CN113907792A - Nonlinear decorrelation fusion ultrasonic multi-parameter thermal coagulation detection and imaging method - Google Patents
Nonlinear decorrelation fusion ultrasonic multi-parameter thermal coagulation detection and imaging method Download PDFInfo
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Abstract
The method comprises the following steps of (1) acquiring multi-beam pulse reversal ultrasonic radio frequency signals in the thermal ablation process and storing imaging parameters; (2) constructing a band-pass filter according to the radio frequency signal and the imaging parameters to extract a signal harmonic component, and obtaining a pulse inversion harmonic signal matrix; (3) selecting proper mother wavelets to perform correlation analysis on all sampling lines of the pulse inversion harmonic signal matrix, and extracting correlation coefficients under the maximum correlation scale to construct a nonlinear decorrelation two-dimensional correlation coefficient matrix; (4) carrying out ultrasonic multi-parameter estimation on the correlation coefficient matrix, and constructing an ultrasonic multi-parameter thermal coagulation identification image on the basis of an ultrasonic B mode image; the invention aims to extract and enhance the nonlinear characteristics of an ultrasonic thermal coagulation area, inhibit background tissue information, effectively improve the accuracy, sensitivity and imaging quality of thermal coagulation detection and improve the accuracy of ultrasonic monitoring and identification.
Description
Technical Field
The invention relates to the technical field of ultrasonic data processing and imaging, in particular to a thermal coagulation detection and imaging method for nonlinear decorrelation ultrasonic radio-frequency signal processing and fusion of ultrasonic multi-parameter.
Background
The thermal ablation technology is an important means for treating solid tumors in a clinical minimally invasive manner, and local tissues are rapidly heated through energy conversion, so that the purpose of thermal coagulation necrosis of tumor cells is achieved. The puncture needle guide in the thermal ablation process, the monitoring in the ablation operation and the postoperative evaluation all need the assistance of imaging, so that the operation efficiency and the safety can be improved, and the possibility of tumor recurrence can be reduced. The traditional ultrasound imaging B-mode, referred to as B-ultrasound, has been widely used in clinical imaging detection due to its advantages of non-invasiveness, portability, low cost, real-time property, and the like. In clinical application, an imaging target is often represented by nonlinear features, and the imaging target can be detected and identified more accurately and clearly by utilizing the nonlinear features of the imaging target different from background tissues for imaging. The tissue is thermally damaged under the radiation of microwave, high-intensity focused ultrasound, laser and other energy, so that the microstructure of the tissue is changed, including cell nucleus shrinkage, protein coagulation, nuclear membrane and cell membrane damage, cytoplasm outflow and the like. This microscopic change causes the interaction between the ultrasound and the tissue to change, reflecting the non-linear characteristics. The clinical common ultrasonic B-mode imaging monitors the thermal coagulation, and the method often adopts a single pulse emission fundamental wave imaging mode which cannot highlight the nonlinear characteristic, so that the imaging target detection result with high resolution, high contrast and high accuracy cannot be provided. In addition, B-mode ultrasound images are susceptible to bubbles, acoustic artifacts and other images, thereby affecting accurate diagnosis of thermally coagulated regions. At present, methods for extracting acoustic parameters from ultrasonic backscatter signals to identify thermal coagulation include backscatter integral, attenuation coefficient, ultrasonic Nakagami parameters, ultrasonic homodyne k distribution parameters and the like, and although the methods can improve the accuracy of thermal coagulation identification to a certain extent, the methods ignore the nonlinear characteristics of a thermal coagulation region and cause loss of imaging contrast, so that development of an imaging technology capable of extracting and highlighting nonlinear information of the thermal coagulation region and fusing ultrasonic parameters is required.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a thermal coagulation detection and imaging method of nonlinear decorrelation fusion ultrasonic multi-parameter, and provides a fusion ultrasonic multi-parameter imaging method on the basis of further improving ultrasonic backscattering nonlinear radio frequency signals, aiming at extracting and enhancing nonlinear characteristics of an ultrasonic thermal coagulation area, inhibiting background tissue information, effectively improving the accuracy, sensitivity and imaging quality of thermal coagulation detection and improving the accuracy of ultrasonic monitoring and identification.
In order to achieve the purpose, the invention provides the following scheme:
a nonlinear decorrelation fusion ultrasonic multi-parameter thermal coagulation detection and imaging method comprises the following steps:
(1) acquiring multi-beam pulse reversal ultrasonic radio frequency signals in the thermal ablation process and storing imaging parameters; the thermal ablation process is local coagulative necrosis caused by tissue rapid heating up caused by certain energy transformation, and comprises microwave thermal ablation, radio frequency thermal ablation, laser thermal ablation and high-intensity focused ultrasound thermal ablation;
(2) constructing a band-pass filter according to the radio frequency signal and the imaging parameters, and extracting a signal harmonic component to obtain a pulse reversal harmonic signal matrix;
(3) selecting proper mother wavelets to perform correlation analysis on all sampling lines of the pulse inversion harmonic signal matrix, and extracting correlation coefficients under the maximum correlation scale to construct a nonlinear decorrelation two-dimensional correlation coefficient matrix;
(4) and carrying out ultrasonic multi-parameter estimation on the correlation coefficient matrix, and constructing an ultrasonic multi-parameter thermal coagulation identification image on the basis of the ultrasonic B mode image.
The step (1) is specifically as follows:
(1.1) placing the ultrasonic imaging and data acquisition equipment on a thermal ablation profile, driving an ultrasonic transducer to transmit ultrasonic pulse beam groups of 0 phase and 180 phase twice, namely, transmitting the ultrasonic pulse beam groups of 0 phase and 180 phase once respectively, repeating the operation, respectively compounding echo signals of the two wave beam groups, and adding two composite signals to suppress linear signals;
(1.2) acquiring front, middle and rear ultrasonic B-mode images in real time, and synchronously acquiring and storing ultrasonic radio frequency data and imaging parameters;
the ultrasonic radio frequency data is a signal after beam synthesis and before envelope detection, and is discretely stored as a two-dimensional data point matrix with the size of M x N;
the imaging parameters include an ultrasound transmit frequency fcSampling rate fsField size (D × W), number of scan lines N, number of sample points M for a single scan line.
The step (2) is specifically as follows:
(2.1) drawing an ultrasonic radio frequency data power spectrum to obtain frequency domain information of the data;
(2.2) constructing a band-pass filter, and extracting harmonic waves of the radio frequency data and frequency components of the harmonic waves within a certain bandwidth range;
the harmonic being a second harmonic, i.e. twice the ultrasonic transmission frequency 2fcTaking the frequency as the center frequency of the band-pass filter;
the band-pass filter comprises a pass band and a stop band, the attenuation of signals in the pass band is at most 3dB, and the attenuation outside the stop band is at least 40 dB;
and (2.3) reconstructing the extracted harmonic frequency components into a pulse inversion harmonic data matrix with the size of M N.
The step (3) is specifically as follows:
(3.1) selecting a representative data point Q at a corresponding position in the acquired pulse inversion harmonic data matrix according to the thermosetting position in the ultrasonic B-mode image, and recording the position as (m, n);
(3.2) extracting scanning lines passing through a point Q, namely an nth scanning line, and p scanning lines on the left side and the right side of the scanning line, wherein 2p +1 scanning lines are obtained;
(3.3) according to the sampling rate f in the imaging parameterssPerforming continuous wavelet transform on the 2p +1 scanning lines to extract the frequency f corresponding to the maximum correlation coefficientM；
(3.4) carrying out continuous wavelet transformation on all N scanning lines in the data matrix to obtain corresponding N time-frequency graphs;
(3.5) extracting f in N time-frequency graphsMCorresponding correlation coefficient vectors of size M x 1;
and (3.6) replacing the scanning lines in the original data matrix with corresponding correlation coefficient vectors to obtain a nonlinear decorrelation two-dimensional correlation coefficient matrix.
The specific operation of the step (3.3) comprises the following steps:
(3.3.1) selecting proper mother wavelets, wherein the mother wavelets comprise Haar wavelets, Daubechies wavelets, Morlet wavelets, Mexican Hat wavelets and Bump wavelets, and are selected according to the characteristics of original signals and expected analysis targets;
(3.3.2) carrying out continuous wavelet transform on the nth scanning line by the selected mother wavelet, wherein the mother wavelet forms a series of wavelet functions capable of carrying out multi-scale analysis on the original signal through scale transform, and the wavelet functions are subjected to correlation operation with a certain section of the original signal and are subjected to operation with the whole signal line through time shift to obtain corresponding correlation coefficients;
(3.3.3) drawing a time-frequency distribution graph according to the correlation coefficient obtained by continuous wavelet transform, wherein the abscissa of the time-frequency distribution graph is the number of sampling points of a scanning line and ranges from 1 to M, the ordinate of the time-frequency distribution graph is a group of frequencies obtained by the mother wavelet in the continuous wavelet transform through scale transform, and the amplitude of the time-frequency distribution graph is the correlation coefficient of the continuous wavelet transform;
(3.3.4) extracting the mth column of the time-frequency distribution matrix to draw a frequency curve, wherein the abscissa of the curve is continuous wavelet transform frequency, the ordinate is continuous wavelet transform correlation coefficient, and m is the ordinate position of a representative data point Q in the two-dimensional data matrix;
(3.3.5) identifying the frequency f corresponding to the maximum correlation coefficient point in the frequency curvemJudging that the wavelet function at the frequency scale has the same value asRepresenting the most similar characteristic of the signal segment in which the data point Q is located;
(3.3.6) the steps (3.3.2), (3.3.3), (3.3.4) and (3.3.5) are performed for each of the extracted 2p +1 scan lines, and the obtained 2p +1 fmAveraging to obtain the frequency fMThe wavelet function at this frequency scale is judged to have the most similar characteristics to those of the thermal freezing region.
The step (4) is specifically as follows:
(4.1) performing down-sampling on the decorrelated data matrix, wherein the down-sampling method comprises average sampling, point sampling and maximum value sampling according to different data characteristics, and normalizing the down-sampled correlation coefficient matrix to a data range of 0-1;
(4.2) performing single-window-width sliding window traversal on the normalized data, and performing ultrasonic multi-parameter estimation to obtain an ultrasonic multi-parameter image under the single window width;
(4.3) selecting rectangular sliding windows with three window widths to perform traversal and ultrasonic parameter estimation, and compounding the parameter images obtained under the three window widths to obtain a multi-window-width compounded ultrasonic multi-parameter image;
and (4.4) superposing the parametric image on the ultrasonic B mode image to obtain an ultrasonic B mode fusion multi-parametric recognition thermal coagulation area image.
The step (4.2) is specifically operated as follows:
(4.2.1) selecting a rectangular sliding window, wherein the window width L is k times of the ultrasonic incident wavelength, and the obtained sliding window comprises i x j pixel points, so that the sliding window traverses the whole two-dimensional correlation coefficient matrix by taking a single pixel as a step length, and performing parameter estimation on i x j data points contained in the window;
(4.2.2) assigning the parameter estimation value as a new pixel value to the central pixel covered by the current rectangular window;
(4.2.3) traversing the whole normalized data matrix by a sliding window to obtain an ultrasonic multi-parameter image under a single window width, and determining a thermal solidification region identification parameter image;
the step length of the sliding window is selected according to the size of the data matrix, the imaging speed is increased on the premise of higher sampling rate, traversal can be performed by using a plurality of pixels as the step length, and when the sampling rate is increased by two times, the step length can be correspondingly increased by two times on the premise of keeping the imaging resolution;
the multi-window width refers to traversing the data matrix by a plurality of sliding windows with different window widths to respectively obtain ultrasonic multi-parameter images;
the ultrasonic multi-parameter imaging comprises ultrasonic normalized entropy parameter imaging, ultrasonic weighted entropy parameter imaging, ultrasonic Nakagami parameter imaging and ultrasonic homodyne K distribution imaging.
The invention has the advantages that:
(1) compared with the traditional ultrasonic B-mode imaging, the nonlinear decorrelation fusion ultrasonic multi-parameter thermal coagulation detection and imaging method provided by the invention can extract the nonlinear characteristics of the thermal coagulation region, inhibit the linear characteristics of the background tissue, improve the detection sensitivity of the thermal coagulation region and improve the contrast between the thermal coagulation region and the background tissue.
(2) The nonlinear decorrelation fusion ultrasonic multi-parameter thermal coagulation detection and imaging method provided by the invention combines the specific characterization of the ultrasonic multi-parameter imaging method on the tissue on the basis of extracting the thermal coagulation nonlinear characteristics, further improves the accuracy and sensitivity of thermal coagulation area detection and imaging, and provides a feasible scheme for accurately monitoring the thermal fusion process of clinical ultrasonic images.
Drawings
FIG. 1 is a flow chart of a nonlinear decorrelation fusion ultrasonic multi-parameter thermal coagulation detection and imaging method.
Fig. 2 is a flow chart of the construction of a correlation coefficient matrix of a nonlinear decorrelation two-dimensional continuous wavelet transform.
Fig. 3 is a flow chart of ultrasonic multi-parameter thermal coagulation area identification and monitoring imaging.
FIG. 4 is a living rabbit liver microwave thermal ablation ultrasound B-mode image contrast nonlinear decorrelation fusion ultrasound normalized entropy parameter image.
FIG. 5 is a live rabbit liver microwave thermal ablation ultrasound B-mode image contrast nonlinear decorrelation fusion ultrasound Nakagami parametric image.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
The invention provides a nonlinear decorrelation fusion ultrasonic multi-parameter thermal coagulation detection and imaging method, aiming at the problems that the contrast between thermal coagulation and background tissues is low, the thermal coagulation and background tissues are easily affected by artifacts, the recognition accuracy is low and the like in the thermal ablation monitoring process of the traditional ultrasonic B-mode imaging.
According to the method provided by the invention, on the basis of extracting nonlinear signals of a thermal coagulation region through pulse inversion of an ultrasonic multi-beam group and inhibiting linear signals of background tissues, correlation analysis is further carried out on the signals by adopting continuous wavelet transform, a maximum correlation coefficient matrix of the thermal coagulation region is constructed to replace an original signal matrix, and finally an ultrasonic multi-parameter imaging method is fused to obtain a thermal coagulation fixed quantity ultrasonic detection and identification image.
Fig. 1 is a thermal coagulation monitoring and imaging method of nonlinear decorrelation fusion ultrasound multi-parameter provided by the present invention, the steps include:
(1) acquiring multi-beam pulse reversal ultrasonic radio frequency signals in the thermal ablation process and storing imaging parameters;
the step 1 specifically comprises the following steps:
(1.1) placing an ultrasonic imaging and data acquisition device on a section of a microwave liver thermal ablation position, driving an ultrasonic transducer to emit ultrasonic pulse beam groups of 0 phase and 180 phase twice, namely, emitting the 0 phase and the 180 phase once respectively, repeating the operation, respectively compounding echo signals of the two beam groups, and adding the two composite signals to suppress linear signals;
(1.2) acquiring front, middle and rear ultrasonic B-mode images in real time, and synchronously acquiring and storing ultrasonic radio frequency data and imaging parameters;
the thermal ablation process is local coagulative necrosis caused by rapid tissue temperature rise caused by certain energy transformation, and comprises microwave thermal ablation, radio frequency thermal ablation, laser thermal ablation and high-strength focused ultrasound thermal ablation;
the ultrasonic radio-frequency signal is a signal after beam synthesis and before envelope detection, and is discretely stored as a two-dimensional data point matrix with the size of M x N;
the imaging parameters include an ultrasound transmit frequency fcSampling rate fsThe field size (D x W), the number of scanning lines N, the number of sampling points M of a single scanning line and the like;
the values of the imaging parameters are shown in Table 1
Table 1: imaging parameters obtained during radio frequency signal acquisition
(2) Constructing a band-pass filter according to the radio frequency signal and the imaging parameters to extract harmonic components of the signal, and obtaining a pulse inversion harmonic signal matrix;
the step 2) specifically comprises the following steps:
(2.1) drawing the power spectrum of the ultrasonic radio frequency data obtained in the step 1) by using a periodogram method to obtain frequency domain information of the data;
(2.2) constructing a band-pass filter, and extracting harmonic waves of the ultrasonic radio frequency data and frequency components of the harmonic waves within a certain bandwidth range;
(2.3) reconstructing the extracted harmonic frequency components into a pulse inversion harmonic data matrix with the size of M N;
the harmonic being a second harmonic, i.e. twice the ultrasonic transmission frequency 2fcTaking the frequency as the center frequency of the band-pass filter;
the passband bandwidth range of the band-pass filter is 0.8 multiplied by 2fc～1.5×2fcThe stop band is 0.6 multiplied by 2fc～1.7×2fcThe attenuation inside the pass band is more than 3dB, and the attenuation outside the stop band is at least 40 dB;
(3) selecting proper mother wavelets to perform correlation analysis on all sampling lines of the pulse inversion harmonic signal matrix, and extracting correlation coefficients under the maximum correlation scale to construct a nonlinear decorrelation two-dimensional correlation coefficient matrix;
referring to fig. 2, the step (3) specifically includes:
(3.1) selecting a representative data point Q at a corresponding position in a pulse inversion harmonic data matrix according to the thermosetting position in the ultrasonic B-mode image acquired in the step 1), and recording the position as (m, n);
(3.2) extracting scanning lines passing through a point Q, namely an nth scanning line, and p scanning lines on the left side and the right side of the scanning line, wherein 2p +1 scanning lines are obtained, and the value of p is 5 according to the data size embodiment;
(3.3) selecting proper mother wavelets according to the sampling rate f in the imaging parameterssPerforming continuous wavelet transform on the 2p +1 scan lines to extract frequency f corresponding to maximum correlation coefficientMAccording to the data characteristic, the mother wavelet is Morlet wavelet;
the step (3.3) comprises the following specific operations:
(3.3.1) carrying out continuous wavelet transform on the nth scanning line according to the selected mother wavelet, wherein the mother wavelet forms a series of wavelet functions capable of carrying out multi-scale analysis on the original signal through scale transform, the wavelet functions are subjected to correlation operation with a certain section of the original signal and are subjected to operation with the whole signal line through time shift to obtain corresponding correlation coefficients;
(3.3.2) drawing a time-frequency distribution graph according to the correlation coefficient obtained by the continuous wavelet transform, wherein the abscissa of the time-frequency distribution graph is the number of sampling points of a scanning line and ranges from 1 to M, the ordinate of the time-frequency distribution graph is a group of frequencies obtained by the mother wavelet in the continuous wavelet transform through scale transform, and the amplitude of the time-frequency distribution graph is the correlation coefficient of the continuous wavelet transform;
(3.3.3) extracting the mth column of the time-frequency distribution matrix to draw a frequency curve, wherein the abscissa of the curve is continuous wavelet transform frequency, and the ordinate is a continuous wavelet transform correlation coefficient, wherein m is the ordinate position of a representative data point Q in the two-dimensional data matrix;
(3.3.4) identifying the frequency f corresponding to the maximum correlation coefficient point in the frequency curvemJudging that the wavelet function under the frequency scale has the most similar characteristics to the signal segment where the representative data point Q is located;
(3.3.5) performing the time-frequency analysis, the frequency curve extraction and the maximum correlation frequency identification on the extracted 2p +1 scanning lines,for the obtained 2p +1 fmAveraging and compounding to obtain frequency fMJudging that the wavelet function under the frequency scale has the most similar characteristics with the thermal solidification region;
(3.4) carrying out continuous wavelet transformation on all N scanning lines in the data matrix to obtain corresponding N time-frequency graphs;
(3.5) extracting f in N time-frequency graphsMCorresponding correlation coefficient vectors of size M x 1;
(3.6) replacing the scanning lines in the original data matrix with corresponding correlation coefficient vectors to obtain a nonlinear decorrelation two-dimensional correlation coefficient matrix;
(4) carrying out ultrasonic multi-parameter estimation on the correlation coefficient matrix, and constructing an ultrasonic multi-parameter thermal coagulation identification image on the basis of an ultrasonic B mode image;
referring to fig. 3, the step (4) specifically includes:
(4.1) carrying out triple average sampling on the decorrelation data matrix to obtain a correlation coefficient matrix after down sampling;
(4.2) normalizing the correlation coefficient matrix after down sampling to a data range of 0-1;
(4.3) performing single-window-width sliding window traversal on the normalized data, and performing ultrasonic multi-parameter estimation to obtain an ultrasonic multi-parameter image under the single window width;
the specific operation of the step (4.3) is as follows:
(4.3.1) selecting a rectangular sliding window, wherein the window width L is k times of the ultrasonic incident wavelength, and the obtained sliding window comprises i x j pixel points, so that the sliding window traverses the whole two-dimensional correlation coefficient matrix by taking a single pixel as a step length, and performing parameter estimation on i x j data points contained in the window;
(4.3.2) assigning the parameter estimation value as a new pixel value to the central pixel covered by the current rectangular window;
(4.3.3) traversing the whole normalized data matrix by a sliding window to obtain an ultrasonic multi-parameter image under a single window width, and determining a thermal solidification region identification parameter image;
the ultrasonic multi-parameter imaging comprises ultrasonic normalized entropy parameter imaging, ultrasonic weighted entropy parameter imaging, ultrasonic Nakagami parameter imaging and ultrasonic homodyne K distribution imaging;
(4.4) traversing and ultrasonic parameter estimation are carried out by selecting rectangular sliding windows with three window widths, and the obtained parameter images under the three window widths are compounded to obtain a multi-window width compounded ultrasonic multi-parameter image, wherein the three window widths are 3 times of incident wavelength, 4 times of incident wavelength and 5 times of incident wavelength respectively;
and (4.5) superposing the parametric image on the ultrasonic B mode image to obtain an ultrasonic B mode fusion multi-parametric recognition thermal coagulation area image.
Referring to fig. 4, the two live new zealand white rabbit livers are subjected to microwave thermal ablation, and ultrasonic B-mode imaging and nonlinear decorrelation fusion ultrasonic normalization entropy parameter imaging are performed after the ablation is finished, wherein ablation parameters of the rabbit liver thermal ablation process represented in the figure are respectively microwave power 30W, ablation time length 25s, microwave power 20W and ablation time length 19 s. Referring to fig. 5, when the two sets of data shown in fig. 4 are subjected to B-mode imaging and nonlinear decorrelation fused ultrasound Nakagami parametric imaging, it can be seen that, compared with the low contrast and low resolution of the ultrasound B-mode image, the normalized entropy parametric image and the Nakagami parametric image are displayed in pseudo-color at the thermal coagulation position, providing an imaging result with higher thermal coagulation-tissue ratio and more accurate region identification.
Claims (9)
1. A nonlinear decorrelation fusion ultrasonic multi-parameter thermal coagulation detection and imaging method is characterized by comprising the following steps:
(1) acquiring multi-beam pulse reversal ultrasonic radio frequency signals in the thermal ablation process and storing imaging parameters; the thermal ablation process is local coagulative necrosis caused by tissue rapid heating up caused by certain energy transformation, and comprises microwave thermal ablation, radio frequency thermal ablation, laser thermal ablation and high-intensity focused ultrasound thermal ablation;
(2) constructing a band-pass filter according to the radio frequency signal and the imaging parameters, and extracting a signal harmonic component to obtain a pulse reversal harmonic signal matrix;
(3) selecting proper mother wavelets to perform correlation analysis on all sampling lines of the pulse inversion harmonic signal matrix, extracting correlation coefficients under the maximum correlation scale, and constructing a nonlinear decorrelation two-dimensional correlation coefficient matrix;
(4) and carrying out ultrasonic multi-parameter estimation on the correlation coefficient matrix, and constructing an ultrasonic multi-parameter thermal coagulation identification image on the basis of the ultrasonic B-mode image.
2. The method for detecting and imaging the thermal coagulation of the nonlinear decorrelation fusion ultrasonic multi-parameter according to claim 1, wherein the step (1) is specifically as follows:
(1.1) placing the ultrasonic imaging and data acquisition equipment on a thermal ablation profile, driving an ultrasonic transducer to transmit ultrasonic pulse beam groups of 0 phase and 180 phase twice, namely, transmitting the ultrasonic pulse beam groups of 0 phase and 180 phase once respectively, repeating the operation, respectively compounding echo signals of the two wave beam groups, and adding the two compound signals to suppress linear signals;
(1.2) acquiring front, middle and later ultrasonic B-mode images in real time, and synchronously acquiring and storing ultrasonic radio frequency data and imaging parameters;
the ultrasonic radio frequency data is a signal after beam synthesis and before envelope detection, and is discretely stored as a two-dimensional data point matrix with the size of M x N;
the imaging parameters include an ultrasound transmit frequency fcSampling rate fsField size (D × W), number of scan lines N, number of sample points M for a single scan line.
3. The method for detecting and imaging the thermal coagulation of the nonlinear decorrelation fusion ultrasonic multi-parameter according to claim 1, wherein the step (2) specifically comprises:
(2.1) drawing an ultrasonic radio frequency data power spectrum to obtain frequency domain information of the data;
(2.2) constructing a band-pass filter, and extracting harmonic waves of the radio frequency data and frequency components of the harmonic waves within a certain bandwidth range;
the harmonic being a second harmonic, i.e. twice the ultrasonic transmission frequency 2fcTaking the frequency as the center frequency of the band-pass filter;
the band-pass filter comprises a pass band and a stop band, the attenuation of signals in the pass band is at most 3dB, and the attenuation outside the stop band is at least 40 dB;
and (2.3) reconstructing the extracted harmonic frequency components into a pulse inversion harmonic data matrix with the size of M N.
4. The method for detecting and imaging the thermal coagulation of the nonlinear decorrelation fusion ultrasonic multi-parameter according to claim 1, wherein the step (3) is specifically as follows:
(3.1) selecting a representative data point Q at a corresponding position from the acquired pulse inversion harmonic data matrix according to the thermosetting position in the ultrasonic B-mode image, and recording the position as (m, n);
(3.2) extracting scanning lines passing through a point Q, namely an nth scanning line, and p scanning lines on the left side and the right side of the scanning line, wherein 2p +1 scanning lines are obtained;
(3.3) according to the sampling rate f in the imaging parameterssPerforming continuous wavelet transform on the 2p +1 scan lines to extract frequency f corresponding to maximum correlation coefficientM；
(3.4) carrying out continuous wavelet transformation on all N scanning lines in the data matrix to obtain corresponding N time-frequency graphs;
(3.5) extracting f in N time-frequency graphsMCorresponding correlation coefficient vectors of size M x 1;
and (3.6) replacing the scanning lines in the original data matrix with corresponding correlation coefficient vectors to obtain a nonlinear decorrelation two-dimensional correlation coefficient matrix.
5. The method for detecting and imaging the thermal coagulation with the nonlinear decorrelation fused ultrasound multi-parameters according to claim 4, wherein the step (3.3) comprises the following specific operations:
(3.3.1) selecting proper mother wavelets, wherein the mother wavelets comprise Haar wavelets, Daubechies wavelets, Morlet wavelets, Mexican Hat wavelets and Bump wavelets, and are selected according to the characteristics of original signals and expected analysis targets;
(3.3.2) carrying out continuous wavelet transform on the nth scanning line by the selected mother wavelet, wherein the mother wavelet forms a series of wavelet functions capable of carrying out multi-scale analysis on the original signal through scale transform, and the wavelet functions are subjected to correlation operation with a certain section of the original signal and are subjected to operation with the whole signal line through time shift to obtain corresponding correlation coefficients;
(3.3.3) drawing a time-frequency distribution graph according to the correlation coefficient obtained by continuous wavelet transform, wherein the abscissa of the time-frequency distribution graph is the number of sampling points of a scanning line and ranges from 1 to M, the ordinate of the time-frequency distribution graph is a group of frequencies obtained by the mother wavelet in the continuous wavelet transform through scale transform, and the amplitude of the time-frequency distribution graph is the correlation coefficient of the continuous wavelet transform;
(3.3.4) extracting the mth column of the time-frequency distribution matrix to draw a frequency curve, wherein the abscissa of the curve is continuous wavelet transform frequency, the ordinate is continuous wavelet transform correlation coefficient, and m is the ordinate position of a representative data point Q in the two-dimensional data matrix;
(3.3.5) identifying the frequency f corresponding to the maximum correlation coefficient point in the frequency curvemJudging that the wavelet function under the frequency scale has the most similar characteristics to the signal segment where the representative data point Q is located;
(3.3.6) the steps (3.3.2), (3.3.3), (3.3.4) and (3.3.5) are performed for each of the extracted 2p +1 scan lines, and the obtained 2p +1 fmAveraging to obtain the frequency fMThe wavelet function at this frequency scale is judged to have the most similar characteristics to those of the thermal freezing region.
6. The method for detecting and imaging the thermal coagulation of the nonlinear decorrelation fusion ultrasonic multi-parameter according to claim 1, wherein the step (4) specifically comprises:
(4.1) performing down-sampling on the decorrelated data matrix, wherein the down-sampling method comprises average sampling, point sampling and maximum value sampling according to different data characteristics, and normalizing the down-sampled correlation coefficient matrix to a data range of 0-1;
(4.2) performing single-window-width sliding window traversal on the normalized data, and performing ultrasonic multi-parameter estimation to obtain an ultrasonic multi-parameter image under the single window width;
(4.3) selecting rectangular sliding windows with three window widths to perform traversal and ultrasonic parameter estimation, and compounding the parameter images obtained under the three window widths to obtain a multi-window-width compounded ultrasonic multi-parameter image;
and (4.4) superposing the parametric image on the ultrasonic B mode image to obtain an ultrasonic B mode fusion multi-parametric recognition thermal coagulation area image.
7. The method of claim 6, wherein said step (4.2) is operable to:
(4.2.1) selecting a rectangular sliding window, wherein the window width L is k times of the ultrasonic incident wavelength, and the obtained sliding window comprises i x j pixel points, so that the sliding window traverses the whole two-dimensional correlation coefficient matrix by taking a single pixel as a step length, and performing parameter estimation on i x j data points contained in the window;
(4.2.2) assigning the parameter estimation value as a new pixel value to the central pixel covered by the current rectangular window;
and (4.2.3) traversing the whole normalized data matrix by a sliding window to obtain an ultrasonic multi-parameter image under a single window width, and determining an identification parameter image of the thermal solidification region.
8. The method of claim 7, wherein the step size of the sliding window is selected according to the size of the data matrix, and the traversal is performed with a plurality of pixels as the step size to increase the imaging rate under the premise of a higher sampling rate.
9. The thermal coagulation detection and imaging method of nonlinear decorrelation fusion ultrasonic multi-parameter according to claim 7, wherein the multi-window width refers to traversing a data matrix by a plurality of sliding windows with different window widths to respectively obtain ultrasonic multi-parameter images;
the ultrasonic multi-parameter imaging comprises ultrasonic normalized entropy parameter imaging, ultrasonic weighted entropy parameter imaging, ultrasonic Nakagami parameter imaging and ultrasonic homodyne K distribution imaging.
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