CN113768539B - Ultrasonic three-dimensional imaging method and device, computer equipment and storage medium - Google Patents
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Abstract
An ultrasonic three-dimensional imaging method and device, computer equipment and storage medium solve the problem of poor image quality 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 each secondary lobe of the plurality of initial sparse arrays based on the array directivity; updating the positions of array elements in the previous generation of sparse array based on an optimization algorithm to obtain the next generation of sparse array; determining a maximum level value of each secondary lobe of a plurality of sparse arrays in a next generation sparse array based on the array directivity; when the maximum level value of the secondary lobe shows a convergence trend, determining the sparse array corresponding to the convergence point as a target sparse array with optimal directivity; controlling the target sparse array to transmit and receive ultrasonic waves; determining an original signal based on ultrasonic waves received by a target sparse array; an ultrasound image is formed based on the raw signals.
Description
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, an ultrasound two-dimensional matrix transducer is typically composed of thousands of transducer elements (hereinafter referred to as elements). In the case of a large-scale cell array, if the acquisition channels are in one-to-one correspondence with the array elements, the hardware cost and the circuit complexity of the acquisition system will be greatly increased. Meanwhile, huge pressure is caused to data transmission, storage and calculation of a system by massive radio frequency data of thousands of channels, 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 compressed sensing theory provides a distinctive idea for simplifying the acquisition system and reducing the data acquisition amount, namely, under the assumption of sparseness of the sampled signal, it is possible to recover the complete signal in a nonlinear manner with the sampled data far below the nyquist limit. The compressed sensing sparse imaging strategy can effectively reduce the number of acquisition channels, reduce the complexity of the system and improve the imaging speed.
However, current compressed sensing sparse imaging strategies based on poor image quality.
Disclosure of Invention
In view of this, embodiments of the present application are directed to providing 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 the image quality obtained based on the compressed sensing sparse imaging strategy is poor.
The first aspect of the application provides an ultrasonic three-dimensional imaging method, 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 each sub-lobe of the plurality of initial sparse arrays based on the array directivity, the maximum level value of the sub-lobe being equal to a sum of the maximum level value of the azimuth sub-lobe and the maximum level value of the elevation sub-lobe; the steps of repeatedly executing are as follows: updating the positions of array elements in the previous generation of sparse arrays based on an optimization algorithm to obtain next generation sparse arrays, wherein initial values of the previous generation sparse arrays are a plurality of initial sparse arrays, and the next generation sparse arrays comprise a plurality of sparse arrays; and determining a maximum level value of each secondary lobe of a plurality of sparse arrays in the next generation sparse array based on the array directivity; when the maximum level value of the secondary lobe shows a convergence trend, determining the sparse array corresponding to the convergence point as a target sparse array with optimal directivity; controlling the target sparse array to transmit and receive ultrasonic waves; determining an original signal based on ultrasonic waves received by a target sparse array; an ultrasound image is formed based on the raw signals.
In one embodiment, determining the maximum level value of each secondary lobe of the plurality of initial sparse arrays based on array directivity comprises: determining a description function of 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 pitching angle of each array element in the two-dimensional matrix transducer, total number of the array elements in azimuth and pitching directions, and respective array element spacing in azimuth and pitching directions; obtaining a plurality of azimuth lobe level values from the azimuth traversal description function, and determining the second largest value in the azimuth lobe level values as the largest level value of the azimuth secondary lobes; obtaining a plurality of pitching lobe level values from the pitching traversal description function, and determining the second largest value in the pitching lobe level values as the maximum level value of the pitching secondary lobe; the sum of the maximum level value of the azimuth sub-lobe and the maximum level value of the elevation sub-lobe is taken as the maximum level value of the sub-lobe.
In one embodiment, the descriptive function is:
wherein I (m, n) represents the excitation amplitude of the (m, n) th element in the two-dimensional matrix transducer; m and N respectively represent the total number of array elements in azimuth and pitching directions; d, d y 、d z Respectively representing array element spacing in azimuth and pitching directions; θ and
the azimuth and elevation angles are indicated, respectively.
In one embodiment, updating the positions of the array elements in the previous generation sparse array based on the optimization algorithm, the obtaining the next generation sparse array includes: 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 of the secondary lobe; screening a predetermined number of sparse arrays according to a predetermined genetic selectivity to obtain a plurality of optimized sparse arrays; and carrying out genetic crossover and genetic variation based on the plurality of optimized sparse arrays to obtain a next generation sparse array.
In one embodiment, performing genetic crossover based on a plurality of optimized sparse arrays includes: and executing the operations of the two-by-two array element positions on the optimized sparse arrays to obtain a plurality of cross sparse arrays.
In one embodiment, performing genetic variation based on a plurality of optimized sparse arrays includes: changing the enabling states of array elements in the optimized sparse array and the cross sparse array according to a preset mutation rate to obtain a mutation sparse array; the next generation sparse array comprises an optimized sparse array, a cross sparse array and a variant sparse array.
In one embodiment, the ultrasonic waves emitted by the target sparse array have periodicity, and a predetermined number of wave planes are included in one period, and an included angle is formed between two adjacent wave planes in time sequence.
In one embodiment, determining the raw signal based on the ultrasound received by the target sparse array includes: based on the received ultrasonic waves, determining an observed value of the compressed sensing model; determining an observation matrix of the compressed sensing model based on array element positions of the target sparse array; the original signal is determined based on the observations, the observation matrix, and a fixed or adaptive sparse transform basis.
In one embodiment, determining an observation matrix of the compressed sensing model based on array element positions of the target sparse array includes: binarizing array element positions 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.
A second aspect of the present application provides an ultrasound 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 so as to obtain a plurality of initial sparse arrays; a first determining module, configured to determine a maximum level value of each sub-lobe of the plurality of initial sparse arrays based on the array directivity, where the maximum level value of the sub-lobe is equal to a sum of a maximum level value of the azimuth sub-lobe and a maximum level value of the elevation sub-lobe; the updating module is used for repeatedly executing the following steps: updating the positions of array elements in the previous generation of sparse arrays based on an optimization algorithm to obtain next generation sparse arrays, wherein initial values of the previous generation sparse arrays are a plurality of initial sparse arrays, and the next generation sparse arrays comprise a plurality of sparse arrays; and determining a maximum level value of each secondary lobe of a plurality of sparse arrays in the next generation 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 determining 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 an ultrasonic image based on the original signal.
A third aspect of the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory for execution by the processor, wherein the processor, when executing the computer program, implements the steps of the ultrasound three-dimensional imaging method provided by any of the embodiments described above.
A fourth aspect of the present application provides a computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the ultrasound three-dimensional imaging method provided by any of the embodiments described above.
According to the ultrasonic three-dimensional imaging method and device, the computer equipment and the storage medium, on one hand, the target sparse array with optimal directivity is determined by utilizing the directivity optimization strategy, and ultrasonic beams are transmitted and collected by utilizing the target sparse array, namely, the transmitted and collected ultrasonic beams are constrained by physical characteristics of the beams, so that side lobes and grating lobe levels of the collected beams can be restrained; on the other hand, based on a mathematical model of target sparse array drive compressed sensing reconstruction, missing channel data is recovered, artifact and noise influence caused by downsampling are further reduced, and superposition of double benefits of two stages of acquisition and reconstruction is realized. Therefore, the ultrasonic three-dimensional imaging method provided by the embodiment utilizes the observation matrix as a bridge, so that the combination of the array directivity optimization strategy and the compressed sensing strategy is realized, and the imaging quality is improved.
Drawings
FIG. 1 shows a schematic diagram of an exemplary system architecture to which the ultrasound three-dimensional imaging method or apparatus of embodiments of the present application may be applied.
Fig. 2 is a flowchart of an ultrasound three-dimensional imaging method according to an embodiment of the present application.
Fig. 3 is a flowchart of an ultrasound three-dimensional imaging method according to another embodiment of the present application.
Fig. 4 is a flowchart of an ultrasound three-dimensional imaging method according to another embodiment of the present application.
Fig. 5 is a block diagram of an ultrasound three-dimensional imaging device 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 following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Summary of the application
As described in the background, the image quality obtained based on the compressed sensing sparse imaging strategy is poor. The inventor researches find that the current compressed sensing sparse imaging strategy is only satisfied with the mathematical irrelevance of the construction 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 side lobe and grating lobe of the ultrasonic three-dimensional imaging beam are greatly interfered, the signal to noise ratio is seriously reduced, and the imaging quality is poor.
In view of this, the embodiments of the present application provide an ultrasound three-dimensional imaging method and apparatus, a computer device, and a storage medium, which combine a directivity optimization strategy of a sparse array with compressed sensing by using an observation matrix as a bridge, on the one hand, determine a target sparse array with optimal directivity by using the directivity optimization strategy, and transmit and collect ultrasound beams by using the target sparse array, that is, the transmitted and collected ultrasound beams are constrained by physical characteristics of the beams, so that side lobes and grating lobe levels of the collected beams can be suppressed; on the other hand, based on a mathematical model of target sparse array drive compressed sensing reconstruction, missing channel data is recovered, artifact and noise influence caused by downsampling are further reduced, and superposition of double benefits of two stages of acquisition and reconstruction is realized.
Exemplary System
FIG. 1 shows a schematic diagram of an exemplary system architecture to which the ultrasound three-dimensional imaging method or apparatus of embodiments of the present application may be applied. As shown in fig. 1, the system architecture 100 includes an ultrasound two-dimensional matrix transducer 110 and a host 120. Wherein the ultrasound two-dimensional matrix transducer 110 can transmit and receive ultrasound beams under the control of the host computer 120, the host computer 120 can form ultrasound images based on the ultrasound beams received by the ultrasound two-dimensional matrix transducer 110.
Specifically, the host 120 may select a portion of array elements in the ultrasonic two-dimensional matrix transducer 110 by executing the ultrasonic three-dimensional imaging method provided in the embodiment of the present application, so as to form a target sparse array with optimal array directivity. Meanwhile, the host computer 120 may reconstruct an original signal based on the ultrasonic beam acquired by the target sparse array, and form an ultrasonic image based on the original signal, and then display the ultrasonic image on the display 130. Accordingly, the ultrasonic three-dimensional imaging device is disposed in the host computer 120.
Exemplary method
Fig. 2 is a flowchart of an ultrasound 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 the ultrasonic two-dimensional matrix transducer to activate, and obtaining a plurality of initial sparse arrays.
In one embodiment, the predetermined number is 1/4 of the number of array elements in the ultrasound two-dimensional matrix transducer. For example, an ultrasound two-dimensional matrix transducer comprising 32×32=1024 array elements, from which 1/4 array element activation is randomly selected, can be obtained
An initial sparse array.
Step S220 of determining maximum level value MSLL of each sub-lobe of the plurality of initial sparse arrays based on the array directivity, the maximum level value MSLL of the sub-lobe being equal to the azimuth sub-lobeMaximum level value MSLL of lobe az And maximum level value MSLL of pitching sub-lobe el Is added to the sum of (3).
Specifically, first, 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 include the excitation amplitude, azimuth angle and elevation angle of each array element in the two-dimensional matrix transducer, the total number of the respective array elements in azimuth and elevation, and the respective array element pitches in azimuth and elevation. Second, a plurality of azimuth lobe level values are obtained from the azimuth traversal description function, and the second largest value among the azimuth lobe level values is determined as the maximum level value MSLL of the azimuth secondary lobe az . Next, a plurality of pitching lobe level values are obtained from the pitching traversal description function, and the second largest value among the plurality of pitching lobe level values is determined as the maximum level value MSLL of the pitching secondary lobe el . Then, the sum of the maximum level value of the azimuth sub-lobe and the maximum level value of the elevation sub-lobe is taken as the maximum level value MSLL of the sub-lobe.
For example, the descriptive function is:
wherein I (m, n) represents the excitation amplitude of the (m, n) th element in the two-dimensional matrix transducer; m and N respectively represent the total number of array elements in azimuth and pitching directions; d, d y 、d z Respectively representing array element spacing in azimuth and pitching directions; θ and
the azimuth and elevation angles are indicated, respectively.
For the following
Let->
Get->
Theta is at->
Within the range, a ++is calculated every predetermined angular value, e.g. 1 radian>
The value of the maximum level value MSLL of the azimuth sub-lobe is obtained by obtaining the level values of a plurality of azimuth lobes and taking the second largest value in the level values of the azimuth lobes as the maximum level value MSLL of the azimuth sub-lobe az 。
For the following
Let θ take->
At->
Within the range, a ++is calculated every predetermined angular value, e.g. 1 radian>
The value of the maximum level value MSLL of the pitching sub-lobe is obtained by obtaining a plurality of pitching lobe level values, and taking the second largest value of the pitching lobe level values as the maximum level value MSLL of the pitching sub-lobe el 。
Msll=msll az +MSLL el 。
Step S230, repeatedly executed step S231 and step S232.
Step S231, updating the positions of array elements in the previous generation of sparse arrays based on an optimization algorithm to obtain a next generation of sparse arrays, wherein initial values of the previous generation of sparse arrays are a plurality of initial sparse arrays, and the next generation of sparse arrays comprise a plurality of sparse arrays.
In one embodiment, the optimization algorithm is a genetic algorithm that includes 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 marking the sparse array as an optimized sparse array. At the same time, sparse arrays with relatively poor performance are filtered out. The genetic crossover is similar to the reproduction process and is used for mutually exchanging array element positions for individuals in the new population obtained after genetic selection, so that a new sparse array is obtained and is marked as a crossover sparse array. Genetic variation is similar to genetic variation process, and is used to randomly change the enabling state of one or some array elements, so as to obtain a new sparse array, which is named as variation sparse array. The next generation sparse array comprises an optimized sparse array, a cross sparse array and a variant sparse array.
Step S232, determining a maximum level value MSLL of each sub-lobe of the plurality of sparse arrays in the next generation sparse array based on the array directivity. The specific execution process is referred to step S220, and will not be described here again.
As can be seen, step S230 is equivalent to iteratively updating the positions of the array elements of each of the plurality of initial sparse arrays based on the optimization algorithm, and calculating the maximum level value MSLL of each sub-lobe of the plurality of sparse arrays in each generation of sparse arrays.
In step S240, when the maximum level value MSLL of the secondary lobe presents a convergence trend, the sparse array corresponding to the convergence point is determined to be the target sparse array with optimal directivity. The convergence point refers to the starting point at which the maximum level value MSLL of the secondary lobe converges to a certain predetermined value. Equivalently, taking a secondary lobe maximum level value MSLL as a constraint target, and establishing an optimization model as follows: min { MSLL az +MSLL el }. And optimizing the sparse array based on the optimization model to obtain the target sparse array.
Step S250, controlling the target sparse array to transmit and receive ultrasonic waves.
In one embodiment, the ultrasonic wave emitted by the target sparse array has periodicity by controlling the emission time sequence of the array elements in the target sparse array, wherein a predetermined number of wave planes are included in one period, a certain included angle is formed between two adjacent wave planes in time sequence, and the included angle between each of the predetermined number of wave planes and the array element plane of the ultrasonic two-dimensional matrix transducer is between [ -30 degrees, 30 degrees ]. In one example, 7 wave planes are included in one period, and the included angles between the 7 wave planes and the array element planes of the ultrasonic two-dimensional matrix transducer are sequentially-30 degrees, -20 degrees, -10 degrees, 0 degrees, 10 degrees, 20 degrees and 30 degrees. In this way, sidelobe and grating lobe effects can be further reduced, thereby improving the signal-to-noise ratio.
In step S260, the original signal is determined 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 may be implemented by conventional means, and is not the point of the invention of the present application, and is not described in detail here.
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 utilizing a directivity optimization strategy, and ultrasonic beams are transmitted and collected by utilizing the target sparse array, namely, the transmitted and collected ultrasonic beams are constrained by physical characteristics of the beams, so that side lobes and grating lobe levels of the collected beams can be restrained; on the other hand, based on a mathematical model of target sparse array drive compressed sensing reconstruction, missing channel data is recovered, artifact and noise influence caused by downsampling are further reduced, and superposition of double benefits of two stages of acquisition and reconstruction is realized. Therefore, the ultrasonic three-dimensional imaging method provided by the embodiment utilizes the observation matrix as a bridge, and realizes the combination of the array directivity optimization strategy and the compressed sensing strategy.
Fig. 3 is a flowchart of an ultrasound three-dimensional imaging method according to another embodiment of the present application. As shown in fig. 3, the ultrasonic three-dimensional imaging method 300 differs from the ultrasonic three-dimensional imaging method 200 shown in fig. 2 only in that, in the present embodiment, step S231 is specifically performed as:
step S310, selecting a predetermined number of sparse arrays from the previous generation sparse arrays.
A predetermined number of sparse arrays are randomly selected from the previous generation of sparse arrays. For example, from
10 sparse arrays are randomly selected from the initial sparse arrays.
Step S320, the predetermined number of sparse arrays are ordered according to the order of the fitness value from the big to the small, and 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 plurality of sparse arrays randomly selected from a previous generation of sparse arrays. For example, in the above example, 10 sparse arrays selected randomly from the initial sparse arrays are arranged in order of fitness value from large to small.
And step S330, screening a predetermined number of sparse arrays according to a predetermined genetic selectivity to obtain a plurality of optimized sparse arrays.
In one embodiment, the genetic selectivity is 0.5, i.e., the first 5 out of 10 sparse arrays are selected as the optimal sparse array, and the remaining 5 sparse arrays are eliminated.
And step S340, performing genetic crossover 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: and step S341, performing the operation of pairwise exchange of array element positions on the plurality of optimized sparse arrays to obtain a plurality of crossed sparse arrays. In this case, 5 optimized sparse arrays have
And (3) combining, wherein the two optimized sparse arrays in each combination exchange partial array element positions to obtain 2 new sparse arrays, namely the cross sparse arrays.
Whether the step of genetic crossing is performed depends on the crossing probability, i.e. each iteration generates a random number, when the random number is greater than or equal to a threshold value, the step of genetic crossing is performed; when the random number is less than the threshold, the step of genetic crossing is not performed. In one example, the step of performing a genetic crossover takes 0.6 as the crossover probability, i.e., the generated random number is greater than 0.6; otherwise, the method is not executed.
In one embodiment, the process of genetic variation comprises: and S342, changing the enabling states of the array elements in the optimized sparse array and the cross sparse array according to a preset mutation rate to obtain a mutation sparse array.
The variation rate refers to that a certain proportion of array elements in each iteration process can generate variation of the enabling state, for example, the enabling state of one or some array elements in the optimized sparse array and the cross sparse array is changed from activation variation to inhibition. In one example, the variability is 0.005.
Fig. 4 is a flowchart of an ultrasound three-dimensional imaging method according to another embodiment of the present application. As shown in fig. 4, the ultrasonic three-dimensional imaging method 400 differs from the ultrasonic three-dimensional imaging method 200 shown in fig. 2 only in that, in the present embodiment, step S260 is specifically performed as:
step S410, determining an observation value of the compressed sensing model based on the received ultrasonic wave.
For an ultrasonic two-dimensional matrix transducer, ultrasonic waves are transmitted and received by utilizing a target sparse array formed by partial array elements in the ultrasonic two-dimensional matrix transducer, and undersampled ultrasonic radio frequency data RF is obtained bp As observations of the compressed sensing model.
Step S420, determining an observation matrix phi of the compressed sensing model based on array element positions of the target sparse array bp 。
Specifically, first, the array element positions of the target sparse array are binarized to obtain a binarized matrix. For example, the ultrasonic two-dimensional matrix transducer comprises 2×2 array elements, and the matrix obtained by binarizing the determined target sparse array is
The number 1 indicates that the array element at the position is in an activated state, and the number 0 indicates that the array element at the position is in a suppressed 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 is equal to the number of array elements in the ultrasonic two-dimensional matrix transducer. Is connected withFor example, when the matrix obtained by binarizing the target sparse array is
When the corresponding observation matrix is +.>
Step S430, determining the original signal based on the observed value, the observation matrix, and the fixed or adaptive sparse transform basis.
The sparse transformation base ψ is selected from any one of a discrete Fourier transformation base, a wavelet transformation base and a KSVD self-adaptive transformation base, and the sparse transformation base ψ and an observation matrix phi bp And the equidistant constraint condition is satisfied.
Specifically, first, a sparse transform basis ψ is used as a sparse matrix, and an observation matrix Φ bp Together forming a measurement matrix a, i.e. a=Φ bp ψ, then RF hp ＝Φ bp ψs=as, where s is the original signal RF bpcs Sparse coefficients on a sparse matrix.
Secondly, calculating to obtain s by solving the optimization norm problem:
min||s|| 0 s.t.RF bp ＝Φ bp Ψs＝As
the optimization norm may be solved herein using a selection orthogonal matching pursuit (Orthogonal Matching Pursuit, OMP) algorithm.
Then, the original signal RF is reconstructed by using sparse inverse transform bpcs ＝Ψs。
Exemplary apparatus
The application also provides an ultrasonic three-dimensional imaging device. Fig. 5 is a block diagram of an ultrasound three-dimensional imaging device 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 two-dimensional ultrasonic matrix transducer for activation, so as to obtain a plurality of initial sparse arrays. The first determining module 52 is configured to determine a maximum level value of each sub-lobe of the plurality of initial sparse arrays based on the array directivity, the maximum level value of the sub-lobe being equal to a sum of the maximum level value of the azimuth sub-lobe and the maximum level value of the elevation sub-lobe. The update module 53 is configured to repeatedly perform the following steps: updating the positions of array elements in the previous generation of sparse arrays based on an optimization algorithm to obtain next generation sparse arrays, wherein initial values of the previous generation sparse arrays are a plurality of initial sparse arrays, and the next generation sparse arrays comprise a plurality of sparse arrays; the maximum level value of each secondary lobe of a plurality of sparse arrays in a next generation sparse array 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 shows a convergence trend, that the sparse array corresponding to the convergence point is a target sparse array with optimal directivity. The control module 55 is used to control the target sparse array to transmit and receive ultrasound. The third determination module 56 is configured to determine the raw signal based on the ultrasound 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 selectivity to obtain a plurality of optimized sparse arrays. The genetic cross unit is used for executing the operation of exchanging array element positions for the optimized sparse arrays to obtain a plurality of cross sparse arrays. And the genetic variation unit changes 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.
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 determining unit is used for determining an observation matrix of the compressed sensing model based on the array element positions of the target sparse array. The third determination unit is configured to determine the original signal based on the observation value, the observation matrix, and a fixed or adaptive sparse transform basis.
The ultrasonic three-dimensional imaging device provided by the embodiment belongs to the same application conception as the ultrasonic three-dimensional imaging method provided by the embodiment of the application, and can execute the ultrasonic three-dimensional imaging method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of executing the ultrasonic three-dimensional imaging method. Technical details not described in detail in this embodiment may be referred to the ultrasound three-dimensional imaging method provided in the embodiment of the present application, and will not be described in detail herein.
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 memory 62.
The processor 61 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities and may control other components in the electronic device 60 to perform the desired functions.
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) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer readable storage medium that can be executed by the processor 11 to implement the ultrasound three-dimensional imaging methods and/or other desired functions of the various embodiments of the present application described above. Various contents such as an input signal, a signal component, a noise component, and the like 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 forms of connection mechanisms (not shown).
For example, the input device 63 may be a microphone or a microphone array for capturing an input signal of a sound source. When the electronic device is a stand-alone device, the input means 63 may be a communication network connector. In addition, the input device 63 may also include, for example, a keyboard, a mouse, and the like.
The output device 64 may output various information to the outside, including the determined distance information, direction information, and the like. Output devices 64 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 60 that are relevant to the present application are shown in fig. 6 for simplicity, components such as buses, input/output interfaces, etc. 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 methods and apparatus described above, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in an ultrasound three-dimensional imaging method according to various embodiments of the present application described in the "exemplary methods" section of the present specification.
The computer program product may be written 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, 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 an ultrasound three-dimensional imaging method according to various embodiments of the present application described in the above "exemplary method" section of the present specification.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is 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 would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not intended to be limited to the details disclosed herein as such.
The block diagrams of the devices, apparatuses, devices, systems referred to in this application are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent to 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.
Claims (12)
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 each secondary lobe 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 the maximum level value of the azimuth secondary lobe and the maximum level value of the elevation secondary lobe; and
the following steps are repeatedly executed:
updating the positions of array elements in a previous generation of sparse array based on an optimization algorithm to obtain a next generation of sparse array, wherein the initial value of the previous generation of sparse array is the plurality of initial sparse arrays, and the next generation of sparse array comprises a plurality of sparse arrays; and
determining a maximum level value of each of the secondary lobes 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 ultrasound three-dimensional imaging method of claim 1, wherein the determining the maximum level value of each secondary lobe of the plurality of initial sparse arrays based on array directivity comprises:
determining a description function of 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 pitching angle of each array element in the two-dimensional matrix transducer, total number of the respective array elements in azimuth and pitching directions, and respective array element spacing in azimuth and pitching directions;
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 azimuth lobe level values as the maximum level value of the azimuth secondary lobe;
traversing the description function in the pitching direction to obtain a plurality of pitching lobe level values, and determining the second largest value in the pitching lobe level values as the maximum level value of the pitching secondary lobe;
and adding the maximum level value of the azimuth secondary lobe and the maximum level value of the pitching secondary lobe as the maximum level value of the secondary lobe.
3. The ultrasound three-dimensional imaging method of claim 2, wherein the descriptive function is:
wherein I (m, n) represents the excitation amplitude of the (m, n) th element in the two-dimensional matrix transducer; m and N respectively represent the total number of array elements in azimuth and pitching directions; d, d y 、d z Respectively representing array element spacing in azimuth and pitching directions; θ and
the azimuth and elevation angles are indicated, respectively.
4. The ultrasonic three-dimensional imaging method according to claim 1, wherein updating the positions of the array elements 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 sparse arrays;
sorting the predetermined number of sparse arrays according to the order 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 selectivity to obtain a plurality of optimized sparse arrays;
and performing genetic crossover and genetic variation based on the plurality of optimized sparse arrays to obtain the next-generation sparse array.
5. The ultrasound three-dimensional imaging method of claim 4, wherein said performing genetic crossover based on the plurality of optimized sparse arrays comprises:
and executing the operations of the two-by-two array element positions on the optimized sparse arrays to obtain a plurality of cross sparse arrays.
6. The ultrasound three-dimensional imaging method of claim 5, wherein said performing genetic variation 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 mutation rate to obtain a mutation sparse array;
the next generation sparse array includes the optimized sparse array, the cross sparse array, and the variant sparse array.
7. The ultrasonic three-dimensional imaging method according to claim 1, wherein the ultrasonic waves emitted by the target sparse array have periodicity, and a predetermined number of wave planes are included in one period, and an included angle is formed between two adjacent wave planes in time sequence.
8. The ultrasound three-dimensional imaging method of claim 1, wherein the determining the raw signal based on the ultrasound received by the target sparse array comprises:
determining an observed 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 positions of the target sparse array;
the raw signal is determined based on the observations, the observation matrix, and a fixed or adaptive sparse transform basis.
9. The ultrasound three-dimensional imaging method of claim 8, wherein the determining the observation matrix of the compressed sensing model based on the array element positions of the target sparse array comprises:
binarizing array element positions 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 so as to obtain a plurality of initial sparse arrays;
a first determining module, configured to determine a maximum level value of each secondary lobe of the plurality of initial sparse arrays based on array directivity, where the maximum level value of the secondary lobe is equal to a sum of a maximum level value of a azimuth secondary lobe and a maximum level value of a pitch secondary lobe;
the updating module is used for repeatedly executing the following steps: updating the positions of array elements in a previous generation of sparse array based on an optimization algorithm to obtain a next generation of sparse array, wherein the initial value of the previous generation of sparse array is the plurality of initial sparse arrays, and the next generation of sparse array comprises a plurality of sparse arrays; and determining a maximum level value of each of the secondary lobes 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;
the third determining 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 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 processor, when executing the computer program, implements the steps of the ultrasound three-dimensional imaging method according to any one of claims 1 to 9.
12. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the ultrasound three-dimensional imaging method according to any one of claims 1 to 9.
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