A non-invasive detection system and method for portal hypertension based on fractal theory

Abstract

The application provides a portal hypertension noninvasive detection system and method based on fractal theory, and belongs to the noninvasive detection field.The system comprises a division module, which is used for dividing a portal vein blood flow process into a pipe flow section and a fractal flow section according to the blood vessel diameter distribution characteristics from a liver blood sinusoid to a portal vein section, and determining the geometric parameters of the pipe flow section and the fractal flow section according to the obtained CT image and angiographic image; an establishment module, which is used for establishing a blood flow model of blood from the liver blood sinusoid to the portal vein according to the determined geometric parameters, combining the fractal theory and the flow law, and obtaining a pressure-flow relationship of the portal vein; a statistical module, which is used for statistically processing Doppler ultrasound images of a user to obtain an average flow value of the portal vein; and a determination module, which is used for obtaining a portal vein pressure value of the user based on the obtained pressure-flow relationship and average flow value of the portal vein. By using the application, the portal vein pressure value can be accurately and quickly predicted.

Description

Technical Field

[0001] This invention relates to the field of non-invasive detection, and in particular to a non-invasive detection system and method for portal hypertension based on fractal theory. Background Technology

[0002] Portal hypertension is caused by various etiologies that obstruct portal vein blood flow, leading to increased portal vein pressure and symptoms such as rectal bleeding, splenomegaly, and hypersplenism. It is particularly prone to causing rupture of esophageal varices, resulting in massive bleeding that can be life-threatening. Portal hypertension is often a direct complication of cirrhosis, clinically manifesting as splenomegaly, hypersplenism, thrombocytopenia, ascites, and systemic symptoms such as anemia, fatigue, weight loss, and emaciation. Therefore, early diagnosis of portal hypertension can effectively reduce the risks associated with surgery for patients.

[0003] Traditional methods for diagnosing portal hypertension include invasive and non-invasive approaches. The invasive method, the hepatic vein pressure gradient method, requires insertion of a catheter through the jugular vein into the patient's body. A balloon catheter is used to measure the pressure of the hepatic veins using a pressure sensor at its tip. This method is not only invasive but also requires sophisticated medical equipment. Non-invasive methods include indirect parameter measurements such as serum tests, anatomical imaging (e.g., Doppler ultrasound, computed tomography, and magnetic resonance imaging), physical parameters of tissue properties (e.g., elastography), and quantitative assessment of radiological imaging characteristics (e.g., radiomics). However, these methods can only provide a preliminary diagnosis based on biochemical indicators and cannot determine the specific portal vein pressure value. Therefore, research on how to combine existing non-invasive methods to derive portal vein pressure values ​​is of great significance. Patent CN103976720B uses CT images to generate a three-dimensional shape of the portal venous system, sets corresponding inlet and outlet boundary conditions, and solves the blood flow equation through computational fluid dynamics. However, this method only extracts the major vessels of the portal venous system, so the simulated boundary conditions are not accurate, and the calculated pressure contains errors. Summary of the Invention

[0004] This invention provides a non-invasive detection system and method for portal hypertension based on fractal theory, which can accurately and quickly predict portal vein pressure values. The technical solution is as follows:

[0005] On the one hand, a non-invasive detection system for portal hypertension based on fractal theory is provided, including:

[0006] The segmentation module is used to divide the portal vein blood flow process into a tubular flow segment and a fractal flow segment based on the distribution characteristics of the vessel diameter from the hepatic sinusoids to the portal vein. The geometric parameters of the tubular flow segment and the fractal flow segment are determined based on the acquired CT images and angiography images.

[0007] A module is established to create a blood flow model from the hepatic sinusoids to the portal vein based on the geometric parameters of the determined pipe flow section and fractal flow section, combined with fractal theory and flow laws, and to obtain the pressure-flow relationship of the portal vein.

[0008] The statistics module is used to analyze the user's Doppler ultrasound images to obtain the average portal vein flow value;

[0009] The determination module is used to obtain the user's portal vein pressure value based on the obtained portal vein pressure-flow relationship and the user's average portal vein flow value.

[0010] Furthermore, the partitioning module includes:

[0011] The division unit is used to divide the portal vein blood flow process into a tubular flow segment and a fractal flow segment based on the distribution characteristics of the vessel diameter from the hepatic sinusoids to the portal vein.

[0012] The first determining unit is used to determine the geometric parameters of the ductal flow segment based on CT images; wherein the geometric parameters of the ductal flow segment include the length and diameter of the first-order and second-order vessels in the portal vein;

[0013] The second determining unit is used to determine the geometric parameters of the fractal flow segment based on the angiography image; wherein the geometric parameters of the fractal flow segment include: the tree fractal dimension and the capillary number fractal dimension of the fractal flow portion.

[0014] Furthermore, the establishment module includes:

[0015] The first establishment unit is used to establish the pressure-flow relationship of portal vein blood in the channel flow segment based on the length and diameter of the first and second level vessels in the determined portal vein channel, combined with Hagenpoise flow.

[0016] The second establishment unit is used to establish the pressure-flow relationship of portal vein blood in the fractal flow segment based on the fractal dimension of the determined fractal flow segment, combined with fractal theory and Hagenpoeuille flow.

[0017] The third determining unit is used to determine the pressure-flow relationship of the portal vein based on the established pressure-flow relationship of portal vein blood in the pipe flow segment and the pressure-flow relationship of portal vein blood in the fractal flow segment.

[0018] Furthermore, the established pressure-flow relationship of portal vein blood in the pipeline flow segment is as follows:

[0019]

[0020] Where, ΔP pipeline For the pressure drop in the pipe flow section, Δp pThe pressure drop in the first-order vessels of the portal vein, Δp s For the pressure drop in the second-order vessels of the portal vein, l p l s These represent the lengths of the first-order and second-order vessels in the portal vein, respectively, d. p and d s , where are the diameters of the first-order and second-order vessels in the portal vein, respectively; Q is the average portal vein flow rate; and μ is the viscosity of the vessel.

[0021] Furthermore, the second establishing unit is specifically used to characterize the hierarchical relationship of blood vessels in a single parent duct using tree fractal theory:

[0022]

[0023] Where Δp is the pressure drop of a single dendritic fractal network; Δp k The pressure drop of the k-th grade vessel; m is the number of vessel grades, determined by the smallest parent vessel diameter and the diameter of the hepatic sinusoids; n is the number of bifurcations; l f0 d f0 Let be the length and diameter of the parent tube of the dendritic fractal network, respectively; γ and β are the diameter scaling factor and length scaling factor of the dendritic fractal network, respectively, and the value of γ is related to the dimension D of the dendritic fractal. l They are related and satisfy the following relationship:

[0024]

[0025] Using capillary number fractal theory, the number of mother tubes dN(d) of different diameters was analyzed. f0 ) To conduct statistics:

[0026]

[0027] Where, d f0max D is the diameter of the largest mother tube in the portal venous system. f The number of capillaries is the fractal dimension;

[0028] Based on the vascular classification of individual mother tubes and the number of mother tubes of different diameters, the pressure-flow relationship of portal vein blood in the fractal flow segment is established, expressed as:

[0029]

[0030] Where, ΔP fractal For the pressure drop in the fractal flow section; d f0min denoted as the minimum diameter of the portal vein; Q represents the average flow rate of the portal vein.

[0031] Furthermore, the capillary number fractal dimension D f Represented as:

[0032]

[0033] in, Porosity identified from angiographic images, d f0max d f0min These are the maximum and minimum diameters of the portal vein, respectively.

[0034] Furthermore, the pressure-flow relationship of the portal vein is expressed as follows:

[0035] ΔP=ΔP pipeline +ΔP fractal

[0036] Where ΔP represents the sum of the pressure drops in the pipe flow section and the fractal flow section, ΔP pipeline ΔP fractal These represent the pressure drop in the pipe flow section and the pressure drop in the fractal flow section, respectively.

[0037] Furthermore, the user's portal vein pressure value is expressed as:

[0038] P = ΔP + 665

[0039] Where P represents the user's portal vein pressure value, ΔP represents the sum of pressure drops in the tubular flow section and the fractal flow section, and 665 represents the fixed pressure value at the hepatic sinusoids.

[0040] On the other hand, a non-invasive detection method for portal hypertension based on fractal theory is also provided, including:

[0041] Based on the distribution characteristics of vessel diameter from the hepatic sinusoids to the portal vein, the portal vein blood flow process is divided into a tubular flow segment and a fractal flow segment. The geometric parameters of the tubular flow segment and the fractal flow segment are determined based on the acquired CT images and angiography images.

[0042] Based on the determined geometric parameters of the pipe flow section and fractal flow section, combined with fractal theory and flow laws, a blood flow model from the hepatic sinusoids to the portal vein is established, and the pressure-flow relationship of the portal vein is obtained.

[0043] The average portal vein flow rate was obtained by statistically analyzing the user's Doppler ultrasound images.

[0044] Based on the obtained portal vein pressure-flow relationship and the user's average portal vein flow value, the user's portal vein pressure value is obtained.

[0045] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0046] 1) To address the difficulty in characterizing complex multi-scale blood vessels, this system establishes a complete blood flow model from the hepatic sinusoids to the portal vein based on fractal theory and flow laws. It provides a complete characterization of blood flow in the segment from the hepatic sinusoids to the portal vein, with accurate outlet pressure boundary conditions and physical meaning. Combined with the average portal vein flow value of a specific user, it can accurately and quickly predict the portal vein pressure value. At the same time, it avoids the lack of physical meaning in general non-invasive pressure measurement methods, effectively solving the problem of low accuracy in non-invasive detection, and has important guiding significance for clinical medicine.

[0047] 2) Based on CT images and angiography images, this system proposes a new non-invasive pressure measurement method that can be applied to the rapid non-invasive prediction of portal vein pressure with high accuracy. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0049] Figure 1 A schematic diagram of the structure of a non-invasive portal hypertension detection system based on fractal theory provided in an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of a blood flow model including two segments: portal vein ductal flow and fractal flow, provided in an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of the geometric model of the three-dimensional reconstructed portal vein flow segment provided in an embodiment of the present invention;

[0052] Figure 4 This is a schematic diagram illustrating the geometric parameters of the portal vein flow segment for identification, provided in an embodiment of the present invention.

[0053] Figure 5 This is a schematic diagram of the portal vein fractal flow segment provided in an embodiment of the present invention;

[0054] Figure 6 This is a schematic diagram of the dimension of the portal vein fractal flow segment identification provided in an embodiment of the present invention, wherein a) is a schematic diagram of the angiographic images of different sizes covered by grids, and b) is the number of grids corresponding to different sizes in a double logarithmic coordinate system;

[0055] Figure 7 This is a flowchart illustrating a non-invasive detection method for portal hypertension based on fractal theory, provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0057] like Figure 1 As shown, to address the problems of current invasive portal vein pressure detection methods being difficult to implement and the low accuracy of non-invasive detection, this invention provides a non-invasive portal hypertension detection system based on fractal theory. This system can be applied to the rapid non-invasive detection of portal vein pressure values, thereby non-invasively screening for portal hypertension. The system includes:

[0058] The segmentation module 11 is used to divide the portal vein blood flow process into a tubular flow segment and a fractal flow segment based on the distribution characteristics of the vessel diameter from the hepatic sinusoids to the portal vein segment, and to determine the geometric parameters of the tubular flow segment and the fractal flow segment based on the acquired CT images and angiography images.

[0059] In this embodiment, the partitioning module includes:

[0060] The system is divided into units, considering that the main portal vein and its branches (left and right) can be clearly obtained directly from CT images. The blood flow from the main portal vein to its branches is divided into tubular flow segments, and the blood flow from the branches to the hepatic sinusoids is divided into fractal flow segments, consistent with the portal vein's diameter distribution characteristics. Figure 2 As shown;

[0061] In this embodiment, the diameter of the main portal vein is about 10 mm, which is used as the ductal flow section; the diameter of the hepatic sinusoids is about 5 μm, and the vessels leading to the main portal vein are used as the fractal flow section.

[0062] The first determining unit is used to determine the geometric parameters of the ductal flow segment based on the CT images of the user (specifically, the case); wherein the geometric parameters of the ductal flow segment include the length and diameter of the first-order and second-order vessels in the portal vein;

[0063] In this embodiment, 3D reconstruction is performed on CT images of actual clinical cases. Through thresholding and filtering operations, the geometry of the portal vein flow segment is obtained, such as... Figure 3 As shown; further, the geometric parameters l of the pipe flow section are measured using CT model measurement tools. p l s d p d s Extract, such as Figure 4 As shown, where l p l s d represents the lengths (in cm) of the first-order and second-order vessels in the portal vein. p and ds These are the diameters (in cm) of the first and second order vessels in the portal vein.

[0064] The second determining unit is used to determine the geometric parameters of the fractal flow segment based on the angiography image; wherein the geometric parameters of the fractal flow segment include: the tree fractal dimension and the capillary number fractal dimension of the fractal flow portion.

[0065] In this embodiment, angiography images from actual clinical cases are processed, and fractal flow vessels are obtained through thresholding, filtering, and other operations. Figure 5 As shown; further, the tree-like fractal dimension of the fractal flow portion is identified using box counting.

[0066] In this embodiment, the box counting method involves placing blood vessels on a uniformly divided grid, such as... Figure 6 As shown in a), the minimum number of grid cells required to cover the object is counted. By progressively refining the grid, the change in the required number of cells to cover is observed, thereby calculating the tree-like fractal dimension. Figure 6 As shown in b).

[0067] In this embodiment, assuming the side length of the lattice is δ, and the space is divided into N lattice cells, then the dimension of the tree fractal is:

[0068]

[0069] Module 12 is established to create a blood flow model from the hepatic sinusoids to the portal vein based on the geometric parameters of the determined pipe flow section and fractal flow section, combined with fractal theory and flow law, and to obtain the pressure-flow relationship of the portal vein.

[0070] In this embodiment, the establishment module includes:

[0071] The first unit is used to establish the pressure-flow relationship of portal vein blood in the channel flow segment based on the determined lengths and diameters of the first and second-order vessels in the portal vein, combined with Hagenpoisee flow; wherein the established pressure-flow relationship of portal vein blood in the channel flow segment is as follows:

[0072]

[0073] Where, ΔP pipeline Δp is the pressure drop (in Pa) in the section with pipe flow. p Δp is the pressure drop (in Pa) in the first-order vessels of the portal vein. s The pressure drop (in Pa) in the second-order vessels of the portal vein, l p l s These represent the lengths of the first-order and second-order vessels in the portal vein, respectively, d.p and d s Here, θ represents the diameters of the first and second order vessels in the portal vein, respectively, and Q represents the average portal vein flow rate (in cm³). 3 μ is the viscosity of blood vessels (in Pa·s).

[0074] The second establishment unit is used to establish the pressure-flow relationship of portal vein blood in the fractal flow segment based on the fractal dimension of the determined fractal flow segment, combined with fractal theory and Hagenpoeuille flow.

[0075] In this embodiment, the hierarchical relationship of blood vessels in a single parent duct is characterized using tree-like fractal theory:

[0076]

[0077] Where Δp is the pressure drop (in Pa) of a single dendritic fractal network; Δp k The pressure drop (in Pa) is the pressure drop of the k-th grade vessel; m is the number of vessel grades, determined by the smallest parent vessel diameter and the diameter of the hepatic sinusoids; n is the number of bifurcations; l f0 d f0 , , represent the length and diameter (in cm) of the parent tube of the dendritic fractal network, respectively; γ and β are the diameter scaling factor and length scaling factor of the dendritic fractal network, respectively. β is set to 0.7 according to Murray's Law, and the value of γ is related to the dimension D of the dendritic fractal. l They are related and satisfy the following relationship:

[0078]

[0079] D l This was identified using the box counting method described above;

[0080] Furthermore, using capillary number fractal theory, the number of mother tubes dN(d) of different diameters was analyzed. f0 ) To conduct statistics:

[0081]

[0082] Where, d f0max D is the diameter (in cm) of the largest mother venous tube in the portal venous system. f The capillary number is the fractal dimension, and the porosity is identified from angiographic images. From the formula, we can derive:

[0083]

[0084] Where, d f0min This is the minimum diameter of the portal vein's main duct;

[0085] Based on the vascular classification of individual mother tubes and the number of mother tubes of different diameters, the pressure-flow relationship of portal vein blood in the fractal flow segment is established, expressed as:

[0086]

[0087] Where, ΔP fractal denoted as ρ, where ρ is the pressure drop in the fractal flow section; Q is the average portal vein flow rate.

[0088] The third determining unit is used to determine the pressure-flow relationship of the portal vein based on the established pressure-flow relationship of portal vein blood in the pipe flow segment and the pressure-flow relationship of portal vein blood in the fractal flow segment.

[0089] In this embodiment, the pressure-flow relationship of the portal vein is represented by the pressure drop ΔP. pipeline and ΔP fractal The sum is expressed as:

[0090]

[0091] Where ΔP represents the sum of the pressure drops in the pipe flow section and the fractal flow section, ΔP pipeline ΔP fractal These represent the pressure drop in the pipe flow section and the pressure drop in the fractal flow section, respectively.

[0092] The statistics module 13 is used to perform statistics on the user's Doppler ultrasound images to obtain the average portal vein flow value;

[0093] In this embodiment, the Doppler ultrasound images of the case were statistically analyzed to obtain the average portal vein flow value Q (in cm). 3 / s);

[0094] Module 14 is used to determine the user's portal vein pressure value based on the obtained portal vein pressure-flow relationship and the user's average portal vein flow value Q, combined with the fixed pressure value (665 Pa) at the hepatic sinusoids.

[0095] P = ΔP + 665

[0096] Where P represents the user's portal vein pressure value, and ΔP represents the sum of the pressure drops in the pipe flow section and the fractal flow section.

[0097] In this embodiment, the average flow rate Q and geometric parameter values ​​of the case are substituted into the pressure-flow relationship of the portal vein to obtain the portal vein pressure value of the case.

[0098] The non-invasive portal hypertension detection system based on fractal theory described in this invention has at least the following beneficial effects:

[0099] 1) To address the difficulty in characterizing complex multi-scale blood vessels, this system establishes a complete blood flow model from the hepatic sinusoids to the portal vein based on fractal theory and flow laws. It provides a complete characterization of blood flow in the segment from the hepatic sinusoids to the portal vein, with accurate outlet pressure boundary conditions (specifically, fixed pressure values ​​at the hepatic sinusoids) and physical meaning. Combined with the average portal vein flow rate of a specific user, it can accurately and quickly predict portal vein pressure values. At the same time, it avoids the lack of physical meaning in general non-invasive pressure measurement methods, effectively solving the problem of low accuracy in non-invasive detection, and has important guiding significance for clinical medicine.

[0100] 2) Based on CT images and angiography images, this system proposes a new non-invasive pressure measurement method that can be applied to the rapid non-invasive prediction of portal vein pressure with high accuracy.

[0101] This invention also provides a specific implementation of a non-invasive detection method for portal hypertension based on fractal theory. Since the non-invasive detection method for portal hypertension based on fractal theory provided by this invention corresponds to the aforementioned specific implementation of a non-invasive detection system for portal hypertension based on fractal theory, the non-invasive detection method for portal hypertension based on fractal theory can achieve the purpose of this invention by executing the process steps in the above specific implementation method. Therefore, the explanations and descriptions in the above specific implementation of the non-invasive detection system for portal hypertension based on fractal theory also apply to the specific implementation of the non-invasive detection method for portal hypertension based on fractal theory provided by this invention, and will not be repeated in the following specific implementations of this invention.

[0102] like Figure 7 As shown, this embodiment of the invention also provides a non-invasive detection method for portal hypertension based on fractal theory, comprising:

[0103] S101. Based on the distribution characteristics of vessel diameter from the hepatic sinusoids to the portal vein, the portal vein blood flow process is divided into a tubular flow segment and a fractal flow segment. The geometric parameters of the tubular flow segment and the fractal flow segment are determined based on the acquired CT images and angiography images.

[0104] S102. Based on the determined geometric parameters of the pipe flow section and fractal flow section, combined with fractal theory and flow law, a blood flow model from the hepatic sinusoids to the portal vein is established to obtain the pressure-flow relationship of the portal vein.

[0105] S103, Statistically analyze the user's Doppler ultrasound images to obtain the average portal vein flow value;

[0106] S104. Based on the obtained portal vein pressure-flow relationship and the user's average portal vein flow value, the user's portal vein pressure value is obtained.

[0107] The non-invasive detection method for portal hypertension based on fractal theory described in this invention has at least the following beneficial effects:

[0108] 1) To address the difficulty in characterizing complex multi-scale blood vessels, this system establishes a complete blood flow model from the hepatic sinusoids to the portal vein based on fractal theory and flow laws. It provides a complete characterization of blood flow in the segment from the hepatic sinusoids to the portal vein, with accurate outlet pressure boundary conditions and physical meaning. Combined with the average portal vein flow value of a specific user, it can accurately and quickly predict the portal vein pressure value. At the same time, it avoids the lack of physical meaning in general non-invasive pressure measurement methods, effectively solving the problem of low accuracy in non-invasive detection, and has important guiding significance for clinical medicine.

[0109] 2) Based on CT images and angiography images, this system proposes a new non-invasive pressure measurement method that can be applied to the rapid non-invasive prediction of portal vein pressure with high accuracy.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A non-invasive detection system for portal hypertension based on fractal theory, characterized in that, include: The segmentation module is used to divide the portal vein blood flow process into a tubular flow segment and a fractal flow segment based on the distribution characteristics of the vessel diameter from the hepatic sinusoids to the portal vein. The geometric parameters of the tubular flow segment and the fractal flow segment are determined based on the acquired CT images and angiography images. A module is established to create a blood flow model from the hepatic sinusoids to the portal vein based on the geometric parameters of the determined pipe flow section and fractal flow section, combined with fractal theory and flow laws, and to obtain the pressure-flow relationship of the portal vein. The statistics module is used to analyze the user's Doppler ultrasound images to obtain the average portal vein flow value; The determination module is used to obtain the user's portal vein pressure value based on the obtained portal vein pressure-flow relationship and the user's average portal vein flow value.

2. The non-invasive portal hypertension detection system based on fractal theory according to claim 1, characterized in that, The partitioning module includes: The division unit is used to divide the portal vein blood flow process into a tubular flow segment and a fractal flow segment based on the distribution characteristics of the vessel diameter from the hepatic sinusoids to the portal vein. The first determining unit is used to determine the geometric parameters of the ductal flow segment based on CT images; wherein the geometric parameters of the ductal flow segment include the length and diameter of the first-order and second-order vessels in the portal vein; The second determining unit is used to determine the geometric parameters of the fractal flow segment based on the angiography image; wherein the geometric parameters of the fractal flow segment include: the tree fractal dimension and the capillary number fractal dimension of the fractal flow portion.

3. The non-invasive portal hypertension detection system based on fractal theory according to claim 1, characterized in that, The establishment module includes: The first establishment unit is used to establish the pressure-flow relationship of portal vein blood in the channel flow segment based on the length and diameter of the first and second level vessels in the determined portal vein channel, combined with Hagenpoise flow. The second establishment unit is used to establish the pressure-flow relationship of portal vein blood in the fractal flow segment based on the determined fractal dimension of the fractal flow segment, combined with fractal theory and Hagenpoeuille flow. The third determining unit is used to determine the pressure-flow relationship of the portal vein based on the established pressure-flow relationship of portal vein blood in the pipe flow segment and the pressure-flow relationship of portal vein blood in the fractal flow segment.

4. The non-invasive portal hypertension detection system based on fractal theory according to claim 3, characterized in that, The established pressure-flow relationship of portal vein blood in the pipeline flow section is as follows: ； in, For the pressure drop in the pipeline flow section, This is due to the pressure drop in the first-order vessels of the portal vein. This is due to the pressure drop in the second-order vessels of the portal vein. , These represent the lengths of the first-order and second-order vessels within the portal vein. and These are the diameters of the first-order and second-order vessels in the portal vein, respectively. The average portal vein flow rate is [value missing]. This refers to the viscosity of blood vessels.

5. The non-invasive portal hypertension detection system based on fractal theory according to claim 3, characterized in that, The second establishment unit is specifically used to characterize the hierarchical relationship of blood vessels in a single parent duct using tree fractal theory: ； in, For the pressure drop of a single tree-like fractal network; For the first Pressure drop in blood vessels; The number of vascular grades. Determined by the minimum diameter of the mother tube and the diameter of the hepatic sinusoids; Number of branches; , These represent the length and diameter of the parent tube of the tree-like fractal network, respectively. The viscosity of blood vessels; , These are the diameter scaling factor and length scaling factor of the tree-like fractal network, respectively. Values ​​and tree fractal dimension They are related and satisfy the following relationship: ； Using capillary number fractal theory to determine the number of mother tubes of different diameters Statistical analysis: = ； in, The diameter of the largest venous tube in the portal vein system. The number of capillaries is the fractal dimension; Based on the vascular classification of individual mother tubes and the number of mother tubes of different diameters, the pressure-flow relationship of portal vein blood in the fractal flow segment is established, expressed as: ； in, For the pressure drop in the fractal flow section; This is the minimum diameter of the portal vein's main duct; This represents the average portal vein flow.

6. The non-invasive portal hypertension detection system based on fractal theory according to claim 5, characterized in that, Capillary number fractal dimension Represented as: ； in, Porosity identified from angiographic images. , These are the maximum and minimum diameters of the portal vein, respectively.

7. The non-invasive portal hypertension detection system based on fractal theory according to claim 1, characterized in that, The pressure-flow relationship of the portal vein is expressed as follows: ； in, This represents the sum of the pressure drops in the pipe flow section and the fractal flow section. , These represent the pressure drop in the pipe flow section and the pressure drop in the fractal flow section, respectively.

8. The non-invasive portal hypertension detection system based on fractal theory according to claim 1, characterized in that, The user's portal vein pressure value is expressed as follows: ； in, This indicates the user's portal vein pressure value. This represents the sum of the pressure drops in the pipe flow section and the fractal flow section. This indicates the fixed pressure value at the hepatic sinusoids.

9. A non-invasive detection method for portal hypertension based on fractal theory, characterized in that, include: Based on the distribution characteristics of vessel diameter from the hepatic sinusoids to the portal vein, the portal vein blood flow process is divided into a tubular flow segment and a fractal flow segment. The geometric parameters of the tubular flow segment and the fractal flow segment are determined based on the acquired CT images and angiography images. Based on the determined geometric parameters of the pipe flow section and fractal flow section, combined with fractal theory and flow law, a blood flow model from the hepatic sinusoids to the portal vein is established, and the pressure-flow relationship of the portal vein is obtained. The average portal vein flow rate was obtained by statistically analyzing the user's Doppler ultrasound images. Based on the obtained portal vein pressure-flow relationship and the user's average portal vein flow value, the user's portal vein pressure value is obtained.

Images