CN110992436A - Image reconstruction method and device, and image data processing method and device - Google Patents
Image reconstruction method and device, and image data processing method and device Download PDFInfo
- Publication number
- CN110992436A CN110992436A CN201911077399.2A CN201911077399A CN110992436A CN 110992436 A CN110992436 A CN 110992436A CN 201911077399 A CN201911077399 A CN 201911077399A CN 110992436 A CN110992436 A CN 110992436A
- Authority
- CN
- China
- Prior art keywords
- image
- inversion recovery
- gradient echo
- liquid attenuation
- attenuation inversion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention discloses an image reconstruction method and device, an image data processing method and device, electronic equipment and a storage medium. The image reconstruction method comprises the following steps: cutting the gradient echo image into a plurality of gradient echo image blocks to be reconstructed; inputting the gradient echo image blocks into a liquid attenuation inversion recovery image reconstruction model trained in advance for image reconstruction; and splicing the liquid attenuation inversion recovery image blocks output by the liquid attenuation inversion recovery image reconstruction model into a liquid attenuation inversion recovery image. Therefore, the gradient echo image containing complete nuclear magnetic quantization information is input into the liquid attenuation inversion recovery image reconstruction model established based on the deep learning without calculating quantization parameters, so that the high-resolution liquid attenuation inversion recovery image can be quickly generated, and the method has better robustness and accuracy.
Description
Technical Field
The present invention relates to the field of medical imaging technologies, and in particular, to an image reconstruction method and apparatus, an image data processing method and apparatus, an electronic device, and a storage medium.
Background
In the prior art, a magnetic resonance imaging liquid attenuation inversion recovery (FLAIR) image can be obtained by performing image reconstruction on scanning data acquired by a FLAIR sequence, however, the FLAIR sequence has a long scanning time, and a clinical 1.5T nuclear magnetic scanner needs about more than 3.5 minutes. As can be seen, it is time consuming to reconstruct FLAIR images using a FLAIR sequence.
Currently, in order to improve the efficiency of FLAIR image reconstruction, a FLAIR image reconstruction method based on the Gradient Echo (Stage) technique has been proposed. By adopting the Stage technology, T1, PD and T2 equivalent quantization parameters are calculated, and then the FLAIR image is generated according to the quantization parameters. Inevitably, both the PD and T2 parameters have magnetic sensitivity artifacts, and in the image reconstruction process, the magnetic sensitivity artifacts of the two parameters are enhanced by multiplication, so that the obtained FLAIR image has relatively strong magnetic sensitivity artifacts, and the resolution cannot meet the clinical application requirement.
Disclosure of Invention
The invention provides an image reconstruction method and equipment, an image data processing method and device, electronic equipment and a storage medium, which are used for rapidly reconstructing a high-resolution FLAIR image.
Specifically, the invention is realized by the following technical scheme:
in a first aspect, an image reconstruction method is provided, which includes:
cutting the gradient echo image into a plurality of gradient echo image blocks to be reconstructed;
inputting the gradient echo image blocks into a liquid attenuation inversion recovery image reconstruction model trained in advance for image reconstruction;
and splicing the liquid attenuation inversion recovery image blocks output by the liquid attenuation inversion recovery image reconstruction model into a liquid attenuation inversion recovery image.
In a second aspect, a method for processing image data is provided, the method comprising:
acquiring a plurality of groups of image data; each set of image data comprises a gradient echo image and a liquid attenuation inversion recovery image of an imaging object;
cutting the gradient echo image and the liquid attenuation inversion recovery image based on the same cutting strategy aiming at each group of image data to respectively obtain a plurality of gradient echo image blocks and a plurality of liquid attenuation inversion recovery image blocks;
taking the gradient echo image blocks as input, taking the liquid attenuation inversion recovery image blocks as labels to train a neural network model, and obtaining a liquid attenuation inversion recovery image reconstruction model; the liquid attenuation inversion recovery image reconstruction model is used for image reconstruction.
In a third aspect, an image reconstruction apparatus is provided, the image reconstruction apparatus comprising:
the cutting module is used for cutting the gradient echo image into a plurality of gradient echo image blocks to be reconstructed;
the input module is used for inputting the gradient echo image blocks into a liquid attenuation inversion recovery image reconstruction model trained in advance for image reconstruction;
and the splicing module is used for splicing the liquid attenuation inversion recovery image blocks output by the liquid attenuation inversion recovery image reconstruction model into a liquid attenuation inversion recovery image.
In a fourth aspect, there is provided a processing apparatus of image data, the processing apparatus including:
the acquisition module is used for acquiring a plurality of groups of image data; each set of image data comprises a gradient echo image and a liquid attenuation inversion recovery image of an imaging object;
the cutting module is used for cutting the gradient echo image and the liquid attenuation inversion recovery image based on the same cutting strategy aiming at each group of image data to respectively obtain a plurality of gradient echo image blocks and a plurality of liquid attenuation inversion recovery image blocks;
the model training module is used for taking the gradient echo image blocks as input and taking the liquid attenuation inversion recovery image blocks as labels to train a neural network model so as to obtain a liquid attenuation inversion recovery image reconstruction model; the liquid attenuation inversion recovery image reconstruction model is used for image reconstruction.
In a fifth aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the image reconstruction method according to the first aspect when executing the computer program.
A sixth aspect provides a computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the image reconstruction method of the first aspect.
In a seventh aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the method for processing image data according to the second aspect.
In an eighth aspect, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of processing image data of the second aspect.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects: according to the embodiment of the invention, the Stage image containing complete nuclear magnetic quantization information is input into the FLAIR image reconstruction model established based on deep learning without calculating quantization parameters, so that the FLAIR image with high resolution can be rapidly generated, and the robustness and the accuracy are better.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart illustrating a method of image reconstruction according to an exemplary embodiment of the present invention;
FIG. 2a shows two Stage images at different flip angles and TE (echo time) according to an exemplary embodiment of the present invention;
FIG. 2b is a FLAIR image obtained using the image reconstruction method of the present invention, in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a flow chart illustrating a method of processing image data in accordance with an exemplary embodiment of the present invention;
FIG. 4a is a schematic diagram of Stage image cropping in step 302 of FIG. 3;
FIG. 4b is a schematic diagram of the FLAIR image cropped in step 302 of FIG. 3;
FIG. 5 is a block diagram of an image reconstruction device according to an exemplary embodiment of the present invention;
FIG. 6 is a block schematic diagram of an apparatus for processing image data according to an exemplary embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
Fig. 1 is a flowchart illustrating an image reconstruction method according to an exemplary embodiment of the present invention, the image reconstruction method including the steps of:
The Stage image is obtained based on a Stage technology, the Stage technology adopts a plurality of groups (generally two groups) of different reversal angles and Stage data acquired by multi-gradient echoes to establish the Stage image, and nuclear magnetic signal physical parameters of an imaging object (such as brain tissue and spinal cord) can be completely quantized to obtain complete nuclear magnetic quantization information.
In step 101, the Stage image may be randomly cropped into a plurality of Stage image blocks by using, but not limited to, an image cropping function crop.
And 102, inputting the Stage image block into a pre-trained FLAIR image reconstruction model for image reconstruction.
The FLAIR image reconstruction model is obtained based on deep learning training, can quickly and accurately reconstruct the input Stage image block and outputs the FLAIR image block.
And 103, splicing the FLAIR image blocks output by the FLAIR image reconstruction model into a FLAIR image.
In step 103, FLAIR image blocks may be spliced into a FLAIR image by, but not limited to, using a linear superposition algorithm.
Referring to fig. 2a and fig. 2b, fig. 2a shows two Stage images under different flip angles and TE (echo time), and fig. 2b is a FLAIR image obtained by using the image reconstruction method of the present embodiment, and it can be seen from fig. 2b that the FLAIR image has good uniformity, high contrast, and clear edges of organ boundaries in the image, and does not introduce the problem of image over-smoothing, and the details of the anatomical structure of the image are well preserved, so that the method can meet the requirements of clinical application, and has good practicability.
In the embodiment, the Stage image containing complete nuclear magnetic quantization information is input into the FLAIR image reconstruction model established based on deep learning without calculating quantization parameters, so that the FLAIR image with high resolution can be quickly generated, and the robustness and the accuracy are better.
Fig. 3 is a flowchart illustrating a processing method of image data according to an exemplary embodiment of the present invention, the processing method including the steps of:
Wherein, the multiple sets of image data can be magnetic resonance image data of each volunteer or clinical patient, the multiple sets of image data are used as samples for model training, and each set of image data comprises a Stage image and a FLAIR image of an imaging object. Stage images may be, but are not limited to being, generated based on a Stage scan sequence and FLAIR images may be, but are not limited to being, generated based on a FLAIR sequence.
It should be noted that different cropping strategies may be adopted for different sets of image data, but the Stage image and the FLAIR image in each set of image data need to be cropped by using the same cropping strategy, specifically:
randomly selecting a first area on the Stage image and cutting to obtain a Stage image block, wherein the size of the first area can be the same or different during each cutting; and aiming at each cutting of the Stage image, selecting a second area with the same size at the position corresponding to the FLAIR image, and cutting to obtain a FLAIR image block. That is, if an area is randomly selected from the Stage image as a Stage image block, an area with the same size is selected from a position corresponding to the FLAIR image as a FLAIR image block, see fig. 4a and 4b, and if an area a of the Stage image is selected as a Stage image block a, an area a' with the same size is selected from a position corresponding to the FLAIR image as a FLAIR image block a; similarly, if a Stage image block B is selected in the B area of the Stage image, the B' area with the same size is selected as the FLAIR image block B at the position corresponding to the FLAIR image.
Similar to step 101, the Stage image and the FLAIR image may be, but are not limited to, image cropped using an image cropping function crop. Through the crop function, the number of the image blocks obtained by cutting is increased, and the depth learning effect on the model is better when the number of the image blocks is larger.
In this embodiment, the model is trained based on the deep learning method, and the model training process is further described as follows:
and (3) building a neural network model, wherein the input end of the model is a Stage image block, and the output end of the model is a FLAIR image. And training a neural network model by combining a plurality of groups of Stage image blocks and FLAIR image blocks, wherein the FLAIR image blocks are used as training labels, in each iterative training process, the parameters of each neuron node in the neural network model are calculated and adjusted, and the model training is completed until the loss function of the model training meets the preset requirement, so that a FLAIR image reconstruction model is obtained for image reconstruction.
Further, the Stage image and the FLAIR image obtained in the model using process can be added into a model training sample, and the FLAIR image reconstruction model is optimized periodically to improve the accuracy of image reconstruction. The neural network model may be but not limited to a Convolutional Neural Network (CNN), the number of layers and the number of nodes of the neural network may be set according to actual requirements, for example, a three-layer CNN network is used, 64 nodes are set in a first layer of the model, 32 nodes are set in a second layer, 1 node is set in a last layer, and the loss function may be but not limited to a peak signal-to-noise ratio PSNR:
wherein, MAX represents the maximum value of pixels in the FLAIR image block output by the model, and MSE represents the mean square error between the training label and the FLAIR image block output by the model.
The invention also provides embodiments of an image reconstruction device and an image data processing device, corresponding to the embodiments of the image reconstruction method and the image data processing method.
Fig. 5 is a block diagram of an image reconstruction apparatus according to an exemplary embodiment of the present invention, the image reconstruction apparatus including the steps of: a cropping module 51, an input module 52 and a stitching module 53.
The cropping module 51 is used for cropping the Stage image into a plurality of Stage image blocks to be reconstructed.
Optionally, the cropping module 51 employs an image cropping function to crop a Stage image patch into a plurality of Stage image patches.
The input module 52 is used for inputting the Stage image blocks into a FLAIR image reconstruction model trained in advance for image reconstruction.
The splicing module 53 is configured to splice FLAIR image blocks output by the FLAIR image reconstruction model into a FLAIR image.
Optionally, the stitching module stitches the FLAIR image blocks into the FLAIR image by using a linear superposition algorithm.
In another example, the image reconstruction device further comprises an acquisition module and a model training module for implementing model building.
The acquisition module is used for acquiring a plurality of groups of training data, and each group of training data comprises a Stage image and a FLAIR image of an imaging object;
the cutting module is further used for cutting the Stage image and the FLAIR image based on the same cutting strategy aiming at each group of training data to respectively obtain a plurality of Stage image blocks and a plurality of FLAIR image blocks;
and the model training module is used for training the neural network model by taking the Stage image block as input and taking the FLAIR image block as a label to obtain the FLAIR image reconstruction model.
Fig. 6 is a block diagram of a processing apparatus of image data according to an exemplary embodiment of the present invention, the processing apparatus including: an acquisition module 61, a cropping module 62, and a model training module 63.
The obtaining module 61 is configured to obtain multiple sets of image data; each set of image data comprises a Stage image and a FLAIR image of the imaging subject;
the cutting module 62 is configured to cut a Stage image and a FLAIR image based on the same cutting strategy for each group of image data to obtain a plurality of Stage image blocks and a plurality of FLAIR image blocks, respectively;
the model training module 63 is used for training a neural network model by taking the Stage image block as input and the FLAIR image block as a label to obtain a FLAIR image reconstruction model; the FLAIR image reconstruction model is used for image reconstruction.
Optionally, the cropping module is specifically configured to:
randomly selecting a first area on the Stage image and cutting to obtain a Stage image block;
and aiming at each cutting of the Stage image, selecting a second area with the same size at the relative position of the FLAIR image and cutting to obtain a FLAIR image block.
Fig. 7 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present invention, and the electronic device 70 shown in fig. 7 is only an example and should not bring any limitation to the functions and the scope of the embodiment of the present invention.
As shown in FIG. 7, the electronic device 70 may take the form of a general purpose computing device, which may be a server device, for example. The components of the electronic device 70 may include, but are not limited to: the at least one processor 71, the at least one memory 72, and a bus 73 connecting the various system components (including the memory 72 and the processor 71).
The bus 73 includes a data bus, an address bus, and a control bus.
The memory 72 may include volatile memory, such as Random Access Memory (RAM)721 and/or cache memory 722, and may further include Read Only Memory (ROM) 723.
The processor 71 executes various functional applications and data processing, such as the image reconstruction method described in any of the above embodiments, by running a computer program stored in the memory 72.
The electronic device 70 may also communicate with one or more external devices 74 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 75. Also, the model-generating electronic device 70 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 76. As shown, the network adapter 76 communicates with the other modules of the model-generating electronic device 70 via a bus 73. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating electronic device 70, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the image reconstruction method according to any of the above embodiments.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the image data processing method according to any of the above embodiments is implemented.
The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the image data processing method according to any one of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (16)
1. An image reconstruction method, characterized in that the image reconstruction method comprises:
cutting the gradient echo image into a plurality of gradient echo image blocks to be reconstructed;
inputting the gradient echo image blocks into a liquid attenuation inversion recovery image reconstruction model trained in advance for image reconstruction;
and splicing the liquid attenuation inversion recovery image blocks output by the liquid attenuation inversion recovery image reconstruction model into a liquid attenuation inversion recovery image.
2. The image reconstruction method of claim 1, further comprising:
acquiring a plurality of groups of training data, wherein each group of training data comprises a gradient echo image and a liquid attenuation inversion recovery image of an imaging object;
cutting the gradient echo image and the liquid attenuation inversion recovery image based on the same cutting strategy aiming at each group of training data to respectively obtain a plurality of gradient echo image blocks and a plurality of liquid attenuation inversion recovery image blocks;
and taking the gradient echo image block as input, and taking the liquid attenuation inversion recovery image block as a label to train a neural network model, so as to obtain the liquid attenuation inversion recovery image reconstruction model.
3. The image reconstruction method of claim 1 wherein the gradient echo tiles are cropped into a plurality of gradient echo tiles based on an image cropping function.
4. The image reconstruction method of claim 1 wherein the liquid attenuation inversion recovery image blocks are stitched into a liquid attenuation inversion recovery image based on a linear superposition algorithm.
5. A method of processing image data, the method comprising:
acquiring a plurality of groups of image data; each set of image data comprises a gradient echo image and a liquid attenuation inversion recovery image of an imaging object;
cutting the gradient echo image and the liquid attenuation inversion recovery image based on the same cutting strategy aiming at each group of image data to respectively obtain a plurality of gradient echo image blocks and a plurality of liquid attenuation inversion recovery image blocks;
taking the gradient echo image blocks as input, taking the liquid attenuation inversion recovery image blocks as labels to train a neural network model, and obtaining a liquid attenuation inversion recovery image reconstruction model; the liquid attenuation inversion recovery image reconstruction model is used for image reconstruction.
6. The method of processing image data according to claim 5, wherein cropping the gradient echo image and the liquid attenuated inversion restored image based on the same cropping strategy comprises:
randomly selecting a first area on the gradient echo image and cutting to obtain a gradient echo image block;
and aiming at each cutting of the gradient echo image, selecting a second area with the same size at the relative position of the liquid attenuation inversion recovery image, and cutting to obtain a liquid attenuation inversion recovery image block.
7. An image reconstruction apparatus characterized by comprising:
the cutting module is used for cutting the gradient echo image into a plurality of gradient echo image blocks to be reconstructed;
the input module is used for inputting the gradient echo image blocks into a liquid attenuation inversion recovery image reconstruction model trained in advance for image reconstruction;
and the splicing module is used for splicing the liquid attenuation inversion recovery image blocks output by the liquid attenuation inversion recovery image reconstruction model into a liquid attenuation inversion recovery image.
8. The image reconstruction device of claim 7, further comprising:
the acquisition module is used for acquiring a plurality of groups of training data, and each group of training data comprises a gradient echo image and a liquid attenuation inversion recovery image of an imaging object;
the cutting module is further used for cutting the gradient echo image and the liquid attenuation inversion recovery image based on the same cutting strategy aiming at each group of training data to respectively obtain a plurality of gradient echo image blocks and a plurality of liquid attenuation inversion recovery image blocks;
and the model training module is used for training a neural network model by taking the gradient echo image blocks as input and the liquid attenuation inversion recovery image blocks as labels to obtain the liquid attenuation inversion recovery image reconstruction model.
9. The image reconstruction device of claim 7, wherein the cropping module is specifically to crop the gradient echo patch into a plurality of gradient echo patches based on an image cropping function.
10. The image reconstruction device of claim 7 wherein the stitching module is specifically configured to stitch the liquid attenuation inversion recovery image blocks into a liquid attenuation inversion recovery image based on a linear superposition algorithm.
11. An apparatus for processing image data, the apparatus comprising:
the acquisition module is used for acquiring a plurality of groups of image data; each set of image data comprises a gradient echo image and a liquid attenuation inversion recovery image of an imaging object;
the cutting module is used for cutting the gradient echo image and the liquid attenuation inversion recovery image based on the same cutting strategy aiming at each group of image data to respectively obtain a plurality of gradient echo image blocks and a plurality of liquid attenuation inversion recovery image blocks;
the model training module is used for taking the gradient echo image blocks as input and taking the liquid attenuation inversion recovery image blocks as labels to train a neural network model so as to obtain a liquid attenuation inversion recovery image reconstruction model; the liquid attenuation inversion recovery image reconstruction model is used for image reconstruction.
12. The image data processing apparatus according to claim 11, wherein the cropping module is specifically configured to:
randomly selecting a first area on the gradient echo image and cutting to obtain a gradient echo image block;
and aiming at each cutting of the gradient echo image, selecting a second area with the same size at the relative position of the liquid attenuation inversion recovery image, and cutting to obtain a liquid attenuation inversion recovery image block.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the image reconstruction method of any one of claims 1 to 4 when executing the computer program.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the image reconstruction method according to any one of claims 1 to 4.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of processing image data according to claim 5 or 6 when executing the computer program.
16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of processing image data of claim 5 or 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911077399.2A CN110992436B (en) | 2019-11-06 | 2019-11-06 | Image reconstruction method and device, and image data processing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911077399.2A CN110992436B (en) | 2019-11-06 | 2019-11-06 | Image reconstruction method and device, and image data processing method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110992436A true CN110992436A (en) | 2020-04-10 |
| CN110992436B CN110992436B (en) | 2023-08-29 |
Family
ID=70083275
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911077399.2A Active CN110992436B (en) | 2019-11-06 | 2019-11-06 | Image reconstruction method and device, and image data processing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110992436B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140009156A1 (en) * | 2011-03-24 | 2014-01-09 | Koninklijke Philips N.V. | Compressed sensing mr image reconstruction using constraint from prior acquisition |
| US20160313422A1 (en) * | 2013-12-19 | 2016-10-27 | Koninklijki Philips N.V. | Phase sensitive inversion recovery mri with water/fat separation |
| US20170061620A1 (en) * | 2015-09-01 | 2017-03-02 | Samsung Electronics Co., Ltd. | Method and apparatus for processing magnetic resonance image |
| US20180128891A1 (en) * | 2016-11-09 | 2018-05-10 | Samsung Electronics Co., Ltd. | Magnetic resonance imaging (mri) apparatus and method of obtaining magnetic resonance image |
| US20180374246A1 (en) * | 2017-06-22 | 2018-12-27 | Canon Medical Systems Corporation | Image processing apparatus, magnetic resonance imaging apparatus, and storage medium |
| CN109671129A (en) * | 2018-12-14 | 2019-04-23 | 深圳先进技术研究院 | A kind of the dynamic magnetic resonance image method for reconstructing and device of auto-adaptive parameter study |
| US20190172230A1 (en) * | 2017-12-06 | 2019-06-06 | Siemens Healthcare Gmbh | Magnetic resonance image reconstruction with deep reinforcement learning |
| CN111656392A (en) * | 2018-02-15 | 2020-09-11 | 通用电气公司 | System and method for synthesizing magnetic resonance images |
-
2019
- 2019-11-06 CN CN201911077399.2A patent/CN110992436B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140009156A1 (en) * | 2011-03-24 | 2014-01-09 | Koninklijke Philips N.V. | Compressed sensing mr image reconstruction using constraint from prior acquisition |
| US20160313422A1 (en) * | 2013-12-19 | 2016-10-27 | Koninklijki Philips N.V. | Phase sensitive inversion recovery mri with water/fat separation |
| US20170061620A1 (en) * | 2015-09-01 | 2017-03-02 | Samsung Electronics Co., Ltd. | Method and apparatus for processing magnetic resonance image |
| US20180128891A1 (en) * | 2016-11-09 | 2018-05-10 | Samsung Electronics Co., Ltd. | Magnetic resonance imaging (mri) apparatus and method of obtaining magnetic resonance image |
| US20180374246A1 (en) * | 2017-06-22 | 2018-12-27 | Canon Medical Systems Corporation | Image processing apparatus, magnetic resonance imaging apparatus, and storage medium |
| US20190172230A1 (en) * | 2017-12-06 | 2019-06-06 | Siemens Healthcare Gmbh | Magnetic resonance image reconstruction with deep reinforcement learning |
| CN111656392A (en) * | 2018-02-15 | 2020-09-11 | 通用电气公司 | System and method for synthesizing magnetic resonance images |
| CN109671129A (en) * | 2018-12-14 | 2019-04-23 | 深圳先进技术研究院 | A kind of the dynamic magnetic resonance image method for reconstructing and device of auto-adaptive parameter study |
Non-Patent Citations (2)
| Title |
|---|
| WEN WEN ET AL: "Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis" * |
| 麻高超: "磁共振多对比度成像技术的研究" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110992436B (en) | 2023-08-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Johnson et al. | Conditional generative adversarial network for 3D rigid‐body motion correction in MRI | |
| US10915990B2 (en) | Systems and methods for denoising medical images with deep learning network | |
| US11250543B2 (en) | Medical imaging using neural networks | |
| CN110991636B (en) | Training method and device of generated countermeasure network, image enhancement method and device | |
| Du et al. | Accelerated super-resolution MR image reconstruction via a 3D densely connected deep convolutional neural network | |
| CN113160380B (en) | Three-dimensional magnetic resonance image super-resolution reconstruction method, electronic equipment and storage medium | |
| CN116402865B (en) | Multi-mode image registration method, device and medium using diffusion model | |
| CN116823625B (en) | Cross-contrast magnetic resonance super-resolution method and system based on variational self-encoder | |
| CN109146813A (en) | Multitask image reconstruction method, device, equipment and medium | |
| CN110874855B (en) | Collaborative imaging method and device, storage medium and collaborative imaging equipment | |
| CN111243052A (en) | Image reconstruction method and device, computer equipment and storage medium | |
| CN104504657B (en) | Method and device for de-noising magnetic resonance diffusion tensor | |
| WO2019224800A1 (en) | Method and system for simulating and constructing original medical images from one modality to other modality | |
| Li et al. | 3D High-Quality Magnetic Resonance Image Restoration in Clinics Using Deep Learning | |
| CN110992436B (en) | Image reconstruction method and device, and image data processing method and device | |
| CN115761134A (en) | Twin model generation method, system, nuclear magnetic resonance device, and medium | |
| US11941732B2 (en) | Multi-slice MRI data processing using deep learning techniques | |
| CN112184850B (en) | Image processing method, device, console device and CT system | |
| CN110930394B (en) | Method and terminal equipment for measuring slope and pinnate angle of muscle fiber bundle line | |
| CN113689353A (en) | Three-dimensional image enhancement method and device and training method and device of image enhancement model | |
| CN110545373B (en) | Spatial environment sensing method and device | |
| Sable et al. | A Novel Approach for Super Resolution in Medical Imaging | |
| US20230019733A1 (en) | Motion artifact correction using artificial neural networks | |
| US12039699B2 (en) | Method and system for simulating and constructing original medical images from one modality to other modality | |
| CN114114116B (en) | Magnetic resonance imaging generation method, system, storage medium and computer equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |