CN112649773A - Magnetic resonance scanning method, device, equipment and storage medium - Google Patents
Magnetic resonance scanning method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN112649773A CN112649773A CN202011528321.0A CN202011528321A CN112649773A CN 112649773 A CN112649773 A CN 112649773A CN 202011528321 A CN202011528321 A CN 202011528321A CN 112649773 A CN112649773 A CN 112649773A
- Authority
- CN
- China
- Prior art keywords
- magnetic resonance
- artifact
- image
- determining
- scanning
- 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
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
Abstract
The embodiment of the invention discloses a magnetic resonance scanning method, a magnetic resonance scanning device, magnetic resonance scanning equipment and a storage medium. The method comprises the following steps: acquiring artifact identification data in a magnetic resonance scanning process; wherein the artifact identification data comprises a set of magnetic resonance images and/or motion data corresponding to at least one magnetic resonance image of the set of magnetic resonance images, respectively; determining artifact images in the magnetic resonance image set based on the artifact identification data and a trained target artifact identification model; determining a rescanning parameter corresponding to the artifact image based on a magnetic resonance scanning mode, and executing rescanning operation on the measured object based on the rescanning parameter to obtain a corrected image; and updating the magnetic resonance image set based on the correction image to obtain a target magnetic resonance image set. The embodiment of the invention solves the problem of poor timeliness of artifact image compensation scanning operation and improves the quality of magnetic resonance scanning.
Description
Technical Field
The embodiment of the invention relates to the technical field of magnetic resonance imaging, in particular to a magnetic resonance scanning method, a magnetic resonance scanning device, magnetic resonance scanning equipment and a storage medium.
Background
Magnetic resonance imaging is a tomographic imaging technique that acquires an electromagnetic signal from a subject to be measured by using a magnetic resonance phenomenon and reconstructs tissue information based on the electromagnetic signal. Due to the long time of the magnetic resonance scan, the moving behavior of the measured object inevitably occurs during the magnetic resonance scan, and the moving behavior may cause artifacts in the obtained magnetic resonance image, thereby affecting the subsequent diagnosis result.
The traditional motion artifact identification scheme needs to depend on a scanning technician to perform manual screening, the scanning technician judges whether the magnetic resonance image has motion artifacts or not according to personal experience, whether the artifacts affect clinical diagnosis or not, and whether rescanning is possible to acquire images with higher quality or not is judged according to the condition of a patient. The conventional artifact identification scheme requires a scan technician to have a rich image judgment capability and clinical experience, increases the workload of the scan technician, and has a large error in identification results. And the process of identifying the artifact image has certain time delay, so that the subsequent supplementary scanning operation possibly exceeds the requirement of the magnetic resonance scanning on the timeliness.
Disclosure of Invention
The embodiment of the invention provides a magnetic resonance scanning method, a magnetic resonance scanning device, magnetic resonance scanning equipment and a storage medium, which are used for improving the timeliness of a supplementary scanning operation in a magnetic resonance scanning process and the image quality obtained by magnetic resonance scanning.
In a first aspect, an embodiment of the present invention provides a magnetic resonance scanning method, including:
acquiring artifact identification data in a magnetic resonance scanning process; wherein the artifact identification data comprises a set of magnetic resonance images and/or motion data corresponding to at least one magnetic resonance image of the set of magnetic resonance images, respectively;
determining artifact images in the magnetic resonance image set based on the artifact identification data and a trained target artifact identification model;
determining a rescanning parameter corresponding to the artifact image based on a magnetic resonance scanning mode, and executing rescanning operation on the measured object based on the rescanning parameter to obtain a corrected image;
and updating the magnetic resonance image set based on the correction image to obtain a target magnetic resonance image set.
In a second aspect, an embodiment of the present invention further provides a magnetic resonance scanning apparatus, including:
the artifact identification data acquisition module is used for acquiring artifact identification data in the magnetic resonance scanning process; wherein the artifact identification data comprises a set of magnetic resonance images and/or motion data corresponding to at least one magnetic resonance image of the set of magnetic resonance images, respectively;
an artifact image identification module, configured to determine an artifact image in the magnetic resonance image set based on the artifact identification data and a trained target artifact identification model;
the correction image determining module is used for determining a rescanning parameter corresponding to the artifact image based on a magnetic resonance scanning mode and executing rescanning operation on the detected object based on the rescanning parameter to obtain a correction image;
and the target magnetic resonance image set determining module is used for updating the magnetic resonance image set based on the correction image to obtain a target magnetic resonance image set.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any of the magnetic resonance scanning methods referred to above.
In a fourth aspect, embodiments of the present invention also provide a storage medium containing computer-executable instructions for performing any of the magnetic resonance scanning methods referred to above when executed by a computer processor.
According to the embodiment of the invention, the artifact image in the magnetic resonance image set obtained by magnetic resonance scanning is identified, the rescanning parameter corresponding to the artifact image is determined based on the magnetic resonance scanning mode, and the magnetic resonance image set is updated based on the correction image obtained by rescanning, so that the problem of poor timeliness of artifact image complementary scanning operation is solved, and the image quality obtained by magnetic resonance scanning is improved.
Drawings
Fig. 1 is a flowchart of a magnetic resonance scanning method according to an embodiment of the present invention;
fig. 2 is a flowchart of a magnetic resonance scanning method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a magnetic resonance scanning method corresponding to a multiple breath-hold scanning mode according to a second embodiment of the present invention;
fig. 4 is a flowchart of a specific example of a magnetic resonance scanning method according to a second embodiment of the present invention;
fig. 5 is a schematic diagram of a magnetic resonance scanning apparatus according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to a fourth 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.
Example one
Fig. 1 is a flowchart of a magnetic resonance scanning method according to an embodiment of the present invention, where the embodiment is applicable to a case of performing artifact identification on a magnetic resonance image, the method may be performed by a magnetic resonance scanning apparatus, the apparatus may be implemented in a software and/or hardware manner, and the apparatus may be configured in a magnetic resonance device or a terminal device. The method specifically comprises the following steps:
and S110, acquiring artifact identification data in the magnetic resonance scanning process.
In particular, the artifact identification data is used to determine the presence or absence of artifact identification data during the magnetic resonance scan. In this embodiment, the artifact identification data comprises the set of magnetic resonance images and/or motion data corresponding to at least one respective magnetic resonance image of the set of magnetic resonance images. Specifically, the magnetic resonance image set includes at least one magnetic resonance image.
Illustratively, the motion data includes, but is not limited to, at least one of respiration data, heartbeat data, and motion data of the target site. Specifically, the respiration data includes data such as respiration signals, respiration frequency, respiration amplitude and the like, the heartbeat data includes data such as heartbeat signals, heartbeat frequency, heartbeat amplitude and the like, the target part can be a hand, a head, a chest, a foot, legs and the like, the motion data of the target part can be pose motion data, and the pose motion data includes pose angle data and/or position data. The selection of the motion data is not particularly limited herein.
And S120, determining artifact images in the magnetic resonance image set based on the artifact identification data and the trained target artifact identification model.
The target artifact identification model includes, but is not limited to, a K-nearest neighbor algorithm-based network model, a support vector machine, a bayesian network model, a decision tree, a convolutional neural network model, a cyclic neural network model, a deep learning network model, and the like, where a specific model type of the target artifact identification model is not limited.
Based on the foregoing embodiment, optionally, the object artifact identification model includes a first object artifact identification model and/or a second object artifact identification model, and accordingly, determining the artifact image in the magnetic resonance image set based on the artifact identification data and the trained object artifact identification model includes: respectively inputting each magnetic resonance image into a first target artifact identification model to obtain an output first identification result, and/or respectively inputting each motion data into a second target artifact identification model to obtain an output second identification result; based on the first recognition result and/or the second recognition result, an artifact image in the magnetic resonance image set is determined.
The first target artifact identification model and the second target artifact identification model include, but are not limited to, a network model based on a K-nearest neighbor algorithm, a support vector machine, a bayesian network model, a decision tree, a convolutional neural network model, a cyclic neural network model, a deep learning network model, and the like, and specific model types of the first target artifact identification model and the second target artifact identification model are not limited herein. Specifically, the types of the first target artifact identification model and the second target artifact identification model may be the same or different. For example, the first identification result is whether the magnetic resonance image is an artifact image, and the second identification result is whether the magnetic resonance image corresponding to the motion data is an artifact image.
In one embodiment, optionally, the first identification result includes a first artifact level corresponding to each magnetic resonance image, and the second identification result includes a second artifact level corresponding to each motion data. The number of division of the artifact level is not limited, and the artifact level includes 3 levels, specifically, a no-artifact level, a slight-artifact level, and a severe-artifact level. Specifically, the second artifact level corresponding to the motion data is used to describe the fluctuation condition of the motion data. Illustratively, no artifact level, a slight artifact level, and a severe artifact level correspond to no fluctuation, slight fluctuation, and strong fluctuation, respectively, of the motion data.
On the basis of the above embodiment, optionally, a sample magnetic resonance image and sample motion data are obtained, the sample magnetic resonance image is input into the first initial artifact identification model, iterative training is performed on the first initial artifact identification model according to the output first predicted identification result and the first standard identification result, and a trained first target artifact identification model is obtained until a first preset requirement is met; and inputting the sample motion data into a second initial artifact identification model, and performing iterative training on the second initial artifact identification model according to an output second predicted identification result and a second standard identification result until a second preset requirement is met to obtain a trained second target artifact identification model.
In one embodiment, optionally, determining an artifact image in the set of magnetic resonance images based on the first recognition result and/or the second recognition result comprises: for each magnetic resonance image, determining a target artifact level based on the first artifact level and the second artifact level; and if at least one of the target artifact level, the first artifact level and the second artifact level reaches a preset level threshold, taking the magnetic resonance image as an artifact image.
Specifically, the first artifact level and the second artifact level are weighted and calculated according to the weighting coefficients corresponding to the first artifact level and the second artifact level respectively, so that a target artifact level is obtained.
In one embodiment, optionally, the preset requirement is that a consistency ratio calculated based on the standard recognition result and the predicted recognition result is less than or equal to a preset ratio threshold, or the preset requirement is that a loss function value calculated based on the output predicted recognition result and the standard recognition result converges. Specifically, if the consistency ratio is greater than a preset ratio threshold, the sample size of the sample magnetic resonance image is increased, and the iterative training of the initial artifact identification model is continued.
And S130, determining rescanning parameters corresponding to the artifact image based on the magnetic resonance scanning mode, and executing rescanning operation on the measured object based on the rescanning parameters to obtain a corrected image.
Specifically, the magnetic resonance scanning mode includes, but is not limited to, a multiple breath-hold scanning mode, a single breath-hold scanning mode, or a non-breath-hold scanning mode, and the like, and the corresponding determination of the rescanning parameters in different magnetic resonance scanning modes is different. The specific steps for carrying out this part are specifically explained in the following examples.
Exemplary rescan parameters include, but are not limited to, magnetic resonance receive coil parameters, magnetic field homogeneity compensation parameters, and excitation times, among others.
And S140, updating the magnetic resonance image set based on the corrected image to obtain a target magnetic resonance image set.
In one embodiment, optionally, the artifact image in the magnetic resonance image set is replaced by the corrected image, resulting in the target magnetic resonance image set. Illustratively, the magnetic resonance image set includes an image 1, an image 2, and an image 3, where the image 2 is an artifact image, and the corrected image corresponding to the artifact image is an image 2 ', and the target magnetic resonance image set includes the image 1, the image 2', and the image 3.
In another embodiment, optionally, the correction image is added to the set of magnetic resonance images resulting in a set of target magnetic resonance images. In this case, the correction image may be stored corresponding to the artifact image in the target magnetic resonance image set, which is, for example, image 1, image 2', and image 3. In the target magnetic resonance image set, the correction image may be stored separately from the artifact image, and the target magnetic resonance image set is image 1, image 2, image 3, and image 2', for example. The manner in which the correction image is added to the set of magnetic resonance images is not limited here.
According to the technical scheme of the embodiment, the artifact images in the magnetic resonance image set obtained through magnetic resonance scanning are identified, the rescanning parameters corresponding to the artifact images are determined based on the magnetic resonance scanning mode, and the magnetic resonance image set is updated based on the correction images obtained through rescanning, so that the problem of poor timeliness of artifact image complementary scanning operation is solved, and the image quality obtained through magnetic resonance scanning is improved.
Example two
Fig. 2 is a flowchart of a magnetic resonance scanning method according to a second embodiment of the present invention, and the technical solution of the present embodiment is further detailed based on the above-mentioned second embodiment. Optionally, the determining the rescan parameters corresponding to the artifact image based on the magnetic resonance scanning mode includes: and if the magnetic resonance scanning mode is a single breath-holding scanning mode or a non-breath-holding scanning mode, determining rescanning parameters corresponding to the artifact image according to the image position of the artifact image.
The specific implementation steps of this embodiment include:
s210, acquiring artifact identification data in the magnetic resonance scanning process.
S220, determining artifact images in the magnetic resonance image set based on the artifact identification data and the trained target artifact identification model.
And S230, if the magnetic resonance scanning mode is the single breath-holding scanning mode or the non-breath-holding scanning mode, determining rescanning parameters corresponding to the artifact image according to the image position of the artifact image.
Specifically, the single breath-holding scanning mode or the non-breath-holding scanning mode belongs to a continuous scanning mode, and the continuous scanning mode can perform one-time continuous scanning on the detected object to obtain a magnetic resonance image set. In the magnetic resonance scanning process, a plurality of magnetic resonance images on different layers can be obtained through scanning, the image position can be used for describing the position of a scanned slice layer, and the scanning parameters corresponding to different slice layers can be different.
In one embodiment, optionally, determining a rescan parameter corresponding to the artifact image according to the image position of the artifact image comprises: determining at least one slice group according to the image position of at least one artifact image; for each slice group, rescanning parameters corresponding to the slice group are determined based on the slice position of the slice group and the number of artifact images in the slice group.
In the present embodiment, each slice group includes position-continuous artifact images, and for example, a slice group may include only one artifact image, or include at least two position-continuous artifact images.
The magnetic resonance images in the magnetic resonance image set are, illustratively, image 1, image 2, image 3, image 4, image 5, image 6, and image 7 in order according to the image position. Assuming that images 3, 4 and 6 of the set of magnetic resonance images are artifact images, two slice groups can be determined, slice group 1 containing images 3 and 4 and slice group 2 containing image 6.
For example, the slice position of the slice group may be an image position of any artifact image in the slice group. The rescanning parameter is related to the slice position of the slice group and the number of the artifact images in the slice group, so that parameters such as signal-to-noise ratio and contrast of the artifact images corresponding to the corrected image and the corrected image obtained by scanning based on the rescanning parameter are consistent.
On the basis of the foregoing embodiment, optionally, determining rescanning parameters corresponding to the artifact image based on the magnetic resonance scanning mode includes: and if the magnetic resonance scanning mode is a multi-breath-holding scanning mode, taking the single breath-holding scanning parameter corresponding to the artifact image as a rescanning parameter.
Specifically, when the magnetic resonance scanning mode is the multiple breath holding scanning mode, the measured object needs to keep a breath holding state in each breath holding period, and performs the magnetic resonance scanning operation based on a single breath holding scanning parameter in the breath holding period, and after the scanning operation is completed, a magnetic resonance image set corresponding to the breath holding period is obtained. Specifically, the single breath-hold scanning parameters corresponding to each breath-hold period may be the same or different.
In one embodiment, optionally, acquiring artifact identification data during a magnetic resonance scan comprises: and when the magnetic resonance scanning mode is a multi-breath-holding scanning mode, acquiring artifact identification data corresponding to a single breath-holding period. The advantage that sets up like this lies in, can in time carry out the artifact to the magnetic resonance image set that single breath-holding cycle was gathered after every breath-holding cycle was ended to in time carry out subsequent benefit to sweep the step to this breath-holding cycle, reduce and mend the degree of difficulty of sweeping, guarantee to mend the timeliness of sweeping the step.
Fig. 3 is a flowchart of a magnetic resonance scanning method corresponding to a multiple breath-hold scanning mode according to a second embodiment of the present invention. Specifically, the multiple gas-holding scanning mode comprises m gas-holding periods, after the nth (n is less than or equal to m) scanning is completed, magnetic resonance image reconstruction is performed on scanning data obtained by scanning to obtain a magnetic resonance image set, at least one magnetic resonance image in the magnetic resonance image set is respectively input into the target artifact identification model, and whether an artifact image exists in the magnetic resonance image set or not is determined. If yes, performing a complementary scanning operation, specifically, re-performing the nth scanning based on the nth scanning parameter, and if no, n is equal to n +1 and continuing to perform the nth scanning if n is equal to or less than m.
S240, rescanning operation is carried out on the measured object based on the rescanning parameters to obtain a corrected image, and the magnetic resonance image set is updated based on the corrected image to obtain a target magnetic resonance image set.
On the basis of the foregoing embodiment, optionally, before determining the rescan parameters corresponding to the artifact image based on the magnetic resonance scanning mode, the method further includes: displaying the artifact image and/or the motion data corresponding to the artifact image on an interactive interface; and if a supplementary scanning instruction input by the user based on the interactive interface is received, performing the operation of determining the rescan parameters.
For example, the artifact risk prompt information corresponding to the artifact image may also be displayed on the interactive interface. Illustratively, the complement scan instruction includes a target artifact image corresponding to the user selection operation. The advantage of this arrangement is that the magnetic resonance scanning error is reduced, thereby avoiding the execution of unnecessary supplementary scanning steps, and improving the scanning efficiency of the magnetic resonance scanning.
Fig. 4 is a flowchart of a specific example of a magnetic resonance scanning method according to a second embodiment of the present invention. Specifically, the multi-breath-holding scanning mode includes m breath-holding periods, after the nth (n is equal to or less than m) scanning is completed, the motion data is input into a second target artifact identification model to obtain a second identification result, specifically, whether the motion data fluctuates or not is determined according to the second identification result, if yes, an artifact risk is prompted, if not, a magnetic resonance image obtained by reconstructing a magnetic resonance image based on scanning data obtained by scanning is input into the first target artifact identification model to obtain whether the magnetic resonance image has an artifact or not, if yes, an artifact risk is prompted, if no, n is n +1, and the nth scanning is continuously executed under the condition that n is equal to or less than m. After the fact that the artifact risk exists is prompted, whether a supplementary scanning instruction is received or not is determined, if yes, the nth scanning is executed again based on the nth scanning parameter, if not, n is n +1, and the nth scanning is executed continuously under the condition that n is not larger than m.
According to the technical scheme, the rescanning parameters corresponding to the artifact images are determined based on the magnetic resonance scanning mode, the rescanning parameters are rescanned on the detected object based on the rescanning parameters, the problem of determining the rescanning parameters in different magnetic resonance scanning modes is solved, the magnetic resonance scanning mode applicable to the compensation scanning operation is widened, and the image quality obtained by magnetic resonance scanning is further improved.
EXAMPLE III
Fig. 5 is a schematic diagram of a magnetic resonance scanning apparatus according to a third embodiment of the present invention. The present embodiment is applicable to the case of performing artifact identification on a magnetic resonance image, and the apparatus may be implemented in a software and/or hardware manner, and the apparatus may be configured in a magnetic resonance device or a terminal device. The magnetic resonance scanning apparatus includes: an artifact identification data acquisition module 310, an artifact image identification module 320, a correction image determination module 330, and a target magnetic resonance image set determination module 340.
The artifact identification data acquiring module 310 is configured to acquire artifact identification data in a magnetic resonance scanning process; wherein the artifact identification data comprises a set of magnetic resonance images and/or motion data corresponding to at least one magnetic resonance image in the set of magnetic resonance images, respectively;
an artifact image identification module 320, configured to determine an artifact image in the magnetic resonance image set based on the artifact identification data and the trained target artifact identification model;
a corrected image determining module 330, configured to determine a rescanning parameter corresponding to the artifact image based on the magnetic resonance scanning mode, and perform a rescanning operation on the object to be tested based on the rescanning parameter to obtain a corrected image;
a target magnetic resonance image set determining module 340, configured to update the magnetic resonance image set based on the corrected image, so as to obtain a target magnetic resonance image set.
According to the technical scheme of the embodiment, the artifact images in the magnetic resonance image set obtained through magnetic resonance scanning are identified, the rescanning parameters corresponding to the artifact images are determined based on the magnetic resonance scanning mode, and the magnetic resonance image set is updated based on the correction images obtained through rescanning, so that the problem of poor timeliness of artifact image complementary scanning operation is solved, and the image quality obtained through magnetic resonance scanning is improved.
On the basis of the above technical solution, optionally, the corrected image determining module 330 includes:
and the rescanning parameter determining unit is used for determining rescanning parameters corresponding to the artifact image according to the image position of the artifact image if the magnetic resonance scanning mode is the single breath-holding scanning mode or the non-breath-holding scanning mode.
On the basis of the above technical solution, optionally, the rescan parameter determining unit is specifically configured to:
determining at least one slice group according to the image position of at least one artifact image;
for each slice group, rescanning parameters corresponding to the slice group are determined based on the slice position of the slice group and the number of artifact images in the slice group.
Based on the above technical solution, optionally, the object artifact identification model includes a first object artifact identification model and/or a second object artifact identification model, and correspondingly, the artifact image identification module 320 includes:
the identification result determining unit is used for respectively inputting each magnetic resonance image into the first target artifact identification model to obtain an output first identification result, and/or respectively inputting each motion data into the second target artifact identification model to obtain an output second identification result;
an artifact image determination unit for determining an artifact image in the set of magnetic resonance images based on the first recognition result and/or the second recognition result.
On the basis of the above technical solution, optionally, the apparatus further includes:
the compensation scanning instruction receiving module is used for displaying the artifact image and/or the motion data corresponding to the artifact image on the interactive interface before determining the rescanning parameters corresponding to the artifact image based on the magnetic resonance scanning mode; and if a supplementary scanning instruction input by the user based on the interactive interface is received, performing the operation of determining the rescan parameters.
On the basis of the above technical solution, optionally, the first identification result includes a first artifact level corresponding to each magnetic resonance image, and the second identification result includes a second artifact level corresponding to each motion data.
On the basis of the above technical solution, optionally, the artifact image determination unit is specifically configured to:
for each magnetic resonance image, determining a target artifact level based on the first artifact level and the second artifact level;
and if at least one of the target artifact level, the first artifact level and the second artifact level reaches a preset level threshold, taking the magnetic resonance image as an artifact image.
The magnetic resonance scanning device provided by the embodiment of the invention can be used for executing the magnetic resonance scanning method provided by the embodiment of the invention, and has corresponding functions and beneficial effects of the executing method.
It should be noted that, in the embodiment of the magnetic resonance scanning apparatus, the units and modules included in the embodiment are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
Example four
Fig. 6 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention, where the embodiment of the present invention provides a service for implementing the magnetic resonance scanning method according to the foregoing embodiment of the present invention, and the magnetic resonance scanning apparatus according to the foregoing embodiment may be configured. Fig. 6 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 6 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present invention.
As shown in FIG. 6, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes programs stored in the system memory 28 to perform various functional applications and data processing, such as implementing a magnetic resonance scanning method provided by an embodiment of the present invention.
Through the electronic equipment, the problem of large error of the artifact image identification result is solved, and the magnetic resonance scanning efficiency and the identification quality are improved.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a magnetic resonance scanning method, the method including:
acquiring artifact identification data in a magnetic resonance scanning process; wherein the artifact identification data comprises a set of magnetic resonance images and/or motion data corresponding to at least one magnetic resonance image in the set of magnetic resonance images, respectively;
determining artifact images in the magnetic resonance image set based on the artifact identification data and the trained target artifact identification model;
determining rescanning parameters corresponding to the artifact image based on the magnetic resonance scanning mode, and performing rescanning operation on the detected object based on the rescanning parameters to obtain a corrected image;
and updating the magnetic resonance image set based on the corrected image to obtain a target magnetic resonance image set.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Of course, the storage medium provided by the embodiments of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the above method operations, and may also perform related operations in the magnetic resonance scanning method provided by any embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A magnetic resonance scanning method, comprising:
acquiring artifact identification data in a magnetic resonance scanning process; wherein the artifact identification data comprises a set of magnetic resonance images and/or motion data corresponding to at least one magnetic resonance image of the set of magnetic resonance images, respectively;
determining artifact images in the magnetic resonance image set based on the artifact identification data and a trained target artifact identification model;
determining a rescanning parameter corresponding to the artifact image based on a magnetic resonance scanning mode, and executing rescanning operation on the measured object based on the rescanning parameter to obtain a corrected image;
and updating the magnetic resonance image set based on the correction image to obtain a target magnetic resonance image set.
2. The method of claim 1, wherein determining rescanning parameters corresponding to the artifact image based on the magnetic resonance scan pattern comprises:
and if the magnetic resonance scanning mode is a single breath-holding scanning mode or a non-breath-holding scanning mode, determining rescanning parameters corresponding to the artifact image according to the image position of the artifact image.
3. The method of claim 2, wherein determining rescanning parameters corresponding to the artifact image based on the image location of the artifact image comprises:
determining at least one slice group according to the image position of at least one artifact image;
for each slice group, determining a rescan parameter corresponding to the slice group based on a slice position of the slice group and a number of artifact images in the slice group.
4. The method of claim 1, wherein the object artifact identification model comprises a first object artifact identification model and/or a second object artifact identification model, and wherein determining the artifact images in the set of magnetic resonance images based on the artifact identification data and the trained object artifact identification model comprises:
respectively inputting each magnetic resonance image into a first target artifact identification model to obtain an output first identification result, and/or respectively inputting each motion data into a second target artifact identification model to obtain an output second identification result;
determining an artifact image in the set of magnetic resonance images based on the first and/or second identification results.
5. The method of claim 4, wherein prior to determining rescanning parameters corresponding to the artifact image based on a magnetic resonance scan mode, the method further comprises:
displaying the artifact image and/or the motion data corresponding to the artifact image on an interactive interface;
and if a supplementary scanning instruction input by the user based on the interactive interface is received, executing the operation of determining the rescanning parameter.
6. The method of claim 4, wherein the first identification result comprises a first artifact level corresponding to each of the magnetic resonance images, and the second identification result comprises a second artifact level corresponding to each of the motion data.
7. The method of claim 6, wherein determining artifact images in the set of magnetic resonance images based on the first and/or second identification comprises:
for each magnetic resonance image, determining a target artifact level based on the first and second artifact levels;
and if at least one of the target artifact level, the first artifact level and the second artifact level reaches a preset level threshold, taking the magnetic resonance image as an artifact image.
8. A magnetic resonance scanning apparatus, comprising:
the artifact identification data acquisition module is used for acquiring artifact identification data in the magnetic resonance scanning process; wherein the artifact identification data comprises a set of magnetic resonance images and/or motion data corresponding to at least one magnetic resonance image of the set of magnetic resonance images, respectively;
an artifact image identification module, configured to determine an artifact image in the magnetic resonance image set based on the artifact identification data and a trained target artifact identification model;
the correction image determining module is used for determining a rescanning parameter corresponding to the artifact image based on a magnetic resonance scanning mode and executing rescanning operation on the detected object based on the rescanning parameter to obtain a correction image;
and the target magnetic resonance image set determining module is used for updating the magnetic resonance image set based on the correction image to obtain a target magnetic resonance image set.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a magnetic resonance scanning method as recited in any one of claims 1-7.
10. A storage medium containing computer executable instructions for performing the magnetic resonance scanning method of any one of claims 1-7 when executed by a computer processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011528321.0A CN112649773B (en) | 2020-12-22 | 2020-12-22 | Magnetic resonance scanning method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011528321.0A CN112649773B (en) | 2020-12-22 | 2020-12-22 | Magnetic resonance scanning method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112649773A true CN112649773A (en) | 2021-04-13 |
| CN112649773B CN112649773B (en) | 2023-05-26 |
Family
ID=75359105
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011528321.0A Active CN112649773B (en) | 2020-12-22 | 2020-12-22 | Magnetic resonance scanning method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112649773B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114170166A (en) * | 2021-11-26 | 2022-03-11 | 四川大学华西医院 | Magnetic resonance head scanning image quality evaluation method and equipment |
| CN114280517A (en) * | 2021-12-27 | 2022-04-05 | 深圳市联影高端医疗装备创新研究院 | Magnetic resonance imaging method, device, equipment and storage medium |
Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4855910A (en) * | 1986-10-22 | 1989-08-08 | North American Philips Corporation | Time-clustered cardio-respiratory encoder and method for clustering cardio-respiratory signals |
| AU5117699A (en) * | 1998-07-21 | 2000-02-14 | Acoustic Sciences Associates | Synthetic structural imaging and volume estimation of biological tissue organs |
| AU5160300A (en) * | 1999-05-21 | 2000-12-12 | Government Of The United States Of America, As Represented By The Secretary Of The Department Of Health And Human Services, The | Determination of an empirical statistical distribution of the diffusion tensor in mri |
| CN101023878A (en) * | 2006-02-17 | 2007-08-29 | 通用电气公司 | Method and device for compensating image data using simultaneously collected motion data |
| CN101711672A (en) * | 2009-06-23 | 2010-05-26 | 华东师范大学 | Method for acquiring detailed physiological information of testee in magnetic resonance imaging |
| CN102077572A (en) * | 2008-06-19 | 2011-05-25 | 松下电器产业株式会社 | Method and apparatus for motion blur and ghosting prevention in imaging system |
| CN102483850A (en) * | 2009-09-08 | 2012-05-30 | 皇家飞利浦电子股份有限公司 | Motion Monitoring System For Monitoring Motion Within A Region Of Interest |
| CN102871660A (en) * | 2012-10-12 | 2013-01-16 | 上海卡勒幅磁共振技术有限公司 | Method for restraining shadow of magnetic resonance imaging respiratory movement |
| CN103189896A (en) * | 2010-10-27 | 2013-07-03 | 皇家飞利浦电子股份有限公司 | Image artifact identification and mitigation |
| CN103315739A (en) * | 2013-05-22 | 2013-09-25 | 华东师范大学 | Magnetic resonance imaging method and system for eliminating motion artifact based on dynamic tracking technology |
| CN103340621A (en) * | 2013-06-04 | 2013-10-09 | 中国科学院苏州生物医学工程技术研究所 | Device and method for removing motion artifacts |
| CN104135931A (en) * | 2012-02-22 | 2014-11-05 | 皇家飞利浦有限公司 | Method and system for reducing localized artifacts in imaging data |
| CN105938616A (en) * | 2015-12-23 | 2016-09-14 | 上海奕瑞光电子科技有限公司 | Identification and correction method for tremor or tapping artifact in dark-field image template of detector |
| CN106551703A (en) * | 2015-09-30 | 2017-04-05 | 上海联影医疗科技有限公司 | Computer tomography method and computed tomography imaging system |
| CN108291950A (en) * | 2015-12-03 | 2018-07-17 | 皇家飞利浦有限公司 | The removal of image artifacts in SENSE-MRI |
| CN109658469A (en) * | 2018-12-13 | 2019-04-19 | 深圳先进技术研究院 | A kind of neck joint imaging method and device based on the study of depth priori |
| CN110503698A (en) * | 2018-05-16 | 2019-11-26 | 西门子医疗有限公司 | The method for determining image mode and its parameter, device and imaging system |
| CN110866880A (en) * | 2019-11-14 | 2020-03-06 | 上海联影智能医疗科技有限公司 | Image artifact detection method, device, equipment and storage medium |
| CN111031909A (en) * | 2017-08-29 | 2020-04-17 | 通用电气公司 | System and method for correcting artifacts in magnetic resonance data |
| CN111443318A (en) * | 2019-01-16 | 2020-07-24 | 上海联影医疗科技有限公司 | Magnetic resonance image processing method, magnetic resonance image processing device, storage medium and magnetic resonance imaging system |
| CN111542853A (en) * | 2017-10-31 | 2020-08-14 | 皇家飞利浦有限公司 | Motion artifact prediction during data acquisition |
| CN111693914A (en) * | 2020-06-19 | 2020-09-22 | 上海联影医疗科技有限公司 | Magnetic resonance imaging system, non-contact motion monitoring method, and storage medium |
| CN112424835A (en) * | 2020-05-18 | 2021-02-26 | 上海联影医疗科技股份有限公司 | System and method for image reconstruction |
-
2020
- 2020-12-22 CN CN202011528321.0A patent/CN112649773B/en active Active
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4855910A (en) * | 1986-10-22 | 1989-08-08 | North American Philips Corporation | Time-clustered cardio-respiratory encoder and method for clustering cardio-respiratory signals |
| AU5117699A (en) * | 1998-07-21 | 2000-02-14 | Acoustic Sciences Associates | Synthetic structural imaging and volume estimation of biological tissue organs |
| AU5160300A (en) * | 1999-05-21 | 2000-12-12 | Government Of The United States Of America, As Represented By The Secretary Of The Department Of Health And Human Services, The | Determination of an empirical statistical distribution of the diffusion tensor in mri |
| CN101023878A (en) * | 2006-02-17 | 2007-08-29 | 通用电气公司 | Method and device for compensating image data using simultaneously collected motion data |
| CN102077572A (en) * | 2008-06-19 | 2011-05-25 | 松下电器产业株式会社 | Method and apparatus for motion blur and ghosting prevention in imaging system |
| CN101711672A (en) * | 2009-06-23 | 2010-05-26 | 华东师范大学 | Method for acquiring detailed physiological information of testee in magnetic resonance imaging |
| CN102483850A (en) * | 2009-09-08 | 2012-05-30 | 皇家飞利浦电子股份有限公司 | Motion Monitoring System For Monitoring Motion Within A Region Of Interest |
| CN103189896A (en) * | 2010-10-27 | 2013-07-03 | 皇家飞利浦电子股份有限公司 | Image artifact identification and mitigation |
| CN104135931A (en) * | 2012-02-22 | 2014-11-05 | 皇家飞利浦有限公司 | Method and system for reducing localized artifacts in imaging data |
| CN102871660A (en) * | 2012-10-12 | 2013-01-16 | 上海卡勒幅磁共振技术有限公司 | Method for restraining shadow of magnetic resonance imaging respiratory movement |
| CN103315739A (en) * | 2013-05-22 | 2013-09-25 | 华东师范大学 | Magnetic resonance imaging method and system for eliminating motion artifact based on dynamic tracking technology |
| CN103340621A (en) * | 2013-06-04 | 2013-10-09 | 中国科学院苏州生物医学工程技术研究所 | Device and method for removing motion artifacts |
| CN106551703A (en) * | 2015-09-30 | 2017-04-05 | 上海联影医疗科技有限公司 | Computer tomography method and computed tomography imaging system |
| CN108291950A (en) * | 2015-12-03 | 2018-07-17 | 皇家飞利浦有限公司 | The removal of image artifacts in SENSE-MRI |
| CN105938616A (en) * | 2015-12-23 | 2016-09-14 | 上海奕瑞光电子科技有限公司 | Identification and correction method for tremor or tapping artifact in dark-field image template of detector |
| CN111031909A (en) * | 2017-08-29 | 2020-04-17 | 通用电气公司 | System and method for correcting artifacts in magnetic resonance data |
| CN111542853A (en) * | 2017-10-31 | 2020-08-14 | 皇家飞利浦有限公司 | Motion artifact prediction during data acquisition |
| CN110503698A (en) * | 2018-05-16 | 2019-11-26 | 西门子医疗有限公司 | The method for determining image mode and its parameter, device and imaging system |
| CN109658469A (en) * | 2018-12-13 | 2019-04-19 | 深圳先进技术研究院 | A kind of neck joint imaging method and device based on the study of depth priori |
| CN111443318A (en) * | 2019-01-16 | 2020-07-24 | 上海联影医疗科技有限公司 | Magnetic resonance image processing method, magnetic resonance image processing device, storage medium and magnetic resonance imaging system |
| CN110866880A (en) * | 2019-11-14 | 2020-03-06 | 上海联影智能医疗科技有限公司 | Image artifact detection method, device, equipment and storage medium |
| CN112424835A (en) * | 2020-05-18 | 2021-02-26 | 上海联影医疗科技股份有限公司 | System and method for image reconstruction |
| CN111693914A (en) * | 2020-06-19 | 2020-09-22 | 上海联影医疗科技有限公司 | Magnetic resonance imaging system, non-contact motion monitoring method, and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| CHRISTIAN DÁVID: "Motion Compensation for Free-Breathing Diffusion-Weighted Imaging (MoCo DWI) in Whole Body Integrated PET-MRI", 《2018 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE PROCEEDINGS (NSS/MIC)》, 5 September 2019 (2019-09-05) * |
| YANQIU FENG: "Affine motion compensation with improved reconstruction in PROPELLER MRI", 《2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY》, 13 November 2009 (2009-11-13) * |
| 叶慧慧: "快速定量磁共振成像的多层同时成像技术", 《中国博士学位论文全文数据库 信息科技辑》, 31 August 2017 (2017-08-31) * |
| 徐孝秋: "呼吸导航3D BTFE与双反转TSE序列对冠状动脉成像质量的评价", 《临床放射学杂志》, 20 October 2010 (2010-10-20) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114170166A (en) * | 2021-11-26 | 2022-03-11 | 四川大学华西医院 | Magnetic resonance head scanning image quality evaluation method and equipment |
| CN114280517A (en) * | 2021-12-27 | 2022-04-05 | 深圳市联影高端医疗装备创新研究院 | Magnetic resonance imaging method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112649773B (en) | 2023-05-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109961491B (en) | Multi-mode image truncation compensation method, device, computer equipment and medium | |
| CN109567843B (en) | Imaging scanning automatic positioning method, device, equipment and medium | |
| US11250543B2 (en) | Medical imaging using neural networks | |
| US11900594B2 (en) | Methods and systems for displaying a region of interest of a medical image | |
| CN112649773B (en) | Magnetic resonance scanning method, device, equipment and storage medium | |
| CN111462020A (en) | Method, system, storage medium and device for correcting motion artifact of heart image | |
| CN111968110B (en) | CT imaging method, device, storage medium and computer equipment | |
| CN110910465B (en) | Motion artifact correction method and system | |
| US20200096592A1 (en) | Magnetic resonance diffusion tensor imaging method and device, and fiber tracking method and device | |
| CN109671036A (en) | A kind of method for correcting image, device, computer equipment and storage medium | |
| CN112767505A (en) | Image processing method, training method, device, electronic terminal and storage medium | |
| CN113658175A (en) | Method and device for determining symptom data | |
| CN110176047B (en) | Method and system for improving CT image quality | |
| CN111882544A (en) | Medical image display method based on artificial intelligence and related device | |
| CN110717958A (en) | Image reconstruction method, device, equipment and medium | |
| CN113269849A (en) | Method and apparatus for reconstructing magnetic resonance | |
| WO2023232068A1 (en) | Systems and methods for image processing | |
| CN115249279A (en) | Medical image processing method, medical image processing device, computer equipment and storage medium | |
| CN110728732A (en) | Image reconstruction method, device, equipment and medium | |
| CN112950734B (en) | Coronary reconstruction method, device, electronic equipment and storage medium | |
| CN115761134A (en) | Twin model generation method, system, nuclear magnetic resonance device, and medium | |
| CN111968112B (en) | CT three-dimensional positioning image acquisition method and device and computer equipment | |
| CN114202516A (en) | Foreign matter detection method and device, electronic equipment and storage medium | |
| CN109493396B (en) | CT image display method, device, equipment and medium | |
| EP3764118A1 (en) | An apparatus and method for medical image correction |
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 |