CN116524134A - Three-dimensional blood vessel modeling method based on intravascular image and FFR (fringe field switching) calculation method and system - Google Patents
Three-dimensional blood vessel modeling method based on intravascular image and FFR (fringe field switching) calculation method and system Download PDFInfo
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Abstract
The invention discloses a three-dimensional blood vessel modeling method based on an intravascular image and an FFR calculation method and system, comprising the steps of obtaining an intravascular image and image parameters of a continuous blood vessel to be detected, and dividing target elements in each frame of intravascular image according to the intravascular image; calculating the image characteristics of each frame of intravascular image according to the target elements obtained by segmentation; initializing the three-dimensional center line of the blood vessel to be detected according to the image parameters, and evolving the initialized three-dimensional curve according to the image characteristics of each frame of the intravascular image; reconstructing a blood vessel model according to the image parameters, the image characteristics of each frame of blood vessel image and the three-dimensional central line of the blood vessel after evolution; and carrying out hydrodynamic simulation based on the reconstructed vascular model, and calculating FFR. According to the method, the fluid mechanics simulation is carried out based on the accurate blood vessel model, the calculated FFR is closer to the real numerical value, the risk and cost of patient examination are reduced, and the method is convenient and fast.
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to a three-dimensional blood vessel modeling method based on intravascular images and an FFR (fringe field reflection) calculation method and system.
Background
Fractional flow reserve (Fractional Flow Reserve, FFR for short) refers to the ratio of the maximum blood flow supplied to a coronary artery in its corresponding myocardial region when lesions are occurring and when theoretically not occurring. Scientific researches and clinical experiments show that FFR as a functional evaluation index can accurately judge the myocardial ischemia severity of a patient, so as to guide whether coronary intervention treatment is needed or not and perform postoperative evaluation. However, FFR measurement belongs to invasive measurement, and needs to be performed in a state of maximum congestion of blood vessels, and in clinical operation, a vasodilation drug such as adenosine needs to be injected, and the drug can cause a certain injury to a human body, is at risk, and cannot be used by patients with liver and kidney functions deficiency or drug allergy. In addition, the cost of the pressure guide wire required by FFR measurement is high, and the burden of patients is increased, so that the current FFR popularization rate is relatively low.
In order to solve the above-described problems with FFR, related researchers have developed methods for calculating FFR based on images. Intravascular optical coherence tomography (Intravascular Optical Coherence Tomography, abbreviated as IVOCT) and intravascular ultrasound imaging (Intravenous Ultrasound, abbreviated as IVUS) are common vascular detection techniques in which an imaging probe is positioned within a blood vessel in a minimally invasive manner to obtain a cross-sectional image of a coronary vessel, known as intravascular imaging. The intravascular image shot by the method can realize intravascular and vascular wall microstructure imaging, and makes up the defect that the coronary angiography technology lacks intravascular information. In the existing method for carrying out fluid mechanics simulation calculation of FFR according to intravascular image modeling, the modeling process ignores bending information of a real blood vessel, so that the calculated FFR has lower precision; or additional contrast images are needed to supplement the vessel bending information, increasing the complexity of the algorithm.
Disclosure of Invention
It is therefore an object of the present invention to provide a three-dimensional blood vessel modeling method, FFR calculation method and system based on intravascular images to improve the accuracy of three-dimensional modeling of blood vessels and to improve the accuracy and stability of fractional flow reserve calculation.
In order to achieve the above object, the three-dimensional blood vessel modeling method based on an intravascular image provided by the invention comprises the following steps:
s1, acquiring continuous intravascular images and image parameters of blood vessels to be detected, and dividing target elements in each intravascular image according to the intravascular images;
s2, calculating image characteristics of each frame of intravascular image according to the target elements obtained by segmentation;
s3, initializing the three-dimensional center line of the blood vessel to be detected according to the image parameters, and evolving the initialized three-dimensional center line according to the image characteristics of each frame of the intravascular image;
s4, reconstructing a blood vessel model according to the image parameters, the image characteristics and the three-dimensional central line of the blood vessel after evolution.
Further preferably, in S1, acquiring intravascular images and image parameters of a continuous blood vessel to be detected includes:
the intravascular image is an image under a rectangular coordinate system of a blood vessel segment;
the image parameters include the image size in rectangular coordinates, the actual distance represented by each pixel in the image, and the actual distance between adjacent frame images.
Further preferably, in S1, the method further includes preprocessing the acquired intravascular image, where the preprocessing includes: median filtering and adaptive histogram equalization.
Further preferably, in S1, when dividing the target element in each intravascular image, different methods are adopted to divide according to the category of the target element; target elements include lumens, imaging catheters, plaque.
Further preferably, in S2, calculating the image feature of each frame of the intravascular image according to the segmentation result of the target element in the intravascular image includes the steps of:
s201, acquiring a lumen center and a lumen area according to a lumen segmentation result, wherein the lumen center is a point with the largest shortest distance from all points in the lumen to the profile; acquiring a catheter center based on a catheter segmentation result, wherein the catheter center is the center of mass of a catheter area; acquiring a plaque center and a plaque area based on a plaque segmentation result, wherein the plaque center is the mass center of the plaque area;
s202, correcting the lumen center based on the lumen center, the lumen area, the plaque center and the plaque area.
Further, in S202, the lumen center is corrected based on the lumen center, the lumen area, the plaque center, and the plaque area according to the following formula:
wherein ,for the corrected lumen center, +.>For the lumen center before correction, +.> and />Lumen area and plaque area, respectively, +.>Is plaque center (I/O)>Is->To->Vector of (3)，/>Is a correction coefficient.
Further preferably, in S3, when the initialized three-dimensional curve is evolved according to the image features of each frame of intravascular image, a deflection angle is calculated according to the corrected relative positional relationship between the lumen center and the catheter center, and the three-dimensional center line of the blood vessel corresponding to the calculated deflection angle is bent, and a deflection angle calculation formula is as follows:
wherein ,trepresent the firsttThe frame image is displayed in a frame image,is the deflection angle at the t-th frame, +.>For the actual distance between the shooting intervals of adjacent frame images, < >>For the corrected lumen center and catheter center distance of frame t-1, +.>For the corrected lumen center and catheter center distance of the t-th frame, +.>For the corrected lumen center and catheter center distance of frame t+1,/for>Is the deflection coefficient.
The invention also provides an FFR calculation method based on the intravascular image, which is characterized in that a three-dimensional blood vessel model is built based on the three-dimensional blood vessel modeling method based on the intravascular image, and the built three-dimensional blood vessel model is utilized to perform fluid mechanics simulation to calculate FFR.
Further preferably, the boundary conditions set in the hydrodynamic simulation may include parameters such as pressure at the inlet and outlet of the blood vessel, blood flow rate, or blood flow rate.
The invention also provides an FFR calculation system based on the intravascular image, which is used for implementing the FFR calculation method of the intravascular image and comprises the following steps: the device comprises an image acquisition module, a feature extraction module, a model establishment module and an FFR calculation module;
the image acquisition module is used for acquiring intravascular images and image parameters of blood vessels to be detected and dividing target elements in each intravascular image according to the intravascular images;
the feature extraction module is used for calculating the image features of each frame of intravascular image according to the target elements obtained by segmentation;
the model building module is used for initializing the three-dimensional center line of the blood vessel to be detected according to the image parameters, and evolving the initialized three-dimensional center line according to the image characteristics of each frame of the intravascular image; constructing a blood vessel model according to the image parameters, the image characteristics of each frame of blood vessel image and the three-dimensional central line of the blood vessel after evolution;
and the FFR calculation module is used for carrying out hydrodynamic simulation according to the constructed blood vessel model and preset boundary conditions, calculating the pressure at the corresponding position of each blood vessel section in the target blood vessel, and calculating the FFR value at the corresponding position of each blood vessel section in the target blood vessel.
Compared with the prior art, the three-dimensional blood vessel modeling method, FFR calculation method and system based on the intravascular image have at least the following advantages:
1. the intravascular image-based vessel modeling method is convenient and quick, is simple to operate, can estimate the real vessel morphology only according to a single-mode image, and avoids the problems of difficult acquisition and fusion of multi-mode images;
2. according to the FFR calculation method based on the intravascular images, the fluid mechanics simulation is carried out based on the accurate blood vessel model, the calculated FFR is closer to the real numerical value, the non-invasive method is adopted integrally, the risk and cost of patient examination are reduced, the method is convenient and fast, and assistance can be provided for clinical coronary heart disease diagnosis and interventional therapy postoperative examination.
Drawings
Fig. 1 is a flowchart of a blood vessel modeling method based on an intravascular image according to the present invention.
Fig. 2 is a graph showing image segmentation and feature calculation results according to an embodiment of the present invention.
FIG. 3 is a graph showing the result of the lumen center correction according to one embodiment of the present invention.
Fig. 4 is a schematic view of a three-dimensional centerline deflection angle of a blood vessel according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of initializing a three-dimensional centerline of a blood vessel according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of three-dimensional centerline evolution of a blood vessel according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a reconstructed three-dimensional blood vessel model according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of an FFR calculation system based on intravascular image vessel modeling according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
As shown in fig. 1, a three-dimensional blood vessel modeling method based on an intravascular image according to an embodiment of the present invention includes the following steps:
s1, acquiring continuous intravascular images and image parameters of blood vessels to be detected, and dividing target elements in each intravascular image according to the intravascular images; further, acquiring the intravascular images of the continuous blood vessel to be detected comprises acquiring intravascular images of the blood vessel segment under a rectangular coordinate system, wherein the image parameters comprise the image size under the rectangular coordinate system, the actual distance represented by each pixel in the image and the actual distance of the shooting interval of the adjacent frame images;
also included is preprocessing of the intravascular images, including but not limited to: the pretreatment method comprises the following steps: median filtering and adaptive histogram equalization.
Further, target elements in the intravascular image are segmented, including lumens, imaging catheters, plaque, and the like. For example, in fig. 2, a segmented target element is shown, where a is the lumen center, B is the catheter center, and C is the catheter profile; d is the lumen contour.
Segmentation methods include, but are not limited to: a method based on a Markov random field, a method based on an active contour model, a method based on a neural network and the like are used for segmenting a lumen; dividing an imaging catheter by a prior condition-based method, a region growing-based method, a neural network-based method and the like; plaque is segmented based on a region growing method, a neural network based method, and the like.
S2, calculating the image characteristics of each frame of intravascular image according to the segmentation result of the target element in the intravascular image;
further, step S2 includes:
step S21, obtaining image features of intravascular images, wherein the image features comprise a lumen center and a lumen area which are obtained based on a lumen segmentation result, and the lumen center is a point with the largest shortest distance from all points in the lumen to the outline; a catheter center acquired based on a catheter segmentation result, the catheter center being a centroid of a catheter region; the plaque center and the plaque area are obtained based on the plaque segmentation result, wherein the plaque center is the mass center of the plaque area;
step S22, correcting the center of the lumen according to the following formula:
wherein ,for the corrected lumen center, +.>For the lumen center before correction, +.> and />Lumen area and plaque area, respectively, +.>Is plaque center (I/O)>Is->To->Vector of->The correction coefficient is obtained by experiments, and the value range is 0.2-0.6; FIG. 3 shows the result of the lumen center correction according to one embodiment of the present invention, in which +.>0.36, where E is the plaque profile.
S3, initializing the three-dimensional center line of the blood vessel to be detected according to the image parameters, and evolving the initialized three-dimensional center line according to the image characteristics of each frame of the intravascular image;
the method specifically comprises the following steps:
step S31, initializing a three-dimensional centerline of a blood vessel based on the image parameters, wherein the initialized three-dimensional centerline of the blood vessel is a straight line in space, and the distance between adjacent sampling points of the three-dimensional centerline of the blood vessel is the actual distance between the image capturing intervals of the adjacent frames, as shown in fig. 5, which is a schematic diagram of the initialization of the three-dimensional centerline of the blood vessel.
Step S32, evolving a three-dimensional central line of the blood vessel based on the corrected relative positions of the lumen center and the catheter center, and FIG. 6 is a schematic diagram of the blood vessel after evolving the three-dimensional central line; specifically, the deflection angle of the three-dimensional central line of the blood vessel has a mapping relation with the relative position change of the corrected lumen center and the catheter center.
Fig. 4 is a schematic diagram showing the three-dimensional center line deflection angle of a blood vessel and the relative positions of the corrected center of the lumen and the center of the catheter according to an embodiment of the present invention, after repeated verification according to a plurality of experiments, in this example,
wherein ,trepresent the firsttThe frame image is displayed in a frame image,is the deflection angle at the t-th frame, +.>For the actual distance between the shooting intervals of adjacent frame images, < >>For the corrected lumen center and catheter center distance of frame t-1, +.>For the corrected lumen center and catheter center distance of the t-th frame, +.>For the corrected lumen center and catheter center distance of frame t+1,/for>Is the deflection coefficient. Obtained by experiments, the value range is 3-6 degrees, in this example 5 degrees. The three-dimensional centerline of the blood vessel is curved according to the deflection angle as shown in fig. 6.
S4, reconstructing a blood vessel model according to the image parameters, the image characteristics of each frame of blood vessel image and the three-dimensional central line of the blood vessel after evolution. Fig. 7 is a reconstructed three-dimensional vascular model.
In another embodiment of the present application, there is provided an FFR calculation method based on an intravascular image, including:
establishing a target blood vessel model based on the blood vessel model establishing method;
performing hydrodynamic simulation based on the target vessel model, and calculating FFR;
further, the boundary conditions set by the hydrodynamic simulation may include parameters such as pressure at the inlet and outlet of the blood vessel, blood flow rate or blood flow, and the like, and may be obtained according to experimental or empirical values. The hydrodynamic simulation is performed according to the three-dimensional model and boundary conditions of the blood vessel, which belongs to the mature prior art, and various solving methods exist, such as: finite Element Method (FEM), finite Volume Method (FVM), finite Difference Method (FDM), etc.
As shown in fig. 8, an embodiment of the present application further provides an FFR calculation system based on an intravascular image, for implementing the FFR calculation method described above, including: the device comprises an image acquisition module, a feature extraction module, a model establishment module and an FFR calculation module;
the image acquisition module is used for acquiring intravascular images and image parameters of blood vessels to be detected and dividing target elements in each intravascular image according to the intravascular images; it should be noted that, the obtained intravascular image of the blood vessel to be detected is an intravascular image sequence under a rectangular coordinate system obtained according to the scanning frequency; the acquired image parameters comprise the image size under a rectangular coordinate system, the actual distance represented by each pixel in the image and the actual distance of the shooting interval of the adjacent frame images; image preprocessing is carried out on an image of a rectangular coordinate system, and the preprocessing method comprises the following steps: median filtering and adaptive histogram equalization.
The feature extraction module is used for calculating the image features of each frame of intravascular image according to the target elements obtained by segmentation;
the method comprises the steps of dividing a lumen, a catheter and a plaque in each frame of image of an intravascular image sequence output by an image acquisition module, calculating the center of the lumen, the area of the lumen, the center of the catheter, the center of the plaque and the area of the plaque based on the division result, and correcting the center of the lumen according to the center of the plaque and the area of the plaque. See the detailed description of step S2 above for specific procedures.
The model building module is used for initializing the three-dimensional center line of the blood vessel to be detected according to the image parameters, and evolving the initialized three-dimensional center line according to the image characteristics of each frame of the intravascular image; constructing a blood vessel model according to the image parameters, the image characteristics of each frame of blood vessel image and the three-dimensional central line of the blood vessel after evolution; for specific procedures, see the description of step S3 in the above method embodiment.
And the FFR calculation module is used for carrying out hydrodynamic simulation according to the constructed blood vessel model and preset boundary conditions, calculating the pressure at the corresponding position of each blood vessel section in the target blood vessel, and calculating the FFR value at the corresponding position of each blood vessel section in the target blood vessel, wherein the specific process is described in the step S4 in the embodiment of the method.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (9)
1. The three-dimensional blood vessel modeling method based on the intravascular image is characterized by comprising the following steps of:
s1, acquiring continuous intravascular images and image parameters of blood vessels to be detected, and dividing target elements in each intravascular image according to the intravascular images;
s2, calculating image characteristics of each frame of intravascular image according to the target elements obtained by segmentation;
s3, initializing the three-dimensional center line of the blood vessel to be detected according to the image parameters, and evolving the initialized three-dimensional center line according to the image characteristics of each frame of the intravascular image;
s4, reconstructing a blood vessel model according to the image parameters, the image characteristics and the three-dimensional central line of the blood vessel after evolution.
2. The three-dimensional blood vessel modeling method based on the blood vessel image according to claim 1, wherein in S1, acquiring the blood vessel image and the image parameters of the blood vessel to be detected continuously, comprises:
the intravascular image is an image under a rectangular coordinate system of a blood vessel segment;
the image parameters comprise the image size in a rectangular coordinate system, the actual distance represented by each pixel in the image and the actual distance of the shooting interval of the adjacent frame images.
3. The three-dimensional blood vessel modeling method based on intravascular images according to claim 1, further comprising preprocessing the acquired intravascular images in S1, wherein the preprocessing comprises: median filtering and adaptive histogram equalization.
4. The three-dimensional blood vessel modeling method based on the intravascular image according to claim 1, wherein in S1, different methods are adopted to segment different types of target elements when segmenting the target elements in each frame of intravascular image; the target elements include lumens, imaging catheters, plaque.
5. The three-dimensional blood vessel modeling method based on the intravascular image according to claim 1, wherein in S2, calculating the image features of each frame of the intravascular image based on the segmentation result of the target element in the intravascular image comprises the steps of:
s201, acquiring a lumen center and a lumen area according to a lumen segmentation result, wherein the lumen center is a point with the largest shortest distance from all points in the lumen to the outline; acquiring a catheter center based on a catheter segmentation result, wherein the catheter center is the center of mass of a catheter area; acquiring a plaque center and a plaque area based on a plaque segmentation result, wherein the plaque center is the mass center of the plaque area;
s202, correcting the lumen center according to the lumen center, the lumen area, the plaque center and the plaque area.
6. The three-dimensional blood vessel modeling method based on the intravascular image according to claim 5, wherein in S202, the lumen center is corrected according to the following formula:
wherein ,for the corrected lumen center, +.>For the lumen center before correction, +.> and />Lumen area and plaque area, respectively, +.>Is plaque center (I/O)>Is->To->Vector of->Is a correction coefficient.
7. The three-dimensional blood vessel modeling method based on intravascular images according to claim 1, wherein in S3, when the initialized three-dimensional center line is evolved according to the image features of each frame of intravascular images, a deflection angle is calculated according to the corrected relative positional relationship between the lumen center and the catheter center, and the corresponding three-dimensional center line of the blood vessel is bent according to the calculated deflection angle, and a deflection angle calculation formula is as follows:
wherein ,trepresent the firsttThe frame image is displayed in a frame image,is the deflection angle at the t-th frame, +.>For the actual distance between the shooting intervals of adjacent frame images, < >>For the corrected lumen center and catheter center distance of frame t-1, +.>For the corrected lumen center and catheter center distance of the t-th frame, +.>For the corrected lumen center and catheter center distance of frame t+1,/for>Is the deflection coefficient.
8. An FFR calculation method based on intravascular images, wherein a three-dimensional blood vessel model is established based on the three-dimensional blood vessel modeling method based on intravascular images according to any one of claims 1 to 7, and fluid mechanics simulation is performed to calculate FFR using the established three-dimensional blood vessel model.
9. An intravascular image based FFR calculation system for performing the intravascular image based FFR calculation method according to claim 8, comprising: the device comprises an image acquisition module, a feature extraction module, a model building module and an FFR calculation module;
the image acquisition module is used for acquiring intravascular images and image parameters of blood vessels to be detected and dividing target elements in each intravascular image according to the intravascular images;
the feature extraction module is used for calculating the image features of each frame of intravascular image according to the target elements obtained by segmentation;
the model building module is used for initializing the three-dimensional center line of the blood vessel to be detected according to the image parameters, and evolving the initialized three-dimensional center line according to the image characteristics of each frame of the intravascular image; constructing a blood vessel model according to the image parameters, the image characteristics of each frame of blood vessel image and the three-dimensional central line of the blood vessel after evolution;
and the FFR calculation module is used for carrying out hydrodynamic simulation according to the constructed blood vessel model and preset boundary conditions, calculating the pressure at the corresponding position of each blood vessel section in the target blood vessel, and calculating the FFR value at the corresponding position of each blood vessel section in the target blood vessel.
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