A method and system for motion detection in magnetic resonance imaging

Abstract

The embodiment of the present specification provides a motion detection method in magnetic resonance imaging, the method comprising: acquiring at least two motion detection subsequences; acquiring motion detection data of a region of interest based on the at least two motion detection subsequences, the at least two motion detection subsequences being inserted in at least two imaging subsequences, the imaging subsequences being used for magnetic resonance imaging of the region of interest; and determining a motion state of the region of interest according to the motion detection data.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of medical technology, and in particular, to a motion detection method and system in magnetic resonance imaging. BACKGROUND

[0002] Magnetic resonance imaging (MRI) is an imaging method widely used in the medical field. In the magnetic resonance imaging process, the scanned person often needs to maintain a certain stationary state to prevent motion artifacts on the image and affect the imaging effect, but it is difficult to know whether the scanned person moves before the sequence scanning is completed in the existing magnetic resonance imaging scanning. Motion detection methods based on magnetic resonance sequences can be used to obtain the motion state of the scanned person in real time during scanning. For large amplitude motion, scanning can be terminated in advance to avoid time waste, or the motion-affected K-space data can be supplemented and reacquired, and the motion data can be excluded for reconstruction to obtain the corrected image. The current motion detection method based on magnetic resonance sequences compares and calculates the magnetic resonance signals collected at different times to obtain the real-time motion state, but there are the following problems: due to factors such as field drift and temperature rise during scanning, the collected magnetic resonance signals have certain phase errors, which affect the motion curve calculation; when scanning parts such as the head, the motion curve changes due to swallowing and other movements of the mouth, which is usually a non-region of interest; when scanning parts such as the pelvic cavity and abdomen, the motion curve changes due to involuntary movements such as peristalsis.

[0003] Therefore, it is desirable to provide a motion detection method and system in magnetic resonance imaging. SUMMARY

[0004] One of the embodiments of the present specification provides a motion detection method in magnetic resonance imaging. The method comprises: acquiring at least two motion detection sub-sequences; acquiring motion detection data of a region of interest based on the at least two motion detection sub-sequences, the at least two motion detection sub-sequences being inserted in at least two imaging sub-sequences, the imaging sub-sequences being used for magnetic resonance imaging of the region of interest; and determining a motion state of the region of interest according to the motion detection data.

[0005] One of the embodiments of the present specification provides another motion detection method in magnetic resonance imaging. The method comprises: acquiring motion detection data of a region of interest; acquiring first data of a first direction and second data of a second direction of the region of interest in the motion detection data, the first direction being more affected by interference motion than the second direction; and determining a motion state of the region of interest according to the first data and / or the second data.

[0006] One of the embodiments of the present specification provides another motion detection method in magnetic resonance imaging. The method comprises: acquiring motion detection data of a region of interest; determining boundary data in the motion detection data; and determining a motion state of the region of interest according to the boundary data.

[0007] One of the embodiments of the present specification provides a motion detection system in magnetic resonance imaging, comprising a motion sequence acquisition module, a motion data acquisition module, and a motion state determination module; the motion sequence acquisition module is configured to acquire at least two motion detection sub-sequences; the motion data acquisition module is configured to acquire motion detection data of a region of interest based on the at least two motion detection sub-sequences, the at least two motion detection sub-sequences being inserted in at least two imaging sub-sequences, the imaging sub-sequences being configured to perform magnetic resonance imaging on the region of interest; and the motion state determination module is configured to determine a motion state of the region of interest according to the motion detection data.

[0008] One of the embodiments of the present specification provides a computer readable storage medium, the storage medium stores computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes the motion detection method in magnetic resonance imaging as described in some embodiments of the present specification. BRIEF DESCRIPTION OF DRAWINGS

[0009] The present specification will be further illustrated in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, wherein:

[0010] Figure 1 is a schematic diagram of an application scenario of a motion detection system in magnetic resonance imaging according to some embodiments of the present specification;

[0011] Figure 2 is a schematic diagram of a motion detection system in magnetic resonance imaging according to some embodiments of the present specification;

[0012] Figure 3 is an exemplary flowchart of a motion detection method in magnetic resonance imaging according to some embodiments of the present specification;

[0013] Figure 4 is an exemplary flowchart of a motion detection method in magnetic resonance imaging according to some embodiments of the present specification;

[0014] Figure 5 is an exemplary flowchart of a motion detection method in magnetic resonance imaging according to some embodiments of the present specification;

[0015] Figure 6is an exemplary flowchart of a method of motion detection in magnetic resonance imaging according to some embodiments of the present specification;

[0016] Figure 7 is a schematic diagram of an imaging sub-sequence and a motion detection sub-sequence according to some embodiments of the present specification;

[0017] Figure 8 is a schematic diagram of a method of motion detection in magnetic resonance imaging according to some embodiments of the present specification;

[0018] Figure 9 is a schematic diagram of another method of motion detection in magnetic resonance imaging according to some embodiments of the present specification;

[0019] Figure 10 is a schematic diagram of a motion state according to some embodiments of the present specification;

[0020] Figure 11 is a schematic diagram of another method of motion detection in magnetic resonance imaging according to some embodiments of the present specification;

[0021] Figure 12 is a schematic diagram of a motion state according to some embodiments of the present specification. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can also be applied to other similar scenarios without creative labor. Unless it is clear from the language context or otherwise indicated, the same reference numbers in the drawings represent the same structures or operations.

[0023] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0024] As shown in the specification and claims, unless the context clearly indicates otherwise, "one", "a", "an", and / or "the" do not refer to the singular, but can also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0025] Flow diagrams in the present specification are used to illustrate the operations performed by systems according to embodiments of the present specification. It should be understood that the preceding or following operations are not necessarily performed in the exact order. Instead, various steps can be processed in reverse order, or at the same time. Meanwhile, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0026] In some application scenarios, the motion detection system in magnetic resonance imaging can include a processing device, a medical imaging device, and the motion detection system in magnetic resonance imaging can acquire the motion state of the scanned object in magnetic resonance imaging by implementing the methods and / or processes disclosed in the present specification through the processing device and the like, so as to obtain the corrected image of the scanned object according to the motion state, and obtain accurate data reflecting the physical condition of the scanned object.

[0027] Figure 1 is a schematic diagram of an application scenario of the motion detection system in magnetic resonance imaging according to some embodiments of the present specification.

[0028] As Figure 1 shown, in some embodiments, the system 100 can include a medical imaging device 110, a processing device 120, a storage device 130, a terminal 140, and a network 150.

[0029] The medical imaging device 110 refers to a device that reproduces the structure inside the human body as an image using different media in a medical manner. In some embodiments, the medical imaging device 110 can be any medical device that images a specified body part of a patient based on a magnetic resonance imaging method, for example, MRI, PET-MR (Positron Emission Tomography-Magnetic Resonance), etc. The medical imaging device 110 provided above is for illustrative purposes only and is not intended to limit the scope thereof. The medical imaging device 110 includes a plurality of imaging modules / sequences for imaging a scanned object (e.g., a human body, etc.). In some embodiments, the medical imaging device 110 can include a motion detection sub-sequence inserted in each repetition time (TR) of a scan sequence to acquire a magnetic resonance signal for motion detection. In some embodiments, the medical imaging device 110 can transmit the acquired magnetic resonance signal (e.g., a magnetic resonance signal for motion detection, a magnetic resonance signal for imaging, etc.) to the processing device 120. In some embodiments, the medical imaging device 110 can receive an instruction, etc. sent by a doctor through the terminal 140 and perform a related operation according to the instruction, for example, irradiation imaging, etc. In some embodiments, the medical imaging device 110 can exchange data and / or information with other components (e.g., the processing device 120, the storage device 130, the terminal 140) in the system 100 through the network 150. In some embodiments, the medical imaging device 110 can be directly connected with other components in the system 100. In some embodiments, one or more components (e.g., the processing device 120, the storage device 130) in the system 100 can be included in the medical imaging device 110.

[0030] The processing device 120 can process data and / or information obtained from other devices or system components, perform the motion detection method in magnetic resonance imaging shown in some embodiments of the present specification based on the data, information and / or processing results, and complete one or more functions described in some embodiments of the present specification. For example, the processing device 120 can obtain the motion state (e.g., motion curve, etc.) of the subject based on the magnetic resonance signal of the medical imaging device 110. For another example, the processing device 120 can correct the magnetic resonance signal / data for imaging based on the motion state (e.g., motion curve, etc.) of the subject, and obtain the corrected magnetic resonance image by reconstruction. In some embodiments, the processing device 120 can send the processed data (e.g., motion curve, etc.) to the storage device 130 for storage. In some embodiments, the processing device 120 can obtain the pre-stored data and / or information (e.g., magnetic resonance signal for motion detection, formula for motion detection, etc.) from the storage device 130 for performing the motion detection method in magnetic resonance imaging shown in some embodiments of the present specification, such as obtaining the motion state of the subject.

[0031] In some embodiments, the processing device 120 can include one or more sub-processing devices (e.g., single-core processing devices or multi-core multi-core processing devices). For example only, the processing device 120 can include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, or the like, or any combination thereof.

[0032] The storage device 130 can store data or information generated by other devices. In some embodiments, the storage device 130 can store the data and / or information collected by the medical imaging device 110, such as the magnetic resonance signal for motion detection, the magnetic resonance signal for imaging, etc. In some embodiments, the storage device 130 can store the data and / or information processed by the processing device 120, such as the motion curve, etc. The storage device 130 can include one or more storage components, each of which can be a separate device or a part of other devices. The storage device can be local or implemented through the cloud.

[0033] The terminal 140 can control the operation of the medical imaging device 110. A physician can issue an operation instruction to the medical imaging device 110 through the terminal 140, so that the medical imaging device 110 completes a specified operation, for example, irradiates a body part of a scanned object for imaging. In some embodiments, the terminal 140 can instruct the processing device 120 to perform a motion detection method in magnetic resonance imaging as shown in some embodiments of the present specification. In some embodiments, the terminal 140 can receive a corrected magnetic resonance image and / or a motion curve diagram from the processing device 120, so that the physician can accurately determine the physical condition and / or motion state of the scanned object. In some embodiments, the terminal 140 can be one or any combination of a mobile device 140-1, a tablet computer 140-2, a laptop computer 140-3, a desktop computer, and other devices with input and / or output functions.

[0034] The network 150 can connect the components of the system and / or connect the system with external resource parts. The network 150 enables communication between the components and other parts outside the system, and facilitates exchange of data and / or information. In some embodiments, one or more components (for example, the medical imaging device 110, the processing device 120, the storage device 130, and the terminal 140) in the system 100 can send data and / or information to other components through the network 150. In some embodiments, the network 150 can be any one or more of a wired network or a wireless network.

[0035] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of the present specification. Various changes and modifications can be made by those of ordinary skill in the art under the guidance of the present specification. The features, structures, methods, and other characteristics of the exemplary embodiments described in the present specification can be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the processing device 120 can be based on a cloud computing platform, such as a public cloud, a private cloud, a community cloud, and a hybrid cloud. However, these changes and modifications will not depart from the scope of the present specification.

[0036] Figure 2 is a schematic diagram of a motion detection system in magnetic resonance imaging according to some embodiments of the present specification.

[0037] As shown in Figure 2 some embodiments, the motion detection system in magnetic resonance imaging 200 can include a motion sequence acquisition module 210, a motion data acquisition module 220, and a motion state determination module 230.

[0038] In some embodiments, the motion sequence acquisition module 210 can be configured to acquire at least two motion detection sub-sequences.

[0039] In some embodiments, the motion data acquisition module 220 can be configured to acquire motion detection data of the region of interest based on at least two motion detection subsequences, wherein the motion detection subsequences are inserted in at least two imaging subsequences, and the imaging subsequences can be used for magnetic resonance imaging of the region of interest.

[0040] In some embodiments, the motion state determination module 230 can be configured to determine a motion state (e.g., a motion profile, etc.) of the region of interest according to the motion detection data.

[0041] In some embodiments, the motion state determination module 230 can determine reference data according to data acquired by a first motion detection subsequence of the at least two motion detection subsequences; select data acquired by a second motion detection subsequence of the at least two motion detection subsequences as target data; determine a difference between the reference data and the target data; and determine the motion state according to the difference.

[0042] In some embodiments, the motion state determination module 230 can perform phase filtering processing on the motion detection data, wherein the phase filtering processing can be used to reduce or eliminate phase differences in the motion detection data.

[0043] In some embodiments, after acquiring the motion detection data of the region of interest, the motion state determination module 230 can acquire first data in a first direction and second data in a second direction of the region of interest in the motion detection data, wherein the first direction is more affected by the interfering motion (e.g., swallowing action, etc.) than the second direction; and determine the motion state of the region of interest according to the first data and the second data.

[0044] In some embodiments, the motion state determination module 230 can divide the first data into at least two first segments along the first direction; determine a first motion state of each first segment; divide the second data into at least two second segments along the second direction; determine a second motion state of each second segment; determine a first difference between the first motion states; determine a second difference between the second motion states; and determine the influence of the interfering motion on the motion detection data according to the first difference and the second difference.

[0045] In some embodiments, after determining that the motion detection data is influenced by the interfering motion, the motion state determination module 230 can set a first weight of the first data and a second weight of the second data, wherein the first weight is less than the second weight; and determine the motion state according to the first weight and the second weight.

[0046] In some embodiments, after determining that the motion detection data is influenced by the interfering motion, the motion state determination module 230 can exclude the first data and determine the motion state according to the second data.

[0047] In some embodiments, after obtaining the motion monitoring data of the region of interest (e.g., head, etc.), the motion state determination module 230 can determine boundary data in the motion monitoring data; and determine the motion state according to the boundary data.

[0048] Figure 3 is an exemplary flowchart of a method of motion detection in magnetic resonance imaging according to some embodiments of the present specification.

[0049] As shown in Figure 3 , the flow 300 includes the following steps. In some embodiments, the flow 300 can be performed by the processing device 120.

[0050] Step 310: Obtain at least two motion detection sub-sequences. In some embodiments, the step 310 can be performed by the motion sequence obtaining module 210.

[0051] The motion detection sub-sequences are magnetic resonance scan sequences (operations) for motion detection in a medical imaging device (e.g., MRI), and can be multiple. The magnetic resonance scan sequence includes multiple repetition times (TRs), and each TR includes an imaging sub-sequence. In some embodiments, one or more motion detection sub-sequences (e.g., at least two motion detection sub-sequences corresponding to different directions) can be inserted in each TR of the magnetic resonance scan sequence, and these motion detection sub-sequences can be adjacent to each other or can be performed simultaneously. In some embodiments, the motion detection sub-sequences inserted in a TR can be adjacent to the imaging sub-sequence in the TR, for example, at least one motion detection sub-sequence can be inserted before or after the imaging sub-sequence in a TR. Therefore, the at least one motion detection sub-sequence is equivalent to being inserted between two adjacent imaging sub-sequences, and the two adjacent imaging sub-sequences belong to two adjacent TRs, respectively. The imaging sub-sequence is a scan sequence of the medical imaging device for imaging, and the imaging sub-sequence can be multiple, each imaging sub-sequence corresponding to a TR. Figure 7 is a schematic diagram of an imaging sub-sequence and a motion detection sub-sequence according to some embodiments of the present specification. As shown in Figure 7 , the motion detection sub-sequence 720 can be inserted between the imaging sub-sequences 710 and 730, and the motion detection sub-sequence 740 can be inserted between the imaging sub-sequences 730 and 750, wherein each of the imaging sub-sequences 710, 730, and 750 corresponds to a TR.

[0052] In some embodiments of the present disclosure, motion detection is added to the imaging sub-sequence with a motion detection sub-sequence of short duration. By adjusting the parameters of the motion detection sub-sequence, the motion detection sub-sequence can be made to substantially not interfere with the imaging sub-sequence and substantially not increase the scan time. In some embodiments, the motion detection sub-sequence can be shorter in duration than the imaging sub-sequence, for example, the motion detection sub-sequence can be 1-20 milliseconds in duration. In some embodiments, the magnetic resonance signals acquired from the motion detection sub-sequence are used for motion detection of the subject and not for imaging of the subject.

[0053] In some embodiments, the motion detection sub-sequence can employ any kind of echo sequence, for example, a Gradient Echo (GRE) sequence, a Fast Spin Echo (FSE) sequence, an Echo Planar Imaging (EPI) sequence, etc. In some embodiments, the motion detection sub-sequence and the imaging sub-sequence can be of the same kind or different kinds. In some embodiments, the motion detection sub-sequence can include radio frequency excitation pulses, spatial encoding gradients (including slice selection gradients, readout gradients, or phase encoding gradients), etc. In some embodiments, the parameters of the motion detection sub-sequence can be adjusted in conjunction with the inserted imaging sub-sequence and the current scan location, for example, the excitation slice, the flip angle, the readout direction, etc., in order to acquire magnetic resonance signals that reflect the motion of the subject in real time during the execution of the imaging sub-sequence for subsequent computation to obtain a curve that reflects the motion of the subject.

[0054] In some embodiments, the excitation slice of the motion detection sub-sequence can be the same as or different from the imaging sub-sequence. In some embodiments, because the data acquired from the motion detection sub-sequence is not used for imaging, only one-dimensional spatial encoding can be performed, for example, encoding gradients can be applied in only one direction (for example, the slice selection direction, the readout direction, or the phase encoding direction) in the motion detection sub-sequence. Alternatively, encoding gradients can be applied in at least two directions (for example, the slice selection direction, the readout direction, and the phase encoding direction) in the motion detection sub-sequence.

[0055] As an example, if the motion detection sub-sequence employs a spin echo sequence (for example, an SE or FSE sequence), the flip angle of the excitation pulse in the motion detection sub-sequence does not need to be 90 degrees and can be a small angle flip angle (for example, 3-5 degrees) to reduce the acquisition time of the motion detection sub-sequence while not affecting the imaging sub-sequence.

[0056] As a further example, if the imaging sub-sequences are GRE sequences, to guarantee the steady state of the GRE signals of the imaging sub-sequences, the motion detection sub-sequences can also be GRE sequences, and the flip angles, the excitation slices of the motion detection sub-sequences can be similar to those of the imaging sub-sequences.

[0057] At step 320, motion detection data of the region of interest is acquired based on the at least two motion detection sub-sequences. The motion detection sub-sequences are inserted in the at least two imaging sub-sequences, which are used for magnetic resonance imaging of the region of interest. In some embodiments, step 320 can be performed by the motion data acquisition module 220. In some embodiments, the acquired magnetic resonance signals (or echo signals) of the region of interest during the execution of the motion detection sub-sequences are the motion detection data of the region of interest.

[0058] The region of interest is a target scanning region of the medical imaging device, which can be any part of the human body, such as the head, the abdomen, etc. The motion detection data refers to the magnetic resonance signals used for motion detection, which can be the signals acquired by the medical imaging device (e.g., MRI, etc.) when scanning any part of the human body (e.g., the head, the abdomen, etc.).

[0059] In some embodiments, at least two sets of motion detection data of the region of interest can be acquired by executing two or more motion detection sub-sequences during the scanning of the region of interest by the medical imaging device, wherein each motion detection sub-sequence corresponds to a set of motion detection data, and each motion detection sub-sequence is inserted in two imaging sub-sequences, which can be used for magnetic resonance imaging of the region of interest (e.g., the head, the abdomen, etc.).

[0060] At step 330, the motion state of the region of interest is determined based on the motion detection data. In some embodiments, step 330 can be performed by the motion state determination module 230.

[0061] In some embodiments, the motion state of the region of interest can be determined in multiple ways based on the acquired at least one set of motion detection data, such as by acquiring a motion curve graph. The motion curve graph is an image that can reflect the motion state of the scanned object (i.e., the target object) during scanning, such as a curve graph with time as the horizontal coordinate and the r value in formula 3 as the vertical coordinate.

[0062] In some embodiments, the two sets of acquired motion detection data can be compared, and the motion state of the region of interest can be determined based on the difference between the two sets of motion detection data. For more information about determining the motion state based on the difference between the two sets of motion detection data, please refer to the related description of Figure 4 、 Figure 8 , which will not be repeated here.

[0063] In the process of determining the motion state of the region of interest, the movement of non-interested parts may affect the judgment of the motion state. Taking the swallowing action of the head as an example, swallowing action occasionally occurs during the scanning process. However, under normal circumstances, the region of interest is scanned in the brain. Therefore, swallowing does not need to be reflected in the motion state in motion detection.

[0064] In some embodiments, the motion state of a region of interest (ROI), such as the motion state of a head, can be determined based on data from multiple directions within the motion detection data. For more information on how to determine the motion state of a ROI based on data from multiple directions within the motion detection data, please refer to [link to relevant documentation]. Figure 5 , Figure 9 The relevant descriptions will not be repeated here.

[0065] Involuntary movements can also affect the assessment of motion status in the region of interest. For example, abdominal or pelvic peristalsis is often beyond the control of the subject being scanned and is quite common during the scan. Reflecting these movements would frequently prompt the subject to move. Furthermore, even if these movements were reflected, it would be difficult to interact with the subject and get them to cooperate with the scan. Therefore, for these involuntary movements, it is necessary to avoid their influence and preserve the true motion status.

[0066] In some embodiments, the motion state of a region of interest (ROI), such as the abdomen or pelvis, can be determined based on boundary data in motion detection data. For more information on how to determine the motion state of a ROI based on boundary data in motion detection data, please refer to [link to relevant documentation]. Figure 6 , Figure 11 The relevant descriptions will not be repeated here.

[0067] In some embodiments, the motion state can be used to determine whether the scanned object has moved. For large-scale motion, the scan can be terminated in advance, or the motion data can be discarded. The motion-affected data can be supplemented and re-acquired to reconstruct the image, such as a magnetic resonance image.

[0068] Some embodiments in this specification acquire signals in real time by inserting motion detection subsequences into the imaging subsequence. The acquired magnetic resonance signals for motion detection are then processed by algorithms in combination with the motion state characteristics of different parts of the body, which can more accurately and better reflect the motion state of the scanned subject.

[0069] Figure 4 This is an exemplary flowchart of a motion detection method in magnetic resonance imaging according to some embodiments of this specification.

[0070] like Figure 4As shown, process 400 includes the following steps. In some embodiments, process 400 may be executed by motion state determination module 230.

[0071] Step 410, determine reference data according to the data obtained from the first motion detection subsequence among at least two motion detection subsequences.

[0072] Reference data refers to motion detection data used as a reference, which is data obtained before obtaining target data. Target data is the motion detection data obtained at the current time (latest) and used as a target. In some embodiments, reference data may include a set of data, and a set of data is data obtained from one or more motion detection subsequences. In some embodiments, reference data may include the average value of multiple sets of data, that is, the average value of data obtained from multiple motion detection subsequences. In some embodiments, reference data may also be other forms of data.

[0073] Reference data can be determined from the motion detection data obtained from the motion detection subsequence. For example, as Figure 8 shown, reference data 820 can be obtained from motion detection data 810. In some embodiments, one or more motion detection subsequences before the latest (current) motion detection subsequence can be selected from multiple motion detection subsequences as the first motion detection subsequence, and then reference data is determined based on the data obtained from the first motion detection subsequence.

[0074] In some embodiments, one motion detection subsequence before the latest motion detection subsequence can be selected as the first motion detection subsequence, and the data obtained from this motion detection subsequence is used as reference data. For example, assuming that the motion detection subsequences are arranged in order of time from front to back as S1, S2, S3... S d (e.g., motion detection data 810), then the latest (current) motion detection subsequence is S d , any one of S1 to S d-1 can be selected as the first motion detection subsequence, and the reference data is the data obtained from the selected first motion detection subsequence.

[0075] In some embodiments, multiple motion detection subsequences before the latest motion detection subsequence can be selected, and the average value of the data obtained from these motion detection subsequences is determined as reference data. For example, c (1 < c ≤ d - 1) motion detection subsequences can be selected from the motion detection subsequences S1, S2, S3... S d-1 in the previous example as the first motion detection subsequence, and then the average value of the data obtained from these c motion detection subsequences is used as reference data. Here, c and d are natural numbers.

[0076] Step 420: Select the data obtained from the second motion detection subsequence out of at least two motion detection subsequences as the target data.

[0077] Target data can be determined from motion detection data obtained from motion detection subsequences. For example, such as Figure 8 As shown, target data 830 can be obtained from motion detection data 810. In some embodiments, the latest motion detection subsequence can be selected as the second motion detection subsequence from a plurality of motion detection subsequences, and then the data obtained from this motion detection subsequence can be used as the target data. For example, the target data can be obtained from motion detection subsequences S1, S2, S3...S... d (Motion detection data 810) Select S d As the second motion detection subsequence, then S d The acquired data is used as the target data.

[0078] Step 430: Perform phase filtering on the motion detection data.

[0079] During the acquisition of motion detection data, various factors such as field drift and temperature rise can cause phase differences in the acquired magnetic resonance signals (even if the scanned subject is not moving). Therefore, in some embodiments, the motion detection data can be first subjected to phase filtering processing, and then the motion state can be calculated using algorithms. Phase filtering is used to reduce or eliminate phase differences in the motion detection data. For example, ... Figure 8 As shown, for reference data 820 and target data 830, a low-frequency phase 840 corresponding to reference data 820 and a low-frequency phase 850 corresponding to target data 830 can be obtained through a low-pass filter. Then, the amplitude of the data is kept unchanged, and the phases are subtracted. That is, the phase of reference data 820 is subtracted from the low-frequency phase 840 to obtain the phase of reference data 820 (i.e., the filtered result 860), and the phase of target data 830 is subtracted from the low-frequency phase 850 to obtain the phase of target data 830 (i.e., the filtered result 870). The filtered results 860 and 870 are input into the algorithm for calculation to obtain the motion state 880.

[0080] As an example, for motion detection data X(m,n) obtained in K-space (e.g., reference data or target data), where m is the number of sampling points and n is the number of channels, a low-pass filter V(m,1) is applied to the frequency domain and multiplied by the data X channel by channel to obtain the filtered data Z (low-frequency phase). Then, X and Z are subjected to inverse Fourier transform to obtain the image domain data x(m,n) and z(m,n). For the data x(m,n), calculated channel by channel, keeping the amplitude of the data unchanged, the phase becomes the phase difference between x and z. The filtered result x'(m,n) can then be obtained using the following formula:

[0081] x'(m, n) = |A| * exp(i * θ) (1)

[0082] where A is the amplitude of x(m, n); i is the imaginary unit; θ is the phase, which can be calculated by the following formula:

[0083] θ = angle(x) - angle(z) (2)

[0084] where angle(x) and angle(z) are the phases of x and z, respectively.

[0085] In some embodiments of the present specification, by performing phase filtering processing on the obtained motion detection data, the phase difference of the magnetic resonance signal caused by various factors such as field drift and temperature rise is reduced or eliminated, the influence of additional factors is removed, and the accuracy and reliability of the motion detection data are improved, thereby improving the accuracy of the obtained motion state.

[0086] Step 440, determining the difference between the reference data and the target data.

[0087] After obtaining the reference data and the target data, the difference between the reference data and the target data can be determined. In some embodiments, the difference between the reference data and the target data can be determined in various ways such as algorithms, for example, correlation calculation, etc.

[0088] In some embodiments, if the reference data is the data of one data acquisition point (i.e., a motion detection sub-sequence), the correlation between the reference data and the target data can be calculated by the following formula:

[0089]

[0090] where r is the correlation of the reference data U and the target data Y; U i refers to the data of one data acquisition point in the reference data U, refers to the average value of the data of all data acquisition points in the reference data U; Y i refers to the data of one data acquisition point in the target data Y; refers to the average value of the data of all data acquisition points in the target data Y; n is the number of data acquisition points in the reference data U and / or the target data Y.

[0091] Step 450, determining the motion state according to the difference.

[0092] In some embodiments, the motion state can be determined according to the difference between the reference data and the target data (for example, the correlation of the reference data and the target data, etc.), and the motion state can be represented by a motion curve graph. For example, the horizontal coordinate is time, and the vertical coordinate is the curve graph of the r value in formula 3.

[0093] The correlation r of the reference data and the target data can reflect the correlation of the target data and the reference data, the greater the correlation, the greater the r, and the smaller the motion amplitude. In some embodiments, a motion curve can be directly plotted according to r or 1-r, where the horizontal coordinate is time, and the vertical coordinate can be r or 1-r.

[0094] In some embodiments of the present specification, the difference between the current data and the previous data in the motion detection data is obtained by comparing the current data with the previous data, the motion state is determined according to the difference, and the phase difference caused by various uncertain factors such as field drift and temperature rise is reduced or even eliminated through phase filtering, so that the change of the data over time can be more accurately reflected, the robustness of the process is improved, and the motion state changing over time is accurately obtained.

[0095] In some embodiments, step 430 can be omitted.

[0096] In some embodiments, the processing device 120 obtains the motion detection data collected by the current motion detection sub-sequence from the medical imaging device 110 after the medical imaging device 110 performs a motion detection sub-sequence, processes the motion detection data according to the procedure 400, and determines the current motion state of the region of interest, thereby realizing real-time motion monitoring of the region of interest. For example, if the r value corresponding to the current motion detection data is less than the motion detection threshold, it indicates that the current scanning object has a large amplitude motion, and the processing device 120 can issue a prompt, automatically terminate the scan, or automatically exclude the magnetic resonance signals collected by the imaging sub-sequence before the current motion detection sub-sequence and adjacent to the current motion detection sub-sequence.

[0097] Figure 5 is an exemplary flowchart of a motion detection method in magnetic resonance imaging according to some embodiments of the present specification.

[0098] As shown in Figure 5 , the flow 500 includes the following steps. In some embodiments, the flow 500 can be performed by the processing device 120.

[0099] Step 510, obtaining motion detection data of a region of interest. In some embodiments, step 510 can be performed by the motion data acquisition module 220.

[0100] In some embodiments, the motion detection data of the region of interest can be obtained by scanning the region of interest by the medical imaging device, for example, Figure 9 motion detection data 910 as shown. For more information on how to obtain the motion detection data of the region of interest, please refer to the relevant description of step 320, which will not be repeated here.

[0101] In some embodiments, motion in a region of non-interest can affect the motion state of the region of interest, necessitating the removal of interfering motion (i.e., motion in a region of non-interest). For example, if the region of interest is the brain, and swallowing affects the brain's motion state, thus being included in the motion detection data, it is necessary to remove the influence of swallowing from the acquired motion detection data.

[0102] In some embodiments, the motion detection data can be compared in different directions (e.g., two directions, three directions, etc.) to determine if the data includes interfering motion (e.g., swallowing motion, etc.), and then the influence of the motion can be removed by processing the data to obtain the motion state of the scanned object.

[0103] Step 520: Acquire first data in a first direction and second data in a second direction from the motion detection data for the region of interest. In some embodiments, step 520 may be performed by the motion state determination module 230.

[0104] In some embodiments, data from two different directions of a region of interest (e.g., head) in motion detection data can be acquired, wherein one direction is more affected by the interfering motion than the other direction. For example, first data of a first direction and second data of a second direction of the region of interest can be acquired, wherein the first direction is more affected by the interfering motion than the second direction.

[0105] like Figure 9 As shown, in some embodiments, the AP direction data 920 of the patient coordinate system (the scanned object is the patient) can be obtained from the motion detection data 910 as the first data, and the RL direction data 930 of the patient coordinate system can be obtained as the second data. The AP direction is the direction connecting the back of the head and the forehead (front-back direction); the RL direction is the left-right direction of the body. The AP direction is more susceptible to influences such as head swallowing movements than the RL direction.

[0106] In some embodiments, at least two motion detection subsequences can be inserted into each TR of the magnetic resonance imaging sequence to acquire data in different directions. In some embodiments, the first motion detection subsequence can be used to excite the sagittal plane of the region of interest, and then a readout gradient can be applied along the AP direction to acquire data in the AP direction; the second motion detection subsequence can be used to excite the coronal plane of the region of interest, and then a readout gradient can be applied along the RL direction to acquire data in the RL direction.

[0107] Step 530: Determine the motion state of the region of interest based on the first data and the second data. In some embodiments, step 330 may be performed by the motion state determination module 230.

[0108] For example, in the AP and RL directions of the patient coordinate system, if swallowing motion occurs, in the AP direction, the signal change mainly occurs in the A direction (frontal lobe); in the RL direction, the signal change has no obvious directional tendency. Therefore, in some embodiments, the motion state of the region of interest can be determined according to the first data and the second data, for example, whether swallowing motion occurs can be determined according to the data in the AP direction and the data in the RL direction, so as to determine the motion state of the head.

[0109] In some embodiments, the data in each direction (for example, the AP and RL directions) can be respectively segmented and calculated, and then the differences between the segmented calculation results in each direction are compared. If there is a significant difference in the AP direction and a small difference in the RL direction, it can be determined that swallowing motion occurs.

[0110] The swallowing motion of the head causes vibration in the A direction in the AP direction, and the P direction (back of the brain) has little change, so the data in the A direction and the P direction (which can be divided into two segments along the AP direction, one segment representing A direction data and the other segment representing P direction data) will have a significant difference. However, the swallowing motion has no significant difference in the R (right) and L (left) directions in the RL direction (because the throat is in the middle), so the data in the R direction and the L direction have little difference.

[0111] In some embodiments, the first data can be divided into at least two first segments along the first direction; the first motion state of each first segment is determined; the second data is divided into at least two second segments along the second direction; the second motion state of each second segment is determined; the first difference between the first motion states is determined; the second difference between the second motion states is determined; and the motion detection data is determined to be affected by the interference motion according to the first difference and the second difference, that is, if the first difference is a significant difference and the second difference is a non-significant difference, the motion detection data is determined to be affected by the interference motion. For example, the magnetic resonance signal X in the AP direction can be divided into two segments along the AP direction, and the magnetic resonance signal Y in the RL direction can be divided into two segments along the RL direction according to the physical position (which can be divided equally or combined with the specific scanning part), and then the motion state of each segment is calculated (for example, the data corresponding to each segment is obtained in the parameter data, which is obtained by the method shown in formula 3), to obtain the motion results X1, X2 (for example, the r value in formula 3) in the AP direction and the motion results Y1, Y2 (for example, the r value in formula 3) in the RL direction. Figure 4 The calculation results of X1, X2 and Y1, Y2 are compared respectively, if there is a significant difference in the AP direction and no significant difference in the RL direction, that is, X1 and X2 have a significant difference, and Y1 and Y2 have no significant difference, it can be determined that swallowing motion occurs.

[0112] In some embodiments, determining whether there is a significant difference can be based on whether the ratio or difference of the motion results of different segments reaches a preset threshold. For example, assuming that the motion results X1 and X2 in the AP direction are the correlation r values ​​in Formula 3, and X1 is greater than X2, if the ratio or difference of X1 and X2 is greater than or equal to the preset threshold, it can be determined that there is a significant difference between X1 and X2.

[0113] like Figure 9 As shown, in some embodiments, the data in direction 920 (AP) can be segmented and calculated, and then the segmented calculation results in direction AP are compared to obtain a judgment result 940, which indicates a significant difference. At the same time, the data in direction 930 (RL) can be segmented and calculated, and then the segmented calculation results in direction RL are compared to obtain a judgment result 950, which indicates no significant difference. Based on 940 and 950, it can be determined that a swallowing movement has occurred. If the comparison of the segmented calculation results in both directions AP and RL is not significant (i.e., 950), it can be determined that no swallowing movement has occurred.

[0114] In some embodiments, if it is determined that there is interfering motion, that is, the motion detection data is affected by interfering motion, multiple data can be processed in various ways (e.g., weighted calculation of data in different directions, removal of data in affected directions, etc.) to remove the influence of interfering motion, thereby obtaining the motion state of the region of interest based on the processed data.

[0115] like Figure 9 As shown, in some embodiments, after determining that a swallowing motion has occurred, the data in 920 (AP direction) and 930 (RL direction) can be weighted / removed through step 960, and the motion state 970 can be obtained based on the processed data.

[0116] In some embodiments, if it is determined that the motion detection data is affected by interfering motion, i.e., the first difference is a significant difference and the second difference is a non-significant difference, a first weight of the first data and a second weight of the second data can be set, where the first weight is less than the second weight; the motion state is determined based on the first weight and the second weight. For example, for motion results X1 and X2 in the AP direction and motion results Y1 and Y2 in the RL direction, X1 and X2 can be combined into a single value X' (e.g., by averaging), and Y1 and Y2 can be combined into a single value Y' (e.g., by averaging). Then, a weighted sum is performed on X' and Y', and the result of the weighted sum is used as the data for determining the motion state. The weight of X' can be less than the weight of Y', i.e., the weight of the AP direction can be less than the weight of the RL direction, thereby reducing the influence of the AP direction data. Another example is that the motion state can be determined based on the AP direction data and the RL direction data through methods such as... Figure 4 The methods shown calculate the r value respectively, for example, r AP and r RLr AP and r RL respectively, and w AP and w RL are set respectively, w AP may be less than w RL , and then the weighted sum of r AP and r RL is obtained, and the result of the weighted sum is used as the data for determining the motion state.

[0117] In some embodiments, if it is judged that the motion detection data is affected by the interfering motion, i.e., the first difference is the significant difference and the second difference is the non-significant difference, the first data can be rejected, and the motion state is determined according to the second data (for example, the motion state diagram shown in FIG. 9B). For example, for the motion results X1, X2 in the AP direction and Y1, Y2 in the RL direction, X1 and X2 can be rejected, and Y1 and Y2 can be combined into a value Y' by methods such as averaging, and then Y' is used as the data for determining the motion state. For another example, the data in the AP direction can be directly rejected, and only the data in the RL direction is used to calculate the r value by the method shown in FIG. 9C, and the motion curve diagram is generated based on the r value. Figure 10 Figure 4

[0118] Figure 10 The curve diagram shown in FIG. 9D is the data in the AP direction in the motion detection data, and the data obtained in the AP direction is subjected to inverse Fourier transform to generate the image domain data. The horizontal coordinate is the coordinate (0-256) in the image domain along the AP direction, and the vertical coordinate is the signal intensity. As shown in FIG. 9E, it is divided into two segments along the AP direction, the horizontal coordinate 0-128 corresponds to the data in the A direction, and 129-256 corresponds to the data in the P direction. It can be seen that due to the swallowing action, there is a group of data in the A direction which has a large difference from other data (the rate of change of the vertical coordinate value of the group of data is much greater than that of other groups of data), and the data in the P direction is less affected by the swallowing action, so the coincidence degree is good. Figure 10

[0119] In some embodiments, after it is judged that the swallowing motion does not occur, the motion state 970 can be directly obtained based on 910. For example, the motion state is obtained by the method shown in FIG. 9F. Figure 4

[0120] In some embodiments of the present specification, in order to avoid the interference of the motion of the non-interest region caused by the head swallowing and other actions on the motion state of the interest region, the motion state in different directions is obtained by segmenting and calculating the data in different directions of the motion detection data, and the motion state in different directions is subjected to weighted or rejected processing, so as to avoid unnecessary motion state reflection such as swallowing motion, and a more real, accurate and reliable motion state can be obtained. ​​​​

[0121] Figure 6 is an exemplary flowchart of a method of motion detection in magnetic resonance imaging according to some embodiments illustrated in the present specification.

[0122] As shown in FIG. 6, the flow 600 includes the following steps. In some embodiments, the flow 600 can be performed by the processing device 120. Figure 6

[0123] Step 610, acquiring motion detection data of a region of interest. In some embodiments, the step 610 can be performed by the motion data acquisition module 220.

[0124] In some embodiments, the motion detection data of the region of interest can be acquired by scanning the region of interest by a medical imaging device, for example, the motion detection data 1110 (target data) shown in FIG. 11. Figure 11 For more information about how to acquire the motion detection data of the region of interest, please refer to the relevant description of step 320, which will not be repeated here.

[0125] For non-autonomous motion, for example, peristalsis of the abdomen or pelvic cavity, heart beat, etc., these motions are often not controlled by the scanned object, and are relatively common during scanning. If these motion states are reflected, the scanned object will be frequently prompted to move. In addition, even if these motion states are reflected, it is difficult to interact with the scanned object to cooperate with the scanning. Therefore, for these non-autonomous motion cases, they need to be regarded as unnecessary motion states to avoid their influence and retain the true motion state.

[0126] Because non-autonomous motion such as peristalsis and heart beat usually occurs in non-boundary regions and does not cause the boundary region to move, while autonomous motion (for example, respiratory motion, body movement, etc.) can usually be embodied in the boundary region, the true motion state can be reflected by the data of the boundary region. In some embodiments, the boundary region of the motion detection data (magnetic resonance signal) can be obtained using a boundary extraction algorithm, and then the motion state of the scanned object can be calculated. In some embodiments, the boundary region can include tissues such as fat.

[0127] Step 620, determining boundary data in the motion detection data. In some embodiments, the step 620 can be performed by the motion state determination module 230.

[0128] The boundary data refers to the motion detection data belonging to the boundary region, for example, the motion detection data of fat tissues in the abdomen, pelvic cavity, heart and other parts, etc. In some embodiments, the boundary data can be determined from the acquired motion detection data.

[0129] ​In some embodiments, the boundary range of the motion detection data, i.e. the range of the data of the boundary region, can be obtained by various ways, such as edge extraction algorithm, etc. As shown in Figure 11 The motion detection data 1110 can be obtained by extracting the boundary to obtain the boundary range of 1110, so as to determine the boundary data.

[0130] In some embodiments, for the motion detection data X, the inverse Fourier transform is performed to obtain the image domain signal x, the two sides are found point by point to the center, the demarcation points L1 and R1 of the noise and the actual signal of the two sides are found, then the data is segmented to obtain two segments of data XL and XR. For XL and XR, the maximum values in the respective regions are obtained and recorded as MaxL and MaxR, then the position points of the half-width XL(N) = MaxL / 2 and XR(N) = MaxR / 2 from the outside to the inside are found and recorded as L2 and R2, and the boundary range is the interval between (L1, L2) and (R2, R1), and the boundary data is the data between the interval (L1, L2) and (R2, R1).

[0131] In step 630, the motion state of the region of interest is determined according to the boundary data. In some embodiments, step 630 can be performed by the motion state determination module 230.

[0132] As shown in Figure 11 The boundary of the motion detection data 1110 can be extracted to obtain the boundary data 1120 and the non-boundary data 1130, and the motion state 1140 can be determined according to the boundary data 1120 (excluding the non-boundary data 1130).

[0133] In some embodiments, for the image domain magnetic resonance signal x of the motion detection data X after the inverse Fourier transform, when the boundary data is determined to be the data between the interval (L1, L2) and (R2, R1), the data x(L1, L2) and x(R2, R1) of the interval (L1, L2) and (R2, R1) can be taken for calculating the motion curve. For example, the same boundary extraction processing can be performed on the reference data, and then the difference between the boundary data in the target data and the reference data is determined according to the method shown in Figure 4 to determine the motion curve.

[0134] Figure 12 As shown in the graph, the motion detection data is subjected to the inverse Fourier transform to generate the image domain data. The horizontal coordinate is the coordinate (0-256) in the image domain, and the vertical coordinate is the signal intensity. As shown in Figure 12As shown, the data in the circled area 1210 represents non-boundary region data of the motion detection data, and the data outside the circled area 1210 represents boundary region data of the motion detection data. Due to non-autonomous motion such as peristalsis, the data in the non-boundary region has low coincidence degree, while the data in the boundary region has high coincidence degree because the boundary region is less affected by non-autonomous motion.

[0135] In some embodiments of the present specification, for non-autonomous motion (e.g., peristalsis of the abdomen or pelvic cavity, heart beat, etc.), a boundary range is obtained by using a boundary extraction algorithm on the motion detection data, and then a motion state is calculated according to the data of the boundary, so as to avoid the influence of non-autonomous motion such as peristalsis, and to obtain a more real, accurate and reliable motion state.

[0136] It should be noted that the above description of the flow 300, the flow 400, the flow 500 and the flow 600 is only for example and illustration, and does not limit the scope of the present specification. Various modifications and changes can be made to the flow 300, the flow 400, the flow 500 and the flow 600 under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification. For example, the order of steps 410 and 420 can be exchanged. For another example, step 620 can be performed between steps 430 and 440.

[0137] The beneficial effects that may be brought by the embodiments of the present specification include but are not limited to: (1) by inserting a motion detection sub-sequence with very short duration in the imaging sub-sequence, and by combining the motion state characteristics of different parts, the magnetic resonance signals for motion detection are acquired and processed in real time, and the algorithm processing removes unnecessary interference, so as to obtain a more accurate and reliable real-time motion curve, to better reflect the motion state of the scanned object, and to obtain a more accurate reconstructed image; (2) by processing the acquired signals through the phase filtering algorithm, the phase interference caused by factors such as field drift and temperature rise is removed, and the accuracy of the data is improved, so as to obtain a more accurate motion state; (3) by using methods such as segmented calculation and boundary extraction algorithm when detecting the motion state, the influence of unnecessary motion (e.g., motion of non-interesting parts such as head swallowing, non-autonomous motion such as peristalsis, etc.) is detected and removed, so as to obtain a more accurate motion state; (4) the above-mentioned various means are simple and easy to implement, and have good practicability, and through the comprehensive use of the above-mentioned various means, the obtainability, reliability, credibility and accuracy of the motion state are ensured. It should be noted that different embodiments may have different beneficial effects, and in different embodiments, the beneficial effects that may be produced can be any one or a combination of the above, or any other beneficial effects that can be obtained.

[0138] Having described the basic concepts, it is obvious to those skilled in the art that the foregoing detailed disclosure is intended to be illustrative only and not limiting of the scope of the description. Although the foregoing has been described in some detail for purposes of clarity, it will be apparent that certain changes and modifications can be made to the descriptions, illustrations, and specific embodiments without departing from the scope of the description. Such changes and modifications are therefore intended to be covered by the foregoing description. The scope of the description is to be afforded the broadest interpretation so as to encompass all compatible modifications and equivalent structures.

[0139] Also, the description has used specific terminology in describing embodiments of the description. For the purposes of this description, "an embodiment" or "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described is included in at least one embodiment of the description. Therefore, the appearance of "an embodiment" or "one embodiment" or "some embodiments" in various places in the description are not necessarily all referring to the same embodiment. Furthermore, some of the features, structures, or characteristics can be combined in an appropriate manner in one or more embodiments.

[0140] In addition, the order of presentation of the processing elements and sequences described in this description, unless specifically stated to the contrary, is not intended to imply that such order is essential to the described processes and methods. Although the above disclosure discusses some presently preferred embodiments of the application, it is to be understood that the details disclosed herein are by way of example and that other embodiments can be practiced within the scope of the description. For example, although the system components described above can be implemented by hardware devices, they can also be implemented by software solutions only, such as installing the described system on an existing server or mobile device.

[0141] Similarly, it is to be noticed that the term "comprising" or "comprises", when used in this specification, is taken to specify the presence of stated features, integers, steps, or components, but does not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof. It is also to be understood that the word "a" or "an" when used in the context of this patent document

[0142] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates 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 be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0143] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0144] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1.A method for motion detection in magnetic resonance imaging, comprising: acquiring at least two motion detection sub-sequences; acquiring motion detection data of a region of interest based on the at least two motion detection sub-sequences, the at least two motion detection sub-sequences being inserted in at least two imaging sub-sequences, the imaging sub-sequences being used for magnetic resonance imaging of the region of interest, the motion detection data not being used for the magnetic resonance imaging of the region of interest; determining a motion state of the region of interest according to the motion detection data, comprising: determining the motion state of the region of interest according to first data of a first direction and second data of a second direction of the region of interest in the motion detection data, the first direction being more affected by a disturbing motion than the second direction. 2.The method of claim 1, wherein the determining the motion state of the region of interest according to the first data of the first direction and the second data of the second direction of the region of interest in the motion detection data, comprises: acquiring the first data and the second data from the motion detection data; dividing the first data into at least two first segments along the first direction; determining a first motion state of each of the first segments; dividing the second data into at least two second segments along the second direction; determining a second motion state of each of the second segments; determining a first difference between the first motion states; determining a second difference between the second motion states; determining that the motion detection data is affected by the disturbing motion according to the first difference and the second difference. 3.The method of claim 2, wherein the determining the motion state of the region of interest according to the first data of the first direction and the second data of the second direction of the region of interest in the motion detection data, further comprises: setting a first weight of the first data and a second weight of the second data, the first weight being less than the second weight; determining the motion state according to the first weight and the second weight. 4.The method of claim 2, wherein the determining the motion state of the region of interest according to the first data of the first direction and the second data of the second direction of the region of interest in the motion detection data, further comprises: determining the motion state according to the second data. 5.The method of claim 1, wherein after the determining the motion state of the region of interest according to the first data of the first direction and the second data of the second direction of the region of interest in the motion detection data, comprises: determining reference data according to data acquired by a first motion detection sub-sequence of the at least two motion detection sub-sequences; selecting data acquired by a second motion detection sub-sequence of the at least two motion detection sub-sequences as target data; determining a difference between the reference data and the target data; determining the motion state according to the difference. 6.The method of claim 5, wherein determining the motion state of the region of interest based on first data of a first direction and second data of a second direction of the region of interest in the motion detection data further comprises: performing phase filtering on the motion detection data. 7.A motion detection system in magnetic resonance imaging, comprising a motion sequence acquisition module, a motion data acquisition module, and a motion state determination module; the motion sequence acquisition module is configured to acquire at least two motion detection sub-sequences; the motion data acquisition module is configured to acquire motion detection data of a region of interest based on the at least two motion detection sub-sequences, the at least two motion detection sub-sequences being inserted in at least two imaging sub-sequences, the imaging sub-sequences being configured to perform magnetic resonance imaging on the region of interest, the motion detection data not being used for the magnetic resonance imaging on the region of interest; the motion state determination module is configured to determine a motion state of the region of interest based on the motion detection data, comprising: determining the motion state of the region of interest based on first data of a first direction and second data of a second direction of the region of interest in the motion detection data, the first direction being more affected by a disturbing motion than the second direction. 8.The system of claim 7, wherein determining the motion state of the region of interest based on first data of a first direction and second data of a second direction of the region of interest in the motion detection data comprises: acquiring the first data and the second data from the motion detection data; dividing the first data into at least two first segments along the first direction; determining a first motion state of each of the first segments; dividing the second data into at least two second segments along the second direction; determining a second motion state of each of the second segments; determining a first difference between the first motion states; determining a second difference between the second motion states; determining that the motion detection data is affected by the disturbing motion based on the first difference and the second difference. 9.The system of claim 8, wherein determining the motion state of the region of interest based on first data of a first direction and second data of a second direction of the region of interest in the motion detection data further comprises: setting a first weight of the first data and a second weight of the second data, the first weight being less than the second weight; determining the motion state based on the first weight and the second weight. 10.The system of claim 8, wherein determining the motion state of the region of interest based on first data of a first direction and second data of a second direction of the region of interest in the motion detection data further comprises: determining the motion state based on the second data.

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