and angle of the nuclide image to make the kidney anatomical structures of the two images completely aligned; the three-dimensional nuclide distribution image after noise suppression and spatial registration is combined with the original multi-angle ray data to integrate into a structured nuclide image data set, solving the problems of image noise interference and spatial misalignment and laying a foundation for subsequent region recognition and count rate analysis.

[0025] Further, only comprehensive ray data can provide sufficient raw materials for three-dimensional image reconstruction; based on the reconstruction of three-dimensional nuclear species distribution image, the original data is converted into an intuitive image form, which is convenient for subsequent processing; and noise suppression and spatial registration processing are the optimization of the reconstructed image, which eliminates interference and ensures accuracy, and the three are carried out in turn, and gradually build high-quality nuclear species image data set, to provide reliable input for data analysis module.

[0026] The data analysis module 102 is connected with the nuclear species image acquisition module, and is used for identifying the target region and the background region of the kidney in the nuclear species image data set, and analyzing the first radioactivity count rate and the second radioactivity count rate of the target region and the background region, respectively.

[0027] In the embodiment of the application, the target region refers to the region of interest of the kidney, covering the functional region of the kidney, and the background region refers to the ipsilateral psoas major muscle region, which is adjacent to the kidney and has stable tissue characteristics, and is not affected by the specific uptake of the radioactive nuclide marker.

[0028] In the embodiment of the application, when identifying the target region and the background region of the kidney in the nuclear species image data set, the data analysis module 102 is specifically used for: Based on the anatomical landmarks in the nuclear species image data set, the contour of the kidney is drawn, and the initial target region of the kidney is determined according to the contour; The homogenous tissue region is selected as the initial background region on the periphery boundary of the initial target region, wherein the homogenous tissue region is the ipsilateral psoas major muscle region; Morphological operation is performed on the initial target region and the initial background region, and whether the initial target region and the initial background region after morphological operation meet the preset anatomical standard is verified; When the anatomical standard is met, the region consistency of the initial target region and the initial background region is analyzed, and the target region and the background region of the kidney are determined according to the region consistency.

[0029] In detail, by image recognition algorithm (such as Canny operator in edge detection algorithm), anatomical landmarks of the kidney are extracted in the radionuclide image dataset, such as identifying the slightly lower radioactive area at the renal hilum (because the renal hilum is mainly blood vessels and connective tissue, which has less uptake of radionuclide markers), the endpoints of the upper and lower poles of the kidney; based on the anatomical landmarks, a closed contour is formed by connecting the pixel points along the edge of the kidney using a polygon fitting algorithm, ensuring that the contour completely covers the kidney parenchymal region and avoids missing or including peripheral non-kidney tissue; the region inside the contour is determined as the initial target region of the kidney, solving the defect of strong subjectivity of manual delineation of existing devices and improving the accuracy of initial target region delineation. The peripheral boundary of the initial target region (kidney) is located in the radionuclide image dataset, and a search range of 5-10 mm extending outward from the boundary is set; then in the search range, the ipsilateral psoas major muscle region is found by a tissue density recognition algorithm (with the aid of CT anatomical images), which appears as a long strip, uniform density and stable radioactive intensity in the image; finally, the region is delineated as the initial background region, which is standardized to define the background region as the ipsilateral psoas major muscle, solving the problem of random background region selection and ensuring the homogeneity of the background region.

[0030] Specifically, the morphological operation adopts an open-close operation of erosion followed by dilation, the erosion operation uses a 3x3 structure element to eliminate small protrusions and noise points at the region boundary, and the dilation operation also uses a 3x3 structure element to fill small cavities inside the region, making the region contour more regular; during verification, the morphological parameters of the processed region (such as the length, short diameter, area of the kidney target region, and the length, width of the background region) are extracted and compared with the preset anatomical standards (such as kidney length 9-12 cm, short diameter 3-5 cm, and the length of the psoas major muscle background region to kidney length ratio 0.8-1.2); if all parameters are within the standard range, it is determined to meet the anatomical standards, otherwise the region delineation is adjusted, the initial region is optimized and corrected to ensure that the region morphology complies with the standards. When the anatomical standards are met, the region consistency of the target region and the background region is analyzed, and the target region and the background region of the kidney are determined according to the region consistency, the coefficient of variation (coefficient of variation = standard deviation / average value) of the pixel values in the target region and the background region is calculated, and the coefficient of variation threshold (target region ≤0.2, background region ≤0.15) is set; if the coefficient of variation of the target region is ≤0.2, it indicates that the radioactive distribution in the region is uniform and there is no abnormal interference, and the coefficient of variation of the background region is ≤0.15, which indicates that the radioactivity in the region is stable and meets the background characteristics, then it is determined that the region consistency meets the standards, and the current initial target region and initial background region are determined as the final kidney target region and background region; if the coefficient of variation exceeds the threshold, the morphological operation or the region boundary is adjusted until the region consistency meets the standards, ensuring that the selected region accurately reflects the true radioactivity distribution and excludes interference for subsequent count rate analysis.

[0031] In the embodiment of the present application, the first radioactivity count rate is the first radioactivity index after smoothing filtering and standardization processing, and its unit is usually count / second·square centimeter, which can accurately reflect the radionuclide count in the unit time and unit area of the kidney target region, and is directly related to the uptake activity of the marker by the renal sympathetic nerve; the second radioactivity count rate is the second radioactivity index after smoothing filtering and standardization processing, and its unit is the same as that of the first radioactivity count rate, which is used for comparison with the first radioactivity count rate, eliminates background interference, and accurately reflects the specific radioactivity distribution of the target region.

[0032] In the embodiment of the present application, when the data analysis module 102 analyzes the first radioactivity count rate and the second radioactivity count rate of the target region and the background region respectively, it is specifically used for: extracting the first pixel value of the target region, and calculating the first radioactivity index of the target region according to the first pixel value; performing smoothing filtering processing on the first radioactivity index, and performing standardization processing on the first radioactivity index after smoothing filtering processing to obtain the first radioactivity count rate of the target region; extracting the second pixel value of the background region, and calculating the second radioactivity index of the background region according to the second pixel value; performing smoothing filtering processing on the second radioactivity index, and performing standardization processing on the second radioactivity index after smoothing filtering processing to obtain the second radioactivity count rate of the background region.

[0033] In detail, through the image pixel extraction algorithm, all pixel values corresponding to the pixels in the kidney target region are collected point by point, and stored as a pixel value array; the average value of the array is calculated by using the arithmetic average method, and the average value is taken as the first radioactivity index of the target region; if there are a small number of abnormally high / low radioactive pixels in the region due to pathological factors (such as infarction), the average value is calculated after removing the abnormal values by using the median filtering, so as to ensure that the index can reflect the overall radioactivity intensity of the target region; the first radioactivity index is subjected to smoothing filtering processing, and the first radioactivity index after smoothing filtering processing is subjected to standardization processing to obtain the first radioactivity count rate of the target region; the smoothing filtering adopts mean filtering, and if it is dynamic acquisition, the filter window size is set to 5 consecutive time points, the first radioactivity index is subjected to sliding average processing to eliminate short-term noise fluctuation; when the standardization processing is performed, the average radioactivity index of the kidney target region of the preset healthy control group is obtained, and the formula δ1=Z1τ1×α is used, wherein δ1 is the standardized first radioactivity count rate, Z1 is the smoothed first radioactivity index, τ1 is the average radioactivity index, and α is the standard coefficient, and α is generally 100.

[0034] Specifically, all second pixel values in the background area (ipsilateral psoas major muscle) are collected by a pixel extraction algorithm to form a pixel value array, the average value of the array is calculated (if there is an abnormal value, it is removed first), and a second radioactivity index is obtained, which reflects the background radioactivity intensity of the background area and is used to offset the influence of the background on the target area count rate in the subsequent process. The second radioactivity index is subjected to smoothing filter processing to eliminate noise and individual differences, and an accurate count rate is obtained. The second radioactivity index after smoothing filter processing is subjected to standardization processing to obtain the second radioactivity count rate of the background area. The processing process is the same as that of the first radioactivity count rate: mean filter is used to eliminate noise, and the average radioactivity index of the background area of the healthy control group is used for standardization to obtain the second radioactivity count rate, so as to ensure the calculation standard of the background count rate and the target count rate is unified, and to provide accurate comparison data for subsequent kinetic parameter analysis.

[0035] Further, through standardized region identification and targeted count rate processing, the defects of subjective region identification and large count rate deviation are solved, the radioactivity count rate is ensured to be accurate, and the accuracy from region identification to count rate analysis is gradually realized, thereby providing reliable data for the radionuclide kinetic parameter analysis module.

[0036] The radionuclide kinetic parameter analysis module 103 is configured to analyze, based on the first radioactivity count rate and the second radioactivity count rate, an uptake index and a retention index of the radionuclide marker by using a kinetic model built in an imaging device.

[0037] In the embodiment of the present application, the uptake index is a quantitative index reflecting the uptake efficiency of the radionuclide marker by the renal sympathetic nerve ending, which is obtained by analyzing the early and delayed uptake ratio by using the kinetic model. The value of the uptake index is positively correlated with the sympathetic nerve activity, that is, the higher the uptake index, the stronger the ability of the sympathetic nerve to uptake the radionuclide marker, and the higher the nerve activity. The retention index is a quantitative index reflecting the retention time and amount of the radionuclide marker in the renal sympathetic nerve ending, which is also obtained by analyzing the kinetic model. The higher the retention index, the stronger the retention ability of the radionuclide marker in the nerve ending, and the higher the level of the sustained activity of the sympathetic nerve.

[0038] In the embodiment of the present application, when analyzing the uptake index and the retention index of the radionuclide marker based on the first radioactivity count rate and the second radioactivity count rate by using the kinetic model built in the imaging device, the radionuclide kinetic parameter analysis module 103 is specifically configured to: analyze an early uptake ratio of the radionuclide marker based on the first radioactivity count rate and the second radioactivity count rate, wherein the early uptake ratio is the ratio of the first radioactivity count rate to the second radioactivity count rate; acquire a target nuclide image data set at a preset delay time point, and analyze the delay uptake ratio of the radionuclide marker based on the target nuclide image data set; analyze the early uptake ratio and the delay uptake ratio through the kinetic model to obtain an uptake index and a retention index of the radionuclide marker.

[0039] In detail, the first radioactivity count rate of a target region of a kidney acquired at an early stage (such as 1 hour after injection of 123I-MIBG) and the second radioactivity count rate of a background region The early uptake ratio is calculated according to the formula wherein Q1 is the early uptake ratio, which preliminarily reflects the early uptake efficiency of the nuclide marker in the kidney. According to a preset delay time point (such as 3 hours after injection of 123I-MIBG), the nuclide image data of the kidney region is acquired by using the same scanning parameters (multi-angle, time window) as in the early stage, and the target nuclide image data set is obtained after reconstruction, noise suppression and spatial registration processing; then the first radioactivity count rate of the target region of the kidney at the delay stage and the second radioactivity count rate of the background region The delay uptake ratio is calculated according to the formula wherein Q2 is the delay uptake ratio, and the delay stage data is acquired to provide a basis for analyzing the retention characteristics of the nuclide marker.

[0040] Further, the kinetic model is a mathematical model constructed based on the physiological processes such as uptake, distribution, metabolism and clearance of the radionuclide marker in the kidney, and is used for quantitative analysis of kinetic parameters (such as uptake index and retention index) of the nuclide marker. The two-compartment kinetic model divides the kidney into a blood perfusion compartment and a nerve ending uptake compartment, which is more consistent with the actual metabolic process of the nuclide marker in the kidney.

[0041] In the embodiment of the present application, when the nuclide kinetic parameter analysis module 103 analyzes the early uptake ratio and the delay uptake ratio through the kinetic model to obtain the uptake index and the retention index of the radionuclide marker, it is specifically used for: constructing a two-compartment kinetic model according to preset blood perfusion rate constants and clearance rate constants; constructing a differential equation system of the uptake and clearance kinetic process of the radionuclide marker in the kidney region based on the two-compartment kinetic model; fitting the differential equation system to obtain optimized values of the blood perfusion rate constants and the clearance rate constants; According to the optimization value analysis of the standardized uptake value and the standardized retention rate of the radionuclide marker, the standardized uptake value and the standardized retention rate are determined as uptake index and retention index, respectively.

[0042] In detail, the two-compartment kinetic model is a mathematical model that simplifies the kidney into two interrelated compartments (blood perfusion compartment and nerve ending uptake compartment), in which the radionuclide marker first enters the blood perfusion compartment and then perfuses into the nerve ending uptake compartment through blood flow, and part of the radionuclide marker is removed from the two compartments. This model can accurately simulate the dynamic metabolism of the radionuclide marker in the kidney. The initial value of the blood perfusion rate constant (denoted as K1) is 0.2 min⁻¹, and the initial value of the removal rate constant (denoted as K2) is 0.1 min⁻¹. Based on the principle of the two-compartment model, a differential equation for the concentration change of the radionuclide marker is constructed: dC(t) / dt=K1×C_blood(t)-K2×C(t), where C(t) is the radionuclide concentration of the nerve ending uptake compartment at time t, and C_blood(t) is the radionuclide concentration of the blood perfusion compartment at time t, which can be converted through the early uptake ratio Q1, i.e., Cbloodt=Q1×ε, where ε is the background radionuclide concentration. By constructing a two-compartment model that fits the physiology of the kidney, the problem of simple existing device models that cannot accurately simulate radionuclide metabolism is solved.

[0043] Specifically, another differential equation of the two-compartment model describes the change of the concentration of the radionuclide in the blood perfusion compartment: dC blood(t) / dt=K0xC in(t)-(K1+K3)xC blood(t), where K0 is the rate constant of the radionuclide marker entering the blood perfusion compartment, which is set based on the injection dose and body weight, such as 0.05 min-1, C in(t) is the radionuclide concentration at the injection site at time t, which decays over time, in accordance with the half-life of the radionuclide, the half-life of 123I is about 13.2 hours, C int=C0xe-λt, C0 is the initial concentration, λ is the decay constant, and K3 is the rate constant of direct clearance of the radionuclide in the blood perfusion compartment (preset as 0.03 min-1); the equation is integrated with the previously constructed differential equation of the nerve terminal uptake compartment to form a complete differential equation system that comprehensively describes the entire process of the radionuclide marker from entering the blood to being taken up by the nerve terminal and then being cleared. The least squares method is used to fit the differential equation system, and the actual collected early and delayed stage radionuclide count rate data are used as the fitting target, the values of K1 and K2 are adjusted, so that the deviation (such as root mean square error) between the radionuclide concentration change curve calculated by the equation and the actual count rate change curve is minimized; for example, when the initial K1=0.2 min-1 and K2=0.1 min-1, the calculated delayed radionuclide concentration corresponds to a count rate of 100, which has a large deviation from the actual K3=100. Through iterative adjustment, when K1=0.25 min-1 and K2=0.08 min-1 are finally determined, the calculated delayed count rate is 118, which has the smallest deviation from the actual value. At this time, K1 and K2 are the optimized values.

[0044] Further, the standardized uptake value (i.e., uptake index) is calculated, and the formula is: S=a x v / nm, where S is the standardized uptake value, a is the radionuclide concentration of the target region, v is the volume of the target region, n is the total dose of injected radionuclide, and m is the body weight of the target object, where the radionuclide concentration of the target region can be calculated by the optimized K1 (the larger K1 is, the faster the radionuclide enters the nerve terminal, and the higher the concentration is); the standardized retention rate (i.e., retention index) is calculated, and the formula is: R=H1H2x 100%, where R is the standardized retention rate, H1 is the radionuclide concentration of the target region in the delay stage, and H2 is the radionuclide concentration of the target region in the early stage. Through standardized calculation, the index has more clinical reference value.

[0045] Further, through the construction and parameter optimization of the two-compartment model, the uptake and retention indexes are accurately calculated, the model parameters are converted into intuitive nerve activity related indexes, the accurate conversion from data to quantitative parameters is realized, and the problems of simple model and low parameter calculation accuracy of existing devices are solved.

[0046] The nerve activity quantification module 104 is configured to analyze the sympathetic nerve activity quantification index of the kidney region according to the uptake index, the retention index, and pre-received electrocardiophysiological data of the target object.

[0047] In the embodiment of the present application, the sympathetic nerve activity quantification index is a value reflecting the initial activity level of the sympathetic nerve in the kidney region, which is obtained by linear calculation based on the uptake index and the retention index.

[0048] In the embodiment of the present application, when the nerve activity quantification module 104 analyzes the sympathetic nerve activity quantification index of the kidney region according to the uptake index, the retention index, and pre-received electrocardiophysiological data of the target object, it is specifically configured to: linearly calculate the initial sympathetic nerve activity value of the kidney region based on the uptake index and the retention index; weight and correct the initial sympathetic nerve activity value according to the atrial fibrillation induction rate in the electrocardiophysiological data, and take the corrected initial sympathetic nerve activity value as the sympathetic nerve activity quantification index of the kidney region.

[0049] In detail, the linear calculation formula is Y0=α×S+β×R, wherein Y0 is the initial sympathetic nerve activity value, and α and β are weight coefficients (determined based on clinical data statistics, α=0.6 and β=0.4, because the uptake index reflects the nerve activity more directly, a higher weight is given), which comprehensively reflects the uptake and retention characteristics of the nerve activity by linearly combining the two core indexes, solving the one-sidedness problem of relying on a single index for evaluation.

[0050] Specifically, the corresponding relationship between the atrial fibrillation induction rate and the weight coefficient is set (for example, when the atrial fibrillation induction rate is ≤20%, the weight coefficient is 1.0; when 20%< the induction rate ≤50%, the weight coefficient is 1.2; and when the induction rate >50%, the weight coefficient is 1.5); the electrocardiophysiological data of the target object is obtained, and the atrial fibrillation induction rate is calculated (for example, the induction rate is 60%, and the corresponding weight coefficient is 1.5); and then the correction formula Y1=Y0×θ is used to weight and correct the initial sympathetic nerve activity value, wherein Y1 is the sympathetic nerve activity quantification index, and θ is the weight coefficient, so that the quantification index is more in line with the actual situation of atrial fibrillation of the target object by weighting and correcting the initial sympathetic nerve activity value in association with the atrial fibrillation induction rate.

[0051] Further, the initial sympathetic nerve activity value is calculated first, which is based on the basic activity level obtained from the radionuclide kinetics parameters; and then the weight correction is combined with the atrial fibrillation induction rate, which adjusts the basic value according to the pathological state of the heart of the target object, so that the final sympathetic nerve activity quantification index is more accurate and has more clinical significance, providing a core evaluation index for the dynamic evaluation and decision-making module.

[0052] The dynamic evaluation and decision module 105 is configured to determine a dynamic threshold of the sympathetic nerve activity quantification index based on the activity expression level index of the target object, and dynamically analyze the sympathetic nerve activity quantification index according to the dynamic threshold, so as to obtain an evaluation index of the renal sympathetic nerve nuclide, and output the evaluation index to a display interface of an imaging device.

[0053] In the embodiment of the present application, the dynamic threshold is a personalized threshold for judging whether the sympathetic nerve activity quantification index is normal, which is determined based on the activity expression level index of the target object, rather than a fixed universal threshold. The value of the dynamic threshold is adjusted according to the change of the protein expression level of the individual, so as to ensure the personalization and accuracy of the evaluation.

[0054] In the embodiment of the present application, when the dynamic evaluation and decision module 105 determines the dynamic threshold of the sympathetic nerve activity quantification index based on the activity expression level index of the target object, the dynamic evaluation and decision module 105 is specifically configured to: detect the protein expression level of tyrosine hydroxylase in the renal tissue sample of the target object, and determine the relative gray value of tyrosine hydroxylase based on the protein expression level; construct a baseline reference range based on the statistical distribution of the expression level of tyrosine hydroxylase of a preset healthy control group; analyze a personalized adjustment coefficient of the sympathetic nerve activity quantification index according to the degree of value deviation of the relative gray value from the baseline reference range; determine the dynamic threshold of the sympathetic nerve activity quantification index according to the upper limit value of the baseline reference range and the personalized adjustment coefficient.

[0055] In detail, the protein expression level refers to the content of tyrosine hydroxylase protein in the renal tissue sample, which is quantitatively represented by the gray value of the protein band. The higher the gray value, the higher the protein expression level. Western-blot technology is used for detection. First, total protein in the renal tissue sample is extracted, and the protein is separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to a polyvinylidene fluoride (PVDF) membrane. The specific primary antibody (anti-tyrosine hydroxylase antibody) is incubated with the PVDF membrane, and then the fluorescently labeled secondary antibody is incubated. Finally, the protein band image is obtained by a chemiluminescence imaging system. The gray value of the tyrosine hydroxylase protein band (denoted as G1) and the gray value of the internal reference protein GAPDH band (denoted as G2) are measured by image analysis software (such as ImageJ), and the relative gray value is calculated as G1 / G2. Through standardized detection and calculation, accurate protein expression quantification data is obtained to provide individual basis for the determination of the dynamic threshold.

[0056] The healthy control group is a pre-selected healthy population without atrial fibrillation and abnormal renal sympathetic nerve function (usually 50-100 cases, age and gender matched with the target object group), which is used to provide a normal reference range of tyrosine hydroxylase expression level, and 60 healthy people (age 40-60 years old, matched with the target object group) are selected as the healthy control group, and the relative gray value of tyrosine hydroxylase of each case is calculated according to the Western-blot detection method; the relative gray values are statistically analyzed to calculate the mean (denoted as μ) and the standard deviation (denoted as σ); according to the formula of the reference range μ±2σ, the reference range is calculated, and through the statistics of a large sample of healthy people, a reference standard of normal protein expression is constructed, which provides a basis for judging the deviation degree of the individual.

[0057] Specifically, the bit value deviation index P is calculated, that is, P=A-B / σ, wherein A is the relative gray value of the target object, and B is the median of the reference range; the corresponding relationship between the deviation index and the individual adjustment coefficient (denoted as L) is set: when P≤-1.0, L=0.8; when -1.0

[0058] In the embodiment of the present application, the evaluation index is a comprehensive index containing the renal sympathetic nerve activity state (level) and the clinical treatment suggestion generated based on the activity level and the preset clinical decision rule, which is the core clinical reference information of the device output and is directly used to guide the doctor to formulate the treatment scheme.

[0059] In the embodiment of the present application, when the dynamic evaluation and decision module 105 dynamically analyzes the sympathetic nerve activity quantization index according to the dynamic threshold to obtain the evaluation index of the renal sympathetic nerve nuclide, it is specifically used for: determining the activity deviation of the renal sympathetic nerve nuclide according to the difference between the sympathetic nerve activity quantization index and the dynamic threshold; performing multi-parameter decision weighted correction on the activity deviation according to the atrial fibrillation induction rate data of the target object, and dividing the activity level of the renal sympathetic nerve based on the numerical range of the corrected activity deviation; Evaluation indicators for renal sympathetic nuclides are generated based on the mapping relationship between the activity level and preset clinical decision rules.

[0060] In detail, the sympathetic nerve activity quantification index is obtained. The activity deviation is the difference between the sympathetic nerve activity quantification index and the dynamic threshold. This difference initially reflects the relationship between activity and individual thresholds, providing a basis for subsequent grading. The atrial fibrillation induction rate of the target subjects is obtained, and a corresponding correction coefficient (consistent with the weighting coefficient) is determined based on the atrial fibrillation induction rate. The activity deviation is multiplied by the correction coefficient to obtain the corrected activity deviation. The activity grading criteria are as follows: a corrected deviation less than or equal to zero indicates normal activity; a corrected deviation greater than zero or less than or equal to 2 indicates mild overactivation; and a corrected deviation greater than 2 indicates severe overactivation. Thus, by combining the atrial fibrillation induction rate with correction and grading, the activity assessment becomes more relevant to the actual clinical situation.

[0061] Specifically, the clinical decision-making rules are as follows: Normal activity → Renal sympathetic nerve activity is normal, no special intervention is required, and it is recommended to repeat radionuclide imaging and electrophysiological data every 6 months to monitor changes in the actual situation of atrial fibrillation; Mild overactivation → Mild overactivation of the renal sympathetic nerves, combined with the actual situation of atrial fibrillation, it is recommended to give priority to the use of receptor blockers for drug intervention, monitor blood pressure and heart rate monthly, and repeat radionuclide imaging every 3 months; Severe overactivation → Severe overactivation of the renal sympathetic nerves is a high-risk factor for atrial fibrillation, and it is recommended to perform renal denervation. Radionuclide imaging and electrophysiological data should be repeated at 1 month, 3 months and 6 months after surgery to evaluate the surgical effect. Finally, the evaluation indicators are output to the display interface of the imaging equipment, allowing doctors to view and export them. This step generates evaluation indicators through standardized decision-making rules, providing clear and actionable suggestions for clinical treatment, and solving the deficiency of existing equipment that only outputs data and lacks decision-making guidance.

[0062] Furthermore, by establishing dynamic thresholds based on individual protein expression levels, adjusting the activity deviation in conjunction with actual conditions, and generating clinical recommendations, the shortcomings of fixed thresholds and the disconnect between assessment and decision-making are resolved. Overall, this achieves accurate and personalized assessment of renal sympathetic nerve activity, providing reliable clinical evidence for atrial fibrillation treatment (such as renal denervation) and promoting the standardized application of autonomic nerve function assessment in atrial fibrillation treatment.

[0063] Reference Figure 2 The diagram shown is a flowchart illustrating the operation method of a renal sympathetic neuron radionuclide assessment system for RDN according to an embodiment of the present invention. In this embodiment, the operation method of the renal sympathetic neuron radionuclide assessment system for RDN includes: S1. Acquire a dataset of radionuclide images of the kidney region of the target object using a radionuclide detection device with preset radionuclide markers. S2, identifying a target region and a background region of the kidney in the radionuclide image data set, and analyzing first and second radioactivity count rates of the target region and the background region, respectively; S3, based on the first and second radioactivity count rates, analyzing an uptake index and a retention index of the radionuclide marker by a kinetic model built in the imaging device; S4, analyzing a sympathetic nerve activity quantification index of the kidney region according to the uptake index, the retention index, and pre-received electrocardiophysiological data of the target object; S5, determining a dynamic threshold of the sympathetic nerve activity quantification index based on an activity expression level index of the target object, and dynamically analyzing the sympathetic nerve activity quantification index according to the dynamic threshold to obtain an evaluation index of the renal sympathetic nerve radionuclide, and outputting the evaluation index to a display interface of the imaging device.

[0064] In several embodiments provided in the present application, it should be understood that the disclosed system and method can be implemented in other manners. For example, the above-described system embodiments are merely illustrative, and the division of the modules is merely a logical function division, and there can be another division manner in actual implementation.

[0065] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0066] In addition, each function module in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware, or in the form of hardware plus software function modules.

[0067] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0068] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is not limited only by the above description, and therefore all changes within the meaning and scope of equivalent elements falling within the scope of protection are intended to be included in the present application.

[0069] Furthermore, the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural and vice-versa, unless the context clearly requires these exclusions. The conjunction "or" is used to link items in a list or a set of alternatives, and is not disjunctive, unless the context clearly requires it to be disjunctive. The conjunction "and" is used to link items in a list or a set of alternatives, and is not conjunctive, unless the context clearly requires it to be conjunctive. The prefix "first", "second", "third", etc. is used to identify similar items and does not require or imply any actual temporal or chronological order among the items, unless the context clearly requires it.

[0070] Finally, it should be noted that the above-mentioned embodiments illustrate rather than limit the application, since the scope of the application is determined by the appended claims.

Claims

1. A renal sympathetic neuron radionuclide assessment system for RDN, characterized in that, The system is integrated into a nuclear medicine imaging device and includes: The radionuclide image acquisition module is used to acquire a dataset of radionuclide images of the kidney region of a target object using a radiodetector. The data analysis module is signal-connected to the radionuclide image acquisition module and is used to identify the target area and background area of ​​the kidney in the radionuclide image dataset, and to analyze the first radioactivity count rate and the second radioactivity count rate of the target area and the background area respectively. The radionuclide kinetic parameter analysis module is used to analyze the uptake index and retention index of the radionuclide marker based on the first radionuclide count rate and the second radionuclide count rate using the kinetic model built into the imaging device. A neural activity quantification module is used to analyze the sympathetic neural activity quantification index of the kidney region based on the uptake index, the retention index, and pre-received electrophysiological data of the target subject. The dynamic evaluation and decision-making module is used to determine the dynamic threshold of the sympathetic nerve activity quantification index based on the activity expression level index of the target object, and to perform dynamic analysis on the sympathetic nerve activity quantification index according to the dynamic threshold to obtain the evaluation index of the renal sympathetic nerve nuclide, and output the evaluation index to the display interface of the imaging device.

2. The renal sympathetic neuron radionuclide assessment system for RDN as described in claim 1, characterized in that, The radionuclide image acquisition module, when acquiring a radionuclide image dataset of a preset radionuclide marker in the kidney region of a target object using a radiodetector, is specifically used for: Within a preset time window, the radioactive detector in the imaging device is controlled to perform multi-angle scanning of the radionuclide markers in the kidney region of the target object to obtain multi-angle X-ray data. A three-dimensional nuclide distribution image of the kidney region was reconstructed based on the multi-angle X-ray data; The three-dimensional nuclide distribution image is subjected to noise suppression and spatial registration processing to obtain a nuclide image dataset of the kidney region.

3. The renal sympathetic neuron radionuclide assessment system for RDN as described in claim 1, characterized in that, When identifying the target area and background area of ​​the kidney in the radionuclide image dataset, the data analysis module is specifically used for: The outline of the kidney is delineated based on anatomical landmarks in the radionuclide image dataset, and the initial target region of the kidney is determined based on the outline. A homogeneous tissue region is selected as the initial background region at the outer boundary of the initial target region, wherein the homogeneous tissue region is the ipsilateral psoas major muscle region. Morphological operations are performed on the initial target region and the initial background region, and the results are verified to show whether the initial target region and the initial background region after morphological operations meet the preset anatomical standards. When anatomical criteria are met, the regional consistency between the initial target region and the initial background region is analyzed, and the target region and background region of the kidney are determined based on the regional consistency.

4. The renal sympathetic neuron radionuclide assessment system for RDN as described in claim 1, characterized in that, When analyzing the first and second radioactivity count rates of the target region and the background region, respectively, the data analysis module is specifically used for: Extract the first pixel value of the target region, and calculate the first radioactivity index of the target region based on the first pixel value; The first radioactivity index is smoothed and filtered, and the smoothed and filtered first radioactivity index is standardized to obtain the first radioactivity count rate of the target area. Extract the second pixel value of the background region, and calculate the second radioactivity index of the background region based on the second pixel value; The second radioactivity index is smoothed and filtered, and then the smoothed and filtered second radioactivity index is standardized to obtain the second radioactivity count rate in the background region.

5. The renal sympathetic neuron radionuclide assessment system for RDN as described in claim 1, characterized in that, The radionuclide kinetic parameter analysis module, when analyzing the uptake and retention indices of the radionuclide markers based on the first and second radionuclide count rates using the kinetic model built into the imaging device, is specifically used for: The early uptake ratio of the radionuclide marker is analyzed based on the first radionuclide count rate and the second radionuclide count rate, wherein the early uptake ratio is the ratio of the first radionuclide count rate to the second radionuclide count rate. A target nuclide image dataset is acquired at a preset delay time point, and the delayed uptake ratio of the radionuclide marker is analyzed based on the target nuclide image dataset. The early uptake ratio and the delayed uptake ratio were analyzed using the kinetic model to obtain the uptake index and retention index of the radionuclide marker.

6. The renal sympathetic neuron radionuclide assessment system for RDN as described in claim 5, characterized in that, The radionuclide kinetic parameter analysis module, when analyzing the early uptake ratio and the delayed uptake ratio through the kinetic model to obtain the uptake index and retention index of the radionuclide marker, is specifically used for: A two-compartment dynamic model was constructed based on preset blood perfusion rate constants and clearance rate constants; Based on the aforementioned two-compartment kinetic model, a differential equation system was constructed to determine the uptake and clearance kinetics of the radionuclide marker in the renal region. The differential equation system is fitted based on the early uptake ratio and the delayed uptake ratio to obtain the optimized values ​​of the blood perfusion rate constant and the clearance rate constant; Based on the optimized values, the standardized uptake value and standardized retention rate of the radionuclide marker are analyzed, and the standardized uptake value and the standardized retention rate are determined as the uptake index and the retention index, respectively.

7. The renal sympathetic neuron radionuclide assessment system for RDN as described in claim 1, characterized in that, When the neural activity quantification module analyzes the sympathetic neural activity quantification index of the kidney region based on the uptake index, the retention index, and pre-received electrophysiological data of the target subject, it is specifically used for: The initial sympathetic nerve activity value of the kidney region is calculated linearly based on the uptake index and the retention index. The initial sympathetic activity value is weighted and corrected based on the atrial fibrillation induction rate in the electrophysiological data, and the corrected initial sympathetic activity value is used as the quantitative index of sympathetic activity in the kidney region.

8. The renal sympathetic neuron radionuclide assessment system for RDN as described in claim 1, characterized in that, The dynamic evaluation and decision-making module, when determining the dynamic threshold of the sympathetic nerve activity quantification index based on the target object's activity expression level index, is specifically used for: The protein expression level of tyrosine hydroxylase in the kidney tissue sample of the target object was detected, and the relative gray value of tyrosine hydroxylase was determined based on the protein expression level. A benchmark reference range was constructed based on the statistical distribution of tyrosine hydroxylase expression levels in a pre-defined healthy control group. The personalized adjustment coefficient of the sympathetic nerve activity quantification index is analyzed based on the degree of deviation between the relative gray value and the bit value of the reference range. The dynamic threshold of the sympathetic nerve activity quantification index is determined based on the upper limit of the benchmark reference range and the personalized adjustment coefficient.

9. The renal sympathetic neuron radionuclide assessment system for RDN as described in claim 1, characterized in that, When the dynamic assessment and decision-making module performs dynamic analysis on the sympathetic nerve activity quantification index based on the dynamic threshold to obtain the assessment index of renal sympathetic nerve nuclei, it is specifically used for: The activity deviation of renal sympathetic nuclide is determined based on the difference between the sympathetic activity quantification index and the dynamic threshold. The activity deviation is corrected by multi-parameter decision weighting based on the atrial fibrillation induction rate data of the target subjects, and the activity level of the renal sympathetic nerve is divided based on the numerical range of the corrected activity deviation. Evaluation indicators for renal sympathetic nuclides are generated based on the mapping relationship between the activity level and preset clinical decision rules.

10. A method for operating a renal sympathetic neuron radionuclide assessment system for RDN, characterized in that, For implementing the renal sympathetic neuron radionuclide assessment system for RDN as described in any one of claims 1-9, the method comprises: A dataset of radionuclide images of the kidney region of a target subject was acquired using a radiodetection device with preset radionuclide markers. Identify the target region and background region of the kidney in the radionuclide image dataset, and analyze the first radioactivity count rate and the second radioactivity count rate of the target region and the background region respectively; Based on the first radioactivity count rate and the second radioactivity count rate, the uptake index and retention index of the radionuclide marker are analyzed by the kinetic model built into the imaging device. The sympathetic nerve activity quantification index of the renal region is analyzed based on the uptake index, the retention index, and the pre-received electrophysiological data of the target subjects. The dynamic threshold of the sympathetic nerve activity quantification index is determined based on the activity expression level index of the target object, and the sympathetic nerve activity quantification index is dynamically analyzed according to the dynamic threshold to obtain the evaluation index of renal sympathetic nerve nuclide, and the evaluation index is output to the display interface of the imaging device.

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