Constrained joint-based frequency-preferred magnetic particle imaging reconstruction method and system

Abstract

This invention relates to the field of magnetic particle imaging technology. To address the spatial resolution degradation caused by excessive retention of redundant low-frequency components and discarding of high-frequency components in existing magnetic particle imaging methods, this invention provides a frequency-optimized magnetic particle imaging reconstruction method and system based on constraint joint analysis. The frequency-optimized magnetic particle imaging reconstruction method based on constraint joint analysis includes adjusting a preset correlation threshold, selecting the correlation threshold corresponding to the peak value of the reconstruction index as the final determined optimal correlation threshold, recalculating the correlation between each candidate frequency component and the already selected frequency components to complete the final selection of frequency components, and constructing a system matrix and corresponding measurement voltage signals using the frequency components selected by the optimal correlation threshold. This forms a system of linear equations for the system state and is solved to obtain a particle concentration distribution image, completing the reconstruction. This achieves high-resolution, high-fidelity real-time magnetic particle imaging reconstruction with very few frequency components.

Description

Technical Field

[0001] This invention relates to the field of magnetic particle imaging technology, and in particular to a frequency-optimized magnetic particle imaging reconstruction method and system based on constraint combination. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Magnetic particle imaging (MPI) is an emerging molecular imaging technique that enables non-invasive, real-time tracking of the distribution of superparamagnetic iron-oxide nanoparticles (SPIONs) within biological organisms. This technique offers significant advantages such as high sensitivity, absence of background interference, rapid dynamic imaging capabilities, lack of tissue depth limitations, and absence of ionizing radiation, and has garnered widespread attention in medical imaging and clinical translation in recent years.

[0004] Currently, the most commonly used magnetic nanoparticle imaging reconstruction algorithm is based on the system matrix approach. Its basic principle is to utilize iterative algorithms, most commonly the Kaczmarz algorithm, to solve the inverse problem, mapping the acquired voltage signal to a particle concentration distribution. Since the reconstruction time is linearly related to the number of rows in the system matrix (i.e., the number of frequency components), frequency component reduction is usually necessary to achieve real-time imaging. Existing mainstream techniques are frequency selection methods based on signal-to-noise ratio (SNR). This method uses empty measurements as a noise reference, calculates the SNR value for each row (each frequency component) in the system matrix, sorts them from high to low SNR, and truncates frequency components above a specific threshold or a fixed number for reconstruction. The distribution of SNR values ​​has inherent characteristics: low-frequency components encoding image contour information typically have higher SNR values, while high-frequency components encoding image details and edges typically have lower SNR values. Truncation selection based solely on SNR magnitude will preferentially retain low-frequency components while systematically discarding detail-rich high-frequency components. When the frequency retention ratio is compressed (e.g., below 10%) in pursuit of real-time performance, the image spatial resolution drops drastically, resulting in severe blurring. Furthermore, the high SNR low-frequency components selected based on the SNR method exhibit high correlation, leading to information redundancy. If the row vectors of the system matrices corresponding to two frequencies are highly similar, their encoded spatial information overlaps. Repeatedly selecting these components contributes little to improving reconstruction quality, instead increasing the computational burden and resulting in insufficient effective independent information for reconstruction, thus failing to support high-quality sparse reconstruction. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a frequency-optimized magnetic nanoparticle imaging reconstruction method and system based on constraint joint. By introducing a correlation constraint mechanism into the SNR evaluation system, it can eliminate highly redundant frequency components and replace them with highly independent high-frequency components while ensuring the signal-to-noise ratio, thereby achieving high-resolution, high-fidelity real-time magnetic nanoparticle imaging reconstruction with very few frequency components.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a frequency-optimized magnetic particle imaging reconstruction method based on constraint joint.

[0008] In one or more embodiments, a frequency-optimized magnetic particle imaging reconstruction method based on constraint joint is provided, comprising:

[0009] The system matrix and measurement voltage signal of the magnetic nanoparticle imaging system are obtained, and then the measurement voltage signal is converted into a frequency domain voltage signal and preprocessed.

[0010] Using the empty measurement signal as a noise reference, the SNR values ​​of all frequency components in the preprocessed frequency domain voltage signal are calculated and sorted from high to low accordingly.

[0011] Based on the sorted frequency components, the selected frequency components are determined, and the correlation between each candidate frequency component and the selected frequency component is calculated so that the correlation between the selected frequency component and the selected frequency component is less than the preset correlation threshold. Then, the corresponding reconstruction index is calculated.

[0012] Adjust the preset correlation threshold, select the correlation threshold corresponding to the peak value of the reconstruction index as the final determined optimal correlation threshold, and recalculate the correlation between each candidate frequency component and the already selected frequency component to complete the final selection of frequency components.

[0013] By using the frequency components selected by the optimal correlation threshold, a system matrix after frequency selection and the corresponding measured voltage signal are constructed, forming a system of linear equations for the system state and solving them to obtain a particle concentration distribution image, thus completing the reconstruction.

[0014] As one implementation method, the process for determining the optimal correlation threshold is as follows:

[0015] During the training phase, set a threshold loop, specifying the threshold range and gradient.

[0016] Frequency components are selected at each threshold, the image is reconstructed, and the reconstruction index is calculated.

[0017] After the threshold cycle ends, based on the recorded reconstruction metrics, the threshold corresponding to the highest reconstruction metric is selected as the optimal threshold for the final selection frequency correlation.

[0018] As one implementation, the reconstruction metrics include the peak signal-to-noise ratio and structural similarity of the reconstructed image.

[0019] As one implementation method, the Kaczmarz algorithm is used to solve the system's linear state equations. The calculation process is as follows:

[0020] ;

[0021] in, Represents the first in the system matrix OK, For vectors The One element; This indicates the selected calculation row in the system matrix. Represents the remainder function. Indicates the current iteration number. This represents the total number of rows in the matrix; This represents the concentration distribution vector of superparamagnetic iron oxide nanoparticles. Indicates the first The estimated particle concentration distribution after the next iteration. Indicates the first The estimated particle concentration distribution at the next iteration; This represents the actual measured voltage signal. Represents the system matrix. Indicates the index of the selected system matrix row in the current iteration step; Describe the Euclidean norm. This indicates the conjugate transpose.

[0022] As one implementation method, the expression for calculating the SNR value of all frequency components is:

[0023] ;

[0024] in, and These represent the frequency spectra of signals carrying particle information and those measured in space, respectively. Represents the sampling points in the system matrix; Indicates the first Each frequency component.

[0025] As one implementation method, in the process of calculating the correlation between the current candidate frequency component and the already selected frequency components:

[0026] ;

[0027] in, These are the frequency components to be selected. These are the frequency components that have already been selected. It is the conjugate transpose. It's the Euclidean norm; a correlation threshold is set. Ensure that the selected frequency components match the already selected set of frequency components. The correlation of each frequency component is less than the set correlation threshold.

[0028] As one implementation method, the measured voltage signal is subjected to Fourier transform to convert it to the frequency domain, and high-pass filtering is performed to filter out components below the set frequency or below the third harmonic of the fundamental frequency, so as to remove interference from the directly coupled excitation signal and obtain all frequency components in the preprocessed frequency domain voltage signal.

[0029] A second aspect of the present invention provides a frequency-optimized magnetic particle imaging reconstruction system based on constraint coherence.

[0030] In one or more embodiments, a frequency-preferred magnetic particle imaging reconstruction system based on constraint joint methods includes:

[0031] The signal preprocessing module is used to acquire the system matrix and measurement voltage signal of the magnetic nanoparticle imaging system, and then convert the measurement voltage signal into a frequency domain voltage signal and preprocess it.

[0032] The SNR value calculation module is used to calculate the SNR value of all frequency components in the preprocessed frequency domain voltage signal using the empty measurement signal as a noise reference, and sort the frequency components from high to low accordingly.

[0033] The correlation calculation module is used to determine the selected frequency components based on the sorted frequency components, and then calculate the correlation between each candidate frequency component and the selected frequency components, so that the correlation between the selected frequency components and the selected frequency components is less than the preset correlation threshold, and then calculate the corresponding reconstruction index.

[0034] The optimal threshold determination module is used to adjust the preset correlation threshold, select the correlation threshold corresponding to the peak value of the reconstruction index as the final determined optimal correlation threshold, and then recalculate the correlation between each candidate frequency component and the already selected frequency component to complete the final selection of frequency components.

[0035] The image reconstruction module is used to construct the frequency-filtered system matrix and the corresponding measured voltage signal by using the frequency components filtered by the optimal correlation threshold, form a system of linear equations for the system state and solve them to obtain the particle concentration distribution image and complete the reconstruction.

[0036] A third aspect of the present invention provides a computer-readable storage medium.

[0037] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the frequency-optimized magnetic particle imaging reconstruction method based on constraint joint as described above.

[0038] A fourth aspect of the present invention provides an electronic device.

[0039] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the constraint-based frequency-optimized magnetic particle imaging reconstruction method described above.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] This invention aims to address the problem of decreased spatial resolution caused by excessive retention of redundant low-frequency components and discarding of high-frequency components in existing SNR-based frequency selection methods while pursuing real-time imaging. By introducing a correlation constraint mechanism into the SNR evaluation system, highly redundant frequency components are eliminated and replaced with highly independent high-frequency components while ensuring the signal-to-noise ratio. This allows for high-resolution, high-fidelity real-time magnetic nanoparticle imaging reconstruction with very few frequency components. Attached Figure Description

[0042] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0043] Figure 1 This is a flowchart of the frequency-optimized magnetic particle imaging reconstruction method based on constraint joint according to an embodiment of the present invention;

[0044] Figure 2 This is a schematic diagram of the frequency-optimized magnetic particle imaging reconstruction method based on constraint joint according to an embodiment of the present invention;

[0045] Figure 3 This is a reconstructed image of the frequency-optimized magnetic particle imaging reconstruction method based on constraint joint according to an embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram of the structure of the frequency-optimized magnetic particle imaging reconstruction system based on constraint joint according to an embodiment of the present invention;

[0047] Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0050] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0051] Combination Figure 1 and Figure 2 The frequency-optimized magnetic particle imaging reconstruction method based on constraint joint in this embodiment may include the following steps S101 to S105.

[0052] The specific implementation process of steps S101 to S105 is as follows:

[0053] Step S101: Obtain the system matrix and measurement voltage signal of the magnetic nanoparticle imaging system, and then convert the measurement voltage signal into a frequency domain voltage signal and preprocess it.

[0054] Specifically, the measured voltage signal is subjected to Fourier transform to convert it to the frequency domain, and high-pass filtering is performed to filter out components below the set frequency (e.g., 80kHz) or below the third harmonic of the fundamental frequency, so as to remove interference from the directly coupled excitation signal and obtain all frequency components in the preprocessed frequency domain voltage signal.

[0055] It should be noted that, in addition to Fourier transform, other methods can also be used for frequency domain transformation, which will not be detailed here.

[0056] Step S102: Using the empty measurement signal as a noise reference, calculate the SNR value of all frequency components in the preprocessed frequency domain voltage signal and sort the frequency components from high to low accordingly.

[0057] Since the SNR value of the frequency components ultimately affects the quality of the reconstructed image, if the SNR value of the frequency components is low, the image quality obtained by reconstructing at such a frequency will be very low. Therefore, before selecting a frequency, the frequency components should be sorted from high to low according to their SNR values, and then the correlation should be used for screening to reduce the noise in the reconstructed image.

[0058] Specifically, the expression for calculating the SNR value of all frequency components is:

[0059] ;

[0060] in, and These represent the frequency spectra of signals carrying particle information and those measured in space, respectively. Represents the sampling points in the system matrix; Indicates the first Each frequency component.

[0061] Step S103: Based on the sorted frequency components, determine the selected frequency components, and then calculate the correlation between each candidate frequency component and the selected frequency components, so that the correlation between the selected frequency components and the selected frequency components is less than the preset correlation threshold, and then calculate the corresponding reconstruction index.

[0062] In one or more embodiments, frequency components that are above a set SNR threshold or a fixed number are selected as the frequency components.

[0063] In the process of calculating the correlation between the current candidate frequency component and the already selected frequency components:

[0064] ;

[0065] in, These are the frequency components to be selected. These are the frequency components that have already been selected. It is the conjugate transpose. It's the Euclidean norm; a correlation threshold is set. Ensure that the selected frequency components match the already selected set of frequency components. The correlation of each frequency component is less than the set correlation threshold.

[0066] Step S104: Adjust the preset correlation threshold, select the correlation threshold corresponding to the peak value of the reconstruction index as the final determined optimal correlation threshold, and recalculate the correlation between each candidate frequency component and the already selected frequency component to complete the final selection of frequency components.

[0067] Specifically, the process for determining the optimal correlation threshold is as follows:

[0068] Set a threshold loop during the training phase, and set the threshold range and gradient; for example, set the threshold range to 0 to 1 and the gradient to 0.01.

[0069] Frequency components are selected at each threshold, the image is reconstructed, and reconstruction metrics are calculated; wherein, reconstruction metrics include, but are not limited to, the peak signal-to-noise ratio and structural similarity of the reconstructed image.

[0070] After the threshold cycle ends, based on the recorded reconstruction metrics, the threshold corresponding to the highest reconstruction metric is selected as the optimal threshold for the final selection frequency correlation.

[0071] Step S105: Using the frequency components selected by the optimal correlation threshold, construct the system matrix after frequency selection and the corresponding measured voltage signal, form a system of linear equations for the system state, and solve them to obtain the particle concentration distribution image, thus completing the reconstruction. The reconstructed image is shown below. Figure 3 As shown.

[0072] In one or more embodiments, the Kaczmarz algorithm is used to solve the system's linear state equations, and the calculation process is as follows:

[0073] ;

[0074] in, Represents the first in the system matrix OK, For vectors The One element; This indicates the selected calculation row in the system matrix. Represents the remainder function. Indicates the current iteration number. This represents the total number of rows in the matrix; This represents the concentration distribution vector of superparamagnetic iron oxide nanoparticles. Indicates the first The estimated particle concentration distribution after the next iteration. Indicates the first The estimated particle concentration distribution at the next iteration; This represents the actual measured voltage signal. Represents the system matrix. Indicates the index of the selected system matrix row in the current iteration step; Describe the Euclidean norm. This indicates the conjugate transpose.

[0075] It should be noted that other algorithms can also be used to solve the system's linear state equations.

[0076] Table 1 compares the reconstruction method of the present invention with the signal-to-noise ratio-based method and the signal-to-noise ratio-based method in terms of evaluation metrics such as peak signal-to-noise ratio and structural similarity.

[0077] Table 1. Comparison of evaluation indicators;

[0078]

[0079] As shown in Table 1, the reconstruction method of this invention outperforms traditional methods in both peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) when compared with signal-to-noise ratio (SNR)-based methods. The algorithm proposed in this invention has been experimentally validated on the OpenMPI dataset. Two-dimensional slice reconstruction experiments were conducted on the data provided by the OpenMPI dataset. For this dataset, the optimal correlation threshold was 0.71, and only 5% of the frequency components were selected for reconstruction. Comparison with traditional SNR algorithms was performed. Experimental results show that the algorithm proposed in this invention achieves higher quality reconstruction with the same number of frequencies, and the calculated peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) both show superior performance.

[0080] like Figure 4 As shown, the frequency-optimized magnetic particle imaging reconstruction system based on constraint joint principles provided in this embodiment of the invention can be implemented in software. The frequency-optimized magnetic particle imaging reconstruction system based on constraint joint principles includes the following software modules:

[0081] The signal preprocessing module 401 is used to acquire the system matrix and measurement voltage signal of the magnetic nanoparticle imaging system, and then convert the measurement voltage signal into a frequency domain voltage signal and preprocess it.

[0082] SNR value calculation module 402 is used to calculate the SNR value of all frequency components in the preprocessed frequency domain voltage signal using the empty measurement signal as a noise reference and sort the frequency components from high to low accordingly.

[0083] The correlation calculation module 403 is used to determine the selected frequency components based on the sorted frequency components, and then calculate the correlation between each candidate frequency component and the selected frequency component, so that the correlation between the selected frequency component and the selected frequency component is less than the preset correlation threshold, and then calculate the corresponding reconstruction index.

[0084] The optimal threshold determination module 404 is used to adjust the preset correlation threshold, select the correlation threshold corresponding to the peak value of the reconstruction index as the final determined optimal correlation threshold, and recalculate the correlation between each candidate frequency component and the already selected frequency component to complete the final selection of frequency components.

[0085] The image reconstruction module 405 is used to construct the frequency-filtered system matrix and the corresponding measured voltage signal by using the frequency components filtered by the optimal correlation threshold, form a system of linear equations of the system state and solve them to obtain the particle concentration distribution image and complete the reconstruction.

[0086] It should be noted that each module in the frequency-optimized magnetic particle imaging reconstruction system based on constraint joint in this embodiment of the invention corresponds one-to-one with each step in the frequency-optimized magnetic particle imaging reconstruction method based on constraint joint in the above embodiment, and their specific implementation processes are the same, so they will not be repeated here.

[0087] The structure of the electronic device according to an embodiment of the present invention will be described in detail below. Figure 5 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of the present invention. It can be understood that... Figure 5 The diagram shows only an exemplary structure of the electronic device, not the entire structure. Some or all of the structures shown may be implemented as needed.

[0088] The electronic device provided in this embodiment of the invention includes: at least one processor 501, a memory 502, a user interface 503, and at least one network interface 504. The various components in the constraint-based frequency-preferred magnetic particle imaging reconstruction system are coupled together via a bus system 505. It is understood that the bus system 505 is used to realize communication between these components. In addition to a data bus, the bus system 505 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 5 The general designated all buses as Bus System 505.

[0089] The user interface 503 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0090] It is understood that memory 502 can be volatile memory or non-volatile memory, or both. In this embodiment of the invention, memory 502 is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as framework layers, core library layers, driver layers, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0091] In some embodiments, the frequency-optimized magnetic particle imaging reconstruction system based on constraint joint principles provided in this invention can be implemented using a combination of hardware and software. For example, the frequency-optimized magnetic particle imaging reconstruction system based on constraint joint principles provided in this invention can be a processor in the form of a hardware decoding processor, programmed to execute the frequency-optimized magnetic particle imaging reconstruction method based on constraint joint principles provided in this invention. For instance, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0092] As an example, processor 501 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0093] As an example of the hardware implementation of the frequency-optimized magnetic particle imaging reconstruction system based on constraint joint provided in the embodiments of the present invention, the device provided in the embodiments of the present invention can be directly executed by a processor 501 in the form of a hardware decoding processor. For example, it can be executed by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the frequency-optimized magnetic particle imaging reconstruction method based on constraint joint provided in the embodiments of the present invention.

[0094] The memory 502 in this embodiment of the invention is used to store various types of data to support the operation of the frequency-optimized magnetic particle imaging reconstruction system based on constraint joint, or to store data for execution. Figure 1The program code for the method shown. Examples of this data include: any executable instructions for operation on a constraint-joint frequency-optimized magnetic particle imaging reconstruction system, such as executable instructions that can be included in the executable instructions to implement the constraint-joint frequency-optimized magnetic particle imaging reconstruction method of the embodiments of the present invention.

[0095] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0096] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0097] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A frequency-optimized magnetic particle imaging reconstruction method based on constrained joint methods, characterized in that, include: The system matrix and measurement voltage signal of the magnetic nanoparticle imaging system are obtained, and then the measurement voltage signal is converted into a frequency domain voltage signal and preprocessed. Using the empty measurement signal as a noise reference, the SNR values ​​of all frequency components in the preprocessed frequency domain voltage signal are calculated and sorted from high to low accordingly. Based on the sorted frequency components, the selected frequency components are determined, and the correlation between each candidate frequency component and the selected frequency component is calculated so that the correlation between the selected frequency component and the selected frequency component is less than the preset correlation threshold. Then, the corresponding reconstruction index is calculated. Adjust the preset correlation threshold, select the correlation threshold corresponding to the peak value of the reconstruction index as the final determined optimal correlation threshold, and recalculate the correlation between each candidate frequency component and the already selected frequency component to complete the final selection of frequency components. By using the frequency components filtered by the optimal correlation threshold, a system matrix after frequency filtering and the corresponding measured voltage signal are constructed, forming a system of linear equations for the system state and solving them to obtain a particle concentration distribution image, thus completing the reconstruction. In the process of calculating the correlation between the current candidate frequency component and the already selected frequency components: ； in, These are the frequency components to be selected. These are the frequency components that have already been selected. It is the conjugate transpose. It's the Euclidean norm; a correlation threshold is set. Ensure that the selected frequency components match the already selected set of frequency components. The correlation of each frequency component is less than the set correlation threshold.

2. The frequency-optimized magnetic particle imaging reconstruction method based on constrained joint methods as described in claim 1, characterized in that, The process for determining the optimal correlation threshold is as follows: During the training phase, set a threshold loop, specifying the threshold range and gradient. Frequency components are selected at each threshold, the image is reconstructed, and the reconstruction index is calculated. After the threshold cycle ends, based on the recorded reconstruction metrics, the threshold corresponding to the highest reconstruction metric is selected as the optimal threshold for the final selection frequency correlation.

3. The frequency-optimized magnetic particle imaging reconstruction method based on constrained joint methods as described in claim 2, characterized in that, The reconstruction metrics include the peak signal-to-noise ratio and structural similarity of the reconstructed image.

4. The frequency-optimized magnetic particle imaging reconstruction method based on constrained joint methods as described in claim 1, characterized in that, The Kaczmarz algorithm is used to solve the system's linear state equations. The calculation process is as follows: ； in, Represents the first in the system matrix OK, For vectors The One element; This indicates the selected calculation row in the system matrix. Represents the remainder function. Indicates the current iteration number. This represents the total number of rows in the matrix; This represents the concentration distribution vector of superparamagnetic iron oxide nanoparticles. Indicates the first The estimated particle concentration distribution after the next iteration. Indicates the first The estimated particle concentration distribution at the next iteration; This represents the actual measured voltage signal. Represents the system matrix. Indicates the index of the selected system matrix row in the current iteration step; Describe the Euclidean norm. This indicates the conjugate transpose.

5. The frequency-optimized magnetic particle imaging reconstruction method based on constrained joint methods as described in claim 1, characterized in that, The expression for calculating the SNR values ​​of all frequency components is: ； in, and These represent the frequency spectra of signals carrying particle information and those measured in space, respectively. Represents the sampling points in the system matrix; Indicates the first Each frequency component.

6. The frequency-optimized magnetic particle imaging reconstruction method based on constrained joint methods as described in claim 1, characterized in that, The measured voltage signal is subjected to Fourier transform to be converted to the frequency domain. High-pass filtering is then performed to filter out components below the set frequency or below the third harmonic of the fundamental frequency, so as to remove interference from the directly coupled excitation signal and obtain all frequency components in the preprocessed frequency domain voltage signal.

7. A frequency-optimized magnetic particle imaging reconstruction system based on constrained joint methods, characterized in that, The frequency-optimized magnetic particle imaging reconstruction method based on constrained joint as described in any one of claims 1-6 includes: The signal preprocessing module is used to acquire the system matrix and measurement voltage signal of the magnetic nanoparticle imaging system, and then convert the measurement voltage signal into a frequency domain voltage signal and preprocess it. The SNR value calculation module is used to calculate the SNR value of all frequency components in the preprocessed frequency domain voltage signal using the empty measurement signal as a noise reference, and sort the frequency components from high to low accordingly. The correlation calculation module is used to determine the selected frequency components based on the sorted frequency components, and then calculate the correlation between each candidate frequency component and the selected frequency components, so that the correlation between the selected frequency components and the selected frequency components is less than the preset correlation threshold, and then calculate the corresponding reconstruction index. The optimal threshold determination module is used to adjust the preset correlation threshold, select the correlation threshold corresponding to the peak value of the reconstruction index as the final determined optimal correlation threshold, and then recalculate the correlation between each candidate frequency component and the already selected frequency component to complete the final selection of frequency components. The image reconstruction module is used to construct the frequency-filtered system matrix and the corresponding measured voltage signal by using the frequency components filtered by the optimal correlation threshold, form a system of linear equations for the system state and solve them to obtain the particle concentration distribution image and complete the reconstruction.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the frequency-preferred magnetic particle imaging reconstruction method based on constraint joint as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the frequency-optimized magnetic particle imaging reconstruction method based on constraint joint as described in any one of claims 1-6.

Images