Blood flow imaging method, system, and storage medium

Abstract

The present disclosure provides a blood flow imaging method, system and storage medium, which obtains a plurality of initial ultrasonic blood flow images and a plurality of target detection groups, filters each target detection group based on a filter set 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 filtered data groups, and generates an ultrasonic blood flow image based on the blood flow average speed value. The present disclosure utilizes a filter set composed of multiple filters with different cutoff frequencies to process ultrasonic echo signals, screens out target data groups corresponding to blood flow regions to generate an ultrasonic blood flow image, effectively reduces high-frequency noise caused by hardware and low-frequency clutter caused by tissue motion, makes the calculated blood flow velocity more close to the true value, and can enhance the blood flow of a certain region in a targeted manner, so as to facilitate the judgment of the degree and starting position of regurgitation.

Description

Technical Field

[0001] This disclosure relates to the field of ultrasound imaging, and more particularly to a blood flow imaging method, system, and storage medium. Background Technology

[0002] The main method of existing color ultrasound imaging technology is to perform wall filtering on the acquired data, and then perform cross-correlation calculation on the filtered data to obtain the blood flow velocity at the ultrasound acquisition location. The measured blood flow velocity value is the mean value at that location.

[0003] Generally, the parameters of wall filtering are global, and the cutoff frequency of the wall filter used at each position in the field of view is the same. If a wall filter with a lower cutoff frequency is used globally, the low-frequency tissue motion signal in some areas with more intense tissue movement cannot be removed, resulting in a lower measured velocity and poor image quality. On the other hand, if a wall filter with a higher cutoff frequency is used globally, the measured velocity value will be higher, failing to reflect the true blood flow value. It may also lead to insufficient blood flow due to excessive suppression, affecting the doctor's judgment. Summary of the Invention

[0004] The technical problem to be solved by this disclosure is to overcome the defect of the existing technology that uses global wall filtering parameters, which cannot reflect the true blood flow value. The purpose is to provide an ultrasound blood flow imaging scheme that can effectively reduce high-frequency noise from hardware and low-frequency noise from tissue movement, so that the calculated blood flow velocity is closer to the true value. Specifically, a blood flow imaging method, system and storage medium are provided.

[0005] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0006] According to a first aspect of this disclosure, a blood flow imaging method is provided, the method comprising:

[0007] Acquire several frames of initial ultrasound blood flow images;

[0008] Multiple target detection groups are acquired, each target detection group includes several detection points at the same location in the initial ultrasound blood flow images, and different target detection groups correspond to different locations;

[0009] A preset number of data sets are obtained for each target detection group, wherein the data sets are obtained by filtering the target detection groups based on a filter group, the filter group includes the preset number of filters with different cutoff frequencies, and the data sets include a pair of energy values ​​and velocity values.

[0010] Based on the data set, the average blood flow velocity value corresponding to each target detection group is obtained;

[0011] An ultrasound blood flow image is generated based on the average blood flow velocity values ​​corresponding to multiple target detection groups.

[0012] Preferably, the filter bank includes the preset number of bandpass filters;

[0013] The passband frequencies of the preset number of bandpass filters increase sequentially, or the passband frequencies of the preset number of bandpass filters increase continuously in sequence.

[0014] Preferably, each of the bandpass filters has the same passband width;

[0015] And / or,

[0016] The passband width B of the filter bank is [0, PRF / 2];

[0017] Where B is the passband width of the filter bank, and PRF is the ultrasonic pulse repetition frequency.

[0018] Preferably, the step of obtaining a preset number of data groups corresponding to each of the target detection groups includes:

[0019] The cross-correlation algorithm is used to calculate any two adjacent ultrasound blood flow data output by the same filter for each target detection group to obtain the preset number of data groups corresponding to each target detection group;

[0020] Wherein, any two adjacent ultrasound blood flow data are two detection points at the same location in two adjacent frames of the initial ultrasound blood flow image, output through the same filter.

[0021] Preferably, the step of obtaining the average blood flow velocity value corresponding to each target detection group based on the data group includes:

[0022] The preset number of data groups are filtered to obtain the target data group corresponding to each target detection group;

[0023] Based on the target data set, the average blood flow velocity value corresponding to each target detection set is obtained.

[0024] Preferably, the step of filtering the preset number of data groups to obtain the target data group corresponding to each target detection group includes:

[0025] The data group whose energy value is greater than a first threshold and whose energy value is less than a second threshold is selected as the target data group;

[0026] Wherein, the first threshold is less than the second threshold.

[0027] Preferably, before the step of filtering the preset number of data groups to obtain the target data group corresponding to each target detection group, the method further includes:

[0028] Based on the energy values ​​in the data groups, the preset number of data groups are arranged sequentially according to the order of the energy values.

[0029] Preferably, the step of obtaining the average blood flow velocity value corresponding to each target detection group based on the target data group includes:

[0030] The energy values ​​corresponding to each target data group are accumulated to obtain the accumulated energy value;

[0031] Based on each target data group and the accumulated energy value, the average blood flow velocity value corresponding to each target detection group is calculated.

[0032] Preferably, before the step of generating an ultrasound blood flow image based on the average blood flow velocity values ​​corresponding to the plurality of target detection groups, the method further includes:

[0033] Determine whether the average blood flow velocity value is greater than a preset threshold;

[0034] If so, the first threshold is increased, and the preset number of data groups are filtered based on the increased first threshold and the second threshold to obtain the target data group corresponding to each target detection group;

[0035] Wherein, the increased first threshold is smaller than the second threshold.

[0036] Preferably, the step of acquiring several frames of initial ultrasound blood flow images includes:

[0037] The initial ultrasound blood flow images in several frames are generated based on the ultrasound echo signals fed back by the target object;

[0038] The ultrasonic echo signal includes several initial ultrasonic echo signal segments with equal time intervals;

[0039] The initial ultrasound blood flow images of several frames are generated based on several initial ultrasound echo signal segments.

[0040] According to a second aspect of this disclosure, a blood flow imaging system is provided, the system comprising a first acquisition module, a second acquisition module, a third acquisition module, a calculation module, and a generation module:

[0041] The first acquisition module is used to acquire several frames of initial ultrasound blood flow images;

[0042] The second acquisition module is used to acquire multiple target detection groups, each target detection group including several detection points at the same location in the initial ultrasound blood flow images, and different target detection groups correspond to different locations;

[0043] The third acquisition module is used to acquire a preset number of data groups corresponding to each target detection group. The data groups are obtained by filtering the target detection groups based on a filter group. The filter group includes the preset number of filters with different cutoff frequencies. The data groups include a pair of energy values ​​and velocity values.

[0044] The calculation module is used to obtain the average blood flow velocity value corresponding to each target detection group based on the data group;

[0045] The generation module is used to generate an ultrasound blood flow image based on the average blood flow velocity values ​​corresponding to multiple target detection groups. According to a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the blood flow imaging method described in the first aspect of this disclosure.

[0046] Based on common knowledge in the field, the preferred conditions described can be combined arbitrarily to obtain the preferred embodiments of this disclosure.

[0047] The positive and progressive effects of this disclosure are as follows: A filter bank consisting of multiple filters with different cutoff frequencies is used to process multiple consecutive frames of initial ultrasound blood flow images. Based on the filtered data sets, the average blood flow velocity value corresponding to each target detection group is obtained. Ultrasound blood flow images are generated based on the average blood flow velocity value. This effectively reduces high-frequency noise from hardware and low-frequency noise from tissue movement, making the calculated blood flow velocity closer to the true value and truly reflecting the main velocity components of the blood flow region. By increasing the filtering threshold of the data sets, blood flow in a specific region can be enhanced in a targeted manner, thus facilitating the determination of the degree and starting location of reflux. Attached Figure Description

[0048] Figure 1 This is a flowchart of the blood flow imaging method in Example 1;

[0049] Figure 2 This is a schematic diagram of the frequency band elimination mechanism of the blood flow imaging method in Example 1;

[0050] Figure 3 This is a schematic diagram of the first module of the blood flow imaging system in Example 2;

[0051] Figure 4 This is a schematic diagram of the second module of the blood flow imaging system in Example 2. Detailed Implementation

[0052] The present disclosure is further illustrated below by way of embodiments, but is not intended to limit the scope of the embodiments.

[0053] The wall filtering parameters used in current color ultrasound imaging technology are global and cannot reflect the true blood flow value. In addition, excessive suppression may lead to insufficient blood flow, affecting the doctor's judgment.

[0054] In view of this, the present disclosure provides a blood flow imaging method, system and storage medium to solve the problem that existing color ultrasound imaging technology uses global wall filtering parameters, which leads to the inability to reflect the true blood flow value.

[0055] Example 1

[0056] In one specific embodiment of this disclosure, a blood flow imaging method is provided, such as... Figure 1 As shown, the method includes:

[0057] S1. Acquire several frames of initial ultrasound blood flow images;

[0058] S2. Obtain multiple target detection groups. Each target detection group includes detection points at the same location in several frames of initial ultrasound blood flow images. Different target detection groups correspond to different locations.

[0059] S3. Obtain a preset number of data sets corresponding to each target detection group. The data sets are obtained by filtering the target detection groups based on a filter group. The filter group includes a preset number of filters with different cutoff frequencies. The data sets include a pair of energy values ​​and velocity values.

[0060] S4. Based on the data set, obtain the average blood flow velocity value corresponding to each target detection group;

[0061] S5. Generate ultrasound blood flow images based on the average blood flow velocity values ​​corresponding to multiple target detection groups.

[0062] Specifically, in step S1, the initial ultrasound blood flow images can be continuous multi-frame IQ (ultrasound blood flow) images, or multi-frame IQ images obtained by emitting ultrasound at preset time intervals.

[0063] In step S2, after obtaining X frames of IQ images via step S1, X detection points at the same location in the X frames of IQ images are grouped into a target detection group. Different locations correspond to different target detection groups. For example, if 10 frames of IQ images are obtained via step S1, the first detection point in each frame of IQ images constitutes a target detection group, which includes 10 detection points. That is, detection points at the same location in the X frames of IQ images are grouped into a target detection group. It should be noted that the detection points in this embodiment refer to pixels in the IQ images. To improve the accuracy of blood flow detection, every pixel in the IQ images can be detected.

[0064] In step S3, each target detection group is processed by a filter bank consisting of N filters with different cutoff frequencies to obtain N data groups. Each data group includes a pair of energy and velocity values, which correspond to the blood flow energy and velocity values ​​at the detection point. In other words, each target detection group yields N data groups after filtering. Alternatively, in step S3, a filter bank consisting of N filters with different cutoff frequencies can be set, and each target detection group can be input into the filter bank to obtain N data groups corresponding to each target detection group. The specific data processing procedure is not specifically limited in this embodiment.

[0065] In step S4, the average blood flow velocity at the corresponding position of the target detection group can be obtained based on the velocity and energy values ​​in the data group. For example, if the target detection group consists of the first detection point of each frame of IQ image, then 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.

[0066] In step S5, after obtaining the average blood flow velocity value at each location in the IQ image, an ultrasound blood flow image that is closer to the true value can be generated through color coding. For example, after obtaining the average blood flow velocity value at each detection point in the IQ image, color coding can be performed based on the blood flow velocity value to generate an ultrasound blood flow image.

[0067] This embodiment uses a filter bank composed of multiple filters with different cutoff frequencies to process multiple consecutive frames of initial ultrasound blood flow images. Based on the filtered data, the average blood flow velocity value corresponding to each target detection group is obtained. Based on the average blood flow velocity value, an ultrasound blood flow image is generated. This can effectively reduce high-frequency noise from hardware and low-frequency noise from tissue movement, making the calculated blood flow velocity closer to the true value and truly reflecting the main velocity components of the blood flow region.

[0068] In one feasible implementation, the filter bank includes a predetermined number of bandpass filters;

[0069] The passband frequencies of a preset number of bandpass filters increase sequentially, or the passband frequencies of a preset number of bandpass filters increase continuously.

[0070] In this case, each bandpass filter has the same passband width;

[0071] The passband width of the filter bank is B∈[0,PRF / 2];

[0072] Where B is the passband width of the filter bank and PRF is the ultrasonic pulse repetition frequency.

[0073] In this embodiment, a preset number of bandpass filters are selected to form a filter bank. The passband width of each bandpass filter is the same and increases sequentially. The passband width of the entire filter bank is B∈[0,PRF / 2], where B is the passband width of the filter bank and PRF is the ultrasonic pulse repetition frequency. For example, the filter bank includes N bandpass filters, and the passband frequencies of each bandpass filter are [0,PRF / 2N], [PRF / 2N,2PRF / 2N], [2PRF / 2N,3PRF / 2N], [3PRF / 2N,4PRF / 2N]....[(N-1)PRF / 2N,NPRF / 2N]. By using the upper cutoff frequency of the previous bandpass filter as the lower cutoff frequency of the next bandpass filter, it can be ensured that the filter bank contains bandpass filters of the entire frequency band.

[0074] It should be noted that the number of bandpass filters in the filter bank can be set and adjusted according to the calculation requirements. In other words, engineers can customize the passband width of the bandpass filters.

[0075] Of course, the filter bank may also consist of only a portion of the frequency band composed of multiple bandpass filters; this implementation does not impose any specific limitations here.

[0076] In one feasible implementation, step S3 includes:

[0077] The cross-correlation algorithm is used to calculate any two adjacent ultrasound blood flow data output by the same filter for each target detection group to obtain a preset number of data sets for each target detection group.

[0078] Among them, any two adjacent ultrasound blood flow data are two detection points at the same location in two adjacent initial ultrasound blood flow images, output through the same filter.

[0079] Specifically, a set of target detection groups is input into a filter bank containing N bandpass filters for filtering. Each bandpass filter outputs a set of filtered ultrasound blood flow data. The correlation between any two adjacent filtered ultrasound blood flow data is calculated using a cross-correlation algorithm. Any two adjacent filtered ultrasound blood flow data refer to two detection points at the same location in two adjacent IQ frames that are output by the same filter.

[0080] For example, a target detection group comprising 10 detection points is input into the i-th bandpass filter. These 10 detection points correspond to the same location in 10 frames of IQ image data. The i-th bandpass filter outputs filtered ultrasound blood flow data corresponding to each detection point; that is, it outputs 10 filtered ultrasound blood flow data. Any two adjacent data points in the output are cross-correlated. For example, the filtered ultrasound blood flow data corresponding to a detection point in the first frame of IQ image data and the filtered ultrasound blood flow data corresponding to a detection point in the second frame of IQ image data are cross-correlated. The filtered ultrasound blood flow data corresponding to the detection points in the second frame of IQ image data and the filtered ultrasound blood flow data corresponding to the detection points in the third frame of IQ image data are cross-correlated. This process is repeated for the next step. The filtered ultrasound blood flow data corresponding to the detection points in the (N-1)th frame of IQ image data and the filtered ultrasound blood flow data corresponding to the detection points in the Nth frame of IQ image data are then cross-correlated, resulting in N-1 cross-correlation results. The average of these N-1 cross-correlation results is then calculated to obtain the power value of the i-th bandpass filter corresponding to the target detection group. i ) and speed value (Vel i By doing so, N data sets can be obtained for each target detection group.

[0081] In one feasible implementation, step S4 includes:

[0082] The preset number of data groups are filtered to obtain the target data group corresponding to each target detection group;

[0083] Based on the target data set, the average blood flow velocity value corresponding to each target detection set is obtained.

[0084] Specifically, since the proportion of different components of ultrasound blood flow data varies in different flow velocity ranges, the calculated energy values ​​are also different. Therefore, the component corresponding to each data group can be determined by the energy value in each data group. For example, it can be determined whether each data group corresponds to a tissue area or a blood flow area based on the energy value in each data group. Thus, the target data group corresponding to the blood flow area can be screened out, while the data group corresponding to the non-blood flow area can be screened out. Then, the average blood flow velocity corresponding to the target detection group can be calculated based on the target data group to improve the accuracy of blood flow velocity.

[0085] In one feasible implementation, step S4 includes:

[0086] The data group with an energy value greater than the first threshold and an energy value less than the second threshold is selected as the target data group.

[0087] The first threshold is less than the second threshold.

[0088] To improve screening efficiency, in step S4, a preset number of data groups can be arranged sequentially according to their energy values.

[0089] In this embodiment, since a bandpass filter is used, the calculated velocity value is related to the passband region of the filter used; the higher the frequency of the passband region, the larger the calculated velocity value. However, because the proportions of different components of ultrasound blood flow data in each velocity range are different, the calculated energy values ​​are not the same. Energy data obtained by using a filter with a lower passband frequency often shows higher energy in tissue areas, energy data obtained by using a filter with a moderate passband frequency shows that the high-energy areas are concentrated in the medium-velocity blood flow portion, and energy data obtained by using a filter with a higher passband frequency shows that the high-energy areas are concentrated in the high-velocity or aliased regions.

[0090] Therefore, this embodiment sorts the N data groups corresponding to each target detection group according to their energy values ​​in order, for example, by sorting them from largest to smallest energy value, or by sorting them from smallest to largest energy value, and by setting a frequency band elimination mechanism to eliminate the frequency bands of tissue and noise.

[0091] like Figure 2 As shown, after obtaining X frames of IQ images via step S1, each target detection group (Data) consisting of detection points at the same location in the X frames of IQ images is input into several filters (WF1, WF2, WF3…WF…) with different cutoff frequencies. N-1 WF NFrom the filter bank composed of (Vel1-Power1, Vel2-Power2, Vel3-Power3…Vel), the data group corresponding to each target detection group is obtained. N-1 -Power N-1 Vel N -Power N The N data groups corresponding to each target detection group are arranged in descending order of energy value (1, 2, 3...N-1, N). Detection points with energy higher than T2 are removed using a preset second threshold T2 (the highest energy part in the frequency band is generally the DC part and the moving tissue part; generally, the more vigorously moving tissue part will be removed first). Detection points with energy lower than T1 are removed using a preset first threshold T1 (the velocity in normal blood flow areas is generally concentrated in one frequency band; for example, the lower energy frequency band of medium-speed blood flow is generally in the high frequency band, while the lower energy frequency band of aliasing areas is often in the mid-frequency band. The purpose of using the T1 threshold to remove data is mainly to remove the noise frequency band in non-blood flow areas to make the assessed velocity more accurate). This results in the target data group corresponding to the target detection group that does not include the noise frequency band in non-blood flow areas and tissue parts. The average blood flow velocity value Vel-Power is then calculated based on the energy and velocity values ​​in the target data group.

[0092] It should be noted that engineers can adjust T1 and T2 through the threshold setting entry to make the calculated blood flow value closer to the actual value.

[0093] In one feasible implementation, step S4 includes:

[0094] The energy values ​​corresponding to each target data group are accumulated to obtain the accumulated energy value.

[0095] Based on each target data set and the accumulated energy value, the average blood flow velocity value corresponding to each target detection set is calculated.

[0096] Specifically, the energy and velocity values ​​corresponding to the retained target data set will be processed further:

[0097] The energy values ​​of the target data groups are accumulated to obtain the final energy image. For example, if there are K target data groups, the energy values ​​in each data group are accumulated using the following formula to obtain the accumulated energy value:

[0098]

[0099] Where Power is the accumulated energy value, K is the number of target data sets, and Power i Let be the energy value corresponding to the i-th target data group, i = 1, 2, ..., K.

[0100] Next, the energy and velocity values ​​of the target data set are weighted and summed using the following formula to obtain the final average blood flow velocity:

[0101]

[0102] Where Power is the accumulated energy value, Vel is the average blood flow velocity value, K is the number of target data sets, and Vel is... i For the velocity value corresponding to the i-th target data set, Power i Let be the energy value corresponding to the i-th target data group, i = 1, 2, ..., K.

[0103] After calculating the average blood flow velocity corresponding to each target detection group, color coding can be performed based on the average blood flow velocity value corresponding to each target detection group, thereby generating an ultrasound blood flow image of the target object.

[0104] In one particular implementable embodiment, prior to step S5, the method further includes:

[0105] Determine whether the average blood flow velocity is greater than a preset threshold;

[0106] If so, the first threshold is increased, and a preset number of data groups are filtered based on the increased first and second thresholds to obtain the target data group corresponding to each target detection group.

[0107] Among them, the increased first threshold is smaller than the second threshold.

[0108] In certain preset scenarios, users may want to particularly enhance blood flow in a specific feature. For example, in a cardiac preset scenario, doctors often want reflux to be brighter (higher flow velocity), making it easier to determine the area, degree, and origin of reflux. However, typical color Doppler ultrasound displays the average velocity at a certain point. The averaging effect of low-to-mid-frequency signals lowers the actual velocity value of reflux. Therefore, engineers can add new criteria to the frequency band elimination mechanism. For instance, if the average velocity value in the retained data is greater than a certain value, the first threshold T1 is automatically increased. Based on the increased first threshold T1 and second threshold T2, a preset number of data sets corresponding to each target detection group are filtered. By eliminating more low-velocity components, the areas with higher flow velocities are enhanced. In this way, the high-velocity reflux areas on the generated ultrasound blood flow image will have an enhanced effect. Similarly, engineers can use energy sorting and frequency band elimination mechanisms to set enhancement controls for certain preset scenarios, such as reflux enhancement and coronary artery enhancement in the cardiac preset scenario.

[0109] This implementation method enhances blood flow in a specific area by increasing the screening threshold of the data set, thereby facilitating the determination of the degree of reflux and the starting location of reflux.

[0110] In one feasible implementation, step S1 includes:

[0111] Several initial ultrasound blood flow images are generated based on ultrasound echo signals fed back by the target object;

[0112] An ultrasonic echo signal consists of several initial ultrasonic echo signal segments with equal time intervals.

[0113] Several initial ultrasound blood flow images are generated based on several initial ultrasound echo signal segments.

[0114] Specifically, ultrasound waves can be emitted to specific parts of the human body through an ultrasound probe, such as the heart, neck, abdomen, and limbs, where blood flow ultrasound examinations are required. The ultrasound probe receives the echo signals and then performs amplification, analog-to-digital conversion, demodulation, and beamforming on the echo signals to obtain X-frame IQ images.

[0115] The ultrasound echo signal can be divided into multiple initial ultrasound echo signal segments with equal time intervals. The time interval between these initial ultrasound echo signal segments can be customized by the engineer, for example, by setting the time interval through an ensemble (fixed packet length). A larger ensemble indicates higher frequency resolution of the data, and the number of filter groups can be appropriately increased. Thus, based on multiple initial ultrasound echo signal segments with equal time intervals, multiple frames of initial ultrasound blood flow images are generated.

[0116] This embodiment utilizes a filter bank composed of multiple filters with different cutoff frequencies to process multiple consecutive frames of initial ultrasound blood flow images. Based on the filtered data sets, the average blood flow velocity value corresponding to each target detection group is obtained. Ultrasound blood flow images are generated based on the average blood flow velocity value. This can effectively reduce high-frequency noise from hardware and low-frequency noise from tissue movement, making the calculated blood flow velocity closer to the true value and truly reflecting the main velocity components of the blood flow region. By increasing the filtering threshold of the data sets, blood flow in a specific region can be enhanced in a targeted manner, thereby facilitating the determination of the degree of reflux and the starting location of reflux.

[0117] Example 2

[0118] In one specific embodiment of this disclosure, a blood flow imaging system is provided for implementing the blood flow imaging method in Embodiment 1, such as... Figure 3As shown, the system 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.

[0119] The first acquisition module 100 is used to acquire several frames of initial ultrasound blood flow images;

[0120] The second acquisition module 200 is used to acquire multiple target detection groups. Each target detection group includes detection points at the same location in several frames of initial ultrasound blood flow images. Different target detection groups correspond to different locations.

[0121] The third acquisition module 300 is used to acquire a preset number of data groups corresponding to each target detection group. The data groups are obtained by filtering the target detection groups based on a filter group. The filter group includes a preset number of filters with different cutoff frequencies. The data group includes a pair of energy values ​​and velocity values.

[0122] The calculation module 400 is used to obtain the average blood flow velocity value corresponding to each target detection group based on the data group;

[0123] The generation module 500 is used to generate ultrasound blood flow images based on the average blood flow velocity values ​​corresponding to multiple target detection groups.

[0124] In one feasible implementation, the filter bank includes a predetermined number of bandpass filters;

[0125] The passband frequencies of a preset number of bandpass filters increase sequentially, or the passband frequencies of a preset number of bandpass filters increase continuously.

[0126] In this case, each bandpass filter has the same passband width;

[0127] The passband width of the filter bank is B∈[0,PRF / 2];

[0128] Where B is the passband width of the filter bank and PRF is the ultrasonic pulse repetition frequency.

[0129] In a specific feasible manner, such as Figure 4 As shown, the system also includes a filter bank setting module 600, which is used to set the number of bandpass filters in the filter bank.

[0130] In one feasible implementation, the third acquisition module 300 is further used to calculate any two adjacent ultrasound blood flow data output by the same filter for each target detection group using a cross-correlation algorithm, so as to obtain a preset number of data groups corresponding to each target detection group.

[0131] Among them, any two adjacent ultrasound blood flow data are two detection points at the same location in two adjacent initial ultrasound blood flow images, output through the same filter.

[0132] In one feasible implementation, the calculation module 400 is also used to filter a preset number of data groups to obtain the target data group corresponding to each target detection group;

[0133] Based on the target data set, the average blood flow velocity value corresponding to each target detection group is obtained.

[0134] In one feasible implementation, the calculation module 400 is further configured to use data groups with energy values ​​greater than a first threshold and energy values ​​less than a second threshold as target data groups;

[0135] The first threshold is less than the second threshold.

[0136] In one feasible implementation, the calculation module 400 is further configured to arrange a preset number of data groups in order of energy value based on the energy value in the data group.

[0137] In one feasible implementation, the calculation module 400 is also used to accumulate the energy value corresponding to each target data group to obtain the accumulated energy value;

[0138] Based on each target data set and the accumulated energy value, the average blood flow velocity value corresponding to each target detection set is calculated.

[0139] In a specific feasible manner, such as Figure 4 As shown, the system also includes a threshold adjustment module 700, which is used to determine whether the average blood flow velocity value is greater than a preset threshold.

[0140] If so, the first threshold is increased, and a preset number of data groups are filtered based on the increased first and second thresholds to obtain the target data group corresponding to each target detection group.

[0141] Among them, the increased first threshold is smaller than the second threshold.

[0142] In one feasible manner, several frames of the initial ultrasound blood flow images are generated based on ultrasound echo signals fed back by the target object;

[0143] An ultrasonic echo signal consists of several initial ultrasonic echo signal segments with equal time intervals.

[0144] Several initial ultrasound blood flow images are generated based on several initial ultrasound echo signal segments.

[0145] This embodiment utilizes a filter bank composed of multiple filters with different cutoff frequencies to process multiple consecutive frames of initial ultrasound blood flow images. Based on the filtered data sets, the average blood flow velocity value corresponding to each target detection group is obtained. Ultrasound blood flow images are generated based on the average blood flow velocity value. This can effectively reduce high-frequency noise from hardware and low-frequency noise from tissue movement, making the calculated blood flow velocity closer to the true value and truly reflecting the main velocity components of the blood flow region. By increasing the filtering threshold of the data sets, blood flow in a specific region can be enhanced in a targeted manner, thereby facilitating the determination of the degree of reflux and the starting location of reflux.

[0146] Example 3

[0147] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the method described above.

[0148] The readable storage medium may be more specifically adopted, 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 thereof.

[0149] In a possible implementation, the present invention can also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the method implemented in the above embodiments.

[0150] The program code for executing the present invention can be written using any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0151] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A blood flow imaging method, characterized in that, The method includes: Acquire several frames of initial ultrasound blood flow images; Multiple target detection groups are acquired, each target detection group includes several detection points at the same location in the initial ultrasound blood flow images, and different target detection groups correspond to different locations; A preset number of data sets are obtained for each target detection group, wherein the data sets are obtained by filtering the target detection groups based on a filter group, the filter group includes the preset number of filters with different cutoff frequencies, and the data sets include a pair of energy values ​​and velocity values. Based on the data set, the average blood flow velocity value corresponding to each target detection group is obtained; An ultrasound blood flow image is generated based on the average blood flow velocity values ​​corresponding to multiple target detection groups. The filter bank includes the preset number of bandpass filters; The passband frequencies of the preset number of bandpass filters increase sequentially, or the passband frequencies of the preset number of bandpass filters increase continuously. Each of the bandpass filters has the same passband width.

2. The method according to claim 1, characterized in that, The passband width B of the filter bank is [0, PRF / 2]; Where B is the passband width of the filter bank, and PRF is the ultrasonic pulse repetition frequency.

3. The method according to claim 1, characterized in that, The step of obtaining a preset number of data sets corresponding to each target detection group includes: The cross-correlation algorithm is used to calculate any two adjacent ultrasound blood flow data output by the same filter for each target detection group to obtain the preset number of data groups corresponding to each target detection group; Wherein, any two adjacent ultrasound blood flow data are two detection points at the same location in two adjacent frames of the initial ultrasound blood flow image, output through the same filter.

4. The method according to claim 1, characterized in that, The step of obtaining the average blood flow velocity value corresponding to each target detection group based on the data group includes: The preset number of data groups are filtered to obtain the target data group corresponding to each target detection group; Based on the target data set, the average blood flow velocity value corresponding to each target detection set is obtained.

5. The method according to claim 4, characterized in that, The step of filtering the preset number of data groups to obtain the target data group corresponding to each target detection group includes: The data group whose energy value is greater than a first threshold and whose energy value is less than a second threshold is selected as the target data group; Wherein, the first threshold is less than the second threshold.

6. The method according to claim 4, characterized in that, Before the step of filtering the preset number of data groups to obtain the target data group corresponding to each target detection group, the method further includes: Based on the energy values ​​in the data groups, the preset number of data groups are arranged sequentially according to the order of the energy values.

7. The method according to claim 4, characterized in that, The step of obtaining the average blood flow velocity value corresponding to each target detection group based on the target data group includes: The energy values ​​corresponding to each target data group are accumulated to obtain the accumulated energy value; Based on each target data group and the accumulated energy value, the average blood flow velocity value corresponding to each target detection group is calculated.

8. The method according to claim 5, characterized in that, Before the step of generating an ultrasound blood flow image based on the average blood flow velocity values ​​corresponding to multiple target detection groups, the method further includes: Determine whether the average blood flow velocity value is greater than a preset threshold; If so, the first threshold is increased, and the preset number of data groups are filtered based on the increased first threshold and the second threshold to obtain the target data group corresponding to each target detection group; Wherein, the increased first threshold is smaller than the second threshold.

9. The method according to any one of claims 1 to 8, characterized in that, The step of acquiring several frames of initial ultrasound blood flow images includes: The initial ultrasound blood flow images in several frames are generated based on the ultrasound echo signals fed back by the target object; The ultrasonic echo signal includes several initial ultrasonic echo signal segments with equal time intervals; The initial ultrasound blood flow images of several frames are generated based on several initial ultrasound echo signal segments.

10. A blood flow imaging system, characterized in that, The system includes 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 to acquire several frames of initial ultrasound blood flow images; The second acquisition module is used to acquire multiple target detection groups, each target detection group including several detection points at the same location in the initial ultrasound blood flow images, and different target detection groups correspond to different locations; The third acquisition module is used to acquire a preset number of data groups corresponding to each target detection group. The data groups are obtained by filtering the target detection groups based on a filter group. The filter group includes the preset number of filters with different cutoff frequencies. The data groups include a pair of energy values ​​and velocity values. The calculation module is used to obtain the average blood flow velocity value corresponding to each target detection group based on the data group; The generation module is used to generate ultrasound blood flow images based on the average blood flow velocity values ​​corresponding to multiple target detection groups; The filter bank includes the preset number of bandpass filters; The passband frequencies of the preset number of bandpass filters increase sequentially, or the passband frequencies of the preset number of bandpass filters increase continuously. Each of the bandpass filters has the same passband width.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the blood flow imaging method according to any one of claims 1 to 9.

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