CN115393339A - Ultrasonic imaging method and device, electronic equipment and storage medium - Google Patents

Abstract

The invention discloses an ultrasonic imaging method, an ultrasonic imaging device, electronic equipment and a storage medium, wherein the method comprises the following steps: converting the received ultrasonic echo signals representing time change into beam domain signals representing frequency change; compressing the wave beam domain signal by adopting a preset sampling matrix to obtain a virtual measurement signal; wherein the preset sampling matrix satisfies a finite equidistant property; the virtual measurement signal has sparsity; restoring the virtual measurement signal to a full array element state to obtain a virtual frequency domain signal; carrying out offset processing on the virtual frequency domain signal to obtain an actual frequency domain signal; the actual frequency domain signal is converted into a target image matrix for generating an ultrasound image. The technical scheme provided by the invention can improve the image quality of ultrasonic imaging to a certain extent.

Description

Ultrasonic imaging method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of ultrasound imaging technologies, and in particular, to a method and an apparatus for ultrasound imaging, an electronic device, and a storage medium.

Background

Ultrasonic imaging is to scan a human body by using an ultrasonic sound beam, and obtain an image of an internal organ by receiving and processing a reflected signal. Plane wave imaging provides an ultra-fast ultrasound imaging method by acquiring a planar signal at a very high frame rate. All array elements of the transducer are excited simultaneously in plane wave imaging, echo signals are received simultaneously by all the array elements after tissue scattering, and the ultrasonic image of the whole area can be obtained through the echo signals. Beamforming is an important component of a plane wave imaging system, and the performance of a beamforming algorithm affects the quality of ultrasound imaging.

In the prior art, the beam forming method is mainly a Delay and Sum (DAS) algorithm. The DAS algorithm applies a fixed weight to the delayed echo signal to reduce signal side lobes. However, the weights of the conventional window function are usually a set of fixed parameters preset according to the depth, and the characteristics of the echo signal itself are not fully considered. Therefore, the side lobes are attenuated to some extent, and the main lobe width is also increased, resulting in poor quality of ultrasound imaging.

Disclosure of Invention

In view of this, the embodiments of the present invention provide a method and an apparatus for ultrasound imaging, an electronic device, and a storage medium, so as to improve image quality of ultrasound imaging.

The invention provides an ultrasonic imaging method in one aspect, which comprises the following steps: converting the received ultrasonic echo signals representing time variation into beam domain signals representing frequency variation; compressing the wave beam domain signal by adopting a preset sampling matrix to obtain a virtual measurement signal; wherein the preset sampling matrix satisfies a finite equidistant property; the virtual measurement signal has sparsity; restoring the virtual measurement signal to a full array element state to obtain a virtual frequency domain signal; carrying out offset processing on the virtual frequency domain signal to obtain an actual frequency domain signal; the actual frequency domain signal is converted into a target image matrix for generating an ultrasound image.

In one embodiment, converting the received ultrasound echo signals representing the time variation into beam domain signals representing the frequency variation comprises: and carrying out two-dimensional fast Fourier change on the ultrasonic echo signal to obtain a beam domain signal.

In one embodiment, the transformation matrix of direction vectors constructed by the number of ultrasound transducer elements comprises: equally dividing the-90-degree region range according to twice of the number of the ultrasonic transducer array elements to obtain array element intervals; constructing direction vectors of the ultrasonic transducer array elements in different emission directions according to the array element spacing; and generating a transformation matrix according to the direction vectors of the different transmitting directions.

In one embodiment, solving for the projection coefficient vector comprises: constructing a cost function; wherein the cost function is used for representing the sparsity degree of the projection coefficient vector; constructing a Lagrangian operator according to the cost function, the measurement matrix, the projection coefficient vector and the virtual measurement signal; and solving the projection coefficient vector by performing minimization operation on the Lagrangian operator.

In one embodiment, the shifting the virtual frequency domain signal to obtain an actual frequency domain signal includes: calculating an actual sound velocity and an actual coordinate according to an included angle between a plane wave emitted by an ultrasonic transducer and the ultrasonic transducer; converting the virtual frequency domain signal to an actual frequency domain signal based on the actual speed of sound and the actual coordinates.

In one embodiment, converting the actual frequency domain signals to a target image matrix for generating an ultrasound image comprises: and carrying out two-dimensional Fourier inversion on the actual frequency domain signal to obtain a target image matrix for generating an ultrasonic image.

In another aspect, the present invention provides an ultrasound imaging apparatus, including: the signal conversion unit is used for converting the received ultrasonic echo signals representing time change into beam domain signals representing frequency change; the signal compression unit is used for compressing the frequency domain signal by adopting a preset sampling matrix to obtain a virtual measurement signal; wherein the preset sampling matrix satisfies a finite equidistant property; the virtual measurement signal has sparsity; the signal recovery unit is used for recovering the virtual measurement signal to a full array element state to obtain a virtual frequency domain signal; the signal offset unit is used for carrying out offset processing on the virtual frequency domain signal to obtain an actual frequency domain signal; and the signal inverse conversion unit is used for converting the actual frequency domain signal into a target image matrix for generating an ultrasonic image.

In another aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used for storing a computer program, and the computer program realizes the above-mentioned ultrasonic imaging method when being executed by the processor.

In another aspect, the present invention also provides a computer-readable storage medium for storing a computer program, which when executed by a processor, implements the above-mentioned ultrasound imaging method.

The technical scheme provided by the application carries out compressed sensing to the wave beam domain through converting the echo signal received by the ultrasonic transducer into the form of the wave beam domain, obtains the virtual frequency domain signal, then carries out offset processing to the virtual frequency domain signal, obtains the actual frequency domain signal, and finally converts the actual frequency domain signal into the target image matrix for ultrasonic imaging, thereby to a certain extent, the image quality of ultrasonic imaging is carried out in the process of using compressed sensing to the echo signal is improved.

Drawings

The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are schematic and are not to be understood as limiting the invention in any way, and in which:

FIG. 1 shows a schematic flow diagram of an ultrasound imaging method in one embodiment of the invention;

FIG. 2 is a schematic diagram of a plane wave transmitting and receiving model and a seismic wave explosion model in an embodiment of the invention;

FIG. 3 is a diagram illustrating simulation comparison of imaging results of a frequency domain beam synthesis method and a DAS method based on compressed sensing according to an embodiment of the present invention;

FIG. 4 shows a low echo contrast diagram of a compressed sensing-based frequency domain beamforming method (fkCS) and a compressed sensing-based time domain beamforming method (tmCS) according to an embodiment of the present invention;

FIG. 5 is a speckle contrast diagram of a frequency domain beamforming method (fkCS) based on compressed sensing and a time domain beamforming method based on compressed sensing according to an embodiment of the present invention;

FIG. 6 is a diagram showing the horizontal resolution and the variation with compression ratio of a frequency-domain beamforming method based on compressed sensing (fkCS) and a time-domain beamforming method based on compressed sensing (tmCS) according to an embodiment of the present invention;

FIG. 7 shows a simulated B-mode image at a compression ratio of 50% for a frequency-domain beamforming based on compressed sensing (fkCS) and a time-domain beamforming based on compressed sensing (tmCS) in an embodiment of the present invention;

FIG. 8 shows a schematic diagram of an ultrasound imaging apparatus in an embodiment of the invention;

fig. 9 shows a schematic structural diagram of an electronic device in an embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.

Plane wave imaging provides an ultra-fast ultrasound imaging method by acquiring a plane signal at a very high frame rate, but this sacrifices image quality and causes a problem of an excessive amount of data. Compared with a focused wave imaging mode, the plane wave transmission mode greatly improves the frame rate. However, since the emission beam is not focused, the signal-to-noise ratio, image resolution, and contrast of the echo signal are deteriorated to some extent. In practical applications, a multi-angle compound imaging method is often used to improve image quality, and the imaging quality of such an imaging mode is improved with increasing angles, but the frame rate is also decreased linearly. In order to keep the advantage of high frame rate of plane wave imaging as much as possible, different beam forming algorithms are applied in the plane wave imaging, and the image quality can be improved without reducing the frame rate.

Beamforming is an important component of medical ultrasound imaging systems, and the performance of the beamforming algorithm affects the quality of ultrasound imaging. Currently, common beamforming methods are Delay and Sum (DAS) and adaptive beamforming algorithms. The DAS algorithm is the most basic beamforming algorithm that applies fixed weights to the delayed echo signals to reduce signal side lobes. However, the weights of the conventional window function are usually a set of fixed parameters preset according to the depth, and the characteristics of the echo signal itself are not fully considered. Therefore, the side lobes are attenuated to some extent, and the main lobe width is also increased, i.e., the resolution is deteriorated. Compared with the DAS, the adaptive beamforming algorithm utilizes the echo signals received by the transducer array to adaptively calculate the dynamic weighting value applied to each array element according to environmental changes, so that the adaptive beamforming algorithm has better resolution and anti-interference capability. The traditional self-adaptive beam forming has the advantages of high resolution, strong anti-interference capability and the like, but the receiving performance is greatly reduced due to the problems of high side lobe level and high angle mismatch sensitivity. In addition, adaptive beamforming involves a large number of matrix operations, which occupy a large amount of computation space and reduce the computation speed.

In addition to image quality, high frame rates also present data loading and memory storage problems. Compared with MV-based algorithms, the introduction of the fourier domain by the plane wave imaging process provides a new direction for beamforming. Compared to the beamforming method in the time domain, beamforming in the fourier domain may improve the computational efficiency by applying a fast fourier transform, while not occupying too much computational space. Thereafter, a fourier domain beamforming process based on the Explosive Reflection Model (ERM) is proposed, which assumes that the echoes of the back scatterers propagate only in the sensor direction, and the principle of Stolt's shift in this model is more realistic, thereby improving the imaging quality.

Compressed Sensing (CS) is a new sampling theory, which exploits the sparsity of signals to make signals able to acquire discrete samples of signals by random sampling under the condition of far less than nyquist sampling rate, and then perfectly reconstruct signals by nonlinear reconstruction algorithms. The theory of compressed sensing states that, as long as the signal is compressible or sparse in a transform domain, the transformed high-dimensional signal can be projected onto a low-dimensional space by an observation matrix unrelated to the transform basis, and then the original signal can be reconstructed with high probability from these few projections by solving an optimization problem, which can prove that such projections contain enough information for reconstructing the signal. The compressed sensing theory has the characteristic that the number of sampling channels can be reduced or the signal dimension can be reduced on the premise of keeping the original characteristics of the signal. However, in the conventional method of applying compressed sensing to time-domain beam synthesis, the image quality rapidly deteriorates with the reduction of the compression rate, and there is a clear contradictory relationship between the image quality and the data compression rate.

The embodiment of the specification provides a scene example of an ultrasonic imaging method. First, a two-dimensional Fast Fourier Transform (FFT) is performed on a received ultrasonic echo signal, and an echo signal X (X, z =0,t) received by a transducer is transformed to a beam domain G (k) _x Z =0,f). And then, determining the size of a transformation matrix according to the number of the array elements, and constructing a transformation matrix H, wherein in order to ensure that the projection vector is a sparse vector, the number of rows of the transformation matrix is less than the number of columns. Dividing the area from-90 degrees to 90 degrees into equal parts to obtain

And N is the number of the ultrasonic transducer elements. By using

Constructing a directional vector

Where d is the ultrasonic transducer element spacing, λ is the ultrasonic wavelength, and is defined by the direction vector δ (φ) _k ) Constructing a transformation matrix H.

The projection coefficient vector is obtained by projecting the array element receiving signal on the transformation matrix, and the array element receiving signal can be expressed as the product of the transformation matrix and the projection coefficient vector.

G(k _x ,0,f)＝HS(k _x ,0,f)

G(k _x 0, f) denotes the frequency domain signal after two-dimensional Fourier transformation of the transducer received signal, H denotes the transformation matrix, S (k) _x 0, f) denotes the transducer receive signal G (k) _x 0, f) the vector of projection coefficients on the transformation matrix H. Most of the projection coefficient vectors are 0 or the minimum value can be approximate to 0, and the vector is a sparse vector.

Design sampling matrix Λ = [ kappa ] k ₁ ,κ ₂ ,…,κ _M ]The size of Λ is M multiplied by N, wherein M is smaller than N to play a role in compressing the frequency domain of the original signal, and the sampling matrix in the algorithm is a random Gaussian matrix. The sampling matrix can be used for down-sampling the original sampling data to obtain a new sampling matrix G _s (k _x ,0,f)。

Z(k _x ,0,f)＝ΛG(k _x ,0,f)＝ΛHS(k _x ,0,f)

Matrix Z (k) _x 0, f) is the downsampling matrix that the algorithm needs to recover, Z (k) _x The dimension of 0, f) is M.times.L. When the signal has sparsity and the sampling matrix has finite equidistant Property (RIP), the original signal can be transformed from Z (k) _x And 0, f) are recovered. Due to Z (k) _x Dimension of 0, f) is much smaller than G (k) _x 0, f), so the compressed sensing theory can play an important role in reducing the sampling frequency, reducing the sampling channel, and the like.

After the transformation matrix and the sampling matrix are obtained, the measurement matrix P is calculated from the sampling matrix.

P＝ΛH

The measurement matrix is used for sampling observation values so as to reconstruct received signals. To reconstruct the original signal from the compressed signal, the requirements need to be met: the product of the measurement matrix and the sparse matrix satisfies the RIP. This property ensures that the original space has a one-to-one mapping to the sparse spaceThis requires that the sampling matrix randomly drawn from the observation matrix must be non-singular. The measurement matrix may measure the signals to obtain measurement vectors, and then reconstruct the original signals from the measurement values using a reconstruction algorithm. And carrying out compression sampling on the array element receiving signals, and obtaining a compression sampling vector through a sampling matrix. When a measurement matrix is designed, the measurement value does not influence the information of an original signal in the process of sparse representation of the signal, so that the signal can be accurately reconstructed. At this time, the down-sampling matrix Z (k) _x 0, f) can be expressed as:

Z(k _x ,0,f)＝PS(k _x ,0,f)

and after obtaining a compressed sampling value, estimating a projection coefficient vector by adopting a multipoint regularization underdetermined system focus solution (RM-FOCUSS) algorithm. In order to solve the projection coefficient vector of analytic sparseness, a cost function J is firstly constructed ^(p) (S), the smaller the cost function, the more sparse the signal. Assuming that there are L plane wave signals,

when p is closer to 0, is J ^(p) The more sparse S (t) is represented by (S).

Lagrangian L (S, Ω),

wherein ω is _l For lagrangian operator vectors, L =1,2, \8230, L, S (t) can be obtained by minimizing L (S, Ω), and the specific calculation process is as follows:

calculating to obtain sparse solution S (k) _x 0, f) by the formula G _s (k _x ,0,f)＝HS(k _x 0, f) to obtain the recovered full array element signal. At this time G _s (k _x 0, f) is a full array element signal, but is compressed with the original frequency domain signal G (k) _x 0, f) are different.

According to the seismic wave model, the ultrasonic imaging process can be converted into that a virtual target in an imaging area transmits ultrasonic waves to the ultrasonic transducer, and the ultrasonic transducer receives signals of the virtual target and then images. In the conversion process, the sound velocity c in the original process and the coordinates of the imaging area are changed, so that the corresponding relation between the real imaging target and the virtual target needs to be found, and the coordinates of the virtual target and the sound velocity of the reflected signal need to be solved.

Where θ is the angle between the emitted plane wave and the ultrasonic transducer.

G _s (k _x 0, f) is the result after the compressed sensing is restored. Suppose psi _θ (x, z, t = 0) final imaging signal, phi _θ (k _x ,k _z T = 0) is ψ _θ (x, z, t = 0), that is, the frequency domain signal of the image of the real imaging target.

It is the frequency domain signal of the "virtual target" imaging signal. Now it is necessary to mix G _s (k _x 0, f) into psi _θ (x, z, t = 0).

Wherein

It is known that

Deducing phi from the _θ (k _x ,k _z ,t＝0)。

Finally, two-dimensional Fourier inversion is carried out

Obtain the final image psi _θ (x,z,t＝0)。

The above description is only exemplary of the present disclosure and should not be construed as limiting the present disclosure, and any modifications, equivalents and the like that are within the spirit and principle of the present disclosure are intended to be included within the scope of the present disclosure.

Referring to fig. 1, one embodiment of the present application provides an ultrasound imaging method, which may include the following steps.

S110: the received ultrasonic echo signals representing the time variation are converted into beam domain signals representing the frequency variation.

In some cases, if the compressed sensing is directly applied to the time-domain beamforming method, the image quality deteriorates rapidly as the compression rate decreases, and there is a clear contradictory relationship between the image quality and the data compression rate. Thus, the received ultrasound echo signals may first be converted into beam domain signals characterizing the frequency change.

In this embodiment, a two-dimensional fast fourier transform is performed on the received ultrasonic echo signal, and the echo signal X (X, z =0,t) received by the ultrasonic transducer is transformed into the beam domain G (k) _x ,z＝0,f)。

S120: compressing the wave beam domain signal by adopting a preset sampling matrix to obtain a virtual measurement signal; wherein the preset sampling matrix satisfies a finite equidistant property; the virtual measurement signal has sparsity.

In this embodiment, the preset sampling matrix is composed of M rows, i.e., M sampling bases. Preset sampling matrix Λ = [ gamma ] ₁ ,γ ₂ ,…,γ _M ] ^T . Each miningThe basis is an N-dimensional vector, i.e. gamma _i ＝[γ _i1 ,γ _i2 ,…,γ _iN ](i =1,2, \8230;, M). The sampling of the ith row indicates that the output of all array elements is projected onto the sampling base, corresponding to one compressed sampling point. For example, Z _i (t)＝γ _i1 G ₁ (t)+γ _i2 G ₂ (t)+…+γ _iN G _N And (t) represents the received signal of the ith compression sampling point corresponding to the compression sampling. The sampling matrix Lambda has M rows in total, and only M compression sampling points are needed for representation, namely M array elements are selected from N array elements of the original array to carry out spatial sampling. The sampling matrix may be a gaussian random matrix. Of course, the sampling matrix may also be a Hadamard matrix, a sparse random matrix, a partial fourier matrix, or the like. The original sampling matrix data can be down-sampled by using the sampling matrix to obtain a virtual measurement signal Z (k) _x ,0,f)。

Z(k _x ,0,f)＝ΛG(k _x ,0,f)

S130: and restoring the virtual measurement signal to a full array element state to obtain a virtual frequency domain signal.

In some cases, the number of sampling channels may be reduced or the dimensionality of the signal may be reduced on the premise of maintaining the original characteristics of the signal through a compressed sensing theory, and then the compressed data needs to be restored to obtain a reply signal.

In this embodiment, the size of the transformation matrix is determined according to the number of array elements, and a transformation matrix H is constructed, where the number of rows of the transformation matrix is smaller than the number of columns in order to ensure that the projection vector is a sparse vector. Dividing the area from-90 degrees to 90 degrees into equal parts to obtain

And N is the number of the ultrasonic transducer elements. By using

Constructing a directional vector

Where d is the ultrasonic transducer array element spacing, λ is the ultrasonic wavelength, free spaceVector delta phi _k ) Constructing a transformation matrix H.

The projection coefficient vector is obtained by projecting the array element receiving signal on the transformation matrix, and the array element receiving signal can be expressed as the product of the transformation matrix and the projection coefficient vector. By constructing the measurement matrix P = Λ H. Wherein, Z (k) _x ,0,f)＝PS(k _x 0, f). By solving for the projection sparse vector S (k) _x 0, f) and then using G _s (k _x ,0,f)＝HS(k _x 0, f) obtaining a virtual measurement signal.

S140: and carrying out offset processing on the virtual frequency domain signal to obtain an actual frequency domain signal.

In some cases, the process of ultrasound plane wave imaging can be summarized as: the ultrasonic transducer transmits ultrasonic waves to an imaging area, an imaging target reflects ultrasonic signals, the transducer receives reflected echo signals, and the received signals are processed and imaged. According to the seismic wave model, the ultrasonic imaging process can be converted into that a virtual target in an imaging area transmits ultrasonic waves to the ultrasonic transducer, and the ultrasonic transducer receives signals of the virtual target and then images.

Referring to fig. 2 (a), the ultrasound transducer emits ultrasound waves to cover the imaging area, the imaging target reflects the ultrasound signals, the reflected echo signals return to the ultrasound transducer, and the signal received at the ultrasound transducer has an ordinate z =0 and is denoted as X (X, z =0, t). Referring to fig. 2 (b), in the seismic wave explosion model, it is assumed that the imaging target itself emits a signal, which is the emission signal when t =0, i.e., ψ _θ (x, z, t = 0). The real ultrasonic imaging result can also be regarded as that the imaging target transmits an ultrasonic signal to the transducer for imaging, but applying the explosion model to plane wave imaging needs Stolt's migration for coordinate transformation.

In this embodiment, the sound velocity c and the coordinates of the imaging region in the original process are changed, so that the corresponding relationship between the real imaging target and the "virtual target" needs to be found, and the coordinates of the "virtual target" and the sound velocity of the reflected signal need to be solved.

Wherein

The "virtual sound velocity" of an ultrasonic signal transmitted for a "virtual target", (x) _s ,z _s ) It is true that the coordinates of the object being imaged,

are the coordinates of the "virtual target". α, β and γ are transform coefficients defined as follows:

where θ is the angle between the emitted plane wave and the ultrasound transducer.

S150: and converting the actual frequency domain signal into a target image matrix for generating an ultrasonic image.

In this embodiment, G _s (k _x 0, f) is the result after compressed sensing recovery. Suppose psi _θ (x, z, t = 0) final imaging signal, phi _θ (k _x ,k _z T = 0) is ψ _θ (x, z, t = 0), i.e. the frequency domain signal of the image of the real imaging target.

It is the frequency domain signal of the "virtual target" imaging signal. Now it is necessary to mix G _s (k _x 0, f) into psi _θ (x, z, t = 0).

Wherein

It is known that

Deducing phi from the _θ (k _x ,k _z ,t＝0)。

Finally, two-dimensional Fourier inversion is carried out

Obtain the final image psi _θ (x,z,t＝0)。

Referring to FIG. 3, the imaging result with a compression rate of 80% (abbreviated as fkCS-80%) and a time Delay-and-Sum beamforming (DAS) are compared with a Stolt's f-k migration-based frequency-domain beamforming algorithm (abbreviated as f-k) for uncompressed data volume. The compression ratio (R) is calculated by the following method:

wherein N is _c And N _o The amount of data after compression and before compression, respectively. In the simulation results, gCNR is calculated using regions a, B and C, respectively, to represent the imaged hypoechoic region and bright spot contrast, the gCNR calculation method is as follows:

wherein p is _t Is the probability density distribution of the target region, p _b (x) Is the probability density distribution of the background region.

The transverse resolution of the scattering sub-point with the coordinate of (10, 70) mm in the area D is calculated to compare the resolution situations of different imaging methods. The transverse resolution of the point D is respectively 0.5985mm (DAS), 0.5262mm (f-k) and 0.5451mm (fkCS-80%) by the frequency domain beam synthesis method based on compressed sensing, and the frequency domain beam synthesis imaging method is improved by 12% compared with the transverse resolution of the DAS and the imaging result with the compression rate of 80% is only reduced by 3.6% compared with the transverse resolution of the uncompressed state. The contrast of the three methods in the hypoechoic area is respectively 1.7482 (DAS), 2.5791 (f-k) and 2.4915 (fkCS-80%), the f-k method is improved by 47.5% in hypoechoic contrast compared with the DAS, and the fkCS-80% method with the compression rate of 80% is reduced by 3.4% in contrast compared with the uncompressed (f-k). The images of the three methods are respectively 2.9104 (DAS), 3.1455 (f-k) and 3.1081 (fkCS-80%) on the bright spot contrast, the f-k method is improved by 8% on the low echo contrast compared with the DAS, and the fkCS-80% method with the compression ratio of 80% is reduced by 1.2% on the contrast compared with the uncompressed (f-k). Therefore, the frequency domain beam synthesis method based on compressed sensing has certain improvement on image quality compared with DAS. And when the number of imaging channels is compressed to 80%, the influence on the imaging quality is small. This is because the ultrasonic signal itself has sparsity, and original data can be restored to some extent even after a part of channels are compressed for the number of signal channels.

Referring to fig. 4 to 7, simulation results and image quality parameter analysis results of a frequency domain beam synthesis method (fkCS) based on compressive sensing and a time domain beam synthesis method (tmCS) based on compressive sensing are shown. Fig. 4, 5 and 6 are graphs showing the variation trend of the hypoechoic contrast, the bright spot contrast and the lateral resolution of the two imaging methods with the variation of the compression ratio respectively. FIG. 7 is a simulated B-mode image of fkCS and tmCS at a compression ratio of 50%. As can be seen from the B-mode image, when the compression ratio is 50%, the tmCS — 50% image quality is poor, the hypoechoic area is almost covered, and the bright spots generate obvious artifacts; and low echo areas and scattering sub-points can still be distinguished in the fkCS-50% imaging result, bright spots have slight artifacts, and the artifact degree is obviously weaker than that of fkCS. As can be seen from fig. 4-6, as the compression ratio decreases, the contrast and lateral resolution of tmCS decreases faster than fkCS. It can be concluded that using compressed sensing in the frequency domain results in better image quality than using compressed sensing in the time domain when the compression rates are the same.

In some cases, M is chosen to be between 0.3N and 0.8N, i.e., a compression ratio of between 30% and 80%, with M rounded. In this range, the number of channels of data can be reduced, and better imaging quality can be maintained.

In one embodiment, converting the received ultrasound echo signals representing the time variations into beam domain signals representing the frequency variations may comprise: and carrying out two-dimensional fast Fourier change on the ultrasonic echo signal to obtain a beam domain signal.

In the present embodiment, the fast fourier transform is a general term for an efficient and fast calculation method using a computer discrete fourier transform. It is obtained by improving the algorithm of discrete Fourier transform according to the characteristics of odd, even, imaginary and real of the discrete Fourier transform.

In one embodiment, restoring the virtual measurement signal to a full array element state, and obtaining a virtual frequency domain signal may include: constructing a measurement matrix; wherein the virtual measurement signal is represented as a product of the measurement matrix and a projection coefficient vector; the projection coefficient vector is used for representing the projection of the ultrasonic echo signal on a transformation matrix of a direction vector constructed by the number of ultrasonic transducer elements; solving the projection coefficient vector; and performing product operation on the transformation matrix and the projection coefficient vector to obtain the virtual frequency domain signal.

In the present embodiment, the measurement matrix is an M × 2N matrix. In case the measurement matrix satisfies the finite equidistant property, the projection coefficient vector S (k) can be solved _x 0, f) from the virtual measurement signal Z (k) _x 0, f) accurate reconstruction of the beam domain signal G (k) at full array elements _x 0, f). The measurement matrix may be expressed as a product of a preset sampling matrix and a transformation matrix.

In one embodiment, the transformation matrix of the direction vectors constructed by the number of ultrasound transducer elements may include: equally dividing the-90-degree region range according to twice of the number of the ultrasonic transducer array elements to obtain array element intervals; constructing direction vectors of the ultrasonic transducer array elements in different emission directions according to the array element spacing; and generating a transformation matrix according to the direction vectors of the different transmitting directions.

In the present embodiment, considering a full array of ultrasonic beams, N array elements are uniformly arranged, and the array element spacing is equal to

(λ is the operating wavelength of the ultrasound transducer). Now, K far-field echo signals are received, and the complex amplitude and the incident angle of the signals are S respectively _k (t) and θ _sk (K =1,2, \8230;, K). Of which 1 is the desired signal and the remaining K-1 are the interfering signals. The received signal of each array element of the ultrasonic transducer is represented by a vector X (t) with N dimensions, wherein X (t) = [ X = [ [ X ] X ₁ (t),x ₂ (t),…,x _N (t)] ^T . Then there is

Wherein, a (theta) _sk ) For arrays at theta _sk The directivity of the (K =1,2, \8230;, K) direction is appropriate.

According to equal sin (theta) _sk ) Dividing an airspace 2N of-90 degrees into equal parts for a division principle to obtain theta ₁ ,θ ₂ ,…,θ _2N The 2N directivity appropriate quantities are used to construct a transformation matrix H.

H＝[a(θ ₁ ),a(θ ₂ ),…,a(θ _2N )]

Writing the array received signal vector X (t) into a matrix form represented by a transformation matrix H, having

X(t)＝HS(t)

Wherein, S (t) is a projection coefficient vector of the array element received signal vector X (t) on the transformation matrix H. In some cases, θ _sk (K =1,2, \ 8230;, K) are each θ ₁ ,θ ₂ ,…,θ _2N One of themThen the projection coefficient vector S (t) has a value similar to S (t) = [0, \8230 ], S ₁ (t),0,…,s _K (t),0,…,0]In the form of (1). Only a few elements of the vector S (t) are non-zero and the rest are zero elements, i.e. S (t) is sparse. Therefore, according to the theory of compressed sensing, the array element received signal vector X (t) can be accurately recovered by a reconstruction algorithm after compressed sampling.

In one embodiment, solving for the projection coefficient vector may comprise: constructing a cost function; wherein the cost function is used for representing the sparsity degree of the projection coefficient vector; constructing a Lagrangian operator according to the cost function, the measurement matrix, the projection coefficient vector and the virtual measurement signal; and solving the projection coefficient vector by performing minimization operation on the Lagrangian operator.

In the present embodiment, compressed sample values Z of M compressed sample array elements are obtained _M×L Then, a projection coefficient vector S is estimated by adopting a multipoint regularization underdetermined system focus solution (RM-FOCUSS) algorithm _2N×L And then reconstructing a full array element received signal vector G according to G = HS _S 。

To solve S _2N×L Constructing the following cost function:

when p is close to 0, J ^(p) (S) is represented by S _2N×L Non-zero column of (J) ^(p) The smaller (S), the more sparse the signal. Then define Lagrangian L (S, Ω) as

Wherein ω is _l For Lagrange operator vector, L =1,2, \8230, L, S (t) can be obtained by minimizing L (S, omega), and the sparse solution S can be obtained _2N×L . And solving by a regularized M-FOCUSS iterative algorithm.

Calculating to obtain sparse solution S (k) _x 0, f) by the formula G _s (k _x ,0,f)＝HS(k _x 0, f) to obtain the recovered virtual frequency domain signal G of full array element _s (k _x ,0,f)。

In one embodiment, the shifting the virtual frequency domain signal to obtain an actual frequency domain signal may include: calculating actual sound velocity and actual coordinates according to an included angle between plane waves emitted by an ultrasonic transducer and the ultrasonic transducer; converting the virtual frequency domain signal to an actual frequency domain signal based on the actual speed of sound and the actual coordinates.

In this embodiment, G _s (k _x 0, f) is the result after compressed sensing recovery. Suppose psi _θ (x, z, t = 0) final imaging signal, phi _θ (k _x ,k _z T = 0) is ψ _θ (x, z, t = 0), i.e. the frequency domain signal of the image of the real imaging target.

It is the frequency domain signal of the "virtual target" imaging signal. Now it is necessary to mix G _s (k _x 0, f) into psi _θ (x, z, t = 0).

Wherein

It is known that

Deducing phi from the _θ (k _x ,k _z ,t＝0)。

Finally, two-dimensional Fourier inversion is carried out

Obtain the final image psi _θ (x,z,t＝0)。

In one embodiment, converting the actual frequency domain signals to a target image matrix for generating an ultrasound image may comprise: and carrying out two-dimensional Fourier inversion on the actual frequency domain signal to obtain a target image matrix for generating an ultrasonic image.

In the present embodiment, the inverse fourier transform is performed in two dimensions

Obtain the final image psi _θ (x,z,t＝0)。

Referring to fig. 8, an embodiment of the present application further provides an ultrasound imaging apparatus, which may include: the device comprises a signal conversion unit, a signal compression unit, a signal recovery unit, a signal offset unit and a signal inverse conversion unit.

And the signal conversion unit is used for converting the received ultrasonic echo signals representing the time change into beam domain signals representing the frequency change.

The signal compression unit is used for compressing the wave beam domain signal by adopting a preset sampling matrix to obtain a virtual measurement signal; wherein the preset sampling matrix satisfies a finite equidistant property; the virtual measurement signal has sparsity.

And the signal recovery unit is used for recovering the virtual measurement signal to a full array element state to obtain a virtual frequency domain signal.

And the signal offset unit is used for carrying out offset processing on the virtual frequency domain signal to obtain an actual frequency domain signal.

And the signal inverse conversion unit is used for converting the actual frequency domain signal into a target image matrix for generating an ultrasonic image.

With regard to specific functions and effects achieved by the ultrasonic imaging device, reference may be made to other embodiments of the present specification for comparative explanation, and details are not repeated here. The various modules in the ultrasound imaging apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

Referring to fig. 9, an embodiment of the present application further provides an electronic device, which includes a processor and a memory, where the memory is used to store a computer program, and the computer program is executed by the processor to implement the above-mentioned ultrasound imaging method.

The processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof.

The memory, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the methods in the embodiments of the present invention. The processor executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory, that is, the method in the above method embodiment is realized.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be coupled to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

An embodiment of the present application further provides a computer-readable storage medium for storing a computer program which, when executed by a processor, implements the ultrasound imaging method described above.

It will be understood by those skilled in the art that all or part of the processes of the method for implementing the embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods as described. Any reference to memory, storage, databases, or other media used in the various embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example.

It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The various embodiments of the present disclosure are described in a progressive manner. The different embodiments focus on the different parts described compared to the other embodiments. After reading this specification, one skilled in the art can appreciate that many embodiments and many features disclosed in the embodiments can be combined in many different ways, and for the sake of brevity, all possible combinations of features in the embodiments are not described. However, as long as there is no contradiction between combinations of these technical features, the scope of the present specification should be considered as being described.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.

In the present specification, the embodiments are mainly intended to emphasize different portions from other embodiments, and the embodiments can be explained with reference to each other. Any combination of the embodiments in the present specification by a person skilled in the art based on general technical common knowledge is covered in the disclosure of the present specification.

The above description is only an embodiment of the present disclosure, and is not intended to limit the scope of the claims of the present disclosure. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims (10)

1. A method of ultrasound imaging, the method comprising:

converting the received ultrasonic echo signals representing time variation into beam domain signals representing frequency variation;

compressing the wave beam domain signal by adopting a preset sampling matrix to obtain a virtual measurement signal; wherein the preset sampling matrix satisfies a finite equidistant property; the virtual measurement signal has sparsity;

restoring the virtual measurement signal to a full array element state to obtain a virtual frequency domain signal;

carrying out offset processing on the virtual frequency domain signal to obtain an actual frequency domain signal;

the actual frequency domain signal is converted into a target image matrix for generating an ultrasound image.

2. The method of claim 1, wherein converting the received ultrasound echo signals representing the time variations into beam domain signals representing the frequency variations comprises:

and carrying out two-dimensional fast Fourier change on the ultrasonic echo signal to obtain a wave beam domain signal.

3. The method of claim 1, wherein restoring the virtual measurement signal to a full array element state to obtain a virtual frequency domain signal comprises:

constructing a measurement matrix; wherein the virtual measurement signal is represented as a product of the measurement matrix and a projection coefficient vector; the projection coefficient vector is used for representing the projection of the ultrasonic echo signal on a transformation matrix of a direction vector constructed by the number of ultrasonic transducer elements;

solving the projection coefficient vector;

and performing product operation on the transformation matrix and the projection coefficient vector to obtain the virtual frequency domain signal.

4. The method of claim 3, wherein the transformation matrix of direction vectors constructed from the number of ultrasound transducer elements comprises:

equally dividing the-90-degree region range according to twice of the number of the ultrasonic transducer array elements to obtain array element intervals;

constructing direction vectors of the ultrasonic transducer array elements in different emission directions according to the array element spacing;

and generating a transformation matrix according to the direction vectors of the different transmitting directions.

5. The method of claim 3, wherein solving for the projection coefficient vector comprises:

constructing a cost function; wherein the cost function is used for representing the sparsity degree of the projection coefficient vector;

constructing a Lagrangian operator according to the cost function, the measurement matrix, the projection coefficient vector and the virtual measurement signal;

and solving the projection coefficient vector by performing minimization operation on the Lagrangian operator.

6. The method of claim 1, wherein the shifting the virtual frequency domain signal to obtain an actual frequency domain signal comprises:

calculating an actual sound velocity and an actual coordinate according to an included angle between a plane wave emitted by an ultrasonic transducer and the ultrasonic transducer;

converting the virtual frequency domain signal to an actual frequency domain signal based on the actual speed of sound and the actual coordinates.

7. The method of claim 1, wherein converting the actual frequency domain signals to a target image matrix for generating an ultrasound image comprises:

and carrying out two-dimensional Fourier inversion on the actual frequency domain signal to obtain a target image matrix for generating an ultrasonic image.

8. An ultrasonic imaging apparatus, characterized in that the ultrasonic imaging apparatus comprises:

the signal conversion unit is used for converting the received ultrasonic echo signals representing time change into beam domain signals representing frequency change;

the signal compression unit is used for compressing the wave beam domain signal by adopting a preset sampling matrix to obtain a virtual measurement signal; wherein the preset sampling matrix satisfies a finite equidistant property; the virtual measurement signal has sparsity;

the signal recovery unit is used for recovering the virtual measurement signal to a full array element state to obtain a virtual frequency domain signal;

the signal offset unit is used for carrying out offset processing on the virtual frequency domain signal to obtain an actual frequency domain signal;

and the signal inverse conversion unit is used for converting the actual frequency domain signal into a target image matrix for generating an ultrasonic image.

9. An electronic device, characterized in that the electronic device comprises a processor and a memory for storing a computer program which, when executed by the processor, implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium is used for storing a computer program which, when executed by a processor, implements the method of any one of claims 1 to 7.

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