Data collection optimization method and device, electronic device and machine-readable storage medium

Abstract

A data acquisition optimization method is provided, which includes: constructing an acquisition strategy objective function including an acquisition signal and a noise signal associated with imaging data; the data acquisition optimization method also includes: executing the following loop process until a preset convergence condition is met: inputting the signal value of a predetermined acquisition signal associated with predetermined imaging data in this loop process into the acquisition strategy objective function to obtain the acquisition strategy objective function value in this loop process; determining whether the acquisition strategy objective function value in this loop process meets the preset convergence condition; wherein, when the acquisition strategy objective function value in this loop process meets the preset convergence condition, the predetermined imaging data in this loop process is determined as the optimal imaging data. A data acquisition optimization device is also provided. The present invention determines the optimal imaging data by constructing an acquisition strategy objective function, thereby optimizing the acquisition strategy of imaging data.

Description

Technical Field

[0001] The present invention belongs to the field of image processing technology, and in particular, relates to a data acquisition optimization method and device, an electronic device, and a machine-readable storage medium. Background Art

[0002] At present, the application of magnetic resonance fingerprint technology (MRF) to chemical exchange saturation transfer (CEST) imaging can improve the accuracy of tissue parameter quantification by using non-steady-state signals and numerical simulation results, while shortening the imaging time. MRF technology leaves a high degree of freedom for the design of pulse sequence parameters, making how to achieve optimal data acquisition a research-worthy issue. At the same time, the difference between CEST imaging and conventional T1-weighted imaging and T2-weighted imaging also further highlights the importance of this issue in CEST-MRF.

[0003] In traditional MRF methods, a fast acquisition method is usually used to obtain hundreds or thousands of undersampled images. In CEST imaging, a saturation pulse needs to be applied for a certain period of time before signal sampling, and a recovery time needs to be set aside after acquisition to meet the specific absorption rate limit. The lengths of both are in the order of seconds, which limits the acquisition efficiency of a single image. In this case, the improvement of acquisition efficiency by using the undersampling method is also very limited. At the same time, since the amplitude of the CEST effect is smaller than that of the water signal, when the noise caused by undersampling is too large, it may submerge the effective signal. Therefore, the main path of traditional CEST-MRF research is to use a small number (usually 20 to 30) of fully sampled randomized imaging parameter images to achieve tissue parameter quantification. In order to achieve accurate quantification in a limited number of acquisitions, the optimization of the acquisition strategy is crucial. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned prior art, the present invention provides a data acquisition optimization method and a data acquisition optimization device for optimizing the acquisition strategy of imaging data.

[0005] According to one aspect of an embodiment of the present invention, a data acquisition optimization method is provided, the data acquisition optimization method comprising: constructing an acquisition strategy objective function including an acquisition signal and a noise signal, the acquisition signal being associated with imaging data; the data acquisition optimization method further comprising: executing the following loop process until a preset convergence condition is met: inputting a signal value of a predetermined acquisition signal associated with predetermined imaging data in this loop process into the acquisition strategy objective function to obtain an acquisition strategy objective function value in this loop process; determining whether the acquisition strategy objective function value in this loop process meets the preset convergence condition; wherein, when the acquisition strategy objective function value in this loop process meets the preset convergence condition, the predetermined imaging data in this loop process is determined as optimal imaging data.

[0006] In an example of the data acquisition optimization method provided in the above aspect, when the acquisition strategy objective function value does not meet the preset convergence condition, the signal value of the predetermined acquisition signal associated with another predetermined imaging data is used as the signal value of the predetermined acquisition signal in the next cycle.

[0007] In an example of the data acquisition optimization method provided in the above aspect, the constructing of the acquisition strategy objective function including the acquisition signal and the noise signal includes: constructing the acquisition strategy objective function including the acquisition signal and the noise signal based on a mixed a posteriori Cramer-Rao bound.

[0008] In one example of the data acquisition optimization method provided in the above aspect, based on the mixed posterior Cramer-Rao bound, the acquisition strategy objective function including the acquisition signal and the noise signal is constructed using the following formula 1:

[0009] [Formula 1]

[0010] Among them, θ represents all the changing parameters in the dictionary simulation, z represents the observed signal value, d represents the true signal value, p(d0) is the prior probability distribution of the system state at the beginning of the acquisition, p(θ) is the prior probability distribution of θ, L represents the number of acquisitions, const represents a constant, σ represents the standard deviation of the noise signal, and f j (θ, α) represents the predetermined acquisition signal associated with the predetermined imaging data α, E[·] represents the expected value, Represents the second-order partial derivative operation.

[0011] According to another aspect of an embodiment of the present invention, a data acquisition optimization device is provided, which includes: an objective function construction module, and an objective function value acquisition module and a determination module that operate in a loop until a preset convergence condition is met; the objective function construction module is used to construct an acquisition strategy objective function including an acquisition signal and a noise signal, and the acquisition signal is associated with imaging data; the objective function value acquisition module is used to input the signal value of a predetermined acquisition signal associated with predetermined imaging data in this cycle process into the acquisition strategy objective function to obtain the acquisition strategy objective function value in this cycle process; the determination module is used to determine whether the acquisition strategy objective function value in this cycle process meets the preset convergence condition; wherein, when the acquisition strategy objective function value in this cycle process meets the preset convergence condition, the determination module is also used to determine the predetermined imaging data in this cycle process as the optimal imaging data.

[0012] In an example of the data acquisition optimization device provided in the above another aspect, when the acquisition strategy objective function value does not meet the preset convergence condition, the signal value of a predetermined acquisition signal associated with another predetermined imaging data is used as the signal value of the predetermined acquisition signal in the next cycle.

[0013] In an example of the data acquisition optimization device provided in the above another aspect, the objective function construction module is further used to construct the acquisition strategy objective function including the acquisition signal and the noise signal based on the mixed posterior Cramer-Rao bound.

[0014] In an example of the data acquisition optimization device provided in the above another aspect, the objective function construction module is further used to construct the acquisition strategy objective function including the acquisition signal and the noise signal based on the mixed posterior Cramer-Rao bound and using the following formula 1:

[0015] [Formula 1]

[0016] Among them, θ represents all the changing parameters in the dictionary simulation, z represents the observed signal value, d represents the true signal value, p(d0) is the prior probability distribution of the system state at the beginning of the acquisition, p(θ) is the prior probability distribution of θ, L represents the number of acquisitions, const represents a constant, σ represents the standard deviation of the noise signal, and f j (θ, α) represents the predetermined acquisition signal associated with the predetermined imaging data α, E[·] represents the expected value, Represents the second-order partial derivative operation.

[0017] According to another aspect of an embodiment of the present invention, an electronic device is provided, which includes: at least one processor, and a memory coupled to the at least one processor, the memory storing instructions, and when the instructions are executed by the at least one processor, the at least one processor executes the data acquisition optimization method as described above.

[0018] According to another aspect of an embodiment of the present invention, a machine-readable storage medium is provided, which stores executable instructions, and is characterized in that when the instructions are executed, the machine executes the data acquisition optimization method as described above.

[0019] Beneficial effect: The present invention determines the optimal imaging data by constructing an acquisition strategy objective function, thereby optimizing the acquisition strategy of the imaging data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other aspects, features and advantages of the embodiments of the present invention will become more apparent through the following description in conjunction with the accompanying drawings, in which:

[0021] Figure 1 is a flow chart of a data acquisition optimization method according to an embodiment of the present invention;

[0022] Figure 2 is a block diagram of a data acquisition optimization device according to an embodiment of the present invention;

[0023] Figure 3 is a block diagram showing an electronic device for implementing a data acquisition optimization method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] Hereinafter, specific embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention may be implemented in many different forms, and the present invention should not be construed as being limited to the specific embodiments set forth herein. On the contrary, these embodiments are provided to explain the principles of the present invention and their practical applications, so that other persons skilled in the art can understand the various embodiments of the present invention and various modifications suitable for specific intended applications.

[0025] As used herein, the term "including" and its variations represent open terms, meaning "including but not limited to". The terms "based on", "according to", etc. mean "based at least in part on", "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other definitions may be included below, whether explicit or implicit. Unless the context clearly indicates otherwise, the definition of a term is consistent throughout the specification.

[0026] As described above, in order to optimize the acquisition strategy of imaging data, a data acquisition optimization method and a data acquisition optimization device for optimizing the acquisition strategy of imaging data are provided according to an embodiment of the present invention. In the data acquisition optimization method, the data acquisition optimization method includes: constructing an acquisition strategy objective function including an acquisition signal and a noise signal, wherein the acquisition signal is associated with the imaging data; the data acquisition optimization method also includes: executing the following loop process until a preset convergence condition is met: inputting the signal value of a predetermined acquisition signal associated with the predetermined imaging data in this loop process into the acquisition strategy objective function to obtain the acquisition strategy objective function value in this loop process; determining whether the acquisition strategy objective function value in this loop process meets the preset convergence condition; wherein, when the acquisition strategy objective function value in this loop process meets the preset convergence condition, the predetermined imaging data in this loop process is determined as the optimal imaging data.

[0027] Therefore, in the data acquisition optimization method, the optimal imaging data is determined by constructing an acquisition strategy objective function, thereby optimizing the acquisition strategy of the imaging data.

[0028] The data acquisition optimization method and data acquisition optimization device for optimizing the acquisition strategy of imaging data according to an embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

[0029] The data acquisition optimization method according to an embodiment of the present invention can be executed by an electronic device, and the electronic device may include a smart phone, a tablet computer, a personal computer, a cloud server, a server, etc.

[0030] Figure 1 is a flow chart of a data acquisition optimization method according to an embodiment of the present invention.

[0031] Reference Figure 1 , in block 101 , an acquisition strategy objective function including an acquisition signal and a noise signal is constructed, wherein the acquisition signal is associated with imaging data.

[0032] In one example, the imaging data may be an imaging data set, which may include parameters such as saturation energy, saturation time, and saturation frequency at each acquisition. The acquisition signal is associated with the imaging data. Here, the acquisition signal may be a function of the imaging data.

[0033] The specific process of constructing the acquisition strategy objective function is described in detail below.

[0034] Firstly, an evaluation index based on the mixed posterior Cramer-Rao bound is proposed to measure the sequence encoding ability of CEST-MRF. The evaluation index CRB for the encoding ability of any tissue parameter is globIt can be expressed as the following formula 1.

[0035] [Formula 1]

[0036] Where θ has a length of r, which represents all the parameters that change in the dictionary simulation (these parameters may include exchange rate, exchangeable proton concentration, B0 field offset, B1 field offset, etc.). V(θ) is the Cramé-Rao matrix. Assuming that the first parameter in θ is the parameter to be quantified, the first value on the diagonal of the Cramé-Rao matrix [V(θ)] 1,1 Represents the lower bound of the variance for parameter estimation. The Cramer-Rao matrix can be expressed as the inverse of the Fisher information matrix I(θ), see equation 2 below for details.

[0037] [Formula 2]

[0038] Among them, z and d represent the observed signal value and the true signal value respectively. E[·] represents the expected value. Represents the second-order partial derivative operation, as shown in Equation 3 and Equation 4 below.

[0039] [Formula 3]

[0040] [Formula 4]

[0041] Furthermore, p(z, d, θ) represents the joint probability density of z, d, θ. Here, assuming that the noise at each acquisition is uncorrelated, the logarithm of p(z, d, θ) is expressed as the following equation 5.

[0042] [Formula 5]

[0043] Where p(d0) is the prior probability distribution of the system state at the beginning of the acquisition. L represents the number of acquisitions.

[0044] In practical applications, since not every parameter has a prior probability distribution, the mixed posterior Cramer-Rao bound is used here. In the solution process, the derivative terms containing non-existent prior probabilities can be set to 0 directly based on the above equation 5.

[0045] In one example, first, it is assumed that the real signal value d is an ideal value calculated by the Bloch-McConnell equation. At the same time, in actual imaging, in addition to the noise in the signal reception and transmission process, the factors that cause the magnetic resonance signal to deviate from the ideal value are relatively complex. In order to simplify the model, it is usually assumed that the influence of all factors superimposed can be, for example, a Gaussian white noise signal. In this case, the above formula 5 can be simplified to the following formula 6.

[0046] [Formula 6]

[0047] Among them, const represents a constant, σ represents the standard deviation of the Gaussian white noise signal, and f j (θ, α) represents the acquisition signal associated with the imaging data α, and j represents the jth acquisition.

[0048] Substituting equation 6 and equation 2 into equation 1, we can obtain the objective function for optimizing the acquisition strategy, namely, CRB glob It can be directly used as the objective function for optimizing the acquisition strategy, that is, the acquisition strategy objective function, and is expressed as the following formula 7.

[0049] [Formula 7]

[0050] In the above, according to the embodiment of the present invention, the mixed posterior Cramer-Rao bound is used to construct the CEST-MRF sequence coding ability evaluation index in the process of constructing the acquisition strategy objective function. Since the propagation of noise is taken into account, the relative size of its value has a clear correlation with the quantitative accuracy. At the same time, the prior knowledge about the parameters is introduced by using the prior probability, which can realize the evaluation of the global coding ability. Below, we will explain in detail the acquisition optimization of imaging data based on the acquisition strategy objective function constructed above.

[0051] Continue to refer to Figure 1 In box 103, the signal value of the predetermined acquisition signal associated with the predetermined imaging data in the current cycle is input into the acquisition strategy objective function to obtain the acquisition strategy objective function value in the current cycle.

[0052] In one example, after a predetermined imaging data is selected, the corresponding predetermined acquisition signal is determined. That is, given an α, the corresponding predetermined acquisition signal f of the jth acquisition is j The function value (or signal value) of (θ, α) is determined. Substituting the function value into the acquisition strategy objective function, a corresponding acquisition strategy objective function value can be obtained.

[0053] In block 105 , it is determined whether the acquisition strategy objective function value in the current cycle satisfies the preset convergence condition.

[0054] In one example, the preset convergence condition includes: the acquisition strategy objective function value is minimum.

[0055] If the preset convergence condition is met, the loop ends, and the predetermined imaging data in the current loop is determined as the optimal imaging data, and the corresponding imaging data acquisition strategy is also the optimal acquisition strategy.

[0056] If the preset convergence condition is not met, the signal value of the predetermined acquisition signal associated with another predetermined imaging data is used as the signal value of the predetermined acquisition signal in the next cycle.

[0057] Figure 2 is a block diagram of a data acquisition optimization device according to an embodiment of the present invention.

[0058] Reference Figure 2 According to an embodiment of the present invention, the data acquisition optimization device 200 includes: an objective function construction module 210, an objective function value acquisition module 220 and a determination module 230. The objective function construction module 210 is used to construct an acquisition strategy objective function including an acquisition signal and a noise signal, wherein the acquisition signal is associated with the imaging data. The objective function value acquisition module 220 and the determination module 230 operate in a loop until a preset convergence condition (i.e., a loop end condition) is met.

[0059] In one example, the objective function construction module 210 is further used to construct the acquisition strategy objective function including the acquisition signal and the noise signal based on the mixed a posteriori Cramer-Rao bound. In another example, the objective function construction module 210 is further used to construct the acquisition strategy objective function including the acquisition signal and the noise signal based on the mixed a posteriori Cramer-Rao bound and using the above formula 7,

[0060] The objective function value acquisition module 220 is used to input the signal value of the predetermined acquisition signal associated with the predetermined imaging data in the current cycle into the acquisition strategy objective function to obtain the acquisition strategy objective function value in the current cycle.

[0061] In one example, after the objective function value acquisition module 220 selects a predetermined imaging data, the corresponding predetermined acquisition signal is determined. That is, given an α, the corresponding predetermined acquisition signal f collected for the jth time is j The function value (or signal value) of (θ, α) is determined. The objective function value acquisition module 220 inputs the function value into the acquisition strategy objective function to obtain a corresponding acquisition strategy objective function value.

[0062] The determination module 230 is used to determine whether the acquisition strategy objective function value in the current cycle satisfies the preset convergence condition. In one example, the preset convergence condition includes: the acquisition strategy objective function value is the minimum.

[0063] If the preset convergence condition is met, the determination module 230 determines that the loop ends, and the determination module 230 determines that the predetermined imaging data in the current loop is determined as the optimal imaging data, and the corresponding imaging data acquisition strategy is also the optimal acquisition strategy.

[0064] If the preset convergence condition is not met, the signal value of the predetermined acquisition signal associated with another predetermined imaging data is used as the signal value of the predetermined acquisition signal in the next cycle.

[0065] Reference above Figure 1 and Figure 2 , a data acquisition optimization method and a data acquisition optimization device according to an embodiment of the present invention are described.

[0066] The data acquisition optimization device according to the embodiment of the present invention can be implemented by hardware, or by software or a combination of hardware and software. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of the device in which it is located reading the corresponding computer program instructions in the memory into the memory and running them. In the embodiment of the present invention, the device for data acquisition optimization can be implemented by electronic devices, for example.

[0067] Figure 3 is a block diagram showing an electronic device for implementing a data acquisition optimization method according to an embodiment of the present invention.

[0068] Reference Figure 3 , the electronic device 300 may include at least one processor 310, a memory (e.g., a non-volatile memory) 320, a storage 330, and a communication interface 340, and the at least one processor 310, the storage 320, the storage 330, and the communication interface 340 are connected together via a bus 350. At least one processor 310 executes at least one computer-readable instruction stored or encoded in the memory (i.e., the above-mentioned element implemented in the form of software).

[0069] In one example, computer executable instructions are stored in a memory, which when executed cause at least one processor 310 to perform the following process: construct an acquisition strategy objective function including an acquisition signal and a noise signal, wherein the acquisition signal is associated with the imaging data; and also perform the following loop process until a preset convergence condition is met: input the signal value of a predetermined acquisition signal associated with the predetermined imaging data in this loop process into the acquisition strategy objective function to obtain the acquisition strategy objective function value in this loop process; determine whether the acquisition strategy objective function value in this loop process satisfies the preset convergence condition; wherein, if the acquisition strategy objective function value in this loop process satisfies the preset convergence condition, the predetermined imaging data in this loop process is determined as the optimal imaging data.

[0070] It should be understood that the computer executable instructions stored in the memory, when executed, cause at least one processor 310 to perform the above operations in accordance with various embodiments of the present invention. Figure 1 and Figure 2 Describes the various operations and functions.

[0071] According to one embodiment, a program product such as a machine-readable medium is provided. The machine-readable medium may have instructions (i.e., the above-mentioned elements implemented in the form of software), which, when executed by a machine, causes the machine to perform the various embodiments of the present invention in combination with the above-mentioned Figure 1 and Figure 2 Describes the various operations and functions.

[0072] Specifically, a system or device equipped with a readable storage medium can be provided, on which software program codes that implement the functions of any of the above-mentioned embodiments are stored, and a computer or processor of the system or device can read and execute instructions stored in the readable storage medium.

[0073] In this case, the program code itself read from the machine-readable medium can realize the function of any one of the above embodiments, so the machine-readable code and the machine-readable storage medium storing the machine-readable code constitute a part of the embodiments of the present invention.

[0074] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD-RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or a cloud via a communication network.

[0075] The above describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] Not all steps and units in the above-mentioned processes and system structure diagrams are necessary, and some steps or units can be ignored according to actual needs. The execution order of each step is not fixed and can be determined according to needs. The device structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some units may be implemented by the same physical entity, or some units may be implemented by multiple physical entities, or some components in multiple independent devices may be implemented together.

[0077] The terms "exemplary," "example," and the like used throughout this specification mean "used as an example, instance, or illustration" and do not mean "preferred" or "advantageous" over other embodiments. The detailed description includes specific details for the purpose of providing an understanding of the described techniques. However, these techniques can be implemented without these specific details. In some instances, in order to avoid obscuring the concepts of the described embodiments, well-known structures and devices are shown in block diagram form.

[0078] The optional implementation modes of the embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation modes. Within the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

[0079] The above description of the contents of this specification is provided to enable any person of ordinary skill in the art to implement or use the contents of this specification. Various modifications to the contents of this specification will be apparent to those of ordinary skill in the art, and the general principles defined herein may be applied to other variations without departing from the scope of protection of the contents of this specification. Therefore, the contents of this specification are not limited to the examples and designs described herein, but are consistent with the widest range of principles and novel features disclosed herein.

Claims

1. A data acquisition optimization method, characterized in that: The data acquisition optimization method comprises: constructing an acquisition strategy objective function including an acquisition signal and a noise signal, wherein the acquisition signal is associated with imaging data; the data acquisition optimization method further comprises: executing the following cycle until a preset convergence condition is met: Inputting the signal value of the predetermined acquisition signal associated with the predetermined imaging data in the current cycle into the acquisition strategy objective function to obtain the acquisition strategy objective function value in the current cycle; Determine whether the acquisition strategy objective function value in this cycle satisfies the preset convergence condition; Wherein, when the acquisition strategy objective function value in the current cycle process satisfies the preset convergence condition, the predetermined imaging data in the current cycle process is determined as the optimal imaging data; The step of constructing an acquisition strategy objective function including acquisition signals and noise signals includes: Constructing the acquisition strategy objective function including the acquisition signal and the noise signal based on the mixed posterior Cramer-Rao bound; Among them, based on the mixed posterior Cramer-Rao bound, the acquisition strategy objective function including the acquisition signal and the noise signal is constructed using the following formula 1: [Formula 1] , in, θ represents all the parameters that change in the dictionary simulation, z represents the observed signal value, d represents the real signal value, is the prior probability distribution of the system state at the beginning of acquisition, for θ The prior probability distribution of L Indicates the number of acquisitions, const indicates a constant, σ represents the standard deviation of the noise signal, f j ( θ, α ) indicates the predetermined imaging data α The associated predetermined acquisition signal, represents the expected value, Represents the second-order partial derivative operation.

2. The data acquisition optimization method according to claim 1, characterized in that: When the acquisition strategy objective function value does not satisfy a preset convergence condition, a signal value of a predetermined acquisition signal associated with another predetermined imaging data is used as a signal value of a predetermined acquisition signal in a next cycle.

3. A data acquisition optimization device, characterized in that: The data acquisition optimization device comprises: an objective function construction module and an objective function value acquisition module and a determination module which operate cyclically until a preset convergence condition is met; The objective function construction module is used to construct an acquisition strategy objective function including an acquisition signal and a noise signal, wherein the acquisition signal is associated with the imaging data; The objective function value acquisition module is used to input the signal value of the predetermined acquisition signal associated with the predetermined imaging data in the current cycle into the acquisition strategy objective function to obtain the acquisition strategy objective function value in the current cycle; The determination module is used to determine whether the acquisition strategy objective function value in this cycle satisfies the preset convergence condition; Wherein, when the acquisition strategy objective function value in the current cycle satisfies the preset convergence condition, the determination module is further used to determine the predetermined imaging data in the current cycle as the optimal imaging data; Wherein, the objective function construction module is further used to construct the acquisition strategy objective function including the acquisition signal and the noise signal based on the mixed posterior Cramer-Rao bound; The objective function construction module is further used to construct the acquisition strategy objective function including the acquisition signal and the noise signal based on the mixed posterior Cramer-Rao bound and using the following formula 1: [Formula 1] , in, θ represents all the parameters that change in the dictionary simulation, z represents the observed signal value, d represents the real signal value, is the prior probability distribution of the system state at the beginning of acquisition, for θ The prior probability distribution of L Indicates the number of acquisitions, const indicates a constant, σ represents the standard deviation of the noise signal, f j ( θ, α ) indicates the predetermined imaging data α The associated predetermined acquisition signal, represents the expected value, Represents the second-order partial derivative operation.

4. The data acquisition optimization device according to claim 3, characterized in that: In the case that the acquisition strategy objective function value does not satisfy the preset convergence condition, the signal value of the predetermined acquisition signal associated with another predetermined imaging data is used as the signal value of the predetermined acquisition signal in the next cycle.

5. An electronic device, characterized in that: include: at least one processor, and A memory coupled to the at least one processor, the memory storing instructions, which, when executed by the at least one processor, enables the at least one processor to perform the data acquisition optimization method as claimed in claim 1 or 2.

6. A machine-readable storage medium storing executable instructions, characterized in that: When the instructions are executed, the machine performs the data acquisition optimization method according to claim 1 or 2.

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