WO2021184350A1 - 一种基于神经网络的网格化磁共振图像重建方法和装置 - Google Patents

一种基于神经网络的网格化磁共振图像重建方法和装置 Download PDF

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WO2021184350A1
WO2021184350A1 PCT/CN2020/080400 CN2020080400W WO2021184350A1 WO 2021184350 A1 WO2021184350 A1 WO 2021184350A1 CN 2020080400 W CN2020080400 W CN 2020080400W WO 2021184350 A1 WO2021184350 A1 WO 2021184350A1
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data
neural network
frequency band
magnetic resonance
resampled
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French (fr)
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王珊珊
郑海荣
荣楚誉
刘新
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—Two-dimensional [2D] image generation

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  • This application belongs to the field of image reconstruction technology, and in particular relates to a neural network-based gridded magnetic resonance image reconstruction method, device, terminal equipment, and computer storage medium.
  • Magnetic Resonance Imaging is currently one of the most advanced medical diagnostic methods. Compared with X-ray and X-ray computed tomography imaging, MRI does not produce harmful radiation to the human body and can image human soft tissues. , Provide early diagnosis for a variety of lesions.
  • MRI has the disadvantages of slow imaging speed and long imaging time, which limit the promotion and application of MRI.
  • MRI reconstruction of irregular sampling trajectory is mainly realized by gridding algorithm.
  • the gridding algorithm mainly includes two types. One is the nearest neighbor point gridding algorithm, which first compensates for the density of the unevenly sampled data, and then resamples the data to the grid matrix in the Cartesian coordinate system through the nearest neighbor interpolation method , And then reconstruct the magnetic resonance image.
  • This method can realize the rapid reconstruction of the magnetic resonance image, but the reconstruction quality needs to be improved; the other type is the matrix inversion method, which has a higher accuracy of the magnetic resonance image reconstruction, but The reconstruction speed is slow.
  • the existing MRI reconstruction methods have the problems of low reconstruction quality and long reconstruction time.
  • the embodiments of the present application provide a neural network-based gridded magnetic resonance image reconstruction method, device, terminal equipment, and computer storage medium, which can solve the problems of low reconstruction quality and long reconstruction time of existing magnetic resonance image reconstruction methods.
  • the first aspect of the embodiments of the present application provides a neural network-based gridded magnetic resonance image reconstruction method, including:
  • Image reconstruction processing is performed on the target data to obtain a magnetic resonance image.
  • each of the resampled data is input to a weighted neural network to obtain the weight corresponding to the resampled data, and the weight of each resampled data is calculated according to the weight corresponding to each of the resampled data.
  • the target data obtained includes:
  • the data of each of the resampled data in the preset frequency band is input into the weight neural network corresponding to the preset frequency band to obtain the weight corresponding to each of the resampled data in the preset frequency band, wherein the number of the preset frequency bands More than two
  • the target data is determined according to the fusion data corresponding to each of the preset frequency bands.
  • the weighted neural network is a convolutional neural network.
  • the performing image reconstruction processing on the target data to obtain a magnetic resonance image includes:
  • the interpolation algorithm is a linear interpolation algorithm.
  • the interpolation algorithm includes two or more of the nearest neighbor interpolation method, the bilinear interpolation method, and the cubic spline interpolation method.
  • the second aspect of the embodiments of the present application provides a neural network-based gridded magnetic resonance image reconstruction device, including:
  • a density compensation module configured to obtain initial sampling data, perform density compensation on the initial sampling data, and obtain compensation data
  • the re-sampling module is configured to re-sample the compensation data by using at least two interpolation algorithms to obtain the re-sampled data corresponding to each interpolation algorithm;
  • the weight learning module is used to input each of the resampled data into the weight neural network to obtain the weight corresponding to the resampled data, and perform weighting calculation on each of the resampled data according to the weight corresponding to each of the resampled data And, get the target data;
  • the image reconstruction module is used to perform image reconstruction processing on the target data to obtain a magnetic resonance image.
  • the weight learning module includes:
  • the weight calculation sub-module is used to input the data of each resampled data in the preset frequency band into the weight neural network corresponding to the preset frequency band to obtain the weight corresponding to each of the resampled data in the preset frequency band, wherein,
  • the number of the preset frequency bands is more than two;
  • the weighted fusion sub-module is used to perform a weighted summation of the data of each resampled data in the preset frequency band according to the weight corresponding to each of the resampled data in the preset frequency band to obtain the fusion corresponding to the preset frequency band data;
  • the target data module is configured to determine the target data according to the fusion data corresponding to each of the preset frequency bands.
  • the weighted neural network is a convolutional neural network.
  • the image reconstruction module is specifically configured to perform an inverse fast Fourier transform on the target data to obtain a magnetic resonance image.
  • the interpolation algorithm is a linear interpolation algorithm.
  • the interpolation algorithm includes two or more of the nearest neighbor interpolation method, the bilinear interpolation method, and the cubic spline interpolation method.
  • the third 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 running on the processor.
  • a terminal device including a memory, a processor, and a computer program stored in the memory and running on the processor.
  • the processor executes the computer program, Implement the steps as described above.
  • the fourth aspect of the embodiments of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the foregoing method are implemented.
  • the 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 implement the steps of the above-mentioned method.
  • the neural network-based gridded magnetic resonance image reconstruction method of the present application after density compensation is performed on the initial sampling data to obtain the compensation data, at least two interpolation algorithms are used to resample the compensation data to obtain the reconstruction corresponding to each interpolation algorithm. Sampling data. Then, the weights corresponding to different resampled data are learned through the neural network, and each resampled data is weighted and summed according to the weights corresponding to each resampled data to obtain the target data.
  • the target data is used for image reconstruction, thereby ensuring a higher Under the premise of the reconstruction speed, the quality of the reconstructed image is improved, which solves the problems of low reconstruction quality and long reconstruction time of the existing magnetic resonance image reconstruction methods.
  • FIG. 1 is a schematic flowchart of a method for reconstructing a gridded magnetic resonance image based on a neural network according to an embodiment of the present application
  • FIG. 2 is a schematic structural diagram of a neural network-based meshed magnetic resonance image reconstruction device provided by an embodiment of the present application
  • Fig. 3 is a schematic diagram of a terminal device provided by an embodiment of the present application.
  • the term “if” can be construed as “when” or “once” or “in response to determination” or “in response to detecting “.
  • the phrase “if determined” or “if detected [described condition or event]” can be interpreted as meaning “once determined” or “in response to determination” or “once detected [described condition or event]” depending on the context ]” or “in response to detection of [condition or event described]”.
  • the magnetic resonance image reconstruction method provided by the embodiments of this application can be applied to mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR)/virtual reality (VR) devices, notebook computers, and super mobiles.
  • AR augmented reality
  • VR virtual reality
  • terminal devices such as ultra-mobile personal computers (UMPC), netbooks, and personal digital assistants (personal digital assistants, PDAs)
  • UMPC ultra-mobile personal computers
  • PDAs personal digital assistants
  • the terminal device may be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a wireless local loop (Wireless Local Loop, WLL) station, Personal Digital Assistant (PDA) devices, handheld devices with wireless communication functions, computing devices or other processing devices connected to wireless modems, in-vehicle devices, car networking terminals, computers, laptop computers, handheld communication devices , Handheld computing equipment, satellite wireless equipment, wireless modem cards, TV set top boxes (STB), customer premise equipment (customer premise equipment, CPE), and/or other equipment used to communicate on wireless systems and download
  • a first-generation communication system for example, a mobile terminal in a 5G network or a mobile terminal in a public land mobile network (PLMN) network that will evolve in the future.
  • PLMN public land mobile network
  • the following describes a neural network-based meshed magnetic resonance image reconstruction method provided in the first embodiment of the present application. Please refer to FIG. 1 for the neural network-based meshed magnetic resonance image reconstruction method in the first embodiment of the present application.
  • Magnetic resonance imaging is currently one of the most advanced medical diagnosis and treatment methods.
  • Magnetic resonance imaging technology uses the principle of nuclear magnetic resonance to perform cross-sectional imaging or three-dimensional imaging of specific parts of the human body or organisms to obtain the anatomical structure, physiological function and pathological changes of tissues and organs. information.
  • Current studies have shown that the human body will not be harmed by the action of a magnetic field. Therefore, magnetic resonance imaging can be regarded as a harmless human tissue imaging diagnosis and treatment method, which will not produce harmful radiation to the human body.
  • the user can use a magnetic resonance scanner to perform a magnetic resonance scanning operation to obtain initial sampling data, that is, k-space data.
  • the terminal device that executes the magnetic resonance image reconstruction method may be a magnetic resonance scanner, or other terminal devices that can obtain initial sampling data through wired communication, wireless communication, transfer of storage media, and the like.
  • the terminal device that executes the magnetic resonance image reconstruction method may be an image workstation, and the image workstation may communicate with the magnetic resonance scanner through a data line, WiFi, Bluetooth, etc., to obtain initial sampling data, and perform magnetic resonance image reconstruction work.
  • the terminal device can divide the initial sampling data into a grid and compensate by preset density
  • the function density compensation function, DCF) performs density compensation on the initial sampling data to obtain compensation data.
  • the type of the preset density compensation function can be selected according to the actual situation. For example, Jackson area density compensation function can be selected as the preset density compensation function; alternatively, the same image density compensation function can also be selected as the preset density compensation function; or, other density compensation functions can also be selected as the preset density Compensation function.
  • the interpolation algorithm can be used to resample the compensation function and sample the data from the non-Cartesian coordinate system. To the grid matrix under the Cartesian coordinate system, in order to quickly reconstruct the magnetic resonance image.
  • the compensation data is globally resampled using the same interpolation method. Using the same interpolation method for resampling globally will make the reconstructed MRI image less accurate.
  • the terminal device of this embodiment uses at least two interpolation algorithms to resample the compensation data to obtain the resampled data in the Cartesian coordinate system corresponding to each interpolation algorithm.
  • the type of the above interpolation algorithm can be selected according to the actual situation.
  • the above-mentioned interpolation algorithm may be a linear interpolation algorithm or a non-linear interpolation algorithm.
  • the terminal equipment can use the linear interpolation algorithm.
  • the terminal device selects a linear interpolation algorithm, at least two of linear interpolation algorithms such as the nearest neighbor interpolation method, bilinear interpolation method, and cubic spline interpolation method can be selected as the above interpolation algorithm.
  • the terminal device After the terminal device obtains each resampled data, it can input each resampled data into the weighted neural network, and the weighted neural network learns each resampled data to obtain the weight corresponding to each resampled data.
  • the terminal device After that, the terminal device performs a weighted summation on each resampled data according to the weight corresponding to each resampled data.
  • the network type of the above-mentioned weighted neural network can be selected according to the actual situation.
  • the weighted neural network can be any of neural networks such as Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), etc. A sort of.
  • the user can set the initialized neural network and sample data, and then the terminal device can use the sample data and sample labels to train the neural network.
  • the sample data may be sample resampling data, and the sample label is the weight corresponding to the sample data.
  • the terminal device inputs the sample data into the neural network and obtains the sample output. According to the sample label and sample output, the loss value is calculated by the preset loss function, and the neural network is iteratively updated according to the loss value, and the iteration is repeated until the preset suspension condition is satisfied. Weighted neural network.
  • the preset suspension condition can be set according to the actual situation.
  • the preset suspension condition may be set as the loss value is less than the preset loss threshold or the number of iterations is greater than or equal to the preset iteration threshold.
  • the data collected clinically can be used as the verification set to verify the above-mentioned weighted neural network. If the verification passes, the weighted neural network can be used; if the verification fails, the sample data in the sample set can be replaced. Retrain the weighted neural network.
  • the terminal device can split each resampled data according to the frequency band preset by the user to obtain the data of each resampled data in each preset frequency band. .
  • the terminal device can split the first resampled data according to frequency band A, frequency band B, and frequency C to obtain the first frequency division data in frequency band A, the second frequency division data in frequency band B, and the frequency division data in frequency band C.
  • the third frequency division data at the same time, the terminal device can split the second resampling data according to frequency band A, frequency band B, and frequency C to obtain the fourth frequency division data in frequency band A and the fifth frequency division data in frequency band B , Is located in the sixth frequency division data of frequency band C.
  • the split method of the aforementioned preset frequency band can be selected according to the actual situation. For example, suppose the user wants to split the frequency band where the k-space data is located into a high-frequency band, an intermediate frequency band, and a low-frequency band. The user can divide the frequency band where the k-space data is located by three equal divisions to obtain the high-frequency band. , Mid-frequency band and low-frequency band, where the frequency of the high-frequency band is greater than the frequency of the mid-frequency band, and the frequency of the mid-frequency band is greater than the frequency of the low-frequency band; The method splits the frequency band where the spatial data is located into multiple frequency bands.
  • the preset frequency bands may not intersect with each other, or there may be partial intersections. For example, suppose that the frequency band where the k-space data is located is split, and frequency band A, frequency band B, and frequency band C are obtained. Frequency band A, frequency band B, and frequency band C may not intersect each other; or, it is also possible that frequency band A and frequency band B do not intersect with frequency band C, and frequency band A and frequency band B partially intersect.
  • the terminal device can perform a weighted sum operation respectively.
  • the terminal device may input the data of each resampled data in the preset frequency band into the weight neural network corresponding to the preset frequency band to obtain the weight corresponding to each of the resampled data in the preset frequency band.
  • the terminal device performs a weighted summation of the data of each resampled data in the preset frequency band according to the weight corresponding to each resampled data in the preset frequency band to obtain the fusion data corresponding to the preset frequency band.
  • the calculation method of fusion data can be expressed as:
  • C r represents the fusion data corresponding to a certain preset frequency band
  • a 1 represents the weight of the first type of resampling data in the aforementioned preset frequency band
  • a i represents the weight of the i-th type of resampling data in the aforementioned preset frequency band, i Is the number of interpolation algorithms
  • C 1 represents the data of the first type of resampling data in the preset frequency band
  • C i represents the data of the i-th resampled data within the preset frequency band.
  • the first resampled data will be split to obtain the first frequency division data in frequency band A, the second frequency division data in frequency band B, and the third frequency division data in frequency band C.
  • the second resampled data is split to obtain the fourth frequency division data in frequency band A, the fifth frequency division data in frequency band B, and the sixth frequency division data in frequency band C.
  • the weight of the first resampled data in frequency band A is 0.5
  • the weight in frequency band B is 0.7
  • the weight in frequency band C is 0.2
  • the weight of the second resampled data in frequency band A is 0.5
  • the weight in frequency band B is 0.5
  • the weight is 0.3
  • the weight in band C is 0.8.
  • the terminal device can obtain the first frequency division data and the fourth frequency division data corresponding to frequency band A, and add the value obtained by multiplying the first frequency division data by 0.5 and the value obtained by multiplying the fourth frequency division data by 0.5 to obtain frequency band A.
  • the terminal device can obtain the second frequency division data and the fifth frequency division data corresponding to frequency band B, and the value obtained by multiplying the second frequency data by 0.7 is the same as the value obtained by multiplying the fifth frequency data by 0.3
  • the terminal device can obtain the third frequency division data and the sixth frequency division data corresponding to frequency band C, and multiply the value obtained by multiplying the third frequency data by 0.2 and the sixth frequency data by The value obtained by 0.8 is added to obtain the fusion data corresponding to frequency band C.
  • the number of weighted neural networks described above may be the same as the number of preset frequency bands, that is, one preset frequency band corresponds to one weighted neural network.
  • frequency band A corresponds to weighted neural network 1
  • frequency band B corresponds to weighted neural network 2
  • frequency band C corresponds to weighted neural network 3.
  • the terminal device can calculate the fusion data corresponding to frequency band B.
  • the second frequency division data is input to the weighting neural network 2 to obtain a weight of 0.7 corresponding to the second frequency division data
  • the fifth frequency division data corresponding to frequency band B is input to the weighting neural network 2 to obtain a weight of 0.3 corresponding to the fifth frequency division data.
  • the terminal device adds the value obtained by multiplying the second frequency division data by 0.7 and the value obtained by multiplying the fifth frequency division data by 0.3 to obtain the fusion data corresponding to frequency band B.
  • the terminal device After obtaining the fusion data corresponding to each preset frequency band, the terminal device can determine the target data from the fusion data corresponding to each preset frequency band, and perform image reconstruction processing on the target data to obtain a magnetic resonance image.
  • the preset frequency bands may not intersect each other, or they may partially intersect. At this time, if the preset frequency bands do not intersect each other, the target data can be obtained directly according to the fusion data combination corresponding to each preset frequency band. If there are intersecting frequency bands in each preset frequency band, the intersecting frequency band is used as the first frequency band, and the disjoint frequency bands in each preset frequency band are used as the second frequency band; Fusion data, the updated fusion data of the first frequency band is determined by a preset calculation method.
  • the preset calculation method can be set according to the actual situation. For example, the preset calculation method can be calculation of simple arithmetic average, calculation of weighted arithmetic average, etc.
  • Method According to the fusion data corresponding to each second frequency band and the updated fusion data corresponding to each first frequency band, the target data is combined to obtain the target data.
  • the target data can be obtained directly according to the combination of the fusion data corresponding to frequency band A and the fusion data corresponding to frequency band B.
  • the frequency band that intersects frequency band A and frequency B is regarded as the first frequency band
  • the frequency band that does not intersect with frequency band B in frequency band A is regarded as the first second frequency band
  • the frequency band that does not intersect with frequency band B is regarded as the second frequency band.
  • the intersecting frequency band is used as the second second frequency band.
  • S104 Perform image reconstruction processing on the target data to obtain a magnetic resonance image.
  • the terminal device can select an appropriate reconstruction method according to the actual situation. For example, the terminal device can perform inverse Fourier transform on the target data to obtain a magnetic resonance image; or, the terminal device can also perform inverse fast Fourier transform on the target data to obtain a magnetic resonance image; or, the terminal device can also use other
  • the image reconstruction method performs image reconstruction processing on the target data to obtain a magnetic resonance image.
  • the terminal device divides the frequency domain of the collected initial sampling data (ie, k-space data) into frequency domain 1, frequency domain 2, and frequency domain 3.
  • Frequency domain 1, frequency domain 2, and frequency domain 3 do not intersect each other.
  • Frequency domain 1 corresponds to weighted neural network 1
  • frequency domain 2 corresponds to weighted neural network 2
  • frequency domain 3 corresponds to weighted neural network 3.
  • the terminal device performs density compensation on the initial sampling data to obtain the compensation data.
  • the terminal device uses the nearest neighbor interpolation method, bilinear interpolation method, and cubic spline interpolation method to resample the compensation data to obtain resampled data 1, resampled data 2, and resampled data 3.
  • the terminal equipment uses the data of resampling data 1 in frequency domain 1 as frequency division data 1.1, the data of resampling data 1 in frequency domain 2 as frequency division data 1.2, and the data of resampling data 1 in frequency domain 3 as frequency division data.
  • the terminal device inputs frequency division data 1.1, frequency division data 2.1 and frequency division data 3.1 into weight neural network 1, to obtain weight 1.1, weight 2.1 and weight 3.1, and compare frequency division data 1.1 according to weight 1.1, weight 2.1 and weight 3.1 ,
  • the frequency division data 2.1 and the frequency division data 3.1 are weighted and summed to obtain the fusion data 1;
  • the terminal device inputs the frequency division data 1.2, frequency division data 2.2, and frequency division data 3.2 into the weighted neural network 2 to obtain the weight 1.2, weight 2.2, and weight 3.2.
  • Frequency data 2.2 and frequency division data 3.2 are weighted and summed to obtain fusion data 2;
  • the terminal device inputs the frequency division data 1.3, frequency division data 2.3, and frequency division data 3.3 into the weighted neural network 3 to obtain the weight 1.3, weight 2.3, and weight 3.3, and compare the frequency division data 1.3 and division according to the weight 1.3, weight 2.3, and weight 3.3.
  • the frequency data 2.3 and the frequency division data 3.3 are weighted and summed to obtain the fusion data 3.
  • the terminal device combines the fusion data 1, the fusion data 2 and the fusion data 3 into target data, and performs an inverse fast Fourier transform on the target data to obtain a magnetic resonance image.
  • the terminal device may use at least two interpolation algorithms to resample the compensation data to obtain the resampled data corresponding to each interpolation algorithm.
  • the weight corresponding to each resampled data is calculated through the weighted neural network, and then the weighted summation of each resampled data is performed according to the weight corresponding to each resampled data to obtain the target
  • the magnetic resonance image is reconstructed according to the target data, so that the quality of the reconstructed image is improved under the premise of ensuring a higher reconstruction speed, and the problem of low reconstruction quality and long reconstruction time of the existing magnetic resonance image reconstruction method is solved.
  • the weight of each resampled data is learned through machine learning.
  • the use of neural networks can better learn the characteristics of each resampled data, and more accurately determine the weight of each resampled data, thereby improving the subsequent reconstruction of the magnetic field.
  • the accuracy of the resonance image is a feature that is a feature that is a feature that is a feature that is a feature that is a feature that is a feature that is a feature that is a feature size of the weight of each resampled data.
  • the above-mentioned interpolation algorithm may adopt a linear interpolation algorithm, thereby improving the speed of magnetic resonance image reconstruction.
  • the second embodiment of this application provides a neural network-based gridded magnetic resonance image reconstruction device.
  • the gridded magnetic resonance image based on neural network includes,
  • the density compensation module 201 is configured to obtain initial sampling data, perform density compensation on the initial sampling data, and obtain compensation data;
  • the re-sampling module 202 is configured to re-sample the compensation data by using at least two interpolation algorithms to obtain the re-sampled data corresponding to each interpolation algorithm;
  • the weight learning module 203 is configured to input each of the resampled data into a weighted neural network to obtain the weight corresponding to the resampled data, and weight each of the resampled data according to the weight corresponding to each of the resampled data Sum and get the target data;
  • the image reconstruction module 204 is configured to perform image reconstruction processing on the target data to obtain a magnetic resonance image.
  • the weight learning module 203 includes:
  • the weight calculation sub-module is used to input the data of each resampled data in the preset frequency band into the weight neural network corresponding to the preset frequency band to obtain the weight corresponding to each of the resampled data in the preset frequency band, wherein,
  • the number of the preset frequency bands is more than two;
  • the weighted fusion sub-module is used to perform a weighted summation of the data of each resampled data in the preset frequency band according to the weight corresponding to each of the resampled data in the preset frequency band to obtain the fusion corresponding to the preset frequency band data;
  • the target data module is configured to determine the target data according to the fusion data corresponding to each of the preset frequency bands.
  • the weighted neural network is a convolutional neural network.
  • the image reconstruction module 204 is specifically configured to perform an inverse fast Fourier transform on the target data to obtain a magnetic resonance image.
  • the interpolation algorithm is a linear interpolation algorithm.
  • the interpolation algorithm includes two or more of the nearest neighbor interpolation method, the bilinear interpolation method, and the cubic spline interpolation method.
  • FIG. 3 is a schematic diagram of a terminal device provided in Embodiment 3 of the present application.
  • the terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and running on the processor 30.
  • the processor 30 executes the computer program 32, the steps in the embodiment of the neural network-based gridded magnetic resonance image reconstruction method are implemented, such as steps S101 to S104 shown in FIG. 1.
  • the processor 30 executes the computer program 32, the functions of the modules/units in the foregoing device embodiments, for example, the functions of the modules 201 to 204 shown in FIG. 2 are realized.
  • the computer program 32 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 31 and executed by the processor 30 to complete This application.
  • the one or more modules/units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the terminal device 3.
  • the computer program 32 can be divided into a density compensation module, a resampling module, a weight learning module, and an image reconstruction module.
  • the specific functions of each module are as follows:
  • a density compensation module configured to obtain initial sampling data, perform density compensation on the initial sampling data, and obtain compensation data
  • the re-sampling module is configured to re-sample the compensation data by using at least two interpolation algorithms to obtain the re-sampled data corresponding to each interpolation algorithm;
  • the weight learning module is used to input each of the resampled data into the weight neural network to obtain the weight corresponding to the resampled data, and perform weighting calculation on each of the resampled data according to the weight corresponding to each of the resampled data And, get the target data;
  • the image reconstruction module is used to perform image reconstruction processing on the target data to obtain a magnetic resonance image.
  • the terminal device 3 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the terminal device may include, but is not limited to, a processor 30 and a memory 31.
  • FIG. 3 is only an example of the terminal device 3, and does not constitute a limitation on the terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components.
  • the terminal device may also include input and output devices, network access devices, buses, and so on.
  • the so-called processor 30 may be a central processing unit (Central Processing Unit, CPU), other general processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3.
  • the memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk equipped on the terminal device 3, a smart memory card (Smart Media Card, SMC), or Secure Digital (SD). Card, Flash Card, etc.
  • the memory 31 may also include both an internal storage unit of the terminal device 3 and an external storage device.
  • the memory 31 is used to store the computer program and other programs and data required by the terminal device.
  • the memory 31 can also be used to temporarily store data that has been output or will be output.
  • the disclosed device/terminal device and method may be implemented in other ways.
  • the device/terminal device embodiments described above are merely illustrative.
  • the division of the modules or units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units.
  • components can be combined or integrated into another system, or some features can be omitted or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, 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 the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • this application implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware through a computer program.
  • the computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, it can implement the steps of the foregoing method embodiments.
  • 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 forms.
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Abstract

本申请适用于图像重建技术领域，提供了一种基于神经网络的网格化磁共振图像重建方法、装置、终端设备及计算机存储介质，所述方法包括：获取初始采样数据，对所述初始采样数据进行密度补偿，得到补偿数据；使用至少两种插值算法对所述补偿数据进行重采样，得到各插值算法对应的重采样数据；将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据；对所述目标数据进行图像重建处理，得到磁共振图像。本申请可以解决现有的磁共振图像重建方法重建质量低，重建时间长的问题。

Description

一种基于神经网络的网格化磁共振图像重建方法和装置 技术领域
本申请属于图像重建技术领域，尤其涉及一种基于神经网络的网格化磁共振图像重建方法、装置、终端设备及计算机存储介质。
背景技术
磁共振成像(Magnetic Resonance Imaging，MRI)是目前最先进的医疗诊断手段之一，与X光和X射线计算机断层扫描成像相比，MRI不会产生对人体有害的辐射，并且可以对人体软组织成像，对多种病变提供早期诊断。
然而，MRI存在成像速度慢、成像时间长的缺陷，这些缺陷限制了MRI的推广和应用。目前不规则采样轨迹磁共振重建主要是通过网格化算法实现的。网格化算法主要包括两类，一类最相邻点网格化算法，先对不均匀采样数据进行密度补偿，然后通过最邻近插值法将数据重采样至笛卡尔坐标系下的网格矩阵，再进行磁共振图像重建，这种方式可以实现磁共振图像的快速重建，但是重建质量有待提高；另一类是矩阵反演法，这种方法重建得到的磁共振图像的精度较高，但是重建速度较慢。
综上，现有的磁共振重建方法存在重建质量低，重建时间长的问题。
发明概述
技术问题
本申请实施例提供了一种基于神经网络的网格化磁共振图像重建方法、装置、终端设备及计算机存储介质，可以解决现有的磁共振图像重建方法重建质量低，重建时间长的问题。
问题的解决方案
技术解决方案
为解决上述技术问题，本申请实施例采用的技术方案是：
本申请实施例的第一方面提供了一种基于神经网络的网格化磁共振图像重建方法，包括：
获取初始采样数据，对所述初始采样数据进行密度补偿，得到补偿数据；
使用至少两种插值算法对所述补偿数据进行重采样，得到各插值算法对应的重采样数据；
将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据；
对所述目标数据进行图像重建处理，得到磁共振图像。
可选地，所述将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据包括：
将各所述重采样数据在预设频段内的数据输入所述预设频段对应的权重神经网络，得到各所述重采样数据在预设频段对应的权重，其中，所述预设频段的数量为两个以上；
根据各所述重采样数据在预设频段对应的权重，对各所述重采样数据在预设频段内的数据进行加权求和，得到所述预设频段对应的融合数据；
根据各所述预设频段对应的融合数据确定所述目标数据。
可选地，所述权重神经网络为卷积神经网络。
可选地，所述对所述目标数据进行图像重建处理，得到磁共振图像包括：
对所述目标数据进行快速傅里叶逆变换，得到磁共振图像。
可选地，所述插值算法为线性插值算法。
可选地，所述插值算法包括最邻近插值法、双线性插值法以及三次样条插值法中的两种以上。
本申请实施例的第二方面提供了一种基于神经网络的网格化磁共振图像重建装置，包括：
密度补偿模块，用于获取初始采样数据，对所述初始采样数据进行密度补偿，得到补偿数据；
重采样模块，用于使用至少两种插值算法对所述补偿数据进行重采样，得到各插值算法对应的重采样数据；
权重学习模块，用于将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据；
图像重建模块，用于对所述目标数据进行图像重建处理，得到磁共振图像。
可选地，所述权重学习模块包括：
权重计算子模块，用于将各所述重采样数据在预设频段内的数据输入所述预设频段对应的权重神经网络，得到各所述重采样数据在预设频段对应的权重，其中，所述预设频段的数量为两个以上；
加权融合子模块，用于根据各所述重采样数据在预设频段对应的权重，对各所述重采样数据在预设频段内的数据进行加权求和，得到所述预设频段对应的融合数据；
目标数据模块，用于根据各所述预设频段对应的融合数据确定所述目标数据。
可选地，所述权重神经网络为卷积神经网络。
可选地，所述图像重建模块，具体用于对所述目标数据进行快速傅里叶逆变换，得到磁共振图像。
可选地，所述插值算法为线性插值算法。
可选地，所述插值算法包括最邻近插值法、双线性插值法以及三次样条插值法中的两种以上。
本申请实施例的第三方面提供了一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如上述方法的步骤。
本申请实施例的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上述方法的步骤。
本申请实施例的第五方面提供了一种计算机程序产品，当计算机程序产品在终端设备上运行时，使得终端设备实现如上述方法的步骤。
发明的有益效果
有益效果
在本申请的基于神经网络的网格化磁共振图像重建方法中，对初始采样数据进行密度补偿得到补偿数据后，使用至少两种插值算法对补偿数据进行重采样，得到各插值算法对应的重采样数据。然后，通过神经网络学习不同的重采样数据对应的权重，根据各重采样数据对应的权重对各重采样数据进行加权求和，得到目标数据，使用目标数据进行图像重建，从而在保证较高的重建速度的前提下，提升重建图像的质量，解决了现有的磁共振图像重建方法重建质量低，重建时间长的问题。
对附图的简要说明
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种基于神经网络的网格化磁共振图像重建方法的流程示意图；
图2是本申请实施例提供的一种基于神经网络的网格化磁共振图像重建装置的结构示意图；
图3是本申请实施例提供的终端设备的示意图。
发明实施例
本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
应当理解，当在本申请说明书和所附权利要求书中使用时，术语“包括”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。
还应当理解，在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合。
如在本申请说明书和所附权利要求书中所使用的那样，术语“如果”可以依据上下文被解释为“当...时”或“一旦”或“响应于确定”或“响应于检测到”。类似地，短语“如果确定”或“如果检测到[所描述条件或事件]”可以依据上下文被解释为意指“一旦确定”或“响应于确定”或“一旦检测到[所描述条件或事件]”或“响应于检测到[所描述条件或事件]”。
另外，在本申请说明书和所附权利要求书的描述中，术语“第一”、“第二”、“第三”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
在本申请说明书中描述的参考“一个实施例”或“一些实施例”等意味着在本申请的一个或多个实施例中包括结合该实施例描述的特定特征、结构或特点。由此，在本说明书中的不同之处出现的语句“在一个实施例中”、“在一些实施例中”、“在其他一些实施例中”、“在另外一些实施例中”等不是必然都参考相同的实施例，而是意味着“一个或多个但不是所有的实施例”，除非是以其他方式另外特别强调。术语“包括”、“包含”、“具有”及它们的变形都意味着“包括但不限于”，除非是以其他方式另外特别强调。
本申请实施例提供的磁共振图像重建方法可以应用于手机、平板电脑、可穿戴设备、车载设备、增强现实(augmented reality，AR)/虚拟现实(virtual reality，VR)设备、笔记本电脑、超级移动个人计算机(ultra-mobile personal computer，UMPC)、上网本、个人数字助理(personal digital assistant，PDA)等终端设备上，本申请实施例对终端设备的具体类型不作任何限制。
例如，所述终端设备可以是WLAN中的站点(STAION，ST)，可以是蜂窝电话、无绳电话、会话启动协议(Session InitiationProtocol，SIP)电话、无线本地环路(Wireless Local Loop，WLL)站、个人数字处理(Personal Digital Assistant，PDA)设备、具有无线通信功能的手持设备、计算设备或连接到无线调制解调器的其它处理设备、车载设备、车联网终端、电脑、膝上型计算机、手持式通信设备、手持式计算设备、卫星无线设备 、无线调制解调器卡、电视机顶盒(set top box，STB)、用户驻地设备(customer premise equipment，CPE)和/或用于在无线系统上进行通信的其它设备以及下一代通信系统，例如，5G网络中的移动终端或者未来演进的公共陆地移动网络(Public Land Mobile Network，PLMN)网络中的移动终端等。
实施例一：
下面对本申请实施例一提供的一种基于神经网络的网格化磁共振图像重建方法进行描述，请参阅附图1，本申请实施例一中的基于神经网络的网格化磁共振图像重建方法包括：
S101、获取初始采样数据，对所述初始采样数据进行密度补偿，得到补偿数据；
磁共振成像是当前最先进的医学诊疗手段之一，磁共振成像技术利用核磁共振原理对人体或生物体的特定部分进行断面成像或立体成像，可获得组织和器官的解剖结构、生理功能和病变信息。目前的研究表明，人体在磁场作用中不会受到伤害，因此，磁共振成像可以被认为是一种无害的人体组织成像诊疗手段，不会对产生对人体有害的辐射。
在进行磁共振成像时，用户可以使用磁共振扫描仪执行磁共振扫描操作，得到初始采样数据，即k空间数据。
在本实施例中，执行磁共振图像重建方法的终端设备可以是磁共振扫描仪，也可以是其他可以通过有线通信、无线通信、转移存储介质等方式获取到初始采样数据的终端设备。例如，执行磁共振图像重建方法的终端设备可以是图像工作站，图像工作站可以通过数据线、wifi、蓝牙等方式与磁共振扫描仪通信连接，获取初始采样数据，执行磁共振图像重建工作。
由于磁共振扫描仪在采样的过程中使用了不等间隔的采样轨迹，因此需要对初始采样数据的不均匀的采样密度进行补偿，终端设备可以将初始采样数据划分网格，通过预设密度补偿函数(density compensation function，DCF)对初始采样数据进行密度补偿，得到补偿数据。
预设密度补偿函数的类型可以根据实际情况进行选择。例如，可以选用杰克森(Jackson)面积密度补偿函数作为预设密度补偿函数；或者，也可以选用同像 密度补偿函数作为预设密度补偿函数；或者，也可以选择其他密度补偿函数作为预设密度补偿函数。
S102、使用至少两种插值算法对所述补偿数据进行重采样，得到各插值算法对应的重采样数据；
终端设备获取到补偿数据之后，由于补偿数据位于非笛卡尔坐标系，难以快速重建为磁共振图像，因此，可以使用插值算法对补偿函数进行重采样，将采样数据从非笛卡尔坐标系重采样至笛卡尔坐标系下的网格矩阵，以便快速进行磁共振图像的重建。
当前的磁共振图像重建方法中，对补偿数据全局使用同一种插值方法进行重采样。全局使用同一种插值方法进行重采样，会使得重建得到磁共振图像精度较低。
为此，本实施例的终端设备使用至少两种插值算法对补偿数据进行重采样，得到各插值算法对应的笛卡尔坐标系下的重采样数据。
其中，上述插值算法的类型可以根据实际情况进行选择。上述插值算法可以是线性插值算法，也可以是非线性插值算法。
当需要提高磁共振图像的重建速度时，终端设备可以选用线性插值算法。当终端设备选用线性插值算法时，可以选用最邻近插值法、双线性插值法以及三次样条插值法等线性插值算法中的至少两种作为上述插值算法。
S103、将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据；
终端设备获取到各重采样数据之后，可以将各重采样数据分别输入权重神经网络中，由权重神经网络学习各重采样数据，得到各重采样数据对应的权重。
之后，终端设备再根据各重采样数据对应的权重，对各重采样数据进行加权求和。
可以理解的是，上述权重神经网络的网络类型可以根据实际情况进行选择。例如，该权重神经网络可以是卷积神经网络(Convolutional Neural Networks，CNN)、循环神经网络(Recurrent Neural Network，RNN)、长短期记忆网络(Long  Short-Term Memory，LSTM)等神经网络中的任意一种。
用户可以设置初始化的神经网络以及样本数据，然后，终端设备可以使用样本数据以及样本标签对神经网络进行训练。样本数据可以为样本重采样数据，样本标签为该样本数据对应的权重。终端设备将样本数据输入神经网络，得到样本输出，根据样本标签和样本输出，通过预设损失函数计算损失值，根据损失值对神经网络进行迭代更新，重复迭代，直至满足预设中止条件，得到权重神经网络。
该预设中止条件可以根据实际情况进行设置。例如，预设中止条件可以设置为损失值小于预设损失阈值或迭代次数大于或等于预设迭代阈值。
并且，训练完成后，还可以使用临床采集到的数据作为验证集对上述权重神经网络进行验证，若验证通过，则可以使用该权重神经网络；若验证不通过，则可以更换样本集中样本数据，重新对该权重神经网络进行训练。
进一步地，由于k空间数据在不同的频段存在一定的差异，因此，终端设备可以根据用户预先设置的频段，对各重采样数据进行拆分，得到各重采样数据在各预设频段内的数据。
例如，假设用户可以预先将k空间数据所在频段划分为频段A、频段B和频段C，并设置终端设备通过两种插值方法对补偿数据进行重采样，得到第一重采样数据和第二重采样数据。此时，终端设备可以根据频段A、频段B和频段C对第一重采样数据进行拆分，得到位于频段A的第一分频数据，位于频段B的第二分频数据，位于频段C的第三分频数据；同时，终端设备可以根据频段A、频段B和频段C对第二重采样数据进行拆分，得到位于频段A的第四分频数据，位于频段B的第五分频数据，位于频段C的第六分频数据。
上述预设频段的拆分方式可以根据实际情况进行选择。例如，假设用户想要把k空间数据所在的频段拆分成高频频段、中频频段和低频频段，用户可以通过三等分分割的方式对k空间数据所在的频段进行分割，得到高频频段、中频频段和低频频段，其中，高频频段的频率大于中频频段的频率，中频频段的频率大于低频频段的频率；或者，用户也可以自行设置拆分点，通过不等分分割的方式将空间数据所在的频段拆分成多个频段。
此外，对k空间数据所在的频段进行拆分，得到各个预设频段时，各个预设频段可能互不相交，或者，也可能存在部分相交的情况。例如，假设对k空间数据所在的频段进行拆分，得到频段A、频段B和频段C。频段A、频段B和频段C可能互不相交；或者，也可能频段A和频段B与频段C不想交，频段A和频段B部分相交。
之后，对于各个预设频段，终端设备可以分别执行加权求和操作。此时，终端设备可以将各重采样数据在预设频段内的数据输入所述预设频段对应的权重神经网络，得到各所述重采样数据在预设频段对应的权重。
然后，终端设备根据各重采样数据在预设频段对应的权重，对各重采样数据在预设频段内的数据进行加权求和，得到预设频段对应的融合数据。
融合数据的计算方式可以表示为：
C r＝a 1C 1+...+a iC i
其中，C r表示某一预设频段对应的融合数据；a 1表示第1种重采样数据在上述预设频段的权重；a i表示第i种重采样数据在上述预设频段的权重，i为插值算法的数量；C 1表示第1种重采样数据在上述预设频段内的数据；C i表示第i种重采样数据在上述预设频段内的数据。
例如，如上所述，假设将对第一重采样数据进行拆分，得到位于频段A的第一分频数据、位于频段B的第二分频数据以及位于频段C的第三分频数据，对第二重采样数据进行拆分，得到位于频段A的第四分频数据、位于频段B的第五分频数据以及位于频段C的第六分频数据。
此时，假设第一重采样数据在频段A的权重为0.5，在频段B的权重为0.7，在频段C的权重为0.2；第二重采样数据在频段A的权重为0.5，在频段B的权重为0.3，在频段C的权重为0.8。
终端设备可以获取频段A对应的第一分频数据和第四分频数据，将第一分频数据乘以0.5得到的值与第四分频数据乘以0.5得到的值相加，得到频段A对应的融合数据；终端设备可以获取频段B对应的第二分频数据和第五分频数据，将第二分频数据乘以0.7得到的值与第五分频数据乘以0.3得到的值相加，得到频段B对应的融合数据；终端设备可以获取频段C对应的第三分频数据和第六分频数据， 将第三分频数据乘以0.2得到的值与第六分频数据乘以0.8得到的值相加，得到频段C对应的融合数据。
上述描述的权重神经网络的数量可以与预设频段的数量一致，即一个预设频段对应一个权重神经网络。
例如，参见上述示例，假设频段A对应权重神经网络1，频段B对应权重神经网络2，频段C对应权重神经网络3，则终端设备在计算频段B对应的融合数据时，可以将频段B对应的第二分频数据输入权重神经网络2，得到第二分频数据对应的权重0.7，将频段B对应的第五分频数据输入权重神经网络2，得到第五分频数据对应的权重0.3。然后，终端设备将第二分频数据乘以0.7得到的值与第五分频数据乘以0.3得到的值相加，得到频段B对应的融合数据。
得到各个预设频段对应的融合数据之后，终端设备可以将各个预设频段对应的融合数据确定目标数据，对目标数据进行图像重建处理，得到磁共振图像。
如上所述，各个预设频段可能互不相交，也可能存在部分相交。此时，若各个预设频段互不相交，可以直接根据各个预设频段对应的融合数据组合得到目标数据。若各个预设频段中，存在相交的频段，则将相交频段作为第一频段，将各个预设频段中互不相交的频段作为第二频段；对于各个第一频段，根据第一频段对应的各个融合数据，通过预设计算方式确定第一频段更新后的融合数据，预设计算方式可以根据实际情况进行设置，比如，预设计算方式可以为计算简单算术平均值、计算加权算术平均值等计算方式；根据各个第二频段对应的融合数据以及各个第一频段对应的更新后的融合数据组合得到目标数据。
例如，假设预设频段包括频段A和频段B，如果频段A和频段B不相交，则可以直接根据频段A对应的融合数据和频段B对应的融合数据组合得到目标数据。
如果频段A和频段B相交，则将频段A和频段B相交的频段作为第一频段，将频段A中不与频段B相交的频段作为第一个第二频段，将频段B中不与频段A相交的频段作为第二个第二频段。此时，由于频段A对应的融合数据中存在一份关于第一频段的融合数据，频段B对应的融合数据中存在一份关于第一频段的融合数据，则可以计算第一频段对应的两份融合数据的简单算术平均值，得到第一频段对应的新的融合数据。然后，根据第一个第二频段对应的融合数据、第二个第 二频段对应的融合数据以及第一频段对应的新的融合组合得到目标数据。
S104、对所述目标数据进行图像重建处理，得到磁共振图像。
对目标数据进行图像重建处理时，终端设备可以根据实际情况选择合适的重建方式。例如，终端设备可以对目标数据进行傅里叶逆变换，得到磁共振图像；或者，终端设备也可以对目标数据进行快速傅里叶逆变换，得到磁共振图像；或者，终端设备也可以通过其他图像重建方式对目标数据进行图像重建处理，得到磁共振图像。
以下结合具体的应用场景对本实施例的磁共振图像重建方法进行说明：
假设终端设备上设置了使用最邻近插值法、双线性插值法以及三次样条插值法进行重采样。并且，终端设备将采集到的初始采样数据(即k空间数据)的频域划分为频域1、频域2以及频域3。频域1、频域2和频域3互不相交。频域1对应权重神经网络1，频域2对应权重神经网络2，频域3对应权重神经网络3。
1、终端设备对初始采样数据进行密度补偿，得到补偿数据。
2、终端设备使用最邻近插值法、双线性插值法以及三次样条插值法对补偿数据进行重采样，得到重采样数据1、重采样数据2和重采样数据3。
3、终端设备将重采样数据1在频域1的数据作为分频数据1.1，将重采样数据1在频域2的数据作为分频数据1.2，重采样数据1在频域3的数据作为分频数据1.3；将重采样数据2在频域1的数据作为分频数据2.1，将重采样数据2在频域2的数据作为分频数据2.2，重采样数据2在频域3的数据作为分频数据2.3；将重采样数据3在频域1的数据作为分频数据3.1，将重采样数据3在频域2的数据作为分频数据3.2，重采样数据3在频域3的数据作为分频数据3.3。
4、终端设备将分频数据1.1、分频数据2.1和分频数据3.1输入权重神经网络1，得到权重1.1、权重2.1和权重3.1，并根据权重1.1、权重2.1和权重3.1对分频数据1.1、分频数据2.1和分频数据3.1进行加权求和，得到融合数据1；
终端设备将分频数据1.2、分频数据2.2和分频数据3.2输入权重神经网络2，得到权重1.2、权重2.2和权重3.2，并根据权重1.2、权重2.2和权重3.2对分频数据1.2、分频数据2.2和分频数据3.2进行加权求和，得到融合数据2；
终端设备将分频数据1.3、分频数据2.3和分频数据3.3输入权重神经网络3，得到 权重1.3、权重2.3和权重3.3，并根据权重1.3、权重2.3和权重3.3对分频数据1.3、分频数据2.3和分频数据3.3进行加权求和，得到融合数据3。
5、终端设备将融合数据1、融合数据2和融合数据3组合成目标数据，对目标数据进行快速傅里叶逆变换，得到磁共振图像。
本实施例一提供的磁共振图像重建方法中，终端设备可以采用至少两种插值算法对补偿数据进行重采样，得到各插值算法对应的重采样数据。并且，在根据各重采样数据计算目标数据的过程中，通过权重神经网络计算各重采样数据对应的权重，然后根据各重采样数据对应的权重，对各重采样数据进行加权求和，得到目标数据，根据目标数据进行磁共振图像重建，从而在保证较高的重建速度的前提下，提升重建图像的质量，解决了现有的磁共振图像重建方法重建质量低，重建时间长的问题。
其中，各重采样数据的权重是通过机器学习的方式学习得到，采用神经网络可以更好地学习各重采样数据的特点，更为准确地确定各重采样数据的权重，从而提高后续重建的磁共振图像的精度。
上述插值算法可以采用线性插值算法，从而提高磁共振图像重建的速度。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
实施例二：
本申请实施例二提供了一种基于神经网络的网格化磁共振图像重建装置，为便于说明，仅示出与本申请相关的部分，如图2所示，基于神经网络的网格化磁共振图像重建装置包括，
密度补偿模块201，用于获取初始采样数据，对所述初始采样数据进行密度补偿，得到补偿数据；
重采样模块202，用于使用至少两种插值算法对所述补偿数据进行重采样，得到各插值算法对应的重采样数据；
权重学习模块203，用于将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样 数据进行加权求和，得到目标数据；
图像重建模块204，用于对所述目标数据进行图像重建处理，得到磁共振图像。
进一步地，所述权重学习模块203包括：
权重计算子模块，用于将各所述重采样数据在预设频段内的数据输入所述预设频段对应的权重神经网络，得到各所述重采样数据在预设频段对应的权重，其中，所述预设频段的数量为两个以上；
加权融合子模块，用于根据各所述重采样数据在预设频段对应的权重，对各所述重采样数据在预设频段内的数据进行加权求和，得到所述预设频段对应的融合数据；
目标数据模块，用于根据各所述预设频段对应的融合数据确定所述目标数据。
进一步地，所述权重神经网络为卷积神经网络。
进一步地，所述图像重建模块204，具体用于对所述目标数据进行快速傅里叶逆变换，得到磁共振图像。
进一步地，所述插值算法为线性插值算法。
进一步地，所述插值算法包括最邻近插值法、双线性插值法以及三次样条插值法中的两种以上。
需要说明的是，上述装置/单元之间的信息交互、执行过程等内容，由于与本申请方法实施例基于同一构思，其具体功能及带来的技术效果，具体可参见方法实施例部分，此处不再赘述。
实施例三：
图3是本申请实施例三提供的终端设备的示意图。如图3所示，该实施例的终端设备3包括：处理器30、存储器31以及存储在所述存储器31中并可在所述处理器30上运行的计算机程序32。所述处理器30执行所述计算机程序32时实现上述基于神经网络的网格化磁共振图像重建方法实施例中的步骤，例如图1所示的步骤S101至S104。或者，所述处理器30执行所述计算机程序32时实现上述各装置实施例中各模块/单元的功能，例如图2所示模块201至204的功能。
示例性的，所述计算机程序32可以被分割成一个或多个模块/单元，所述一个 或者多个模块/单元被存储在所述存储器31中，并由所述处理器30执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序32在所述终端设备3中的执行过程。例如，所述计算机程序32可以被分割成密度补偿模块、重采样模块、权重学习模块以及图像重建模块，各模块具体功能如下：
密度补偿模块，用于获取初始采样数据，对所述初始采样数据进行密度补偿，得到补偿数据；
重采样模块，用于使用至少两种插值算法对所述补偿数据进行重采样，得到各插值算法对应的重采样数据；
权重学习模块，用于将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据；
图像重建模块，用于对所述目标数据进行图像重建处理，得到磁共振图像。
所述终端设备3可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述终端设备可包括，但不仅限于，处理器30、存储器31。本领域技术人员可以理解，图3仅仅是终端设备3的示例，并不构成对终端设备3的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述终端设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器30可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器31可以是所述终端设备3的内部存储单元，例如终端设备3的硬盘或内存。所述存储器31也可以是所述终端设备3的外部存储设备，例如所述终端设备3上配备的插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。进一步 地，所述存储器31还可以既包括所述终端设备3的内部存储单元也包括外部存储设备。所述存储器31用于存储所述计算机程序以及所述终端设备所需的其他程序和数据。所述存储器31还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (14)

  1. 一种基于神经网络的网格化磁共振图像重建方法，其特征在于，包括：
    获取初始采样数据，对所述初始采样数据进行密度补偿，得到补偿数据；
    使用至少两种插值算法对所述补偿数据进行重采样，得到各插值算法对应的重采样数据；
    将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据；
    对所述目标数据进行图像重建处理，得到磁共振图像。
  2. 如权利要求1所述的基于神经网络的网格化磁共振图像重建方法，其特征在于，所述将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据包括：
    将各所述重采样数据在预设频段内的数据输入所述预设频段对应的权重神经网络，得到各所述重采样数据在预设频段对应的权重，其中，所述预设频段的数量为两个以上；
    根据各所述重采样数据在预设频段对应的权重，对各所述重采样数据在预设频段内的数据进行加权求和，得到所述预设频段对应的融合数据；
    根据各所述预设频段对应的融合数据确定所述目标数据。
  3. 如权利要求1所述的基于神经网络的网格化磁共振图像重建方法，其特征在于，所述权重神经网络为卷积神经网络。
  4. 如权利要求1所述的基于神经网络的网格化磁共振图像重建方法，其特征在于，所述对所述目标数据进行图像重建处理，得到磁共振图像包括：
    对所述目标数据进行快速傅里叶逆变换，得到磁共振图像。
  5. 如权利要求1所述的基于神经网络的网格化磁共振图像重建方法，其特征在于，所述插值算法为线性插值算法。
  6. 如权利要求5所述的基于神经网络的网格化磁共振图像重建方法，其特征在于，所述插值算法包括最邻近插值法、双线性插值法以及三次样条插值法中的两种以上。
  7. 一种基于神经网络的网格化磁共振图像重建装置，其特征在于，包括：
    密度补偿模块，用于获取初始采样数据，对所述初始采样数据进行密度补偿，得到补偿数据；
    重采样模块，用于使用至少两种插值算法对所述补偿数据进行重采样，得到各插值算法对应的重采样数据；
    权重学习模块，用于将各所述重采样数据输入权重神经网络，得到所述重采样数据对应的权重，并根据各所述重采样数据对应的权重，对各所述重采样数据进行加权求和，得到目标数据；
    图像重建模块，用于对所述目标数据进行图像重建处理，得到磁共振图像。
  8. 如权利要求7所述的基于神经网络的网格化磁共振图像重建装置，其特征在于，所述权重学习模块包括：
    权重计算子模块，用于将各所述重采样数据在预设频段内的数据输入所述预设频段对应的权重神经网络，得到各所述重采样数据在预设频段对应的权重，其中，所述预设频段的数量为两个以上；
    加权融合子模块，用于根据各所述重采样数据在预设频段对应的权重，对各所述重采样数据在预设频段内的数据进行加权求和，得到所述预设频段对应的融合数据；
    目标数据模块，用于根据各所述预设频段对应的融合数据确定所述目标数据。
  9. 如权利要求7所述的基于神经网络的网格化磁共振图像重建装置， 其特征在于，所述权重神经网络为卷积神经网络。
  10. 如权利要求7所述的基于神经网络的网格化磁共振图像重建装置，其特征在于，所述图像重建模块，具体用于对所述目标数据进行快速傅里叶逆变换，得到磁共振图像。
  11. 如权利要求7所述的基于神经网络的网格化磁共振图像重建装置，其特征在于，所述插值算法为线性插值算法。
  12. 如权利要求11所述的基于神经网络的网格化磁共振图像重建装置，其特征在于，所述插值算法包括最邻近插值法、双线性插值法以及三次样条插值法中的两种以上。
  13. 一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至6任一项所述的方法。
  14. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至6任一项所述的方法。
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