Motion state determination method, system and device and storage medium

Abstract

The embodiment of the invention provides a motion state determination method, system and device and a storage medium, and the method comprises the steps: obtaining first scanning data of a scanning object in a first scanning stage, the first scanning data corresponding to a first imaging view; second scanning data of the scanned object in the first scanning stage are obtained, the second scanning data correspond to a second imaging view field, and the second imaging view field is smaller than the first imaging view field; based on the first scanning data and the second scanning data, determining a data corresponding relation between a first imaging view and a second imaging view of the scanning object in at least one motion state in the first scanning stage; and determining the motion state of the scanning object in the second scanning stage based on the data correspondence.

Description

Technical Field

[0001] The present specification relates to the field of medical imaging, and in particular to a motion state determination method, system, device and storage medium. Background Art

[0002] Magnetic resonance imaging (MRI) is widely used in the imaging of liver, brain and other parts of the body due to its excellent soft tissue imaging capabilities and safety features without ionizing radiation. Since the speed of MRI is limited by factors such as phase encoding and repetition time TR, the imaging of certain moving organs is often accompanied by motion artifacts. In the process of MRI-guided radiotherapy, three-dimensional MRI scanning is required because there must be no gaps between the imaging layers. However, conventional three-dimensional scanning sequences cannot meet the requirements of MRI real-time imaging. In the case of high real-time requirements, in order to achieve real-time motion state capture of certain organs that are greatly affected by moving tissues such as respiratory movement and heartbeat, and to improve the motion tracking accuracy of target organ tissues, the three-dimensional motion state capture of tissues using two-dimensional image navigation is usually used to locate the current tissue motion state. However, this method has the problem that the speed of two-dimensional magnetic resonance imaging limits the real-time imaging frame rate.

[0003] In addition, in two-dimensional imaging, some high-speed imaging sequences (for example, the fastest echo planar imaging technology currently) can be combined with different sequences to achieve the effect of fast imaging. However, since this technology requires a series of gradient alternations to achieve continuous signal acquisition, the technology has extremely high requirements for gradient performance and extremely precise gradient switching rates. Due to the loss of the correction of the nuclear phase by the refocusing pulse, the accumulated phase error will cause the image deformation and related artifacts to become more serious. This makes it difficult for such technologies to meet the requirements of real-time imaging over a long period of time, and the stability of image quality, gradient performance, and the risk of quenching will raise the technical threshold.

[0004] The second technical means to speed up imaging is the widely used parallel MRI imaging technology. MRI's parallel imaging technology uses coil information with known position and sensitivity parameters to assist in locating the spatial position of MR signals. This method reduces the number of phase encoding steps and reduces imaging time. The simultaneous acquisition of magnetic resonance signals by multi-channel coils will be limited by hardware performance limitations such as system memory, and multi-channel operations and reconstruction also bring computing pressure to the system. In small-scale local imaging, due to the limitation of the size of a single-channel coil, parallel imaging of multiple sampling channels is difficult to achieve good results in local small-scale imaging.

[0005] The third technical means to speed up imaging is compressed sensing technology. Compressed sensing technology uses the data conjugation characteristics in K space and adopts an undersampling method. It calculates and compensates the undersampled high-frequency position information in K space based on the central full-sampled kernel data in the later stage to achieve the imaging acceleration process by reducing the encoding and sampling times. However, due to its relatively complex reconstruction algorithm and the need for training data, it also creates certain obstacles to the implementation of this technology.

[0006] In view of this, it is hoped to provide a motion state determination method, system, device and storage medium, so as to quickly locate the current motion state of the tissue by acquiring high real-time signals of the moving tissue, and ultimately achieve high-speed real-time three-dimensional magnetic resonance imaging. Summary of the invention

[0007] One or more embodiments of this specification provide a method for determining a motion state, a method for high-speed capture of motion state guided by the K-space data difference caused by the image curling phenomenon in a small FOV (Field of view). This method can effectively reduce the time consumption of phase encoding and image reconstruction steps, reduce the computing power consumption of the computer caused by signal processing and image reconstruction, and achieve high-speed real-time three-dimensional magnetic resonance imaging by acquiring high-real-time signals of moving tissues to quickly locate the current motion state of the tissues.

[0008] The method includes: acquiring first scanning data of a scanning object in a motion cycle, the first scanning data corresponding to a first imaging field of view; acquiring second scanning data of the scanning object in a first scanning phase, the second scanning data corresponding to a second imaging field of view, the second imaging field of view being smaller than the first imaging field of view; determining a data correspondence between the first imaging field of view and the second imaging field of view of the scanning object in at least one motion state in the first scanning phase based on the first scanning data and the second scanning data; and determining a motion state of the scanning object in the second scanning phase based on the data correspondence.

[0009] One or more embodiments of the present specification provide a motion state determination system, the system comprising: an analysis module, for acquiring first scanning data of a scanning object in a first scanning phase, the first scanning data corresponding to a first imaging field of view; acquiring second scanning data of the scanning object in the first scanning phase, the second scanning data corresponding to a second imaging field of view, the second imaging field of view being smaller than the first imaging field of view; determining a data correspondence between the first imaging field of view and the second imaging field of view of the scanning object in at least one motion state in the first scanning phase based on the first scanning data and the second scanning data; a determination module, for determining the motion state of the scanning object in the second scanning phase based on the data correspondence.

[0010] One or more embodiments of the present specification provide a method for determining a motion state, the method comprising: determining a data correspondence between a first imaging field of view and a second imaging field of view under at least one motion state, the first imaging field of view corresponding to a three-dimensional scanning image, the second imaging field of view corresponding to a K-space data set, the data correspondence comprising a correspondence between the three-dimensional scanning image and the K-space data set; performing a small-field magnetic resonance scan on a scanned object in real time to obtain a real-time K-space data set; determining a physiological phase of the scanned object based on the real-time K-space data set and the data correspondence; and determining a radiotherapy plan for the scanned object based on the physiological phase of the scanned object. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0012] Figure 1 It is an application scenario diagram of the motion state determination system shown in some embodiments of this specification;

[0013] Figure 2 is an exemplary module diagram of a motion state determination system according to some embodiments of this specification;

[0014] Figure 3 is an exemplary flow chart of a motion state determination method according to some embodiments of this specification;

[0015] Figure 4 is an exemplary schematic diagram of obtaining a three-dimensional scanned image through three-dimensional state segmentation according to some embodiments of this specification;

[0016] Figure 5 is an exemplary schematic diagram of the relationship between the second imaging field of view and the scanned object according to some embodiments of this specification;

[0017] Figure 6 is an exemplary schematic diagram of dividing a K-space data set using K-space data change characteristics according to some embodiments of this specification;

[0018] Figure 7 is an exemplary schematic diagram of the relationship between image warping and a K-space data set according to some embodiments of the present specification;

[0019] Figure 8 is an exemplary schematic diagram of obtaining data correspondence according to some embodiments of this specification;

[0020] Fig. 9It is an exemplary schematic diagram of a motion state determination method according to some embodiments of this specification. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0022] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0023] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0024] 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 preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0025] Figure 1 is an application scenario diagram of the motion state determination system shown in some embodiments of this specification. Figure 1 As shown, the application scenario 100 of the motion state determination system may include a magnetic resonance device 110, a processing device 120, a network 130, a terminal device 140, and a storage device 150. In some embodiments, the components in the application scenario 100 may be interconnected via the network 130. In some embodiments, some components in the application scenario 100 may be directly connected.

[0026] The magnetic resonance device 110 can be used to perform magnetic resonance scanning on a scanned object. In some embodiments, the scanned object can be biological or non-biological. For example, the scanned object can include a patient, an artificial object, etc. In some embodiments, the scanned object can include a specific part of the body, for example, the head, neck, chest, etc. or any combination thereof. In some embodiments, the scanned object can include a specific organ, for example, the liver, kidney, pancreas, bladder, uterus, rectum, etc. or any combination thereof. In some embodiments, the scanned object can include a region of interest (ROI), for example, a tumor, a nodule, etc.

[0027] In some embodiments, when determining the motion state of a certain scanned object, the scanned object can be first scanned according to the first imaging field of view and the second imaging field of view, and the data correspondence relationship under the first imaging field of view and the second imaging field of view under each motion state in the first scanning stage of the scanned object is obtained through the preprocessing stage, and then the motion state of the scanned object in the second scanning stage can be determined in real time according to the data correspondence relationship. For example, the motion state of the scanned object in a similar motion cycle can be determined. Among them, the similar motion cycle can be a motion cycle with a similar motion law as the motion cycle corresponding to the first scanning stage in the scanning process other than the first scanning stage. Among them, the preprocessing stage is a stage for obtaining the data correspondence relationship under the first imaging field of view and the second imaging field of view under each motion state in the first scanning stage. The application stage is a stage for determining the motion state of the scanned object in the second scanning stage in real time according to the data correspondence relationship. As a non-limiting example, the motion-related data of the scanned object in the second scanning stage can be obtained by various means such as sensors and imaging devices, and combined with the data correspondence relationship, so as to determine the motion state of the scanned object in the second scanning stage (and optionally, other motion cycles similar to the motion cycle corresponding to the scanning process).

[0028] In some embodiments, the magnetic resonance device 110 can obtain a large amount of unprocessed raw data (e.g., K-space data, etc.) by performing a magnetic resonance scan on the scanned object. In some embodiments, the magnetic resonance device 110 can obtain image data by performing a magnetic resonance scan on the scanned object. In some embodiments, the magnetic resonance device 110 can perform a three-dimensional magnetic resonance scan on the scanned object to obtain three-dimensional scanning data (e.g., a three-dimensional scanning image, etc.). In some embodiments, the magnetic resonance device 110 can perform a four-dimensional magnetic resonance scan on the scanned object to obtain four-dimensional scanning data (e.g., a three-dimensional scanning image containing time dimension information, etc.).

[0029] In some embodiments, the magnetic resonance device 110 can perform magnetic resonance scanning on the scanned object according to a certain scanning sequence, wherein the scanning sequence may include but is not limited to a free induction decay sequence (FID), a self-selected echo sequence (SE), a gradient echo sequence (GRE), a hybrid sequence (HS), etc. In some embodiments, different scanning sequences may be used for scanning different scanned objects. For example, an Ax SE T1 scanning sequence may be used for scanning a conventional head, and a Cor SE T1 scanning sequence may be used for scanning a pituitary gland.

[0030] In some embodiments, the magnetic resonance device 110 may include a 1.5T magnetic resonance device, a 3.0T magnetic resonance device, a 5.0T magnetic resonance device, a 7.0T magnetic resonance device, etc.

[0031] The above description of the magnetic resonance apparatus 110 is for illustration purposes only and is not intended to limit the scope of the present description.

[0032] In some embodiments, the magnetic resonance device 110 may send scan data to the processing device 120 and / or other components of the application scenario 100. In some embodiments, the magnetic resonance device 110 may receive relevant data or instructions from the processing device 120 and / or other components of the application scenario 100 to perform magnetic resonance scanning.

[0033] The processing device 120 can process the data and / or information obtained from the magnetic resonance device 110, the terminal device 140 and / or the storage device 150. For example, the processing device 120 can obtain the first scanning data and the second scanning data of the scanned object in the first scanning phase, and based on the first scanning data and the second scanning data, obtain the data correspondence relationship between the first imaging field of view and the second imaging field of view in each motion state of the scanned object in the first scanning phase. For another example, the processing device 120 can obtain the third scanning data of the scanned object in the second scanning phase, and determine the target motion state of the scanned object when acquiring the third scanning data based on the third scanning data and the data correspondence relationship.

[0034] In some embodiments, the processing device 120 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device 120 may be local or remote. For example, the processing device 120 may access information and / or data from the magnetic resonance device 110, the terminal device 140, and / or the storage device 150 via the network 130. For another example, the processing device 120 may be directly connected to the magnetic resonance device 110, the terminal device 140, and / or the storage device 150 to access information and / or data. In some embodiments, the processing device 120 may be arranged in the magnetic resonance device 110. In some embodiments, the processing device 120 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, a cloud-to-cloud cloud, a multi-cloud, etc., or any combination thereof.

[0035] The network 130 may include any suitable network that facilitates information and / or data exchange. In some embodiments, one or more components of the application scenario 100 may exchange information and / or data via the network 130. For example, the processing device 120 may receive scan data from the magnetic resonance device 110 via the network 130. The network 130 may include a local area network (LAN), a wide area network (WAN), a wired network, a wireless network, etc., or any combination thereof.

[0036] The terminal device 140 can communicate and / or connect with the magnetic resonance device 110, the processing device 120 and / or the storage device 150. For example, the terminal device 140 can send one or more control instructions to the magnetic resonance device 110 through the network 130 to control the magnetic resonance device 110 to perform magnetic resonance scanning on the scanned object according to the instructions. In some embodiments, the terminal device 140 may include one or any combination of other devices with input and / or output functions such as a mobile device 140-1, a tablet computer 140-2, a laptop computer 140-3, a desktop computer 140-4, etc. In some embodiments, the terminal device 140 may include an input device, an output device, etc. The input device may include a keyboard, a touch screen, a mouse, a voice device, etc., or any combination thereof. The output device may include a display, a speaker, a printer, etc., or any combination thereof. In some embodiments, the terminal device 140 may be a part of the processing device 120. In some embodiments, the terminal device 140 may be integrated with the processing device 120 as an operating console of the magnetic resonance device 110.

[0037] The storage device 150 may store data, instructions, and / or any other information. In some embodiments, the storage device 150 may store data acquired from the magnetic resonance device 110, the processing device 120, and / or the terminal device 140. For example, the storage device 150 may store raw data acquired from the magnetic resonance device 110. In some embodiments, the storage device 150 may store data and / or instructions that the processing device 120 uses to execute or use to complete the exemplary methods described in this specification. For example, the storage device 150 may store instructions for controlling the magnetic resonance device 110 to perform scanning.

[0038] In some embodiments, the storage device 150 may 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 may be implemented on a cloud platform. In some embodiments, the storage device 150 may be part of the processing device 120.

[0039] It should be noted that the above description 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 may be made under the guidance of the contents of this specification. The features, structures, methods and other features of the exemplary embodiments described in this specification may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the magnetic resonance device 110, the processing device 120 and the terminal device 140 may share a storage device 150, or may have their own storage devices. However, these changes and modifications will not deviate from the scope of this specification.

[0040] Figure 2 is an exemplary module diagram of a motion state determination system according to some embodiments of this specification. Figure 2 As shown, the motion state determination system 200 may include an acquisition module 210, an analysis module 220, and a determination module 230. In some embodiments, the motion state determination system 200 may be implemented by the processing device 120.

[0041] In some embodiments, the acquisition module 210 can be used to: acquire first scan data of the scanned object in the first scanning phase, the first scan data corresponding to the first imaging field of view; acquire second scan data of the scanned object in the first scanning phase, the second scan data corresponding to the second imaging field of view. For more information on acquiring the first scan data and the second scan data, see Figure 3 and its related description.

[0042] In some embodiments, the first scanning data includes at least one set of three-dimensional scanning images of the scanning object in at least one motion state in the first scanning phase. Accordingly, the acquisition module 210 can be further used to: acquire first raw data of the scanning object in the first scanning phase, the first raw data corresponding to the first imaging field of view; perform three-dimensional state segmentation based on the first raw data to obtain at least one set of three-dimensional scanning images, each set of the at least one set of three-dimensional scanning images corresponding to a specific motion state of the scanning object. For more information on acquiring at least one set of three-dimensional scanning images, see Figure 3 , Figure 4 and its related description.

[0043] In some embodiments, the second scanning data includes at least one set of K-space data sets in at least one motion state of the scanning object in the first scanning phase. Accordingly, the acquisition module 210 can be further used to: acquire second original data of the scanning object in the first scanning phase, the second original data corresponding to the second imaging field of view; divide the second original data based on the K-space data change characteristics to obtain at least one set of K-space data sets, each set of the at least one set of K-space data sets corresponds to a set of three-dimensional scanning images in at least one set of three-dimensional scanning images. For more information on acquiring at least one set of K-space data sets, see Figure 3 and its related description.

[0044] In some embodiments, the analysis module 220 can be used to: obtain the data correspondence between the first imaging field of view and the second imaging field of view in each motion state of the scanned object in the first scanning stage based on the first scanning data and the second scanning data. In some embodiments, the analysis module 220 can be further used to: determine the data correspondence between the scanned object in each motion state in the first scanning stage through a matching model based on at least one set of three-dimensional scanning images and at least one set of K-space data sets. For more information on obtaining data correspondence, see Figure 3 and its related description.

[0045] In some embodiments, the determination module 230 may be used to: determine the motion state of the scanned object in the second scanning phase based on the data correspondence. For more information on determining the motion state of the scanned object in the second scanning phase, see Figure 3 and its related description.

[0046] In some embodiments, the determination module 230 may be further used to: obtain third scan data of the scanned object in the second scan phase, the third scan data corresponding to the second imaging field of view; and determine the target motion state of the scanned object when the third scan data is acquired based on the third scan data and the data correspondence relationship. For more information about the third scan data and determining the target motion state of the scanned object when the third scan data is acquired, see Figure 3and its related description.

[0047] In some embodiments, the determination module 230 may be further used to: determine a set of K-space data sets in the second scan data that matches the third scan data based on the third scan data; determine the target motion state corresponding to the third scan data based on the data correspondence relationship and the K-space data sets that match the third scan data. For more information on determining the target motion state, see Figure 3 and its related description.

[0048] It should be noted that the above description of the motion state determination system 200 and its modules is only for convenience of description, and this specification can not be limited to the scope of the embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various modules without deviating from this principle, or to form a subsystem connected with other modules. In some embodiments, the acquisition module 210, the analysis module 220 and the determination module 230 can be different modules in a system, or a module can realize the functions of two or more modules mentioned above. For example, each module can share a storage module, and each module can also have its own storage module respectively. Such deformations are all within the protection scope of this specification.

[0049] Figure 3 It is a flowchart of an exemplary motion state determination method shown in some embodiments of this specification. In some embodiments, the motion state determination method can be executed by the processing device 120 or the motion state determination system 200. For example, process 300 can be stored in a storage device (e.g., storage device 150) in the form of a program or instruction, and when the processing device 120 or the motion state determination system 200 executes the program or instruction, process 300 can be implemented. The operational diagram of process 300 presented below is illustrative. In some embodiments, the process can be completed using one or more additional operations not described and / or one or more operations not discussed. In addition, Figure 3 The order in which the operations of process 300 are illustrated and described below is not intended to be limiting.

[0050] Step 301 , acquiring first scan data of a scanned object in a first scanning phase, where the first scan data corresponds to a first imaging field of view. In some embodiments, step 301 may be performed by the acquisition module 210 .

[0051] In some embodiments, the scanned object is affected by physiological activities. For example, when breathing or the heart beats, the motion state (also known as the physiological phase) of the scanned object may change accordingly. When the scanned object is the lung, it may contract and relax under the influence of physiological activities. When the scanned object is the heart, it may move under the influence of physiological activities. For more information about the scanned object, see Figure 1 and its related description.

[0052] In some embodiments, the first scanning phase may be a scanning phase for acquiring data. The data acquired in the first scanning phase may be used to determine the data correspondence. For more information, see below.

[0053] In some embodiments, the first scanning phase may include one or more motion cycles.

[0054] The motion cycle refers to the period of time from a certain moment when the displacement, speed, acceleration, etc. of the scanned object completely or roughly recovers to the same level as that moment. The breathing, heartbeat, etc. of the scanned object will cause the tissue to undergo organ displacement and organ expansion. For example, when the scanned object is the heart, the motion cycle can be the heartbeat cycle of the heart. For another example, when the scanned object is the lung, the motion cycle can be the period of time when the lung contracts and relaxes, that is, the breathing cycle. For another example, when the scanned object is the gastrointestinal tract, the motion cycle can be the gastrointestinal motility cycle, etc.

[0055] In some embodiments, the first scanning data may be scanning data obtained after performing a four-dimensional magnetic resonance scan on the scanned object. The first scanning data may be four-dimensional scanning data. For example, the first scanning data may be a three-dimensional scanning image sequence including time dimension information.

[0056] In some embodiments, the first scan data corresponds to a first imaging field of view. Accordingly, the first scan data may be scan data obtained after a four-dimensional magnetic resonance scan is performed on the scan object in the first imaging field of view. The first imaging field of view is the field size when the first scan data is obtained.

[0057] In some embodiments, the first imaging field of view may be the field of view size of a four-dimensional magnetic resonance scan of a scanned object. In some embodiments, the first imaging field of view may be greater than or equal to the range where the scanned object is located. For example, the first imaging field of view may exceed the four sides or the periphery of the scanned object by a certain distance. For another example, the first imaging field of view may be set at the edge of the range where the scanned object is located.

[0058] In some embodiments, the first imaging field of view can be set according to actual needs. For example, the first imaging field of view can be set to a maximum range that can determine the condition of a certain scanned object according to the diagnosis needs of a doctor.

[0059] In some embodiments, the first imaging field of view is limited by hardware limitations of the magnetic resonance device.

[0060] In some embodiments, the acquisition module 210 can perform a four-dimensional magnetic resonance scan on the scanned object in a first imaging field of view through a magnetic resonance device (e.g., the magnetic resonance device 110), acquire raw data (e.g., K-space data) of the scanned object in the first scanning phase, and reconstruct the raw data to obtain the first scanning data. Exemplary reconstruction methods include, but are not limited to, a partial Fourier reconstruction method, a parallel reconstruction method, a compressed sensing reconstruction method, and a deep learning-based reconstruction method.

[0061] In some embodiments, the first scanning data includes a set of three-dimensional scanning images of the scanned object in multiple motion states in one or more motion cycles included in the first scanning phase. The motion state can be represented by position (i.e., the position of the scanned object at a certain moment in the motion cycle relative to the position of the scanned object at the initial moment of the motion cycle), form (e.g., volume), etc. In some embodiments, different ranges of position or form can be considered as different motion states.

[0062] For example, if the scanned object is a heart, the motion state within the cardiac cycle can be divided based on the change in the size of the heart or the change in the position of a certain point on the edge of the heart. As an example only, when the heart moves, the motion state within the cardiac cycle can be divided into a light beating state, a moderate beating state, and a violent beating state, etc. Among them, in the light beating state, the change in the size of the heart or the change in the position of a certain point on the edge of the heart is less than the corresponding first threshold; in the moderate beating state, the change in the size of the heart or the change in the position of a certain point on the edge of the heart is less than the corresponding second threshold, and is greater than or equal to the aforementioned first threshold; in the violent beating state, the change in the size of the heart or the change in the position of a certain point on the edge of the heart is less than the corresponding third threshold, and is greater than or equal to the aforementioned second threshold. The first threshold, the second threshold, and the third threshold can be system settings, system default values, or manually set values, etc.

[0063] For another example, if the scanned object is the lung, the motion state of the lung in the contraction and expansion cycle can be divided based on the change in the lung volume or the change in the position of a certain part of the lung edge. Just as an example, when the lung contracts and expands, its motion state can be divided into a primary contraction state, a secondary contraction state, etc., and / or a primary expansion state, a secondary expansion state, etc. The division method of each motion state can refer to the above method of dividing the motion state in the heartbeat cycle, which will not be repeated here.

[0064] In some embodiments, the first scanning data may include one or more groups of three-dimensional scanning images. The scanning object includes one or more motion states within a motion cycle, and each group of three-dimensional scanning images may correspond to a specific motion state in the one or more motion states. A group of three-dimensional scanning images may include one or more continuous three-dimensional scanning images. The position or shape of the scanning object in a group of three-dimensional scanning images corresponding to a specific motion state changes less. The position or shape of the scanning object in multiple groups of three-dimensional scanning images corresponding to different motion states changes more or more obviously.

[0065] See also Figure 4 In some embodiments, the acquisition module 210 may acquire first original data of the scanned object in the first scanning phase, and perform three-dimensional state segmentation based on the first original data to obtain at least one set of three-dimensional scanned images.

[0066] The first original data refers to the four-dimensional image data obtained after performing a four-dimensional magnetic resonance scan on the scanned object. Four-dimensional image data refers to image data including time dimension information. In some embodiments, the first original data can be obtained by reconstructing the raw data (for example, K-space data set) obtained after performing a four-dimensional magnetic resonance scan on the scanned object. For instructions on reconstruction, see the above. In this embodiment, the first scanning data includes at least one three-dimensional scanning image of the scanned object in each motion state within one or more motion cycles included in the first scanning stage. Accordingly, the first scanning data can also be obtained based on processing the first original data, that is, three-dimensional state segmentation.

[0067] In some embodiments, the first original data corresponds to the first imaging field of view. Accordingly, the first original data may be four-dimensional image data obtained after performing a four-dimensional magnetic resonance scan on the scanned object in the first imaging field of view.

[0068] In some embodiments, the acquisition module 210 can segment the first original data into at least one group of three-dimensional scanned images by performing three-dimensional state segmentation on the first original data. In some embodiments, the acquisition module 210 can segment the first original data into three-dimensional states by image matching. For example, the acquisition module 210 can preset reference three-dimensional scanned images (which may include one or more images) corresponding to different motion states, match the three-dimensional scanned images in the first original data with the reference three-dimensional scanned images corresponding to different motion states, and thus segment the first original data into at least one group of three-dimensional scanned images corresponding to different motion states.

[0069] In some embodiments, the acquisition module 210 can segment the first original data into one or more groups of three-dimensional scanning images by image comparison. For example, the acquisition module 210 can slide and intercept multiple time segments, compare the similarity of multiple three-dimensional scanning images corresponding to each time segment, segment the three-dimensional scanning images with image similarity less than the similarity threshold to form a group of three-dimensional scanning images, and correspond to a specific motion state, thereby segmenting the first original data into one or more groups of three-dimensional scanning images.

[0070] Step 302 , obtaining second scan data of the scanned object in the first scanning phase, where the second scan data corresponds to a second imaging field of view. In some embodiments, step 302 may be performed by the obtaining module 210 .

[0071] The second scan data refers to scan data obtained after performing a rapid magnetic resonance scan on the scan object. In some embodiments, the second scan data may be scan data containing time dimension information. For example, the second scan data may be a K-space data set containing time dimension information.

[0072] In some embodiments, the second scanning data corresponds to the second imaging field of view. Accordingly, the second scanning data may be scanning data obtained after a rapid magnetic resonance scanning is performed on the scanning object in the second imaging field of view.

[0073] In some embodiments, when performing rapid magnetic resonance scanning, the phase encoding direction may be adjusted, for example, the phase encoding direction may be set on the anatomical short axis of the human body.

[0074] In some embodiments, acquiring the first scanning data and acquiring the second scanning data may be performed simultaneously or non-simultaneously.

[0075] The second imaging field of view refers to the size of the field of view for rapid magnetic resonance scanning of the scanned object. In some embodiments, the second imaging field of view may be smaller than the range where the scanned object is located. In some embodiments, the second imaging field of view may be smaller than the first imaging field of view. By setting the second imaging field of view to be smaller than the first imaging field of view, the signal intensity of the central low frequency and the peripheral high frequency parts of the collected signal in the K space of the second scanning data can be higher than that of the first scanning data, which can better reflect the motion state of the scanned object. In some embodiments, since the second imaging field of view is smaller than the range where the scanned object is located, image curling may occur at this time.

[0076] The second imaging field of view can be determined in a variety of ways. In some embodiments, the second imaging field of view can be preset by the system (e.g., the motion state determination system 200) or manually. For example, the system or the human can preset the size of the second imaging field of view based on prior knowledge or historical data.

[0077] In some embodiments, the second imaging field of view may be determined based on the first imaging field of view. For example, the system may perform operations such as proportional reduction on the field of view size of the first imaging field of view to obtain the second imaging field of view.

[0078] In some embodiments, the second imaging field of view may be determined based on the organ type of the scanned object. For example, the system may preset a correspondence between different organ types and different second imaging fields of view, and determine the actual second imaging field of view applied to the scanned object according to the actual organ type of the scanned object.

[0079] In some embodiments, the second imaging field of view may be determined based on the first scanning data and a preset condition.

[0080] The preset condition may be in various forms. In some embodiments, the preset condition may include: setting the field size of the second imaging field of view so that the second imaging field of view satisfies the motion type of the scanned object. The motion type of the scanned object may refer to the motion direction of the scanned object, for example, including up and down movement, left and right movement, etc. In some embodiments, the motion type of the scanned object may be determined based on the first scanning data. For example, the motion type, i.e., the motion direction, of the scanned object may be determined based on a plurality of adjacent three-dimensional scanning images.

[0081] In some embodiments, satisfying the motion type of the scanned object means that the length of one side of the second imaging field of view is the same as or similar to the length of one side of the scanned object perpendicular to the motion direction. Figure 5 , wherein R is the scanning object, a is the side of the second imaging field of view that is perpendicular to the movement direction of the scanning object R, and b is the side of the scanning object R that is perpendicular to its movement direction. Then, satisfying the movement type of the scanning object means that the length of the side b in the second imaging field of view is the same as or similar to the length of the side a on the scanning object R.

[0082] In some embodiments, the preset condition may include: setting the field size of the second imaging field of view so that the image obtained based on the first scanning data does not include image curling, while the image obtained based on the second scanning data includes image curling. Figure 5 , there is image curling in the second scanning data corresponding to the second imaging field of view.

[0083] In some embodiments, the preset conditions may include: setting the field size of the second imaging field of view so that the time resolution of the second scanning data in the first scanning stage meets the preset time resolution conditions and / or image curling appears in the direction in which the movement of the scanning object has the greatest impact on the imaging.

[0084] In some embodiments, the preset time resolution condition may be that the time resolution of the second scan data in the first scan stage is greater than or equal to a time resolution threshold. The time resolution threshold may be a system default value, an experience value, a manually preset value, or any combination thereof, and may be set according to actual needs, and this specification does not limit this.

[0085] In some embodiments, the direction in which the motion of the scanned object has the greatest impact on the imaging may be the same as the motion direction of the scanned object. In some embodiments, the direction in which the motion of the scanned object has the greatest impact on the imaging may be the phase encoding direction. In some embodiments, the direction in which the motion of the scanned object has the greatest impact on the imaging may be the frequency encoding direction.

[0086] In some embodiments, the system can adjust the field size of the first imaging field of view multiple times so that the image obtained based on the first scanning data does not include image curling, while the image obtained based on the second scanning data includes image curling, and the adjusted field size is used as the second imaging field of view.

[0087] In some embodiments, the system can adjust the size of the first imaging field of view multiple times so that the time resolution of the second scanning data in the first scanning stage meets the preset time resolution condition and / or the image wrapping appears in the direction in which the movement of the scanning object has the greatest impact on the imaging, and use the adjusted field of view size as the second imaging field of view.

[0088] In some embodiments, alternatively or additionally, the preset condition may include: setting the field size of the second imaging field of view so that the spatial resolution of the second scan data meets the preset spatial resolution condition. In some embodiments, the preset spatial resolution condition may be that the spatial resolution of the second scan data in the first scanning stage is less than a spatial resolution threshold. The spatial resolution threshold may be a system default value, an empirical value, a manually preset value, or any combination thereof, and may be set according to actual needs, and this specification does not limit this.

[0089] The preset conditions may also be in other feasible forms, which may be set according to actual needs.

[0090] In some embodiments, the system can adjust the field size of the first imaging field of view multiple times so that the spatial resolution of the second scanning data meets a preset spatial resolution condition, and use the adjusted field size as the second imaging field of view.

[0091] In some embodiments, the acquisition module 210 can perform one or more rapid magnetic resonance scans on the scanned object in the second imaging field of view through a magnetic resonance device (e.g., the magnetic resonance device 110) to acquire second scan data of the scanned object in the first scan phase. The second scan data can be acquired without reconstruction.

[0092] In some embodiments, the second scanning data includes one or more sets of K-space data sets in at least one motion state of the scanned object in one or more motion cycles included in the first scanning stage. The scanned object includes at least one motion state in the motion cycle, and each set of K-space data sets corresponds to a specific motion state in at least one motion state. A set of K-space data sets may include one or more continuous K-space data sets. The position or morphology of the scanned object in a set of K-space data sets corresponding to a specific motion state changes less. The position or morphology of the scanned object in multiple sets of K-space data sets corresponding to different motion states changes more or more obviously.

[0093] Since each group of K-space data sets corresponds to a motion state, each three-dimensional scanning image corresponds to a motion state, accordingly, each group of K-space data sets in at least one group of K-space data sets corresponds to a group of three-dimensional scanning images in at least one group of three-dimensional scanning images.

[0094] In some embodiments, the acquisition time length corresponding to each group of K-space data sets in the second scan data is the same. In some embodiments, the acquisition time length corresponding to each group of K-space data sets in the second scan data may be different. In some embodiments, the acquisition time length corresponding to each group of three-dimensional scanned images in the first scan data is the same. In some embodiments, the acquisition time length corresponding to each group of three-dimensional scanned images in the first scan data may be different. In some embodiments, the acquisition time length corresponding to each group of three-dimensional scanned images in the first scan data is the same as the acquisition time length corresponding to each group of K-space data sets in the second scan data. In some embodiments, the acquisition time length corresponding to each group of three-dimensional scanned images in the first scan data may be different from the acquisition time length corresponding to each group of K-space data sets in the second scan data.

[0095] In some embodiments, the number of groups of K-space data sets in the second scanning data is the same as the number of groups of three-dimensional scanning images in the first scanning data. Figure 6 In some embodiments, the acquisition module 210 may obtain second original data of the scanned object in one or more motion cycles included in the first scanning phase, and divide the second original data based on the K-space data change characteristics to obtain at least one set of K-space data sets.

[0096] The second raw data refers to raw data obtained after a rapid magnetic resonance scan is performed on the scanned object. In some embodiments, the second raw data corresponds to a second imaging field of view. Accordingly, the second raw data may be raw data obtained after a rapid magnetic resonance scan is performed on the scanned object in the second imaging field of view.

[0097] In some embodiments, the acquisition module 210 may divide the second original data based on the K-space data variation characteristics, and divide the second original data into at least one group of K-space data sets.

[0098] In some embodiments, the K-space data variation characteristics may include differences in the distribution of high-frequency and low-frequency signals.

[0099] Due to the existence of image convolution, there will be differences in the distribution of high-frequency and low-frequency signals in the K-space data set. Figure 7 , the left side shows the image curling with different curling degrees, and the right side shows the imaging difference between the K-space dataset corresponding to the image curling and the reference K-space dataset. The reference K-space dataset is the K-space dataset when there is no image curling. Figure 7 As shown in the figure, when the image curling is more severe, the central low-frequency component and its peripheral high-frequency component of the K-space dataset are more enhanced relative to the reference K-space dataset, while when the curling is less severe, the central low-frequency component and its peripheral high-frequency component of the K-space dataset are less enhanced relative to the reference K-space dataset. In other words, as the curling increases, the signal strength of the central low-frequency and peripheral high-frequency parts of the K-space dataset is significantly improved.

[0100] Accordingly, the acquisition module 210 can divide the second original data into one or more groups of K-space data sets by distinguishing the distribution differences of high-frequency and low-frequency signals in the second original data. The signal strength changes of the central low-frequency and peripheral high-frequency parts of the K-space data collected at different times in a group of K-space data sets corresponding to a specific motion state are small or unobvious. For multiple groups of K-space data sets under different motion states, the signal strength changes of the central low-frequency and peripheral high-frequency parts of different K-space data sets are large or obvious.

[0101] For example, the K-space data sets whose low-frequency signal strength enhancement amounts (which may be the average enhancement amounts of all low-frequency signals) are within the same range among the multiple K-space data sets in the second original data can be divided into the same group of K-space data sets. For another example, the K-space data sets whose high-frequency signal strength enhancement amounts (which may be the average enhancement amounts of all high-frequency signals) are within the same range among the multiple K-space data sets in the second original data can be divided into the same group of K-space data sets. For another example, the K-space data sets whose low-frequency signal strength enhancement amounts are within the same range and whose high-frequency signal strength enhancement amounts are within the same range among the multiple K-space data sets in the second original data can be divided into the same group of K-space data sets.

[0102] Step 303 , based on the first scan data and the second scan data, obtain the data correspondence between the first imaging field of view and the second imaging field of view of the scanned object in at least one motion state in the first scanning phase. In some embodiments, step 303 may be performed by the analysis module 220 .

[0103] The data correspondence relationship may reflect the correspondence relationship between one or more data in the first scan data acquired under the first imaging field of view and one or more data in the second scan data acquired under the second imaging field of view. For example, the data correspondence relationship may reflect the correspondence relationship between each data in the first scan data and the associated data in the second scan data. For example, in the case where the first scan data includes at least one set of three-dimensional scan images and the second scan data includes at least one set of K-space data sets, see Figure 8 , the data correspondence may include a correspondence between at least one set of three-dimensional scanning images and at least one set of K-space data sets. For another example, the first scanning data may include at least one set of three-dimensional or two-dimensional scanning images, and the second scanning data may include at least one set of three-dimensional or two-dimensional scanning images with a smaller imaging field of view than the first scanning data, and the data correspondence may include a correspondence between the two sets of three-dimensional or two-dimensional scanning images with different imaging fields of view. For another example, the first scanning data may include at least one set of two-dimensional scanning images, and the second scanning data may include at least one set of K-space data sets, and the data correspondence may include a correspondence between at least one set of two-dimensional scanning images and at least one set of K-space data sets.

[0104] There are multiple ways to obtain the data correspondence. In some embodiments, the analysis module 220 can determine the motion state corresponding to each group of three-dimensional scanning images and the motion state corresponding to each group of K-space data sets, determine the correspondence between the three-dimensional scanning images and the K-space data sets according to the motion state, and obtain the data correspondence between the first imaging field of view and the second imaging field of view under at least one motion state of the scanned object in the first scanning stage. For example, each group of three-dimensional scanning images in at least one group of three-dimensional scanning images corresponds to a motion state of the scanned object in the first scanning stage, and each group of K-space data sets in at least one group of K-space data sets corresponds to a motion state of the scanned object in the first scanning stage, then the analysis module 220 can associate the three-dimensional scanning images and K-space data sets with the same motion state to determine the data correspondence. For another example, the analysis module 220 can label at least one three-dimensional scanning image and at least one group of K-space data sets based on the motion state, and associate the three-dimensional scanning images and K-space data sets with the same label by analyzing the labels of different three-dimensional scanning images and K-space data sets to determine the data correspondence. As an example only, when the scanned object is the lungs, the label types may include "first-level inhalation state (such as indicating less inhalation)", "second-level inhalation state (such as indicating more inhalation)", "third-level inhalation state (such as indicating that inhalation has reached the limit)", etc.

[0105] In this embodiment, the motion state corresponding to the K-space data set can be obtained by performing image reconstruction on the K-space data set and then performing image registration to obtain the corresponding relationship between the K-space data set and the motion state. The image registration process includes image transformation, feature extraction, similarity measurement, search optimization, etc.

[0106] In some embodiments, the analysis module 220 can determine the data correspondence between the K-space dataset and the three-dimensional scanning image group in at least one motion state of the scanned object in the first scanning phase through a matching model based on at least one set of three-dimensional scanning images and at least one set of K-space datasets.

[0107] The matching model may be a machine learning model. In some embodiments, the matching model may include any one or combination of various feasible models such as a recurrent neural network (RNN) model, a deep neural network (DNN) model, a convolutional neural network (CNN) model, etc.

[0108] In some embodiments, the input of the matching model may include at least one three-dimensional scanning image and at least one set of K-space data sets, and the output of the matching model may include data correspondences under at least one motion state of the scanned object in the first scanning phase.

[0109] In some embodiments, the matching model can be an existing image registration model. Exemplary image registration models include but are not limited to an unsupervised registration model based on a fully convolutional network (FCNet), an unsupervised registration model based on a generative adversarial network, and the like.

[0110] In some embodiments, the matching model can be obtained by training a large number of labeled training samples. In some embodiments, the training samples include at least one set of sample three-dimensional scanning images and at least one set of sample K-space data sets, and the labels include the actual data correspondences corresponding to the training samples. In some embodiments, the training samples can be acquired based on historical data, and the labels can be determined by the system or human annotation. For example, the system or humans can determine the sample three-dimensional scanning images and sample K-space data sets that have a corresponding relationship by image matching or image comparison (see above for details), and obtain the actual data correspondences between at least one sample three-dimensional scanning image and at least one set of sample K-space data sets.

[0111] In some embodiments of the present specification, by adopting a machine learning model to determine data correspondence, the self-learning ability of machine learning can be used to find patterns from a large amount of data, obtain the correlation between the three-dimensional scanning image and the K-space data set, and improve the accuracy and efficiency of determining the data correspondence.

[0112] Step 304 , based on the data correspondence, determine the motion state of the scanned object in the second scanning phase. In some embodiments, step 304 may be performed by the determination module 230 .

[0113] In some embodiments, the second scanning phase may be a real-time scanning phase. The second scanning phase is different from the first scanning phase.

[0114] In some embodiments, the second scanning stage can be a complete imaging process (for example, a situation where the motion state of the scanned object is determined while capturing an image), or it can be an incomplete imaging process or a non-imaging process for acquiring specific data (for example, K-space data) (for example, a situation where only the motion state of the scanned object needs to be determined without capturing an image).

[0115] In some embodiments, the determination module 230 may acquire third scan data of the scanned object in the second scan stage. The third scan process for acquiring the third scan data may not overlap in time with the first scan process for acquiring the first scan data and the second scan process for acquiring the second scan data, or may partially overlap in time with the first scan data and / or the second scan data. The third scan data may correspond to the second imaging field of view. Alternatively, the third scan data may correspond to a third imaging field of view that is different from the second imaging field of view, for example, the third imaging field of view may be smaller than the second imaging field of view, or the third imaging field of view may be larger than the second imaging field of view but smaller than the first imaging field of view, and so on. In some embodiments, the determination module 230 may determine the target motion state of the scanned object when acquiring the third scan data based on the third scan data and the data correspondence.

[0116] The third scanning data may refer to scanning data obtained after a rapid magnetic resonance scan is performed on the scanned object. In some embodiments, the third scanning data may be obtained in the application phase. For more information about the application phase, see Figure 1 and its related description.

[0117] In some embodiments, the third scan data may be scan data including time dimension information. For example, the third scan data may be a K-space data set including time dimension information. In some embodiments, the third scan data may be scan data not including time dimension information, that is, scan data obtained during a certain rapid magnetic resonance scan. For example, the third scan data may be a K-space data set not including time dimension information.

[0118] In some embodiments, the third scanning data corresponds to the second imaging field of view. Accordingly, the third scanning data may be scanning data obtained after a rapid magnetic resonance scanning is performed on the scanning object in the second imaging field of view.

[0119] In some embodiments, the determination module 230 may perform a rapid magnetic resonance scan on the scanned object in the second imaging field of view through a magnetic resonance device (e.g., the magnetic resonance device 110) to obtain third scan data of the scanned object. The third scan data may be obtained without reconstruction. The third scan data may be obtained in real time.

[0120] In some embodiments, the determination module 230 may determine a set of K-space data sets in the second scan data that matches the third scan data. The determination module 230 may determine a set of three-dimensional image data in the first scan data that corresponds to the matched K-space data set in the data correspondence. The determination module 230 may determine the motion state corresponding to the third scan data based on the determined set of three-dimensional image data. For example, the determination module 230 may determine the motion state of the scanned object represented by the three-dimensional scanned image in the determined set of three-dimensional image data as the target motion state of the scanned object when the third scan data is collected. The target motion state corresponding to the third scan data may be the corresponding position, shape, etc. of the scanned object when the third scan data is collected.

[0121] In some embodiments, the determination module 230 may determine a set of K-space data sets in the second scan data that matches the third scan data based on the third scan data, and determine the target motion state corresponding to the third scan data based on the data correspondence relationship and the K-space data set that matches the third scan data, wherein the target motion state is the motion state corresponding to the three-dimensional scan image corresponding to the K-space data set.

[0122] In some embodiments, the determination module 230 may determine the three-dimensional imaging result of the scanned object based on the third scan data. For example, the determination module 230 may determine a set of K-space data sets in the second scan data that matches the third scan data. The determination module 230 may determine a set of three-dimensional image data in the first scan data that corresponds to the matched K-space data set in the data correspondence relationship. The determination module 230 may determine the three-dimensional imaging result of the scanned object corresponding to the third scan data based on the determined set of three-dimensional image data.

[0123] It should be noted that, although in the above embodiments, the first scan data (and the first original data) are four-dimensional scan data, and the second scan data (and the second original data) and the third scan data are scan data obtained after rapid magnetic resonance scanning, the above related description is only for illustrative purposes and is not intended to limit the scope of this specification. The first scan data, the second scan data and the third scan data can be other types of scan data, for example, the first scan data can be a two-dimensional scan image sequence containing time dimension information, and the second scan data and the third scan data can be scan data obtained by normal magnetic resonance scanning.

[0124] Some embodiments of the present specification can achieve at least the following beneficial effects: (1) by performing a pre-scanning stage and an application stage, the real-time motion state of certain organs that are greatly affected by moving tissues such as respiratory movement and heartbeat can be captured, the motion tracking accuracy of target organ tissues can be improved, and high real-time requirements can be guaranteed; (2) the data correspondence between the K-space data set and the three-dimensional scanning image is constructed through the scanning results of the pre-scanning stage, so that the motion state of the moving tissue can be obtained in the application stage without reconstructing the K-space data set, effectively reducing the time cost of image reconstruction and reducing the computing power consumption of the computer caused by signal processing and image reconstruction; (3) by using small field of view imaging in the pre-scanning stage, the number of phase encoding steps in the imaging process can be effectively reduced, greatly reducing the phase (4) Image curling can reflect the enhancement of high-frequency components in the image caused by image overlap in the K-space data set, so as to locate the correspondence between different motion states and the variation law of K-space data; (5) By acquiring the K-space data set of the scanned object in real time during the application stage, and by the correspondence between the real-time K-space data set and the data obtained in the preprocessing stage, the K-space data can be used to directly guide and capture the motion state, greatly reducing the time consumed by two-dimensional image reconstruction, and quickly locating the current motion state of the moving tissue, so as to finally realize high-speed real-time three-dimensional imaging of magnetic resonance.

[0125] The following combination Fig. 9 The embodiments of the present invention provide an exemplary description of the motion state determination method described in some embodiments of the present specification.

[0126] First, the motion state determination system can obtain the data correspondence between the first imaging field of view and the second imaging field of view under each motion state in the motion cycle through the preprocessing stage. The preprocessing stage can include the following steps S11 to S13.

[0127] Step S11, performing a four-dimensional magnetic resonance scan on the scan object in a large scanning field of view to obtain first original data; and based on the first original data, obtaining at least one set of three-dimensional scanning images corresponding to different motion states within a motion cycle.

[0128] Step S12, setting a suitable small field of view (wherein the field of view sizes of the large field of view and the small field of view are relative), and performing a rapid magnetic resonance scan of a specific plane on the scanned object in the small scanning field of view to obtain second original data; and based on the second original data, obtaining at least one set of K-space data sets corresponding to the specific plane in different motion states within the motion cycle.

[0129] It should be noted that step S11 and step S12 may be performed simultaneously or non-simultaneously.

[0130] Step S13, based on at least one group of three-dimensional scanning images corresponding to different motion states and at least one group of K-space data sets corresponding to specific planes in different motion states, use a matching model to determine the correspondence between the K-space data sets and the three-dimensional scanning image groups under different motion states, and obtain a data correspondence.

[0131] The above is the preprocessing stage. After the preprocessing stage is completed, the motion state determination system can enter the application stage. The application stage includes the following steps S14 to S15.

[0132] Step S14: Perform a real-time rapid magnetic resonance scan on the scan object in a small scanning field of view to obtain a real-time K-space data set (ie, the aforementioned third scanning data).

[0133] Step S15, based on the real-time K-space data set and the data correspondence obtained in the preprocessing stage, locate the current motion state. In this process, if there is a K-space data set matching the real-time K-space data set in the data correspondence, the current motion state can be determined according to the three-dimensional scan image corresponding to the matched K-space data set, and then the current motion state is output. If there is no K-space data set matching the real-time K-space data set in the data correspondence, it is determined that the patient has a large deviation and needs to be re-scanned.

[0134] One or more embodiments of the present specification provide a motion state method, which includes the following steps S21-S24.

[0135] Step S21: Determine the data correspondence between a first imaging field of view and a second imaging field of view in at least one motion state, wherein the first imaging field of view corresponds to a three-dimensional scanning image, and the second imaging field of view corresponds to a K-space data set, and the data correspondence includes a correspondence between the three-dimensional scanning image and the K-space data set.

[0136] It should be noted that step S21 can be implemented based on a database, and the database includes relevant scan data obtained from historical scans. For more information on obtaining data correspondence, please refer to the above text.

[0137] Step S22: Perform a small-field magnetic resonance scan on the scan object in real time to obtain a real-time K-space data set.

[0138] In this step, the processor can control the imaging device to perform a rapid magnetic resonance scan on the scan object within a small scanning field of view. For more information, see the previous text.

[0139] Step S23: Based on the real-time K-space data set and the data correspondence, the physiological phase of the scanned object is determined. For more details, please refer to the above text.

[0140] Step S24: Determine a radiotherapy plan for the scanned object based on the physiological phase of the scanned object.

[0141] In some embodiments, the processor can determine the radiotherapy plan of the scanned object in a variety of ways based on the physiological phase of the scanned object. In some embodiments, the processor can determine the radiotherapy plan of the scanned object by querying a preset plan table based on the physiological phase of the scanned object. In some embodiments, the preset plan table may include a correspondence between multiple physiological phases of multiple organ types and multiple radiotherapy plans. In some embodiments, the preset plan table can be predetermined based on prior knowledge or historical data. In some embodiments, the processor can match a historical radiotherapy plan corresponding to a similar historical physiological phase of the same organ type from historical radiotherapy data based on the physiological phase of the scanned object, and determine the historical radiotherapy plan as the current radiotherapy plan. The radiotherapy plan can also be determined in other feasible ways, which are not limited here.

[0142] Some embodiments of the present specification can determine a radiotherapy plan in a targeted manner by analyzing the physiological phase of the scanned object, thereby improving the accuracy of radiotherapy.

[0143] One or more embodiments of the present specification provide a motion state determination device, including a processor, wherein the processor is used to execute a motion state determination method described in any of the above embodiments.

[0144] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a motion state determination method described in any of the above embodiments.

[0145] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of 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 suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0146] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in 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.

[0147] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with 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 by software solutions, such as installing the described system on an existing server or mobile device.

[0148] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0149] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0150] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0151] 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, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for determining a motion state, characterized in that: include: Acquire first scanning data of a scanned object in a first scanning phase, wherein the first scanning data corresponds to a first imaging field of view, and the scanned object is affected by physiological activities; Acquire second scanning data of the scanned object in the first scanning phase, wherein the second scanning data corresponds to a second imaging field of view, and the second imaging field of view is smaller than the first imaging field of view; Determining, based on the first scanning data and the second scanning data, a data correspondence between the first imaging field of view and the second imaging field of view in at least one motion state of the scanned object in the first scanning phase; as well as Based on the data correspondence, the motion state of the scanned object in the second scanning phase is determined in real time.

2. The motion state determination method according to claim 1, characterized in that: The step of determining the motion state of the scanned object in the second scanning phase in real time based on the data correspondence includes: Acquiring third scanning data of the scanned object in the second scanning phase; and Based on the third scanning data and the data correspondence, a target motion state of the scanning object when the third scanning data is collected is determined.

3. The motion state determination method according to claim 2, characterized in that: The determining, based on the third scanning data and the data correspondence, the target motion state of the scanning object when the third scanning data is collected comprises: Determining, based on the third scan data, a set of K-space data sets in the second scan data that matches the third scan data, wherein the third scan data corresponds to the second imaging field of view; and The target motion state is determined based on the data correspondence and a K-space data set matching the third scan data.

4. The motion state determination method according to claim 1, characterized in that: The method for determining the second imaging field of view includes: The second imaging field of view is determined based on the first scanning data and preset conditions.

5. The motion state determination method according to claim 4, characterized in that: The preset condition includes: setting the field size of the second imaging field of view so that the image obtained based on the first scanning data does not include image curling, and the image obtained based on the second scanning data includes image curling.

6. The motion state determination method according to claim 4, characterized in that: The preset conditions include: setting the field size of the second imaging field of view so that the time resolution of the second scanning data in the first scanning stage meets the preset time resolution condition and / or image curling occurs in the direction in which the movement of the scanning object has the greatest impact on the imaging.

7. The motion state determination method according to claim 1, characterized in that: The first scanning data includes at least one set of three-dimensional scanning images of the scanning object in at least one motion state in the first scanning phase; the first scanning data of the scanning object in the first scanning phase includes: Acquiring first raw data of the scanned object in the first scanning phase, wherein the first raw data corresponds to the first imaging field of view; and A three-dimensional state segmentation is performed based on the first original data to obtain at least one group of three-dimensional scanning images, wherein each group of the at least one group of three-dimensional scanning images corresponds to a specific motion state of the scanning object.

8. The motion state determination method according to claim 7, characterized in that: The second scanning data includes at least one set of K-space data sets in at least one motion state of the scanning object in the first scanning phase; and acquiring the second scanning data of the scanning object in the first scanning phase includes: Acquiring second raw data of the scanned object in the first scanning phase, the second raw data corresponding to the second imaging field of view; and The second original data is divided based on the K-space data change characteristics to obtain the at least one group of K-space data sets, each group in the at least one group of K-space data sets corresponds to a group of three-dimensional scanning images in the at least one group of three-dimensional scanning images.

9. A motion state determination system, characterized in that: include: Get module for Acquire first scanning data of a scanned object in a first scanning phase, wherein the first scanning data corresponds to a first imaging field of view, and the scanned object is affected by physiological activities; Acquire second scanning data of the scanned object in the first scanning phase, wherein the second scanning data corresponds to a second imaging field of view, and the second imaging field of view is smaller than the first imaging field of view; an analysis module, configured to determine, based on the first scanning data and the second scanning data, a data correspondence between the first imaging field of view and the second imaging field of view in at least one motion state of the scanned object in the first scanning phase; as well as The determination module is used to determine the motion state of the scanned object in the second scanning phase in real time based on the data correspondence.

10. A method for determining a motion state, characterized in that: The method comprises: Determine a data correspondence between a first imaging field of view and a second imaging field of view in at least one motion state, wherein the first imaging field of view corresponds to a three-dimensional scanning image, and the second imaging field of view corresponds to a K-space data set, and the data correspondence includes a correspondence between the three-dimensional scanning image and the K-space data set; Perform small field of view magnetic resonance scanning on the scanned object in real time to obtain real-time K-space data set; Determining the physiological phase of the scanned object based on the real-time K-space data set and the data correspondence relationship; A radiotherapy plan for the scanned object is determined based on the physiological phase of the scanned object.

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