CN117679074A - Blood flow imaging method, system and storage medium - Google Patents
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Abstract
The blood flow imaging method obtains a plurality of frames of initial ultrasonic blood flow images and a plurality of target detection groups, performs filtering processing on each target detection group based on a filter group to obtain a corresponding preset number of data groups, obtains a blood flow average speed value corresponding to each target detection group based on the data groups obtained after filtering, and generates an ultrasonic blood flow image based on the blood flow average speed value. According to the method, the ultrasonic echo signals are processed by utilizing the filter bank formed by the filters with different cut-off frequencies, the target data set corresponding to the blood flow area is screened out to generate an ultrasonic blood flow image, high-frequency noise caused by hardware and low-frequency clutter caused by tissue motion are effectively reduced, the calculated blood flow velocity is enabled to be more approximate to a true value, and blood flow of a certain area can be pertinently enhanced, so that the reflux degree and the starting position can be conveniently judged.
Description
Technical Field
The present disclosure relates to the field of ultrasound imaging, and in particular, to a blood flow imaging method, system, and storage medium.
Background
The main method of the existing color ultrasonic imaging technology is to perform wall filtering on the acquired data, and perform cross-correlation calculation on the data after the wall filtering to obtain the blood flow velocity of the ultrasonic acquisition position, wherein the measured blood flow velocity value is the average value of the position.
The parameters of general wall filtering are global, the cut-off frequencies of wall filters used at all positions under a visual field are the same, if the wall filters with lower cut-off frequencies are used globally, low-frequency tissue motion signals of areas with more severe tissue motion cannot be removed, the measured speed is small, and the image quality is poor; if a wall filter with a higher cut-off frequency is used globally, the measured speed value is larger and cannot reflect the real blood flow value, and the blood flow is not full due to excessive inhibition, so that the judgment of doctors is affected.
Disclosure of Invention
The technical problem to be solved by the present disclosure is to overcome the defect that the actual blood flow value cannot be reflected due to the use of global wall filtering parameters in the prior art, and aims to provide a solution for ultrasonic blood flow imaging, which can effectively reduce high-frequency noise caused by hardware and low-frequency clutter caused by tissue motion, so that the calculated blood flow velocity is more approximate to the actual value, and in particular provides a blood flow imaging method, a blood flow imaging system and a storage medium.
The technical problems are solved by the following technical scheme:
according to a first aspect of the present disclosure, there is provided a blood flow imaging method, the method comprising:
acquiring a plurality of frames of initial ultrasonic blood flow images;
acquiring a plurality of target detection groups, wherein each target detection group comprises detection points at the same position in a plurality of frames of initial ultrasonic blood flow images, and different target detection groups correspond to different positions;
acquiring a preset number of data sets corresponding to each target detection group, wherein the data sets are obtained by filtering the target detection groups based on filter groups, the filter groups comprise filters with different cut-off frequencies, and the data sets comprise a pair of energy values and speed values;
obtaining a blood flow average speed value corresponding to each target detection group based on the data group;
an ultrasound blood flow image is generated based on the blood flow average velocity values corresponding to the plurality of target detection groups.
Preferably, the filter bank includes the predetermined number of band pass filters;
the pass band frequencies of the preset number of the band pass filters are sequentially increased, or the pass band frequencies of the preset number of the band pass filters are sequentially and continuously increased.
Preferably, the passband width of each of the bandpass filters is the same;
and/or the number of the groups of groups,
the passband width B epsilon [0, PRF/2] of the filter bank;
where B is the pass-band width of the filter bank and PRF is the ultrasonic pulse repetition frequency.
Preferably, the step of obtaining a preset number of data sets corresponding to each target detection set includes:
calculating any two adjacent ultrasonic blood flow data output by each target detection group through the same filter by using a cross-correlation algorithm to obtain the preset number of data groups corresponding to each target detection group;
the two detection points at the same position in the two adjacent frames of initial ultrasonic blood flow images are output by the same filter.
Preferably, the step of obtaining a blood flow average speed value corresponding to each target detection group based on the data sets includes:
screening the preset number of data sets to obtain target data sets corresponding to each target detection set;
and obtaining the average blood flow speed value corresponding to each target detection group based on the target data group.
Preferably, the step of screening the preset number of data sets to obtain target data sets corresponding to each target detection set includes:
taking the data set with the energy value larger than a first threshold value and the energy value smaller than a second threshold value as the target data set;
wherein the first threshold is less than the second threshold.
Preferably, before the step of screening the preset number of the data sets to obtain the target data set corresponding to each target detection set, the method further includes:
and according to the energy values in the data sets, sequentially arranging the data sets with the preset quantity according to the energy value sequence.
Preferably, the step of obtaining the average velocity value of the blood flow corresponding to each target detection group based on the target data group includes:
accumulating the energy values corresponding to each target data set to obtain accumulated energy values;
and calculating the average blood flow speed value corresponding to each target detection group based on each target data group and the accumulated energy value.
Preferably, before the step of generating an ultrasound blood flow image based on the blood flow average velocity values corresponding to the plurality of target detection groups, the method further comprises:
judging whether the average blood flow speed value is larger than a preset threshold value or not;
if yes, the first threshold value is increased, and the preset number of data sets are screened based on the increased first threshold value and the increased second threshold value, so that the target data set corresponding to each target detection set is obtained;
wherein the increased first threshold is less than the second threshold.
Preferably, the step of acquiring a plurality of frames of initial ultrasound blood flow images includes:
generating a plurality of frames of initial ultrasonic blood flow images based on ultrasonic echo signals fed back by a target object;
the ultrasonic echo signal comprises a plurality of initial ultrasonic echo signal segments with equal time intervals;
a number of frames of the initial ultrasound blood flow image is generated based on a number of the initial ultrasound echo signal segments.
According to a second aspect of the present disclosure, there is provided a blood flow imaging system, the system comprising a first acquisition module, a second acquisition module, a third acquisition module, a calculation module, a generation module:
the first acquisition module is used for acquiring a plurality of frames of initial ultrasonic blood flow images;
the second acquisition module is used for acquiring a plurality of target detection groups, each target detection group comprises a plurality of detection points at the same position in the initial ultrasonic blood flow image, and different target detection groups correspond to different positions;
the third acquisition module is configured to acquire a preset number of data sets corresponding to each target detection group, where the data sets are obtained by performing filtering processing on the target detection groups based on a filter set, the filter set includes filters with different cut-off frequencies and the preset number of filters, and the data sets include a pair of energy values and speed values;
the calculation module is used for obtaining a blood flow average speed value corresponding to each target detection group based on the data groups;
the generation module is used for generating an ultrasonic blood flow image based on the blood flow average speed values corresponding to the target detection groups. According to a third aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the blood flow imaging method of the first aspect of the present disclosure.
On the basis of conforming to the common knowledge in the art, the preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.
The positive progress effect of the present disclosure is: processing the initial ultrasonic blood flow image of a plurality of continuous frames by using a filter bank formed by a plurality of filters with different cut-off frequencies, obtaining a blood flow average speed value corresponding to each target detection group based on the data set obtained after filtering, generating an ultrasonic blood flow image based on the blood flow average speed value, and effectively reducing high-frequency noise brought by hardware and low-frequency clutter brought by tissue movement, so that the calculated blood flow velocity is more approximate to a true value, and truly reflecting the main flow velocity components of a blood flow region; the blood flow of a certain area is pertinently enhanced by improving the screening threshold value of the data set, so that the information such as the reflux degree and the reflux starting position can be conveniently judged.
Drawings
FIG. 1 is a flow chart of a blood flow imaging method in example 1;
fig. 2 is a schematic diagram of a frequency band rejection mechanism of the blood flow imaging method in embodiment 1;
FIG. 3 is a first block diagram of a blood flow imaging system in example 2;
fig. 4 is a second block diagram of the blood flow imaging system in embodiment 2.
Detailed Description
The present disclosure is further illustrated by way of examples below, but is not thereby limited to the scope of the examples.
The parameters of wall filtering adopted by the existing color ultrasonic imaging technology are global, cannot reflect the real blood flow value, and can influence the judgment of doctors due to insufficient blood flow caused by excessive inhibition.
In view of this, the present disclosure provides a blood flow imaging method, system and storage medium to solve the problem that the current color ultrasound imaging technology cannot reflect the real blood flow value due to the use of global wall filtering parameters.
Example 1
In one embodiment of the present disclosure, there is provided a blood flow imaging method, as shown in fig. 1, comprising:
s1, acquiring a plurality of frames of initial ultrasonic blood flow images;
s2, acquiring a plurality of target detection groups, wherein each target detection group comprises detection points at the same position in a plurality of frames of initial ultrasonic blood flow images, and different target detection groups correspond to different positions;
s3, acquiring a preset number of data sets corresponding to each target detection group, wherein the data sets are obtained by filtering the target detection groups based on filter groups, the filter groups comprise filters with different preset numbers and cut-off frequencies, and the data sets comprise a pair of energy values and speed values;
s4, obtaining a blood flow average speed value corresponding to each target detection group based on the data group;
s5, generating an ultrasonic blood flow image based on the blood flow average speed values corresponding to the target detection groups.
Specifically, in step S1, the plurality of frames of initial ultrasound blood flow images may be continuous multi-frame IQ (ultrasound blood flow) images, or may be multi-frame IQ images obtained by transmitting ultrasound waves at preset time intervals.
In step S2, after the X-frame IQ image is acquired in step S1, the X detection points at the same position in the X-frame IQ image are grouped into a target detection group, and different positions correspond to different target detection groups, for example, the 10-frame IQ image is acquired in step S1, where the first detection point in each frame IQ image forms a target detection group, and the target detection group includes 10 detection points, that is, the detection points at the same position in the X-frame IQ image are grouped into a target detection group. Note that, the detection point in the present embodiment refers to a pixel point in the IQ image, and in order to improve the accuracy of blood flow detection, each pixel point in the IQ image may be detected.
In step S3, N data sets obtained by filtering each target detection set with a filter set comprising N filters with different cut-off frequencies are obtained, each data set includes a pair of energy values and speed values, the energy values and speed values in the data set correspond to the blood flow energy values and blood flow speed values of the detection points at the position, that is, each target detection set can obtain corresponding N data sets after filtering with the filter. Of course, in step S3, a filter group composed of N filters having different cut-off frequencies may be provided, and each target detection group may be input into the filter group, thereby obtaining N data groups corresponding to each target detection group. The specific data processing procedure is not specifically limited in this embodiment.
In step S4, the average blood flow velocity at the position corresponding to the target detection group may be obtained according to the velocity value and the energy value in the data set, for example, the target detection group is formed by the first detection point of each frame of IQ image, and the average blood flow velocity value corresponding to the target detection group is the average blood flow velocity value corresponding to the first detection point in each frame of IQ image.
In step S5, after the average velocity value of the blood flow at each position in the IQ image is obtained, an ultrasound blood flow image that is more close to the true value may be generated by color coding, for example, after the average velocity value of the blood flow at each detection point in the IQ image is obtained, color coding may be performed according to the velocity value of the blood flow to generate the ultrasound blood flow image.
According to the method, the initial ultrasonic blood flow image of a plurality of continuous frames is processed by utilizing the filter bank formed by the filters with different cut-off frequencies, the blood flow average speed value corresponding to each target detection group is obtained based on the data bank obtained after filtering, the ultrasonic blood flow image is generated based on the blood flow average speed value, high-frequency noise brought by hardware and low-frequency clutter brought by tissue movement can be effectively reduced, the calculated blood flow velocity is more approximate to a true value, and the main flow velocity components of a blood flow region are truly reflected.
In a specific embodiment, the filter bank comprises a preset number of bandpass filters;
the pass band frequencies of the preset number of the band pass filters are sequentially increased, or the pass band frequencies of the preset number of the band pass filters are sequentially and continuously increased;
wherein the pass band width of each band-pass filter is the same;
the passband width B epsilon [0, PRF/2] of the filter bank;
where B is the passband width of the filter bank and PRF is the ultrasonic pulse repetition frequency.
In this embodiment, a preset number of band pass filters are selected to form a filter bank, the pass band width of the pass band frequency of each band pass filter is the same and continuously increases in sequence, and the pass band width B e [0, PRF/2] of the whole filter bank is the pass band width of the filter bank, and PRF is the ultrasonic pulse repetition frequency, for example, the filter bank includes N band pass filters, the pass band frequency of each band pass filter is [0, PRF/2N ], [ PRF/2N,2PRF/2N ], [2PRF/2N,3PRF/2N ], [3PRF/2N,4PRF/2N ] … [ (N-1) PRF/2N ], nprf/2N ], and by taking the upper limit cut-off frequency of the previous band pass filter as the lower limit cut-off frequency of the next band pass filter, the filter bank can be ensured to include band pass filters of the whole band.
It should be noted that the number of bandpass filters in the filter bank may be set and adjusted according to the calculation requirement, that is, the engineer may set the passband width of the bandpass filters in a self-defined manner.
Of course, the filter bank may include only a partial band constituted by a plurality of band pass filters, and the present embodiment is not particularly limited herein.
In a specific implementation manner, step S3 includes:
calculating any two adjacent ultrasonic blood flow data output by each target detection group through the same filter by using a cross-correlation algorithm to obtain a preset number of data groups corresponding to each target detection group;
wherein, any two adjacent ultrasonic blood flow data are two detection points at the same position in two adjacent frames of initial ultrasonic blood flow images and are output by the same filter.
Specifically, a group of target detection groups is input into a filter group comprising N band-pass filters for filtering operation, each band-pass filter outputs a group of filtered ultrasonic blood flow data, correlation of any adjacent two filtered ultrasonic blood flow data is calculated through a cross-correlation algorithm, and any adjacent two filtered ultrasonic blood flow data refer to that two detection points at the same position in two adjacent frames of IQ images are output through the same filter.
For example, a target detection group including 10 detection points is input to the i-th band-pass filter,the 10 detection points correspond to the same position in the 10 frames of IQ image data, the ith band-pass filter outputs the filtered ultrasonic blood flow data corresponding to each detection point, namely, outputs the 10 filtered ultrasonic blood flow data, carries out cross-correlation calculation on any two adjacent data in the output data, for example, carries out cross-correlation calculation on the filtered ultrasonic blood flow data corresponding to the detection point in the 1 st frame of IQ image data and the filtered ultrasonic blood flow data corresponding to the detection point in the 2 nd frame of IQ image data, carries out cross-correlation calculation on the filtered ultrasonic blood flow data corresponding to the detection point in the 2 nd frame of IQ image data and the filtered ultrasonic blood flow data corresponding to the detection point in the 3 rd frame of IQ image data, … carries out cross-correlation calculation on the filtered ultrasonic blood flow data corresponding to the detection point in the N-1 st frame of IQ image data to obtain N-1 cross-correlation calculated results, and carries out accumulated average value on the N-1 cross-correlation calculated results to obtain the energy value of the target detection group corresponding to the ith band-pass filter (Powerer) i ) And velocity value (Vel i ). And so on, N data sets corresponding to each target detection set can be obtained.
In a specific implementation manner, step S4 includes:
screening the preset number of data sets to obtain target data sets corresponding to each target detection set;
and obtaining the average blood flow speed value corresponding to each target detection group based on the target data group.
Specifically, because the ratio of the ultrasonic blood flow data of different components in each flow velocity interval is different, the calculated energy value is also different, so that the component corresponding to each data set can be determined through the energy value in each data set, for example, whether the data set corresponds to a tissue region or a blood flow region can be determined according to the energy value in each data set, thus, the target data set corresponding to the blood flow region can be screened out, the data set corresponding to the non-blood flow region can be screened out, and the average blood flow velocity corresponding to the target detection set can be calculated based on the target data set, so that the accuracy of the blood flow velocity is improved.
In a specific implementation manner, step S4 includes:
taking a data group with the energy value larger than a first threshold value and the energy value smaller than a second threshold value as a target data group;
wherein the first threshold is less than the second threshold.
In order to improve the screening efficiency, in step S4, a preset number of data sets may be sequentially arranged according to the energy values in the data sets.
In the present embodiment, since a band-pass filter is used, the calculated velocity value is related to the pass band region in which the filter is used, and the higher the frequency of the pass band region is, the larger the calculated velocity value is. However, because the ultrasonic blood flow data of different components have different duty ratios in each flow velocity interval, the calculated energy values are different, the energy data obtained by filtering by using a filter with a lower passband frequency often has higher tissue area energy, the energy data obtained by filtering by using a filter with a moderate passband frequency is concentrated in a middle speed blood flow part, the energy data obtained by filtering by using a filter with a higher passband frequency is concentrated in a high speed or aliasing area.
Therefore, in this embodiment, the obtained N data sets corresponding to each target detection set are sequentially arranged according to the energy values, for example, from large to small according to the energy values, or from small to large according to the energy values, and the frequency band rejection mechanism is set to reject the tissue part frequency band and the noise frequency band.
As shown in fig. 2, after the X-frame IQ image is acquired in step S1, each target detection group (Data) composed of the same position detection point in the X-frame IQ image is respectively input to a filter (WF) having a plurality of cut-off frequencies different from each other 1 、WF 2 、WF 3 …WF N-1 、WF N ) In the filter bank formed, a data set (Vel 1 -Power 1 、Vel 2 -Power 2 、Vel 3 -Power 3 …Vel N-1 -Power N-1 、Vel N -Power N ) N data sets corresponding to each target detection set are sequentially arranged (1, 2,3 … N-1, N) from large to small according to energy values, detection points with energy higher than T2 are removed (the highest energy part in the frequency band is generally a direct current part and a moving tissue part, and the tissue part with more intense movement is preferentially removed) through a preset second threshold T2, detection points with energy lower than T1 are removed through a preset first threshold T1 (the speed of a normal blood flow area is generally concentrated in a frequency band area, such as medium-speed blood flow, the frequency band with lower energy is generally in a high frequency band, the lower energy frequency band of an aliasing area is often in a medium frequency band, the noise frequency band of a non-blood flow area is removed mainly through the effect of T1 threshold removal data, so that the estimated speed is more accurate), and then the target data set corresponding to the target detection set which does not comprise the noise frequency band and the tissue part of the non-blood flow area is obtained, and the average speed Vel-Powere value is obtained through calculation according to the energy values and the speed values in the target data sets.
It should be noted that, the engineer may adjust T1 and T2 through the threshold setting entry, so that the calculated blood flow value may be more similar to the actual value.
In a specific implementation manner, step S4 includes:
accumulating the energy value corresponding to each target data group to obtain an accumulated energy value;
and calculating to obtain the average blood flow speed value corresponding to each target detection group based on each target data group and the accumulated energy value.
Specifically, the energy value and the speed value corresponding to the reserved target data set are processed in the next step:
the energy values of the target data sets are accumulated to obtain a final energy image, for example, K target data sets are reserved, and the energy values in each data set are accumulated to obtain accumulated energy values through the following formula:
wherein Power is the accumulated energy value, K is the number of target data sets, power i I=1, 2, … K for the energy value corresponding to the i-th target data set.
And then carrying out weighted summation on the energy value and the speed value of the target data set to obtain the final average blood flow speed through the following formula:
wherein Power is the accumulated energy value, vel is the average velocity value of blood flow, K is the number of target data sets, vel i Power for the speed value corresponding to the ith target data set i I=1, 2, … K for the energy value corresponding to the i-th target data set.
After the average blood flow velocity corresponding to each target detection group is obtained through calculation, color coding can be carried out according to the average blood flow velocity value corresponding to each target detection group, and then an ultrasonic blood flow image of the target object is generated.
In a specific embodiment, before step S5, the method further includes:
judging whether the average blood flow speed value is larger than a preset threshold value or not;
if yes, a first threshold value is increased, and a preset number of data sets are screened based on the increased first threshold value and the increased second threshold value, so that target data sets corresponding to each target detection set are obtained;
wherein the increased first threshold is less than the second threshold.
In some preset situations, the user may wish to particularly enhance the blood flow of a certain feature, for example, in a heart preset situation, a doctor often wants regurgitation to be brighter (the flow velocity is higher), so that the regurgitation area is better judged, and the degree and the starting position are better judged; the average speed of a certain point is generally displayed by color Doppler ultrasound, the average effect brought by signals of middle and low frequency bands is reduced by the real reflux speed value, so that engineers can add new judgment in a frequency band rejection mechanism, for example, if the average speed value in reserved data is greater than a certain value, a first threshold value T1 is automatically increased, a preset number of data sets corresponding to each target detection group are screened based on the increased first threshold value T1 and a second threshold value T2, and a region with higher flow velocity is enhanced by rejecting more low-speed components, so that the high-speed reflux region on the generated ultrasonic blood flow image can generate an enhancement effect. Similarly, engineers can set enhancement controls for certain presets, such as regurgitation enhancement, coronary enhancement, etc. at heart presets, targeted through energy sequencing and frequency band rejection mechanisms.
In the embodiment, the blood flow in a certain area is purposefully enhanced by increasing the screening threshold of the data set, so that the information such as the reflux degree and the reflux starting position can be conveniently judged.
In a specific embodiment, step S1 includes:
generating a plurality of frames of initial ultrasonic blood flow images based on ultrasonic echo signals fed back by a target object;
the ultrasonic echo signal comprises a plurality of initial ultrasonic echo signal segments with equal time intervals;
a number of frames of initial ultrasound blood flow images are generated based on a number of initial ultrasound echo signal segments.
Specifically, an ultrasonic probe can be used to transmit ultrasonic waves to a specific part of a human body, such as a part of the heart, the neck, the abdomen, limbs and the like, which is required to be subjected to blood flow ultrasonic examination, and the ultrasonic probe is used to receive a feedback ultrasonic echo signal, and the ultrasonic echo signal is subjected to operations such as front end amplification, analog-to-digital conversion, demodulation, beam synthesis and the like to obtain an X-frame IQ image.
The ultrasonic echo signal can be split into a plurality of initial ultrasonic echo signal segments with equal time intervals, and the time intervals between the initial ultrasonic echo signal segments can be customized by an engineer, for example, the time intervals between the initial ultrasonic echo signal segments are set through an ensembe (fixed packet length). The more ensable, the higher the frequency resolution of the data, and the set filter bank number can be properly adjusted. Thus, a multi-frame initial ultrasound blood flow image is generated based on a plurality of equally spaced initial ultrasound echo signal segments.
According to the method, the initial ultrasonic blood flow image of a plurality of continuous frames is processed by utilizing the filter bank formed by the filters with different cut-off frequencies, the blood flow average speed value corresponding to each target detection group is obtained based on the data bank obtained after filtering, the ultrasonic blood flow image is generated based on the blood flow average speed value, high-frequency noise caused by hardware and low-frequency clutter caused by tissue movement can be effectively reduced, the calculated blood flow velocity is more approximate to a true value, and the main flow velocity components of a blood flow region are truly reflected; the blood flow of a certain area is pertinently enhanced by improving the screening threshold value of the data set, so that the information such as the reflux degree and the reflux starting position can be conveniently judged.
Example 2
In a specific embodiment of the present disclosure, a blood flow imaging system is provided for implementing the blood flow imaging method in embodiment 1, as shown in fig. 3, and includes a first acquisition module 100, a second acquisition module 200, a third acquisition module 300, a calculation module 400, and a generation module 500:
the first acquisition module 100 is configured to acquire a plurality of frames of initial ultrasound blood flow images;
the second obtaining module 200 is configured to obtain a plurality of target detection groups, where each target detection group includes detection points at a same position in a plurality of frames of initial ultrasound blood flow images, and different target detection groups correspond to different positions;
the third obtaining module 300 is configured to obtain a preset number of data sets corresponding to each target detection group, where the data sets are obtained by performing filtering processing on the target detection groups based on a filter set, the filter set includes a preset number of filters with different cut-off frequencies, and the data sets include a pair of energy values and a speed value;
the calculation module 400 is configured to obtain, based on the data sets, a blood flow average speed value corresponding to each target detection set;
the generation module 500 is configured to generate an ultrasound blood flow image based on the blood flow average velocity values corresponding to the plurality of target detection groups.
In a specific embodiment, the filter bank comprises a preset number of bandpass filters;
the pass band frequencies of the preset number of the band pass filters are sequentially increased, or the pass band frequencies of the preset number of the band pass filters are sequentially and continuously increased;
wherein the pass band width of each band-pass filter is the same;
the passband width B epsilon [0, PRF/2] of the filter bank;
where B is the passband width of the filter bank and PRF is the ultrasonic pulse repetition frequency.
In a specific implementation manner, as shown in fig. 4, the system further includes a filter bank setting module 600, where the filter bank setting module 600 is configured to set the number of bandpass filters in the filter bank.
In a specific implementation manner, the third obtaining module 300 is further configured to calculate, by using a cross-correlation algorithm, any two adjacent ultrasound blood flow data output by each target detection group via the same filter, to obtain a preset number of data sets corresponding to each target detection group;
wherein, any two adjacent ultrasonic blood flow data are two detection points at the same position in two adjacent frames of initial ultrasonic blood flow images and are output by the same filter.
In a specific implementation manner, the calculation module 400 is further configured to screen a preset number of data sets to obtain a target data set corresponding to each target detection set;
and obtaining a blood flow average speed value corresponding to each target detection group based on the target data group.
In a specific implementation manner, the calculation module 400 is further configured to take, as the target data set, a data set having an energy value greater than the first threshold and an energy value less than the second threshold;
wherein the first threshold is less than the second threshold.
In a specific implementation manner, the calculation module 400 is further configured to sequentially arrange the preset number of data sets according to the energy values in the data sets.
In a specific implementation manner, the calculation module 400 is further configured to perform accumulation processing on the energy value corresponding to each target data set to obtain an accumulated energy value;
and calculating to obtain the average blood flow speed value corresponding to each target detection group based on each target data group and the accumulated energy value.
In a specific implementation manner, as shown in fig. 4, the system further includes a threshold adjustment module 700, where the threshold adjustment module 700 is configured to determine whether the average velocity value of the blood flow is greater than a preset threshold;
if yes, a first threshold value is increased, and a preset number of data sets are screened based on the increased first threshold value and the increased second threshold value, so that target data sets corresponding to each target detection set are obtained;
wherein the increased first threshold is less than the second threshold.
In a specific implementation manner, a plurality of frames of initial ultrasonic blood flow images are generated based on ultrasonic echo signals fed back by a target object;
the ultrasonic echo signal comprises a plurality of initial ultrasonic echo signal segments with equal time intervals;
a number of frames of initial ultrasound blood flow images are generated based on a number of initial ultrasound echo signal segments.
According to the method, the initial ultrasonic blood flow image of a plurality of continuous frames is processed by utilizing the filter bank formed by the filters with different cut-off frequencies, the blood flow average speed value corresponding to each target detection group is obtained based on the data bank obtained after filtering, the ultrasonic blood flow image is generated based on the blood flow average speed value, high-frequency noise caused by hardware and low-frequency clutter caused by tissue movement can be effectively reduced, the calculated blood flow velocity is more approximate to a true value, and the main flow velocity components of a blood flow region are truly reflected; the blood flow of a certain area is pertinently enhanced by improving the screening threshold value of the data set, so that the information such as the reflux degree and the reflux starting position can be conveniently judged.
Example 3
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method in the above-described embodiment.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation manner, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of the method as in the above-described embodiments, when the program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partially on the user device, as a stand-alone software package, partially on the user device, partially on a remote device or entirely on the remote device.
While specific embodiments of the present disclosure have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the disclosure is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the disclosure, but such changes and modifications fall within the scope of the disclosure.
Claims (12)
1. A method of blood flow imaging, the method comprising:
acquiring a plurality of frames of initial ultrasonic blood flow images;
acquiring a plurality of target detection groups, wherein each target detection group comprises detection points at the same position in a plurality of frames of initial ultrasonic blood flow images, and different target detection groups correspond to different positions;
acquiring a preset number of data sets corresponding to each target detection group, wherein the data sets are obtained by filtering the target detection groups based on filter groups, the filter groups comprise filters with different cut-off frequencies, and the data sets comprise a pair of energy values and speed values;
obtaining a blood flow average speed value corresponding to each target detection group based on the data group;
an ultrasound blood flow image is generated based on the blood flow average velocity values corresponding to the plurality of target detection groups.
2. The method of claim 1, wherein the filter bank comprises the predetermined number of bandpass filters;
the pass band frequencies of the preset number of the band pass filters are sequentially increased, or the pass band frequencies of the preset number of the band pass filters are sequentially and continuously increased.
3. The method of claim 2, wherein the pass band width of each of the band pass filters is the same;
and/or the number of the groups of groups,
the passband width B epsilon [0, PRF/2] of the filter bank;
where B is the pass-band width of the filter bank and PRF is the ultrasonic pulse repetition frequency.
4. The method of claim 1, wherein the step of obtaining a predetermined number of data sets for each of the target detection groups comprises:
calculating any two adjacent ultrasonic blood flow data output by each target detection group through the same filter by using a cross-correlation algorithm to obtain the preset number of data groups corresponding to each target detection group;
the two detection points at the same position in the two adjacent frames of initial ultrasonic blood flow images are output by the same filter.
5. The method of claim 1, wherein the step of obtaining a corresponding mean velocity value of the blood flow for each of the target test sets based on the data sets comprises:
screening the preset number of data sets to obtain target data sets corresponding to each target detection set;
and obtaining the average blood flow speed value corresponding to each target detection group based on the target data group.
6. The method of claim 5, wherein the step of screening the predetermined number of the data sets to obtain a target data set corresponding to each of the target detection groups comprises:
taking the data set with the energy value larger than a first threshold value and the energy value smaller than a second threshold value as the target data set;
wherein the first threshold is less than the second threshold.
7. The method of claim 5, wherein prior to the step of screening the predetermined number of the data sets to obtain a target data set for each of the target detection sets, the method further comprises:
and according to the energy values in the data sets, sequentially arranging the data sets with the preset quantity according to the energy value sequence.
8. The method of claim 5, wherein the step of obtaining the mean velocity value of the blood flow for each of the target test sets based on the target data sets comprises:
accumulating the energy values corresponding to each target data set to obtain accumulated energy values;
and calculating the average blood flow speed value corresponding to each target detection group based on each target data group and the accumulated energy value.
9. The method of claim 6, wherein prior to the step of generating an ultrasound blood flow image based on the blood flow average velocity values corresponding to the plurality of target detection sets, the method further comprises:
judging whether the average blood flow speed value is larger than a preset threshold value or not;
if yes, the first threshold value is increased, and the preset number of data sets are screened based on the increased first threshold value and the increased second threshold value, so that the target data set corresponding to each target detection set is obtained;
wherein the increased first threshold is less than the second threshold.
10. The method according to any one of claims 1 to 9, wherein the step of acquiring a number of frames of initial ultrasound blood flow images comprises:
generating a plurality of frames of initial ultrasonic blood flow images based on ultrasonic echo signals fed back by a target object;
the ultrasonic echo signal comprises a plurality of initial ultrasonic echo signal segments with equal time intervals;
a number of frames of the initial ultrasound blood flow image is generated based on a number of the initial ultrasound echo signal segments.
11. A blood flow imaging system, wherein the system comprises a first acquisition module, a second acquisition module, a third acquisition module, a calculation module, and a generation module:
the first acquisition module is used for acquiring a plurality of frames of initial ultrasonic blood flow images;
the second acquisition module is used for acquiring a plurality of target detection groups, each target detection group comprises a plurality of detection points at the same position in the initial ultrasonic blood flow image, and different target detection groups correspond to different positions;
the third acquisition module is configured to acquire a preset number of data sets corresponding to each target detection group, where the data sets are obtained by performing filtering processing on the target detection groups based on a filter set, the filter set includes filters with different cut-off frequencies and the preset number of filters, and the data sets include a pair of energy values and speed values;
the calculation module is used for obtaining a blood flow average speed value corresponding to each target detection group based on the data groups;
the generation module is used for generating an ultrasonic blood flow image based on the blood flow average speed values corresponding to the target detection groups.
12. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the blood flow imaging method of any of claims 1 to 10.
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