Method for automatic identification and removal of liver blood vessels in attenuation imaging

Abstract

The present application provides a method for automatic identification and removal of liver blood vessels in attenuation imaging (ATI), comprising: obtaining ultrasonic echo data of a target identification region, and setting an ROI box; performing ATI calculation on points in the ROI box to generate an initial ATI image; constructing a Nakagami distribution graph based on the initial ATI image, determining blood vessel tissue in the liver by means of a preset liver blood vessel threshold, and performing tissue marking; and on the basis of the tissue marking and the ATI image, removing liver blood vessels to generate a target ATI image.

Description

A method for automatic identification and removal of liver blood vessels in acoustic attenuation imaging Technical Field

[0001] The present application relates to the technical field of liver recognition, and in particular to a method for automatically recognizing and eliminating liver blood vessels in acoustic attenuation imaging. Background Art

[0002] Currently, ultrasonic attenuation imaging provides a quantitative tool for the non-invasive diagnosis of fatty liver and liver fibrosis. Ultrasound is a mechanical wave. During its propagation in human tissue, it is attenuated by small interface scattering, large interface reflection, beam diffusion, and absorption of ultrasonic energy by soft tissue. Fat is an acoustic attenuation medium, and the propagation of ultrasonic waves in fatty liver will be attenuated. The greater the proportion of fat tissue in the liver, the higher the ultrasonic attenuation. The amount of acoustic attenuation is a function of the attenuation coefficient generated during the propagation of ultrasonic waves. Using the law of acoustic attenuation to form a two-dimensional image and perform attenuation measurement of human tissue is of great significance, especially for the diagnosis of fatty liver and liver fibrosis.

[0003] In the prior art, the ultrasonic attenuation detection algorithm mainly uses the reflected echo of the B image to predict the acoustic attenuation coefficient through signal processing technology.

[0004] There is an attenuation imaging method based on a hybrid evaluation algorithm, which calculates the attenuation coefficient by finding the relationship between the center frequency and the power spectrum amplitude at different depths.

[0005] There is an attenuation imaging method based on the least squares evaluation algorithm. This method introduces a model containing attenuation parameters to fit the power spectrum ratio, and uses the least squares algorithm to minimize the cost function, thereby evaluating the local attenuation coefficient.

[0006] The amplitude value of the ultrasonic echo data is subjected to logarithmic fitting processing according to the least mean square estimation method, the attenuation coefficient is calculated, and ultrasonic attenuation imaging is finally realized.

[0007] However, while all of the aforementioned approaches are applied to tissues such as the liver, they focus on the design, signal chain, and processing of ultrasound attenuation imaging. No technology or patent has yet addressed the actual liver application environment, nor has it considered the impact of liver blood vessels on ultrasound attenuation imaging.

[0008] The calculation of acoustic attenuation imaging requires uniform liver tissue to avoid interference from vascular echoes. The liver contains blood vessels such as the hepatic artery, portal vein, hepatic vein, interlobular veins, and interlobular arteries. These vessels can produce abnormal echoes, affecting the calculation of acoustic attenuation imaging.

[0009] Application Contents

[0010] The present application provides a method for automatically identifying and removing liver blood vessels in acoustic attenuation imaging to solve the problems in the above-mentioned background technology.

[0011] A method for automatically identifying and removing liver blood vessels in acoustic attenuation imaging, comprising:

[0012] Acquire ultrasonic echo data of the target identification area and set the ROI frame;

[0013] Perform ATI imaging calculation on the points within the ROI frame to generate the initial ATI image;

[0014] Construct a Nakagami distribution map based on the initial ATI image, and determine the vascular tissue in the liver through a preset intra-liver vascular threshold, and perform tissue labeling;

[0015] Based on tissue markers and ATI images, liver blood vessels are removed to generate target ATI images.

[0016] Preferably, the ultrasonic echo data is a plane wave of B-ultrasound reflection echo.

[0017] Preferably, the ATI imaging calculation includes performing single-point calculation on points within the ROI frame, and arranging the results of the single-point calculation into an ATI data matrix; wherein,

[0018] In single-point calculation, the current calculation point is taken as the center of the ATI data matrix to determine the amplitude matrix of the current calculation point;

[0019] The length and width of the ATI data matrix are determined by the length and width of the ROI box;

[0020] The value of each point in the ATI data matrix is ​​the sound attenuation coefficient.

[0021] Preferably, the value of the amplitude matrix is ​​the logarithm of the current calculation point; wherein,

[0022] The logarithm includes the following calculation steps:

[0023] Based on the ROI frame, determine the real and imaginary parts of the current calculation point and determine the amplitude value

[0024] Where A represents the amplitude value of the ultrasonic echo data, I represents the real part, and Q represents the imaginary part;

[0025] According to the amplitude value, determine the depth logarithm column of the current calculation point and determine the logarithm: Y 1...n =20log10(A)

[0026] Among them, 1, 2, 3…n represent different column values; Y is the logarithm.

[0027] Preferably, the sound attenuation coefficient includes the following calculation steps:

[0028] Perform weighted averaging on the amplitude matrix and convert it into a one-dimensional row vector; Y=a1Y1+a2Y2+a3Y3...+a n Y n

[0029] Among them, a1, a2, ...a n , represents the weight coefficients of different columns; Y is a one-dimensional row vector along the depth direction;

[0030] According to the one-dimensional row vector, the least square method is used to fit along the ultrasonic depth direction to determine the slope of the fitting coefficient;

[0031] The coefficient slope conforms to the following formula: Y=k×x+b

[0032] Divide the coefficient slope by the B-ultrasound transmission frequency to determine the attenuation coefficient; where,

[0033] The attenuation coefficient conforms to the following formula: β=k / f0=0.4521dB / (cm.MHz)

[0034] In the above example, k = 1.3563 dB / cm, divided by the transmission frequency f0 = 3M, the attenuation coefficient β = 0.4521 dB / (cm.MHz).

[0035] Preferably, the Nakagami distribution map is a tissue scattering distribution map of vascular tissue in the liver.

[0036] Preferably, the tissue scattering distribution map includes the scattering distribution coefficient of each calculated single point; wherein,

[0037] When the scattering distribution coefficient is greater than the blood vessel threshold in the liver, it is normal liver tissue;

[0038] When the scattering distribution coefficient is less than the threshold of blood vessels in the liver, it is a blood vessel and has strong echoes.

[0039] Preferably, the tissue marker is an ATI image marker of attenuation imaging; wherein,

[0040] ATI image marking generates tissue marking synchronously from the blood vessel coordinate position. Tissue marking includes blood vessel marking and strong echo tissue marking. The tissue marking method is coordinate marking.

[0041] Preferably, the liver devascularization comprises:

[0042] By tissue marking, blood vessels and strong echoes are marked on the ATI image to obtain the marked ATI marked image;

[0043] The ATI marker image is overlaid on the B-ultrasound image of the ultrasonic echo data to generate a target ATI image.

[0044] The beneficial effects of this application are:

[0045] This application is based on the ATI calculation method, in which the data acquisition method of using plane waves to collect echo data can effectively avoid the interference of the focusing mode on the focus energy, ensure that the echo signal is relatively balanced, and ensure the accuracy and stability of the ATI calculation.

[0046] The weighted summation of columns in the ATI calculation of this application can effectively reduce data interference and ensure the stability of the algorithm.

[0047] In the functional application of acoustic attenuation, the present application automatically identifies and eliminates blood vessels in the liver, ensuring the accuracy and stability of AT1 acoustic attenuation measurement.

[0048] The algorithm of this application has high real-time performance and meets the functional requirements of detecting blood vessels in the liver and real-time calculation.

[0049] The solution of removing blood vessels and strong echoes from the acoustic attenuation image in this application and replacing them with B-images is not only beneficial for the measurement of acoustic attenuation, but also can effectively enrich the information of B-images, display blood vessels and strong echo points in the liver, and produce better clinical effects of B-images.

[0050] This application further deduces that blood vessels and strong echoes in other tissues of the human body can be identified and removed using this method.

[0051] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0052] The technical solution of the present application is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings:

[0054] FIG1 is a flow chart of a method for automatically identifying and removing liver blood vessels in acoustic attenuation imaging according to an embodiment of the present application;

[0055] FIG2 is an image of the liver collected in an embodiment of the present application;

[0056] FIG3 is a slope diagram of the attenuation coefficient in an embodiment of the present application;

[0057] FIG4 is an ATI image according to an embodiment of the present application;

[0058] FIG5 is a diagram B in the embodiment of the present application without removing blood vessels;

[0059] FIG6 is a fusion effect diagram of the ATI image and the B image with blood vessels removed in an embodiment of the present application;

[0060] FIG7 is a flow chart of the steps for generating FIG. B in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application.

[0062] (Attenuation Imaging - ATI)

[0063] This application proposes a method for automatically identifying and removing liver blood vessels in acoustic attenuation imaging, including:

[0064] Acquire ultrasonic echo data of the target identification area and set the ROI frame;

[0065] Perform ATI (attenuation imaging) imaging calculation on the points within the ROI (Region of Interest) box to generate an initial ATI image;

[0066] Construct a Nakagami distribution map based on the initial ATI image (Nakagami-m distribution is a fast fading model proposed by Japanese scholar Nakagami in a 1960 paper). Use a preset intra-liver vascular threshold to identify and label the vascular tissue within the liver.

[0067] Based on tissue markers and ATI images, liver blood vessels are removed to generate target ATI images.

[0068] The working principle of the above technical solution is:

[0069] As shown in FIG1 , the present application is a method for automatically identifying and removing liver blood vessels during imaging based on acoustic attenuation, thereby preventing interference from tube echoes and liver tissue in the acquired B-ultrasound images.

[0070] In specific implementations, acoustic attenuation calculation requires uniform liver tissue to avoid interference from vascular echoes. In order to ensure the accuracy and stability of the acoustic attenuation function, it is necessary to automatically remove blood vessels and strong echo points in the liver.

[0071] In specific implementation, the accuracy of acoustic attenuation function measurement is ensured by automatically identifying and eliminating blood vessels and strong echo points in the liver.

[0072] In specific implementations, the identification of blood vessels in the liver is mainly applied to the theory of tissue scattering distribution imaging. Because blood vessel scattering satisfies the Nakagami distribution, the Nakagami parameter m of each point in the ROI box is calculated, and the calculated Nakagami parameter m is compared with the blood vessel threshold in the liver. If it is lower than a specific threshold, it is judged as blood vessel tissue in the liver and identified.

[0073] In the specific implementation, the blood vessels in the liver are eliminated. After the blood vessel position is successfully identified in the liver, the blood vessel coordinate position is synchronized to the attenuation imaging image, the blood vessel position is eliminated, and the B image is used to replace it. The attenuation imaging is calculated after the blood vessels are eliminated to ensure the accuracy of the acoustic attenuation measurement.

[0074] In the above embodiment, the B image refers to the B mode (Brightness-mode) of the ultrasound device, in which the ultrasound probe is used to transmit ultrasound to the tissue, record the echo of the internal structure of the tissue, and process the echo to form a grayscale image to reflect the internal structure of the tissue.

[0075] Medical ultrasound imaging systems utilize the propagation of ultrasound waves through the human body to obtain ultrasonic signatures of human tissue and organ structures. In current medical ultrasound imaging systems, high-voltage pulse waves are applied to each probe element, stimulating them to generate high-frequency ultrasound waves, which then form a transmit beam that enters the body. The probe elements receive echoes scattered or reflected by tissue structures, forming a receive beam. The ultrasound imaging system extracts information from the ultrasound echoes, performs beam synthesis, and processes the echoes to form a grayscale image, known as a B-image, that reflects the internal structure of the tissue.

[0076] In the implementation process of this application, Figure B is constructed through the following six steps, as shown in Figure 7:

[0077] (1) At the receiving end of the ultrasonic probe, after obtaining the ultrasonic echo data, ADC sampling is performed to collect RF (Radio Frequency) radio frequency data;

[0078] (2) Perform quadrature demodulation on the RF data to generate I and Q data;

[0079] (3) Sampling channel data at a given sampling rate (typical value 15M);

[0080] (4) After channel data sampling, beam synthesis is performed;

[0081] (5) Scan conversion after beamforming;

[0082] (6) After the scan conversion is completed, the B image is displayed and is visible on the ultrasound device interface.

[0083] The beneficial effects of the above technical solution are:

[0084] This application is based on the ATI calculation method, in which the data acquisition method of using plane waves to collect echo data can effectively avoid the interference of the focusing mode on the focus energy, ensure that the echo signal is relatively balanced, and ensure the accuracy and stability of the ATI calculation.

[0085] The weighted summation of columns in the ATI calculation of this application can effectively reduce data interference and ensure the stability of the algorithm.

[0086] In the application of the acoustic attenuation function, the present application automatically identifies and eliminates blood vessels in the liver, ensuring the accuracy and stability of ATI acoustic attenuation measurement.

[0087] The algorithm of this application has high real-time performance and meets the functional requirements of detecting blood vessels in the liver and real-time calculation.

[0088] The solution of removing blood vessels and strong echoes from the acoustic attenuation image in this application and replacing them with B-images is not only beneficial for the measurement of acoustic attenuation, but also can effectively enrich the information of B-images, display blood vessels and strong echo points in the liver, and produce better clinical effects of B-images.

[0089] This application further deduces that blood vessels and strong echoes in other tissues of the human body can be identified and removed using this method.

[0090] Preferably, the ultrasonic echo data is a plane wave of B-ultrasound reflection echo.

[0091] The working principle of the above technical solution is:

[0092] In this embodiment, the calculation of ATI (Attenuation Imaging) acoustic attenuation is a process of obtaining ultrasound echo data of a target area based on B-ultrasound reflection echoes and performing signal processing.

[0093] Ultrasonic waves mainly emit focused waves and plane waves. The sound field generated by the focused wave has stronger energy and higher signal-to-noise ratio near the focal area, while the sound field of the plane wave is more balanced in the imaging area.

[0094] ATI acoustic attenuation imaging requires a plane wave pattern for transmission and reception. When focusing mode is used to collect data at the same depth, the energy at the focal point is high and the energy outside the focal point is low. This affects the ATI energy attenuation assessment.

[0095] Plane waves are not affected by focus. Setting the TGC (Time Gain Compensate) value to the same at different depths allows for effective evaluation of the attenuation coefficient based on the echo energy and depth fit.

[0096] Preferably, the ATI imaging calculation includes performing single-point calculation on points within the ROI frame, and arranging the results of the single-point calculation into an ATI data matrix; wherein,

[0097] In single-point calculation, the current calculation point is taken as the center of the ATI data matrix to determine the amplitude matrix of the current calculation point;

[0098] The length and width of the ATI data matrix are determined by the length and width of the ROI box;

[0099] The value of each point in the ATI data matrix is ​​the sound attenuation coefficient.

[0100] The working principle of the above technical solution is:

[0101] As shown in FIG2 , in actual implementation, the liver image captured shows clearly visible blood vessels and liver capsule in the liver; the ROI box is in the middle of FIG2 , and the position and size of the ROI box can be determined, and the size of the ROI box is set by the user;

[0102] During the specific calculation process, each point within the ROI frame needs to be calculated using the ATI algorithm. The single-point calculation of each point is combined into the ATI calculation results of all points within the ROI frame, which are organized into an ATI data matrix to form a complete ATI image.

[0103] In a specific implementation, as shown in FIG4 , an acoustic attenuation coefficient is calculated for all points within the ROI by moving the index from the upper left corner to the lower right corner. The calculated acoustic attenuation coefficients for all points are stored in an ATI data matrix and displayed, forming an ATI image within the ROI.

[0104] For single-point calculation of each point, the input of the algorithm is a matrix centered on the point, and the length and width of the matrix are given by the calculation system.

[0105] Preferably, the value of the amplitude matrix is ​​the logarithm of the current calculation point; wherein,

[0106] The logarithm includes the following calculation steps:

[0107] Based on the ROI frame, determine the real and imaginary parts of the current calculation point and determine the amplitude value

[0108] Where A represents the amplitude value of the ultrasonic echo data, l represents the real part, and Q represents the imaginary part;

[0109] According to the amplitude value, determine the depth logarithm column of the current calculation point and determine the logarithm: Y 1...n =20log10(A)

[0110] Among them, 1, 2, 3…n represent different column values; Y is the logarithm.

[0111] The working principle of the above technical solution is:

[0112] The above calculation of logarithms is to calculate the ATI value of a single point. During the calculation process, a matrix of given length and width is used as input with the calculation point as the center and input into the ATI single-point calculation algorithm function. First, the amplitude of each point in the matrix is ​​calculated, and then the amplitude value is taken logarithm by finding the law of amplitude change with depth. The matrix is ​​logarithmized by column to determine the logarithm of each point in the amplitude matrix.

[0113] Preferably, the sound attenuation coefficient includes the following calculation steps:

[0114] Step 1: Perform weighted averaging on the amplitude matrix and convert it into a one-dimensional row vector; Y = a1Y1+a2Y2+a3Y3…+a n Y n

[0115] Among them, a1, a2, ...a n , represents the weight coefficients of different columns; Y is a one-dimensional row vector along the depth direction;

[0116] Step 2: According to the one-dimensional row vector, the least square method is used to fit along the ultrasonic depth direction to determine the slope of the fitting coefficient; where,

[0117] The coefficient slope conforms to the following formula: Y=k×x+b

[0118] Step 3: Divide the coefficient slope by the B-ultrasound transmission frequency to determine the attenuation coefficient; where,

[0119] The attenuation coefficient conforms to the following formula: β=k / f0=0.4521dB / (cm.MHz)

[0120] In the above example, k = 1.3563 dB / cm, divided by the transmission frequency f0 = 3M, the attenuation coefficient β = 0.4521 dB / (cm.MHz).

[0121] The principle of the above technical solution is:

[0122] During implementation, to effectively minimize data interference and ensure algorithm stability, a weighted average is performed on the amplitude matrix, averaging the data along the column dimension. The values ​​of each row are weighted and summed across different columns, with the center weighted higher and the weights on both sides gradually decreasing. This transforms the data from a two-dimensional matrix into a one-dimensional row vector. During this process, the center weight of the measurement point is higher, while the weights on both sides gradually decrease. The weight coefficients conform to a normal distribution, as shown in step 1.

[0123] Then in step 2, the weighted summed one-dimensional row vector data Y is linearly fitted along the ultrasonic depth direction, as shown in Figure 3. The horizontal coordinate x is the point sequence along the depth direction, and the vertical coordinate Y is the weighted logarithmic amplitude value. Using the linear fitting model, the least squares fitting method is used to obtain the fitting coefficient slope.

[0124] In step 3, the slope result obtained by the fitting calculation is divided by the transmission frequency to obtain the attenuation coefficient.

[0125] Preferably, the Nakagami distribution map is a tissue scattering distribution map of vascular tissue in the liver.

[0126] The principle of the above technical solution is:

[0127] In practice, the application of acoustic attenuation in tissues such as the liver requires uniform liver tissue to avoid interference from vascular echoes. However, the liver contains vessels such as the hepatic artery, portal vein, hepatic vein, interlobular veins, and interlobular arteries. These vessels can generate abnormal echoes, affecting the calculation of acoustic attenuation imaging.

[0128] Strong reflective points, such as liver blood vessels or the liver capsule, can create abnormal echoes and affect ATI calculations. This algorithm effectively identifies liver blood vessels and other strong reflective points and removes them from ATI images. This prevents strong reflective points, such as blood vessels and the liver capsule, from affecting ATI imaging.

[0129] The tissue scattering distribution map is used to identify blood vessels within the liver. According to the theory of tissue scattering distribution, blood vessel scattering satisfies the Nakagami distribution. The Nakagami parameter m is calculated for each point within the ROI frame and compared with the blood vessel threshold within the liver. If the calculated Nakagami parameter m is below a specific threshold, it is judged to be vascular tissue within the liver.

[0130] Preferably, the tissue scattering distribution map includes a scattering distribution coefficient of each calculated single point;

[0131] in,

[0132] When the scattering distribution coefficient is greater than the blood vessel threshold in the liver, it is normal liver tissue;

[0133] When the scattering distribution coefficient is less than the threshold of blood vessels in the liver, it is a blood vessel and has strong echoes.

[0134] The principle of the above technical solution is:

[0135] The tissue scattering distribution is a pixel-by-pixel map based on the shape parameters of the Nakagami distribution, which reflects the arrangement and concentration of scatterers. The scattering distribution coefficient is also defined as the "Nakagami parameter m." As the Nakagami parameter m changes, the distribution of the backscattered signal envelope changes. From the anterior Rayleigh distribution to the posterior Rayleigh distribution, the anterior Rayleigh distribution indicates that several scatterers are randomly distributed in the tissue. In contrast, the posterior Rayleigh distribution indicates that there are localized high concentrations of scatterers with periodic distribution.

[0136] Therefore, the change of the Nakagami parameter m from 0 to greater than 1 indicates that the backscatter signal envelope changes from the pre-Rayleigh distribution to the post-Rayleigh distribution (Rayleigh distribution). The blood vessels and homogeneous tissues in the liver have different scattering characteristics, and their scattering distribution coefficients, that is, the Nakagami parameter m, will also be different. Based on this difference, by calculating the Nakagami parameter m, the blood vessels and homogeneous tissues in the liver can be effectively distinguished.

[0137] The calculation process for the Nakagami parameter is as follows:

[0138] The result of calculating the Nakagami parameter m of a single point is centered on the single point and a matrix of length and width given by the system is used as input to the single point calculation algorithm function of the Nakagami parameter m:

[0139] Blood vessel scattering satisfies the Nakagami distribution. The calculation formula of the Nakagami fading parameter m is as follows:

[0140] Where R represents the amplitude value matrix of a two-dimensional area with given length and width.

[0141] Where I represents the real part and Q represents the imaginary part.

[0142] Molecule [E(R 2 )] 2 Represents the mean of R squared, then squared.

[0143] Denominator E(R 4 )-[E(R 2 )] 2 Represents the difference between the mean and the numerator of R fourth power.

[0144] Then, the single point scattering distribution coefficient is calculated and a threshold judgment is performed. If it is less than the threshold, it is identified as a blood vessel and a strong echo. If it is greater than the threshold, it is identified as normal liver tissue.

[0145] The threshold is set according to the tissue being identified. For blood vessels and strong echo points in the liver, the threshold is set to 0.5. The scattering distribution coefficients of all points are stored in the Nakagami two-dimensional matrix.

[0146] Preferably, the tissue marker is an ATI image marker of attenuation imaging; wherein,

[0147] ATI image marking generates tissue marking synchronously from the blood vessel coordinate position. Tissue marking includes blood vessel marking and strong echo tissue marking. The tissue marking method is coordinate marking.

[0148] The working principle of the above technical solution is:

[0149] In the specific implementation process, the scattering distribution coefficient value and threshold are used for judgment. For points less than the liver threshold, the vascular coordinate position is synchronized to the attenuation imaging image and marked in the ATI matrix as blood vessels and highly echogenic tissue.

[0150] The marked index is the basis for processing blood vessels and strong echoes in the ATI image.

[0151] Preferably, the liver devascularization comprises:

[0152] By tissue marking, blood vessels and strong echoes are marked on the ATI image to obtain the marked ATI marked image;

[0153] The ATI marker image is overlaid on the B-ultrasound image of the ultrasonic echo data to generate a target ATI image.

[0154] The working principle of the above technical solution is:

[0155] As shown in Figures 5 and 6, which are the effect images after removing blood vessels and strong echoes, the ATI image is fused with the B image to remove the liver blood vessels. Specifically, the ATI image is overlaid on the B image, wherein the indexes of the blood vessels and strong echo tissue marked in the ATI are replaced with the results of the B image, and the remaining normal tissues are displayed with the ATI image.

[0156] After the ATI image and B image are superimposed, blood vessels and strong echoes can be clearly seen on the B image.

[0157] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for automatically identifying and removing liver blood vessels in acoustic attenuation imaging, characterized in that: include: Acquire the ultrasonic echo data of the target identification area and set the ROI frame; Perform ATI imaging calculation on the points within the ROI frame to generate an initial ATI image; Constructing a Nakagami distribution map based on the initial ATI image, and determining the vascular tissue in the liver through a preset intra-liver vascular threshold, and performing tissue labeling; According to tissue markers and ATI images, liver blood vessels are removed to generate target ATI images.

2. The method for automatic identification and elimination of liver blood vessels in acoustic attenuation imaging according to claim 1, characterized in that: The ultrasonic echo data is a plane wave of B-ultrasound reflection echo.

3. The method for automatic identification and elimination of liver blood vessels in acoustic attenuation imaging according to claim 1, characterized in that: The ATI imaging calculation includes performing single-point calculation on points within the ROI frame, and arranging the results of the single-point calculation into an ATI data matrix; wherein, In single-point calculation, the current calculation point is taken as the center of the ATI data matrix to determine the amplitude matrix of the current calculation point; The length and width of the ATI data matrix are determined by the length and width of the ROI box; The value of each point in the ATI data matrix is ​​the sound attenuation coefficient.

4. The method for automatic identification and elimination of liver blood vessels in acoustic attenuation imaging according to claim 3, characterized in that: The value of the amplitude matrix is ​​the logarithm of the current calculation point; wherein, The logarithm includes the following calculation steps: Based on the ROI frame, determine the real and imaginary parts of the current calculation point and determine the amplitude value Wherein, A represents the amplitude value of ultrasonic echo data, I represents the real part, and Q represents the imaginary part; According to the amplitude value, determine the depth logarithm column of the current calculation point and determine the logarithm: Y 1...n =20log10(A) Among them, 1, 2, 3…n represent different column values; Y is the logarithm.

5. The method for automatic identification and elimination of liver blood vessels in acoustic attenuation imaging according to claim 3, characterized in that: The sound attenuation coefficient comprises the following calculation steps: Perform weighted averaging on the amplitude matrix and convert it into a one-dimensional row vector; <h2 style=";text-align:left;direction:ltr">Y = a1Y1 + a2Y2 + a3Y3… + a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> Y<h2 style=";text-align:left;direction:ltr"> n Among them, a1, a2, ...a n , represents the weight coefficients of different columns; Y is a one-dimensional row vector along the depth direction; According to the one-dimensional row vector, the least square method is used to fit along the ultrasonic depth direction to determine the slope of the fitting coefficient; where, The coefficient slope conforms to the following formula: Y=k×x+b Divide the coefficient slope by the transmission frequency of B-ultrasound to determine the attenuation coefficient; where, The attenuation coefficient conforms to the following formula: β=k / f0=0.4521dB / (cm.MHz) In the above example, k = 1.3563db / cm, divided by the transmission frequency f0 = 3M, the attenuation coefficient β = 0.4521dB / (cm.MHz).

6. The method for automatic identification and elimination of liver blood vessels in acoustic attenuation imaging according to claim 1, characterized in that: The Nakagami distribution map is a tissue scattering distribution map of vascular tissue in the liver.

7. The method for automatic identification and elimination of liver blood vessels in acoustic attenuation imaging according to claim 6, characterized in that: The tissue scattering distribution map includes the scattering distribution coefficient of each calculated single point; wherein, When the scattering distribution coefficient is greater than the blood vessel threshold in the liver, it is normal liver tissue; When the scattering distribution coefficient is less than the threshold of blood vessels in the liver, it is a blood vessel with strong echoes.

8. The method for automatic identification and elimination of liver blood vessels in acoustic attenuation imaging according to claim 1, characterized in that: The tissue marker is marked by an ATI image of attenuation imaging; wherein, ATI image marking generates tissue marking synchronously from the blood vessel coordinate position. The tissue marking includes blood vessel marking and strong echo tissue marking. The tissue marking method is coordinate marking.

9. The method for automatic identification and elimination of liver blood vessels in acoustic attenuation imaging according to claim 1, characterized in that: The liver devascularization comprises: By tissue marking, blood vessels and strong echoes are marked on the ATI image to obtain the marked ATI marked image; The ATI marker image is overlaid on the B-ultrasound image of the ultrasonic echo data to generate a target ATI image.

Images