阅读说明：本技术 磁共振成像方法、装置、系统及存储介质 (Magnetic resonance imaging method, device, system and storage medium ) 是由 梁栋 程静 王海峰 郑海荣 刘新 于 2019-04-24 设计创作，主要内容包括：本发明公开了一种磁共振成像方法、装置、系统及存储介质。其中方法包括：获取磁共振成像的原始模型,根据用于求解所述原始模型的迭代算法建立初始成像模型,所述迭代算法中包括待定参数、待定求解算子和待定结构关系中至少一个；基于样本数据对所述初始成像模型进行训练,生成磁共振成像模型,其中,所述初始成像模型的训练用于学习所述迭代算法中的待定参数、待定求解算子和待定结构关系中至少一个；获取待处理的欠采样K空间数据,将所述欠采样K空间数据输入至所述磁共振成像模型,生成磁共振图像。提高了磁共振成像模型的自由度,相对于传统方式,通过学习得到的磁共振成像模型可提高磁共振重建图像的质量。(The invention discloses a magnetic resonance imaging method, a magnetic resonance imaging device, a magnetic resonance imaging system and a storage medium. The method comprises the following steps: acquiring an original model of magnetic resonance imaging, and establishing an initial imaging model according to an iterative algorithm for solving the original model, wherein the iterative algorithm comprises at least one of undetermined parameters, undetermined solving operators and undetermined structural relations; training the initial imaging model based on sample data to generate a magnetic resonance imaging model, wherein the training of the initial imaging model is used for learning at least one of a parameter to be determined, a solution operator to be determined and a structural relation to be determined in the iterative algorithm; and acquiring undersampled K space data to be processed, and inputting the undersampled K space data into the magnetic resonance imaging model to generate a magnetic resonance image. The degree of freedom of the magnetic resonance imaging model is improved, and compared with the traditional mode, the magnetic resonance imaging model obtained through learning can improve the quality of a magnetic resonance reconstruction image.)

1. A magnetic resonance imaging method, comprising:

acquiring an original model of magnetic resonance imaging, and establishing an initial imaging model according to an iterative algorithm for solving the original model, wherein the iterative algorithm comprises at least one of undetermined parameters, undetermined solving operators and undetermined structural relations;

training the initial imaging model based on sample data to generate a magnetic resonance imaging model, wherein the training of the initial imaging model is used for learning at least one of a parameter to be determined, a solution operator to be determined and a structural relation to be determined in the iterative algorithm;

And acquiring undersampled K space data to be processed, and inputting the undersampled K space data into the magnetic resonance imaging model to generate a magnetic resonance image.

2. The method of claim 1, wherein training the initial imaging model based on sample data generates a magnetic resonance imaging model comprising:

processing first sample data, initial parameters and initial images in a sample set based on undetermined parameters and a preset structural relationship to obtain first input information;

and performing iterative training on the initial imaging model based on the first input information, learning the undetermined parameters, and generating a first magnetic resonance imaging model for magnetic resonance imaging, wherein the initial imaging model comprises a preset number of iterative modules, the iterative modules are sequentially connected to perform iterative processing on the first input information, and the iterative modules comprise preset solving operators.

3. The method of claim 1, wherein the initial imaging model is trained based on sample data to generate a magnetic resonance imaging model, further comprising:

processing second sample data, initial parameters and initial images in the sample set based on the undetermined parameters and the preset structural relationship to obtain second input information;

And performing iterative training on the initial imaging model based on the second input information, determining the undetermined parameters and network parameters of the initial imaging model, and generating a second magnetic resonance imaging model for magnetic resonance imaging, wherein the initial imaging model comprises a preset number of iterative sub-network models, the iterative sub-network models are sequentially connected and used for performing iterative processing on the second input information, and the network parameters of the initial imaging model are used for replacing a preset solving operator.

4. The method of claim 3, further comprising, after generating the second magnetic resonance imaging model for magnetic resonance imaging:

taking third sample data, initial parameters and initial images in the unprocessed sample set as third input information;

and training the second magnetic resonance imaging model based on the third input information, learning the undetermined structure relationship and network parameters in the second magnetic resonance imaging model, and generating a third magnetic resonance imaging model for magnetic resonance imaging.

5. The method of any of claims 1-4, wherein the original model of magnetic resonance imaging further comprises a sparse transform algorithm, and wherein the method comprises:

Establishing a sub-network model for executing the sparse transformation algorithm, wherein the sub-network model is connected with the output end of the magnetic resonance imaging model;

training the sub-network model based on fourth sample data in the sample set, and generating a fourth magnetic resonance imaging model based on the trained sub-network model.

6. The method of claim 1, wherein training the initial imaging model based on sample data generates a magnetic resonance imaging model comprising:

inputting the sample data into the initial imaging model to obtain an output magnetic resonance image of the magnetic resonance imaging model, and determining a loss function according to the output magnetic resonance image and a standard magnetic resonance image generated by full-sampling K space data corresponding to the sample data, wherein the loss function loss is determined according to the following formula:

wherein, theFor the output magnetic resonance image of the magnetic resonance imaging model, said x_refA standard magnetic resonance image generated for full-sampling K-space data of the sample;

and adjusting network parameters in the initial imaging model according to the loss function to generate a magnetic resonance imaging model.

7. The method of claim 1, wherein the iterative algorithm comprises a basic dual algorithm, an alternating direction multiplier algorithm, and an iterative threshold shrink algorithm.

8. A magnetic resonance imaging apparatus, characterized by comprising:

the initial imaging model establishing module is used for acquiring an original model of magnetic resonance imaging and establishing an initial imaging model according to an iterative algorithm for solving the original model, wherein the iterative algorithm comprises at least one of undetermined parameters, undetermined solving operators and undetermined structural relations;

the model training module is used for training the initial imaging model based on sample data to generate a magnetic resonance imaging model, wherein the training of the initial imaging model is used for learning at least one of a to-be-determined parameter, a to-be-determined solution operator and a to-be-determined structural relationship in the iterative algorithm;

and the magnetic resonance imaging module is used for acquiring the undersampled K space data to be processed, inputting the undersampled K space data into the magnetic resonance imaging model and generating a magnetic resonance image.

9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the magnetic resonance imaging method as set forth in any one of claims 1-7.

10. A magnetic resonance imaging system comprising a magnetic resonance apparatus and a computer apparatus, wherein the computer apparatus comprises a memory, one or more processors and a computer program stored on the memory and executable on the processors, characterized in that the processor when executing the program is operable to perform the magnetic resonance imaging method of any one of claims 1-7.

Technical Field

Embodiments of the present invention relate to deep learning technologies, and in particular, to a magnetic resonance imaging method, apparatus, system, and storage medium.

Background

Magnetic resonance images human tissue using static and radio frequency magnetic fields, which not only provides rich tissue contrast, but also is harmless to the human body, thus becoming a powerful tool for medical clinical diagnosis. However, the low imaging speed has been a big bottleneck limiting the rapid development.

In terms of fast imaging, the currently common techniques are parallel imaging and compressed sensing. Parallel imaging uses the correlation between multi-channel coils to accelerate acquisition, and compressed sensing uses the prior information of sparsity of an imaged object to reduce k-space sampling points. However, the parallel imaging acceleration times are limited due to the conditions such as hardware and the like, and the phenomenon of noise amplification of the image can occur along with the increase of the acceleration times; the compressed sensing technology has very long reconstruction time due to the adoption of iterative reconstruction, and is difficult to select sparse transformation and reconstruction parameters.

Disclosure of Invention

The invention provides a magnetic resonance imaging method, a magnetic resonance imaging device, a magnetic resonance imaging system and a storage medium, which are used for improving the quality of a magnetic resonance image.

In a first aspect, an embodiment of the present invention provides a magnetic resonance imaging method, including:

acquiring an original model of magnetic resonance imaging, and establishing an initial imaging model according to an iterative algorithm for solving the original model, wherein the iterative algorithm comprises at least one of undetermined parameters, undetermined solving operators and undetermined structural relations;

Training the initial imaging model based on sample data to generate a magnetic resonance imaging model, wherein the training of the initial imaging model is used for learning at least one of a parameter to be determined, a solution operator to be determined and a structural relation to be determined in the iterative algorithm;

and acquiring undersampled K space data to be processed, and inputting the undersampled K space data into the magnetic resonance imaging model to generate a magnetic resonance image.

In a second aspect, an embodiment of the present invention further provides a magnetic resonance imaging apparatus, including:

the initial imaging model establishing module is used for acquiring an original model of magnetic resonance imaging and establishing an initial imaging model according to an iterative algorithm for solving the original model, wherein the iterative algorithm comprises at least one of undetermined parameters, undetermined solving operators and undetermined structural relations;

the model training module is used for training the initial imaging model based on sample data to generate a magnetic resonance imaging model, wherein the training of the initial imaging model is used for learning at least one of a to-be-determined parameter, a to-be-determined solution operator and a to-be-determined structural relationship in the iterative algorithm;

and the magnetic resonance imaging module is used for acquiring the undersampled K space data to be processed, inputting the undersampled K space data into the magnetic resonance imaging model and generating a magnetic resonance image.

In a third aspect, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements a magnetic resonance imaging method as provided in any of the embodiments of the present invention.

In a fourth aspect, embodiments of the present invention further provide a magnetic resonance imaging system, including a magnetic resonance apparatus and a computer apparatus, where the computer apparatus includes a memory, one or more processors, and a computer program stored in the memory and executable on the processors, and when the computer program is executed by the processors, the magnetic resonance imaging method as provided in any of the embodiments of the present invention is implemented.

According to the technical scheme provided by the embodiment, the initial imaging model is established according to the iterative algorithm comprising at least one of the undetermined parameters, the undetermined solution operator and the undetermined structural relationship, undetermined factors in the iterative algorithm are learned through training of the initial imaging model, the undetermined factors in the iterative algorithm are replaced based on the fixed solution operator, the fixed parameters and the fixed structural relationship in the prior art, the degree of freedom of the magnetic resonance imaging model is improved, and compared with the traditional mode, the quality of the magnetic resonance reconstructed image can be improved through the magnetic resonance imaging model obtained through learning.

Drawings

FIG. 1 is a schematic diagram of a magnetic resonance imaging system;

fig. 2 is a flowchart illustrating a magnetic resonance imaging method according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an initial imaging model provided by an embodiment of the invention;

FIG. 4 is a schematic diagram of another initial imaging model provided in accordance with an embodiment of the present invention;

FIG. 5 is a schematic diagram of a first sub-model in an initial imaging model established based on the basic dual algorithm (3) according to an embodiment of the present invention;

FIG. 6 is a diagram illustrating a second sub-model and associated modules in the initial imaging model established based on the basic dual algorithm (3) according to an embodiment of the present invention;

FIG. 7 is a diagram illustrating a first sub-model in an initial imaging model established based on the basic dual algorithm (4) according to an embodiment of the present invention;

FIG. 8 is a diagram of a second sub-model in the initial imaging model established based on the basic dual algorithm (4) according to the embodiment of the present invention;

fig. 9 is a schematic structural diagram of a magnetic resonance imaging apparatus according to a second embodiment of the present invention;

fig. 10 is a schematic structural diagram of a magnetic resonance system according to a third embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

A magnetic resonance imaging system typically comprises a magnet having an aperture, a transmit coil for transmitting radio frequency signals and a receive coil for receiving magnetic resonance signals, gradient coils for spatially localizing the magnetic resonance signals, a pulse generator for generating a scan sequence, and a control system. The magnetic resonance imaging system is operated by an operator (clinician) controlling a console connected to the control system, which may include a keyboard or other input device, a control panel and a display to input commands and display the generated images.

Fig. 1 is a schematic structural diagram of a magnetic resonance imaging system in which a clinician first places a subject 3 on a bed 1 and places a local coil for receiving magnetic resonance signals on the body surface of the subject 3 when performing a magnetic resonance examination; then the clinician controls the scanning bed 1 to move towards the aperture formed by the magnet 2 by operating the console connected with the control system 5, after the magnetic resonance imaging system monitors that the clinician sends out the instruction of the movement of the scanning bed 1, the control system 5 monitors the movement range of the scanning bed 1 immediately, when the scanning bed 1 enters the edge of the scanning imaging area 4, the control system 5 controls the pulse sequence generator to generate a corresponding sequence for scanning, the sequence can control the excitation to generate the radio frequency pulse, and the radio frequency pulse can excite the body area of the examinee 3 to generate the precession nuclear spin. In the moving process of the scanning bed 1, the gradient magnetic field generated by the gradient coil can carry out phase encoding, frequency encoding or slice selection encoding on the precession nuclear spin, the receiving coil placed on the surface of the body of the detected object can move in the inner space of the magnet space along with the scanning bed 1, and the receiving coils at different positions are in an open state or a closed state under the action of the control system so as to receive corresponding magnetic resonance signals.