Tissue parameter mapping method based on multi-slice joint magnetic resonance fingerprints and related device

Abstract

The invention discloses a multi-slice combined magnetic resonance fingerprint tissue parameter mapping method and a related device, and relates to the field of magnetic resonance fingerprint imaging and deep learning, and the method comprises the steps: carrying out the preprocessing of the magnetic resonance fingerprint data of different slices, and obtaining the noise-free magnetic resonance fingerprint data of different slices; inputting the noise-free magnetic resonance fingerprint data of the three adjacent slices into a multi-slice feature fusion network, and outputting a fused magnetic resonance fingerprint feature map; and inputting the fused magnetic resonance fingerprint feature map into a parameter reconstruction network, and outputting a tissue parameter mapping result of the noise-free magnetic resonance fingerprint data of the middle slice. According to the method, the features of the adjacent slices are utilized to enrich the features of the middle slice, the learning of the local features is enhanced through crossover and attention operation, the feature dimension is reduced by using the full-connection network, and the global receptive field is improved through the DRD-Net network, the learning of the global features is enhanced, and the tissue parameter reconstruction quality of the magnetic resonance fingerprints is improved.

Description

Technical Field

[0001] The present application relates to the field of magnetic resonance fingerprint imaging and deep learning, and in particular to a method and related device for mapping tissue parameters based on multi-slice joint magnetic resonance fingerprint. Background Art

[0002] Magnetic Resonance Fingerprinting (MRF) was first published in Nature magazine in 2013. MRF is a novel rapid quantitative imaging method that can simultaneously quantify multiple tissue characteristics in a short period of time. The implementation process of traditional MRF is: first, magnetic resonance fingerprint data is quickly obtained through a special data acquisition method, then all possible tissue characteristics are simulated and measured through the Bloch equation to build a dictionary, and finally, the dot product of each fingerprint signal and all signal vectors in the dictionary is calculated. The larger the dot product, the higher the match between this dictionary and the fingerprint signal. However, under the traditional MRF method, as the size of the dictionary increases, the difficulty of storage and calculation will also increase.

[0003] To solve the above problems, deep learning-based magnetic resonance fingerprint imaging technology was proposed. Deep learning-based magnetic resonance fingerprint imaging technology can achieve direct mapping from measured magnetic resonance fingerprint signals to underlying parameters with fewer time series points, but this technology still has the problem of low quality of magnetic resonance fingerprint tissue parameter reconstruction. Summary of the invention

[0004] The applicant's research found that the magnetic resonance fingerprint imaging technology based on deep learning mainly uses a fully convolutional network, lacks the extraction of global features, and does not consider the auxiliary role of the magnetic resonance fingerprints of adjacent slices in the reconstruction of the magnetic resonance fingerprint parameter map of the intermediate slice, resulting in low quality of magnetic resonance fingerprint tissue parameter reconstruction. In view of this, the purpose of this application is to provide a method and related devices for mapping tissue parameters based on multi-slice joint magnetic resonance fingerprints, aiming to improve the quality of magnetic resonance fingerprint tissue parameter reconstruction.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint, comprising:

[0007] Acquire magnetic resonance fingerprint data of different slices; the magnetic resonance fingerprint data is time series data;

[0008] Preprocessing the magnetic resonance fingerprint data of the different slices to obtain noise-free magnetic resonance fingerprint data of the different slices;

[0009] Determine the noise-free magnetic resonance fingerprint data of three adjacent slices in the noise-free magnetic resonance fingerprint data of different slices as a group, and input the noise-free magnetic resonance fingerprint data of different slices into a multi-slice feature fusion network in groups, and output a fused magnetic resonance fingerprint feature map; the multi-slice feature fusion network is used to perform cross and attention operations on the noise-free magnetic resonance fingerprint data of three adjacent slices, and obtain a magnetic resonance fingerprint feature map after feature fusion of the noise-free magnetic resonance fingerprint data of three adjacent slices;

[0010] The fused magnetic resonance fingerprint feature map is input into a parameter reconstruction network, and a tissue parameter mapping result of noise-free magnetic resonance fingerprint data of an intermediate slice is output.

[0011] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-slice joint magnetic resonance fingerprint tissue parameter mapping method described in the first aspect.

[0012] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint described in the first aspect.

[0013] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the multi-slice joint magnetic resonance fingerprint tissue parameter mapping method described in the first aspect.

[0014] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0015] The present application provides a method and related device for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint. The present application obtains magnetic resonance fingerprint data of different slices; pre-processes the magnetic resonance fingerprint data of different slices to obtain noise-free magnetic resonance fingerprint data of different slices; determines the noise-free magnetic resonance fingerprint data of three adjacent slices in the noise-free magnetic resonance fingerprint data of different slices as a group, and inputs the noise-free magnetic resonance fingerprint data of different slices into a multi-slice feature fusion network in groups, outputs the fused magnetic resonance fingerprint feature map; inputs the fused magnetic resonance fingerprint feature map into a parameter reconstruction network, and outputs the tissue parameter mapping result of the noise-free magnetic resonance fingerprint data of the middle slice. The present application enriches the features of the middle slice with the features of the adjacent slices, strengthens the learning of local features through cross and attention operations, uses a fully connected network to reduce the feature dimension, and improves the global receptive field through the DRD-Net network, enhances the learning of global features, and improves the quality of tissue parameter reconstruction of magnetic resonance fingerprints. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is a diagram of the application environment of the method for mapping tissue parameters based on multi-slice joint magnetic resonance fingerprints in one embodiment of the present application;

[0018] Figure 2 A schematic diagram of a process of a multi-slice joint magnetic resonance fingerprint tissue parameter mapping method provided in an embodiment of the present application;

[0019] Figure 3 A schematic diagram of a network structure of a method for mapping tissue parameters based on multi-slice joint magnetic resonance fingerprints provided in an embodiment of the present application;

[0020] Figure 4 A schematic diagram of each Temporal SwinTransformer block in a multi-slice fusion network provided in an embodiment of the present application;

[0021] Figure 5 A schematic diagram of each dense block in a parameter reconstruction network provided in an embodiment of the present application;

[0022] Figure 6 A multi-slice joint magnetic resonance fingerprint tissue parameter mapping result diagram provided in an embodiment of the present application;

[0023] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0026] The method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the magnetic resonance fingerprint data of different slices to the server 104, and the server 104 receives the magnetic resonance fingerprint data of different slices. For the magnetic resonance fingerprint data of different slices, the server 104 pre-processes the magnetic resonance fingerprint data of the different slices to obtain noise-free magnetic resonance fingerprint data of different slices; select the magnetic resonance fingerprint data of three adjacent slices from the noise-free magnetic resonance fingerprint data of the different slices, input them into the multi-slice feature fusion network, and output the fused magnetic resonance fingerprint feature map; the multi-slice feature fusion network is used to perform cross and attention operations on the magnetic resonance fingerprint data of the three adjacent slices to achieve feature fusion of the magnetic resonance fingerprint data of the three adjacent slices; the fused magnetic resonance fingerprint feature map is input into the parameter reconstruction network, and the tissue parameter mapping result of the noise-free magnetic resonance fingerprint data of the middle slice is output. The server 104 may feed back the parameter mapping result of the noise-free magnetic resonance fingerprint data of the intermediate slice to the terminal 102. In addition, in some embodiments, the tissue parameter mapping method based on multi-slice joint magnetic resonance fingerprint may also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 may directly process the magnetic resonance fingerprint data of different slices, or the server 104 may obtain the magnetic resonance fingerprint data of different slices from the data storage system and process the magnetic resonance fingerprint data of different slices.

[0027] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0028] In an exemplary embodiment, Figure 2 As shown, a method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 204. Among them:

[0029] Step 201, obtaining magnetic resonance fingerprint data of different slices.

[0030] Among them, the magnetic resonance fingerprint data of different slices are all time series data.

[0031] When acquiring magnetic resonance fingerprint data of different slices, it is necessary to design a sampling sequence with constantly changing parameters such as RF pulses, repetition time, and sampling trajectory, so that each different tissue type has a unique signal response under the sampling sequence.

[0032] Step 202: preprocess the magnetic resonance fingerprint data of different slices to obtain noise-free magnetic resonance fingerprint data of different slices.

[0033] In an exemplary embodiment, step 202 specifically includes:

[0034] Step 2021: Select some segments in the magnetic resonance fingerprint data of different slices, and determine the some segments in the magnetic resonance fingerprint data of different slices as data to be processed.

[0035] The purpose of step 2021 is to reduce the amount of data and shorten the data processing time, thereby improving efficiency. For example, the magnetic resonance fingerprint data of each slice originally includes 2000 frames of images, and after selecting some fragments to obtain the data to be processed, the data to be processed only includes 200 frames of images.

[0036] Step 2022: extracting the foreground area of ​​the data to be processed, separating the target area of ​​interest from the background area, and obtaining noise-free magnetic resonance fingerprint data.

[0037] As an optional implementation, step 2022 specifically includes:

[0038] (1) According to a preset global threshold, the proton density parameter map of the data to be processed is filtered and binarized to obtain a first proton density parameter map.

[0039] (2) extracting the maximum connected domain of the first proton density parameter map based on a connected domain extraction algorithm, removing noise in the first proton density parameter map, and obtaining a second proton density parameter map.

[0040] (3) Filling holes in the second proton density parameter map to repair missing parts in the second proton density parameter map to obtain a third proton density parameter map; the third proton density parameter map is a reference map for extracting the foreground area.

[0041] (4) Performing a matrix dot multiplication operation on each frame of the data to be processed and the reference image of the extracted foreground area to obtain noise-free magnetic resonance fingerprint data.

[0042] like Figure 3As shown, the overall network of the multi-slice joint magnetic resonance fingerprint tissue parameter mapping method provided by the present application includes two parts: a multi-slice feature fusion network and a parameter reconstruction network. Before practical application, it is necessary to set a suitable learning rate, optimizer and loss function, and use the noise-free magnetic resonance fingerprint data of multiple slices to train the overall network. In practical application, the magnetic resonance fingerprint data of different slices are first preprocessed to obtain noise-free magnetic resonance fingerprint data of different slices; then, the magnetic resonance fingerprint data of three adjacent slices are selected from the noise-free magnetic resonance fingerprint data of the different slices and input into the overall network, and the magnetic resonance fingerprint data of the three adjacent slices are sequentially subjected to the multi-slice feature fusion and parameter reconstruction process, and the tissue parameter mapping result of the magnetic resonance fingerprint data of the middle slice is output.

[0043] Step 203, the noise-free magnetic resonance fingerprint data of three adjacent slices in the noise-free magnetic resonance fingerprint data of different slices are determined as a group, and the noise-free magnetic resonance fingerprint data of different slices are input into a multi-slice feature fusion network in groups, and a fused magnetic resonance fingerprint feature map is output; the multi-slice feature fusion network is used to perform cross and attention operations on the noise-free magnetic resonance fingerprint data of three adjacent slices, and obtain a magnetic resonance fingerprint feature map after feature fusion of the noise-free magnetic resonance fingerprint data of three adjacent slices.

[0044] In an exemplary embodiment, Figure 4 As shown in the figure, the multi-slice feature fusion network includes three attention branches, namely the upper attention branch, the middle attention branch and the lower attention branch; each attention branch is composed of an even number of Temporal SwinTransformer blocks.

[0045] The Temporal SwinTransformer block includes a block embedding unit, a channel merging unit, a first linear mapping unit, a multi-head self-attention unit, a second linear mapping unit, a first residual connection unit, a layer normalization unit, a multi-layer perceptron, a second residual connection unit, a block de-embedding unit and a 3×3 convolution unit.

[0046] The multi-head self-attention unit is a window multi-head self-attention unit or a sliding window multi-head self-attention unit; in the same attention branch, the multi-head self-attention units in adjacent Temporal SwinTransformer blocks are different. For example, in the middle attention branch, the first Temporal Swin Transformer block uses a W-MSA unit, and the second Temporal Swin Transformer block uses a SW-MSA unit. Moreover, the three TemporalSwinTransformer blocks in the same column use the same type of multi-head self-attention unit.

[0047] The first end of the block embedding unit is the input end of the Temporal SwinTransformer block; the second end of the block embedding unit is connected to the first end of the channel merging unit; the second end of the channel merging unit is connected to one end of the first linear mapping unit; the other end of the first linear mapping unit is connected to one end of the first residual connection unit; the other end of the first residual connection unit is connected to one end of the layer normalization unit; the other end of the layer normalization unit is connected to one end of the multilayer perceptron; the other end of the multilayer perceptron is connected to one end of the second residual connection unit; the other end of the second residual connection unit is connected to one end of the block de-embedding unit; the other end of the block de-embedding unit is connected to one end of the 3×3 convolution unit; the other end of the 3×3 convolution unit is the output end of the Temporal SwinTransformer block.

[0048] Since each attention branch is composed of an even number of Temporal SwinTransformer blocks, this ensures that W-MSA (Windows Multi-Head Self-Attention, W-MSA) and SW-MSA (Shifted Windows Multi-Head Self-Attention, SW-MSA) can be used alternately. Among them, W-MSA stands for the window multi-head self-attention mechanism, which constrains the calculation range of the self-attention mechanism by setting a fixed-size window in the Transformer architecture, and SW-MSA stands for the sliding window multi-head self-attention mechanism, which introduces a sliding window and calculates W-MSA multiple times to cover the entire sequence. Each time the sliding window is passed, the self-attention weights within the window are recalculated. The number of windows is calculated as:

[0049]

[0050] Where window_size represents the set window size, W and H represent the length and height of the input feature map respectively, and the setting of the window size needs to satisfy the calculated number of windows N is an integer value.

[0051] The block embedding unit of the upper attention branch, the block embedding unit of the lower attention branch and the block embedding unit of the middle attention branch perform channel merging in the channel merging unit of the middle attention branch; the block embedding unit of the middle attention branch and the block embedding unit of the upper attention branch perform channel merging in the channel merging unit of the upper attention branch; the block embedding unit of the middle attention branch and the block embedding unit of the lower attention branch perform channel merging in the channel merging unit of the lower attention branch.

[0052] Furthermore, the third end of the block embedding unit of the upper attention branch is connected to the third end of the channel merging unit of the intermediate attention branch; the third end of the block embedding unit of the lower attention branch is connected to the fourth end of the channel merging unit of the intermediate attention branch; the third end of the block embedding unit of the intermediate attention branch is connected to the third end of the channel merging unit of the upper attention branch; the fourth end of the block embedding unit of the intermediate attention branch is connected to the third segment of the channel merging unit of the lower attention branch.

[0053] It should be noted that when the magnetic resonance fingerprint data of three adjacent slices are selected from the noise-free magnetic resonance fingerprint data of the multiple slices and input into the multi-slice feature fusion network, the magnetic resonance fingerprint data of three adjacent slices with shortened lengths are input into the upper attention branch, the middle attention branch and the lower attention branch. For example, assuming that the number of time frames of the original magnetic resonance fingerprint data is 2000, the inner product calculation is performed using a preset dictionary and each voxel signal of the magnetic resonance fingerprint data to obtain an accurate tissue parameter mapping result, which will be used as the target of the multi-slice feature fusion network and the parameter reconstruction network prediction. When training the network, in order to achieve acceleration, only a part of the data is selected, and each slice obtains, for example, 200 time frames of fingerprint data, and normalization and data enhancement are performed. The acceleration factor at this time is 10.

[0054] In an exemplary embodiment, the noise-free magnetic resonance fingerprint data of different slices are input into the multi-slice feature fusion network in groups as follows: the first time, 200 frames of noise-free magnetic resonance fingerprint data corresponding to the first, second and third slices are selected, respectively, and the upper attention branch, the middle attention branch and the lower attention branch are input, and the tissue parameter mapping result of the second slice is output; the second time, 200 frames of noise-free magnetic resonance fingerprint data corresponding to the second, third and fourth slices are selected, respectively, and the upper attention branch, the middle attention branch and the lower attention branch are input, and the tissue parameter mapping result of the third slice is output, and so on. The optimization goal of the network of the present application is to make the result of parameter mapping for each slice using 200 frames of magnetic resonance fingerprint data close to the tissue parameter mapping result obtained by dictionary matching using 2000 frames of magnetic resonance fingerprint data.

[0055] Dictionary matching is performed under the condition of longer sequence length, and the result of dictionary matching will be used as the gold standard graph to supervise the learning process of the network. The optimization goal of the method provided in this application is to still be able to approach this gold standard graph under the condition of shorter sequence length.

[0056] Further, Figure 4 The details of the multi-slice network are shown. First, the block embedding unit is used to complete the block embedding process, where the block size is P. The block embedding process can transform the feature dimensions of the input three slice fingerprints from [L, H, W] to [(L×H) / P 2 ,W], thereby adjusting the original image size and adding the absolute position encoding information to each block. Where W and H represent the length and height of the input feature map, L represents the sequence length of the fingerprint map, and the size of P is 1. It can be seen that Figure 4 Each block embedding unit draws 8 rounded rectangles, and every 2 rounded rectangles are drawn together, one is used to represent the block information, and the other is used to represent the position coding information added to the block, that is, Figure 4 A total of 4 blocks are shown in the block embedding unit in FIG. However, those skilled in the art should know that 4 blocks are drawn in the figure only for the convenience of showing the structure of the multi-slice feature network, and are not limited to 4 in actual application, and may also be other numbers of blocks.

[0057] After the block embedding process is completed, the feature cross-merging process needs to be performed. Taking the intermediate attention branch as an example, C in the figure represents the channel merging unit, and the cross-merging of features is completed in the channel merging unit. After merging, the dimension of the image information will increase by 3 times. Therefore, it is necessary to set the first linear mapping unit ( Figure 4 The linear mapping 1 in the figure is used to reduce the dimension of the cross-merged information. Then, the multi-head self-attention unit is used to perform attention operation, and the second linear mapping unit ( Figure 4 The linear mapping in 2) adjusts the dimension of the image.

[0058] Next, the output of the second linear mapping unit is residually connected to strengthen the learning of features. After that, the features are normalized using the layer normalization unit to scale the data to a smaller range, making the training process more stable. Then, a multi-layer perceptron is used to further strengthen the learning of features, and the residual connection is completed again through the second residual connection unit.

[0059] Finally, the de-embedding process is completed by the block de-embedding unit, and batch normalization and activation are performed through a 3x3 convolutional layer with a stride of 1 to further learn the features and obtain the output of the Temporal Swin Transformer block. Among them, the block de-embedding unit restores the feature map of size [L×H,W] to [L,H,W]. The output of the Temporal Swin Transformer block is input to the next Temporal Swin Transformer block (if it exists) or to the parameter reconstruction network.

[0060] Step 204: input the fused magnetic resonance fingerprint feature map into a parameter reconstruction network, and output a tissue parameter mapping result of noise-free magnetic resonance fingerprint data of a middle slice, wherein the middle slice refers to a slice in the middle of three adjacent slices.

[0061] In an exemplary embodiment, Figure 3 As shown, the parameter reconstruction network is a DRD-Net network; the DRD-Net network includes a fully connected layer, n dense blocks, a global feature fusion layer, a global residual connection layer and an upsampling convolution layer. One end of the fully connected layer is connected to the output end of the multi-slice feature fusion network; the other end of the fully connected layer is connected to one end of the n dense blocks; the other end of the n dense blocks is connected to one end of the global feature fusion layer; the other end of the global feature fusion layer is connected to one end of the global residual connection layer; the other end of the global residual connection layer is connected to one end of the upsampling convolution layer; the other end of the upsampling convolution layer is used to output the parameter mapping result of the noise-free magnetic resonance fingerprint data of the intermediate slice.

[0062] As an optional implementation, step 204 specifically includes:

[0063] Step 2041: input the fused magnetic resonance fingerprint feature map into the fully connected layer of the parameter reconstruction network, and output a low-dimensional feature F0; the fully connected layer is a 1x1 convolutional layer.

[0064] Step 2042: Input the low-dimensional feature F0 into the n dense blocks for processing, and output n sub-features F1, F2, ..., F n In this embodiment, the value of n is 4, that is, the number of dense blocks is 4. Of course, the number of n can also be other values.

[0065] Step 2043: Input the n sub-features into the global feature fusion layer for global feature fusion, and output the global feature. The feature fusion is completed through the 1x1 convolution layer and the 3x3 convolution layer, and the global feature fusion is output. GThe global feature fusion layer includes n sub-features F1, F2, ..., F n The combined feature pooling layer, a 1x1 convolutional layer, and a 3x3 convolutional layer.

[0066] Step 2044: Combine the low-dimensional feature F0 and the global feature F G Input the global residual connection layer and output the features after global residual connection;

[0067] Step 2045: Input the features after global residual connection into the upsampling convolution layer, and output the tissue parameter mapping result of the noise-free magnetic resonance fingerprint data of the middle slice. The upsampling convolution layer includes two 3x3 convolution layers.

[0068] As an optional implementation, Figure 5 As shown, the specific details of each dense block in the parameter reconstruction network are as follows:

[0069] First, feature F n After the dense block is input, it passes through a 3x3 convolution layer with a dilation rate of 1 to obtain feature 1. Then, feature 1 is combined with feature F n Combine the combined features as a whole, perform a 3x3 convolution with a dilation rate of 2, and output feature 2. Then, feature 2, feature 1, and feature F n Combine the combined features as a whole, perform a 3x3 convolution with a dilation rate of 4, and output feature 3. Next, feature 3, feature 2, feature 1, and feature F n Combine and treat the combined features as a whole, perform a 3x3 convolution with a dilation rate of 4, and output feature 4. Then, feature 4, feature 3, feature 2, feature 1, and feature F n Combine, take the combined features as a whole, perform 3x3 convolution (expansion rate is 1), realize the fusion of local features, and output feature 5. Finally, input the fused feature 5 of the local features into the channel attention network to calculate the channel attention. The calculation of the channel attention includes the following process:

[0070] (1) Through global average pooling, the data of each channel is directly converted into one value, achieving data compression in the spatial dimension.

[0071] (2) Assign weights to each feature channel. The number of output weight parameters is the same as the number of channels in the input feature map. Two fully connected layers are used to assign weights. The first fully connected layer is followed by a ReLU function, and the second fully connected layer is followed by a Sigmoid function, thereby achieving effective control of data scale.

[0072] (3) Assign the normalized weight of each channel to each channel of the feature map.

[0073] After completing the calculation of channel attention, the features after channel attention are residually connected with the input features of the dense block to obtain the output features of a single dense block.

[0074] By implementing the above steps 201 to 204, the present application first uses the features of the adjacent slices to enrich the features of the middle slice, then uses a fully connected network to reduce the feature dimension, and then uses the DRD-Net network to improve the receptive field and enhance the learning of global features; finally, the mapping of tissue parameters is achieved through two 3×3 convolutions, thereby improving the quality of tissue parameter reconstruction of magnetic resonance fingerprints.

[0075] In an exemplary embodiment, the multi-slice joint magnetic resonance fingerprint tissue parameter mapping result is as follows: Figure 6 shown. Figure 6 The T1 truth map of the synthetic data of five people, the T2 truth map of the synthetic data of five people, the tissue parameter mapping results of the traditional Drone method, and the parameter mapping results of the multi-slice joint magnetic resonance fingerprint tissue parameter mapping method of the present application are shown. Figure 6 The T1 truth map and T2 truth map in the figure are obtained by the traditional dictionary matching method. Dictionary matching is performed under the condition of longer sequence length and takes a long time to complete. The result obtained by dictionary matching will be used as the gold standard map.

[0076] from Figure 6 It can be seen from the 4-fold residual map and the 5-fold residual map that, whether it is the T1 truth map or the T2 truth map, the error of the tissue parameter mapping result of the present application is significantly lower than the error of the tissue parameter mapping result of the traditional Drone method, which indicates that the method provided in the present application significantly improves the quality of tissue parameter reconstruction of magnetic resonance fingerprints.

[0077] The present application also provides an application scenario. Specifically, the method for tissue parameter mapping based on multi-slice combined magnetic resonance fingerprint provided in this embodiment can be applied to the display of magnetic resonance fingerprint data tissue parameter mapping results in the medical field, thereby assisting doctors in conducting research on diseases. For example, research on nervous system diseases, research on cardiovascular system diseases, research on musculoskeletal system diseases, and research on abdominal and pelvic organ diseases. The present application is based on a method for tissue parameter mapping based on multi-slice combined magnetic resonance fingerprint, which can still have a high parameter reconstruction quality when the amount of magnetic resonance fingerprint data is small. Since the amount of data required is small, the time required to collect magnetic resonance fingerprint data is also significantly reduced, thereby improving the efficiency of disease diagnosis and detection.

[0078] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store magnetic resonance fingerprint data of different slices. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for mapping tissue parameters based on multi-slice joint magnetic resonance fingerprint is implemented.

[0079] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0080] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0081] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0082] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0083] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0084] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0085] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0086] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint, characterized in that: The method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint includes: Acquire magnetic resonance fingerprint data of different slices; the magnetic resonance fingerprint data is time series data; Preprocessing the magnetic resonance fingerprint data of the different slices to obtain noise-free magnetic resonance fingerprint data of the different slices; Determine the noise-free magnetic resonance fingerprint data of three adjacent slices in the noise-free magnetic resonance fingerprint data of different slices as a group, and input the noise-free magnetic resonance fingerprint data of different slices into a multi-slice feature fusion network in groups, and output a fused magnetic resonance fingerprint feature map; the multi-slice feature fusion network is used to perform cross and attention operations on the noise-free magnetic resonance fingerprint data of three adjacent slices, and obtain a magnetic resonance fingerprint feature map after feature fusion of the noise-free magnetic resonance fingerprint data of three adjacent slices; The fused magnetic resonance fingerprint feature map is input into a parameter reconstruction network, and a tissue parameter mapping result of noise-free magnetic resonance fingerprint data of an intermediate slice is output.

2. The method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint according to claim 1, characterized in that: Preprocessing the magnetic resonance fingerprint data of the different slices to obtain noise-free magnetic resonance fingerprint data of the different slices specifically includes: Selecting some segments in the magnetic resonance fingerprint data of different slices, and determining the some segments in the magnetic resonance fingerprint data of different slices as data to be processed; The foreground area of ​​the data to be processed is extracted, and the target area of ​​interest is separated from the background area to obtain noise-free magnetic resonance fingerprint data.

3. The method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint according to claim 2, characterized in that: Extracting the foreground area of ​​the data to be processed, separating the target area of ​​interest from the background area, and obtaining noise-free magnetic resonance fingerprint data, specifically includes: According to a preset global threshold, filtering and binarizing the proton density parameter map of the data to be processed to obtain a first proton density parameter map; Extracting the maximum connected domain of the first proton density parameter map based on a connected domain extraction algorithm, removing noise in the first proton density parameter map, and obtaining a second proton density parameter map; Filling holes in the second proton density parameter map, repairing missing parts in the second proton density parameter map, and obtaining a third proton density parameter map; the third proton density parameter map is a reference map for extracting a foreground area; A matrix point multiplication operation is performed on each frame of the image in the data to be processed and the reference image of the extracted foreground area to obtain noise-free magnetic resonance fingerprint data.

4. The method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint according to claim 1, characterized in that: The multi-slice feature fusion network includes three attention branches, namely, an upper attention branch, a middle attention branch, and a lower attention branch; each attention branch is composed of an even number of Temporal Swin Transformer blocks; The Temporal SwinTransformer block includes a block embedding unit, a channel merging unit, a first linear mapping unit, a multi-head self-attention unit, a second linear mapping unit, a first residual connection unit, a layer normalization unit, a multi-layer perceptron, a second residual connection unit, a block de-embedding unit and a 3×3 convolution unit; The multi-head self-attention unit is a window multi-head self-attention unit or a sliding window multi-head self-attention unit; in the same attention branch, the multi-head self-attention units in adjacent Temporal SwinTransformer blocks are different; The first end of the block embedding unit is the input end of the Temporal Swin Transformer block; the second end of the block embedding unit is connected to the first end of the channel merging unit; the second end of the channel merging unit is connected to one end of the first linear mapping unit; the other end of the first linear mapping unit is connected to one end of the first residual connection unit; the other end of the first residual connection unit is connected to one end of the layer normalization unit; the other end of the layer normalization unit is connected to one end of the multi-layer perceptron; the other end of the multi-layer perceptron is connected to one end of the second residual connection unit; the other end of the second residual connection unit is connected to one end of the block de-embedding unit; the other end of the block de-embedding unit is connected to one end of the 3×3 convolution unit; the other end of the 3×3 convolution unit is the output end of the Temporal Swin Transformer block; The block embedding unit of the upper attention branch, the block embedding unit of the lower attention branch and the block embedding unit of the middle attention branch perform channel merging in the channel merging unit of the middle attention branch; the block embedding unit of the middle attention branch and the block embedding unit of the upper attention branch perform channel merging in the channel merging unit of the upper attention branch; the block embedding unit of the middle attention branch and the block embedding unit of the lower attention branch perform channel merging in the channel merging unit of the lower attention branch.

5. The method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint according to claim 1, characterized in that: The parameter reconstruction network is a DRD-Net network; the DRD-Net network includes a fully connected layer, n dense blocks, a global feature fusion layer, a global residual connection layer and an upsampling convolution layer; One end of the fully connected layer is connected to the output end of the multi-slice feature fusion network; the other end of the fully connected layer is connected to one end of the n dense blocks; the other end of the n dense blocks is connected to one end of the global feature fusion layer; the other end of the global feature fusion layer is connected to one end of the global residual connection layer; the other end of the global residual connection layer is connected to one end of the upsampling convolution layer; the other end of the upsampling convolution layer is used to output the tissue parameter mapping result of the noise-free magnetic resonance fingerprint data of the intermediate slice.

6. The method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint according to claim 5, characterized in that: Inputting the fused magnetic resonance fingerprint feature map into a parameter reconstruction network, and outputting the tissue parameter mapping result of the noise-free magnetic resonance fingerprint data of the middle slice, specifically includes: Inputting the fused magnetic resonance fingerprint feature map into a fully connected layer of a parameter reconstruction network to output low-dimensional features; Inputting the low-dimensional features into the n dense blocks for processing, and outputting n sub-features; Input n sub-features into the global feature fusion layer for global feature fusion and output the global feature; Inputting the low-dimensional features and the global features into a global residual connection layer, and outputting the features after global residual connection; The features after global residual connection are input into the upsampling convolution layer, and the tissue parameter mapping result of the noise-free magnetic resonance fingerprint data of the middle slice is output.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-slice joint magnetic resonance fingerprint tissue parameter mapping method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint described in any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for tissue parameter mapping based on multi-slice joint magnetic resonance fingerprint described in any one of claims 1 to 6 is implemented.

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