阅读说明：本技术 超声三维成像方法和装置、计算机设备、存储介质 (Ultrasonic three-dimensional imaging method and device, computer equipment and storage medium ) 是由 汪帝 张珏 于 2021-09-15 设计创作，主要内容包括：一种超声三维成像方法和装置、计算机设备,以及存储介质,解决了基于压缩感知稀疏成像策略得到的图像质量较差的问题。方法包括：从超声二维矩阵换能器中随机选择预定数量的阵元激活,得到多个初始稀疏阵列；基于阵列指向性确定多个初始稀疏阵列各自的次波瓣的最大电平值；基于优化算法更新上一代稀疏阵列中阵元的位置,得到下一代稀疏阵列；基于阵列指向性确定下一代稀疏阵列中的多个稀疏阵列各自的次波瓣的最大电平值；当次波瓣的最大电平值呈现收敛趋势时,确定收敛点对应的稀疏阵列为指向性最优的目标稀疏阵列；控制目标稀疏阵列发射和接收超声波；基于目标稀疏阵列接收到的超声波确定原始信号；基于原始信号形成超声影像。(An ultrasonic three-dimensional imaging method and device, computer equipment and a storage medium solve the problem of poor image quality obtained based on a compressed sensing sparse imaging strategy. The method comprises the following steps: randomly selecting a preset number of array elements from an ultrasonic two-dimensional matrix transducer for activation to obtain a plurality of initial sparse arrays; determining a maximum level value of a sub-lobe of each of the plurality of initial sparse arrays based on the array directivity; updating the position of an array element in the previous generation of sparse array based on an optimization algorithm to obtain a next generation of sparse array; determining maximum level values of respective minor lobes of a plurality of sparse arrays in a next generation of sparse array based on array directivity; when the maximum level value of the secondary lobe shows a convergence trend, determining that the sparse array corresponding to the convergence point is a target sparse array with optimal directivity; controlling a target sparse array to transmit and receive ultrasonic waves; determining an original signal based on the ultrasonic waves received by the target sparse array; an ultrasound image is formed based on the raw signal.)

1. An ultrasonic three-dimensional imaging method, comprising:

randomly selecting a preset number of array elements from an ultrasonic two-dimensional matrix transducer for activation to obtain a plurality of initial sparse arrays;

determining a maximum level value of a secondary lobe of each of the plurality of initial sparse arrays based on array directivity, the maximum level value of the secondary lobe being equal to a sum of a maximum level value of a azimuth secondary lobe and a maximum level value of a pitch secondary lobe; and

the following steps are repeatedly executed:

updating the position of an array element in a previous-generation sparse array based on an optimization algorithm to obtain a next-generation sparse array, wherein the initial value of the previous-generation sparse array is the plurality of initial sparse arrays, and the next-generation sparse array comprises a plurality of sparse arrays; and

determining a maximum level value of the secondary lobe of each of the plurality of sparse arrays in the next generation sparse array based on array directivity;

when the maximum level value of the secondary lobe shows a convergence trend, determining that the sparse array corresponding to the convergence point is a target sparse array with optimal directivity;

controlling the target sparse array to transmit and receive ultrasonic waves;

determining an original signal based on the ultrasonic waves received by the target sparse array;

an ultrasound image is formed based on the raw signal.

2. The method of claim 1, wherein said determining maximum level values of the minor lobes of each of said plurality of initial sparse arrays based on array directivity comprises:

determining a description function of the array directivity of each initial sparse array based on attribute parameters of the two-dimensional matrix transducer, wherein the attribute parameters comprise excitation amplitude, azimuth angle and pitch angle of each array element in the two-dimensional matrix transducer, the total number of the array elements in the azimuth direction and the pitch direction, and the array element spacing in the azimuth direction and the pitch direction;

traversing the description function in the azimuth direction to obtain a plurality of azimuth lobe level values, and determining the second largest value in the plurality of azimuth lobe level values as the maximum level value of the azimuth secondary lobe;

traversing the description function in the elevation direction to obtain a plurality of elevation lobe level values, and determining a second largest value in the plurality of elevation lobe level values as a maximum level value of the elevation secondary lobe;

and taking the sum of the maximum level value of the azimuth secondary lobe and the maximum level value of the elevation secondary lobe as the maximum level value of the secondary lobe.

3. The ultrasonic three-dimensional imaging method according to claim 2, characterized in that the describing function is:

wherein I (m, n) represents the excitation amplitude of the (m, n) th array element in the two-dimensional matrix transducer; m and N respectively represent the total number of array elements in the azimuth direction and the pitch direction; d_y、d_zArray element spacing in azimuth and elevation directions is respectively represented; theta andrepresenting the azimuth and elevation angles, respectively.

4. The ultrasonic three-dimensional imaging method according to claim 1, wherein the updating the position of the array element in the previous-generation sparse array based on the optimization algorithm to obtain the next-generation sparse array comprises:

selecting a predetermined number of sparse arrays from the previous generation of sparse arrays;

sequencing the sparse arrays in the preset number according to the sequence of the fitness value from large to small, wherein the fitness value is the reciprocal of the maximum level value of the secondary lobe;

screening the sparse arrays with the preset number according to a preset genetic selection rate to obtain a plurality of optimized sparse arrays;

and performing genetic crossing and genetic variation based on the plurality of optimized sparse arrays to obtain the next-generation sparse array.

5. The method of ultrasonic three-dimensional imaging according to claim 4, wherein said genetically crossing based on said plurality of optimized sparse arrays comprises:

and executing pairwise exchange operation of the array element positions on the optimized sparse arrays to obtain a plurality of crossed sparse arrays.

6. The method of ultrasonic three-dimensional imaging according to claim 5, wherein said genetically mutating based on said plurality of optimized sparse arrays comprises:

changing the enabling states of the array elements in the optimized sparse array and the cross sparse array according to a preset variation rate to obtain a variation sparse array;

the next-generation sparse array includes the optimized sparse array, the crossed sparse array, and the variant sparse array.

7. The method according to claim 1, wherein the ultrasonic waves emitted by the target sparse array have periodicity, and each period comprises a predetermined number of wave planes, and two adjacent wave planes in time sequence form an included angle.

8. The method of claim 1, wherein the determining a raw signal based on the ultrasound waves received by the sparse array of targets comprises:

determining an observation value of a compressed sensing model based on the received ultrasonic waves;

determining an observation matrix of the compressed sensing model based on the array element position of the target sparse array;

determining the original signal based on the observations, the observation matrix, and a fixed or adaptive sparse transform basis.

9. The method according to claim 8, wherein the determining an observation matrix of the compressed sensing model based on the array element positions of the target sparse array comprises:

carrying out binarization on the array element position of the target sparse array to obtain a binarization matrix;

and converting the binarization matrix into an observation matrix, wherein the observation matrix is used for indicating the position information of the array elements in the target sparse array.

10. An ultrasonic three-dimensional imaging device, comprising:

the activation module is used for randomly selecting a preset number of array elements from the ultrasonic two-dimensional matrix transducer to activate to obtain a plurality of initial sparse arrays;

a first determining module for determining a maximum level value of a secondary lobe of each of the plurality of initial sparse arrays based on the array directivity, the maximum level value of the secondary lobe being equal to the sum of the maximum level value of the azimuth secondary lobe and the maximum level value of the pitch secondary lobe;

an update module for repeatedly performing the following steps: updating the position of an array element in a previous-generation sparse array based on an optimization algorithm to obtain a next-generation sparse array, wherein the initial value of the previous-generation sparse array is the plurality of initial sparse arrays, and the next-generation sparse array comprises a plurality of sparse arrays; and determining a maximum level value of the secondary lobe of each of the plurality of sparse arrays in the next generation sparse array based on array directivity;

the second determining module is used for determining that the sparse array corresponding to the convergence point is a target sparse array with optimal directivity when the maximum level value of the secondary lobe shows a convergence trend;

the control module is used for controlling the target sparse array to transmit and receive ultrasonic waves;

a third determining module, configured to determine an original signal based on the ultrasonic waves received by the target sparse array;

and the forming module is used for forming an ultrasonic image based on the original signal.

11. A computer device comprising a memory, a processor and a computer program stored on the memory for execution by the processor, characterized in that the steps of the method of ultrasound three-dimensional imaging according to any of claims 1 to 9 are implemented when the computer program is executed by the processor.

12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the ultrasound three-dimensional imaging method according to any one of claims 1 to 9.

Technical Field

The application relates to the technical field of medical equipment, in particular to an ultrasonic three-dimensional imaging method and device, computer equipment and a storage medium.

Background

To obtain a sufficient three-dimensional imaging field of view, ultrasonic two-dimensional matrix transducers are typically composed of thousands of transducer elements (hereinafter referred to as array elements). In the case of a large-scale unit array, if the acquisition channels correspond to the array elements one to one, the hardware cost and circuit complexity of the acquisition system are greatly increased. Meanwhile, mass radio frequency data of thousands of channels also cause huge pressure on data transmission, storage and calculation of a system, so that the acquisition frame rate and the reconstruction frame rate of three-dimensional imaging of the matrix probe are seriously reduced.

In recent years, the compressive sensing theory provides a distinctive idea for simplifying an acquisition system and reducing data acquisition amount, namely under the assumption of sparsity of a sampled signal, a complete signal can be recovered in a nonlinear mode by sampling data far below a nyquist limit. By adopting a compressed sensing sparse imaging strategy, the number of acquisition channels can be effectively reduced, the system complexity is reduced, and the imaging speed is increased.

However, the image quality obtained by the current compressed sensing-based sparse imaging strategy is poor.

Disclosure of Invention

In view of this, embodiments of the present application aim to provide an ultrasound three-dimensional imaging method and apparatus, a computer device, and a storage medium, so as to solve the problem in the prior art that an image quality obtained based on a compressed sensing sparse imaging strategy is poor.

The application provides an ultrasonic three-dimensional imaging method in a first aspect, which comprises the following steps: randomly selecting a preset number of array elements from an ultrasonic two-dimensional matrix transducer for activation to obtain a plurality of initial sparse arrays; determining a maximum level value of a secondary lobe of each of the plurality of initial sparse arrays based on the array directivity, the maximum level value of the secondary lobe being equal to the sum of the maximum level value of the azimuth secondary lobe and the maximum level value of the pitch secondary lobe; and repeatedly executing the following steps: updating the position of an array element in a previous-generation sparse array based on an optimization algorithm to obtain a next-generation sparse array, wherein the initial value of the previous-generation sparse array is a plurality of initial sparse arrays, and the next-generation sparse array comprises a plurality of sparse arrays; and determining a maximum level value of a sub-lobe of each of a plurality of sparse arrays in the next generation of sparse array based on the array directivity; when the maximum level value of the secondary lobe shows a convergence trend, determining that the sparse array corresponding to the convergence point is a target sparse array with optimal directivity; controlling a target sparse array to transmit and receive ultrasonic waves; determining an original signal based on the ultrasonic waves received by the target sparse array; an ultrasound image is formed based on the raw signal.

In one embodiment, determining the maximum level value of the sub-lobe for each of the plurality of initial sparse arrays based on the array directivity comprises: determining a description function of the array directivity of each initial sparse array based on attribute parameters of the two-dimensional matrix transducer, wherein the attribute parameters comprise the excitation amplitude, the azimuth angle and the pitch angle of each array element in the two-dimensional matrix transducer, the total number of the array elements in the azimuth direction and the pitch direction, and the array element spacing in the azimuth direction and the pitch direction; traversing the description function in the azimuth direction to obtain a plurality of azimuth lobe level values, and determining the second largest value in the plurality of azimuth lobe level values as the maximum level value of the azimuth secondary lobe; obtaining a plurality of pitching lobe level values in a pitching traversal description function, and determining the second largest value in the plurality of pitching lobe level values as the maximum level value of a pitching secondary lobe; and the sum of the maximum level value of the azimuth secondary lobe and the maximum level value of the pitching secondary lobe is used as the maximum level value of the secondary lobe.

In one embodiment, the describing function is:

wherein I (m, n) represents the excitation amplitude of the (m, n) th array element in the two-dimensional matrix transducer; m and N respectively represent the total number of array elements in the azimuth direction and the pitch direction; d_y、d_zArray element spacing in azimuth and elevation directions is respectively represented; theta andrepresenting the azimuth and elevation angles, respectively.

In one embodiment, updating the position of the array element in the previous-generation sparse array based on the optimization algorithm to obtain the next-generation sparse array comprises: selecting a predetermined number of sparse arrays from a previous generation of sparse arrays; sequencing a predetermined number of sparse arrays according to the sequence of the fitness value from large to small, wherein the fitness value is the reciprocal of the maximum level value of the secondary lobe; screening a predetermined number of sparse arrays according to a predetermined genetic selection rate to obtain a plurality of optimized sparse arrays; and performing genetic crossing and genetic variation based on the plurality of optimized sparse arrays to obtain a next-generation sparse array.

In one embodiment, genetically crossing based on a plurality of optimized sparse arrays comprises: and executing pairwise exchange operation of the array element positions on the optimized sparse arrays to obtain a plurality of crossed sparse arrays.

In one embodiment, performing genetic variation based on a plurality of optimized sparse arrays comprises: changing and optimizing the enabling states of array elements in the sparse array and the cross sparse array according to a preset variation rate to obtain a variation sparse array; next generation sparse arrays include optimized sparse arrays, cross-sparse arrays, and variant sparse arrays.

In one embodiment, the ultrasonic waves emitted by the target sparse array have periodicity, one period comprises a predetermined number of wave planes, and two wave planes which are adjacent in time sequence form a certain included angle.

In one embodiment, determining the raw signal based on the ultrasound received by the sparse array of interest comprises: determining an observation value of a compressed sensing model based on the received ultrasonic waves; determining an observation matrix of a compressed sensing model based on the array element position of the target sparse array; the original signal is determined based on the observations, the observation matrix, and a fixed or adaptive sparse transformation basis.

In one embodiment, determining an observation matrix of a compressed sensing model based on array element positions of a target sparse array comprises: carrying out binarization on the array element position of the target sparse array to obtain a binarization matrix; and converting the binarization matrix into an observation matrix, wherein the observation matrix is used for indicating the position information of the array elements in the target sparse array.

The second aspect of the present application provides an ultrasonic three-dimensional imaging apparatus, comprising: the activation module is used for randomly selecting a preset number of array elements from the ultrasonic two-dimensional matrix transducer to activate to obtain a plurality of initial sparse arrays; a first determining module for determining a maximum level value of a secondary lobe of each of the plurality of initial sparse arrays based on the array directivity, the maximum level value of the secondary lobe being equal to the sum of the maximum level value of the azimuth secondary lobe and the maximum level value of the pitch secondary lobe; an update module for repeatedly performing the following steps: updating the position of an array element in a previous-generation sparse array based on an optimization algorithm to obtain a next-generation sparse array, wherein the initial value of the previous-generation sparse array is a plurality of initial sparse arrays, and the next-generation sparse array comprises a plurality of sparse arrays; and determining a maximum level value of a sub-lobe of each of a plurality of sparse arrays in the next generation of sparse array based on the array directivity; the second determining module is used for determining that the sparse array corresponding to the convergence point is a target sparse array with optimal directivity when the maximum level value of the secondary lobe shows a convergence trend; the control module is used for controlling the target sparse array to transmit and receive ultrasonic waves; the third determination module is used for determining an original signal based on the ultrasonic waves received by the target sparse array; and the forming module is used for forming the ultrasonic image based on the original signal.

A third aspect of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executed by the processor, wherein the processor implements the steps of the ultrasonic three-dimensional imaging method provided in any one of the above embodiments when executing the computer program.

A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the ultrasonic three-dimensional imaging method provided in any one of the above embodiments.

According to the ultrasonic three-dimensional imaging method and device, the computer equipment and the storage medium, on one hand, a target sparse array with optimal directivity is determined by using a directivity optimization strategy, and ultrasonic beams are transmitted and acquired by using the target sparse array, namely the transmitted and acquired ultrasonic beams are restricted by physical characteristics of the beams, so that the side lobes and the grating lobe levels of the acquired beams can be inhibited; on the other hand, a mathematical model for compressed sensing reconstruction is driven based on the target sparse array, missing channel data are recovered, artifact and noise influence caused by down-sampling is further reduced, and superposition of dual benefits of two stages of acquisition and reconstruction is achieved. Therefore, the ultrasonic three-dimensional imaging method provided by the embodiment utilizes the observation matrix as a bridge, realizes the combination of the array directivity optimization strategy and the compression sensing strategy, and improves the imaging quality.

Drawings

Fig. 1 is a schematic diagram illustrating an exemplary system architecture to which the ultrasonic three-dimensional imaging method or apparatus of the embodiments of the present application may be applied.

Fig. 2 is a flowchart of an ultrasonic three-dimensional imaging method according to an embodiment of the present application.

Fig. 3 is a flowchart of an ultrasonic three-dimensional imaging method according to another embodiment of the present application.

Fig. 4 is a flowchart of an ultrasonic three-dimensional imaging method according to another embodiment of the present application.

Fig. 5 is a block diagram of an ultrasonic three-dimensional imaging apparatus according to an embodiment of the present application.

Fig. 6 is a block diagram of an electronic device according to an exemplary embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Summary of the application

As described in the background, the current compressed sensing sparse imaging based strategy results in poor image quality. The inventor researches and discovers that the current compressed sensing sparse imaging strategy only satisfies the mathematical irrelevance between the constructed observation matrix and the sparse matrix, but ignores the influence of the physical characteristics of the ultrasonic three-dimensional imaging beam on the imaging quality, so that the interference of side lobes and grating lobes of the ultrasonic three-dimensional imaging beam is larger, the signal-to-noise ratio is seriously reduced, and the imaging quality is poorer.

In view of this, the embodiment of the present application provides an ultrasonic three-dimensional imaging method and apparatus, a computer device, and a storage medium, where an observation matrix is used as a bridge, and a directivity optimization strategy of a sparse array is combined with compressed sensing, on one hand, a target sparse array with optimal directivity is determined by using the directivity optimization strategy, and an ultrasonic beam is emitted and collected by using the target sparse array, that is, the emitted and collected ultrasonic beam is constrained by beam physical characteristics, so that side lobes and grating lobe levels of a collected beam can be suppressed; on the other hand, a mathematical model for compressed sensing reconstruction is driven based on the target sparse array, missing channel data are recovered, artifact and noise influence caused by down-sampling is further reduced, and superposition of dual benefits of two stages of acquisition and reconstruction is achieved.

Exemplary System

Fig. 1 is a schematic diagram illustrating an exemplary system architecture to which the ultrasonic three-dimensional imaging method or apparatus of the embodiments of the present application may be applied. As shown in fig. 1, the system architecture 100 includes an ultrasonic two-dimensional matrix transducer 110 and a host computer 120. The ultrasonic two-dimensional matrix transducer 110 can transmit and receive ultrasonic beams under the control of the host computer 120, and the host computer 120 can form an ultrasonic image based on the ultrasonic beams received by the ultrasonic two-dimensional matrix transducer 110.

Specifically, the host 120 may select a part of array elements in the ultrasonic two-dimensional matrix transducer 110 to form a target sparse array with optimal array directivity by performing the ultrasonic three-dimensional imaging method provided in the embodiment of the present application. Meanwhile, the host 120 may reconstruct the original signal based on the ultrasonic beam acquired by the target sparse array, and form an ultrasonic image based on the original signal, so as to display the ultrasonic image through the display 130. Accordingly, an ultrasonic three-dimensional imaging device is provided in the host computer 120.

Exemplary method

Fig. 2 is a flowchart of an ultrasonic three-dimensional imaging method according to an embodiment of the present application. The method may be used for the terminal device 101 shown in fig. 1. As shown in fig. 2, the three-dimensional ultrasound imaging method 200 includes:

step S210, randomly selecting a preset number of array elements from an ultrasonic two-dimensional matrix transducer for activation to obtain a plurality of initial sparse arrays.

In one embodiment, the predetermined number is 1/4 the number of elements in the ultrasonic two-dimensional matrix transducer. For example, an ultrasonic two-dimensional matrix transducer comprises 32 x 32-1024 array elements, and 1/4 array elements are randomly selected from the array elements to be activated, so that the ultrasonic two-dimensional matrix transducer can obtainAn initial sparse array.

Step S220, determining maximum level values MSLL of respective minor lobes of the plurality of initial sparse arrays based on the array directivity, the maximum level values MSLL of the minor lobes being equal to the maximum level values MSLL of the azimuth minor lobes_azAnd maximum level value MSLL of secondary lobe in pitching direction_elAnd (4) adding.

Specifically, firstly, a description function of the array directivity of each initial sparse array is determined based on the attribute parameters of the two-dimensional matrix transducer, wherein the attribute parameters comprise the excitation amplitude, the azimuth angle and the elevation angle of each array element in the two-dimensional matrix transducer, the total number of the array elements in the azimuth direction and the elevation direction, and the array element spacing in the azimuth direction and the elevation direction. Secondly, traversing the description function in the azimuth direction to obtain a plurality of azimuth lobe level values, and determining the second largest value in the plurality of azimuth lobe level values as the maximum level value MSLL of the azimuth secondary lobe_az. Then, obtaining a plurality of pitching lobe level values in the pitching traversal description function, and determining the second largest value in the plurality of pitching lobe level values as the maximum level value MSLL of the pitching secondary lobe_el. Then, the sum of the maximum level value of the azimuth secondary lobe and the maximum level value of the pitch secondary lobe is used as the maximum level value MSLL of the secondary lobe.

For example, the describing function is:

wherein I (m, n) represents the excitation amplitude of the (m, n) th array element in the two-dimensional matrix transducer; m and N respectively represent the total number of array elements in the azimuth direction and the pitch direction; d_y、d_zArray element spacing in azimuth and elevation directions is respectively represented; theta andrepresenting the azimuth and elevation angles, respectively.

For theOrder toGetTheta is atWithin a range, one is calculated at predetermined angular values, e.g. 1 radianObtaining a plurality of azimuth lobe level values, and taking the second largest value in the plurality of azimuth lobe level values as the maximum level value MSLL of the azimuth secondary lobe_az。

For theLet theta get In thatWithin a range, at predetermined angular intervals, e.g. 1 radianCalculate oneObtaining a plurality of elevation lobe level values, and taking the second largest value in the plurality of elevation lobe level values as the maximum level value MSLL of the elevation secondary lobe_el。

Then MSLL is equal to MSLL_az+MSLL_el。

Step S230, step S231 and step S232 are repeatedly performed.

And S231, updating the position of the array element in the previous-generation sparse array based on an optimization algorithm to obtain a next-generation sparse array, wherein the initial value of the previous-generation sparse array is a plurality of initial sparse arrays, and the next-generation sparse array comprises a plurality of sparse arrays.

In one embodiment, the optimization algorithm is a genetic algorithm, and the genetic algorithm comprises three sub-operations of genetic selection, genetic crossover and genetic variation. The genetic selection is similar to a natural selection process and is used for screening out a sparse array with better performance from the current generation sparse array according to a preset adaptive function and recording the sparse array as an optimized sparse array. At the same time, sparse arrays with relatively poor performance are filtered out. Genetic crossing is similar to a reproduction process and is used for exchanging array element positions of individuals in a new population obtained after genetic selection, so that a new sparse array is obtained and is marked as a crossed sparse array. Genetic variation is similar to a genetic variation process and is used for randomly changing the enabling state of one or some array elements so as to obtain a new sparse array which is marked as a variation sparse array. Next generation sparse arrays include optimized sparse arrays, cross-sparse arrays, and variant sparse arrays.

Step S232 determines maximum level values MSLL of respective minor lobes of a plurality of sparse arrays in the next-generation sparse array based on the array directivity. For a specific implementation process, refer to step S220, which is not described herein again.

As can be seen, step S230 is equivalent to iteratively updating the positions of the array elements of the multiple initial sparse arrays based on the optimization algorithm, and calculating the maximum level value MSLL of the minor lobe of each of the multiple sparse arrays in each generation of sparse array.

And step S240, when the maximum level value MSLL of the secondary lobe presents a convergence trend, determining that the sparse array corresponding to the convergence point is a target sparse array with optimal directivity. The convergence point is the starting point at which the maximum level value MSLL of the secondary lobe converges to a certain predetermined value. Equivalently, the secondary lobe maximum level value MSLL is used as a constraint target, and an optimization model is established as follows: min { MSLL_az+MSLL_el}. And optimizing the current sparse array based on the optimization model to obtain a target sparse array.

And step S250, controlling the target sparse array to transmit and receive ultrasonic waves.

In one embodiment, the emission timing of the array elements in the target sparse array is controlled, so that the ultrasonic waves emitted by the target sparse array have periodicity, one period comprises a predetermined number of wave planes, and an included angle is formed between every two adjacent wave planes in the timing sequence, and the included angle between each wave plane in the predetermined number of wave planes and the array element plane of the ultrasonic two-dimensional array transducer is between [ -30 degrees and 30 degrees ]. In one example, a period comprises 7 wave planes, and the included angles between the 7 wave planes and the array element plane of the ultrasonic two-dimensional matrix transducer are-30 degrees, -20 degrees, -10 degrees, -0 degrees, 10 degrees, 20 degrees and 30 degrees sequentially. In this way, side lobe and grating lobe effects can be further reduced, thereby improving signal-to-noise ratio.

Step S260, determining an original signal based on the ultrasonic waves received by the target sparse array.

In step S270, an ultrasound image is formed based on the original signal. This step can be carried out by conventional means, is not part of the inventive point of the present application, and is not described in detail herein.

According to the ultrasonic three-dimensional imaging method provided by the embodiment, on one hand, a target sparse array with optimal directivity is determined by using a directivity optimization strategy, and ultrasonic beams are transmitted and acquired by using the target sparse array, namely the transmitted and acquired ultrasonic beams are constrained by beam physical characteristics, so that the levels of side lobes and grating lobes of the acquired beams can be suppressed; on the other hand, a mathematical model for compressed sensing reconstruction is driven based on the target sparse array, missing channel data are recovered, artifact and noise influence caused by down-sampling is further reduced, and superposition of dual benefits of two stages of acquisition and reconstruction is achieved. As can be seen, the ultrasonic three-dimensional imaging method provided by this embodiment utilizes the observation matrix as a bridge, and realizes the combination of the array directivity optimization strategy and the compressive sensing strategy.

Fig. 3 is a flowchart of an ultrasonic three-dimensional imaging method according to another embodiment of the present application. As shown in fig. 3, the ultrasound three-dimensional imaging method 300 is different from the ultrasound three-dimensional imaging method 200 shown in fig. 2 only in that, in the present embodiment, the step S231 is specifically performed as:

in step S310, a predetermined number of sparse arrays are selected from the previous generation of sparse arrays.

A predetermined number of sparse arrays are randomly selected from the previous generation of sparse arrays. For example, from10 sparse arrays are randomly selected from the initial sparse arrays.

And step S320, sequencing the sparse arrays with the preset number according to the sequence of the fitness value from large to small, wherein the fitness value is the reciprocal of the maximum level value SMLL of the secondary lobe.

The fitness is used here as an adaptation function in an optimization algorithm for optimizing a number of sparse arrays randomly selected from previous generation sparse arrays. For example, in the above example, 10 sparse arrays randomly selected from the initial sparse arrays are arranged in the order of decreasing fitness values.

And step S330, screening a predetermined number of sparse arrays according to a predetermined genetic selection rate to obtain a plurality of optimized sparse arrays.

In one embodiment, the genetic selection rate is 0.5, i.e. the first 5 out of 10 sparse arrays are selected as optimized sparse arrays and the remaining 5 sparse arrays are eliminated.

And step S340, performing genetic crossing and genetic variation based on the plurality of optimized sparse arrays to obtain a next-generation sparse array.

In one embodiment, the process of genetic crossing comprises: step S341, executing pairwise optimization on a plurality of optimized sparse arraysAnd exchanging the position of the array elements to obtain a plurality of crossed sparse arrays. In this case, 5 optimized sparse arrays haveAnd (4) performing combination, wherein two optimized sparse arrays in each combination interchange partial array element positions to obtain 2 new sparse arrays, namely cross sparse arrays.

Whether the step of genetically crossing is performed depends on the probability of crossing, i.e. a random number is generated at each iteration, the step of genetically crossing is performed when the random number is greater than or equal to a threshold value; when the random number is less than the threshold, the step of genetic crossing is not performed. In one example, when the cross probability is 0.6, i.e. the generated random number is greater than 0.6, the step of genetic crossing is performed; otherwise, it is not executed.

In one embodiment, the process of genetic variation comprises: and step 342, changing and optimizing the enabling states of the array elements in the sparse array and the cross sparse array according to a preset variation rate to obtain a variation sparse array.

The variation rate means that a certain proportion of array elements are subjected to enabling state variation in each iteration process, for example, the enabling state of one or some array elements in an optimized sparse array and a crossed sparse array is suppressed from being subjected to activating variation. In one example, the variance ratio is 0.005.

Fig. 4 is a flowchart of an ultrasonic three-dimensional imaging method according to another embodiment of the present application. As shown in fig. 4, the ultrasound three-dimensional imaging method 400 is different from the ultrasound three-dimensional imaging method 200 shown in fig. 2 only in that, in the present embodiment, the step S260 is specifically performed as:

step S410, based on the received ultrasonic waves, determines an observation value of the compressed sensing model.

For an ultrasonic two-dimensional matrix transducer, a target sparse array formed by partial array elements in the transducer is used for transmitting and receiving ultrasonic waves to obtain undersampled ultrasonic radio frequency data RF_bpAs an observed value of the compressed sensing model.

Step S420, determining an observation matrix phi of the compressed sensing model based on the array element position of the target sparse array_bp。

Specifically, first, the array element position of the target sparse array is binarized to obtain a binarization matrix. For example, an ultrasonic two-dimensional matrix transducer comprises 2 x 2 array elements, and a determined target sparse array is subjected to binarization to obtain a matrix ofThe number 1 indicates that the array element at the position is in the active state, and the number 0 indicates that the array element at the position is in the inhibit state.

And secondly, converting the binarization matrix into an observation matrix, wherein the observation matrix is used for indicating the position information of the array elements in the target sparse array.

The number of rows of the observation matrix is equal to the number of array elements in the target sparse array, and the number of columns of the observation matrix is equal to the number of array elements in the ultrasonic two-dimensional matrix transducer. In the above example, when the target sparse array is binarized, the matrix obtained isWhen the corresponding observation matrix is

Step S430, determining an original signal based on the observation value, the observation matrix, and the fixed or adaptive sparse transformation basis.

The sparse transformation base Ψ is selected from any one of a discrete Fourier transformation base, a wavelet transformation base and a KSVD adaptive transformation base, and the sparse transformation base Ψ and the observation matrix Φ_bpAnd the equidistant constraint condition is satisfied.

Specifically, first, a sparse transformation basis Ψ is used as a sparse matrix, and an observation matrix Φ is used_bpTogether forming a measurement matrix a, i.e. a ═ Φ_bpΨ, then RF_hp＝Φ_bpΨ s ═ As, where s is the original signal RF_bpcsSparse coefficients on a sparse matrix.

Secondly, calculating by solving an optimized norm problem to obtain s:

min||s||₀s.t.RF_bp＝Φ_bpΨs＝As

the optimized norm may be solved here using an Orthogonal Matching Pursuit (OMP) algorithm.

Then, the original signal RF is reconstructed by utilizing sparse inverse transformation_bpcs＝Ψs。

Exemplary devices

The application also provides an ultrasonic three-dimensional imaging device. Fig. 5 is a block diagram of an ultrasonic three-dimensional imaging apparatus according to an embodiment of the present application. As shown in fig. 5, the ultrasonic three-dimensional imaging device 50 includes an activation module 51, a first determination module 52, an update module 53, a second determination module 54, a control module 55, a third determination module 56, and a formation module 57.

The activation module 51 is configured to randomly select a predetermined number of array elements from the ultrasonic two-dimensional matrix transducer for activation, so as to obtain a plurality of initial sparse arrays. The first determination module 52 is configured to determine a maximum level value of a secondary lobe of each of the plurality of initial sparse arrays based on the array directivity, the maximum level value of the secondary lobe being equal to a sum of a maximum level value of the azimuth secondary lobe and a maximum level value of the pitch secondary lobe. The updating module 53 is configured to repeatedly perform the following steps: updating the position of an array element in a previous-generation sparse array based on an optimization algorithm to obtain a next-generation sparse array, wherein the initial value of the previous-generation sparse array is a plurality of initial sparse arrays, and the next-generation sparse array comprises a plurality of sparse arrays; the maximum level value of the respective minor lobes of a plurality of sparse arrays in the next generation of sparse arrays is determined based on the array directivity. The second determining module 54 is configured to determine, when the maximum level value of the secondary lobe exhibits a convergence trend, that the sparse array corresponding to the convergence point is the target sparse array with the optimal directivity. The control module 55 is used to control the target sparse array to transmit and receive ultrasound. The third determining module 56 is configured to determine the original signal based on the ultrasonic waves received by the target sparse array. The forming module 57 is used for forming an ultrasound image based on the original signal.

In one embodiment, the update module 53 includes a genetic selection unit, a genetic crossover unit, and a genetic variation unit. Wherein the genetic selection unit is used for selecting a predetermined number of sparse arrays from the previous generation of sparse arrays; sequencing a predetermined number of sparse arrays according to the sequence of the fitness value from large to small, wherein the fitness value is the reciprocal of the maximum level value SMLL of the secondary lobe; and screening a predetermined number of sparse arrays according to a predetermined genetic selection rate to obtain a plurality of optimized sparse arrays. The genetic crossing unit is used for executing pairwise array element position exchange operation on the optimized sparse arrays to obtain a plurality of crossed sparse arrays. And the genetic variation unit changes and optimizes the enabling states of the array elements in the sparse array and the cross sparse array according to a preset variation rate to obtain a variation sparse array.

In one embodiment, the third determination module 56 includes a first determination unit, a second determination unit, and a third determination unit. The first determination unit is used for determining an observation value of the compressed sensing model based on the received ultrasonic waves. The second determination unit is used for determining an observation matrix of the compressed sensing model based on the array element position of the target sparse array. The third determination unit is used for determining the original signal based on the observation value, the observation matrix and a fixed or self-adaptive sparse transformation base.

The ultrasonic three-dimensional imaging device provided by the embodiment belongs to the same application concept as the ultrasonic three-dimensional imaging method provided by the embodiment of the application, can execute the ultrasonic three-dimensional imaging method provided by any embodiment of the application, and has corresponding functional modules and beneficial effects for executing the ultrasonic three-dimensional imaging method. For details of the technology not described in detail in this embodiment, reference may be made to the ultrasonic three-dimensional imaging method provided in this embodiment, and details are not described here again.

Exemplary electronic device

Fig. 6 is a block diagram of an electronic device according to an exemplary embodiment of the present application. As shown in fig. 6, the electronic device 60 includes one or more processors 61 and a memory 62.

The processor 61 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 60 to perform desired functions.

The memory 62 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM), cache memory (or the like). The non-volatile memory may include, for example, Read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer readable storage medium and executed by the processor 11 to implement the ultrasound three-dimensional imaging methods of the various embodiments of the present application described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.

In one example, the electronic device 60 may further include: an input device 63 and an output device 64, which are interconnected by a bus system and/or other form of connection mechanism (not shown).

The input means 63 may be, for example, a microphone or a microphone array for capturing an input signal of a sound source. The input means 63 may be a communication network connector when the electronic device is a stand-alone device. The input device 63 may also include, for example, a keyboard, a mouse, and the like.

The output device 64 may output various information including the determined distance information, direction information, and the like to the outside. Output devices 64 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.

Of course, for the sake of simplicity, only some of the components of the electronic device 60 relevant to the present application are shown in fig. 6, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 60 may include any other suitable components depending on the particular application.

Exemplary computer program product and computer-readable storage Medium

In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method of ultrasound three-dimensional imaging according to various embodiments of the present application described in the "exemplary methods" section above of this specification.

The computer program product may include program code for carrying out operations for embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present application may also be a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor 11 to perform the steps in the ultrasound three-dimensional imaging method according to various embodiments of the present application described in the "exemplary methods" section above in this description.

The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.

The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.