A three-dimensional magnetic particle imaging system and method for quantitative analysis of fused imaging parameters

Abstract

This invention belongs to the field of magnetic particle imaging, specifically relating to a three-dimensional magnetic particle imaging system, method, and device that fuses imaging parameters for quantitative analysis. It aims to address the problem that existing technologies only focus on the quality analysis of two-dimensional MPI reconstructed images, using a single parameter and failing to comprehensively assess the combined influence of multiple factors on the reconstructed image, resulting in poor quality of the reconstructed MPI three-dimensional image. The system includes: an input module configured to acquire a phantom model of the object to be reconstructed, as well as the imaging parameters and interference parameters of the MPI imaging device; a signal calculation module configured to calculate the induced voltage signal; a reconstruction module configured to perform three-dimensional reconstruction of the object to be imaged based on the induced voltage signal using a three-dimensional image reconstruction method to obtain an MPI three-dimensional reconstructed image; and an output module configured to output the MPI three-dimensional reconstructed image. This invention improves the quality of MPI three-dimensional reconstructed images.

Description

Technical Field

[0001] This invention belongs to the field of magnetic particle imaging, and specifically relates to a three-dimensional magnetic particle imaging system, method, and device for quantitative analysis by fusing imaging parameters. Background Technology

[0002] Magnetic particle imaging (MPI) is an emerging imaging technique in medical tomography. It utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the spatial distribution of nanoparticle contrast agents within the body. Real-time three-dimensional (3D) imaging is one of the most significant innovations of MPI. Compared to other tomographic imaging methods, MPI offers high sensitivity, high spatial resolution, and high temporal resolution, and does not require ionizing radiation. MPI has a very broad application prospect in the medical field and is widely used in cell tracing, cancer detection, atherosclerotic plaque detection, and precision magnetothermal therapy, among other medical applications.

[0003] The characteristics of magnetic particles, the configuration of MPI hardware, and interference with MPI hardware all affect the quality of MPI reconstructed images. Spatial resolution is a crucial imaging characteristic of MPI systems; higher spatial resolution results in better reconstructed image quality. The spatial resolution of an MPI system is positively correlated with particle diameter and the selected field gradient. However, as the diameter of SPIO particles increases, relaxation time increases, and the hardware temperature rises with increasing operating time, leading to lower spatial resolution. Furthermore, the selected field gradient is inversely proportional to the sensitivity of the MPI system. Therefore, MPI spatial resolution cannot be infinitely improved by increasing gradient intensity or particle diameter. In practical imaging, specific magnetic field strengths and apertures need to be designed for different research subjects (e.g., mice, monkeys), and the MPI signal intensity is affected by particle temperature, particle saturation, particle concentration, particle relaxation, and the amplitude of the driving field. In addition, the MPI signal sequence is affected by the driving field frequency and acquisition frequency, and the MPI imaging quality is also affected by noise and background signal (direct feedthrough) interference, as well as magnetocaloric effects. Currently, existing 3D MPI reconstruction methods consider only a single factor or ignore relaxation time, magnetocaloric effects, and noise signals when acquiring voltage signals. Therefore, in order to improve the quality of MPI reconstructed images, it is crucial to rapidly quantify and analyze the above-mentioned different influencing factors before customizing SPIO imaging equipment, which is essential for the design and construction of the equipment. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, namely, to resolve the issue that existing technologies only focus on the quality analysis of two-dimensional MPI reconstructed images, using a single parameter and failing to comprehensively evaluate the combined influence of multiple factors on the reconstructed image, resulting in poor quality of the reconstructed three-dimensional MPI images, this invention proposes a three-dimensional magnetic particle imaging system that fuses imaging parameters for quantitative analysis. This system includes: an input module, a signal calculation module, a reconstruction module, and an output module.

[0005] The input module is configured to acquire a phantom model of the object to be imaged and reconstructed, as well as imaging parameters and interference parameters of the MPI imaging device; the phantom model is a 3D computational image model composed of voxels; the imaging parameters include hardware configuration parameters and magnetic particle characteristic parameters of the MPI imaging device; the interference parameters include signal distortion parameters, noise interference parameters, and direct feedthrough parameters.

[0006] The signal calculation module is configured to transform the position of the phantom model from the natural coordinate system to the magnetic field coordinate system, and take the transformed position as the first position; and calculate the induced voltage signal by combining the first position, the imaging parameters, and the interference parameters.

[0007] The reconstruction module is configured to perform three-dimensional reconstruction of the object to be imaged and reconstructed based on the induced voltage signal using a three-dimensional image reconstruction method, thereby obtaining an MPI three-dimensional reconstruction image of the object to be imaged and reconstructed.

[0008] The output module is configured to output the MPI 3D reconstructed image.

[0009] In some preferred embodiments, the position of the phantom model is transformed from the natural coordinate system to the magnetic field coordinate system by:

[0010]

[0011] Where R represents the position of the phantom model transformed into the magnetic field coordinate system, i.e. R v =(x v y v , z v ) T The phantom model represents the position in the natural coordinate system, TF(·) represents the transformation function from the image pixel position to the magnetic field coordinate position in the actual imaging of the device, Δx, Δy, and Δz represent the actual side lengths of the voxel in the x, y, and z directions, respectively. x A y A z G represents the amplitude of the driving field in the x, y, and z directions. x G y G zThis represents the gradient of the selection field in the x, y, z directions.

[0012] In some preferred embodiments, the method for calculating the magnetic particle concentration at different locations based on the position of the phantom model transformed into a magnetic field coordinate system is as follows:

[0013]

[0014] ΔV=Δx*Δy*Δz

[0015] Where C(R) represents the magnetic particle concentration at different locations in the magnetic field, ΔV represents the actual volume of the voxel, and N P (R) represents the number of magnetic particles at position R.

[0016] In some preferred embodiments, the induced voltage signal is calculated by combining the first position, the imaging parameters, and the interference parameters, and the method is as follows:

[0017] Based on the first position and the magnetic particle characteristic parameters, the particle magnetization intensity is calculated; the particle signal is obtained by differentiating the particle magnetization intensity.

[0018] Obtain the relaxation time and calculate the relaxation effect of the magnetic particles;

[0019] Based on the relaxation effect of the magnetic particles, the direct feedthrough parameters, the noise interference parameters, and the particle signal, the induced voltage signal is calculated.

[0020] In some preferred embodiments, the particle magnetization is calculated by combining the first position and the magnetic particle characteristic parameters, and the method is as follows:

[0021]

[0022] Where R represents the position of the phantom model transformed into the magnetic field coordinate system, M(·) represents the magnetization function, H(·) represents the magnetic field strength function, and β represents the magnetic particle characteristic parameter. Let represent the Langevin function, t represent time, and m represent the magnetic moment modulus.

[0023] In some preferred embodiments, the particle signal is obtained by differentiating the particle magnetization intensity, and the method is as follows:

[0024]

[0025] Where μ0 represents the permeability of free space, ρ R This indicates the sensitivity of the receiving coil. This represents a particle signal.

[0026] In some preferred embodiments, the induced voltage signal is calculated as follows:

[0027] u(t)=[-μf(t) ω K∑ R M′(H(R,t))ΔxΔyΔz]*T(t) τ +αu noise +βu b

[0028] f(t) = k0t

[0029]

[0030] u b =γ n sin(2πnft), n>0

[0031] Where u(t) represents the induced voltage signal, K represents the product of the permeability and the magnetic moment modulus constant, M′ represents the derivative of the magnetization, f(t) represents the influence of the magnetocaloric effect of the MPI imaging device on the generated voltage signal (specifically determined by prior measurement based on the MPI imaging device), k0 represents the magnetocaloric proportionality coefficient (set according to the excitation magnetic field strength), ω is used to control the influence of the magnetocaloric effect on the voltage signal, α is used to control and evaluate the influence of added noise on the voltage signal, β is used to quantitatively evaluate the influence of direct feedthrough, T(t) represents the relaxation effect of magnetic particles, u b Indicates the direct feedthrough parameter, u noise The noise interference parameter is represented by τ, the relaxation time is represented by δ, and the Heaviside function is represented by γ. n denoted by , f represents the magnetic field frequency, and n represents a natural positive integer.

[0032] In some preferred embodiments, during the calculation of the induced voltage signal, the derivative of the particle magnetization intensity and imaging parameters at multiple spatial locations within the phantom model are calculated in parallel using a combination of GPU and multithreading. Specifically:

[0033] After calculating the particle magnetization, the particle magnetization is first copied to the GPU, and the derivative, relaxation effect and magnetocaloric effect of the particle magnetization are calculated in parallel.

[0034] The particle signal is obtained based on the derivative of the particle magnetization.

[0035] Multithreading is used to create a GPU, and the particle signal is copied to the GPU. Multithreading is then used to perform calculations on the relationship between the direct feedthrough signal and the noise signal in the obtained particle signal.

[0036] A second aspect of the present invention proposes a three-dimensional magnetic particle imaging method for quantitative analysis by fusing imaging parameters, the method comprising the following steps:

[0037] S100, acquire the phantom model of the object to be imaged and reconstructed, as well as the imaging parameters and interference parameters of the MPI imaging device; the phantom model is a 3D computational image model composed of voxels; the imaging parameters include the hardware configuration parameters and magnetic particle characteristic parameters of the MPI imaging device; the interference parameters include signal distortion parameters, noise interference parameters, direct feedthrough parameters, and magnetocaloric effect parameters.

[0038] S200, the position of the phantom model is transformed from the natural coordinate system to the magnetic field coordinate system, and the transformed position is taken as the first position; the induced voltage signal is calculated by combining the first position, the imaging parameters, and the interference parameters;

[0039] S300, based on the induced voltage signal, the object to be imaged and reconstructed is reconstructed in three dimensions using a three-dimensional image reconstruction method, thereby obtaining and outputting an MPI three-dimensional reconstructed image of the object to be imaged and reconstructed.

[0040] A third aspect of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor for implementing the three-dimensional magnetic particle imaging method for quantifying the fused imaging parameters described above.

[0041] The beneficial effects of this invention are:

[0042] This invention improves the quality of MPI 3D reconstruction images.

[0043] 1) This invention first transforms the phantom's position from the natural coordinate system to the magnetic field coordinate system, linking the phantom model and MPI hardware. Then, combining the transformed phantom model's position and magnetic particle characteristic parameters, it calculates the particle magnetization intensity and differentiates the particle magnetization intensity to obtain the particle signal. Simultaneously, to make the induced voltage signal more realistic, the particle relaxation effect is considered, acquiring a particle signal including the relaxation effect. Finally, direct feedthrough and noise signals are further incorporated to construct the induced voltage signal. Through more comprehensive and flexible MPI system parameter settings, considering MPI system noise, direct feedthrough, and particle relaxation effects, the quality of MPI reconstructed images is improved.

[0044] 2) This invention uses multiple parameters based on particle characteristics to quantitatively evaluate the influencing parameters of 3D-MPI reconstructed images, and quickly quantifies the influence of different influencing factors on the quality of reconstructed images. This enables rapid and quantitative evaluation of the impact of these factors on the quality of MPI reconstructed images. Furthermore, this method can be used to evaluate the quality of reconstructed images in advance before designing an MPI system, thus making magnetic particle imaging have greater application prospects in the medical field. Attached Figure Description

[0045] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0046] Figure 1 This is a schematic diagram of the framework of a three-dimensional magnetic particle imaging system for quantitative analysis of fused imaging parameters according to an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of a multi-stage GPU-accelerated thread pool parallel computing process based on imaging parameters according to an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of MPI three-dimensional reconstruction image using a three-dimensional magnetic particle imaging system that performs quantitative analysis of fused imaging parameters according to an embodiment of the present invention.

[0049] Figure 4 This is a schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0051] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0052] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0053] The present invention provides a three-dimensional magnetic particle imaging system for quantitative analysis of fused imaging parameters, such as... Figure 1 As shown, the system includes: an input module, a signal calculation module, a reconstruction module, and an output module;

[0054] The input module is configured to acquire a phantom model of the object to be imaged and reconstructed, as well as imaging parameters and interference parameters of the MPI imaging device; the phantom model is a 3D computational image model composed of voxels; the imaging parameters include hardware configuration parameters and magnetic particle characteristic parameters of the MPI imaging device; the interference parameters include signal distortion parameters, noise interference parameters, and direct feedthrough parameters.

[0055] The signal calculation module is configured to transform the position of the phantom model from the natural coordinate system to the magnetic field coordinate system, and take the transformed position as the first position; and calculate the induced voltage signal by combining the first position, the imaging parameters, and the interference parameters.

[0056] The reconstruction module is configured to perform three-dimensional reconstruction of the object to be imaged and reconstructed based on the induced voltage signal using a three-dimensional image reconstruction method, thereby obtaining an MPI three-dimensional reconstruction image of the object to be imaged and reconstructed.

[0057] The output module is configured to output the MPI 3D reconstructed image.

[0058] To more clearly explain the three-dimensional magnetic particle imaging system for quantifying and analyzing fused imaging parameters according to the present invention, the following description is in conjunction with the appendix. Figure 3 The modules of one embodiment of the system of the present invention will be described in detail below.

[0059] In the following embodiments, this invention proposes a three-dimensional magnetic particle imaging system for quantitative analysis by fusing imaging parameters. This system has more comprehensive and flexible MPI system parameter settings, can consider MPI system noise, direct feedthrough, and particle relaxation effects, and can be applied to the quantitative analysis of the quality of three-dimensional MPI reconstructed images. This invention's three-dimensional magnetic particle imaging system for quantitative analysis by fusing imaging parameters includes an input module, a signal calculation module, a reconstruction module, and an output module; specifically as follows:

[0060] The input module is configured to acquire a phantom model of the object to be imaged and reconstructed, as well as imaging parameters and interference parameters of the MPI imaging device; the phantom model is a 3D computational image model composed of voxels; the imaging parameters include hardware configuration parameters and magnetic particle characteristic parameters of the MPI imaging device; the interference parameters include signal distortion parameters, noise interference parameters, and direct feedthrough parameters.

[0061] In this embodiment, the input module is used to obtain the phantom model of the object to be imaged and reconstructed, as well as the parameters for MPI imaging reconstruction, including imaging parameters and interference parameters.

[0062] The signal calculation module is configured to transform the position of the phantom model from the natural coordinate system to the magnetic field coordinate system, and take the transformed position as the first position; and calculate the induced voltage signal by combining the first position, the imaging parameters, and the interference parameters.

[0063] In this embodiment, the phantom's position is first transformed from the natural coordinate system to the magnetic field coordinate system, thus linking the phantom model and the MPI hardware. The transformation process can be represented as follows:

[0064]

[0065] Where R represents the position of the phantom model transformed into the magnetic field coordinate system, i.e. R v The phantom model represents the position in the natural coordinate system, TF(·) represents the transformation function from the image pixel position to the magnetic field coordinate position in the actual imaging of the device, Δx, Δy, and Δz represent the actual side lengths of the voxel in the x, y, and z directions, respectively. x A y A z G represents the amplitude of the driving field in the x, y, and z directions. x G y G z This represents the gradient of the selection field in the x, y, z directions.

[0066] The method for calculating the magnetic particle concentration at different locations, based on the position of the phantom model transformed into a magnetic field coordinate system, is as follows:

[0067]

[0068] ΔV=Δx*Δy*Δz(3)

[0069] Where C(R) represents the magnetic particle concentration at different locations in the magnetic field, ΔV represents the actual volume of the voxel, and N P (R) represents the number of magnetic particles at position R.

[0070] Then, combining the first position and the magnetic particle characteristic parameters, the particle magnetization intensity is calculated; the particle signal is obtained by differentiating the particle magnetization intensity, specifically:

[0071]

[0072]

[0073] Where R represents the position of the phantom model transformed into the magnetic field coordinate system, M(·) represents the magnetization function, H(·) represents the magnetic field strength function, C(R) represents the magnetic particle concentration at different positions, and β represents the magnetic particle characteristic parameters. Let represent the Langevin function, t represent time, and m represent the magnetic moment modulus.

[0074] To make the induced voltage signal closer to reality, this invention considers the particle relaxation effect and calculates the relaxation effect of magnetic particles using the following method:

[0075]

[0076] Where T(t) represents the relaxation effect of the magnetic particle, τ represents the relaxation time, and δ represents the Heaviside function.

[0077] After obtaining the particle signal containing the relaxation effect as described above, the influence of direct feedthrough and noise signals on the particle signal needs to be considered. Therefore, this invention further incorporates direct feedthrough and noise signals into the particle signal to form an induced voltage signal. The noise signal implemented in this invention includes, but is not limited to, random noise, which can be generated using a Gaussian function.

[0078] The direct feedthrough parameters are calculated as shown in formula (7):

[0079] u b =γ n sin(2πnft), n>0(7)

[0080] Among them, u b Indicates the direct feedthrough parameter, γ n denoted by , f represents the magnetic field frequency, and n represents a natural positive integer.

[0081] Finally, to enable computer calculation of particle signals, this invention proposes for the first time a discrete calculation formula for particle signals. Simultaneously, this invention also proposes for the first time to decompose the generation of induced voltage signals into particle signals, relaxation, noise, magnetocaloric effect, and direct feedthrough. The calculation method for the induced voltage signal is shown in the following formula:

[0082] u(t)=[-μf(t) ω K∑ R M′(H(R,t))ΔxΔyΔz]*T(t) τ +αu noise +βu b (8)

[0083] f(t)=k0t (9)

[0084] Where u(t) represents the induced voltage signal, K represents the product of the permeability and the magnetic moment modulus constant, M′ represents the derivative of the magnetization, f(t) represents the influence of the magnetocaloric effect of the MPI imaging device on the generated voltage signal (specifically determined by prior measurement based on the MPI imaging device), k0 represents the magnetocaloric proportionality coefficient (set according to the excitation magnetic field strength), ω is used to control the influence of the magnetocaloric effect on the voltage signal, α is used to control and evaluate the influence of added noise on the voltage signal, β is used to quantitatively evaluate the influence of direct feedthrough, T(t) represents the relaxation effect of magnetic particles, u b Indicates the direct feedthrough parameter, u noise The noise interference parameter is represented by τ, the relaxation time is represented by δ, and the Heaviside function is represented by γ. n Here, f represents the disturbance amplitude, n represents the magnetic field frequency, and n represents a natural positive integer. This invention achieves multi-parameter control of the voltage signal by controlling these parameters. Specifically, it integrates the magnetocaloric effect function, noise signal, direct feedthrough signal, and relaxation effect parameters from the actual process to achieve control and quantitative analysis of the interaction between different parameters in the image signal. This has significant reference value for determining the excitation frequency, magnetic field strength, and particle characteristics in the hardware parameters during the actual equipment setup process, thereby determining the quality of the reconstructed image.

[0085] Furthermore, to reduce the computation time for reconstruction, this invention introduces GPU-accelerated thread pool technology to speed up the processing of large and complex matrix data. Signals obtained from multi-parameter calculations can be reconstructed using the reconstruction algorithm. Finally, quantitative evaluation of the imaging system under different parameters is achieved. Specifically:

[0086] The calculation of particle signals can be performed by a central processing unit (CPU) or a graphics processing unit (GPU). To shorten the calculation time, this invention introduces a parallel computing approach combining GPU and multithreading to achieve rapid calculation of MPI particle voltage patterns. Figure 2 As shown, ( Figure 2 In this context, the 3D phantom (i.e., the phantom model) is used by the GPU to compute the particle magnetization response within multiple voxels in parallel, including the derivatives of the magnetization function at different locations. Multithreading is used to accelerate particle signal computation across multiple time intervals. Multithreading is implemented using thread pool technology. Figure 2 The dashed lines in the figure represent the parallel computation of multithreading. In the above parallel computation, the particle signal at each time step is independent, and the magnetization of each particle in the phantom model is independent of other particles. Therefore, equation (4) can be highly parallelized. Specifically:

[0087] The induced voltage signal is calculated using a proposed multi-stage GPU plus thread pool technique based on MPI imaging parameters to accelerate particle signal computation. It can be seen that the magnetization of each particle within the phantom space (i.e., the phantom model) is independent and undisturbed. Therefore, to accelerate the calculation of particle magnetization within the phantom space, based on the way parameters affect the voltage signal, a multi-threaded approach is used with a GPU to calculate the derivative of particle magnetization intensity and the effect of imaging parameters at multiple spatial locations within the phantom space in parallel at different stages. From the voltage signal calculation method, it can be seen that the particle signals during the scanning process are temporally independent and unaffected by previous signals. Therefore, to accelerate the calculation of particle signals, this invention proposes using thread pool technology to create multi-threaded approaches to parallelize the signal strength of particle signals at multiple time sequences.

[0088] according to The multi-stage acceleration technology mainly involves adding corresponding voltage influence parameters in stages. After the signal is copied to the GPU, the GPU unit first performs rapid parallel calculations of the magnetization derivative, relaxation effect, and magnetocaloric effect. After this stage is completed, the calculated particle signal is copied to the CPU in parallel, and then the relationship between the direct feedthrough signal and the noise signal is calculated through multi-threading.

[0089] The algorithm proposed in this invention is preferably executed on a GPU. However, it should be noted that this invention includes the aforementioned GPU acceleration module, but is not limited to this module. Signal output can also be performed through a CPU unit.

[0090] The reconstruction module is configured to perform three-dimensional reconstruction of the object to be imaged and reconstructed based on the induced voltage signal using a three-dimensional image reconstruction method, thereby obtaining an MPI three-dimensional reconstruction image of the object to be imaged and reconstructed.

[0091] In this embodiment, the commonly used x-space reconstruction method or system matrix reconstruction method is used to reconstruct the three-dimensional image of the object to be imaged.

[0092] The output module is configured to output the MPI 3D reconstructed image. The output module is also configured to quantitatively evaluate the reconstruction result. The quality of the reconstructed image is evaluated by comparing the reconstructed result with the original phantom in three metrics: structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE). Different fusion input parameters will produce different reconstructed image qualities, thereby achieving quantitative evaluation of the imaging system under different parameters.

[0093] The three-dimensional magnetic particle imaging system proposed in this invention, which uses fusion imaging parameters for quantitative analysis, can quickly and quantitatively assess the impact of these factors on the quality of MPI reconstructed images. During the image system reconstruction process, signal calculation tools classify parameters into two categories: imaging parameters and interference parameters. Imaging parameters are mandatory in the calculation process, while interference parameters are optional. The three-dimensional magnetic particle imaging system using fusion imaging parameters for quantitative analysis enables 3D visualization of the reconstructed image. This 3D visualization is implemented using VTK and embedded in a GUI interface. The graphical user interface (GUI), 3D virtual body membrane development, and reconstruction methods can be developed using the Python language.

[0094] It should be noted that the three-dimensional magnetic particle imaging system for quantifying and analyzing fused imaging parameters provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0095] A second embodiment of the present invention provides a three-dimensional magnetic particle imaging method for quantifying and analyzing fused imaging parameters, such as... Figure 2 As shown, the method includes the following steps:

[0096] S100: Acquire the phantom model of the object to be imaged and reconstructed, as well as the imaging parameters and interference parameters of the MPI imaging device; the phantom model is a 3D computational image model composed of voxels; the imaging parameters include the hardware configuration parameters and magnetic particle characteristic parameters of the MPI imaging device; the interference parameters include signal distortion parameters, noise interference parameters, and direct feedthrough parameters.

[0097] S200, the position of the phantom model is transformed from the natural coordinate system to the magnetic field coordinate system, and the transformed position is taken as the first position; the induced voltage signal is calculated by combining the first position, the imaging parameters, and the interference parameters;

[0098] S300, based on the induced voltage signal, the object to be imaged and reconstructed is reconstructed in three dimensions using a three-dimensional image reconstruction method, thereby obtaining and outputting an MPI three-dimensional reconstructed image of the object to be imaged and reconstructed.

[0099] An electronic device according to a third embodiment of the present invention includes: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to implement the above-described three-dimensional magnetic particle imaging method for quantifying and analyzing fused imaging parameters.

[0100] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described three-dimensional magnetic particle imaging method for quantifying and analyzing fused imaging parameters.

[0101] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the electronic devices and computer-readable storage media described above can be referred to the corresponding processes in the aforementioned system examples, and will not be repeated here.

[0102] The following is for reference. Figure 4 It shows a schematic diagram of the structure of a computer system suitable for implementing the methods, systems, and apparatus embodiments of this application. Figure 4 The server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0103] like Figure 4 As shown, the computer system includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read Only Memory (ROM) 402 or programs loaded from storage section 408 into Random Access Memory (RAM) 403. RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0104] The following components are connected to I / O interface 405: an input section 306 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0105] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, 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), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0106] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0109] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0110] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A three-dimensional magnetic particle imaging system for quantitative analysis by fusing imaging parameters, characterized in that, The system includes: an input module, a signal calculation module, a reconstruction module, and an output module; The input module is configured to acquire a phantom model of the object to be imaged and reconstructed, as well as imaging parameters and interference parameters of the MPI imaging device; the phantom model is a 3D computational image model composed of voxels; the imaging parameters include hardware configuration parameters and magnetic particle characteristic parameters of the MPI imaging device; the interference parameters include signal distortion parameters, noise interference parameters, and direct feedthrough parameters. The signal calculation module is configured to transform the position of the phantom model from the natural coordinate system to the magnetic field coordinate system, and take the transformed position as the first position; combining the first position, the imaging parameters, and the interference parameters, it calculates the induced voltage signal, the method of which is as follows: Based on the first position and the magnetic particle characteristic parameters, the particle magnetization intensity is calculated; the particle signal is obtained by differentiating the particle magnetization intensity. Obtain the relaxation time and calculate the relaxation effect of the magnetic particles; Based on the relaxation effect of the magnetic particles, the direct feedthrough parameters, the noise interference parameters, and the particle signal, the induced voltage signal is calculated. The induced voltage signal is calculated as follows: f(t) = k0t you b =c n sin(2πnft),n>0 Where u(t) represents the induced voltage signal, K represents the product of the permeability and the magnetic moment modulus constant, M′ represents the derivative of the magnetization, f(t) represents the influence of the magnetocaloric effect of the MPI imaging device on the generated voltage signal (specifically determined by prior measurement based on the MPI imaging device), k0 represents the magnetocaloric proportionality coefficient (set according to the excitation magnetic field strength), ω is used to control the influence of the magnetocaloric effect on the voltage signal, α is used to control and evaluate the influence of added noise on the voltage signal, β is used to quantitatively evaluate the influence of direct feedthrough, T(t) represents the relaxation effect of magnetic particles, u b Indicates the direct feedthrough parameter, u noise The noise interference parameter is represented by τ, the relaxation time is represented by δ, and the Heaviside function is represented by γ. n denoted by , where f represents the disturbance amplitude, f represents the magnetic field frequency, and n represents a natural positive integer; The reconstruction module is configured to perform three-dimensional reconstruction of the object to be imaged and reconstructed based on the induced voltage signal using a three-dimensional image reconstruction method, thereby obtaining an MPI three-dimensional reconstruction image of the object to be imaged and reconstructed. The output module is configured to output the MPI 3D reconstructed image.

2. The three-dimensional magnetic particle imaging system for quantitative analysis of fused imaging parameters according to claim 1, characterized in that, The method for transforming the position of the phantom model from the natural coordinate system to the magnetic field coordinate system is as follows: Where R represents the position of the phantom model transformed into the magnetic field coordinate system, i.e. R v The phantom model represents the position in the natural coordinate system, TF(·) represents the transformation function from the image pixel position to the magnetic field coordinate position in the actual imaging of the device, Δx, Δy, and Δz represent the actual side lengths of the voxel in the x, y, and z directions, respectively. x A y A z G represents the amplitude of the driving field in the x, y, and z directions. x G y G z This represents the gradient of the selection field in the x, y, z directions.

3. The three-dimensional magnetic particle imaging system for quantitative analysis of fused imaging parameters according to claim 2, wherein the method for calculating the magnetic particle concentration at different locations based on the position of the phantom model transformed into the magnetic field coordinate system is as follows: ΔV=Δx*Δy*Δz in, C(R) represents the magnetic particle concentration at different locations in the magnetic field, ΔV represents the actual volume of the voxel, and N P (R) represents the number of magnetic particles at position R.

4. The three-dimensional magnetic particle imaging system for quantitative analysis of fused imaging parameters according to claim 3, characterized in that, The particle magnetization intensity is calculated by combining the first position and the magnetic particle characteristic parameters, using the following method: Where R represents the position of the phantom model transformed into the magnetic field coordinate system, M(·) represents the magnetization function, H(·) represents the magnetic field strength function, and β represents the magnetic particle characteristic parameter. Let represent the Langevin function, t represent time, and m represent the magnetic moment modulus.

5. The three-dimensional magnetic particle imaging system for quantitative analysis of fused imaging parameters according to claim 4, characterized in that, The particle signal is obtained by differentiating the magnetization of the particle, as follows: Where μ0 represents the permeability of free space, ρ R This indicates the sensitivity of the receiving coil. This represents a particle signal.

6. The three-dimensional magnetic particle imaging system for quantitative analysis of fused imaging parameters according to claim 5, characterized in that, During the calculation of the induced voltage signal, the derivative of the particle magnetization intensity and imaging parameters at multiple spatial locations within the phantom model are calculated in parallel using a combination of GPU and multithreading. Specifically: After calculating the particle magnetization intensity, the particle magnetization intensity is first copied to the GPU, and the derivative of the particle magnetization intensity, as well as the effects of relaxation effect and magnetocaloric effect on the derivative of magnetization intensity are calculated in parallel. The particle signal is obtained based on the derivative of the particle magnetization. Multithreading is created using the GPU to copy the particle signal to the GPU. The obtained particle signal is then calculated using multithreading to perform magnetocaloric and relaxation effects. The calculation results are then copied to the CPU to perform the relationship calculation between the direct feedthrough signal and the noise signal.

7. A three-dimensional magnetic particle imaging method for quantitative analysis by fusing imaging parameters, characterized in that, The method includes the following steps; S100: Acquire the phantom model of the object to be imaged and reconstructed, as well as the imaging parameters and interference parameters of the MPI imaging device; the phantom model is a 3D computational image model composed of voxels; the imaging parameters include the hardware configuration parameters and magnetic particle characteristic parameters of the MPI imaging device; the interference parameters include signal distortion parameters, noise interference parameters, and direct feedthrough parameters. S200, the position of the phantom model is transformed from the natural coordinate system to the magnetic field coordinate system, and the transformed position is taken as the first position; the induced voltage signal is calculated by combining the first position, the imaging parameters, and the interference parameters, as follows: Based on the first position and the magnetic particle characteristic parameters, the particle magnetization intensity is calculated; the particle signal is obtained by differentiating the particle magnetization intensity. Obtain the relaxation time and calculate the relaxation effect of the magnetic particles; Based on the relaxation effect of the magnetic particles, the direct feedthrough parameters, the noise interference parameters, and the particle signal, the induced voltage signal is calculated. The induced voltage signal is calculated as follows: f(t) = k0t you b =c n sin(2πnft),n>0 Where u(t) represents the induced voltage signal, K represents the product of the permeability and the magnetic moment modulus constant, M′ represents the derivative of the magnetization, f(t) represents the influence of the magnetocaloric effect of the MPI imaging device on the generated voltage signal (specifically determined by prior measurement based on the MPI imaging device), k0 represents the magnetocaloric proportionality coefficient (set according to the excitation magnetic field strength), ω is used to control the influence of the magnetocaloric effect on the voltage signal, α is used to control and evaluate the influence of added noise on the voltage signal, β is used to quantitatively evaluate the influence of direct feedthrough, T(t) represents the relaxation effect of magnetic particles, u b Indicates the direct feedthrough parameter, u noise The noise interference parameter is represented by τ, the relaxation time is represented by δ, and the Heaviside function is represented by γ. n denoted by , where f represents the disturbance amplitude, f represents the magnetic field frequency, and n represents a natural positive integer; S300, based on the induced voltage signal, the object to be imaged and reconstructed is reconstructed in three dimensions using a three-dimensional image reconstruction method, thereby obtaining and outputting an MPI three-dimensional reconstructed image of the object to be imaged and reconstructed.

8. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by the processor to implement the three-dimensional magnetic particle imaging method for quantifying and analyzing fused imaging parameters as described in claim 7.

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