A blood flow fraction calculation method based on X-ray angiography image sequence

Abstract

The present invention relates to the field of medical technology, and provides a method for calculating blood flow fraction based on an X-ray contrast image sequence. The method comprises: obtaining at least two X-ray contrast image sequences, wherein the X-ray contrast image sequence includes multiple frames of first contrast images; in each X-ray contrast image sequence, calculating the amount of information of each frame of the first contrast image relative to the first frame of the first contrast image; based on each amount of information, screening each first contrast image to obtain a second contrast image of each X-ray contrast image sequence; based on each second contrast image, reconstructing a three-dimensional vascular image; and based on the three-dimensional vascular image, calculating the blood flow fraction. Through the present invention, the search time for the optimal frame contrast image is shortened, and the calculation accuracy of the blood flow fraction is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a blood flow fraction calculation method based on an X-ray angiography image sequence. Background Art

[0002] Pulmonary artery stenosis refers to the narrowing of the passage between the right ventricle and the pulmonary artery due to congenital malformation, while the ventricular septum is intact. This is one of the common congenital cardiovascular diseases. Common types of stenosis include valvular stenosis, infundibulum stenosis, and pulmonary artery stenosis. They can exist separately or together. The symptoms and progression of pulmonary artery stenosis are related to the degree of stenosis. Mild stenosis may be asymptomatic, while severe stenosis may develop symptoms early and gradually develop cyanosis and heart failure.

[0003] For pulmonary artery stenosis, X-ray pulmonary angiography is used as a common detection method. With the continuous development of science, medical research is also gradually deepening. The evaluation method of pulmonary artery stenosis has expanded from the original medical imaging to hemodynamics. Through medical imaging, the morphology of the blood vessels can be intuitively judged, but the stress inside the blood vessels, that is, the local pressure of the blood vessels, cannot be understood, and hemodynamics can make up for this. According to the X-ray pulmonary angiography image sequence, hemodynamics is used to calculate the pulmonary artery blood flow fraction, and then the pulmonary artery blood flow fraction is used to determine the degree of pulmonary artery stenosis.

[0004] The X-ray pulmonary artery angiography image sequence is a set of continuous images similar to a video. Before image analysis, it is necessary to select the clearest frame of the target blood vessel from a set of image sequences, that is, the optimal frame angiography image. In the prior art, the optimal frame angiography image is selected from the angiography image sequence by manual search. This process is not only time-consuming, but also leads to deviations in hemodynamic analysis due to the subjectivity of manual search. Summary of the invention

[0005] In order to improve the calculation accuracy of blood flow fraction and shorten the search time of optimal frame angiography images, the present invention proposes a blood flow fraction calculation method based on X-ray angiography image sequence.

[0006] In a first aspect, the present invention provides a method for calculating blood flow fraction based on an X-ray angiography image sequence, the method comprising:

[0007] Acquire at least two X-ray contrast imaging sequences, wherein the X-ray contrast imaging sequences include a plurality of frames of first contrast imaging images;

[0008] In each X-ray contrast image sequence, calculating the information amount of each frame of the first contrast image relative to the first frame of the first contrast image;

[0009] Screening each first contrast image according to each amount of information to obtain a second contrast image of each X-ray contrast image sequence;

[0010] reconstructing a three-dimensional blood vessel image according to each second angiography image;

[0011] The blood flow fraction is calculated based on the three-dimensional vascular image.

[0012] Through the above method, by calculating the information amount of each frame of the first contrast image in each X-ray contrast image sequence, the optimal frame contrast image of each X-ray contrast image sequence, that is, the second contrast image, is screened out, the time for searching for the optimal frame contrast image is shortened, and the subjectivity of manually searching for the optimal frame contrast image is overcome, so that the obtained second contrast image is more accurate, and then the three-dimensional vascular image reconstructed according to the second contrast image is closer to the real blood vessel, the calculation accuracy of the blood flow fraction is improved, and a basis is provided for determining the degree of pulmonary artery stenosis.

[0013] In an optional implementation, in each X-ray contrast image sequence, calculating the information amount of each frame of the first contrast image relative to the first frame of the first contrast image includes:

[0014] In the X-ray contrast image sequence, the first contrast image of each frame is subtracted from the first contrast image of the first frame to obtain a third contrast image corresponding to the first contrast image of each frame;

[0015] The variance of each third contrast image is calculated, and each variance is used as the information amount of each frame of the first contrast image relative to the first frame of the first contrast image.

[0016] Through the above implementation, the angiography image of the blood vessel often does not appear in the first frame of the first angiography image. Therefore, the first frame of the first angiography image is used as the background image, and the first angiography images of other frames are subtracted from the first frame of the first angiography image to obtain an image with background noise removed and containing angiography, thereby avoiding the influence of background noise on the angiography image of the blood vessel, improving the accuracy of screening the optimal frame of angiography images, and providing a basis for accurately calculating the blood flow fraction.

[0017] In an optional implementation, screening each first contrast image according to each amount of information to obtain a second contrast image of the X-ray contrast image sequence includes:

[0018] The first contrast image with the largest amount of information is used as the second contrast image in the X-ray contrast image sequence.

[0019] Through the above implementation, the variance is used to characterize the information content of the contrast image. The larger the variance is, the larger the information content of the blood vessels contained in the contrast image is. Thus, the optimal frame contrast image, that is, the second contrast image, is screened out.

[0020] In an optional implementation, reconstructing a three-dimensional blood vessel image according to each second angiography image includes:

[0021] Acquire the two-dimensional coordinates of each pixel point in each second contrast image and the image information of each second contrast image;

[0022] Acquire a first distance from the human body to the detector corresponding to each second contrast image;

[0023] According to each first distance and each image information, convert each two-dimensional coordinate into a first three-dimensional coordinate corresponding to each two-dimensional coordinate;

[0024] Rotate each first three-dimensional coordinate according to a preset rule to obtain a second three-dimensional coordinate corresponding to each first three-dimensional coordinate;

[0025] determining the three-dimensional coordinates of the blood vessel according to each second three-dimensional coordinate;

[0026] A three-dimensional blood vessel image is reconstructed according to the three-dimensional coordinates of the blood vessel.

[0027] In an optional implementation, the image information includes the width, height, first point spacing, and second point spacing of the second contrast image, and converting the two-dimensional coordinates into first three-dimensional coordinates corresponding to the two-dimensional coordinates according to the first distance and the image information includes:

[0028] Converting the first coordinate value in the two-dimensional coordinates into the first coordinate value in the first three-dimensional coordinates according to the width and the first point spacing;

[0029] Convert the second coordinate value in the two-dimensional coordinate into the second coordinate value in the first three-dimensional coordinate according to the height and the distance between the second point;

[0030] Using the first distance as the third coordinate value in the first three-dimensional coordinate;

[0031] The first three-dimensional coordinates are determined according to the first coordinate value in the first three-dimensional coordinates, the second coordinate value in the first three-dimensional coordinates, and the third coordinate value in the first three-dimensional coordinates.

[0032] In an optional implementation, rotating the first three-dimensional coordinate according to a preset rule to obtain a second three-dimensional coordinate corresponding to the first three-dimensional coordinate includes:

[0033] Acquire a first angle and a second angle corresponding to the second angiography image, wherein the first angle is the angle between a straight line formed between the radiation source and the detector and a preset plane in a three-dimensional coordinate system, and the second angle is the angle between a straight line formed between the radiation source and the detector and a preset coordinate axis in the three-dimensional coordinate system;

[0034] Rotate the first three-dimensional coordinate around a first coordinate axis of the three-dimensional coordinate system at a first angle to obtain a third three-dimensional coordinate corresponding to the first three-dimensional coordinate;

[0035] The third three-dimensional coordinate is rotated around the second coordinate axis of the three-dimensional coordinate system according to the second angle to obtain the second three-dimensional coordinate.

[0036] In an optional implementation, determining the three-dimensional coordinates of the blood vessel according to each second three-dimensional coordinate includes:

[0037] Acquire a second distance from the radiation source to the detector corresponding to the second contrast image;

[0038] Determining coordinates of the radiation source corresponding to each second angiography image according to each second distance;

[0039] The three-dimensional coordinates of the blood vessel are determined based on the second three-dimensional coordinates and the coordinates of the radiation source.

[0040] In an optional implementation, determining the three-dimensional coordinates of the blood vessel according to each second three-dimensional coordinate and the coordinates of the radiation source includes:

[0041] Determining a connection line between each second three-dimensional coordinate and the radiation source according to each second three-dimensional coordinate and the coordinate of the radiation source;

[0042] Grouping the lines to obtain a plurality of line groups, wherein the second three-dimensional coordinates corresponding to the lines in the same line group belong to the same position of the blood vessel;

[0043] Determine the target point corresponding to each line group, and the sum of the distances between the target point and each line in the line group is the smallest;

[0044] The three-dimensional coordinates corresponding to each target point are used as the three-dimensional coordinates of the blood vessel.

[0045] Through the above implementation, when the second three-dimensional coordinate belongs to the same position of the blood vessel, the intersection of the line formed by the second three-dimensional coordinate and the radiation source is the three-dimensional coordinate point of the blood vessel. However, when there is an error in the second three-dimensional coordinate, the line formed by the second three-dimensional coordinate and the radiation source often does not have an intersection. At this time, the point with the smallest sum of distances to each line in the line group can be used as the three-dimensional coordinate of the blood vessel, so as to overcome the influence of the distortion of the blood vessel morphology caused by the error, thereby improving the accuracy of calculating the blood flow fraction.

[0046] In a second aspect, the present invention further provides a computer device, comprising a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the steps of the method for calculating blood flow fraction based on an X-ray angiography image sequence of the first aspect or any embodiment of the first aspect by executing the computer instructions.

[0047] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for calculating blood flow fraction based on an X-ray angiography image sequence of the first aspect or any embodiment of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 is a flow chart of a method for calculating blood flow fraction based on an X-ray angiography image sequence according to an exemplary embodiment;

[0050] Figure 2 In one example, a schematic diagram of a scene in which a radiation source and a detector in a DSA system detect blood vessels of a human body;

[0051] Figure 3 is a schematic diagram of a first angle and a second angle in an example;

[0052] Figure 4 is a schematic diagram of determining the three-dimensional coordinates of a blood vessel in an example;

[0053] Figure 5 is a schematic diagram of a line formed by a second three-dimensional coordinate and a radiation source in an example;

[0054] Figure 6 In one example, a real-time pressure map of the right ventricle is acquired;

[0055] Figure 7 It is a structural schematic diagram of a blood flow fraction calculation device based on an X-ray angiography image sequence proposed according to an exemplary embodiment;

[0056] Figure 8 It is a schematic diagram of the hardware structure of a computer device proposed according to an exemplary embodiment. DETAILED DESCRIPTION

[0057] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0059] In order to improve the calculation accuracy of blood flow fraction and shorten the search time of optimal frame angiography images, the present invention proposes a blood flow fraction calculation method based on X-ray angiography image sequence.

[0060] Figure 1 FIG. 1 is a flow chart of a method for calculating blood flow fraction based on an X-ray angiography image sequence according to an exemplary embodiment. Figure 1 As shown, the blood flow fraction calculation method based on the X-ray angiography image sequence includes the following steps S101 to S105.

[0061] Step S101: acquiring at least two X-ray contrast image sequences, wherein the X-ray contrast image sequences include a plurality of frames of first contrast images.

[0062] In an optional embodiment, the X-ray angiography image sequence is obtained by scanning a patient with a digital subtraction angiography (DSA). The DSA system includes a radiation source and a detector. The radiation source is usually an X-ray generator, which generates X-rays and projects them into the human body. These X-rays are partially absorbed when passing through human tissue, while the parts that pass through blood vessels are captured by the detector. The detector is a device that can detect these X-rays, usually composed of one or more pixel arrays, each pixel can detect X-rays passing through the human body. The digital subtraction angiography machine rotates during the scanning process, and different X-ray angiography image sequences are obtained by different scanning angles during the rotation process.

[0063] In an optional embodiment, the number of X-ray angiography image sequences is not specifically limited. The more the number of X-ray angiography image sequences, the closer the obtained three-dimensional blood vessel image is to the actual situation, so that the calculated blood flow fraction is more accurate.

[0064] In an optional embodiment, the number of frames of the first contrast image in each X-ray contrast image sequence is not specifically limited and can be set according to actual conditions.

[0065] In an optional embodiment, the first contrast image is obtained from the X-ray contrast image sequence by an image frame extraction algorithm, for example, a local binary pattern (LBP), a histogram of oriented gradient (HOG), a scale-invariant feature transform (SIFT), etc.

[0066] In an optional embodiment, the first contrast image may be an image after downsampling and noise reduction filtering. The original contrast image is downsampled to reduce the image size and improve the algorithm execution efficiency. The original contrast image is subjected to noise reduction filtering to reduce image noise and improve the accuracy of the algorithm.

[0067] Step S102: in each X-ray contrast image sequence, calculating the information amount of each frame of the first contrast image relative to the first frame of the first contrast image.

[0068] In an optional embodiment, the amount of information can be described by features such as variance, contrast, and texture of the image, which are not specifically limited here.

[0069] Step S103: Screening each first contrast image according to each amount of information to obtain a second contrast image of each X-ray contrast image sequence.

[0070] In an optional embodiment, the first frame of the first contrast image often does not contain a blood vessel contrast image, and the first frame of the first contrast image is a background image containing noise. Therefore, the first frame of the first contrast image can be used as a background image, and the first contrast image can be screened by using the information amount of other frames of the first contrast image compared with the first frame of the first contrast image. The greater the amount of information, the greater the blood vessel information contained in the first contrast image, and the greater the possibility of being the optimal frame of contrast image.

[0071] Step S104: reconstructing a three-dimensional blood vessel image according to each second angiography image.

[0072] In an optional embodiment, a three-dimensional reconstruction algorithm may be used to transform the second angiography image to obtain a three-dimensional blood vessel image.

[0073] Step S105: Calculate the blood flow fraction according to the three-dimensional blood vessel image.

[0074] In an optional embodiment, the degree of pulmonary artery stenosis is determined based on the calculated blood flow fraction, providing a basis for judging the degree of pulmonary artery stenosis. For example, when the blood flow fraction is around 0.5, it indicates that pulmonary artery stenosis will cause functional ischemia of the blood vessels, and interventional treatment is required.

[0075] Through the above method, by calculating the information amount of each frame of the first contrast image in each X-ray contrast image sequence, the optimal frame contrast image of each X-ray contrast image sequence, that is, the second contrast image, is screened out, the time for searching for the optimal frame contrast image is shortened, and the subjectivity of manually searching for the optimal frame contrast image is overcome, so that the obtained second contrast image is more accurate, and then the three-dimensional vascular image reconstructed according to the second contrast image is closer to the real blood vessel, the calculation accuracy of the blood flow fraction is improved, and a basis is provided for diagnosing the degree of pulmonary artery stenosis.

[0076] In one example, the first contrast image is obtained from an X-ray contrast image sequence by an image frame extraction algorithm. For example, assuming an X-ray contrast image sequence A (4*4*4), the matrix data can be represented as follows:

[0077] A(:,:,1)=[1,1,1,1;1,1,1,1;1,1,1,1;1,1,1,1]

[0078] A(:,:,2)=[1,1,1,1;1,1,1,1;1,1,1,1;1,1,1,1]

[0079] A(:,:,3)=[1,1,1,1;1,1,1,1;1,1,1,1;1,1,1,1]

[0080] A(:,:,4)=[1,1,1,1;1,1,1,1;1,1,1,1;1,1,1,1]

[0081] The third dimension in the matrix represents the time axis in the sequence.

[0082] At this time, if the first frame of the contrast image in A is extracted, the matrix data K (4*4*4) of the first frame of the contrast image can be expressed as follows:

[0083] K(:,:,1)=[1,1,1,1;1,1,1,1;1,1,1,1;1,1,1,1]

[0084] K(:,:,2)=[0,0,0,0;0,0,0,0;0,0,0,0;0,0,0,0]

[0085] K(:,:,3)=[0,0,0,0;0,0,0,0;0,0,0,0;0,0,0,0]

[0086] K(:,:,4)=[0,0,0,0;0,0,0,0;0,0,0,0;0,0,0,0]

[0087] If the second frame of the contrast image in A is extracted, the matrix data K (4*4*4) can be expressed as follows:

[0088] K(:,:,1)=[0,0,0,0;0,0,0,0;0,0,0,0;0,0,0,0]

[0089] K(:,:,2)=[1,1,1,1;1,1,1,1;1,1,1,1;1,1,1,1]

[0090] K(:,:,3)=[0,0,0,0;0,0,0,0;0,0,0,0;0,0,0,0]

[0091] K(:,:,4)=[0,0,0,0;0,0,0,0;0,0,0,0;0,0,0,0]

[0092] By analogy, if the Nth frame of the contrast image in A is extracted, the matrix data K (4*4*4) can be expressed as follows:

[0093] K(:,:,N)=[1,1,1,1;1,1,1,1;1,1,1,1;1,1,1,1]

[0094] K(:,:,else)=[0,0,0,0;0,0,0,0;0,0,0,0;0,0,0,0]

[0095] Among them, else represents all frames except the Nth frame.

[0096] Then, by cross-producting the above two three-dimensional matrices, we can get the required two-dimensional matrix, that is, a frame of contrast image X(4*4), and the formula is as follows.

[0097] A(4*4*4)×K(4*4*4)=X(4*4)

[0098] In one example, the first contrast image is an image obtained by performing downsampling processing and noise reduction filtering processing on an original contrast image in an X-ray contrast image sequence.

[0099] For example, assuming a frame of original contrast image K (4*4), its matrix data can be expressed as follows:

[0100] K(:,:)=[A1,B1,C1,D1;A2,B2,C2,D2;A3,B3,C3,D3;A4,B4,C4,D4]

[0101] At this time, if the downsampling factor is 2, the matrix data corresponding to the downsampled image can be expressed as follows:

[0102] K2(:,:)=[(0+A1+B1+0+A2) / 5,(0+B1+C1+C2+D1) / 5; (A2+A3+A4+0+B3) / 5,(C2

[0103] +C3+C4+B3+D3) / 5]

[0104] For example, a Gaussian filter may be used to perform noise reduction filtering on the downsampled image, thereby obtaining a first contrast image. Assuming that a frame of contrast image K (4*4), the corresponding matrix data may be expressed as follows:

[0105] K(:,:)=[A1,B1,C1,D1;A2,B2,C2,D2;A3,B3,C3,D3;A4,B4,C4,D4]

[0106] At this time, the matrix data corresponding to the Gaussian low-pass filter G(3*3) can be expressed as follows:

[0107] G(:,:)=[X1,Y1,Z1;X2,Y2,Z2;X3,Y3,Z3]

[0108] Let G(3*3) start multiplication from the first element of K(4*4), and finally traverse all elements of K(4*4), and the multiplication results form a new matrix K3:

[0109] K3(:,:)=[A13,B13,C13,D13;A23,B23,C23,D23;A33,B33,C33,D33;A43,B43,C43,D43]

[0110] The obtained K3 is the matrix data corresponding to the first angiography image.

[0111] In one example, in the above step S102, the information amount of each frame of the first contrast image relative to the first frame of the first contrast image is calculated in the following manner:

[0112] First, in the X-ray contrast image sequence, a third contrast image corresponding to each frame of the first contrast image is obtained by subtracting the first contrast image of each frame from the first contrast image of the first frame.

[0113] In an optional embodiment, each pixel point in each frame of the first contrast image may be subtracted from the corresponding pixel point in the first frame of the first contrast image to obtain the third contrast image.

[0114] Then, the variance of each third contrast image is calculated, and each variance is used as the amount of information of each frame of the first contrast image relative to the first frame of the first contrast image.

[0115] In an optional embodiment, the variance of the third contrast image is calculated by the following formula:

[0116] Variance_2D=∑(I(x,y)-Mean_2D) 2 / (N*M)

[0117] Among them, Variance_2D represents the variance of the third contrast image; I(x, y) represents the gray value of a point on the third contrast image; Mean_2D represents the mean gray value of the third contrast image; N and M represent the width and height of the third contrast image, respectively.

[0118] In the embodiment of the present invention, the vascular signal-to-noise ratio of the first angiography image may also be calculated based on the variance of the third angiography image. The specific formula is as follows:

[0119] VSNR=10*log10(Variance_2D_A / Variance_2D_B)

[0120] Among them, VSNR represents the vascular signal-to-noise ratio of the first angiography image; Variance_2D_A represents the variance of the third angiography image; Variance_2D_B represents the variance of the first angiography image of the first frame. It can be obtained from the formula that the larger the VSNR value, the greater the probability that the current frame angiography image is the optimal frame angiography image.

[0121] In the embodiment of the present invention, the angiography image of the blood vessel often does not appear in the first frame of the first angiography image. Therefore, the first frame of the first angiography image is used as the background image, and the first frame of the first angiography image is subtracted from the first frame of the first angiography image to obtain an image with background noise removed and containing angiography, thereby avoiding the influence of background noise on the angiography image, improving the accuracy of screening the optimal frame of angiography image, and providing a basis for accurately calculating the blood flow fraction.

[0122] In one example, in the above step S103, the first contrast image with the largest amount of information is used as the second contrast image of the X-ray contrast image sequence. In the embodiment of the present invention, the information amount of the contrast image is characterized by variance. The larger the variance, the greater the amount of information about blood vessels contained in the contrast image. The optimal frame contrast image, i.e., the second contrast image, is thus screened out.

[0123] Figure 2 This is a schematic diagram of the scene in which the radiation source and detector in the DSA system detect human blood vessels. In the DSA system, there is an imaging coordinate system. The angiography image in the X-ray angiography image sequence is a two-dimensional projection of the three-dimensional blood vessels of the human body in the imaging coordinate system of the DSA system. It can be seen that there is a certain correspondence between the angiography image in the X-ray angiography image sequence and the three-dimensional blood vessels of the human body.

[0124] In one example, in the above step S104, the three-dimensional blood vessel image is reconstructed through the following steps:

[0125] Step a1: Acquire the two-dimensional coordinates of each pixel point in each second contrast image and the image information of each second contrast image.

[0126] Step a2: Obtaining the first distance from the human body to the detector corresponding to each second angiography image. In the embodiment of the present invention, the first distance from the human body to the detector can be obtained through the label corresponding to the image sequence in the DSA system.

[0127] Step a3: According to each first distance and each image information, each two-dimensional coordinate is converted into a first three-dimensional coordinate corresponding to each two-dimensional coordinate.

[0128] Step a4: Rotate each first three-dimensional coordinate according to a preset rule to obtain a second three-dimensional coordinate corresponding to each first three-dimensional coordinate.

[0129] Step a5: Determine the three-dimensional coordinates of the blood vessel according to each second three-dimensional coordinate.

[0130] Step a6: Reconstruct a three-dimensional blood vessel image according to the three-dimensional coordinates of the blood vessel.

[0131] In one example, the image information includes a width, a height, a first point spacing, and a second point spacing of the second contrast image.

[0132] The width and height of the second contrast image are basic attributes describing the second contrast image. Generally speaking, the width and height of the second contrast image are in pixels and can be expressed by numbers. For example, an image with a resolution of 720×480 has a width of 720 pixels and a height of 480 pixels.

[0133] The dot pitch of an image refers to the distance between two adjacent pixels in the image. The size of the dot pitch can affect the clarity and resolution of the image. Generally speaking, the smaller the dot pitch, the higher the clarity and resolution of the image, because the distance between adjacent pixels is smaller and the details are better. Exemplarily, the first dot pitch of the second contrast image is the distance between two adjacent pixels parallel to the x-axis in the second contrast image. The second dot pitch of the second contrast image is the distance between two adjacent pixels parallel to the y-axis in the second contrast image.

[0134] In the above step a3, the two-dimensional coordinate is converted into a first three-dimensional coordinate corresponding to the two-dimensional coordinate according to the first distance and the image information in the following manner:

[0135] First, according to the width and the first point spacing, the first coordinate value in the two-dimensional coordinate is converted into the first coordinate value in the first three-dimensional coordinate.

[0136] In an optional embodiment, the first coordinate value in the first three-dimensional coordinates=(the first coordinate value in the two-dimensional coordinates-the width)*the first point spacing. The first point spacing is the point spacing of the x-axis in the second angiography image.

[0137] Secondly, according to the height and the second point distance, the second coordinate value in the two-dimensional coordinate is converted into the second coordinate value in the first three-dimensional coordinate.

[0138] In an optional embodiment, the second coordinate value in the first three-dimensional coordinates=(the second coordinate value in the two-dimensional coordinates-the height)*the second point spacing, wherein the second point spacing is the point spacing of the y-axis in the second angiography image.

[0139] Again, the first distance is used as the third coordinate value in the first three-dimensional coordinates.

[0140] Finally, the first three-dimensional coordinates are determined according to the first coordinate value in the first three-dimensional coordinates, the second coordinate value in the first three-dimensional coordinates, and the third coordinate value in the first three-dimensional coordinates.

[0141] In one example, in the above step a4, the first three-dimensional coordinate is rotated according to a preset rule by the following content to obtain a second three-dimensional coordinate corresponding to the first three-dimensional coordinate:

[0142] First, a first angle and a second angle corresponding to the second angiographic image are obtained, wherein the first angle is the angle between the straight line formed between the radiation source and the detector and the preset plane in the three-dimensional coordinate system, and the second angle is the angle between the straight line formed between the radiation source and the detector and the preset coordinate axis in the three-dimensional coordinate system. Figure 3 As shown, the first angle and the second angle corresponding to the second angiography image are β and α respectively. The preset plane is a plane formed by the x-axis and the y-axis, and the preset coordinate axis is the z-axis. In an embodiment of the present invention, the first angle and the second angle are obtained by labels corresponding to the image sequence in the DSA system.

[0143] Then, the first three-dimensional coordinate is rotated around the first coordinate axis of the three-dimensional coordinate system according to the first angle to obtain a third three-dimensional coordinate corresponding to the first three-dimensional coordinate. In the embodiment of the present invention, the first coordinate axis is the y-axis.

[0144] Finally, the third three-dimensional coordinate is rotated around the second coordinate axis of the three-dimensional coordinate system according to the second angle to obtain the second three-dimensional coordinate. In the embodiment of the present invention, the second coordinate axis is the x-axis.

[0145] In one example, in the above step a5, the three-dimensional coordinates of the blood vessel are determined by the following steps:

[0146] First, a second distance from the radiation source to the detector corresponding to the second angiography image is obtained. In an embodiment of the present invention, the second distance is obtained through a label corresponding to an image sequence in a DSA system.

[0147] Then, the coordinates of the radiation source corresponding to each second angiography image are determined according to each second distance.

[0148] Finally, the three-dimensional coordinates of the blood vessel are determined according to the second three-dimensional coordinates and the coordinates of the radiation source.

[0149] In an optional embodiment, the three-dimensional coordinates of the blood vessel are determined by:

[0150] First, according to each second three-dimensional coordinate and the coordinate of the radiation source, a connection line between each second three-dimensional coordinate and the radiation source is determined.

[0151] Secondly, the lines are grouped to obtain a plurality of line groups, the second three-dimensional coordinates corresponding to the lines in the same line group belong to the same position of the blood vessel, and the lines in the line group correspond one-to-one to each X-ray angiography image sequence.

[0152] Thirdly, the target point corresponding to each line group is determined, and the sum of the distances between the target point and each line in the line group is the smallest.

[0153] Finally, the three-dimensional coordinates corresponding to each target point are used as the three-dimensional coordinates of the blood vessel.

[0154] like Figure 4 As shown in FIG. 1 , when the second three-dimensional coordinate belongs to the same position of the blood vessel, the intersection of the line formed by the second three-dimensional coordinate and the radiation source is the three-dimensional coordinate point of the blood vessel. However, when there is an error in the second three-dimensional coordinate, the line formed by the second three-dimensional coordinate and the radiation source often does not have an intersection, such as Figure 5 As shown, at this time, the point with the smallest sum of distances from each line in the line group can be used as the three-dimensional coordinate of the blood vessel, so as to overcome the influence of the distortion of the blood vessel morphology caused by the error, thereby improving the accuracy of calculating the blood flow fraction.

[0155] In one example, in the above step S105, the blood flow fraction is calculated by the following steps:

[0156] Step b1: Calculate the blood flow velocity of the blood vessels through the three-dimensional blood vessel image.

[0157] In an optional embodiment, the blood flow velocity of the blood vessel is calculated by an integral method, and the specific formula is as follows:

[0158]

[0159] in, is the blood flow velocity; V is the volume of the blood vessel; t1 is the time of flowing into the starting end of the blood vessel; t2 is the time of flowing out of the ending end of the blood vessel; s is the flow rate per unit time.

[0160] Step b2: Collect the real-time pressure corresponding to the blood vessel.

[0161] In an optional embodiment, the blood vessel pressure, such as the real-time right ventricular pressure, may be collected by a disposable invasive blood pressure sensor. Figure 6 This is the real-time pressure map of the right ventricle obtained.

[0162] Step b3: Calculate the pressure at each location of the blood vessel based on the blood flow rate and real-time pressure using a fluid mechanics algorithm.

[0163] In an optional embodiment, in the fluid mechanics algorithm, the pressure at each location of the blood vessel is calculated by using the Navier-Stokes equations (NS equations), wherein the NS equations are expressed as follows:

[0164]

[0165]

[0166] Wherein, u is the blood flow velocity, t is the time, p is the pressure at each location of the blood vessel, ρ is the fluid density, and υ is the kinematic viscosity of the fluid. In the embodiment of the present invention, the fluid parameters (ρ and υ) in the above formula are calculated using the blood flow velocity and the real-time pressure, and then the pressure at each location of the blood vessel is obtained.

[0167] Step b4: Calculate the blood flow fraction based on the pressure at each location of the blood vessel.

[0168] In an optional embodiment, the blood flow fraction is calculated by the following formula:

[0169] PFR=P / Pa

[0170] Among them, PFR is the blood flow fraction, P is the pressure in each part of the blood vessel, and Pa is the average pressure in the aorta (obtained by collection).

[0171] The specific implementation process of reconstructing a three-dimensional blood vessel image based on each second angiography image is described below through a specific embodiment.

[0172] First, the conversion from the two-dimensional origin (0, 0) in the first angiography image to the three-dimensional space point of the world coordinate is realized by the following code.

[0173] double q0[3]={0,0,0-z_distance1};

[0174] / / z_distance1 represents the first distance from the human body to the detector

[0175] double q1[3] = {0};

[0176] this->TrasformY(q0,angle1,q1);

[0177] / / angle1 represents the first angle

[0178] double q2[3]={0};

[0179] this->TrasformX(q1,angle2,q2);

[0180] / / angle2 represents the second angle

[0181] Then, the conversion of the two-dimensional random point (x, y) in the first angiography image to the three-dimensional space point of the world coordinate is realized by the following code.

[0182] double p[3]={x,y,0};

[0183] / / 2D coordinates, the z axis is assumed to be 0

[0184] double p2[3]={0};

[0185] this->Trasform(p,point2d,p2);

[0186] / / p is the two-dimensional coordinate before conversion, p2 is the three-dimensional coordinate after conversion

[0187] Among them, the Trasform function is used to convert a two-dimensional coordinate into a three-dimensional coordinate, which is specifically implemented through the following code definition.

[0188] double pos_2d; / / 2D coordinates to be converted

[0189] double a0[3]={(pos_2d[0]-extent[0])*apacing_temp[0],

[0190] (pos_2d[1]-extent[1])*apacing_temp[1],

[0191] 0+z_distance1};

[0192] / / extent[0] represents the width of the two-dimensional image

[0193] / / extent[1] represents the height of the two-dimensional image

[0194] / / apacing_temp[0] represents the pixel spacing on the x-axis of the two-dimensional image, that is, the first point spacing

[0195] / / apacing_temp[1] represents the pixel spacing on the y-axis of the two-dimensional image, that is, the second pixel spacing / / Get the width and height of the two-dimensional image from tag ID (0028, 0010) / ID (0028, 0011) respectively

[0196] / / Get the first point spacing and the second point spacing of the two-dimensional image from the tag ID (0018, 1164) / / z_distance1 represents the first distance from the human body to the detector

[0197] double a1[3]={0};

[0198] this->TrasformY(a0,angle1,a1);

[0199] / / angle1 represents the first angle

[0200] this->TrasformX(a1,angle2,temp);

[0201] / / angle2 represents the second angle

[0202] / / In this function, temp is the converted three-dimensional coordinate

[0203] Among them, the TrasformY function is used to rotate a three-dimensional point around the Y axis of the three-dimensional coordinate system. Its specific implementation code is as follows:

[0204] temp[0]=cos(angle_l_r)*a0[0]+sin(angle_l_r)*a0[2];

[0205] temp[1] = a0[1];

[0206] temp[2]=cos(angle_l_r)*a0[2]-sin(angle_l_r)*a0[0];

[0207] / / In this function, a0 is the 3D coordinate before conversion; temp is the 3D coordinate after conversion; angle_l_r represents the angle of rotation around the Y axis, i.e. the first angle

[0208] The TrasformX function is used to rotate a 3D point around the X-axis of the 3D coordinate system. The specific implementation code is as follows:

[0209] temp[0] = a0[0];

[0210] temp[1]=cos(angle_t_b)*a0[1]-sin(angle_t_b)*a0[2];

[0211] temp[2]=sin(angle_t_b)*a0[1]+cos(angle_t_b)*a0[2];

[0212] / / In this function, a0 is the 3D coordinate before conversion; temp is the 3D coordinate after conversion; angle_t_b represents the angle of rotation around the X axis, i.e. the second angle

[0213] Based on the same inventive concept, an embodiment of the present invention also provides a blood flow fraction calculation device based on an X-ray angiography image sequence, such as Figure 7 As shown, the device comprises:

[0214] The acquisition module 701 is used to acquire at least two X-ray contrast image sequences, where the X-ray contrast image sequences include multiple frames of first contrast images. For details, please refer to the description of step S101 in the above embodiment, which will not be repeated here.

[0215] The first calculation module 702 is used to calculate the information amount of each frame of the first contrast image relative to the first frame of the first contrast image in each X-ray contrast image sequence; for details, please refer to the description of step S102 in the above embodiment, which will not be repeated here.

[0216] The screening module 703 is used to screen the first contrast images according to the information amounts to obtain the second contrast images of the X-ray contrast image sequences; the details are described in step S103 in the above embodiment and will not be repeated here.

[0217] The reconstruction module 704 is used to reconstruct a three-dimensional blood vessel image according to each second angiography image. For details, please refer to the description of step S104 in the above embodiment, which will not be repeated here.

[0218] The second calculation module 705 is used to calculate the blood flow fraction according to the three-dimensional blood vessel image. For details, please refer to the description of step S105 in the above embodiment, which will not be repeated here.

[0219] Through the above device, by calculating the information amount of each frame of the first contrast image in each X-ray contrast image sequence, the optimal frame contrast image of each X-ray contrast image sequence, that is, the second contrast image, is screened out, thereby shortening the time for searching for the optimal frame contrast image, overcoming the subjectivity of manually searching for the optimal frame contrast image, making the obtained second contrast image more accurate, and then the three-dimensional vascular image reconstructed according to the second contrast image is closer to the real blood vessel, thereby improving the calculation accuracy of the blood flow fraction and providing a basis for diagnosing the degree of pulmonary artery stenosis.

[0220] In one example, the first calculation module 702 includes:

[0221] The first calculation submodule is used to subtract the first contrast image of each frame from the first contrast image of the first frame in the X-ray contrast image sequence to obtain the third contrast image corresponding to each first contrast image; for details, please refer to the description in the above embodiment and will not be repeated here.

[0222] The second calculation submodule is used to calculate the variance of each third contrast image, and use each variance as the information amount of each frame of the first contrast image relative to the first frame of the first contrast image. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0223] In one example, the screening module 703 includes:

[0224] The screening submodule is used to use the first contrast image with the largest amount of information as the second contrast image in the X-ray contrast image sequence. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0225] In one example, the reconstruction module 704 includes:

[0226] The first acquisition submodule is used to acquire the two-dimensional coordinates of each pixel point in each second contrast image, and the image information of each second contrast image; for details, please refer to the description in the above embodiment, which will not be repeated here.

[0227] The second acquisition submodule is used to acquire the first distance from the human body to the detector corresponding to each second angiography image; for details, please refer to the description in the above embodiment, which will not be repeated here.

[0228] The conversion submodule is used to convert each two-dimensional coordinate into a first three-dimensional coordinate corresponding to each two-dimensional coordinate according to each first distance and each image information; the details are described in the above embodiment and will not be repeated here.

[0229] The rotation submodule is used to rotate each first three-dimensional coordinate according to a preset rule to obtain a second three-dimensional coordinate corresponding to each first three-dimensional coordinate; for details, please refer to the description in the above embodiment and will not be repeated here.

[0230] The determination submodule is used to determine the three-dimensional coordinates of the blood vessel according to each second three-dimensional coordinate; the details are described in the above embodiment and will not be repeated here.

[0231] The reconstruction submodule is used to reconstruct a three-dimensional blood vessel image according to the three-dimensional coordinates of the blood vessel. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0232] In one example, the image information includes a width, a height, a first point spacing, and a second point spacing of the second contrast image, and the conversion submodule includes:

[0233] The first conversion unit is used to convert the first coordinate value in the two-dimensional coordinate into the first coordinate value in the first three-dimensional coordinate according to the width and the first point spacing; the details are described in the above embodiment and will not be repeated here.

[0234] The second conversion unit is used to convert the second coordinate value in the two-dimensional coordinate into the second coordinate value in the first three-dimensional coordinate according to the height and the second point distance; the details are described in the above embodiment and will not be repeated here.

[0235] The first determining unit is used to use the first distance as the third coordinate value in the first three-dimensional coordinates; for details, please refer to the description in the above embodiment, which will not be repeated here.

[0236] The second determining unit is used to determine the first three-dimensional coordinates according to the first coordinate value in the first three-dimensional coordinates, the second coordinate value in the first three-dimensional coordinates, and the third coordinate value in the first three-dimensional coordinates. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0237] In one example, the rotation submodule includes:

[0238] The first acquisition unit is used to acquire a first angle and a second angle corresponding to the second contrast image, the first angle being the angle between a straight line formed between the radiation source and the detector and a preset plane in a three-dimensional coordinate system, and the second angle being the angle between a straight line formed between the radiation source and the detector and a preset coordinate axis in a three-dimensional coordinate system; for details, please refer to the description in the above embodiment and will not be repeated here.

[0239] The first rotation unit is used to rotate the first three-dimensional coordinate around the first coordinate axis of the three-dimensional coordinate system at a first angle to obtain a third three-dimensional coordinate corresponding to the first three-dimensional coordinate; for details, please refer to the description in the above embodiment and will not be repeated here.

[0240] The second rotation unit is used to rotate the third three-dimensional coordinate around the second coordinate axis of the three-dimensional coordinate system according to the second angle to obtain the second three-dimensional coordinate. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0241] In one example, determining the submodule includes:

[0242] The second acquisition unit is used to acquire a second distance from the radiation source to the detector corresponding to the second angiography image; for details, please refer to the description in the above embodiment and will not be repeated here.

[0243] The third determination unit is used to determine the coordinates of the radiation source corresponding to each second angiography image according to each second distance; the details are as described in the above embodiment and will not be repeated here.

[0244] The fourth determining unit is used to determine the three-dimensional coordinates of the blood vessel according to the second three-dimensional coordinates and the coordinates of the radiation source. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0245] In one example, the fourth determining unit includes:

[0246] The first determining subunit is used to determine the connection line between each second three-dimensional coordinate and the radiation source according to each second three-dimensional coordinate and the coordinate of the radiation source; the details refer to the description in the above embodiment and will not be repeated here.

[0247] The grouping subunit is used to group the links to obtain multiple link groups, and the second three-dimensional coordinates corresponding to the links in the same link group belong to the same position of the blood vessel; for details, please refer to the description in the above embodiment and will not be repeated here.

[0248] The second determination subunit is used to determine the target point corresponding to each line group, and the sum of the distances between the target point and each line in the line group is the smallest; for details, please refer to the description in the above embodiment and will not be repeated here.

[0249] The third determination subunit is used to use the three-dimensional coordinates corresponding to each target point as the three-dimensional coordinates of the blood vessel. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0250] The specific limitations and beneficial effects of the above-mentioned device can be found in the above-mentioned limitations on the blood flow fraction calculation method based on the X-ray angiography image sequence, which will not be repeated here. The above-mentioned modules can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0251] Figure 8 FIG. 1 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment. Figure 8 As shown, the device includes one or more processors 810 and a memory 820, and the memory 820 includes a persistent memory, a volatile memory, and a hard disk. Figure 8 A processor 810 is taken as an example. The device may also include: an input device 830 and an output device 840.

[0252] The processor 810, the memory 820, the input device 830 and the output device 840 may be connected via a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0253] The processor 810 may be a central processing unit (CPU). The processor 810 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips. A general-purpose processor may be a microprocessor or the processor may be any conventional processor.

[0254] The memory 820 is a non-transient computer-readable storage medium, including a persistent memory, a volatile memory, and a hard disk, and can be used to store non-transient software programs, non-transient computer executable programs, and modules, such as the program instructions / modules corresponding to the blood flow fraction calculation method based on the X-ray angiography image sequence in the embodiment of the present application. The processor 810 executes various functional applications and data processing of the server by running the non-transient software programs, instructions, and modules stored in the memory 820, that is, to implement any of the above-mentioned blood flow fraction calculation methods based on the X-ray angiography image sequence.

[0255] The memory 820 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data used as needed, etc. In addition, the memory 820 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 820 may optionally include a memory remotely arranged relative to the processor 810, and these remote memories may be connected to the data processing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0256] The input device 830 can receive input digital or character information and generate signal input related to user settings and function control. The output device 840 can include display devices such as display screens.

[0257] One or more modules are stored in the memory 820, and when executed by one or more processors 810, the execution is as follows: Figure 1 The method shown.

[0258] The above-mentioned product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to Figure 1 Related description of the illustrated embodiment.

[0259] The embodiment of the present invention further provides a non-transitory computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the method in any of the above method embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0260] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0261] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A method for calculating blood flow fraction based on an X-ray angiography image sequence, characterized in that: The method comprises: Acquire at least two X-ray contrast imaging sequences, wherein the X-ray contrast imaging sequences include a plurality of frames of first contrast imaging images; In each of the X-ray contrast imaging sequences, calculating the amount of information of each frame of the first contrast imaging image relative to the first frame of the first contrast imaging image; Screening each of the first contrast images according to each of the information amounts to obtain a second contrast image of each of the X-ray contrast image sequences; reconstructing a three-dimensional blood vessel image according to each of the second angiography images; Calculating a blood flow fraction according to the three-dimensional vascular image; Wherein, in each of the X-ray contrast image sequences, calculating the information amount of each frame of the first contrast image relative to the first frame of the first contrast image comprises: In the X-ray contrast image sequence, a first contrast image of each frame is subtracted from a first contrast image of the first frame to obtain a third contrast image corresponding to the first contrast image of each frame; Calculating the variance of each of the third contrast images, and using each of the variances as the amount of information of each frame of the first contrast image relative to the first frame of the first contrast image; Reconstructing a three-dimensional blood vessel image according to each of the second angiography images includes: Acquire the two-dimensional coordinates of each pixel point in each of the second contrast images, and the image information of each of the second contrast images; Acquire a first distance from the human body to the detector corresponding to each of the second contrast images; Convert each of the two-dimensional coordinates into a first three-dimensional coordinate corresponding to each of the two-dimensional coordinates according to each of the first distances and each of the image information; Rotate each of the first three-dimensional coordinates according to a preset rule to obtain a second three-dimensional coordinate corresponding to each of the first three-dimensional coordinates; determining the three-dimensional coordinates of the blood vessel according to each of the second three-dimensional coordinates; reconstructing the three-dimensional blood vessel image according to the three-dimensional coordinates of the blood vessel; Determining the three-dimensional coordinates of the blood vessel according to each of the second three-dimensional coordinates includes: Acquire a second distance from the radiation source to the detector corresponding to the second angiography image; determining the coordinates of the radiation source corresponding to each of the second angiography images according to each of the second distances; Determining a connection line between each of the second three-dimensional coordinates and the radiation source according to each of the second three-dimensional coordinates and the coordinates of the radiation source; Grouping the connecting lines to obtain a plurality of connecting line groups, wherein the second three-dimensional coordinates corresponding to the connecting lines in the same connecting line group belong to the same position of the blood vessel; Determine a target point corresponding to each line group, wherein the sum of distances between the target point and each line in the line group is the smallest; The three-dimensional coordinates corresponding to each of the target points are used as the three-dimensional coordinates of the blood vessel.

2. The method according to claim 1, characterized in that Screening each of the first contrast images according to each of the information amounts to obtain a second contrast image of the X-ray contrast image sequence includes: The first contrast image with the largest amount of information is used as the second contrast image of the X-ray contrast image sequence.

3. The method according to claim 1, characterized in that The image information includes a width, a height, a first point spacing, and a second point spacing of the second contrast image, and converting the two-dimensional coordinates into first three-dimensional coordinates corresponding to the two-dimensional coordinates according to the first distance and the image information, including: converting the first coordinate value in the two-dimensional coordinate into the first coordinate value in the first three-dimensional coordinate according to the width and the first point spacing; converting the second coordinate value in the two-dimensional coordinate into the second coordinate value in the first three-dimensional coordinate according to the height and the second point distance; Using the first distance as the third coordinate value in the first three-dimensional coordinate; The first three-dimensional coordinates are determined according to a first coordinate value in the first three-dimensional coordinates, a second coordinate value in the first three-dimensional coordinates, and a third coordinate value in the first three-dimensional coordinates.

4. The method according to claim 1, characterized in that: Rotating the first three-dimensional coordinate according to a preset rule to obtain a second three-dimensional coordinate corresponding to the first three-dimensional coordinate includes: Acquire a first angle and a second angle corresponding to the second angiography image, wherein the first angle is an angle between a straight line formed between the radiation source and the detector and a preset plane in a three-dimensional coordinate system, and the second angle is an angle between a straight line formed between the radiation source and the detector and a preset coordinate axis in the three-dimensional coordinate system; Rotate the first three-dimensional coordinate around the first coordinate axis of the three-dimensional coordinate system according to the first angle to obtain a third three-dimensional coordinate corresponding to the first three-dimensional coordinate; The third three-dimensional coordinate is rotated around the second coordinate axis of the three-dimensional coordinate system according to the second angle to obtain the second three-dimensional coordinate.

5. A computer device, characterized in that: The invention comprises a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the steps of the method for calculating the blood flow fraction based on an X-ray angiography image sequence according to any one of claims 1 to 4 by executing the computer instructions.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for calculating blood flow fraction based on an X-ray contrast imaging sequence according to any one of claims 1 to 4 are implemented.

Images