Cardiovascular image analysis system
By acquiring time-series images using cardiovascular angiography equipment and generating blood flow topology maps using optical flow algorithms, blood flow characteristics are quantified. This solves the problems of subjectivity and single indicators in the assessment of cardiovascular stenosis in existing technologies, and realizes accurate assessment and objective assessment standards for hemodynamic disturbances.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-04-07
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing cardiovascular stenosis assessment techniques rely on subjective visual judgment and single indicators, which make it difficult to quantify hemodynamic disturbances, resulting in insufficient identification of early stenosis or complex lesions, and frequent misdiagnosis or missed diagnosis.
By acquiring temporal images using cardiovascular angiography equipment, combining optical flow algorithms to calculate pixel displacement fields, generating blood flow topology maps, quantifying blood flow velocity and rotation characteristics, and constructing stenosis correlation indices using vortex breakpoint density and energy distribution, an objective assessment standard is provided.
It enables accurate assessment of hemodynamic abnormalities, overcomes the subjective bias and single-indicator limitations of traditional methods, and provides an objective and repeatable assessment of the degree of stenosis.
Smart Images
Figure CN121810646A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical image processing and analysis technology, specifically to a cardiovascular image analysis system. Background Technology
[0002] One of the core pathological features of cardiovascular disease is hemodynamic abnormalities caused by vascular stenosis. Accurate assessment of the degree of stenosis and the state of blood flow disturbance is crucial for clinical intervention decisions. Current vascular stenosis assessment techniques mainly rely on subjective visual judgment from coronary angiography or anatomical measurements from intravascular ultrasound, but these have significant limitations: Traditional coronary angiography judges stenosis by observing the contrast agent filling pattern with the naked eye of the doctor, which is greatly influenced by subjective experience and makes it difficult to quantify hemodynamic disturbances. Although intravascular ultrasound can provide the anatomical structure of the lumen, it cannot directly reflect the functional characteristics of blood flow. In addition, existing quantitative methods mostly rely on a single indicator, ignoring the hemodynamic chain reaction caused by stenosis, such as eddy formation and energy breakage. They are not sensitive enough to identify early stenosis or complex lesions such as diffuse stenosis, which can easily lead to misdiagnosis or missed diagnosis. Summary of the Invention
[0003] This invention addresses the technical problems existing in the prior art by providing a cardiovascular image analysis system.
[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A cardiovascular imaging analysis system, comprising: Image acquisition and preprocessing module: Acquires images of the target blood vessel using cardiovascular angiography equipment, forms a time-series image sequence, synchronously records the contrast agent injection time and heart rate, and sorts them by timestamp; Blood flow topology generation module: Calculates pixel displacement field based on time-series image sequence, obtains displacement amount through local pixel grayscale matching, forms motion vector containing direction and distance, indirectly quantifies blood flow velocity, and then converts it into blood flow topology map, with vector arrows and color encoding of flow velocity, and forms continuous path through streamline interpolation; Topology recognition module: vorticity is calculated by the spatial change rate of velocity components to quantify the blood flow rotation characteristics. Vortex regions are then defined based on vorticity calculations. Within these regions, extreme point detection algorithms are used to locate vortex breakup points. Blood flow energy distribution is calculated based on the amplitude of motion vectors. Obstruction degree assessment module: Based on the density of vortex break points and the length of blood flow interruption lines, a stenosis correlation index is constructed, and the degree of vascular obstruction is determined according to the index range.
[0005] In a preferred embodiment, the image acquisition and preprocessing module uses a digital cardiovascular angiography device to perform dynamic scanning of the cardiovascular system. During the dynamic scanning process, the device continuously acquires images according to set parameters to form a time-series image sequence. Each frame in the time-series image sequence corresponds to the vascular state at a specific moment. The continuity between frames directly reflects the dynamic process of contrast agent propagation with blood flow. The contrast agent filling node displays the lumen state, and the clearance stage reflects the blood flow excretion speed. The flow of contrast agent in the blood vessel and changes in lumen morphology are captured in real time. The image acquisition synchronously records the contrast agent injection time and heart rate parameters. The acquired time-series image sequence is stored in order of timestamps. The contrast agent injection time is marked by the starting time point of the contrast agent entering the blood vessel through the catheter, which is used to associate the contrast agent filling time node in the image. The heart rate is synchronously acquired through the electrocardiogram monitoring unit, reflecting the cardiac pulsation cycle.
[0006] In a preferred embodiment, the blood flow topology generation module calculates the pixel displacement field based on the time-series image sequence using an optical flow algorithm. By matching the local pixel grayscale of adjacent frames, the motion vector of each pixel is obtained. The motion vector directly reflects the trajectory of the contrast agent moving with the blood flow. Local pixel grayscale matching involves defining a local matching window centered on the pixel to be tested in frame t of a time-series image sequence, comparing adjacent frames t and t+1. The grayscale similarity between this local matching window and all windows of the same size within the corresponding search area in frame t+1 is calculated, using the sum of squared differences as the similarity metric. The specific calculation formula is as follows:
[0007] in, Let (x, y) represent the pixel grayscale value at coordinates (x, y) in the t-th frame of the image. (x, y) represents the pixel coordinates within the local matching window, where x is the horizontal coordinate and y is the vertical coordinate in the topology graph. Represents the coordinates in the (t+1)th frame of the image. The pixel grayscale value at point (u, v) represents the pixel displacement component to be determined, where u represents the horizontal displacement and v represents the vertical displacement. This represents the sum of the squared differences between the two windows. When the similarity reaches its maximum value, the difference in the center pixel coordinates of the two windows is the displacement of that pixel. The motion vector of each pixel contains two parameters: direction and distance. The direction is the vector direction from the pixel position in frame t to the corresponding pixel position in frame t+, which directly corresponds to the direction of contrast agent movement with blood flow. The specific calculation formula is as follows:
[0008] Where d represents the physical distance of pixel displacement, and s represents the physical size of the pixel. It represents the distance of pixel displacement, reflecting the amount of spatial movement of pixels between two frames. The distance is the spatial distance between pixels between two frames, which is achieved by converting the pixel size into the actual displacement length. The pixel displacement field is converted into a blood flow topology map. The conversion is based on the global distribution of the pixel displacement field. The displacement data is transformed into graphic features through visualization mapping. The direction of blood flow is marked by motion vector arrows, and the flow velocity is represented by color coding. The color coding is directly related to the distance of the motion vector. The preset color gradient from blue to red corresponds to the flow velocity from low velocity threshold to high velocity threshold. The low velocity threshold corresponds to the laminar flow velocity near the vessel wall in normal blood flow, and the high velocity threshold corresponds to the peak flow velocity at the center of the vessel. The discrete displacement vectors are connected by a streamline interpolation algorithm to form a continuous blood flow path. Based on the distribution of the pixel displacement field, a smooth curve is generated along the motion vector. The specific calculation formula is as follows:
[0009] in, This represents the unit direction vector of the streamline at coordinates (x, y), used to describe the tangent direction of the streamline at that location, and its direction is consistent with the direction of blood flow. This represents the lateral velocity component at coordinates (x, y). This represents the longitudinal velocity component at coordinates (x, y). The velocity modulus at coordinates (x, y) represents the actual velocity of blood flow at that location. The density of the curves reflects the intensity of blood flow. The topology map can distinguish between two types of blood flow characteristics: normal blood flow is characterized by parallel streamlines, consistent motion vector directions, and uniform distribution, while blood flow at narrow points is characterized by distorted streamlines and chaotic motion vector directions.
[0010] In a preferred embodiment, the topology map recognition module determines vorticity based on the spatial rate of change of the velocity component. In the blood flow topology map, vortex regions are identified through vorticity calculation, and the specific calculation formula is as follows:
[0011] in, This represents the vorticity value at coordinates (x, y), reflecting the intensity and direction of the rotation of the blood flow vector. This represents the lateral velocity component at coordinates (x, y), which is the velocity of blood flow in the horizontal direction. This represents the longitudinal velocity component at coordinates (x, y), which is the velocity of blood flow in the vertical direction. This represents the partial derivative of the longitudinal velocity along the transverse direction, reflecting the rate of change of the longitudinal velocity with respect to the horizontal position. The partial derivative of the transverse velocity along the longitudinal direction represents the rate of change of the transverse velocity with respect to the vertical position. Within the defined vortex region, an extreme point detection algorithm is used to locate the vortex breakup point. The specific steps are as follows: S1. Calculate the vorticity gradient. The partial derivatives are obtained using the Sobel operator. The specific calculation formula is as follows:
[0012] in, This represents the vorticity gradient vector at coordinates (x, y), reflecting the spatial variation trend of vorticity values. = This represents the partial derivative of vorticity along the transverse direction. = This represents the partial derivative of vorticity along the longitudinal direction; S2. When the gradient modulus is 0 and the vorticity value at that point is a local extremum in the region, it is marked as a candidate breakup point. The specific formula for calculating the gradient modulus is as follows:
[0013] in, This represents the modulus of the vorticity gradient at coordinates (x, y), reflecting the severity of vorticity changes. When the modulus is 0, it indicates that the point is a candidate for an extreme point of vorticity. This means that the modulus of the vorticity gradient is calculated by taking the square root of the sum of the squares of the lateral and longitudinal partial derivatives of the vorticity. This calculation ensures that the modulus is non-negative and can comprehensively reflect the intensity of vorticity changes in both directions. S3. If there are pixels with opposite vorticity signs in the neighborhood of a candidate break point, it is confirmed as a vortex break point. The vortex break point is the core of the vortex initiation or dissipation, which reflects the sudden change in the direction of the displacement vector and is a landmark node for blood flow disorder caused by narrowing and obstruction. Blood flow energy distribution is calculated based on the amplitude of the motion vector. The blood flow energy value is a quantitative indicator at the coordinate point and directly corresponds to the square of the motion vector amplitude, reflecting the amount of kinetic energy carried by the blood flow. In areas of vascular stenosis, the increased vascular resistance leads to a decrease in blood flow velocity, and the energy value in this area will be significantly lower than that in normal vascular segments. The specific formula for calculating the blood flow energy value is as follows:
[0014] in, This represents the blood flow energy value at coordinates (x, y), reflecting the magnitude of the kinetic energy carried by the blood flow. | | represents the magnitude of the motion vector at coordinates (x, y). Blood flow density defines the blood flow energy per unit area, used to standardize the spatial distribution of energy. The specific formula for calculating blood flow density is as follows:
[0015] in, The blood flow density at coordinates (x, y) represents the blood flow energy per unit area, A represents the physical area corresponding to a single pixel, and s represents the physical size of a pixel, i.e., the actual length corresponding to 1 pixel. The value of blood flow density is a quantitative indicator at the coordinates, obtained by dividing the blood flow energy value by the physical area corresponding to a single pixel. The blood flow interruption line is the continuous boundary where the blood flow density drops sharply, and its extraction rules are as follows: S1. Calculate the blood flow density difference between adjacent pixels. The specific calculation formula is as follows:
[0016] in, This represents the difference in blood flow density between two adjacent pixels. =P( ), =P( () represents the blood flow density between two adjacent pixels, and k represents the preset ratio. This indicates the higher blood flow density value between two pixels. When the difference exceeds k times the higher value, it is marked as a candidate boundary point. S2. When the difference in blood flow density exceeds the preset proportion of the higher blood flow density value between two pixels, mark the adjacent pixel pair as a boundary candidate point. S3. By connecting discrete candidate points into a continuous curve through neighborhood connectivity analysis, an energy flow interruption line is formed. The energy flow interruption line corresponds to the energy break of blood flow due to resistance loss at the stenosis. Its starting point corresponds to the exit position of the stenosis segment, and its extension length is directly related to the range of blood flow attenuation at the distal end of the stenosis, providing key features for the assessment of the degree of vascular stenosis.
[0017] In a preferred embodiment, the obstruction degree determination module statistically analyzes the density of vortex breakpoints and the length of the blood flow interruption line. When the stenosis is severe, it indicates that the blood flow turbulence is more significant. When the breakpoint density is high and the interruption line is long, it indicates that the range of blood flow energy breakage due to resistance loss at the stenosis is wider. The specific calculation formula for the density of vortex breakpoints is as follows:
[0018] in, The density of vortex breakpoints reflects the intensity of blood flow turbulence per unit area. N represents a specific region in the blood flow optical flow topology map, centered on the stenosis, containing all identified vortex breakpoints. Its boundary is naturally enclosed by the vessel wall contour, ensuring coverage of the stenosis-related blood flow turbulence area. M represents the physical area of the specific region. The specific formula for calculating the length of the blood flow interruption line is as follows:
[0019] Where L represents the length of the blood flow interruption line, and k represents the number of segments measured in segments. The linear measurement length of the i-th segment is represented by a correlation index established based on the density of the vortex breakpoint and the length of the blood flow interruption line. The specific calculation formula is as follows:
[0020] Where I represents the narrow correlation index, This represents the density at the vortex breakup point. The weight represents the density of the vortex breakpoint, and L represents the length of the blood flow interruption line. The weight representing the length of the blood flow interruption line, This represents the maximum theoretical value, calculated by substituting the measured maximum vortex breakpoint density and maximum blood flow interruption line length from clinical samples. When the correlation index... At that time, it was determined that there was no significant obstruction, and the corresponding blood flow was normal. This indicates the absence of a significant obstruction threshold. At this point, the density of the vortex break point is extremely low, indicating no significant vortex generation. The length of the blood flow interruption line is close to 0, indicating no significant attenuation of blood flow energy. At this time, it was determined to be mild transverse obstruction, corresponding to local blood flow turbulence but no significant interruption of blood flow. At this time, the density of the vortex breakup point was slightly increased, indicating the presence of local vortices but without a formed density distribution. The length of the blood flow interruption line was relatively short, indicating that the range of blood flow energy attenuation was limited. At this point, it is determined to be moderate obstruction. At this time, the density of vortex breakup points increases significantly, and surface vortices are obvious and densely distributed. At this point, it is determined to be a severe obstruction, and the density of vortex breakup points is extremely high, indicating the presence of dense vortices.
[0021] The beneficial effects of this invention are as follows: By synchronously recording the injection time of contrast agent and heart rate, this invention ensures the temporal consistency and spatial clarity of time-series images, accurately captures the dynamic changes of rapid blood flow, avoids displacement calculation errors caused by frame rate fluctuations, and overcomes the shortcomings of traditional low frame rate images in capturing instantaneous blood flow features. Based on the optical flow algorithm, the pixel displacement field is calculated, and a topology map is generated through vector arrows, color coding, and streamline interpolation. This transforms abstract blood flow motion into intuitive direction, velocity, and path features. Compared with traditional static images, this topology map can simultaneously present the laminar flow characteristics of normal blood flow and the turbulent characteristics of stenosis. Based on the density of vortex breakpoints and the length of energy flow interruption lines, a correlation index is constructed, overcoming the bias of traditional subjective grading and providing an objective and repeatable assessment standard for different degrees of stenosis. Attached Figure Description
[0022] Figure 1 This is a flowchart of the present invention; Figure 2 This is a system block diagram of the present invention. Detailed Implementation
[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0024] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.
[0025] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.
[0026] like Figure 1 - Figure 2 This embodiment provides: a cardiovascular imaging analysis system, comprising: Image acquisition and preprocessing module: Acquires images of the target blood vessel using cardiovascular angiography equipment, forms a time-series image sequence, synchronously records the contrast agent injection time and heart rate, and sorts them by timestamp; In this embodiment, the image acquisition and preprocessing module needs to be specifically described. The image acquisition and preprocessing module uses a digital cardiovascular angiography device to perform dynamic scanning of the cardiovascular system. During the dynamic scanning process, the device continuously acquires images according to set parameters to form a time-series image sequence. Each frame in the time-series image sequence corresponds to the vascular state at a specific moment. The continuity between frames directly reflects the dynamic process of contrast agent propagation with blood flow. The contrast agent filling node displays the lumen state, the clearing stage reflects the blood flow excretion speed, and the flow of contrast agent in the blood vessel and changes in lumen morphology are captured in real time. The image acquisition synchronously records the contrast agent injection time and heart rate parameters. The acquired time-series image sequence is stored in order of timestamps. The contrast agent injection time is marked by the starting time point of the contrast agent entering the blood vessel through the catheter, which is used to associate the contrast agent filling time node in the image. The heart rate is synchronously acquired through the electrocardiogram monitoring unit to reflect the cardiac pulsation cycle.
[0027] It should be noted that during cardiovascular imaging, contrast agents are injected to enhance the image contrast between blood vessels and surrounding tissues, thereby enabling clear visualization of the vascular lumen and blood flow status. The attenuation coefficients of surrounding tissues such as myocardium and connective tissue are relatively similar to those of the blood vessels themselves under X-ray imaging, making it difficult to distinguish vascular outlines or track dynamic changes in blood flow during direct imaging. Contrast agents, being high-density iodine-containing substances, absorb X-rays and exhibit a distinct high-grayscale characteristic in images, creating a strong contrast with the surrounding low-grayscale soft tissues. This clearly delineates the course of blood vessels, the state of the lumen, and their branching structures. The flow of contrast agents with blood flow can be recorded through time-series imaging sequences, and the dynamic changes in filling and clearing directly reflect blood flow velocity, direction, and patency.
[0028] Blood flow topology generation module: Calculates pixel displacement field based on time-series image sequence, obtains displacement amount through local pixel grayscale matching, forms motion vector containing direction and distance, indirectly quantifies blood flow velocity, and then converts it into blood flow topology map, with vector arrows and color encoding of flow velocity, and forms continuous path through streamline interpolation; In this embodiment, the blood flow topology generation module needs to be specifically described. The blood flow topology generation module calculates the pixel displacement field based on the time-series image sequence using an optical flow algorithm. By matching the local pixel grayscale of adjacent frame images, the motion vector of each pixel is obtained. The motion vector directly reflects the trajectory of the contrast agent moving with the blood flow. Local pixel grayscale matching involves defining a local matching window (with a window size of 3) centered on the pixel to be measured in the t-th frame and the (t+1)-th frame of a time-series image sequence, using the adjacent t-th and t+1-th frames. The pixel count is determined based on the spatial resolution of the blood vessel image (ensuring sufficient grayscale features within the window). The grayscale similarity between the local matching window and all windows of the same size within the corresponding search region in frame t+1 is calculated, using the sum of squared differences as the similarity metric. The specific calculation formula is as follows:
[0029] in, Let (x, y) represent the pixel grayscale value at coordinates (x, y) in the t-th frame of the image. (x, y) represents the pixel coordinates within the local matching window, where x is the horizontal coordinate and y is the vertical coordinate in the topology graph. Represents the coordinates in the (t+1)th frame of the image. The pixel grayscale value at point (u, v) represents the pixel displacement component to be determined, where u represents the horizontal displacement and v represents the vertical displacement. This represents the sum of the squared differences between the two windows. When the similarity reaches its maximum value, the difference in the center pixel coordinates of the two windows is the displacement of that pixel. The motion vector of each pixel contains two parameters: direction and distance. The direction is the vector direction from the pixel position in frame t to the corresponding pixel position in frame t+, which directly corresponds to the direction of contrast agent movement with blood flow. The specific calculation formula is as follows:
[0030] Where d represents the physical distance of pixel displacement, and s represents the physical size of the pixel. It represents the distance of pixel displacement, reflecting the amount of spatial movement of pixels between two frames. The distance is the spatial distance between pixels between two frames, which is achieved by converting the pixel size into the actual displacement length. The pixel displacement field is converted into a blood flow topology map. The conversion is based on the global distribution of the pixel displacement field. The displacement data is transformed into graphic features through visualization mapping. The direction of blood flow is marked by motion vector arrows, and the flow velocity is represented by color coding. The color coding is directly related to the distance of the motion vector. The preset color gradient from blue to red corresponds to the flow velocity from low velocity threshold to high velocity threshold. The low velocity threshold corresponds to the laminar flow velocity near the vessel wall in normal blood flow, and the high velocity threshold corresponds to the peak flow velocity at the center of the vessel. The discrete displacement vectors are connected by a streamline interpolation algorithm to form a continuous blood flow path. Based on the distribution of the pixel displacement field, a smooth curve is generated along the motion vector. The specific calculation formula is as follows:
[0031] in, This represents the unit direction vector of the streamline at coordinates (x, y), used to describe the tangent direction of the streamline at that location, and its direction is consistent with the direction of blood flow. This represents the lateral velocity component at coordinates (x, y). This represents the longitudinal velocity component at coordinates (x, y). The velocity modulus at coordinates (x, y) represents the actual velocity of blood flow at that location. The density of the curves reflects the density of blood flow (e.g., dense streamlines in the center of the vessel and sparse streamlines at the edges). The topology map can distinguish between two types of blood flow characteristics: normal blood flow is characterized by parallel streamlines, consistent motion vector directions, and uniform distribution, while blood flow at stenosis is characterized by distorted streamlines and chaotic motion vector directions.
[0032] It should be noted that the basis of grayscale matching is that during the contrast agent flow, the grayscale distribution in a local area remains stable in a short period of time, ensuring that the displacement calculation is consistent with the actual movement trajectory of the contrast agent. The displacement vector is positively correlated with the blood flow velocity. Under a stable inter-frame time interval, the faster the flow velocity, the longer the physical distance that the contrast agent moves per unit time, and the greater the corresponding pixel displacement distance, that is, the longer the displacement vector, thereby achieving indirect quantization of blood flow velocity.
[0033] Topology recognition module: vorticity is calculated by the spatial change rate of velocity components to quantify the blood flow rotation characteristics. Vortex regions are then defined based on vorticity calculations. Within these regions, extreme point detection algorithms are used to locate vortex breakup points. Blood flow energy distribution is calculated based on the amplitude of motion vectors. In this embodiment, the topology map recognition module needs to be specifically explained. This module determines vorticity based on the spatial rate of change of the flow velocity component. In the blood flow topology map, vortex regions are identified through vorticity calculation. The specific calculation formula is as follows:
[0034] in, This represents the vorticity value at coordinates (x, y), reflecting the intensity and direction of the rotation of the blood flow vector. This represents the lateral velocity component at coordinates (x, y), which is the velocity of blood flow in the horizontal direction. This represents the longitudinal velocity component at coordinates (x, y), which is the velocity of blood flow in the vertical direction. This represents the partial derivative of the longitudinal velocity along the transverse direction, reflecting the rate of change of the longitudinal velocity with respect to the horizontal position. The partial derivative of the transverse velocity along the longitudinal direction represents the rate of change of the transverse velocity with respect to the vertical position. Within the defined vortex region, an extreme point detection algorithm is used to locate the vortex breakup point. The specific steps are as follows: S1. Calculate the vorticity gradient. The partial derivatives are obtained using the Sobel operator. The specific calculation formula is as follows:
[0035] in, This represents the vorticity gradient vector at coordinates (x, y), reflecting the spatial variation trend of vorticity values. = This represents the partial derivative of vorticity along the transverse direction. = This represents the partial derivative of vorticity along the longitudinal direction; S2. When the gradient modulus is 0 and the vorticity value at that point is a local extremum in the region, it is marked as a candidate breakup point. The specific formula for calculating the gradient modulus is as follows:
[0036] in, This represents the modulus of the vorticity gradient at coordinates (x, y), reflecting the severity of vorticity changes. When the modulus is 0, it indicates that the point is a candidate for an extreme point of vorticity. This means that the modulus of the vorticity gradient is calculated by taking the square root of the sum of the squares of the lateral and longitudinal partial derivatives of the vorticity. This calculation ensures that the modulus is non-negative and can comprehensively reflect the intensity of vorticity changes in both directions. S3. If there are pixels with opposite vorticity signs in the neighborhood of a candidate break point, it is confirmed as a vortex break point. The vortex break point is the core of the vortex initiation or dissipation, which reflects the sudden change in the direction of the displacement vector and is a landmark node for blood flow disorder caused by narrowing and obstruction. Blood flow energy distribution is calculated based on the amplitude of the motion vector. The blood flow energy value is a quantitative indicator at the coordinate point and directly corresponds to the square of the motion vector amplitude, reflecting the amount of kinetic energy carried by the blood flow. In areas of vascular stenosis, the increased vascular resistance leads to a decrease in blood flow velocity, and the energy value in this area will be significantly lower than that in normal vascular segments. The specific formula for calculating the blood flow energy value is as follows:
[0037] in, This represents the blood flow energy value at coordinates (x, y), reflecting the magnitude of the kinetic energy carried by the blood flow. | | represents the magnitude of the motion vector at coordinates (x, y). Blood flow density defines the blood flow energy per unit area, used to standardize the spatial distribution of energy. The specific formula for calculating blood flow density is as follows:
[0038] in, The blood flow density at coordinates (x, y) represents the blood flow energy per unit area, A represents the physical area corresponding to a single pixel, and s represents the physical size of a pixel, i.e., the actual length corresponding to 1 pixel. The value of blood flow density is a quantitative indicator at the coordinates, obtained by dividing the blood flow energy value by the physical area corresponding to a single pixel. The blood flow interruption line is the continuous boundary where the blood flow density drops sharply, and its extraction rules are as follows: S1. Calculate the blood flow density difference between adjacent pixels. The specific calculation formula is as follows:
[0039] in, This represents the difference in blood flow density between two adjacent pixels. =P( ), =P( () represents the blood flow density between two adjacent pixels, and k represents the preset ratio. This indicates the higher blood flow density value between two pixels. When the difference exceeds k times the higher value, it is marked as a candidate boundary point. S2. When the difference in blood flow density exceeds the preset proportion of the higher blood flow density value between two pixels, mark the adjacent pixel pair as a boundary candidate point. S3. By connecting discrete candidate points into a continuous curve through neighborhood connectivity analysis, an energy flow interruption line is formed. The energy flow interruption line corresponds to the energy break of blood flow due to resistance loss at the stenosis. Its starting point corresponds to the exit position of the stenosis segment, and its extension length is directly related to the range of blood flow attenuation at the distal end of the stenosis, providing key features for the assessment of the degree of vascular stenosis.
[0040] It should be noted that vorticity calculation is used to quantify the rotational characteristics of motion vectors in the blood flow topology map. It is determined based on the spatial rate of change of the velocity component. The vorticity value is a quantitative index at the coordinate. The transverse and longitudinal velocity components are converted from the motion vectors in the blood flow topology map generation module, corresponding to the velocity components of blood flow in the horizontal and vertical directions, respectively. The difference between the partial derivative of the longitudinal velocity along the transverse direction and the partial derivative of the transverse velocity along the longitudinal direction constitutes the vorticity value. Its positive or negative sign represents the direction of rotation, and the absolute value reflects the intensity of rotation. The identification of vortex regions is based on the vorticity calculation results and is achieved through a preset vorticity threshold. The preset vorticity threshold is calibrated based on the vorticity distribution characteristics of normal blood vessel segments. When the absolute value of the vorticity of all pixels in a certain continuous region is greater than the threshold, and the region area meets the minimum range of clinical significance, it is judged as a vortex region. Only the blood flow rotation regions with clinical significance are retained to limit the range for vortex breakage point location.
[0041] Obstruction degree assessment module: Constructs a stenosis correlation index based on the density of vortex break points and the length of blood flow interruption lines, and judges the degree of vascular obstruction based on the index range; In this embodiment, the obstruction degree determination module needs to be specifically explained. This module statistically analyzes the density of vortex breakpoints and the length of the blood flow interruption line. When the stenosis is severe, it indicates that the blood flow turbulence is more significant. When the breakpoint density is high and the interruption line is long, it indicates that the range of blood flow energy breakage due to resistance loss at the stenosis is wider. The specific calculation formula for the density of vortex breakpoints is as follows:
[0042] in, The density of vortex breakpoints reflects the intensity of blood flow turbulence per unit area. N represents a specific region in the blood flow optical flow topology map, centered on the stenosis, containing all identified vortex breakpoints. Its boundary is naturally enclosed by the vessel wall contour, ensuring coverage of the stenosis-related blood flow turbulence area. M represents the physical area of the specific region. The specific formula for calculating the length of the blood flow interruption line is as follows:
[0043] Where L represents the length of the blood flow interruption line, and k represents the number of segments measured in segments. The linear measurement length of the i-th segment is represented by a correlation index established based on the density of the vortex breakpoint and the length of the blood flow interruption line. The specific calculation formula is as follows:
[0044] Where I represents the narrow correlation index, This represents the density at the vortex breakup point. The weight represents the density of the vortex breakpoint, and L represents the length of the blood flow interruption line. The weight representing the length of the blood flow interruption line, This represents the maximum theoretical value, calculated by substituting the measured maximum vortex breakpoint density and maximum blood flow interruption line length from clinical samples. When the correlation index... At that time, it was determined that there was no significant obstruction, and the corresponding blood flow was basically normal. This indicates the absence of a significant obstruction threshold. At this point, the density of the vortex break point is extremely low, indicating no significant vortex generation. The length of the blood flow interruption line is close to 0, indicating no significant attenuation of blood flow energy. At this time, it was determined to be mild transverse obstruction, corresponding to local blood flow turbulence but no significant interruption of blood flow. At this time, the density of the vortex breakup point was slightly increased, indicating the presence of local vortices but without a formed density distribution. The length of the blood flow interruption line was relatively short, indicating that the range of blood flow energy attenuation was limited. At this point, it is determined to be moderate obstruction. At this time, the density of vortex breakup points increases significantly, and surface vortices are obvious and densely distributed. At this point, it is determined to be a severe obstruction, and the density of vortex breakup points is extremely high, indicating the presence of dense vortices.
[0045] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0046] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0047] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0048] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0049] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0050] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.
[0051] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.
Claims
1. A cardiovascular image analysis system, characterized in that, include: Image acquisition and preprocessing module: Acquires images of the target blood vessel using cardiovascular angiography equipment, forms a time-series image sequence, synchronously records the contrast agent injection time and heart rate, and sorts them by timestamp; Blood flow topology generation module: Calculates pixel displacement field based on time-series image sequence, obtains displacement amount through local pixel grayscale matching, forms motion vector containing direction and distance, indirectly quantifies blood flow velocity, and then converts it into blood flow topology map, with vector arrows and color encoding of flow velocity, and forms continuous path through streamline interpolation; Topology recognition module: vorticity is calculated by the spatial change rate of velocity components to quantify the blood flow rotation characteristics. Vortex regions are then defined based on vorticity calculations. Within these regions, extreme point detection algorithms are used to locate vortex breakup points. Blood flow energy distribution is calculated based on the amplitude of motion vectors. Obstruction degree assessment module: Based on the density of vortex break points and the length of blood flow interruption lines, a stenosis correlation index is constructed, and the degree of vascular obstruction is determined according to the index range.
2. The cardiovascular image analysis system according to claim 1, characterized in that, The image acquisition and preprocessing module uses digital cardiovascular angiography equipment to perform dynamic scanning of the cardiovascular system. During the dynamic scanning process, the equipment continuously acquires images according to set parameters, forming a time-series image sequence. Each frame in the time-series image sequence corresponds to the vascular state at a specific moment. The continuity between frames directly reflects the dynamic process of contrast agent propagation with blood flow. The contrast agent filling node displays the lumen state, and the clearance phase reflects the blood flow excretion speed. It captures the flow of contrast agent in the blood vessel and changes in lumen morphology in real time. The image acquisition synchronously records the contrast agent injection time and heart rate parameters. The acquired time-series image sequence is stored in order of timestamps. The contrast agent injection time is marked by the starting time point of the contrast agent entering the blood vessel through the catheter, which is used to associate the contrast agent filling time node in the image. The heart rate is synchronously acquired through the electrocardiogram monitoring unit, reflecting the cardiac pulsation cycle.
3. The cardiovascular image analysis system according to claim 1, characterized in that, The blood flow topology generation module uses an optical flow algorithm to calculate the pixel displacement field based on the time-series image sequence. By matching the local pixel grayscale of adjacent frames, it obtains the motion vector of each pixel. The motion vector directly reflects the trajectory of the contrast agent moving with the blood flow. Local pixel grayscale matching involves defining a local matching window centered on the pixel to be tested in frame t of a time-series image sequence, comparing adjacent frames t and t+1. The grayscale similarity between this local matching window and all windows of the same size within the corresponding search area in frame t+1 is calculated, using the sum of squared differences as the similarity metric. The specific calculation formula is as follows: in, Let (x, y) represent the pixel grayscale value at coordinates (x, y) in the t-th frame of the image. (x, y) represents the pixel coordinates within the local matching window, where x is the horizontal coordinate and y is the vertical coordinate in the topology graph. Represents the coordinates in the (t+1)th frame of the image. The pixel grayscale value at point (u, v) represents the pixel displacement component to be determined, where u represents the horizontal displacement and v represents the vertical displacement. This represents the sum of the squared differences between the two windows. When the similarity reaches its maximum value, the difference in the center pixel coordinates of the two windows is the displacement of that pixel.
4. A cardiovascular imaging analysis system according to claim 3, characterized in that, The motion vector of each pixel contains two parameters: direction and distance. The direction is the vector direction from the pixel position in frame t to the corresponding pixel position in frame t+, which directly corresponds to the direction of contrast agent movement with blood flow. The specific calculation formula is as follows: Where d represents the physical distance of pixel displacement, and s represents the physical size of the pixel. It represents the distance of pixel displacement, reflecting the amount of spatial movement of pixels between two frames. The distance is the spatial distance between pixels between two frames, which is achieved by converting the pixel size into the actual displacement length.
5. A cardiovascular imaging analysis system according to claim 3, characterized in that, The pixel displacement field is converted into a blood flow topology map. The conversion is based on the global distribution of the pixel displacement field. The displacement data is transformed into graphic features through visualization mapping. The direction of blood flow is marked by motion vector arrows, and the flow velocity is represented by color coding. The color coding is directly related to the distance of the motion vector. The preset color gradient from blue to red corresponds to the flow velocity from low velocity threshold to high velocity threshold. The low velocity threshold corresponds to the laminar flow velocity near the vessel wall in normal blood flow, and the high velocity threshold corresponds to the peak flow velocity at the center of the vessel. The discrete displacement vectors are connected by a streamline interpolation algorithm to form a continuous blood flow path. Based on the distribution of the pixel displacement field, a smooth curve is generated along the motion vector. The specific calculation formula is as follows: in, This represents the unit direction vector of the streamline at coordinates (x, y), used to describe the tangent direction of the streamline at that location, and its direction is consistent with the direction of blood flow. This represents the lateral velocity component at coordinates (x, y). This represents the longitudinal velocity component at coordinates (x, y). The velocity modulus at coordinates (x, y) represents the actual velocity of blood flow at that location. The density of the curves reflects the intensity of blood flow. The topology map can distinguish between two types of blood flow characteristics: normal blood flow is characterized by parallel streamlines, consistent motion vector directions, and uniform distribution, while blood flow at narrow points is characterized by distorted streamlines and chaotic motion vector directions.
6. A cardiovascular imaging analysis system according to claim 1, characterized in that, The topology map recognition module determines vorticity based on the spatial rate of change of the velocity component. In the blood flow topology map, vortex regions are identified through vorticity calculation. The specific calculation formula is as follows: in, This represents the vorticity value at coordinates (x, y), reflecting the intensity and direction of the rotation of the blood flow vector. This represents the lateral velocity component at coordinates (x, y), which is the velocity of blood flow in the horizontal direction. This represents the longitudinal velocity component at coordinates (x, y), which is the velocity of blood flow in the vertical direction. This represents the partial derivative of the longitudinal velocity along the transverse direction, reflecting the rate of change of the longitudinal velocity with respect to the horizontal position. The partial derivative of the transverse velocity along the longitudinal direction represents the rate of change of the transverse velocity with the vertical position. Within the defined vortex region, the extreme point detection algorithm is used to locate the vortex breakup point.
7. A cardiovascular image analysis system according to claim 6, characterized in that, The specific steps for locating the vortex breakage point are as follows: S1. Calculate the vorticity gradient. The partial derivatives are obtained using the Sobel operator. The specific calculation formula is as follows: in, This represents the vorticity gradient vector at coordinates (x, y), reflecting the spatial variation trend of vorticity values. = This represents the partial derivative of vorticity along the transverse direction. = This represents the partial derivative of vorticity along the longitudinal direction; S2. When the gradient modulus is 0 and the vorticity value at that point is a local extremum in the region, it is marked as a candidate breakup point. The specific formula for calculating the gradient modulus is as follows: in, This represents the modulus of the vorticity gradient at coordinates (x, y), reflecting the severity of vorticity changes. When the modulus is 0, it indicates that the point is a candidate for an extreme point of vorticity. This means that the modulus of the vorticity gradient is calculated by taking the square root of the sum of the squares of the lateral and longitudinal partial derivatives of the vorticity, and this calculation ensures that the modulus is non-negative. S3. If there are pixels with opposite vorticity signs in the neighborhood of a candidate break point, then it is confirmed as a vortex break point.
8. A cardiovascular imaging analysis system according to claim 1, characterized in that, Blood flow energy distribution is calculated based on the amplitude of the motion vector. The blood flow energy value is a quantitative indicator at the coordinate point and directly corresponds to the square of the motion vector amplitude, reflecting the amount of kinetic energy carried by the blood flow. In areas of vascular stenosis, the increased vascular resistance leads to a decrease in blood flow velocity, and the energy value in this area will be significantly lower than that in normal vascular segments. The specific formula for calculating the blood flow energy value is as follows: in, This represents the blood flow energy value at coordinates (x, y), reflecting the magnitude of the kinetic energy carried by the blood flow. | | represents the magnitude of the motion vector at coordinates (x, y). Blood flow density defines the blood flow energy per unit area, used to standardize the spatial distribution of energy. The specific formula for calculating blood flow density is as follows: in, The value represents the blood flow density at coordinates (x, y), the blood flow energy per unit area, A represents the physical area corresponding to a single pixel, and s represents the physical size of a pixel, i.e., the actual length corresponding to 1 pixel. The value of the blood flow density is a quantitative indicator at the coordinates, obtained by dividing the blood flow energy value by the physical area corresponding to a single pixel. The blood flow interruption line is the continuous boundary where the blood flow density drops sharply.
9. A cardiovascular imaging analysis system according to claim 8, characterized in that, The rules for extracting blood flow interruption lines are as follows: S1. Calculate the blood flow density difference between adjacent pixels. The specific calculation formula is as follows: in, This represents the difference in blood flow density between two adjacent pixels. =P( ), =P( () represents the blood flow density between two adjacent pixels, and k represents the preset ratio. This indicates the higher blood flow density value between two pixels. When the difference exceeds k times the higher value, it is marked as a candidate boundary point. S2. When the difference in blood flow density exceeds the preset proportion of the higher blood flow density value between two pixels, mark the adjacent pixel pair as a boundary candidate point. S3. By connecting discrete candidate points into a continuous curve through neighborhood connectivity analysis, an energy flow interruption line is formed. The energy flow interruption line corresponds to the energy break of blood flow due to resistance loss at the stenosis. Its starting point corresponds to the exit position of the stenosis segment, and its extension length is directly related to the range of blood flow attenuation at the distal end of the stenosis, providing key features for the assessment of the degree of vascular stenosis.
10. A cardiovascular imaging analysis system according to claim 1, characterized in that, The obstruction degree determination module statistically analyzes the density of vortex breakpoints and the length of blood flow interruption lines. When the stenosis is severe, it indicates that the blood flow turbulence is more significant. When the breakpoint density is high and the interruption line is long, it indicates that the range of blood flow energy breakage due to resistance loss at the stenosis is wider. The specific calculation formula for the density of vortex breakpoints is as follows: in, The density of vortex breakpoints reflects the intensity of blood flow turbulence per unit area. N represents a specific region in the blood flow optical flow topology map, centered on the stenosis, containing all identified vortex breakpoints. Its boundary is naturally enclosed by the vessel wall contour, ensuring coverage of the stenosis-related blood flow turbulence area. M represents the physical area of the specific region. The specific formula for calculating the length of the blood flow interruption line is as follows: Where L represents the length of the blood flow interruption line, and k represents the number of segments measured in segments. The linear measurement length of the i-th segment is represented by a correlation index established based on the density of the vortex breakpoint and the length of the blood flow interruption line. The specific calculation formula is as follows: Where I represents the narrow correlation index, This represents the density at the vortex breakup point. The weight represents the density of the vortex breakpoint, and L represents the length of the blood flow interruption line. The weight representing the length of the blood flow interruption line, This represents the maximum theoretical value, when the correlation index... At that time, it was determined that there was no significant obstruction, and the corresponding blood flow was normal. Indicates no significant obstruction threshold, when At that time, it was determined to be mild transverse obstruction, corresponding to local blood flow disturbance but no significant interruption of blood flow. At this point, it is determined to be moderate obstruction. At this time, the density of vortex breakup points increases significantly, and surface vortices are obvious and densely distributed. At this point, it is determined to be a severe obstruction, and the density of vortex breakup points is extremely high, indicating the presence of dense vortices.