CN111612867A - Motion artifact correction method, motion artifact correction device, computer equipment and readable storage medium - Google Patents
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Abstract
The application relates to a motion artifact correction method, a motion artifact correction device, a computer device and a readable storage medium, wherein the method comprises the following steps: acquiring an artifact image to be corrected and a plurality of groups of reconstructed images which are adjacent to the artifact image to be corrected in time phase; carrying out multi-level scale registration on the multiple groups of reconstructed images and the artifact images to be corrected respectively to obtain a motion vector between each reconstructed image and the artifact images to be corrected; and carrying out motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image. According to the method, the reconstructed image and the artifact image to be corrected are registered by adopting different scales to obtain the motion vector, so that different motions can be more accurately estimated, and the accuracy of motion artifact correction is improved.
Description
Technical Field
The present application relates to the field of medical image processing, and in particular, to a method, an apparatus, a computer device, and a readable storage medium for motion artifact correction.
Background
In the scanning process of a certain scanning region of a detected person by using medical imaging equipment, such as Computed Tomography (CT) and Positron Emission Tomography (PET), since imaging requires a certain time, the consistency and integrity of projection data are destroyed by autonomous or non-autonomous movement of a patient during the imaging process, so that various artifacts, called motion artifacts, are shown in a reconstructed image. The occurrence of artifacts can affect the normal diagnosis of a doctor, so that the research of related correction methods has a very important role in further improvement of imaging technology.
At present, by acquiring a motion vector of a reconstructed image in an imaging process, artifact correction is performed on the reconstructed image according to the motion vector. However, because the motion amplitudes of different branches or segments of the coronary artery are different, the motion vector acquired by the traditional method cannot accurately reflect the real motion of the patient in the imaging process, and the problem of poor motion artifact correction effect is caused.
Disclosure of Invention
The application provides a motion artifact correction method, a motion artifact correction device, a computer device and a readable storage medium, which are used for at least solving the problem of low accuracy of motion artifact correction in the related art.
In a first aspect, an embodiment of the present application provides a method for correcting a motion artifact, where the method includes:
acquiring an artifact image to be corrected and a plurality of groups of reconstructed images which are adjacent to the artifact image to be corrected in time phase;
carrying out multi-level scale registration on the multiple groups of reconstructed images and the artifact images to be corrected respectively to obtain a motion vector between each reconstructed image and the artifact images to be corrected;
and carrying out motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image.
In some embodiments, the multi-level scale registration of the plurality of groups of reconstructed images with the artifact image to be corrected respectively includes:
extracting the coronary artery central lines of a plurality of groups of the reconstructed images as target central lines;
extracting a coronary artery central line of the artifact image to be corrected as a reference central line;
and respectively carrying out multi-level scale registration on the multiple groups of target center lines and the reference center line.
In some embodiments, the multi-level scale registration of the target centerlines with the reference centerlines respectively comprises:
respectively registering the multiple groups of target center lines with the reference center line by adopting a first-scale registration algorithm;
adopting a registration algorithm of a second scale to register the registration result; the second dimension is less than the first dimension.
In some embodiments, the multi-level scale registration of the plurality of groups of reconstructed images with the artifact image to be corrected respectively includes:
acquiring a coronary artery region of an artifact image to be corrected;
dividing the coronary region into a plurality of sub-regions;
acquiring prior motion trends of a plurality of sub-regions;
determining a corresponding scale adopted by registration of each sub-region according to the prior motion trend;
and aiming at each group of reconstructed images, respectively carrying out multi-level scale registration on the groups of reconstructed images and the artifact images to be corrected according to the determined scale.
In some embodiments, the acquiring a plurality of groups of reconstructed images temporally adjacent to the artifact image to be corrected includes:
acquiring a time phase of the artifact image to be corrected;
acquiring projection data of a plurality of time phases adjacent to the time phases according to the time phases;
and reconstructing the projection data of the multiple time phases to obtain multiple groups of reconstructed images adjacent to the time phases of the artifact images to be corrected.
In some embodiments, the motion artifact correction of the artifact to be corrected according to the motion vector comprises:
dividing projection data corresponding to the artifact image to be corrected into a plurality of segments of sub-projection data according to a preset angle;
reconstructing each sub-projection data to obtain a plurality of groups of sub-images;
correcting the multiple groups of sub-images according to the multiple motion vectors to obtain multiple groups of corrected sub-images;
and carrying out fusion processing on the plurality of groups of correction sub-images to obtain the artifact-removed image.
In some embodiments, the correcting the plurality of groups of sub-images according to the plurality of motion vectors to obtain a plurality of syndrome images includes:
acquiring a central time phase of each sub-image;
acquiring a first time phase of a reconstructed image corresponding to each motion vector;
and selecting a corresponding motion vector to correct the sub-image according to the corresponding relation between the central time phase and the first time phase.
In a second aspect, an embodiment of the present application provides a motion artifact correction apparatus, including:
the device comprises an acquisition module, a correction module and a correction module, wherein the acquisition module is used for acquiring an artifact image to be corrected and a plurality of groups of reconstructed images which are adjacent to the artifact image to be corrected in time phase;
the registration module is used for respectively carrying out multi-level scale registration on the multiple groups of reconstructed images and the artifact images to be corrected to obtain a motion vector between each reconstructed image and the artifact images to be corrected;
and the correction module is used for carrying out motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the motion artifact correction method according to the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the motion artifact correction method as described in the first aspect above.
Compared with the related art, the motion artifact correction method provided by the embodiment of the application comprises the steps of obtaining an artifact image to be corrected and a plurality of groups of reconstructed images which are adjacent to each other in time phase; carrying out multi-level scale registration on the multiple groups of reconstructed images and the artifact images to be corrected respectively to obtain a motion vector between each reconstructed image and the artifact images to be corrected; and performing motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image, so that the accuracy of motion artifact correction is improved.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of a motion artifact correction method according to an embodiment;
fig. 2 is a schematic diagram of an artifact image to be corrected and a plurality of groups of reconstructed image maps adjacent to each other when the artifact image to be corrected is provided in an embodiment;
FIG. 3a is a schematic view of a centerline of a coronary artery according to an exemplary embodiment;
FIG. 3b is a diagram of smaller scale registration provided by an embodiment;
FIG. 3c is a schematic illustration of a larger scale registration provided by an embodiment;
FIG. 3d is a schematic illustration of an intermediate scale registration provided by an embodiment;
fig. 4a to 4c are schematic diagrams of sub-images in an artifact image to be corrected according to an embodiment;
FIGS. 5 a-5 c are schematic diagrams of motion vectors according to an embodiment;
FIG. 6a is a schematic diagram of an artifact image to be corrected according to an embodiment;
FIG. 6b is a schematic diagram of an artifact-corrected image of FIG. 6a according to an embodiment;
FIG. 7 is a block diagram of a motion artifact correction apparatus in an embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The present embodiment is preferably applied to a medical imaging system, such as CT, DR, MRI, PET-CT, PET-MRI or any multi-modality combination thereof, and the present application is described with reference to CT as an example, and the method can be implemented by software or hardware configured in a smart device, such as a control computer, a personal computer, a doctor workstation, a cloud server, and the like connected to a clinical imaging device.
Fig. 1 is a flowchart of a motion artifact correction method according to an embodiment, as shown in fig. 1, the motion artifact correction method includes steps 110 to 130, where:
The artifact image to be corrected and the plurality of groups of reconstructed images may be three-dimensional images obtained by imaging projection data acquired by a medical imaging system. The projection data described in this embodiment may be MRI data acquired by an MRI apparatus, CT data acquired by an electronic computed tomography CT apparatus, or PET imaging data acquired by a positron emission tomography PET apparatus, and may also be imaging data acquired by a multi-modality imaging apparatus PET-MR, PET-CT, or the like.
The specific process of acquiring the artifact image to be corrected is as follows: projection data of a region of interest is acquired, wherein the projection data may correspond to a plurality of phases. And reconstructing projection data corresponding to one of the phases to obtain an image serving as an artifact image to be corrected. The artifact image to be corrected corresponding to one phase needs to be reconstructed by using projection data of multiple angles, for example, the artifact image to be corrected corresponding to one phase needs to be reconstructed by using projection data of 240 degrees. In the present application, the region of interest is a coronary region.
In order to better reflect the time series characteristics of an organ or tissue in one physiological cycle, the physiological cycle is generally divided into a plurality of time phases, and the state of the organ or tissue at a certain time in one physiological cycle is represented by the time phases. Taking lung tissue as an example, each physiological cycle of the lung tissue generally includes a plurality of phases, i.e., an inspiration initial phase, an inspiration end phase, a breath holding phase, an expiration initial phase, and an expiration end phase, and each motion state corresponds to one phase. As another example, each physiological cycle of the heart typically includes eight phases, including isovolumetric contraction, rapid ejection, slow ejection, pre-diastole, isovolumetric relaxation, rapid filling, slow filling, and atrial contraction, as exemplified by the heart's organs.
In one embodiment, one of the plurality of time phases is used as a reference time phase, and an image obtained by reconstructing projection data corresponding to the reference time phase is used as an artifact image to be corrected. In order to improve the quality of the reconstructed image, a time phase in which the variation amplitude of the target organ or tissue is within a preset threshold range is used as a reference time phase, and since the difference between the state of the organ or tissue of the time phase and the state of the adjacent time phase is small, the influence of the motion of the organ or tissue on the quality of the projection data is small, which is beneficial to acquiring high-quality projection data of the reference time phase.
The artifact in the artifact image to be corrected is caused by the movement of the region of interest during imaging, which destroys the consistency and integrity of the projection data. Therefore, in order to remove the artifact of the artifact image to be corrected, it is necessary to know the motion vector of the phase in the region of interest, and perform artifact correction on the artifact image to be corrected according to the motion vector. In order to obtain a motion vector during imaging, a plurality of groups of reconstructed images adjacent to each other in time phase of an artifact image to be corrected need to be obtained, and more comprehensive physiological cycle motion information of an organ or tissue can be obtained through the plurality of groups of reconstructed images adjacent to each other in time phase, so that an accurate motion vector is obtained. The adjacent groups of reconstructed images comprise reconstructed images of the artifact image to be corrected relative to a previous adjacent time phase and reconstructed images of the artifact image to be corrected relative to a next adjacent time phase.
It is to be understood that the adjacent sets of reconstructed images may further include reconstructed images of a plurality of time phases before the reference time phase and reconstructed images of a plurality of time phases after the reference time phase, and the present embodiment is described by taking as an example that the adjacent sets of reconstructed images include a set of reconstructed images adjacent before the reference time phase and a set of reconstructed images adjacent after the reference time phase, so as to obtain a first reconstructed image of a t1 period (corresponding to the first time phase), an artifact image to be corrected of a t2 period (corresponding to the reference time phase), and a second reconstructed image of a t3 period (corresponding to the second time phase). As shown in fig. 2, the first reconstructed image, the artifact image to be corrected, and the second reconstructed image are sequentially arranged from left to right.
It will be appreciated that, since a set of projection data corresponds to a state of the region of interest within the motion cycle, a reconstructed image from sets of projection data corresponding to phases adjacent to the phase of the artifact image to be corrected may better represent a trend of motion of the region of interest.
And 120, respectively carrying out multi-level scale registration on the multiple groups of reconstructed images and the artifact images to be corrected to obtain a motion vector between each reconstructed image and the artifact images to be corrected.
Since the motion amplitudes of different branches and segments of the coronary artery are different, the motion vectors obtained by using the rigid registration method cannot accurately estimate different motions. In the embodiment, a plurality of groups of reconstructed images are respectively subjected to multi-level scale registration with an artifact image to be corrected, a large scale is firstly adopted for rough registration, a small scale is then adopted for fine registration, and registration is carried out through multi-level scales, so that the real motion state of a coronary artery region can be more accurately simulated, and a more accurate motion vector can be obtained.
In some embodiments, the multi-level scale registration of the plurality of groups of reconstructed images with the artifact images to be corrected respectively comprises: and extracting the coronary artery central lines of the multiple groups of reconstructed images as target central lines. As shown in fig. 3 a; extracting a coronary artery central line of an artifact image to be corrected as a reference central line; and respectively carrying out multi-level scale registration on the multiple groups of target center lines and the reference center line. When representing the motion of the coronary artery between two time phases by using the point on the centerline of the coronary artery as a reference, if the motion between two sets of centerlines is to be accurately approximated, the influence range is not a small range (as shown in fig. 3 b), and when the simulation range is large, as shown in fig. 3c, the approximation to the motion is affected, and the grid point is often not completely coincident with the spatial position of the data point. Fig. 3d shows a scale between that of fig. 3b and that of fig. 3c, with a range of action between them.
Assuming that the region of interest is the heart, for the coronary region, the target centerline may be extracted from a plurality of reconstructed images obtained based on different sets of projection data, respectively. And then, performing multi-level scale registration on the target central line extracted from the reconstructed image and the reference central line extracted from the artifact image to be corrected, and determining the motion vector of the coronary artery region based on the change of the coordinate of the reference central line relative to the coordinate of the target central line. The motion vector includes the magnitude of the motion (for representing the intensity of the motion) and the direction (for representing the direction of the motion). From the different motion vectors which may correspond to different branches and segments of the coronary region, a plurality of different motion vectors may be combined into a motion vector field.
Therefore, the embodiment performs registration under different scales to realize accurate approximation of motion, so as to obtain more accurate motion vectors and improve the motion artifact correction effect.
In some of these embodiments, the multi-level scale registration of the sets of target centerlines with the reference centerlines respectively comprises: respectively registering a plurality of groups of target center lines with a reference center line by adopting a first-scale registration algorithm; adopting a registration algorithm of a second scale to register the registration result; the second dimension is smaller than the first dimension. The size of the scale represents the range influenced by the central line, and the embodiment firstly adopts the large scale to carry out registration to simulate the approximate state of the motion, then adjusts the size of the scale on the basis of the registration result to gradually approach the motion between the central lines, thereby accurately simulating the motion state of each branch in the coronal region during imaging.
It should be noted that, in this embodiment, registration is performed only in two dimensions, namely, the first dimension and the second dimension, and in other embodiments, registration may be performed in more dimensions, such as 3 dimensions and 4 dimensions.
Registering the images of the first reconstruction image and the artifact image to be corrected in two time phases by the multi-level scale registration method to obtain a first motion vector of the tissue organ when the time t2 is compared with the time t 1; registering the second reconstructed image and the image of the artifact image to be corrected in two time phases to obtain a motion vector of the tissue organ at the time t2 compared with the time t 3; by analogy, a plurality of motion vectors at each time instant before the previous time instant can be obtained.
And step 130, performing motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image.
And applying the motion vector with the motion information to the artifact image to be corrected, and performing motion artifact correction on the artifact image to be corrected so as to obtain a de-artifact image. Specifically, for the coordinates and corresponding motion vectors of each pixel point in the artifact image to be corrected, the coordinates of each pixel point are subjected to size compensation and direction compensation, so that artifact removal processing can be performed on the artifact image to be corrected.
Compared with the prior art, the motion artifact correction method provided by the embodiment can be used for registering the reconstructed image and the artifact image to be corrected by adopting different scale levels to obtain the motion vector, so that different motions can be more accurately estimated, and the accuracy of motion artifact correction is improved.
In some embodiments, the multi-level scale registration of the plurality of groups of reconstructed images with the artifact image to be corrected respectively comprises;
acquiring a coronary artery region of an artifact image to be corrected;
dividing a coronary region into a plurality of sub-regions;
acquiring prior motion trends of a plurality of sub-regions;
determining a corresponding scale adopted by registration of each sub-region according to the prior motion trend;
and aiming at each group of reconstructed images, respectively carrying out multi-level scale registration on the groups of reconstructed images and the artifact images to be corrected according to the determined scale.
The amplitude of motion of different branches or segments in the coronary region is different, but the motion trends of the same segment or branch can be considered to be approximately the same. In addition, since the motion state of each person is related to a plurality of factors such as heart rate and pathology, the prior motion state is difficult to obtain. But for anatomical segmentation of the coronary region, the corresponding motion trends should be consistent or not very different. The coronary artery region is anatomically segmented, and then the obtained different segmented motion trends are used as prior knowledge for a subsequent registration process. Different anatomical segments of the coronary artery region can be understood as a sub-region of the coronary artery region, a proper registration scale is determined according to the prior motion trend corresponding to the sub-region, namely, the range of motion vector fields in different sub-regions needing to be acted is determined, and then the sub-regions are registered by adopting the determined registration scale to obtain motion vectors. The multi-level scale registration in this embodiment may be understood as registration using different scales for different segments of the coronary.
In the embodiment, the registration scale is determined by adopting the priori knowledge, and multi-level scale registration is performed according to the determined scale, so that the registration efficiency is improved.
In some embodiments, performing motion artifact correction on the artifact to be corrected according to the motion vector comprises:
dividing projection data corresponding to the artifact image to be corrected into a plurality of segments of sub-projection data according to a preset angle;
reconstructing each sub-projection data to obtain a plurality of groups of sub-images;
correcting the multiple groups of sub-images according to the multiple motion vectors to obtain multiple groups of corrected sub-images;
and carrying out fusion processing on the plurality of groups of correction sub-images to obtain the artifact-removed image.
Specifically, according to a preset angle, the projection data used for reconstructing the artifact image to be corrected is divided into a plurality of sub-projection data, for example, if the projection data of M degrees is needed for reconstructing the image in one time phase, the projection data of M degrees can be divided into N segments, each segment includes the projection data of M/N degrees, and then the sub-projection data of each segment is reconstructed to obtain a corresponding sub-image. And correcting the sub-images reconstructed by each section of projection data by using the corresponding motion vectors to obtain a plurality of groups of syndrome images, and finally fusing the plurality of groups of correction sub-images to obtain the artifact-removed images corresponding to the artifact images to be corrected.
It should be noted that each group of sub-images includes a plurality of images, for example, each group of sub-images includes 280 images. Fig. 4a, 4b and 4c each show 3 images of one of the partial images of the artifact image to be corrected. The image shown in fig. 4b can be understood as an image corresponding to the central phase, and since the time of the front and back projection data acquisition hardly has too much time difference from the central position, when the segmentation is enough, there is almost no motion artifact, and therefore, it is considered that artifact correction is not needed for this segmentation. And each reconstructed image and the artifact image to be corrected correspond to different motion vectors, and the sub-images are subjected to alignment correction processing through a plurality of motion vectors.
It should be noted that, since the filtered back-projection reconstruction algorithm is a process that can be linearly superimposed, if N is equal to 3, adding fig. 4a, 4b and 4c can obtain a complete heart image corresponding to one of the phases in fig. 2.
In some embodiments, correcting the plurality of sub-images according to the plurality of motion vectors to obtain a plurality of syndrome images comprises: acquiring a central time phase of each sub-image; acquiring a first time phase of a reconstructed image corresponding to each motion vector; and selecting a corresponding motion vector to correct the sub-image according to the corresponding relation between the central time phase and the first time phase. Preferably, the motion vector corresponding to the reconstructed image most adjacent to the center phase of each group of sub-images may be selected to correct each sub-image. The motion vector shown in fig. 5a is used to perform artifact removal processing on the image shown in fig. 4a to obtain a first correction sub-image, and the motion vector shown in fig. 5c is used to perform artifact removal processing on the image shown in fig. 4c to obtain a second correction sub-image. And carrying out fusion processing on the first correction sub-image, the second correction sub-image and the image corresponding to the central time phase to obtain an artifact-removed image. The fusion process may be understood as adding to each of the images other than the image corresponding to the center time.
Fig. 6a is a schematic diagram of an artifact image to be corrected, and fig. 6b is a schematic diagram of a de-artifact image obtained by correcting the artifact image to be corrected by the motion artifact correction method provided by the present application. As can be seen from fig. 6a, in order to be corrected for the artifacts, a part of the coronary region, for example, three regions in fig. 6a, appears blurred. As can be seen from fig. 6b, after the correction is performed on fig. 6a by the above scheme, the three regions in fig. 6a are displayed more clearly, so that the effectiveness of the motion artifact correction method provided by the present application can be proved.
In the embodiment, the artifact image to be corrected is divided into the group image according to the preset angle; selecting a motion vector corresponding to a reconstructed image which is most adjacent to the central time phase of each group of sub-images to correct each sub-image; the sub-images at each angle can be corrected specifically, so that the artifact correction effect can be further improved.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
In one embodiment, as shown in fig. 7, there is provided a motion artifact correction apparatus, including: an acquisition module 710, a registration module 720, and a correction module 730, wherein:
an obtaining module 710, configured to obtain an artifact image to be corrected and multiple groups of reconstructed images that are adjacent to the artifact image to be corrected in time phase;
a registration module 720, configured to perform multi-level scale registration on the multiple groups of reconstructed images and the artifact images to be corrected, respectively, to obtain a motion vector between each reconstructed image and the artifact image to be corrected;
and the correcting module 730 is configured to perform motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image.
The motion artifact correction apparatus provided in this embodiment includes an obtaining module 710, a registration module 720, and a correction module 730, where the obtaining module 710 obtains an artifact image to be corrected and a plurality of groups of reconstructed images that are adjacent to each other in time phase of the artifact image to be corrected; the registration module 720 performs multi-level scale registration on the multiple groups of reconstructed images and the artifact images to be corrected respectively to obtain a motion vector between each reconstructed image and the artifact image to be corrected; the correction module 730 performs motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image, and the reconstructed image and the artifact image to be corrected are registered by adopting different hierarchical scales to obtain the motion vector by considering that the motion amplitudes of the coronary artery in different divisions or sections are different, so that different motions can be estimated more accurately, and the accuracy of the motion artifact correction is improved.
In some embodiments, the registration module 720 is further configured to extract the coronary artery centerlines of the plurality of sets of reconstructed images as the target centerline; extracting a coronary artery central line of an artifact image to be corrected as a reference central line; and respectively carrying out multi-level scale registration on the multiple groups of target center lines and the reference center line.
In some embodiments, the registration module 720 is further configured to register the sets of target centerlines with the reference centerlines respectively using a first scale registration algorithm; adopting a registration algorithm of a second scale to register the result of the coarse registration; the second dimension is smaller than the first dimension.
In some embodiments, the registration module is further configured to acquire a coronary region of the artifact image to be corrected; dividing the coronary region into a plurality of sub-regions; acquiring prior motion trends of a plurality of sub-regions; determining a corresponding scale adopted by registration of each sub-region according to the prior motion trend; and aiming at each group of reconstructed images, respectively carrying out multi-level scale registration on the groups of reconstructed images and the artifact images to be corrected according to the determined scale.
In some embodiments, the correction module 730 is further configured to divide the projection data corresponding to the artifact image to be corrected into multiple segments of sub-projection data according to a preset angle; reconstructing each sub-projection data to obtain a plurality of groups of sub-images; correcting the multiple groups of sub-images according to the multiple motion vectors to obtain multiple groups of corrected sub-images; and carrying out fusion processing on the plurality of groups of correction sub-images to obtain the artifact-removed image.
In some embodiments, the correction module 730 is further configured to obtain a central phase of each of the sub-images; acquiring a first time phase of a reconstructed image corresponding to each motion vector; and selecting a corresponding motion vector to correct the sub-image according to the corresponding relation between the central time phase and the first time phase.
For the specific definition of the motion artifact correction means, reference may be made to the above definition of the motion artifact correction method, which is not described herein again. The various modules in the motion artifact correction apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In addition, the motion artifact correction method described in conjunction with fig. 1 in the embodiment of the present application may be implemented by a computer device. Fig. 8 is a hardware structure diagram of a computer device according to an embodiment of the present application.
The computer device may comprise a processor 81 and a memory 82 in which computer program instructions are stored.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possibly computer program instructions for execution by the processor 82.
The processor 81 implements any of the motion artifact correction methods in the above embodiments by reading and executing computer program instructions stored in the memory 82.
In some of these embodiments, the computer device may also include a communication interface 83 and a bus 80. As shown in fig. 8, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The computer device may execute the motion artifact correction method in the embodiment of the present application based on the obtained program instruction, thereby implementing the motion artifact correction method described in conjunction with fig. 1.
In addition, in combination with the motion artifact correction method in the foregoing embodiments, the embodiments of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the motion artifact correction methods in the above embodiments.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of motion artifact correction, the method comprising:
acquiring an artifact image to be corrected and a plurality of groups of reconstructed images which are adjacent to the artifact image to be corrected in time phase;
carrying out multi-scale registration on the multiple groups of reconstructed images and the artifact images to be corrected respectively to obtain a motion vector between each reconstructed image and the artifact images to be corrected;
and carrying out motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image.
2. The method of claim 1, wherein the multi-level scale registration of the plurality of sets of reconstructed images with the artifact-to-be-corrected images respectively comprises:
extracting the coronary artery central lines of a plurality of groups of the reconstructed images as target central lines;
extracting a coronary artery central line of the artifact image to be corrected as a reference central line;
and respectively carrying out multi-level scale registration on the multiple groups of target center lines and the reference center line.
3. The method of claim 2, wherein the multi-level scale registering the sets of target centerlines with the reference centerlines respectively comprises:
respectively registering the multiple groups of target center lines with the reference center line by adopting a first-scale registration algorithm;
adopting a registration algorithm of a second scale to register the registration result; the second dimension is less than the first dimension.
4. The method of claim 1, wherein the multi-level scale registration of the plurality of sets of reconstructed images with the artifact-to-be-corrected images respectively comprises:
acquiring a coronary artery region of an artifact image to be corrected;
dividing the coronary region into a plurality of sub-regions;
acquiring prior motion of a plurality of the sub-regions;
determining a corresponding scale adopted by registration of each sub-region according to the prior motion trend;
and aiming at each group of reconstructed images, respectively carrying out multi-level scale registration on the groups of reconstructed images and the artifact images to be corrected according to the determined scale.
5. The method of claim 1, wherein the acquiring sets of reconstructed images temporally adjacent to the artifact image to be corrected comprises:
acquiring a time phase of the artifact image to be corrected;
acquiring projection data of a plurality of time phases adjacent to the time phases according to the time phases;
and reconstructing the projection data of the multiple time phases to obtain multiple groups of reconstructed images adjacent to the time phases of the artifact images to be corrected.
6. The method of claim 1, wherein the motion artifact correction of the artifact to be corrected according to the motion vector comprises:
dividing projection data corresponding to the artifact image to be corrected into a plurality of segments of sub-projection data according to a preset angle;
reconstructing each sub-projection data to obtain a plurality of groups of sub-images;
correcting the multiple groups of sub-images according to the multiple motion vectors to obtain multiple groups of corrected sub-images;
and carrying out fusion processing on the plurality of groups of correction sub-images to obtain the artifact-removed image.
7. The method of claim 6, wherein said correcting the plurality of sub-images according to the plurality of motion vectors to obtain a plurality of syndrome images comprises:
acquiring a central time phase of each sub-image;
acquiring a first time phase of a reconstructed image corresponding to each motion vector;
and selecting a corresponding motion vector to correct the sub-image according to the corresponding relation between the central time phase and the first time phase.
8. A motion artifact correction apparatus, said apparatus comprising:
the device comprises an acquisition module, a correction module and a correction module, wherein the acquisition module is used for acquiring an artifact image to be corrected and a plurality of groups of reconstructed images which are adjacent to the artifact image to be corrected in time phase;
the registration module is used for respectively carrying out multi-level scale registration on the multiple groups of reconstructed images and the artifact images to be corrected to obtain a motion vector between each reconstructed image and the artifact images to be corrected;
and the correction module is used for carrying out motion artifact correction on the artifact image to be corrected according to the motion vector to obtain an artifact-removed image.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. 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 any one of claims 1 to 7.
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| CN202010482915.6A CN111612867B (en) | 2020-06-01 | 2020-06-01 | Motion artifact correction method, motion artifact correction device, computer equipment and readable storage medium |
| PCT/CN2021/078556 WO2021170147A1 (en) | 2020-02-28 | 2021-03-01 | Systems and methods for correcting motion artifacts in images |
| EP21759961.2A EP4111418A4 (en) | 2020-02-28 | 2021-03-01 | Systems and methods for correcting motion artifacts in images |
| US17/823,062 US20230190216A1 (en) | 2020-02-28 | 2022-08-29 | Systems and methods for correcting motion artifacts in images |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021170147A1 (en) * | 2020-02-28 | 2021-09-02 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for correcting motion artifacts in images |
| CN117953095A (en) * | 2024-03-25 | 2024-04-30 | 有方（合肥）医疗科技有限公司 | CT data processing method, electronic equipment and readable storage medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140056497A1 (en) * | 2012-08-23 | 2014-02-27 | General Electric Company | System and method for correcting for metal artifacts using multi-energy computed tomography |
| US20140212011A1 (en) * | 2013-01-28 | 2014-07-31 | Koninklijke Philips Electronics N.V. | Spect motion-correction |
| CN104318536A (en) * | 2014-10-21 | 2015-01-28 | 沈阳东软医疗系统有限公司 | Method and device for CT image correction |
| CN106056645A (en) * | 2016-05-25 | 2016-10-26 | 天津商业大学 | CT image translational motion artifact correction method based on frequency domain analysis |
| US20170365047A1 (en) * | 2016-06-15 | 2017-12-21 | General Electric Company | Artifact management in imaging |
| US20180260981A1 (en) * | 2017-03-07 | 2018-09-13 | Children's Medical Center Corporation | Registration-based motion tracking for motion-robust imaging |
| CN109754448A (en) * | 2018-12-29 | 2019-05-14 | 深圳安科高技术股份有限公司 | A kind of CT heart scanning artifact correction method and its system |
| KR20190103816A (en) * | 2018-02-28 | 2019-09-05 | 삼성전자주식회사 | Method and apparatus for correcting the computed tomographic image |
| CN110378982A (en) * | 2019-07-23 | 2019-10-25 | 上海联影医疗科技有限公司 | Reconstruction image processing method, device, equipment and storage medium |
| CN110473269A (en) * | 2019-08-08 | 2019-11-19 | 上海联影医疗科技有限公司 | A kind of image rebuilding method, system, equipment and storage medium |
| CN110910465A (en) * | 2019-11-21 | 2020-03-24 | 上海联影医疗科技有限公司 | Motion artifact correction method and system |
| CN110940943A (en) * | 2019-12-06 | 2020-03-31 | 上海联影医疗科技有限公司 | Training method of pulsation artifact correction model and pulsation artifact correction method |
-
2020
- 2020-06-01 CN CN202010482915.6A patent/CN111612867B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140056497A1 (en) * | 2012-08-23 | 2014-02-27 | General Electric Company | System and method for correcting for metal artifacts using multi-energy computed tomography |
| US20140212011A1 (en) * | 2013-01-28 | 2014-07-31 | Koninklijke Philips Electronics N.V. | Spect motion-correction |
| CN104318536A (en) * | 2014-10-21 | 2015-01-28 | 沈阳东软医疗系统有限公司 | Method and device for CT image correction |
| CN106056645A (en) * | 2016-05-25 | 2016-10-26 | 天津商业大学 | CT image translational motion artifact correction method based on frequency domain analysis |
| US20170365047A1 (en) * | 2016-06-15 | 2017-12-21 | General Electric Company | Artifact management in imaging |
| US20180260981A1 (en) * | 2017-03-07 | 2018-09-13 | Children's Medical Center Corporation | Registration-based motion tracking for motion-robust imaging |
| KR20190103816A (en) * | 2018-02-28 | 2019-09-05 | 삼성전자주식회사 | Method and apparatus for correcting the computed tomographic image |
| CN109754448A (en) * | 2018-12-29 | 2019-05-14 | 深圳安科高技术股份有限公司 | A kind of CT heart scanning artifact correction method and its system |
| CN110378982A (en) * | 2019-07-23 | 2019-10-25 | 上海联影医疗科技有限公司 | Reconstruction image processing method, device, equipment and storage medium |
| CN110473269A (en) * | 2019-08-08 | 2019-11-19 | 上海联影医疗科技有限公司 | A kind of image rebuilding method, system, equipment and storage medium |
| CN110910465A (en) * | 2019-11-21 | 2020-03-24 | 上海联影医疗科技有限公司 | Motion artifact correction method and system |
| CN110940943A (en) * | 2019-12-06 | 2020-03-31 | 上海联影医疗科技有限公司 | Training method of pulsation artifact correction model and pulsation artifact correction method |
Non-Patent Citations (1)
| Title |
|---|
| 黄敏;覃兴婕;李清园;: "MRI运动伪影校正方法与实现", no. 04 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021170147A1 (en) * | 2020-02-28 | 2021-09-02 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for correcting motion artifacts in images |
| CN117953095A (en) * | 2024-03-25 | 2024-04-30 | 有方（合肥）医疗科技有限公司 | CT data processing method, electronic equipment and readable storage medium |
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|---|---|
| CN111612867B (en) | 2023-08-15 |
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