Vascular profile symmetry quantification method and device, medium and program product

Abstract

The embodiment of the invention provides a blood vessel section symmetry quantification method and device, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the steps of obtaining a geometric model of a to-be-tested blood vessel; calculating the area and perimeter of the blood vessel cross section; calculating circle measurement according to the ratio of the area to the perimeter; calculating an asymmetric coefficient of the blood vessel cross section; a regularity value is obtained through calculation according to the reciprocal of the circle measurement and the asymmetric coefficient; and outputting a result whether the shape of the cross section corresponding to the blood vessel is regular according to the regularity value, and if an irregular result is obtained, quantifying irregular classification levels according to the regularity value. According to the method, the symmetric distribution characteristics of the blood vessel section are objectively quantified, the dependence of a traditional parameterized model (such as ellipse fitting) on a regular shape is overcome, the asymmetric form of the calcified plaque or the eccentric lesion is accurately described, and the method is suitable for quantitative analysis of asymmetric stenosis such as the eccentric plaque.

Description

Technical Field

[0001] This invention relates to the field of intelligent healthcare, and more specifically, to a method, apparatus, medium, and program product for quantifying the symmetry of vascular cross-sections. Background Technology

[0002] In the diagnosis and treatment of vascular diseases, vascular morphology has a crucial impact on blood flow. The geometry of blood vessels, including their diameter, tortuosity, and structural changes in their walls, directly determines the speed and resistance of blood flow. In particular, vascular stenosis can significantly alter blood flow dynamics, leading to local blood flow disturbances, blood pressure changes, and even complications such as thrombosis and tissue ischemia.

[0003] The symmetry of blood vessels (especially their geometric shape and hemodynamic symmetry) is crucial in assessing vascular health. For example, vascular symmetry is a normal marker of physiological function and an early warning sign of disease. However, traditionally, when assessing vascular symmetry or arterial stenosis, the focus has been primarily on the degree of stenosis along the blood flow direction, with less consideration given to geometric indicators such as the symmetry of the cross-sectional shape perpendicular to the flow direction. Nevertheless, studying the cross-sectional geometry of arterial stenosis is of great significance in both kinetic models and clinical practice. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention provides a method, device, medium, and program product for quantifying the symmetry of vascular cross-sections; the method of this invention objectively quantifies the symmetry distribution characteristics of vascular cross-sections, overcomes the dependence of traditional parametric models (such as ellipse fitting) on ​​regular shapes, accurately describes the asymmetric morphology of calcified plaques or eccentric lesions, and is suitable for the quantitative analysis of asymmetric stenosis such as eccentric plaques.

[0005] The first aspect of this application discloses a method for quantifying the symmetry of vascular cross-sections, the method comprising:

[0006] S101, Obtain the geometric model of the blood vessel to be tested;

[0007] S102, calculate the area and perimeter of the blood vessel cross-section; calculate the circle measurement based on the ratio of area to perimeter;

[0008] S103, calculate the asymmetry coefficient of the blood vessel cross section;

[0009] S104, the regularity value is calculated based on the reciprocal of the circularity measure and the asymmetry coefficient;

[0010] S105: Output the result of whether the shape of the cross-section corresponding to the blood vessel is regular based on the regularity value. If the result is irregular, quantify the classification level of irregularity based on the regularity value.

[0011] In some embodiments, the method for calculating the asymmetry coefficient includes: calculating a first moment of inertia representing the area distribution of the blood vessel cross-section and a second moment of inertia representing the eccentricity of the cross-section; and calculating the ratio of the second moment of inertia to the first moment of inertia to obtain the asymmetry coefficient.

[0012] In some embodiments, the method for calculating the first moment of inertia includes:

[0013] or Where A is the region containing the cross-section, and dA is an infinitesimally small area unit on the region; x and y are the distances from each tiny area element to the corresponding axis, respectively. 2 and y 2 It is the squared distance of each area element to the origin (or centroid);

[0014] Optional methods for calculating the second moment of inertia include:

[0015] or Where A is the region containing the cross-section, and dA is an infinitesimally small area unit on the region; x and y are the distances from each tiny area element to the corresponding axis, respectively. 2 and y 2 It is the squared distance of each area element to the origin (or centroid);

[0016] Optionally, the method further includes: when the asymmetry coefficient is between 1-a and 1+a, the result of good cross-sectional symmetry corresponding to the narrow position is obtained; when the asymmetry coefficient is greater than 1+a, the result of poor cross-sectional symmetry corresponding to the narrow position is obtained, where a is a positive number greater than 0.

[0017] In some embodiments, the method for calculating the circle measurement is: the ratio of the area to the square of the perimeter;

[0018] Alternatively, the method for calculating the circularity measurement is as follows: , where A is the area of ​​the cross-section and p is the perimeter of the cross-section.

[0019] In some embodiments, the method further includes: determining the shape irregularity of the cross section corresponding to the narrow segment based on the circularity metric; when the circularity metric C is 1, the cross section is circular; the smaller the value of the circularity metric C, the higher the shape irregularity.

[0020] In some embodiments, the regularity value is calculated as follows:

[0021] , where C compact It's about compactness, S asymmetry β is the asymmetric coefficient, and β is the weighting factor that adjusts the influence of symmetry on the irregularity coefficient.

[0022] In some embodiments, between S101 and S102, the method further includes: the diameter of the blood vessel at different cross sections perpendicular to the center line of the trunk, and locating the stenotic segment according to the change in the blood vessel diameter; the blood vessel cross section in S102 and S103 is the blood vessel cross section of the stenotic segment.

[0023] Optionally, the criteria for locating stenotic segments include: extracting the vessel wall contour line of a cross section perpendicular to the trunk centerline; constructing a normal plane at point C(s) on the trunk centerline, perpendicular to the tangent direction T(s) of the centerline, and intercepting the cross section contour perpendicular to the trunk centerline to obtain the radius distribution function R(θ,s), where θ is the polar angle; calculating the maximum and minimum radii of the vessel wall contour line of the cross section at each position s along the trunk centerline; calculating the stenosis index based on the ratio of the minimum radius to the reference radius of the proximal normal vessel; and identifying positions with a stenosis index greater than or equal to a first threshold as stenotic segments.

[0024] A second aspect of this application discloses a computer device, comprising: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the above-described method.

[0025] A third aspect of this application discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0026] The fourth aspect of this application discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0027] This application has the following beneficial effects:

[0028] 1. This application innovatively discloses a method for quantifying the symmetry of vascular cross-sections. This method is based on the quantification of the asymmetry of vascular cross-sections using moments of inertia. Specifically, it uses the ratio of a first moment of inertia to a second moment of inertia to define an asymmetry coefficient, objectively quantifying the symmetry distribution characteristics of the vascular cross-section. This method effectively overcomes the dependence of traditional parametric models (such as ellipse fitting) on ​​regular shapes, accurately describing the asymmetric morphology of calcified plaques or eccentric lesions.

[0029] 2. The quantification method for vascular cross-sectional symmetry proposed in this application is well-suited to the diversity of actual clinical stenosis, as actual stenosis may exhibit irregular geometric distribution, leading to discrepancies between the calculated results and the actual blood flow resistance. For example, calcified plaques may cause localized vessel wall stiffness, but the diameter change may not be significant; in such cases, traditional methods may underestimate the clinical significance of the stenosis. This application provides a basis for more accurate prediction of hemodynamic parameters from both a kinetic modeling and clinical perspective.

[0030] From a dynamic modeling perspective, the shape of the blood vessel cross-section (such as elliptical or irregular shape) significantly affects blood flow velocity distribution and shear stress. The traditional circular cross-section assumption may underestimate the impact of stenosis on local turbulence, energy loss, and plaque stress. However, computational fluid dynamics (CFD) models based on the actual shape can more accurately predict hemodynamic parameters (such as pressure gradient and plaque vulnerability), providing a more reliable basis for assessing ischemic risk.

[0031] Traditional models assume the blood vessel has a circular cross-section and calculate the flow rate Q based on the Hagen-Poiseuille equation. Where ΔP is the pressure gradient, r is the radius, η is the blood viscosity, and L is the length of the stenosis.

[0032] If the actual cross-section is elliptical (major axis a, minor axis b), its effective radius reff needs to be corrected to the equivalent hydraulic diameter: For an ellipse, the area A = πab, and the perimeter is approximately P ≈ π[3(a+b)−(3a+b)(a+3b)]. The flow rate formula then becomes: ;

[0033] Clearly, the resistance of a non-circular cross-section (such as an ellipse) is significantly higher than that of a circular blood vessel with an equivalent diameter, and traditional models underestimate the actual pressure gradient (ΔP) and energy loss.

[0034] From a clinical perspective, irregular cross-sections may reflect the spatial heterogeneity of plaque components (such as biased distribution of the lipid core or uneven thickness of the fibrous cap), which is directly related to the risk of plaque rupture. In addition, three-dimensional morphological features (such as the steepness of the stenosis inlet / outlet) may affect interventional treatment (such as stent apposition effect). Optimized cross-sectional analysis helps in individualized assessment and treatment decisions, thereby improving prognosis and reducing the risk of acute cardiovascular events. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram of the method flow provided in the first aspect of the present invention;

[0037] Figure 2 This is a schematic diagram of a quantification system for vascular cross-sectional symmetry provided in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of the architecture of an exemplary computing device provided in an embodiment of the present invention;

[0040] Figure 5 This is a schematic diagram of the storage medium provided in an embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram of the vascular correlation coefficient results provided in an embodiment of the present invention;

[0042] Figure 7 These are schematic diagrams of different moments of inertia provided in embodiments of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0044] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Figure 1 This is a schematic flowchart of a method for quantifying the symmetry of blood vessel cross-sections provided by an embodiment of the present invention. Specifically, the method includes the following steps:

[0047] S101, Obtain the geometric model of the blood vessel to be tested;

[0048] In some embodiments, the blood vessel to be tested is derived from a subject. As used herein, the terms "subject," "test subject," or "sample to be tested" refer to any animal (e.g., a mammal), including but not limited to humans, non-human primates, rodents, etc., which will become the recipient of a particular treatment. Generally, the terms "subject" and "patient" are used interchangeably herein when referring to a human subject. Preferably, the subject is a human.

[0049] In some embodiments, between S101 and S102, the method further includes: the diameter of the blood vessel at different cross sections perpendicular to the center line of the trunk, and locating the stenotic segment according to the change in the blood vessel diameter; the blood vessel cross section in S102 and S103 is the blood vessel cross section of the stenotic segment.

[0050] Optionally, the criteria for locating stenotic segments include: extracting the vessel wall contour line of a cross section perpendicular to the trunk centerline; constructing a normal plane at point C(s) on the trunk centerline, perpendicular to the tangent direction T(s) of the centerline, and intercepting the cross section contour perpendicular to the trunk centerline to obtain the radius distribution function R(θ,s), where θ is the polar angle; calculating the maximum and minimum radii of the vessel wall contour line of the cross section at each position s along the trunk centerline; calculating the stenosis index based on the ratio of the minimum radius to the reference radius of the proximal normal vessel; and identifying positions with a stenosis index greater than or equal to a first threshold as stenotic segments.

[0051] Optionally, the main trunk centerline of the blood vessel can be extracted based on the geometric model. The main trunk centerline is the centerline after removing irrelevant branches.

[0052] Optionally, the geometric model is constructed based on medical image data of the blood vessel to be tested;

[0053] Optionally, the geometric model is a model after denoising using a Gaussian kernel local polynomial filter.

[0054] S102, calculate the area and perimeter of the blood vessel cross-section; calculate the circle measurement based on the ratio of area to perimeter;

[0055] In some embodiments, the method for calculating the circle measurement is: the ratio of the area to the square of the perimeter;

[0056] Optionally, the circular measurement can be calculated as follows: C = 4πA / p^2, where A is the area of ​​the cross-section and p is the perimeter of the cross-section;

[0057] The method also includes: determining the shape irregularity of the cross section corresponding to the narrow segment based on the circularity metric; when the circularity metric C is 1, the cross section is circular; the smaller the value of the circularity metric C, the higher the shape irregularity.

[0058] S103, calculate the asymmetry coefficient of the blood vessel cross section; in some embodiments, the method for calculating the asymmetry coefficient includes: calculating a first moment of inertia representing the area distribution of the blood vessel cross section and a second moment of inertia representing the eccentricity of the cross section; calculating the ratio of the second moment of inertia to the first moment of inertia to obtain the asymmetry coefficient.

[0059] In some embodiments, the method for calculating the first moment of inertia includes:

[0060] or Where A is the region containing the cross-section, dA is an infinitesimal area unit on the region; x and y are the distances from each tiny area element to the corresponding axis, and x² and y² are the squared distances from each area element to the origin (or centroid).

[0061] Optional methods for calculating the second moment of inertia include:

[0062] or Where A is the region containing the cross-section, dA is an infinitesimal area unit on the region; x and y are the distances from each tiny area element to the corresponding axis, and x² and y² are the squared distances from each area element to the origin (or centroid).

[0063] Optionally, the method further includes: when the asymmetry coefficient is between 1-a and 1+a, the result of good cross-sectional symmetry corresponding to the narrow position is obtained; when the asymmetry coefficient is greater than 1+a, the result of poor cross-sectional symmetry corresponding to the narrow position is obtained, where a is a positive number greater than 0.

[0064] S104, the regularity value is calculated based on the reciprocal of the circularity measure and the asymmetry coefficient;

[0065] In some embodiments, the regularity value is calculated as follows: I irregularity =1 / C compact ×(1+βS asymmetry ), where C compact It is the reciprocal of the circle's measure, S asymmetry β is the asymmetric coefficient, and β is the weighting factor that adjusts the influence of symmetry on the irregularity coefficient.

[0066] S105: Output the result of whether the shape of the cross-section corresponding to the blood vessel is regular based on the regularity value. If the result is irregular, quantify the classification level of irregularity based on the regularity value.

[0067] Figure 3 This is a schematic diagram of a computer device provided in an embodiment of the present invention, such as... Figure 3As shown, the device 2000 may include: one or more processors 2010 and one or more memories 2020; wherein the memories store computer-readable code that, when run by the one or more processors, can perform the methods described above.

[0068] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, operations, and logic block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or any conventional processor, and can be based on an x86 or ARM architecture.

[0069] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0070] For example, the method or apparatus according to embodiments of this disclosure can also be used by means of Figure 4 The architecture of the computing device 3000 shown is used for implementation. For example... Figure 4 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage devices in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for processing and / or communication of the methods provided in this disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 4 One or more components in the computing device shown.

[0071] This invention also includes a computer-readable storage medium, such as... Figure 5The diagram illustrates a storage medium 4000 provided in an embodiment of the present invention. The computer storage medium 4020 stores computer-readable instructions 4010. When the computer-readable instructions 4010 are executed by a processor, the method described above according to embodiments of the present disclosure can be performed. The computer-readable storage medium in the embodiments of the present disclosure may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM), and Direct Memory Bus Random Access Memory (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0072] This disclosure also provides a computer program product or system, including a computer program that, when executed by a processor, implements the steps of the above-described method, such as... Figure 2 As shown, the computer program product or computer program includes:

[0073] Model acquisition module 201 is used or configured to acquire the geometric model of the blood vessel to be tested;

[0074] The circularity measurement calculation module 202 is used or configured to calculate the area and perimeter of the blood vessel cross-section; and calculate the circularity measurement based on the ratio of the area to the perimeter.

[0075] Asymmetry coefficient calculation module 203 is used or configured to calculate the asymmetry coefficient of the blood vessel cross section;

[0076] Regularity value calculation module 204 is used or configured to calculate the regularity value based on the reciprocal of the circularity metric and the asymmetry coefficient;

[0077] The blood vessel rule judgment module 205 is used or configured to output the result of whether the shape of the corresponding cross-section of the blood vessel is regular based on the regularity value. If the result is irregular, the irregularity classification level is quantified based on the regularity value.

[0078] Specifically:

[0079] Step 1: Acquisition of medical image data and construction of vascular geometric models (taking the extraction of the aorta as an example)

[0080] 1. Acquisition of medical image data:

[0081] High-resolution medical imaging equipment, such as computed tomography (CT), magnetic resonance imaging (MRI), angiography, and ultrasound, is used to acquire three-dimensional medical images of the human aorta to be evaluated. These medical imaging devices provide accurate vascular imaging, ensuring the accuracy of subsequent analysis. The acquired medical image data must be stored in DICOM format (a standard format for digital imaging and communications medicine) to ensure standardization and high quality of the image data.

[0082] 2. Construction of the vascular geometric model:

[0083] In MIMICS software, thresholding techniques are used to extract a mask of the aorta from medical image data. This process separates the aortic region from the surrounding tissue in the image data, laying the foundation for subsequent segmentation and modeling. A series of segmentation algorithms (such as random walk algorithms based on image intensity and gradients along the tracking path, region growing algorithms, interval binary segmentation algorithms, thresholding algorithms, voxel growing algorithms, or deep learning segmentation algorithms) are used to generate the aortic vascular path. These algorithms achieve accurate segmentation of the aortic vascular region by analyzing the features of different regions in the image.

[0084] 3. Remove small branch vessels while preserving the main trunk:

[0085] In the segmented vascular model, some small branch vessels, such as the three branches of the aortic arch, the renal artery, the celiac trunk, and the superior mesenteric artery, are removed. Only the ascending aorta, the aortic arch, and the descending aorta leading to the iliac branches are retained to obtain a simpler geometric model of the aortic trunk. This process helps reduce interference from details, allowing subsequent vessel wall roughness quantification to focus on the analysis of the aortic trunk.

[0086] 4. Generation and denoising of 3D reconstructed geometry:

[0087] The extracted vascular model was used to generate the geometry of the aorta through 3D reconstruction technology. This 3D model reflects the morphology and structure of the blood vessel, providing a basis for subsequent accurate analysis. To improve model quality and reduce the impact of image noise, a Gaussian kernel local polynomial filter was used to denoise the 3D reconstructed geometry. This denoising process effectively removes high-frequency noise from the vascular model, making the model smoother and more stable. The denoised 3D model of the aortic trunk provides a high-quality geometric data foundation for subsequent vascular wall roughness measurement. At this point, the model not only has an accurate morphological description but also provides a clearer and more stable vascular geometry for subsequent analysis.

[0088] Step Two: Confirmation and Extraction of Narrow Segment Profile:

[0089] After obtaining a complete 3D vascular model, the next step is to extract the cross-section of the stenotic segment. First, the stenotic region is located by calculating the variation in vessel diameter at different locations. Vascular stenosis is typically characterized by a significant reduction in vessel diameter; therefore, the location of the stenotic segment can be identified by calculating the diameter of each cross-section in the model. Specifically, the variation in the vessel's maximum diameter at each cross-section can be tracked to find the narrowest part of the vessel, which is the stenotic segment.

[0090] Once the narrowed area is accurately identified, a cross-section of that area can be obtained based on its lateral location. To extract the cross-sectional shape of this area, the vessel needs to be cut along a plane perpendicular to its main axis, and the cross-sectional data on that plane needs to be acquired. In this way, the cross-section of the narrowed segment can be obtained, thus accurately reflecting the geometric characteristics of the area.

[0091] 3.1 Centerline Extraction:

[0092] The centerline of the main blood vessel trunk is extracted from the 3D blood vessel model using either a skeletonization algorithm or a minimum path tracing method. The centerline is parameterized as an arc length *s*, as follows: ;

[0093] 3.2 Extraction of external sectional lines of the blood vessel wall:

[0094] Based on the segmented blood vessel model, the contour line (external section line) of the blood vessel wall is extracted on a plane perpendicular to the centerline. At the centerline point C(s), a normal plane (perpendicular to the tangent direction of the centerline T(s)) is constructed to extract the cross-sectional contour of the blood vessel, obtaining the radius distribution function R(θ,s), where θ is the polar angle.

[0095] 3.3 Location and Profile Determination of Narrow Segments:

[0096] Radius calculation: Calculate the maximum radius Rmax(s) and minimum radius Rmin(s) of the cross-section at each position s along the centerline. Narrowness quantification: Define the narrowness index: Among them, R normal This is the reference radius for normal proximal blood vessels.

[0097] Narrow location identification: Find the S corresponding to the maximum value of SI(s) stenosis This is the location where the narrowing is most severe. In S... stenosis Extract the cross-section at the point and extract the coordinates and shape geometry of the points at the cross-section.

[0098] 3.4 Extraction of Area A and Perimeter P: Code was written using the MATLAB app. This code first generates a simulated circular blood vessel cross-sectional image (saved as a PNG file; a real blood vessel PNG file can be used instead). Then, the image is read and uniformly converted to double type for preprocessing, including binarization, median filtering for noise reduction, and hole filling. Next, the regionprops function is used to extract the area and perimeter of the processed image. Combined with the assumed pixel resolution, this is converted to physical units (square millimeters and millimeters). Finally, the results are output and the cleaned blood vessel cross-sectional image is visualized to verify the accuracy of the geometric parameters. Figure 6 As shown.

[0099] Step 3: Calculate the compactness coefficient, which is the reciprocal of the circle measurement;

[0100] The irregularity coefficient is defined based on the circularity metric of the shape: the circularity metric is a parameter that measures how close a shape is to a circle. We can define the irregularity coefficient based on the circularity metric, ensuring that the coefficient for a circle is 1, and the coefficient for other shapes is greater than 1.

[0101] 1. The circularity metric C is defined as: Where A is the area of ​​the shape and P is the perimeter of the shape.

[0102] For circles, the circle metric has the largest value, C=1. For irregular shapes, the circle metric will be less than 1, and the smaller the value, the more irregular the shape.

[0103] 2. Irregularity Coefficient Definition: Irregularity Coefficient = 1 / C; The irregularity coefficient is calculated using a circularity metric. To ensure that the coefficient for a circle is 1, and the coefficients for other shapes are greater than 1, the irregularity coefficient can be defined using the above formula. Thus, for a circle, C = 1, so the irregularity coefficient is 1; for other shapes, C is less than 1, therefore the irregularity coefficient is greater than 1.

[0104] Step 4: Calculate the asymmetry coefficients:

[0105] The first moment of inertia I1 represents the area distribution of the shape, which can be understood as a measure of the mass distribution at the geometric center of the shape.

[0106] The second moment of inertia, I², represents the centrifugality of the shape, that is, how the mass (area) of the shape is distributed relative to its geometric center. The more asymmetrical the shape, the larger I² is.

[0107] Therefore, the degree of asymmetry can be defined as: S asymmetry =I2 / I1; where: A is the region containing the shape, x and y are the distances from each tiny area element to the corresponding axis, x 2 and y 2 It is the squared distance of each area element to the origin (or centroid). The principal axes of inertia are orthogonal coordinate axes passing through the centroid of the shape, and their directions are determined by the geometric distribution of the shape. For symmetrical shapes (such as circles), the principal axes of inertia can be in any direction; for asymmetrical shapes (such as ellipses), the principal axes of inertia are along the major and minor axes. (In the calculation, the origin of the coordinate system is located at the centroid of the shape, and the x-axis and y-axis correspond to the directions of the principal axes of inertia, respectively).

[0108] or ;

[0109] or ;

[0110] For perfectly symmetrical shapes (such as circles), I2 and I1 will be very close, so S asymmetry It will approach 1. For asymmetrical shapes (such as rectangles, curved shapes, etc.), I2 will be significantly greater than I1, therefore S asymmetry It will be larger. dA is an infinitesimally small unit of area in terms of shape;

[0111] Symmetry value: For a circle (perfectly symmetric), since I2 and I1 are approximately equal, therefore S asymmetry ≈1.

[0112] Asymmetry value: For asymmetric shapes (such as ellipses, rectangles, etc.), since I2 will be much larger than I1, therefore S asymmetry A value greater than 1 indicates that these shapes are more asymmetrical than circles. For example... Figure 7 As shown.

[0113] Step 5: Calculate the regularity value: ;

[0114] Among them, C compact It is the compactness coefficient (the reciprocal of the circle measurement), S asymmetry It is the degree of asymmetry, and β is the weighting factor that adjusts the influence of symmetry on the irregularity coefficient.

[0115] Step Six: Combined Narrowness of Compactness Coefficient and Regularity Value: ; where D shape It is a comprehensive stenosis based on the cross-sectional shape, where A1 is the average cross-sectional area of ​​a normal blood vessel, A2 is the average cross-sectional area of ​​the stenotic region, and I... irregularity It is the regularity value calculated through compactness coefficient and asymmetry.

[0116] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0117] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0118] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0122] The exemplary embodiments of this disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art will understand that various modifications and combinations can be made to these embodiments or their features without departing from the principles and spirit of this disclosure, and such modifications should fall within the scope of this disclosure.

Claims

1. A method for quantifying the symmetry of blood vessel cross-sections, characterized in that, The methods include: S101, Obtain the geometric model of the blood vessel to be tested; S102, calculate the area and perimeter of the blood vessel cross-section; calculate the circularity measure based on the ratio of area to perimeter; S103, Calculate the asymmetry coefficient of the blood vessel cross-section; S104, The regularity value is calculated based on the reciprocal of the circularity measure and the asymmetry coefficient; S105, output the result of whether the shape of the cross-section corresponding to the blood vessel is regular according to the regularity value. If the result is irregular, quantify the classification level of irregularity according to the regularity value.

2. The method for quantifying the symmetry of vascular cross-sections according to claim 1, characterized in that, The method for calculating the asymmetry coefficient includes: calculating a first moment of inertia representing the area distribution of the blood vessel cross-section and a second moment of inertia representing the eccentricity of the cross-section; and calculating the ratio of the second moment of inertia to the first moment of inertia to obtain the asymmetry coefficient.

3. The method for quantifying the symmetry of vascular cross-sections according to claim 2, characterized in that, The method for calculating the first moment of inertia includes: or Where A is the region containing the cross-section, and dA is an infinitesimally small area unit on the region; x and y are the distances from each tiny area element to the corresponding axis, respectively. 2 and y 2 It is the squared distance of each area element from the origin; Optionally, the method for calculating the second moment of inertia includes: or Where A is the region containing the cross-section, and dA is an infinitesimally small area unit on the region; x and y are the distances from each tiny area element to the corresponding axis, respectively. 2 and y 2 It is the squared distance of each area element from the origin; Optionally, the method further includes: when the asymmetry coefficient is between 1-a and 1+a, a result is obtained that the cross-sectional symmetry of the narrow position is good; when the asymmetry coefficient is greater than 1+a, a result is obtained that the cross-sectional symmetry of the narrow position is poor, where a is a positive number greater than 0.

4. The method for quantifying the symmetry of vascular cross-sections according to claim 1, characterized in that, The method for calculating the circularity is: the ratio of the area to the square of the perimeter; Optionally, the method for calculating the circle measurement is as follows: , where A is the area of ​​the cross-section and p is the perimeter of the cross-section.

5. The method for quantifying the symmetry of vascular cross-sections according to claim 1, characterized in that, The method further includes: determining the shape irregularity of the cross section corresponding to the narrow segment based on the circularity metric; when the circularity metric C is 1, the cross section is circular; the smaller the value of the circularity metric C, the higher the shape irregularity.

6. The method for quantifying the symmetry of vascular cross-sections according to claim 1, characterized in that, The method for calculating the regularity value is as follows: , where C compact It's about compactness, S asymmetry β is the asymmetric coefficient, and β is the weighting factor that adjusts the influence of symmetry on the irregularity coefficient.

7. The method for quantifying the symmetry of vascular cross-sections according to claim 1, characterized in that, Between S101 and S102, the method further includes: different cross-sections of the blood vessel diameter perpendicular to the center line of the main trunk, and locating the stenotic segment according to the change in blood vessel diameter; the blood vessel cross-section in S102 and S103 is the blood vessel cross-section of the stenotic segment.

8. A computer device, characterized in that, The device includes: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-7.

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