WO2022198655A1

WO2022198655A1 - Magnetic resonance oxygen-17 metabolism imaging method, apparatus, storage medium, and terminal device

Info

Publication number: WO2022198655A1
Application number: PCT/CN2021/083359
Authority: WO
Inventors: 王珊珊; 荣楚誉; 郑海荣; 刘新
Original assignee: 中国科学院深圳先进技术研究院
Priority date: 2021-03-23
Filing date: 2021-03-26
Publication date: 2022-09-29
Also published as: CN112967297A; CN112967297B

Abstract

A magnetic resonance oxygen-17 metabolism imaging method, an apparatus, a computer-readable storage medium, and a terminal device. The method comprises: obtaining magnetic resonance oxygen-17 metabolism golden angle radial sampling data, and dividing the radial sampling data into a plurality of pieces of partition domain radial sampling data according to preset frequency partitions (S101); causing different radial interpolation networks to respectively perform processing on each piece of partition domain radial sampling data, and obtaining filled radial sampling data (S102); performing imaging processing on the filled radial sampling data, and obtaining a target image (S103). Due to introducing different radial interpolation networks to respectively perform filling on sampling data on each frequency partition, final imaging precision is effectively improved.

Description

Magnetic resonance oxygen seventeen metabolic imaging method, device, storage medium and terminal equipment

technical field

The present application belongs to the field of computer technology, and in particular relates to a magnetic resonance oxygen seventeen metabolic imaging method, device, computer-readable storage medium and terminal device.

Background technique

The regulation of cerebral fluid plays an important role in the brain. The dynamic image of cerebral fluid can be obtained by magnetic resonance oxygen seventeen (oxygen-17) metabolic imaging, which can be used as a basis for judging whether the brain is in a normal state or a diseased state. In the prior art, an imaging method based on a gridding algorithm can be used. In this method, after density compensation is performed on the sampled data, the data is resampled to a large grid matrix by interpolation, and then the magnetic resonance image is reconstructed. Although this method can achieve fast imaging, the imaging accuracy is low.

technical problem

In view of this, embodiments of the present application provide a magnetic resonance oxygen seventeen metabolic imaging method, device, computer-readable storage medium, and terminal device, so as to solve the problem of low accuracy of the existing imaging method.

technical solutions

A first aspect of the embodiments of the present application provides a magnetic resonance oxygen seventeen metabolic imaging method, which may include:

Obtaining the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and dividing the radial sampling data into several sub-regional radial sampling data according to preset frequency partitions;

Different radial interpolation networks are used to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition;

Perform imaging processing on the filled radial sampling data to obtain a target image.

In a possible implementation manner of the first aspect, the use of different radial interpolation networks to respectively process the radial sampling data of each sub-region to obtain padded radial sampling data may include:

Rearrange the radial sampling data of each sub-region into a matrix form to obtain matrix data of each sub-region;

Use different radial interpolation networks to process the matrix data of each subregion, and obtain the augmented matrix data of each subregion;

The augmented matrix data of each sub-region is restored to the form of radial spokes, and the filled radial sampling data is obtained.

In a possible implementation manner of the first aspect, before using different radial interpolation networks to process the respective subregional matrix data, the method may further include:

Construct the training sample set of the gth radial interpolation network, 1≤g≤G, G is the number of frequency partitions; the training sample set includes several training samples, and each training sample includes input matrix data and expected output matrix data, the input matrix data is consistent with the size of the gth subregion matrix data, and the expected output matrix data is consistent with the size of the gth subregion augmented matrix data;

The g-th radial interpolation network is trained by using the training sample set to obtain the g-th radial interpolation network after training.

In a possible implementation manner of the first aspect, the construction of the training sample set of the gth radial interpolation network may include:

Obtain fully sampled data;

performing data deletion on the full-collected data to obtain under-sampled data corresponding to the full-collected data;

Rearranging the fully collected data to obtain the input matrix data;

rearranging the undersampled data to obtain the expected output matrix data;

A training sample consisting of the input matrix data and the expected output matrix data is added to the training sample set.

In the first specific implementation of the embodiment of the present application, the process of using different radial interpolation networks to process the matrix data of each sub-region to obtain the augmented matrix data of each sub-region may include:

Use different radial interpolation networks to expand the number of rows of each subregional matrix data to α _g times, and obtain each subregional augmented matrix data; where α _g is the augmentation coefficient corresponding to the gth frequency partition, and α _g is an integer greater than 1.

In the second specific implementation of the embodiment of the present application, the process of using different radial interpolation networks to process the matrix data of each sub-region to obtain the augmented matrix data of each sub-region may include:

Use different radial interpolation networks to expand the number of columns of each subregional matrix data to _βg times, and obtain each subregional augmented matrix data; where _βg is the augmentation coefficient corresponding to the gth frequency partition, and β _g is an integer greater than 1.

In the third specific implementation of the embodiment of the present application, the process of using different radial interpolation networks to process the matrix data of each sub-region to obtain the augmented matrix data of each sub-region may include:

Using different radial interpolation networks, the number of rows of each subregional matrix data is enlarged to α _g times, and the number of columns is enlarged to β _g times, and the augmented matrix data of each subregion is obtained.

In a possible implementation manner of the first aspect, performing imaging processing on the padded radial sampling data to obtain a target image may include:

Perform inverse fast Fourier transform processing on the padded radial sampling data to obtain the target image.

A second aspect of the embodiments of the present application provides a magnetic resonance oxygen seventeen metabolic imaging device, which may include:

The sampling data division module is used to obtain the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divide the radial sampling data into several sub-regional radial sampling data according to the preset frequency division;

The radial interpolation processing module is used to process the radial sampling data of each sub-region by using different radial interpolation networks to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition;

The imaging processing module is configured to perform imaging processing on the filled radial sampling data to obtain a target image.

In a possible implementation manner of the second aspect, the radial interpolation processing module may include:

The data rearrangement unit is used to rearrange the radial sampling data of each sub-region into a matrix form respectively, and obtain the matrix data of each sub-region;

The radial interpolation processing unit is used to process the matrix data of each sub-region by using different radial interpolation networks to obtain the augmented matrix data of each sub-region;

A data restoration unit, configured to restore the augmented matrix data of each sub-region to the form of radial spokes, and obtain the filled radial sampling data.

In a possible implementation manner of the second aspect, the magnetic resonance oxygen seventeen metabolic imaging device may further include:

The training sample set building module is used to construct the training sample set of the gth radial interpolation network, 1≤g≤G, G is the number of frequency partitions; the training sample set includes several training samples, each training sample Including input matrix data and expected output matrix data, the input matrix data is consistent with the size of the gth subregional matrix data, and the expected output matrix data is consistent with the size of the gth subregion augmented matrix data;

The radial interpolation network training module is used for training the g th radial interpolation network by using the training sample set to obtain the g th radial interpolation network after training.

In a possible implementation manner of the second aspect, the training sample set building module may include:

The full sampling data acquisition unit is used to obtain fully sampled full sampling data;

an under-sampling data processing unit, configured to perform data deletion on the fully-sampled data to obtain under-sampled data corresponding to the fully-sampled data;

a first rearranging unit, used for rearranging the fully collected data to obtain the input matrix data;

a second rearranging unit, configured to rearrange the undersampled data to obtain the expected output matrix data;

A training sample adding unit, configured to add a training sample composed of the input matrix data and the expected output matrix data to the training sample set.

In the first specific implementation of the embodiment of the present application, the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of rows of each subregional matrix data to α _g times, respectively, to obtain each subregional matrix data. Region augmentation matrix data; where α _g is the augmentation coefficient corresponding to the g-th frequency partition, and α _g is an integer greater than 1.

In the second specific implementation of the embodiment of the present application, the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of columns of each subregional matrix data to β _g times, respectively, to obtain each subregional matrix data. Region augmentation matrix data; where β _g is the augmentation coefficient corresponding to the g-th frequency partition, and β _g is an integer greater than 1.

In a third specific implementation of the embodiment of the present application, the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of rows and columns of each subregional matrix data to α _g times, respectively. to β _g times to obtain the augmented matrix data of each sub-region.

In a possible implementation manner of the second aspect, the imaging processing module is specifically configured to perform inverse fast Fourier transform processing on the padded radial sampling data to obtain the target image.

A third aspect of the embodiments of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any one of the above-mentioned magnetic resonance oxygen seventeen metabolism is realized The steps of the imaging method.

A fourth aspect of the embodiments of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program The steps of realizing any one of the above-mentioned magnetic resonance oxygen seventeen metabolic imaging methods.

A fifth aspect of the embodiments of the present application provides a computer program product, which, when the computer program product runs on a terminal device, enables the terminal device to perform any of the steps of the above-mentioned magnetic resonance oxygen seventeen metabolic imaging method.

beneficial effect

Compared with the prior art, the embodiment of the present application has the following beneficial effects: the embodiment of the present application obtains the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divides the radial sampling data according to preset frequency divisions is the radial sampling data of several sub-regions; different radial interpolation networks are used to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency Partition; perform imaging processing on the filled radial sampling data to obtain a target image. Through the embodiments of the present application, different radial interpolation networks are introduced to fill in the sampled data in each frequency partition respectively, so that the sampled data in each different frequency partition has a stronger pertinence, and the filled data is more accurate, thereby effectively improving the the final imaging accuracy.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only for the present application. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

1 is a flowchart of an embodiment of a magnetic resonance oxygen seventeen metabolic imaging method in an embodiment of the present application;

Fig. 2 is the schematic diagram of golden angle radial sampling trajectory;

Fig. 3 is the schematic flow chart of using different radial interpolation networks to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data;

Fig. 4 is the schematic diagram of frequency domain data filling process;

5 is a structural diagram of an embodiment of a magnetic resonance oxygen seventeen metabolic imaging device in an embodiment of the present application;

FIG. 6 is a schematic block diagram of a terminal device in an embodiment of the present application.

Embodiments of the present invention

In order to make the purpose, features and advantages of the invention of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the following The described embodiments are only some, but not all, embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

It is to be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described feature, integer, step, operation, element and/or component, but does not exclude one or more other features , whole, step, operation, element, component and/or the presence or addition of a collection thereof.

It should also be understood that the terminology used in the specification of the application herein is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural unless the context clearly dictates otherwise.

It should also be further understood that, as used in this specification and the appended claims, the term "and/or" refers to and including any and all possible combinations of one or more of the associated listed items .

As used in this specification and the appended claims, the term "if" may be contextually interpreted as "when" or "once" or "in response to determining" or "in response to detecting" . Similarly, the phrases "if it is determined" or "if the [described condition or event] is detected" may be interpreted, depending on the context, to mean "once it is determined" or "in response to the determination" or "once the [described condition or event] is detected. ]" or "in response to detection of the [described condition or event]".

In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

In the embodiment of the present application, in view of the problem of low accuracy of the existing imaging methods, different radial interpolation networks are introduced to fill in the sampled data in each frequency partition respectively, so that the sampled data in each different frequency partition has a stronger performance. The pertinence of the filled data is more accurate, thereby effectively improving the final imaging accuracy.

Referring to FIG. 1, an embodiment of a magnetic resonance oxygen seventeen metabolic imaging method in the embodiment of the present application may include:

Step 101: Acquire the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divide the radial sampling data into several sub-regional radial sampling data according to preset frequency partitions.

Magnetic Resonance Imaging (Magnetic Resonance Imaging (MRI) is one of the most advanced medical diagnosis and treatment methods. Compared with X-ray and X-ray computed tomography (X-CT), MRI does not produce harmful radiation to the human body, and can image human soft tissue at the same time. , can provide early diagnosis of a variety of diseases. Among them, dynamic imaging, which requires high temporal resolution, causes motion artifacts due to the slow imaging speed of MRI, which limits its wide application. The original data collected by MRI is frequency domain data (that is, k-space data). At the level of k-space sampling technology, in order to improve the sampling speed, researchers have done a lot of research on undersampling, non-uniform sampling, and sampling trajectory design. Correspondingly, it is also important to study the image domain reconstruction corresponding to different sampling modes. The golden angle radial sampling targeted by the embodiments of the present application is different from the ordinary radial sampling. Because the ordinary radial sampling is densely sampled in the low-frequency region in the center, and sparsely sampled in the high-frequency region, this causes certain difficulties for reconstruction. The golden angle radial sampling adopts different sampling densities for different annular areas according to the distance from the center, that is, reducing the sampling spokes in the low frequency area and increasing the sampling spokes in the high frequency area, which improves the sampling efficiency.

Figure 2 shows a schematic diagram of the golden angle radial sampling trajectory. The golden angle radial sampling rotates a fixed angle of 111.25 degrees each time. This value is calculated based on the golden ratio, so it is called the golden angle. The advantage of the golden angle radial sampling data is that the k-space at any time in the sampling process presents a state of relatively uniform sampling, which is conducive to image reconstruction based on the data of any number of sampling lines.

The terminal device that executes the embodiments of the present application may be a magnetic resonance scanner, or may be other terminal devices that can acquire radial sampling data through wired communication, wireless communication, transfer storage media, or the like. For example, the executive body can be an image workstation, and the image workstation can communicate and connect with the magnetic resonance scanner through data lines, wifi, bluetooth, etc., to obtain radial sampling data, and perform magnetic resonance imaging work.

After acquiring the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, the radial sampling data may be divided into several sub-regional radial sampling data according to preset frequency partitions. The specific number of frequency partitions and the frequency range of each frequency partition may be set according to actual conditions, which are not specifically limited in this embodiment of the present application. For example, three frequency partitions can be set in the low frequency area, the intermediate frequency area and the high frequency area, and the radial sampling data can be divided into the low frequency area radial sampling data, the intermediate frequency area radial sampling data and the high frequency area radial sampling data correspondingly.

Step 102 , using different radial interpolation networks to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data.

In a specific implementation of the embodiment of the present application, step 102 may include the process shown in FIG. 3 :

Step 1021: Rearrange the radial sampling data of each sub-region into a matrix form to obtain matrix data of each sub-region.

Taking the radial sampling data of any sub-region as an example, the number of radial spokes is denoted as N, and the number of sampling points on each spoke is denoted as M. In the process of data rearrangement, the sampling on each spoke is If the data is taken as a row of the matrix, a matrix data with a size of N×M (N rows and M columns) can be obtained, that is, subregional matrix data.

Step 1022: Use different radial interpolation networks to process the matrix data of each sub-region respectively to obtain the augmented matrix data of each sub-region.

Due to the regional differences of k-space data, the importance of low-frequency regions and high-frequency regions to the final reconstructed image is not the same. In this embodiment of the present application, radial interpolation networks corresponding to each frequency partition may be preset. For example, as shown in Figure 4, for the three frequency partitions of the low frequency region, the intermediate frequency region and the high frequency region, corresponding radial interpolation networks can be set respectively (that is, the radial interpolation module 1 shown in the figure, the radial interpolation module 2 and radial interpolation module 3). In the embodiment of the present application, any neural network in the prior art may be selected and used as the radial interpolation network according to the actual situation, which is not specifically limited here.

When the radial interpolation network processes the subregional matrix data, the size of the subregional matrix data can be enlarged. Size expansion can specifically include the following three situations:

Case 1. Use different radial interpolation networks to expand the number of rows of each sub-regional matrix data to α _g times, that is, increase the number of spokes, and obtain the _augmented matrix data of each sub-region ; The augmentation coefficient corresponding to the frequency partition, and α _g is an integer greater than 1, and its specific value can be set according to the actual situation, which is not specifically limited in this embodiment of the present application, 1≤g≤G, G is the number of frequency partitions .

Case 2: Use different radial interpolation networks to expand the number of columns of each subregional matrix data to _βg times, that is, increase the number of sampling points on each spoke, and obtain the augmented matrix data of each subregion; among them, _βg is the augmentation coefficient corresponding to the g-th frequency partition, and β _g is an integer greater than 1, and its specific value can be set according to the actual situation, which is not specifically limited in this embodiment of the present application.

Case 3: Using different radial interpolation networks to expand the number of rows of each subregional matrix data to α _g times, and the number of columns to β _g times, that is to say, the number of spokes and the number of sampling points on each spoke are increased at the same time. Each subregion augments the matrix data.

In the embodiment of the present application, any one of the above-mentioned situations may be selected according to the actual situation to process the matrix data of each subregion.

The radial interpolation network used in the embodiments of the present application has undergone the training process of deep learning in advance. Processing Radial Interpolation Network) as an example to describe its training process in detail.

First, construct the training sample set of the gth radial interpolation network. The training sample set includes several training samples. Each training sample includes input matrix data and expected output matrix data. The size of the input matrix data is the same as that of the gth subregional matrix data, and the expected output matrix data is increased with the gth subregional data. The size of the wide matrix data is consistent.

In a specific implementation of the embodiment of the present application, the fully sampled data of the corresponding frequency partition, that is, the fully sampled data, may be obtained first, and then the fully sampled data is deleted according to the preset undersampling ratio to obtain the same data as the fully sampled data. The undersampled data corresponding to the data. Next, rearrange the fully sampled data to obtain input matrix data; and rearrange the undersampled data to obtain expected output matrix data. Finally, the training samples consisting of the input matrix data and the expected output matrix data are added to the training sample set. In the initial state, the training sample set is empty, that is, there is no training sample. By repeating the above process, new training samples can be continuously constructed and added to the training sample set until the predetermined number of training samples is reached.

After the construction of the training sample set is completed, the g th radial interpolation network can be trained by using the training sample set to obtain the g th radial interpolation network after training.

During the training process, for each training sample in the training data set, the radial interpolation network can be used to process the input matrix data in the training sample to obtain the actual output matrix data, and then according to the expectations in the training sample The output matrix data and the actual output matrix data calculate the training loss value. The specific calculation method of the training loss value can be set according to the actual situation. In a specific implementation of the embodiment of the present application, the squared error between the expected output matrix data and the actual output matrix data can be calculated, and the squared error can be determined. is the training loss value.

After the training loss value is calculated, the model parameters of the radial interpolation network can be adjusted according to the training loss value. In the embodiment of the present application, it is assumed that the model parameter of the radial interpolation network is W1, and the training loss value is back-propagated to modify the model parameter W1 of the radial interpolation network to obtain the modified model parameter W2. After modifying the parameters, continue to perform the next training process. In this training process, recalculate the training loss value, and backpropagate the training loss value to modify the model parameter W2 of the radial interpolation network to obtain the modified model parameter. W3, ..., and so on, repeat the above process continuously, and the model parameters can be modified in each training process until the preset training conditions are met, wherein the training conditions can be that the number of training times reaches the preset number of times threshold, the number of times The threshold can be set according to the actual situation, for example, it can be set to thousands, tens of thousands, hundreds of thousands or even larger values; the training condition can also be that the radial interpolation network converges; it may occur that the number of training has not yet reached the number of times threshold value, but the radial interpolation network has converged, which may lead to unnecessary repetition of work; or the radial interpolation network can never converge, which may lead to an infinite loop and cannot end the training process. Based on the above two situations, the training condition can also be training. The degree reaches the degree threshold or the radial interpolation network converges. When the training conditions are met, the trained radial interpolation network can be obtained.

Step 1023 , restore the augmented matrix data of each sub-region to the form of radial spokes, and obtain the filled radial sampling data.

It is easy to understand that step 1023 is the inverse process of step 1021. Taking any sub-region augmented matrix data as an example, assuming its size is N′×M′ (N′ rows and M′ columns), in the process of data restoration, Taking the data of each row of the matrix as the sampling data on one spoke, the radial sampling data after corresponding sub-region filling can be obtained, including N′ spokes, and the number of sampling points on each spoke is M′. After restoring and obtaining the filled radial sampling data of each sub-region, these data can be combined into complete filled radial sampling data.

Figure 4 shows a schematic diagram of the data rearrangement, radial interpolation network processing and data restoration process. After such a process, the data filling in the frequency domain is effectively realized, and the finally obtained radial sampling data after filling is compared with the original. The radially sampled data has higher data precision.

Step 103: Perform imaging processing on the padded radial sampling data to obtain a target image.

Specifically, inverse fast Fourier transform processing may be performed on the padded radial sampling data, so as to obtain the final imaging result, that is, the target image.

To sum up, the embodiment of the present application acquires the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divides the radial sampling data into several sub-regional radial sampling data according to preset frequency partitions; using Different radial interpolation networks respectively process the radial sampling data of each subregion to obtain filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition; The data is subjected to imaging processing to obtain the target image. Through the embodiments of the present application, different radial interpolation networks are introduced to fill in the sampled data in each frequency partition respectively, so that the sampled data in each different frequency partition has a stronger pertinence, and the filled data is more accurate, thereby effectively improving the the final imaging accuracy.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Corresponding to the magnetic resonance oxygen seventeen metabolic imaging method described in the above embodiment, FIG. 5 shows a structural diagram of an embodiment of a magnetic resonance oxygen seventeen metabolic imaging device provided in an embodiment of the present application.

In this embodiment, a magnetic resonance oxygen seventeen metabolic imaging device may include:

The sampling data division module 501 is used to obtain the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divide the radial sampling data into several sub-regional radial sampling data according to preset frequency divisions;

The radial interpolation processing module 502 is used to process the radial sampling data of each sub-region by using different radial interpolation networks to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition ;

The imaging processing module 503 is configured to perform imaging processing on the filled radial sampling data to obtain a target image.

In a possible implementation manner of this embodiment, the radial interpolation processing module may include:

In a possible implementation of this embodiment, the magnetic resonance oxygen seventeen metabolic imaging device may further include:

In a possible implementation manner of this embodiment, the training sample set building module may include:

In a possible implementation manner of this embodiment, the imaging processing module is specifically configured to perform inverse fast Fourier transform processing on the padded radial sampling data to obtain the target image.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described devices, modules and units can be referred to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

FIG. 6 shows a schematic block diagram of a terminal device provided by an embodiment of the present application. For convenience of description, only parts related to the embodiment of the present application are shown.

As shown in FIG. 6 , the terminal device 6 in this embodiment includes: a processor 60 , a memory 61 , and a computer program 62 stored in the memory 61 and running on the processor 60 . When the processor 60 executes the computer program 62 , the steps in each of the above embodiments of the magnetic resonance oxygen seventeen metabolic imaging method are implemented, for example, steps 101 to 103 shown in FIG. 1 . Alternatively, when the processor 60 executes the computer program 62, the functions of the modules/units in each of the foregoing apparatus embodiments, such as the functions of the modules 501 to 503 shown in FIG. 5, are implemented.

Exemplarily, the computer program 62 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 61 and executed by the processor 60 to complete the this application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the terminal device 6 .

Those skilled in the art can understand that FIG. 6 is only an example of the terminal device 6, and does not constitute a limitation on the terminal device 6, and may include more or less components than the one shown, or combine some components, or different components For example, the terminal device 6 may further include an input and output device, a network access device, a bus, and the like.

The processor 60 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processors). Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 61 may be an internal storage unit of the terminal device 6 , such as a hard disk or a memory of the terminal device 6 . The memory 61 may also be an external storage device of the terminal device 6, such as a plug-in hard disk equipped on the terminal device 6, a smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc. Further, the memory 61 may also include both an internal storage unit of the terminal device 6 and an external storage device. The memory 61 is used to store the computer program and other programs and data required by the terminal device 6 . The memory 61 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

In the embodiments provided in this application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units. Or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only). Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer-readable Storage media exclude electrical carrier signals and telecommunications signals.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the above-mentioned embodiments, those of ordinary skill in the art should understand that: it can still be used for the above-mentioned implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the application, and should be included in the within the scope of protection of this application.

Claims

A magnetic resonance oxygen seventeen metabolic imaging method, comprising:

Obtaining the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and dividing the radial sampling data into several sub-regional radial sampling data according to preset frequency partitions;

Different radial interpolation networks are used to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition;

Perform imaging processing on the filled radial sampling data to obtain a target image.
The magnetic resonance oxygen seventeen metabolic imaging method according to claim 1, wherein the radial sampling data of each sub-region is processed separately by using different radial interpolation networks to obtain the filled radial sampling data, include:

Rearrange the radial sampling data of each sub-region into a matrix form to obtain matrix data of each sub-region;

Use different radial interpolation networks to process the matrix data of each subregion, and obtain the augmented matrix data of each subregion;

The augmented matrix data of each sub-region is restored to the form of radial spokes, and the filled radial sampling data is obtained.
The magnetic resonance oxygen seventeen metabolic imaging method according to claim 2, characterized in that, before using different radial interpolation networks to process the matrix data of each subregion, the method further comprises:

Construct the training sample set of the gth radial interpolation network, 1≤g≤G, G is the number of frequency partitions; the training sample set includes several training samples, and each training sample includes input matrix data and expected output matrix data, the input matrix data is consistent with the size of the gth subregion matrix data, and the expected output matrix data is consistent with the size of the gth subregion augmented matrix data;

The g-th radial interpolation network is trained by using the training sample set to obtain the g-th radial interpolation network after training.
The magnetic resonance oxygen seventeen metabolic imaging method according to claim 3, wherein the construction of the training sample set of the gth radial interpolation network comprises:

Obtain fully sampled data;

performing data deletion on the full-collected data to obtain under-sampled data corresponding to the full-collected data;

Rearranging the fully collected data to obtain the input matrix data;

rearranging the undersampled data to obtain the expected output matrix data;

A training sample consisting of the input matrix data and the expected output matrix data is added to the training sample set.
The magnetic resonance oxygen seventeen metabolic imaging method according to claim 2, wherein the different radial interpolation networks are used to process the matrix data of each sub-region respectively to obtain the augmented matrix data of each sub-region, comprising:

Use different radial interpolation networks to expand the number of rows of each subregional matrix data to α g times, and obtain each subregional augmented matrix data; where α g is the augmentation coefficient corresponding to the gth frequency partition, and α g is an integer greater than 1, 1≤g≤G, and G is the number of frequency partitions.
The magnetic resonance oxygen seventeen metabolic imaging method according to claim 2, wherein the different radial interpolation networks are used to process the matrix data of each sub-region respectively to obtain the augmented matrix data of each sub-region, comprising:

Use different radial interpolation networks to expand the number of columns of each subregional matrix data to βg times, and obtain each subregional augmented matrix data; where βg is the augmentation coefficient corresponding to the gth frequency partition, and β g is an integer greater than 1, 1≤g≤G, and G is the number of frequency partitions.
The magnetic resonance oxygen seventeen metabolic imaging method according to claim 2, wherein the different radial interpolation networks are used to process the matrix data of each sub-region respectively to obtain the augmented matrix data of each sub-region, comprising:

Use different radial interpolation networks to expand the number of rows of each sub-regional matrix data to α g times, and the number of columns to β g times, to obtain the augmented matrix data of each sub-region; where α g and β g are the same as the first The augmentation coefficients corresponding to the g frequency partitions, and both α g and β g are integers greater than 1, 1≤g≤G, and G is the number of frequency partitions.
The magnetic resonance oxygen seventeen metabolic imaging method according to any one of claims 1 to 7, wherein, performing imaging processing on the filled radial sampling data to obtain a target image, comprising:

Perform inverse fast Fourier transform processing on the padded radial sampling data to obtain the target image.
A magnetic resonance oxygen seventeen metabolic imaging device, comprising:

The sampling data division module is used to obtain the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divide the radial sampling data into several sub-regional radial sampling data according to the preset frequency division;

The radial interpolation processing module is used to process the radial sampling data of each sub-region by using different radial interpolation networks to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition;

The imaging processing module is configured to perform imaging processing on the filled radial sampling data to obtain a target image.
The magnetic resonance oxygen seventeen metabolic imaging device according to claim 9, wherein the radial interpolation processing module comprises:

The data rearrangement unit is used to rearrange the radial sampling data of each sub-region into a matrix form respectively, and obtain the matrix data of each sub-region;

The radial interpolation processing unit is used to process the matrix data of each sub-region by using different radial interpolation networks to obtain the augmented matrix data of each sub-region;

A data restoration unit, configured to restore the augmented matrix data of each sub-region to the form of radial spokes, and obtain the filled radial sampling data.
The magnetic resonance oxygen seventeen metabolic imaging device according to claim 10, further comprising:

The training sample set building module is used to construct the training sample set of the gth radial interpolation network, 1≤g≤G, G is the number of frequency partitions; the training sample set includes several training samples, each training sample Including input matrix data and expected output matrix data, the input matrix data is consistent with the size of the gth subregional matrix data, and the expected output matrix data is consistent with the size of the gth subregion augmented matrix data;

The radial interpolation network training module is used for training the g th radial interpolation network by using the training sample set to obtain the g th radial interpolation network after training.
The magnetic resonance oxygen seventeen metabolic imaging device according to claim 11, wherein the training sample set building module comprises:

The full sampling data acquisition unit is used to obtain fully sampled full sampling data;

an under-sampling data processing unit, configured to perform data deletion on the fully-sampled data to obtain under-sampled data corresponding to the fully-sampled data;

a first rearranging unit, used for rearranging the fully collected data to obtain the input matrix data;

a second rearranging unit, configured to rearrange the undersampled data to obtain the expected output matrix data;

A training sample adding unit, configured to add a training sample composed of the input matrix data and the expected output matrix data to the training sample set.
A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, the magnetic resonance oxygen according to any one of claims 1 to 8 is implemented Seventeen steps of the metabolic imaging method.
A terminal device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, when the processor executes the computer program, the process according to claim 1 to The steps of the magnetic resonance oxygen seventeen metabolic imaging method described in any one of 8.