Signal processing method

Abstract

The invention relates to a signal processing method. The method comprises the following steps: acquiring a first measured value of a first measurement point; determining a first predicted value of a second measurement point according to the first measured value; the second measuring point is adjacent to the first measuring point and is behind the first measuring point; determining a difference value between the first predicted value and a second measured value of the second measurement point; and performing compression processing on the difference value to obtain target compression data corresponding to the second measurement point. By adopting the method, the data volume can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of medical image processing, and in particular to a signal processing method. Background Art

[0002] Conventional ultrasound systems can generate ultrasound waves by controlling and stimulating the ultrasonic transducer, receiving the returned echo, converting the echo into an electrical signal, sampling the electrical signal through an analog-to-digital converter, and then performing a series of digital signal processing processes, such as beamforming, extracting amplitude information or phase information, etc.

[0003] With the continuous expansion and deepening of ultrasound applications, the requirements for data processing performance are increasing, resulting in an explosive growth in the amount of ultrasound signal data. For example, in order to improve image quality and resolution, it is necessary to increase the number of transducer channels or increase the sampling rate of the analog-to-digital converter. This will inevitably lead to an increase in the amount of ultrasound signal data, which will not only increase the cost of data storage, but may also increase the bandwidth of data storage and processing as well as system power consumption.

[0004] Therefore, the current ultrasonic signal processing technology has the problem of large data volume. Summary of the invention

[0005] Based on this, it is necessary to provide a signal processing method, apparatus, computer device, computer-readable storage medium and computer program product that can reduce the amount of data in response to the above technical problems.

[0006] In a first aspect, the present application provides a signal processing method, comprising:

[0007] Obtaining a first measured value of a first measuring point;

[0008] Determine a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point;

[0009] determining a difference between the first predicted value and a second measured value at the second measurement point;

[0010] The difference is compressed to obtain target compressed data corresponding to the second measuring point.

[0011] In a second aspect, the present application further provides a signal processing device, comprising:

[0012] An acquisition module, used for acquiring a first measured value of a first measurement point;

[0013] A prediction module, configured to determine a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point;

[0014] a calculation module, configured to determine a difference between the first predicted value and a second measured value of the second measurement point;

[0015] A compression module is used to compress the difference to obtain target compressed data corresponding to the second measurement point.

[0016] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0017] Obtaining a first measured value of a first measuring point;

[0018] Determine a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point;

[0019] determining a difference between the first predicted value and a second measured value at the second measurement point;

[0020] The difference is compressed to obtain target compressed data corresponding to the second measuring point.

[0021] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0022] Obtaining a first measured value of a first measuring point;

[0023] Determine a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point;

[0024] determining a difference between the first predicted value and a second measured value at the second measurement point;

[0025] The difference is compressed to obtain target compressed data corresponding to the second measuring point.

[0026] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0027] Obtaining a first measured value of a first measuring point;

[0028] Determine a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point;

[0029] determining a difference between the first predicted value and a second measured value at the second measurement point;

[0030] The difference is compressed to obtain target compressed data corresponding to the second measuring point.

[0031] The above-mentioned signal processing method, device, computer equipment, computer-readable storage medium and computer program product obtain a first measured value of a first measuring point, determine a first predicted value of a second measuring point according to the first measured value, the second measuring point is adjacent to the first measuring point and after the first measuring point, determine the difference between the first predicted value and the second measured value of the second measuring point, compress the difference to obtain target compressed data corresponding to the second measuring point; the predicted value of the second measuring point can be determined among multiple adjacent measuring points, and the second measuring point can be measured to obtain the measured value of the second measuring point, and the difference between the predicted value and the measured value can be compressed. Since the amount of data after compression is small, the amount of data in the storage, transmission and processing processes is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 is a flow chart of a signal processing method in one embodiment;

[0034] Figure 2 is a schematic diagram of two-point linear extrapolation in one embodiment;

[0035] Figure 3 is a schematic diagram of difference data in one embodiment;

[0036] Figure 4 is a flow chart of a signal processing method in another embodiment;

[0037] Figure 5 is a schematic diagram of the similarity of medical ultrasound signals;

[0038] Figure 6 is a schematic diagram of a signal compression and decompression ultrasound system in one embodiment;

[0039] Figure 7 is a flow chart of a signal compression process in one embodiment;

[0040] Figure 8is a flow chart of a signal decompression process in one embodiment;

[0041] Fig. 9 is a schematic diagram of a difference calculation process in one embodiment;

[0042] Fig.10 is a probability distribution of difference data in one embodiment;

[0043] Fig.11 is a flow chart of a signal processing method in another embodiment;

[0044] Fig.12 is a structural block diagram of a signal processing device in one embodiment;

[0045] Fig.13 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0048] In an exemplary embodiment, Figure 1 As shown, a signal processing method is provided. This embodiment takes the method applied to a terminal as an example for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0049] Step S102: obtaining a first measured value of a first measuring point.

[0050] The first measurement point may be a measurement point at the same depth as the current measurement point in a preset direction, and the first measurement point and the current measurement point are arranged front and back along the preset direction. It can be understood that the received signal of the first measurement point corresponds to the same image coordinate as the received signal of the current measurement point, wherein the preset direction includes but is not limited to the slow time direction of ultrasonic scanning, the direction of adjacent scanning lines, the direction of adjacent channels, etc. The first measured value may be data measured at the first measurement point. For example, the ultrasonic probe performs multiple scans on the same scanning position and receives echo signals. According to the specified depth, the current measurement point is determined from a certain scan. Then, the first measurement point can be determined from the echo signals corresponding to one or more transmissions before the scan in the preset direction. Accordingly, the data scanned at the first measurement point is the first measured value; for one transmission of the ultrasonic probe, the echo signals can also be received through multiple channels. The channel before the current channel at the specified scanning depth can be used as the first measurement point. For example, the first two channels adjacent to the current channel can be used as the first measurement point. Accordingly, the first measured value can be obtained according to the echo signal received at the first measurement point. It is understandable that the first measurement point may be one point or multiple points, and the first measured value is data measured at each first measurement point, wherein different depths correspond to different first measurement points, or in other words, different image coordinates correspond to different first measurement points.

[0051] In a specific implementation, in order to avoid directly compressing the measured value of the current measurement point, the terminal may obtain a corresponding first measured value for a first measurement point before the current measurement point.

[0052] In practical applications, for the scanning data of the ultrasound system in the slow time direction, the adjacent scanning line direction or the adjacent channel direction, the current measurement point can be recorded as , take one or more measurement points before the current measurement point as the first measurement point, then the first measurement point can be recorded as , , ..., the data scanned at the first measurement point , , ...is the first measured value.

[0053] Step S104: determining a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point.

[0054] The second measurement point may be a current measurement point on a receiving line in a preset direction. The first predicted value may be a predicted value of the second measurement point in a compression phase.

[0055] In a specific implementation, the terminal may determine a first predicted value for a second measurement point adjacent to and after the first measurement point based on the first measured value of the first measurement point. The specific prediction method includes but is not limited to using a preset filter.

[0056] In actual applications, the terminal can , , ..., one or more, input the preset filter, and get the current scan point The predicted value of , which is the first predicted value of the second measurement point.

[0057] Step S106, determining a difference between the first predicted value and a second measured value of the second measuring point.

[0058] The second measured value may be data measured at the second measuring point. It is understandable that the first measured value and the second measured value may be pixel values ​​in the scanned image, or voltage values ​​or current values ​​of the original received signal, and the present application does not limit this.

[0059] In a specific implementation, the terminal may obtain a second measured value of a second measurement point, and calculate a difference between the first predicted value and the second measured value.

[0060] In practical applications, the current scan point Scanned data Recorded as the second measured value, calculate the second measured value With the first prediction value The difference between .

[0061] Step S108: compress the difference to obtain target compressed data corresponding to the second measuring point.

[0062] The target compressed data may be compressed data corresponding to the current measurement point in a preset direction, and is used to reflect the actual measurement value of the current measurement point.

[0063] In a specific implementation, the terminal may compress the difference between the first predicted value and the second measured value to obtain target compressed data. Since the difference between the first predicted value and the second measured value is usually small, compressing the difference may reduce the amount of compressed data.

[0064] In practical applications, the difference Compression is performed, and the compression method includes but is not limited to lossless compression methods such as Huffman coding, algorithmic coding, and run length coding, and lossy compression methods such as JPEG (Joint Photographic Experts Group) compression.

[0065] The above-mentioned signal processing method obtains the first measured value of the first measuring point, determines the first predicted value of the second measuring point according to the first measured value, the second measuring point is adjacent to the first measuring point and after the first measuring point, determines the difference between the first predicted value and the second measured value of the second measuring point, compresses the difference, and obtains the target compressed data corresponding to the second measuring point; the predicted value of the second measuring point can be determined among multiple adjacent measuring points, and the second measuring point is measured to obtain the measured value of the second measuring point, and the difference between the predicted value and the measured value is compressed. Since the amount of data after compression is small, the amount of data in the storage, transmission and processing processes is reduced.

[0066] In an exemplary embodiment, the above step S104 may specifically include: performing linear extrapolation processing according to first measured values ​​of two adjacent first measurement points to obtain a first predicted value.

[0067] In a specific implementation, the terminal may select two adjacent first measurement points, obtain a first measured value of each first measurement point, and perform linear extrapolation according to the first measured values ​​of the two adjacent first measurement points to obtain a first predicted value.

[0068] Figure 2 A schematic diagram of a two-point linear extrapolation is provided. Figure 2 , taking the slow time direction as an example, for the current scan point , select the first two adjacent scan points , , and the corresponding first measured values ​​are , , the difference between the two first measured values ​​is

[0069] ,

[0070] According to the linear extrapolation of the two points, the scan point is obtained The first prediction value is

[0071] ,

[0072] Then you can calculate the scan points The difference between the second measured value and the first predicted value is obtained

[0073] ,

[0074] right to compress.

[0075] In this embodiment, by performing linear extrapolation processing according to the first measured values ​​of two adjacent first measurement points to obtain the first predicted value, the first predicted value can be quickly determined, thereby improving the efficiency of signal compression.

[0076] In an exemplary embodiment, the above step S104 may specifically include: obtaining a target prediction value of a first measurement point; and performing a weighted summation of the target prediction value and the first measured value to obtain a first prediction value.

[0077] The target prediction value may be a prediction value of the first measurement point.

[0078] In a specific implementation, compression can be iterated in a chain manner between multiple measurement points. The terminal can use the predicted value of the first measurement point obtained when the first measurement point was compressed last time as the target predicted value, and perform weighted summation of the target predicted value and the first measured value of the first measurement point according to a pre-set weight to obtain the first predicted value of the second measurement point.

[0079] In practical applications, compression can be iterated in a chained manner between multiple measurement points at the same depth. The predicted value of the previous scan point can be obtained The predicted value of , before obtaining the previous scan point The measured value After that, the weights of the predicted value and the measured value can be set as and , , then the first prediction value of the current scan point is

[0080] .

[0081] In this embodiment, by obtaining a target prediction value of the first measurement point and performing weighted summation on the target prediction value and the first measured value to obtain a first prediction value, the prediction value can be flexibly determined based on signal characteristics to improve the accuracy of signal compression.

[0082] In an exemplary embodiment, after the above step S106, it may further include: when the difference exceeds a preset range, determining a substitute value of the difference; the substitute value includes a near average value, a near maximum value, a preset value or a second measured value; compressing the substitute value to obtain target compressed data.

[0083] The preset range may be a valid range of the difference value. The replacement value may be a value used to replace the difference value. The adjacent average value may be an average value of adjacent measured values. The adjacent maximum value may be a maximum value among adjacent measured values. The preset value may be a preset value, for example, 0.

[0084] In a specific implementation, the terminal can determine whether the difference between the first predicted value and the second measured value exceeds a preset range. If it does not exceed the preset range, the difference can be compressed to obtain target compressed data. Otherwise, if the difference exceeds the preset range, a predetermined substitute value can be used to replace the difference, and the substitute value can be compressed to obtain target compressed data. The substitute value includes but is not limited to a near average value, a near maximum value, a preset value or a second measured value, etc. When the second measured value is used, the obtained second measured value is directly compressed.

[0085] In this embodiment, when the difference exceeds the preset range, a substitute value of the difference is determined, and the substitute value is compressed to obtain target compressed data, so as to ensure that the calculated difference meets the preset range and improve the accuracy of signal compression.

[0086] In an exemplary embodiment, after the above step S106, the method may further include: performing noise reduction processing on the difference to obtain the noise-reduced difference; and performing compression processing on the noise-reduced difference to obtain target compressed data.

[0087] In a specific implementation, the difference between the first predicted value and the second measured value may contain random broadband noise. The terminal may perform noise reduction processing such as filtering on the difference to obtain the noise-reduced difference, and compress the noise-reduced difference to obtain target compressed data.

[0088] In this embodiment, by performing noise reduction processing on the difference to obtain the noise-reduced difference, and performing compression processing on the noise-reduced difference to obtain target compressed data, the noise in the difference can be removed and the accuracy of signal compression can be improved.

[0089] In an exemplary embodiment, after the above step S106, the method may further include: filtering the difference to obtain a filtered difference; fusing the difference with the filtered difference to obtain a fused difference; and compressing the fused difference to obtain target compressed data.

[0090] In a specific implementation, the terminal can further predict and calculate the difference of the difference signal. Specifically, the difference signal can be first Input the preset filter and get the filtered difference , the difference The difference between the filtered Subtract to get the difference after fusion , the difference after fusion The target compressed data is obtained by compressing. It can be understood that this process can be regarded as a second measured value. Perform second-order differentiation.

[0091] In this embodiment, the difference is filtered to obtain a filtered difference, the difference is fused with the filtered difference to obtain a fused difference, the fused difference is compressed to obtain target compressed data, and the second-order differential of the second measured value can be compressed to further reduce the amount of data for signal compression.

[0092] In an exemplary embodiment, the above-mentioned step S108 may specifically include: determining the mask data corresponding to the difference, compressing the mask data to obtain compressed mask data; reducing the non-zero value in the difference to obtain the reduced difference, using the reduced difference as the new difference, returning to the step of determining the mask data corresponding to the difference, compressing the mask data to obtain the compressed mask data, until the first termination condition is met; determining the target compressed data based on the compressed mask data finally obtained.

[0093] The mask data may be data obtained by representing zero values ​​with 0 and non-zero values ​​with 1. The first termination condition may be that there are no non-zero values ​​in the difference, or the number of non-zero values ​​in the difference is less than a preset threshold, or there are only 0 and 1 in the difference, and the present application does not limit this.

[0094] In a specific implementation, the terminal may record the zero value in the difference as 0 and the non-zero value as 1 to obtain the corresponding mask data, compress the mask data to obtain the first compressed mask data, and then, if the first termination condition is not met, the non-zero value in the difference is reduced to obtain the reduced difference, the reduced difference is used as the new difference, and the process returns to the process of recording the zero value in the difference as 0 and the non-zero value as 1 to obtain the corresponding mask data, and compressing the mask data to obtain the second compressed mask data. If the first termination condition is not met, the above process is repeated to obtain several compressed mask data in turn until there is no non-zero value in the difference, and finally the compressed mask data obtained can be used as the target compressed data.

[0095] Figure 3 Schematic diagram of difference data in one embodiment. Figure 3, the difference between the first predicted value and the second measured value can be a two-dimensional matrix. Taking the first termination condition that there is no non-zero value in the difference as an example, the non-zero values ​​in the difference can be recorded as 1 to obtain the first mask data. After compression, the first compressed mask data can be obtained. Then, all non-zero values ​​in the difference can be reduced by 1, for example, 1-1=0, 23-1=22, 18-1=17..., to obtain a new difference, and all non-zero values ​​in the new difference are recorded as 1 to obtain the second mask data. After compression, the second compressed mask data can be obtained. Then, all non-zero values ​​in the new difference can be reduced by 1 again, for example, 22-1=21, 17-1=16..., and the above process is repeated until there is no non-zero value in the difference. Assuming that it is repeated N times, the N compressed mask data obtained can be used as the target compressed data. It should be noted that the second and subsequent compressions mentioned above may compress all 0s and 1s in the current mask data, or may only compress 1s in the current mask data, and this application does not impose any limitation on this.

[0096] In this embodiment, by determining the mask data corresponding to the difference, compressing the mask data to obtain compressed mask data, reducing the non-zero value in the difference to obtain the reduced difference, using the reduced difference as the new difference, returning to the step of determining the mask data corresponding to the difference, compressing the mask data to obtain compressed mask data, until the first termination condition is met, determining the target compressed data based on the compressed mask data finally obtained, which can increase the compression ratio and reduce the amount of compressed data.

[0097] In an exemplary embodiment, the first measuring point and the second measuring point are both in a preset direction; the preset direction includes a slow time direction of ultrasonic scanning, a direction of adjacent scanning lines, or a direction of adjacent channels.

[0098] The slow time direction may be scanning data obtained by performing multiple scans on the same part at the same depth each time. The adjacent scan line direction may be scanning data obtained in the direction of multiple adjacent scan lines. The adjacent channel direction may be scanning data obtained in the direction of multiple adjacent channels.

[0099] In a specific implementation, the terminal may, but is not limited to, acquire the first measurement point and the second measurement point in the slow time direction, the adjacent scanning line direction, or the adjacent channel direction of the ultrasonic scanning. and the scan point before the current scan point , , ... can be in the same slow time direction, the direction of adjacent scan lines, or the direction of adjacent channels.

[0100] In this embodiment, by making the first measuring point and the second measuring point both in the preset direction, since the measured values ​​in the preset direction do not change much, the difference between adjacent measured values ​​is small, and the small difference is compressed to further reduce the amount of compressed data.

[0101] In an exemplary embodiment, the above step S102 may specifically include: acquiring raw data obtained by scanning at the first measuring point; preprocessing the raw data to obtain a first measured value; the preprocessing includes at least one of amplification, filtering, analog-to-digital conversion, beamforming, coherent compounding, and envelope detection.

[0102] The original data may be data obtained from the original scan.

[0103] In a specific implementation, after acquiring the original data of the first measurement point, the terminal may amplify, filter, perform analog-to-digital conversion, beamform, coherent recombination, envelope detection and other processing on the original data to obtain the first measured value.

[0104] In this embodiment, by acquiring the original data obtained by scanning at the first measuring point and preprocessing the original data to obtain the first measured value, the accuracy of the first measured value can be increased, thereby improving the accuracy of signal compression.

[0105] In an exemplary embodiment, Figure 4 As shown, after the above step S108, the following steps may be specifically included:

[0106] Step S202, determining a second predicted value of a second measurement point, and decompressing the target compressed data to obtain decompressed data;

[0107] Step S204: obtaining a target measured value of the second measuring point according to the decompressed data and the second predicted value.

[0108] The second predicted value may be a predicted value of the second measurement point in the decompression stage. The decompressed data may be the target compressed data after decompression. The target measured value may be the actual measured value of the second measurement point obtained after decompression.

[0109] In a specific implementation, the terminal can use the same method as compression to determine the predicted value of the second measurement point, obtain the second predicted value, and decompress the target compressed data to obtain the decompression processing. Based on the decompressed data and the second predicted value, the terminal can determine the target measured value of the second measurement point.

[0110] In practical applications, taking two-point linear extrapolation as an example, for the current scan point , the first measured value of the first two adjacent scanning points can be calculated , The difference between

[0111] ,

[0112] The second prediction value is

[0113] ,

[0114] According to the decompressed data With the second predicted value The target measured value of the current scan point is

[0115] .

[0116] In this embodiment, by determining the second predicted value of the second measuring point and decompressing the target compressed data to obtain decompressed data, the target measured value of the second measuring point is obtained based on the decompressed data and the second predicted value, thereby achieving reliable transmission of the actual measured value of the current scanning point.

[0117] In an exemplary embodiment, the first predicted value and the second predicted value are obtained by inputting the first measured value into a filter; after the above step S204, it can also specifically include: adjusting the coefficient of the filter according to the target measured value to obtain an adjusted filter; obtaining a new first predicted value and a new second predicted value according to the adjusted filter; taking the second measurement point as a new first measurement point, and returning to the step of obtaining the first measured value of the first measurement point until the second termination condition is met.

[0118] The second termination condition may be, but is not limited to, the end of the scan.

[0119] In a specific implementation, both the first predicted value and the second predicted value can be obtained by inputting the first measured value into a preset filter. After determining the target measured value, the filter coefficient can be adjusted according to the difference between the target measured value and the second measured value to obtain an adjusted filter. At the next scanning point, the adjusted filter can be used to obtain a new first predicted value and a new second predicted value. The aforementioned compression and decompression process is repeated to obtain new target compressed data and a new target measured value until the second termination condition is reached, for example, the scanning is completed.

[0120] In practical applications, after decompression is completed, the filter coefficient can be adjusted according to the difference between the target measured value and the second measured value. In the next compression stage of the iterative process, the new first prediction value can be determined based on the adjusted filter coefficient, and compression processing can be performed using the new first prediction value. Similarly, the next decompression stage of the iterative process can also determine the new second prediction value based on the adjusted filter coefficient, and decompression processing can be performed using the new second prediction value.

[0121] In this embodiment, the coefficient of the filter is adjusted according to the target measured value to obtain an adjusted filter, and a new first predicted value and a new second predicted value are obtained according to the adjusted filter. The second measurement point is used as the new first measurement point, and the process returns to the step of obtaining the first measured value of the first measurement point until the second termination condition is met. The filter used in the compression process and the decompression process can be updated in real time, thereby improving the accuracy of the first predicted value and the second predicted value, thereby improving the accuracy of signal compression.

[0122] In an exemplary embodiment, before the above step S102, it may further include: determining a first measurement point according to the reduced pulse repetition frequency; after the above step S204, it may further include: interpolating the target measured value according to the reduced pulse repetition frequency.

[0123] In a specific implementation, in order to further reduce the amount of data, during the compression process, the terminal can reduce the pulse repetition frequency, and perform sampling in a preset direction according to the reduced pulse repetition frequency to obtain a first measurement point. Correspondingly, during the decompression process, the terminal can interpolate the obtained target measured value according to the reduced pulse repetition frequency. The interpolation methods include but are not limited to linear interpolation, spline interpolation, multi-phase filtering interpolation, etc.

[0124] In this embodiment, by reducing the pulse repetition frequency in the compression stage, sampling is performed in a preset direction according to the reduced pulse repetition frequency to obtain a first measurement point, and in the decompression stage, the target measured value is interpolated according to the reduced pulse repetition frequency, which can further reduce the amount of data and improve the compression ratio of signal compression.

[0125] In order to facilitate those skilled in the art to have a deeper understanding of the embodiments of the present application, a specific example will be described below.

[0126] The present application proposes a signal compression method, which compresses the signal by performing adaptive or non-adaptive predictive filtering in the slow time direction, the adjacent scan line direction or the adjacent channel direction, which helps to reduce the amount of ultrasonic signal data and the power consumption of the ultrasonic system, and can also remove ultrasonic signal noise and improve the data signal-to-noise ratio. After experimental verification, the compression ratio can reach more than 40%. The method can be applied to, but not limited to, desktop ultrasound systems, portable ultrasound systems, handheld ultrasound systems, etc. It can be understood that based on the same theory, through appropriate modifications and adjustments, the method can also be applied to other signal acquisition systems, such as radar, light laser detection and ranging (LiDAR), sonar (sound navigation and ranging, SONAR), computed tomography (CT), magnetic resonance (MR), etc.

[0127] Medical ultrasound signals between adjacent channels, adjacent scan lines, adjacent transmissions, or adjacent frames have great similarity. Figure 5 It is the data after beam synthesis of multiple consecutive transmissions at the same location, with a pulse repetition frequency PRF = 4400Hz. It can be seen that the data changes slowly between adjacent transmissions. Based on this principle, the application predicts the next set of data based on the previous set or sets of data, compresses and stores the difference between the predicted value and the actual measured value, thereby effectively reducing the amount of signal data.

[0128] Figure 6 A signal compression and decompression system for an ultrasound system is provided. Figure 6 The ultrasound system includes a front-end processing module (Front End Processing) and a back-end processing module (Back End Processing). The front-end processing module controls the TX&TR switch (TR switch) to switch the ultrasound array transducer (Ultrasound array) to the transmitting or receiving state. In the receiving stage, the front-end processing module controls the TR switch to receive the echo signal from the transducer element, and the analog front-end module (Analog Front Ended) amplifies and filters the signal, and converts the analog signal into a digital signal through the analog to digital converter (ADC), and transmits it to the front-end processing module for compression processing. After the front-end processing module completes data compression, the compressed data is sent to the back-end processing module, and the back-end processing module completes data decompression and restores the original signal.

[0129] Optionally, the ultrasound system can perform any digital signal processing on the signal after ADC sampling, including but not limited to beamforming, coherent compounding, envelope detection, etc., and can send the ultrasound data after any stage to the front-end processing module for compression, for example, it can be the beamformed data of the previous one or several frames, or it can be the original channel data of the previous one or several "transmit / receive" cycles. The system and method compress the signal after ADC, not limited to the signal after beamforming.

[0130] The front-end processing module is a processing module directly connected to the ADC, usually one or more FPGAs, or an ASIC. The front-end processing module contains three computing units: a prediction filtering unit, a difference calculation unit, and a data compression unit, which realize the function of compressing the sampled data and transmit the compressed data to the back-end sampling module. Among them, the prediction filtering unit is used to generate prediction data, the difference calculation unit is used to calculate the difference between the prediction data and the collected data: DeltaData =MeasuredData – PredicatedData, and the data compression unit is used to compress the difference calculation unit.

[0131] The back-end processing module is another signal processing module connected to the front-end processing module, including but not limited to FPGA, GPU, CPU, DSP, ASIC and other chips. The back-end processing module includes a prediction filtering unit, a filter coefficient calculation unit and a data decompression unit, which realizes the decompression function of the compressed data and transmits the filter coefficient to the front-end processing module. Among them, the prediction filtering unit is used to generate prediction data, the data decompression unit is used to restore the collected data: MeasuredData = DeltaData + PredicatedData, and the filter coefficient calculation unit is used to generate appropriate filter coefficients.

[0132] Among them, the signal transmission method includes but is not limited to Wi-Fi, USB, PCI computer bus (PCI Express, PCI-E), Bluetooth and other wired or wireless data transmission media.

[0133] Figure 7 and Figure 8 The specific processes of signal compression and decompression are provided respectively. The signal compression process can be performed by the front-end processing module, including the following steps:

[0134] Step S301, firstly, the scanning parameters need to be initialized;

[0135] Step S302, start scanning sequence;

[0136] Step S303, predicting the signal using the updated filter coefficient and previous data (first measured value) to obtain predicted data (first predicted value);

[0137] Step S304, receiving collected data (second measured value);

[0138] Step S305, calculating the difference between the predicted data and the collected data;

[0139] Step S306, compressing the difference data;

[0140] Step S307, sending the compressed difference data (target compressed data) to the backend processing module;

[0141] Step S308, determining whether the scan is finished, if not, updating the filter coefficients and the collected data as the filter input at the next moment;

[0142] The signal decompression process may be performed by a backend processing module, including the following steps:

[0143] Step S401, firstly, the scanning parameters need to be initialized;

[0144] Step S402, sending scanning parameters such as filter coefficients to a front-end processing module;

[0145] Step S403, predicting the signal using the filter and previous data (first measured value) to obtain predicted data (second predicted value);

[0146] Step S404, receiving compressed difference data (target compressed data) from the front-end processing module;

[0147] Step S405, decompressing the compressed difference data to obtain decompressed data;

[0148] Step S406, calculate the sum of the decompressed data and the predicted data (second predicted value), restore the collected data (obtain the target measured value), and complete the decompression process.

[0149] It can be understood that after step S406, subsequent signal processing processes can be performed. For example, step S407 can be added to determine whether the scan is ended. If the scan is not ended, the filter parameters and collected data are updated as the filter input for the next scan moment. Otherwise, if the scan is ended, no further processing is performed.

[0150] The front-end processing module and the back-end processing module may use the same set of prediction filter coefficients, which are generated by the back-end processing module and sent to the front-end processing module. The prediction filter generates prediction data (first prediction value) through the previously collected data (first measured value). Fig. 9Taking compressed frame image data as an example, the data of the previous frame or multiple frames are input into the prediction filter to generate predicted frame data. The difference between the predicted data (predicted frame) and the acquired data (measured frame) is calculated to obtain the difference data (difference frame).

[0151] Both the front-end processing module and the back-end processing module include a calculation unit for predictive filtering, which can be based on any adaptive filtering algorithm, including but not limited to Wiener Filter, Kalman Filter, Linear Prediction Filter, etc. It can also be a non-adaptive prediction filter, including but not limited to filtering based on finite impulse response (FIR) or infinite impulse response (IIR) or other predictive calculation units. By selecting a suitable prediction algorithm, the difference between the predicted data and the collected data can be minimized to facilitate data compression.

[0152] The specific implementation of the prediction filter is described in detail below. Optionally, a simple non-adaptive prediction can reduce the computational burden. One way is to use an FIR filter, such as two-point linear extrapolation, such as Figure 2 As shown, for the front-end processing module, the following calculations are performed:

[0153] ,

[0154] ,

[0155] ,

[0156] in, , , ...is the difference between adjacent measurements in the slow time direction, that is, the differential sequence in the slow time direction. , , ...is the measured value in the slow time direction, that is, the ideal value superimposed with noise. , , ...are the predicted values ​​in the slow time direction. , , ...is the difference between the measured and predicted values ​​in the slow time direction. Represents different moments.

[0157] The backend processing module performs the following calculations:

[0158] ,

[0159] ,

[0160] .

[0161] The above is only an example. In practical applications, a higher-order FIR filter or a polynomial extrapolation method can be used to implement it.

[0162] Another way is IIR filter, for example

[0163] .

[0164] in, The range is 0~1, which is the weight of the previous measured value and the previous predicted value. , it means that the weight of the measured value is the largest. If , it means that the weight of the predicted value is the largest. The value of can be set in advance according to the characteristics of the signal. In practical applications, more orders of IIR filters can also be used.

[0165] In some examples, an adaptive filter may be used to achieve a higher compression ratio. Linear Predictive Coding (LPC) is a commonly used adaptive prediction algorithm, which is often used for encoding, recognition and reconstruction of speech signals. The basic composition of LPC is an FIR-based extrapolation filter, which adaptively adjusts the FIR coefficients by minimizing the prediction error. Fig.10 The predicted difference probability distribution of image data using two prediction methods is shown. It can be seen that compared with the difference directly generated by linear extrapolation of two frames of images, the linear prediction method makes the difference distribution more concentrated, contains more zero values, and is easier to compress.

[0166] In calculation or When or Whether the signal is within the set range. If it exceeds the effective value, the collected data (the second measured value) can be directly transmitted, or it can be replaced by other values. The replacement methods include but are not limited to the average of adjacent values, the maximum effective value, and the value of zero.

[0167] Furthermore, in medical ultrasound imaging, the difference between the predicted value and the acquired value (actual value, measured value) Contains random broadband noise, which can be generated in the slow time direction or at each moment The data is filtered, but it cannot pass Fully restore the measured value ,use Express The result obtained after filtering, that is, the difference after filtering, is calculated according to the second prediction value. and To recover the measured value, we can use the formula To achieve this, it is understandable that With measured value There is still a certain error between them, wherein the filtering method includes but is not limited to FIR filter, IIR filter, adaptive filtering and other noise suppression related algorithms.

[0168] In order to further reduce the amount of data, under the condition of meeting the set threshold (such as the difference is less than a certain threshold), the pulse repetition frequency PRF can be reduced, that is, the sampling rate in the slow time direction can be reduced, and after the back-end processing module restores the original data (obtains the target measured value), new data can be generated by interpolation. Interpolation methods include but are not limited to linear interpolation, spline interpolation, polyphase filter interpolation, etc.

[0169] Based on the same principle, the difference signal can be further predicted and the difference can be calculated. In the front-end processing module, the following calculation is performed

[0170] ,

[0171] in, is the input to the prediction filter , the predicted value after filtering (the difference after filtering). This is equivalent to the second-order differential of the original signal in the slow time direction to a certain extent. In the back-end processing module, by inputting The prediction filter with the same coefficients is obtained , and then according to , we can get The specific formula is

[0172] .

[0173] There are many methods for compressing difference signals. Lossless compression methods such as Huffman coding, algorithmic coding, run-length coding, etc. can be used, and lossy compression methods such as various JPEG compressions can also be used. The following provides a difference compression method.

[0174] Take the frame image difference data as an example. Like ultrasound images, this data is a two-dimensional data. Each pixel has a value. The directions are the horizontal and vertical coordinates of the image, The direction is the slow time direction. Figure 3The figure shows the difference data matrix of a certain frame, and there is data with a difference greater than 0. First, filter out all zero values ​​and encode all non-zero data as 1, so that a 1-bit matrix of the same size as the difference frame can be obtained as the Mask(0) matrix. If the size of a frame of data is 500×500, the number of bits of the Mask(0) matrix is ​​500×500×1 bits. Encode and compress the Mask(0) matrix. Since the proportion of 0 in the matrix is ​​large, the mask matrix can be encoded using run length encoding, Golomb encoding, or Golomb-Rice encoding.

[0175] Next, all non-zero data in the previous step are subtracted by 1, and all non-zero data after subtraction are encoded as 1, resulting in a Mask(1) matrix whose size is the number of 1s in Mask(0). Mask(1) is encoded in a similar way as above, and so on, until the number of non-zero data is less than the set threshold.

[0176] According to the compression method adopted, the compressed difference data is decompressed accordingly in the decompression unit of the back-end processing module. For example, Mask (0), Mask (1), ... can be extracted from the compressed difference data in sequence, and Mask (0), Mask (1), ... can be summed to obtain the decompressed data.

[0177] In one embodiment, Fig.11 As shown, a signal processing method is provided, comprising the following steps:

[0178] Step S501, obtaining a first measured value of a first measuring point;

[0179] Step S502, determining a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point;

[0180] Step S503, determining the difference between the first predicted value and the second measured value of the second measuring point;

[0181] Step S504, compressing the difference to obtain target compressed data corresponding to the second measurement point;

[0182] Step S505, determining a second predicted value of a second measurement point, and decompressing the target compressed data to obtain decompressed data;

[0183] Step S506, obtaining a target measured value of the second measuring point according to the decompressed data and the second predicted value;

[0184] Step S507, adjusting the coefficient of the filter according to the target measured value to obtain an adjusted filter;

[0185] Step S508, obtaining a new first prediction value and a new second prediction value according to the adjusted filter;

[0186] Step S509: taking the second measurement point as a new first measurement point, and returning to step S501 until the second termination condition is met.

[0187] In a specific implementation, the terminal can determine the first predicted value of the current second measurement point based on the first measured value of the previous first measurement point, calculate the difference between the first predicted value and the second measured value of the second measurement point, compress the difference, and obtain target compressed data. The target compressed data can then be stored, transmitted, processed, etc. In the corresponding decompression process, the second predicted value of the second measurement point can be determined, and the target compressed data can be decompressed. The target measured value of the second measurement point can be obtained based on the sum of the decompressed data and the second predicted value, and the decompression process is completed. Afterwards, the filter coefficients of the compression and decompression processes can be adjusted based on the difference between the target measured value and the second measured value, the filter, the first predicted value and the second predicted value are updated, and the second measurement point is used as the new first measurement point. The above compression and decompression processes are repeated until a preset termination condition is reached.

[0188] Since the specific processing process of the terminal has been described in detail in the above embodiments, it will not be repeated here.

[0189] The above-mentioned signal processing method can determine the predicted value of the second measurement point among multiple adjacent measurement points, measure the second measurement point to obtain the actual measured value of the second measurement point, and compress the difference between the predicted value and the actual measured value. Since the amount of data after compression is small, the amount of data in the storage, transmission and processing process is reduced.

[0190] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0191] Based on the same inventive concept, the embodiment of the present application also provides a signal processing device for implementing the signal processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more signal processing device embodiments provided below can refer to the limitations on the signal processing method above, and will not be repeated here.

[0192] In an exemplary embodiment, Fig.12 As shown, a signal processing device is provided, including: an acquisition module 502, a prediction module 504, a calculation module 506 and a compression module 508, wherein:

[0193] An acquisition module 502 is used to acquire a first measured value of a first measurement point;

[0194] A prediction module 504 is used to determine a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point;

[0195] A calculation module 506, configured to determine a difference between the first predicted value and a second measured value of the second measurement point;

[0196] The compression module 508 is used to compress the difference to obtain target compressed data corresponding to the second measurement point.

[0197] In an exemplary embodiment, the prediction module 504 is further configured to perform linear extrapolation processing according to the first measured values ​​of two adjacent first measurement points to obtain the first predicted value.

[0198] In an exemplary embodiment, the prediction module 504 is further configured to obtain a target prediction value of the first measurement point; and perform a weighted summation of the target prediction value and the first measured value to obtain the first prediction value.

[0199] In an exemplary embodiment, the compression module 508 is also used to determine a substitute value of the difference when the difference exceeds a preset range; the substitute value includes a near average value, a near maximum value, a preset value or the second measured value; and compress the substitute value to obtain the target compressed data.

[0200] In an exemplary embodiment, the compression module 508 is further configured to perform noise reduction processing on the difference to obtain the noise-reduced difference; and perform compression processing on the noise-reduced difference to obtain the target compressed data.

[0201] In an exemplary embodiment, the compression module 508 is further used to filter the difference to obtain a filtered difference; merge the difference with the filtered difference to obtain a merged difference; and compress the merged difference to obtain the target compressed data.

[0202] In an exemplary embodiment, the compression module 508 is also used to determine the mask data corresponding to the difference, compress the mask data to obtain compressed mask data; reduce the non-zero value in the difference to obtain the reduced difference, use the reduced difference as the new difference, return to the mask data corresponding to the determined difference, compress the mask data to obtain the compressed mask data, until the first termination condition is met; determine the target compressed data based on the compressed mask data finally obtained.

[0203] In an exemplary embodiment, the first measuring point and the second measuring point are both in a preset direction; the preset direction includes a slow time direction of ultrasonic scanning, a direction of adjacent scanning lines, or a direction of adjacent channels.

[0204] In an exemplary embodiment, the acquisition module 502 is also used to acquire the original data obtained by scanning the first measurement point; preprocess the original data to obtain the first measured value; the preprocessing includes at least one of amplification, filtering, analog-to-digital conversion, beamforming, coherent compounding, and envelope detection.

[0205] In an exemplary embodiment, the signal processing device further includes a decompression module for determining a second predicted value of the second measurement point, and performing decompression processing on the target compressed data to obtain decompressed data; and obtaining a target measured value of the second measurement point based on the decompressed data and the second predicted value.

[0206] In an exemplary embodiment, the first predicted value and the second predicted value are obtained by inputting the first measured value into a filter; the above-mentioned signal processing device also includes an iteration module, which is used to adjust the coefficient of the filter according to the target measured value to obtain an adjusted filter; according to the adjusted filter, a new first predicted value and a new second predicted value are obtained; the second measurement point is used as a new first measurement point, and the step of obtaining the first measured value of the first measurement point is returned to until the second termination condition is met.

[0207] In an exemplary embodiment, the signal processing device further includes a sampling module, which is used to determine the first measurement point according to the reduced pulse repetition frequency; and to perform interpolation processing on the target measured value according to the reduced pulse repetition frequency.

[0208] Each module in the above signal processing device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0209] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.13 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a signal processing method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0210] Those skilled in the art will understand that Fig.13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0211] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0212] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0213] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0214] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0215] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0216] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 application.

[0217] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A signal processing method, characterized in that: The method comprises: Obtaining a first measured value of a first measuring point; Determine a first predicted value of a second measurement point according to the first measured value; the second measurement point is adjacent to the first measurement point and is after the first measurement point; determining a difference between the first predicted value and a second measured value at the second measurement point; The difference is compressed to obtain target compressed data corresponding to the second measuring point.

2. The method according to claim 1, characterized in that Determining a first predicted value of a second measuring point according to the first measured value includes: Linear extrapolation is performed based on the first measured values ​​of two adjacent first measuring points to obtain the first predicted value.

3. The method according to claim 1, characterized in that Determining a first predicted value of a second measuring point according to the first measured value includes: Obtaining a target prediction value of the first measurement point; The target predicted value and the first measured value are weightedly summed to obtain the first predicted value.

4. The method according to claim 1, characterized in that: After determining the difference between the first predicted value and the second measured value of the second measuring point, the method further includes: In the case where the difference value exceeds a preset range, determining a substitute value for the difference value; the substitute value includes a near average value, a near maximum value, a preset value or the second measured value; The replacement value is compressed to obtain the target compressed data.

5. The method according to claim 1, characterized in that After determining the difference between the first predicted value and the second measured value of the second measuring point, the method further includes: Performing noise reduction processing on the difference to obtain a noise-reduced difference; The noise-reduced difference is compressed to obtain the target compressed data.

6. The method according to claim 1, characterized in that After determining the difference between the first predicted value and the second measured value of the second measuring point, the method further includes: Performing filtering on the difference to obtain a filtered difference; Merging the difference with the filtered difference to obtain a fused difference; The fused difference is compressed to obtain the target compressed data.

7. The method according to claim 1, characterized in that The compressing the difference to obtain target compressed data corresponding to the second measurement point includes: Determine the mask data corresponding to the difference value, and compress the mask data to obtain compressed mask data; A step of reducing non-zero values ​​in the difference to obtain reduced difference, taking the reduced difference as a new difference, returning to the mask data corresponding to the determined difference, compressing the mask data to obtain compressed mask data, until a first termination condition is met; The target compressed data is determined according to the compressed mask data finally obtained.

8. The method according to claim 1, characterized in that The first measuring point and the second measuring point are both in a preset direction; the preset direction includes a slow time direction of ultrasonic scanning, a direction of adjacent scanning lines, or a direction of adjacent channels.

9. The method according to claim 1, characterized in that: The obtaining of a first measured value of a first measuring point includes: Acquire raw data obtained by scanning at the first measuring point; The raw data is preprocessed to obtain the first measured value; the preprocessing includes at least one of amplification, filtering, analog-to-digital conversion, beamforming, coherent compounding, and envelope detection.

10. The method according to claim 1, characterized in that After compressing the difference to obtain target compressed data corresponding to the second measurement point, the method further includes: determining a second predicted value of the second measurement point, and decompressing the target compressed data to obtain decompressed data; A target measured value of the second measuring point is obtained according to the decompressed data and the second predicted value.

11. The method according to claim 10, characterized in that The first predicted value and the second predicted value are obtained by inputting the first measured value into a filter; after obtaining the target measured value of the second measuring point according to the decompressed data and the second predicted value, the method further includes: According to the target measured value, adjusting the coefficient of the filter to obtain an adjusted filter; Obtaining a new first prediction value and a new second prediction value according to the adjusted filter; The second measuring point is used as a new first measuring point, and the process returns to the step of obtaining the first measured value of the first measuring point until a second termination condition is met.

12. The method according to claim 10, characterized in that Before obtaining the first measured value of the first measuring point, the method further includes: determining the first measurement point according to the reduced pulse repetition frequency; After obtaining the target measured value of the second measuring point according to the decompressed data and the second predicted value, the method further includes: The target measured value is interpolated according to the reduced pulse repetition frequency.

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