Microcirculatory disorder assessment method, microcirculatory disorder resistance calculation method and system

Abstract

The present invention discloses a method for assessing myocardial ischemia, comprising: acquiring a first image (CCTA) and a second image (CTP); obtaining a mapping image based on the first image and the second image; calibrating a target vascular perfusion area in the mapping image; and obtaining myocardial blood flow (MBF) in the target vascular perfusion area under stenosis conditions based on the second image and the target vascular perfusion area. hyper‑stenosis ; Obtain myocardial blood flow MBF under simulated normal conditions hyper‑normal MBF is the myocardial blood flow in the target vascular perfusion area under stenosis conditions. hyper‑stenosis and myocardial blood flow (MBF) under simulated normal conditions hyper‑normal , assessing the proportion of myocardial ischemia caused by coronary artery stenosis using CTP-FFR. The present invention, through the aforementioned method, enables rapid and accurate quantitative assessment of overall myocardial ischemia. The present invention also discloses a myocardial ischemia assessment system, a microcirculatory disorder assessment method and system, a microcirculatory disorder resistance calculation method and system, an electronic device, and a computer-readable storage medium.

Description

Technical Field

[0001] The present invention relates to the field of biomedical engineering technology, and in particular to a method and system for evaluating myocardial ischemia, a method and system for evaluating microcirculation disorders, and a method and system for calculating microcirculation disorders. Background Art

[0002] Myocardial ischemia refers to an inadequate blood supply to the heart muscle, resulting in a lack of oxygen and nutrients for myocardial cells, thus impairing normal heart function. Myocardial ischemia is a serious cardiovascular disease that, if left untreated, can lead to myocardial infarction and even be life-threatening. Chronic coronary syndrome (CCS) refers to a series of symptoms and signs of myocardial ischemia caused by the ongoing progression of coronary atherosclerosis. Coronary artery disease can manifest as epicardial coronary artery stenosis and / or microvascular dysfunction.

[0003] The myocardial ischemia assessment method and system have non-therapeutic purposes. The myocardial ischemia assessment method and system are based on the processing of medical images, that is, their direct objects are medical images, such as CCTA and CTP.

[0004] Chinese invention patent CN117476238B, Computational fluid dynamics simulation analysis method and system for evaluating myocardial ischemia, discloses a method of computational fluid dynamics based on patient-specific images and protects a method of calculating the corresponding myocardial perfusion area of ​​blood vessels based on images.

[0005] The paper "A Novel CT Perfusion-Based Fractional Flow Reserve Algorithm for Detecting Coronary Artery Disease" published by Xuelian Gao et al. discloses the use of CTP images to calculate MBF and then uses fluid dynamics simulation methods to optimize the CT-FFR (blood flow reserve fraction based on coronary CT angiography) calculation.

[0006] Existing noninvasive ischemia assessment methods for epicardial coronary artery stenosis mostly calculate blood flow and pressure based on computational fluid dynamics principles, such as the CT angiography-derived-Fractional Flow Reserve (CT-FFR) technique. Existing methods employ mechanical simulation to calculate the proportion of myocardial ischemia caused by coronary artery stenosis. CT-FFR typically involves the following steps: Image acquisition: Acquiring CT images of the heart and blood vessels; Image segmentation: Using image processing algorithms to segment tissue, blood vessels, and blood within the CT images and identify the internal and external boundaries of the blood vessels, as well as the cardiac wall layers; Fluid dynamics modeling: Using the segmented image data and individual hemodynamic parameters, a three-dimensional model of the heart and blood vessels is constructed. Pressure gradient calculation: Within the established fluid dynamics model, the pressure gradient at the stenotic site is calculated. The pressure gradient refers to the change in pressure from the proximal to the distal end of a vessel as blood flows through it. FFR calculation: In the absence of stenosis, the pressure gradient within a vessel is inversely proportional to its radius. Therefore, the FFR value can be calculated by comparing the pressure gradients before and after stenosis. Therefore, the existing method of calculating CT-FFR by fluid dynamics simulation is to process the acquired CT images and obtain the pressure gradient in the blood vessels before and after stenosis, and calculate the blood flow reserve fraction FFR based on the pressure gradient in the blood vessels before and after stenosis.

[0007] Existing mainstream computational methods primarily include fluid dynamics simulation and machine learning. Fluid dynamics simulation requires specific boundary conditions as input, consumes significant time and computing power, is costly, and is slow. Furthermore, the input model parameters are subject to uncertainty, impacting the reliability of the simulation results. Machine learning methods, while fast, require specialized software and technical expertise, and are complex to operate. However, the vast amount of data required to train the model also comes from mechanical simulation results, making it data-dependent. Furthermore, while trained models typically perform well on the training dataset, they may perform poorly on unseen data, impacting the reliability of the learning results.

[0008] Therefore, existing methods of fluid mechanics simulation and training models have problems such as complex calculations, high costs, slow speeds, inaccurate results, and unreliable results. Summary of the Invention

[0009] The object of the present invention is to solve the above-mentioned problems.

[0010] The myocardial ischemia assessment method and system, microcirculation disorder assessment method and system, and microcirculation disorder calculation method and system provided by the present invention all have non-therapeutic purposes.

[0011] The myocardial ischemia assessment method and system, microcirculation disorder assessment method and system, and microcirculation disorder calculation method and system provided by the present invention are all based on the processing of medical images, that is, their direct objects are medical images, such as CCTA and CTP.

[0012] First, compared to calculating CT-FFR through CT images, the present invention provides a non-invasive method for assessing myocardial ischemia based solely on imaging. That is, the method can obtain CTP-FFR directly based on CTP image processing. CTP-FFR is the blood flow reserve fraction based on CT myocardial perfusion images under load, which quantifies the degree of ischemia caused by epicardial vascular stenosis. The method for assessing myocardial ischemia provided by the present invention does not require the use of fluid mechanics. Since mechanical simulation is a simulation calculation, the assessment of myocardial ischemia obtained based on mechanical simulation is a simulation result. Compared with the assessment of myocardial ischemia obtained based on mechanical simulation, the results obtained by the image processing-based quantification method of myocardial ischemia provided by the present invention are more accurate and faster. The present invention uses CTP-FFR to assess the ischemic condition of actual myocardial perfusion, which has the advantage of rapid and accurate quantitative assessment of overall myocardial ischemia.

[0013] An embodiment of the present invention discloses a method for assessing myocardial ischemia, comprising: acquiring a first image and a second image, wherein the first image is a coronary CT angiography image (CCTA), and the second image is a CT myocardial perfusion image (CTP) under load; mapping a left ventricular myocardial region to each vessel in a coronary artery tree using an image processing method based on the first image and the second image to obtain a mapped image; calibrating a target vascular perfusion region in the mapped image; acquiring a time-attenuation curve of myocardial tissue in the myocardial perfusion region based on the second image, acquiring a quantitative myocardial blood flow in the target vascular perfusion region based on the time-attenuation curve of the myocardial tissue and the target vascular perfusion region, and using the quantitative myocardial blood flow in the target vascular perfusion region as the myocardial blood flow (MBF) in the target vascular perfusion region under stenosis. hyper-stenosis ; Obtain the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel, and use the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions hyper-normal MBF is the myocardial blood flow in the target vascular perfusion area under stenosis conditions. hyper-stenosis MBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal , CTP-FFR is used to assess the proportion of myocardial ischemia caused by coronary artery stenosis.

[0014] Using the above technical solution, the proportion of myocardial ischemia due to coronary artery stenosis (CTP-FFR) is calculated by processing the first and second images and using the myocardial blood flow (MBF) obtained from the second CTP image. In other words, the myocardial ischemia assessment method provided by the present invention directly obtains CTP-FFR based on image processing of CTP, quantifying the degree of ischemia caused by epicardial vascular stenosis. Unlike CT-FFR, which is used to assess ischemia caused by epicardial vascular stenosis, CTP-FFR can more quickly, accurately, and reliably quantitatively assess the ischemic state of actual myocardial perfusion.

[0015] According to another specific embodiment of the present invention, a time-attenuation curve of the myocardial tissue of the myocardial perfusion region is obtained based on the second image, and a quantitative myocardial blood flow of the target vascular perfusion region is obtained based on the time-attenuation curve of the myocardial tissue and the target vascular perfusion region. The quantitative myocardial blood flow of the target vascular perfusion region is used as the myocardial blood flow MBF of the target vascular perfusion region in the case of stenosis. hyper-stenosis , including: obtaining the quantitative myocardial blood flow of the target vessel perfusion area based on the rising slope, peak value and target vessel area of ​​interest of the myocardial tissue time-decay curve of the myocardial perfusion area, and using the quantitative myocardial blood flow of the target vessel perfusion area as the myocardial blood flow MBF of the target vessel perfusion area under stenosis hyper-stenosis .

[0016] According to another specific embodiment of the present invention, the quantitative myocardial blood flow of the target vessel perfusion area includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel, and / or the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel.

[0017] According to another embodiment of the present invention, the myocardial blood flow MBF of the target vascular perfusion area under stenosis is hyper-stenosis MBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal Get MBF hyper-stenosis and MBF hyper-normal The ratio of blood flow in the diseased state to that in the healthy state is CTP-FFR:

[0018]

[0019] The CTP-FFR value is used to evaluate the proportion of myocardial ischemia caused by coronary artery stenosis. The proportion of myocardial ischemia caused by coronary artery stenosis is (1-CTP-FFR)*100%.

[0020] According to another specific embodiment of the present invention, based on the first image and the second image, the left ventricular myocardial region is mapped to each blood vessel in the coronary artery vascular tree by an image processing method to obtain a mapped image, including: segmenting the first image by a first image segmentation algorithm to obtain a coronary artery vascular tree image, and segmenting the second image by a second image segmentation algorithm to obtain a left ventricular myocardial structure image; registering the first image and the second image by an image registration method to obtain a registered image, wherein the registered image is the registered first image; and mapping the left ventricular myocardial structure region of the left ventricular myocardial structure image to each blood vessel of the coronary artery vascular tree in the coronary artery vascular tree image by an image mapping method based on the registered image and the second image to obtain a mapped image.

[0021] According to another specific embodiment of the present invention, based on the registered image and the second image, the left ventricular myocardial structure region of the left ventricular myocardial structure image is mapped to each vessel of the coronary artery vascular tree in the coronary artery vascular tree image by an image mapping method to obtain a mapped image, including: mapping the left ventricular myocardial region to each vessel in the coronary artery vascular tree by bifurcation principle mapping to obtain a mapped image; mapping the left ventricular myocardial region to each vessel in the coronary artery vascular tree by bifurcation principle mapping to obtain a mapped image, preferably, further including: mapping the left ventricular myocardial region to all coronary arteries with a lumen diameter ≥1 mm in the coronary artery vascular tree by the divide-and-conquer principle.

[0022] In a second aspect, an embodiment of the present invention discloses a myocardial ischemia assessment system, comprising: an image acquisition module for acquiring a first image and a second image, the first image being a coronary CT angiography image (CCTA), and the second image being a CT myocardial perfusion image (CTP) under load; an image processing module for mapping the left ventricular myocardial area to each blood vessel in the coronary artery vascular tree by an image processing method based on the first image and the second image, to obtain a mapping image; an image calibration module for calibrating the target vascular perfusion area in the mapping image; a myocardial blood flow acquisition module for the target vascular perfusion area, for acquiring a time-attenuation curve of the myocardial tissue in the myocardial perfusion area based on the second image, acquiring a quantitative myocardial blood flow in the target vascular perfusion area based on the time-attenuation curve of the myocardial tissue and the target vascular perfusion area, and using the quantitative myocardial blood flow in the target vascular perfusion area as the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis ; Used to obtain the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel, and use the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions hyper-normal Myocardial ischemia assessment module, used to evaluate myocardial blood flow (MBF) in the presence of stenosis based on the target vascular perfusion area hyper-stenosisMBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal , CTP-FFR is used to assess the proportion of myocardial ischemia caused by coronary artery stenosis.

[0023] By adopting the above technical solution, the image acquisition module, the image processing module, the image calibration module, the myocardial blood flow acquisition module of the target vascular perfusion area and the myocardial ischemia assessment module cooperate with each other to perform a non-invasive assessment of myocardial ischemia based only on the first image and the second image. The myocardial ischemia assessment system can quantify the degree of ischemia caused by epicardial vascular stenosis without the help of fluid mechanics. In addition, the present invention evaluates the ischemic condition of the actual myocardial perfusion through CTP-FFR, and has the advantages of rapid, accurate and reliable quantitative assessment of overall myocardial ischemia.

[0024] According to another specific embodiment of the present invention, the myocardial blood flow acquisition module of the target vascular perfusion area is used to obtain the quantitative myocardial blood flow of the target vascular perfusion area based on the rising slope, peak value and target vascular area of ​​interest of the myocardial tissue time-decay curve of the target vascular perfusion area, and use the quantitative myocardial blood flow of the target vascular perfusion area as the myocardial blood flow MBF of the target vascular perfusion area in the case of stenosis. hyper-stenosis .

[0025] According to another specific embodiment of the present invention, the quantitative myocardial blood flow of the target vessel perfusion area includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel, and / or the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel.

[0026] According to another embodiment of the present invention, the myocardial ischemia assessment module is used to evaluate the myocardial blood flow (MBF) of the target vascular perfusion area under stenosis. hyper-stenosis MBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal Get MBF hyper-stenosis and MBF hyper-normal The ratio of blood flow in the diseased state to that in the healthy state is CTP-FFR:

[0027]

[0028] The CTP-FFR value is used to evaluate the proportion of myocardial ischemia caused by coronary artery stenosis. The proportion of myocardial ischemia caused by coronary artery stenosis is (1-CTP-FFR)*100%.

[0029] According to another specific embodiment of the present invention, the image processing module includes: an image segmentation module for segmenting the first image using a first image segmentation algorithm to obtain a coronary artery vascular tree image, and segmenting the second image using a second image segmentation algorithm to obtain a left ventricular myocardial structure image; an image registration module for registering the first image and the second image using an image registration method to obtain a registered image, wherein the registered image is the registered first image; and an image mapping module for mapping the left ventricular myocardial structure region of the left ventricular myocardial structure image to each vessel of the coronary artery vascular tree in the coronary artery vascular tree image using an image mapping method based on the registered image and the second image to obtain a mapped image.

[0030] According to another specific embodiment of the present invention, the image mapping module is used to map the left ventricular myocardial area to each blood vessel in the coronary artery vascular tree through bifurcation principle mapping to obtain a mapping image. Furthermore, the image mapping module is used to map the left ventricular myocardial area to all coronary arteries with a lumen diameter ≥1 mm in the coronary artery vascular tree through the divide-and-conquer principle.

[0031] In a third aspect, the present invention provides a method for assessing microcirculatory disorders, comprising: obtaining a proportion of myocardial ischemia due to coronary artery stenosis (CTP-FFR) using the myocardial ischemia assessment method according to any embodiment of the first aspect; obtaining a proportion of myocardial ischemia due to epicardial vascular stenosis (CT-FFR) using a CT-FFR method for assessing epicardial vascular stenosis; and assessing a proportion of microcirculatory disorders (ΔCT-FFR) based on the proportion of myocardial ischemia due to coronary artery stenosis (CTP-FFR) and the proportion of myocardial ischemia due to epicardial vascular stenosis (CT-FFR).

[0032] △CT-FFR=CT-FFR-CTP-FFR formula 2.

[0033] Using the above technical solution, since CT-FFR evaluates ischemia caused by epicardial vascular stenosis, and CTP-FFR can evaluate the ischemic condition of actual myocardial perfusion, the numerical difference between CT-FFR and CTP-FFR is used to indicate the degree of ischemia caused by microcirculatory disorders. This is simple and efficient, and since this microcirculatory disorder assessment method adopts a non-invasive approach, it is safer.

[0034] In a fourth aspect, the present invention provides a microcirculation disorder assessment system, comprising: a myocardial ischemia assessment module for obtaining a proportion of myocardial ischemia (CTP-FFR) due to coronary artery stenosis using a myocardial ischemia assessment method according to any one of the embodiments of the first aspect; the myocardial ischemia assessment module is further configured to obtain a proportion of myocardial ischemia (CT-FFR) due to epicardial vascular stenosis using a CT-FFR method for assessing epicardial vascular stenosis; and a microcirculation disorder assessment module for assessing a microcirculation disorder proportion (ΔCT-FFR) based on the proportion of myocardial ischemia (CTP-FFR) due to coronary artery stenosis and the proportion of myocardial ischemia (CT-FFR) due to epicardial vascular stenosis obtained by the myocardial ischemia assessment module.

[0035] △CT-FFR=CT-FFR-CTP-FFR formula 2.

[0036] By adopting the above technical solution, CT-FFR and CTP-FFR are obtained through the joint action of the myocardial ischemia assessment module and the microcirculation disorder assessment module. Since CT-FFR assesses ischemia caused by epicardial vascular stenosis, and CTP-FFR can assess the ischemia of actual myocardial perfusion, the numerical difference between CT-FFR and CTP-FFR indicates the degree of ischemia caused by microcirculation disorder. This is simple and efficient, and because the microcirculation disorder assessment system adopts a non-invasive method, it is safer.

[0037] In a fifth aspect, the present invention provides a method for calculating microcirculatory resistance, comprising: obtaining the aortic pressure P0 of the coronary system through the systolic and diastolic pressures of the target subject; obtaining the venous pressure P2 of the coronary system, wherein the venous pressure P2 of the coronary system under load is 5 mmHg; obtaining a second image, which is a CT myocardial perfusion image CTP under load; and obtaining the myocardial blood flow MBF of the target vascular perfusion area under stenosis according to the second image by the myocardial ischemia assessment method according to any embodiment of the first aspect. hyper-stenosis , obtain the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal According to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal Obtain the resistance R of the microcirculation to the blood flow under normal conditions; obtain the pressure P1 of the distal epicardial blood according to the aortic pressure P0 of the coronary system and the proportion of myocardial ischemia caused by epicardial vascular stenosis obtained by the microcirculatory disorder assessment method in any embodiment of the third aspect; obtain the myocardial blood flow MBF of the target vascular perfusion area under stenosis according to the pressure P1 of the distal epicardial blood vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis, the resistance R caused by microcirculation to blood flow in normal conditions, and the aortic pressure P0 of the coronary system to obtain the resistance R2 caused by microcirculation disorders to blood flow.

[0038] This technical approach takes into account the resistance of the coronary microcirculation under normal conditions and only calculates the resistance caused by microcirculatory disturbances. This means that the assessment not only considers the impact of epicardial vascular stenosis on blood flow, but also the inherent resistance of the microcirculation itself. This method can more accurately assess the impact of microcirculatory disturbances on myocardial perfusion, thereby providing a more comprehensive assessment of myocardial ischemia.

[0039] According to another embodiment of the present invention, the myocardial blood flow MBF of the target vascular perfusion area under normal conditions is simulated according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, and the hyper-normal Obtain the resistance R caused by microcirculation to blood flow under normal conditions, and the calculation method is:

[0040]

[0041] Alternatively, the pressure P1 at the distal end of the epicardial blood is obtained based on the aortic pressure P0 of the coronary system and the proportion of myocardial ischemia caused by epicardial vascular stenosis obtained by the microcirculatory disorder assessment method in any embodiment of the third aspect, as calculated by:

[0042] P1=P0*CT-FFR Formula 4.

[0043] According to another embodiment of the present invention, the myocardial blood flow MBF of the target vascular perfusion area under stenosis is calculated based on the pressure P1 at the distal end of the epicardial vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis , the resistance R caused by microcirculation to blood flow in normal state, the aortic pressure P0 of the coronary system, and the resistance R2 caused by microcirculation disorder to blood flow are calculated as follows:

[0044]

[0045] According to another specific embodiment of the present invention, the aortic pressure P0 of the coronary system is obtained by the systolic pressure and diastolic pressure of the target object, including at least one of the following calculation methods: aortic pressure P0 = diastolic pressure + 1 / 3 (systolic pressure - diastolic pressure) + 5 mmHg; aortic pressure P0 = diastolic pressure + 0.412 (systolic pressure - diastolic pressure).

[0046] In a sixth aspect, the present invention provides a system for calculating microcirculatory resistance, comprising: a data acquisition module for acquiring the aortic pressure P0 of the coronary system through the systolic and diastolic pressures of the target subject; acquiring the venous pressure P2 of the coronary system, wherein the venous pressure P2 of the coronary system under load is 5 mmHg; an image acquisition and analysis module for acquiring a second image, wherein the second image is a CT myocardial perfusion image CTP under load; based on the second image, acquiring the myocardial blood flow MBF of the target vascular perfusion area under stenosis by the myocardial ischemia assessment method as described in any embodiment of the first aspect. hyper-stenosis , obtain the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal ; Data processing module, used to calculate the myocardial blood flow MBF under normal conditions according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, and the target vascular perfusion area hyper-normal Obtain the resistance R of the microcirculation to the blood flow under normal conditions; obtain the pressure P1 of the distal epicardial blood according to the aortic pressure P0 of the coronary system and the proportion CT-FFR of myocardial ischemia caused by epicardial vascular stenosis obtained by the microcirculatory disorder assessment method of any embodiment of the second aspect; obtain the myocardial blood flow MBF of the target vascular perfusion area under stenosis according to the pressure P1 of the distal epicardial blood vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis , the resistance R caused by microcirculation to blood flow in normal conditions, and the aortic pressure P0 of the coronary system to obtain the resistance R2 caused by microcirculation disorders to blood flow.

[0047] Using this technical solution, the microcirculatory resistance calculation system takes into account the resistance existing in the normal coronary microcirculation and only calculates the resistance caused by microcirculatory disturbances. This means that the assessment not only considers the impact of epicardial vascular stenosis on blood flow, but also the inherent resistance of the microcirculation itself. This system can more accurately assess the impact of microcirculatory disturbances on myocardial perfusion, thereby providing a more comprehensive assessment of myocardial ischemia.

[0048] According to another embodiment of the present invention, the myocardial blood flow MBF of the target vascular perfusion area under normal conditions is simulated according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, and the hyper-normal Obtain the resistance R caused by microcirculation to blood flow under normal conditions, and the calculation method is:

[0049]

[0050] Alternatively, the pressure P1 at the distal end of the epicardial blood is obtained based on the aortic pressure P0 of the coronary system and the proportion of myocardial ischemia caused by epicardial vascular stenosis obtained by the microcirculatory disorder assessment method in any embodiment of the third aspect, as calculated as follows:

[0051] P1=P0*CT-FFR Formula 4.

[0052] According to another embodiment of the present invention, the myocardial blood flow MBF of the target vascular perfusion area under stenosis is calculated based on the pressure P1 at the distal end of the epicardial vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis , the resistance R caused by microcirculation to blood flow in normal state, the aortic pressure P0 of the coronary system, and the resistance R2 caused by microcirculation disorder to blood flow are calculated as follows:

[0053]

[0054] According to another specific embodiment of the present invention, the aortic pressure P0 of the coronary system is obtained by the systolic pressure and diastolic pressure of the target object, including at least one of the following calculation methods: aortic pressure P0 = diastolic pressure + 1 / 3 (systolic pressure - diastolic pressure) + 5 mmHg; aortic pressure P0 = diastolic pressure + 0.412 (systolic pressure - diastolic pressure).

[0055] The beneficial effects of the present invention are as follows:

[0056] 1. The present invention proposes a non-invasive assessment method for myocardial ischemia based solely on imaging, which can quantify the degree of ischemia caused by epicardial vascular stenosis without the aid of fluid mechanics.

[0057] The myocardial ischemia assessment method provided by the present invention processes a first image (i.e., coronary CT angiography (CCTA)) and a second image (a CT myocardial perfusion (CTP) image under stress), and uses the myocardial blood flow (MBF) obtained from the second CTP image to calculate the proportion of myocardial ischemia caused by coronary artery stenosis (CTP-FFR). In other words, the myocardial ischemia assessment method provided by the present invention directly obtains CTP-FFR based on image processing of CTP, thereby quantifying the degree of ischemia caused by epicardial vascular stenosis.

[0058] Compared to calculating CT-FFR through CT images, the present invention provides a non-invasive assessment method for myocardial ischemia based solely on imaging. That is, the myocardial ischemia assessment method can obtain CTP-FFR directly based on CTP image processing. CTP-FFR is the blood flow reserve fraction based on CT myocardial perfusion images CTP under load, which quantifies the degree of ischemia caused by epicardial vascular stenosis. The myocardial ischemia assessment method provided by the present invention does not require the use of fluid mechanics. Since mechanical simulation is a simulation calculation, the assessment of myocardial ischemia obtained based on mechanical simulation is a simulation result. Compared to the assessment of myocardial ischemia obtained based on mechanical simulation, the results obtained by the image processing-based quantification method for myocardial ischemia provided by the present invention are more accurate and faster. The present invention assesses the ischemic condition of actual myocardial perfusion through CTP-FFR, and has the advantage of rapid and accurate quantitative assessment of overall myocardial ischemia.

[0059] 2. The myocardial ischemia assessment system provided by the present invention cooperates with an image acquisition module, an image processing module, an image calibration module, a myocardial blood flow acquisition module of the target vascular perfusion area and a myocardial ischemia assessment module to perform non-invasive assessment of myocardial ischemia based only on the first image and the second image. The myocardial ischemia assessment system can quantify the degree of ischemia caused by epicardial vascular stenosis without the aid of fluid mechanics, and the present invention evaluates the ischemic condition of actual myocardial perfusion through CTP-FFR, which has the advantages of rapid, accurate and reliable quantitative assessment of overall myocardial ischemia.

[0060] 3. The present invention provides a method and system for assessing microcirculatory disturbances. This method uses CTP-FFR (the ratio of myocardial ischemia due to coronary artery stenosis) and CT-FFR (the ratio of myocardial ischemia due to epicardial vascular stenosis) to assess the proportion of myocardial ischemia due to epicardial vascular stenosis. Because CT-FFR assesses ischemia due to epicardial vascular stenosis, while CTP-FFR can assess actual myocardial perfusion ischemia, the difference between CT-FFR and CTP-FFR values ​​indicates the degree of ischemia caused by microcirculatory disturbances. This method is simple, efficient, and non-invasive, making it safer.

[0061] 4. The present invention proposes a noninvasive imaging-based method for assessing myocardial ischemia, enabling quantitative evaluation of microcirculatory resistance. The method and system for calculating microcirculatory resistance provided by the present invention take into account the resistance present in the normal coronary microcirculation and only calculate the resistance caused by microcirculatory disturbances. This means that the assessment not only considers the impact of epicardial vascular stenosis on blood flow, but also the inherent resistance of the microcirculation itself. This method can more accurately assess the impact of microcirculatory disturbances on myocardial perfusion, thereby providing a more comprehensive assessment of myocardial ischemia. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The process of the myocardial ischemia assessment method in the embodiment of the present invention is shown as follows: Figure 1 ;

[0063] Figure 2 Shows a coronary CT angiography CCTA image in an embodiment of the present invention;

[0064] Figure 3 1. It shows a CT myocardial perfusion image CTP under load state in an embodiment of the present invention;

[0065] Figure 4 Shows the myocardial blood flow image MBF in an embodiment of the present invention;

[0066] Figure 5 The process of the myocardial ischemia assessment method in the embodiment of the present invention is shown as follows: Figure 2 ;

[0067] Figure 6A Shows a coronary CT angiography image CCTA1 according to another embodiment of the present invention;

[0068] Figure 6B shows a second coronary CT angiography image CCTA2 according to another embodiment of the present invention;

[0069] Figure 7 FIG2 shows a second coronary artery tree image according to another embodiment of the present invention;

[0070] Figure 8A Shows a left ventricular myocardial structure image 1 according to another embodiment of the present invention;

[0071] Figure 8B Shows a second left ventricular myocardial structural image according to another embodiment of the present invention;

[0072] Figure 9 FIG1 shows a coronary artery tree image 1 according to an embodiment of the present invention;

[0073] Figure 10A FIG. 2 shows a registered image according to an embodiment of the present invention;

[0074] Figure 10B shows a mapping image of each vessel in the coronary artery tree and the left ventricular myocardial area according to one embodiment of the present invention;

[0075] Figure 11 A time-attenuation curve diagram of myocardial tissue in a myocardial perfusion area according to an embodiment of the present invention is shown;

[0076] Figure 12 The process of the myocardial ischemia assessment method in the embodiment of the present invention is shown as follows: Figure 3 ;

[0077] Figure 13 The VoxelMorph registration algorithm process and the UNet structure type network design in the embodiment of the present invention are shown;

[0078] Figure 14 A schematic structural diagram of a myocardial ischemia assessment system according to an embodiment of the present invention is shown;

[0079] Figure 15 A flow chart showing a method for evaluating microcirculation disorders according to an embodiment of the present invention is shown;

[0080] Figure 16 A schematic structural diagram of a microcirculation disorder assessment system according to an embodiment of the present invention is shown;

[0081] Figure 17 A flow chart showing a method for calculating microcirculatory disturbances according to an embodiment of the present invention is shown;

[0082] Figure 18 A schematic diagram showing the physical relationship between pressure and blood flow in the coronary system according to an embodiment of the present invention;

[0083] Figure 19 A schematic structural diagram of a microcirculation disorder calculation system according to an embodiment of the present invention is shown;

[0084] Figure 20 A schematic structural diagram of an electronic device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0085] The following is an explanation of the embodiments of the present invention by specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this embodiment. On the contrary, the purpose of introducing the invention in conjunction with the embodiment is to cover other options or modifications that may be extended based on the claims of the present invention. In order to provide a deep understanding of the present invention, the following description will include many specific details. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0086] It should be noted that in this specification, similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0087] The terms “first”, “second”, etc. are only used for distinguishing descriptions and should not be understood as indicating or implying relative importance.

[0088] In the description of this embodiment, it should be noted that, unless otherwise specified or limited, the terms "disposed," "connected," and "connected" should be understood broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this embodiment based on specific circumstances.

[0089] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0090] The myocardial ischemia assessment method and system, microcirculation disorder assessment method and system, microcirculation disorder calculation method and system, electronic equipment, and computer storage medium provided by the present invention all have non-therapeutic purposes.

[0091] The myocardial ischemia assessment method and system, microcirculation disorder assessment method and system, and microcirculation disorder calculation method and system provided by the present invention are all based on the processing of medical images, that is, their direct objects are medical images, such as CCTA and CTP.

[0092] First, reference Figure 1 The present invention provides a method for evaluating myocardial ischemia, comprising:

[0093] S1: Acquire a first image and a second image.

[0094] In this embodiment, the first image is a coronary CT angiography image CCTA, and the second image is a CT myocardial perfusion image CTP under load.

[0095] Among them, such as Figure 2 The first image, Coronary Computed Tomography Angiography (CCTA), is a non-invasive imaging procedure used to detect narrowing or blockage in the coronary arteries. This test uses specialized X-rays and computer processing techniques to produce high-resolution images of the heart and coronary arteries. CCTA images are acquired under non-stress conditions, such as without medication, to visualize the coronary artery structure.

[0096] like Figure 3As shown, the second image is a CT myocardial perfusion (CTP) image under stress, which is a state of increased cardiac stress induced by medication or exercise. Because true myocardial ischemia is determined under stress, this test is typically performed during a stress test, such as one that uses medication to increase heart rate or exercise to increase cardiac stress. This state simulates the workings of the heart during actual activity, allowing for a better assessment of cardiac blood supply and myocardial perfusion. CT myocardial perfusion (CTP) is a non-invasive imaging procedure that images the myocardium to assess its blood supply and perfusion. Cardiac perfusion, including blood flow distribution and any possible areas of ischemia, is crucial for diagnosing coronary artery disease, assessing cardiac risk, and developing treatment plans. Figure 4 The figure shows the myocardial blood flow (MBF) obtained from CT myocardial perfusion images (CTP) under stress conditions.

[0097] S2: Mapping the left ventricular myocardial area to each blood vessel in the coronary artery tree using an image processing method according to the first image and the second image to obtain a mapping image.

[0098] In this embodiment, Figure 2 A first image, a coronary CT angiogram (CCTA), is shown. Figure 3 The second image is shown, namely a CT myocardial perfusion image CTP under stress state. Figure 10B The mapping image shows the mapping relationship between the left ventricular myocardial area and each vessel in the coronary artery tree. Figure 2 The first image shown and Figure 3 The second image shown is obtained as Figure 10B Mapping image shown.

[0099] For example, refer to Figure 10B , vessel b in the coronary artery tree corresponds to the left ventricular myocardial area B, vessel c in the coronary artery tree corresponds to the left ventricular myocardial area C, vessel d in the coronary artery tree corresponds to the left ventricular myocardial area D, and vessel e in the coronary artery tree corresponds to the left ventricular myocardial area E.

[0100] Continue to refer Figure 1 , executing step S3: calibrating the target vascular perfusion area in the mapping image.

[0101] Step S3 implements the calibration of pixel points of the target blood vessel perfusion area on the mapping image.

[0102] In image processing, calibration involves using a series of mathematical procedures to determine the correspondence between image pixels and actual physical dimensions in the real world. Simply put, the purpose of calibration is to enable computers to accurately obtain actual dimensional information from images. Before image calibration, computers process pixel-level information, which cannot be directly converted into physical dimensions such as length and width in the real world. Once calibration is complete, these parameters can be used to convert pixel coordinates in the image to a real-world coordinate system, enabling functions such as length measurement and position location.

[0103] Continue to refer Figure 1 , executing step S4: obtaining a time-attenuation curve of the myocardial tissue of the myocardial perfusion area according to the second image, obtaining a quantitative myocardial blood flow of the target vascular perfusion area based on the time-attenuation curve of the myocardial tissue and the target vascular perfusion area, and using the quantitative myocardial blood flow of the target vascular perfusion area as the myocardial blood flow MBF of the target vascular perfusion area under stenosis hyper-stenosis .

[0104] Step S4 achieves the acquisition of the myocardial blood flow MBF of each pixel point on the second image.

[0105] Furthermore, the myocardial blood flow MBF of each pixel point on the second image obtained in step S4 includes the pixel points of the target vascular perfusion area calibrated on the mapping image in step S3. That is, the myocardial blood flow MBF of each pixel point on the second image has a spatial mapping relationship with the pixel points of the target vascular perfusion area calibrated on the mapping image. By mapping the pixel points of the target vascular perfusion area to the myocardial blood flow MBF of each pixel point in the corresponding area on the second image, the myocardial blood flow MBF of the target vascular perfusion area can be obtained, and the target vascular perfusion area is also the stenotic vascular area. In this embodiment, the mean of the myocardial blood flow of the target vascular perfusion area is calculated and used as the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis .

[0106] In some other possible embodiments, the median of the myocardial blood flow of the target vascular perfusion area is calculated, and the median is used as the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis .

[0107] The time-attenuation curve of myocardial tissue in the myocardial perfusion area is obtained by observing and recording the temporal changes in the absorption and attenuation of a radioactive tracer by myocardial tissue during myocardial perfusion imaging. This curve reflects the perfusion status of myocardial tissue at different times, that is, the blood supply. Analysis of this curve can reveal the temporal and spatial distribution of myocardial perfusion and assess its functional status, which is crucial for diagnosing the severity of coronary artery disease and evaluating treatment efficacy.

[0108] In this embodiment, the time-attenuation curve of the myocardial tissue in the myocardial perfusion area obtained according to the second image is as follows: Figure 11 The horizontal axis represents time in seconds, and the vertical axis represents attenuation in MBq (megabecquerels). The arterial input curve shows the decay of the signal intensity or radionuclide activity in the aorta over time; the myocardial curve shows the decay of the signal intensity or radionuclide activity in the myocardium over time.

[0109] Execute step S5: obtain the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel, and use the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions. hyper- normal.

[0110] The second image can directly distinguish the stenotic vascular area from the normal vascular area. Based on the myocardial blood flow MBF of each pixel in the normal vascular area, in this embodiment, the mean of the myocardial blood flow of each pixel in the normal vascular area is calculated and used as the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions. hyper-normal .

[0111] In some possible embodiments provided by the present invention, the quantitative myocardial blood flow of the target vessel perfusion area includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel, and / or the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel.

[0112] That is to say, the following three technical solutions are included. Technical Solution 1: The quantitative myocardial blood flow of the target vessel perfusion area includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel, and the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel. Technical Solution 2: The quantitative myocardial blood flow of the target vessel perfusion area includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel. Technical Solution 3: The quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel.

[0113] In this embodiment, the mean is selected to represent the myocardial blood flow in the normally perfused area of ​​the target vessel. The selection of the mean is based on the following considerations: First, the mean can provide a measure of the average level of myocardial blood flow in a region or group, which is considered to be the general level of myocardial blood flow in the region and is statistically representative. Second, in actual clinical applications, the mean is a simple quantitative indicator that allows doctors or researchers to quickly understand the status of myocardial perfusion. Furthermore, the mean can integrate information from multiple different perfusion areas, thereby reducing the impact of abnormal values ​​in individual areas on the overall assessment. Even if certain areas may exhibit different perfusion levels due to local lesions or other factors, the overall mean can still provide a relatively stable estimate.

[0114] In some other possible embodiments, the median of the myocardial blood flow in the normal perfusion area of ​​the target vessel is calculated, and the median is used as the myocardial blood flow MBF in the perfusion area of ​​the target vessel under stenosis. hyper-stenosis .

[0115] The median myocardial blood flow of the target vascular perfusion area is used as the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis The following factors are taken into consideration: when there are many numerically abnormal pixels in the region of interest of the image, the statistical measurement based on the mean will have significant deviations. At this time, the use of the median can effectively eliminate or weaken the interference caused by abnormal pixels, and more realistically reflect the overall situation of myocardial blood flow in the target area, thereby improving the accuracy and reliability of the analysis.

[0116] The median myocardial blood flow of the target vessel in the normal perfusion area is taken as the myocardial blood flow MBF of the target vessel in the normal perfusion area. hyper-normalThe following factors are taken into consideration: when there are many numerically abnormal pixels in the region of interest of the image, the statistical measurement based on the mean will have significant deviations. At this time, the use of the median can effectively eliminate or weaken the interference caused by abnormal pixels, and more realistically reflect the overall situation of myocardial blood flow in the target area, thereby improving the accuracy and reliability of the analysis.

[0117] Execute step S6: according to the myocardial blood flow MBF of the target vascular perfusion area under stenosis hyper-stenosis MBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal , CTP-FFR is used to assess the proportion of myocardial ischemia caused by coronary artery stenosis.

[0118] That is, MBF hyper-stenosis MBF is the myocardial blood flow in the target vascular perfusion area under stenosis. hyper-norma l is the myocardial blood flow in the target vascular perfusion area under simulated normal conditions.

[0119] Using the above technical solution, the proportion of myocardial ischemia due to coronary artery stenosis (CTP-FFR) is calculated by processing the first and second images and using the myocardial blood flow (MBF) obtained from the second CTP image. In other words, the myocardial ischemia assessment method provided by the present invention directly obtains CTP-FFR based on image processing of CTP, quantifying the degree of ischemia caused by epicardial vascular stenosis. Unlike CT-FFR, which is used to assess ischemia caused by epicardial vascular stenosis, CTP-FFR can more quickly, accurately, and reliably quantitatively assess the ischemic state of actual myocardial perfusion.

[0120] Compared to calculating CT-FFR through CT images, the present invention provides a non-invasive assessment method for myocardial ischemia based only directly on imaging. That is to say, the myocardial ischemia assessment method can obtain CTP-FFR directly based on CTP image processing, thereby quantifying the degree of ischemia caused by epicardial vascular stenosis. The myocardial ischemia assessment method provided by the present invention does not require the use of fluid mechanics. Since mechanical simulation is a simulation calculation, the assessment of myocardial ischemia obtained based on mechanical simulation is a simulation result. Compared to the assessment of myocardial ischemia obtained based on mechanical simulation, the results obtained by the image processing-based quantification method for myocardial ischemia provided by the present invention are more accurate and faster. The present invention assesses the ischemic condition of actual myocardial perfusion through CTP-FFR, and has the advantage of rapid and accurate quantitative assessment of overall myocardial ischemia.

[0121] refer to Figure 1In some possible embodiments provided by the present invention, step S4: obtaining a time-attenuation curve of the myocardial tissue of the myocardial perfusion region according to the second image, obtaining a quantitative myocardial blood flow of the target vascular perfusion region based on the time-attenuation curve of the myocardial tissue and the target vascular perfusion region, and using the quantitative myocardial blood flow of the target vascular perfusion region as the myocardial blood flow MBF of the target vascular perfusion region under stenosis. hyper-stenosis ,include:

[0122] S41: Based on the rising slope and peak of the time-decay curve of the myocardial tissue in the myocardial perfusion area and the target vessel area of ​​interest, the quantitative myocardial blood flow in the target vessel perfusion area is obtained, and the quantitative myocardial blood flow in the target vessel perfusion area is used as the myocardial blood flow MBF in the target vessel perfusion area under stenosis. hyper-stenosis .

[0123] In this embodiment, since the rising slope of the contrast agent-time attenuation curve of the pixel point is related to the blood flow, the myocardial blood flow can be indirectly reflected by comparing the maximum slopes of the time-attenuation curves of the myocardium and the aorta during the contrast agent filling stage, and dividing the maximum slope of the myocardial curve by the maximum slope of the aortic curve.

[0124] In some possible embodiments provided by the present invention, the myocardial blood flow MBF of the target vascular perfusion area under stenosis is determined. hyper-stenosis MBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal , assessing the proportion of myocardial ischemia caused by coronary artery stenosis, including: myocardial blood flow (MBF) according to the target vascular perfusion area under stenosis hyper-stenosis MBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal Get MBF hyper-stenosis and MBF hyper-normal The ratio of blood flow in the diseased state to that in the healthy state is CTP-FFR:

[0125]

[0126] The CTP-FFR value is used to evaluate the proportion of myocardial ischemia caused by coronary artery stenosis. The proportion of myocardial ischemia caused by coronary artery stenosis is (1-CTP-FFR)*100%.

[0127] For example, a CTP-FFR result of 0.70 indicates that epicardial vascular stenosis under load will result in a blood flow reduction of (1-0.70)*100%, ie, a 30% reduction.

[0128] refer to Figure 5Step S2: Mapping the left ventricular myocardial area to each vessel in the coronary artery tree using an image processing method according to the first image and the second image to obtain a mapping image, including:

[0129] S21: Segmenting the first image using a first image segmentation algorithm to obtain a coronary artery tree image, and segmenting the second image using a second image segmentation algorithm to obtain a left ventricular myocardial structure image.

[0130] Image segmentation algorithms are a key technology in computer vision. They aim to subdivide digital images into regions or objects. These algorithms typically work based on certain image features, such as pixel intensity, color, texture, or other properties. Common image segmentation algorithms include threshold segmentation, edge detection, region growing, level set methods, graph cut algorithms, cluster segmentation, and deep learning methods.

[0131] Threshold segmentation identifies objects in an image by setting one or more thresholds. Regions with pixel values ​​above or below a specific threshold are classified as foreground or background.

[0132] Edge detection uses edge detection algorithms to find points in an image where brightness changes significantly. It is often used to identify object boundaries.

[0133] Region growing, an algorithm that gradually merges neighboring pixels to form objects based on similarity criteria, such as color or brightness similarity between pixels.

[0134] Level set methods use mathematical models to evolutionarily label objects in images. They define objects in the form of a zero level set and evolve this zero level set through diffusion and refocusing operations.

[0135] The graph cut algorithm, based on graph theory, regards image pixels as nodes in a graph and constructs a graph to find the optimal segmentation by minimizing the energy function.

[0136] Clustering segmentation uses clustering algorithms such as K-means to divide image pixels into multiple clusters, each cluster represents an object. The clustering algorithm does not rely on the boundary information of the image and can be used to separate the background and foreground.

[0137] Deep learning methods,With the development of deep learning, segmentation methods based on convolutional neural networks (CNN) have become increasingly popular.,For example, network architectures such as FCN (Fully Convolutional Network), U-Net, and SegNet are designed to handle image segmentation tasks.

[0138] These methods perform well in processing complex scenes and achieving refined segmentation; morphological-based segmentation, through morphological operations such as dilation, erosion, opening and closing operations, can be used to enhance specific structures in the image and assist in segmentation.

[0139] In this embodiment, the first image segmentation algorithm is a deep learning algorithm, and the deep learning algorithm includes but is not limited to the U-Net deep learning network.

[0140] For example, Figure 6A and Figure 6B A first image is shown, wherein Figure 6A and Figure 6B The area indicated by A is the left ventricular myocardium area. Figure 6A and Figure 6B The first image shown is obtained as Figure 7 Image of the coronary artery vascular tree is shown.

[0141] For example, reference Figure 2 and Figure 9 , Figure 2 The first image is shown, segmented by the U-Net deep learning network Figure 2 The first image shown is obtained as Figure 9 An image of the coronary artery vascular tree is shown.

[0142] In this embodiment, the second image segmentation algorithm is a traditional image segmentation method or a deep learning algorithm. The traditional image segmentation method includes but is not limited to a threshold segmentation algorithm, and the deep learning algorithm includes but is not limited to a U-Net deep learning network.

[0143] For example, Figure 3 The second image is shown, and the second image is segmented by a deep learning algorithm such as a U-Net deep learning network to obtain Figure 8A and Figure 8B Image of the left ventricular myocardial structure is shown.

[0144] It is important to note that the first segmentation algorithm used to segment the first image is different from the second segmentation algorithm used to segment the second image. For example, even though both the first and second segmentation algorithms can use the U-Net deep learning network, the U-Net deep learning network is only a model framework, and the specific model parameters and detailed design will vary depending on the segmentation objects, such as the first and second images. Therefore, the first segmentation algorithm is also different from the second segmentation algorithm.

[0145] In other possible embodiments of the present invention, the first and second images may be segmented using other image segmentation methods not limited to those described above, to obtain a coronary artery tree image and a left ventricular myocardial structure image, respectively. The present invention does not limit the image segmentation method employed; as long as the first image can be segmented to obtain a coronary artery tree image and the second image can be segmented to obtain a left ventricular myocardial structure image, the image segmentation method will suffice.

[0146] Continue to refer Figure 5 S22: registering the first image and the second image by an image registration method to obtain a registered image, where the registered image is the registered first image.

[0147] Image registration is a technique in computer vision and medical image processing that aims to align corresponding points in two images so that information from one image can be seen in the other. Image registration methods are often used to merge image data from different time points, different viewpoints, or different modalities.

[0148] The present invention does not limit the image registration method used, as long as the first image and the second image can be registered by the image registration method to obtain a registered image.

[0149] refer to Figure 13 , Figure 13 The CCTA image shown in is the first image. Figure 13 The CTP image shown in is the second image. Figure 13 The registered image shown in is the registered image, that is, the first image after registration.

[0150] Depend on Figure 13 As can be seen, there are no significant differences between the first and second images, as well as the registered images. This is because both images were acquired with the patient in the same position. In other words, the CCTA and CTP scans were performed on the same patient in the same position. This means the patient's physical position did not change much, and therefore the resulting images were similar. It's only because the patient's blood vessels, affected by factors such as breathing and heartbeat, shifted, causing pixel-level shifts in the acquired images.

[0151] Specifically, the heartbeat itself has a cardiac cycle. When acquiring medical images of the same patient in the same position, the images are acquired at the same relative position during different cardiac cycles to minimize the position shift between the two images. However, position shifts caused by the patient's breathing or breath-holding state (e.g., sufficient breath-holding at the beginning of the image, insufficient breath-holding at the end of the image) are unavoidable. Therefore, the acquired first and second images experience position shifts, or displacements, at the pixel level. Therefore, it is necessary to register the first and second images to achieve spatial correspondence between their pixels.

[0152] Continue to refer Figure 5 S23: Mapping the left ventricular myocardial structure region of the left ventricular myocardial structure image to each vessel of the coronary artery tree in the coronary artery tree image by an image mapping method according to the registered image and the second image to obtain a mapping image.

[0153] Image mapping is a technique for mapping pixels in one image (called the source image) to corresponding locations in another image (called the target image). This mapping is commonly used in fields such as image processing, image analysis, computer vision, and medical imaging. Common image mapping methods include direct mapping, interpolation mapping, affine transformation mapping, learning-based mapping, inverse mapping, and optimization methods.

[0154] Direct mapping assumes a one-to-one relationship between the pixels in the source and target images. It calculates the corresponding position of each pixel in the source image in the target image and then directly maps the pixel value to the corresponding position in the target image.

[0155] Interpolation mapping: When direct mapping is not possible, for example, when the source and target images have different resolutions, interpolation methods can be used to calculate the value of each pixel in the target image. Common interpolation methods include nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation.

[0156] Affine transformation mapping is based on geometric transformations such as rotation, scaling, translation, and tilt. It calculates the corresponding position of each pixel in the source image under the geometric transformation and then maps these positions to the target image.

[0157] Learning-based mapping uses machine learning methods such as deep learning to learn the mapping relationship between source and target images by training neural networks or other learning models. For example, a convolutional neural network (CNN) can be used to learn the transformation between images.

[0158] Inverse mapping, which calculates the corresponding position of each pixel in the target image in the source image and then maps these positions to the source image, can improve the accuracy of the mapping in some cases.

[0159] Optimization methods use optimization algorithms to calculate the mapping relationship by minimizing the difference between the source image and the target image. For example, methods such as mutual information maximization and gradient descent can be used to optimize the mapping.

[0160] For example, in this embodiment, based on the registered images, the left ventricular myocardial region is mapped to individual vessels in the coronary artery tree based on the bifurcation principle. Specifically, the myocardial perfusion regions corresponding to the vessel segments in the coronary artery tree are mapped, and the extent of the left ventricle perfused by each vessel is determined based on the distribution of the coronary artery tree.

[0161] Figure 10A shows the registered image, Figure 10B The mapping of each vessel in the coronary artery tree to the left ventricular myocardial area is shown. Figure 10B, vessel b in the coronary artery tree corresponds to the left ventricular myocardial area B, vessel c in the coronary artery tree corresponds to the left ventricular myocardial area C, vessel d in the coronary artery tree corresponds to the left ventricular myocardial area D, and vessel e in the coronary artery tree corresponds to the left ventricular myocardial area E.

[0162] In some possible embodiments provided by the present invention, step S23: mapping the left ventricular myocardial structure region of the left ventricular myocardial structure image to each vessel of the coronary artery tree in the coronary artery tree image using an image mapping method based on the registered image and the second image to obtain a mapped image, includes:

[0163] S231: Mapping the left ventricular myocardial area to each blood vessel in the coronary artery tree by bifurcation principle mapping to obtain a mapping image.

[0164] The bifurcation principle is a method used in mathematics and computer science to map and analyze complex network structures, particularly tree-like structures such as coronary arteries. In the field of cardiology, this principle can be used to study and simulate the blood supply relationship between the coronary arteries and the myocardium.

[0165] In some other possible embodiments provided by the present invention, S231: mapping the left ventricular myocardial area to each vessel in the coronary artery tree by the bifurcation principle to obtain a mapping image includes:

[0166] S2311: Map the left ventricular myocardial area to all coronary arteries with a lumen diameter ≥ 1 mm in the coronary artery tree using the divide-and-conquer principle.

[0167] In some other possible embodiments, the Voronoi algorithm may be used to map the myocardial area of ​​the left ventricle to all coronary arteries with a lumen diameter of ≥1 mm in the coronary artery tree.

[0168] In some possible embodiments provided by the present invention, step S22: registering the first image and the second image using an image registration method to obtain a registered image, where the registered image includes the coronary artery tree and the left ventricular myocardial area.

[0169] Among them, the image registration method includes: at least one of: registration based on traditional feature extraction method, registration based on deep learning feature extraction method and registration method based on deep learning direct learning geometric transformation.

[0170] This registration method, based on traditional feature extraction methods, uses operators such as SIFT (scale-invariant feature transform) to extract image features and transform the two images to align the feature points. This method is highly interpretable, has low hardware requirements, and can achieve high accuracy and efficiency for simpler tasks.

[0171] Registration methods based on deep learning feature extraction train deep learning networks to extract image features, replacing operators such as SIFT used in traditional methods. When traditional feature extraction algorithms contain many outliers or are unable to match a sufficient number of feature points, deep learning methods can be used to improve the accuracy and stability of feature extraction. For example, when CT images have severe local geometric distortion and artifacts, shallow information such as grayscale and gradient cannot support accurate feature point matching. In this case, deep learning networks can be used to extract the high-dimensional semantic information of the images.

[0172] Registration methods based on deep learning that directly learn geometric transformations align two images directly through deep networks, eliminating the need for feature extraction and feature point matching. This approach, such as the VoxelMorph method, requires training based on paired labeled data. When the data volume is large enough, the model's performance can be fully utilized to ensure stability.

[0173] Exemplarily, in this embodiment, a deep learning-based registration method is used to register the first image and the second image, so that the first image and the second image are spatially aligned to obtain a registered image, which includes the coronary artery vascular tree and the left ventricular myocardial area.

[0174] refer to Figure 12 Step S22: registering the first image and the second image by an image registration method to obtain a registered image, where the registered image is the registered first image. The image registration method based on deep learning includes:

[0175] S221: Acquire a first image and use the first image as a source image.

[0176] S222: Acquire a second image and use the second image as a target image.

[0177] S223: Taking the source image and the target image as input, training the registration network, and obtaining the registration deformation field from the source image to the target image.

[0178] S224: Obtain the registered image through spatial transformation, and obtain a similarity loss function according to the registered image, the source image, and the target image.

[0179] S225: Obtain a smooth regularization loss function based on the gradients of the registered deformation field and displacement field.

[0180] S226: Obtain a network loss function according to the similarity loss function and the smoothing regularization loss function.

[0181] S227: Minimize the network loss function through training iterations, and obtain the final registration deformation field and registered image when the network loss function converges.

[0182] Using this technical solution, the first and second images are spatially aligned through registration, resulting in a registered image. Based on the registered image, the left ventricular myocardium region is mapped to the various vessels in the coronary artery tree, ensuring a reliable and accurate mapping relationship.

[0183] In some possible embodiments provided by the present invention, the image registration method is a VoxelMorph method.

[0184] VoxelMorph is a popular medical image registration software that provides a fast and efficient method for 3D image registration. VoxelMorph achieves image registration using a learning-based optimization method. This method combines traditional gradient-based optimization with learning-based feature matching to automatically learn the transformation between the images to be registered. VoxelMorph uses a convolutional neural network (CNN) to estimate the non-rigid transformation between images, which allows it to handle complex transformations such as distortion and deformation.

[0185] In some other possible embodiments provided by the present invention, the image registration method used is a registration based on a traditional feature extraction method or a registration based on a deep learning feature extraction method. The registration method based on the traditional feature extraction method uses operators such as SIFT (scale-invariant feature transform) to extract image features and transform the two images to align the feature points. This method is highly interpretable and has low hardware requirements. It can achieve higher accuracy and efficiency when processing simpler tasks. The registration method based on deep learning feature extraction is to train a deep learning network to extract image features to replace operators such as SIFT in traditional methods. When the traditional feature extraction algorithm contains more outliers or cannot match a sufficient number of feature points, a deep learning method can be used to improve the accuracy and stability of feature extraction.

[0186] In some possible embodiments provided by the present invention, such as Figure 12Step S223 shown: taking the source image and the target image as input, training the registration network, and obtaining the registration deformation field from the source image to the target image, including: taking the source image and the target image as input, training the registration network of the U-Net network structure, and obtaining the registration deformation field from the source image to the target image.

[0187] In some possible embodiments provided by the present invention, such as Figure 12 Before step S224 shown, step S2241 is performed: obtaining the mean square error loss between the registered image and the target image according to the registered image and the target image.

[0188] In some possible embodiments provided by the present invention, such as Figure 12 Before step S225 shown, step S2251 is executed: the gradient of the displacement field u is calculated.

[0189] For example, refer to Figure 12 , and combined with Figure 13 The registration algorithm flow and network design are detailed in the VoxelMorph method. A UNet structure type network is used to generate a three-dimensional deformation field from CTP images to CCTA images, as follows:

[0190] S221: Acquire a first image CCTA and use the first image CCTA as a source image (f).

[0191] S222: Acquire a second image CTP and use the second image as the target image (m).

[0192] S223: Take the source image (f) and the target image (m) as input, train the registration network gθ(f, m) of the U-Net network structure, and obtain the registration deformation field (φ) from the source image to the target image.

[0193] S2241: Based on the registered image Get the mean square error loss between the target image (m) and the two The calculation method is:

[0194]

[0195] Among them, n is the number of pixels that need to be registered, y i is the gray value of the i-th pixel of the target image (m), The image after registration The grayscale value of the i-th pixel.

[0196] Continue to refer Figure 12 Combined with Figure 13 , S224: Obtain the registered image through spatial transformation According to the registered image The source image (f) and the target image (m) obtain the similarity loss function The calculation is as follows:

[0197]

[0198] Among them, (p) is each pixel, Ω is all pixels, is the mean square error loss between the registered image (m°φ) and the target image (m).

[0199] Execute step S2251: Gradient of displacement field u is calculated as follows:

[0200]

[0201] in, is the partial derivative of the displacement u with respect to x, is the partial derivative of displacement u with respect to y, is the partial derivative of displacement u with respect to z, and p is each pixel point.

[0202] Continue to refer Figure 12 Combined with Figure 13 , execute step S225: obtain the smooth regularization term loss function L according to the gradient of the registration deformation field (φ) and the displacement field u smooth (φ), calculated as follows:

[0203]

[0204] in, is the gradient of the displacement field u, (p) is each pixel, and Ω is all pixels.

[0205] S226: Based on the similarity loss function Smooth regularization loss function L smooth (φ) Get the network loss function L(f,n,φ), which is calculated as follows:

[0206]

[0207] Among them, λ is an adjustable parameter used to assign weights to the two loss functions.

[0208] S227: Minimize the network loss function L(f,m,φ) through training iterations. When the network loss function L(f,m,φ) converges, the final registration deformation field (φ) and the registered image are obtained.

[0209] Second, reference Figure 14The embodiment of the present invention discloses a myocardial ischemia assessment system 01, comprising: an image acquisition module 011, an image processing module 012, an image calibration module 013, a myocardial blood flow acquisition module 014 of a target vascular perfusion area, and a myocardial ischemia assessment module 015.

[0210] The image acquisition module 011 is used to acquire a first image and a second image, wherein the first image is a coronary CT angiography image CCTA, and the second image is a CT myocardial perfusion image CTP under load state.

[0211] The image processing module 012 is configured to obtain registration of the coronary artery tree and the left ventricular myocardial structure based on the first image and the second image.

[0212] The image processing module 012 includes an image segmentation module 0121, an image registration module 0122 and an image mapping module 0123.

[0213] The image segmentation module 0121 is configured to segment the first image using a first image segmentation algorithm to obtain a coronary artery tree image, and segment the second image using a second image segmentation algorithm to obtain a left ventricular myocardial structure image.

[0214] The image registration module 0122 is used to register the first image and the second image using an image registration method to obtain a registered image, where the registered image is the registered first image.

[0215] The image mapping module 0123 is used to map the left ventricular myocardial structure region of the left ventricular myocardial structure image to each vessel of the coronary artery tree in the coronary artery tree image using an image mapping method according to the registered image and the second image, thereby obtaining a mapped image.

[0216] The image calibration module 013 is used to calibrate the target blood vessel perfusion area in the mapping image.

[0217] The myocardial blood flow acquisition module 014 of the target vascular perfusion area is used to obtain the time-attenuation curve of the myocardial tissue in the myocardial perfusion area based on the second image, and obtain the quantitative myocardial blood flow of the target vascular perfusion area based on the time-attenuation curve of the myocardial tissue, and use the quantitative myocardial blood flow of the target vascular perfusion area as the myocardial blood flow MBF of the target vascular perfusion area in the case of stenosis. hyper-stenosis .

[0218] The myocardial blood flow acquisition module 014 of the target vessel perfusion area is used to obtain the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel, and use the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions. hyper-normal .

[0219] Myocardial ischemia assessment module 015 is used to evaluate myocardial blood flow (MBF) in the target vascular perfusion area under stenosis conditions. hyper-stenosis MBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal , CTP-FFR is used to assess the proportion of myocardial ischemia caused by coronary artery stenosis.

[0220] Using the above technical solution, the image acquisition module 011, image processing module 012, image calibration module 013, myocardial blood flow acquisition module 014 in the target vascular perfusion area, and myocardial ischemia assessment module 015 work together to directly obtain CTP-FFR based on CTP image processing, thereby quantifying the degree of ischemia caused by epicardial vascular stenosis. Unlike CT-FFR, which is used to assess ischemia caused by epicardial vascular stenosis, CTP-FFR can more quickly, accurately, and reliably quantitatively assess the ischemic state of actual myocardial perfusion.

[0221] Compared to calculating CT-FFR through CT images, the present invention provides a non-invasive myocardial ischemia assessment system 01 that is directly based only on imaging. That is, the myocardial ischemia assessment system 01 can obtain CTP-FFR directly based on CTP image processing, thereby quantifying the degree of ischemia caused by epicardial vascular stenosis. The myocardial ischemia assessment method provided by the present invention does not require the use of fluid mechanics. Since mechanical simulation is a simulation calculation, the assessment of myocardial ischemia obtained based on mechanical simulation is a simulation result. Compared to the assessment of myocardial ischemia obtained based on mechanical simulation, the results obtained by the quantification method of myocardial ischemia based on image processing of the non-invasive myocardial ischemia assessment system 01 provided by the present invention are more accurate and faster. The non-invasive myocardial ischemia assessment system 01 assesses the ischemic condition of actual myocardial perfusion through CTP-FFR, and has the advantage of rapid and accurate quantitative assessment of overall myocardial ischemia.

[0222] It should be noted that the myocardial ischemia assessment method and system, microcirculation disorder assessment method and system, microcirculation disorder calculation method and system, electronic device and computer storage medium provided by the present invention all have non-therapeutic purposes.

[0223] The myocardial ischemia assessment method and system, microcirculation disorder assessment method and system, and microcirculation disorder calculation method and system provided by the present invention are all based on the processing of medical images, that is, their direct objects are medical images, such as CCTA and CTP.

[0224] In some possible embodiments provided by the present invention, the myocardial blood flow acquisition module 014 of the target vascular perfusion region is used to obtain the quantitative myocardial blood flow of the target vascular perfusion region based on the rising slope and peak of the time-decay curve of the myocardial tissue in the myocardial perfusion region and the target vascular region of interest, and the quantitative myocardial blood flow of the target vascular perfusion region is used as the myocardial blood flow MBF of the target vascular perfusion region in the case of stenosis. hyper-stenosis . This has been described in detail in the previous article and will not be repeated here.

[0225] In some possible embodiments provided by the present invention, the quantitative myocardial blood flow of the target vessel perfusion area includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel, and / or the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel.

[0226] For example, the mean value of the myocardial blood flow of the normal perfusion area of ​​the target vessel is used as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions. hyper-normal The reasons have been explained in detail above and will not be elaborated on here.

[0227] In some other possible embodiments, the median of the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel or other statistical data may be used as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions according to actual needs. hyper-normal， The present invention is not limited to this.

[0228] In some possible embodiments provided by the present invention, the myocardial ischemia assessment module 015 is used to calculate the myocardial blood flow MBF according to the target vascular perfusion area under stenosis. hyper-stenosis MBF and target vascular perfusion area in simulated normal myocardial blood flow hyper-normal Get MBF hyper-stenosis and MBF hyper-normal The ratio of blood flow in the diseased state to that in the healthy state is CTP-FFR:

[0229]

[0230] The CTP-FFR value is used to evaluate the proportion of myocardial ischemia caused by coronary artery stenosis. The proportion of myocardial ischemia caused by coronary artery stenosis is (1-CTP-FFR)*100%.

[0231] For example, a CTP-FFR result of 0.70 indicates that epicardial vascular stenosis under load will result in a blood flow reduction of (1-0.70)*100%, ie, a 30% reduction.

[0232] In some possible embodiments provided by the present invention, reference is made to Figure 14 The image processing module 012 includes an image segmentation module 0121 , an image registration module 0122 and an image mapping module 0123 .

[0233] The image segmentation module 0121 is used to segment the first image using an image segmentation algorithm to obtain a coronary artery tree image and a left ventricular myocardial structure image.

[0234] The image registration module 0122 is used to register the first image and the second image using an image registration method to obtain a registered image, where the registered image includes the coronary artery tree and the left ventricular myocardial area.

[0235] The image mapping module 0123 is used to map the left ventricular myocardial area to each blood vessel in the coronary artery tree through an image mapping method according to the registered image, so as to obtain a mapping image.

[0236] In some possible embodiments provided by the present invention, the image mapping module 0123 is configured to map the left ventricular myocardial area to each blood vessel in the coronary artery tree by bifurcation principle mapping to obtain a mapping image.

[0237] In some possible embodiments provided by the present invention, the image mapping module 0123 is configured to map the myocardial area of ​​the left ventricle to all coronary arteries with a lumen diameter ≥ 1 mm in the coronary artery tree by using the divide-and-conquer principle.

[0238] In some possible embodiments provided by the present invention, the image registration method includes at least one of: registration based on traditional feature extraction methods, registration based on deep learning feature extraction methods, and registration methods based on direct learning of geometric transformations through deep learning. These have been described in detail above and will not be repeated here.

[0239] For example, in this embodiment, a deep learning-based registration method is used to register the first and second images, spatially aligning the first and second images to obtain a registered image. The registered image includes the coronary artery tree and the left ventricular myocardium region. This has been described in detail above and will not be repeated here.

[0240] Thirdly, reference Figure 15 The present invention provides a method for evaluating microcirculation disorders, comprising:

[0241] S61: Obtaining the myocardial ischemia fraction (CTP-FFR) due to coronary artery stenosis using the myocardial ischemia assessment method according to any of the embodiments of the first aspect. CTP-FFR is the fractional flow reserve (FFR) based on a CT myocardial perfusion image (CTP) under stress, i.e., the fractional flow reserve (FFR) obtained directly from the CTP image through image processing.

[0242] S62: The proportion of myocardial ischemia due to epicardial vascular stenosis (CT-FFR) is obtained using the CT-FFR method for evaluating epicardial vascular stenosis. CT-FFR stands for Fractional Flow Reserve (FFR) based on coronary CT angiography. This FFR is derived from CT images through image processing, mechanical simulation, and pressure gradient calculation.

[0243] S63: Evaluate the proportion of microcirculatory impairment based on the proportion of myocardial ischemia caused by coronary artery stenosis (CTP-FFR) and the proportion of myocardial ischemia caused by epicardial vascular stenosis (CT-FFR):

[0244] △CT-FFR=CT-FFR-CTP-FFR formula 2.

[0245] CT-FFR evaluates ischemia caused by epicardial vascular stenosis, while CTP-FFR can evaluate the actual ischemic state of myocardial perfusion.

[0246] For example, if the CTP-FFR result is 0.70 and the CT-FFR result is 0.80, it means that epicardial stenosis causes (1-0.80)*100%, or a 20% blood flow reduction, and microcirculatory disorder causes (0.80-0.70)*100%, or a 10% blood flow reduction.

[0247] Using the above technical solution, since CT-FFR evaluates ischemia caused by epicardial vascular stenosis, and CTP-FFR can evaluate the ischemic condition of actual myocardial perfusion, the numerical difference between CT-FFR and CTP-FFR is used to indicate the degree of ischemia caused by microcirculatory disorders. This is simple and efficient, and since this microcirculatory disorder assessment method adopts a non-invasive approach, it is safer.

[0248] In some possible embodiments provided by the present invention, the CT-FFR method for evaluating epicardial vascular stenosis includes at least one of CT-QFR based on simplified fluid dynamics equations, FFRct based on computational fluid dynamics simulation, and CT-FFR based on machine learning.

[0249] CT-QFR is a method based on simplified fluid dynamics equations that uses CT images to estimate vascular geometry and hemodynamic parameters, and then calculates FFR. CT-QFR typically requires input of certain assumptions, such as that blood is a Newtonian fluid and that the flow is laminar.

[0250] FFRct uses computational fluid dynamics (CFD) simulation to evaluate blood flow dynamics. It takes into account more complex blood flow characteristics, such as non-Newtonian fluid behavior and turbulent flow.

[0251] CT-FFR, based on machine learning methods such as deep learning, can automatically learn vascular anatomy and hemodynamic parameters from CT images to predict FFR. These methods can more accurately estimate FFR, especially in the presence of complex vascular lesions.

[0252] Fourthly, reference Figure 16 The present invention provides a microcirculation disorder assessment system 02, including a myocardial ischemia assessment module 015 and a microcirculation disorder assessment module 022.

[0253] The myocardial ischemia assessment module 015 is used to obtain the ratio of myocardial ischemia caused by coronary artery stenosis (CTP-FFR) by using the myocardial ischemia assessment method according to any one of the embodiments of the first aspect.

[0254] The myocardial ischemia assessment module 015 is further configured to obtain a CT-FFR ratio of myocardial ischemia caused by epicardial vascular stenosis based on a CT-FFR method for assessing epicardial vascular stenosis.

[0255] The microcirculation disorder assessment module 022 is used to assess the microcirculation disorder ratio ΔCT-FFR based on the myocardial ischemia ratio CTP-FFR caused by coronary artery stenosis and the myocardial ischemia ratio CT-FFR caused by epicardial vascular stenosis obtained by the myocardial ischemia assessment module:

[0256] △CT-FFR=CT-FFR-CTP-FFR formula 2.

[0257] By adopting the above technical solution, CT-FFR and CTP-FFR are obtained through the joint action of the myocardial ischemia assessment module 015 and the microcirculation disorder assessment module 022. Since CT-FFR assesses ischemia caused by epicardial vascular stenosis, and CTP-FFR can assess the ischemia of actual myocardial perfusion, the numerical difference between CT-FFR and CTP-FFR indicates the degree of ischemia caused by microcirculation disorder. This is simple and efficient, and since the microcirculation disorder assessment system 02 adopts a non-invasive method, it is safer.

[0258] In some possible embodiments provided by the present invention, the CT-FFR method for evaluating epicardial vascular stenosis includes at least one of CT-QFR using simplified fluid dynamics equations, FFRct based on computational fluid dynamics simulation, and CT-FFR based on machine learning. The specific contents of the aforementioned CT-FFR method for evaluating epicardial vascular stenosis are as described above.

[0259] In their "Chinese Expert Consensus on the Diagnosis and Treatment of Coronary Microvascular Disease," the Basic Research Group of the Chinese Medical Association's Cardiology Branch recommends the use of indices such as the index of microvascular resistance (IMR) and coronary flow reserve (CFR) to assess coronary microcirculatory dysfunction. IMR effectively reflects distal microcirculatory resistance, while CFR reflects myocardial ischemia caused by both epicardial vascular and microcirculatory dysfunction. However, for patients with microcirculatory dysfunction and concurrent obstructive coronary artery disease, clinicians are unable to distinguish the respective impacts of these two indicators on myocardial ischemia based on existing indices to formulate appropriate treatment strategies.

[0260] In addition, it is not easy to obtain indicators such as coronary blood flow reserve CFR and microcirculatory resistance index IMR clinically due to the high requirements for technical equipment and technical support usually required for measuring coronary blood flow reserve CFR and microcirculatory resistance index IMR; the complexity and professionalism of the operation; and the interference of the patient's physiological and pathological conditions, such as the patient's heart rate, blood pressure, electrolyte imbalance and other physiological conditions, and pathological conditions such as arrhythmia, myocardial infarction, and cardiac tamponade.

[0261] Therefore, the fifth aspect of the present invention provides a non-invasive assessment method for myocardial ischemia based on imaging, which realizes a method for quantitative assessment of microcirculatory resistance.

[0262] refer to Figure 17 Combined with Figure 18 The present invention provides a method for calculating microcirculatory resistance, comprising:

[0263] S8: Obtain the aortic pressure P0 of the coronary system through the systolic and diastolic blood pressures of the target subject.

[0264] In some possible embodiments provided by the present invention, the aortic pressure P0 of the coronary system is obtained by the systolic pressure and diastolic pressure of the target object, including at least one of the following calculation methods: aortic pressure P0 = diastolic pressure + 1 / 3 (systolic pressure - diastolic pressure) + 5 mmHg; aortic pressure P0 = diastolic pressure + 0.412 (systolic pressure - diastolic pressure).

[0265] In some other possible embodiments provided by the present invention, the systolic and diastolic blood pressures of the target object may be obtained through other calculations.

[0266] In the present invention, since the systolic and diastolic blood pressure values ​​of the target subject are specific, the calculated aortic pressure P0 is personalized.

[0267] S9: Obtain the venous pressure P2 of the coronary system. The venous pressure P2 of the coronary system under load is 5 mmHg.

[0268] In this embodiment, the venous pressure P2 of the coronary system under load is 5 mmHg, which is a value obtained based on past experience.

[0269] S10: Acquire a second image, which is a CT myocardial perfusion image (CTP) under a stress state.

[0270] S11: Based on the second image, the myocardial blood flow (MBF) of the target vascular perfusion area under stenosis is obtained by the myocardial ischemia assessment method according to any embodiment of the first aspect. hyper-stenosis , obtain the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal .

[0271] That is, step S11 includes steps S4, S41 and S5 in the aforementioned myocardial ischemia assessment method of the first aspect. Specifically,

[0272] Execute step S4: Based on the second image, obtain the time-attenuation curve of the myocardial tissue in the myocardial perfusion area, obtain the quantitative myocardial blood flow of the target vascular perfusion area based on the time-attenuation curve of the myocardial tissue, and use the quantitative myocardial blood flow of the target vascular perfusion area as the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis ,include:

[0273] Execute step S41: obtain the quantitative myocardial blood flow of the target vessel perfusion area based on the slope and peak value of the time-decay curve of the myocardial tissue in the myocardial perfusion area, and use the quantitative myocardial blood flow of the target vessel perfusion area as the myocardial blood flow MBF of the target vessel perfusion area under stenosis. hyper-stenosis .

[0274] S5: Obtain the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel, and use the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions hyper-normal .

[0275] Continue to refer Figure 17 Combined with Figure 18, execute step S12: according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, the myocardial blood flow MBF of the target vascular perfusion area under the simulated normal condition hyper-normal Obtain the resistance R caused by microcirculation to blood flow under normal conditions.

[0276] Under normal conditions, the resistance to blood flow posed by the microcirculation is determined by multiple factors, including vascular geometry, such as radius, length, and branching pattern; physical properties of blood, such as viscosity and density; and physiological properties of the vessel wall, such as elasticity and smooth muscle tone. The microcirculation, the smallest part of the cardiovascular system, includes capillaries, arterioles, and venules, which are crucial for maintaining tissue perfusion and metabolism. Microcirculatory resistance is not usually measured directly but is assessed indirectly by calculating the resistance of the entire cardiac circulation. This is achieved by measuring aortic pressure and coronary venous pressure and calculating myocardial blood flow.

[0277] Execute step S13: obtain the pressure P1 of the distal epicardial blood according to the aortic pressure P0 of the coronary system and the proportion CT-FFR of myocardial ischemia caused by epicardial vascular stenosis obtained by the microcirculation disorder assessment method in any embodiment of the third aspect.

[0278] S14: Based on the pressure P1 at the distal end of the epicardial vessels, the venous pressure P2 of the coronary system, and the myocardial blood flow (MBF) in the target vascular perfusion area under stenosis hyper-stenosis , the resistance R caused by microcirculation to blood flow in normal conditions, and the aortic pressure P0 of the coronary system to obtain the resistance R2 caused by microcirculation disorders to blood flow.

[0279] Clinicians do face a challenge in patients with microcirculatory disorders and obstructive coronary artery disease: existing indices may not adequately distinguish the separate effects of coronary stenosis and microcirculatory impairment on myocardial ischemia. This is because myocardial ischemia may be the result of coronary stenosis (i.e., macrovascular disease) or microcirculatory dysfunction, or a combination of both.

[0280] Traditional assessment methods, such as coronary angiography, focus primarily on the degree of large vessel stenosis. However, even in the absence of significant large vessel stenosis, microcirculatory disturbances can lead to myocardial ischemia. Microcirculatory disturbances can be caused by a variety of factors, including endothelial dysfunction, inflammation, platelet activation, vasoconstriction, and oxidative stress.

[0281] The calculation method of microcirculatory resistance provided by the present invention is a non-invasive assessment method for myocardial ischemia based on imaging, which realizes the quantitative assessment of microcirculatory resistance.

[0282] The microcirculatory resistance index (IMR) is considered to be the reference standard for evaluating microcirculatory resistance and is defined as the distal coronary artery pressure (Pdist ) and distal blood flow (Q dist ) ratio.

[0283] refer to Figure 18 The calculation method of microcirculatory resistance provided by the present invention takes into account the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis It is affected by three kinds of resistance: the resistance R1 caused by the epicardium, the resistance R2 caused by microcirculation disorders, and the resistance R caused by microcirculation in normal conditions.

[0284] Among them, the aortic pressure P0 is weakened to the pressure P1 at the distal end of the epicardial blood vessels through the resistance R1 caused by the epicardium to blood flow.

[0285] Due to the resistance R2 caused by microcirculatory disorders and the resistance R caused by microcirculation to blood flow in normal conditions, the vascular pressure will decrease from the pressure P1 at the distal end of the epicardial vessels to the venous pressure P2. According to the pressure difference between the two and the myocardial blood flow MBF in the target vascular perfusion area under stenosis, hyper-stenosis The total resistance R2 caused by microcirculation disorder to blood flow and the resistance R caused by microcirculation to blood flow under normal conditions can be calculated.

[0286] The resistance R caused by microcirculation to blood flow is objective and can be calculated by aortic pressure P0, venous pressure P2 and target vascular perfusion area in a simulated normal myocardial blood flow MBF. hyper-normal Obtained by calculation.

[0287] Therefore, the resistance R2 caused by microcirculation disorders to blood flow can be obtained by the sum of the aforementioned resistances (the resistance R2 caused by microcirculation disorders to blood flow and the resistance R caused by microcirculation to blood flow in normal conditions) and the resistance R caused by microcirculation to blood flow in normal conditions, that is, the resistance R2 caused by microcirculation disorders to blood flow is equal to the difference between the sum of the aforementioned resistances and the resistance R caused by microcirculation to blood flow in normal conditions.

[0288] The microcirculatory disorder resistance calculation method provided by the present invention not only takes into account the impact of epicardial vascular stenosis on blood flow, but also takes into account the inherent resistance of the microcirculation itself. It can more accurately assess the impact of microcirculatory disorders on myocardial perfusion, thereby providing a more comprehensive myocardial ischemia assessment.

[0289] In some possible embodiments provided by the present invention, reference is made to Figure 17 and Figure 18 ,like Figure 17 Step S12 is shown: according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, the myocardial blood flow MBF of the target vascular perfusion area under the simulated normal condition hyper-normalObtain the resistance R caused by microcirculation to blood flow under normal conditions, and the calculation method is:

[0290]

[0291] The resistance R caused by microcirculation to blood flow is objective and can be calculated by the aortic pressure P0, venous pressure P2 and target vascular perfusion area in simulating the myocardial blood flow MBF under normal conditions. hyper-normal Obtained by calculation.

[0292] In some possible embodiments provided by the present invention, reference is made to Figure 17 and Figure 18 ,like Figure 17 Step S13 is shown: based on the aortic pressure P0 of the coronary system and the proportion of myocardial ischemia caused by epicardial vascular stenosis obtained by the microcirculatory disorder assessment method in any embodiment of the third aspect, the pressure P1 at the distal end of the epicardial blood is obtained, and the calculation method is:

[0293] P1=P0*CT-FFR Formula 4.

[0294] In some possible embodiments provided by the present invention, reference is made to Figure 17 and Figure 18 ,like Figure 17 Step S14 is shown: according to the pressure P1 at the distal end of the epicardial vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis , the resistance R caused by microcirculation to blood flow in normal state, the aortic pressure P0 of the coronary system, and the resistance R2 caused by microcirculation disorder to blood flow are calculated as follows:

[0295]

[0296] As mentioned above, due to the resistance R2 caused by microcirculatory disorders and the resistance R caused by microcirculation to blood flow in normal conditions, the vascular pressure will decrease from the pressure P1 at the distal end of the epicardial vessels to the venous pressure P2. According to the pressure difference between the two and the myocardial blood flow MBF of the target vascular perfusion area under stenosis, hyper-stenosis The total resistance R2 caused by microcirculation disorder to blood flow and the resistance R caused by microcirculation to blood flow under normal conditions can be calculated.

[0297] The resistance R2 caused by microcirculation disorders to blood flow can be obtained by adding the aforementioned resistance sum (the resistance R2 caused by microcirculation disorders and the resistance R caused by microcirculation to blood flow under normal conditions) to the resistance R caused by microcirculation to blood flow under normal conditions, that is, the resistance R2 caused by microcirculation disorders to blood flow is equal to the difference between the aforementioned resistance sum and the resistance R caused by microcirculation to blood flow under normal conditions.

[0298] Sixth aspect, reference Figure 19 The present invention provides a microcirculation barrier resistance calculation system 03, which includes a data acquisition module 031, an image acquisition and analysis module 032 and a data processing module 033.

[0299] The data acquisition module 031 is used to obtain the aortic pressure P0 of the coronary system through the systolic and diastolic pressures of the target object; and obtain the venous pressure P2 of the coronary system. The venous pressure P2 of the coronary system under load is 5 mmHg.

[0300] The image acquisition and analysis module 032 is used to acquire a second image, which is a CT myocardial perfusion image CTP under load; based on the second image, the myocardial blood flow MBF of the target vascular perfusion area under stenosis is obtained by the myocardial ischemia assessment method as described in any embodiment of the first aspect. hyper-stenosis , obtain the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal .

[0301] The data processing module 033 is used to calculate the myocardial blood flow MBF in the target vascular perfusion area under normal conditions according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, and the hyper-normal Obtain the resistance R of the microcirculation to the blood flow under normal conditions; obtain the pressure P1 of the distal epicardial blood according to the aortic pressure P0 of the coronary system and the proportion CT-FFR of myocardial ischemia caused by epicardial vascular stenosis obtained by the microcirculatory disorder assessment method in any embodiment of the third aspect; obtain the myocardial blood flow MBF of the target vascular perfusion area under stenosis according to the pressure P1 of the distal epicardial blood vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis , the resistance R caused by microcirculation to blood flow in normal conditions, and the aortic pressure P0 of the coronary system to obtain the resistance R2 caused by microcirculation disorders to blood flow.

[0302] Using the above technical solution, the microcirculatory resistance calculation system 03 takes into account the resistance R of the coronary microcirculation to blood flow under normal conditions, thereby calculating the resistance R2 caused by microcirculatory disturbances. This means that the assessment not only considers the epicardial resistance R1, but also the resistance R of the coronary microcirculation under normal conditions. This system 03 can more accurately assess the impact of microcirculatory disturbances on myocardial perfusion, thereby providing a more comprehensive assessment of myocardial ischemia.

[0303] In some possible embodiments provided by the present invention, according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions, hyper-normal Obtain the resistance R caused by microcirculation to blood flow under normal conditions, and the calculation method is:

[0304]

[0305] In some possible embodiments provided by the present invention, the pressure P1 at the distal end of the epicardial blood is obtained based on the aortic pressure P0 of the coronary system and the proportion of myocardial ischemia caused by epicardial vascular stenosis obtained by the microcirculatory disorder assessment method in any embodiment of the third aspect, and the calculation method is as follows:

[0306] P1=P0*CT-FFR Formula 4.

[0307] In some possible embodiments provided by the present invention, the myocardial blood flow MBF of the target vascular perfusion area under stenosis is calculated based on the pressure P1 at the distal end of the epicardial vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis , the resistance R caused by microcirculation to blood flow in normal state, the aortic pressure P0 of the coronary system, and the resistance R2 caused by microcirculation disorder to blood flow are calculated as follows:

[0308]

[0309] In some possible embodiments provided by the present invention, the aortic pressure P0 of the coronary system is obtained by the systolic pressure and diastolic pressure of the target object, including at least one of the following calculation methods: aortic pressure P0 = diastolic pressure + 1 / 3 (systolic pressure - diastolic pressure) + 5 mmHg; aortic pressure P0 = diastolic pressure + 0.412 (systolic pressure - diastolic pressure).

[0310] Seventh aspect, reference Figure 20 The present invention provides an electronic device 2, comprising a memory 201, a processor 202, and a computer program stored in the memory 201 and executable on the processor 202. When the processor 202 executes the computer program, it implements the myocardial ischemia assessment method described in any embodiment of the first aspect, the microcirculatory disorder assessment method described in any embodiment of the third aspect, or the microcirculatory disorder resistance calculation method described in any embodiment of the fifth aspect. The memory 201 may include, for example, system memory, a fixed non-volatile storage medium, and the like. The system memory may store, for example, an operating system, application programs, a boot loader, and other programs.

[0311] In this embodiment, the electronic device realizes a rapid, accurate and reliable quantitative assessment of the overall myocardial ischemia through the myocardial ischemia assessment method provided by the first aspect of the present invention; through the microcirculation disorder assessment method provided by the third aspect, the numerical difference between the ischemia CT-FFR caused by epicardial vascular stenosis and the ischemia CTP-FFR of the actual myocardial perfusion ischemia indicates the degree of ischemia caused by microcirculation disorders, which is simple and efficient, and because the microcirculation disorder assessment method adopts a non-invasive method, it is safer; through the calculation method of microcirculation disorder resistance provided by the fifth aspect, not only the effect of epicardial vascular stenosis on blood flow is taken into account, but also the inherent resistance of the microcirculation itself is taken into account, so that the effect of microcirculation disorders on myocardial perfusion can be more accurately assessed, thereby providing a more comprehensive myocardial ischemia assessment.

[0312] In an eighth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the myocardial ischemia assessment method in any embodiment of the first aspect, or implements the microcirculation disorder assessment method in any embodiment of the third aspect, or implements the microcirculation disorder resistance calculation method in any embodiment of the fifth aspect.

[0313] By adopting the above-mentioned technical scheme, the computer-readable storage medium realizes a rapid, accurate and reliable quantitative assessment of the overall myocardial ischemia through the myocardial ischemia assessment method provided by the first aspect of the present invention; through the microcirculation disorder assessment method provided by the third aspect, the numerical difference between the ischemia CT-FFR caused by epicardial vascular stenosis and the ischemia CTP-FFR of the actual myocardial perfusion is used to indicate the degree of ischemia caused by microcirculation disorders, which is simple and efficient, and because the microcirculation disorder assessment method adopts a non-invasive method, it is safer; through the calculation method of microcirculation disorder resistance provided by the fifth aspect, not only the effect of epicardial vascular stenosis on blood flow is taken into account, but also the inherent resistance of the microcirculation itself is taken into account, so that the effect of microcirculation disorders on myocardial perfusion can be more accurately assessed, thereby providing a more comprehensive myocardial ischemia assessment.

[0314] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0315] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0316] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0317] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0318] Although the present invention has been illustrated and described with reference to certain preferred embodiments thereof, it should be understood by those skilled in the art that the above description is provided as a further detailed description of the present invention in conjunction with specific embodiments thereof, and that the specific implementation of the present invention is not limited to these descriptions. Those skilled in the art may make various changes in form and details, including simple deductions or substitutions, without departing from the spirit and scope of the present invention.

Claims

1. A method for calculating microcirculatory resistance, characterized in that: include: The aortic pressure P0 of the coronary system is obtained by the systolic and diastolic blood pressures of the target subject; Acquiring a venous pressure P2 of the coronary system, wherein the venous pressure P2 of the coronary system is 5 mmHg under a load state; Acquiring a second image, where the second image is a CT myocardial perfusion image (CTP) under a stress state; Based on the second image, the myocardial blood flow (MBF) of the target vascular perfusion area under stenosis is obtained by the myocardial ischemia assessment method. hyper-stenosis , obtain the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal ; According to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal Obtain the resistance R of microcirculation to blood flow under normal conditions; Obtaining a pressure P1 at the distal end of the epicardial vessel based on the aortic pressure P0 of the coronary system and the proportion of myocardial ischemia caused by epicardial vessel stenosis obtained by a CT-FFR method for evaluating epicardial vessel stenosis; According to the pressure P1 at the distal end of the epicardial vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis hyper-stenosis , the resistance R caused by microcirculation to blood flow in the normal state, and the aorta pressure P0 of the coronary system to obtain the resistance R2 caused by microcirculation disorder to blood flow; Wherein, the myocardial ischemia assessment method includes: Acquiring a first image and a second image, wherein the first image is a coronary CT angiography (CCTA) image, and the second image is a CT myocardial perfusion (CTP) image under stress; mapping the left ventricular myocardial area to each blood vessel in the coronary artery tree using an image processing method according to the first image and the second image to obtain a mapping image; calibrating a target vascular perfusion area in the mapping image; A time-attenuation curve of the myocardial tissue in the myocardial perfusion region is obtained according to the second image, and a quantitative myocardial blood flow of the target vascular perfusion region is obtained based on the time-attenuation curve of the myocardial tissue and the target vascular perfusion region. The quantitative myocardial blood flow of the target vascular perfusion region is used as the myocardial blood flow MBF of the target vascular perfusion region in the case of stenosis. hyper-stenosis ; Obtain the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel, and use the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions. hyper-normal ; MBF according to the target vascular perfusion area under stenosis hyper-stenosis and the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal , CTP-FFR is used to assess the proportion of myocardial ischemia caused by coronary artery stenosis.

2. The method for calculating microcirculatory resistance according to claim 1, wherein: In the myocardial ischemia assessment method, the time-attenuation curve of the myocardial tissue in the myocardial perfusion area is obtained based on the second image, and the quantitative myocardial blood flow of the target vascular perfusion area is obtained based on the time-attenuation curve of the myocardial tissue and the target vascular perfusion area. The quantitative myocardial blood flow of the target vascular perfusion area is used as the myocardial blood flow MBF of the target vascular perfusion area in the case of stenosis. hyper-stenosis ,include: The quantitative myocardial blood flow of the target vessel perfusion area is obtained based on the rising slope and peak value of the time-decay curve of the myocardial tissue in the myocardial perfusion area and the target vessel area of ​​interest, and the quantitative myocardial blood flow of the target vessel perfusion area is used as the myocardial blood flow MBF of the target vessel perfusion area under stenosis. hyper-stenosis .

3. The method for calculating microcirculatory resistance according to claim 1, wherein: In the myocardial ischemia assessment method, the quantitative myocardial blood flow of the target vessel perfusion area includes at least one of the mean and median of the myocardial blood flow of the target vessel perfusion area, and / or the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel.

4. The method for calculating microcirculatory resistance according to claim 1, wherein: In the myocardial ischemia assessment method, the myocardial blood flow MBF of the target vascular perfusion area under stenosis is measured. hyper-stenosis and the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal Get MBF hyper-stenosis and MBF hyper-normal The ratio of blood flow in the diseased state to that in the healthy state is CTP-FFR: The CTP-FFR value is used to evaluate the proportion of myocardial ischemia caused by coronary artery stenosis. The proportion of myocardial ischemia caused by coronary artery stenosis is (1-CTP-FFR)*100%.

5. The method for calculating microcirculatory resistance according to claim 1, wherein: In the myocardial ischemia assessment method, mapping the left ventricular myocardial area to each vessel in the coronary artery tree using an image processing method based on the first image and the second image to obtain a mapping image includes: Segmenting the first image using a first image segmentation algorithm to obtain a coronary artery tree image, and segmenting the second image using a second image segmentation algorithm to obtain a left ventricular myocardial structure image; Registering the first image and the second image by an image registration method to obtain a registered image, wherein the registered image is the registered first image; The left ventricular myocardial structure region of the left ventricular myocardial structure image is mapped to each vessel of the coronary artery tree in the coronary artery tree image by an image mapping method according to the registered image and the second image, so as to obtain a mapping image.

6. The method for calculating microcirculatory resistance according to claim 5, wherein: In the myocardial ischemia assessment method, mapping the left ventricular myocardial structure region of the left ventricular myocardial structure image to each vessel of the coronary artery tree in the coronary artery tree image using an image mapping method based on the registered image and the second image to obtain a mapped image includes: The left ventricular myocardial area is mapped to each blood vessel in the coronary artery tree by bifurcation principle mapping to obtain a mapping image.

7. The method for calculating microcirculatory resistance according to claim 5, wherein: In the myocardial ischemia assessment method, mapping the left ventricular myocardial structure region of the left ventricular myocardial structure image to each vessel of the coronary artery tree in the coronary artery tree image using an image mapping method based on the registered image and the second image to obtain a mapped image further includes: The myocardial area of ​​the left ventricle is mapped to all coronary arteries with a lumen diameter of ≥1 mm in the coronary artery tree by the divide-and-conquer principle.

8. The method for calculating microcirculatory resistance according to claim 1, wherein: The aortic pressure P0 of the coronary system is obtained by using the systolic and diastolic pressures of the target subject, including at least one of the following calculation methods: Aortic pressure P0 = diastolic pressure + 1 / 3 (systolic pressure - diastolic pressure) + 5 mmHg; Aortic pressure P0 = diastolic pressure + 0.412 (systolic pressure - diastolic pressure).

9. A method for evaluating microcirculatory disorders, characterized in that: include: Obtaining the proportion of myocardial ischemia caused by coronary artery stenosis CTP-FFR by the myocardial ischemia assessment method in the method for calculating microcirculatory barrier resistance according to any one of claims 1 to 7; The proportion of myocardial ischemia caused by epicardial vascular stenosis was obtained according to the CT-FFR method for evaluating epicardial vascular stenosis; The proportion of myocardial ischemia caused by coronary artery stenosis (CTP-FFR) and the proportion of myocardial ischemia caused by epicardial vascular stenosis (CT-FFR) were used to evaluate the proportion of microcirculatory disturbances (ΔCT-FFR): △CT-FFR=CT-FFR-CTP-FFR Formula 2.

10. The method for calculating microcirculatory resistance according to claim 1, wherein: The aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions are described. hyper-normal Obtain the resistance R caused by microcirculation to blood flow under normal conditions, and the calculation method is: Alternatively, the pressure P1 at the distal end of the epicardial vessel is obtained based on the aortic pressure P0 of the coronary system and the proportion of myocardial ischemia caused by epicardial vessel stenosis obtained by the microcirculatory disorder assessment method according to claim 9, CT-FFR, and the calculation method is: P1=P0*CT-FFR Formula 4.

11. The method for calculating microcirculatory resistance according to claim 10, wherein: According to the pressure P1 at the distal end of the epicardial vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis hyper-stenosis , the resistance R caused by microcirculation to blood flow in the normal state, and the aorta pressure P0 of the coronary system to obtain the resistance R2 caused by microcirculation disorder to blood flow, which is calculated as follows:

12. A microcirculatory disorder assessment system, characterized in that: include: a myocardial ischemia assessment module, configured to obtain a myocardial ischemia ratio (CTP-FFR) caused by coronary artery stenosis by using the myocardial ischemia assessment method in the method for calculating microcirculatory resistance according to any one of claims 1 to 7; The myocardial ischemia assessment module is further configured to obtain a CT-FFR ratio of myocardial ischemia caused by epicardial vascular stenosis based on a CT-FFR method for assessing epicardial vascular stenosis; The microcirculation disorder assessment module is used to assess the microcirculation disorder ratio ΔCT-FFR based on the myocardial ischemia ratio CTP-FFR caused by coronary artery stenosis and the myocardial ischemia ratio CT-FFR caused by epicardial vascular stenosis obtained by the myocardial ischemia assessment module: △CT-FFR=CT-FFR-CTP-FFR formula 2.

13. A system for calculating microcirculatory resistance, characterized in that: include: A data acquisition module is configured to acquire the aortic pressure P0 of the coronary system through the systolic and diastolic pressures of the target subject; and acquire the venous pressure P2 of the coronary system, wherein the venous pressure P2 of the coronary system is 5 mmHg under a load state; The image acquisition and analysis module is used to acquire a second image, which is a CT myocardial perfusion image (CTP) under load; based on the second image, the myocardial blood flow (MBF) of the target vascular perfusion area under stenosis is obtained by a myocardial ischemia assessment method. hyper-stenosis , obtain the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal ; The data processing module is used to calculate the myocardial blood flow MBF of the target vascular perfusion area under normal conditions according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, and the hyper-norma l Obtain the resistance R of microcirculation to blood flow under normal conditions; for obtaining a pressure P1 at the distal end of the epicardial vessel according to the aortic pressure P0 of the coronary system and the proportion CT-FFR of myocardial ischemia caused by epicardial vessel stenosis obtained by a CT-FFR method for evaluating epicardial vessel stenosis; For calculating the myocardial blood flow MBF according to the pressure P1 at the distal end of the epicardial vessel, the venous pressure P2 of the coronary system, and the target vascular perfusion area under stenosis. hyper-stenosis , the resistance R caused by microcirculation to blood flow in the normal state, and the aorta pressure P0 of the coronary system to obtain the resistance R2 caused by microcirculation disorder to blood flow; Wherein, the myocardial ischemia assessment method comprises: Acquiring a first image and a second image, wherein the first image is a coronary CT angiography (CCTA) image, and the second image is a CT myocardial perfusion (CTP) image under stress, performed by an image acquisition module of a myocardial ischemia assessment system; Mapping the left ventricular myocardial area to each vessel in the coronary artery tree using an image processing method based on the first image and the second image to obtain a mapping image, which is executed by an image processing module of the myocardial ischemia assessment system; calibrating the target vascular perfusion area in the mapping image, performed by an image calibration module of the myocardial ischemia assessment system; A time-attenuation curve of the myocardial tissue in the myocardial perfusion region is obtained according to the second image, and a quantitative myocardial blood flow of the target vascular perfusion region is obtained based on the time-attenuation curve of the myocardial tissue and the target vascular perfusion region. The quantitative myocardial blood flow of the target vascular perfusion region is used as the myocardial blood flow MBF of the target vascular perfusion region in the case of stenosis. hyper-stenosis , performed by a myocardial blood flow acquisition module of a target vascular perfusion area of ​​the myocardial ischemia assessment system; Obtain the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel, and use the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel as the myocardial blood flow MBF of the target vessel perfusion area under simulated normal conditions. hyper-normal , performed by a myocardial blood flow acquisition module of a target vascular perfusion area of ​​the myocardial ischemia assessment system; MBF according to the target vascular perfusion area under stenosis hyper-stenosis and the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal , the proportion CTP-FFR of myocardial ischemia caused by coronary artery stenosis is evaluated, which is performed by the myocardial ischemia evaluation module of the myocardial ischemia evaluation system.

14. The microcirculatory resistance calculation system according to claim 13, wherein: In the myocardial ischemia assessment system, the myocardial blood flow acquisition module of the target vascular perfusion area is used to obtain the quantitative myocardial blood flow of the target vascular perfusion area based on the rising slope and peak value of the time-decay curve of the myocardial tissue in the myocardial perfusion area and the target vascular area of ​​interest, and use the quantitative myocardial blood flow of the target vascular perfusion area as the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis .

15. The microcirculatory resistance calculation system according to claim 13, wherein: In the myocardial ischemia assessment system, the quantitative myocardial blood flow of the target vessel perfusion area includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel, and / or the quantitative myocardial blood flow of the normal perfusion area of ​​the target vessel includes at least one of the mean and median of the myocardial blood flow of the normal perfusion area of ​​the target vessel.

16. The microcirculatory resistance calculation system according to claim 13, wherein: In the myocardial ischemia assessment system, the myocardial ischemia assessment module is used to measure the myocardial blood flow (MBF) of the target vascular perfusion area under stenosis. hyper-stenosis and the myocardial blood flow MBF of the target vascular perfusion area under simulated normal conditions hyper-normal Get MBF hyper-stenosis and MBF hyper-normal The ratio of blood flow in the diseased state to that in the healthy state is CTP-FFR: The CTP-FFR value is used to evaluate the proportion of myocardial ischemia caused by coronary artery stenosis. The proportion of myocardial ischemia caused by coronary artery stenosis is (1-CTP-FFR)*100%.

17. The microcirculatory resistance calculation system according to claim 13, wherein: In the myocardial ischemia assessment system, the image processing module includes: an image segmentation module, configured to segment the first image using a first image segmentation algorithm to obtain a coronary artery tree image, and segment the second image using a second image segmentation algorithm to obtain a left ventricular myocardial structure image; an image registration module, configured to register the first image and the second image using an image registration method to obtain a registered image, wherein the registered image is the registered first image; An image mapping module is configured to map the left ventricular myocardial structure region of the left ventricular myocardial structure image to each vessel of the coronary artery tree in the coronary artery tree image using an image mapping method based on the registered image and the second image, thereby obtaining a mapped image.

18. The microcirculatory resistance calculation system according to claim 17, wherein: In the myocardial ischemia assessment system, the image mapping module is used to map the left ventricular myocardial area to each blood vessel in the coronary artery tree through bifurcation principle mapping to obtain a mapping image.

19. The microcirculatory resistance calculation system according to claim 17, wherein: The myocardial ischemia assessment system further includes: the image mapping module is used to map the myocardial area of ​​the left ventricle to all coronary arteries with a lumen diameter of ≥1 mm in the coronary artery tree by the divide-and-conquer principle.

20. The microcirculatory resistance calculation system according to claim 13, wherein: The data processing module is used to calculate the myocardial blood flow MBF of the target vascular perfusion area under normal conditions according to the aortic pressure P0 of the coronary system, the venous pressure P2 of the coronary system, and the target vascular perfusion area. hyper-normal Obtain the resistance R caused by microcirculation to blood flow under normal conditions, and the calculation method is: Alternatively, the data processing module is configured to obtain the pressure P1 at the distal end of the epicardial vessel based on the aortic pressure P0 of the coronary system and the proportion CT-FFR of myocardial ischemia caused by epicardial vessel stenosis obtained by the microcirculatory disorder assessment method according to claim 8 or 11, and the calculation method is: R1=R0*CT-FFR Formula 4.

21. The microcirculatory resistance calculation system according to claim 13, wherein: The data processing module is used to calculate the myocardial blood flow MBF of the target vascular perfusion area under stenosis according to the pressure P1 at the distal end of the epicardial vessel, the venous pressure P2 of the coronary system, and the myocardial blood flow MBF of the target vascular perfusion area under stenosis. hyper-stenosis , the resistance R caused by microcirculation to blood flow in the normal state, and the aorta pressure P0 of the coronary system to obtain the resistance R2 caused by microcirculation disorder to blood flow, which is calculated as follows:

22. The microcirculatory resistance calculation system according to claim 13, wherein: The data acquisition module is used to obtain the aortic pressure P0 of the coronary system through the systolic and diastolic pressures of the target subject, including at least one of the following calculation methods: Aortic pressure P0 = diastolic pressure + 1 / 3 (systolic pressure - diastolic pressure) + 5 mmHg; Aortic pressure P0 = diastolic pressure + 0.412 (systolic pressure - diastolic pressure).

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