Ultrasonic imaging method, device, equipment and storage medium based on adjustable shannon entropy

Abstract

The application relates to the technical field of ultrasonic detection and ultrasonic imaging, and discloses an ultrasonic imaging method, device and equipment based on adjustable Shannon entropy and a storage medium, which comprises the following steps: performing ultrasonic imaging on an imaging target to obtain original radio frequency data of the imaging target; performing envelope demodulation on the original radio frequency data to obtain an ultrasonic echo signal matrix; constructing a sliding window, traversing coordinate points in the echo signal matrix by using the sliding window, and sequentially calculating adjustable Shannons entropy corresponding to each coordinate point; and constructing an entropy image of the imaging target based on the adjustable Shannons entropy corresponding to each coordinate point in the ultrasonic echo signal matrix. The application enhances the sensitivity of Shannon entropy to information disorder, and can improve the imaging effect. By imaging based on the adjustable weight value of Shannon entropy, the accuracy of ultrasonic diagnosis and detection can be improved, and the image can be adjusted according to the acoustic characteristics and positions of different imaging targets by adjusting the weight value, so that the optimal imaging effect is achieved.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic testing and ultrasonic imaging technology, specifically to ultrasonic imaging methods, apparatus, equipment, and storage media based on adjustable Shannon entropy. Background Technology

[0002] Ultrasound imaging has garnered significant attention due to its safety, real-time performance, and low cost; however, low contrast has become a limitation in soft tissue examinations. Furthermore, uniform scattering regions can appear within certain tissue structures, leading to speckle scattering in ultrasound images. The signals generating these speckles are related to the underlying tissue microstructure, so characterizing these signals can provide information about the tissue microstructure if the ultrasound signal depends on it. Thus, to enhance the contrast and specificity of ultrasound imaging, quantitative ultrasound (QUS) technology has emerged. QUS encompasses various categories, including spectral-based ultrasound signal parameterization, Doppler-based flux estimation, tissue elastography, and envelope statistics. Traditional parametric imaging methods based on statistical distributions generally require the scatterer to follow a typical distribution, and their estimation accuracy and spatial resolution are limited by the sliding window side length. Therefore, entropy has been proposed for ultrasound backscatter signal analysis and its effectiveness in characterizing tissue structures has been demonstrated.

[0003] In related technologies, for ultrasound parametric imaging, the imaging results of different methods will vary when the target is imaged at different depths and with different acoustic reflection intensities. Among existing parametric imaging methods, it is difficult to find a method suitable for examining all parts and acoustic characteristics of the target. Summary of the Invention

[0004] In view of this, the present invention provides an ultrasound imaging method, apparatus, device and storage medium based on adjustable Shannon entropy to solve the problem that it is difficult to find a method suitable for examining all parts and acoustic features of a target.

[0005] In a first aspect, the present invention provides an ultrasound imaging method based on adjustable Shannon entropy, the method comprising:

[0006] Ultrasonic imaging is performed on the imaging target to obtain the raw radio frequency data of the imaging target;

[0007] The raw radio frequency data is de-encapsulated to obtain the ultrasonic echo signal matrix;

[0008] Construct a sliding window and use it to traverse the coordinate points in the echo signal matrix, and calculate the tunable Shannon entropy corresponding to each coordinate point in turn;

[0009] An entropy image of the imaging target is constructed based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix.

[0010] In this invention, raw radio frequency (RF) data of the imaging target is acquired, and the RF data is unencapsulated. A sliding window is used to traverse and calculate the adjustable Shannon entropy for each pixel, constructing an entropy image corresponding to the imaging target. The influence of the histogram's horizontal axis distribution on the statistical distribution is considered, enhancing the sensitivity of Shannon entropy to information disorder and improving imaging performance. Imaging based on Shannon entropy with adjustable weights not only improves the accuracy of ultrasound diagnosis and detection but also allows for image adjustment based on the acoustic characteristics and location of different imaging targets, thereby achieving optimal imaging results.

[0011] In one optional implementation, the raw radio frequency data is unencapsulated to obtain an ultrasonic echo signal matrix, including:

[0012] Beamforming and Hilbert transform were performed on the raw radio frequency data to obtain the ultrasonic echo signal matrix.

[0013] In this method, the raw radio frequency data is deencapsulated through beamforming and Hilbert transform, and the acquired ultrasound signals are demodulated so that the echo signals are arranged according to the position of the final image, thereby improving the image quality.

[0014] In one alternative implementation, the method further includes:

[0015] The number of rows in the sliding window is less than the number of rows in the echo signal matrix; the number of rows in the sliding window is calculated based on the wavelength multiple of the ultrasonic echo signal matrix, the sampling frequency, the ultrasonic signal wavelength, and the ultrasonic signal velocity.

[0016] The number of columns in the sliding window is less than the number of columns in the echo signal matrix. The number of columns in the sliding window is calculated based on the wavelength multiple of the ultrasonic echo signal matrix, the ultrasonic signal wavelength, the number of ultrasonic transducer elements, and the ultrasonic image width.

[0017] In this method, a sliding window is set to facilitate subsequent traversal of the ultrasonic echo signal matrix, thereby enabling the calculation of the adjustable Shannon entropy at each coordinate point of the ultrasonic echo signal matrix.

[0018] In one optional implementation, a sliding window is used to traverse the coordinate points in the echo signal matrix, and the tunable Shannon entropy corresponding to each coordinate point is calculated sequentially, including:

[0019] Based on all signals within the current sliding window, the adjustable Shannon entropy of the center coordinates of the current sliding window is calculated;

[0020] Set the step size, and based on the step size, use a sliding window to traverse all coordinate points in the echo signal matrix from left to right and from top to bottom to obtain the adjustable Shannon entropy corresponding to each coordinate point.

[0021] In this method, by using a sliding window to traverse each coordinate point in the echo signal matrix, the adjustable Shannon entropy of each coordinate point is calculated to realize the adjustable Shannon entropy calculation of the image corresponding to the imaging target, which facilitates subsequent imaging using the adjustable Shannon entropy of each pixel of the image corresponding to the imaging target.

[0022] In one optional implementation, the adjustable Shannon entropy of the center coordinates of the current sliding window is calculated based on all signals within the current sliding window, including:

[0023] Calculate the probability density histogram of all coordinate points within the current sliding window;

[0024] Normalize the probability density histogram along the horizontal direction to obtain the midpoint of the horizontal coordinate of each bar in the probability density histogram.

[0025] The range of gradient values ​​is determined based on the acoustic characteristics and depth of the imaging target.

[0026] Based on the range of gradient values ​​and the midpoint of the horizontal coordinate of each bar in the probability density histogram, the adjustable Shannon entropy of the center coordinate of the current sliding window is calculated.

[0027] This approach enhances the sensitivity of Shannon entropy to information disorder by considering the influence of the histogram's horizontal axis distribution on the statistical distribution, thereby improving imaging performance. Imaging based on Shannon entropy with adjustable weights not only improves the accuracy of ultrasound diagnosis and detection but also allows for the adjustment of the weights according to the acoustic characteristics and location of different imaging targets, ultimately achieving optimal imaging results.

[0028] In one alternative implementation, the adjustable Shannon entropy of the center coordinates of the current sliding window is calculated based on the range of gradient values ​​and the midpoint of the x-coordinate of each bar in the probability density histogram, using the following formula:

[0029]

[0030] Among them, H hWA y' is the adjustable Shannon entropy of the center coordinates of the current sliding window, w(y') is the midpoint of the x-coordinate of each bar in the probability density histogram, and n is the gradient.

[0031] In this method, by accumulating the probability density histogram of each bar within the normalized sliding window to obtain a probability of 1, it is ensured that the horizontally normalized weighted Shannon entropy is calculated for each pixel within the sliding window. Based on the horizontally normalized weighted Shannon entropy calculation for each pixel within the sliding window, by adjusting the gradient n, the image can achieve a better imaging effect that is more suitable for the characteristics of the imaging target.

[0032] In one optional implementation, an entropy image of the imaging target is constructed based on the tunable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix, including:

[0033] The adjustable Shannon entropy corresponding to each coordinate point in the echo signal matrix is ​​determined as the pixel value of that coordinate point, thus obtaining the entropy image of the imaging target.

[0034] In this approach, by using adjustable Shannon entropy for imaging, the generated entropy image can better monitor or diagnose the imaging target, improve the image contrast corresponding to the imaging target and suppress background noise, better depict the target area, thereby improving the diagnostic accuracy, or providing real-time and accurate monitoring for treatment, ensuring the treatment effect and the patient's safety during the treatment process.

[0035] Secondly, the present invention provides an ultrasound imaging device based on adjustable Shannon entropy, the device comprising:

[0036] The target imaging module is used to perform ultrasonic imaging on the imaging target to obtain the raw radio frequency data of the imaging target;

[0037] The data unenveloping module is used to unenvelop the raw radio frequency data to obtain the ultrasonic echo signal matrix;

[0038] The tunable Shannon entropy calculation module is used to construct a sliding window and use the sliding window to traverse the coordinate points in the echo signal matrix to calculate the tunable Shannon entropy corresponding to each coordinate point in turn.

[0039] The image construction module is used to construct an entropy image of the imaging target based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix.

[0040] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the ultrasound imaging method based on adjustable Shannon entropy described in the first aspect or any corresponding embodiment thereof.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the ultrasound imaging method based on adjustable Shannon entropy described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 This is a schematic flowchart of an ultrasound imaging method based on adjustable Shannon entropy according to an embodiment of the present invention.

[0044] Figure 2 This is a flowchart of an ultrasound imaging method based on adjustable Shannon entropy with level normalized weights according to an embodiment of the present invention.

[0045] Figure 3 This is a schematic flowchart of another ultrasound imaging method based on adjustable Shannon entropy according to an embodiment of the present invention.

[0046] Figure 4 This is a schematic flowchart of another ultrasound imaging method based on adjustable Shannon entropy according to an embodiment of the present invention.

[0047] Figure 5 This is a simulated image area for a target and a simulated setup according to an embodiment of the present invention.

[0048] Figure 6 This is a schematic diagram of the simulation results of the effect of gradient parameter n on imaging according to an embodiment of the present invention.

[0049] Figure 7 This is a schematic diagram illustrating the comparison results of horizontally normalized weighted adjustable Shannon entropy imaging with B-ultrasound images, WSE images, and hNSE images according to an embodiment of the present invention.

[0050] Figure 8 This is a structural block diagram of an ultrasound imaging device based on adjustable Shannon entropy according to an embodiment of the present invention.

[0051] Figure 9 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] In related technologies, for ultrasound parametric imaging, the imaging results of different methods will vary when the target is imaged at different depths and with different acoustic reflection intensities. Among existing parametric imaging methods, it is difficult to find a method suitable for examining all parts and acoustic characteristics of the target.

[0054] To address the aforementioned problems, this invention provides an ultrasound imaging method based on adjustable Shannon entropy, used in a computer device. It should be noted that the executing entity can be an ultrasound imaging device based on adjustable Shannon entropy. This device can be implemented as part or all of the computer device through software, hardware, or a combination of both. The computer device can be a terminal, client, or server. The server can be a single server or a server cluster composed of multiple servers. In this embodiment, the terminal can be a smartphone, personal computer, tablet computer, or other smart hardware device. The following method embodiments all use a computer device as the executing entity for illustration.

[0055] The computer equipment in this embodiment is suitable for use in scenarios where an ultrasound imaging system is used to monitor ablated tissue or diagnose tumor tissue. The present invention provides an ultrasound imaging method based on adjustable Shannon entropy. This method acquires the raw radio frequency data of the imaging target, unwraps the raw radio frequency data, and uses a sliding window to traverse and calculate the adjustable Shannon entropy of each pixel, constructing an entropy image corresponding to the imaging target. It considers the influence of the histogram's horizontal axis distribution on the statistical distribution, enhancing the sensitivity of Shannon entropy to information disorder and improving imaging performance. Imaging based on Shannon entropy with adjustable weights not only improves the accuracy of ultrasound diagnosis and detection but also allows for image adjustment based on the acoustic characteristics and location of different imaging targets, thereby achieving optimal imaging results.

[0056] According to an embodiment of the present invention, an embodiment of an ultrasound imaging method based on adjustable Shannon entropy is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0057] This embodiment provides an ultrasound imaging method based on adjustable Shannon entropy, which can be used in the aforementioned computer equipment. Figure 1 This is a flowchart of an ultrasound imaging method based on tunable Shannon entropy according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps:

[0058] Step S101: Perform ultrasound imaging on the imaging target to obtain the raw radio frequency data of the imaging target.

[0059] In one example, the target is imaged using an ultrasonic signal acquisition system, and radio frequency data of the raw echo signal is obtained.

[0060] Step S102: Unenvelop the original radio frequency data to obtain the ultrasonic echo signal matrix.

[0061] In one example, the raw radio frequency data is demodulated into envelope data by beamforming and Hilbert transform. For easier comparison, the envelope signal after Hilbert transform can also be logarithmically compressed to obtain an ultrasound image.

[0062] Step S103: Construct a sliding window and use the sliding window to traverse the coordinate points in the echo signal matrix, and calculate the tunable Shannon entropy corresponding to each coordinate point in turn.

[0063] In one example, the sliding window is set to a size of M×N. The sliding window traverses the entire image in a step of one pixel. The level-normalized weighted Shannon entropy is calculated using the two-dimensional signal within the window and used as the pixel value of the window center coordinates.

[0064] Step S104: Construct an entropy image of the imaging target based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix.

[0065] In one example, the calculated horizontally normalized weight-adjustable Shannon entropy is used as the pixel value of the window center coordinates to construct a horizontally normalized weight-adjustable Shannon entropy (hNWASE) image of the imaging target.

[0066] In one implementation scenario, Figure 2 This is a flowchart of an ultrasound imaging method based on adjustable Shannon entropy with level normalized weights, according to an embodiment of the present invention. Figure 2 As shown, an ultrasound imaging method based on level-normalized weighted Shannon entropy can include:

[0067] 1) The ultrasonic signal acquisition system is used to image the target and obtain the radio frequency data of the raw echo signal.

[0068] 2) Beamforming and Hilbert transform the raw radio frequency data to demodulate it into envelope data.

[0069] 3) Set the size of the sliding window to M×N, and traverse the entire image with a step size of one pixel. The sliding window size is set to 1 to 10 times the wavelength of the ultrasound signal.

[0070] 4) Calculate the level-normalized weighted adjustable Shannon entropy hNWASE using the two-dimensional signal within the window, and use it as the pixel value at the center coordinate of the window. Specifically, the calculation method is as follows:

[0071] (1) Calculate the probability density histogram: Divide the window into 20 groups and calculate the probability density histogram. The height of each bar in the histogram is w(y), and the midpoint of the horizontal coordinate of each bar is y.

[0072] (2) Horizontal normalization: Normalize the horizontal axis range of the probability density histogram to 0 to 1:

[0073] y'=(yy min ) / (y max -y min )

[0074] Where y' is the midpoint of the x-coordinate of each bar in the normalized histogram, y max The maximum x-coordinate of the midpoint of the histogram bars is y. min It represents the minimum x-coordinate of the midpoint of the histogram bars.

[0075] (3) Calculate the level-normalized weighted Shannon entropy hNWASE:

[0076]

[0077] Where n is the gradient that can adjust the weights of y', and the magnitude of n can adjust the sensitivity of the level-normalized weights, the adjustable Shannon entropy hNWASE, to the degree of signal disorder, thereby adjusting the image quality. hWA The horizontally normalized weights are adjustable Shannon entropy.

[0078] Based on the acoustic characteristics and depth of the imaging target, n is chosen within the range of 0.1 to 2. When n is small, the level normalization weights can be adjusted using the Shannon entropy H. hWA More sensitive to signal clutter, for targets with low signal strength or deep imaging locations, reducing n can improve image contrast; when n is large, the horizontal normalization weights can be adjusted using Shannon entropy H. hWA The reduced sensitivity to signal distortion allows for better suppression of background noise, making it suitable for targets with strong echo signals or shallow imaging positions.

[0079] 5) Calculate the hNWASE level-normalized weighted Shannon entropy for all pixels to obtain the hNWASE level-normalized weighted Shannon entropy image.

[0080] The ultrasound imaging method based on adjustable Shannon entropy provided in this embodiment acquires the raw radio frequency data of the imaging target, unencapsulates the raw radio frequency data, and uses a sliding window to traverse and calculate the adjustable Shannon entropy of each pixel to construct an entropy image corresponding to the imaging target. It considers the influence of the histogram's horizontal axis distribution on the statistical distribution, enhancing the sensitivity of Shannon entropy to information disorder and improving imaging performance. Imaging based on Shannon entropy with adjustable weights not only improves the accuracy of ultrasound diagnosis and detection but also allows for image adjustment based on the acoustic characteristics and location of different imaging targets, thereby achieving optimal imaging results.

[0081] This embodiment provides an ultrasound imaging method based on adjustable Shannon entropy, which can be used in the aforementioned computer equipment. Figure 3 This is a flowchart of an ultrasound imaging method based on tunable Shannon entropy according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps:

[0082] Step S301: Perform ultrasound imaging on the imaging target to obtain the raw radio frequency data of the imaging target. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0083] Step S302: Unenveil the original radio frequency data to obtain the ultrasonic echo signal matrix.

[0084] Specifically, step S302 includes:

[0085] Step S3021: Beamforming and Hilbert transform are performed on the original radio frequency data to obtain the ultrasonic echo signal matrix.

[0086] In one example, the original radio frequency data is beamformed, and the envelope data is extracted using the Hilbert transform to obtain the de-envelope echo signal matrix.

[0087] In this method, the raw radio frequency data is deencapsulated through beamforming and Hilbert transform, and the acquired ultrasound signals are demodulated so that the echo signals are arranged according to the position of the final image, thereby improving the image quality.

[0088] Step S303: Construct a sliding window and use the sliding window to traverse the coordinate points in the echo signal matrix, and calculate the tunable Shannon entropy corresponding to each coordinate point in turn.

[0089] Specifically, constructing a sliding window includes:

[0090] Step S3031, the number of rows of the sliding window is less than the number of rows of the echo signal matrix; based on the wavelength multiple, sampling frequency, ultrasonic signal wavelength, and ultrasonic signal sound speed of the ultrasonic echo signal matrix, the number of rows of the sliding window is calculated.

[0091] Step S3032, the number of columns of the sliding window is less than the number of columns of the echo signal matrix; based on the wavelength multiple, ultrasonic signal wavelength, number of ultrasonic transducer elements, and ultrasonic image width of the ultrasonic echo signal matrix, the number of columns of the sliding window is calculated.

[0092] In one example, assuming that the size of the unpacked ultrasonic echo signal is X×Y, the size of the sliding window is set to M×N, and the relationship between the size of the sliding window and the size of the ultrasonic echo signal is (M < X, N < Y). Traverse the entire echo signal matrix from left to right and top to bottom with a step size of one pixel, and calculate the horizontally normalized weight-adjustable Shannon entropy for the signal matrix with a size of M×N in each window.

[0093] The calculation process of M and N for the window size is as follows:

[0094]

[0095]

[0096] where k is the selected wavelength multiple, f s is the sampling frequency of the imaging system, λ is the ultrasonic signal wavelength, c is the ultrasonic signal sound speed, L is the number of ultrasonic transducer elements, and W is the ultrasonic image width.

[0097] In this method, by setting the sliding window, it is convenient to traverse the ultrasonic echo signal matrix subsequently, and calculate the adjustable Shannon entropy of each coordinate point of the ultrasonic echo signal matrix.

[0098] Step S304, based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix, construct an entropy image of the imaging target. For details, please refer to Figure 1 Step S103 of the illustrated embodiment, which will not be elaborated here.

[0099] The ultrasonic imaging method based on adjustable Shannon entropy provided in this embodiment unpacks the original RF data through beamforming and Hilbert transform, demodulates the collected ultrasonic signals, arranges the echo signals according to the positions of the final image, and improves the image quality. By setting the sliding window, it is convenient to traverse the ultrasonic echo signal matrix subsequently, and calculate the adjustable Shannon entropy of each coordinate point of the ultrasonic echo signal matrix.

[0100] In this embodiment, an ultrasonic imaging method based on adjustable Shannon entropy is provided, which can be used in the above computer device. Figure 4 This is a flowchart of an ultrasound imaging method based on tunable Shannon entropy according to an embodiment of the present invention, as shown below. Figure 4 As shown, the process includes the following steps:

[0101] Step S401: Perform ultrasound imaging on the imaging target to obtain the raw radio frequency data of the imaging target. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0102] Step S402: Unenvelop the raw radio frequency data to obtain the ultrasonic echo signal matrix. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0103] Step S403: Construct a sliding window and use the sliding window to traverse the coordinate points in the echo signal matrix, and calculate the tunable Shannon entropy corresponding to each coordinate point in turn.

[0104] Specifically, in step S403 above, the coordinate points in the echo signal matrix are traversed using a sliding window, and the tunable Shannon entropy corresponding to each coordinate point is calculated sequentially, including:

[0105] Step S4031: Based on all signals within the current sliding window, calculate the adjustable Shannon entropy of the center coordinates of the current sliding window.

[0106] In some optional implementations, step S4031 above includes:

[0107] Step a1: Calculate the probability density histogram of all coordinate points within the current sliding window.

[0108] Step a2: Normalize the probability density histogram along the horizontal direction to obtain the midpoint of the horizontal coordinate of each bar in the probability density histogram.

[0109] Step a3: Determine the range of gradient values ​​based on the acoustic characteristics and depth of the imaging target.

[0110] Step a4: Based on the range of gradient values ​​and the midpoint of the horizontal coordinate of each bar in the probability density histogram, calculate the adjustable Shannon entropy of the center coordinate of the current sliding window.

[0111] In some alternative implementations, step a4 above includes:

[0112] Based on the range of gradient values ​​and the midpoint of the x-coordinate of each bar in the probability density histogram, the adjustable Shannon entropy of the center coordinate of the current sliding window is calculated using the following formula:

[0113]

[0114] Among them, H hWA y' is the adjustable Shannon entropy of the center coordinates of the current sliding window, w(y') is the midpoint of the x-coordinate of each bar in the probability density histogram, and n is the gradient.

[0115] In one example, the level-normalized weighted adjustable Shannon entropy hNWASE is calculated using the two-dimensional signal within the window, and used as the pixel value at the center coordinate of the window. Specifically, the calculation method is as follows:

[0116] (1) Calculate the probability density histogram: Divide the window into 20 groups and calculate the probability density histogram. The height of each bar in the histogram is w(y), and the midpoint of the horizontal coordinate of each bar is y.

[0117] (2) Horizontal normalization: Normalize the horizontal axis range of the probability density histogram to 0 to 1:

[0118] y'=(yy min ) / (y max -y min )

[0119] Where y' is the midpoint of the x-coordinate of each bar in the normalized histogram, y max The maximum x-coordinate of the midpoint of the histogram bars is y. min It represents the minimum x-coordinate of the midpoint of the histogram bars.

[0120] (3) Calculate the level-normalized weighted Shannon entropy hNWASE:

[0121]

[0122] Where n is the gradient that can adjust the weights of y', and the magnitude of n can adjust the sensitivity of the level-normalized weights, the adjustable Shannon entropy hNWASE, to the degree of signal disorder, thereby adjusting the image quality. hWA The horizontally normalized weights are adjustable Shannon entropy.

[0123] Based on the acoustic characteristics and depth of the imaging target, n is chosen within the range of 0.1 to 2. When n is small, the level normalization weights can be adjusted using the Shannon entropy H. hWA More sensitive to signal clutter, for targets with low signal strength or deep imaging locations, reducing n can improve image contrast; when n is large, the horizontal normalization weights can be adjusted using Shannon entropy H. hWA The reduced sensitivity to signal distortion allows for better suppression of background noise, making it suitable for targets with strong echo signals or shallow imaging positions.

[0124] In one implementation scenario, a simulation experiment was conducted to investigate the impact of different gradient parameters n on the imaging results. Figure 5This refers to a simulated image area for a target and a simulated setup according to an embodiment of the present invention. For example... Figure 5 As shown, the simulation model is set up as follows:

[0125] 1) Use the Field II open-source software to generate phantoms with different regions of interest (ROIs) of scattering amplitude. Set the total size of the phantoms to be square with a size of 40×40mm, and embed the imaging target into the background as the ROI.

[0126] 2) Two circles with diameters of 10 mm and depths of 15 mm and 30 mm were embedded in the phantom, respectively. Four squares with sides of 2 mm were placed at the top, bottom, left, and right vertices of the circles to form Regions of Interest (ROIs). The two ROIs were named A and B, representing lesions at different depths, respectively. The background and ROIs had the same scattering concentration. The actual area of ​​the ROI set in the simulation was 35.154 mm².

[0127] 3) Define the scattering amplitude of the ROI region as A. r The scattering amplitude of the background is defined as A. b , This is the ratio of the echo signal intensity within the ROI to the background, reflecting the actual contrast between the ROI and the background. A value closer to 1 indicates a smaller difference in echo signal intensity between the ROI and the background, resulting in a more blurred ROI boundary. Due to tissue heterogeneity, the contrast between normal and diseased tissues in different organs varies; therefore, the range of C is set to [0.2, 4] to simulate different scenarios. When C is less than 1, the ROI is considered a hypoechoic region; when C is greater than 1, the ROI is considered a hyperechoic region.

[0128] 4) Define a linear array with 128 elements using the "xdc_linear_array" function in the Field II program. Each element has a width of 0.27 mm, a height of 8 mm, and a notch size of 0.3 mm. The transducer is excited by an excitation pulse with a center frequency of 6.25 MHz.

[0129] 5) To avoid the influence of non-uniform sound field, the phantom was placed at a depth of 10mm to 50mm, and the weight gradient n was set to 0.2 to 2 to explore its effect on entropy imaging.

[0130] Figure 6 This is a schematic diagram illustrating the simulation results of the effect of the gradient parameter n on imaging according to an embodiment of the present invention. Figure 6As shown, increasing the gradient parameter n has different effects on ROIs of different depths and reflection intensities. The weighted gradient n can adjust the influence of weights on entropy, thereby adjusting the sensitivity of entropy to information disorder. The normalized y' value is between 0 and 1; the smaller n is, the larger y' will be, and the more sensitive hNWASE will be to the degree of information disorder. In ultrasound imaging, adjusting n can improve image contrast, contour sharpness, and noise suppression. Due to the different locations and characteristics of lesions in clinical ultrasound scenarios, the influence of weighted gradient n on ultrasound images under different conditions was explored by simulating different echo intensities and depths. As entropy increases, noise in both the background and the ROI decreases simultaneously. When C equals 0.2, 0.4, and 0.6, the ROI is a hypoechoic cyst within the tissue; when C equals 2, 3, and 4, the ROI is a hyperechoic lesion. Area A is an ROI with a depth of 25 mm, and area B is an ROI with a depth of 40 mm. For quantitative analysis, the contrast ratio (CNR) of ROIs at different n, C, and location was calculated to reflect contrast strength. Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of ROI depiction under different conditions, and the area under the ROC curve (AUC), F1 score, accuracy, Matthews correlation coefficient (MCC), and ROI area were calculated for quantitative evaluation. Simulation experiments demonstrate that for hyperechoic ROIs, when the ROI depth decreases or the echo intensity difference between the ROI and the background decreases, n needs to be increased to suppress background noise. Conversely, as the depth of hypoechoic ROIs increases, n needs to be decreased to improve boundary contrast and sharpness.

[0131] Figure 7 This is a schematic diagram illustrating the comparison results of horizontally normalized weighted adjustable Shannon entropy imaging with B-ultrasound images, WSE images, and hNSE images according to an embodiment of the present invention. Figure 7As shown, based on the simulation experiments demonstrating the influence of n on the imaging results, n was set to 0.6 for imaging low-echo ROIs and 1.4 for imaging high-echo ROIs. The hNWASE imaging method was compared with ultrasound imaging and two other entropy WSE and hNSE imaging methods. When there is a significant difference in acoustic characteristics between the ROI and the background, ultrasound images can also depict the ROI's contour well. However, when the difference in reflection intensity is small, the ultrasound image boundaries are blurred and difficult to distinguish. hNSE imaging shows significant differences in contour depiction for objects with the same shape but different acoustic characteristics. The contour of high-echo targets in hNSE imaging is larger than that of low-echo regions. Compared to hNWASE, WSE images contain more noticeable noise. For shallow, low-echo ROIs, hNWASE achieved the highest F1 score (0.8348±0.043), accuracy (99.270±0.184%), and MCC (83.246±4.400%), with the calculated area (36.229±4.876 mm²) showing the best match to the actual area. Furthermore, for deep, low-echo ROIs, hNWASE imaging obtained the highest F1 score (0.767±0.047) and the area closest to the true value (37.693±2.850 mm²). These results indicate that hNWASE has the highest accuracy in ROI delineation. When imaging hyperechoic targets, hNSE had the highest CNR (10.390±7.324), but the ROI area (27.318±4.146 mm²) was significantly smaller than the true value, indicating that strong acoustic reflection from the background caused boundary artifacts. Quantitative results for hyperechoic targets. Regardless of the ROI location, hNWASE achieved the highest F1 score, accuracy, and MCC for hyperechoic ROI imaging, indicating that hNWASE is the most accurate in delineating hyperechoic targets. However, it should be noted that due to artifacts caused by hyperechoic targets, the ROI area depicted by all methods is usually larger than the actual area. Among these methods, hNSE imaging exhibits the largest area deviation, indicating that the hNSE method is significantly affected by hyperechoic conditions.

[0132] In this approach, by considering the influence of the histogram's horizontal axis distribution on the statistical distribution, the sensitivity of Shannon entropy to information disorder is enhanced, thereby improving imaging performance. Imaging based on Shannon entropy with adjustable weights not only improves the accuracy of ultrasound diagnosis and detection but also allows for the adjustment of the weights according to the acoustic characteristics and location of different imaging targets, ultimately achieving optimal imaging results. By accumulating probabilities of 1 for each bar in the normalized sliding window's probability density histogram, it is ensured that the horizontally normalized weighted adjustable Shannon entropy is calculated for each pixel within the sliding window. Based on the horizontally normalized weighted adjustable Shannon entropy calculation for each pixel within the sliding window, adjusting the gradient n allows for better imaging results that are more suitable for the characteristics of the imaging target.

[0133] Step S4032: Set the step size, and based on the step size, use a sliding window to traverse all coordinate points in the echo signal matrix from left to right and from top to bottom to obtain the adjustable Shannon entropy corresponding to each coordinate point.

[0134] In this method, by using a sliding window to traverse each coordinate point in the echo signal matrix, the adjustable Shannon entropy of each coordinate point is calculated to realize the adjustable Shannon entropy calculation of the image corresponding to the imaging target, which facilitates subsequent imaging using the adjustable Shannon entropy of each pixel of the image corresponding to the imaging target.

[0135] Step S404: Construct an entropy image of the imaging target based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix.

[0136] Specifically, step S404 includes:

[0137] Step S4041: Determine the adjustable Shannon entropy corresponding to each coordinate point in the echo signal matrix as the pixel value of that coordinate point to obtain the entropy image of the imaging target.

[0138] In one example, the calculated level-normalized weighted adjustable Shannon entropy is used as the pixel value for the window center coordinates. When the sliding window extends beyond the boundary of the original echo signal during the sliding process, the sub-signal matrix value is set to 0 for the portion extending beyond the boundary.

[0139] In this approach, by using adjustable Shannon entropy for imaging, the generated entropy image can better monitor or diagnose the imaging target, improve the image contrast corresponding to the imaging target and suppress background noise, better depict the target area, thereby improving the diagnostic accuracy, or providing real-time and accurate monitoring for treatment, ensuring the treatment effect and the patient's safety during the treatment process.

[0140] The ultrasound imaging method based on adjustable Shannon entropy provided in this embodiment calculates the adjustable Shannon entropy for each coordinate point by traversing the echo signal matrix using a sliding window. This allows for the calculation of adjustable Shannon entropy for the image corresponding to the imaging target, facilitating subsequent imaging using the adjustable Shannon entropy of each pixel in the image. By considering the influence of the histogram's horizontal axis distribution on the statistical distribution, the sensitivity of Shannon entropy to information disorder is enhanced, improving imaging performance. Imaging based on Shannon entropy with adjustable weights not only improves the accuracy of ultrasound diagnosis and detection but also allows for the adjustment of the adjustable Shannon entropy according to the acoustic characteristics and location of different imaging targets, thereby achieving optimal imaging results. By accumulating each bar in the normalized probability density histogram within the sliding window with a probability of 1, it is ensured that the horizontally normalized weighted adjustable Shannon entropy is calculated for each pixel within the sliding window. By calculating the horizontally normalized weighted Shannon entropy for each pixel within the sliding window, gradient adjustment is achieved, resulting in a better imaging effect that is more suitable for the characteristics of the imaging target. Imaging using adjustable Shannon entropy generates entropy images that can better monitor or diagnose the imaging target, improving image contrast and suppressing background noise, thus better depicting the target region and improving diagnostic accuracy. Alternatively, it can provide real-time and accurate monitoring for treatment, ensuring treatment effectiveness and patient safety during the treatment process.

[0141] This embodiment also provides an ultrasound imaging device based on adjustable Shannon entropy, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0142] This embodiment provides an ultrasound imaging device based on adjustable Shannon entropy, such as... Figure 8 As shown, it includes:

[0143] The target imaging module 801 is used to perform ultrasonic imaging on the imaging target to obtain the raw radio frequency data of the imaging target. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0144] The data unencapsulation module 802 is used to unencapsulate the raw radio frequency data to obtain the ultrasonic echo signal matrix. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0145] The tunable Shannon entropy calculation module 803 is used to construct a sliding window and traverse the coordinate points in the echo signal matrix using the sliding window to calculate the tunable Shannon entropy corresponding to each coordinate point. For details, please refer to [link to module 803]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0146] Image construction module 804 is used to construct an entropy image of the imaging target based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasound echo signal matrix. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0147] In some alternative implementations, the data unpacking module 802 includes:

[0148] The data de-enveloping unit is used to perform beamforming and Hilbert transform on the raw radio frequency data to obtain the ultrasonic echo signal matrix.

[0149] In some alternative implementations, the ultrasound imaging device based on adjustable Shannon entropy further includes:

[0150] The sliding window row count calculation unit is used when the number of rows in the sliding window is less than the number of rows in the echo signal matrix. Based on the wavelength multiple of the ultrasonic echo signal matrix, the sampling frequency, the ultrasonic signal wavelength, and the ultrasonic signal velocity, the number of rows in the sliding window is calculated.

[0151] The sliding window column number calculation unit is used when the number of columns in the sliding window is less than the number of columns in the echo signal matrix. Based on the wavelength multiple of the ultrasonic echo signal matrix, the ultrasonic signal wavelength, the number of ultrasonic transducer elements, and the ultrasonic image width, the number of columns in the sliding window is calculated.

[0152] In some alternative implementations, the adjustable shannon entropy calculation module 803 includes:

[0153] The adjustable Shannon entropy calculation unit is used to calculate the adjustable Shannon entropy of the center coordinate of the current sliding window based on all signals within the current sliding window.

[0154] The coordinate traversal unit is used to set the step size and, based on the step size, uses a sliding window to traverse all coordinate points in the echo signal matrix from left to right and from top to bottom to obtain the adjustable Shannon entropy corresponding to each coordinate point.

[0155] In some alternative implementations, the current adjustable Shannon entropy calculation unit includes:

[0156] The histogram calculation sub-unit is used to calculate the probability density histogram of all coordinate points within the current sliding window.

[0157] The histogram normalization sub-unit is used to normalize the probability density histogram along the horizontal direction to obtain the midpoint of the horizontal coordinate of each bar in the probability density histogram.

[0158] The gradient value range determination sub-unit is used to determine the gradient value range based on the acoustic characteristics and depth of the imaging target.

[0159] The adjustable Shannon entropy calculation subunit is used to calculate the adjustable Shannon entropy of the center coordinate of the current sliding window based on the range of gradient values ​​and the midpoint of the horizontal coordinate of each bar in the probability density histogram.

[0160] In some optional implementations, the adjustable Shannon entropy of the center coordinates of the current sliding window is calculated based on the range of gradient values ​​and the midpoint of the x-coordinate of each bar in the probability density histogram, as shown in the following formula:

[0161]

[0162] Among them, H hWA y' is the adjustable Shannon entropy of the center coordinates of the current sliding window, w(y') is the midpoint of the x-coordinate of each bar in the probability density histogram, and n is the gradient.

[0163] In some alternative implementations, the image construction module 804 includes:

[0164] The image construction unit is used to determine the adjustable Shannon entropy corresponding to each coordinate point in the echo signal matrix as the pixel value of that coordinate point, thereby obtaining the entropy image of the imaging target.

[0165] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0166] In this embodiment, the ultrasound imaging device based on adjustable Shannon entropy is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0167] This invention also provides a computer device having the above-described features. Figure 8 The ultrasound imaging device shown is based on adjustable Shannon entropy.

[0168] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 9As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0169] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0170] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0171] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0172] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0173] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0174] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0175] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An ultrasound imaging method based on adjustable Shannon entropy, characterized in that, The method includes: Ultrasonic imaging is performed on the imaging target to obtain the raw radio frequency data of the imaging target; The original radio frequency data is de-encapsulated to obtain the ultrasonic echo signal matrix; Construct a sliding window and use the sliding window to traverse the coordinate points in the echo signal matrix, and calculate the tunable Shannon entropy corresponding to each coordinate point in turn; Based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix, an entropy image of the imaging target is constructed. The process of unenveloping the original radio frequency data to obtain the ultrasonic echo signal matrix includes: Beamforming and Hilbert transform are performed on the raw radio frequency data to obtain the ultrasonic echo signal matrix; The step of using the sliding window to traverse the coordinate points in the echo signal matrix and sequentially calculating the adjustable Shannon entropy corresponding to each coordinate point includes: Based on all signals within the current sliding window, the adjustable Shannon entropy of the center coordinates of the current sliding window is calculated; Set a step size, and based on the step size, use the sliding window to traverse all coordinate points in the echo signal matrix from left to right and from top to bottom to obtain the adjustable Shannon entropy corresponding to each coordinate point; The adjustable Shannon entropy, calculated based on all signals within the current sliding window to obtain the center coordinates of the current sliding window, includes: Calculate the probability density histogram of all coordinate points within the current sliding window; The probability density histogram is normalized along the horizontal direction to obtain the midpoint of the horizontal coordinate of each bar in the probability density histogram. The range of gradient values ​​is determined based on the acoustic characteristics and depth of the imaging target. Based on the range of the gradient and the midpoint of the horizontal coordinate of each bar in the probability density histogram, the adjustable Shannon entropy of the center coordinate of the current sliding window is calculated. The adjustable Shannon entropy of the center coordinate of the current sliding window is calculated based on the range of the gradient and the midpoint of the horizontal coordinate of each bar in the probability density histogram, using the following formula: in, The adjustable Shannon entropy is the center coordinate of the current sliding window. Let be the midpoint of the x-coordinate of each bar in the probability density histogram. Let be the height of each bar in the probability density histogram. The gradient; The step of constructing an entropy image of the imaging target based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix includes: The adjustable Shannon entropy corresponding to each coordinate point in the echo signal matrix is ​​determined as the pixel value of that coordinate point, thereby obtaining the entropy image of the imaging target.

2. The method according to claim 1, characterized in that, The method further includes: The number of rows in the sliding window is less than the number of rows in the echo signal matrix; the number of rows in the sliding window is calculated based on the wavelength multiple, sampling frequency, ultrasonic signal wavelength, and ultrasonic signal velocity of the ultrasonic echo signal matrix. The number of columns in the sliding window is less than the number of columns in the echo signal matrix; the number of columns in the sliding window is calculated based on the wavelength multiple of the ultrasonic echo signal matrix, the ultrasonic signal wavelength, the number of ultrasonic transducer elements, and the ultrasonic image width.

3. An ultrasound imaging device based on adjustable Shannon entropy, characterized in that, The apparatus for implementing the ultrasound imaging method based on adjustable Shannon entropy as described in claim 1 or 2, the apparatus comprising: The target imaging module is used to perform ultrasonic imaging on the imaging target to obtain the raw radio frequency data of the imaging target; The data unencapsulation module is used to unencapsulate the original radio frequency data to obtain an ultrasonic echo signal matrix. The adjustable Shannon entropy calculation module is used to construct a sliding window and use the sliding window to traverse the coordinate points in the echo signal matrix to calculate the adjustable Shannon entropy corresponding to each coordinate point in turn. An image construction module is used to construct an entropy image of the imaging target based on the adjustable Shannon entropy corresponding to each coordinate point in the ultrasonic echo signal matrix.

4. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the ultrasound imaging method based on adjustable Shannon entropy as described in claim 1 or 2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the ultrasound imaging method based on adjustable Shannon entropy as described in claim 1 or 2.

Images