Magnetic Particle Imaging Signal Enhancement Method Based on Circumferential Multi-Channel Receiver Coils

Abstract

The present invention belongs to the field of magnetic particle imaging, and particularly relates to a method for enhancing magnetic particle imaging signals based on a circumferential multi-channel receiving coil, aiming to solve the problem of how to deeply combine multi-channel acquisition technology with graph neural network algorithms. The present invention includes: constructing a dynamic graph structure based on the positions of multiple pairs of receiving coils of a circumferential receiving mechanism; performing signal encoding on the response signals generated by multiple pairs of receiving coils of the circumferential receiving structure; encoding the spatial representation of the response signals into the node features of the dynamic graph structure, and using a graph neural network to perform information fusion based on the node features to obtain the output features of each pair of receiving coils in each convolutional layer of the graph neural network; mapping the output features of the convolutional layer of the last layer of the graph neural network back to the time domain space using a one-dimensional linear layer and an inverse Fourier transform to obtain the final enhanced signal. The present invention is based on a receiving coil, uses a graph model to achieve complementary fusion of multi-channel signals, and finally improves the MPI fast-scanning imaging ability.

Description

Background Art

[0002] Magnetic particle imaging (MPI) is an emerging medical imaging technology that has been widely used in various biomedical fields. There is a mutually restrictive relationship between the scanning speed and signal quality of MPI. Although MPI can theoretically achieve a high scanning speed, in the actual imaging process, short-time scanning is often accompanied by more noise, resulting in a decrease in signal quality. Therefore, MPI usually needs to reduce noise by extending the scanning time, which makes it difficult to achieve the expected high-speed imaging effect, limiting the application of MPI in vascular imaging, functional imaging and other applications that require fast imaging. How to improve the quality of acquired signals while shortening the scanning time and achieve an optimal balance between scanning speed and signal quality is a key challenge in accelerating MPI imaging. Multi-channel parallel acquisition technology has significantly improved the fast scanning capabilities of various medical imaging modalities, and intelligent algorithms based on graph neural networks have shown unique advantages in multi-channel signal relationship modeling and complex data structure processing, especially in sparse data reconstruction, fast imaging and other tasks. These advances provide new solutions for further improving the fast imaging capabilities of MPI. During the scanning process of MPI, single-channel sparse and fast scanning signals often cannot fully characterize the spatial distribution information of magnetic particles. However, graph neural networks, with their powerful graph structure modeling capabilities, can effectively capture the complex relationship between multi-channel signals, and use the joint modeling of global and local signals to compensate for information loss and further improve signal quality and robustness. Therefore, how to deeply combine multi-channel acquisition technology with graph neural network algorithms is the core idea to improve the scanning speed and quality of MPI. Summary of the invention

[0003] In order to solve the above-mentioned problem in the prior art, that is, how to deeply combine the multi-channel acquisition technology with the graph neural network algorithm, the present invention provides a magnetic particle imaging signal enhancement method based on a toroidal multi-channel receiving coil, the method comprising:

[0004] Based on the positions of multiple pairs of receiving coils of the toroidal receiving mechanism, a dynamic graph structure including multiple nodes is constructed;

[0005] Signal encoding is performed on response signals generated by multiple pairs of receiving coils of the toroidal receiving structure to obtain a spatial representation of the response signals;

[0006] The spatial representation of the response signal is encoded into the node features of the dynamic graph structure, and the graph neural network is used to fuse information based on the node features to obtain the output features of each pair of receiving coils in each convolutional layer of the graph neural network;

[0007] The output features of the convolutional layer in the last layer of the graph neural network are mapped back to the time domain space using a one-dimensional linear layer and an inverse Fourier transform to obtain the final output enhanced signal.

[0008] In a preferred embodiment, the circumferential receiving mechanism is: stacking multiple pairs of receiving coils in the circumferential aperture direction to form a circumferential receiving structure with multiple different directions.

[0009] In a preferred embodiment, constructing a dynamic graph structure including multiple nodes based on the positions of multiple pairs of receiving coils of the circumferential receiving mechanism includes:

[0010] Each node of the dynamic graph structure represents a pair of receiving coils;

[0011] Calculating the connection strength between any two pairs of receiving coils based on the offset angle between them;

[0012] Calculating the cosine similarity between any two pairs of receiving coils;

[0013] Determining the final dynamic graph structure based on the cosine similarity and connection strength between any two pairs of receiving coils.

[0014] In a preferred embodiment, calculating the connection strength between any two pairs of receiving coils includes:

[0015] ;

[0016] Where, is the offset angle of the kth pair of coils, is the offset angle of the jth pair of coils, is the connection strength between the kth pair of coils and the jth pair of coils; k and j are both the numbers of coil pairs, is the connection strength.

[0017] In a preferred embodiment, calculating the cosine similarity between any two pairs of receiving coils includes:

[0018] ;

[0019] Where, is the cosine similarity between the kth pair of coils and the jth pair of coils, is the signal representation of the kth pair of coils, is the signal representation of the jth pair of coils, is the transpose of the signal representation of the jth pair of coils, is the 2-norm of the signal representation of the kth pair of coils, is the 2-norm of the signal representation of the jth pair of coils, is the cosine similarity.

[0020] In a preferred embodiment, the final dynamic graph structure is:

[0021] ;

[0022] wherein, is the connection weight between the k-th pair of coils and the j-th pair of coils, is the harmonic weight.

[0023] In a preferred embodiment, obtaining the spatial representation of the response signal by signal encoding the response signals generated by multiple pairs of receiving coils includes:

[0024] ;

[0025] wherein, is the spatial representation of the response signal, is the convolution operator in the time domain, is the convolution operator in the frequency domain, is the activation function, represents the Fourier transform.

[0026] In a preferred embodiment, using a graph neural network to perform information fusion based on node features to obtain the output features of each pair of receiving coils in each convolutional layer of the graph neural network includes:

[0027] The spatial representation of the response signal is used as the input feature of the first convolutional layer;

[0028] For the output feature of the k-th pair of receiving coils in the o-th convolutional layer is:

[0029] ;

[0030] wherein, is the output feature of the k-th pair of receiving coils in the o-th convolutional layer, W and are both parameters of the convolutional layer, is the connection weight between the k-th pair of coils and the j-th pair of coils, is the input feature of the k-th pair of receiving coils in the o-th convolutional layer, is the input feature of the j-th pair of receiving coils in the o-th convolutional layer.

[0031] In a preferred embodiment, mapping the output feature of the last convolutional layer of the graph neural network back to the time domain space using a one-dimensional linear layer and the inverse Fourier transform to obtain the finally output enhanced signal includes:

[0032] ;

[0033] wherein, and are all trainable parameters, is the enhanced signal of the final output, is the time-domain representation of the output features of the last layer, is the frequency-domain representation of the output features of the last layer, is the inverse Fourier transform.

[0034] In a second aspect of the present invention, a magnetic particle imaging signal enhancement system based on a circumferential multi-channel receiving coil is proposed. The system includes:

[0035] A graph structure construction module, configured to construct a dynamic graph structure including multiple nodes based on the positions of multiple pairs of receiving coils of a circumferential receiving mechanism;

[0036] A signal encoding module, configured to perform signal encoding on the response signals generated by multiple pairs of receiving coils of a circumferential receiving structure to obtain a spatial representation of the response signals;

[0037] An information fusion module, configured to encode the spatial representation of the response signals into the node features of the dynamic graph structure, and perform information fusion based on the node features using a graph neural network to obtain the output features of each pair of receiving coils in each convolutional layer of each layer of the graph neural network;

[0038] A signal mapping module, configured to map the output features of the convolutional layer of the last layer of the graph neural network back to the time-domain space using a one-dimensional linear layer and an inverse Fourier transform to obtain the enhanced signal of the final output.

[0039] Advantages of the present invention:

[0040] (1) By constructing a multi-channel parallel receiving coil, the present invention obtains more signal information within the same time, realizes joint analysis of multi-channel signals based on a graph neural network, and improves the magnetic particle imaging scanning speed on the basis of ensuring signal quality; at the same time, based on the construction of a circumferential parallel receiving coil, the present invention uses a graph model to realize complementary fusion of multi-channel signals, reduces the scanning time on the basis of ensuring high-quality signals, and finally realizes the goal of improving the fast scanning imaging ability of MPI;

[0041] (2) The present invention proposes a circumferentially stacked receiving coil structure, which reduces the number of turns of the opposing coils and stacks multiple pairs of receiving coils in the circumferential aperture direction; this receiving coil structure improves the space utilization rate without changing the overall thickness and enriches the direction information of the magnetic particle response signals;

[0042] (3) The present invention realizes an algorithm for improving the quality of sparse fast signals, constructs a circumferential multi-channel receiving coil, stacks the receiving coils in the circumferential aperture direction to form multiple circumferential receiving structures in different directions, and after obtaining receiving signals in different directions within the same time, uses a graph neural network for joint complementary analysis to obtain high-quality signals. Description of the Drawings

[0043] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0044] Figure 1 It is a schematic diagram of a method for enhancing magnetic particle imaging signals based on a circumferential multi-channel receiving coil according to an embodiment of the present invention;

[0045] Figure 2 It is a schematic diagram of the design concept of multiple receiving coils of a circumferential receiving mechanism according to an embodiment of the present invention;

[0046] Figure 3 It is a schematic diagram of a method for enhancing magnetic particle imaging signals based on a circumferential multi-channel receiving coil using a graph neural network according to an embodiment of the present invention;

[0047] Figure 4 It is a schematic diagram of the structure of a computer system of a server for implementing the method, system, and device embodiments of the present application. Detailed Embodiments

[0048] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention and are not intended to limit the invention. Additionally, it should be noted that for the sake of description, only parts related to the relevant invention are shown in the drawings.

[0049] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0050] The present invention provides a method for enhancing magnetic particle imaging signals based on a circumferential multi-channel receiving coil, specifically including:

[0051] Construct a dynamic graph structure including multiple nodes based on the positions of multiple pairs of receiving coils of a circumferential receiving mechanism;

[0052] Perform signal encoding on the response signals generated by multiple pairs of receiving coils of the circumferential receiving structure to obtain a spatial representation of the response signals;

[0053] Encode the spatial representation of the response signals into the node features of the dynamic graph structure, and use a graph neural network to perform information fusion based on the node features to obtain the output features of each pair of receiving coils in each convolutional layer of the graph neural network;

[0054] Map the output features of the last convolutional layer of the graph neural network back to the time domain space using a one-dimensional linear layer and an inverse Fourier transform to obtain the finally output enhanced signals.

[0055] To more clearly illustrate the method for enhancing magnetic particle imaging signals based on a circumferential multi-channel receiving coil of the present invention, the following will elaborate on each step in the embodiments of the present invention. Figure 1 The steps in the embodiments of the present invention will be described in detail as follows.

[0056] The method for enhancing magnetic particle imaging signals based on a circumferential multi-channel receiving coil in the first embodiment of the present invention is described in detail as follows:

[0057] Construct a dynamic graph structure containing multiple nodes based on the positions of multiple pairs of receiving coils of the circumferential receiving mechanism;

[0058] In the present invention, the receiving coils always appear in pairs in MPI. As Figure 2 shown, it is a schematic diagram of the design concept of multiple receiving coils of the circumferential receiving mechanism in the embodiment of the present invention; in this embodiment, the circumferential receiving mechanism is: stacking multiple pairs of receiving coils in the circumferential aperture direction to form a circumferential receiving structure with multiple different directions. This receiving coil reduces the number of turns of the opposing coils and stacks multiple pairs of receiving coils in the circumferential aperture direction. This receiving coil structure improves the space utilization rate without changing the overall thickness and enriches the direction information of the magnetic particle response signal. The number of receiving coils can be initially set to 8 - 12 pairs according to actual situations.

[0059] In this embodiment, constructing a dynamic graph structure containing multiple nodes based on the positions of multiple pairs of receiving coils of the circumferential receiving mechanism includes:

[0060] Each node of the dynamic graph structure represents a pair of receiving coils;

[0061] In this embodiment, the purpose of constructing the dynamic graph structure is to construct the correlation between signals of different channels and perform complementary enhancement of signals between channels accordingly. The construction idea of the graph structure is as follows: Denote the offset angle of the th pair of coils as , construct a graph structure containing N nodes, each node represents a pair of receiving coils, and the connection strength between any two pairs of receiving coils and is defined by the angle difference between the two.

[0062] Calculate the connection strength between any two pairs of receiving coils based on the offset angle between any two pairs of receiving coils; in this embodiment, calculating the connection strength between any two pairs of receiving coils includes:

[0063] ;

[0064] where is the offset angle of the kth pair of coils, is the offset angle of the j-th pair of coils, is the connection strength between the k-th pair of coils and the j-th pair of coils; both k and j are the numbers of coil pairs, is the connection strength.

[0065] The graph structure constructed by the offset angle is fixed and cannot be dynamically adjusted according to the feature transformation during the model training process. Therefore, a dynamic graph structure is constructed based on the cosine similarity of the spatial representations of the response signals of each pair of coils, and the cosine similarity of any two pairs of receiving coils is calculated;

[0066] In this embodiment, calculating the cosine similarity of any two pairs of receiving coils includes:

[0067] ;

[0068] Among them, is the cosine similarity between the k-th pair of coils and the j-th pair of coils, is the signal representation of the k-th pair of coils, is the signal representation of the j-th pair of coils, is the transpose of the signal representation of the j-th pair of coils, is the 2-norm of the signal representation of the k-th pair of coils, is the 2-norm of the signal representation of the j-th pair of coils, is the cosine similarity.

[0069] Determine the final dynamic graph structure based on the cosine similarity and connection strength of any two pairs of receiving coils.

[0070] In this embodiment, the final graph structure is determined by the linear combination of their connection strength and their cosine similarity:

[0071] ;

[0072] Among them, is the connection weight between the k-th pair of coils and the j-th pair of coils, is the harmonic weight, which can be initially set to 0.5, is the connection weight between the k-th node and the j-th node.

[0073] Perform signal encoding on the response signals generated by multiple pairs of receiving coils in the circumferential receiving structure to obtain the spatial representation of the response signals;

[0074] In this embodiment, the response signal generated by each pair of coils is denoted as, which is essentially a one-dimensional time series signal. In the present invention, through one-dimensional convolution operation, dimension compression is performed in the frequency domain and the time domain respectively, and then spliced into a vector representation of the signal. Then, performing signal encoding on the response signals generated by multiple pairs of receiving coils to obtain the spatial representation of the response signals includes:

[0075] ;

[0076] wherein, is the spatial representation of the response signal, is the convolution operator in the time domain, is the convolution operator in the frequency domain, is the activation function, represents the Fourier transform.

[0077] Encode the spatial representation of the response signal into the node features of the dynamic graph structure, and use the graph neural network to perform information fusion based on the node features to obtain the output features of each pair of receiving coils in each convolutional layer of the graph neural network;

[0078] The graph neural network is a new type of model that performs convolutional operations on non-Euclidean data and can utilize both the structural information of the graph and the node feature information simultaneously. As Figure 3 shown, it is a schematic diagram of the magnetic particle imaging signal enhancement method based on the circumferential multi-channel receiving coil using the graph neural network in the embodiment of the present invention. The present invention encodes the hardware information into the graph structure and encodes the response signal information into the node features, and realizes the fusion of software and hardware information through the graph neural network. In this embodiment, using the graph neural network to perform information fusion based on the node features to obtain the output features of each pair of receiving coils in each convolutional layer of the graph neural network includes:

[0079] The spatial representation of the response signal is used as the input feature of the first convolutional layer;

[0080] For the output feature of the k-th pair of receiving coils in the o-th convolutional layer is:

[0081] ;

[0082] wherein, is the output feature of the k-th pair of receiving coils in the o-th convolutional layer, and are both parameters of the convolutional layer, is the connection weight between the k-th pair of coils and the j-th pair of coils, is the input feature of the k-th pair of receiving coils in the o-th convolutional layer, is the input feature of the j-th pair of receiving coils in the o-th convolutional layer.

[0083] Specifically, the spatial representation of each pair of coils is affected by the signals of the remaining coils, and the degree of influence is dynamically adjusted by the connection strength between the two, which enables the signals of each pair of coils to be jointly complemented with other signals during the training process, reducing the impact of undersampling on the data quality.

[0084] The output features of the convolutional layer of the last layer of the graph neural network are mapped back to the time domain space using a one-dimensional linear layer and the inverse Fourier transform to obtain the finally output enhanced signal.

[0085] In this embodiment, after passing through multiple layers of convolution, the dimension of the received signal remains unchanged, and the output features of its last layer can be expressed as:

[0086] ;

[0087] In this embodiment, mapping the output features of the convolutional layer of the last layer of the graph neural network back to the time domain space using a one-dimensional linear layer and the inverse Fourier transform to obtain the finally output enhanced signal includes:

[0088] ;

[0089] where and are both trainable parameters, is the finally output enhanced signal, is the time domain representation of the output features of the last layer, is the frequency domain representation of the output features of the last layer, is the inverse Fourier transform.

[0090] Although the above steps are described in the above order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of this embodiment, different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are all within the protection scope of the present invention.

[0091] The magnetic particle imaging signal enhancement system based on a circumferential multi-channel receiving coil according to the second embodiment of the present invention, the system includes:

[0092] A graph structure construction module, configured to construct a dynamic graph structure including multiple nodes based on the positions of multiple pairs of receiving coils of the circumferential receiving mechanism;

[0093] A signal encoding module, configured to perform signal encoding on the response signals generated by multiple pairs of receiving coils of the circumferential receiving structure to obtain the spatial representation of the response signals;

[0094] An information fusion module, configured to encode the spatial representation of the response signals into the node features of the dynamic graph structure, and perform information fusion based on the node features using a graph neural network to obtain the output features of each pair of receiving coils in each convolutional layer of the graph neural network;

[0095] A signal mapping module, which is used to map the output features of the convolutional layer of the last layer of the graph neural network back to the time domain space using a one-dimensional linear layer and an inverse Fourier transform to obtain the finally output enhanced signal.

[0096] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes and related descriptions of the above-described systems can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0097] It should be noted that the magnetic particle imaging signal enhancement system based on a circumferential multi-channel receiving coil provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules according to needs, 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 combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. For the names of the modules and steps involved in the embodiments of the present invention, they are only used to distinguish each module or step, and are not regarded as an improper limitation of the present invention.

[0098] 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, and the instructions are used to be executed by the processor to implement the above-mentioned magnetic particle imaging signal enhancement method based on a circumferential multi-channel receiving coil.

[0099] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned magnetic particle imaging signal enhancement method based on a circumferential multi-channel receiving coil.

[0100] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes and related descriptions of the above-described storage device and processing device can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0101] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the technical field. To clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in the form of electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0102] Reference is made below to Figure 4 , which shows a schematic structural diagram of a computer system of a server for implementing the method, system, and device embodiments of the present application. Figure 4 The server shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0103] As Figure 4 shown, the computer system includes a central processing unit (CPU, Central Processing Unit) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM, Read Only Memory) 602 or the program loaded from the storage section 608 into the random access memory (RAM, Random Access Memory) 603. In the RAM 603, various programs and data required for system operation are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. The input / output (I / O, Input / Output) interface 605 is also connected to the bus 604.

[0104] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as required. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 610 as required so that a computer program read therefrom is installed into the storage section 608 as required.

[0105] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above functions defined in the methods of the present application are performed. It should be noted that the computer-readable medium described above in the present 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 of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0106] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include 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, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

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

[0108] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or represent a specific order or sequence.

[0109] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, so that a process, method, article, or device / equipment that comprises a series of elements not only includes those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent in these processes, methods, articles, or devices / equipment.

[0110] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle 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 these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil, characterized in that: The method comprises: Based on the positions of multiple pairs of receiving coils of the toroidal receiving mechanism, a dynamic graph structure including multiple nodes is constructed; Signal encoding is performed on response signals generated by multiple pairs of receiving coils of the toroidal receiving structure to obtain a spatial representation of the response signals; The spatial representation of the response signal is encoded into the node features of the dynamic graph structure, and the graph neural network is used to fuse information based on the node features to obtain the output features of each pair of receiving coils in each convolutional layer of the graph neural network; The output features of the last convolutional layer of the graph neural network are mapped back to the time domain space using a one-dimensional linear layer and inverse Fourier transform to obtain the final output enhanced signal.

2. The method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil according to claim 1, characterized in that: The circumferential receiving mechanism is: a plurality of pairs of receiving coils are stacked and placed in a direction surrounding the aperture to form a circumferential receiving structure with a plurality of different directions.

3. The method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil according to claim 2, characterized in that: Based on the positions of multiple pairs of receiving coils of the toroidal receiving mechanism, a dynamic graph structure containing multiple nodes is constructed including: Each node of the dynamic graph structure represents a pair of receiving coils; Calculating the connection strength between any two pairs of receiving coils based on the offset angle between any two pairs of receiving coils; Calculate the cosine similarity of any two pairs of receiving coils; The final dynamic graph structure is determined based on the cosine similarity and connection strength of any two pairs of receiving coils.

4. The method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil according to claim 3, characterized in that: Calculating the connection strength between any two pairs of receiving coils involves: ； in, is the offset angle of the kth pair of coils, is the offset angle of the jth pair of coils, is the connection strength between the kth pair of coils and the jth pair of coils; k and j are the numbers of the coil pairs, For connection strength.

5. The method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil according to claim 4, characterized in that: Calculating the cosine similarity of any two pairs of receiving coils involves: ； in, is the cosine similarity between the kth pair of coils and the jth pair of coils, is the signal representation of the kth pair of coils, is the signal representation of the jth pair of coils, is the transpose of the signal representation of the j-th pair of coils, is the 2-norm of the signal representation of the k-th pair of coils, is the 2-norm of the signal representation of the j-th pair of coils, is the cosine similarity.

6. The method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil according to claim 5, characterized in that: The final dynamic graph structure is: ； in, is the connection weight between the kth pair of coils and the jth pair of coils, To reconcile the weights.

7. The method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil according to claim 6, characterized in that: Signal encoding of response signals generated by multiple pairs of receiving coils to obtain spatial representation of response signals includes: ； in, is the spatial representation of the response signal, is the convolution operator in the time domain, is the convolution operator in the frequency domain, is the activation function, represents Fourier transform.

8. The method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil according to claim 7, characterized in that: The output features of each pair of receiving coils in each convolutional layer of the graph neural network are obtained by using the graph neural network to fuse information based on node features, including: The spatial representation of the response signal is used as the input feature of the first convolutional layer; For the kth pair of receiving coils, the output features of the oth convolutional layer are: ； in, is the output feature of the kth pair of receiving coils at the oth convolutional layer, W and are the parameters of the convolutional layer. is the connection weight between the kth pair of coils and the jth pair of coils, is the input feature of the kth pair of receiving coils in the oth convolutional layer, is the input feature of the j-th pair of receiving coils in the o-th convolutional layer.

9. The method for enhancing magnetic particle imaging signals based on a toroidal multi-channel receiving coil according to claim 8, characterized in that: The output features of the convolutional layer of the last layer of the graph neural network are mapped back to the time domain space using a one-dimensional linear layer and inverse Fourier transform to obtain the final output enhanced signal including: ； in, and are all trainable parameters. is the final output enhanced signal, is the time domain representation of the output feature of the last layer, is the frequency domain representation of the output features of the last layer, is the inverse Fourier transform.

10. A magnetic particle imaging signal enhancement system based on a toroidal multi-channel receiving coil, characterized in that: The system comprises: A graph structure building module, used for building a dynamic graph structure containing multiple nodes based on the positions of multiple pairs of receiving coils of the circumferential receiving mechanism; A signal encoding module, used for encoding the response signals generated by the multiple pairs of receiving coils of the toroidal receiving structure to obtain the spatial representation of the response signals; An information fusion module is used to encode the spatial representation of the response signal into the node features of the dynamic graph structure, and use the graph neural network to perform information fusion based on the node features to obtain the output features of each pair of receiving coils at each convolution layer of the graph neural network; The signal mapping module is used to map the output features of the convolutional layer of the last layer of the graph neural network back to the time domain space using a one-dimensional linear layer and inverse Fourier transform to obtain the final output enhanced signal.

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