A method and system for simulating motion artifacts

Abstract

An embodiment of the present application discloses a method for simulating motion artifacts. The method may include determining a segmented motion vector field of a target object based on an artifact simulation model. The method may include determining a segmented reconstructed image by segmentally reconstructing a target image, and the method may further include generating a motion artifact simulation image based on the segmented motion vector field and the segmented reconstructed image.

Description

Technical Field

[0001] This specification relates to the field of image processing, and particularly to a method and system for simulating motion artifacts. Background Art

[0002] Artifacts refer to images that appear during the imaging process or information processing process due to certain reasons and cause a decrease in image quality that do not exist in the scanned object itself. Motion artifacts refer to artifacts caused by the movement of the scanned object. Taking the scanned object as the heart as an example, cardiac motion artifacts will affect the observation of the normal condition of the heart. Therefore, reducing motion artifacts is becoming increasingly important in the image processing process. The usual solution is to correct the image containing motion artifacts by using a motion artifact removal model. However, the training of the motion artifact removal model requires a large number of images including motion artifacts.

[0003] Therefore, it is desirable to provide a method and system for simulating motion artifacts to obtain a large number of images including motion artifacts through simulation. Summary of the Invention

[0004] One embodiment of the present application provides a method for simulating motion artifacts. The method may include determining a segmented motion vector field of a target object based on an artifact simulation model. The method may include determining a segmented reconstructed image by segmentally reconstructing the target image, and the method may further include generating a motion artifact simulation image based on the segmented motion vector field and the segmented reconstructed image.

[0005] In some embodiments, determining a segmented motion vector field of a target object based on an artifact simulation model may include extracting the centerline of a tubular structure in the target image, and determining the segmented motion vector field based on the centerline and the artifact simulation model.

[0006] In some embodiments, the target image may be an image with a quality score higher than a preset threshold.

[0007] In some embodiments, determining a segmented motion vector field of a target object based on an artifact simulation model may include dividing a preset time period into multiple sub-time periods, and determining the segmented motion vector field for each sub-time period based on the artifact simulation model.

[0008] In some embodiments, the artifact simulation model may include a motion function or a machine learning model.

[0009] In some embodiments, determining a segmented reconstructed image by segmentally reconstructing the target image may include obtaining projection data of the target image centered on the phase of the target image, and performing segmented reconstruction on the projection data to determine the segmented reconstructed image.

[0010] In some embodiments, generating a motion artifact simulation image based on a segmented motion vector field and a segmented reconstructed image may include generating a segmented motion compensation image based on the segmented motion vector field and the segmented reconstructed image, and determining the motion artifact simulation image by superimposing the segmented motion compensation images.

[0011] In some embodiments, generating a segmented motion compensation image based on a segmented motion vector field and a segmented reconstructed image may include generating a segmented motion compensation image based on the segmented motion vector field, the segmented reconstructed image, and weights related to projection data corresponding to the segmented reconstructed image.

[0012] In some embodiments, the method may further include training a motion artifact removal model based on the motion artifact simulation image.

[0013] One embodiment of the present application provides a motion artifact simulation system. The system may include a determination module, a reconstruction module, and a generation module. The determination module may be configured to determine a segmented motion vector field of a target object based on an artifact simulation model. The reconstruction module may be configured to determine a segmented reconstructed image by segmentally reconstructing a target image. The generation module may be configured to generate a motion artifact simulation image based on the segmented motion vector field and the segmented reconstructed image. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] This specification will be further described by way of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0015] Figure 1 is an exemplary application scenario diagram of a motion artifact simulation system shown in some embodiments of this specification;

[0016] Figure 2 is an exemplary module diagram of a motion artifact simulation system shown in some embodiments of this specification;

[0017] Figure 3 is an exemplary flowchart of a motion artifact simulation method shown in some embodiments of this specification;

[0018] Figure 4 is a schematic diagram of an exemplary weight curve shown in some embodiments of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To more clearly illustrate the technical solutions of the embodiments of this specification, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0020] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0021] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0022] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in order. On the contrary, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0023] The term "image" in this specification is used to refer to image data (e.g., scan data, projection data) and / or various forms of images, including two-dimensional (2D) images, three-dimensional (3D) images, four-dimensional (4D) images, etc. The terms "pixel" and "voxel" in this specification can be used interchangeably to refer to the elements of an image. The term "anatomical structure" in this specification can refer to gas (e.g., air), liquid (e.g., water), solid (e.g., stone), cells, tissues, organs of the imaging object, etc., or any combination thereof, which can be displayed in the image and actually exist in or on the body of the imaging object. The terms "range", "position" and "region" in this specification can refer to the position of the anatomical structure shown in the image or the actual position of the anatomical structure existing in or on the body of the imaging object, because the image can indicate the actual position of a certain anatomical structure existing in or on the body of the imaging object.

[0024] Embodiments of this specification relate to a motion artifact simulation method and system. The motion artifact simulation method and system can be applied to motion artifact simulation in medical imaging (such as CT, X-ray machine, MRI, PET, etc.). For example, the motion artifact simulation method and system can be applied to cardiac motion artifact simulation, respiratory motion artifact simulation, head motion artifact simulation, etc., and can simulate the artifacts generated by medical imaging devices when photographing organs and / or tissues such as the heart, lungs, abdomen, etc. It should be understood that the application scenarios of the system and method in this specification are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Although this specification mainly describes the examples of the heart and lungs, it should be noted that the principles of this specification can also be applied to more biological organs or tissues, such as bones, blood vessels, nerves, spleen, stomach, etc.

[0025] Figure 1 is an exemplary application scenario diagram of a motion artifact simulation system according to some embodiments of this specification. The motion artifact simulation system 100 can simulate motion artifacts by implementing the methods and / or processes disclosed in this specification to generate motion artifact simulation images (i.e., images containing motion artifacts). As Figure 1 shown, the motion artifact simulation system 100 can include: a scanning device 110, a processing device 120, a terminal device 130, a network 140, and a storage device 150.

[0026] In some embodiments, two or more components of the motion artifact simulation system 100 can be connected to and / or communicate with each other through a wireless connection (e.g., the network 140), a wired connection, or any combination thereof. The connections between the components of the motion artifact simulation system 100 can be variable. Only as an example, as Figure 1 shown, the scanning device 110 can be connected to the processing device 120 through the network 140 and / or directly (as shown by the dashed two-way arrow connecting the scanning device 110 and the processing device 120). For another example, the storage device 150 can be directly and / or connected to the processing device 120 through the network 140. For yet another example, the terminal device 130 can be directly (as shown by the dashed two-way arrow connecting the terminal device 130 and the processing device 120) and / or connected to the processing device 120 through the network 140.

[0027] The scanning device 110 can acquire scanning data. The scanning device 110 can scan a target object or a part thereof and generate scanning data (e.g., an image) related to the target object or the part thereof. In some embodiments, the scanning device 110 can include a single-modal imaging device. For example, the scanning device 110 can include digital subtraction angiography (DSA), positron emission tomography (PET) device, single photon emission computed tomography (SPECT) device, magnetic resonance imaging (MRI) device (also referred to as an MR scanner), computed tomography (CT) device, ultrasonic scanner, digital radiography (DR) scanner, etc., or any combination thereof. In some embodiments, the scanning device 110 can include a multi-modal imaging device. Exemplary multi-modal imaging devices can include PET-CT devices, PET-MR devices, etc., or combinations thereof. For illustrative purposes, this specification is described in conjunction with a CT device.

[0028] The processing device 120 can process data and / or information obtained from the scanning device 110, the terminal device 130, and / or the storage device 150. For example, the processing device 120 can acquire a target image generated by the scanning device 110 (e.g., an image that does not include motion artifacts or includes motion artifacts below a preset threshold). The processing device 120 can determine a segmented motion vector field of the target object based on an artifact simulation model and determine a segmented reconstructed image by segmentally reconstructing the target image. Further, based on the segmented motion vector field and the segmented reconstructed image, the processing device 120 can generate a motion artifact simulation image. In some embodiments, the processing device 120 can include a computer, a user console, a single server, or a server group, etc. The server group can be centralized or distributed. In some embodiments, the processing device 120 can be local or remote. For example, the processing device 120 can access information and / or data stored in the scanning device 110, the terminal device 130, and / or the storage device 150 via the network 140. Again, for example, the processing device 120 can directly connect to the scanning device 110, the terminal device 130, and / or the storage device 150 to access the stored information and / or data. In some embodiments, the processing device 120 can be implemented on a cloud platform. By way of example only, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, etc., or any combination thereof. In some embodiments, the processing device 120 or a part of the processing device 120 can be integrated into the scanning device 110.

[0029] The terminal device 130 can implement user interaction between the user and the motion artifact simulation system 100. In some embodiments, the terminal device 130 can be connected to and / or communicate with the scanning device 110, the processing device 120, and / or the storage device 150. For example, the terminal device 130 can obtain a target image from the scanning device 110. As another example, the terminal device 130 can obtain a processing result (e.g., a generated motion artifact simulation image) from the processing device 120 and display the processing result. As yet another example, a user (e.g., a doctor, a radiologist) can send one or more control instructions to the scanning device 110 through the terminal device 130 to control the scanning device 110 to perform scanning according to the instructions. In some embodiments, the terminal device 130 can include a mobile device 131, a tablet computer 132, a notebook computer 133, etc., or any combination thereof. In some embodiments, the terminal device 130 can be a part of the processing device 120 or the scanning device 110.

[0030] The network 140 can include any suitable network that facilitates the exchange of information and / or data of the motion artifact simulation system 100. By way of example only, the network 140 can include a Hospital Information System (HIS), a Picture Archiving and Communication Systems (PACS), or other networks that are independent of the HIS or PACS but connected thereto. In some embodiments, one or more components of the motion artifact simulation system 100 (e.g., the scanning device 110, the processing device 120, the storage device 150, the terminal device 130) can communicate information and / or data with one or more other components of the motion artifact simulation system 100 through the network 140. For example, the processing device 120 can obtain data (e.g., scanning data) from the scanning device 110 via the network 140. As another example, the terminal device 130 can obtain user (e.g., doctor, radiologist) instructions from the terminal device 130 via the network 140. In some embodiments, one or more components of the motion artifact simulation system 100 (e.g., the scanning device 110, the processing device 120, the storage device 150, the terminal device 130) can communicate information and / or data with one or more external resources (e.g., a third-party external database, etc.). For example, the processing device 120 can obtain an artifact simulation model from an external database provided by or updated by a vendor or manufacturer (e.g., the manufacturer of the scanning device 110) that provides and / or updates the artifact simulation model.

[0031] The storage device 150 can store data (such as target images, artifact simulation models, generated motion artifact simulation images, etc.), instructions, and / or any other information. In some embodiments, the storage device 150 can store data obtained from the scanning device 110, the terminal device 130, and / or the processing device 120. For example, the storage device 150 can store the target image obtained from the scanning device 110. In some embodiments, the storage device 150 can store data and / or instructions that the processing device 120 can execute or use to perform the exemplary methods / systems described in this specification. In some embodiments, the storage device 150 can include a mass storage device, a removable storage device, a volatile read / write memory, a read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 150 can be implemented through the cloud platform described in this specification. In some embodiments, the storage device 150 can communicate directly or via the network 140 with one or more other components of the motion artifact simulation system 100 (such as the scanning device 110, the processing device 120, the storage device 150, the terminal device 130). One or more components of the motion artifact simulation system 100 can directly or through the network 140 access the data or instructions stored in the storage device 150. In some embodiments, the storage device 150 can be a part of the processing device 120 or the scanning device 110.

[0032] It should be noted that the above description of the motion artifact simulation system 100 is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various changes and modifications can be made under the guidance of the content of this specification. For example, the motion artifact simulation system 100 can include one or more additional components and / or one or more components of the motion artifact simulation system 100 can be omitted. Also, for example, the components of the motion artifact simulation system 100 can be implemented on two or more sub-components and / or two or more components of the motion artifact simulation system 100 can be integrated into a single component. However, these changes and modifications do not depart from the scope of this specification.

[0033] Figure 2 is an exemplary module diagram of a motion artifact simulation system shown according to some embodiments of this specification. As Figure 2 shown, the motion artifact simulation system 200 can include a determination module 210, a reconstruction module 220, and a generation module 230.

[0034] The determination module 210 can be used to determine the segmented motion vector field of the target object based on the artifact simulation model. More descriptions about determining the segmented motion vector field can be found elsewhere in this specification. For example, see Figure 3 step 310 and its related descriptions in

[0035] The reconstruction module 220 can be used to determine a piecewise reconstructed image by piecewise reconstructing a target image. More descriptions about determining the piecewise reconstructed image can be found elsewhere in this specification. For example, see Figure 3 step 320 and its related descriptions in

[0036] The generation module 230 can be used to generate a motion artifact simulation image based on the piecewise motion vector field and the piecewise reconstructed image. More descriptions about generating the motion artifact simulation image can be found elsewhere in this specification. For example, see Figure 3 step 330 and its related descriptions in

[0037] It should be understood that Figure 2 the system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware (such as a processor), software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented using computer-executable instructions and / or included in processor control code. For example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of this specification can be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips and transistors, or programmable hardware devices such as field programmable gate arrays and programmable logic devices, but also by software executed by various types of processors, or by a combination of the above hardware circuits and software (for example, firmware).

[0038] It should be noted that the above description of the motion artifact simulation system 200 is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various changes and modifications can be made to the application forms and details of the above methods and systems without departing from the principles of this specification. In some embodiments, the motion artifact simulation system 200 may include one or more other modules and / or one or more of the above modules may be omitted. For example, the motion artifact simulation system 200 may further include a transmission module (not shown) for sending signals (e.g., electrical signals, electromagnetic signals) to one or more components of the motion artifact simulation system 100 (e.g., the scanning device 110, the storage device 150, the terminal device 130). Again, for example, the motion artifact simulation system 200 may include a storage module (not shown) for storing information and / or data related to motion artifact simulation (e.g., target images, artifact simulation models, generated motion artifact simulation images, etc.). In some embodiments, two or more modules may be integrated into one module, and / or one module may be divided into two or more units. However, these changes and modifications are also within the scope of this specification.

[0039] Figure 3 is an exemplary flowchart of a motion artifact simulation method according to some embodiments of this specification. In some embodiments, process 300 may be executed by the motion artifact simulation system 100. For example, process 300 may be implemented as a set of instructions (e.g., an application program) stored in a storage device (e.g., the storage device 150). In some embodiments, the processing device 120 may execute the set of instructions and be guided by the set of instructions to execute process 300.

[0040] Step 310, based on the artifact simulation model, determine the segmented motion vector field of the target object. In some embodiments, step 310 may be executed by the determination module 210.

[0041] In some embodiments, the target image may be an image having a quality score higher than a preset threshold. The preset threshold may be the default setting of the motion artifact simulation system 100, or manually set by a user (e.g., a doctor, a radiologist), or adjusted by the processing device 120 according to actual needs. In some embodiments, the target image may be screened from multiple initial images of the scanned object. The scanned object may include tissues, organs, etc. of a living body. For example, the tissue may include a combination of one or more of epithelial tissue, connective tissue, muscle tissue, nerve tissue, etc. Again, for example, the organ may include a combination of one or more of the heart, liver, spleen, lung, stomach, etc. The initial image may be a medical scan image of the scanned object. By way of example only, the initial image may be obtained by a medical imaging device (e.g., the scanning device 110). In the embodiments of this specification, the scanned object may include tissues / organs such as the heart, lungs, etc. that have motion (e.g., respiratory motion, heartbeat) during the image capture process. Due to the motion of the scanned object, it is possible that the captured medical image (i.e., the initial image) may generate artifacts (motion artifacts). In some embodiments, the target image may be an image of a scanned object (e.g., the heart, lungs) that contains tubular structures (e.g., blood vessels (e.g., coronary arteries), respiratory tracts). By way of example only, the target image may be a coronary artery image of the heart.

[0042] In some embodiments, the quality score may be obtained based on at least one of motion artifacts, noise, and CT mean in the image. In some embodiments, based on at least one of motion artifacts, noise, and CT mean, the initial image may be scored to obtain the quality score of the initial image, and the target image may be screened from multiple initial images based on the quality score. For example, a user (e.g., a doctor, a radiologist) may score multiple initial images based on at least one of motion artifacts, noise, and CT mean. In some embodiments, a user (e.g., a doctor, a radiologist) may evaluate the quality of the initial image according to at least one of motion artifacts, noise, and CT mean, and score according to the impact of the quality of the initial image on the diagnosis. In some embodiments, multiple initial images may be scored from 0 to 5. For example, if the quality of the initial image is very good (e.g., without motion artifacts) and suitable for diagnosis, it may be 4 - 5 points; if the quality of the initial image can barely be used for diagnosis, it may be 3 points. If it cannot be confirmed whether the quality of the initial image can be used for diagnosis, it may be 2 points. If the quality of the initial image is completely not available for diagnosis, it may be 1 point. Further, the initial images with a quality score greater than or equal to the preset threshold (e.g., 3 points) may be screened from multiple initial images as the target images.

[0043] In some embodiments, the artifact simulation model may refer to a model for simulating the movement of a scanned object (such as the heart, lungs). In some embodiments, a target object may be extracted from a target image, and a segmented motion vector field may be determined based on the target object and the artifact simulation model. By way of example only, the target object may refer to a scanned object (such as the heart, lungs) in the target image. In some embodiments, the target object may refer to the centerline of a tubular structure (such as a blood vessel (such as a coronary artery), a respiratory tract) extracted from the target image, and a segmented motion vector field may be determined based on the centerline of the tubular structure and the artifact simulation model. In some embodiments, the centerline of the tubular structure may refer to the geometric centerline of the tubular structure along the extension direction of the tubular structure. The extension direction of the tubular structure may refer to the direction in which the length of the tubular structure increases. In some embodiments, the centerline of the tubular structure in the target image may be extracted by a centerline extraction algorithm. By way of example only, the centerline extraction algorithm may include, but is not limited to, at least one or a combination of more than one of a manual centerline extraction algorithm, a centerline extraction algorithm based on a minimum path, a centerline extraction algorithm based on an active contour model, etc.

[0044] In some embodiments, the artifact simulation model may be a machine learning model. By way of example only, the machine learning model may include a neural network model, a deep learning model, etc. In some embodiments, the above-mentioned target image may be input into the machine learning model, and a segmented motion vector field may be determined based on the output of the machine learning model. In some embodiments, the above-mentioned extracted target object (such as a scanned object, a centerline) may be input into the machine learning model, and a segmented motion vector field may be determined based on the output of the machine learning model.

[0045] In some embodiments, the artifact simulation model may include a motion function. In some embodiments, the motion function may be one of the inputs of the artifact simulation model. The motion function may be a random motion function. By way of example only, the random motion function may be expressed as the following formula (1):

[0046] rate*(vec_x,vec_y,vec_z), (1)

[0047] Among them, rate represents the motion speed, vec_x represents the motion vector in the x direction of any point on the extracted target object (for example, the scanned object, the center line), vec_y represents the motion vector in the y direction of any point on the extracted target object (for example, the scanned object, the center line), and vec_z represents the motion vector in the z direction of any point on the extracted target object (for example, the scanned object, the center line). In some embodiments, the x, y, and z directions may be the directions of the three axes of the image coordinate system of the target image. In some embodiments, vec_x, vec_y, and vec_z may be randomly generated. In some embodiments, the random motion function may include at least one of a uniform motion function and a variable-speed motion function. For example, when the random motion function is a uniform motion function, rate is a constant value; when the random motion function is a variable-speed motion function, rate is a randomly changing value.

[0048] In some embodiments, the motion vector field may reflect the motion of the scanned object (such as the heart, the lungs). The motion vector field may be a set including the motion vectors of at least two spatial points on the scanned object in the image domain. The motion vector may represent the displacement of the corresponding voxels of any two spatial points on the scanned object in the image domain from one time point to another time point.

[0049] In some embodiments, the preset time period may be divided into multiple sub-time periods, and based on the extracted target object (for example, the scanned object, the center line) and the artifact simulation model, the segmented motion vector field for each sub-time period may be determined. The preset time period may be the default setting of the motion artifact simulation system 100, or manually set by a user (such as a doctor, a radiologist), or adjusted by the processing device 120 according to actual needs. For example, the preset time period may be the time period for acquiring the target image or a part thereof. In some embodiments, the preset time period may be divided into multiple sub-time periods according to a preset number of time nodes. The preset number of time nodes may be a preset number of time points selected at intervals from the preset time period. The preset number of time nodes may divide the preset time period into several sub-time periods evenly or unevenly. The preset number may be the default setting of the motion artifact simulation system 100, or manually set by a user (such as a doctor, a radiologist), or adjusted by the processing device 120 according to actual needs. For example, the preset number may be 5, namely T0, T1, T2, T3, and T4. These 5 time points divide the preset time period into 4 sub-time periods evenly, and T0 is the time point when acquiring the target image starts, and T4 is the time point when acquiring the target image ends.

[0050] In some embodiments, the extracted target object (e.g., scanned object, centerline), a preset time period, and a preset number of time nodes can be input into an artifact simulation model. The artifact simulation model correspondingly outputs a motion vector field for each sub-time period (i.e., a segmented motion vector field). Specifically, the preset time period can be divided into several sub-time periods (e.g., n - 1 segments) according to the preset number (e.g., n) of time nodes, and then based on the coordinates of each spatial point on the extracted target object in the image domain, the motion vector field for each sub-time period can be obtained according to the motion function.

[0051] Step 320, determining a segmented reconstruction image by segmentally reconstructing the target image. In some embodiments, step 320 can be performed by the reconstruction module 220.

[0052] In some embodiments, the projection data of the target image can be obtained with the phase of the target image as the center. The phase of the target image can refer to the phase at which the scanned object is located when the target image is acquired. Exemplarily, taking the cardiac coronary artery as the scanned object, each physiological cycle of the heart generally includes eight phases such as isovolumic contraction phase, rapid ejection phase, slow ejection phase, pre-diastolic phase, isovolumic relaxation phase, rapid filling phase, slow filling phase, and atrial contraction phase. For example, when the target image is acquired, the heart can be in at least one of the above eight phases. Further, one of the above at least one phase can be designated as the phase of the target image. For example, when the target image is acquired and the heart is in the isovolumic contraction phase, the isovolumic contraction phase can be designated as the phase of the target image. Another example is that during the process of acquiring the target image, the heart experiences the entire isovolumic contraction phase and part of the rapid ejection phase. At this time, the isovolumic contraction phase can be designated as the phase of the target image. Then, with the phase of the target image as the center, the projection data before and after the phase of the target image can be obtained.

[0053] Further, the projection data can be segmentally reconstructed to determine a segmented reconstruction image. Specifically, the projection data can be divided into several data segments (e.g., n - 1 segments) according to the preset number (e.g., n) of time nodes, and then each data segment is reconstructed to obtain a segmented reconstruction image (e.g., n - 1 segments). In some embodiments, each data segment can be reconstructed by a reconstruction algorithm. The reconstruction algorithm is an algorithm that converts the relevant information of the scanned object in the data domain into information in the image domain. Only as an example, the reconstruction algorithm can include, but is not limited to, the Back Projection (BP) algorithm, the Filtered Back Projection (FBP) algorithm, etc.

[0054] Step 330, generating a motion artifact simulation image based on the segmented motion vector field and the segmented reconstruction image. In some embodiments, step 330 can be performed by the generation module 230.

[0055] In some embodiments, the number of segmented motion vector fields is equal to and in one-to-one correspondence with the number of segmented reconstructed images. For example, there are n - 1 segmented reconstructed images, and each of the n - 1 segmented reconstructed images corresponds to a segmented motion vector field, that is, different segmented reconstructed images correspond to different segmented motion vector fields.

[0056] In some embodiments, a segmented motion compensated image can be generated based on the segmented motion vector field and the segmented reconstructed image. Specifically, for at least one segment of the segmented reconstructed image, the motion vector field corresponding thereto is used to motion compensate this segment of the reconstructed image, thereby generating a motion compensated image corresponding to this segment of the reconstructed image. For example, for each voxel in this segment of the reconstructed image, the motion vector field corresponding to this segment of the reconstructed image is used to move the position of the voxel (i.e., stretch or deform the reconstructed image). After all the voxels in this segment of the reconstructed image are moved, the obtained image is the motion compensated image corresponding to this segment of the reconstructed image. Further, a motion artifact simulation image can be determined by superimposing the segmented motion compensated images. Specifically, all the segmented motion compensated images (for example, n - 1 segments) are superimposed, thereby generating a motion artifact simulation image.

[0057] In some embodiments, a segmented motion compensated image can be generated based on the segmented motion vector field, the projection data corresponding to the segmented reconstructed image, and the weight related to the projection data corresponding to the segmented reconstructed image. Specifically, a weight curve can be set for the projection data corresponding to each segmented reconstructed image, and the weighted projection data is obtained based on the weight curve. Then, the weighted projection data is reconstructed to generate a weighted segmented reconstructed image. Further, a segmented motion compensated image is generated based on the motion vector field and the weighted segmented reconstructed image. In some embodiments, the weight curve can be a straight line or a curve, and this specification does not limit this.

[0058] Figure 4 is a schematic diagram of an exemplary weight curve shown in some embodiments of this specification. As Figure 4 shown, each of the 5 time nodes T0, T1, T2, T3, and T4 can correspond to at least one weight curve. The time node T0 corresponds to the weight curve 1a, the time node T1 corresponds to the weight curves 1b and 2a, the time node T2 corresponds to the weight curves 2b and 3a, the time node T3 corresponds to the weight curves 3b and 4a, and the time node T4 corresponds to the weight curve 4b.

[0059] In some embodiments, according to five time nodes T0, T1, T2, T3, and T4, the projection data of the target image 410 can be divided into four projection data segments 411, 412, 413, and 414. The weight curves 1a and 1b can be used to weight the projection data segment 411 to obtain the weighted projection data segment 1; the weight curves 2a and 2b can be used to weight the projection data segment 412 to obtain the weighted projection data segment 2; the weight curves 3a and 3b can be used to weight the projection data segment 413 to obtain the weighted projection data segment 3; the weight curves 4a and 4b can be used to weight the projection data segment 414 to obtain the weighted projection data segment 3. It should be noted that for any projection data segment, the sum of the weights used to weight it can be 1.

[0060] Further, the weighted projection data segments (such as 1-4) are respectively reconstructed to obtain four weighted segmented reconstructed images. For each of the four weighted segmented reconstructed images, motion compensation can be performed on the weighted segmented reconstructed image according to the obtained motion vector field corresponding to the weighted segmented reconstructed image to determine the motion compensation image of the weighted segmented reconstructed image. Then, the obtained motion compensation images are superimposed to generate a motion artifact simulation image.

[0061] According to the motion artifact simulation method disclosed in this specification, segmented simulation of motion artifacts can be realized, which conforms to the actual motion situation of the scanned object (such as the coronary artery). Compared with the traditional motion artifact simulation method of performing forward and backward projections and then imaging after transforming the phantom of the scanned object, the efficiency is higher.

[0062] In addition, according to the motion artifact simulation method disclosed in this specification, a large number of motion artifact simulation images can be obtained, and these images can contain various motion artifacts. In some embodiments, based on the generated motion artifact simulation images, a motion artifact removal model can be trained. The generated motion artifact simulation images can be used as training samples for the motion artifact removal model. The motion artifact removal model can be used to remove or reduce motion artifacts in images. The motion artifact removal model can include deep learning models, machine learning models, etc. For example, the motion artifact removal model can include, but is not limited to, U-NET (U-shaped network), neural network models, etc. Using these large numbers of motion artifact simulation images as training samples can improve the motion artifact removal effect of the motion artifact removal model.

[0063] It should be noted that the above description of process 300 is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various changes and modifications can be made under the guidance of the content of this specification. In some embodiments, process 300 may include one or more additional operations (or steps) and / or omit one or more of the above operations (or steps) to complete. For example, process 300 may include an additional transmission operation (or step) to send the generated motion artifact simulation image to a terminal device (e.g., terminal device 130) for display. Again, for example, process 300 may include an additional storage operation (or step) to store information and / or data related to motion artifact simulation (e.g., target image, artifact simulation model, generated motion artifact simulation image, etc.) in a storage device (e.g., storage device 150) disclosed elsewhere in this specification. However, these changes and modifications do not depart from the scope of this specification.

[0064] The beneficial effects that the embodiments of this specification may bring include, but are not limited to: (1) Piecewise simulation of motion artifacts can be achieved, which conforms to the actual motion of the scanned object (e.g., coronary artery). Compared with the traditional motion artifact simulation method of transforming the phantom of the scanned object and then performing forward and backward projections for imaging, the efficiency is higher; (2) A large number of motion artifact simulation images can be obtained, and these images can contain various motion artifacts; (3) Using these large number of motion artifact simulation images as training samples can improve the motion artifact removal effect of the motion artifact removal model. It should be noted that the beneficial effects that different embodiments may produce are different. In different embodiments, the beneficial effects that may be produced can be any one or several combinations of the above, or any other beneficial effects that may be obtained.

[0065] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are proposed in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0066] At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0067] In addition, those skilled in the art can understand that various aspects of this specification can be described and illustrated by several patentable types or situations, including any new and useful process, machine, product, or composition of matter, or any new and useful improvement thereof. Accordingly, various aspects of this specification can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above-mentioned hardware or software can all be referred to as "data blocks", "modules", "engines", "units", "components", or "systems". In addition, various aspects of this specification may be embodied as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0068] A computer storage medium may contain a propagated data signal that contains computer program code, such as on a baseband or as part of a carrier wave. This propagated signal may have various forms of manifestation, including electromagnetic form, optical form, etc., or a suitable combination thereof. A computer storage medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission for use of a program. The program code located on a computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0069] The computer program code required for the operation of each part of this specification can be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the user's computer, or run as an independent software package on the user's computer, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (such as through the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0070] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical and alphabetical characters, or the use of other names described in this specification are not used to limit the order of the processes and methods in this specification. Although some currently useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details are for illustrative purposes only. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0071] Similarly, it should be noted that, in order to simplify the presentation of the disclosure in this specification and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, multiple features are sometimes grouped into one embodiment, drawing, or description thereof. However, this method of disclosure does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0072] In some embodiments, numbers are used to describe the components and the quantity of attributes. It should be understood that such numbers used to describe the embodiments are modified by the modifiers "about", "approximate" or "substantially" in some examples. Unless otherwise stated, "about", "approximate" or "substantially" indicate that the stated number allows a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are made as precise as possible within the feasible range.

[0073] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification as references. Except for the application history documents that are inconsistent with or conflict with the content of this specification, and also except for the documents that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0074] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly presented and described in this specification.

Claims

1. A method for simulating motion artifacts, characterized in that, The method includes: Determining a segmented motion vector field of a target object based on an artifact simulation model; Determining a segmented reconstruction image by segmentally reconstructing the target image; Generating a segmented motion compensation image based on the segmented motion vector field, the segmented reconstruction image, and weights related to projection data corresponding to the segmented reconstruction image; Generating a motion artifact simulation image by superimposing the segmented motion compensation images.

2. The method according to claim 1, characterized in that, Determining a segmented motion vector field of a target object based on an artifact simulation model includes: Extracting the centerline of a tubular structure in the target image; Determining the segmented motion vector field based on the centerline and the artifact simulation model.

3. The method according to claim 1, wherein The target image is an image having a quality score higher than a preset threshold.

4. The method according to claim 1, characterized in that Determining a segmented motion vector field of a target object based on an artifact simulation model includes: Dividing a preset time period into a plurality of sub-time periods; Determining a segmented motion vector field for each sub-time period based on the artifact simulation model.

5. The method according to claim 4, characterized in that, The artifact simulation model includes a motion function or a machine learning model.

6. The method according to claim 4, characterized in that Determining a segmented reconstruction image by segmentally reconstructing the target image includes: Obtaining projection data of the target image with the phase of the target image as the center; Performing segmented reconstruction on the projection data to determine the segmented reconstruction image.

7. The method according to claim 1, wherein The method further includes: Training a motion artifact removal model based on the motion artifact simulation image.

8. A motion artifact simulation system, characterized in that, The system includes: A determination module configured to determine a segmented motion vector field of a target object based on an artifact simulation model; A reconstruction module configured to determine a segmented reconstruction image by segmentally reconstructing a target image; A generation module configured to generate a segmented motion compensation image based on the segmented motion vector field, the segmented reconstruction image, and weights related to projection data corresponding to the segmented reconstruction image; and generate a motion artifact simulation image by superimposing the segmented motion compensation images.

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