Blood status assessment method and device based on intracranial DSA imaging

Abstract

The present application discloses a method and device for evaluating blood status based on intracranial DSA imaging, the method comprising: acquiring two plane images of the same target blood vessel, wherein the two plane images have different shooting angles; extracting the centerline and boundary line of the target blood vessel in each plane image respectively; determining the matching relationship between the centerlines of the target blood vessel in the two plane images, and determining the three-dimensional centerline of the target blood vessel in three-dimensional space according to the matching relationship; determining the three-dimensional model of the target blood vessel according to the three-dimensional centerline of the target blood vessel and the matching relationship; determining the blood status parameters of the target blood vessel based on the established three-dimensional model of the target blood vessel. The calculation method of the present application is simple, has a small amount of calculation, and is efficient, fast, and accurate, providing a strong basis for evaluating cardiovascular and cerebrovascular ischemia.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a method and device for evaluating blood status based on intracranial DSA images. Background Art

[0002] In recent years, the number of people who die from cardiovascular and cerebrovascular diseases each year in the world has reached 15 million, ranking first among all causes of death, and the trend of younger people suffering from cardiovascular and cerebrovascular diseases has tended to intensify year by year.

[0003] In the field of stroke, the degree of vascular stenosis (such as diameter stenosis rate, area stenosis rate, minimum lumen area, etc.) is still used to assess whether brain tissue is ischemic. From a hemodynamic point of view, whether tissue is ischemic is determined by the geometric shape of the blood vessels and the blood flow state in the blood vessels. There is a certain possibility of misjudgment when diagnosing ischemia based solely on the degree of stenosis.

[0004] In the prior art, with the development of image processing technology, digital modeling has become an effective way to solve the above problems, and the construction technology of vascular three-dimensional models has become the basis for solving the above problems. However, in the prior art, there are few efficient and accurate methods for constructing vascular three-dimensional models. Summary of the invention

[0005] In response to the above problems, the embodiments of the present application provide a method and device for evaluating blood status based on intracranial DSA images.

[0006] In a first aspect, a method for evaluating blood status based on DSA imaging is provided, the method comprising:

[0007] Acquiring two planar images of the same target blood vessel, wherein the two planar images have different shooting angles;

[0008] Extract the center line and boundary line of the target blood vessel in each plane image respectively;

[0009] Determining a matching relationship between center lines of a target blood vessel in two planar images, and determining a three-dimensional center line of the target blood vessel in three-dimensional space according to the matching relationship;

[0010] determining a three-dimensional model of the target blood vessel according to the three-dimensional centerline of the target blood vessel and the matching relationship;

[0011] Based on the established three-dimensional model of the target blood vessel, blood state parameters of the target blood vessel are determined.

[0012] Optionally, in the above method, respectively extracting the center line and the boundary line of the target blood vessel in each plane image includes:

[0013] For a planar image, determine the starting point and end point of the target blood vessel;

[0014] Using a minimum path algorithm, a center line between the starting point and the end point is determined as the center line of the target blood vessel;

[0015] Determine multiple sampling points on the center line;

[0016] At the determined plurality of sampling points, resampling the plane image along the normal direction of the center line to obtain a resampled image;

[0017] In the resampled image, determining the boundary intensity value of each sampling point to obtain a boundary intensity image;

[0018] In the boundary intensity image, a minimum path algorithm is used to determine two boundary lines corresponding to the center line.

[0019] Optionally, in the above method, determining the matching relationship between the center lines of the target blood vessels in the two planar images includes:

[0020] Based on the epipolar line definition in the binocular vision algorithm, the center lines of the target blood vessels in the two plane images are discretized into multiple pairs of corresponding center point pairs;

[0021] For each center point on a center line, along the normal direction of a center point, determine two matching points corresponding to the center point on two boundary lines corresponding to the center line;

[0022] Each pair of center points and the four matching points corresponding to each pair of center points form a matching relationship between the center lines of the target blood vessels in the two plane images.

[0023] Optionally, in the above method, determining two matching points corresponding to the center point on two boundary lines corresponding to the center line along the normal direction of the center point includes:

[0024] Determine the normal vector of the center point and draw a ray along the direction of the normal vector;

[0025] The two intersection points of the ray and the two boundary lines corresponding to the center line are used as two matching points corresponding to the center point.

[0026] Optionally, in the above method, determining the three-dimensional centerline of the target blood vessel in the three-dimensional space according to the matching relationship includes:

[0027] Determine the positions of the shooting light source points corresponding to the two plane images;

[0028] Determine a plurality of spatial three-dimensional points of the target blood vessel according to the position of the shooting light source point and the positions of each center point pair;

[0029] The plurality of spatial three-dimensional points are connected by using a smooth curve to obtain the three-dimensional center line.

[0030] Optionally, in the above method, determining the three-dimensional model of the target blood vessel according to the three-dimensional centerline of the target blood vessel and the matching relationship includes:

[0031] Project the four matching points corresponding to each center point into a cross section of the three-dimensional space;

[0032] Close the projection points of four matching points in the same cross section to obtain the contour of the target blood vessel in the cross section;

[0033] The obtained contours of the target blood vessel in each cross section are connected to obtain a three-dimensional model of the target blood vessel.

[0034] Optionally, in the above method, the difference in shooting angles between the two planar images is not less than 25°.

[0035] Optionally, in the above method, the blood state parameters include: average blood flow velocity and average blood flow rate;

[0036] Determining the blood state parameters of the target blood vessel based on the established three-dimensional model of the target blood vessel includes:

[0037] According to the DSA image, the starting frame and the ending frame of the contrast agent flowing through the target blood vessel are determined; and the average blood flow velocity is determined according to the following formula:

[0038] V = L / ((F2–F1) / fps);

[0039] Wherein, V is the average blood flow velocity, L is the length of the target blood vessel, fps is the temporal resolution of the DSA image, F1 is the number of frames of the start frame in the DSA image, and F2 is the number of frames of the end frame in the DSA image;

[0040] discretize the three-dimensional model of the target blood vessel into a plurality of three-dimensional center points;

[0041] Determine the length from each 3D center point to the starting point of the target blood vessel, as well as the blood vessel cross section corresponding to each center point; and determine the average blood flow according to the following formula:

[0042]

[0043] Where V is the average blood flow velocity in the target blood vessel, S i is the cross-sectional area of ​​the blood vessel corresponding to the i-th center point.

[0044] In a second aspect, a blood status assessment device based on DSA imaging is provided, the device comprising:

[0045] An acquisition unit, used for acquiring two planar images of the same target blood vessel, wherein the two planar images have different shooting angles;

[0046] An extraction unit, used to extract the center line and boundary line of the target blood vessel in each plane image respectively;

[0047] A matching unit, used to determine a matching relationship between the center lines of the target blood vessel in the two plane images, and determine a three-dimensional center line of the target blood vessel in the three-dimensional space according to the matching relationship;

[0048] A model building unit, used for building a three-dimensional model of the target blood vessel according to the three-dimensional center line of the target blood vessel and the matching relationship;

[0049] The estimation unit is used to determine the blood state parameter of the target blood vessel based on the established three-dimensional model of the target blood vessel.

[0050] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor; and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to perform any of the above methods.

[0051] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs, and when the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes any of the above methods.

[0052] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0053] The present application obtains two plane images of the same target blood vessel, wherein the two plane images have different shooting angles; extracts the center line and boundary line of the target blood vessel in each plane image respectively; determines the matching relationship between the center lines of the target blood vessel in the two plane images, and determines the three-dimensional center line of the target blood vessel in three-dimensional space according to the matching relationship; determines the three-dimensional model of the target blood vessel according to the three-dimensional center line of the target blood vessel and the boundary line of the target blood vessel in each plane image, and the blood flow of the diseased blood vessel can be evaluated based on the three-dimensional model. The present application obtains the three-dimensional center line of the target blood vessel in the plane images obtained at different shooting angles by fusing the center line of the target blood vessel in the plane images, and determines the three-dimensional model of the target blood vessel according to the boundary line of the target blood vessel around the obtained three-dimensional center line. The calculation method of the present application is simple, has a small amount of calculation, and is efficient, fast, and accurate, providing a strong basis for evaluating cardiovascular and cerebrovascular ischemia. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0055] Figure 1 A schematic diagram showing a flow chart of a method for evaluating blood status based on DSA imaging according to an embodiment of the present application;

[0056] Figure 2 A schematic structural diagram of a blood status assessment device based on DSA imaging according to an embodiment of the present application is shown;

[0057] Figure 3 This is a schematic diagram of the structure of an electronic device in an embodiment of the present application;

[0058] Figure 4 Two planar images (including the target blood vessel) obtained after image feature extraction at different shooting angles according to an embodiment of the present application are shown. Figure 4 a and Figure 4 b);

[0059] Figure 5 A three-dimensional model of a target blood vessel according to an embodiment of the present application is shown.

[0060] Figure 6 The start frame and end frame images of contrast agent flowing through a blood vessel according to one embodiment of the present application are shown. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0062] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0063] Figure 1 A schematic diagram showing a flow chart of a method for evaluating blood status based on DSA imaging according to an embodiment of the present invention is shown. Figure 1 It can be seen that the present application at least includes steps S110 to S150:

[0064] Step S110: acquiring two planar images of the same target blood vessel, wherein the two planar images have different shooting angles.

[0065] The present application is applicable to the blood vessels throughout the body of the treatment subject, mainly including the coronary blood vessels of the heart and the blood vessels of the brain.

[0066] First, two planar images of the same target blood vessel are acquired, the planar images being obtained during an angiographic procedure, such as digital subtraction angiography.

[0067] The "acquisition" process can be understood as receiving, which can be input by the staff. It should be noted that the two plane images have different shooting angles. If they have the same shooting angle, the acquired images are the same, and there is no meaning of "fusion". In order to obtain better results, in some embodiments of the present application, the difference in shooting angle between the two plane images is not less than 25°.

[0068] Step S120: extracting the center line and boundary line of the target blood vessel in each plane image respectively.

[0069] Blood vessels appear as "earthworms" in planar images, or can be understood as irregular "tubes". Image recognition technology is used to process each planar image to obtain the center line and boundary line of the target blood vessel. It should be noted that the center line and boundary line are also irregular.

[0070] The recognition of the center line and the boundary line can refer to the image recognition technology in the prior art. For example, the image is first binarized, and then the two boundary lines of the target blood vessel are recognized, and the line in the middle of the two boundary lines is used as the center line.

[0071] Step S130: determining a matching relationship between the center lines of the target blood vessel in the two planar images, and determining the three-dimensional center line of the target blood vessel in the three-dimensional space according to the matching relationship.

[0072] The target blood vessels in the two plane images are then fused in a three-dimensional coordinate system to form a three-dimensional model of the blood vessel. In the present application, the position information of the three-dimensional center line of the target blood vessel in the three-dimensional space is first determined, and then the three-dimensional model of the target blood vessel is determined around the three-dimensional center line.

[0073] First, the matching relationship between the center lines of the target blood vessels in the two plane images is determined. In some embodiments of the present application, based on the epipolar line definition in the binocular vision algorithm, the center lines of the target blood vessels in the two plane images are discretized into multiple pairs of corresponding center point pairs.

[0074] The two planar images are respectively recorded as a first image and a second image, the target blood vessel in the first image is recorded as a first blood vessel, and the target blood vessel in the second image is recorded as a second blood vessel.

[0075] Assume that there are multiple first center points P in the first blood vessel i , the second blood vessel has multiple first center points Qi， The first center point P of the first blood vessel i and the first center point Q of the second blood vessel i Correspondingly, a matching relationship between the center lines of the target blood vessels in the two plane images is formed.

[0076] In order to improve modeling accuracy, the center line can be discretized into as many center point pairs as possible.

[0077] According to the matching relationship between the center lines of the target blood vessels in the two plane images, the three-dimensional center line of the target blood vessel in the three-dimensional space is determined. Specifically, in some embodiments of the present application, the positions of the shooting light source points in the two plane images are determined; according to the positions of the shooting light source points and the positions of each center point pair, multiple three-dimensional points of the target blood vessel are determined; and the multiple three-dimensional points are connected by a smooth curve to obtain the three-dimensional center line.

[0078] Assume that when reading the contrast image, we can obtain the X-ray source points O1 and O2 of two viewing angles in two images, and connect the ray O1P i 、O2Q i , the intersection of the two rays is P i With Q i The corresponding three-dimensional point in space. By performing the above operation on the paired center point pairs one by one, the three-dimensional center line of the target diseased blood vessel can be obtained.

[0079] In other embodiments of the present application, the matching relationship of the center line of the target blood vessel in the two plane images also includes four matching points corresponding to each center point pair. The specific determination method is: for a center point on a center line, along the normal direction of its center point, determine the two matching points corresponding to the center point on the two boundary lines corresponding to the center line. For the other center point of the center point pair, the same method is used to obtain two matching points. The four matching points thus determined are the matching points of the center point pair and the four boundary lines. The role of the matching points is to prepare for the subsequent establishment of the boundary lines of the three-dimensional model. Using the above method, the matching points of multiple center point pairs on the two center lines and the boundary lines are determined, and each center point pair corresponds to four matching points.

[0080] Step S140: determining a three-dimensional model of the target blood vessel according to the three-dimensional center line of the target blood vessel and the above matching relationship.

[0081] After the three-dimensional center line of the target blood vessel in the three-dimensional space is obtained, a three-dimensional model of the target blood vessel is determined around the three-dimensional center line, that is, the boundary line of the three-dimensional center line needs to be determined.

[0082] Specifically, the four matching points corresponding to each center point are projected into a cross section in the three-dimensional space respectively; the projection points of the four matching points in the same cross section are closed to obtain the contour of the target blood vessel in the cross section; the contours of the target blood vessels in each cross section are connected to obtain a three-dimensional model of the target blood vessel.

[0083] For a pair of center point pairs, it corresponds to four matching points. These four matching points are projected onto the same cross section around the three-dimensional center line in three-dimensional space to obtain four projection points. Then, the four projection points of the four matching points in the same cross section are closed. It should be noted that it is best to use curve fitting to close. The above operation is performed on multiple center point pairs respectively, and multiple "ring-shaped" contours around the three-dimensional center line can be obtained. Then, the contours of the target blood vessels in each cross section are connected to obtain a three-dimensional model of the target blood vessel.

[0084] Step S150: determining the blood state parameters of the target blood vessel based on the established three-dimensional model of the target blood vessel.

[0085] In some embodiments of the present application, blood state parameters include but are not limited to average blood flow velocity and average blood flow rate, etc. The following method can be used to estimate the average blood flow velocity: according to the DSA image, determine the start frame and the end frame of the contrast agent flowing through the target blood vessel; and determine the average blood flow velocity according to the following formula:

[0086] V = L / ((F2–F1) / fps);

[0087] Wherein, V is the average blood flow velocity, L is the length of the target blood vessel, fps is the temporal resolution of the DSA image, F1 is the number of frames of the start frame in the DSA image, and F2 is the number of frames of the end frame in the DSA image.

[0088] Please refer to Figure 6 (include Figure 6-1 a, Figure 6-1 b, Figure 6-2 a, Figure 6-2 b), Figure 6The start frame and end frame images of the contrast agent flowing through the blood vessels according to one embodiment of the present application, as well as the contrast agent time-concentration curve are shown. For the calculation of F1, after determining the coordinates of the starting point on the DSA image, the pixel average value of the point is calculated within the range of n*n with the point as the center. In particular, the value of n can be 3, 5 or 7. For DSA images, the average grayscale value of the starting point is calculated frame by frame, and then the average grayscale value of each frame image is subtracted from the average grayscale value of the first frame image to obtain the contrast agent concentration corresponding to each frame, and the contrast agent time-concentration curve of the starting point is finally obtained in chronological order. The maximum value point Imax is searched on the curve, and the contrast agent inflow mark threshold σ is set based on the maximum value. The calculation formula of the threshold is: σ=a*Imax, where a is the weight coefficient, and the value range is 0.1–0.5.

[0089] After obtaining the marking threshold σ, search for the first point greater than or equal to σ on the contrast agent time-concentration curve. The image frame number corresponding to this point is the starting frame F1. Similarly, the ending frame F2 can be obtained.

[0090] Then, other parameters may be calculated. In some embodiments of the present application, the calculation of the average blood flow may be performed by the following method: discretizing the three-dimensional model of the target blood vessel into a plurality of three-dimensional center points; determining the length from each three-dimensional center point to the starting point of the target blood vessel, and the blood vessel cross section corresponding to each center point; and determining the average blood flow according to the following formula:

[0091]

[0092] Where V is the average blood flow velocity in the target blood vessel, S i is the blood vessel cross-sectional area corresponding to the i-th center point.

[0093] After the average blood flow velocity is calculated, the hemodynamic parameters of the diseased blood vessels, such as the average flow rate Q, pressure gradient PG, and pressure ratio PR, can be calculated based on the average velocity and the three-dimensional model of the blood vessels.

[0094] Specifically, the target blood vessel 3D model is discretized according to the 3D center point. Assume that the 3D center line of the target blood vessel is composed of P1, P2...P N There are N center points in total, and the lengths L1, L2, ...L from each center point to the starting point of the target blood vessel can be calculated one by one. N , each center point corresponds to the blood vessel cross-sectional area S1, S2...S N .

[0095] The calculation formula of average flow Q is: Where V is the average blood flow velocity in the blood vessel, S i is the cross-sectional area of ​​the blood vessel corresponding to the i-th center point.

[0096] from Figure 1 It can be seen from the method shown that the present application obtains two plane images of the same target blood vessel, wherein the two plane images have different shooting angles; extracts the center line and boundary line of the target blood vessel in each plane image respectively; determines the matching relationship between the center lines of the target blood vessel in the two plane images, and determines the three-dimensional center line of the target blood vessel in three-dimensional space according to the matching relationship; determines the three-dimensional model of the target blood vessel according to the three-dimensional center line of the target blood vessel and the boundary line of the target blood vessel in each plane image, and the blood flow state of the diseased blood vessel can be estimated according to the three-dimensional model. The present application obtains the three-dimensional center line of the target blood vessel in the plane images obtained at different shooting angles by fusing the center line of the target blood vessel in the plane images, and determines the three-dimensional model of the target blood vessel according to the boundary line of the target blood vessel around the obtained three-dimensional center line. The calculation method of the present application is simple, has a small amount of calculation, and is efficient, fast and accurate, providing a strong basis for evaluating cardiovascular and cerebrovascular ischemia.

[0097] In some embodiments of the present application, the following method can also be used to extract the center line and boundary line in the plane image: for a plane image, determine the starting point and the end point of the target blood vessel; use the minimum path algorithm to determine the center line between the starting point and the end point as the center line of the target blood vessel; determine multiple sampling points on the center line; at the determined multiple sampling points, resample the plane image along the normal direction of the center line to obtain a resampled image; in the resampled image, determine the boundary intensity value of each sampling point to obtain a boundary intensity image; in the boundary intensity image, use the minimum path algorithm to determine two boundary lines corresponding to the center line.

[0098] Specifically, for a target blood vessel in an image, the starting point and end point of the target diseased blood vessel can be manually selected, and the initial center line of the target blood vessel can be extracted by applying the Dijkstra minimum path algorithm. With each component point of the center line as the center, the key frame image is resampled along the normal direction of the center line to obtain a resampled image. In the resampled image, the boundary intensity value is calculated point by point to obtain a boundary intensity image. In this image, the minimum path algorithm is used to search for two blood vessel boundary lines corresponding to the center line, and the two blood vessel boundary lines are reversely mapped back to the original image. Then, the center line and boundary line are extracted.

[0099] In some embodiments of the present application, the method of determining two matching points corresponding to the center point on two boundary lines corresponding to the center line along the normal direction of a center point includes: determining the normal vector of the center point and making a ray along the direction of the normal vector; and taking the two intersection points of the ray and the two boundary lines corresponding to the center line as the two matching points corresponding to the center point.

[0100] That is to say, when determining the matching points between the boundary line and the center point, for a center point, first determine its normal direction, then make a ray along the normal direction, and the intersection of the ray and the two boundary lines is the matching point corresponding to the center point. For a center point pair, there are four corresponding matching points.

[0101] In some embodiments of the present application, some blood vessels are particularly curved, and a ray and a boundary line may have more than one intersection point. In this case, the intersection point closest to the center point is used as the matching point.

[0102] Another embodiment of the present application is given below, and its process can be briefly described as follows:

[0103] First, feature extraction: After obtaining the center line of the entire vascular tree, i.e., the skeleton diagram, manually select the starting point and end point of the target diseased blood vessel, and apply the Dijkstra minimum path algorithm to extract the initial center line of the target blood vessel. With each component point of the center line as the center, resample the key frame image along the normal direction of the center line to obtain a resampled image. In the resampled image, calculate the boundary intensity value point by point to obtain a boundary intensity image. In this image, use the minimum path algorithm to search for the two vascular boundary lines corresponding to the center line, and map them back to the original image. Then, the center line and boundary line are extracted, and the image feature extraction is completed. Figure 4 As shown, Figure 4 Two planar images containing target blood vessels obtained after image feature extraction at different shooting angles according to an embodiment of the present application are shown.

[0104] Second, epipolar line matching: Perform the above steps on two sets of intracranial DSA images with an angle difference of ≥25° to obtain the center lines and boundary lines of the two sets of images. In three-dimensional reconstruction, it is necessary to determine the matching relationship between the two sets of center lines at different angles. Therefore, using the epipolar line definition in the binocular vision algorithm, the two center lines can be discretized into paired center points.

[0105] Third, centerline and boundary line matching: After obtaining two sets of matching centerlines, the centerlines and boundary lines of the two perspectives are matched respectively. Specifically, for each center point P on the centerline i , calculate its normal vector, through P i Draw a straight line along the normal vector. The intersection of this straight line and the two boundary lines is the boundary line and the center point P. i The matching points are denoted as L i , R i .

[0106] In particular, when the blood vessel is severely twisted, there may be multiple intersection points on the boundary line. The distance from each point to the center point P is calculated separately. i Distance, the point with the closest distance is taken as the matching point L ior R i .

[0107] Fourth, 3D reconstruction: After completing the first two matching operations, the matching relationship between the two center lines of different perspectives is obtained, and the first perspective center point P is recorded as i Corresponding to the second viewing center point Q i When reading DSA images, two X-ray source points O1 and O2 of different viewing angles can be obtained, and the connecting ray O1P i 、O2Q i , the intersection of the two rays is P i With Q i The corresponding three-dimensional point in space. Perform the above operations on the paired center points one by one to obtain the three-dimensional center line of the target diseased blood vessel, and then project the four two-dimensional boundary points corresponding to each center point into the three-dimensional space to obtain the four boundary points of each cross section of the target diseased blood vessel. Use curves to connect the four spatial boundary points in sequence to obtain the blood vessel contour on the cross section. After the blood vessel contours are found on all cross sections, the three-dimensional reconstruction of the target diseased blood vessel is completed, such as Figure 5 As shown, Figure 5 A three-dimensional model of a target blood vessel according to an embodiment of the present application is shown.

[0108] Fifth, the average blood flow velocity estimation can be calculated according to the aforementioned algorithm.

[0109] Figure 2 A blood status assessment device based on DSA imaging according to an embodiment of the present application is shown, and the device 200 includes:

[0110] An acquisition unit 210 is used to acquire two planar images of the same target blood vessel, wherein the two planar images have different shooting angles;

[0111] An extraction unit 220, used to extract the center line and the boundary line of the target blood vessel in each plane image respectively;

[0112] A matching unit 230, configured to determine a matching relationship between center lines of a target blood vessel in two planar images, and determine a three-dimensional center line of the target blood vessel in a three-dimensional space according to the matching relationship;

[0113] A model building unit 240, configured to determine a three-dimensional model of the target blood vessel according to the three-dimensional center line of the target blood vessel and the matching relationship;

[0114] The estimation unit 250 is used to determine the blood state parameters of the target blood vessel based on the established three-dimensional model of the target blood vessel.

[0115] In some embodiments of the present application, in the above-mentioned device, the extraction unit 220 is used to determine the starting point and the end point of the target blood vessel for a plane image; use the minimum path algorithm to determine the center line between the starting point and the end point as the center line of the target blood vessel; determine multiple sampling points on the center line; at the determined multiple sampling points, resample the plane image along the normal direction of the center line to obtain a resampled image; in the resampled image, determine the boundary strength value of each sampling point to obtain a boundary strength image; in the boundary strength image, use the minimum path algorithm to determine two boundary lines corresponding to the center line.

[0116] In some embodiments of the present application, in the above-mentioned device, the matching unit 230 is used to discretize the center line of the target blood vessel in the two plane images into multiple pairs of corresponding center point pairs based on the polar line definition in the binocular vision algorithm; for each center point on a center line, along the normal direction of a center point, determine two matching points corresponding to the center point on two boundary lines corresponding to the center line; each center point pair, and the four matching points corresponding to each center point pair, form a matching relationship between the center lines of the target blood vessels in the two plane images.

[0117] In some embodiments of the present application, in the above-mentioned device, the matching unit 230 is used to determine the normal vector of the center point and make a ray along the direction of the normal vector; the two intersection points of the ray and the two boundary lines corresponding to the center line are used as two matching points corresponding to the center point.

[0118] In some embodiments of the present application, in the above-mentioned device, the matching unit 230 is used to determine the position of the shooting light source point in the two plane images; determine multiple spatial three-dimensional points of the target blood vessel based on the position of the shooting light source point and the position of each center point pair; and connect the multiple spatial three-dimensional points with a smooth curve to obtain the three-dimensional center line.

[0119] In some embodiments of the present application, in the above-mentioned device, the model building unit 240 is used to project two matching points corresponding to each center point into a cross-section of the three-dimensional space respectively; close the projection points of the four matching points in the same cross-section to obtain the contour of the target blood vessel of the cross-section; connect the contours of the target blood vessels of each cross-section to obtain a three-dimensional model of the target blood vessel.

[0120] In some embodiments of the present application, in the above-mentioned device, the difference in shooting angles between the two planar images is not less than 25°.

[0121] In some embodiments of the present application, in the above device, the blood state parameters include: average blood flow velocity, average blood flow rate;

[0122] The estimation unit is used to determine the start frame and the end frame of the contrast agent flowing through the target blood vessel according to the DSA image; and determine the average blood flow velocity according to the following formula: V = L / ((F2-F1) / fps); wherein V is the average blood flow velocity, L is the length of the target blood vessel, fps is the time resolution of the DSA image, F1 is the number of frames of the start frame in the DSA image, and F2 is the number of frames of the end frame in the DSA image; and is used to discretize the three-dimensional model of the target blood vessel into multiple three-dimensional center points; determine the length from each three-dimensional center point to the starting point of the target blood vessel, and the blood vessel cross section corresponding to each center point; and determine the average blood flow according to the following formula:

[0123] Where V is the average blood flow velocity in the target blood vessel, S i is the cross-sectional area of ​​the blood vessel corresponding to the i-th center point.

[0124] It should be noted that the above-mentioned blood status assessment device based on DSA imaging can implement the blood status assessment method based on DSA imaging one by one.

[0125] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 3 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0126] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0127] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0128] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a blood status assessment device based on DSA images at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0129] Acquiring two planar images of the same target blood vessel, wherein the two planar images have different shooting angles;

[0130] Extract the center line and boundary line of the target blood vessel in each plane image respectively;

[0131] Determining a matching relationship between center lines of a target blood vessel in two planar images, and determining a three-dimensional center line of the target blood vessel in three-dimensional space according to the matching relationship;

[0132] determining a three-dimensional model of the target blood vessel according to the three-dimensional centerline of the target blood vessel and the matching relationship;

[0133] Based on the established three-dimensional model of the target blood vessel, blood state parameters of the target blood vessel are determined.

[0134] The above application Figure 2The method performed by the blood state assessment device based on DSA images disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0135] The electronic device may also perform Figure 2 The method is implemented by the blood status assessment device based on DSA images, and the blood status assessment device based on DSA images is implemented in Figure 2 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0136] The present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to execute Figure 2 The method performed by the blood status assessment device based on DSA imaging in the illustrated embodiment is specifically used to perform:

[0137] Acquiring two planar images of the same target blood vessel, wherein the two planar images have different shooting angles;

[0138] Extract the center line and boundary line of the target blood vessel in each plane image respectively;

[0139] Determining a matching relationship between center lines of a target blood vessel in two planar images, and determining a three-dimensional center line of the target blood vessel in three-dimensional space according to the matching relationship;

[0140] determining a three-dimensional model of the target blood vessel according to the three-dimensional centerline of the target blood vessel and the matching relationship;

[0141] Based on the established three-dimensional model of the target blood vessel, blood state parameters of the target blood vessel are determined.

[0142] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0143] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0146] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0147] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0148] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0149] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0150] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0151] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for evaluating blood status based on intracranial DSA images, characterized in that: The method comprises: Acquiring two planar images of the same target blood vessel, wherein the two planar images have different shooting angles; Extract the center line and boundary line of the target blood vessel in each plane image respectively; Determining a matching relationship between center lines of a target blood vessel in two planar images, and determining a three-dimensional center line of the target blood vessel in three-dimensional space according to the matching relationship; determining a three-dimensional model of the target blood vessel according to the three-dimensional centerline of the target blood vessel and the matching relationship; determining blood state parameters of the target blood vessel based on the established three-dimensional model of the target blood vessel; Wherein, determining the matching relationship between the center lines of the target blood vessels in the two planar images includes: Based on the epipolar line definition in the binocular vision algorithm, the center lines of the target blood vessels in the two plane images are discretized into multiple pairs of corresponding center point pairs; For each center point on a center line, along the normal direction of a center point, determine two matching points corresponding to the center point on two boundary lines corresponding to the center line; Each pair of center points and four matching points corresponding to each pair of center points form a matching relationship between the center lines of the target blood vessels in the two plane images; Determining the three-dimensional model of the target blood vessel according to the three-dimensional center line of the target blood vessel and the matching relationship includes: Project the two matching points corresponding to each center point into a cross section of the three-dimensional space; The projection points of four matching points in the same cross section are closed by curve fitting to obtain the contour of the target blood vessel in the cross section; Connecting the contours of the target blood vessels in each cross section to obtain a three-dimensional model of the target blood vessel; The blood state parameters include: average blood flow velocity and average blood flow rate; Determining the blood state parameters of the target blood vessel based on the established three-dimensional model of the target blood vessel includes: According to the DSA image, the starting frame and the ending frame of the contrast agent flowing through the target blood vessel are determined; and the average blood flow velocity is determined according to the following formula: V = L / ((F2–F1) / fps); Wherein, V is the average blood flow velocity, L is the length of the target blood vessel, fps is the temporal resolution of the DSA image, F1 is the number of frames of the start frame in the DSA image, and F2 is the number of frames of the end frame in the DSA image; Among them, F1 and F2 are determined by the following method: Determine the coordinates of the starting point on the DSA image; Calculate the average pixel value of the starting point within a range of n*n with the starting point as the center; Calculating the average gray value of the starting point frame by frame; The average gray value of each frame of image is subtracted from the average gray value of the first frame of image to obtain the contrast agent concentration corresponding to each frame; Arranging the contrast agent concentrations corresponding to each frame in time sequence to obtain a contrast agent time-concentration curve at the starting point; Searching for a maximum point Imax of the curve, and setting a contrast agent inflow mark threshold σ based on the maximum point; Searching for the first point greater than or equal to σ on the curve, and taking the image frame number corresponding to the point as F1; Similarly, find F2; discretize the three-dimensional model of the target blood vessel into a plurality of three-dimensional center points; Determine the length from each 3D center point to the starting point of the target blood vessel, as well as the blood vessel cross section corresponding to each center point; and determine the average blood flow according to the following formula: Where V is the average blood flow velocity in the target blood vessel, S i is the cross-sectional area of ​​the blood vessel corresponding to the i-th center point, and N is the number of center points.

2. The method according to claim 1, characterized in that The step of respectively extracting the center line and the boundary line of the target blood vessel in each plane image comprises: For a planar image, determine the starting point and end point of the target blood vessel; Using a minimum path algorithm, a center line between the starting point and the end point is determined as the center line of the target blood vessel; Determine multiple sampling points on the center line; At the determined plurality of sampling points, resampling the plane image along the normal direction of the center line to obtain a resampled image; In the resampled image, determining the boundary intensity value of each sampling point to obtain a boundary intensity image; In the boundary intensity image, a minimum path algorithm is used to determine two boundary lines corresponding to the center line.

3. The method according to claim 1, characterized in that The step of determining two matching points corresponding to the center point on two boundary lines corresponding to the center line along the normal direction of the center point comprises: Determine the normal vector of the center point and draw a ray along the direction of the normal vector; The two intersection points of the ray and the two boundary lines corresponding to the center line are used as two matching points corresponding to the center point.

4. The method according to claim 1, characterized in that: Determining the three-dimensional center line of the target blood vessel in the three-dimensional space according to the matching relationship includes: Determine the positions of the shooting light source points corresponding to the two plane images; Determine a plurality of spatial three-dimensional points of the target blood vessel according to the position of the shooting light source point and the positions of each center point pair; The plurality of spatial three-dimensional points are connected by using a smooth curve to obtain the three-dimensional center line.

5. The method according to any one of claims 1 to 4, characterized in that: The difference in shooting angles between the two planar images is not less than 25°.

6. A blood status assessment device based on intracranial DSA imaging, characterized in that: The device comprises: An acquisition unit, used for acquiring two planar images of the same target blood vessel, wherein the two planar images have different shooting angles; An extraction unit, used to extract the center line and boundary line of the target blood vessel in each plane image respectively; A matching unit, used to determine a matching relationship between the center lines of the target blood vessel in the two plane images, and determine a three-dimensional center line of the target blood vessel in the three-dimensional space according to the matching relationship; a model building unit, configured to determine a three-dimensional model of the target blood vessel according to the three-dimensional center line of the target blood vessel and the matching relationship; an estimation unit, configured to determine a blood state parameter of the target blood vessel based on the established three-dimensional model of the target blood vessel; The matching unit is further used to discretize the center lines of the target blood vessels in the two plane images into multiple pairs of corresponding center point pairs based on the epipolar line definition in the binocular vision algorithm; for each center point on a center line, determine two matching points corresponding to the center point on two boundary lines corresponding to the center line along the normal direction of the center point; each center point pair and the four matching points corresponding to each center point pair form a matching relationship of the center lines of the target blood vessels in the two plane images; The model building unit is further used to project two matching points corresponding to each center point into a cross section in the three-dimensional space respectively; close the projection points of the four matching points in the same cross section by curve fitting to obtain the contour of the target blood vessel in the cross section; connect the contours of the target blood vessels in each cross section to obtain a three-dimensional model of the target blood vessel; The blood state parameters include: average blood flow velocity and average blood flow rate; The estimation unit is further used to determine the start frame and the end frame of the contrast agent flowing through the target blood vessel according to the DSA image; and determine the average blood flow velocity according to the following formula: V=L / ((F2-F1) / fps); wherein V is the average blood flow velocity, L is the length of the target blood vessel, fps is the time resolution of the DSA image, F1 is the number of frames of the start frame in the DSA image, and F2 is the number of frames of the end frame in the DSA image; Among them, F1 and F2 are determined by the following method: Determine the coordinates of the starting point on the DSA image; Calculate the average pixel value of the starting point within a range of n*n with the starting point as the center; Calculating the average gray value of the starting point frame by frame; The average gray value of each frame of image is subtracted from the average gray value of the first frame of image to obtain the contrast agent concentration corresponding to each frame; Arranging the contrast agent concentrations corresponding to each frame in time sequence to obtain a contrast agent time-concentration curve at the starting point; Searching for a maximum point Imax of the curve, and setting a contrast agent inflow mark threshold σ based on the maximum point; Searching for the first point greater than or equal to σ on the curve, and taking the image frame number corresponding to the point as F1; Similarly, find F2; and for discretizing the three-dimensional model of the target blood vessel into a plurality of three-dimensional center points; determining the length from each three-dimensional center point to the starting point of the target blood vessel, and the blood vessel cross section corresponding to each center point; and determining the average blood flow according to the following formula: Among them, V is the average blood flow velocity in the target blood vessel, Si is the blood vessel cross-sectional area corresponding to the i-th center point, and N is the number of center points.

7. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor is caused to perform the method of claims 1 to 5.

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