CN115880440B - Magnetic particle three-dimensional reconstruction imaging method based on generation countermeasure network - Google Patents
Magnetic particle three-dimensional reconstruction imaging method based on generation countermeasure network Download PDFInfo
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Abstract
The invention belongs to the technical field of medical imaging, in particular relates to a magnetic particle three-dimensional reconstruction imaging method, system and device based on a generation countermeasure network, and aims to solve the problems of long reconstruction time and low imaging resolution of the existing MPI high-resolution image. The method comprises the following steps: acquiring a multi-view sparse two-dimensional MPI image of an imitation body of an object to be imaged and reconstructed, and performing downsampling to obtain a downsampled sparse two-dimensional MPI image; inputting the downsampled sparse two-dimensional MPI image into a trained generation model of a neighborhood point average diffusion search self-attention generation countermeasure network to obtain a dense two-dimensional MPI image; reconstructing each dense two-dimensional MPI image through a filtering back projection reconstruction algorithm to finally obtain a three-dimensional MPI image of the object to be imaged; the invention accelerates the reconstruction speed of the MPI image, improves the imaging resolution, and ensures that the magnetic particle imaging equipment has a larger application prospect in the medical field.
Description
Technical Field
The invention belongs to the technical field of medical imaging, and particularly relates to a magnetic particle three-dimensional reconstruction imaging method, system and device based on a generation countermeasure network.
Background
Magnetic particle imaging is a novel imaging technology at present, and has the imaging advantages of no radiation, high sensitivity and the like. The current magnetic particle imaging equipment has wide application prospect in clinical research, including early detection of focus, drug targeting treatment and other leading directions. In the existing magnetic particle imaging devices, how to quickly realize the scanning of an object and the reconstruction of a high-resolution three-dimensional image is a challenging problem. At present, part of equipment performs fault scanning on an object by rotating an equipment coil, scans two-dimensional images of the object at different angles by physically rotating an imaged coil, and then performs filtered back projection reconstruction on the scanned images. However, the imaging technology needs to perform coil rotation movement, and the service life of equipment can be reduced due to too high rotation speed. Second, if the time resolution of the scan is to be improved, multiple scans are required, which is time consuming and laborious. Therefore, how to improve the reconstruction speed and the imaging resolution of imaging under the condition of considering the construction cost of hardware equipment is a current challenge. Based on the method, the invention provides a magnetic particle three-dimensional reconstruction imaging method based on generation of an antagonism network.
Disclosure of Invention
In order to solve the problems in the prior art, namely, the problems of long reconstruction time and low imaging resolution of the existing MPI three-dimensional high-resolution image, the invention provides a magnetic particle three-dimensional reconstruction imaging method based on generation of a countermeasure network, which comprises the following steps:
step S100, acquiring a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged, and performing downsampling to obtain a downsampled sparse two-dimensional MPI image; the multi-view sparse two-dimensional MPI images are a plurality of MPI projection images which are collected in sequence according to set angles in a rotating mode;
step S200, inputting the downsampled sparse two-dimensional MPI image into a trained generation model of a self-attention generation countermeasure network by average diffusion search of neighborhood points, and obtaining a dense two-dimensional MPI image;
step S300, reconstructing each dense two-dimensional MPI image by a filtering back projection reconstruction algorithm to finally obtain a three-dimensional MPI image of the object to be imaged;
the neighborhood point average diffusion search self-attention generation countermeasure network comprises a generation model and a discrimination model;
the generating model comprises five diffusion searching attention mechanism convolution modules; the diffusion search attention mechanism convolution module is constructed based on a neighborhood point average diffusion convolution sub-network, a self-attention fusion network and an activation function layer which are connected in sequence; the activation function adopted by the activation function layer in the first four diffusion searching attention mechanism convolution modules is a leakage ReLU, and the activation function adopted by the activation function layer in the fifth diffusion searching attention mechanism convolution module is a hyperbolic tangent function;
The neighborhood point average diffusion convolution sub-network is configured to sequentially encode the downsampled sparse two-dimensional MPI image through an encoding function to obtain a sequentially encoded image; performing regional convolution operation on each sequence coding image to obtain particle concentration information, angle information and association degree information among the images of each sequence coding image;
the self-attention fusion network is configured to multiply the particle concentration information, the angle information and the association degree information among the images of each sequence coding image with the corresponding weight matrix for linear transformation, fuse the images through a multi-head attention mechanism, and up-sample the fused images to obtain a dense two-dimensional MPI image;
the discrimination model comprises five convolutional neural network modules; the convolutional neural network module is constructed based on a convolutional layer, a regularization layer and an activation function layer which are sequentially connected; the activation function adopted by the activation function layer of the first four convolutional neural network modules is a leak ReLU, and the activation function adopted by the activation function layer of the fifth convolutional neural network module is a softmax.
In some preferred embodiments, the training process of the neighborhood point average spread search self-attention generation countermeasure network is as follows:
Step A100, acquiring a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged through MPI imaging equipment, and performing downsampling to obtain a downsampled sparse two-dimensional MPI image; obtaining a training data set based on the downsampled sparse two-dimensional MPI image and a corresponding truth value label thereof;
step A200, inputting the downsampled sparse two-dimensional MPI image into a pre-constructed neighborhood point average diffusion search self-attention generation countermeasure network generation model to obtain a dense two-dimensional MPI image;
step A300, inputting the dense two-dimensional MPI image and the truth value label corresponding to the dense two-dimensional MPI image into the neighborhood point average diffusion search self-attention generation countermeasure network discrimination model, and acquiring discrimination results of the dense two-dimensional MPI image;
step A400, based on the discrimination result, combining each dense two-dimensional MPI image, each sparse two-dimensional MPI image and the corresponding truth value labels thereof, calculating a total loss value through a pre-constructed loss function, and updating the network parameters of the neighborhood point average diffusion search self-attention generation countermeasure network generation model and discrimination model;
and step A500, training the generator network and the discriminator network of the neighborhood point average spread search self-attention generation countermeasure network in a circulating way until the trained neighborhood point average spread search self-attention generation countermeasure network is obtained.
In some preferred embodiments, the number of angles of formation is set to
At an angle of
At the time of the first
Zhang Xishu two-dimensional MPI image has a coding function of
：
In some preferred embodiments, the sequential encoded images are subjected to a region convolution operation by: processing the concentration values of the pixel points in the sequential coding image, and performing operation by adopting a 3 multiplied by 3 convolution kernel after processing;
the method for processing the concentration value of the pixel point in the sequential coding image comprises the following steps:
wherein ,
representing the first of said sequential encoded pictures
Line 1
The pixel points of the column are arranged,
representing a point neighborhood
The average value of all the values within the range,
representation of
The minimum of all values within the neighborhood of points,
representation of
The maximum of all values within the neighborhood of points,
representation of
The variable values in the point neighborhood consist of points that are adjacent laterally, longitudinally and diagonally.
In some preferred embodiments, the particle concentration information, the angle information, and the correlation degree information between images of each sequentially encoded image are multiplied by a corresponding weight matrix to perform linear transformation, and the method includes:
wherein ,
representing self-attention mechanism, vector
The information on the concentration of the particles is indicated,
the method comprises the steps of carrying out a first treatment on the surface of the Vector quantity
The information of the angle is represented by a set of angles,
the method comprises the steps of carrying out a first treatment on the surface of the Vector quantity 
Information indicating the degree of association between images,
，
representing the deformation of a matrix into a one-dimensional column vector
Representing sequentially encoded images of the input neighborhood point average diffusion convolution sub-network;
、
、
respectively representing weight matrices corresponding to particle concentration information, angle information and correlation degree information between images of sequentially encoded images,
representing sequential encoded images
Neurons in all output layers
The sum of the output results from 1 to N,
representing sequential encoded images
Through the first
The output values of the individual neurons are then,
representing sequential encoded images
Through the first
The output values of the individual neurons are then,
representing sequential encoded images
Through the first
The output value of the individual neurons is the total output value
Is a specific gravity of (c).
In some preferred embodiments, the particle concentration information, the angle information and the association degree information between the images of each sequentially encoded image are fused through a multi-head attention mechanism, and the method comprises the following steps:
wherein ,
is a function of the splice-in,
represent the first
The outputs of the individual attention machine sub-modules,
a total weight matrix representing the output of the multi-headed attentiveness mechanism,
and information obtained by fusing the particle concentration information, the angle information and the association degree information between the images of the sequential encoded images.
In some preferred embodiments, the total loss value is calculated by a pre-constructed loss function by:
wherein ,
as a function of the total loss,
representing a pixel loss function and an contrast loss function,
representing an angular sequential ordering optimization function,
representing the error of the pixel information and,
representing the loss of the generative model,
a sparse two-dimensional MPI image is represented,
representation generation of the first
Zhang Miji a two-dimensional MPI image,
representation generation of the first
Zhang Miji a two-dimensional MPI image,
represent the first
Zhang Xishu truth labels corresponding to two-dimensional MPI images,
a coefficient representing the loss of a pixel,
a coefficient representing the countering loss is provided,
indicating the probability of whether the discrimination network discriminates true,
the output of the generative model is represented,
representing the function of the angle difference,
as a function of the hyperbolic tangent,
representing the predicted value of the angle at the corresponding input,
，
representing arbitrary two dense two-dimensional MPI images
An angle judgment function is provided, wherein the angle judgment function is provided with a plurality of angle judgment functions,
and
respectively represent the first
The angle labels corresponding to the densely-generated two-dimensional MPI images,
parameters indicating the richness settings for the information contained in the different mimics.
In a second aspect of the invention, a magnetic particle three-dimensional reconstruction imaging system based on generation of a countermeasure network is presented, the system comprising: an image acquisition module 100, an image reconstruction module 200 and a three-dimensional reconstruction module 300;
The image acquisition module 100 is configured to acquire a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged, and perform downsampling to obtain a downsampled sparse two-dimensional MPI image; the multi-view sparse two-dimensional MPI images are a plurality of MPI projection images which are collected in sequence according to set angles in a rotating mode;
the image reconstruction module 200 is configured to input the downsampled sparse two-dimensional MPI image into a trained generation model of a neighborhood point average diffusion search self-attention generation countermeasure network to obtain a dense two-dimensional MPI image;
the three-dimensional reconstruction module 300 is configured to reconstruct each dense two-dimensional MPI image through a filtered back projection reconstruction algorithm, and finally obtain a three-dimensional MPI image of the reconstructed object to be imaged;
the neighborhood point average diffusion search self-attention generation countermeasure network comprises a generation model and a discrimination model;
the generating model comprises five diffusion searching attention mechanism convolution modules; the diffusion search attention mechanism convolution module is constructed based on a neighborhood point average diffusion convolution sub-network, a self-attention fusion network and an activation function layer which are connected in sequence; the activation function adopted by the activation function layer in the first four diffusion searching attention mechanism convolution modules is a leakage ReLU, and the activation function adopted by the activation function layer in the fifth diffusion searching attention mechanism convolution module is a hyperbolic tangent function;
The neighborhood point average diffusion convolution sub-network is configured to sequentially encode the downsampled sparse two-dimensional MPI image through an encoding function to obtain a sequentially encoded image; performing regional convolution operation on each sequence coding image to obtain particle concentration information, angle information and association degree information among the images of each sequence coding image;
the self-attention fusion network is configured to multiply the particle concentration information, the angle information and the association degree information among the images of each sequence coding image with the corresponding weight matrix for linear transformation, fuse the images through a multi-head attention mechanism, and up-sample the fused images to obtain a dense two-dimensional MPI image;
the discrimination model comprises five convolutional neural network modules; the convolutional neural network module is constructed based on a convolutional layer, a regularization layer and an activation function layer which are sequentially connected; the activation function adopted by the activation function layer of the first four convolutional neural network modules is a leak ReLU, and the activation function adopted by the activation function layer of the fifth convolutional neural network module is a softmax.
In a third aspect of the invention, a storage device is provided in which a plurality of programs are stored, said programs being adapted to be loaded and executed by a processor for implementing a magnetic particle three-dimensional reconstruction imaging method based on generating a countermeasure network as described above.
In a fourth aspect of the present invention, a processing arrangement is provided, comprising a processor, a storage device; a processor adapted to execute each program; a storage device adapted to store a plurality of programs; the program is adapted to be loaded and executed by a processor for carrying out one of the above-mentioned magnetic particle three-dimensional reconstruction imaging methods based on generating a countermeasure network.
The invention has the beneficial effects that:
the invention improves the reconstruction speed and imaging resolution of MPI imaging;
the invention integrates the average diffusion search self-attention mechanism of the neighborhood points into the generation countermeasure network, optimizes by utilizing the angle sequence ordering optimization function, accelerates the reconstruction speed of MPI images, improves the reconstruction quality of the MPI images, overcomes the hardware defects of the current equipment, and ensures that the magnetic particle imaging equipment has a larger application prospect in the medical field.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is a flow diagram of a method for three-dimensional reconstruction imaging of magnetic particles based on generation of a countermeasure network in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a training flow of a magnetic particle three-dimensional reconstruction imaging method based on generating a countermeasure network in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a network module of an attention mechanism of one embodiment of the present invention;
FIG. 4 is a schematic diagram of a network structure of a method for three-dimensional reconstruction imaging of magnetic particles based on generation of a countermeasure network in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a frame of a magnetic particle three-dimensional reconstruction imaging system based on generation of a countermeasure network in accordance with an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The invention provides a magnetic particle three-dimensional reconstruction imaging method based on a generation countermeasure network, which is shown in figure 1 and comprises the following steps:
step S100, acquiring a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged, and performing downsampling to obtain a downsampled sparse two-dimensional MPI image; the multi-view sparse two-dimensional MPI images are a plurality of MPI projection images which are collected in sequence according to set angles in a rotating mode;
step S200, inputting the downsampled sparse two-dimensional MPI image into a trained generation model of a self-attention generation countermeasure network by average diffusion search of neighborhood points, and obtaining a dense two-dimensional MPI image;
step S300, reconstructing each dense two-dimensional MPI image by a filtering back projection reconstruction algorithm to finally obtain a three-dimensional MPI image of the object to be imaged;
the neighborhood point average diffusion search self-attention generation countermeasure network comprises a generation model and a discrimination model;
the generating model comprises five diffusion searching attention mechanism convolution modules; the diffusion search attention mechanism convolution module is constructed based on a neighborhood point average diffusion convolution sub-network, a self-attention fusion network and an activation function layer which are connected in sequence; the activation function adopted by the activation function layer in the first four diffusion searching attention mechanism convolution modules is a leakage ReLU, and the activation function adopted by the activation function layer in the fifth diffusion searching attention mechanism convolution module is a hyperbolic tangent function;
The neighborhood point average diffusion convolution sub-network is configured to sequentially encode the downsampled sparse two-dimensional MPI image through an encoding function to obtain a sequentially encoded image; performing regional convolution operation on each sequence coding image to obtain particle concentration information, angle information and association degree information among the images of each sequence coding image;
the self-attention fusion network is configured to multiply the particle concentration information, the angle information and the association degree information among the images of each sequence coding image with the corresponding weight matrix for linear transformation, fuse the images through a multi-head attention mechanism, and up-sample the fused images to obtain a dense two-dimensional MPI image;
the discrimination model comprises five convolutional neural network modules; the convolutional neural network module is constructed based on a convolutional layer, a regularization layer and an activation function layer which are sequentially connected; the activation function adopted by the activation function layer of the first four convolutional neural network modules is a leak ReLU, and the activation function adopted by the activation function layer of the fifth convolutional neural network module is a softmax.
In order to more clearly describe the three-dimensional reconstruction imaging method of magnetic particles based on the generation of the countermeasure network, each step in the embodiment of the method of the present invention is described in detail below with reference to the accompanying drawings.
In the existing commercial equipment for realizing magnetic particle (Magnetic particle Imaging, MPI) imaging, in order to image the distribution of particles in an object, the object needs to be scanned, but the current physical scanning mode based on coil rotation has long time for reconstructing an image, and multi-angle tomography is limited by the rotation speed of the coil, so that the hardware cost of the equipment is greatly increased, and the service life of the equipment is shortened. In order to effectively reduce reconstruction time and save hardware cost, the invention designs a Multi-angle three-dimensional magnetic particle imaging (Multi-view Three Dimensional Magnetic Particle Imaging, MV-3D-MPI) method and system based on a generated discrimination model. The invention provides a neighborhood point average diffusion search self-attention generation countermeasure network model, which aims to realize rapid reconstruction of an MPI high-resolution image and improve imaging resolution.
In the following embodiments, a training process of generating an countermeasure network by average spread search self-attention of a neighborhood point is described first, and then a testing process of generating an countermeasure network by average spread search self-attention of a neighborhood point is described in detail.
1. The training process of the neighborhood point average diffusion search self-attention generation countermeasure network comprises the following specific steps:
Step A100, acquiring a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged through MPI imaging equipment, and performing downsampling to obtain a downsampled sparse two-dimensional MPI image; obtaining a training data set based on the downsampled sparse two-dimensional MPI image and a corresponding truth value label thereof;
in the embodiment, acquiring multi-view dense MPI projection images by an own MPI device, and taking 15000 images as a training set; for a simulation, an image is preferably acquired every 5 ° (in other embodiments, the images can be acquired at set angle intervals according to actual requirements), and a total of 36-angle projection MPI images are acquired; the pixel size is changed to 12×12 by any conventional downsampling method, and the downsampled image is used as an input object of the network.
Step A200, inputting the downsampled sparse two-dimensional MPI image into a pre-constructed neighborhood point average diffusion search self-attention generation countermeasure network generation model to obtain a dense two-dimensional MPI image;
in this embodiment, the neighborhood point average diffusion search self-attention generation countermeasure network includes a generation model and a discrimination model;
the generating model comprises five diffusion searching attention mechanism convolution modules; the diffusion search attention mechanism convolution module is constructed based on a neighborhood point average diffusion convolution sub-network, a self-attention fusion network and an activation function layer which are connected in sequence; the activation function adopted by the activation function layer in the first four diffusion searching attention mechanism convolution modules is a leakage ReLU, and the activation function adopted by the activation function layer in the fifth diffusion searching attention mechanism convolution module is a hyperbolic tangent function;
The neighborhood point average diffusion convolution sub-network is configured to sequentially encode the downsampled sparse two-dimensional MPI image through an encoding function to obtain a sequentially encoded image; performing regional convolution operation on each sequence coding image to obtain particle concentration information, angle information and association degree information among the images of each sequence coding image;
the self-attention fusion network is configured to multiply the particle concentration information, the angle information and the association degree information among the images of each sequence coding image with the corresponding weight matrix for linear transformation, fuse the images through a multi-head attention mechanism, and up-sample the fused images to obtain a dense two-dimensional MPI image;
in the invention, because of the sequential relation of angles existing in the input, in order to enable the model to learn the relevance of adjacent angles and the global relevance of different angles, the input layer of the network adopts sequential coding, the input of the set model comprises 6 images of different angles, one image is taken every 30 degrees in [0 degree, 180 degrees ], and the angle is
At the time of the first
Zhang Xishu encoding function of two-dimensional MPI image
The method comprises the following steps:
wherein , 
the sine and cosine functions in the expression can ensure that the constructed model learns the relative position relation between different images.
Aiming at the characteristics of an MPI image, the neighborhood point diffusion search value is mainly used for more fully modeling the relation between a certain pixel value of the MPI image and the neighborhood thereof, and as the magnetic particle response of a magnetic field free region can be influenced by the particles beside, the concentration value of imaging at part of points is related to the concentration value of other peripheral pixel points. Performing regional convolution operation on the sequence coding image, wherein the method comprises the following steps: processing the concentration values of the pixel points in the sequential coding images, and preferably adopting a 3 multiplied by 3 convolution kernel to operate after the processing;
the method for processing the concentration value of the pixel point in the sequential coding image comprises the following steps:
to be used for
The method for processing the concentration value by taking the point as an example comprises the following steps:
wherein ,
representing the first of said sequential encoded pictures
Line 1
The pixel points of the column are arranged,
representing a point neighborhood
The average value of all the values within the range,
representation of
The minimum of all values within the neighborhood of points,
representation of
The maximum of all values within the neighborhood of points,
representation of
The variable values in the point neighborhood consist of points that are adjacent laterally, longitudinally and diagonally.
The invention provides a self-attention fusion network module for fusing key information extracted from images by different sub-networks in a network model generation part. Setting the input of each sub-network as the preprocessed image 
Processing input by three sub-networks to obtain different types of information, multiplying the obtained three types of different information with corresponding weight matrixes respectively for linear transformation, wherein the corresponding weight matrixes are respectively
、
、
Then fusing different types of information through a self-attention fusion network module, multiplying the particle concentration information, the angle information and the association degree information among the images of each sequence coding image with a corresponding weight matrix for linear transformation, wherein the method comprises the following steps:
wherein ,
representing self-attention mechanism, vector
The information on the concentration of the particles is indicated,
the method comprises the steps of carrying out a first treatment on the surface of the Vector quantity
The information of the angle is represented by a set of angles,
the method comprises the steps of carrying out a first treatment on the surface of the Vector quantity
Information indicating the degree of association between images,
，
representing the deformation of a matrix into a one-dimensional column vector
Representing sequentially encoded images of the input neighborhood point average diffusion convolution sub-network;
、
、
respectively representing weight matrices corresponding to particle concentration information, angle information and correlation degree information between images of sequentially encoded images,
representing sequential encoded images
Neurons in all output layers
The sum of the output results from 1 to N,
representing sequential encoded images
Through the first
The output values of the individual neurons are then,
representing sequential encoded images
Through the first 
The output values of the individual neurons are then,
representing sequential encoded images
Through the first
The output value of the individual neurons is the total output value
Is a specific gravity of (c).
The particle concentration information, the angle information and the association degree information among the images of each sequence coding image are fused through a multi-head attention mechanism, and the method comprises the following steps:
wherein ,
is a function of the splice-in,
represent the first
The outputs of the individual attention machine sub-modules,
a total weight matrix representing the output of the multi-headed attentiveness mechanism,
information representing the combination of particle concentration information, angle information and correlation information between images of sequentially encoded images, and the output of each attention sub-module is spliced and then passed through a processing unit
Multiplying to perform linear transformation, and finally obtaining output of the generated model through up-sampling
， 
Representing the upsampling function.
Step A300, inputting the dense two-dimensional MPI image and the truth value label corresponding to the dense two-dimensional MPI image into the neighborhood point average diffusion search self-attention generation countermeasure network discrimination model, and acquiring discrimination results of the dense two-dimensional MPI image;
the discrimination model comprises five convolutional neural network modules; the convolutional neural network module is constructed based on a convolutional layer, a regularization layer and an activation function layer which are sequentially connected; the activation function adopted by the activation function layer of the first four convolutional neural network modules is a leak ReLU, and the activation function adopted by the activation function layer of the fifth convolutional neural network module is a softmax.
The convolution layer carries out convolution operation on input data of the judging model, regularization is carried out through the regularization layer, and finally the activation function layer is input to carry out function operation.
Step A400, based on the discrimination result, combining each dense two-dimensional MPI image, each sparse two-dimensional MPI image and the corresponding truth value labels thereof, calculating a total loss value through a pre-constructed loss function, and updating the network parameters of the neighborhood point average diffusion search self-attention generation countermeasure network generation model and discrimination model;
in this embodiment, the total loss value is calculated by a pre-constructed loss function, and the method is as follows:
wherein ,
as a function of the total loss,
representing a pixel loss function and an contrast loss function,
representing an angular sequential ordering optimization function,
representing the error of the pixel information and,
representing the loss of the generative model,
a sparse two-dimensional MPI image is represented,
representation generation of the first
Zhang Miji a two-dimensional MPI image,
representation generation of the first
Zhang Miji a two-dimensional MPI image,
represent the first
Zhang Xishu truth labels corresponding to two-dimensional MPI images,
a coefficient representing the loss of a pixel,
representing the coefficient of countermeasures against losses, in the present embodiment
And
it is preferably set to 1 and,
indicating the probability of whether the discrimination network discriminates true, 
The output of the generative model is represented,
representing the function of the angle difference,
as a function of the hyperbolic tangent,
representing the predicted value of the angle at the corresponding input,
，
representing arbitrary two dense two-dimensional MPI images
The angle judgment function takes a value of 1 if the judgment value is true, takes a value of 0 if the judgment value is not true,
and
respectively represent the first
An angle label corresponding to the generated dense two-dimensional MPI image;
parameters indicating the richness settings of the information contained in the different mimics, in the present invention, if the collected mimics are letter mimics, for the letter mimics
Preferably set to 1, for more complex Chinese character mimics
Preferably set to 2, for a simulation dedicated to evaluating resolution, since the details are rich,
preferably set to 3. And when the image pairs are taken, taking the imitation as a unit, supervising the image pairs taken by each imitation image, and punishing error sequencing. The three sets of imitation body fills preferably have an inner diameter of 0.5mm containing 10 mg/ml Perimag solution. The field of view (FOV) of the three groups of simulations is preferably 6cm ×6× 6cm ×6cm (letters), 6cm ×6cm ×10× 10 cm (spiral, acquisition time of one sparse projection is 3 minutes), 6cm×6cm×cm (container, acquisition time of one sparse projection is 2 minutes), respectively.
Step A500, training a generator network and a discriminator network of the neighborhood point average spread search self-attention generation countermeasure network in a circulating manner until the trained neighborhood point average spread search self-attention generation countermeasure network is obtained;
2. test procedure for generating countermeasure network by neighborhood point average diffusion search self-attention
Step S100, acquiring a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged, and performing downsampling to obtain a downsampled sparse two-dimensional MPI image; the multi-view sparse two-dimensional MPI images are a plurality of MPI projection images which are collected in sequence according to set angles in a rotating mode;
in this embodiment, 200 instance images acquired by the MPI apparatus are taken as a test set.
Step S200, inputting the downsampled sparse two-dimensional MPI image into a trained generation model of a self-attention generation countermeasure network by average diffusion search of neighborhood points, and obtaining a dense two-dimensional MPI image;
in this embodiment, the self-attention generation countermeasure network is searched based on the average diffusion of the trained neighborhood points, and preferably, the sparse two-dimensional MPI image is generated from any angle of [0 °,5 °,10 °,15 °,20 °,25 °, 30 °,35 °,40 °,45 °,50 °,55 °,60 °,65 °, …,170 °,175 ° ], and the pixel size of the generated dense two-dimensional MPI image is 48×48.
Step S300, reconstructing each dense two-dimensional MPI image by a filtering back projection reconstruction algorithm to finally obtain a three-dimensional MPI image of the object to be imaged;
a magnetic particle three-dimensional reconstruction imaging system based on generation of a countermeasure network according to a second embodiment of the present invention, as shown in fig. 4, includes: an image acquisition module 100, an image reconstruction module 200 and a three-dimensional reconstruction module 300;
the image acquisition module 100 is configured to acquire a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged, and perform downsampling to obtain a downsampled sparse two-dimensional MPI image; the multi-view sparse two-dimensional MPI images are a plurality of MPI projection images which are collected in sequence according to set angles in a rotating mode;
the image reconstruction module 200 is configured to input the downsampled sparse two-dimensional MPI image into a trained generation model of a neighborhood point average diffusion search self-attention generation countermeasure network to obtain a dense two-dimensional MPI image;
the three-dimensional reconstruction module 300 is configured to reconstruct each dense two-dimensional MPI image through a filtered back projection reconstruction algorithm, and finally obtain a three-dimensional MPI image of the reconstructed object to be imaged;
the neighborhood point average diffusion search self-attention generation countermeasure network comprises a generation model and a discrimination model;
The generating model comprises five diffusion searching attention mechanism convolution modules; the diffusion search attention mechanism convolution module is constructed based on a neighborhood point average diffusion convolution sub-network, a self-attention fusion network and an activation function layer which are connected in sequence; the activation function adopted by the activation function layer in the first four diffusion searching attention mechanism convolution modules is a leakage ReLU, and the activation function adopted by the activation function layer in the fifth diffusion searching attention mechanism convolution module is a hyperbolic tangent function;
the neighborhood point average diffusion convolution sub-network is configured to sequentially encode the downsampled sparse two-dimensional MPI image through an encoding function to obtain a sequentially encoded image; performing regional convolution operation on each sequence coding image to obtain particle concentration information, angle information and association degree information among the images of each sequence coding image;
the self-attention fusion network is configured to multiply the particle concentration information, the angle information and the association degree information among the images of each sequence coding image with the corresponding weight matrix for linear transformation, fuse the images through a multi-head attention mechanism, and up-sample the fused images to obtain a dense two-dimensional MPI image;
The discrimination model comprises five convolutional neural network modules; the convolutional neural network module is constructed based on a convolutional layer, a regularization layer and an activation function layer which are sequentially connected; the activation function adopted by the activation function layer of the first four convolutional neural network modules is LeakyReLU, and the activation function adopted by the activation function layer of the fifth convolutional neural network module is softmax.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working processes and related descriptions of the above-described system may refer to corresponding processes in the foregoing method embodiments, which are not repeated herein.
It should be noted that, in the magnetic particle three-dimensional reconstruction imaging system provided in the foregoing embodiment, only the division of the foregoing functional modules is illustrated, and in practical application, the foregoing functional allocation may be performed by different functional modules according to needs, that is, the modules or steps in the foregoing embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into a plurality of sub-modules to complete all or part of the functions described above. The names of the modules and steps related to the embodiments of the present invention are merely for distinguishing the respective modules or steps, and are not to be construed as unduly limiting the present invention.
A storage device of a third embodiment of the present invention stores therein a plurality of programs adapted to be loaded by a processor and to implement a magnetic particle three-dimensional reconstruction imaging method based on generation of a countermeasure network as described above.
A processing device according to a fourth embodiment of the present invention includes a processor, a storage device; a processor adapted to execute each program; a storage device adapted to store a plurality of programs; the program is adapted to be loaded and executed by a processor for carrying out one of the above-mentioned magnetic particle three-dimensional reconstruction imaging methods based on generating a countermeasure network.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the storage device and the processing device and the related description of the foregoing description may refer to the corresponding process in the foregoing method example, which is not repeated herein.
Those of skill in the art will appreciate that the various illustrative modules, method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the program(s) corresponding to the software modules, method steps, may be embodied in Random Access Memory (RAM), 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 art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not intended to be limiting.
The terms "first," "second," "third," and the like, are used for distinguishing between similar objects and not for describing a particular sequential or chronological order.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (10)
1. A magnetic particle three-dimensional reconstruction imaging method based on generation of a countermeasure network, the method comprising the steps of:
step S100, acquiring a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged, and performing downsampling to obtain a downsampled sparse two-dimensional MPI image; the multi-view sparse two-dimensional MPI images are a plurality of MPI projection images which are collected in sequence according to set angles in a rotating mode;
step S200, inputting the downsampled sparse two-dimensional MPI image into a trained generation model of a self-attention generation countermeasure network by average diffusion search of neighborhood points, and obtaining a dense two-dimensional MPI image;
Step S300, reconstructing each dense two-dimensional MPI image by a filtering back projection reconstruction algorithm to finally obtain a three-dimensional MPI image of the object to be imaged;
the neighborhood point average diffusion search self-attention generation countermeasure network comprises a generation model and a discrimination model;
the generating model comprises five diffusion searching attention mechanism convolution modules; the diffusion search attention mechanism convolution module is constructed based on a neighborhood point average diffusion convolution sub-network, a self-attention fusion network and an activation function layer which are connected in sequence; the activation function adopted by the activation function layer in the first four diffusion searching attention mechanism convolution modules is a leakage ReLU, and the activation function adopted by the activation function layer in the fifth diffusion searching attention mechanism convolution module is a hyperbolic tangent function;
the neighborhood point average diffusion convolution sub-network is configured to sequentially encode the downsampled sparse two-dimensional MPI image through an encoding function to obtain a sequentially encoded image; performing regional convolution operation on each sequence coding image to obtain particle concentration information, angle information and association degree information among the images of each sequence coding image;
the self-attention fusion network is configured to multiply the particle concentration information, the angle information and the association degree information among the images of each sequence coding image with the corresponding weight matrix for linear transformation, fuse the images through a multi-head attention mechanism, and up-sample the fused images to obtain a dense two-dimensional MPI image;
The discrimination model comprises five convolutional neural network modules; the convolutional neural network module is constructed based on a convolutional layer, a regularization layer and an activation function layer which are sequentially connected; the activation function adopted by the activation function layer of the first four convolutional neural network modules is a leak ReLU, and the activation function adopted by the activation function layer of the fifth convolutional neural network module is a softmax.
2. The method for three-dimensional reconstruction imaging of magnetic particles based on generation of an countermeasure network according to claim 1, wherein the training process of generating the countermeasure network by average spread search self-attention of the neighborhood points is as follows:
step A100, acquiring a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged through MPI imaging equipment, and performing downsampling to obtain a downsampled sparse two-dimensional MPI image; obtaining a training data set based on the downsampled sparse two-dimensional MPI image and a corresponding truth value label thereof;
step A200, inputting the downsampled sparse two-dimensional MPI image into a pre-constructed neighborhood point average diffusion search self-attention generation countermeasure network generation model to obtain a dense two-dimensional MPI image;
step A300, inputting the dense two-dimensional MPI image and the truth value label corresponding to the dense two-dimensional MPI image into the neighborhood point average diffusion search self-attention generation countermeasure network discrimination model, and acquiring discrimination results of the dense two-dimensional MPI image;
Step A400, based on the discrimination result, combining each dense two-dimensional MPI image, each sparse two-dimensional MPI image and the corresponding truth value labels thereof, calculating a total loss value through a pre-constructed loss function, and updating the network parameters of the neighborhood point average diffusion search self-attention generation countermeasure network generation model and discrimination model;
and step A500, training the generator network and the discriminator network of the neighborhood point average spread search self-attention generation countermeasure network in a circulating way until the trained neighborhood point average spread search self-attention generation countermeasure network is obtained.
3. The method for three-dimensional reconstruction imaging of magnetic particles based on generation of countermeasure network according to claim 1, wherein the number of generation angles is set to be
At an angle of +>
At the time->
Zhang Xishu two-dimensional MPI imageIs a function of the code of (a)
The method comprises the following steps: />
。
4. The method for three-dimensional reconstruction imaging of magnetic particles based on generation of countermeasure network according to claim 1, wherein the sequentially encoded images are subjected to a region convolution operation, which comprises the following steps: processing the concentration values of the pixel points in the sequential coding image, and performing operation by adopting a 3 multiplied by 3 convolution kernel after processing;
The method for processing the concentration value of the pixel point in the sequential coding image comprises the following steps:
wherein ,
representing the +.o in the sequentially encoded pictures>
Line->
Pixel points of column->
Representing the neighborhood of points->
Mean value of all values in>
Representation->
Minimum of all values in the point neighborhood, +.>
Representation->
Maximum value of all values in the point neighborhood, +.>
Representation->
Variable values in the point neighborhood.
5. The method for three-dimensional reconstruction imaging of magnetic particles based on generation of countermeasure network according to claim 1, wherein the particle concentration information, angle information, and correlation degree information between images of each sequentially encoded image are multiplied by corresponding weight matrices to perform linear transformation, the method is as follows:
wherein ,
representing the self-attention mechanism, vector->
Information indicating particle concentration>
The method comprises the steps of carrying out a first treatment on the surface of the Vector->
The information of the angle is represented by a set of angles,/>
the method comprises the steps of carrying out a first treatment on the surface of the Vector->
Information representing the degree of association between images, +.>
，/>
Representing the deformation of the matrix into a one-dimensional column vector, vector +.>
Representing sequentially encoded images of the input neighborhood point average diffusion convolution sub-network; />
、/>
、/>
Respectively representing weight matrices corresponding to particle concentration information, angle information and correlation degree information between images of sequentially encoded images, ++>
Representing each sequentially encoded image +. >
Neurons in all output layers +.>
Sum of output results from 1 to N, < ->
Representing each sequentially encoded image +.>
Through->
Output values of individual neurons, < >>
Representing each sequentially encoded image +.>
Through->
Output values of individual neurons, < >>
Representing each sequentially encoded image +.>
Through->
The output value of the individual neurons is the total output value +.>
Is a specific gravity of (c).
6. The method for three-dimensional reconstruction imaging of magnetic particles based on generation of countermeasure network according to claim 5, wherein the particle concentration information, the angle information and the association degree information between images of each sequentially encoded image are fused by a multi-head attention mechanism, the method comprising:
wherein ,
is a splicing function->
Indicate->
Output of the attention machine submodule, +.>
Total weight matrix representing the output of the multi-head attention mechanism,/->
And information obtained by fusing the particle concentration information, the angle information and the association degree information between the images of the sequential encoded images.
7. A method of three-dimensional reconstruction imaging of magnetic particles based on generation of a countermeasure network according to claim 2, characterized in that the total loss value is calculated by a pre-constructed loss function by:
wherein ,
for the total loss function- >
Representing pixel loss function and contrast loss function, < ->
Representing an angular sequential ordering optimization function,/->
Representing pixel information error,/>
Representing the loss of the generative model->
A sparse two-dimensional MPI image is represented,
representing the generated->
Zhang Miji two-dimensional MPI image,>
representing the generated->
Zhang Miji two-dimensional MPI image,>
indicate->
Zhang Xishu truth label corresponding to two-dimensional MPI image, < ->
Coefficients representing pixel loss, < >>
Coefficient representing countermeasures against losses->
Represents the probability of judging whether or not it is true by the judging network, < >>
Representing the output of the generative model,/>
Representing the angle difference function, ++>
As hyperbolic tangent function, +.>
Representing the predicted value of the angle at the corresponding input,
，/>
representing any two dense two-dimensional MPI images +.>
Angle judging function (F)>
and />
Respectively represent +.>
Angle label corresponding to dense two-dimensional MPI image generated by sheet,/->
Parameters indicating the richness settings for the information contained in the different mimics.
8. A magnetic particle three-dimensional reconstruction imaging system based on a generation countermeasure network, the system comprising: an image acquisition module 100, an image reconstruction module 200 and a three-dimensional reconstruction module 300;
the image acquisition module 100 is configured to acquire a multi-view sparse two-dimensional MPI image of an imitation body of a reconstructed object to be imaged, and perform downsampling to obtain a downsampled sparse two-dimensional MPI image; the multi-view sparse two-dimensional MPI images are a plurality of MPI projection images which are collected in sequence according to set angles in a rotating mode;
The image reconstruction module 200 is configured to input the downsampled sparse two-dimensional MPI image into a trained generation model of a neighborhood point average diffusion search self-attention generation countermeasure network to obtain a dense two-dimensional MPI image;
the three-dimensional reconstruction module 300 is configured to reconstruct each dense two-dimensional MPI image through a filtered back projection reconstruction algorithm, and finally obtain a three-dimensional MPI image of the reconstructed object to be imaged;
the neighborhood point average diffusion search self-attention generation countermeasure network comprises a generation model and a discrimination model;
the generating model comprises five diffusion searching attention mechanism convolution modules; the diffusion search attention mechanism convolution module is constructed based on a neighborhood point average diffusion convolution sub-network, a self-attention fusion network and an activation function layer which are connected in sequence; the activation function adopted by the activation function layer in the first four diffusion searching attention mechanism convolution modules is a leakage ReLU, and the activation function adopted by the activation function layer in the fifth diffusion searching attention mechanism convolution module is a hyperbolic tangent function;
the neighborhood point average diffusion convolution sub-network is configured to sequentially encode the downsampled sparse two-dimensional MPI image through an encoding function to obtain a sequentially encoded image; performing regional convolution operation on each sequence coding image to obtain particle concentration information, angle information and association degree information among the images of each sequence coding image;
The self-attention fusion network is configured to multiply the particle concentration information, the angle information and the association degree information among the images of each sequence coding image with the corresponding weight matrix for linear transformation, fuse the images through a multi-head attention mechanism, and up-sample the fused images to obtain a dense two-dimensional MPI image;
the discrimination model comprises five convolutional neural network modules; the convolutional neural network module is constructed based on a convolutional layer, a regularization layer and an activation function layer which are sequentially connected; the activation function adopted by the activation function layer of the first four convolutional neural network modules is a leak ReLU, and the activation function adopted by the activation function layer of the fifth convolutional neural network module is a softmax.
9. A storage device in which a plurality of programs are stored, characterized in that the programs are adapted to be loaded and executed by a processor to implement a method of generating a countermeasure network-based magnetic particle three-dimensional reconstruction imaging as claimed in any of claims 1 to 7.
10. A processing device, comprising a processor and a storage device; a processor adapted to execute each program; a storage device adapted to store a plurality of programs; characterized in that the program is adapted to be loaded and executed by a processor for implementing a method for three-dimensional reconstruction imaging of magnetic particles based on generation of a countermeasure network as claimed in any of claims 1 to 7.
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