Method and device for adjusting ultrasonic image gain and ultrasonic equipment

Abstract

The invention relates to the technical field of ultrasonic imaging, and discloses a method for adjusting ultrasonic image gain, which comprises the following steps: carrying out noise estimation on frame data of an ultrasonic image frequency spectrum signal, and determining a noise estimation result; performing double-integral processing on the ultrasonic image spectrum signal based on the noise estimation result to obtain a double-integral processing result; performing low-pass filtering on the ultrasonic image frequency spectrum signal based on the double-integration result to generate a smooth mask; and adjusting the ultrasonic image frequency spectrum signal by using the smooth mask to weaken a noise signal in the ultrasonic image frequency spectrum signal or enhance an effective signal in the ultrasonic image frequency spectrum signal. According to the scheme, the gain of effective signals in the ultrasonic image is effectively improved, meanwhile, the gain of noise signals is reduced, detail loss of target tissue in the ultrasonic image can be reduced, and therefore the quality and diagnosis accuracy of the ultrasonic image are improved. The invention further discloses a device for adjusting the ultrasonic image gain and ultrasonic equipment.

Description

Technical Field

[0001] The present application relates to the field of ultrasonic imaging technology, for example, to a method and device for adjusting ultrasonic image gain, and ultrasonic equipment. Background Art

[0002] Ultrasonic pulse Doppler technology is an important branch of ultrasonic diagnostic technology. It is based on the pulse Doppler effect, which sends ultrasonic pulses to the target tissue, receives the reflected echo signals between pulses, and then analyzes these signals to obtain the movement information of the target tissue (such as blood flow). The ultrasound images obtained by ultrasonic pulse Doppler imaging enable doctors to accurately and quickly obtain the movement status information of relevant tissues in the patient's body, providing an important basis for the diagnosis and treatment of diseases. In related technologies, in the process of ultrasonic pulse Doppler imaging, signal filtering is used to remove the introduced noise.

[0003] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related art:

[0004] Removing introduced noise through signal filtering can easily cause loss of effective signals, resulting in loss of details of target tissues in ultrasound images, affecting the accuracy of doctors' disease diagnosis based on ultrasound images.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] The embodiments of the present disclosure provide a method and apparatus for adjusting the gain of an ultrasound image, and an ultrasound device to increase the gain of effective signals in an ultrasound image and reduce the gain of noise signals, thereby removing noise and reducing the loss of details of target tissues in the ultrasound image.

[0008] In some embodiments, the method for adjusting the gain of an ultrasonic image includes: performing noise estimation on frame data of an ultrasonic image spectrum signal to determine a noise estimation result; performing double integration processing on the ultrasonic image spectrum signal based on the noise estimation result to obtain a double integration processing result; performing low-pass filtering on the ultrasonic image spectrum signal based on the double integration result to generate a smoothing mask; and adjusting the ultrasonic image spectrum signal using the smoothing mask to weaken the noise signal in the ultrasonic image spectrum signal or enhance the effective signal in the ultrasonic image spectrum signal.

[0009] In some embodiments, a device for adjusting ultrasound image gain includes a processor and a memory storing program instructions, and the processor is configured to perform the aforementioned method for adjusting ultrasound image gain when executing the program instructions.

[0010] In some embodiments, the ultrasound device includes: an ultrasound device body; and the aforementioned device for adjusting ultrasound image gain, which is installed on the ultrasound device body.

[0011] The method and apparatus for adjusting ultrasound image gain and ultrasound equipment provided by the embodiments of the present disclosure can achieve the following technical effects:

[0012] In the technical solution of the present application, the frame data of the ultrasonic image spectrum signal is firstly estimated for noise to determine the noise estimation result, then the ultrasonic image spectrum signal is double-integrated based on the noise estimation result to obtain the double-integrated processing result, then the ultrasonic image spectrum signal is low-pass filtered based on the double-integrated result to generate a smoothing mask, and then the ultrasonic image spectrum signal is adjusted using the smoothing mask to weaken the noise signal in the ultrasonic image spectrum signal or enhance the effective signal in the ultrasonic image spectrum signal. In this way, through a series of steps such as noise estimation, double-integrated processing, low-pass filtering and smoothing mask adjustment, the gain of the effective signal in the ultrasonic image is effectively improved, while the gain of the noise signal is reduced. While removing noise, due to the accuracy of the smoothing mask and the enhancement of the low-frequency components by the double-integrated processing, the solution can reduce the loss of details of the target tissue in the ultrasonic image, thereby improving the quality of the ultrasonic image and the diagnostic accuracy.

[0013] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0015] Figure 1is a flow chart of a method for adjusting ultrasound image gain provided by an embodiment of the present disclosure;

[0016] Figure 2 It is a flowchart of performing noise estimation on frame data of an ultrasound image spectrum signal and determining a noise estimation result provided by an embodiment of the present disclosure;

[0017] Figure 3 It is a flowchart of performing double integration processing on an ultrasound image spectrum signal based on a noise estimation result to obtain a double integration processing result provided by an embodiment of the present disclosure;

[0018] Figure 4 is a flow chart of another method for adjusting ultrasound image gain provided by an embodiment of the present disclosure;

[0019] Figure 5 is a flow chart of another method for adjusting ultrasound image gain provided by an embodiment of the present disclosure;

[0020] FIG6( a ) is an ultrasound image without adjusting the ultrasound image gain provided by an embodiment of the present disclosure;

[0021] FIG6( b ) is an ultrasound image after adjusting the ultrasound image gain provided by an embodiment of the present disclosure;

[0022] Figure 7 is a structural schematic diagram of a device for adjusting ultrasound image gain provided by an embodiment of the present disclosure;

[0023] Figure 8 It is a schematic diagram of an ultrasonic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0025] The terms "first", "second", etc. in the specification and claims of the embodiments 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 terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0026] Unless otherwise specified, the term "plurality" means two or more than two. In the disclosed embodiment, the character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B. The term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, the three relationships of A and B. The term "corresponding" may refer to an association relationship or a binding relationship, and the correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0027] The basic path of ultrasonic Doppler imaging is to perform a fast Fourier transform on the received echo signal after windowing, and map its amplitude to a grayscale value of 0-255 for display on the screen. In ultrasonic Doppler imaging, there are different presets for different scanning parts. The blood flow velocity and intensity of different scanning parts are similar. This can be used as prior information in the preset to provide parameters for Doppler imaging. The technical solution disclosed in the present invention uses this prior knowledge to segment the data of the ultrasonic image spectrum into noise and effective signals, increase the gain of the effective signal, and reduce the gain of the noise part, thereby removing noise while ensuring less loss of other details.

[0028] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for adjusting ultrasound image gain, comprising the following steps:

[0029] S101, performing noise estimation on frame data of an ultrasonic image spectrum signal to determine a noise estimation result.

[0030] The result of noise estimation is iterated continuously with the increase of frame number. The noise estimation result of the first frame signal affects the iteration speed. After receiving the frame data of the ultrasound image spectrum signal, the noise estimation is performed on it. The purpose of noise estimation is to quantify the noise level in the ultrasound image and provide a benchmark for subsequent processing steps. Noise estimation involves statistical analysis of the signal to determine the statistical characteristics of the noise (such as mean, variance, etc.).

[0031] S102, performing double integration processing on the ultrasound image spectrum signal based on the noise estimation result to obtain a double integration processing result.

[0032] After obtaining the noise estimation result, the ultrasound image spectrum signal is subjected to double integration processing. Double integration processing can enhance the low-frequency components in the signal, because the low-frequency components will be accumulated during the integration process, while the high-frequency noise components will be weakened, which helps to initially improve the gain of the effective signal and reduce the gain of the noise signal.

[0033] S103, performing low-pass filtering on the ultrasound image spectrum signal based on the double integration result to generate a smoothing mask.

[0034] Based on the double integration results, the ultrasound image spectrum signal is low-pass filtered. The low-pass filter allows low-frequency signals to pass through, while blocking high-frequency signals (i.e., noise) from passing through. Through low-pass filtering, high-frequency noise can be further weakened while retaining the effective low-frequency signal. The generated smoothing mask (SoftMask) can more accurately reflect the location and intensity of the effective signal, providing guidance for subsequent adjustment steps.

[0035] S104: Use a smoothing mask to adjust the ultrasound image spectrum signal to weaken the noise signal in the ultrasound image spectrum signal or enhance the effective signal in the ultrasound image spectrum signal.

[0036] Optionally, adjusting the ultrasound image spectrum signal using a smoothing mask includes: applying the smoothing mask to the ultrasound image spectrum signal as a bias compensation.

[0037] Applying a smoothing mask as a bias compensation to the ultrasound image spectrum signal includes calculating the ultrasound image output spectrum signal according to the following formula:

[0038] Output=fft_log+SoftMask

[0039] Among them, Output is the output spectrum signal of the ultrasound image, fft_log is the spectrum signal of the ultrasound image, and SoftMask is the smoothing mask.

[0040] Optionally, the ultrasound image spectrum signal is adjusted using a smoothing mask, including: determining an offset according to the smoothing mask and a spectrum enhancement parameter, and applying the offset to the ultrasound image spectrum signal as a bias compensation.

[0041] Here, the spectral enhancement parameter (SpectralBoost) is an important preset parameter recorded in the preset file regarding the scanning site, and is used to specify the desired signal enhancement degree under specific circumstances.

[0042] Determining an offset according to a smoothing mask and a spectrum enhancement parameter includes calculating the offset according to the following formula:

[0043] Offset=min((SpectralBoost-SoftMask), 0)

[0044] Among them, Offset is the offset, SpectralBoost is the spectrum enhancement parameter, and SoftMask is the smoothing mask.

[0045] The goal of this calculation is to find an offset that is the minimum difference between SpectralBoost and SoftMask, without exceeding 0. This means that if the value of SoftMask is greater than SpectralBoost, offset will be 0; if the value of SpectralBoost is greater than SoftMask, offset will be equal to the difference between the two.

[0046] Applying the offset as bias compensation to the ultrasound image spectrum signal includes calculating the ultrasound image output spectrum signal according to the following formula:

[0047] Output=fft_log+Offset

[0048] Among them, Output is the ultrasound image output spectrum signal, fft_log is the ultrasound image spectrum signal, and Offset is the offset.

[0049] Offset acts as the final bias compensation in the spectrum signal, adjusting the gain of the spectrum signal so that the noise is weakened and the effective signal is enhanced. If Offset is positive, it will be subtracted from SpectralBoost, thereby reducing the gain of those areas indicated as noise by SoftMask; if Offset is 0 or negative, it will not affect SpectralBoost, so it will not change the gain of those areas indicated as effective signals by SoftMask.

[0050] In the technical solution of the present application, firstly, by estimating the noise of the frame data of the ultrasound image spectrum signal, an accurate noise benchmark is provided for subsequent processing. Through double integration processing, the low-frequency components (usually corresponding to the effective signal) in the signal are enhanced, while the high-frequency noise components are weakened, thereby improving the signal-to-noise ratio and laying the foundation for subsequent low-pass filtering and smoothing mask generation. Secondly, the low-pass filtering based on the double integration result further weakens the high-frequency noise while retaining the low-frequency effective signal. The generated smoothing mask can more accurately reflect the spatial distribution and intensity of the effective signal, providing a reliable basis for subsequent signal adjustment. Therefore, the technical solution of the present application can effectively enhance the effective signal in the ultrasound image while weakening the noise signal, which helps to improve the clarity and contrast of the ultrasound image and make the target tissue easier to identify and diagnose. In addition, due to the accuracy of the smoothing mask and the flexibility of the signal adjustment, the technical solution can minimize the loss of details of the target tissue, thereby providing more accurate and reliable diagnostic information.

[0051] In some embodiments, in combination Figure 2 As shown, performing noise estimation on frame data of ultrasound image spectrum signals and determining a noise estimation result comprises the following steps:

[0052] S201 : when the frame data of the ultrasound image spectrum signal is the first frame data, perform noise estimation using the spectrum centroid to determine a first noise estimation result.

[0053] Here, the first frame of data is the first frame of data of the ultrasound image spectrum signal. The spectrum centroid is the weighted average frequency of the signal power spectrum, where the weight of each frequency component is its corresponding power or amplitude, reflecting the main energy distribution of the signal. The spectrum centroid can be regarded as the "balance point" of the ultrasound image spectrum signal, which can be used to estimate the main frequency components or overall frequency trend of the ultrasound image spectrum signal.

[0054] Optionally, noise estimation is performed using the spectrum centroid to determine a first noise estimation result, including: calculating a power spectrum of an ultrasound image spectrum signal; determining the centroid of the ultrasound image spectrum signal based on the power spectrum of the ultrasound image spectrum signal; and treating frequency components in the ultrasound image spectrum signal that are higher than the centroid as noise to obtain a first noise estimation result.

[0055] The power spectrum P[k] of the ultrasound image spectrum signal is calculated by performing a fast Fourier transform (FFT) on the ultrasound image input signal to obtain the power of each frequency component.

[0056] In some possible implementations, the centroid is calculated according to the following formula:

[0057]

[0058] in, is the centroid of the spectrum, N is the length of the ultrasound image spectrum signal, P[k] is the power of the kth frequency component, and k is the frequency index.

[0059] After calculating the centroid, the frequency components in the ultrasound image spectrum signal that are higher than the centroid are considered as noise. The sum of the powers of the noise components is calculated as the initial value of the noise estimation. In some practical applications, the initial value of the noise estimation is determined according to the following formula:

[0060]

[0061] Among them, NoisePower is the initial value of the noise estimation, and cei1() represents rounding up (ceiling function).

[0062] S202: When the frame data of the ultrasound image spectrum signal is the second frame data, perform noise estimation using the spectrum amplitude to determine a second noise estimation result.

[0063] Here, the second frame data is subsequent frame data (second frame and later) of the ultrasonic image spectrum signal.

[0064] Optionally, the spectrum amplitude is used to perform noise estimation to determine a second noise estimation result, including: using a noise segmentation threshold to determine the effective signal segment and the noise segment in the power spectrum of the ultrasound image spectrum signal; merging the effective signal segment and the noise segment, and then respectively calculating the power sum of the effective signal and the power sum of the noise; based on the power sum of the effective signal and the power sum of the noise, obtaining a noise estimation value of the ultrasound image spectrum signal through an infinite impulse response filter; and treating the frequency components in the ultrasound image spectrum signal that are higher than the noise estimation value as noise to obtain a second noise estimation result.

[0065] In some practical applications, the amplitudes of elements in the frequency spectrum of the ultrasound image frequency spectrum signal whose amplitudes are in a preset order are found as noise segmentation thresholds, and the noise segmentation thresholds are used to distinguish noise from valid signals.

[0066] When identifying noise segments and valid signal segments, traverse the power spectrum and look for intersections in the sequence, that is, the points where the power spectrum value changes from below the threshold to above the threshold, or from above the threshold to below the threshold. Each intersection indicates the beginning or end of a potential noise segment. Through continuous intersections, the start and end indexes of noise segments and valid signal segments can be determined. In this way, a signal can be divided into several parts, such as signal1, noise1, signal2, noise2, ... signalN, noiseN.

[0067] When processing valid signal segments and noise segments, find the shortest valid signal segment (represented by S) and noise segment (represented by N). Combine this segment with the segments on the left and right until the sum of the noise segment and the valid signal segment is less than or equal to 3, that is, the final result is the following: 1) N; 2) S; 3) NS; 4) SN; 5) SNS; 6) NSN.

[0068] The power sum of all valid signals and the power sum of noise are calculated, and the noise estimation value of the ultrasound image spectrum signal is obtained through an infinite impulse response (IIR) filter for the next round of iteration. Among them, the algorithm used by the IIR filter includes signal preprocessing, filter application, noise floor extraction, etc. The algorithm used can be adaptively adjusted and optimized according to the specific situation.

[0069] In an embodiment of the present application, for the first frame data, the spectrum centroid is cleverly used to estimate the noise. By calculating the power spectrum of the ultrasound image spectrum signal, its centroid position is determined, and then the frequency components higher than the centroid are regarded as noise, which can quickly and effectively provide a reliable noise estimation result for the first frame data; for subsequent frame data, the spectrum amplitude is used to estimate the noise. By setting the noise segmentation threshold, the power spectrum is divided into a valid signal segment and a noise segment, and their power sums are calculated respectively. The infinite impulse response filter is used to further extract or suppress the noise to obtain a noise estimation value, which can more finely analyze the spectrum signal and improve the accuracy of noise estimation.

[0070] In some embodiments, in combination Figure 3 As shown, performing noise estimation on frame data of ultrasound image spectrum signals and determining a noise estimation result comprises the following steps:

[0071] S301, determining a first threshold, a second threshold, and a clipping value based on a noise estimation result.

[0072] Here, the first threshold thr1 is set between the average noise level and the peak noise level. The first threshold thr1 is set low so that even a weak signal can make the mean value exceed the threshold, thereby making the integral more increased rather than decreased in a specific area.

[0073] The clipping value clip is used to calculate the difference between the data and the threshold and clip this difference to ensure that strong signals behave similarly to weak signals. The clipping value clip helps prevent strong signals from taking too much weight in the integration process.

[0074] S302: Perform a first integral calculation on the ultrasound image spectrum signal using a first threshold to obtain a first multi-integral.

[0075] Optionally, a first integral calculation is performed on the ultrasound image spectrum signal using a first threshold to obtain a first multiple integral, including: determining a power spectrum of a fast Fourier transform of the ultrasound image spectrum signal; and determining the first multiple integral using a difference between the power spectrum of the fast Fourier transform of the ultrasound image spectrum signal and the first threshold.

[0076] In some practical applications, the first-order integral is determined according to the following formula:

[0077] Integ1=Integ1+(FFTPower-thr1)

[0078] Wherein, Integ1 is the first multi-integral, FFTPower is the power spectrum of the fast Fourier transform, and thr1 is the first threshold.

[0079] The purpose of the first integration is to enhance the characteristics of the signal while preserving the dynamic range of the signal.

[0080] S303, performing a first clipping operation based on the first multi-integral and the clipping value to obtain a first integral processing result.

[0081] The first clipping operation includes applying two threshold operations to the first multiple integral Integ1: values ​​greater than the clipping value clip are clipped to clip, and values ​​less than 0 are clipped to 0.

[0082] S304: Perform a second integral calculation on the ultrasound image spectrum signal using a second threshold to obtain a second integral.

[0083] Optionally, performing a second integral calculation on the ultrasound image spectrum signal using a second threshold to obtain a second integral includes: determining the second integral using a difference between the first integral and the second threshold.

[0084] In some practical applications, the second integral is determined according to the following formula:

[0085] Integ2=Integ2+(Integ1-thr2)

[0086] Among them, Integ2 is the second integral, and thr2 is the second threshold.

[0087] The purpose of the second integration is to further enhance the characteristics of the signal while further suppressing the noise.

[0088] S305, performing a second clipping operation based on the second integral and the clipping value to obtain a double integral processing result.

[0089] The second clipping operation includes applying two threshold operations to the second multi-integral Integ2: values ​​greater than the clipping value clip are clipped to clip, and values ​​less than 0 are clipped to 0.

[0090] In this embodiment, the double integration processing method is used to distinguish the signal from the noise, further remove the noise component, and retain the main characteristics of the signal. In each step, threshold and clipping operations are used to ensure that the integral value is within a reasonable range and to avoid the influence of too strong or too weak signals on the overall result. The output of Integ2 is a signal processed by double integration, which helps to distinguish between signals and noise in spectrum analysis and provides important information for subsequent signal processing.

[0091] In some embodiments, the ultrasound image spectrum signal is determined as follows: windowing the ultrasound image input signal; performing a fast Fourier transform on the windowed ultrasound image input signal; adding a preset bias coefficient as a gain to the spectrum amplitude of the transformed ultrasound image input signal to obtain the ultrasound image spectrum signal.

[0092] Windowing refers to reducing spectrum leakage by applying a smooth window function at the beginning and end of the signal, the purpose of which is to reduce spectrum energy leakage and reduce spectrum side lobes.

[0093] The order of the window function is related to the pulse Doppler sweep rate (DopSweepRate) and the pulse repetition frequency (prf). In some practical applications, the order of the window function satisfies the following relationship with the pulse Doppler sweep rate (DopSweepRate) and the pulse repetition frequency (prf):

[0094]

[0095] Where WinSize is the order of the window function, DopSweepRate is the scanning speed of the pulse Doppler, prf is the pulse repetition frequency, and K is the reference coefficient.

[0096] The windowed ultrasound image input signal x[n] is subjected to a fast Fourier transform, including performing a fast Fourier transform according to the following formula:

[0097]

[0098] In some possible implementations, the ultrasound image spectrum signal is determined according to the following formula:

[0099] fft_log=8*(log 2 (X i 2 +X q 2 )+LogOffset)

[0100] Among them, fft_log is the spectrum signal of ultrasound image, log 2 (X i 2 +X q 2 ) is the amplitude of the ultrasound image input signal spectrum, and LogOffset is the preset offset coefficient.

[0101] By adding the preset offset coefficient (LogOffset) as a gain to the spectrum amplitude of the transformed ultrasound image input signal, the visualization effect of the spectrum signal can be significantly enhanced, which not only improves the contrast of the spectrum signal, but also makes the details in the spectrum diagram clearer, helping doctors or operators to more accurately identify and analyze the information in the ultrasound image.

[0102] In some possible implementations, the preset bias coefficient is determined as follows: a noise difference between an empirical value of noise intensity and an estimated value of noise intensity is determined; and a preset bias coefficient is determined according to the noise difference based on an overall gain of a pulse Doppler of the ultrasound device.

[0103] In some practical applications, the preset bias coefficient is determined according to the following formula:

[0104] LogOffset=recGain+PwSpectralNoise-NoisePower

[0105] Among them, LogOffset is the preset bias coefficient, recGain is the overall gain of the pulse Doppler of the ultrasound device, PwSpectralNoise is the empirical value of the noise intensity, and NoisePower is the estimated value of the noise intensity.

[0106] The preset offset coefficient (LogOffset) is determined by taking into account the overall pulse Doppler gain (recGain) of the ultrasound device, the empirical value of the noise intensity (PwSpectralNoise), and the estimated value of the noise intensity (NoisePower). This dynamic adjustment mechanism enables the spectrum signal to be adaptively optimized according to different device parameters and noise conditions, further improving the accuracy and reliability of spectrum analysis.

[0107] In some possible implementations, the ultrasound image input signal is determined in the following manner: an initial ultrasound image input signal is obtained; and the initial ultrasound image input signal is wall filtered to obtain the ultrasound image input signal.

[0108] The initial ultrasound image input signal is generally a Doppler signal, which contains both high-frequency, low-intensity blood flow signals and low-frequency, high-intensity tissue motion signals. The function of wall filtering is to remove low-frequency tissue signals, thereby highlighting high-frequency blood flow signals. This makes the blood flow signal more clearly visible in the ultrasound image, helping doctors or operators to more accurately identify and analyze vascular structure and blood flow status.

[0109] Wall filtering can be achieved by a wall filter. The wall filter is essentially a high-pass filter, and a Butterworth high-pass filter can be used. The order is adjustable (for example, the available orders are 2nd order, 4th order and 8th order).

[0110] The cutoff frequency bw of the wall filter is related to the pulse repetition frequency prf and the external input parameter bandwExp, satisfying the following relationship:

[0111]

[0112] In this way, users can actively adjust the effect of the filter according to actual conditions. By adjusting these parameters, users can achieve precise control of different frequency components to achieve the best filtering effect, so that the wall filter technology can maintain excellent performance when processing different types of ultrasound images.

[0113] Combination Figure 4 As shown, in some embodiments, a method for adjusting ultrasound image gain is provided, comprising the following steps:

[0114] S401, obtaining an initial ultrasound image input signal.

[0115] S402: Perform wall filtering on the initial ultrasonic image input signal to obtain an ultrasonic image input signal.

[0116] S403, windowing the ultrasonic image input signal.

[0117] S404, performing fast Fourier transform on the windowed ultrasound image input signal.

[0118] S405 , adding the preset bias coefficient as a gain to the spectrum amplitude of the transformed ultrasound image input signal to obtain an ultrasound image spectrum signal.

[0119] S406, performing noise estimation on the frame data of the ultrasound image spectrum signal to determine a noise estimation result.

[0120] S407, performing double integration processing on the ultrasound image spectrum signal based on the noise estimation result to obtain a double integration processing result.

[0121] S408 , performing low-pass filtering on the ultrasound image spectrum signal based on the double integration result to generate a smoothing mask.

[0122] S409: Use a smoothing mask to adjust the ultrasound image spectrum signal to weaken the noise signal in the ultrasound image spectrum signal or enhance the effective signal in the ultrasound image spectrum signal.

[0123] By adopting the method for adjusting the gain of an ultrasonic image provided by the embodiment of the present disclosure, wall filtering can effectively remove the interference of wall signals in the ultrasonic image, so that the subsequent processing can focus more on the signal of the target tissue or structure, which is helpful to improve the clarity and contrast of the image; windowing processing can reduce the spectrum leakage phenomenon in the fast Fourier transform process, so that the spectrum analysis is more accurate; by adding a preset bias coefficient as a gain to the spectrum amplitude of the transformed ultrasonic image input signal, the overall or local brightness of the image can be flexibly adjusted; the combination of noise estimation and double integral processing can accurately identify and suppress the noise component in the ultrasonic image spectrum signal, and at the same time, the double integral processing can also enhance the effective signal, so that the target tissue or structure in the image is more prominent; low-pass filtering based on the double integral result can generate a smoothing mask, which can reflect the spatial distribution and intensity of the effective signal. The smoothing mask is used to adjust the ultrasonic image spectrum signal, which can further weaken the noise signal and enhance the effective signal, thereby significantly improving the image quality. In this way, the target tissue or structure in the ultrasonic image will become clearer and easier to identify.

[0124] Combination Figure 5 As shown, in some embodiments, a method for adjusting ultrasound image gain is provided, comprising the following steps:

[0125] S501: Obtain an initial ultrasound image input signal.

[0126] S502: Perform wall filtering on the initial ultrasonic image input signal to obtain an ultrasonic image input signal.

[0127] S503: Add a window to the ultrasonic image input signal.

[0128] S504, performing fast Fourier transform on the windowed ultrasound image input signal.

[0129] S505 , adding the preset bias coefficient as a gain to the spectrum amplitude of the transformed ultrasound image input signal to obtain an ultrasound image spectrum signal.

[0130] S506: When the frame data of the ultrasound image spectrum signal is the first frame data, use the spectrum centroid to perform noise estimation to determine a first noise estimation result.

[0131] S507 : When the frame data of the ultrasound image spectrum signal is the second frame data, perform noise estimation using the spectrum amplitude to determine a second noise estimation result.

[0132] S508 : Determine a first threshold, a second threshold, and a clipping value based on the first noise estimation result or the second noise estimation result.

[0133] S509 , performing a first integral calculation on the ultrasound image spectrum signal using a first threshold to obtain a first multi-integral, and performing a first clipping operation based on the first multi-integral and the clipping value to obtain a first integral processing result.

[0134] S510, performing a second integral calculation on the ultrasound image spectrum signal using a second threshold to obtain a second integral, and performing a second clipping operation based on the second integral and the clipping value to obtain a double integral processing result.

[0135] S511, performing low-pass filtering on the ultrasound image spectrum signal based on the double integration result to generate a smoothing mask.

[0136] S512: Use a smoothing mask to adjust the ultrasonic image spectrum signal to weaken the noise signal in the ultrasonic image spectrum signal or enhance the effective signal in the ultrasonic image spectrum signal.

[0137] The method for adjusting the gain of an ultrasound image provided by the embodiment of the present disclosure significantly improves the quality of the ultrasound image through a series of carefully designed steps, especially significantly suppresses noise and enhances effective signals. The following further explains its beneficial technical effects from several aspects:

[0138] First, after obtaining the initial ultrasound image input signal, the wall signal interference that is irrelevant to the tissue structure is effectively removed through the wall filtering step, making the subsequent processed ultrasound image input signal purer, which is conducive to the precise execution of subsequent steps. The application of wall filtering not only improves the signal-to-noise ratio of ultrasound images, but also lays a solid foundation for subsequent spectrum analysis.

[0139] Secondly, the combination of the windowing step and the fast Fourier transform enables a more detailed analysis of the signal in the frequency domain. The windowing operation reduces spectrum leakage and ensures the accuracy of the transformation results, while the fast Fourier transform efficiently converts the time domain signal into a frequency domain signal, which is convenient for subsequent gain adjustment and noise processing. At the same time, by adding a preset bias coefficient to the spectrum amplitude, flexible gain control of the ultrasound image input signal is achieved, which can be precisely adjusted according to actual needs, thereby optimizing the image display effect.

[0140] Thirdly, according to the characteristics of different frame data, the spectral centroid and spectral amplitude are used for noise estimation respectively, which improves the accuracy and adaptability of noise estimation. This dynamic noise estimation strategy helps to maintain a stable processing effect in a complex and changeable ultrasound image environment. After determining the first threshold, the second threshold and the clipping value, the noise signal and the effective signal are effectively separated through two integral calculations and clipping operations, which not only further reduces the noise interference, but also retains the key information in the image, providing a reliable basis for subsequent image processing.

[0141] Finally, a smoothing mask is generated through low-pass filtering, and the mask is used to adjust the spectrum signal of the ultrasound image, which effectively weakens the noise signal and significantly enhances the effective signal. The introduction of the smoothing mask not only improves the efficiency of image processing, but also ensures the smoothness and continuity of the processing results, making the final generated ultrasound image clearer and more delicate.

[0142] FIG6(a) is an ultrasound image without adjusting the ultrasound image gain provided by an embodiment of the present disclosure, in which the effective signal and noise are mixed together, and the target tissue is displayed fuzzy. FIG6(b) is an ultrasound image after adjusting the ultrasound image gain provided by an embodiment of the present disclosure, in which the noise is suppressed, the effective signal is enhanced, and the target tissue is displayed more clearly. It can be seen that the solution for adjusting the ultrasound image gain provided by an embodiment of the present disclosure can improve the clarity of the ultrasound image by weakening the noise signal and enhancing the effective signal, making the structure and details in the image more obvious.

[0143] Combination Figure 7 As shown, the embodiment of the present disclosure provides a device 700 for adjusting the gain of an ultrasonic image, including a processor 70 and a memory 71, and may also include a communication interface 72 and a bus 73. The processor 70, the communication interface 72, and the memory 71 may communicate with each other through the bus 73. The communication interface 72 may be used for information transmission. The processor 70 may call the logic instructions in the memory 71 to execute the method for adjusting the gain of an ultrasonic image in the above embodiment.

[0144] In addition, the logic instructions in the above-mentioned memory 71 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0145] The memory 71 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 70 executes the function application and data processing by running the program instructions / modules stored in the memory 71, that is, the method for adjusting the ultrasound image gain in the above method embodiment is implemented.

[0146] The memory 71 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 71 may include a high-speed random access memory and may also include a non-volatile memory.

[0147] The device for adjusting the gain of an ultrasonic image provided by the embodiment of the present disclosure, firstly, by estimating the noise of the frame data of the ultrasonic image spectrum signal, an accurate noise reference is provided for subsequent processing. Through double integration processing, the low-frequency component (usually corresponding to the effective signal) in the signal is enhanced, while the high-frequency noise component is weakened, thereby improving the signal-to-noise ratio and laying a foundation for subsequent low-pass filtering and smoothing mask generation. Secondly, the low-pass filtering based on the double integration result further weakens the high-frequency noise while retaining the effective signal of the low frequency. The generated smoothing mask can more accurately reflect the spatial distribution and intensity of the effective signal, providing a reliable basis for subsequent signal adjustment. Therefore, the technical solution of the present application can effectively enhance the effective signal in the ultrasonic image while weakening the noise signal, which helps to improve the clarity and contrast of the ultrasonic image and make the target tissue easier to identify and diagnose. In addition, due to the accuracy of the smoothing mask and the flexibility of the signal adjustment, the technical solution can minimize the loss of details of the target tissue, thereby providing more accurate and reliable diagnostic information.

[0148] The embodiment of the present disclosure provides an ultrasound device 80 , comprising an ultrasound device body 800 , and the aforementioned device 700 for adjusting ultrasound image gain, which is installed on the ultrasound device body 800 .

[0149] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for adjusting ultrasound image gain.

[0150] An embodiment of the present disclosure provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the above-mentioned method for adjusting the gain of an ultrasound image.

[0151] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0152] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0153] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, individual components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. The scope of the embodiments of the present disclosure includes the entire scope of the claims, and all available equivalents of the claims. When used in this application, although the terms "first", "second", etc. may be used in this application to describe each element, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, without changing the meaning of the description, the first element can be called the second element, and similarly, the second element can be called the first element, as long as all occurrences of the "first element" are renamed consistently and all occurrences of the "second element" are renamed consistently. The first element and the second element are both elements, but may not be the same element. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates, the singular forms "a", "an" and "the" are intended to include the plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method or device including the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0154] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0155] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0156] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for adjusting ultrasound image gain, characterized in that: include: Performing noise estimation on frame data of the ultrasound image spectrum signal to determine a noise estimation result; Performing double integration processing on the ultrasound image spectrum signal based on the noise estimation result to obtain a double integration processing result; Based on the double integration result, the spectrum signal of the ultrasound image is low-pass filtered to generate a smoothing mask; The ultrasonic image spectrum signal is adjusted by using a smoothing mask to weaken the noise signal in the ultrasonic image spectrum signal or enhance the effective signal in the ultrasonic image spectrum signal.

2. The method according to claim 1, characterized in that Performing noise estimation on frame data of the ultrasound image spectrum signal and determining a noise estimation result includes: When the frame data of the ultrasound image spectrum signal is the first frame data, performing noise estimation using the spectrum centroid to determine a first noise estimation result; and / or, When the frame data of the ultrasound image spectrum signal is the second frame data, the spectrum amplitude is used to perform noise estimation to determine a second noise estimation result.

3. The method according to claim 2, characterized in that The noise estimation is performed using the spectrum centroid to determine a first noise estimation result, including: Calculate the power spectrum of the ultrasound image spectrum signal; Determine the centroid of the ultrasound image spectrum signal according to the power spectrum of the ultrasound image spectrum signal; The frequency components higher than the centroid in the ultrasound image spectrum signal are regarded as noise to obtain a first noise estimation result; The method of performing noise estimation by using the spectrum amplitude to determine a second noise estimation result includes: Using the noise segmentation threshold, determining the effective signal segment and the noise segment in the power spectrum of the ultrasound image spectrum signal; After merging the effective signal segment and the noise segment, the power sum of the effective signal and the power sum of the noise are calculated respectively; Based on the power sum of the effective signal and the power sum of the noise, a noise estimation value of the ultrasound image spectrum signal is obtained through an infinite impulse response filter; The frequency components in the ultrasound image spectrum signal that are higher than the noise estimation value are regarded as noise to obtain a second noise estimation result.

4. The method according to claim 1, characterized in that: Based on the noise estimation result, double integration processing is performed on the ultrasound image spectrum signal to obtain a double integration processing result, including: Based on the noise estimation result, determining a first threshold, a second threshold and a clipping value; Performing a first integral calculation on the ultrasound image spectrum signal using a first threshold to obtain a first multi-integral, and performing a first clipping operation based on the first multi-integral and the clipping value to obtain a first integral processing result; A second integral calculation is performed on the ultrasound image spectrum signal using the second threshold to obtain a second integral, and a second clipping operation is performed based on the second integral and the clipping value to obtain a double integral processing result.

5. The method according to claim 4, characterized in that Performing a first integral calculation on the ultrasound image spectrum signal using a first threshold to obtain a first multi-integral includes: Determine the power spectrum of the fast Fourier transform of the ultrasound image spectrum signal; Determine a first multi-integral by using a difference between a power spectrum of a fast Fourier transform of an ultrasonic image spectrum signal and a first threshold; and / or, Performing a second integral calculation on the ultrasound image spectrum signal using a second threshold to obtain a second integral includes: Using the difference between the first multi-integral and the second threshold, a second multi-integral is determined.

6. The method according to claim 1, characterized in that The smoothing mask is used to adjust the spectrum signal of the ultrasound image, including: applying a smoothing mask to the ultrasound image spectrum signal as a bias compensation; or, An offset is determined according to a smoothing mask and a spectrum enhancement parameter, and the offset is applied to the ultrasound image spectrum signal as a bias compensation.

7. The method according to any one of claims 1 to 6, characterized in that: The ultrasound image spectrum signal is determined as follows: Windowing the ultrasound image input signal; Performing fast Fourier transform on the windowed ultrasound image input signal; The preset bias coefficient is added as a gain to the spectrum amplitude of the transformed ultrasonic image input signal to obtain an ultrasonic image spectrum signal.

8. The method according to claim 7, characterized in that The ultrasound image input signal is determined as follows: obtaining an initial ultrasound image input signal; Performing wall filtering on the initial ultrasonic image input signal to obtain an ultrasonic image input signal; and / or, The preset bias coefficients are determined as follows: determining a noise difference between an empirical value of noise intensity and an estimated value of noise intensity; The preset bias coefficient is determined according to the noise difference based on the overall gain of the pulse Doppler of the ultrasound device.

9. A device for adjusting ultrasound image gain, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to perform the method for adjusting ultrasound image gain according to any one of claims 1 to 8 when executing the program instructions.

10. An ultrasonic device, characterized in that: include; Ultrasonic equipment body; The device for adjusting the gain of an ultrasonic image as claimed in claim 9 is installed on a body of an ultrasonic device.

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