Ultrasonic composite imaging methods, devices and computer equipment

Abstract

This application relates to the field of ultrasound imaging technology, and provides an ultrasound composite imaging method, apparatus, computer equipment, storage medium, and program product, which can reduce the interference of tissue movement relative to the transducer and ensure the resolution of the ultrasound image. In this application, a beamforming result sequence of pixels within the imaging area is obtained; in the beamforming result sequence, a target beamforming result corresponding to the target sub-emission is determined; based on the tissue movement velocity at the pixel, a corresponding weighted Gaussian distribution sequence is obtained; the target weight in the weighted Gaussian distribution sequence is assigned to the target beamforming result, and the weights on both sides of the target weight are correspondingly assigned to the beamforming results on both sides of the target beamforming result, thus obtaining the composite result of the pixels and forming an ultrasound image.

Description

Technical Field

[0001] This application relates to the field of ultrasound imaging technology, and in particular to an ultrasound composite imaging method, apparatus, computer equipment, storage medium, and computer program product. Background Technology

[0002] Ultrasound imaging is a non-invasive, radiation-free medical imaging technique that can provide two-dimensional or three-dimensional real-time imaging of soft tissues and human organs. Compared to other imaging methods, ultrasound imaging suffers from a relatively low signal-to-noise ratio. Coherent / incoherent compounding is a widely used method to improve the signal-to-noise ratio of ultrasound imaging and plays an important role in this field.

[0003] However, coherent / incoherent recombination, especially coherent recombination, is particularly affected by the movement of tissue relative to the transducer, resulting in a sharp decrease in the resolution of ultrasound images. Summary of the Invention

[0004] Therefore, it is necessary to provide an ultrasonic composite imaging method, device, computer equipment, storage medium, and computer program product to address the aforementioned technical problems.

[0005] This application provides a method for ultrasound composite imaging, the method comprising:

[0006] Obtain the beamforming result sequence of pixels within the imaging area; the beamforming results in the beamforming result sequence are arranged according to the emission order corresponding to the beamforming results;

[0007] In the beamforming result sequence, a target beamforming result corresponding to the target sub-emission is determined; the target sub-emission is an emission perpendicular to or nearly perpendicular to the pixel in multiple emission sequences.

[0008] Based on the tissue movement velocity at the pixel, obtain the corresponding weighted Gaussian distribution sequence;

[0009] The target weight in the weight Gaussian distribution sequence is assigned to the target beamforming result, and the weights on both sides of the target weight are assigned to the beamforming results on both sides of the target beamforming result to obtain the composite result of the pixel points, so as to form the ultrasound image of the imaging area; the target weight is the weight at the expected value or the weight near the expected value in the weight Gaussian distribution sequence.

[0010] This application provides an ultrasound composite imaging device, the device comprising:

[0011] The beamforming result acquisition module is used to acquire the beamforming result sequence of pixels within the imaging area; in the beamforming result sequence, each beamforming result is arranged according to the emission order corresponding to the beamforming result.

[0012] The target synthesis result determination module is used to determine the target beam synthesis result corresponding to the target sub-emission in the beam synthesis result sequence; the target sub-emission is an emission perpendicular to or nearly perpendicular to the pixel in multiple emission sequences;

[0013] The weight acquisition module is used to acquire the corresponding weight Gaussian distribution sequence based on the tissue movement speed at the pixel.

[0014] The composite module is used to assign the target weight in the weight Gaussian distribution sequence to the target beamforming result, and to assign the weights on both sides of the target weight to the beamforming results on both sides of the target beamforming result, so as to obtain the composite result of the pixel points to form the ultrasound image of the imaging area; the target weight is the weight at the expected value or the weight near the expected value in the weight Gaussian distribution sequence.

[0015] This application provides a computer device, including a memory and a processor, wherein the memory stores a computer program and the processor executes the above-described method.

[0016] This application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor using the methods described above.

[0017] This application provides a computer program product having a computer program stored thereon, the computer program being executed by a processor using the above-described method.

[0018] In the aforementioned ultrasound composite imaging method, apparatus, computer equipment, storage medium, and computer program product, a sequence of beamforming results for pixels within the imaging area is acquired. Each beamforming result in this sequence is arranged according to its corresponding emission order. When multiple beamforming results for a pixel are composited, a corresponding weighted Gaussian distribution sequence is obtained based on the tissue movement velocity at the pixel. Since the target beamforming result corresponds to the target sub-emission perpendicular to the pixel in multiple transmissions, and the target weight is the weight at the expected value or near the expected value in the weighted Gaussian distribution sequence, with the weights on both sides of the target weight gradually decreasing, assigning the target weight from the weighted Gaussian distribution sequence to the target beamforming result, and correspondingly assigning the weights on both sides of the target weight to the beamforming results on both sides of the target beamforming result, increases the proportion of beamforming results closer to the target sub-emission in the composite, while decreasing the proportion of beamforming results farther from the target sub-emission, thus avoiding interference caused by excessively fast tissue movement and ensuring the resolution of the ultrasound image. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the focused wave in one embodiment;

[0020] Figure 2 This is a schematic diagram of the transmission sequence in one embodiment;

[0021] Figure 3 This is a schematic diagram illustrating the beamforming result of a single emission corresponding to a pixel in one embodiment.

[0022] Figure 4 This is a flowchart illustrating an ultrasound composite imaging method in one embodiment;

[0023] Figure 5 This is a schematic diagram illustrating the application of the ultrasound composite imaging method in one embodiment;

[0024] Figure 6 This is a schematic diagram of a plane wave in one embodiment;

[0025] Figure 7 This is a flowchart illustrating the process of obtaining an ultrasound image based on an emission sequence in one embodiment;

[0026] Figure 8 This is a structural block diagram of an ultrasound composite imaging device in one embodiment;

[0027] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0029] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0030] In some scenarios, when it's necessary to observe a specific area, ultrasound imaging can be used; this area can be referred to as the imaging region. Ultrasound imaging methods include ultrasound B-mode imaging, which can be based on... Figure 1 The focused wave is shown. The main characteristics of the focused wave are: multiple array elements of the probe work together to emit sound waves, the sound waves will focus after propagating forward a certain distance, and after focusing, the sound waves will diverge again as they continue to propagate forward; the point of focus can be called the focal point.

[0031] By adjusting the coordination between the array elements when emitting sound waves, the focal point position can be adjusted. In ultrasound imaging using focused waves, emitting a focused wave at a specific focal point can be considered as completing one emission; conversely, emitting focused waves at two focal points can be considered as completing two focused wave emissions. Figure 2 The imaging sequence shown, namely the focused wave sequence numbered 9 to 136, is considered as two focused wave transmissions because the focal position of focused wave number 9 is different from that of focused wave number 10.

[0032] To form a complete ultrasound image, multiple focused wave emissions are required. Figure 2 The focused waves shown are numbered 9 to 136, which means 128 focused wave transmissions were performed. During multiple focused wave transmissions, after one focused wave transmission was completed, the focal point of the focused wave was changed, and the next focused wave transmission was performed.

[0033] In a single-focused wave emission, the area covered by the sound wave from divergence to focusing and then back to emission can be called the effective imaging region of the single-focused wave emission, such as... Figure 3 As shown.

[0034] During a focused wave emission, if a point within the imaging area is covered by that emission, echo signal synthesis can be performed to obtain the beamforming result corresponding to that point in the focused wave emission. In some scenarios, if a point within the imaging area needs to be reflected in the image, that point can be called a pixel.

[0035] like Figure 3 As shown, pixel a in the imaging area falls within the effective imaging area of ​​the focused wave emission. At this time, by synthesizing the echo signals received by the corresponding array elements of the probe, the beamforming result corresponding to pixel a for the focused wave emission can be obtained. The beamforming result is denoted as sum.

[0036] Therefore, a single focused wave emission yields one beamforming result for pixel a. If multiple focused wave emissions are performed, and pixel a falls within the effective imaging area of ​​each emission, multiple beamforming results for pixel a will be obtained. The method provided in this application can be used to combine these multiple beamforming results for pixel a.

[0037] The ultrasonic composite imaging method provided in this application includes Figure 4 The steps shown are as follows:

[0038] Step S401: Obtain the beamforming result sequence of pixels within the imaging area.

[0039] Step S402: In the beamforming result sequence, determine the target beamforming result corresponding to the target sub-transmission.

[0040] The beamforming results in the beamforming result sequence are arranged according to the transmission order corresponding to the beamforming results, such as... Figure 5 As shown, pixel a in the imaging region falls within the effective imaging area of ​​focused wave emission ① to ⑦. Therefore, seven beamforming results for pixel a can be obtained: sum_1, sum_2, sum_3, sum_4, sum_5, sum_6, and sum_7. Among them, sum_1 corresponds to the first focused wave emission, sum_2 corresponds to the second focused wave emission, and so on, with each beamforming result corresponding to a specific focused wave emission. According to the order of focused wave emission corresponding to the beamforming results, the above seven beamforming results for pixel a can be sorted to obtain the beamforming result sequence {sum_1, sum_2, sum_3, sum_4, sum_5, sum_6, sum_7}.

[0041] In the first to seventh focused wave emissions, if pixel a is located in the emission center region of the fourth focused wave emission, then the fourth focused wave emission can be called an emission perpendicular to pixel a. Therefore, the emission perpendicular to the pixel in the multiple emissions is called the target emission.

[0042] However, in some scenarios, the following situation may occur: In the first to seventh focused wave emission, pixel a is not located in the emission center region of any focused wave emission, that is, in multiple emission, there is no emission perpendicular to the pixel. In this case, it can be determined which focused wave emission is closest to the emission center region of the pixel a, and that focused wave emission is called the emission that is close to the pixel a, and is taken as the target emission.

[0043] Accordingly, the beamforming result corresponding to the target emission can be called the target beamforming result. For example, the beamforming result sum_4 corresponding to the fourth focused wave emission can be called the target beamforming result.

[0044] Step S403: Obtain the corresponding weighted Gaussian distribution sequence based on the tissue movement speed at the pixel.

[0045] Since multiple beamforming results for the same pixel need to be combined, the weights assigned to each beamforming result can form a weight sequence; the distribution of this weight sequence can be set according to a Gaussian distribution, thus obtaining a weight Gaussian distribution sequence.

[0046] In one embodiment, obtaining the corresponding weighted Gaussian distribution sequence based on the tissue movement speed at the pixel includes: determining the level of tissue movement speed at the pixel; and obtaining the corresponding weighted Gaussian distribution sequence based on the positive correlation between the level of tissue movement speed and the variance of the weighted Gaussian distribution sequence, and the level of tissue movement speed at the pixel.

[0047] In this embodiment, when the tissue movement speed at multiple pixels is different, the tissue movement speed at each pixel can have a corresponding variance and a corresponding weighted Gaussian distribution sequence. The higher the tissue movement speed, the greater the variance of the corresponding weighted Gaussian distribution sequence, and the more discrete the weights in the weighted Gaussian distribution sequence are.

[0048] To ensure that the variance of the weighted Gaussian distribution sequence is positively correlated with the tissue movement velocity, the following steps can be performed: First, set the variance based on the tissue movement velocity; the higher the tissue movement velocity, the higher the set variance. The larger the value, the better; additionally, you can set an expected value. According to expected value This forms a Gaussian distribution curve, which is continuous. To obtain a discrete weighted Gaussian distribution sequence, the Gaussian distribution curve can be discretized. The discretization process involves selecting a weighted average value on the horizontal axis of the Gaussian distribution curve. Or the interval centered at or near it, then divide the length of this interval by N (N is the number of beamforming results to be combined), and take the result as k; then, the expected value Take a weight at or near a given location, and then every k intervals... Take a weight at or near the location, Take a weight at or near the location, Take a weight at or near the location, We take a weight at or near the point, and so on, to obtain the desired Gaussian distribution sequence of weights.

[0049] In some scenarios, to improve composite efficiency, the composite effect can be slightly compromised. In this case, the tissue motion speed can be divided into multiple speed levels, such as high speed, medium speed, and low speed. Each speed level has a corresponding variance and a corresponding weighted Gaussian distribution sequence. In this case, if the tissue motion speeds at two pixels belong to the same speed level, then these two tissue motion speeds can share the weighted Gaussian distribution sequence corresponding to that speed level.

[0050] Therefore, based on the positive correlation between the speed of tissue movement and the variance of the weighted Gaussian distribution sequence, and the speed of tissue movement at each pixel, the corresponding weighted Gaussian distribution sequence is obtained, including: determining the speed level of the tissue movement speed at each pixel based on its speed level; and obtaining the corresponding weighted Gaussian distribution sequence based on the positive correlation between the speed level and the variance of the weighted Gaussian distribution sequence, and the speed level of the tissue movement speed at each pixel.

[0051] Furthermore, the method provided in this application also includes the following steps: obtaining multiple pre-divided speed levels; generating a weighted Gaussian distribution sequence corresponding to each speed level based on the variance set for each speed level; wherein, the higher the speed level, the larger the corresponding variance is set.

[0052] In this embodiment, the specific process of generating a weighted Gaussian distribution corresponding to each speed level based on the variance set for each speed level is similar to that described in the above embodiment, and will not be repeated here.

[0053] Specific to Figure 5 The pixel a shown can be used to obtain the tissue movement speed at pixel a. Figure 5 The weighted Gaussian distribution sequence shown is { , , , , , , The higher the tissue movement speed at pixel a, the greater the variance of the weighted Gaussian distribution sequence.

[0054] After obtaining the Gaussian distribution sequence of weights corresponding to the pixels, determine which weight is the expected value. The weight at the location, or the nearest expected value. The weight at the point; the expected value can be... ± Set as the range of values ​​close to the expected value, and the non-expected values ​​within that range. Any point at is the nearest expected value The point, that is, determining which weight is The weight at a certain point in the sequence is used as the target weight. For example, in the above Gaussian distribution sequence of weights, { , , , , , , In}, if determined yes If the weight is at a certain point, then it can be... As the target weight.

[0055] Step S404: Assign the target weight in the weighted Gaussian distribution sequence to the target beamforming result, and assign the corresponding weights on both sides of the target weight to the beamforming results on both sides of the target beamforming result to obtain the composite result of the pixels, so as to form the ultrasound image of the imaging area.

[0056] After determining the target weight, the target weight is assigned to the target beamforming result. As for the weights on both sides of the target weight, they are assigned to the beamforming results on both sides of the target beamforming result.

[0057] The beamforming result sequence of pixel a is {sum_1,sum_2,sum_3,sum_4,sum_5,sum_6,sum_7}, and the weighted Gaussian distribution sequence is { , , , , , , For example, let's take} as an example:

[0058] If the target beamforming result is sum_4, the target weight is... Then Assign it to sum_4; One side , , Then, they are respectively assigned to sum_1, sum_2, and sum_3 on the sum_4 side. The other side , , Then, these values ​​are assigned to sum_5, sum_6, and sum_7 on the other side of sum_4, forming the following compound formula: ×sum_1+ ×sum_2+ ×sum_3+ ×sum_4+ ×sum_5+ ×sum_6+ ×sum_7; This calculates the weighted sum as the composite result for pixel a.

[0059] Following the above method, the composite results of other pixels are obtained, and the ultrasound image of the imaging area is obtained based on the composite results of each pixel.

[0060] In the aforementioned ultrasound composite imaging method, a beamforming result sequence of pixels within the imaging area is acquired. Each beamforming result in this sequence is arranged according to its corresponding emission order. When combining multiple beamforming results of a pixel, a corresponding weighted Gaussian distribution sequence is obtained based on the tissue movement velocity at the pixel. Since the target beamforming result corresponds to the target sub-emission perpendicular to the pixel in multiple emission events, and the target weight is the weight at the expected value or near the expected value in the weighted Gaussian distribution sequence, with the weights on both sides of the target weight gradually decreasing, assigning the target weight from the weighted Gaussian distribution sequence to the target beamforming result, and correspondingly assigning the weights on both sides of the target weight to the beamforming results on both sides of the target beamforming result, increases the proportion of beamforming results closer to the target sub-emission in the composite, while decreasing the proportion of beamforming results farther from the target sub-emission, thus avoiding interference caused by excessively fast tissue movement and ensuring the resolution of the ultrasound image.

[0061] In one embodiment, determining the velocity level of the tissue movement velocity at a pixel based on the magnitude of the tissue movement velocity at the pixel includes: determining the imaging sub-region where the pixel is located in a plurality of pre-divided imaging sub-regions; and determining the velocity level to which the tissue movement velocity at the pixel belongs based on the velocity level corresponding to the imaging sub-region where the pixel is located.

[0062] Each imaging sub-region has a corresponding velocity level; in some scenarios, two or more imaging sub-regions may correspond to the same velocity level.

[0063] In this embodiment, the imaging area is first pre-divided to obtain multiple imaging sub-regions. Since each imaging sub-region has a corresponding velocity level, the velocity level corresponding to the imaging sub-region where the pixel is located can be directly used as the velocity level to which the tissue movement velocity at that pixel belongs, thereby improving processing efficiency.

[0064] Furthermore, under the three speed levels, the speed level of the tissue movement speed at the pixel is determined according to the speed level corresponding to the imaging sub-region where the pixel is located, including: when the speed level corresponding to the imaging sub-region where the pixel is located is high speed, the speed level of the tissue movement speed at the pixel is determined to be high speed; when the speed level corresponding to the imaging sub-region where the pixel is located is medium speed, the speed level of the tissue movement speed at the pixel is determined to be medium speed; when the speed level corresponding to the imaging sub-region where the pixel is located is low speed, the speed level of the tissue movement speed at the pixel is determined to be low speed.

[0065] The imaging area can be pre-divided according to the speed of tissue movement, resulting in multiple pre-divided imaging sub-regions. If the tissue movement speed is divided into three levels, namely high speed, medium speed and low speed, then it can be determined whether the speed level corresponding to each imaging sub-region is high speed, medium speed or low speed.

[0066] Next, it can be determined which imaging sub-region the pixel is located in. When the imaging sub-region where the pixel is located corresponds to a high speed, it can be determined that the tissue movement speed at that pixel belongs to a high speed. When the imaging sub-region where the pixel is located corresponds to a medium speed, it can be determined that the tissue movement speed at that pixel belongs to a medium speed. When the imaging sub-region where the pixel is located corresponds to a low speed, it can be determined that the tissue movement speed at that pixel belongs to a low speed.

[0067] Understandably, some scenarios may allow for more or fewer levels of classification of tissue movement speed.

[0068] Obtaining tissue movement velocity is part of the motion detection section; through the motion detection section, the tissue movement velocity of multiple points within the imaging area can be obtained, thereby dividing the imaging area and obtaining multiple imaging sub-regions.

[0069] One method of motion detection is to utilize the Doppler effect generated by tissue motion to obtain the tissue motion velocity. In this case, the method provided in this application further includes the following steps: based on the Doppler theorem, obtaining the frequency shift of multiple points in the imaging region relative to the transducer; and dividing the imaging region into multiple imaging sub-regions according to the proportional relationship between the frequency shift and the tissue motion velocity, so as to determine that each imaging sub-region has a corresponding velocity level.

[0070] Based on the Doppler theorem, the frequency shift of each point in the imaging region relative to the transducer can be obtained. Since the frequency is directly proportional to the tissue movement speed, the tissue movement speed at each of the above points can be obtained. Then, if three speed levels are set, the imaging region can be divided according to the tissue movement speed at each of the above points to obtain multiple imaging sub-regions. The speed level corresponding to each imaging sub-region is one of high speed, medium speed and low speed.

[0071] Furthermore, based on the Doppler theorem, the step of obtaining the frequency shift of multiple points in the imaging region relative to the transducer can specifically include: emitting a plane wave targeting the imaging region, and obtaining the frequency shift of multiple points in the imaging region relative to the transducer based on the plane wave data and the Doppler theorem.

[0072] Specifically, it can launch such as Figure 6 The plane wave shown is used to obtain the frequency shift of each of the above points relative to the transducer based on the collected plane wave data and the Doppler theorem.

[0073] Furthermore, based on the Doppler theorem, the step of obtaining the frequency shifts of multiple points in the imaging region relative to the transducer can specifically include: based on the Doppler theorem, obtaining multiple frequency shifts of the same point in the imaging region relative to the transducer; averaging the multiple frequency shifts to obtain the average frequency shift of the same point relative to the transducer.

[0074] To improve the accuracy of tissue motion velocity calculation, multiple plane wave transmissions can be performed to obtain multiple frequency shifts at the same point, and then the average can be calculated to reduce the error in tissue motion velocity calculation.

[0075] If the motion detection part utilizes plane waves and performs multiple plane wave emissions, reducing the calculation error of tissue movement velocity through averaging, then the motion detection sequence used can be: Figure 2 Numbered 1 to 8 as shown, eight plane wave emissions are performed. In this case, to obtain a complete ultrasound image, the motion detection sequence and imaging sequence used are combined to obtain, as shown... Figure 2 The complete launch sequence.

[0076] In this case, ultrasound composite imaging methods include Figure 7 The steps shown are as follows:

[0077] Step S701: Using the motion detection sequence, obtain the frequency shift at multiple points within the imaging area;

[0078] Step S702: Based on the frequency shift at multiple points and the proportional relationship between the frequency shift and the tissue movement speed, the imaging region is divided to obtain multiple imaging sub-regions.

[0079] Step S703: Based on the imaging sub-region where the pixel is located, determine the level of tissue movement velocity at the pixel and determine the corresponding weighted Gaussian distribution sequence.

[0080] Step S704: Using the imaging sequence, obtain the beamforming result at the pixel point;

[0081] Step S705: Use the weighted Gaussian distribution sequence to combine the beamforming results at the pixel point to obtain the composite result at that pixel point.

[0082] Step S706: Based on the composite results of each pixel, a complete ultrasound image is obtained.

[0083] Theoretically, the more plane wave emissions used for motion detection, the better. However, once the number of plane wave emissions reaches a certain level, it will not have a significant impact on the results. When using motion detection to make a coarse division of the imaging area, the accuracy requirement for frequency shift calculation is not high, and the number of emissions can be around 10.

[0084] Another approach to motion detection is optical flow based on image sequences. This method utilizes the correlation between adjacent frames and calculates the motion information between adjacent frames based on the correspondence between the current frame and the previous frame.

[0085] In this case, the method provided by this application further includes: emitting a focused wave targeting the imaging region to acquire multiple frames of ultrasound images; obtaining tissue motion information between adjacent frames of ultrasound images based on the correspondence between adjacent frames of ultrasound images; and dividing the imaging region into multiple imaging sub-regions based on the tissue motion information to determine that each imaging sub-region has a corresponding velocity level.

[0086] The ultrasound image sequence described above can be obtained using an imaging sequence. Motion detection using the ultrasound image sequence does not require adding an additional motion detection sequence to the transmission sequence, resulting in no additional storage burden for the hardware. The transmission sequence can be used for both ultrasound B-mode imaging and motion detection. However, motion detection is required for the first few ultrasound images to guide the coherent composite settings for the current frame's ultrasound imaging, so the initial few frames may have poor image resolution.

[0087] In one embodiment, the beamforming result corresponds to a focused wave emission, and the focal positions of focused wave emissions with adjacent emission sequences are adjacent.

[0088] For example, the focal positions of focused waves numbered 9 and 10 are adjacent, therefore, the emission order of focused waves numbered 9 and 10 is adjacent.

[0089] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0090] In one embodiment, such as Figure 8 As shown, an ultrasound composite imaging device is provided, comprising:

[0091] The beamforming result acquisition module 801 is used to acquire the beamforming result sequence of pixels in the imaging area; in the beamforming result sequence, each beamforming result is arranged according to the emission order corresponding to the beamforming result.

[0092] The target synthesis result determination module 802 is used to determine the target beam synthesis result corresponding to the target sub-emission in the beam synthesis result sequence; the target sub-emission is an emission perpendicular to or nearly perpendicular to the pixel in multiple emission sequences;

[0093] The weight acquisition module 803 is used to acquire the corresponding weight Gaussian distribution sequence based on the tissue movement speed at the pixel.

[0094] The composite module 804 is used to assign the target weight in the weight Gaussian distribution sequence to the target beamforming result, and to assign the weights on both sides of the target weight to the beamforming results on both sides of the target beamforming result, so as to obtain the composite result of the pixel points to form the ultrasound image of the imaging area; the target weight is the weight at the expected value or the weight near the expected value in the weight Gaussian distribution sequence.

[0095] In one embodiment, the weight acquisition module 803 is used to determine the level of tissue movement speed at the pixel; based on the positive correlation between the level of tissue movement speed and the variance of the weighted Gaussian distribution sequence, and the level of tissue movement speed at the pixel, a corresponding weighted Gaussian distribution sequence is obtained.

[0096] In one embodiment, the weight acquisition module 803 is used to determine the speed level of the tissue movement speed at the pixel based on the level of the tissue movement speed at the pixel; and to obtain the corresponding weight Gaussian distribution sequence based on the positive correlation between the speed level and the variance of the weight Gaussian distribution sequence, and the speed level of the tissue movement speed at the pixel.

[0097] In one embodiment, the weight acquisition module 803 is further configured to acquire multiple pre-divided speed levels; and generate a weighted Gaussian distribution sequence corresponding to each speed level based on the variance set for each speed level; wherein, the higher the speed level, the larger the corresponding variance is set.

[0098] In one embodiment, the weight acquisition module 803 is further configured to determine the imaging sub-region where the pixel is located among multiple pre-divided imaging sub-regions; wherein each imaging sub-region has a corresponding velocity level; and determine the velocity level to which the tissue movement velocity at the pixel belongs based on the velocity level corresponding to the imaging sub-region where the pixel is located.

[0099] In one embodiment, the weight acquisition module 803 is further configured to determine that the speed level of the tissue movement speed at the pixel is high-speed when the speed level corresponding to the imaging sub-region where the pixel is located is high-speed; determine that the speed level of the tissue movement speed at the pixel is medium-speed when the speed level corresponding to the imaging sub-region where the pixel is located is medium-speed; and determine that the speed level of the tissue movement speed at the pixel is low-speed when the speed level corresponding to the imaging sub-region where the pixel is located is low-speed.

[0100] In one embodiment, the device further includes a region division module for obtaining the frequency shift of multiple points within the imaging region relative to the transducer based on the Doppler theorem; and dividing the imaging region into multiple imaging sub-regions according to the fact that the frequency shift is proportional to the tissue movement speed, so as to determine that each imaging sub-region has a corresponding speed level.

[0101] In one embodiment, the region segmentation module is further configured to obtain multiple frequency shifts of the same point within the imaging region relative to the transducer based on the Doppler theorem; and to average the multiple frequency shifts to obtain the average frequency shift of the same point relative to the transducer.

[0102] In one embodiment, the region segmentation module is further configured to transmit a plane wave targeting the imaging region, and obtain the frequency shift of multiple points within the imaging region relative to the transducer based on the plane wave data and the Doppler theorem.

[0103] In one embodiment, the region segmentation module is further configured to emit focused waves targeting the imaging region to acquire multiple frames of ultrasound images; obtain tissue motion information between adjacent frames of ultrasound images based on the correspondence between adjacent frames of ultrasound images; and divide the imaging region into multiple imaging sub-regions based on the tissue motion information to determine that each imaging sub-region has a corresponding velocity level.

[0104] In one embodiment, the beamforming result corresponds to a focused wave emission, and the focal positions of focused wave emissions with adjacent emission sequences are adjacent.

[0105] Specific limitations regarding the ultrasound composite imaging device can be found in the limitations of the ultrasound composite imaging method described above, and will not be repeated here. Each module in the aforementioned ultrasound composite imaging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independently of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0106] In one embodiment, a computer device is provided, the internal structure of which can be shown as follows: Figure 9 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores ultrasound composite imaging data. The network interface communicates with external terminals via a network connection. The computer device also includes input / output interfaces (I / O interfaces), which are connection circuits between the processor and external devices for exchanging information; they are connected to the processor via a bus. When the computer program is executed by the processor, it implements an ultrasound composite imaging method.

[0107] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the various method embodiments described above.

[0109] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the various method embodiments described above.

[0110] In one embodiment, a computer program product is provided having a computer program stored thereon, the computer program being executed by a processor of the steps described in the various method embodiments above.

[0111] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0112] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The above embodiments are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for ultrasound composite imaging, characterized in that, The method includes: Obtain the beamforming result sequence of pixels within the imaging area; the beamforming results in the beamforming result sequence are arranged according to the emission order corresponding to the beamforming results; In the beamforming result sequence, a target beamforming result corresponding to the target sub-emission is determined; the target sub-emission is an emission perpendicular to or nearly perpendicular to the pixel in multiple emission sequences. Based on the tissue movement speed at the pixel, the corresponding weighted Gaussian distribution sequence is obtained; the higher the tissue movement speed, the greater the variance of the corresponding weighted Gaussian distribution sequence. The target weight in the weight Gaussian distribution sequence is assigned to the target beamforming result, and the weights on both sides of the target weight are assigned to the beamforming results on both sides of the target beamforming result to obtain the composite result of the pixel points, so as to form the ultrasound image of the imaging area; the target weight is the weight at the expected value or the weight near the expected value in the weight Gaussian distribution sequence.

2. The method according to claim 1, characterized in that, Based on the tissue movement velocity at the pixel, obtain the corresponding weighted Gaussian distribution sequence, including: Determine the speed of tissue movement at the pixel; Based on the positive correlation between the tissue movement speed and the variance of the weighted Gaussian distribution sequence, and the tissue movement speed at the pixel, the corresponding weighted Gaussian distribution sequence is obtained.

3. The method according to claim 2, characterized in that, Based on the positive correlation between the tissue movement speed and the variance of the weighted Gaussian distribution sequence, and considering the tissue movement speed at the pixel, a corresponding weighted Gaussian distribution sequence is obtained, including: Based on the speed of tissue movement at the pixel, determine the speed level to which the tissue movement speed at the pixel belongs; Based on the positive correlation between the speed level and the variance of the weighted Gaussian distribution sequence, and the speed level to which the tissue movement speed at the pixel belongs, the corresponding weighted Gaussian distribution sequence is obtained.

4. The method according to claim 3, characterized in that, Before obtaining the corresponding weighted Gaussian distribution sequence based on the positive correlation between the speed level and the variance of the weighted Gaussian distribution sequence, and the speed level to which the tissue movement speed at the pixel belongs, the method further includes: Obtain multiple pre-defined speed levels; Based on the variance set for each speed level, a weighted Gaussian distribution sequence corresponding to each speed level is generated; where the higher the speed level, the larger the corresponding variance is set.

5. The method according to claim 3, characterized in that, Based on the tissue movement velocity at the pixel, the velocity level to which the tissue movement velocity at the pixel belongs is determined, including: In a plurality of pre-divided imaging sub-regions, the imaging sub-region in which the pixel is located is determined; wherein, each imaging sub-region has a corresponding velocity level; Based on the velocity level corresponding to the imaging sub-region where the pixel is located, the velocity level to which the tissue movement velocity at the pixel belongs is determined.

6. The method according to claim 5, characterized in that, In the case of three speed levels, the speed level to which the tissue movement speed at the pixel belongs is determined based on the speed level corresponding to the imaging sub-region where the pixel is located, including: When the velocity level corresponding to the imaging sub-region where the pixel is located is high speed, the velocity level to which the tissue movement velocity at the pixel belongs is determined to be high speed. When the speed level corresponding to the imaging sub-region where the pixel is located is medium speed, the speed level to which the tissue movement speed at the pixel belongs is determined to be medium speed. When the velocity level corresponding to the imaging sub-region where the pixel is located is low, the velocity level of the tissue movement velocity at the pixel is determined to be low.

7. The method according to claim 5, characterized in that, Before determining the imaging sub-region where the pixel is located within a plurality of pre-divided imaging sub-regions, the method further includes: Based on the Doppler theorem, the frequency shift of multiple points within the imaging region relative to the transducer is obtained; Based on the fact that frequency shift is directly proportional to tissue movement speed, the imaging region is divided into multiple imaging sub-regions to determine that each imaging sub-region has a corresponding speed level.

8. The method according to claim 7, characterized in that, The frequency shift of multiple points within the imaging region relative to the transducer, based on the Doppler theorem, includes: Based on the Doppler theorem, multiple frequency shifts of the same point in the imaging region relative to the transducer are obtained; The average frequency shift of the multiple frequency shifts is calculated to obtain the average frequency shift of the same point relative to the transducer.

9. The method according to claim 7, characterized in that, The frequency shift of multiple points within the imaging region relative to the transducer, based on the Doppler theorem, includes: A plane wave is emitted targeting the imaging region, and the frequency shift of multiple points within the imaging region relative to the transducer is obtained based on the plane wave data and the Doppler theorem.

10. The method according to claim 5, characterized in that, Before determining the imaging sub-region where the pixel is located within a plurality of pre-divided imaging sub-regions, the method further includes: A focused wave is emitted targeting the imaging region to acquire multiple frames of ultrasound images; Based on the correspondence between adjacent ultrasound images, the motion information of tissues between adjacent ultrasound images is obtained; Based on the motion information of the tissue, the imaging region is divided into multiple imaging sub-regions to determine that each imaging sub-region has a corresponding velocity level.

11. The method according to claim 1, characterized in that, The beamforming result corresponds to a focused wave emission, and the focal positions of focused wave emissions with adjacent emission sequences are adjacent.

12. An ultrasonic composite imaging device, characterized in that, The device comprises: The beamforming result acquisition module is used to acquire the beamforming result sequence of pixels within the imaging area; in the beamforming result sequence, each beamforming result is arranged according to the emission order corresponding to the beamforming result. The target synthesis result determination module is used to determine the target beam synthesis result corresponding to the target sub-emission in the beam synthesis result sequence; the target sub-emission is an emission perpendicular to or nearly perpendicular to the pixel in multiple emission sequences; The weight acquisition module is used to obtain the corresponding weight Gaussian distribution sequence based on the tissue movement speed at the pixel; the higher the tissue movement speed, the greater the variance of the corresponding weight Gaussian distribution sequence. The composite module is used to assign the target weight in the weight Gaussian distribution sequence to the target beamforming result, and to assign the weights on both sides of the target weight to the beamforming results on both sides of the target beamforming result, so as to obtain the composite result of the pixel points to form the ultrasound image of the imaging area; the target weight is the weight at the expected value or the weight near the expected value in the weight Gaussian distribution sequence.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 11.

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