WO2024230129A1 - Method and apparatus for processing blood vessel ultrasound image, and device and medium - Google Patents

Abstract

A method and apparatus for processing a blood vessel ultrasound image, and a device and a medium. The method comprises: acquiring an ultrasonic signal of a target blood vessel object, and obtaining an initial blood vessel ultrasound image on the basis of the ultrasonic signal (S110); determining a target brightness mapping parameter that matches the ultrasonic signal (S120); and adjusting the brightness of the initial blood vessel ultrasound image on the basis of the target brightness mapping parameter, so as to obtain a target blood vessel ultrasound image (S130), wherein the target brightness mapping parameter is a parameter that is determined by means of analyzing, in a preset hyperspace, a plurality of sample ultrasonic signals collected under the same sampling condition as the ultrasonic signal.

Description

Vascular ultrasound image processing method, device, equipment and medium

This application claims the priority of the Chinese patent application filed with the China Patent Office on May 11, 2023, with application number 202310531607.1, the entire contents of which are incorporated by reference into this application.

Technical Field

The embodiments of the present application relate to the technical field of medical image processing, for example, to a method, device, equipment and medium for processing vascular ultrasound images.

Background Art

Intravascular ultrasound (IVUS) is an intravascular imaging method. Since IVUS images are affected by the signal-to-noise ratio of the ultrasound system and the complex imaging environment, the recognition of different tissue components such as blood, fibrous plaques, and calcified plaques in blood vessels is poor. In addition, the dynamic range of the image displayed by the display device cannot match the dynamic range of the transducer, which will also increase the loss of the dynamic range of the intravascular ultrasound image.

Usually, in the brightness mode of intravascular ultrasound imaging, time gain compensation and logarithmic compression methods are mostly used to control the brightness and contrast of the image to improve the quality of vascular ultrasound images. However, the above methods rely more on experience and subjective evaluation, and the image processing effect is unstable.

Summary of the invention

The embodiments of the present application provide a vascular ultrasound image processing method, device, equipment and medium, which can improve the brightness contrast of different components in the blood vessel and enhance the vascular ultrasound image effect.

In a first aspect, an embodiment of the present application provides a method for processing a vascular ultrasound image, the method comprising:

Acquire an ultrasonic signal of a target blood vessel object, and obtain an initial blood vessel ultrasonic image based on the ultrasonic signal;

Determine a target brightness mapping parameter that matches the ultrasonic signal; wherein the target brightness mapping parameter is a parameter determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace;

The brightness of the initial blood vessel ultrasonic image is adjusted based on the target brightness mapping parameter to obtain a target blood vessel ultrasonic image.

In a second aspect, an embodiment of the present application further provides a vascular ultrasound image processing device, the device comprising:

an image generation module, configured to acquire an ultrasonic signal of a target blood vessel object and obtain an initial blood vessel ultrasonic image based on the ultrasonic signal;

An image processing parameter acquisition module, configured to determine a target brightness mapping parameter that matches the ultrasonic signal; wherein the target brightness mapping parameter is a parameter determined by analyzing a plurality of groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace;

The image processing module is configured to adjust the brightness of the initial blood vessel ultrasonic image based on the target brightness mapping parameter to obtain a target blood vessel ultrasonic image.

In a third aspect, an embodiment of the present application further provides a computer device, the computer device comprising:

at least one processor;

a memory configured to store at least one program;

When the at least one program is executed by the at least one processor, the at least one processor implements the vascular ultrasound image processing method provided in any embodiment of the present application.

In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, a vascular ultrasound image processing method as provided in any embodiment of the present application is implemented.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for processing vascular ultrasound images provided in an embodiment of the present application;

FIG2 is a flow chart of a method for processing a vascular ultrasound image provided by an embodiment of the present application;

FIG3 is a schematic diagram of the structure of a blood vessel ultrasonic image processing device provided in an embodiment of the present application;

FIG4 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application.

DETAILED DESCRIPTION

The present application is described below in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present application, rather than to limit the present application. For ease of description, only parts related to the present application, rather than all structures, are shown in the accompanying drawings.

FIG1 is a flow chart of a vascular ultrasound image processing method provided by an embodiment of the present application. This embodiment is applicable to a scenario in which a vascular ultrasound image is obtained by signal processing based on an intravascular ultrasound signal. The method can be executed by a vascular ultrasound image processing device, which can be implemented by software and/or hardware. Integrated into computer equipment with application development capabilities.

As shown in FIG1 , the blood vessel ultrasound image processing method of this embodiment includes the following steps:

S110 , acquiring an ultrasonic signal of a target blood vessel object, and obtaining an initial blood vessel ultrasonic image based on the ultrasonic signal.

The target blood vessel object may be a blood vessel object that can be imaged by signal acquisition and imaging by an intravascular ultrasound imaging device to reflect the condition of the inner wall of the blood vessel.

After acquiring the ultrasonic signal of the target vascular object, the ultrasonic signal can be processed to obtain the initial vascular ultrasonic image, and the data signal can be converted into an image for representation. Each vascular ultrasonic image has a corresponding brightness mapping curve, which reflects the mapping relationship between the pixel gray value in the vascular ultrasonic image and the vascular ultrasonic signal intensity. Correspondingly, the initial vascular ultrasonic image corresponds to an initial brightness mapping curve.

In an optional manner, the acquired ultrasound signals can be respectively input into a plurality of preset bandpass filters of different filtering frequency bands to obtain ultrasound filter signals of corresponding frequency bands; then, time gain compensation is performed on the plurality of ultrasound filter signals respectively, and the compensated signals are superimposed to obtain processed ultrasound signals; and further, an initial vascular ultrasound image is obtained according to the processed ultrasound signals.

The gain value of the time gain compensation can be calculated by the formula T(r)=1-e- ^βr , where r is the distance from the ultrasonic transducer, β=ln10 ^αf/20 , and α is the attenuation parameter, and its unit is f is the transducer frequency in MHz.

During intravascular ultrasound signal imaging, the ultrasound signal emitted by the ultrasound transducer usually penetrates the blood first and then the tissue; its actual attenuation rate is an unknown variable, and different attenuation rates may be applied at different wavelengths (frequencies). The attenuation rate may be adjusted based on the experience of technicians in the field, or the gain value of the time gain compensation may be adjusted directly based on the empirical value.

S120: Determine a target brightness mapping parameter that matches the ultrasonic signal.

In this embodiment, in order to enable the final vascular ultrasound image to have a larger dynamic display range and present a better image visual effect, the grayscale value display effect of the initial vascular ultrasound image is further adjusted, that is, the initial brightness mapping curve is changed.

The target brightness mapping parameter is a parameter determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace. The same sampling conditions can be The situation of signal acquisition of blood vessels by the same signal acquisition device. Each signal acquisition device with different models or equipment parameters will have different signal dynamic ranges, and the corresponding target brightness mapping parameters are different. Among them, the signal acquisition device can be an ultrasonic transducer that can realize ultrasonic signal conversion, transmission and reception. For the corresponding signal acquisition device, the target brightness mapping parameter is a parameter that has been analyzed and verified and is used to process ultrasonic images.

The preset signal analysis dimensions may include parameters such as the intensity (brightness) of the ultrasound signal, the mean value of the signal intensity, the variance of the signal intensity, and the signal texture characteristics, which present different performance results for different intravascular tissue components, thereby enabling different vascular tissue components to be well distinguished. Among them, the preset hyperspace is a multi-dimensional stereoscopic space established based on the signals of the above multiple dimensions. Furthermore, the analysis results based on the multiple dimensions can be analyzed in the preset hyperspace to obtain a set of brightness mapping parameters that enable different vascular tissue components to be well distinguished.

In an optional implementation, ultrasonic signal sample data can be collected for different signal acquisition devices in advance, and a universal brightness mapping parameter matching the corresponding signal acquisition device can be determined based on a large amount of data analysis. When the relevant brightness mapping parameters need to be used, the preset brightness mapping parameter table can be queried based on the device parameters of the signal acquisition device corresponding to the initial ultrasonic image to be processed to obtain the target brightness mapping parameters.

S130: Adjust the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image.

The adjusting the brightness of the initial vascular ultrasound image based on the target brightness mapping parameters may be a process of updating an initial brightness mapping curve according to the target brightness mapping parameters, and obtaining a target brightness mapping curve through the target brightness mapping parameters, thereby obtaining a target vascular ultrasound image.

Based on the adjustment of this step, different vascular tissue components in the vascular ultrasound image can be more distinguished and the visual effect can be better.

The technical solution of this embodiment is to obtain an initial vascular ultrasound image based on the ultrasound signal after acquiring the ultrasound signal of the target vascular object; then determine the target brightness mapping parameters that match the ultrasound signal; and adjust the brightness of the initial vascular ultrasound image based on the target brightness mapping parameters to obtain the target vascular ultrasound image to improve the image quality; wherein the target brightness mapping parameters are parameters determined by analyzing multiple groups of sample ultrasound signals collected under the same sampling conditions as the ultrasound signal in a preset hyperspace. The technical solution of this embodiment solves the problem of intravascular ultrasound image in the related art. The problem of unstable image processing effect when the dynamic display range of the image is adjusted can be solved by efficiently and stably adjusting the dynamic range of the vascular ultrasound image during high real-time intravascular ultrasound imaging to improve the quality of the vascular image.

FIG2 is a flow chart of a vascular ultrasound image processing method provided in an embodiment of the present application. This embodiment and the vascular ultrasound image processing method in the above embodiment belong to the same inventive concept, and further describes the determination process of the target brightness mapping parameter. The method can be executed by a vascular ultrasound image processing device, which can be implemented by software and/or hardware and integrated into a computer device with application development function.

As shown in FIG2 , the blood vessel ultrasound image processing method of this embodiment includes the following steps:

S210 , acquiring multiple groups of sample ultrasonic signals collected by the target signal collection device, and performing signal preprocessing on the multiple groups of sample ultrasonic signals respectively to obtain corresponding spatial frequency signal graphs.

The target signal acquisition device can be any type of device used for intravascular ultrasound imaging and that requires exploration of more suitable brightness mapping parameters, such as an ultrasonic transducer that can realize ultrasound signal conversion, transmission, and reception. This is because different devices have different corresponding ultrasound signal conversion and signal acquisition capabilities, so it is necessary to determine a brightness mapping parameter that is more suitable for each different type of signal acquisition device. Optionally, determining the brightness mapping parameters that are suitable for each different type of signal acquisition device can be achieved by analyzing a large number of sample ultrasound signals collected by each signal acquisition device for the target vascular object.

In this embodiment, the process of performing signal preprocessing on multiple groups of sample ultrasonic signals to obtain corresponding spatial frequency signal graphs may include the following steps:

First, synchronous compression wavelet transform is performed on the signals at each sampling angle in the multiple groups of sample ultrasonic signals to obtain the signal time-frequency diagrams corresponding to each sampling angle in the multiple groups of sample ultrasonic signals.

Based on the form and characteristics of vascular ultrasound signal acquisition, ultrasound signals are usually represented by polar coordinate data. Then, in each group of sample ultrasound signals, each angle in the polar coordinate system corresponds to a time signal sequence. In this embodiment, with the angle as the dimension, the sample ultrasound signal of each angle is synchronously compressed by wavelet transform to construct channels corresponding to multiple ultrasound signals of different frequencies. Usually, the number of channels is set to 3-5, so as to obtain the signal time-frequency diagram corresponding to each sampling angle of the sample ultrasound signal, which is a three-dimensional diagram determined by time-frequency-signal intensity value.

Then, the signal time-frequency diagrams corresponding to multiple sampling angles are combined to obtain a spatial frequency signal diagram in the form of a three-dimensional grid, that is, the signal time-frequency diagrams at different angles are superimposed and displayed in one space to form a four-dimensional data diagram.

S220 , identifying different vascular tissues in the spatial frequency signal image, and counting data of multiple preset signal analysis dimensions for each vascular tissue.

When different vascular tissues are identified in the spatial frequency signal graph, neural network image recognition can be used, for example, different vascular components can be identified, such as blood, tissue, fibrous plaque, calcified plaque, stent and other tissue components. Different vascular tissues in multiple spatial frequency signal graphs are marked in advance, a neural network model that can identify different vascular tissue components is trained, and the spatial frequency signal graph is input into the neural network model to obtain the corresponding recognition results of different vascular tissue components. When different vascular tissues are identified in the spatial frequency signal graph, manual labeling or other methods that can realize vascular tissue component identification can also be used.

Based on the recognition results of different vascular tissues, data of multiple preset signal analysis dimensions corresponding to different data can be further analyzed, such as the signal intensity value, signal intensity mean, signal intensity variance of data points corresponding to different vascular tissue components in the spatial frequency signal map, and texture description parameters of the area where each vascular tissue is located. The texture description parameter can be at least one parameter of a gray level co-occurrence matrix, a homogeneous texture descriptor, and an edge histogram descriptor.

S230 . Based on the data of the multiple preset signal analysis dimensions of each vascular tissue, analyze and determine the target brightness mapping parameters in a hyperspace formed by the multiple preset signal analysis dimensions.

First, a hyperspace can be established with multiple preset signal analysis dimensions, and then the distribution area of each vascular tissue in the hyperspace can be determined based on the data of the multiple preset signal analysis dimensions of each vascular tissue. A vascular tissue component in a spatial frequency signal graph can correspond to a point in the hyperspace, and vascular tissue components of the same category in multiple spatial frequency signal graphs obtained after processing multiple sample ultrasound signals correspond to multiple points in the hyperspace, which can form a point cloud.

Then, the brightness mapping curve value corresponding to the initial vascular ultrasound image (ie, the initial brightness mapping curve value) is adjusted, and each time the brightness mapping curve value is adjusted, the distance between the centers of the point clouds composed of different types of vascular tissue components is calculated.

Finally, the brightness mapping curve value that makes the distance between the center points of the distribution areas of the multiple vascular tissues the maximum distance value is determined as the target brightness mapping parameter. The brightness curve mapping parameters can maximize the distance between the centers of the point clouds composed of different categories of vascular tissue components, that is, the different categories of vascular tissue components have better distinction.

The distance calculation may be implemented by using Euclidean distance or other distance calculation rules, or may be calculated and determined by using artificial intelligence image recognition.

Once the target brightness mapping parameters are determined, they can be applied in the process of processing relevant intravascular ultrasound images.

S240. When an ultrasonic signal to be processed of a target blood vessel object acquired by the target signal acquisition device is acquired, an initial blood vessel ultrasonic image is obtained based on the ultrasonic signal to be processed.

S250: Adjust the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image.

The technical solution of this embodiment is to first use the target signal acquisition device to perform a large number of ultrasonic signal sampling, collect multiple groups of sample ultrasonic signals, and perform signal preprocessing on the multiple groups of sample ultrasonic signals respectively to obtain the corresponding spatial frequency signal map; then identify different vascular tissues in the spatial frequency signal map, and count the data of multiple preset signal analysis dimensions of each vascular tissue; based on the data of the multiple preset signal analysis dimensions of each vascular tissue, analyze and determine the target brightness mapping parameters in the hyperspace formed by the multiple preset signal analysis dimensions. So that after the ultrasonic signal of the target signal acquisition device imaging the target vascular object is obtained, an initial vascular ultrasonic image is obtained based on the ultrasonic signal; then determine the target brightness mapping parameters that match the signal acquisition device that collects the ultrasonic signal; and adjust the brightness of the initial vascular ultrasonic image based on the target brightness mapping parameters to obtain the target vascular ultrasonic image to improve the image quality; wherein the target brightness mapping parameters are parameters determined based on the analysis of the multiple groups of sample ultrasonic signals collected by the signal acquisition device in the preset hyperspace. The technical solution of this embodiment solves the problem of unstable image processing effect when adjusting the dynamic display range of intravascular ultrasound images in the related technology. It can achieve efficient and stable adjustment of the dynamic range of vascular ultrasound images in the highly real-time intravascular ultrasound imaging process, thereby improving the quality of vascular images.

Figure 3 is a structural schematic diagram of a vascular ultrasound image processing device provided in an embodiment of the present application. This embodiment can be applied to a scenario in which a vascular ultrasound image is obtained by performing signal processing based on an intravascular ultrasound signal. The device can be implemented by software and/or hardware and integrated into a computer terminal device with application development capabilities.

As shown in FIG3 , the blood vessel ultrasound image processing device includes: an image generation module 310, an image processing parameter A data acquisition module 320 and an image processing module 330.

The image generation module 310 is configured to acquire an ultrasonic signal of a target vascular object and obtain an initial vascular ultrasonic image based on the ultrasonic signal; the image processing parameter acquisition module 320 is configured to determine a target brightness mapping parameter that matches the ultrasonic signal; the image processing module 330 is configured to adjust the brightness of the initial vascular ultrasonic image based on the target brightness mapping parameter to obtain a target vascular ultrasonic image; wherein the target brightness mapping parameter is a parameter determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace.

The technical solution of this embodiment is to obtain an initial vascular ultrasound image based on the ultrasound signal after acquiring the ultrasound signal of the target vascular object; then determine the target brightness mapping parameters that match the ultrasound signal; and adjust the brightness of the initial vascular ultrasound image based on the target brightness mapping parameters to obtain the target vascular ultrasound image to improve the image quality; wherein the target brightness mapping parameters are parameters determined by analyzing multiple groups of sample ultrasound signals collected under the same sampling conditions as the ultrasound signal in a preset hyperspace. The technical solution of this embodiment solves the problem of unstable image processing effect when adjusting the dynamic display range of intravascular ultrasound images in related technologies, and can achieve efficient and stable adjustment of the dynamic range of vascular ultrasound images in a highly real-time intravascular ultrasound imaging process to improve the quality of vascular images.

Optionally, the vascular ultrasound image processing device further includes a parameter analysis module configured to analyze and determine the target brightness mapping parameters in a preset hyperspace based on a plurality of groups of sample ultrasound signals acquired under the same sampling conditions as the ultrasound signal;

The parameter analysis module is set to:

Performing signal preprocessing on the multiple groups of sample ultrasonic signals respectively to obtain corresponding spatial frequency signal graphs;

Identifying different vascular tissues in the spatial frequency signal map, and counting data of multiple preset signal analysis dimensions for each vascular tissue;

Based on the data of the multiple preset signal analysis dimensions of each vascular tissue, the target brightness mapping parameters are analyzed and determined in a hyperspace formed by the multiple preset signal analysis dimensions.

Optionally, the parameter analysis module is set to:

Performing synchronous compression wavelet transform on the signals at each sampling angle in the multiple groups of sample ultrasonic signals to obtain a signal time-frequency diagram corresponding to each sampling angle in the multiple groups of sample ultrasonic signals;

The signal time-frequency diagrams corresponding to multiple sampling angles are combined to obtain a spatial frequency signal diagram in the form of a three-dimensional grid.

Optionally, the parameter analysis module is set to:

The signal strength value, signal strength mean, signal strength variance corresponding to the vascular tissue and the texture description parameters of the region where each vascular tissue is located are respectively counted.

Optionally, the parameter analysis module is set to:

Determine the distribution area of each vascular tissue in the hyperspace based on the data of the multiple preset signal analysis dimensions of each vascular tissue;

The brightness mapping curve value corresponding to the initial blood vessel ultrasonic image is adjusted, and the brightness mapping curve value that makes the distance between the center points of the distribution areas of the plurality of blood vessel tissues the maximum distance value is determined as the target brightness mapping parameter.

Optionally, the image generation module 310 is configured to:

Inputting the ultrasonic signal into a plurality of preset bandpass filters of different filtering frequency bands respectively to obtain ultrasonic filtering signals of corresponding frequency bands;

Performing time gain compensation on the plurality of ultrasonic filter signals respectively, and superimposing the compensated signals to obtain processed ultrasonic signals;

The initial blood vessel ultrasonic image is obtained according to the processed ultrasonic signal.

Optionally, the ultrasonic signal is acquired by a signal acquisition device, and the image processing parameter acquisition module 320 is set to:

The preset brightness mapping parameter table is queried according to the device parameters of the signal acquisition device to obtain the target brightness mapping parameters.

The vascular ultrasonic image processing device provided in the embodiment of the present application can execute the vascular ultrasonic image processing method provided in any embodiment of the present application, and has the corresponding functional modules and effects of the execution method.

FIG4 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. FIG4 shows a block diagram of an exemplary computer device 12 suitable for implementing the implementation of the present application. The computer device 12 shown in FIG4 is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present application. The computer device 12 can be any terminal device with computing capabilities, such as intelligent controllers and servers, mobile phones and other terminal devices.

4, computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and bus 18 connecting various system components (including system memory 28 and processing unit 16).

Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include, but are not limited to, an Instruction Set Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 4 , commonly referred to as a “hard drive”). Although not shown in FIG. 4 , a disk drive for reading and writing removable non-volatile disks (such as “floppy disks”) and an optical disk drive for reading and writing removable non-volatile optical disks (such as read-only memories (Compact Disc Read-Only Memory, CD-ROM), digital video disks (Digital Video Disc-Read Only Memory, DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data media interfaces. The system memory 28 may include at least one program product having a set (eg, at least one) of program modules. The program modules are configured to perform the functions of various embodiments of the present application.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.

The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input/output (I/O) interface 22. In addition, the computer device 12 may also communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and/or a public network (e.g., the Internet) through a network adapter 20. As shown in FIG. 4 , the network adapter 20 communicates with other modules of the computer device 12 through a bus 18. It should be understood that although not shown in Figure 4, other hardware and/or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, redundant arrays of independent disks (Redundant Arrays of Independent Disks, RAID) systems, tape drives, and data backup storage systems.

The processing unit 16 executes a variety of functional applications and data processing by running the program stored in the system memory 28, for example, implementing the vascular ultrasound image processing method provided by the embodiment of the present invention, the method comprising:

Acquire an ultrasonic signal of a target blood vessel object, and obtain an initial blood vessel ultrasonic image based on the ultrasonic signal;

Determine a target brightness mapping parameter that matches the ultrasonic signal; wherein the target brightness mapping parameter is a parameter determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace;

The brightness of the initial blood vessel ultrasonic image is adjusted based on the target brightness mapping parameter to obtain a target blood vessel ultrasonic image.

The present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for processing a vascular ultrasound image provided in any embodiment of the present application is implemented. The method includes:

Acquire an ultrasonic signal of a target blood vessel object, and obtain an initial blood vessel ultrasonic image based on the ultrasonic signal;

Determine a target brightness mapping parameter that matches the ultrasonic signal; wherein the target brightness mapping parameter is based on a plurality of groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset Parameters determined by analysis in hyperspace;

The brightness of the initial blood vessel ultrasonic image is adjusted based on the target brightness mapping parameter to obtain a target blood vessel ultrasonic image.

The computer storage medium of the embodiment of the present application may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, RAM, ROM, an erasable programmable read-only memory (EPROM) or flash memory, optical fiber, CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, radio frequency (RF), etc., or any suitable combination of the above.

The computer program code for performing the operations of the present application may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a LAN or WAN. Alternatively, it can be connected to an external computer (for example, through the Internet using an Internet service provider).

The above-mentioned multiple modules or multiple steps of the present application can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into multiple integrated circuit modules respectively, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

Claims (10)

A method for processing a vascular ultrasound image, comprising: Acquire an ultrasonic signal of a target blood vessel object, and obtain an initial blood vessel ultrasonic image based on the ultrasonic signal; Determine a target brightness mapping parameter that matches the ultrasonic signal; wherein the target brightness mapping parameter is a parameter determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace; The brightness of the initial blood vessel ultrasonic image is adjusted based on the target brightness mapping parameter to obtain a target blood vessel ultrasonic image. The method according to claim 1, wherein the process of analyzing and determining the target brightness mapping parameters in a preset hyperspace based on multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal comprises: Performing signal preprocessing on the multiple groups of sample ultrasonic signals respectively to obtain corresponding spatial frequency signal graphs; Identifying different vascular tissues in the spatial frequency signal map, and counting data of multiple preset signal analysis dimensions for each vascular tissue; Based on the data of the multiple preset signal analysis dimensions of each vascular tissue, the target brightness mapping parameters are analyzed and determined in a hyperspace formed by the multiple preset signal analysis dimensions. The method according to claim 2, wherein the performing signal preprocessing on the multiple groups of sample ultrasonic signals respectively to obtain corresponding spatial frequency signal graphs comprises: Performing synchronous compression wavelet transform on the signals at each sampling angle in the multiple groups of sample ultrasonic signals to obtain a signal time-frequency diagram corresponding to each sampling angle in the multiple groups of sample ultrasonic signals; The signal time-frequency diagrams corresponding to multiple sampling angles are combined to obtain the spatial frequency diagram in the form of a three-dimensional grid. Rate signal diagram. The method according to claim 2, wherein the data of multiple preset signal analysis dimensions of each vascular tissue are counted, comprising: The signal intensity value, signal intensity mean, signal intensity variance corresponding to each vascular tissue and the texture description parameters of the area where each vascular tissue is located are counted respectively. The method according to claim 2, wherein the data of the multiple preset signal analysis dimensions based on each vascular tissue, analyzing and determining the target brightness mapping parameters in a hyperspace formed by the multiple preset signal analysis dimensions, comprises: Determine the distribution area of each vascular tissue in the hyperspace based on the data of the multiple preset signal analysis dimensions of each vascular tissue; The brightness mapping curve value corresponding to the initial blood vessel ultrasonic image is adjusted, and the brightness mapping curve value that makes the distance between the center points of the distribution areas of the plurality of blood vessel tissues the maximum distance value is determined as the target brightness mapping parameter. The method according to claim 1, wherein obtaining an initial blood vessel ultrasound image based on the ultrasound signal comprises: Inputting the ultrasonic signal into a plurality of preset bandpass filters of different filtering frequency bands respectively to obtain ultrasonic filtering signals of corresponding frequency bands; Performing time gain compensation on multiple ultrasonic filter signals respectively, and superimposing the compensated signals to obtain processed ultrasonic signals; The initial blood vessel ultrasonic image is obtained according to the processed ultrasonic signal. The method according to claim 1, wherein the ultrasonic signal is acquired by a signal acquisition device; The determining of the target brightness mapping parameter matching the ultrasonic signal comprises: The preset brightness mapping parameter table is queried according to the device parameters of the signal acquisition device to obtain the target brightness mapping parameters. A blood vessel ultrasonic image processing device, comprising: an image generation module, configured to acquire an ultrasonic signal of a target blood vessel object and obtain an initial blood vessel ultrasonic image based on the ultrasonic signal; An image processing parameter acquisition module, configured to determine a target brightness mapping parameter that matches the ultrasonic signal; wherein the target brightness mapping parameter is a parameter determined by analyzing a plurality of groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace; The image processing module is configured to adjust the brightness of the initial blood vessel ultrasonic image based on the target brightness mapping parameter to obtain a target blood vessel ultrasonic image. A computer device comprising: at least one processor; a memory configured to store at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the vascular ultrasound image processing method according to any one of claims 1 to 7. A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the vascular ultrasound image processing method as claimed in any one of claims 1 to 7.