CN112754527B - Data processing method for low-frequency ultrasonic thoracic imaging - Google Patents
Data processing method for low-frequency ultrasonic thoracic imaging Download PDFInfo
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- A61B8/5207—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of raw data to produce diagnostic data, e.g. for generating an image
Abstract
The invention discloses a data processing method for low-frequency ultrasonic thoracic imaging, which comprises the steps of modeling through COMSOL, neglecting a spine and a heart in the model, merging two lungs into an ellipse with a long axis length of 0.15m and a short axis length of 0.1m, arranging a circle of annular skeletal muscle outside the ellipse of the lungs, uniformly arranging 12 ultrasonic transducers around the outer part of the skeletal muscle, and setting the material to be PZT-5H; one transducer is regarded as the sound source S to be identified k Seven opposite transducers are used as receiving array elements, and the signals received by the array elements are subjected to sound source identification processing by utilizing a multiple signal classification (Multiple Signal Characterization, MUSIC) algorithm; through COMSOL modeling, a data processing technology applied to the field of low-frequency ultrasonic chest imaging is creatively discovered according to a seven-transmission-reception cyclic transmitting-receiving mode. The technology can improve the signal-to-noise ratio of the data received by the array and reduce the mean square error of the data.
Description
Technical Field
The invention relates to the technical field of low-frequency ultrasonic thoracic imaging, in particular to a processing method of array received data.
Background
At present, the traditional medical ultrasonic imaging technology becomes one of the main methods of in-vivo imaging due to the noninvasive property and the non-radiation property, but the ultrasonic wave in the frequency band cannot penetrate the thoracic cavity and cannot be used for imaging the thoracic cavity due to the fact that the ultrasonic wave used by the traditional ultrasonic imaging technology is in the range of 2-10 MHz. The low-frequency ultrasonic imaging technology is different from the traditional ultrasonic imaging, the emitted ultrasonic frequency is between 20k and 100k, the low-frequency ultrasonic imaging technology can penetrate the thoracic cavity, and can be used for imaging the thoracic cavity.
The low-frequency ultrasonic thoracic imaging technology uses transmission ultrasonic data for imaging, and the development of the technology belongs to a preliminary research stage worldwide, and the related research results are less. In addition, due to the fact that acoustic properties of tissues and organs in the chest are different and data interference is generated by measuring means, a large amount of noise is contained in acquired transmission data, and imaging quality is affected.
Disclosure of Invention
The invention aims to provide a data processing method for low-frequency ultrasonic thoracic imaging, which solves part of the problems in the existing low-frequency ultrasonic thoracic imaging technology, is suitable for transmitting by one ultrasonic probe, and a plurality of ultrasonic probes form a combined transmitting-receiving mode of a receiving array, so that the signal-to-noise ratio of data received by the array can be improved, and the mean square error of the data can be reduced.
In order to achieve the above purpose, the present invention provides the following technical solutions: a data processing method for low frequency ultrasound thoracic imaging, comprising
Modeling
Modeling is carried out through COMSOL, spine and heart are ignored in the model, two lungs are combined into an ellipse with the long axial length of 0.15m and the short axial length of 0.1m, a circle of annular skeletal muscle is arranged outside the ellipse of the lungs, 12 ultrasonic transducers are uniformly placed around the outside of the skeletal muscle, and the material is PZT-5H;
by adding physical fields of pressure acoustics, transients, solid mechanics, static electricity, sound-structure boundary and piezoelectric effect into COMSOL, a two-dimensional chest cavity model is built; a circle of 12 transceiver integrated ultrasonic transducers are uniformly arranged outside the thoracic cavity, every two transducers are spaced at an interval of 30 degrees, and the outermost part of the simulation area is set to be a perfect matching layer through water and thoracic cavity coupling;
during simulation, one transducer is adopted for transmitting, seven transducers are directly opposite to each other for receiving, and a mode of successive transmitting-receiving is adopted; the transmitting transducer is changed clockwise, and 12 times of transmitting-receiving are sequentially carried out to achieve 360-degree annular scanning;
treatment method
A transducer emitting ultrasonic signals is regarded as the sound source S to be identified k Seven opposite transducers are used as receiving array elements, and the signals received by the array elements are subjected to sound source identification processing by utilizing a multiple signal classification algorithm;
the multiple signal classification algorithm is an array signal processing algorithm, and the distance between the information source and the center of the array satisfies that r is less than or equal to 2L 2 When lambda is detected, the information source is in the near field range, wherein L is the array aperture, lambda is the signal wavelength; the algorithm can be applied to a near-field spherical wave model;
and carrying out sound source identification processing on the received signals, and taking out a group of received signals with the minimum identification error as processed signals.
Compared with the prior art, the invention has the beneficial effects that: through COMSOL modeling, a data processing technology applied to the field of low-frequency ultrasonic chest imaging is creatively discovered according to a seven-transmission-reception mode. The technology can improve the signal-to-noise ratio of the data received by the array and reduce the mean square error of the data.
Drawings
Fig. 1 is a chest model geometry of the present invention.
FIG. 2 is a simplified two-dimensional chest model of the present invention.
Fig. 3 is a cut-away view of a model mesh of the present invention.
Fig. 4 is a partial grid cut-away view of the present invention.
Fig. 5 is a waveform diagram of a received signal when the transducer No. 1 of the present invention is transmitting.
Fig. 6 is a waveform diagram of a received signal when the transducer No. 7 of the present invention is transmitting.
Fig. 7 is a waveform diagram of a received signal when the transducer No. 4 of the present invention is transmitting.
Fig. 8 is a waveform diagram of a signal received when the transducer No. 10 of the present invention is transmitting.
Fig. 9 is a schematic diagram of an ultrasonic signal model according to the present invention.
Fig. 10 is a graph of signal to noise ratio comparison of the present invention.
Fig. 11 is a mean square error comparison graph of the present invention.
1. A first PML; 2. a second PML; 3. a third PML; 4. a fourth PML; 5. skeletal muscle; 6. and (3) lung.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-11, the present invention provides a technical solution: a data processing method for low frequency ultrasound thoracic imaging, comprising
Modeling
Modeling is carried out through COMSOL, spine and heart are ignored in the model, two lungs are combined into an ellipse with the long axial length of 0.15m and the short axial length of 0.1m, a circle of annular skeletal muscle is arranged outside the ellipse of the lungs, 12 ultrasonic transducers are uniformly placed around the outside of the skeletal muscle, and the material is PZT-5H; the actual thoracic cavity interior includes lung, heart, spine, skeletal muscle, and other tissue organs, and the wavelength of the ultrasound used in this study is small compared to thoracic and abdominal movements. So to simplify the problem, it can be assumed that the propagation medium is stationary, irrespective of the movement of the lungs and heart during breathing. The model includes a geometry as shown in fig. 1.
A circle of perfect matching layer is also arranged outside the model, and the perfect matching layer has two functions: during actual measurement, ultrasonic waves emitted by the transducer and transmitted from the human body are not reflected back from the external environment. Secondly, simulation calculation must be performed in a limited area, in order to simulate an ultrasound propagation process that is open in reality, a cut-off boundary needs to be set around the simulation area, and an absorption boundary condition at the boundary needs to be given for setting the cut-off boundary.
By adding physical fields of pressure acoustics, transients, solid mechanics, static electricity, sound-structure boundary and piezoelectric effect into COMSOL, a two-dimensional chest cavity model is built; a circle of 12 transceiver-integrated ultrasonic transducers are uniformly arranged outside the thoracic cavity, every two transducers are spaced by 30 degrees, and the outermost part of the simulation area is set to be a Perfect Matching Layer (PML) through water and thoracic cavity coupling.
During simulation, one transducer is adopted for transmitting, seven transducers are directly opposite to each other for receiving, and a mode of successive transmitting-receiving is adopted; the transmitting transducer is changed clockwise, and 12 times of transmitting-receiving are sequentially carried out to achieve 360-degree annular scanning.
Treatment method
A transducer emitting ultrasonic signals is regarded as the sound source S to be identified k Seven opposite transducers are used as receiving array elements, and the signals received by the array elements are subjected to sound source identification processing by utilizing a multiple signal classification algorithm;
the multiple signal classification algorithm is an array signal processing algorithm, and the distance between the information source and the center of the array satisfies that r is less than or equal to 2L 2 When lambda is detected, the information source is in the near field range, wherein L is the array aperture, lambda is the signal wavelength; the algorithm can be applied to a near-field spherical wave model;
and carrying out sound source identification processing on the received signals, and taking out a group of received signals with the minimum identification error as processed signals.
Mesh subdivision
In finite element simulation, mesh dissection needs to be performed on the modeled model, and the size, shape and time stepping of the mesh dissection limit the accuracy of numerical calculation to a great extent. In the fluid domain, 5 grid cells are used per wavelength, setting the maximum cell size to wavelength/5, i.e. grid size = wavelength/5; the perfect matching layer and the ultrasonic transducer are split in a mapping mode, and because the two parts are regular in shape, the number of grid splitting can be reduced by using mapping grids, and the later operation speed can be accelerated to a certain extent.
Time stepping
The method for determining the time step is a backward differential formula and a generalized alpha. The backward differential formula algorithm produces scattering and waveform distortion is proportional to computation time, so in transient computation, the generalized alpha method is typically used. Generalized α is effective to circumvent waveform distortion using the first 5 time-step solution and to predict the next time-step solution. The calculation adopts a generalized alpha method, and the time stepping is controlled to be 0.5us.
Acquisition process
Firstly, the transducer 1 starts to emit ultrasonic waves, and the opposite transducer 4-10 receives ultrasonic wave data; then, transducer No. 2 transmits ultrasound, the opposite transducer No. 5-11 receives ultrasound data, and so on, until transducer No. 12 transmits and transducer No. 3-9 receives. The sampling step length is set to 2us, the sampling time is set to 1000us, and after 12 times of emission, transmission original data of each receiving transducer are obtained. Fig. 5, 6, 7 and 8 are waveforms of signals received by the 7 transducers facing each other when the transducers No. 1, no. 7, no. 4 and No. 10 transmit.
The ultrasonic wave propagates at different speeds in different mediums, propagates fast in medium that density is high, and it is clear from fig. 5, fig. 6, fig. 7, fig. 8 that the same transducer is launched, and the signal amplitude that different positions received is different, because the shielding of barrier to the ultrasonic wave for the signal difference that the transducer received is great. And when the ultrasonic wave is transmitted at the symmetrical position, the corresponding receiving transducer waveform also has symmetry. The established model is proved to be in accordance with the ideal situation.
A schematic diagram of the ultrasonic signal model of the present invention is shown in fig. 9. Assuming that a center point O of a rectangular coordinate system is used as a reference point, k sound sources are arranged in the three-dimensional space, and theta is respectively used as k Is provided with a plurality of directional angles,is incident on an array of N array elements, OP is the target sound source S k Perpendicular projection line of r k Is the target sound source S k Distance to reference array element, r n The distance from the nth array element to the reference array element; sound speed is defined as c, carrier frequency is f, and sound wave angular frequency ω=2pi f;
the output signal expression of the receiving array is represented by formula (1.1);
in formula (1.1):
X(t)＝[x 1 (t)x 2 (t)…x N (t)] T (1.2)
e (t) is a noise vector, represented by formula (1.3);
E(t)＝[e 1 (t)e 2 (t)…e N (t)] T (1.3)
is an array flow pattern vector, represented by formula (1.4);
in the formula (1.4), the amino acid sequence,representing a target sound source S k (t) a direction vector corresponding to the frequency ω, represented by formula (1.5);
in the formula (1.5), τ nk The time difference from the sound source signal to the nth array element and the reference array element is represented by formula (1.6);
then the nth (n=1, 2, …, N) element coordinates (x n ，y n ，z n ) Combining the two-point distance formula to obtain the distance d between the nth array element and the target sound source nk Represented by formula (1.7);
the output signals X (t) of the array can be obtained by combining the columns (1.1) - (1.7).
Multiple signal classification (MUSIC) algorithms are widely used in the fields of communications, radar, mechanical manufacturing, medical, etc. As a very popular sound source recognition method, MUSIC algorithm is applicable to any array shape. Meanwhile, the MUSIC algorithm also has higher spatial resolution in the field of sound source identification.
In an ideal case, the signal subspace corresponding to the signal component and the noise subspace corresponding to the noise component are mutually orthogonal. Covariance operation is carried out on the array received data to obtain a covariance matrix R shown in a formula (1.8).
R＝E[X(t)X H (t)] (1.8)
In general, in practical cases, the received data of the array is limited, and then the expression of the covariance of the array data with L data length is shown as the expression (1.9).
Wherein R is S And R is E Covariance matrices of the target sound source and noise, respectively.
Covariance matrix for received array dataDecomposing characteristic value, and adding->The feature values of (2) are sorted in order of magnitude, and equation (1.10) can be obtained.
λ 1 ≥λ 2 ≥…≥λ n ＞0 (1.10)
The first k eigenvalues correspond to the target sound source signal in order of magnitude, and the other n-k eigenvalues correspond to the noise signal. And according to the magnitudes of the characteristic values of the sound source signal and the noise signal, distinguishing the corresponding characteristic vectors into signal vectors and noise vectors.
Let lambda set i Is thatIs the ith eigenvalue of v i Representation and lambda i The corresponding feature vector is expressed by formula (1.11).
Let lambda set i ＝σ 2 Is thatIs the minimum feature value of (1)
Substituting (1.13) into (1.12) to obtain:
σ 2 v i ＝(AR S A H +σ 2 I)v i (1.14)
simplifying to obtain (1.15).
AR S A H v i ＝0 (1.15)
Because of AA H Is a full order matrix of k, so both sides of the formula (1.15) are multiplied byThe formula (1.16) can be obtained.
Then, the formula (1.17) is shown.
A H v i ＝0 i＝k+1，k+2，…n (1.17)
Formula (1.17) shows that: feature vector (referred to as noise feature vector) v corresponding to noise feature value i Orthogonal to the column vector (a (θ)) of the matrix a, and each column of a corresponds to the direction of the sound source.
Constructing a noise subspace E by using n-k noise eigenvectors n Represented by formula (1.18).
E n ＝[v k+1 ，v k+2 ，…，v n ] (1.18)
Equation (1.19) defines the spatial spectral function P of the MUSIC algorithm music (θ)。
The denominator of equation (1.19) is the product of the direction vector of the target sound source and the noise space, a (θ) and E N If the orthogonal relationship exists, the denominator value of the expression (1.19) is desirably 0. Due to interference of other factors which are unavoidable in the actual environment, P music The denominator value of (θ) is not practically zero, so P music A maximum value of (θ) occurs. The formula (1.19) is subjected to traversal search, the incoming wave direction of the target sound source can be estimated by searching a maximum value, and the position information of the target sound source can be obtained through conversion of polar coordinates and rectangular coordinates in a near-field model.
Application of algorithm
And using the two-dimensional simulation model to enable the ultrasonic transducer to sequentially transmit and receive, taking out 12 sets of received data, and respectively named as transmission 1, transmission 2, … … and transmission 12. The 12 sets of data are sequentially subjected to sound source identification by using a MUSIC algorithm, and the identification results are shown in table 1.
TABLE 1 Sound source identification results
Since the chest contains different acoustic properties of the medium, the speed of sound wave propagation in each medium is different, and the traditional application field of MUSIC algorithm is performed in the same medium, the sound speed is a constant value. So applying MUSIC algorithm in the model will produce a certain error.
The specific application of the data processing method studied will be described below taking the data of transmission 1 as an example. In the application occasion of actual low-frequency ultrasonic chest imaging, the data of the transmission 1 are repeatedly expanded for 10 times in consideration of a series of unknown noise in the chest and noise in the measuring process, measuring methods and devices and external environment, after the 10 times of expanded data are covered with Gaussian noise with the signal-to-noise ratio of 10, the 10 times of expanded data are respectively subjected to sound source identification by using a MUSIC algorithm, and a group of primary data with the minimum identification error is selected to be used as processed data. The signal-to-noise ratio (SNR) and Mean Square Error (MSE) of the data were calculated as shown in fig. 10 and 11.
As can be seen from fig. 10, the signal to noise ratio of the data processed by the MUSIC algorithm is greater than the average value of the spread data, where the signal to noise ratio of the processed data of the transmission 1, the transmission 2, the transmission 6, the transmission 8, the transmission 9, the transmission 10, the transmission 11 and the transmission 12 can reach the maximum value of the signal to noise ratio of the spread data.
As can be seen from fig. 11, the mean square error of the data processed by the MUSIC algorithm is smaller than the average value of the spread data, wherein the mean square error of the processed spread data can reach the minimum value of the mean square error of the spread data in the transmission 2, the transmission 6, the transmission 8, the transmission 9, the transmission 10 and the transmission 11.
Working principle: and (3) establishing a model through COMSOL, sequentially acquiring 12 groups of received data clockwise according to the transmission-reception mode mentioned above, taking the 12 groups of received data as ideal data, taking a group of ideal data, copying the ideal data for a plurality of times, and adding noise to simulate a group of data acquired under actual conditions.
And re-segmenting the data according to the copying times, and respectively carrying out sound source identification processing on each segment according to a multiple signal classification algorithm to obtain a segment with the minimum identification error as a group of processed acquisition data. Repeating the above operation to obtain 12 groups of processed data.
Claims (2)
1. A data processing method for low-frequency ultrasonic thoracic imaging, characterized by: comprising
Establishing a model;
modeling is carried out through COMSOL, spine and heart are ignored in the model, two lungs are combined into an ellipse with the long axial length of 0.15m and the short axial length of 0.1m, a circle of annular skeletal muscle is arranged outside the ellipse of the lungs, 12 ultrasonic transducers are uniformly placed around the outside of the skeletal muscle, and the material is PZT-5H;
by adding physical fields of pressure acoustics, transients, solid mechanics, static electricity, sound-structure boundary and piezoelectric effect into COMSOL, a two-dimensional chest cavity model is built; a circle of 12 transceiver integrated ultrasonic transducers are uniformly arranged outside the thoracic cavity, every two transducers are spaced at an interval of 30 degrees, and the outermost part of the simulation area is set to be a perfect matching layer through water and thoracic cavity coupling;
during simulation, one transducer is adopted for transmitting, seven transducers are directly opposite to each other for receiving, and a mode of successive transmitting-receiving is adopted; the transmitting transducer is changed clockwise, and 12 times of transmitting-receiving are sequentially carried out to achieve 360-degree annular scanning;
a treatment method;
an ultrasonic transducer is regarded as a sound source S to be recognized k Seven opposite transducers are used as receiving array elements, and the signals received by the array elements are subjected to sound source identification processing by utilizing a multiple signal classification algorithm;
the multiple signal classification algorithm is an array signal processing algorithm, and the distance between the information source and the center of the array satisfies that r is less than or equal to 2L 2 When lambda is detected, the information source is in the near field range, wherein L is the array aperture, lambda is the signal wavelength; the algorithm can be applied to a near-field spherical wave model;
performing sound source identification processing on the received signals, and taking out a group of received signals with the minimum identification error as processed signals;
establishing a model through COMSOL, sequentially acquiring 12 groups of received data clockwise according to a transmitting-receiving mode, taking the 12 groups of received data as ideal data, taking a group of ideal data, copying the ideal data for a plurality of times, and adding noise to simulate a group of data acquired under actual conditions;
re-segmenting the data according to the copying times, and respectively carrying out sound source identification processing on each segment according to a multiple signal classification algorithm to obtain a segment with the minimum identification error as a group of processed acquisition data; repeating the above operation to obtain 12 groups of processed data;
the near-field spherical wave model is as follows:
assuming that a center point O of a rectangular coordinate system is used as a reference point, k sound sources are arranged in the three-dimensional space, and theta is respectively used as k Is provided with a plurality of directional angles,is incident on an array of N array elements, OP is the target sound source S k Perpendicular projection line of r k Is the target sound source S k Distance to reference array element, r n The distance from the nth array element to the reference array element; sound speed is defined as c, carrier frequency is f, and sound wave angular frequency ω=2pi f;
the output signal expression of the receiving array is represented by formula (1.1);
in formula (1.1):
X(t)＝[x 1 (t) x 2 (t)…x N (t)] T (1.2)
e (t) is a noise vector, represented by formula (1.3);
E(t)＝[e 1 (t) e 2 (t)…e N (t)] T (1.3)
is an array flow pattern vector, represented by formula (1.4);
in the formula (1.4), the amino acid sequence,representing a target sound source S k (t) a direction vector corresponding to the frequency ω, represented by formula (1.5);
in the formula (1.5), τ nk The time difference from the sound source signal to the nth array element and the reference array element is represented by formula (1.6);
then the nth (n=1, 2, …, N) element coordinates (x n ,y n ,z n ) Combining the two-point distance formula to obtain the distance d between the nth array element and the target sound source nk Represented by formula (1.7);
the output signals X (t) of the array can be obtained by combining the columns (1.1) - (1.7).
2. The data processing method for low frequency ultrasound thoracic imaging of claim 1, wherein: further comprises:
meshing;
in the fluid domain at the time of simulation, 5 grid cells are used for each wavelength; the perfect matching layer and the ultrasonic transducer are split in a mapping mode;
time stepping;
the method of generalized alpha is used, waveform distortion is effectively avoided, and time stepping is controlled to be 0.5us;
a collection process;
firstly, the transducer 1 starts to emit ultrasonic waves, and the opposite transducer 4-10 receives ultrasonic wave data; then, the transducer No. 2 transmits ultrasonic waves, the transducer No. 5-11 which is opposite receives ultrasonic wave data, and so on until the transducer No. 12 transmits and the transducer No. 3-9 receives; the sampling step length is set to be 2us, the sampling time is set to be 1000us, and after 12 times of emission, the original transmission data of the corresponding receiving transducer when each transducer emits are obtained.
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