CN114519707B - Blood flow perfusion parameter extraction method and equipment - Google Patents
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Abstract
The present disclosure relates to a blood flow perfusion parameter extraction method and apparatus, the blood flow perfusion parameter extraction method of the present disclosure includes: determining a target area 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 subarea based on the ultrasound contrast image, wherein the original scatter data describe the relation between time and the ultrasound intensity of the scatter; generating a time intensity curve of the subarea based on the distribution relation of the original scattered point data; extracting blood flow perfusion related parameters of the subarea based on the time intensity curve; and generating a blood flow perfusion parameter image of the target region based on the blood flow perfusion related parameters of each sub-region. By utilizing the method disclosed by the invention, the change condition of blood perfusion can be reflected in a large range, and more accurate blood perfusion change details are expressed through the blood perfusion parameter image of the target area.
Description
Technical Field
The present disclosure relates to medical ultrasound technology, and more particularly, to a blood perfusion parameter extraction method and apparatus.
Background
Ultrasound contrast imaging generally extracts a Time intensity profile (Time-INTENSITY CURVE, TIC) over a relatively small region for a selected region of interest (ROI). Parameters describing the curve are related to blood perfusion (blood flow) and blood volume (blood volume). Through blood flow perfusion related information, extra dimensional information can be provided for focus or tumor detection and diagnosis, and the accuracy of diagnosis is improved.
Parameters that may be extracted based on the time intensity curve generally include blood flow perfusion related parameters such as area under the curve (AUC), peak enhancement (PEAK ENHANCEMENT, PE), time To Peak (TP), rise Time (RT), fall Time (FT), and the like. The above parameters generally reflect directly or indirectly the pathological condition of the lesion or tumor.
Current ultrasound imaging does not exhibit blood perfusion changes throughout the imaging region, as the TIC curve is extracted over a relatively small area.
Disclosure of Invention
The method, the device and the equipment for extracting the blood flow perfusion parameters are used for extracting the blood flow perfusion parameters in the target area, and blood flow perfusion parameter images of the target area can be generated based on blood flow perfusion related parameters of all the subareas, so that the change condition of blood flow perfusion can be reflected in a large range, and more accurate blood flow perfusion change details can be expressed through the blood flow perfusion parameter images of the target area.
In a first aspect, embodiments of the present disclosure provide a method of extracting a perfusion parameter, comprising: determining a target area 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 subarea based on the ultrasound contrast image, wherein the original scatter data describe the relation between time and the ultrasound intensity of the scatter; generating a time intensity curve of the subarea based on the distribution relation of the original scattered point data; extracting blood flow perfusion related parameters of the subarea based on the time intensity curve; and generating a blood flow perfusion parameter image of the target region based on the blood flow perfusion related parameters of each sub-region.
In a second aspect, embodiments of the present disclosure provide an ultrasound contrast device comprising a processor and a memory, the processor configured to execute one or more computer program implementations stored in the memory: determining a target area 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 subarea based on the ultrasound contrast image, wherein the original scatter data describe the relation between time and the ultrasound intensity of the scatter; generating a time intensity curve of the subarea based on the distribution relation of the original scattered point data; extracting blood flow perfusion related parameters of the subarea based on the time intensity curve; and generating a blood flow perfusion parameter image of the target region based on the blood flow perfusion related parameters of each sub-region.
In a third aspect, embodiments of the present disclosure provide a non-transitory computer readable storage medium having a computer program stored thereon, which when executed by at least one processor implements the blood perfusion parameter extraction method according to embodiments of the present disclosure.
The method of the present disclosure divides a target area into a plurality of sub-areas; generating a time intensity curve of the sub-region based on a plurality of original scatter data of the ultrasound contrast image; the method can embody the change condition of blood perfusion in a large range, and more accurate blood perfusion change details are expressed through the blood perfusion parameter images of the target areas.
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In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. The same reference numerals with letter suffixes or different letter suffixes may represent different instances of similar components. The accompanying drawings illustrate various embodiments by way of example in general and not by way of limitation, and together with the description and claims serve to explain the disclosed embodiments. Such embodiments are illustrative and not intended to be exhaustive or exclusive of the present apparatus or method.
FIG. 1a shows a basic flow diagram of a method of extracting perfusion parameters according to an embodiment of the present disclosure;
FIG. 1b shows a specific flow diagram of a method of extracting perfusion parameters according to an embodiment of the present disclosure;
FIG. 2 illustrates a schematic view of ultrasound imaging region selection target region in accordance with an embodiment of the present disclosure;
FIG. 3 illustrates an original scatter data distribution state and a fitted TIC curve schematic according to an embodiment of the present disclosure;
FIG. 4 illustrates one example of dividing a target region into a number of sub-regions according to an embodiment of the present disclosure;
fig. 5 illustrates a basic structural schematic of an ultrasound contrast device according to an embodiment of the present disclosure.
Detailed Description
In order to better understand the technical solutions of the present disclosure, the following detailed description of the present disclosure is provided with reference to the accompanying drawings and the specific embodiments. Embodiments of the present disclosure will be described in further detail below with reference to the drawings and specific embodiments, but not by way of limitation of the present disclosure.
The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises" and the like means that elements preceding the word encompass the elements recited after the word, and not exclude the possibility of also encompassing other elements.
The embodiment of the present disclosure provides a blood perfusion parameter extraction method, as shown in fig. 1a, which starts with step S101, determining a target region in an ultrasound contrast image. The target area may be an area with a larger imaging range, and the setting of the area size may be accomplished by software operation according to the needs of a doctor, for example, a designated area may be selected as the target area, for example, the target area 201 is selected in the imaging area of an ultrasound Contrast (CEUS) image shown in fig. 2.
Then in step S102, the target area is divided into a plurality of sub-areas based on a preset dividing method. For example, the target area 201 in fig. 2 may be divided into a plurality of sub-areas, the specific size of the divided area may be set according to the actual requirement, in the limit case, one pixel point may be used as one sub-area, or the target area 201 may be divided into a plurality of sub-areas according to the size of the sub-area set by the user. The multiple target areas can be selected according to the requirements of the user, the multiple target areas can not be communicated, the multiple target areas are divided into multiple sub-areas, and the specific dividing method is not limited one by one.
Parameter extraction may then be performed for each sub-region, and in step S103 several raw scatter data of the sub-region describing a time-to-ultrasound intensity relationship with the scatter are determined based on the ultrasound contrast image. During ultrasound contrast imaging, the ultrasound intensity of a certain region with the time of filling with contrast agent can generate scattered echo enhancement, and by utilizing the characteristics, the ultrasound contrast device can acquire a plurality of original data 301 shown by dots in fig. 3, and the blood flow perfusion parameters can be extracted based on the original data 301. In step S104, a time intensity curve of the sub-region is generated based on the distribution relation of the original scatter data. A trend fit may be made to obtain a time intensity curve, for example based on the distribution positional relationship of the raw data 301 as in fig. 3. The blood perfusion-related parameters of the sub-region may thereby be extracted based on the time intensity curve in step S105. Specific types of blood perfusion-related parameters may include, for example, area under the curve (AUC), peak enhancement (PEAK ENHANCEMENT, PE), and blood volume-related parameters, including average instantaneous time (MEAN TRANSIT TIME, MTT), time To Peak (TTP), rise Time (RT), fall Time (FT), etc., without limitation herein. After extracting the blood perfusion related parameters of the current sub-region as shown in fig. 1b, it may be further determined whether the traversal of all sub-regions is completed, and if not, a TIC curve is generated for the next sub-region and the blood perfusion related parameters thereof are extracted.
After the blood flow perfusion-related parameters of each sub-region are extracted, a blood flow perfusion parameter image of the target region is generated based on the blood flow perfusion-related parameters of each sub-region in step S106.
The blood flow perfusion parameter extraction method can extract the blood flow perfusion related parameters of the target area, and output the blood flow perfusion related parameters to a user by generating the blood flow perfusion parameter image of the target area, and simultaneously, for the same blood flow perfusion parameters such as TTP, the TTP parameter distribution condition of the whole target area can be output to a doctor more intuitively based on the TTP parameters of all the subareas, and the distribution condition is not limited in a very small local range. That is, the method of the present disclosure may automatically divide the region of interest of 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) may more fully describe the blood perfusion related information of the whole ROI region, and simultaneously display more details of the blood perfusion condition.
The method disclosed by the invention needs to divide the target area into a plurality of sub-areas, can be completed by adopting various methods, for example, the division can be performed according to the size of the sub-area set by a user, for example, the size of the sub-area is set to be 1 pixel by the user, then all the sub-areas can be divided into 1 pixel, and the target area can be divided into a plurality of regular small rectangular areas, so that the division operation is simple. In some embodiments, the area of the divided sub-regions may be not fixed, and may decrease with increasing speed of image change in the ultrasound contrast image and increase with decreasing speed of image change in the ultrasound contrast image based on the area of each sub-region divided by the preset dividing method. As shown in fig. 4, the target region may be adaptively divided 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 target region is reflected. The dividing method can be used for adaptively dividing a region (a sub-region with larger curvature) with a relatively sharp change of the graph in a target region; on the other hand, in a relatively gentle region of interest, a thicker division (sub-region with a larger triangle equivalent diameter) may be employed. As in fig. 4, the target area 401 may be divided into sub-areas 401a, 401b, 401c of different sizes. By adopting the method for dividing the subareas, the calculation amount of the subsequent traversal calculation can be further reduced, and meanwhile, the blood flow perfusion parameter image generated subsequently can be focused on the area with more intense graphic change, so that a doctor can read the focus conveniently.
Common mathematical perfusion models generally include Lognormal, GAMMA VARIATE, local density random walk, FIRST PASSAGE TIME and Lagged-normal models, notably each model having its applicable organs, for example Lognormal is generally considered to be applicable to breast and heart TIC fits, and Lagged-normal models are adapted to liver perfusion curve fits. However, a mathematical perfusion model cannot be used to extract blood flow perfusion parameters and achieve optimal performance for different organs, and there is no uniform standard how to evaluate the model fitting results, although different mathematical models have the organs to which they are applied. Referring to fig. 3, TIC curve a 302 and TIC curve b 303 are fitted to the same set of raw data. It is apparent that the blood perfusion parameters available for TIC curve a 302 and TIC curve b 303, respectively, are not the same. The existing scheme considers that both curves are fitted correctly, and intuitively, the TIC curve b 303 is more approximate to the trend of data distribution, so that a doctor is more expected to fit to obtain the TIC curve b 303. The perfusion parameters extracted from TIC curves fitted by different models are sensitive to the curve itself, and in some embodiments, generating a time intensity curve for the sub-region based on the distribution relationship of the raw scatter data may include: in step S501, a plurality of different perfusion models are determined, a plurality of designated mathematical models may be set according to the needs of the user, or all the perfusion models in the database maintained in advance may be used for subsequent fitting of blood flow perfusion parameters of the same sub-region, and specific selection may be set according to actual needs.
Next, in step S502, a time intensity curve of the sub-region is generated based on the distribution relation of the original scatter data and the perfusion model. The specific manner of generating the time intensity curve of the sub-region may be a mathematical fitting manner, or may be other manners, which are not limited herein. The limitation of a single perfusion model to organs is effectively solved by fitting the same subregion through a plurality of perfusion models, and the optimal perfusion model corresponding to each subregion can be determined through the subsequent error judging step (step S504), so that the blood flow 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 original scatter data and the generated time intensity curve. The specific error determination method may include calculating the distance deviation R 2, which may be specifically determined according to the actual requirement.
Finally, in step S504, a time intensity profile of the sub-region is determined on the basis of the perfusion model with the smallest error. For example, in fig. 3, TIC curve a 302 and TIC curve b 303, based on the determination method with the minimum error, it may be determined that the currently ideal TIC curve should be TIC curve b 303, and if TIC curve b 303 is taken as the time intensity curve of the sub-region, and the like, under the condition of having more perfusion models, the problem that the fitting of TIC curves is easily polluted by noise can be solved, and the TIC curve with the best fitting effect (for example, the minimum error) can be obtained through the fitting of several perfusion models, and the blood flow perfusion parameters extracted based on the TIC curve are more fit to the desire of the doctor, so as to facilitate the doctor to study the focus of the patient.
Based on the foregoing embodiments, there are a number of ways to determine the error of the scatter data with the fitted TIC curve, and in some embodiments, determining the error of each time intensity curve based on the time intensity data may include: an error of the corresponding perfusion model is determined based on a least squares error between each raw scatter data and the time intensity curve, wherein the error increases as the distance deviation increases. Taking TIC curve a 302 and TIC curve b 303 in fig. 3 as an example, the distances from the original data of each scattered point to TIC curve a 302 and TIC curve b 303 can be calculated, and it can be determined that the error of TIC curve a 302 (the distance between most of the scattered points) is greater than the error of TIC curve b 303, so that it can be determined that TIC curve b 303 is the TIC curve fitted by the preferred perfusion model, and the blood perfusion parameters more matched with the actual situation can be extracted based on TIC curve b 303.
The blood flow perfusion parameter extraction method according to the embodiments of the present disclosure may extract a blood flow perfusion related parameter of a target area, and output to a user by generating a blood flow perfusion parameter image of the target area, the generating the blood flow perfusion parameter image of the target area based on the blood flow perfusion related parameter of each sub-area may include:
and generating a blood flow perfusion parameter sub-image at a position corresponding to each sub-region based on the blood flow perfusion related parameter of the sub-region. For example, the value of the blood perfusion-related parameter MTT is extracted at the current sub-region. The corresponding blood perfusion parameter sub-image may be generated according to the MTT value of the sub-region at the corresponding position of the sub-region, e.g. the MTT value of the sub-region is 100, 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, the blood flow perfusion parameter image of the target region can be generated at the corresponding position according to the MTT value of each subarea, and the generated blood flow perfusion parameter image comprises the position of each subarea and the blood flow perfusion parameter value, so that the difference of the blood flow perfusion region distribution displayed based on different characteristics of pathological or normal tissues is more intuitively displayed for doctors.
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 each subarea into corresponding color values so as to realize mapping each subarea into a blood flow perfusion parameter sub-image of a corresponding color. For example, in the case that the subareas are regularly divided, the colors corresponding to the subareas can be directly displayed in the subareas, so that the colors of the subareas are combined, and the blood flow perfusion parameter sub-image with the colors of the target area can be obtained. For irregular division, for example, triangular division, when a two-dimensional blood flow perfusion map is generated, a rectangular coordinate system is combined to determine which triangular division a sub-region (pixel) belongs to, and a blood flow perfusion parameter corresponding to the triangular division is 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 processing can be performed on the adjacent small regions, and contrast enhancement processing and the like can be performed on the whole image. The readability of the blood flow perfusion parameter image is improved in a mode of mapping colors on the subareas, and particularly, under the condition that the resolution of the subareas is a pixel point, the accurate blood flow perfusion parameter image containing the mapped colors can be output, so that a doctor can better and intuitively understand the current pathological condition of a patient.
Upon obtaining each of the blood flow perfusion parameter sub-images, a blood flow perfusion parameter image of the target region may be generated based on each of the blood flow perfusion parameter sub-images. For example, in the case that the target area is plural and not connected, the blood flow perfusion parameter images of the respective target areas may be generated separately, and then the blood flow perfusion parameter images may be presented on the basis of the ultrasound image, for example, the blood flow perfusion parameter images may be presented on the basis of the target area. For example, when it is necessary to generate a blood perfusion parameter image including a color, the calculated blood perfusion parameter may be mapped to a color image by using various color maps and superimposed on the B image, and when a plurality of target areas are included, the calculated blood perfusion parameter image may be superimposed.
In addition to mapping to a color image, in some embodiments, generating a blood flow perfusion parameter image based on the blood flow perfusion-related parameters of each sub-region may further include: 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 subarea at the positions corresponding to the subareas. For example, the parameter values of the sub-region may be directly filled into the sub-region, thereby generating a sub-image of the 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. The blood flow perfusion parameter image is presented 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 positions corresponding to the respective regions, respectively, to generate a blood perfusion parameter image of the target region. Referring to fig. 2, a CEUS image is shown on the left side and a B image is shown 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 can be mapped into an RGB color image through a color mapping table, and then fused according to a preset rule (such as threshold comparison, etc.), and the fused image can be natural and smooth by matching with a related edge processing algorithm in the process of image fusion.
According to the method disclosed by the invention, the extracted blood flow perfusion parameters are effectively displayed in a two-dimensional image mode, and in a multi-model fitting mode, the perfusion model with the best fitting effect of different subareas can be obtained to fit the subareas, so that the estimation of the blood flow perfusion related parameters is carried out, and the obtained blood flow perfusion related parameters can more truly reflect the blood flow perfusion condition of the subareas. Meanwhile, a blood flow perfusion parameter image containing blood flow perfusion related parameters of the whole target area is output to a doctor, so that the doctor's grasp of the patient's condition is effectively improved, and the doctor's diagnosis efficiency is improved.
Embodiments of the present disclosure provide an ultrasound contrast device, as shown in fig. 5, comprising a processor 501 and a memory 502, the processor 501 being connected to the memory 502 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 area 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 subarea based on the ultrasound contrast image, wherein the original scatter data describe the relation between time and the ultrasound intensity of the scatter; generating a time intensity curve of the subarea based on the distribution relation of the original scattered point data; extracting blood flow perfusion related parameters of the subarea based on the time intensity curve; and generating a blood flow perfusion parameter image of the target region based on the blood flow perfusion related parameters of each sub-region. The processor 501 in this example may be a processing device including one or more general purpose processing devices, such as a microprocessor, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or the like. More specifically, the processor may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, a processor running other instruction sets, or a processor 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 device, 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 an external I/O device, for example, the I/O interface 1 may be connected to the display 503, the external I/O device may be a keyboard and a mouse, etc., and the specific connected devices may be set according to actual needs, which is not specifically described herein.
In some embodiments, the processor 501 may be further configured to: generating a blood flow perfusion parameter sub-image based on the blood flow perfusion related parameters of each sub-region at the position corresponding to the sub-region; and generating a blood flow perfusion parameter image of the target area based on each blood flow perfusion parameter sub-image.
The ultrasound contrast device further comprises a display 503 configured to present the blood flow perfusion parameter image based on the target region.
In some embodiments, the area of each sub-region divided by the preset division method decreases with increasing speed of image change in the ultrasound contrast image and increases with decreasing speed of image change in the ultrasound contrast image.
In some implementations, the processor may be further configured to: determining a number of different perfusion models; based on the distribution relation of each original scattered point data, respectively based on each perfusion model, generating a time intensity curve of the subarea; determining the error of each perfusion model based on the position relation between each original scattered point data and the generated time intensity curve; a time intensity profile for the sub-region is determined based on the perfusion model with the least error.
In some implementations, the processor may be further configured to: an error of the corresponding perfusion model is determined based on a distance deviation between each raw scatter data and a time intensity curve, wherein the error increases with increasing distance deviation.
In some implementations, the processor may be further configured to: mapping the blood flow perfusion related parameters of each subarea into corresponding color values so as to realize mapping each subarea into a blood flow perfusion parameter sub-image with 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 flow perfusion parameter image based on the target region.
In some implementations, 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 subarea at the positions corresponding to the subareas; 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 flow perfusion parameter image based on the target region.
The embodiments of the present disclosure also provide a non-volatile computer readable storage medium, where a computer program is stored, where the computer program is executed by at least one processor to implement the blood perfusion parameter extraction method described in 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 memory disk or other forms of flash memory, cache, registers, static memory, compact disk read-only memory (CD-ROM), digital Versatile Disk (DVD) or other optical storage, magnetic cassettes or other magnetic storage devices, or any other possible non-transitory medium which can be used to store information or instructions that can be accessed by a computer device.
Furthermore, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of the various embodiments across schemes), adaptations or alterations based on the present disclosure. The elements in the claims are to be construed broadly based on the language employed in the claims and are not limited to examples described in the present specification or during the practice 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 aspects thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the above detailed description, various features may be grouped together to streamline the disclosure. This is not to be interpreted as an intention that the disclosed features not being claimed are essential to any claim. Rather, the disclosed subject matter may include less than all of the 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 one another 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 equivalent arrangements of parts may be made by those skilled in the art, which modifications and equivalents are intended to be within the spirit and scope of the present disclosure.
Claims (9)
1. A method for extracting perfusion parameters, comprising:
Determining a target area 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 subarea based on the ultrasound contrast image, wherein the original scatter data describe the relation between time and the ultrasound intensity of the scatter;
generating a time intensity curve of the subarea based on the distribution relation of the original scattered point data;
Extracting blood flow perfusion related parameters of the subarea based on the time intensity curve;
Generating a blood flow perfusion parameter image of the target region based on the blood flow perfusion related parameters of each sub-region;
Generating a time intensity curve for the sub-region based on the distribution relationship of the original scatter data comprises:
Determining a number of different perfusion models;
based on the distribution relation of each original scattered point data, respectively based on each perfusion model, generating a time intensity curve of the subarea;
determining an error of each perfusion model based on each original scatter data and the R 2 of the generated time intensity curve;
A time intensity profile for the sub-region is determined based on the perfusion model with the least error.
2. The method of claim 1, wherein the areas of the sub-regions divided by the predetermined dividing method decrease with an increase in the rate of change of the image in the ultrasound contrast image and increase with a decrease in the rate of change of the image in the ultrasound contrast image.
3. The method of claim 1, wherein the error increases as a distance deviation between each raw scatter data and the time intensity curve increases.
4. The blood perfusion parameter extraction method of claim 1, wherein generating a blood perfusion parameter image of the target region based on blood perfusion-related parameters of each sub-region comprises:
Generating a blood flow perfusion parameter sub-image based on the blood flow perfusion related parameters of each sub-region at the position corresponding to the sub-region;
generating a blood flow perfusion parameter image of the target region based on each blood flow perfusion parameter sub-image;
the blood flow perfusion parameter image is presented based on the target region.
5. The blood perfusion parameter extraction method of claim 1 or 4, wherein generating a blood perfusion parameter image of the target region based on blood perfusion-related parameters of each sub-region comprises:
mapping the blood flow perfusion related parameters of each subarea into corresponding color values so as to realize mapping each subarea into a blood flow perfusion parameter sub-image with corresponding color;
generating a blood flow perfusion parameter image of the target area based on each blood flow perfusion parameter sub-image;
the blood flow perfusion parameter image is presented based on the target region.
6. The blood perfusion parameter extraction method of claim 1, wherein generating a blood perfusion parameter image of the target region based on blood perfusion-related parameters of each sub-region 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 subarea at the positions corresponding to the subareas;
generating a blood flow perfusion parameter image of the target region based on each blood flow perfusion parameter sub-image;
the blood flow perfusion parameter image is presented based on the target region.
7. An ultrasound contrast device comprising a processor and a memory, the processor configured to execute one or more computer program implementations stored in the memory:
Determining a target area 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 scattered point data of the subarea based on the ultrasonic contrast image, wherein the original scattered point data describe the relation between time and echo intensity of the scattered point;
generating a time intensity curve of the subarea based on the distribution relation of the original scattered point data;
Extracting blood flow perfusion related parameters of the subarea based on the time intensity curve;
Generating a blood flow perfusion parameter image of the target region based on the blood flow perfusion related parameters of each sub-region;
Generating a time intensity curve for the sub-region based on the distribution relationship of the original scatter data comprises:
Determining a number of different perfusion models;
based on the distribution relation of each original scattered point data, respectively based on each perfusion model, generating a time intensity curve of the subarea;
determining an error of each perfusion model based on each original scatter data and the R 2 of the generated time intensity curve;
A time intensity profile for the sub-region is determined based on the perfusion model with the least error.
8. The ultrasound contrast device of claim 7, wherein the processor is further configured to:
Generating a blood flow perfusion parameter sub-image based on the blood flow perfusion related parameters of each sub-region at the position corresponding to the sub-region;
generating a blood flow perfusion parameter image of the target region based on each blood flow perfusion parameter sub-image;
The ultrasound contrast device further comprises a display configured to present the blood flow perfusion parameter image based on the target region.
9. A non-transitory computer readable storage medium, having stored thereon a computer program which, when executed by at least one processor, implements the blood perfusion parameter extraction method according to any one of claims 1 to 6.
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