CN114519707A - Method and equipment for extracting blood perfusion parameters - Google Patents
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Abstract
The present disclosure relates to a method and an apparatus for extracting blood perfusion parameters, the method for extracting blood perfusion parameters of the present disclosure includes: determining a target region in an ultrasound contrast image; dividing the target area into a plurality of sub-areas based on a preset dividing method; determining a plurality of original scatter data of the sub-region based on the ultrasonic contrast image, wherein the original scatter data describes a relation between time and ultrasonic intensity of the scatter; generating a time intensity curve of the sub-region based on the distribution relation of the original scatter data; extracting blood flow perfusion related parameters of the sub-region based on the time intensity curve; and generating a blood flow perfusion parameter image of the target area based on the blood flow perfusion related parameters of the sub-areas. By the method, the change condition of blood flow perfusion can be presented in a large range, and more accurate blood flow perfusion change details are expressed through the blood flow perfusion parameter image of the target area.
Description
Technical Field
The present disclosure relates to medical ultrasound technology, and more particularly, to a method and apparatus for extracting blood perfusion parameters.
Background
Ultrasound contrast imaging, in general, extracts a Time-intensity curve (TIC) in a relatively small region for a selected region of interest (ROI). The parameters describing this curve are related to blood perfusion (blood flow) and blood volume (blood volume). Through the relevant information of blood flow perfusion, additional dimension information can be provided for lesion or tumor detection and diagnosis, and the diagnosis accuracy is improved.
Parameters that can be extracted based on the time-intensity curve generally include blood flow perfusion related parameters such as area under the curve (AUC), Peak Enhancement (PE), time To Peak (TP), Rise Time (RT), Fall Time (FT), and the like. The above parameters generally directly or indirectly reflect the pathological condition of the lesion or tumor.
The TIC curves extracted in a relatively small area by existing ultrasound contrast imaging do not exhibit the condition of blood perfusion variation in the whole imaging area.
Disclosure of Invention
The blood perfusion parameter extraction method, the device and the equipment are used for extracting the blood perfusion parameters in a target area, and a blood perfusion parameter image of the target area can be generated based on blood perfusion related parameters of each sub-area, so that the change condition of blood perfusion can be presented in a large range, and more accurate blood perfusion change details can be expressed through the blood perfusion parameter image of the target area.
In a first aspect, embodiments of the present disclosure provide a method for extracting blood flow perfusion parameters, including: determining a target region in an ultrasound contrast image; dividing the target area into a plurality of sub-areas based on a preset dividing method; determining a plurality of original scatter data of the sub-region based on the ultrasonic contrast image, wherein the original scatter data describes a relation between time and ultrasonic intensity of the scatter; generating a time intensity curve of the sub-region based on the distribution relation of the original scatter data; extracting blood flow perfusion related parameters of the sub-region based on the time intensity curve; and generating a blood flow perfusion parameter image of the target area based on the blood flow perfusion related parameters of the sub-areas.
In a second aspect, embodiments of the present disclosure provide an ultrasound contrast device comprising a processor and a memory, the processor being configured to execute one or more computer programs stored in the memory to implement: determining a target region in an ultrasound contrast image; dividing the target area into a plurality of sub-areas based on a preset dividing method; determining a plurality of original scatter data of the sub-region based on the ultrasonic contrast image, wherein the original scatter data describes the relation between time and the ultrasonic intensity of the scatter; generating a time intensity curve of the sub-region based on the distribution relation of the original scatter data; extracting blood flow perfusion related parameters of the sub-region based on the time intensity curve; and generating a blood flow perfusion parameter image of the target area based on the blood flow perfusion related parameters of the sub-areas.
In a third aspect, embodiments of the present disclosure provide a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by at least one processor, implements a blood flow perfusion parameter extraction method as described in embodiments of the present disclosure.
The method of the present disclosure divides a target area into a number of sub-areas; generating a time intensity curve of the sub-region based on a plurality of original scatter data of the ultrasonic contrast image; the blood perfusion parameter image of the target area is generated based on the blood perfusion related parameters of the sub-areas, the change condition of blood perfusion can be represented in a large range by using the method disclosed by the invention, and more accurate blood perfusion change details can be expressed through the blood perfusion parameter image of the target area.
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In the drawings, which are not necessarily drawn to scale, like reference numerals may describe similar parts throughout the different views. Like reference numerals having alphabetic suffixes or different alphabetic suffixes may represent different instances of similar components. The drawings illustrate various embodiments generally by way of example and not by way of limitation, and together with the description and claims serve to explain the disclosed embodiments. Such embodiments are illustrative, and are not intended to be exhaustive or exclusive embodiments of the present apparatus or method.
Fig. 1a shows a basic flow diagram of a method of blood flow perfusion parameter extraction according to an embodiment of the present disclosure;
fig. 1b shows a detailed flow diagram of a blood flow perfusion parameter extraction method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating selection of a target region for an ultrasound contrast imaging region according to an embodiment of the present disclosure;
FIG. 3 illustrates an original scatter data distribution and a fitted TIC curve in accordance with an embodiment of the present disclosure;
FIG. 4 illustrates one example of dividing a target area into several sub-areas in accordance with an embodiment of the present disclosure;
fig. 5 shows a basic configuration diagram of an ultrasound contrast apparatus according to an embodiment of the present disclosure.
Detailed Description
For a better understanding of the technical aspects of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings. Embodiments of the present disclosure are described in further detail below with reference to the figures and the detailed description, but the present disclosure is not limited thereto.
The use of "first," "second," and similar terms in this disclosure are not intended to indicate any order, quantity, or importance, but rather are used for distinction. The word "comprising" or "comprises", and the like, means that the element preceding the word covers the element listed after the word, and does not exclude the possibility that other elements are also covered.
An embodiment of the present disclosure provides a blood perfusion parameter extraction method, as shown in fig. 1a, the blood perfusion parameter extraction method of the present disclosure starts with step S101 of determining a target region in an ultrasound contrast image. The target region may be a region with a larger imaging range, and the setting of the size of the region may be accomplished by software operation according to the needs of the doctor, for example, a designated region may be selected as the target region, for example, in the imaging region of the ultrasound Contrast (CEUS) image shown in fig. 2, the target region 201 is selected.
Then, in step S102, the target area is divided into a plurality of sub-areas based on a preset division method. For example, the target area 201 in fig. 2 may be divided into a plurality of sub-areas, the size of the specific divided area may be set according to actual needs, one pixel point may be used as one sub-area under the limit condition, and the target area 201 may also be divided into a plurality of sub-areas according to the size of the sub-area set by the user. Or a plurality of target areas can be selected according to the requirements of the user, the plurality of target areas can be disconnected, and the plurality of target areas are divided into a plurality of sub-areas, and the specific division method is not limited one by one.
Parameter extraction may then be performed for each sub-region, and a number of original scatter data of the sub-region, which describes the relationship of time to the ultrasound intensity of the scatter, is determined based on the ultrasound contrast image in step S103. The ultrasonic contrast equipment can acquire a plurality of raw data 301 shown as dots in figure 3 by utilizing the characteristic that the ultrasonic intensity of a certain area along with the time of filling the contrast agent can generate scattering echo enhancement in the process of ultrasonic contrast imaging, and the raw data 301 can be used for extracting blood flow perfusion parameters. In step S104, a time intensity curve of the sub-region is generated based on the distribution relationship of the original scatter data. For example, a time intensity curve may be obtained by fitting a trend based on the distribution position relationship of the raw data 301 as in fig. 3. Thereby, blood flow perfusion related parameters of the sub-region may be extracted based on the time intensity curve in step S105. Specific types of blood flow perfusion-related parameters may include, for example, area under the curve (AUC), Peak Enhancement (PE), and blood volume-related parameters, including Mean Transit Time (MTT), Time To Peak (TTP), Rise Time (RT), Fall Time (FT), and the like, which are not limited herein. After obtaining the blood flow perfusion related parameters of the current sub-region by extraction as shown in fig. 1b, it may be further determined whether traversal of all sub-regions is completed, and if not, a TIC curve is generated for the next sub-region and the blood flow perfusion related parameters thereof are extracted.
After the blood flow perfusion related parameters of the sub-regions are extracted, in step S106, a blood flow perfusion parameter image of the target region is generated based on the blood flow perfusion related parameters of the sub-regions.
The blood flow perfusion parameter extraction method can extract blood flow perfusion related parameters of the target area, generates blood flow perfusion parameter images of the target area and outputs the blood flow perfusion related parameters to a user, and can output the TTP parameter distribution condition of the whole target area to a doctor more intuitively based on the TTP parameters of all the subareas for the same blood flow perfusion parameters such as TTP, and the blood flow perfusion related parameters are not limited in a small local range. That is, the method of the present disclosure can automatically divide the region concerned by the doctor into different small regions, and then extract the TIC curve in each small local region and perform parameter estimation, so that the obtained two-dimensional parameter image (function of spatial variable) can more comprehensively describe the blood perfusion related information of the whole ROI region, and show more details of the blood perfusion condition at the same time.
The method of the present disclosure needs to divide the target area into a plurality of sub-areas, and the division may be completed by using a plurality of methods, for example, the division may be performed according to the size of the sub-area set by the user, for example, if the user sets the size of the sub-area to 1 pixel, all the sub-areas may be divided into 1 pixel, and the target area may also be divided into a plurality of regular small rectangular areas, so the division operation is simple. In some embodiments, the area of each of the divided sub-regions may be not fixed, and the area of each of the sub-regions divided based on the preset dividing method decreases as the image change speed in the ultrasound contrast image increases, and increases as the image change speed in the ultrasound contrast image decreases. As shown in fig. 4, the adaptive division may be performed according to the shape of the target region, and the smaller the area of the target region, the faster the image change speed of the region is reflected. By the dividing method, in a region (a sub-region with larger curvature) with more drastic pattern change of a target region, finer division can be used in a self-adaptive manner; on the other hand, in a relatively flat region of interest, a coarser division (a sub-region with a larger triangle equivalent diameter) may be used. As in fig. 4, the target region 401 may be divided into sub-regions 401a, 401b, 401c of different sizes. By adopting the method for dividing the sub-regions, the calculation amount of subsequent traversal calculation can be further reduced, and meanwhile, the subsequently generated blood flow perfusion parameter images can focus more on regions with more severe graph changes, so that doctors can conveniently read focuses.
Commonly used mathematical perfusion models generally include the Lognormal, Gamma variate, Local intensity random walk, First passage time and Lagged-normal models, each of which is noteworthy for its applicable organs, e.g., Lognormal is generally considered suitable for breast and cardiac TIC fitting, and Lagged-normal is suitable for liver perfusion curve fitting. However, one mathematical perfusion model cannot be used to extract blood perfusion parameters for different organs and achieve optimal performance, and there is no uniform standard for how to evaluate model fitting results although different mathematical models have applicable organs. Referring to fig. 3, TIC curve a 302 and TIC curve b 303 are fitted to the same set of raw data. However, it is clear that the blood perfusion parameters obtained by the TIC curve a 302 and the TIC curve b 303 are different. The existing scheme considers that the two curves are correctly fitted, and intuitively speaking, the TIC curve b 303 is closer to the trend of data distribution, so that doctors expect to obtain the TIC curve b 303 in a fitting mode. The blood perfusion parameters extracted from the TIC curves fitted by the different models are sensitive to the curves themselves, and in some embodiments, generating the time intensity curve for the sub-region based on the distribution of the raw scatter data may include: in step S501, a plurality of different perfusion models are determined, several designated mathematical models may be set according to the needs of the user, or all perfusion models in the pre-maintained database may be used for subsequent fitting of blood flow perfusion parameters of the same sub-region, and the specific selection may be set according to actual needs.
Next, in step S502, based on the distribution relationship of the original scatter data, a time intensity curve of the sub-region is generated based on each perfusion model. The specific manner of generating the time intensity curve of the sub-region may be a mathematical fitting manner, or may also be another manner, which is not limited herein. The limitation of a single perfusion model to an organ is effectively solved by fitting the same sub-region through a plurality of perfusion models, and the optimal perfusion model corresponding to each sub-region can be determined through the subsequent error judgment step (step S504), so that the blood perfusion parameter extraction method disclosed by the invention can achieve an ideal effect on any imaging part (organ).
Next, in step S503, an error of each perfusion model is determined based on the positional relationship between each raw scatter data and the generated time intensity curve. A particular way of determining the error may include calculating a distance deviation R2And the concrete can be determined according to actual needs.
Finally, in step S504, the time intensity curve of the subregion is determined on the basis of the perfusion model with the smallest error. For example, a TIC curve a 302 and a TIC curve b 303 in fig. 3, based on the minimum error determination method, it may be determined that a currently ideal TIC curve should be a TIC curve b 303, and then the TIC curve b 303 is used as a time intensity curve of the sub-region, and in the case of having more perfusion models, the same may be done, so as to solve the problem that the fitting of the TIC curve is easily contaminated by noise, and a TIC curve with the best fitting effect (e.g., the minimum error) may be obtained through the fitting of several perfusion models, and blood flow perfusion parameters extracted based on the TIC curve are more suitable for the expectation of the doctor, so that the doctor can conveniently study the focus of the patient.
Based on the foregoing embodiments, there are a variety of ways to determine the error of the scatter data from the fitted TIC curve, and in some embodiments, determining the error of each time intensity curve based on the time intensity data may include: determining an error of the corresponding perfusion model based on a least squares error between each raw scatter data and the time-intensity curve, wherein the error increases with increasing distance deviation. Continuing with the TIC curve a 302 and the TIC curve b 303 in fig. 3 as an example, the distance from each scatter point raw data to the TIC curve a 302 and the TIC curve b 303 may be calculated, and it may be determined that the error of the TIC curve a 302 (the distance between most of scatter points) is greater than the error of the TIC curve b 303, so it may be determined that the TIC curve b 303 is a TIC curve fitted by a preferred perfusion model, and based on the TIC curve b 303, blood perfusion parameters more matched with the actual condition may be extracted.
The blood flow perfusion parameter extraction method according to embodiments of the present disclosure may extract blood flow perfusion related parameters of a target region, and output the blood flow perfusion related parameters to a user by generating a blood flow perfusion parameter image of the target region, where generating the blood flow perfusion parameter image of the target region based on the blood flow perfusion related parameters of each sub-region may include:
And generating blood perfusion parameter sub-images based on the blood perfusion related parameters of the sub-areas at the corresponding positions of the sub-areas. For example, values of the blood perfusion-related parameter MTT are extracted in the current sub-region. The corresponding blood flow perfusion parameter sub-image may be generated according to the position of the MTT value of the sub-region corresponding to the sub-region, for example, if the MTT value of the sub-region is 100, then the MTT 100 may be identified at the corresponding position.
And generating a blood flow perfusion parameter image of the target area based on each blood flow perfusion parameter sub-image, and presenting the blood flow perfusion parameter image based on the target area. For example, a blood perfusion parameter image of the target region may be generated at a corresponding position according to the MTT value of each sub-region, and the generated blood perfusion parameter image includes the position and the blood perfusion parameter value of each sub-region, so as to more intuitively demonstrate the difference in distribution of the blood perfusion region displayed based on different characteristics of pathological or normal tissues for a doctor.
In some embodiments, generating a blood flow perfusion parameter image of the target region based on the blood flow perfusion-related parameters of the sub-regions may further comprise: and mapping the blood flow perfusion related parameters of the sub-areas into corresponding color values so as to realize that the sub-areas are mapped into blood flow perfusion parameter sub-images of the corresponding colors. For example, when the sub-regions are divided regularly, the color corresponding to the sub-region may be directly displayed in the sub-region, so that a color-assigned blood flow perfusion parameter sub-image of the target region may be obtained by combining the colors of the sub-regions. For irregular partition, such as triangulation, when generating a two-dimensional blood flow perfusion map, it may be determined which triangulation the sub-region (pixel) belongs to in conjunction with a rectangular coordinate system, and a blood flow perfusion parameter corresponding to the triangulation may be used as the blood flow perfusion parameter of the pixel. After obtaining the blood flow perfusion parameters of each small region, in the process of generating the two-dimensional blood flow perfusion map, smoothing may be performed on adjacent small regions, contrast enhancement may be performed on the whole image, and the like. Through the mode of mapping colors in the subareas, the readability of the blood flow perfusion parameter image is improved, and particularly, under the condition that the resolution of the subareas is a pixel point, the fine blood flow perfusion parameter image containing the mapping colors can be output, so that a doctor can better and intuitively understand the current pathological condition of a patient.
After obtaining the blood flow perfusion parameter sub-images, a blood flow perfusion parameter image of the target region may be generated based on the blood flow perfusion parameter sub-images. For example, in the case that the target regions are multiple and are not connected, blood perfusion parameter images of the respective target regions may be generated respectively, and then the blood perfusion parameter images may be presented on the basis of the ultrasound images, for example, the blood perfusion parameter images may be presented based on the target regions. For example, when a blood flow perfusion parameter image including colors needs to be generated, the calculated blood flow perfusion parameters may be mapped to a color image by using various color maps and superimposed on the B image, and when a plurality of target regions are included, the images may be correspondingly superimposed.
In addition to mapping as color images, in some embodiments, generating a blood perfusion parameter image based on the blood perfusion related parameters of the sub-regions may further comprise: and generating a blood flow perfusion parameter sub-image with parameter values based on the values of the blood flow perfusion related parameters of the sub-region at the position corresponding to the sub-region. For example, the parameter values of the sub-region may be directly filled into the sub-region, so as to generate a sub-image of the blood perfusion parameter with the parameter values. And generating a blood flow perfusion parameter image of the target area based on each blood flow perfusion parameter sub-image. Rendering the blood perfusion parameter image based on the target region. Similarly, in the case of including a plurality of target regions, the blood perfusion parameter sub-images with parameter values may be combined at corresponding positions of the corresponding regions, respectively, to generate blood perfusion parameter images of the target regions. Referring to fig. 2, the CEUS image is on the left side and the B image is on the right side in fig. 2, when the CEUS image and the B image are superimposed, the B image and the CEUS gray image may be first mapped into an RGB color image through a color mapping table, and then fused according to a preset rule (for example, threshold comparison, etc.), and a related edge processing algorithm may be further used in the process of image fusion to make the fused image natural and smooth.
The method effectively displays the extracted blood perfusion parameters in a two-dimensional image mode, and can obtain perfusion models with the best fitting effect of different sub-regions in a multi-model fitting mode to fit the sub-regions, so that the blood perfusion related parameters are estimated, and the obtained blood perfusion related parameters can reflect the blood perfusion condition of the sub-regions more truly. Meanwhile, a blood perfusion parameter image containing blood perfusion related parameters of the whole target area is output to a doctor, so that the doctor can effectively grasp the state of an illness of a patient, and the diagnosis efficiency of the doctor is improved.
An embodiment of the present disclosure provides an ultrasound contrast apparatus, as shown in fig. 5, including a processor 501 and a memory 502, the processor 501 and the memory 502 being connected by a communication bus, the processor 501 being configured to execute one or more computer program implementations stored in the memory 502: determining a target region in an ultrasound contrast image; dividing the target area into a plurality of sub-areas based on a preset dividing method; determining a plurality of original scatter data of the sub-region based on the ultrasonic contrast image, wherein the original scatter data describes the relation between time and the ultrasonic intensity of the scatter; generating a time intensity curve of the sub-region based on the distribution relation of the original scatter data; extracting blood flow perfusion related parameters of the sub-region based on the time intensity curve; and generating a blood flow perfusion parameter image of the target area based on the blood flow perfusion related parameters of the sub-areas. The processor 501 in this example may be a processing device including more than one general purpose processing device, such as a microprocessor, Central Processing Unit (CPU), Graphics Processing Unit (GPU), or the like. More specifically, the processor may be a Complex Instruction Set Computing (CISC) microprocessor, Reduced Instruction Set Computing (RISC) microprocessor, Very Long Instruction Word (VLIW) microprocessor, processor running other instruction sets, or processors running a combination of instruction sets. The processor may also be one or more special-purpose processing devices such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a system on a chip (SoC), or the like. The ultrasound contrast apparatus, as shown in fig. 5, may further include a plurality of I/O interfaces, where the I/O interfaces may be used to connect to external I/O devices, for example, the I/O interface 1 may be connected to the display 503, the external I/O devices may be a keyboard and a mouse, and the specific connected devices may be set according to actual needs, which is not listed here.
In some embodiments, the processor 501 may be further configured to: generating blood perfusion parameter sub-images based on the blood perfusion related parameters of the sub-areas at the corresponding positions of the sub-areas; and generating a blood flow perfusion parameter image of the target area based on each blood flow perfusion parameter sub-image.
The ultrasound contrast apparatus further comprises a display 503 configured to present the blood perfusion parameter image based on the target region.
In some embodiments, the area of each sub-region divided based on the preset dividing method decreases with increasing image change speed in the ultrasound contrast image, and increases with decreasing image change speed in the ultrasound contrast image.
In some embodiments, the processor may be further configured to: determining a plurality of different perfusion models; generating a time intensity curve of the sub-region based on the distribution relation of the original scatter data and the perfusion models respectively; determining the error of each perfusion model based on the position relation between each original scatter data and the generated time intensity curve; the time intensity curve for the sub-region is determined based on the perfusion model with the smallest error.
In some embodiments, the processor may be further configured to: determining an error of the corresponding perfusion model based on a distance deviation between each raw scatter data and the time-intensity curve, wherein the error increases as the distance deviation increases.
In some embodiments, the processor may be further configured to: mapping the blood flow perfusion related parameters of each sub-area to corresponding color values so as to realize that each sub-area is mapped to a blood flow perfusion parameter sub-image of the corresponding color; and generating a blood flow perfusion parameter image of the target area based on each blood flow perfusion parameter sub-image. The display is further configured to present the blood perfusion parameter image based on the target region.
In some embodiments, the processor may be further configured to: generating a blood flow perfusion parameter sub-image with parameter values based on the values of the blood flow perfusion related parameters of the sub-area at the position corresponding to the sub-area; and generating a blood flow perfusion parameter image of the target area based on each blood flow perfusion parameter sub-image. The display is further configured to present the blood perfusion parameter image based on the target region.
The disclosed embodiments also propose a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by at least one processor, implements the blood flow perfusion parameter extraction method of the foregoing embodiments. The computer-readable storage medium in this example may be a non-transitory computer-readable medium, such as read-only memory (ROM), random-access memory (RAM), phase-change random-access memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), electrically-erasable programmable read-only memory (EEPROM), other types of random-access memory (RAM), flash disks or other forms of flash memory, caches, registers, static memory, compact-disc read-only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, cartridges or other magnetic storage devices, or any other possible non-transitory medium that may be used to store information or instructions that may be accessed by a computer device, and so forth.
Moreover, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments based on the disclosure having equivalent elements, modifications, omissions, combinations (e.g., of various embodiments across), adaptations or alterations. The elements of the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more versions thereof) may be used in combination with each other. For example, other embodiments may be utilized by those of ordinary skill in the art upon reading the foregoing description. In addition, in the foregoing detailed description, various features may be grouped together to streamline the disclosure. This should not be interpreted as an intention that a disclosed feature not claimed is essential to any claim. Rather, the subject matter of the present disclosure may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above embodiments are only exemplary embodiments of the present disclosure, and are not intended to limit the present invention, the scope of which is defined by the claims. Various modifications and equivalents of the invention which are within the spirit and scope of the disclosure may occur to persons skilled in the art and are considered to be within the scope of the invention.
Claims (10)
1. A blood perfusion parameter extraction method is characterized by comprising the following steps:
determining a target region in an ultrasound contrast image;
dividing the target area into a plurality of sub-areas based on a preset dividing method;
determining a plurality of original scatter data of the sub-region based on the ultrasonic contrast image, wherein the original scatter data describes a relation between time and ultrasonic intensity of the scatter;
generating a time intensity curve of the sub-area based on the distribution relation of each original scattered point data;
extracting blood flow perfusion related parameters of the sub-region based on the time intensity curve;
and generating a blood flow perfusion parameter image of the target area based on the blood flow perfusion related parameters of the sub-areas.
2. The method according to claim 1, wherein the area of each sub-region divided by the predetermined division method decreases with increasing image change speed in the ultrasound contrast image and increases with decreasing image change speed in the ultrasound contrast image.
3. The method of claim 1, wherein generating the time-intensity curve for the sub-region based on the distribution of the raw scatter data comprises:
determining a plurality of different perfusion models;
generating a time intensity curve of the sub-region based on the distribution relation of the original scatter data and the perfusion models respectively;
r based on each original scatter data and generated time intensity curve2Determining an error for each perfusion model;
the time intensity curve for the sub-region is determined based on the perfusion model with the smallest error.
4. The method of claim 3, wherein determining an error for each time-intensity curve based on the time-intensity data comprises:
an error of the corresponding perfusion model is determined based on a distance deviation between each raw scatter data and the time-intensity curve, wherein in general the error increases with increasing distance deviation.
5. The method of claim 1, wherein generating a blood perfusion parameter image of the target region based on the blood perfusion related parameters of the sub-regions comprises:
Generating blood perfusion parameter sub-images based on the blood perfusion related parameters of the sub-areas at the corresponding positions of the sub-areas;
generating a blood perfusion parameter image of the target area based on each blood perfusion parameter sub-image;
rendering the blood perfusion parameter image based on the target region.
6. The method of claim 1 or 5, wherein generating the blood perfusion parameter image of the target region based on the blood perfusion related parameters of the sub-regions comprises:
mapping the blood perfusion related parameters of each sub-area into corresponding color values so as to realize that each sub-area is mapped into a blood perfusion parameter sub-image of the corresponding color;
generating a blood perfusion parameter image of the target area based on each blood perfusion parameter sub-image;
rendering the blood perfusion parameter image based on the target region.
7. The method of claim 1, wherein generating a blood perfusion parameter image based on the blood perfusion related parameters of the sub-regions comprises:
generating a blood flow perfusion parameter sub-image with parameter values based on the values of the blood flow perfusion related parameters of the sub-area at the position corresponding to the sub-area;
Generating a blood perfusion parameter image of the target area based on each blood perfusion parameter sub-image;
rendering the blood perfusion parameter image based on the target region.
8. An ultrasound contrast device comprising a processor and a memory, the processor configured to execute one or more computer programs stored in the memory to implement:
determining a target region in an ultrasound contrast image;
dividing the target area into a plurality of sub-areas based on a preset dividing method;
determining a plurality of original scatter data of the sub-region based on the ultrasonic contrast image, wherein the original scatter data describes the relation between time and the echo intensity of the scatter;
generating a time intensity curve of the sub-region based on the distribution relation of the original scatter data;
extracting blood flow perfusion related parameters of the sub-region based on the time intensity curve;
and generating a blood flow perfusion parameter image of the target area based on the blood flow perfusion related parameters of the sub-areas.
9. The ultrasound contrast device of claim 8, wherein the processor is further configured to:
generating blood perfusion parameter sub-images at the corresponding positions of the sub-areas based on the blood perfusion related parameters of the sub-areas;
Generating a blood perfusion parameter image of the target area based on each blood perfusion parameter sub-image;
the ultrasound contrast apparatus further comprises a display configured to present the blood flow perfusion parameter image based on the target region.
10. A non-transitory computer-readable storage medium, having stored thereon a computer program which, when executed by at least one processor, implements the method of blood perfusion parameter extraction according to any one of claims 1-7.
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