A quantitative calculation method for intravascular distribution regularity of embolization microspheres based on in-vitro simulation test device

Abstract

This invention provides a quantitative calculation method for the intravascular distribution of embolic microspheres based on an in vitro simulation testing device, belonging to the field of medical device testing technology. The method includes the following steps: Based on the Glisson system's liver segmentation method and pre-acquired physiological data of the target human hepatic artery, a vascular model is constructed and optimized to reflect the hemodynamic characteristics of the real human hepatic artery; based on the optimized vascular model, an in vitro simulation testing device is constructed; based on the in vitro simulation testing device, liver simulation tests are performed under different microsphere characteristics and injection conditions, and microsphere state data for each liver segment are collected; based on the microsphere state data for each liver segment, the distribution of microspheres in the blood vessels of each liver segment is quantitatively predicted by fitting the correlation between microsphere characteristics, injection conditions, and microsphere distribution data, thus obtaining the intravascular distribution data of embolic microspheres. This invention provides methodological guidance for the clinical application of interventional embolization microspheres.

Description

Technical Field

[0001] This invention relates to the field of medical device testing technology, and more specifically, to a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device. Background Technology

[0002] Transarterial chemoembolization (TACE) is an interventional procedure in which an embolic agent is injected into a target blood vessel through an intra-arterial catheter under the guidance of medical imaging equipment. This causes occlusion and interrupts blood supply, aiming to control bleeding and treat tumors and vascular lesions. It is currently the mainstream clinical treatment for inoperable hepatocellular carcinomas. Embolization microspheres are the main embolic agent recommended in the "Chinese Clinical Practice Guidelines for Transarterial Chemoembolization (TACE) Therapy for Hepatocellular Carcinoma".

[0003] In interventional embolization procedures, the selection of embolization microspheres and injection conditions directly affect the effectiveness and safety of clinical treatment. Improper injection of embolization microspheres may fail to block the blood supply to the tumor at the lesion site, leading to surgical failure or ectopic embolization, causing complications such as ischemic necrosis of healthy liver tissue. Currently, in clinical practice, the selection of microsphere parameters and injection conditions largely rely on the operator's experience and judgment, lacking quantified clinical standards and norms, making it difficult to precisely control the spatial distribution of microspheres in the target area's blood vessels.

[0004] To further standardize the use of embolic microspheres, several studies have disclosed in vitro testing devices for embolic therapy or methods for evaluating the distribution of embolic agents, for example:

[0005] Chinese patent application CN115112560A discloses an in vitro embolization agent simulation device and its usage method. However, the device is relatively simple, does not use a real blood vessel model, and differs significantly from real blood vessels, making it unsuitable for studying the distribution patterns of microspheres in blood vessels.

[0006] Chinese patent application CN112562474A discloses an extracorporeal simulation device for vascular interventional embolization therapy. This device has a simple structure and clear connections between modules. The organ artery module allows modification of vascular model parameters (such as branch angles or diameter) to suit specific conditions, offering strong flexibility and practicality. However, it suffers from the following problems: 1) When the device is divided into upper and lower layers, liquid may leak from the upper module gaps to the lower layer due to gravity, affecting device stability; 2) The vascular model in the device is made of transparent rigid plastic, which cannot fully simulate the elasticity of real blood vessels. Thinner branches or sharp angles can affect the movement and passage of microspheres. Furthermore, the vascular model is a static device and cannot simulate the dynamic contraction process of real blood vessels, resulting in inaccurate data collection; 3) The device operates at room temperature or ambient temperature, lacking temperature control components, and cannot simulate real blood temperature. Temperature affects blood viscosity, which in turn affects the flow resistance and distribution of microspheres; 4) The device lacks a microsphere collection module, allowing only qualitative observation of microsphere distribution through the transparent shell, preventing quantitative analysis.

[0007] Chinese patent application CN117304539A discloses a vascular model, its preparation method, and an in vitro simulation testing device for embolic microspheres. This device is relatively complete, using a realistic vascular model capable of simulating the geometry and physical properties of human blood vessels. It also utilizes a pressure-sensing platform to detect microsphere displacement distance and injection force. However, this work focuses on studying the permeability of various microspheres in blood vessels, resulting in relatively limited data, insufficient for mathematical relationship analysis, and failing to develop a predictive methodology.

[0008] Chinese patent application CN118392757A discloses a method for evaluating the distribution of embolic microspheres in animal models. This method mainly involves administering the drug via hepatic artery perfusion through interventional surgery, and then evaluating the distribution of the embolic microspheres in the animal models after administration. However, it has the following problems: 1) Animal vascular models differ significantly from human vascular models, and the conclusions obtained cannot be directly applied to human experiments; 2) It only uses a counting method, which is a single detection method and the results are prone to large deviations.

[0009] In summary, although some studies have explored the distribution of interventional embolization microspheres, the following problems generally exist: 1) Device level: The lack of a real vascular model or the absence of certain modules prevents a full simulation of the real human body, resulting in incomplete and inaccurate data collection; 2) Methodological level: Due to device limitations, the monitored indicators are too intuitive and singular, making it difficult to form a comprehensive, accurate, and universally applicable quantitative analysis logic for microsphere distribution. This leads to most studies remaining at the qualitative level, failing to meet the clinical need for precise quantification of microsphere distribution patterns.

[0010] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0011] In view of this, the present invention provides a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device, in order to solve the aforementioned problems.

[0012] To solve the above problems, the specific technical solution adopted by the present invention is as follows:

[0013] This invention provides a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device, comprising the following steps:

[0014] S1. Based on the Glisson system liver segmentation method and pre-acquired physiological data of the target human hepatic artery, a vascular model is constructed and optimized to target the hemodynamic characteristics of the real human hepatic artery; based on the optimized vascular model, an in vitro simulation test device is constructed.

[0015] S2. Based on the in vitro simulation testing device, liver simulation tests were conducted under different microsphere characteristics and injection conditions, and microsphere status data of each liver segment were collected.

[0016] S3. Based on the microsphere status data of each liver segment, the distribution of microspheres in the blood vessels of each liver segment is quantitatively predicted by fitting the correlation between microsphere characteristics, injection conditions and microsphere distribution data, thus obtaining the intravascular distribution data of embolized microspheres.

[0017] S4. Determine the basic threshold range using an in vitro simulation testing device;

[0018] S5. Based on preoperative ultrasound images, obtain the volume of the target liver segment in the target human body, calculate the ratio of the target liver segment volume to the standard liver segment volume, and set the liver size correction coefficient.

[0019] S6. Assess the liver function status of the target human body based on the Child-Pugh classification and set the liver status correction coefficient;

[0020] S7. Based on the liver size correction coefficient and the liver state correction coefficient, calculate the dynamic threshold range and compare the distribution ratio of microspheres in each liver segment with the dynamic threshold range. If the distribution ratio of microspheres in each liver segment is within the threshold range, perform embolization surgery on the target human body according to the set microsphere parameters and injection parameters. Otherwise, adjust at least one parameter among microsphere density, microsphere particle size, injection speed or injection location until the distribution ratio of microspheres in each liver segment is within the threshold range.

[0021] Preferably, based on the Glisson system's liver segmentation method and pre-acquired physiological data of the target human hepatic artery, a vascular model is constructed and optimized to target the hemodynamic characteristics of the real human hepatic artery; based on the optimized vascular model, an in vitro simulation testing device is constructed, including the following steps:

[0022] S11. Using the Glisson system for liver segmentation, the liver is divided into five lobes and eight segments. By extracting the vascular model of the hepatic artery, the vascular model of the hepatic artery is made using 3D printing technology to obtain the initial vascular model.

[0023] S12. The preset shape memory alloy wire and pressure sensor are configured on the initial blood vessel model to detect the initial dynamic parameters of the initial blood vessel model.

[0024] S13. Collect the physiological data of the hepatic artery of the target human body, compare it with the initial dynamic parameters, and based on the comparison results, adjust the initial dynamic parameters of the initial vascular model with the hemodynamic characteristics of the real human hepatic artery as the target, and obtain the optimized vascular model.

[0025] S14. Based on the optimized blood vessel model, construct an in vitro simulation testing device.

[0026] Preferably, configuring a pre-set shape memory alloy wire and a pressure sensor on the initial vascular model to detect the initial dynamic parameters of the initial vascular model includes the following steps:

[0027] S121. Embed the preset shape memory alloy wire into the inner layer of the blood vessel wall in the initial blood vessel model, and connect the preset shape memory alloy wire to the pulse current controller.

[0028] S122. A pressure sensor is placed on the outside of the initial vascular model, and a periodic pulse current is passed through the shape memory alloy wire using a pulse current controller so that the initial vascular model performs periodic movements during systole and diastole.

[0029] S123. Based on the pressure sensor and pulse current controller, obtain the dynamic parameters of the initial blood vessel model during the cyclical movement of systole and diastole, including the current intensity of the shape memory alloy, the pulse period, the peak pressure during systole, and the minimum pressure during diastole.

[0030] Preferably, the process involves collecting hepatic artery physiological data from the target human body, comparing it with initial dynamic parameters, and adjusting the initial dynamic parameters of the initial vascular model based on the comparison results and the hemodynamic characteristics of the real human hepatic artery to obtain an optimized vascular model, including the following steps:

[0031] S131. Collect hepatic artery physiological data of the target human body and determine the baseline values ​​of the hepatic artery physiological data of the target human body, including the baseline value of the human body during systole, the baseline value of the human body during diastole, and the pulse pressure difference of the human body.

[0032] S132. Compare the initial dynamic parameters with the baseline values ​​of the target human hepatic artery physiological data to obtain the comparison results;

[0033] S133. Based on the comparison results, and following the preset adjustment strategy, the initial dynamic parameters of the initial vascular model are adjusted and optimized with the hemodynamic characteristics of the real human hepatic artery as the target, to obtain the optimized vascular model.

[0034] The preset adjustment strategies include:

[0035] If the peak pressure during systole is greater than the baseline value for systole in the human body, it indicates excessive vasoconstriction, and the current intensity should be reduced.

[0036] If the minimum diastolic pressure is less than the human body's diastolic baseline value, it indicates that the blood vessels are not dilating sufficiently. In this case, the current pulse interval should be adjusted or the diastolic current intensity should be reduced.

[0037] If the difference between the peak systolic pressure minus the minimum diastolic pressure and the human pulse pressure exceeds a preset threshold, the current intensity or pulse interval during systole and diastole will be adjusted synchronously to correct the pressure variation within the cycle.

[0038] Preferably, the in vitro simulation testing device includes:

[0039] The simulated blood storage and control unit is used to store simulated human blood and regulate the temperature of the simulated blood.

[0040] The cardiac pulse simulation drive unit is used to simulate cardiac pulses;

[0041] Microsphere delivery control unit, used to simulate the microsphere injection process;

[0042] The liver segment vascular simulation unit is used to simulate the human hepatic artery based on an optimized vascular model.

[0043] The microsphere collection unit is used to collect microspheres from each liver segment in the optimized vascular model.

[0044] Preferably, the liver simulation test under different microsphere properties and injection conditions, based on an in vitro simulation testing device, and the collection of microsphere status data for each liver segment, includes the following steps:

[0045] S21. Select the microspheres to be simulated and measure the characteristic parameters of the microspheres. According to the preset injection conditions and injection positions, inject the microspheres to be simulated into the in vitro simulation test device.

[0046] S22. Simulate the human liver artery using an in vitro simulation testing device, and obtain blood flow viscosity, blood flow velocity, and blood vessel diameter of the vascular model.

[0047] S23. Based on blood viscosity, blood velocity, and the diameter of the blood vessel in the vascular model, calculate the blood shear force that affects the trajectory of the microspheres.

[0048] S24. Integrate the microsphere's characteristic parameters, injection conditions, injection location, and blood flow shear force into microsphere state data.

[0049] Preferably, based on the microsphere status data of each liver segment, the distribution of microspheres in the blood vessels of each liver segment is quantitatively predicted by fitting the correlation between microsphere characteristics, injection conditions, and microsphere distribution data, and the embolization microsphere intravascular distribution data includes the following steps:

[0050] S31. Obtain the angle of blood vessel branches and the horizontal distance between the target blood vessel and the central axis of the blood vessel model. Combine this with the blood vessel diameter of the blood vessel model to calculate the correction coefficient of blood vessel anatomy parameters.

[0051] S32. Based on the microsphere status data and vascular anatomy parameter correction coefficients of each liver segment, the microsphere distribution coefficient calculation model is fitted to obtain the microsphere distribution ratio calculation model, and the correlation between microsphere characteristics, injection conditions and microsphere distribution data is determined according to the microsphere distribution ratio calculation model.

[0052] S33. Based on the correlation between microsphere characteristics, injection conditions, and microsphere distribution data, the distribution of microspheres in blood vessels of each liver segment is quantitatively predicted to obtain intravascular distribution data of embolized microspheres.

[0053] Preferably, the formula for calculating the correction coefficient for vascular anatomy parameters is as follows:

[0054] ;

[0055] In the formula, k represents the correction coefficient for vascular anatomy parameters, and k0 and γ are both correction factor constants, k0=0.2, γ=0.2, D v θ represents the diameter of the blood vessel, e represents the angle of the blood vessel branching, e represents the Euler number, i.e. the natural constant, which is usually taken as 2.72, and s represents the horizontal distance of the axis in the calculation model of the distribution ratio of the target blood vessel and the microsphere, in mm.

[0056] The preferred formula for calculating the distribution ratio of microspheres is:

[0057] ;

[0058] In the formula, P represents the distribution ratio of microspheres in each liver segment, k represents the correction coefficient for vascular anatomy parameters, V represents the injection speed, L represents the injection location, d represents the microsphere diameter, ρ represents the microsphere density, and τ represents the blood flow shear force.

[0059] The beneficial effects of this invention are as follows:

[0060] 1. This invention can simulate the embolization interventional procedure. By changing the characteristics and injection conditions of the injected microspheres, it collects the distribution data of microspheres at the outlet of blood vessels in each liver segment and establishes a correlation formula, thereby achieving the goal of accurately predicting the distribution data of microspheres with different characteristics in blood vessels at all levels in the liver segment, and providing methodological guidance for the clinical application of interventional embolization microspheres.

[0061] 2. This invention obtains microsphere distribution data at the outlet of blood vessels in each hepatic segment under different conditions by changing experimental conditions such as the characteristics of embolized microspheres (e.g., particle size and density) and injection conditions (e.g., speed and location). Statistical analysis is performed on various data such as microsphere characteristics, injection conditions, and microsphere distribution data to construct correlation formulas and quantitatively predict the distribution data of microspheres in blood vessels of each hepatic segment, thereby improving the accuracy and safety of embolization treatment and reducing the risk of damage to normal tissues. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0063] Figure 1 This is one of the flowcharts of a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device according to an embodiment of the present invention;

[0064] Figure 2 This is the second flowchart of a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device according to an embodiment of the present invention;

[0065] Figure 3 This is an in vitro experimental diagram of a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device according to an embodiment of the present invention.

[0066] Figure 4 This is a vascular model diagram in a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device according to an embodiment of the present invention.

[0067] Figure 5 This is a flowchart of dynamic threshold calculation in a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device according to an embodiment of the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0069] According to an embodiment of the present invention, a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device is provided.

[0070] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1-2 As shown in the embodiments of the present invention, a method for quantitatively calculating the intravascular distribution pattern of embolic microspheres based on an in vitro simulation testing device is provided, comprising the following steps:

[0071] S1. Based on the Glisson system liver segmentation method and pre-acquired physiological data of the target human hepatic artery, a vascular model is constructed and optimized to target the hemodynamic characteristics of the real human hepatic artery; based on the optimized vascular model, an in vitro simulation test device is constructed.

[0072] As a preferred implementation, based on the Glisson system's liver segmentation method and pre-acquired hepatic artery physiological data of the target human body, a vascular model is constructed and optimized to target the hemodynamic characteristics of the real human hepatic artery; based on the optimized vascular model, an in vitro simulation testing device is constructed, including the following steps:

[0073] S11. Using the Glisson system for liver segmentation, the liver is divided into five lobes and eight segments. By extracting the vascular model of the hepatic artery, the vascular model of the hepatic artery is made using 3D printing technology to obtain the initial vascular model.

[0074] It should be noted that, to determine the vascular model for interventional embolization surgery, taking the hepatic artery, which supplies blood and nutrients to the tumor, as an example, the Glisson system for liver segmentation is used. This allows doctors to accurately plan the puncture path and treatment area based on the location of the lesion in the liver segment, reducing surgical complications. The liver is divided into five lobes and eight segments. A model of the hepatic artery is extracted and 3D printed. The geometric parameters of the vascular model include vessel diameter, vessel length, vessel tortuosity, and vessel thickness. This vascular model can also be connected to silicone tubes of different inner diameters or lengths to simulate capillaries. The material used is medical-grade silicone, simulating the morphology and mechanical properties of blood vessels in specific parts of the human body, and maintaining stable parameters in multiple experiments. This vascular model can also be replaced with vascular models of other parts of the human body, such as… Figure 4 As shown.

[0075] S12. The preset shape memory alloy wire and pressure sensor are configured on the initial blood vessel model to detect the initial dynamic parameters of the initial blood vessel model.

[0076] It should be noted that, in order to further simulate the real state of human blood vessels, the 3D-printed liver segment blood vessel model was optimized: a 0.1 mm thick shape memory alloy wire was embedded in the inner layer of the blood vessel wall. A Ni-Ti alloy was selected, with a phase transition temperature of 37℃, which is consistent with the human body temperature. The alloy wire was connected to a "pulse current controller" to pass periodic pulse currents through the alloy wire, driving the blood vessel wall to move in a periodic motion of "contraction phase (diameter shrinks by 12%) - diastole phase (diameter recovers)," thus replicating the dynamic mechanical characteristics of the human hepatic artery. A pressure sensor (accuracy 0.1 mmHg) was placed outside the blood vessel model to monitor the blood vessel wall pressure in real time and adjust the current intensity accordingly to ensure that the dynamic parameters are consistent with the physiological data of the human hepatic artery.

[0077] In a preferred embodiment, the initial dynamic parameters of the initial blood vessel model are detected by configuring a pre-set shape memory alloy wire and a pressure sensor on the initial blood vessel model, including the following steps:

[0078] S121. Embed the preset shape memory alloy wire into the inner layer of the blood vessel wall in the initial blood vessel model, and connect the preset shape memory alloy wire to the pulse current controller.

[0079] S122. A pressure sensor is placed on the outside of the initial vascular model, and a periodic pulse current is passed through the shape memory alloy wire using a pulse current controller so that the initial vascular model performs periodic movements during systole and diastole.

[0080] S123. Based on the pressure sensor and pulse current controller, obtain the dynamic parameters of the initial blood vessel model during the cyclical movement of systole and diastole, including the current intensity of the shape memory alloy, the pulse period, the peak pressure during systole, and the minimum pressure during diastole.

[0081] S13. Collect the physiological data of the hepatic artery of the target human body, compare it with the initial dynamic parameters, and based on the comparison results, adjust the initial dynamic parameters of the initial vascular model with the hemodynamic characteristics of the real human hepatic artery as the target, and obtain the optimized vascular model.

[0082] As a preferred embodiment, the physiological data of the hepatic artery of the target human body are collected and compared with the initial dynamic parameters. Based on the comparison results, the initial dynamic parameters of the initial vascular model are adjusted with the hemodynamic characteristics of the real human hepatic artery as the target, and the optimized vascular model is obtained by the following steps:

[0083] S131. Collect hepatic artery physiological data of the target human body and determine the baseline values ​​of the hepatic artery physiological data of the target human body, including the baseline value of the human body during systole, the baseline value of the human body during diastole, and the pulse pressure difference of the human body.

[0084] S132. Compare the initial dynamic parameters with the baseline values ​​of the target human hepatic artery physiological data to obtain the comparison results;

[0085] S133. Based on the comparison results, and following the preset adjustment strategy, the initial dynamic parameters of the initial vascular model are adjusted and optimized with the hemodynamic characteristics of the real human hepatic artery as the target, to obtain the optimized vascular model.

[0086] The preset adjustment strategies include: if the peak systolic pressure is greater than the human body's systolic baseline value, it indicates excessive vasoconstriction, so the current intensity is reduced; if the minimum diastolic pressure is less than the human body's diastolic baseline value, it indicates insufficient vasodilation, so the current pulse interval is adjusted or the diastolic current intensity is reduced; if the difference between the peak systolic pressure and the minimum diastolic pressure and the human body's pulse pressure exceeds a preset threshold, the current intensity or current pulse interval during systole and diastole is adjusted synchronously to correct the pressure change amplitude within the cycle.

[0087] Specifically, to ensure that the dynamic parameters are consistent with the physiological data of the human hepatic artery, a closed-loop feedback mechanism of "monitoring data acquisition - data comparison and analysis - current feedback adjustment" is constructed to achieve precise control through real-time data comparison and feedback adjustment. The specific implementation process is as follows:

[0088] Obtain clinical human hepatic artery physiological data: vessel wall pressure parameters, including peak systolic pressure, minimum diastolic pressure, and pulse pressure. Peak systolic pressure (P1) is 80-120 mmHg; minimum diastolic pressure (P2) is 60-80 mmHg; pulse pressure (ΔP=P1-P2) is 20-40 mmHg.

[0089] Using a high-precision pressure sensor (accuracy 0.1 mmHg) placed outside the vascular model, the pressure changes of the model's vascular wall during the "systole-diastole" cycle are recorded in real time, generating a pressure-time curve, and extracting the peak pressure during systole (Pmold systole) and the minimum pressure during diastole (Pmold diastole).

[0090] The pulse current controller records the current intensity (I1) and pulse period (T1) of the Ni-Ti alloy wire. These parameters directly determine the amplitude and frequency of blood vessel contraction.

[0091] The collected model data is compared with preset human physiological data benchmarks to determine the direction and magnitude of the deviation. If Pmode systolic > the human systolic baseline value, it indicates excessive vasoconstriction, and the current intensity needs to be reduced to decrease the vasoconstriction amplitude. If Pmode diastolic < the human diastolic baseline value, it indicates insufficient vasodilation, and the current pulse interval needs to be adjusted or the diastolic current intensity reduced. If the pulse pressure difference (Pmode systolic - Pmode diastolic) deviates from the human pulse pressure difference by more than 5 mmHg, the current parameters during systole and diastole need to be adjusted simultaneously to correct the pressure change amplitude within the cycle.

[0092] S14. Based on the optimized blood vessel model, construct an in vitro simulation testing device.

[0093] As a preferred embodiment, the in vitro simulation testing device includes:

[0094] The simulated blood storage and control unit is used to store simulated human blood and regulate its temperature. It includes a simulated blood storage tank equipped with a thermometer and an adjustable temperature control device to ensure the simulated blood maintains the same temperature as real human blood. The simulated blood collection graduated cylinder group consists of a graduated cylinder at each outlet of the blood vessel, used to collect the microspheres.

[0095] The cardiac pulse simulation drive unit is used to simulate cardiac pulses; through a pulse pump with a specific structure, it reproduces the blood supply pattern of the human heart, providing the entire vascular simulation system with blood flow dynamics that conform to physiological characteristics, rather than simply continuous fluid delivery.

[0096] The microsphere delivery control unit is used to simulate the microsphere injection process; specifically, it pushes embolized microspheres at a simulated clinical rate and includes an injection pump, injection needle, and injection catheter.

[0097] The hepatic segment vascular simulation unit is used to simulate the human hepatic artery based on an optimized vascular model. The liver can be divided into five lobes and eight segments. This vascular model is designed according to the Glisson system and can simulate the interventional embolization process of different hepatic segments. A vascular model with fixed geometric parameters is fabricated using 3D printing technology. These parameters include vessel diameter, length, tortuosity, and thickness. Specifically, the vessel diameter ranges from 1 mm to 4 mm, the length from 14 mm to 41 mm, the tortuosity from 45° to 99°, and the wall thickness is 2 mm. This vascular model can also be connected to silicone tubes of different inner diameters or lengths to simulate capillaries. The material used is medical-grade silicone, simulating the morphology and mechanical properties of blood vessels in specific parts of the human body, and maintaining stable parameters in multiple experiments. This vascular model can also be used to replace vascular models of other parts of the human body.

[0098] The microsphere collection unit is used to collect microspheres from each liver segment of the optimized vascular model. This involves collecting microspheres from each liver segment of the vascular model and observing the distribution of embolic microspheres. After injection, the microspheres in the graduated cylinder are collected into a small, precisely graduated cylinder containing three removable filters: a primary filter (500 μm pore size) filters impurities from the simulated blood; a secondary filter (pore size = microsphere size to be tested - 100 μm, e.g., a 200 μm filter for 300 μm microspheres) retains intact microspheres; and a tertiary filter (pore size = microsphere size to be tested - 20 μm, e.g., a 280 μm filter for 300 μm microspheres) retains smaller microspheres. This triple protection ensures the integrity of the collected microspheres (preventing loss of active ingredients) while also removing ineffective impurities through graded filtration. Furthermore, to facilitate microsphere counting, the microspheres need to be arranged in a single layer in an orderly manner. A movable device is installed at the bottom of the graduated cylinder, connected to components that regulate the moving speed and displacement to prevent microsphere accumulation. After the simulation experiment, the microspheres on the filter screen are arranged in a single layer. The microspheres are then transferred to an optical glass counting plate, photographed under a microscope, and imported into ImageJ software for counting to obtain the number of microspheres. The specific operating steps are as follows:

[0099] Image preprocessing is fundamental to improving recognition accuracy by reducing noise, balancing the background, and enhancing the contrast between the microspheres and the background, providing high-quality images for subsequent segmentation. First, grayscale conversion is performed to convert the image to grayscale, eliminating color channel redundancy and reducing computational load. Background correction is then applied to eliminate background gradients, preventing edge microspheres from being missed due to insufficient brightness.

[0100] Target segmentation: Separating microspheres from the background. If the image grayscale shows a bimodal distribution (significant difference between microspheres and background), the Otsu algorithm is used to automatically determine the optimal threshold. If the background grayscale fluctuates greatly, an adaptive threshold is used, dynamically segmenting the target through local region grayscale statistics to avoid missed or false detections caused by global thresholding. When microspheres are highly concentrated and adhere together, conventional segmentation may misclassify multiple microspheres as a single target; a specialized algorithm can be used to automatically segment the adhered regions.

[0101] Feature filtering; Particle analysis settings: Execute Analyze>AnalyzeParticles, key parameter settings include: microsphere size, set upper and lower limits according to the actual particle size of the microspheres; shape filtering, set the roundness range to 0.7-1.0 to exclude irregular impurities; check the box to display contours and output results to intuitively verify the recognition accuracy and obtain counting data.

[0102] Counting: View the total count in the Results window. Manually sample edge areas using the contour overlay image. If the missed detection rate exceeds 5%, the threshold or size parameters need to be re-optimized. After counting, collect the microspheres in a small graduated cylinder and measure their volume. After measurement, dehydrate the collected microspheres with anhydrous ethanol, dry them in a 50℃ oven for 48 hours, and then weigh them to obtain the number, volume, and weight data of microspheres in each region.

[0103] S2. Based on an in vitro simulation testing device, conduct liver simulation tests under different microsphere characteristics and injection conditions, and collect microsphere state data for each liver segment; such as Figure 3 As shown.

[0104] As a preferred embodiment, liver simulation tests are conducted under different microsphere characteristics and injection conditions based on an in vitro simulation testing device, and microsphere status data of each liver segment are collected, including the following steps:

[0105] S21. Select the microspheres to be simulated and measure the characteristic parameters of the microspheres. According to the preset injection conditions and injection positions, inject the microspheres to be simulated into the in vitro simulation test device.

[0106] The core parameters of the microspheres were determined, including the microsphere density ρ (unit: kg / m³). 3 The microsphere diameter d (in μm) and microsphere size d were determined. Based on the target vessel region parameters, the microsphere injection speed V (in ml / min) and injection position parameter L (in cm, defined as the straight-line distance between the injection catheter port and the vessel model inlet) were set. To further improve data accuracy, a blood flow shear force τ (in Pa) was introduced to influence the microsphere trajectory. The higher the shear force, the easier it is for the microsphere to deviate towards the vessel wall. This value is related to the vessel diameter D. v (Unit: cm) Blood viscosity μ (unit: Pa·s) is related to blood flow velocity v (unit: cm / s). The simulated blood viscosity used in the experiment is the same as the actual viscosity of human blood, and the blood flow velocity is measured by a flow meter and is consistent with the blood flow velocity of the human hepatic artery.

[0107] It should be noted that the microsphere density ρ was determined by the water displacement method, calculated by measuring the mass and volume of the microspheres; the microsphere diameter d, the equivalent sphere diameter of the microsphere, was determined by a laser particle size analyzer, and the average of three measurements was taken. The injection speed v was precisely controlled by an interventional surgical infusion pump, with a speed adjustment accuracy of no less than 0.1 ml / min; the injection position parameter L was measured using a precision ruler, with a positioning error not exceeding 0.5 mm.

[0108] Among these changes were altering the experimental conditions for the in vitro embolization simulation test, including microsphere size (100-500 μm) and microsphere density (1.0-1.6 kg / m³). 3), injection speed (1-5 ml / min) and injection location (3-10 cm).

[0109] Specifically, the formula for calculating blood flow shear force is:

[0110] ;

[0111] In the formula, τ represents blood shear force, μ represents blood viscosity, v represents blood velocity, and D... v Indicates the diameter of the blood vessel.

[0112] S22. Simulate the human liver artery using an in vitro simulation testing device, and obtain blood flow viscosity, blood flow velocity, and blood vessel diameter of the vascular model.

[0113] S23. Based on blood viscosity, blood velocity, and the diameter of the blood vessel in the vascular model, calculate the blood shear force that affects the trajectory of the microspheres.

[0114] S24. Integrate the microsphere's characteristic parameters, injection conditions, injection location, and blood flow shear force into microsphere state data.

[0115] S3. Based on the microsphere status data of each liver segment, the distribution of microspheres in the blood vessels of each liver segment is quantitatively predicted by fitting the correlation between microsphere characteristics, injection conditions and microsphere distribution data, thus obtaining the intravascular distribution data of embolized microspheres.

[0116] As a preferred embodiment, based on the microsphere state data of each liver segment, the distribution of microspheres in the blood vessels of each liver segment is quantitatively predicted by fitting the correlation between microsphere characteristics, injection conditions, and microsphere distribution data, and the embolization microsphere intravascular distribution data includes the following steps:

[0117] S31. Obtain the angle of blood vessel branches and the horizontal distance between the target blood vessel and the central axis of the blood vessel model. Combine this with the blood vessel diameter of the blood vessel model to calculate the correction coefficient of blood vessel anatomy parameters.

[0118] S32. Based on the microsphere status data and vascular anatomy parameter correction coefficients of each liver segment, the microsphere distribution coefficient calculation model is fitted to obtain the microsphere distribution ratio calculation model, and the correlation between microsphere characteristics, injection conditions and microsphere distribution data is determined according to the microsphere distribution ratio calculation model.

[0119] S33. Based on the correlation between microsphere characteristics, injection conditions, and microsphere distribution data, the distribution of microspheres in blood vessels of each liver segment is quantitatively predicted to obtain intravascular distribution data of embolized microspheres.

[0120] Specifically, the distribution patterns of different types of embolic microspheres are tested in an in vitro simulation testing device, data are collected, and then the data are fitted into a microsphere distribution coefficient calculation model.

[0121] The formula for calculating the distribution ratio of microspheres is as follows:

[0122] ;

[0123] Where k represents the correction coefficient for vascular anatomy parameters, ranging from 0.8 to 1.2, determined by fitting the target vessel diameter and the vessel branch angle; d represents the microsphere diameter; ρ represents the microsphere density; V represents the injection speed; L represents the injection location; and a, b, c, and m are influencing factors reflecting the degree of influence of each variable on the proportion P, all of which are positive numbers determined by experimental fitting. After multiple fittings, the values ​​for a were set to 0.4, b to 0.2, c to 1, and m to 1.2. This yields the microsphere distribution proportion calculation model.

[0124] As a preferred embodiment, the formula for calculating the correction coefficient for vascular anatomy parameters is:

[0125] ;

[0126] In the formula, k represents the correction coefficient for vascular anatomy parameters, and k0 and γ are both correction factor constants, k0=0.2, γ=0.2, D v θ represents the diameter of the blood vessel, e represents the angle of the blood vessel branching, e represents the Euler number, i.e. the natural constant, which is usually taken as 2.72, and s represents the horizontal distance of the axis in the calculation model of the distribution ratio of the target blood vessel and the microsphere, in mm.

[0127] As a preferred embodiment, the formula for calculating the distribution ratio of microspheres is:

[0128] ;

[0129] In the formula, P represents the distribution ratio of microspheres in each liver segment. The larger the P value in the target liver area, the higher the aggregation degree of microspheres in the target lesion area and the more concentrated the distribution. k represents the vascular anatomy parameter correction coefficient, V represents the injection speed, L represents the injection location, d represents the microsphere diameter, ρ represents the microsphere density, and τ represents the blood flow shear force.

[0130] S4. Determine the basic threshold range using an in vitro simulation testing device;

[0131] S5. Based on preoperative ultrasound images, obtain the volume of the target liver segment in the target human body, calculate the ratio of the target liver segment volume to the standard liver segment volume, and set the liver size correction coefficient.

[0132] S6. Assess the liver function status of the target human body based on the Child-Pugh classification and set the liver status correction coefficient;

[0133] S7. Based on the liver size correction coefficient and the liver state correction coefficient, calculate the dynamic threshold range and compare the distribution ratio of microspheres in each liver segment with the dynamic threshold range. If the distribution ratio of microspheres in each liver segment is within the threshold range, perform embolization surgery on the target human body according to the set microsphere parameters and injection parameters. Otherwise, adjust at least one parameter among microsphere density, microsphere particle size, injection speed or injection location until the distribution ratio of microspheres in each liver segment is within the threshold range.

[0134] It should be noted that, as Figure 5 As shown, due to significant differences in liver anatomical features (such as liver volume) and physiological states (such as liver function classification) among individual patients, personalized treatment plans should be developed for each patient. This invention proposes a personalized dynamic threshold. The core feature of this dynamic threshold is that it is not a fixed value, but rather a patient-specific threshold range determined collaboratively by a baseline threshold, a liver size correction coefficient, and a liver state correction coefficient. The specific steps are as follows:

[0135] Step 1: Determining the baseline threshold. The baseline threshold range is determined using an in vitro simulation testing device. When the distribution percentage of microspheres falls within this range, the blood flow obstruction rate in the target area of ​​the model is ≥90%, meaning the blood flow after embolization is 90% of the pre-embolization blood flow, achieving the therapeutic target. If it exceeds this range, microsphere reflux occurs in the model, posing a risk of misembolization in non-target areas in vivo; if it falls below this range, the blood flow obstruction rate in the target area of ​​the model is less than 80%, indicating incomplete embolization in vivo. Through multiple in vitro experimental gradient verifications, this baseline threshold range was determined to be 60%-70%.

[0136] Step 2, Liver Size Correction: Based on preoperative CT / MRI 3D reconstruction, the volume of the target liver segment is obtained, and its ratio (R) to the standard liver segment volume is calculated. A size correction coefficient (K1) is then set accordingly. Hepatic artery models corresponding to different liver volumes (large, moderate, and small) are used for in vitro simulation experiments to observe the blood flow occlusion rate and reflux rate under different conditions, thus determining the influence of liver size on the microsphere deposition threshold. As shown in Table 1, when R > 1.2 (large liver volume), K1 is set to 1.10-1.15 to compensate for microsphere dilution caused by increased vascular bed volume by increasing the dynamic threshold; when R < 0.8 (small liver volume), K1 is set to 0.85-0.90 to avoid the risk of reflux caused by excessive microspheres by decreasing the dynamic threshold.

[0137] Step 3: Liver Status Correction. Based on the Child-Pugh classification, the patient's liver function status is assessed, and a status correction coefficient (K2) is set to modify the simulated vascular resistance. The worse the liver function, the higher the vascular resistance. Reducing the vascular radius and increasing the simulated blood viscosity increases vascular resistance, while decreasing vascular resistance has the opposite effect. In vitro simulation experiments are conducted using hepatic artery models with different vascular resistances (high resistance, moderate resistance, and low resistance) to observe the blood flow occlusion rate and reflux rate under different conditions, thus determining the influence of liver size on the microsphere deposition threshold. As shown in the table below, when liver function is grade B, K2 is set to 1.08-1.12; when liver function is grade C, K2 is set to 1.20-1.25, increasing the dynamic threshold to ensure preferential deposition of microspheres in the target area and avoiding low tolerance to microspheres in patients with impaired liver function; when liver function is grade A, K2 is set to 1.0, maintaining the baseline threshold.

[0138] Table 1: Values ​​of each correction factor

[0139] Step 4: Establish the dynamic threshold formula:

[0140] Dynamic threshold P 动 =Basic threshold P 基 ×K1×K2;

[0141] Upper limit of dynamic threshold range P 动max =Basic threshold P 基max ×K1×K2;

[0142] Lower limit P of dynamic threshold range 动min =Basic threshold P 基min ×K1×K2;

[0143] The system determines whether the calculated P value falls within a preset threshold range. If it does, the embolization procedure is performed according to the set microsphere and injection parameters. If it does not fall within this range, at least one parameter—microsphere density ρ, particle size d, injection speed V, or injection position L—is adjusted and substituted into the microsphere distribution coefficient calculation model for fitting analysis until P falls within the preset threshold range. The basic threshold P... 基max This represents the upper limit of the basic threshold, where P is the basic threshold. 基min This indicates the lower limit of the basic threshold.

[0144] To better understand the technical solution of the present invention, the following embodiments are provided for further explanation:

[0145] Example 1: The effect of different material elasticities on the permeability of microspheres;

[0146] Biomimetic blood vessel models with a uniform inner diameter (15 μm) and a length of 2 cm were prepared using polymer materials. They were divided into 5 groups according to their elastic modulus (E) to simulate the elasticity of blood vessels under different physiological / pathological conditions: the extremely high elasticity group simulated capillaries, the high elasticity group simulated healthy arteries, the medium elasticity group simulated normal veins, the low elasticity group simulated mildly sclerotic blood vessels, and the extremely low elasticity group simulated severely sclerotic blood vessels. The specific elastic modulus values ​​are shown in Table 2.

[0147] A suspension containing microspheres was injected into the vascular model using a syringe pump at a fixed flow rate (1 ml / min), while a peristaltic pump injected simulated blood at a flow rate close to that of blood (60-80 rpm / min). A microscope and a high-speed camera were simultaneously used to continuously film the flow of the microspheres within the channels. Each temperature group was repeated three times, with each filming session lasting 15 minutes. After each experiment, the vascular model channels were rinsed three times with PBS to prevent residual microspheres from affecting subsequent experiments. Ten fields of view were randomly selected from the video footage, and the average flow velocity of the microspheres (mm / s) (average time taken to travel the channel length) and the wall collision frequency (number of collisions between the microspheres and the vascular wall per unit time) were statistically analyzed. The experimental results are shown in Table 3. The average flow velocity of the microspheres in the high-elasticity group was closer to the blood flow velocity of human capillaries (approximately 1-2 mm / s), therefore, the high-elasticity PDMS soft silicone material was selected for fabricating the vascular model.

[0148] Table 2: Elastic modulus values ​​of different elasticity groups

[0149] Table 3: Average flow velocity and wall collision frequency of microspheres for different elastic groups

[0150] Example 2: Effect of different experimental temperatures on microsphere permeability;

[0151] To clarify the differences in the permeability of microspheres in a simulated vascular environment under different temperature conditions, and to investigate how temperature affects blood viscosity and thus the flow resistance and distribution of microspheres, the following experiments were conducted:

[0152] Five temperature gradients were set up: 25℃ (room temperature control), 32℃, 37℃ (normal human body temperature), 40℃, and 45℃, covering physiological and extreme temperature ranges. Sufficient simulated blood was prepared according to the original formula (glycerol: pure water = 1:4), and placed at each set temperature for equilibration for 30 minutes. The viscosity of the simulated blood at different temperatures was measured and recorded using a viscometer.

[0153] A suspension containing microspheres was injected into the vascular model using a syringe pump at a fixed flow rate (1 ml / min), while a peristaltic pump injected simulated blood at a flow rate close to that of blood (60-80 rpm / min). A microscope and a high-speed camera were simultaneously used to continuously film the flow of microspheres within the channels. Each temperature group was repeated five times, with each filming session lasting five minutes. After each experiment, the vascular model channels were rinsed three times with PBS to prevent residual microspheres from affecting subsequent experiments. Ten fields of view were randomly selected from the video footage, and the average flow velocity of the microspheres (the average time taken to travel the channel length) was statistically analyzed. The viscosity values ​​of the simulated blood at different temperatures are shown in Table 4, and the average flow velocity of the microspheres is shown in Table 5. At 37°C, this flow velocity corresponds to the capillary blood flow velocity in humans (approximately 1-2 mm / s), realistically simulating the in vivo flow state after clinical microsphere administration and ensuring the clinical translation value of the experimental results. Therefore, a temperature control device was necessary to ensure that the temperature of the simulated blood was consistent with that of human blood.

[0154] Table 4: Viscosity values ​​of simulated blood at different temperatures

[0155] Table 5: Average flow velocity of microspheres at different temperatures

[0156] Example 3: In vitro simulated interventional surgery embolization microsphere injection;

[0157] Sodium alginate sulfate microspheres prepared in the laboratory were selected, with a particle size of 300 micrometers and a density of 0.9092 g / cm³. 3 500 mg of microspheres were weighed and a suspension was prepared to keep the microspheres in suspension. The microspheres were then injected into the assembled device using a 2.7F microcatheter positioned 3 cm from the vascular inlet. The peristaltic pump was first turned on, and simulated blood was introduced at a flow rate of 60 rpm / min. Once the blood flowed through the vascular model, the injection pump was turned on to inject the microspheres at a rate of 1 ml / min. After complete injection, the injection pump was turned off, followed by the peristaltic pump. The microspheres in each graduated cylinder were collected and dehydrated three times with anhydrous ethanol. After drying in a 50°C oven for 48 hours, the microspheres were weighed. The number, volume, and weight of each liver segment were divided by the total number, volume, and weight, respectively, to obtain the distribution ratio of microspheres in each of the three types of liver segments. The average of these three values ​​was taken as the final distribution data. This value was then mapped to the aforementioned variables, and an AI algorithm was used to analyze the data and derive the formula for calculating the microsphere distribution ratio. The injection conditions for the microspheres are shown in Table 6, and the distribution results of the microspheres are shown in Table 7.

[0158] Table 6: Microsphere Injection Conditions

[0159] Table 7: Microsphere Distribution Results

[0160] Subsequent experiments involved changing the embolization microsphere conditions and injection conditions, and repeating the above steps. The percentage of microspheres in each liver segment was used as the dependent variable, while the parameters of the embolization microspheres, injection conditions, and model size data were used as independent variables. The collected data were analyzed to establish a formula for calculating the distribution ratio of microspheres.

[0161] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0162] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A quantitative calculation method for the intravascular distribution regularity of embolization microspheres based on an in-vitro simulation test device, characterized in that, The method comprises the following steps: S1, based on the Glisson system liver segment division method and the pre-acquired liver artery physiological data of the target human body, a blood vessel model is built with the hemodynamic characteristics of the real human liver artery as the target and is optimized; based on the optimized blood vessel model, an in-vitro simulation test device is constructed; S2, liver simulation tests under different microsphere characteristics and injection conditions are carried out on the in-vitro simulation test device, and microsphere state data of each liver segment in the liver is collected; S3, according to the microsphere state data of each liver segment in the liver, the distribution of microspheres in each liver segment blood vessel is quantitatively predicted by fitting the correlation between the microsphere characteristics, the injection conditions and the microsphere distribution data, and the in-vessel distribution data of the embolization microspheres is obtained.

2. The method according to claim 1, wherein, The method comprises the following steps: S11, the liver is divided into five lobes and eight segments by using the Glisson system liver segment division method, the blood vessel model of the hepatic artery is extracted, and the blood vessel model of the hepatic artery is manufactured by using 3D printing technology to obtain an initial blood vessel model; S12, the preset shape memory alloy wire and the pressure sensor are arranged in the initial blood vessel model to detect the initial dynamic parameters of the initial blood vessel model; S13, the liver artery physiological data of the target human body is collected and compared with the initial dynamic parameters, and according to the comparison result, the initial dynamic parameters of the initial blood vessel model are adjusted with the hemodynamic characteristics of the real human liver artery as the target to obtain an optimized blood vessel model; S14, based on the optimized blood vessel model, an in-vitro simulation test device is constructed.

3. The method according to claim 2, wherein, The method comprises the following steps: S121, the preset shape memory alloy wire is embedded into the inner layer of the blood vessel wall of the initial blood vessel model, and the preset shape memory alloy wire is connected with the pulse current controller; S122, the pressure sensor is arranged outside the initial blood vessel model, and the pulse current controller is used to input periodic pulse current to the shape memory alloy wire to make the initial blood vessel model perform periodic motion in the systole and diastole periods; S123, based on the pressure sensor and the pulse current controller, the dynamic parameters of the initial blood vessel model during the periodic motion in the systole and diastole periods are obtained, including the current intensity of the shape memory alloy, the pulse period, the peak systolic pressure and the diastolic pressure.

4. The method according to claim 2, wherein, The method comprises the following steps: S131, the liver artery physiological data of the target human body is collected, and the reference values of the liver artery physiological data of the target human body are determined, including the human systolic reference value, the human diastolic reference value and the human pulse pressure difference; S132. Compare the initial dynamic parameters with the baseline values ​​of the target human hepatic artery physiological data to obtain the comparison results; S133. Based on the comparison results, and following the preset adjustment strategy, the initial dynamic parameters of the initial vascular model are adjusted and optimized with the hemodynamic characteristics of the real human hepatic artery as the target, to obtain the optimized vascular model.

5. The method according to claim 2, wherein, The in vitro simulation testing device includes: The simulated blood storage and control unit is used to store simulated human blood and regulate the temperature of the simulated blood. The cardiac pulse simulation drive unit is used to simulate cardiac pulses; Microsphere delivery control unit, used to simulate the microsphere injection process; The liver segment vascular simulation unit is used to simulate the human hepatic artery based on an optimized vascular model. The microsphere collection unit is used to collect microspheres from each liver segment in the optimized vascular model.

6. The method according to claim 5, wherein, The method for conducting liver simulation tests under different microsphere properties and injection conditions based on the in vitro simulation testing device, and collecting microsphere status data of each liver segment, includes the following steps: S21. Select the microspheres to be simulated and measure the characteristic parameters of the microspheres. According to the preset injection conditions and injection positions, inject the microspheres to be simulated into the in vitro simulation test device. S22. Simulate the human liver artery using an in vitro simulation testing device, and obtain blood flow viscosity, blood flow velocity, and blood vessel diameter of the vascular model. S23. Based on blood viscosity, blood velocity, and the diameter of the blood vessel in the vascular model, calculate the blood shear force that affects the trajectory of the microspheres. S24. Integrate the microsphere's characteristic parameters, injection conditions, injection location, and blood flow shear force into microsphere state data.

7. The method according to claim 1, wherein, The method of quantitatively predicting the distribution of microspheres in the blood vessels of each liver segment based on microsphere state data of each liver segment, and obtaining intravascular distribution data of embolized microspheres by fitting the correlation between microsphere characteristics, injection conditions, and microsphere distribution data, includes the following steps: S31. Obtain the angle of blood vessel branches and the horizontal distance between the target blood vessel and the central axis of the blood vessel model. Combine this with the blood vessel diameter of the blood vessel model to calculate the correction coefficient of blood vessel anatomy parameters. S32. Based on the microsphere status data and vascular anatomy parameter correction coefficients of each liver segment, the microsphere distribution coefficient calculation model is fitted to obtain the microsphere distribution ratio calculation model, and the correlation between microsphere characteristics, injection conditions and microsphere distribution data is determined according to the microsphere distribution ratio calculation model. S33. Based on the correlation between microsphere characteristics, injection conditions, and microsphere distribution data, the distribution of microspheres in blood vessels of each liver segment is quantitatively predicted to obtain intravascular distribution data of embolized microspheres.

8. The method according to claim 7, wherein, The formula for calculating the correction coefficient for the vascular anatomy parameters is as follows: ； In the formula, k represents a blood vessel anatomical parameter correction coefficient, k0 and γ both represent correction factor constants, k0=0.2, and γ=0.2, D v represents a blood vessel diameter, θ represents a blood vessel branch angle, e represents Euler's number, and s represents an axis horizontal distance in a target blood vessel and microsphere distribution proportion calculation model.

9. The method according to claim 7, wherein, The formula for calculating the distribution ratio of microspheres is as follows: ； In the formula, P represents the distribution ratio of microspheres in each liver segment, k represents the correction coefficient for vascular anatomy parameters, V represents the injection speed, L represents the injection location, d represents the microsphere diameter, ρ represents the microsphere density, and τ represents the blood flow shear force.

10. The method according to claim 9, wherein the method is characterized by, The method also includes: S4. Determine the basic threshold range using an in vitro simulation testing device; S5. Based on preoperative ultrasound images, obtain the volume of the target liver segment in the target human body, calculate the ratio of the target liver segment volume to the standard liver segment volume, and set the liver size correction coefficient. S6. Assess the liver function status of the target human body based on the Child-Pugh classification and set the liver status correction coefficient; S7. Based on the liver size correction coefficient and the liver state correction coefficient, calculate the dynamic threshold range and compare the distribution ratio of microspheres in each liver segment with the dynamic threshold range. If the distribution ratio of microspheres in each liver segment is within the threshold range, perform embolization surgery on the target human body according to the set microsphere parameters and injection parameters. Otherwise, adjust at least one parameter among microsphere density, microsphere particle size, injection speed or injection location until the distribution ratio of microspheres in each liver segment is within the threshold range.

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