CN111616740A - Ultrasonic back scattering homodyne K imaging method based on empirical mode decomposition - Google Patents
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Abstract
The invention discloses an ultrasonic backscatter homodyne K imaging method based on Empirical Mode Decomposition (EMD), and belongs to the field of signal processing. EMD signal decomposition is carried out on an original radio frequency signal acquired from an ultrasonic instrument to obtain an intrinsic mode function (IMF signal), the influence of a back scattering signal of a non-target tissue on parameter estimation is solved primarily, ultrasonic tissue characterization of a biological tissue is carried out on the obtained IMF signal through a homodyne K model, and biological tissue microstructure information is provided, wherein the homodyne K model parameter estimation adopts two algorithms, one algorithm is an RSK algorithm based on a signal-to-noise ratio, skewness and kurtosis, and the other algorithm is an XU algorithm based on X statistics and U statistics. Two kinds of parameter estimation are carried out based on the sliding window technology, and finally an estimation value matrix of the parameter estimation can be obtained, and a visualized parameter image can be generated. The method can be used for ultrasonic tissue characterization of biological tissues such as liver, mammary gland and the like, and provides related microstructure information in a parameter and visualization mode.
Description
Technical Field
The invention belongs to the field of medical signal processing, and particularly relates to an ultrasonic back scattering homodyne K imaging method based on Empirical Mode Decomposition (EMD), which is used for establishing a signal decomposition technology based on EMD, developing effective scattering sub-number and coherent scattering parameter imaging based on a homodyne K model, providing related microstructure information, providing physical significance and parameter value of a specific scattering structure and providing visual parameter imaging.
Background
Ultrasonic imaging has the advantages of good real-time property, no ionizing radiation, low price and the like, and has become a mainstream detection technical means in the imaging detection technology. In today's ultrasonic inspection technology, ultrasonic waves emitted by an ultrasonic probe are scattered if they encounter an interface having a diameter smaller than the incident wavelength, and their energy is radiated in all directions. Wherein the scattered waves that are directed away from the probe are backscattered waves. Therefore, the structure of biological tissues (such as liver, mammary gland and the like) can be regarded as an acoustic structure model formed by a plurality of tiny scatterers, and related information of the scatterers directly reflects related information of the microstructure of the biological tissues, so that ultrasonic tissue characterization of the biological tissues can be completed by measuring the related information of the scatterers. When ultrasonic waves are incident into biological tissues, signals received by the probe are superposed on backscattering signals contributed by the respective scatterers under the interaction of incident waves and the scatterers. When the biological tissue is diseased, the microstructure of the tissue itself will change, which affects the waveform characteristics of the received backscatter signal, and based on the random properties of the backscatter signal, the correlation between the statistical distribution of the backscatter signal and the micro scatterers can be concluded by analyzing the probability distribution pattern. Due to the diversification of scattering modes of organism tissues, a plurality of scattering statistics are derived and described and patterned by using mathematical statistical distribution, so that the research of the microstructure of the organism tissues is carried out, and the characterization of the ultrasonic tissues is carried out.
In recent years, with the continuous development of ultrasonic detection technology and the continuous improvement of the application of related statistical models, domestic and foreign scholars propose a plurality of statistical model-based ultrasonic tissue characterization methods, which mainly include rayleigh models, acoustic structure quantitative ASQ, Nakagami models, composite Nakagami, homodyne K models, and the like. However, in the existing ultrasonic tissue characterization method, regardless of the ASQ technology or the Nakagami statistical parameter imaging technology, both technologies cannot provide much specific information for the physical significance of the scattering structure, since the Nakagami parameter m is very easy to generate parameter saturation in tissue characterization, the estimated microscopic scattering substructure is not accurate enough, and there are limitations, and in the backscatter signal of the biological tissue, there is no backscatter signal of the target biological tissue but also other noise signals, which has a great influence on subsequent ultrasonic biological tissue characterization and microstructure measurement. The invention provides a novel method for ultrasonic backscatter homodyne K imaging biological tissue characterization based on empirical mode decomposition, which can be used for ultrasonic tissue characterization of biological tissues, provide microstructure information and provide visual parameter imaging by quantifying the number of effective scatterers and coherent scattering parameters. Therefore, the quantitative research on the biological tissue microstructure in the research field is met, and the method has important research value and application prospect.
Disclosure of Invention
The invention aims to provide a novel ultrasonic backscatter homodyne K imaging biological tissue characterization method based on empirical mode decomposition. The invention is composed of two parts, the first part is to take empirical mode decomposition to the original radio frequency signal collected by the ultrasonic instrument, so as to obtain the first and the second intrinsic mode function IMF1And IMF2The method mainly aims to cope with the influence of noise backscattering signals generated by non-target tissues on the micro-structure characterization accuracy of the biological tissues and improve the accuracy of parameter estimation. The second part is the core part of the invention, namely parameter estimation and parameter visualization imaging based on homodyne K model, firstly generating a B-mode ultrasonic image before parameter estimation, then delineating an interested area to be subjected to parameter estimation through the ultrasonic image, and performing parameter estimation by applying a sliding window technology in the interested area, wherein the parameter estimation mainly relates to two estimation methods: (1) RSK parameter estimation method based on signal-to-noise ratio, skewness and kurtosis; (2) the other method is an XU parameter estimation method based on X statistics and U statistics, the number mu of effective scatterers and the ratio k of coherent scattering signals to diffuse scattering signals are obtained through estimation, parameters of the XU parameter estimation method can generate a parameter image, the parameters can be used for carrying out characterization on biological tissue structure information to provide microstructure data, and parameter imaging can provide visual information.
The specific technical content comprises the following steps:
an ultrasonic backscattering homodyne K imaging method based on empirical mode decomposition comprises the following steps:
the method comprises the following steps of 1, B-mode ultrasonic imaging and region of interest acquisition, and specifically comprises the following steps:
step 1.1, acquiring original ultrasonic radio frequency data I, wherein the number of columns of the data is L, and the number of rows of the data is D;
step 1.2, demodulating original ultrasonic radio frequency data I by using Hilbert transform to construct an envelope image, and carrying out logarithmic compression by using a dynamic range of 40dB to obtain envelope data ISTo construct a B-mode ultrasonic image B;
step 1.3, selecting polygons from the image B, converting the selected polygons into masks of a binary matrix, wherein the area inside the masks is 1, and the area outside the masks is 0, so that an area of interest R is obtained;
step 2, performing signal decomposition on the original radio frequency data by using empirical mode decomposition to obtain an Intrinsic Mode Function (IMF), which specifically comprises the following steps:
step 2.1, carry on initialization, make r0I, I-1, wherein r0Representing the residual component which is not decomposed, namely the original ultrasonic radio frequency data I at the moment, wherein I is the counting number of the empirical mode decomposition times;
step 2.2. order hj-1＝ri-1J is 1 wherein hj-1The new sequence of the eigenmode function is subtracted after each step of decomposition, which is now the residual component r of the previous stepi-1Wherein j is a count of the number of times the eigenmode function is determined;
step 2.3, find hj-1The local extreme point of the envelope curve is subjected to cubic spline function interpolation on the maximum value point and the minimum value point to form an upper envelope curve and a lower envelope curve, and the average value m of the upper envelope curve and the lower envelope curve is calculatedj-1，hj＝hj-1-mj-1；
Step 2.4.hjIf there are no negative local maxima and no positive local minima, hjIs IMF function, the ith instinctive mode function IMFi＝hjOtherwise, j is increased by one, and the process is restarted from the step 2.3;
step 2.5 residual component r after decompositioni＝ri-1-imfiIf r isiIf the number of extreme points is still more than 2, i is incremented by one and the process resumes from step 2.2, otherwise the decomposition ends, thus obtaining the eigenmode function imfi。
And 3, performing homodyne K model parameter estimation and parameter imaging on the decomposed data, and specifically comprising the following steps of:
step 3.1. sliding window technique is used as a method for constructing ultrasound parametric images, using imfiEnvelope signal, using square window whose window edge length WSL is 1-9 times of pulse length to obtain signal data, making homodyne K model parameter estimation in said window, making the window move in distance increment in the whole range of image data, respectively making transverse and longitudinal movement, every increment of window contains pixel number correspondent to window overlap rate WOR, and making window movement increment be determined by axial pixel number and transverse pixel number of single window, firstly defining related parameters of window, in which window axial pixel number is WSL p/t, window transverse pixel number is 1 is WSL p/scan _ step, window transverse movement distance is L-inte1, window transverse movement step length L1 is intel1/WOR, window axial movement distance is D-inte, window axial movement step length A1 is intel/WOR, in which p is pulse length, t is axial point distance, scan _ step is the scanning step;
step 3.2, calculating the window transverse movement grid number x as average/L1, and calculating the window axial movement grid number y as axis/A1;
and 3.3, dividing the signal data into m × n matrixes by windows, wherein m is 1, 2 and 3 … x, n is 1, 2 and 3 … y, moving the windows transversely in the 1 st row, and respectively calculating corresponding parameters mu in each window by using an RSK method and an XU method11、μ12、μ13…μ1nAnd so on until the window completes moving at the m-th row, thereby obtaining the parameter matrix mumnAnd kmnThe matrix is an effective scatterer number parameter matrix mu and a ratio parameter matrix k of coherent scattering signals and diffuse scattering signals;
step 3.4, forming parameter images RSK-k, RSK-mu, XU-k and XU-mu by using the parameters mu and k through color mapping, wherein RSK-k is a k parameter image obtained through an RSK method, RSK-mu is a mu parameter image obtained through the RSK method, XU-k is a k parameter image obtained through an XU method, and XU-mu is a mu parameter image obtained through the XU method;
and 3.5, calculating the average values of the parameters mu and k in the region of interest R by using the parameter images RSK-k, RSK-mu, XU-k and XU-mu, and providing related microstructure information for ultrasonic tissue characterization of biological tissues, thereby providing the physical significance and parameter value of the specific scattering structure and providing visual parameter imaging.
Has the advantages that:
1. the method adopts a new homodyne K model parameter imaging method and uses a sliding window technology, thereby providing a more convenient method for parameter estimation and opening up a new research channel for ultrasonic tissue characterization of biological tissues.
2. The invention adopts a signal decomposition technology based on empirical mode decomposition, and can effectively avoid the influence of noise scattering signals of non-target biological tissues on the characterization.
3. The algorithm can visually display the evaluation result, not only realizes quantitative analysis of the related microstructure of the biological tissue, but also provides visual analysis.
Drawings
FIG. 1: a flow chart of the method of the invention;
FIG. 2: a B-mode ultrasound image generated by the envelope signal;
FIG. 3: in the method, a homodyne K ultrasonic backscattering statistical parameter imaging process (based on an RSK and XU parameter estimation method) is adopted;
Detailed Description
The extraction process is specifically described with reference to the accompanying drawings and practical examples. The data used were collected using a Terason machine, the acquisition site was the right lobe of the liver, the image depth was 8cm, and the probe center frequency fcIs 3MHz, the sampling rate fsIs 12MHz, the acoustic velocity v is 1540, and the window overlap ratio WOR is 0.5. The biological tissue in this embodiment is exemplified by liver tissue. A total of 43 liver data were collected. The following steps are introduced:
1. using original ultrasonic radio frequency data I, reading the original radio frequency data in a two-dimensional array form, obtaining the column number of the data as L and the row number of the data as D, demodulating I by using Hilbert transform to construct an envelope signal, and performing logarithmic compression by using the dynamic range as 40dB to obtain the envelope data ISTo construct a B-mode image B, which is shown in fig. 2. According to medical experience, the image B is subjected to polygon selection (a liver parenchyma part is selected), the selected polygons are converted into a matrix (binary image) mask, so that a region of interest of the liver parenchyma is obtained, and meanwhile, a region of interest R is saved as an mat file for later use.
2. Performing signal decomposition on the original ultrasonic radio frequency data I by using an empirical mode decomposition algorithm so as to obtain the first two intrinsic mode functions IMF1And IMF2. The specific implementation steps are as follows:
(1) proceed initialization to let r0＝I,i＝1；
(2) Obtaining the ith IMF
(a)h0＝ri-1,j＝1；
(b) Find out hj-1Local extreme points of;
(c) carrying out cubic spline function interpolation on the maximum value point and the minimum value point to form an upper envelope line and a lower envelope line;
(d) calculate the mean m of the upper and lower envelopesj-1；
(e)hj＝hj-1-mj-1；
(f) If hjIs an IMF function, IMFi＝hjOtherwise, j equals j +1, go to (b);
(3).ri＝ri-1-imfi；
(4) if riIf the number of extreme points is still more than 2, the process is restarted from step (2), otherwise, the decomposition is ended, thereby obtaining the eigenmode function imfi. The algorithm is finally obtained
3. On the basis of homodyne K statistical distribution, effective scatterer imaging and coherent diffuse ratio imaging are carried out by utilizing an RSK and XU parameter estimation method. The parametric imaging flow diagram is detailed in fig. 3. Using the uncompressed envelope signal IMF, a sliding window based algorithm is used. The method comprises the following specific steps:
(a) firstly, determining the size of the sliding window, calculating the axial pixel number of the window, int e ═ round (WSL × p/y), and calculating the transverse pixel number of the window, int 1 ═ round (WSL × p/scan _ step)
(b) Calculating the transverse and axial moving distance of the window, wherein the transverse moving distance is L-inte1, and the axial moving lattice number of the window is D-inte;
(c) calculating the step length of the transverse and axial window movement, wherein the step length L1 of the transverse window movement is intel1/WOR, the step length A1 of the axial window movement is intel/WOR, the grid number x of the transverse window movement is laterall/L1, and the grid number y of the axial window movement is axial/A1;
(d) at this time, the signal data is divided into m × n matrixes by windows, wherein m is 1, 2, 3 … x, n is 1, 2, 3 … y, the windows are firstly transversely moved in the 1 st row, and corresponding parameters mu are respectively calculated and obtained in each window by utilizing an RSK method and an XU method11、μ12、μ13…μ1nAnd so on until the window completes moving at the m-th row, thereby obtaining the parameter matrix mumnAnd kmnThat is, the parameter matrix k of the ratio of the effective scatterer number parameter to the coherent scattering signal and the diffuse scattering signal is shown
The RSK algorithm comprises the following calculation processes:
the RSK method estimates homodyne K distribution model parameters using the signal-to-noise ratio (SNR), skewness (skewness), and kurtosis (kurtosis) of the envelope amplitude a. The relation among the signal-to-noise ratio R, the skewness S and the kurtosis K and any order moment of A is shown in the formula
Where v is a positive real number and E represents a mathematical expectation.
The arbitrary order moment of the homodyne K distribution can be expressed as:
E[Av]＝(2σ2/u)J(k,μ,v) (4)
wherein J (k, u, v) is as defined in formula
In the formula (I), the compound is shown in the specification,pFq(a1,...,ap；b1,...,bqc) represents a generalized hypergeometric series, csc (·) is a cosecant function, η being defined as follows:
calculate J (k, u, v) using linear interpolation:
in the same way, S can also be obtainedvAnd KvFunction on k and μ. By comparing the estimated value of R, S, K with the theoretical value of R, S, K predicted by the homodyne K distribution model, that is, by searching for a contour line (level curve) in a (K, mu) two-dimensional parameter space, when v takes different values, the intersection point of the contour line is the optimal estimation of K and mu.
Wherein the XU algorithm flow is as follows:
defining the parameter β as coherent scatter energy s2And diffuse scattered energy 2 sigma2α ratio β ═ s2/(2σ2α), the XU statistics are based on the mean of the intensity I of the envelope amplitudes
And the values of the X statistic and the U statistic are estimated α and β
U＝E[log I]-log E[I](9)
X＝E[I log I]/E[I]-E[log I](10)
The estimation of the model parameters can be converted into a solution of the following system of nonlinear equations:
in the formula of UHKAnd XHKIs defined as follows:
in the formula, gammaEFor Euler constants, ψ (-) represents the double gamma function estimating k from μ and β:
(e) finally, the matrix data are collected to obtain an integral effective scatterer mu and a coherent diffuse ratio imaging k, the parameters of the integral effective scatterer mu and the coherent diffuse ratio imaging k are subjected to color mapping, and finally parameter images RSK-k, RSK-mu, XU-k and XU-mu can be obtained.
(f) The mean values of the parameters mu and K in the region of interest R are calculated by using the parameter images RSK-K, RSK-mu, XU-K and XU-mu, and the mean values are used for the ultrasonic tissue characterization of the liver, because the change of scatterers in the liver tissue reflects the information of the microstructure of the liver tissue, namely the parameters mu and K, and the parameters can be used for carrying out visualized parameter imaging, so that the ultrasonic backscatter homodyne K imaging method based on empirical mode decomposition is realized.
Claims (1)
1. An ultrasonic backscattering homodyne K imaging method based on empirical mode decomposition is characterized by comprising the following steps of:
the method comprises the following steps of 1, B-mode ultrasonic imaging and region of interest acquisition, and specifically comprises the following steps:
step 1.1, acquiring original ultrasonic radio frequency data I, wherein the number of columns of the data is L, and the number of rows of the data is D;
step 1.2, demodulating original ultrasonic radio frequency data I by using Hilbert transform to construct an envelope image, and carrying out logarithmic compression by using a dynamic range of 40dB to obtain envelope data ISTo construct a B-mode ultrasonic image B;
step 1.3, selecting polygons from the image B, converting the selected polygons into masks of a binary matrix, wherein the area inside the masks is 1, and the area outside the masks is 0, so that an area of interest R is obtained;
step 2, performing signal decomposition on the original radio frequency data by using empirical mode decomposition to obtain an Intrinsic Mode Function (IMF), which specifically comprises the following steps:
step 2.1, carry on initialization, make r0I, I-1, wherein r0Representing the residual component which is not decomposed, namely the original ultrasonic radio frequency data I at the moment, wherein I is the counting number of the empirical mode decomposition times;
step 2.2. order hj-1＝ri-1J is 1 wherein hj-1The new sequence of the eigenmode function is subtracted after each step of decomposition, which is now the residual component r of the previous stepi-1Wherein j is a count of the number of times the eigenmode function is determined;
step 2.3, find hj-1The local extreme point of the envelope curve is subjected to cubic spline function interpolation on the maximum value point and the minimum value point to form an upper envelope curve and a lower envelope curve, and the average value m of the upper envelope curve and the lower envelope curve is calculatedj-1，hj＝hj-1-mj-1；
Step 2.4.hjIf there are no negative local maxima and no positive local minima, hjIs IMF function, the ith instinctive mode function IMFi＝hjOtherwise, j is increased by one, and the process is restarted from the step 2.3;
step 2.5 residual component r after decompositioni＝ri-1-imfiIf r isiIf the number of extreme points is still more than 2, i is incremented by one and the process resumes from step 2.2, otherwise the decomposition ends, thus obtaining the eigenmode function imfi；
And 3, performing homodyne K model parameter estimation and parameter imaging on the decomposed data, and specifically comprising the following steps of:
step 3.1. sliding window technique is used as a method for constructing ultrasound parametric images, using imfiEnvelope signal, using square window whose window edge length WSL is 1-9 times of pulse length to obtain signal data, making homodyne K model parameter estimation in said window, making the window move in distance increment in the whole range of image data, respectively making transverse and longitudinal movement, every increment of window contains pixel number correspondent to window overlap rate WOR, and making window movement increment be determined by axial pixel number and transverse pixel number of single window, firstly defining related parameters of window, in which window axial pixel number is WSL p/t, window transverse pixel number is 1 is WSL p/scan _ step, window transverse movement distance is L-inte1, window transverse movement step length L1 is intel1/WOR, window axial movement distance is D-inte, window axial movement step length A1 is intel/WOR, in which p is pulse length, t is axial point distance, scan _ step is the scanning step;
step 3.2, calculating the window transverse movement grid number x as average/L1, and calculating the window axial movement grid number y as axis/A1;
and 3.3, dividing the signal data into m × n matrixes by windows, wherein m is 1, 2 and 3 … x, n is 1, 2 and 3 … y, moving the windows transversely in the 1 st row, and respectively calculating corresponding parameters mu in each window by using an RSK method and an XU method11、μ12、μ13…μ1nAnd so on until the window completes moving at the m-th row, thereby obtaining the parameter matrix mumnAnd kmnThe matrix is an effective scatterer number parameter matrix mu and a ratio parameter matrix k of coherent scattering signals and diffuse scattering signals;
step 3.4, forming parameter images RSK-k, RSK-mu, XU-k and XU-mu by using the parameters mu and k through color mapping, wherein RSK-k is a k parameter image obtained through an RSK method, RSK-mu is a mu parameter image obtained through the RSK method, XU-k is a k parameter image obtained through an XU method, and XU-mu is a mu parameter image obtained through the XU method;
and 3.5, calculating the average values of the parameters mu and k in the region of interest R by using the parameter images RSK-k, RSK-mu, XU-k and XU-mu, and providing related microstructure information for ultrasonic tissue characterization of biological tissues, thereby providing the physical significance and parameter value of the specific scattering structure and providing visual parameter imaging.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100074496A1 (en) * | 2008-09-23 | 2010-03-25 | Industrial Technology Research Institute | Multi-dimensional empirical mode decomposition (emd) method for image texture analysis |
| US20100256493A1 (en) * | 2007-11-09 | 2010-10-07 | Tomoaki Chono | Ultrasonic diagostic apparatus, operation method thereof, and ultrasonic diagnostic program |
| CN104138277A (en) * | 2014-07-29 | 2014-11-12 | 哈尔滨工业大学 | Tissue microstructure detection method based on empirical mode decomposition in quantitative ultrasound system |
| CN106983524A (en) * | 2017-05-09 | 2017-07-28 | 南京大学 | A kind of parameter and its measuring method for reflecting that biological tissue is abnormal |
| CN110851788A (en) * | 2019-11-07 | 2020-02-28 | 北京工业大学 | Ultrasonic back scattering homodyne K model parameter estimation method based on neural network |
-
2020
- 2020-05-15 CN CN202010410886.2A patent/CN111616740B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100256493A1 (en) * | 2007-11-09 | 2010-10-07 | Tomoaki Chono | Ultrasonic diagostic apparatus, operation method thereof, and ultrasonic diagnostic program |
| US20100074496A1 (en) * | 2008-09-23 | 2010-03-25 | Industrial Technology Research Institute | Multi-dimensional empirical mode decomposition (emd) method for image texture analysis |
| CN104138277A (en) * | 2014-07-29 | 2014-11-12 | 哈尔滨工业大学 | Tissue microstructure detection method based on empirical mode decomposition in quantitative ultrasound system |
| CN106983524A (en) * | 2017-05-09 | 2017-07-28 | 南京大学 | A kind of parameter and its measuring method for reflecting that biological tissue is abnormal |
| CN110851788A (en) * | 2019-11-07 | 2020-02-28 | 北京工业大学 | Ultrasonic back scattering homodyne K model parameter estimation method based on neural network |
Non-Patent Citations (2)
| Title |
|---|
| 张奇宇 等: "超声背散射零差K成像评估肝纤维化" * |
| 欧阳亚丽;周著黄;吴水才;崔博翔;: "超声零差K分布模型仿真研究" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580239A (en) * | 2020-12-15 | 2021-03-30 | 中南大学 | Substructure response reconstruction method and system, and storage medium |
| CN112580239B (en) * | 2020-12-15 | 2022-07-05 | 中南大学 | Substructure response reconstruction method and system, and storage medium |
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