Medical imaging method for detecting motion and magnetic resonance imaging system

Abstract

The present disclosure relates to a medical imaging method for detecting motion and a magnetic resonance imaging system. The method includes the following steps: receiving and recording multiple original first time-domain signals of the motion of an examination object through multiple channels; transforming the multiple first time-domain signals into a vector matrix including the phase representation based on the phases of multiple components of the respiratory frequency; calculating eigenvectors based on the vector matrix; transforming the first time-domain signals into second time-domain signals based on the eigenvectors, and removing the maximum energy term related to respiratory motion in the second time-domain signals; determining whether a part of non-respiratory body motion is detected, and setting the execution of the sequence only based on the first time-domain signals of the part where non-respiratory body motion is not detected. This method provides accurate detection of the motion characteristics of the examination object during the medical image acquisition process, differentiates body motion from various types of respiratory motion, and avoids image blurring and artifact problems caused by body motion, and has high accuracy and robustness.

Description

Technical Field

[0001] The present invention relates to the field of medical imaging, and particularly to a method for detecting the movement of an imaging object in medical imaging. Background Art

[0002] During the medical imaging process, the movement of the examination object during the shooting will cause blurring and artifacts in the final medical image, resulting in a reduction in image quality. Such a reduction in image quality leads to the risk of misdiagnosis when reading the film and leads to repeated scans, etc., thereby reducing the efficiency of medical image acquisition and increasing costs.

[0003] Taking a magnetic resonance imaging system as an example, magnetic resonance imaging (MRI) is a medical imaging technology that irradiates an object with a radio frequency pulse signal using an antenna under certain magnetic field conditions and forms an image based on the received modulated radio frequency signal from the object. The internal structure, material composition, physiological processes, etc. of the object can be studied using magnetic resonance imaging technology. A radio frequency pulse with a Larmor frequency causes the spin nuclei in the irradiated object, such as hydrogen nuclei (i.e., H+), to precess with a deflection angle, generating a magnetic resonance radio frequency signal after excitation, which is received by a receiving coil / antenna and imaged by computer processing.

[0004] In a magnetic resonance imaging system, depending on the pulse sequence (also called a sequence) used, image acquisition in the magnetic resonance imaging system takes from a few milliseconds to several seconds. Therefore, it is meaningful to start image acquisition at the beginning of the stage when the body remains stationary to avoid artifacts caused by movement during image acquisition. However, some non-conventional body movements introduce motion artifacts, blurring the acquired images. And inevitable movements such as breathing and heartbeat. However, after the stage with movement, there is also a calm stage, such as after exhalation or myocardial contraction. Image acquisition in this stage is expected to have a relatively long stage with less movement, thus expecting the best measurement results here.

[0005] In addition, some reference radio frequency signals such as high-frequency signals or radio frequency signals are used to acquire motion information. Here, data or information related to the mechanical movement of the patient can be read out according to some modulation and decoding methods, so as to identify the movement of the patient caused by breathing or heartbeat, etc.

[0006] Of course, the above problems are not limited to magnetic resonance imaging, and the same problems also exist in imaging such as CT (Computed Tomography), PET (Positron Emission Tomography), and SPECT (Single-Photon Emission Computed Tomography). Summary of the Invention

[0007] In view of this, on the one hand, the present disclosure provides a medical imaging method for detecting motion, which is used to detect motion interference other than mechanical physiological motion, such as the body motion of an examination object, etc., so as to exclude image blurring and / or artifacts introduced by non-regular motion during the medical imaging process. The method for detecting object motion in medical imaging includes the following steps: receiving and recording multiple first time-domain signals of the motion of the examination object through multiple channels; transforming the multiple first time-domain signals based on the phases of multiple components of the respiratory frequency to obtain a vector matrix including the phase representation; calculating eigenvectors based on the vector matrix including the phase representation; transforming the first time-domain signals to second time-domain signals based on the eigenvectors, and removing at least one maximum energy term related to respiratory motion in the second time-domain signals, and determining whether a non-respiratory body motion part in the second time-domain signals is detected; after detecting the non-respiratory body motion part, determining to abandon one or more time points or acquisition windows set in the time domain related to the non-respiratory body motion part for triggering the acquisition of magnetic resonance signals, or abandoning post-processing of the acquired magnetic resonance signals related to the non-respiratory body motion part.

[0008] Optionally, removing at least one maximum energy term related to respiratory motion in the second time-domain signals and determining whether a non-respiratory body motion part in the second time-domain signals is detected: removing at least one maximum energy term in the second time-domain signals to obtain a third time-domain signal; segmenting the third time-domain signal based on different sub-time periods, and calculating the correlation coefficients of the third time-domain signals of each sub-time period; based on the comparison between the correlation coefficients and a prior threshold, determining whether a non-respiratory body motion part in the third time-domain signals is detected.

[0009] Optionally, receiving and recording multiple first time-domain signals of the motion of the examination object through multiple channels includes: receiving and recording multiple first time-domain signals of the motion of the examination object from multiple coil units through multiple channels, and the first time-domain signals include pilot tone signals or navigation echo signals.

[0010] Optionally, the receiving of the plurality of first time-domain signals that record the movement of the examined object through a plurality of channels includes: based on a time period, sampling the plurality of first time-domain signals based on a sampling frequency to construct a discrete representation of the first time-domain signals.

[0011] Optionally, the transforming of the plurality of first time-domain signals into a vector matrix including the phase representation based on the breathing frequency includes: segmenting based on a prior range of breathing frequencies, a sampling frequency, and a time period for acquiring the plurality of first time-domain signals to obtain a plurality of components of breathing frequencies; constructing a filter based on the phases of the plurality of components of breathing frequencies, and transforming the first time-domain signals into a vector matrix including the phase representation based on the filter.

[0012] Optionally, calculating the eigenvector based on the vector matrix including the phase representation: calculating the eigenvector by using eigenvalue decomposition on the vector matrix represented by the breathing frequency.

[0013] Another aspect of the present disclosure provides a magnetic resonance imaging system for providing an image representation of an object of interest located in an examination space of the magnetic resonance imaging system, the magnetic resonance imaging system being adapted to execute the medical imaging method for detecting movement according to the foregoing.

[0014] Another aspect of the present disclosure provides an electronic device, including: a processor; and a memory storing a program, the program including instructions that, when executed by the processor, cause the processor to execute the foregoing medical imaging method for detecting movement.

[0015] Another aspect of the present disclosure provides a computer-readable storage medium, the program including instructions that, when executed by a processor of an electronic device, cause the electronic device to perform according to the foregoing medical imaging method for detecting movement.

[0016] Another aspect of the present disclosure provides a system for detecting the movement of an examined object in medical imaging, characterized in that the system includes: an interface unit configured to receive a plurality of first time-domain signals that record the movement of the examined object through a plurality of channels; a filter configured to transform the plurality of first time-domain signals into a vector matrix including the phase representation based on the phases of a plurality of components of breathing frequencies; an eigenvector calculation unit configured to calculate an eigenvector based on the vector matrix including the phase representation; and a movement determination unit configured to calculate a change of the first time-domain signals based on the eigenvector to obtain a second time-domain signal represented in the breathing frequency space, and remove at least one maximum energy term related to the breathing frequency in the second time-domain signal to obtain a third time-domain signal, and calculate the correlation between the third time-domain signals of each sub-time period to determine whether a non-breathing body movement part in the third time-domain signal is detected.

[0017] Optionally, the filter is configured to segment based on a prior range of the respiration rate, a sampling frequency, and a time period for acquiring a plurality of the first time-domain signals, to obtain a plurality of respiration rate components; the filter is constructed based on phases of the plurality of respiration rate components; and the filter is further configured to transform the first time-domain signal into a vector matrix including the phase representation.

[0018] Optionally, the motion determination unit of the system is further configured to segment based on different sub-time periods in the third time-domain signal, calculate a correlation coefficient of the third time-domain signal for each of the sub-time periods, and based on a comparison between the correlation coefficient and a prior threshold, to determine whether a non-respiratory body movement part in the third time-domain signal is detected.

[0019] Optionally, the motion determination unit is further configured to feedback to a control unit whether a non-respiratory body movement part in the third time-domain signal is detected, wherein the control unit is configured to, after accepting that the non-respiratory body movement part is detected, determine to abandon one or more time points set in the time domain related to the non-respiratory body movement part for triggering the acquisition of magnetic resonance signals or abandon setting an acquisition window, or abandon post-processing of the magnetic resonance signals acquired related to the non-respiratory body movement part.

[0020] One advantage of the medical imaging method for detecting motion provided by the present disclosure is that it can further detect non-regular body movements of an examination object, such as non-respiratory body movements. When non-respiratory body movements are detected, the execution of the sequence is abandoned, thereby optimizing the execution of the sequence based on, for example, a respiration motion signal or curve to trigger the acquisition or post-processing of magnetic resonance signals, including the execution of sequences such as trigger control or gate control. In particular, the indexes of the execution of the sequence based on the pilot tone technology are optimized, thereby effectively eliminating artifacts or blurring in the image after imaging caused by non-respiratory body movements and optimizing the efficiency of image acquisition.

[0021] Another advantage is that the medical imaging method for detecting motion provided by the present disclosure can detect a non-respiratory body movement part in the originally acquired first time-domain signal based on the concept of Self-gating, and can achieve continuous signal acquisition.

[0022] Another advantage is that a filter is constructed based on a priori respiratory rate. This filter can transform the time-domain signals collected from multiple channels into the frequency domain to obtain a vector matrix including the phase representation, and calculate the eigenvectors related to the respiratory rate components using eigenvalue decomposition based on this vector matrix. Thus, the originally collected first time-domain signal is transformed into a second time-domain signal described in the respiratory rate space. Based on the difference between the vectors related to the motion components and the vectors related to the respiratory motion in the respiratory rate space, the eigenvectors of the motion-related components are effectively distinguished to obtain the time-domain signal of the motion components. This method or model has high robustness, that is, after using the correlation coefficient to calculate the similarity or correlation of the sub-time segments of the time-domain signal of the motion components, it can further effectively identify or distinguish non-respiratory body movements and some irregular mechanical physiological movements, especially the interference signals from irregular breathing, such as deep / shallow breathing or breath-holding, etc.

[0023] Another advantage is that the eigenvectors related to the respiratory rate components obtained by calculation are used to transform the first time-domain signal into a second time-domain signal characterized in the respiratory rate space, and at least one maximum energy term is removed to obtain a third time-domain signal from which the respiratory rate-related components are eliminated. This third time-domain signal only includes signals unrelated to the respiratory motion, and thus further detects some irregular mechanical physiological movements based on this third time-domain signal. Especially, the body movement has anti-interference signals and high robustness.

[0024] Another advantage is that after constructing the filter, it can quickly be arranged to identify non-respiratory body movements in the originally collected first time-domain signal, and based on this index, it can process in near real-time whether to abandon or execute the execution of the sequence set for the originally collected first time-domain signal. Specifically, it includes determining to abandon one or more time points set in the time domain related to the part of the non-respiratory body movement for triggering the acquisition of magnetic resonance signals or abandoning the setting of the acquisition window, or abandoning the post-processing of the magnetic resonance signals collected related to the part of the non-respiratory body movement.

[0025] Another advantage is that multiple time-domain signals related to the movement of the detection object are collected and recorded using multiple channels, a matrix about the multiple time-domain signals is established using a certain sampling frequency, and a filter about the frequency domain is further constructed based on the segmentation of the sampling frequency and the respiratory rate, etc., so as to obtain a computable discrete model to further calculate more accurate eigenvectors to effectively identify non-respiratory body movements and mechanical physiological movements including breathing, which has more suitable computability and relatively simple model construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings, making the above and other features and advantages of the present invention clearer to those of ordinary skill in the art. In the drawings:

[0027] Figure 1 To show magnetic resonance imaging pictures of a region of interest taken under respiratory motion according to an exemplary embodiment;

[0028] Figure 2 To show magnetic resonance imaging pictures of a region of interest taken under non-respiratory body motion interference according to an exemplary embodiment;

[0029] Figure 3 To show a schematic diagram of the constitution of a magnetic resonance imaging system including a pilot tone transmitter and a pilot tone receiver according to an exemplary embodiment;

[0030] Figure 4 To show a functional schematic diagram of a system for detecting the motion of an examination object in medical imaging according to an exemplary embodiment;

[0031] Figure 5 To show a flowchart of a medical imaging method for detecting motion according to an exemplary embodiment;

[0032] Figure 6 To show a flowchart of a medical imaging method for detecting motion according to another exemplary embodiment;

[0033] Figure 7 To show a flowchart of a method for constructing a filter that transforms a first time-domain signal into a frequency-domain space related to the respiratory frequency according to an exemplary embodiment;

[0034] Figure 8 To show a schematic diagram of a time-domain signal for detecting non-respiratory body motion in a multi-channel pilot tone signal according to an exemplary embodiment;

[0035] Figure 9 To show a schematic diagram of a time-domain signal of respiratory motion detected in a multi-channel pilot tone signal according to an exemplary embodiment;

[0036] Figure 10 To show a structural diagram of a computing device that can be applied to an exemplary embodiment.

[0037] Wherein, the reference numerals are as follows:

[0038] 100 Magnetic resonance imaging system

[0039] 102 Magnet

[0040] 104 Gradient coil

[0041] 106 Radio frequency coil

[0042] 108 Examination area

[0043] 110 Gradient emission unit

[0044] 112 Radio frequency unit

[0045] 114 Radio frequency switching unit

[0046] 116 Pilot tone transmitter

[0047] 118 Pilot tone receiver

[0048] 120 Local coil

[0049] 122 Region of interest

[0050] 126 Control unit

[0051] 1261 Detection motion system

[0052] 1262 Interface section

[0053] 1264 Filter

[0054] 1266 Eigenvector calculation section

[0055] 1268 Motion determination section

[0056] 128 Display unit

[0057] 130 Image reconstruction unit

[0058] 132 First window

[0059] 134 Second window

[0060] 136 Portion of non - respiratory body movement

[0061] 140 Curve of respiratory motion signal

[0062] 142 Curve of respiratory motion signal affected by non - respiratory body movement

[0063] P Examination object Detailed implementation manners

[0064] For a clearer understanding of the technical features, objectives, and effects of the present disclosure, the detailed implementation manners of the present disclosure are now described with reference to the accompanying drawings. The same reference numerals denote the same parts in the respective figures.

[0065] In this document, "schematic" means "serving as an example, instance, or illustration", and any illustration or implementation manner described as "schematic" in this document should not be construed as a more preferred or more advantageous technical solution.

[0066] For the sake of simplicity of the drawings, only the parts related to the present invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, for the sake of simplicity and easy understanding of the drawings, in some figures, for components with the same structure or function, only one of them is schematically shown, or only one of them is labeled.

[0067] In this document, "a" not only means "only this one", but also can mean "more than one" situation. In this document, "first", "second", etc. are only used for distinguishing from each other, rather than indicating their importance, order, and the premise of mutual existence, etc. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, these three situations. In addition, the character " / " in this document generally represents an "or" relationship between the related objects before and after.

[0068] In a magnetic resonance tomography system, in order to ensure triggering magnetic resonance image acquisition during breath-holding time to reduce motion artifacts introduced by, for example, respiratory motion, pilot tone technology (such as Pilot Tone) is utilized to detect motion signals including respiration, etc. The pilot tone technology can use signals collected by multiple channels (Channel) as a leading indicator for the execution of a sequence. The execution of the sequence, such as triggering a sequence or implementing gating, etc., includes setting one or more time points for triggering image acquisition, or performing post-processing on the acquired images, or setting gating for the acquired images. The triggering sequence, for example, sets a sequence for triggering the acquisition of magnetic resonance signals regarding an area of interest according to the curve or phase information of the respiratory motion signal, or sets a trigger point or condition for receiving magnetic resonance signals from the area of interest based on the curve or phase information. The post-processing of the acquired magnetic resonance signals includes triggering relevant k-space data for imaging, etc., and gating, for example, sets a window range for signal acquisition based on the respiratory motion curve. In addition, gating also includes, for example, prospective gating, retrospective gating, etc.

[0069] The advantages of the pilot tone technology are obvious, enabling the improvement of image quality without the need for breath holding when acquiring images of the examination object. However, when analyzing the pilot tone signal, even if the respiratory signal can be detected, the respiratory signal is still affected by irregular motions such as body motion, which affects the accuracy of the algorithm or method for setting the trigger for magnetic resonance signal acquisition based on the waveform of the respiratory signal, resulting in image artifacts or blurring. Refer to Figure 1 and Figure 2 where Figure 1Curve 140 of the respiratory motion signal obtained by combining pilot tone signals of multiple channels is shown. The image obtained based on the execution of the sequence set according to this curve has good quality, and curve 140 of this respiratory motion signal is not affected or interfered by non-respiratory body movements; Figure 2 Curve 142 of the respiratory motion signal shown that is affected by non-respiratory body movements is affected by irregular movements such as body movements of the subject P. The image obtained based on the execution of the sequence according to this curve has obvious artifacts or blurs.

[0070] The present disclosure provides a magnetic resonance imaging method. Based on detecting non-respiratory body movements, for example, detecting the part of non-respiratory body movements in pilot tone signals or navigation echo signals, the difficulty lies in simultaneously reducing the influence of respiratory movements such as deep / shallow breathing or irregular breathing, that is, further distinguishing body movements from the movements introduced by deep / shallow breathing or irregular breathing. Based on the generally different characteristics of the vectors of body movement signals and respiratory movement signals, the present disclosure separates body movement signals from respiratory movement signals, thereby detecting body movements.

[0071] Figure 3 To show a schematic diagram of a magnetic resonance imaging system according to the present disclosure having a pilot tone transmitter and a pilot tone receiver according to an exemplary embodiment.

[0072] As Figure 3 As shown, the magnetic resonance imaging system 100 includes: a magnet 102. The magnet 102 provides a uniform static magnetic field B0 in the examination area 108 for aligning the nuclear spins of the measurement object or patient. The uniformity of the static magnetic field B0 particularly relates to the magnetic field strength or magnitude. The magnet 102 has a central bore, and the central bore provides a space around the examination area 108 for the subject P to be positioned therein. In addition, the subject P can be moved by a moving unit (not shown) arranged in the channel of the examination area 108. The magnet 11 is usually a superconducting magnet that can provide a magnetic field with a magnetic flux density of 0.55T, 1.5T, 3.0T, etc., or even higher in the case of the latest equipment. However, for lower field strengths, permanent magnets or electromagnets with normally conductive coils can also be used.

[0073] In addition, the magnetic resonance imaging system 100 further includes a gradient coil 104, which is configured to generate a gradient magnetic field superimposed on the magnetic field B0. The gradient magnetic field can be variable in three spatial directions to spatially distinguish the imaging region in the acquired examination volume. The gradient coil 104 is generally a coil made of a normally conductive wire, which can generate fields orthogonal to each other in the examination volume. The gradient transmitter unit 110 can be configured to receive a set of pulse sequences regarding the gradient field from the control unit 126 for supplying a variable current to the gradient coil 104 via a feeder line. The variable current provides the desired gradient field in the examination volume in a time-coordinated manner.

[0074] In addition, the magnetic resonance imaging system 100 further includes a radio frequency coil 106, also known as a body coil. The radio frequency coil 106 can be designed as an integral coil in a tubular or columnar shape. The radio frequency transmitting coil 106 is configured to radiate a radio frequency signal fed via a signal wire into the examination region 108 during RF (Radiofrequency) transmission to excite the nuclei of the object P to be examined. The radio frequency coil 106 also receives magnetic resonance signals from the excited nuclei during RF reception and transmits them via the signal wire. Under the operation of the control unit 126, the RF transmission stage and the RF reception stage can occur successively. The radio frequency coil 104 is coaxially arranged in the bore of the magnet 102.

[0075] Furthermore, the magnetic resonance imaging system 100 includes: an image reconstruction unit 130, which is used to reconstruct a magnetic resonance image based on the acquired MR signals (magnetic resonance signals), such as k-space data. And a control unit 126, which has a display unit 128 provided to control magnetic resonance scanning. The radio frequency unit 112 is provided to feed the RF power of the magnetic resonance radio frequency to the radio frequency coil 106 via the radio frequency switching unit 114 during the RF transmission stage. The radio frequency switching unit 114 can also be controlled by the control unit 126. During the RF reception stage, after pre-amplification, the radio frequency switching unit 114 feeds the magnetic resonance signal from the radio frequency coil 106 to the image reconstruction unit 130.

[0076] In addition, a local coil 120 can be arranged proximal to the examination object P, particularly in the region of interest 122 such as the chest. The local coil 120 can be connected to the radio frequency unit 112 by a connection line and is configured to transmit an RF magnetic field to excite the nuclei in the region of interest 122 from the radio frequency signal provided by the radio frequency unit 112 during RF transmission, and receive the magnetic resonance signals of the excited nuclei in the region of interest 122 through the local coil 120 during RF reception. After pre-amplification, the radio frequency switching unit 114 transmits the magnetic resonance signal from the local coil 120 to the image reconstruction unit 130.

[0077] In addition, the magnetic resonance imaging system 100 further includes: a receiver (not shown), the receiver includes a receive coil channel selector that can output magnetic resonance signals to corresponding channels respectively, forming multiple channels. After converting the above analog signals into digital signals, the receiver outputs them to the control unit 126 for processing. The image reconstruction unit 130 can reconstruct a spatial distribution image of the anatomical tissues of the examination object P with respect to substances from the magnetic resonance signals by at least using an inverse Fourier transform operation. The display unit 128 can provide the display and playback of the reconstructed image and Cinematic.

[0078] The RF transmitting part of the RF unit 112 further includes: a pilot tone transmitter 116, which can be connected to the local coil 120, or arranged around the local coil 120, or the local coil 120 has a separate transmitting antenna for transmitting pilot tone signals, or is arranged beside the magnet 102, etc. In addition, it is also conceivable to arrange a separate transmitting antenna for pilot tone signals in the examination area 108 or at the positions of some regions of interest 122 of the examination object P. In some applications and technical advantages, the pilot tone transmitter 116 can be arranged in the local coil 120.

[0079] The receiving part of the RF unit 120 can include a pilot tone receiver 118. The pilot tone receiver 118 can be signal-connected to the local coil 120, and the local coil 120 can have a receiving antenna for receiving pilot tone signals. In addition, a receiving antenna for receiving pilot tone signals is arranged in the examination area 108 or at the position of a region of interest 122 of the examination object P, for example, an induction loop coil is used as the receiving antenna and is arranged in a decoupled manner with the antenna of the adjacent local coil 120. In some embodiments, the pilot tone receiver 118 can be set as one or more antenna coils of the local coil 120 for receiving magnetic resonance signals, for example, filtering out the signals in the frequency range of the magnetic resonance signal (MR signal) after analog-to-digital conversion (A / D) of the original signals collected by the local coil 120. The pilot tone receiver 118 can be connected to the receiver for receiving magnetic resonance signals, or can use one or more channels in the above receive coil channel selector to receive pilot tone signals, and the pilot tone receiver 118 can only apply some additional processing steps in the form of filters or algorithms to the signals of the antenna coils to extract pilot tone signals. At the same time, based on some applications and technical advantages, the pilot tone receiver 118 can be arranged in the local coil.

[0080] The pilot tone transmitter 116 generates a pilot tone signal, which can be incident on the position of the region of interest 122 of the detection object P via an induction loop / antenna to sense the mechanical physiological movements of the examination object P, such as mechanical physiological movements like breathing and heartbeat, etc., as an indicator for triggering the sequence of transmitting radio frequency signals, and reducing the artifacts introduced by mechanical physiological movements. The pilot tone transmitter 116 can have a separate oscillator, and this oscillator can generate a high-frequency signal with a suitable frequency. In some illustrated embodiments, when the frequency is preferably within the range of the Larmor Frequency or near the Larmor Frequency used by the magnetic resonance imaging system 100 during imaging, it can replace the oscillator in the pilot tone transmitter 116, and the radio frequency signal is fed by the oscillator of the radio frequency unit 112, or a radio frequency signal is generated according to the fed signal in the pilot tone transmitter 116 to maintain the stability of the pilot tone signal while collecting magnetic resonance signals.

[0081] Now referring to Figure 4 , the present disclosure shows a motion detection system 1261 for detecting the motion of an examination object in medical imaging. The motion detection system 1261 includes: an interface unit 1262 configured to receive a plurality of first time-domain signals for recording the motion of the examination object through a plurality of channels during RF reception by, for example, the radio frequency unit 112; a filter 1264 configured to transform the plurality of first time-domain signals into a vector matrix including the phase representation based on the phases of the components of a plurality of respiration frequencies; an eigenvector calculation unit 1266 configured to calculate eigenvectors based on the vector matrix including the phase representation; and a motion determination unit 1268 configured to transform the first time-domain signals into second time-domain signals based on the eigenvector calculation, and remove at least one maximum energy term related to the respiration frequency in the second time-domain signals to obtain third time-domain signals, and detect the non-respiratory body motion part of the third time-domain signals by calculating the correlation between the third time-domain signals of each sub-period. Here, the second time-domain signal represented in the respiration frequency space is equivalent to representing the part based on the correlation with the respiration frequency or respiration movement in the first time-domain signal through scaling, so that the component associated with the respiration movement can be identified in the second time-domain signal, that is, the maximum energy term in the second time-domain signal, which has the greatest correlation with the respiration movement, is determined. Therefore, after removing one or more maximum energy terms from the second time-domain signal, a third time-domain signal unrelated to the respiration movement can be obtained, and the third time-domain signal can include signals representing, for example, non-respiratory body motion, heartbeat motion, etc.

[0082] In addition, the filter 1264 is configured to segment based on a prior range of respiratory rates, a sampling frequency, and a time period for acquiring a plurality of first time-domain signals to obtain a plurality of respiratory rate components; the filter 1264 is further configured to construct based on the corresponding phases of the plurality of respiratory rate components, and the filter 1264 is further configured to transform the first time-domain signal into a vector matrix including a phase representation. Herein, the filter 1264 can be implemented by constructing a filter circuit or by a signal processing algorithm.

[0083] In addition, the motion determination unit 1268 is further configured to segment the obtained third time-domain signal related to the non-respiratory motion component based on different sub-time periods to obtain several sub-time periods or a plurality of time windows, calculate the correlation coefficient of the time-domain signals of each sub-time period, and determine whether to detect a non-respiratory body motion part in the time-domain signal based on a comparison between the correlation coefficient and a prior threshold. For example, when the correlation coefficient is less than the threshold, it is determined that a non-respiratory body motion part is detected in a sub-time period of the third time-domain signal.

[0084] In addition, the motion determination unit 1268 can be further configured to feedback to the control unit 126 whether a non-respiratory body motion part is detected in the third time-domain signal. And when the control unit 126 receives that a non-respiratory body motion part is detected, the control unit 126 is configured to abandon the execution of the sequence, including determining to abandon one or more time points set in the time domain related to the non-respiratory body motion part for triggering the acquisition of magnetic resonance signals or abandoning the setting of the acquisition window, or abandoning the post-processing of the magnetic resonance signals acquired related to the non-respiratory body motion part, etc.

[0085] Now referring to Figure 5 , a flowchart of a medical imaging method for detecting motion based on the magnetic resonance imaging system 100 is shown. This method uses a pilot tone transmitter 116 to send a certain high-frequency signal to the region of interest 122 to detect the motion signal of the examination object P. At the receiver end, a plurality of channels are used to receive the high-frequency signal returned by the region of interest 122. The motion signal contained in the returned high-frequency signal can include a respiratory signal related to breathing or / and a heartbeat signal related to the heartbeat, as well as a motion signal unrelated to the above mechanical physiological motion, such as a non-respiratory signal related to the body motion of the examination object, etc. This method can distinguish mechanical physiological motion from irregular non-mechanical physiological motion, that is, general body motion, to more accurately detect the motion of the detection object, and based on the type of the detected motion as an index for triggering a sequence such as acquiring MR signals, reduce the artifact and blurring problems of the image during the magnetic resonance imaging process caused by the influence of non-mechanical physiological motion.

[0086] In step S110, multiple original first time-domain signals that record the movement of the detection object P are received through multiple channels.

[0087] Taking the illustrated magnetic resonance imaging system 100 as an example, multiple original first time-domain signals that record the movement related to the region of interest 122 of the examination object P are received through multiple channels from multiple coil units such as local coils 120, for example, by a receiver or a pilot tone receiver 118. Taking the first time-domain signal including the pilot tone signal S(t) as an example. Here, a pilot tone transmitter 116 can use multiple induction loop coils provided on the local coil 120 to transmit the above high-frequency signal to the region of interest 122 of the detection object P. The high-frequency signal returned by the region of interest 122 is received by the pilot tone receiver 118, and multiple pilot tone signals that record the movement of the detection object P are received at the receiver through multiple channels. Refer to Figure 7 and Figure 8 , for the illustrated pilot tone signal S(t) of 24 channels, the pilot tone signal S(t) can be expressed as pilot tone signals of multiple channels. Taking the pilot tone signal of 24 signals as an example, S(t) can also be expressed as [S(t)1,..., S(t) 24 . Here, the first time-domain signal is a signal collected originally, and may include a breathing signal, a heartbeat signal, and signals related to non-breathing body movements of the examination object P, etc.

[0088] It should be noted that the method or device for receiving the first time-domain signal or time-domain signal that records the movement of the examination object P is not limited to the pilot tone technology. For example, a navigation echo signal that identifies the breathing movement by detecting the movement of the diaphragm, or detecting the mechanical physiological movement of the examination object P based on an optical sensor or an ultrasonic sensor, or providing a breathing belt to provide multi-channel mechanical physiological signals that reflect the region of interest of the examination object P, etc. In the present disclosure, the method and technology for receiving and recording the original time-domain signal of the movement of the examination object using multiple channels are not limited.

[0089] In step S120, the multiple first time-domain signals are transformed into a vector matrix including phase representations based on the phases of multiple components of the breathing frequency. Here, the range of the breathing frequency can be a priori, and multiple components of the breathing frequency and corresponding phase representations can be obtained by considering the segmentation based on this range of the breathing frequency.

[0090] In an illustrated embodiment, specifically, refer to Figure 7 , the method for constructing a filter for transforming the first time-domain signal into a frequency domain space related to the breathing frequency based on a priori breathing frequency range can be as follows:

[0091] Taking the first time-domain signal including the pilot tone signal S(t) as an example, considering that the change of the normal breathing frequency range over time is relatively slow, and assuming that the range of the breathing frequency is taken as [F min , F max .

[0092] In step S122, based on the prior range of the breathing frequency, the sampling frequency, and the time period for collecting the first time-domain signal S(t), the breathing frequency is segmented, and multiple components of the breathing frequency can be obtained.

[0093] Here, based on sampling the pilot tone signal S(t) over a time period T to obtain a matrix R represented by a set of vectors, R = [S(t0); S(t1); …; S(t T*N )], the pilot tone signal S(t) is discretized. Among them, the range of the time period T is [t0, t T , and the sampling frequency is

[0094] Δf = 1 / N (1), that is, N data samples are taken per second in each channel.

[0095] Based on the sampling frequency represented by equation (1),

[0096] J start = Floor(F min / Δf)+1 (2), where Floor represents taking the integer part downwards,

[0097] J end = Floor(F max / Δf)+1 (3),

[0098] Thus, based on the range of the breathing frequency, J terms can be segmented:

[0099] J = J end - J start +1 (4).

[0100] In step S124, a filter W is constructed based on the phases of multiple components of the breathing frequency.

[0101] In this way, a filter W is constructed to transform the first time-domain signal into a vector matrix including the phase representation, or it can be understood that the filter W constructed by using the phases of the components of the breathing frequency as frequency bands decomposes the first time-domain signal into multiple components of the breathing frequency and includes the vector matrix of the phase representation, that is:

[0102] W: J*K, the filter W is in the form of a matrix, and "*" represents multiplication;

[0103] Let j = J start , J start +1, J start+2, …, J end ; k = 1, 2 … K, K = T * N, where T is the time period selected in the first time-domain signal, further obtaining components of multiple respiratory frequencies and the corresponding phases for the above components. The elements in the matrix of the filter W can be expressed based on the phases of the components of the respiratory frequencies as:

[0104] W(j - J start + 1, k) = sin(2 * pi * j * k / K) (5),

[0105] W(j - J start + 1 + J, k) = cos(2 * pi * j * k / K) (6),

[0106] Or each element can be equivalently expressed as:

[0107] Wherein, the phases of the components of multiple respiratory frequencies can be expressed as Or it can be understood as using a group of Expressed as the basis or basis set for the phases of the components of the respiratory frequencies.

[0108] Herein, the filter W can be understood as a band-pass filter obtained based on the frequency band of the prior respiratory frequency, and based on sampling the pilot tone signal S(t) respectively over a time period T to obtain a matrix R represented by a set of row vectors, R = [S(t0); S(t1); …; S(t T*N )] to perform discretization processing on the pilot tone signal S(t), wherein the range of the time period T is [t0, t T .

[0109] In step S126, based on the filter W, the first time-domain signal is transformed into a vector matrix C including phase representation.

[0110] Finally, herein, based on filtering each pilot tone signal S(t), the matrix R represented by row vectors obtained by sampling the above set of pilot tone signals S(t) using the filter W is transformed into a vector matrix C including the phase representation, or referred to as vector coefficients or filtering coefficients, and having corresponding phases That is:

[0111] C = W * R (8), herein obtaining a J × M matrix, where M represents the number of channels for receiving pilot tone signals.

[0112] In step S130, based on the vector matrix C transformed to include phase representation, eigenvectors are calculated.

[0113] Herein, in order to calculate eigenvectors related to respiratory movement or non-respiratory body movement based on the vector C, eigenvalue decomposition can be used, which can be expressed as:

[0114] [V, D] = eig(C' * C) (9), or expressed as a mathematical formula:

[0115] C' * C * V = V * D (10),

[0116] where C' represents the conjugate transpose matrix of vector C, D represents the diagonal matrix composed of eigenvalues, and V represents the eigenvector. D and the eigenvector V can be calculated through equations (9) or (10).

[0117] In step S140, the first time-domain signal S(t) is transformed into the second time-domain signal S resp (t) based on the eigenvector V, where the second time-domain signal S resp (t) represents the characterization or representation of the collected original time-domain signal in the coordinate system of the breathing frequency space.

[0118] In an exemplary embodiment shown, the eigenvector V is obtained based on the matrix of the above vector C, that is:

[0119] S resp (t) = S(t) * V (11),

[0120] wherein, the component associated with the breathing frequency or breathing movement can be measured in the second time-domain signal S resp (t). That is, it is easy to understand that in the coordinate representation of the breathing frequency space, the component highly correlated with the breathing frequency is amplified under the action of the eigenvector V. Here, the eigenvector V can be arranged in ascending order.

[0121] In step S150, at least one maximum energy term related to the breathing movement is removed from the second time-domain signal S resp (t), the second time-domain signal S resp (t) is segmented based on different sub-time periods, and the correlation coefficient of the second time-domain signal S resp (t) in different sub-time periods is calculated to detect the non-breathing body movement signal in the second time-domain signal S resp (t).

[0122] Here, a window (function) with a certain width can be set to segment the second time-domain signal S resp (t) into multiple sub-time periods, and the correlation coefficient of the second time-domain signal S resp (t) in different sub-time periods is calculated, for example, calculating its correlation coefficient to detect the non-breathing body movement signal therein.

[0123] Specifically, S resp (t) in the first window is selected and the discrete R1 matrix is sampled. Similarly, S resp(t) to obtain the R2 matrix and calculate the correlation coefficient of the second time-domain signal S in different sub-time periods within different windows based on a known method, for example, calculate the correlation coefficient of R1 and R2 by normalizing R1 and R2 respectively and then calculating the covariance and variance between them. resp (t), for example, calculate the correlation coefficient of R1 and R2 by normalizing R1 and R2 respectively and then calculating the covariance and variance between them.

[0124] In step S160, compare whether the correlation coefficient between the corresponding second time-domain signals S resp (t) in different sub-time periods is less than a threshold.

[0125] Here, compare the correlation coefficient between the second time-domain signals S resp (t) corresponding to different sub-time periods with a prior threshold to determine whether the second time-domain signals S resp (t) contain motion signals introduced by non-respiratory body movements or non-mechanical physiological movements, that is, when the correlation coefficient is less than the threshold, it is detected that at least one of the second time-domain signals S resp (t) contains the motion signal. If the correlation coefficient is greater than the threshold, it is obvious that it can be determined that there is no part of the non-respiratory body movement. Therefore, the original first time-domain signal S(t) can be used as the execution index of the sequence. For example, set one or more time points or set an acquisition window for triggering the acquisition of MR signals in the time domain, or perform post-processing on the MR signals acquired in this time domain, etc. Refer to Figure 8 , through the above method of calculating the correlation coefficient, a part 136 of non-respiratory body movement (represented by a triangle in the figure) is detected in the first window 132, and the part 136 of non-respiratory body movement does not include respiratory movement signals including non-regular respiratory movement signals.

[0126] In step S170, in response to the comparison that the correlation coefficient is less than the threshold, a part of non-respiratory body movement is detected in the second time-domain signal S resp (t), and feedback this information to the control unit 126 in near real-time to abandon the execution of the sequence.

[0127] Here, the control unit 126 receives the information of detecting the non-respiratory body movement signal and is further configured to abandon the execution of the sequence, such as triggering, gating, etc. That is, the control unit 126 determines to abandon setting one or more time points or setting an acquisition window for triggering the acquisition of MR signals in the time domain of the part of non-respiratory body movement, or abandon performing post-processing on the MR signals acquired in the time domain related to the part of non-respiratory body movement, etc. In addition, motion compensation, etc. can also be included in the post-processing of the acquired MR signals to reduce image artifacts or blurring. After abandoning the execution of the sequence, it can return to step S110 and start continuously detecting the pilot tone signal S(t) acquired in another time period.

[0128] Alternatively, in step S170, the pilot tone signals S(t) of multiple channels can be combined and displayed on the display unit 128. After step S170 is executed, step S110 can be returned to, to perform the step of detecting motion in multiple original first time-domain signals or pilot tone signals S(t) collected from multiple channels for the next time period. The motion may include parts or components of non-respiratory body motion.

[0129] In step S180, in response to the comparison correlation coefficient being greater than the threshold, for the part where no non-respiratory body motion is detected in the second time-domain signal S resp (t), the first time-domain signals of multiple channels are combined to obtain the curve of the respiratory motion signal, and this curve is used as the index (or marker) for sequence execution. Alternatively, in step S170, the curve of the respiratory motion signal obtained by combining the pilot tone signals S(t) of multiple channels can also be displayed on the display unit 128.

[0130] Reference Figure 6 , shows another medical imaging method for detecting motion, aiming to explicitly remove the components related to respiratory motion in the second time-domain signal. Among them, steps S210 to S240 can respectively correspond to Figure 5 the steps S110 to S140 described, which will not be elaborated here.

[0131] In step S250, at least one maximum energy term is removed from the second time-domain signal S resp (t) to obtain the third time-domain signal S mo (t) related to non-respiratory motion components.

[0132] Here, in the second time-domain signal S resp (t) transformed into the respiratory frequency space, the last or maximum several columns of maximum energy terms, that is, the maximum energy terms represented by, for example, the maximum two columns of signals correspond to the maximum respiratory energy. Usually, the last two columns of signals can be removed from the second time-domain signal S resp (t) to further obtain the third time-domain signal S mo (t) only related to non-respiratory motion components.

[0133] In step S260, the third time-domain signal S mo (t) is segmented based on different sub-time periods, and the correlation coefficients between the third time-domain signals S mo (t) of different sub-time periods are calculated to detect the non-respiratory body motion signal in the third time-domain signal S mo (t).

[0134] Here, a window (function) with a certain width can be set in the third time-domain signal S moMultiple sub-time periods are segmented from (t), and the correlation coefficient between the third time-domain signals S of different sub-time periods is calculated, for example, calculating its correlation coefficient to detect non-respiratory body movement signals therein. mo For example, calculate its correlation coefficient to detect non-respiratory body movement signals therein.

[0135] Specifically, select S in the first window mo (t), and sample to obtain a discrete R1 matrix. Similarly, take S in the second window with the same width mo (t) to obtain an R2 matrix, and calculate the correlation coefficient of the third time-domain signals S in the sub-time periods within different windows based on a known method, for example, calculate the correlation coefficient of the two by taking the covariance and variance of the two after normalizing R1 and R2 respectively. mo For example, calculate the correlation coefficient of the two by taking the covariance and variance of the two after normalizing R1 and R2 respectively.

[0136] In step S270, it is determined whether the correlation coefficient between the corresponding third time-domain signals S in different sub-time periods is less than a threshold. mo Is less than a threshold.

[0137] Here, the correlation coefficient between the third time-domain signals S corresponding to different sub-time periods is compared with a priori threshold to determine whether the third time-domain signals S in different sub-time periods contain movement signals introduced by non-respiratory body movement or non-mechanical physiological movement, that is, when the correlation coefficient is less than the threshold, it is detected that at least one segment of the third time-domain signal S mo (t) contains the non-respiratory body movement signal. If the correlation coefficient is greater than the threshold, it is obvious that it can be determined that there is no such non-respiratory body movement signal in a segment of S mo (t). Refer to mo (t) contains the non-respiratory body movement signal. If the correlation coefficient is greater than the threshold, it is obvious that it can be determined that there is no such non-respiratory body movement signal in a segment of S mo (t). Refer to Figure 9 , which indicates that in the result of a part 136 for detecting non-respiratory body movement, even if there are non-regular respiratory signals such as deep / shallow breathing or breath-holding, and the non-respiratory body movement in part 136 is not detected by using this method, showing robustness under certain interference conditions.

[0138] In step S280, in response to detecting a part of non-respiratory body movement in the third time-domain signal S mo (t), the information is fed back to the control unit 126 near real-time, that is, a part of non-respiratory body movement is detected in S mo (t) to abandon the execution of the sequence.

[0139] In step S290, in response to not detecting a part of non-respiratory body movement in the third time-domain signal S mo (t), the first time-domain signals S of multiple channels are merged to obtain the curve of the respiratory movement signal, and this curve is used as an index (or marker) for the execution of the sequence.

[0140] It should be noted that the above method for detecting the movement of the examination object in medical imaging is not limited to magnetic resonance imaging systems, and is equally applicable to medical imaging systems such as CT imaging systems (i.e., computed tomography systems), PET (Positron Emission Tomography), and SPECT (Single-Photon Emission Computed Tomography).

[0141] According to one aspect of the present disclosure, there is also provided an electronic device, including: a processor; and a memory storing a program, the program including instructions that, when executed by the processor, cause the controller to execute the medical imaging method for detecting movement according to the above.

[0142] According to another aspect of the present disclosure, there is also provided a computer-readable storage medium storing a program, the program including instructions that, when executed by a processor of an electronic device, cause the electronic device to perform the medical imaging method for detecting movement according to the above. The computer-readable storage medium may be, for example, a storage device, a floppy disk, a compact disc CD, a digital versatile disc DVD, a Blu-ray disc, or a random access memory RAM, or other medium containing instructions that cause a computer to execute the above method.

[0143] See Figure 10 As shown, the computing device 2000 will now be described, which is an example of an electronic device that can be applied to various aspects of the present disclosure. The computing device 2000 can be any machine configured to perform processing and / or calculations, and can be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a robot, a smart phone, an in-vehicle computer, or any combination thereof. The above medical imaging method for detecting movement can be implemented in whole or at least in part by the computing device 2000 or a similar device or system.

[0144] The computing device 2000 may include (possibly via one or more interfaces) elements connected to or communicating with a bus 2002. For example, the computing device 2000 may include a bus 2002, one or more processors 2004, one or more input devices 2006, and one or more output devices 2008. The one or more processors 2004 can be any type of processor and can include, but are not limited to, one or more general-purpose processors and / or one or more special-purpose processors (such as special processing chips). The input device 2006 can be any type of device that can input information into the computing device 2000 and can include, but are not limited to, a mouse, a keyboard, a touch screen, a microphone, and / or a remote control. The output device 2008 can be any type of device that can present information and can include, but are not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The computing device 2000 may also include a non-transitory storage device 2010 or be connected to the non-transitory storage device 2010. The non-transitory storage device can be any storage device that is non-transitory and can implement data storage and can include, but are not limited to, a disk drive, an optical storage device, a solid-state memory, a floppy disk, a flexible disk, a hard disk, a magnetic tape, or any other magnetic medium, an optical disk or any other optical medium, a ROM (read-only memory), a RAM (random access memory), a cache memory, and / or any other memory chip or cartridge, and / or any other medium from which a computer can read data, instructions, and / or code. The non-transitory storage device 2010 can be removable from the interface. The non-transitory storage device 2010 may have data / programs (including instructions) / code for implementing the above methods and steps. The computing device 2000 may also include a communication device 2012. The communication device 2012 can be any type of device or system that enables communication with external devices and / or with a network and can include, but are not limited to, a modem, a network card, an infrared communication device, a wireless communication device, and / or a chipset, such as Bluetooth TM devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0145] The computing device 2000 may also include a working memory 2014, which can be any type of working memory that can store programs (including instructions) and / or data useful for the operation of the processor 2004 and can include, but are not limited to, a random access memory and / or a read-only memory device.

[0146] The software element (program) can be located in the working memory 2014, including but not limited to the operating system 2016, one or more application programs 2018, drivers, and / or other data and code. The instructions for executing the above methods and steps can be included in one or more application programs 2018, and the above medical imaging method for detecting motion can be implemented by the processor 2004 reading and executing the instructions of one or more application programs 2018. More specifically, in the above medical imaging method for detecting motion, steps 110-180, steps 210-290, and steps 122-126 can be implemented, for example, by the processor 2004 executing the application program 2018 with the instructions of steps 110-180, steps 210-290, and steps 122-126. In addition, other steps in the above medical imaging method for detecting motion can be implemented, for example, by the processor 2004 executing the application program 2018 with the instructions for executing the corresponding steps. The executable code or source code of the instructions of the software element (program) can be stored in a non-transitory computer-readable storage medium (such as the above storage device 2010), and can be loaded into the working memory 2014 (possibly compiled and / or installed) when executed. The executable code or source code of the instructions of the software element (program) can also be downloaded from a remote location.

[0147] It should also be understood that various variations can be made according to specific requirements. For example, custom hardware can also be used, and / or specific elements can be implemented using hardware, software, firmware, middleware, microcode, a hardware description language, or any combination thereof. For example, some or all of the disclosed methods and devices can be implemented by programming hardware (such as programmable logic circuits including field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs)) using assembly language or a hardware programming language (such as VERILOG, VHDL, C++) based on the logic and algorithms according to the present disclosure.

[0148] It should also be understood that the foregoing method can be implemented in a server-client mode. For example, the client can receive the data input by the user and send the data to the server. The client can also receive the data input by the user, perform a part of the processing in the foregoing method, and send the processed data to the server. The server can receive the data from the client and execute the foregoing method or another part of the foregoing method, and return the execution result to the client. The client can receive the execution result of the method from the server and, for example, present it to the user through an output device.

[0149] It should also be understood that the components of the computing device 2000 may be distributed over a network. For example, one processor may be used to perform some processing while another processor, remote from the one processor, may perform other processing. Other components of the computing system 2000 may be similarly distributed. Thus, the computing device 2000 may be interpreted as a distributed computing system that performs processing at multiple locations.

[0150] Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above methods, systems, and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only defined by the authorized claims and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced by their equivalent elements. In addition, the steps may be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. Importantly, as technology evolves, many of the elements described herein may be replaced by equivalent elements that emerge after the present disclosure.

Claims

1. A medical imaging method for detecting motion, characterized in that, The method includes the following steps: Receiving a plurality of original first time-domain signals that record the movement of an object to be examined through a plurality of channels; Transforming the plurality of first time-domain signals into a vector matrix including the phase representation based on the phases of components of a plurality of respiration frequencies; Calculating eigenvectors based on the vector matrix including the phase representation; Transforming the first time-domain signals into second time-domain signals based on the eigenvectors; Removing at least one maximum energy term related to respiratory movement in the second time-domain signal, and determining whether a non-respiratory body movement part in the second time-domain signal is detected; After detecting the non-respiratory body movement part, determining to abandon one or more time points or acquisition windows set in the time domain related to the non-respiratory body movement part for triggering the acquisition of magnetic resonance signals, or abandoning post-processing of the acquired magnetic resonance signals related to the non-respiratory body movement part.

2. The method according to claim 1, wherein, The removing at least one maximum energy term related to respiratory movement in the second time-domain signal and determining whether a non-respiratory body movement part in the second time-domain signal is detected includes: Removing at least one maximum energy term in the second time-domain signal to obtain a third time-domain signal; segmenting the third time-domain signal based on different sub-time periods, and calculating the correlation coefficients of the third time-domain signal in each of the sub-time periods; Based on the comparison between the correlation coefficient and a prior threshold, determining that a non-respiratory body movement part in the third time-domain signal is detected.

3. The method according to claim 1, wherein, The receiving a plurality of original first time-domain signals that record the movement of an object to be examined through a plurality of channels includes: Receiving a plurality of first time-domain signals that record the movement of the object to be examined from a plurality of coil units through a plurality of channels, where the first time-domain signals include pilot tone signals or navigation echo signals.

4. The method according to claim 1, wherein, The receiving a plurality of original first time-domain signals that record the movement of an object to be examined through a plurality of channels includes: Based on a time period, sampling the plurality of first time-domain signals based on the sampling frequency to construct a discrete representation of the first time-domain signals.

5. The method according to claim 1, wherein, The transforming the plurality of first time-domain signals into a vector matrix including the phase representation based on the phases of components of a plurality of respiration frequencies includes: Based on the prior range of the respiration frequencies, the sampling frequency, and the time period for acquiring the plurality of first time-domain signals, segmenting to obtain components of a plurality of respiration frequencies; Constructing a filter based on the phases of components of a plurality of respiration frequencies; Based on the filter, transforming the first time-domain signals into a vector matrix including the phase representation.

6. The method according to claim 1, wherein, The calculating eigenvectors based on the vector matrix including the phase representation includes: Using eigenvalue decomposition based on the vector matrix represented by components of respiration frequencies to calculate the eigenvectors.

7. A magnetic resonance imaging system for providing an image representation of an examination object located in the examination space of the magnetic resonance imaging system, wherein, The magnetic resonance imaging system is adapted to execute the medical imaging method for detecting movement according to any one of claims 1 to 6 of the foregoing method.

8. An electronic device, comprising: A processor; And A memory storing a program, where the program includes instructions that, when executed by the processor, cause the processor to execute the medical imaging method for detecting movement according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a program, the program comprising instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the medical imaging method for detecting motion according to any one of claims 1 to 6.

10. A medical imaging system for detecting motion of an examination object in medical imaging, characterized in that, The system includes: An interface unit (1262) is configured to receive a plurality of original first time-domain signals that record the movement of an object to be examined through a plurality of channels; A filter (1264) is configured to transform the plurality of first time-domain signals into a vector matrix including the phase representation based on the phases of the components of a plurality of respiration frequencies; An eigenvector calculation unit (1266) is configured to calculate eigenvectors based on the vector matrix including the phase representation; and A motion determination unit (1268) is configured to transform the first time-domain signals into second time-domain signals in the respiration frequency space based on the eigenvectors, and remove at least one maximum energy term related to respiratory movement in the second time-domain signals to obtain third time-domain signals, and detect a non-respiratory body movement part in the third time-domain signals by calculating the correlation between the third time-domain signals of each sub-period.

11. The medical imaging system for detecting motion according to claim 10, wherein, The filter (1264) is configured to perform segmentation based on a prior range of the respiration frequencies, a sampling frequency, and a period for acquiring the plurality of first time-domain signals to obtain components of a plurality of respiration frequencies; The filter (1264) is constructed based on the phases of the components of a plurality of respiration frequencies; and The filter (1264) is further configured to transform the first time-domain signals into a vector matrix including the phase representation.

12. The medical imaging system for detecting motion according to claim 10, wherein, The motion determination unit (1268) is further configured to perform segmentation on the third time-domain signals based on different sub-periods, calculate the correlation coefficients between the third time-domain signals of each sub-period, and determine whether a non-respiratory body movement part in the third time-domain signals is detected based on a comparison between the correlation coefficients and a prior threshold.

13. The medical imaging system for detecting motion according to claim 10, wherein, The motion determination unit (1268) is further configured to feedback to a control unit (126) whether a non-respiratory body movement part in the third time-domain signals is detected, wherein the control unit (126) is configured to, after receiving the detection of the non-respiratory body movement part, determine to abandon one or more time points set in the time domain related to the non-respiratory body movement part for triggering the acquisition of magnetic resonance signals or abandon setting an acquisition window, or abandon post-processing of the magnetic resonance signals acquired related to the non-respiratory body movement part.

Images