Blood vessel plane truncation method, device, equipment and medium for intracranial artery geometric model

Abstract

The present application discloses a vascular plane truncation method, device, equipment and medium of an intracranial artery geometric model, and relates to the field of dynamic simulation technology, including: when a hemodynamic simulation request is received, the target intracranial vascular segment is determined according to intracranial image information; the vascular centerline and vascular information of the target intracranial vascular segment are calculated, and the target point is determined by interpolation of the target intracranial vascular segment based on the vascular information; after truncation and extension at different vascular positions, the extended vascular information formed by the extended point after truncation and extension of each current target point is calculated, and the first relative error is determined; the second relative error between the target vascular radius at the current truncation position and the average vascular radius, and the average distance between each extended point of the extended vascular and the corresponding center point are calculated; the truncation target point information and the vascular truncation plane are determined based on the truncation index constructed based on the first relative error, the second relative error and the average distance. Better adapt to the complex geometric morphology of tortuous blood vessels and ensure the rationality of the truncation plane.

Description

Technical Field

[0001] The invention relates to the technical field of dynamics simulation, and in particular to a blood vessel plane truncation method, device, equipment and medium for an intracranial artery geometric model. Background Art

[0002] When using computational fluid dynamics simulation to obtain hemodynamic simulation in human arteries, the following steps are usually required: clinical imaging, geometric reconstruction, meshing, boundary condition setting, model and solution setting, and post-processing. Among them, when performing geometric reconstruction based on clinical imaging, some processing of the geometric model is required, such as smoothing, truncation and extension of the entrance and exit.

[0003] Studies have shown that extending the inlet by 20 times the diameter and the outlet by 2 times the diameter can ensure that the hemodynamic characteristics of the area of ​​interest are not affected by the truncation of the inlet and outlet. However, for very tortuous arteries, such as intracranial arteries, it is not enough to ensure that the outlet is extended by 2 times the diameter, and the curvature of the outlet also needs to be considered. At present, researchers usually use the manual truncation method, by adjusting the position of the truncation plane to make it as perpendicular to the centerline of the blood vessel as possible (visual inspection), and then extend the outlet after the truncation is completed. However, this manual method is greatly affected by the proficiency of the operator. It is difficult to ensure that the truncation plane is completely perpendicular to the centerline. There may be a significant difference between the diameter of the blood vessel at the truncation position and the average diameter of the blood vessel in this section, and there may be a significant direction / angle difference between the extended blood vessel and the original blood vessel. Therefore, the manual truncation method may affect the accuracy of the computational fluid dynamics simulation results. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for truncation of a blood vessel plane of an intracranial artery geometric model, which can automatically determine the location of the blood vessel truncation point of the intracranial artery geometric model to obtain an accurate blood vessel truncation plane. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a method for truncation of a blood vessel plane of an intracranial artery geometric model, comprising:

[0006] When a hemodynamic simulation request for an intracranial artery is received, a target intracranial blood vessel segment is determined according to corresponding intracranial image information; wherein the target intracranial blood vessel segment is a portion of the blood vessel to be cut off that needs geometric model reconstruction;

[0007] Calculating the three-dimensional coordinate information of each center point on the blood vessel centerline of the target intracranial blood vessel segment and the blood vessel radius corresponding to the corresponding blood vessel position, so as to interpolate based on the three-dimensional coordinate information of each center point and the corresponding blood vessel radius to determine the target point representing the geometric characteristics of the blood vessel centerline;

[0008] After truncation and extension at different blood vessel positions, a first average blood vessel radius in a blood vessel region corresponding to the target intracranial blood vessel segment is calculated based on the target point information of each target point after truncation and extension of each target point, so as to determine a first relative error according to the first average blood vessel radius and the target blood vessel radius at the current truncation position;

[0009] Calculating a second relative error between a target blood vessel radius at a current truncation position and a second average blood vessel radius of the target intracranial blood vessel segment;

[0010] Calculating the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment;

[0011] The truncation index constructed based on the first relative error, the second relative error, and the average distance is normalized, and the corresponding truncation target point information is determined based on the normalized truncation index, and the blood vessel truncation plane is determined according to the truncation target point information.

[0012] Optionally, the interpolation based on the three-dimensional coordinate information of each center point and the corresponding blood vessel radius to determine a target point representing a geometric feature of the blood vessel centerline includes:

[0013] The three-dimensional coordinate information of each center point and the corresponding blood vessel radius are respectively subjected to cubic smoothing spline interpolation processing to obtain interpolation points that meet a preset distance condition as target points that characterize the geometric features of the blood vessel centerline.

[0014] Optionally, after truncation and extension at different blood vessel positions, calculating, based on the target point information of each target point, a first average blood vessel radius of an extended blood vessel formed by an extended point after truncation and extension of each target point in a blood vessel region corresponding to the target intracranial blood vessel segment, includes:

[0015] Calculating the distance information between adjacent target points in sequence according to the blood flow direction, and calculating the accumulated distance and the extended distance of each target point based on the distance information, so as to form an extended blood vessel constructed based on each extended point;

[0016] According to the position information of each extension point of the extended blood vessel, a first average blood vessel radius in the corresponding blood vessel region of the extended blood vessel in the target intracranial blood vessel segment is calculated.

[0017] Optionally, the calculating the accumulated distance and the extended distance of each target point based on the distance information to form an extended blood vessel constructed based on each extended point includes:

[0018] Determine each target point between the current target point and the first target point as a cumulative target point, perform cumulative summation processing on the Euclidean distances between each cumulative target point and an adjacent cumulative target point to obtain a cumulative distance of the current target point, calculate an extended distance of the current target point based on the target blood vessel radius of each target point and an extended parameter, determine a corresponding extended point position based on the extended distance and along a tangent direction of a center line of the current target point, and construct an extended blood vessel based on the extended points of each extended point position;

[0019] The initial end point position of the current target point after truncation and extension is determined based on the sum of the accumulated distance of the current target point and the extended distance.

[0020] Optionally, the calculating a first average blood vessel radius within a blood vessel region corresponding to the extended blood vessel in the target intracranial blood vessel segment includes:

[0021] Determine a blood vessel region located before the target point and the initial end point of the truncated extension as a region to be calculated;

[0022] All blood vessel radii in the area to be calculated are averaged to obtain a first average blood vessel radius in the blood vessel area corresponding to the extended blood vessel in the target intracranial blood vessel segment.

[0023] Optionally, the calculating the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment includes:

[0024] Calculating the unit tangent vector at each extension point according to the three-dimensional coordinate information of each extension point of the extended blood vessel;

[0025] Calculating the actual end point position of the blood vessel centerline after being truncated and extended at each target point using the unit tangent vector at each extension point;

[0026] Uniformly inserting corresponding interpolation points between the target point at the current truncation position and the actual end point position, and calculating the coordinate vector of each interpolation point;

[0027] The average distance is calculated based on the coordinate vector of the interpolation point and the three-dimensional coordinate information of each corresponding center point on the blood vessel center line of the target intracranial blood vessel segment.

[0028] Optionally, determining the blood vessel truncation plane according to the truncation target point information includes:

[0029] Calculating the point normal equation of the truncation plane according to the coordinate vector and unit tangent vector of the truncation target point in the truncation target point information, and specifying the size parameters, direction attributes, and relationship attributes of the blood vessel truncation plane with surrounding structures;

[0030] The blood vessel truncation plane is determined according to the point normal equation calculation result, the size parameter, the direction attribute, and the relationship attribute.

[0031] In a second aspect, the present application discloses a vascular plane truncation device for an intracranial artery geometric model, comprising:

[0032] An information extraction module, for determining a target intracranial blood vessel segment according to corresponding intracranial image information when a hemodynamic simulation request for an intracranial artery is received; wherein the target intracranial blood vessel segment is a portion of the blood vessel to be cut off that requires geometric model reconstruction;

[0033] a target point determination module, used to calculate the three-dimensional coordinate information of each center point on the vascular centerline of the target intracranial vascular segment and the vascular radius corresponding to the corresponding vascular position, so as to interpolate based on the three-dimensional coordinate information of each center point and the corresponding vascular radius to determine the target point representing the geometric characteristics of the vascular centerline;

[0034] A first indicator determination module is used to calculate, after truncation and extension at different blood vessel positions, a first average blood vessel radius of an extended blood vessel formed by an extended point after truncation and extension of each target point in a blood vessel region corresponding to the target intracranial blood vessel segment based on the target point information of each target point, so as to determine a first relative error according to the first average blood vessel radius and a target blood vessel radius at the current truncation position;

[0035] A second indicator determination module is used to calculate a second relative error between a target blood vessel radius at a current truncation position and a second average blood vessel radius of the target intracranial blood vessel segment;

[0036] A third indicator determination module is used to calculate the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment;

[0037] A section generation module is used to normalize the truncation index constructed based on the first relative error, the second relative error, and the average distance, and determine the corresponding truncation target point information based on the normalized truncation index, and determine the blood vessel truncation plane according to the truncation target point information.

[0038] In a third aspect, the present application discloses an electronic device, comprising:

[0039] Memory, used to store computer programs;

[0040] The processor is used to execute the computer program to implement the steps of the blood vessel plane truncation method of the intracranial artery geometric model disclosed above.

[0041] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the vascular plane truncation method of the intracranial artery geometric model disclosed above are implemented.

[0042] It can be seen that the present application discloses a method for truncation of a blood vessel plane of an intracranial artery geometric model, comprising: when a hemodynamic simulation request for an intracranial artery is received, determining a target intracranial blood vessel segment according to corresponding intracranial image information; wherein the target intracranial blood vessel segment is a portion of the blood vessel to be truncated that requires geometric model reconstruction; calculating the three-dimensional coordinate information of each center point on the blood vessel centerline of the target intracranial blood vessel segment and the blood vessel radius corresponding to the corresponding blood vessel position, and interpolating based on the three-dimensional coordinate information of each center point and the corresponding blood vessel radius to determine a target point that characterizes the geometric characteristics of the blood vessel centerline; after truncation and extension at different blood vessel positions, calculating the current truncation and extension of each target point based on the target point information of each target point. The first average vessel radius of the extended blood vessel formed by the extension point in the corresponding blood vessel area in the target intracranial blood vessel segment is used to determine the first relative error according to the first average vessel radius and the target blood vessel radius at the current truncation position; the second relative error between the target blood vessel radius at the current truncation position and the second average blood vessel radius of the target intracranial blood vessel segment is calculated; the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment is calculated; the truncation index constructed based on the first relative error, the second relative error, and the average distance is normalized, and the corresponding truncation target point information is determined based on the normalized truncation index, and the blood vessel truncation plane is determined according to the truncation target point information. It can be seen that by quantifying the difference between the blood vessel radius at the truncation position and the average radius of the blood vessel segment, the difference between the radius of the extended blood vessel and the original blood vessel segment, and the difference between the direction of the extended blood vessel and the original blood vessel through three quantitative indicators, and calculating the comprehensive index by assigning different weights to them, the appropriate truncation position can be determined more accurately, avoiding the uncertainty caused by the reliance on the experience of the operator in the manual truncation method. Moreover, the present invention not only takes into account the problem of outlet extension for tortuous intracranial arteries, but also can better adapt to the complex geometric shapes of tortuous blood vessels and ensure the rationality of the truncation plane through processing of the blood vessel centerline and multi-index evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0044] Figure 1 A flow chart of a blood vessel plane truncation method for an intracranial artery geometric model disclosed in the present application;

[0045] FIG2 (a) is a schematic diagram of a blood vessel centerline and center point disclosed in the present application;

[0046] FIG2( b ) is a schematic diagram of interpolation points obtained by using a cubic spline function disclosed in the present application;

[0047] FIG2( c ) is a schematic diagram of a generated target point disclosed in the present application;

[0048] Figure 3 A schematic diagram of mapping of a truncated and extended endpoint disclosed in the present application;

[0049] Figure 4 A schematic diagram of a target point radius and an average radius of a corresponding area disclosed in this application;

[0050] Figure 5 A schematic diagram of a target point radius and an average radius of a whole blood vessel disclosed in the present application;

[0051] Figure 6 A schematic diagram of a pairing distance between an actual extension point and a mapped target point disclosed in the present application;

[0052] Figure 7 A schematic diagram of the structure of a blood vessel plane truncation device of an intracranial artery geometric model disclosed in the present application;

[0053] Figure 8 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. 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 making creative work are within the scope of protection of the present invention.

[0055] When using computational fluid dynamics simulation to obtain hemodynamic simulation in human arteries, the following steps are usually required: clinical imaging, geometric reconstruction, meshing, boundary condition setting, model and solution setting, and post-processing. Among them, when performing geometric reconstruction based on clinical imaging, some processing of the geometric model is required, such as smoothing, truncation and extension of the entrance and exit.

[0056] Studies have shown that extending the inlet by 20 times the diameter and the outlet by 2 times the diameter can ensure that the hemodynamic characteristics of the area of ​​interest are not affected by the truncation of the inlet and outlet. However, for very tortuous arteries, such as intracranial arteries, it is not enough to ensure that the outlet is extended by 2 times the diameter, and the curvature of the outlet also needs to be considered. At present, researchers usually use the manual truncation method, by adjusting the position of the truncation plane to make it as perpendicular to the centerline of the blood vessel as possible (visual inspection), and then extend the outlet after the truncation is completed. However, this manual method is greatly affected by the proficiency of the operator. It is difficult to ensure that the truncation plane is completely perpendicular to the centerline. There may be a significant difference between the diameter of the blood vessel at the truncation position and the average diameter of the blood vessel in this section, and there may be a significant direction / angle difference between the extended blood vessel and the original blood vessel. Therefore, the manual truncation method may affect the accuracy of the computational fluid dynamics simulation results.

[0057] To this end, the present invention provides a blood vessel plane truncation scheme for an intracranial artery geometric model, which can automatically determine the position of the blood vessel truncation point of the intracranial artery geometric model to obtain an accurate blood vessel truncation plane.

[0058] Reference Figure 1 As shown, the embodiment of the present application discloses a blood vessel plane truncation method of an intracranial artery geometric model, comprising:

[0059] Step S11: when a hemodynamic simulation request for an intracranial artery is received, a target intracranial blood vessel segment is determined according to corresponding intracranial image information; wherein the target intracranial blood vessel segment is a portion of the blood vessel to be truncated that requires geometric model reconstruction.

[0060] In this embodiment, when a hemodynamic simulation request for an intracranial artery is received, corresponding clinical imaging information, that is, corresponding intracranial image information, is obtained based on the current hemodynamic simulation request, and then the intracranial image information is preliminarily processed and analyzed to determine the approximate range and characteristics of the target vascular segment. The specific steps are as follows: Image preprocessing: noise reduction, enhancement and other processing are performed on the original intracranial image to improve image quality. Vascular region identification: using specific algorithms or tools, the vascular region in the preprocessed intracranial image is identified to prepare for subsequent centerline extraction. Determine the region of interest: according to the specific simulation area requirements of the hemodynamic simulation request, determine the specific vascular region that needs to be geometrically reconstructed from all vascular regions as the target intracranial vascular segment.

[0061] Step S12: Calculate the three-dimensional coordinate information of each center point on the vascular centerline of the target intracranial vascular segment and the vascular radius corresponding to the corresponding vascular position, and interpolate based on the three-dimensional coordinate information of each center point and the corresponding vascular radius to determine the target point that characterizes the geometric characteristics of the vascular centerline.

[0062] In this embodiment, a preset image processing tool (such as VMTK) is used to extract and interpolate the target intracranial blood vessel segment. Specifically, the three-dimensional coordinates of each center point of the blood vessel centerline and the blood vessel radius at the corresponding position are first calculated and constructed for the target blood vessel segment. The obtained three-dimensional coordinate data and blood vessel radius are as follows: , , , .in, Represents the set of coordinates of each center point in the horizontal direction. represents the set of coordinates of each center in the vertical direction, Represents the coordinate set of each center point in the vertical axis direction, represents the radius of the blood vessel at each center point, where represents the number of center points. Therefore, The three-dimensional coordinate information of the center point is , the radius of the blood vessel at the current position is As shown in FIG2(a), the middle area of ​​the black line portion represents the target intracranial vascular segment, each point is the center point of the target intracranial vascular segment, and the dotted line formed by each center point is the vascular centerline.

[0063] In this embodiment, the three-dimensional coordinate information of each center point and the corresponding blood vessel radius are respectively processed by cubic smoothing spline interpolation to obtain interpolation points that meet the preset distance conditions as target points that characterize the geometric features of the blood vessel centerline. , , , Use cubic smoothing spline interpolation respectively, set Define the optimization problem for the number of discrete points. The optimization problem is as follows:

[0064] ;

[0065] ;

[0066] in, is the interpolation spline function; It is in the parameter The original data points at is the spline function in The value at is the second-order derivative of the spline function, is a smoothing parameter, ranging between [0,1], represents the order of the cubic spline interpolation function, Represents the local unknown coefficients on each interval, and obtains the cubic spline function of x, y, z, and r , , , .

[0067] Further, N target points are generated according to the above interpolation results , and calculate the target point by the following interpolation function Where The value is as follows:

[0068] ;

[0069] Among them, the three-dimensional coordinate information of the target point is recorded as , the blood vessel radius at the target point is recorded as . Through By performing cubic smoothing spline interpolation, a smoother and more continuous coordinate representation of the vascular centerline and the corresponding change in vascular radius can be obtained. As shown in Figure 2 (b), the gray area in the figure is the cubic spline function, and the point in the figure is the center point.

[0070] In this embodiment, in order to ensure that the acquired target point makes the blood vessel centerline smoother and more reasonable, it is necessary to calculate the distance from the target point to the original point (center point). , find the nearest target point , to calculate the Euclidean distance between the two, and calculate the average of the minimum Euclidean distances from all center points to the target points, ensuring that it is less than the specified value (such as 0.1), otherwise increase the number of target points N, where the Euclidean distance The calculation formula is as follows:

[0071] ;

[0072] For each center point, find the target point closest to it. The corresponding minimum Euclidean distance formula is as follows:

[0073] ;

[0074] Calculate the average of the minimum Euclidean distances The formula is as follows:

[0075] ;

[0076] As shown in FIG. 2( c ), the gray area in the target intracranial blood vessel segment is the blood vessel centerline formed by various target points obtained after cubic smoothing spline interpolation, and each point in it is the target point.

[0077] Step S13: After truncation and extension at different blood vessel positions, the first average blood vessel radius of the extended blood vessel formed by the extended point after truncation and extension of each target point is calculated based on the target point information of each target point in the corresponding blood vessel area in the target intracranial blood vessel segment, so as to determine the first relative error according to the first average blood vessel radius and the target blood vessel radius at the current truncation position.

[0078] In this embodiment, after truncation and extension at different blood vessel positions, the difference between the radius of the extended blood vessel and the blood vessel radius in the corresponding blood vessel region in the original target intracranial blood vessel segment after truncation and extension at different blood vessel positions is calculated. Specifically, the distance information between adjacent target points is calculated in sequence according to the direction of blood flow, and the accumulated distance and the extended distance of each target point are calculated based on the distance information to form an extended blood vessel constructed based on each extended point; the first average blood vessel radius in the blood vessel region corresponding to the extended blood vessel in the target intracranial blood vessel segment is calculated. It can be understood that in order to determine the corresponding area in the target intracranial blood vessel segment corresponding to the extended blood vessel, the target points are calculated in sequence. Distance information between in, , representing The target point and The Euclidean distance between target points:

[0079] ;

[0080] In this embodiment, each target point between the current target point and the first target point is determined as an accumulated target point, and the Euclidean distances between each accumulated target point and the adjacent accumulated target point are accumulated and summed to obtain the accumulated distance of the current target point, and the extended distance of the current target point is calculated based on the target blood vessel radius and the extension parameter of each target point, so as to determine the corresponding extension point position based on the extended distance and along the tangent direction of the center line of the current target point, and to construct an extended blood vessel based on the extension points of each extension point position; the initial terminal position of the current target point after truncation and extension is determined based on the sum of the accumulated distance of the current target point and the extension distance. It can be understood that the cumulative distance is calculated for each target point, that is, if the cumulative distance of the fifth target point is calculated, the first distance from the fifth target point to the fourth target point is calculated along the centerline of the blood vessel, and then the second distance from the fourth target point to the third target point is calculated, the third distance from the third target point to the second target point is calculated, and the fourth distance from the second target point to the first target point is calculated. Finally, the first distance, the second distance, the third distance, and the fourth distance are added together to obtain the cumulative distance of the fifth target point. Therefore, from the first target point to The cumulative distance of the target points The calculation formula is as follows:

[0081] ;

[0082] Further, calculate the extended distance of each target point , the calculation formula for the extended distance is as follows:

[0083] ;

[0084] in, Indicated in The target blood vessel radius at the target point, M represents the magnification information of the blood vessel radius.

[0085] According to the summation formula, the accumulated distance of each target point is added to the extended distance to determine the initial end point position after truncation and extension at each target point (corresponding to the mapping in the direction of the blood vessel centerline, not the actual extended end point position). The summation formula is as follows:

[0086] ;

[0087] in, In this way, the initial end point position of each target point after truncation and extension is determined one by one according to the distance addition result. It should be noted that the current initial end point position corresponds to the mapping position in the direction of the blood vessel centerline, not the actual end point position of the extended blood vessel.

[0088] In this embodiment, the blood vessel region located before the target point and the initial end position of the truncated extension is determined as the region to be calculated; all blood vessel radii in the region to be calculated are averaged to obtain the first average blood vessel radius in the blood vessel region corresponding to the extended blood vessel in the target intracranial blood vessel segment. It can be understood that the blood vessel region located before the target point and the end position of the truncated extension is selected as the region to be calculated, and then the target blood vessel radius of each target point in the region to be calculated is averaged with the blood vessel radius of the region to be calculated according to the following averaging formula to obtain the first average blood vessel radius :

[0089] ;

[0090] in, is a set of indices that satisfy the condition (after the target point and before the end point of the truncated extension), where Represents the cumulative distance of any target point, Indicates the current The cumulative distance of the target points, Indicates The initial endpoint position after the target point is truncated and extended. Is a collection The number of elements in The range is , because when the last target point is truncated, no target point is located in the extended mapping area, so the maximum value of the range is N-1.

[0091] like Figure 3 As shown, the accumulated distance of each target point, the distance between adjacent target points, the extended distance of the target point, and the end point position after the target point stage extension (mapped position, not the actual end point position) are respectively shown.

[0092] For each target point, calculate the target vessel radius of the target point (When the vessel is cut off here and extended based on this, the radius of the extended vessel is also ) and the first average vessel radius of the truncated extension area corresponding to the target intracranial vessel segment The first relative error :

[0093] ;

[0094] like Figure 4 As shown, Figure 4 The target vessel radius of the target point and the first average vessel radius of the corresponding area are shown in FIG.

[0095] Step S14: Calculating a second relative error between the target blood vessel radius at the current truncation position and a second average blood vessel radius of the target intracranial blood vessel segment.

[0096] In this embodiment, when different blood vessel positions are cut off, the difference between the target blood vessel radius at the current cutoff position and the average blood vessel radius of the entire target intracranial blood vessel segment (the second average blood vessel radius), i.e., the second relative error, is calculated. First, the average blood vessel radius of the entire target intracranial blood vessel segment is calculated. (Second average vessel radius), the formula is as follows:

[0097] ;

[0098] Then, calculate the second relative error , the formula is as follows:

[0099] ;

[0100] like Figure 5 As shown, the target vessel radius of the target point and the average radius of the entire vessel (the second average vessel radius) are displayed.

[0101] Step S15: Calculate the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment.

[0102] In this embodiment, the unit tangent vector at each extension point is calculated based on the three-dimensional coordinate information of each extension point of the extended blood vessel; the actual end point position of the blood vessel centerline after truncating and extending each target point is calculated using the unit tangent vector of each extension point; the corresponding interpolation points are uniformly inserted between the target point at the current truncation position and the actual end point position, and the coordinate vector of each interpolation point is calculated; the average distance is calculated based on the coordinate vector of the interpolation point and the three-dimensional coordinate information of each center point corresponding to the blood vessel centerline of the target intracranial blood vessel segment. It can be understood that the tangent vector at each target point is first calculated. :

[0103] when When , the tangent vector of the three-dimensional coordinates of each target point is expressed as:

[0104] ;

[0105] when When , the tangent vector of the three-dimensional coordinates of each target point is expressed as:

[0106] ;

[0107] when When , the tangent vector of the three-dimensional coordinates of each target point is expressed as:

[0108] ;

[0109] Finally, we get the tangent vector for and the unit tangent vector for .

[0110] Using the unit tangent vector Calculate the actual center point end position after truncating and extending each target point (assuming it is M times the vessel radius):

[0111] ;

[0112] in, For the The coordinate vector of the target point (the center point of the target cutoff position) .

[0113] Interpolate evenly between the target point at the truncated position and the end point after extension Points, is the aforementioned, in the calculation Used when meeting The number of points represents the number of points included in the mapping of the extended length on the original vessel segment. The target point indivual( ) The coordinate vector of the interpolation point:

[0114] ;

[0115] Extended blood vessel segment extension point and the target intracranial vessel segment Pair the points in pairs and calculate the When the target point is cut off, the first point and the target intracranial vessel segment The distance between the center points:

[0116] ;

[0117] in, The first The target point after The coordinate vector of the point, To extend the vessel segment The coordinate vector of the points.

[0118] Next, for each target point, the average distance between the extended vascular segment within the extension range and the center point of the target intracranial vascular segment is calculated. :

[0119] ;

[0120] like Figure 6 As shown, the result of uniformly interpolating points between the target point at the truncation and the actual end point position, as well as the result of determining the actual end point position are shown.

[0121] Step S16: normalizing the truncation index constructed based on the first relative error, the second relative error, and the average distance, determining the corresponding truncation target point information based on the normalized truncation index, and determining the blood vessel truncation plane according to the truncation target point information.

[0122] In this embodiment, , , They are all normalized to the range of [0,1], and the normalization formula is as follows:

[0123] ;

[0124] ;

[0125] ;

[0126] in, Represents the first relative error minimum value, Indicates the maximum value of the first relative error, Indicates the second minimum relative error, Indicates the second relative error maximum value, Indicates the minimum average distance, Indicates the maximum average distance.

[0127] In this embodiment, after normalization, a corresponding truncation index is constructed according to the above three index information, wherein the truncation index is expressed as follows:

[0128] ;

[0129] in, Indicates The cutoff index at the target point, Respectively represent the first relative error after normalization Weight information, normalized second relative error Weight information, normalized average distance The weight information.

[0130] The smaller the value, the closer the radius of the truncation point is to the average radius of the vessel segment, the closer the vessel radius of the extended part after truncation is to the vessel radius mapped to the corresponding area of ​​the original vessel segment, and the closer the direction of the extended part after truncation is to the original vessel segment. By adjusting the weights of the three indicators, a truncation position that better meets specific aspects can be obtained.

[0131] In this embodiment, the point normal equation of the truncation plane is calculated according to the coordinate vector of the truncation target point in the truncation target point information and the unit tangent vector, and the size parameter, direction attribute, and relationship attribute of the blood vessel truncation plane with the surrounding structure are specified; the blood vessel truncation plane is determined according to the point normal equation calculation result, the size parameter, the direction attribute, and the relationship attribute. It can be understood that the truncation plane selected in the first After the target point is cut off, the The coordinate vector of the target point and the unit tangent vector at that point Compute the point-normal equation of the truncation plane:

[0132] ;

[0133] At the same time, by combining the calculation results of the point normal equation and specifying some properties of the truncation plane (such as radius, direction attribute, relationship attribute), the blood vessel truncation plane can be generated at the target point.

[0134] It can be seen that the present application discloses a method for truncation of a blood vessel plane of an intracranial artery geometric model, comprising: when a hemodynamic simulation request for an intracranial artery is received, determining a target intracranial blood vessel segment according to corresponding intracranial image information; wherein the target intracranial blood vessel segment is a portion of the blood vessel to be truncated that requires geometric model reconstruction; calculating the three-dimensional coordinate information of each center point on the blood vessel centerline of the target intracranial blood vessel segment and the blood vessel radius corresponding to the corresponding blood vessel position, and interpolating based on the three-dimensional coordinate information of each center point and the corresponding blood vessel radius to determine a target point that characterizes the geometric characteristics of the blood vessel centerline; after truncation and extension at different blood vessel positions, calculating the current truncation and extension of each target point based on the target point information of each target point. The first average vessel radius of the extended blood vessel formed by the extension point in the corresponding blood vessel area in the target intracranial blood vessel segment is used to determine the first relative error according to the first average vessel radius and the target blood vessel radius at the current truncation position; the second relative error between the target blood vessel radius at the current truncation position and the second average blood vessel radius of the target intracranial blood vessel segment is calculated; the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment is calculated; the truncation index constructed based on the first relative error, the second relative error, and the average distance is normalized, and the corresponding truncation target point information is determined based on the normalized truncation index, and the blood vessel truncation plane is determined according to the truncation target point information. It can be seen that by quantifying the difference between the blood vessel radius at the truncation position and the average radius of the blood vessel segment, the difference between the radius of the extended blood vessel and the original blood vessel segment, and the difference between the direction of the extended blood vessel and the original blood vessel through three quantitative indicators, and calculating the comprehensive index by assigning different weights to them, the appropriate truncation position can be determined more accurately, avoiding the uncertainty caused by the reliance on the experience of the operator in the manual truncation method. Moreover, the present invention not only takes into account the problem of outlet extension for tortuous intracranial arteries, but also can better adapt to the complex geometric shapes of tortuous blood vessels and ensure the rationality of the truncation plane through processing of the blood vessel centerline and multi-index evaluation.

[0135] Reference Figure 7 As shown, the present invention also discloses a blood vessel plane truncation device of an intracranial artery geometric model, comprising:

[0136] The information extraction module 11 is used to determine the target intracranial blood vessel segment according to the corresponding intracranial image information when receiving a hemodynamic simulation request for the intracranial artery; wherein the target intracranial blood vessel segment is the blood vessel portion to be cut off that needs to be reconstructed with a geometric model;

[0137] A target point determination module 12 is used to calculate the three-dimensional coordinate information of each center point on the vascular centerline of the target intracranial vascular segment and the vascular radius corresponding to the corresponding vascular position, so as to interpolate based on the three-dimensional coordinate information of each center point and the corresponding vascular radius to determine the target point representing the geometric characteristics of the vascular centerline;

[0138] A first indicator determination module 13 is used to calculate, after truncation and extension at different blood vessel positions, a first average blood vessel radius of an extended blood vessel formed by an extended point after truncation and extension of each target point in a blood vessel region corresponding to the target intracranial blood vessel segment based on the target point information of each target point, so as to determine a first relative error according to the first average blood vessel radius and the target blood vessel radius at the current truncation position;

[0139] A second indicator determination module 14 is used to calculate a second relative error between the target blood vessel radius at the current truncation position and a second average blood vessel radius of the target intracranial blood vessel segment;

[0140] A third indicator determination module 15 is used to calculate the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment;

[0141] The section generation module 16 is used to normalize the truncation index constructed based on the first relative error, the second relative error, and the average distance, and determine the corresponding truncation target point information based on the normalized truncation index, and determine the blood vessel truncation plane according to the truncation target point information.

[0142] It can be seen that the present application discloses that when a hemodynamic simulation request for an intracranial artery is received, a target intracranial blood vessel segment is determined according to corresponding intracranial image information; wherein the target intracranial blood vessel segment is the portion of the blood vessel to be truncated that requires geometric model reconstruction; the three-dimensional coordinate information of each center point on the blood vessel centerline of the target intracranial blood vessel segment and the blood vessel radius corresponding to the corresponding blood vessel position are calculated, so as to interpolate based on the three-dimensional coordinate information of each center point and the corresponding blood vessel radius to determine the target point that characterizes the geometric characteristics of the blood vessel centerline; after truncation and extension at different blood vessel positions, the extended blood vessel formed by the extended point after the current truncation and extension of each target point is calculated based on the target point information of each target point. The first average vessel radius in the corresponding vessel area in the target intracranial vessel segment is calculated to determine the first relative error according to the first average vessel radius and the target vessel radius at the current truncation position; the second relative error between the target vessel radius at the current truncation position and the second average vessel radius of the target intracranial vessel segment is calculated; the average distance between each extension point of the extended vessel and each corresponding center point on the vessel centerline of the target intracranial vessel segment is calculated; the truncation index constructed based on the first relative error, the second relative error, and the average distance is normalized, and the corresponding truncation target point information is determined based on the normalized truncation index, and the vessel truncation plane is determined according to the truncation target point information. It can be seen that by quantifying the difference between the vessel radius at the truncation position and the average radius of the vessel segment, the difference between the radius of the extended vessel and the original vessel segment, and the difference between the direction of the extended vessel and the original vessel through three quantitative indicators, and calculating the comprehensive index by assigning different weights to them, the appropriate truncation position can be determined more accurately, avoiding the uncertainty caused by the reliance on the operator's experience in the manual truncation method. Moreover, the present invention not only takes into account the problem of outlet extension for tortuous intracranial arteries, but also can better adapt to the complex geometric shapes of tortuous blood vessels and ensure the rationality of the truncation plane through processing of the blood vessel centerline and multi-index evaluation.

[0143] Furthermore, the present application also discloses an electronic device. Figure 8 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0144] Figure 8A schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the vascular plane truncation method of the intracranial artery geometric model disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0145] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0146] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0147] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0148] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, so as to realize the operation and processing of the massive data 223 in the memory 22 by the processor 21, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the blood vessel plane truncation method of the intracranial artery geometric model executed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks. In addition to data transmitted from an external device received by the electronic device, the data 223 can also include data collected by its own input and output interface 25.

[0149] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the blood vessel plane truncation method of the intracranial artery geometric model disclosed above is implemented. The specific steps of the method can refer to the corresponding contents disclosed in the above embodiments, and will not be repeated here.

[0150] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0151] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be implemented directly with hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory RAM (Random Access Memory), memory, read-only memory ROM (Read Only Memory), electrically programmable EPROM (Electrically Programmable Read Only Memory), electrically erasable programmable EEPROM (ElectricErasable Programmable Read Only Memory), register, hard disk, removable disk, CD-ROM (CompactDisc-Read Only Memory), or any other form of storage medium known in the technical field.

[0152] Finally, it should be noted that, in this article, relational terms such as first and second, etc. 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 presence of other identical elements in the process, method, article or device including the elements.

[0153] The scheme provided by the present invention is introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A blood vessel plane truncation method for an intracranial artery geometric model, characterized in that: include: When a hemodynamic simulation request for an intracranial artery is received, a target intracranial blood vessel segment is determined according to corresponding intracranial image information; wherein the target intracranial blood vessel segment is a portion of the blood vessel to be cut off that needs geometric model reconstruction; Calculating the three-dimensional coordinate information of each center point on the blood vessel centerline of the target intracranial blood vessel segment and the blood vessel radius corresponding to the corresponding blood vessel position, so as to interpolate based on the three-dimensional coordinate information of each center point and the corresponding blood vessel radius to determine the target point representing the geometric characteristics of the blood vessel centerline; After truncation and extension at different blood vessel positions, a first average blood vessel radius in a blood vessel region corresponding to the target intracranial blood vessel segment is calculated based on the target point information of each target point after truncation and extension of each target point, so as to determine a first relative error according to the first average blood vessel radius and the target blood vessel radius at the current truncation position; Calculating a second relative error between the target blood vessel radius at the current truncation position and a second average blood vessel radius of the target intracranial blood vessel segment; the second average blood vessel radius is an average blood vessel radius of the entire target intracranial blood vessel segment; Calculating the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment; Normalizing a truncation index constructed based on the first relative error, the second relative error, and the average distance, determining corresponding truncation target point information based on the normalized truncation index, and determining a blood vessel truncation plane according to the truncation target point information; After truncation and extension at different blood vessel positions, the first average blood vessel radius of the extended blood vessel formed by the extended point after truncation and extension of each target point is calculated based on the target point information of each target point in the corresponding blood vessel region in the target intracranial blood vessel segment, including: Calculating the distance information between adjacent target points in sequence according to the blood flow direction, and calculating the accumulated distance and the extended distance of each target point based on the distance information, so as to form an extended blood vessel constructed based on each extended point; Calculate a first average blood vessel radius of the extended blood vessel in a blood vessel region corresponding to the target intracranial blood vessel segment; The step of calculating a first average blood vessel radius in a blood vessel region corresponding to the extended blood vessel in the target intracranial blood vessel segment comprises: Determine a blood vessel region located before the target point and the initial end point of the truncated extension as a region to be calculated; All blood vessel radii in the area to be calculated are averaged to obtain a first average blood vessel radius in the blood vessel area corresponding to the extended blood vessel in the target intracranial blood vessel segment.

2. The blood vessel plane truncation method of the intracranial artery geometric model according to claim 1, characterized in that: The interpolation based on the three-dimensional coordinate information of each center point and the corresponding blood vessel radius to determine the target point representing the geometric features of the blood vessel centerline includes: The three-dimensional coordinate information of each center point and the corresponding blood vessel radius are respectively subjected to cubic smoothing spline interpolation processing to obtain interpolation points that meet a preset distance condition as target points that characterize the geometric features of the blood vessel centerline.

3. The blood vessel plane truncation method of the intracranial artery geometric model according to claim 1, characterized in that: The step of respectively calculating the accumulated distance and the extended distance of each target point based on the distance information to form an extended blood vessel constructed based on each extended point includes: Determine each target point between the current target point and the first target point as a cumulative target point, perform cumulative summation processing on the Euclidean distances between each cumulative target point and an adjacent cumulative target point to obtain a cumulative distance of the current target point, calculate an extended distance of the current target point based on the target blood vessel radius of each target point and an extended parameter, determine a corresponding extended point position based on the extended distance and along a tangent direction of a center line of the current target point, and construct an extended blood vessel based on the extended points of each extended point position; The initial end point position of the current target point after truncation and extension is determined based on the sum of the accumulated distance of the current target point and the extended distance.

4. The blood vessel plane truncation method of the intracranial artery geometric model according to claim 1, characterized in that: The calculating of the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment comprises: Calculating the unit tangent vector at each extension point according to the three-dimensional coordinate information of each extension point of the extended blood vessel; Calculating the actual end point position of the blood vessel centerline after being truncated and extended at each target point using the unit tangent vector at each extension point; Uniformly inserting corresponding interpolation points between the target point at the current truncation position and the actual end point position, and calculating the coordinate vector of each interpolation point; The average distance is calculated based on the coordinate vector of the interpolation point and the three-dimensional coordinate information of each center point corresponding to the blood vessel center line of the target intracranial blood vessel segment.

5. The blood vessel plane truncation method of the intracranial artery geometric model according to any one of claims 1 to 4, characterized in that: The determining of the blood vessel truncation plane according to the truncation target point information comprises: Calculating the point normal equation of the truncation plane according to the coordinate vector and unit tangent vector of the truncation target point in the truncation target point information, and specifying the size parameters, direction attributes, and relationship attributes of the blood vessel truncation plane with surrounding structures; The blood vessel truncation plane is determined according to the point normal equation calculation result, the size parameter, the direction attribute, and the relationship attribute.

6. A device for truncation of blood vessels in a geometric model of intracranial arteries, characterized in that: include: An information extraction module, for determining a target intracranial blood vessel segment according to corresponding intracranial image information when a hemodynamic simulation request for an intracranial artery is received; wherein the target intracranial blood vessel segment is a portion of the blood vessel to be cut off that requires geometric model reconstruction; a target point determination module, used to calculate the three-dimensional coordinate information of each center point on the vascular centerline of the target intracranial vascular segment and the vascular radius corresponding to the corresponding vascular position, so as to interpolate based on the three-dimensional coordinate information of each center point and the corresponding vascular radius to determine the target point representing the geometric characteristics of the vascular centerline; A first indicator determination module is used to calculate, after truncation and extension at different blood vessel positions, a first average blood vessel radius of an extended blood vessel formed by an extended point after truncation and extension of each target point in a blood vessel region corresponding to the target intracranial blood vessel segment based on the target point information of each target point, so as to determine a first relative error according to the first average blood vessel radius and a target blood vessel radius at the current truncation position; A second indicator determination module is used to calculate a second relative error between the target blood vessel radius at the current truncation position and a second average blood vessel radius of the target intracranial blood vessel segment; the second average blood vessel radius is an average blood vessel radius of the entire target intracranial blood vessel segment; A third indicator determination module is used to calculate the average distance between each extension point of the extended blood vessel and each corresponding center point on the blood vessel centerline of the target intracranial blood vessel segment; a section generation module, configured to normalize a truncation index constructed based on the first relative error, the second relative error, and the average distance, determine corresponding truncation target point information based on the normalized truncation index, and determine a blood vessel truncation plane according to the truncation target point information; The first indicator determination module is specifically used to calculate the distance information between adjacent target points in sequence according to the blood flow direction, and to calculate the accumulated distance and the extended distance of each target point based on the distance information to form an extended blood vessel constructed based on each extended point; and to calculate the first average blood vessel radius of the extended blood vessel in the blood vessel region corresponding to the target intracranial blood vessel segment; The blood vessel plane truncation device also includes determining a blood vessel area located before the target point and the initial end point position of the truncation extension as a to-be-calculated area; averaging all blood vessel radii in the to-be-calculated area to obtain a first average blood vessel radius in the blood vessel area corresponding to the extended blood vessel in the target intracranial blood vessel segment.

7. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the blood vessel plane truncation method of the intracranial artery geometric model as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: Used to store computer programs; wherein, when the computer program is executed by a processor, the steps of the blood vessel plane truncation method of the intracranial artery geometric model as described in any one of claims 1 to 5 are implemented.

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