A magnetic resonance (MR) imaging method and system

Abstract

The present application relates to a magnetic resonance (MR) imaging method and system, the method comprising: acquiring imaging data of an object collected by an MR imaging device within a scanning time; determining multiple reference images corresponding to multiple times within the scanning time based on at least part of the imaging data; determining time information of a dynamic image corresponding to the object within the scanning time based on the multiple reference images; determining spatial information of the dynamic image based on the time information and the imaging data; and reconstructing the dynamic image based on the time information and the spatial information.

Description

Technical Field

[0001] The present invention relates to the field of medical imaging, and in particular to a magnetic resonance (MR) imaging method and system. Background Art

[0002] Magnetic resonance (MR) imaging is a widely used medical technology. This technology uses a strong magnetic field and radio frequency (RF) technology to scan an object and obtain an image of the object or an area of ​​the object. MR (Magnetic Resonance) imaging can achieve real-time dynamic imaging, reflecting the movement of the object over time and / or the change of contrast over time.

[0003] In the dynamic imaging of an object, it is necessary to reconstruct a multi-dimensional, multi-contrast and / or multi-parameter image by scanning and collecting imaging data, and there is a trade-off relationship between temporal resolution and spatial resolution in magnetic resonance imaging. In some embodiments, the trade-off relationship between temporal resolution and spatial resolution can be overcome by interspersing the collection of navigation signals during the image data collection process. However, within a certain acquisition time range, the collected image data is not completely and effectively used for image reconstruction, resulting in an increase in image data acquisition time and reduced acquisition efficiency.

[0004] Therefore, there is an urgent need to provide a magnetic resonance (MR) imaging method and system to achieve dynamic imaging, which can save data acquisition time, while improving temporal resolution and spatial resolution, and improving the quality of reconstructed images. Summary of the invention

[0005] The purpose of the present application is to provide a magnetic resonance (MR) imaging method and system to reconstruct multi-dimensional, multi-contrast and / or multi-parameter images by scanning and acquiring imaging data without acquiring navigation signals, thereby achieving efficient data acquisition and reconstruction of dynamic images and saving acquisition time.

[0006] One of the embodiments of the present application provides a magnetic resonance (MR) imaging method, comprising: acquiring imaging data of an object collected by an MR imaging device within a scanning time; determining, based on at least part of the imaging data, multiple reference images corresponding to multiple times within the scanning time; determining, based on the multiple reference images, time information of a dynamic image corresponding to the object within the scanning time; determining, based on the time information and the imaging data, spatial information of the dynamic image; and reconstructing the dynamic image based on the time information and the spatial information.

[0007] In some embodiments, based on at least part of the imaging data, determining multiple reference images corresponding to multiple times within the scanning time may include: acquiring first imaging data in the imaging data whose frequency is lower than a frequency threshold; and reconstructing the multiple reference images based on second imaging data corresponding to the multiple times in the first imaging data.

[0008] In some embodiments, determining, based on at least part of the imaging data, a plurality of reference images corresponding to a plurality of times within the scanning time may include: acquiring first imaging data located in a central region of k-space within the imaging data;

[0009] Acquire first imaging data located in the center area of ​​k-space in the imaging data; divide the phases based on the multiple times in the first imaging data to obtain multiple second imaging data corresponding to the multiple phases; and reconstruct the multiple reference images based on the multiple second imaging data.

[0010] In some embodiments, based on the multiple reference images, determining the time information of the dynamic image corresponding to the object within the scanning time may include: determining an image representation matrix based on the multiple reference images and the multiple time sequences corresponding to the multiple reference images, the image representation matrix including a spatial dimension and a time dimension, the spatial dimension corresponding to the spatial position in the reference image, and the time dimension representing the multiple times corresponding to the multiple reference images; and determining the time information based on the image representation matrix and the time dimension.

[0011] In some embodiments, based on the time information and the imaging data, determining the spatial information of the dynamic image may include: determining an objective function based on the time information and the imaging data; and solving the objective function value through multiple iterations to determine the spatial information, the objective function including a consistency term and a regularization term, the consistency term being used to constrain the difference between the imaging data and the estimated imaging data determined based on the estimated spatial information generated in each iteration and the time information, and the regularization term being used to constrain the estimated spatial information generated in each iteration.

[0012] In some embodiments, at least one corresponding imaging data acquisition trajectory in the multiple times includes: the imaging data acquisition density with a frequency lower than a frequency threshold is greater than the imaging data acquisition density with a frequency higher than the frequency threshold.

[0013] In some embodiments, there is a rotation angle between the acquisition trajectories of the imaging data corresponding to at least two adjacent times among the multiple times.

[0014] In some embodiments, reconstructing the dynamic image based on the time information and the spatial information may include: obtaining a low-rank representation corresponding to the dynamic image based on the time information and the spatial information; and determining the dynamic image based on the low-rank representation.

[0015] One of the embodiments of the present application provides a magnetic resonance (MR) imaging system, the system comprising: an acquisition module, used to acquire imaging data of an object collected by an MR imaging device within a scanning time; a reference image determination module, used to determine, based on at least part of the imaging data, multiple reference images corresponding to multiple times within the scanning time; a time information determination module, used to determine, based on the multiple reference images, time information of a dynamic image corresponding to the object within the scanning time; a spatial information determination module, used to determine spatial information of the dynamic image based on the time information and the imaging data; and an image reconstruction module, used to reconstruct the dynamic image based on the time information and the spatial information.

[0016] In some embodiments, the reference image determination module can also be used to obtain first imaging data in the imaging data whose frequency is lower than a frequency threshold; and reconstruct the multiple reference images based on second imaging data corresponding to the multiple times in the first imaging data.

[0017] In some embodiments, the reference image determination module can also be used to obtain first imaging data located in the central area of ​​the k-space in the imaging data; divide the phases based on the multiple times in the first imaging data to obtain multiple second imaging data corresponding to the multiple phases; and reconstruct the multiple reference images based on the multiple second imaging data.

[0018] In some embodiments, the imaging data acquisition trajectory corresponding to at least one of the multiple times includes: the imaging data acquisition density of a frequency lower than a frequency threshold is greater than the imaging data acquisition density of a frequency higher than the frequency threshold. In some embodiments, the imaging data acquisition trajectory corresponding to at least two adjacent times of the multiple times has a rotation angle therebetween.

[0019] In some embodiments, the time information determination module can also be used to determine an image representation matrix based on the multiple reference images and the multiple time sequences corresponding to the multiple reference images, the image representation matrix including a spatial dimension and a time dimension, the spatial dimension corresponding to the spatial position in the reference image, and the time dimension representing the multiple times corresponding to the multiple reference images; and determine the time information based on the image representation matrix and the time dimension.

[0020] In some embodiments, the spatial information determination module can also be used to determine an objective function based on the time information and the imaging data; and to solve the objective function value through multiple iterations to determine the spatial information, wherein the objective function includes a consistency term and a regularization term, the consistency term is used to constrain the difference between the imaging data and the estimated imaging data determined based on the estimated spatial information generated in each iteration and the time information, and the regularization term is used to constrain the estimated spatial information generated in each iteration.

[0021] In some embodiments, the image reconstruction module may also be used to obtain a low-rank representation corresponding to the dynamic image based on the time information and the spatial information, and determine the dynamic image based on the low-rank representation.

[0022] One of the embodiments of the present application provides a magnetic resonance (MR) imaging apparatus, which includes at least one processor and at least one storage device, wherein the storage device is used to store instructions, and when the at least one processor executes the instructions, the magnetic resonance (MR) imaging method is implemented.

[0023] The beneficial effects that may be brought about by the magnetic resonance (MR) imaging method and system of the embodiments of the present application include but are not limited to: (1) when reconstructing a dynamic image of an object (for example, a biological organ such as the heart), time information can be determined by part of the collected imaging data, and spatial information can be determined based on the collected imaging data and time information, and then a dynamic image can be reconstructed based on the time information and spatial information, so that only imaging data needs to be collected during the dynamic image imaging process of MR, and no additional other information needs to be collected, such as navigation signals, motion information, heartbeat information and other information that changes with time. Data can be efficiently collected and dynamic images can be reconstructed, which saves collection time, and the collected imaging data can be used to reconstruct images, with high data utilization, which is conducive to high-quality dynamic image reconstruction; (2) by determining time information by part of the collected imaging data, such as low-frequency data with a frequency lower than a threshold or data in the central area of ​​k-space, the amount of calculation for dynamic image reconstruction can be reduced, the image reconstruction speed can be accelerated, and the efficiency of image reconstruction can be improved; (3) by collecting dense trajectories of the imaging data part with a frequency lower than the frequency threshold and sparse trajectories of the imaging data part with a frequency higher than the frequency threshold, the efficiency of data collection can be further improved on the basis of ensuring the quality and efficiency of image reconstruction. It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present application will be further described in the form of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0025] Figure 1 is a schematic diagram of an application scenario of an exemplary magnetic resonance (MR) imaging system according to some embodiments of this specification:

[0026] Figure 2 is a block diagram of an exemplary magnetic resonance (MR) imaging system according to some embodiments of the present application;

[0027] Figure 3 is an exemplary flow chart of a magnetic resonance (MR) imaging method according to some embodiments of the present specification;

[0028] Figure 4 is an exemplary flow chart of determining time information of a dynamic image according to some embodiments of this specification;

[0029] Figure 5 is an exemplary schematic diagram of an image representation matrix according to some embodiments of the present specification;

[0030] Figure 6 is an exemplary schematic diagram of an acquisition trajectory of imaging data according to some embodiments of this specification;

[0031] Figure 7 is an exemplary schematic diagram of a dynamic image corresponding to an object within a scanning time according to some embodiments of this specification;

[0032] Figure 8 It is an exemplary schematic diagram of a multi-dimensional image corresponding to a dynamic image of an object within a scanning time according to some embodiments of the present specification. DETAILED DESCRIPTION

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0034] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0035] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprises" and "includes" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0036] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, the various steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0037] In some embodiments, the magnetic resonance (MR) imaging system and / or the magnetic resonance (MR) imaging method according to the embodiments of the present application may be used for various biological or non-biological magnetic resonance (MR) imaging.

[0038] In some embodiments, the magnetic resonance (MR) imaging system and / or magnetic resonance (MR) imaging method of the embodiments of the present application can be used for cardiovascular magnetic resonance (CMR) quantitative imaging. Cardiovascular magnetic resonance (CMR) quantitative imaging can reflect or evaluate cardiovascular function and anatomical structure through measurement values ​​collected in magnetic resonance (MR) imaging (such as measurement values ​​in the heart) to diagnose and monitor organisms such as humans or human cardiovascular disease animal models. For example, the quantitative relaxation constant T1 of magnetic resonance (MR) can reflect or evaluate cardiovascular fibrosis, edema, inflammation and the like. In some embodiments, the imaged object has various movements (such as heart and respiratory movements), and the presence of movement will affect magnetic resonance (MR) imaging such as cardiovascular magnetic resonance (CMR) quantitative imaging.

[0039] In some embodiments, to process the motion involved in magnetic resonance (MR) imaging, such as the respiratory and cardiac motion involved in cardiovascular magnetic resonance (CMR) quantitative imaging, gating can be applied during the imaging data acquisition process to achieve motion cessation or near cessation during the data acquisition process, such as applying ECG gating to stop or freeze as much motion as possible during the data acquisition process by breath holding and / or short trigger acquisition. In the data acquisition process of magnetic resonance (MR) imaging using gating, one dynamic is selected at a time, useful information about the remaining dynamics is abandoned, and a pause is required between acquisition triggers. For magnetic resonance (MR) imaging using gating, imaging of an object (such as standard cardiac imaging) requires a scanning sequence with a long scanning time and low efficiency, wherein each scanning sequence uses a different combination of motion freezing mechanisms for different dynamic situations. In some embodiments, these motion freezing mechanisms are unreliable (e.g., ECG triggering) or uncomfortable for the subject (e.g., breath holding), and the use of multiple breath holding acquisitions may cause misalignment between scans, and the acquired imaging data has deviations, complicating image fusion or reconstruction. In addition, this strategy of applying gated MR imaging is not applicable to unhealthy subjects who suffer from cardiac arrhythmias or have difficulty holding their breath.

[0040] Through the magnetic resonance (MR) imaging system and / or magnetic resonance (MR) imaging method of the embodiment of the present application, it is possible to collect imaging data through a single scan and reconstruct a multi-dimensional, multi-contrast and / or multi-parameter image, that is, it is possible to efficiently collect data and reconstruct dynamic images, saving acquisition time. And the collected imaging data can be used to reconstruct images, with high data utilization, which is conducive to high-quality dynamic image reconstruction. In some embodiments, through the magnetic resonance (MR) imaging system and / or magnetic resonance (MR) imaging method of the embodiment of the present application, it is possible to avoid the application of gated magnetic resonance (MR) imaging in the data acquisition process, selecting one dynamic at a time, abandoning useful information about the remaining dynamics, and pausing between acquisition triggers to cause a long scanning time and inefficiency, and it is also possible to avoid multiple breath holding acquisitions causing misalignment between scans, and the collected imaging data has deviations. In some embodiments, the magnetic resonance (MR) imaging system and / or magnetic resonance (MR) imaging method of the embodiment of the present application can be applied to subjects in various situations (including unhealthy subjects with arrhythmia or difficulty holding their breath).

[0041] Figure 1 It is a schematic diagram of an application scenario of an exemplary magnetic resonance (MR) imaging system according to some embodiments of the present application.

[0042] In some embodiments, the magnetic resonance (MR) imaging system can perform imaging based on the magnetic resonance (MR) imaging method disclosed in the present application.

[0043] like Figure 1 As shown, a magnetic resonance (MR) imaging system 100 may include an MR scanning apparatus 110 , a network 120 , a terminal 130 , a processing device 140 , and a storage device 150 .

[0044] The MR scanning device 110 may include a magnet module and a radio frequency (RF) module ( Figure 1 In some embodiments, the MR scanning device 110 can scan an object or a region of the object. For more information about the object, see the description of this specification. Figure 3 and its related description. In some embodiments, the scan can be a pre-scan for calibrating an imaging scan. In some embodiments, the scan can be a positioning scan for locating an object or an object region, identifying an object or an object region. In some embodiments, the scan can be an imaging scan for generating an image.

[0045] The magnet module may include a main magnetic field generator and / or a gradient magnetic field generator ( Figure 1 ). The main magnetic field generator can create a static magnetic field B0 during scanning. The main magnet can be of various types, including, for example, permanent magnets, superconducting electromagnets, resistive electromagnets, etc.

[0046] The gradient magnetic field generator can generate a magnetic field gradient along a certain direction (e.g., X, Y, and / or z direction) on the main magnetic field B0. As used herein, the X, Y, and Z directions can represent the X, Y, and Z axes in a coordinate system. As an example only, the X-axis and the X-axis can be in a horizontal plane, the X-axis and the Y-axis can be in a vertical plane, and the X-axis can be along the rotation axis of the gantry. In some embodiments, the X-axis, the Y-axis, and the Z-axis can be specified by a gradient magnetic field generator (i.e., a gradient coil in a gradient magnetic field generator). The gradient magnetic field can encode and / or read out spatial information of an object (or a region of the object) located in the MR scanning device 110.

[0047] In some embodiments, the magnet module may generate a magnetic field gradient in a set of directions during scanning. By way of example only, the magnet module may generate a first magnetic field gradient in a first direction, a second magnetic field gradient in a second direction, and a third magnetic field gradient in a third direction. In some embodiments, the first, second, and third directions may be along the X-axis, the Y-axis, and the X-axis, respectively. In some embodiments, the magnetic field gradients along the X-axis, the Y-axis, and / or the X-axis may correspond to different encoding / readout directions in k-space (e.g., the direction of the kx axis, the direction of the ky axis, the direction of the kz axis, or any other direction).

[0048] The function, size, type, geometry, position, amount, and / or amplitude of the magnet module and / or RF module may be determined or changed according to one or more specific conditions. By way of example only, the magnet module and the radio frequency (RF) module may be designed to surround the object (or a region of the object) to form a tunnel-type MR scanning device 110 (i.e., a closed-bore MR scanning device 110) or an open-bore MR scanning device 110 (i.e., an open-bore MR scanning device 110). In some embodiments, the RF module may be classified as a transmitting coil and / or a receiving coil. These RF coils may transmit or receive RF signals to / from the object (or a region of the object). By way of example only, the transmitting coil may transmit RF energy to the object (or a region of the object) to induce an electrical signal in the region of interest. As another example, the receiving coil may pick up RF electromagnetic radiation generated by nuclear relaxation inside the object (or a region of the object).

[0049] In some embodiments, the RF coils may be classified into volume coils and local coils according to their functions and / or sizes. In some embodiments, the volume coils may include body coils, birdcage coils, transverse electromagnetic coils, saddle coils, etc. In some embodiments of the present disclosure, the local coils may include solenoid coils, saddle coils, flexible coils, surface coils, etc.

[0050] The surface coil may be a coil placed directly on the object (or a region of the object). In some embodiments, the surface coil may be a receiving coil configured to receive signals generated by nuclear relaxation inside the object (or a region of the object). By way of example only, the surface coil may receive multiple MR signals during a pre-scan and / or an imaging scan. For example, the surface coil may be placed directly on a region of interest (ROI) of the object, thereby providing an improved signal-to-noise ratio (SNR) by limiting the spatial range of reception. In some embodiments, the surface coil may be a ring of conductive material. By way of example only, the surface coil may be a copper tube. In some embodiments, the ring may be formed into various shapes. By way of example only, the ring may be bent to conform to the body part to be examined. In some embodiments, a radio frequency (RF) module may include one or more surface coils. By way of example only, a radio frequency (RF) module may include multiple surface coils.

[0051] The body coil may be a coil surrounding an object (or a region of the object). By way of example only, the body coil may surround the head or knee of a patient being examined. In some embodiments, the body coil may be a receive coil configured to receive signals generated by nuclear relaxation inside the object (or a region of the object), and / or a transmit coil configured to transmit RF energy to the object (or a region of the object). By way of example only, the body coil may receive a plurality of MR signals during a pre-scan and / or an imaging scan.

[0052] In some embodiments, a radio frequency (RF) module may include one or more receiving coils. The coil may include a surface coil and / or a body coil. By way of example only, a radio frequency (RF) module may include a first receiving coil and a second receiving coil. Both the first receiving coil and the second receiving coil may be surface coils. As another example, both the first receiving coil and the second receiving coil may be body coils. In some embodiments, a radio frequency (RF) module may include one or more body coils. By way of example only, a radio frequency (RF) module may include a body coil surrounding a patient being examined.

[0053] The network 120 may include any suitable network that can facilitate information and / or data exchange of the magnetic resonance (MR) imaging system 100, and may also be part of a hospital network HIS (Hospital Information System) or PACS (Picturearchiving and communication systems) or other hospital networks, or may be independent of but connected to HIS or PACS or other hospital networks. In some embodiments, one or more components of the magnetic resonance (MR) imaging system 100 (e.g., the MR scanning device 110, the terminal 130, the processing device 140, the storage device 150, etc.) may exchange information and / or data with one or more components of the magnetic resonance (MR) imaging system 100 via the network 120. For example, the processing device 140 may obtain planning data from a data processing planning system via the network 120. The network 120 may include a public network (such as the Internet), a private network (such as a local area network (LAN), a wide area network (WAN), etc.), a wired network (such as Ethernet), a wireless network (such as an 802.11 network, a wireless Wi-Fi network, etc.), a cellular network (such as a long-term evolution (LTE) network), a frame relay network, a virtual private network (VPN), a satellite network, a telephone network, a router, a hub, a server computer, etc., or a combination thereof. For example, the network 120 may include a wired network, a fiber optic network, a telecommunications network, a local area network, a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth TM Network, ZigBee TM In some embodiments, the network 120 may include one or more network access points. For example, the network 120 may include a wired and / or wireless network access point, such as a base station and / or an Internet exchange point, through which one or more components of the magnetic resonance (MR) imaging system 100 may connect to the network 120 to exchange data and / or information.

[0054] The terminal 130 may include a mobile device 131, a tablet computer 132, a laptop computer 133, etc., or any combination thereof. In some embodiments, the terminal 130 may interact with other components in the magnetic resonance (MR) imaging system 100 through a network. For example, the terminal 130 may send one or more control instructions to the MR scanning device 110 to control the MR scanning device 110 to scan according to the instructions. For another example, the terminal 130 may also receive processing results from the processing device 140, for example, 2D images, such as perspective images, and / or 3D images, such as images being reconstructed or reconstructed. In some embodiments, the mobile device 131 may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, etc., or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a smart appliance control device, a smart monitoring device, a smart TV, a smart camera, an intercom, etc., or any combination thereof. In some embodiments, the wearable device may include a bracelet, shoes and socks, glasses, a helmet, a watch, clothes, a backpack, a smart accessory, etc., or any combination thereof. In some embodiments, the mobile device may include a mobile phone, a personal digital assistant (PDA), a gaming device, a navigation device, a POS device, a laptop, a tablet, a desktop, etc., or any combination thereof. In some embodiments, the virtual reality device and / or augmented reality device may include a virtual reality helmet, virtual reality glasses, a virtual reality patch, an augmented reality helmet, augmented reality glasses, an augmented reality patch, etc., or any combination thereof. For example, the virtual reality device and / or augmented reality device may include Google Glass TM 、Oculus Rift TM 、HoloLens TM or Gear VR TM Etc. In some embodiments, the terminal 130 may be a part of the processing device 140 .

[0055] In some embodiments, the processing device 140 may process data and / or information obtained from the MR scanning device 110, the terminal 130 and / or the storage device 150. For example, the processing device 140 may perform weighted processing on part of the scan data, the imaging data (e.g., the scan data of the low-frequency part or the central area of ​​the K space) to determine the data required for reconstructing the image. For another example, the processing device 140 may perform data preprocessing, image reconstruction, reconstruction post-processing, etc. on the scan data and the imaging data. In some embodiments, the processing device 140 may also control the scanning action of the MR scanning device 110. For example, the processing device 140 may control the magnet module and / or RF module of the MRI scanning device 110. As an example only, the processing device 140 may control the magnetic field gradient in the X direction, the Y direction and the Z direction. In some embodiments, the processing device 140 may receive information from the MRI scanning device 110, or send information to the MRI scanning device 110, etc. According to some embodiments, the processing device 140 may receive a command from, for example, a user, and adjust the magnet module and / or the RF module to obtain an image of the object (or a region of the object) according to the received command. In some embodiments, the processing device 140 may process MR signals received from the RF module (e.g., surface coil(s) and / or body coil(s)), and generate one or more MR data sets (the MR data set may include multiple MR data obtained by sampling the MR signals) or imaging data (e.g., k-space data set) based on these signals. By way of example only, the MR signals may be filled into the k-space to generate a k-space data set, and the k-space data set may include part of the data in the k-space (such as data in the central region of the k-space) or all of the data. In some embodiments, these signals may be received by multiple receiving coils. The signals received by the same receiving coil may be filled into multiple k-space lines of the same k-space. In some embodiments, the k-space lines may be in the form of data sets filled with data points. By way of example only, the receiving coil may include a first receiving coil and a second receiving coil. The k-space corresponding to the first receiving coil may be the first k-space, and the k-space corresponding to the second receiving coil may be the second k-space.

[0056] In some embodiments, the processing device 140 may include a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device 140 may be local or remote. For example, the processing device 140 may access information and / or data from the MR scanning device 110, the terminal 130 and / or the storage device 150 through the network 120. For another example, the processing device 140 may be directly connected to the MR scanning device 110, the terminal 130 and / or the storage device 150 to access information and / or data. In some embodiments, the processing device 140 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, a cross-cloud, a multi-cloud, etc., or a combination of several of them.

[0057] The storage device 150 may store data (e.g., scan data, imaging data, etc. of an object), instructions, and / or any other information. In some embodiments, the storage device 150 may store data obtained from the MR scanning device 110, the terminal 130, and / or the processing device 140. For example, the storage device 150 may store scan data and imaging data of an object obtained from the MR scanning device 110. In some embodiments, the storage device 150 may store data and / or instructions executed or used by the processing device 140 to perform the exemplary method described in the present application. For example, the storage device 150 may store data obtained after weighted processing of scan data and imaging data of a scanned area of ​​an object. For another example, the storage device 150 may also store real-time perspective image data and / or image data obtained during and / or after reconstruction. In some embodiments, the storage device 150 may include a large-capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), or a combination of several thereof. Large-capacity storage may include a disk, an optical disk, a solid-state hard disk, a mobile storage, and the like. The removable memory may include a flash drive, a floppy disk, an optical disk, a memory card, a ZIP disk, a magnetic tape, etc. The volatile read-write memory may include a random access memory (RAM). The RAM may include a dynamic random access memory (DRAM), a double data rate synchronous dynamic random access memory (DDR-SDRAM), a static random access memory (SRAM), a thyristor random access memory (T-RAM), a zero capacitance random access memory (Z-RAM), etc. The ROM may include a mask read-only memory (MROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), a digital versatile disc, etc. In some embodiments, the storage device 150 may be implemented by the cloud platform described in the present application. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, a cross-cloud, a multi-cloud, etc., or a combination of several of them.

[0058] In some embodiments, the storage device 150 may be connected to the network 120 to implement communication with one or more components (e.g., the processing device 140, the terminal 130, etc.) in the magnetic resonance (MR) imaging system 100. One or more components in the magnetic resonance (MR) imaging system 100 may read data or instructions in the storage device 150 through the network 120. In some embodiments, the storage device 150 may be part of the processing device 140, or may be independent and directly or indirectly connected to the processing device 140.

[0059] It should be noted that the above description of the magnetic resonance (MR) imaging system 100 is provided for illustrative purposes only and is not intended to limit the scope of the present disclosure. For those of ordinary skill in the art, various variations and modifications may be made under the teachings of the present disclosure. For example, the assembly and / or function of the magnetic resonance (MR) imaging system 100 may be changed or altered according to a specific implementation scenario. As an example only, some other components may be added to the magnetic resonance (MR) imaging system 100, such as a patient positioning module, a gradient amplifier module, and other devices or modules. It should be noted that the magnetic resonance (MR) imaging system 100 may be a conventional or single-modality medical system, or a multi-modality system, including, for example, a positron emission tomography magnetic resonance imaging (PET-MRI) system, a telemedicine MRI system, and other systems, etc. However, these variations and modifications do not depart from the scope of the present disclosure.

[0060] Figure 2 is a block diagram of an exemplary magnetic resonance (MR) imaging system according to some embodiments of the present application.

[0061] like Figure 2 As shown, the magnetic resonance (MR) imaging 200 may include an acquisition module 210 , a reference image determination module 220 , a time information determination module 230 , a space information determination module 240 , and an image reconstruction module 250 .

[0062] In some embodiments, the acquisition module 210 may be used to acquire imaging data of a subject acquired by an MR imaging device within a scanning time.

[0063] In some embodiments, the reference image determination module 220 can be used to determine multiple reference images corresponding to multiple times within the scanning time based on at least part of the data in the imaging data. In some embodiments, the reference image determination module 220 can also be used to obtain first imaging data in the imaging data whose frequency is lower than a frequency threshold; and reconstruct the multiple reference images based on the second imaging data corresponding to the multiple times in the first imaging data. In some embodiments, the reference image determination module 220 can also be used to obtain first imaging data located in the center area of ​​k-space in the imaging data; divide the phases based on the multiple times in the first imaging data to obtain multiple second imaging data corresponding to multiple phases; and reconstruct the multiple reference images based on the multiple second imaging data. In some embodiments, the acquisition trajectory of the imaging data corresponding to at least one of the multiple times includes: the acquisition density of the imaging data with a frequency lower than the frequency threshold is greater than the acquisition density of the imaging data with a frequency higher than the frequency threshold. In some embodiments, the acquisition trajectory of the imaging data corresponding to at least two adjacent times in the multiple times has a rotation angle between them.

[0064] In some embodiments, the time information determination module 230 can be used to determine the time information of the dynamic image corresponding to the object within the scanning time based on the multiple reference images. In some embodiments, the time information determination module 230 can also be used to determine the image representation matrix based on the multiple reference images and the multiple time sequences corresponding to the multiple reference images, the image representation matrix including a spatial dimension and a time dimension, the spatial dimension corresponding to the spatial position in the reference image, and the time dimension representing the multiple times corresponding to the multiple reference images; and determine the time information based on the image representation matrix and the time dimension. In some embodiments, the time information determination module 230 can also be used to perform singular value decomposition on the image representation matrix based on the time dimension to determine the time information.

[0065] In some embodiments, the spatial information determination module 240 can be used to determine the spatial information of the dynamic image based on the time information and the imaging data. In some embodiments, the spatial information determination module 240 can also be used to determine an objective function based on the time information and the imaging data; and to solve the objective function value through multiple iterations to determine the spatial information, the objective function including a consistent term and a regular term, the consistent term is used to constrain the difference between the imaging data and the estimated imaging data determined based on the estimated spatial information generated in each iteration and the time information, and the regular term is used to constrain the estimated spatial information generated in each iteration.

[0066] In some embodiments, the image reconstruction module 250 can be used to reconstruct the dynamic image based on the time information and the spatial information. In some embodiments, the image reconstruction module 250 can also be used to obtain a low-rank representation corresponding to the dynamic image based on the time information and the spatial information, and determine the dynamic image based on the low-rank representation.

[0067] It is understandable that, after understanding the principle of the system, those skilled in the art may arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from the principle. Figure 2 The acquisition module 210 and the reference image determination module 220 disclosed in the embodiment may be implemented by one module to realize the functions of the above two modules. For another example, each module may share a storage module, or each module may have its own storage module. Such variations are all within the protection scope of the present application.

[0068] Figure 3 is an exemplary flow chart of a magnetic resonance (MR) imaging method according to some embodiments of the present specification.

[0069] In some embodiments, method 300 may be performed by processing logic that may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. Figure 3 One or more operations in the illustrated magnetic resonance (MR) imaging method 300 may be performed by Figure 1 The method 300 is implemented by the processing device 140 shown. For example, the method 300 may be stored in the storage device 150 in the form of instructions, and called and / or executed by the processing device 140.

[0070] like Figure 3 As shown, the magnetic resonance (MR) imaging method 300 may include the following operations.

[0071] Step 310 , acquiring imaging data of the object acquired by an MR imaging device within a scanning time.

[0072] In some embodiments, step 310 may be performed by acquisition module 210 .

[0073] An object refers to a subject that can be scanned and imaged. In some embodiments, an object may include a biological subject, such as a human, an animal, a plant, etc. In some embodiments, an object may include a specific part, organ, tissue, etc. of a subject. By way of example only, an object may include a heart, a lung, a pleura, a blood vessel, a liver, a spine, a bone, a pancreas, a cervix, a lymph, a thyroid gland, a spleen, an adrenal gland, a salivary gland, a sebaceous gland, a testis, a thymus gland, a penis, a uterus, a trachea, a skeletal muscle, a smooth muscle, etc. In some embodiments, an object may include a non-biological subject, such as a phantom, an artificial object, etc.

[0074] A magnetic resonance imaging device, or MR imaging device, refers to a device that scans and images an object based on the magnetic resonance phenomenon. For example, the MR imaging device may include an MR scanning device 110, and scans the object through the MR scanning device 110. For more information about MR imaging devices, please refer to Figure 1 Detailed description in .

[0075] In some embodiments, MR parameters of the MR imaging device may be set to perform scanning. The MR parameters may relate to MR scanning, pulse sequence, protocol selection, signal acquisition, data processing, data storage, data calibration, image generation, etc., or any combination thereof. By way of example only, the MR parameters may include image contrast and / or image grayscale ratio, region of interest (ROI), slice thickness, imaging type (e.g., T1-weighted imaging, T2-weighted imaging, proton density weighted imaging, etc.), spin echo type (spin echo, fast spin echo (FSE), fast recovery FSE, single shot FSE, gradient refocusing echo, fast imaging with steady-state shift, etc.), flip angle value, acquisition time (TA), echo time (TE), repetition time (TR), echo train length (ETL), number of phases, number of excitations (NEX), inversion time, bandwidth (e.g., RF receiver bandwidth, RF transmitter bandwidth, etc.), etc., or any combination thereof.

[0076] In some embodiments, the MR imaging device can scan the object based on a scanning sequence including MR parameters such as radio frequency pulses, gradient fields, and signal acquisition timing. The scanning sequence can be, for example, a spin echo (SE) sequence, a fast spin echo (FSE) sequence, an ultra-short echo time (UTE) sequence, a gradient echo (GRE) sequence (such as a two-dimensional spiral spoiled gradient echo sequence), etc.

[0077] During the scanning of the object by the MR imaging device, MR signals (also called echo signals) can be collected by the RF coil (e.g., RF receiving coil). In some embodiments, various signal processing methods can be applied to process the collected MR signals. By way of example only, these signal processing methods may include analog-to-digital conversion, linear fitting, 2D Fourier transform (2D FT), fast Fourier transform (FFT), interpolation algorithms, grid rearrangement, etc., or any combination thereof. In some embodiments, the collected MR signals may be converted into a set of discrete data. The discrete data may be processed to fill in the k-space to generate a k-space data set. Among them, k-space refers to the signal space of the MR signal, which can be obtained by the spatial encoding technology of MR, for example, spatial encoding of the MR signal based on the gradient magnetic field (which may include layer selection, frequency encoding, phase encoding, etc.) to obtain the corresponding frequency space, namely the k-space.

[0078] Scan time refers to the time it takes for an MR imaging device to scan an object. A scan time is a period of time during which an MR imaging device performs continuous scanning on an object, which can be determined based on actual needs or experience. For example, a scan time can be 5s or 10s. In some embodiments, a scan time can include multiple (for example, 10 or 20) acquisition time periods (or referred to as acquisition time), and multiple acquisition time periods can have a certain time (such as natural time) sequence. An MR imaging device (for example, an RF receiving coil) can receive multiple echo signals (i.e., MR signals) generated by an object under the action of a magnetic field and an RF pulse within a scan time. Each of the multiple MR signals can correspond to an acquisition time period or acquisition time. The data acquisition module of the MR imaging device can collect the corresponding MR signals within the acquisition time period to fill the k-space and generate a k-space data set. The acquisition time period can be set based on actual needs or experience, such as 50ms or 60ms.

[0079] In some embodiments, the imaging data may include one or more of MR signals, data processed from MR signals, k-space data sets, and the like.

[0080] Imaging data refers to data used to generate / reconstruct an image of an object. In some embodiments, the imaging data of an object acquired within a scanning time may include imaging data acquired through multiple scanning cycles, and the multiple scanning cycles may have a certain time (such as natural time) sequence. In some embodiments, a scanning cycle may correspond to an acquisition time period. In some embodiments, the imaging data corresponding to the imaging data of a scanning cycle (such as a phase) may correspond to a k-space data set, and the imaging data corresponding to the imaging data of multiple scanning cycles (such as multiple phases) within a scanning time may correspond to multiple k-space data sets.

[0081] In some embodiments, the k-space corresponding to the imaging data of different scanning cycles may be different. For example, the object may move or its structure may change within a scanning period, such as the beating of the heart causing the heart to dilate or contract, the blood flow in the blood vessels causing the blood vessels to swell or shrink, the breathing of the lungs causing the structures such as the alveoli inside the lungs to change, etc. The movement or structural change of the object may cause the spatial position information to change. The spatial position information of the object is different in different scanning cycles, and different k-spaces may be obtained.

[0082] In some embodiments, imaging data can be obtained based on MR signals. In some embodiments, after the MR imaging device scans the object based on the scanning sequence, it can perform data acquisition on the received MR signals and fill the k-space based on a certain data acquisition trajectory (also referred to as a k-space filling trajectory or a k-space data line). In some embodiments, each MR signal or k-space data set can correspond to a data acquisition trajectory. The data acquisition trajectory can be spiral, linear, circular, ray-shaped (i.e., strip-shaped), etc., or any combination thereof.

[0083] In some embodiments, the acquisition trajectory of imaging data (for example, imaging data corresponding to each acquisition time period or each MR signal) may be: the acquisition density of imaging data with a frequency lower than a frequency threshold is greater than the acquisition density of imaging data with a frequency higher than the frequency threshold (that is, the acquisition trajectory is a variable density acquisition trajectory). That is, the acquisition trajectory or k-space data trajectory corresponding to the imaging data portion with a frequency lower than the frequency threshold is dense, and the acquisition trajectory or k-space data trajectory corresponding to the imaging data portion with a frequency higher than the frequency threshold is relatively sparse.

[0084] In some embodiments, there is a rotation angle between acquisition trajectories of imaging data corresponding to at least two adjacent times among the multiple times (for example, imaging data corresponding to adjacent acquisition time periods or two adjacent MR signals).

[0085] In some embodiments, a spiral data acquisition trajectory may be used, and the data filling trajectory of the corresponding acquired imaging data in k-space is a spiral. For more information about the data acquisition trajectory, see Figure 6 In some embodiments, the scanning sequence of the MR imaging device may adopt a scanning sequence whose data acquisition trajectory may be a spiral type, and for example, a gradient echo (GRE) sequence such as a two-dimensional spiral spoiled gradient echo sequence may be adopted.

[0086] In some embodiments, imaging data (such as multiple k-space data sets) within a scanning time can be divided into multiple phases. The imaging data of each phase in the multiple phases can be sufficient to reconstruct an image related to the object. The imaging data corresponding to each phase can correspond to a certain number of k-space data lines. In some embodiments, the imaging data (such as multiple k-space data sets) can be divided into multiple phases based on a certain number of k-space data lines. For example, the imaging data corresponding to two k-space data lines obtained by continuous acquisition are taken as one phase. In some embodiments, the imaging data acquired in one scanning cycle can correspond to one phase. In some embodiments, the phase can correspond to a time period in the motion cycle, such as a cardiac diastole, a cardiac systole, etc. Within a phase, the object, such as the heart and other organs, can be regarded as relatively stationary.

[0087] In some embodiments, imaging data of the object can be acquired in real time during the scanning process of the object by the MR imaging device. In some embodiments, the acquired imaging data of the object can be stored in a storage device or storage space, and can be read from the storage device or storage space to obtain the imaging data of the object.

[0088] In some embodiments, the imaging data of the object acquired by the MR imaging device within a scanning time can be calibrated. In some embodiments, the calibration process can be to calibrate the data acquisition trajectory or k-space data trajectory of the imaging data to avoid or reduce the error in the data acquisition trajectory or k-space data trajectory caused by the magnetic field gradient delay effect of MR. In some embodiments, a reference k-space data trajectory can be obtained by performing a pre-scan using an MR imaging device, and the k-space data trajectory of the imaging data obtained by scanning the object with the MR imaging device for a scanning time can be compared with the k-space data trajectory used for reference, and the k-space data trajectory of the imaging data of the object can be corrected / adjusted to make it consistent with the k-space data trajectory used for reference.

[0089] Step 320: Determine a plurality of reference images corresponding to a plurality of times within the scanning time based on at least a portion of the imaging data.

[0090] In some embodiments, step 320 may be performed by the reference image determination module 220 .

[0091] The reference image refers to an intermediate image obtained in the process of generating or reconstructing a dynamic image of an object. The reference image may be an image of the object in various forms, such as a 2D image or a 3D image.

[0092] In some embodiments, multiple times within a scanning time period may correspond to multiple scanning cycles or multiple phases within the scanning time period, that is, may correspond to imaging data of multiple scanning cycles or multiple phases. In some embodiments, imaging data of a scanning cycle or a phase may correspond to a reference image, and imaging data of multiple scanning cycles or multiple phases may correspond to multiple reference images.

[0093] In some embodiments, the reference image can be reconstructed based on the imaging data. For example, a k-space data set can be reconstructed to obtain a corresponding image, which is a reference image. In some embodiments, the reconstruction process can be to perform Fourier transform on the imaging data, such as the k-space data set, to obtain the reference image. In some embodiments, the imaging data (such as the k-space data set) can be processed with a trained neural network model to output a reference image. In some embodiments, an image reconstruction algorithm (for example, a split Bregman algorithm, a projected neighboring point algorithm, etc.) can be used to process the imaging data (such as the k-space data set) to obtain a reference image. In some embodiments, the imaging data is data in the frequency domain space, and the corresponding reference image is an image in the image domain or pixel domain.

[0094] In some embodiments, the reference image can be reconstructed based on partial data in the imaging data, which may refer to partial imaging data whose frequency meets the requirements or partial imaging data whose spatial position meets the requirements in the k-space data or partial imaging data determined based on other algorithmic rules.

[0095] In some embodiments, a reference image can be reconstructed based on part of the imaging data whose frequency is lower than a frequency threshold. The frequency threshold can be determined according to actual needs or experience, for example, 120 Hz or 130 Hz. Another example is 0.08 Hz or 0.09 Hz. In some embodiments, the frequency threshold can be determined according to the magnetic resonance frequency range corresponding to the scanned object. For example, the threshold corresponding to the low-frequency part in the frequency range is determined as the frequency threshold. In some embodiments, part of the imaging data whose frequency is lower than the frequency threshold can be referred to as the first imaging data.

[0096] In some embodiments, first imaging data having a frequency lower than a frequency threshold may be acquired in the imaging data, and corresponding multiple reference images may be reconstructed based on the imaging data corresponding to multiple times (i.e., multiple scanning cycles) in the first imaging data. In some embodiments, the imaging data corresponding to one time (i.e., one scanning cycle) in the first imaging data may be referred to as second imaging data, that is, corresponding multiple reference images may be reconstructed based on multiple second imaging data included in the first imaging data.

[0097] In some embodiments, a reference image can be reconstructed based on part of the imaging data located in the center area of ​​k-space. The center area can be determined based on actual needs or experience. For example, with the center point of k-space as the origin, the spatial area corresponding to the value of the frequency in the spatial coordinates between 0 and 120 Hz is the center area. It can be understood that the frequency corresponding to the center area of ​​k-space is lower than the frequency threshold. In some embodiments, part of the imaging data located in the center area of ​​k-space can be referred to as the first imaging data.

[0098] In some embodiments, first imaging data located in the center area of ​​k-space in the imaging data can be acquired, and corresponding multiple reference images can be reconstructed based on the imaging data corresponding to multiple times (i.e., multiple scanning cycles or multiple phases) in the first imaging data. As mentioned above, multiple times can correspond to multiple phases, that is, the first imaging data can be divided based on multiple times to obtain imaging data corresponding to multiple phases. In some embodiments, the imaging data corresponding to one time, i.e., one scanning cycle or one phase in the first imaging data can be called second imaging data, that is, multiple corresponding reference images can be reconstructed based on multiple second imaging data included in the first imaging data.

[0099] Step 330: Determine the time information of the dynamic image corresponding to the object within the scanning time based on the multiple reference images.

[0100] In some embodiments, step 330 may be performed by the time information determination module 230 .

[0101] Dynamic images are related to time and space, and dynamic images may include time information and space information. For example, the heart or some organs will move over time, that is, the various spatial positions of the heart and the various structural characteristics of the heart may change over time, and the corresponding dynamic images of the heart are related to time and space. The information related to time in the image information of the dynamic image (for example, the various acquisition times corresponding to each frame image in the dynamic image, etc.) can be called the time information of the dynamic image, and the information related to space (for example, the structural position information of the heart, the boundary position information, etc.) can be called the spatial information of the dynamic image. It can be understood that spatial information is related to time information. For example, the structural position information and boundary position information of the heart can change over time.

[0102] In some embodiments, the temporal information of the dynamic image may include functions, models, vectors, matrices, tensors, etc., or any combination thereof. In some embodiments, the spatial information of the dynamic image may include functions, models, vectors, matrices, tensors, etc., or any combination thereof.

[0103] In some embodiments, the time information of the dynamic image of the object of the scanning time segment can be determined based on multiple reference images corresponding to multiple times. The reference image can be an image corresponding to the image domain or the pixel domain, and the time information of the dynamic image of the object of the scanning time segment can be determined based on the image data in the image domain or the pixel domain corresponding to the multiple times. In some embodiments, data merging, data decomposition, data extraction and other processing can be performed on multiple reference images to extract or determine the time information of the dynamic image of the corresponding object of the scanning time segment. In some embodiments, the time information basis of the dynamic image (which can also be called the time basis, time basis function or time basis model) can be determined based on multiple reference images, and the time information basis of the dynamic image is used as the time information of the dynamic image. In some embodiments, the time information basis can include functions, models, vectors, matrices, tensors, etc., or any combination thereof.

[0104] For more information on determining the timing information of dynamic images within the scan time, see Figure 4 The relevant instructions will not be repeated here.

[0105] Step 340: Determine the spatial information of the dynamic image based on the time information and the imaging data.

[0106] In some embodiments, step 340 may be performed by the spatial information determination module 240 .

[0107] As mentioned above, the spatial information is related to the time information. Based on the time information of a dynamic image of an object during a scanning period and the imaging data of the object during the scanning period, the spatial information of the dynamic image during the scanning period can be determined.

[0108] In some embodiments, the spatial information basis of the dynamic image (also referred to as a spatial basis, a spatial basis function, or a spatial basis model) can be determined based on the time information of the dynamic image and the imaging data of the object during the scanning time, and the time information basis of the dynamic image is used as the time information of the dynamic image. In some embodiments, the spatial information basis can include a function, a model, a vector, a matrix, a tensor, etc., or any combination thereof.

[0109] In some embodiments, an objective function related to spatial information may be determined based on the determined time information and imaging data (such as a k-space data set) of the object acquired during a scanning period of time, and the spatial information may be determined by solving the objective function.

[0110] In some embodiments, the objective function can be used to calculate the difference between the estimated imaging data obtained based on the time information and the estimated spatial information and the imaging data actually acquired, and the objective function is solved by minimizing the difference to obtain the estimated spatial information that satisfies the condition (for example, the difference between the estimated imaging data obtained based on the estimated spatial information and the determined time information and the imaging data actually acquired is 0 or less than a threshold), and use it as the spatial information of the dynamic image. In some embodiments, the objective function can be iteratively solved multiple times based on the imaging data (for example, multiple k-space data sets corresponding to multiple times) to obtain the estimated spatial information that meets the target.

[0111] In some embodiments, the objective function may include a consistency term, which can be used to constrain the difference between the imaging data and the estimated imaging data determined based on the estimated spatial information generated in each iteration and the time information. The objective function can constrain the difference between the estimated imaging data and the actually acquired imaging data through the consistency term to solve the estimated spatial information.

[0112] In some embodiments, the objective function may include a regularization term, and the regularization term may be used to constrain the estimated spatial information generated in each iteration, that is, the regularization term may stabilize the estimated spatial information obtained in the process of multiple iterative solutions, so that the fluctuation of the estimated spatial information obtained by multiple iterative solutions will not be too large to cause inaccurate data. In some embodiments, the regularization term may be determined based on the estimated spatial information. For example, the L1 norm of the coefficients of the wavelet transform of the estimated spatial information may be used as a regularization term. For another example, the total variation of the spatial dimension of the estimated spatial information may be used as a regularization term. For another example, the regularization term (also referred to as a regularization term or a correction term) of the estimated spatial information may also be obtained based on other regularization algorithms, such as the Bayesian algorithm.

[0113] For example, let f represent the objective function, which can be constructed as:

[0114]

[0115] Among them, U x represents estimated spatial information, Φ represents time information, and d represents the imaging data of the object actually acquired (such as a k-space data set). represents the consistent term, R(U x ) represents the regularization term.

[0116] Step 350: reconstruct the dynamic image based on the time information and the spatial information.

[0117] In some embodiments, step 350 may be performed by the image reconstruction module 250 .

[0118] As mentioned above, dynamic images are related to time and space, and dynamic images may include time information and space information. After determining and obtaining the time information and space information of the dynamic image, the dynamic image may be reconstructed based on the time information and space information.

[0119] In some embodiments, time information and space information can be processed, and time information and space information can be associated to obtain image data including time information and space information, and a dynamic image can be reconstructed based on the image data. In some embodiments, functions, models, vectors, matrices, tensors, etc. corresponding to time information can be multiplied or combined with functions, vectors, matrices, tensors, etc. corresponding to space information to obtain functions, vectors, matrices, tensors, etc. including time information and space information, and a dynamic image can be reconstructed based on the obtained functions, vectors, matrices, tensors, etc. In some embodiments, a model corresponding to time information can be combined with a model corresponding to space information to obtain a model including time information and space information, and a dynamic image can be reconstructed based on the model.

[0120] In some embodiments, based on the determined time information (such as the time information basis of the dynamic image) and spatial information (such as the spatial information basis of the dynamic image), a low-rank representation corresponding to the dynamic image can be obtained. The low-rank representation corresponding to the dynamic image can be a function, a model, a vector, a matrix, a tensor, etc., or any combination thereof.

[0121] In some embodiments, a dynamic image may refer to a time series image. A time series image may include multiple MR images of an object continuously acquired within a scanning time. The low-rank representation corresponding to the dynamic image may indicate that the correlation between multiple images in the time series image corresponding to the dynamic image is low-rank. The correlation between multiple images may include spatial correlation and / or temporal correlation. For example, a scanning cycle or a phase may correspond to a motion cycle or change cycle of an object (e.g., a heartbeat cycle of the heart), and in the images corresponding to each scanning cycle or each phase, the changes of the object over time (e.g., the changes of voxels, pixels, etc. of the object over time) may be similar or identical. It can be understood that the correlation or similarity between multiple images can be characterized by a low-rank representation, and based on the low-rank representation, the image data of the dynamic image (which may be a function, a model, a vector, a matrix, a tensor, etc., or any combination thereof) can be obtained, and then the dynamic image is reconstructed. In some embodiments, the low-rank representation of the dynamic image may also represent a modality of the dynamic image, and the dynamic image can be reconstructed based on the modality.

[0122] In some embodiments, the determined time information and spatial information may be corresponding matrices, and the obtained low-rank representation corresponding to the dynamic image may be a low-rank matrix including the time information and the spatial information.(1) for:

[0123] A (1) =U x Φ

[0124] Among them, the matrix Φ is the time information of the dynamic image (such as the time information basis of the dynamic image), and the matrix U x It is the spatial information of the dynamic image (such as the spatial information basis of the dynamic image).

[0125] In some embodiments, the reconstructed dynamic image of the object, which can also be called a movie image of the object (such as a cardiac movie image), can reflect the changes in the structure, morphology and other characteristics of the object within a scanning period. For example, the heartbeat movement of the heart within a scanning period, the heart structure and morphology during diastole, the heart structure and morphology during systole, the changes in the voxels of the heart over time, the changes in the pixels (such as pixel value, contrast, etc.) at various positions over time, etc. Figure 7 As shown, there are respectively a schematic diagram of the cardiac structure and morphology during diastole and a schematic diagram of the cardiac structure and morphology during systole in a dynamic image of the heart.

[0126] The dynamic image of the object is a dynamic image corresponding to the scanning time, which can reflect the changes in the information of different regions or parts of the object within a certain dimension. The dimension may include one or any combination of the spatial position of the image pixel, the heartbeat movement, the respiratory movement, the inversion recovery time, etc. For example, the information of different regions or parts of the object can be presented in the image with pixels. According to the spatial position dimension of the image pixel, the dynamic image can reflect the changes in the information of different regions or parts of the object during the scanning time as the spatial position of the image pixel changes. For another example, according to the respiratory movement dimension, the dynamic image can reflect the changes in the information of different regions or parts of the object during the scanning time as the respiratory movement changes. For another example, according to the heartbeat movement dimension, the dynamic image can reflect the changes in the information of different regions or parts of the object during the scanning time as the heartbeat movement changes. In some embodiments, within a scanning time, the dynamic image of the heart may include multiple dimensions such as the spatial position of the image pixel, the heartbeat movement, the respiratory movement, the inversion recovery time, etc. In some embodiments, dynamic images corresponding to multiple dimensions (i.e., multiple time series images) can be obtained based on the imaging data of the object acquired for a scanning time and / or the reconstructed dynamic image.

[0127] In some embodiments, information corresponding to multiple dimensions can be extracted, decomposed, and calculated from the imaging data and / or dynamic images of the object acquired during a scanning period of time. In some embodiments, the imaging data and / or image data of the dynamic images of the object can be processed based on the trained neural network model to obtain information corresponding to multiple dimensions (such as spatial information and time information). Furthermore, based on the information corresponding to multiple dimensions (such as spatial information and time information), multiple time series images corresponding to multiple dimensions can be reconstructed. Figure 8 As shown, there is a schematic diagram of a respiratory phase image in the time series images of the dimension corresponding to the respiratory movement of the heart, a schematic diagram of a cardiac phase image in the time series images of the dimension corresponding to the heartbeat movement, and a schematic diagram of an inversion recovery time image in the time series images of the dimension corresponding to the inversion recovery time.

[0128] In some embodiments, the low-rank representation of the dynamic image may be decomposed to obtain time information and spatial information corresponding to multiple dimensions.

[0129] In some embodiments, the time information of the aforementioned dynamic image (which can be expressed as a time information tensor or a time factor tensor of the dynamic image) can correspond to the imaging data based on a partial spatial encoding (also referred to as a subset of the spatial encoding) in the imaging data of the object acquired within a scanning time (such as the imaging data of a partial k-space position in the k-space acquired within a scanning time, which can also be referred to as navigation data), and the extracted time information (such as a time information tensor or a time factor tensor). Wherein, the partial k-space position can correspond to the k-space position of the phase information (such as at least one of the cardiac phase or the respiratory phase) used to identify the motion of the object.

[0130] In some embodiments, extracting time information (such as a time information tensor or a time factor tensor) based on partially spatially encoded imaging data (such as imaging data of partial k-space positions in k-space acquired within a scanning time) may include: calculating a tensor (also referred to as a training tensor) representing the partially spatially encoded imaging data (such as imaging data of partial k-space positions in k-space acquired within a scanning time), and extracting time information (such as a time information tensor or a time factor tensor) from the tensor representing the partially spatially encoded imaging data.

[0131] In some embodiments, multiple time information corresponding to multiple time dimensions can be represented as multiple time basis matrices. In some embodiments, extracting time information (such as a time information tensor or a time factor tensor) from a tensor representing the partially spatially encoded imaging data can include: decomposing the tensor into a spatial factor matrix (also referred to as a spatial information matrix) corresponding to the partially spatially encoded imaging data, a core tensor, and one or more time basis matrices, the time factor tensor can be determined based on the core tensor and one or more time reference matrices, for example, the time factor tensor can be calculated as the product of the core tensor and one or more time reference matrices. Among them, each time basis matrix in the one or more time basis matrices can correspond to a different time dimension. For example, each time dimension can be one of a cardiac phase, a respiratory phase, an acquisition time, an imaging sequence parameter, or a timing parameter.

[0132] It should be noted that the above description of process 300 is only for example and illustration, and does not limit the scope of application of the present application. For those skilled in the art, various modifications and changes can be made to process 300 under the guidance of the present application. However, these modifications and changes are still within the scope of the present application.

[0133] Figure 4 This is an exemplary flow chart for determining the time information of a dynamic image according to some embodiments of the present specification.

[0134] In some embodiments, process 400 may be performed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. Figure 4 One or more operations in the process 400 for determining the time information of the dynamic image corresponding to the object within the scanning time can be performed by Figure 1 The process 400 is implemented by the processing device 140 shown. For example, the process 400 may be stored in the storage device 150 in the form of instructions, and called and / or executed by the processing device 140.

[0135] like Figure 4 As shown, the process 400 of determining the time information of a dynamic image may include the following operations.

[0136] Step 410, based on the multiple reference images and the multiple time sequences corresponding to the multiple reference images, determine an image representation matrix, the image representation matrix including a spatial dimension and a time dimension, the spatial dimension corresponds to the spatial position in the reference image, and the time dimension represents the multiple times corresponding to the multiple reference images.

[0137] In some embodiments, step 410 may be performed by the time information determination module 230 .

[0138] In some embodiments, a reference image can be represented as a vector. Based on the time sequence of multiple times corresponding to multiple reference objects, multiple vectors corresponding to multiple reference images can be arranged in sequence to obtain a corresponding image representation matrix.

[0139] In some embodiments, a reference image may be represented as a row vector, and a plurality of row vectors corresponding to a plurality of reference images may be sequentially arranged along a column direction based on a time sequence to obtain an image representation matrix. In some embodiments, a reference image may be represented as a column vector, and a plurality of column vectors corresponding to a plurality of reference images may be sequentially arranged along a row direction based on a time sequence to obtain an image representation matrix.

[0140] by Figure 5 For example, Figure 5 Schematic diagram of an exemplary image representation vector, wherein 8 column vectors are [K11, K12 ... K18, K19], [K21, K22 ... K28, K29], [K31, K32 ... K38, K39], [K41, K42 ... K48, K49], [K51, K52 ... K58, K59], [K61, K62 ... K68, K69], [K71, K72 ... K78, K79], [K81, K82 ... K88, K89], and the 8 column vectors correspond to 8 reference images of consecutive times t1, t2 ... t7, t8, respectively, t1, t2 ... t7, t8 are scanning times for reconstructing imaging data corresponding to the reference images, such as scanning cycles or phases. The 8 column vectors are arranged in the row direction.

[0141] For a reference image, it may include m*n pixels, where m and n are integers. Each pixel includes pixel information (e.g., the spatial position of the pixel, the pixel value, etc.). In some embodiments, the reference image including m*n pixels may be represented as a vector including m*n elements, and each element in the vector may correspond to the pixel information of a pixel (e.g., the spatial position of the pixel). Figure 5 As shown in FIG. 1 , a reference image includes 9 pixels, and the corresponding column vector includes 9 elements, each of which corresponds to the spatial position information of a pixel. That is, in the image representation matrix, the spatial position information (such as the spatial position of the pixel) and time information (such as the scanning time corresponding to the multiple reference images) of multiple reference images can be included.

[0142] It can be understood that based on the arrangement of the vector representation and image representation matrix corresponding to the reference image, the row dimension and column dimension of the image matrix correspond to the spatial dimension and time dimension of the multiple reference images respectively. The spatial dimension corresponds to the spatial position information in the reference image, and the time dimension represents the time information of the multiple reference images, that is, the multiple times corresponding to the multiple reference images. Figure 5 As shown, the 8 column vectors correspond to 8 consecutive time reference images respectively, that is, the time information of the 8 reference images can be reflected in the row dimension. For a column vector, each element corresponds to the spatial position information of a pixel, that is, the spatial position information of a reference image can be reflected in the column dimension.

[0143] Step 420: Determine the time information based on the image representation matrix and the time dimension.

[0144] In some embodiments, step 420 may be performed by the time information determination module 230 .

[0145] As mentioned above, the image representation matrix may include spatial information (such as the spatial position of pixels) and temporal information (such as multiple times corresponding to multiple reference images) of multiple reference images. The row dimension and column dimension of the image matrix correspond to the spatial dimension and temporal dimension of multiple reference images, respectively.

[0146] In some embodiments, the image representation matrix may be subjected to matrix decomposition to obtain a first matrix corresponding to the time dimension (i.e., a matrix representing the time information of the reference image) and a second matrix corresponding to the space dimension (i.e., a matrix representing the space information of the reference image). In some embodiments, the first matrix obtained by decomposition (i.e., a matrix representing the time information of the reference image) may include time information of multiple reference images (e.g., multiple times corresponding to multiple reference images), and the second matrix (i.e., a matrix representing the space information of the reference image) may include space information of multiple reference images (e.g., space positions of pixels in the reference image).

[0147] In some embodiments, the time information of the dynamic image can be determined based on the first matrix. In some embodiments, the time information basis of the dynamic image (also referred to as the time basis, time basis function or time basis model) can be determined based on the first matrix, and the time information basis of the dynamic image can be used as the time information of the dynamic image. In some embodiments, the time information corresponding to each reference image in a plurality of reference images can be related, for example, a scanning cycle or a phase can correspond to a motion cycle or change cycle of the object (such as a heartbeat cycle of the heart), and the changes of the object over time (such as the changes of the voxels, pixels, etc. of the object over time) in the images corresponding to each scanning cycle or each phase can be similar or identical. Based on this time correlation or similarity, the time basis of the dynamic image (such as the time basis function or the matrix representing the time basis) can be determined and used as the time information of the dynamic image.

[0148] In some embodiments, the rank of the first matrix can be determined, and a part of the first matrix can be determined based on the rank of the first matrix as a representation of the time basis of the dynamic image. In some embodiments, the rank of the matrix can be obtained by solving the matrix. For example, the rank of the matrix is ​​obtained by using algorithms such as elementary transformation method and Gaussian elimination method. In some embodiments, the rank of the matrix corresponds to a range of values, which can be set according to experience or actual needs. For example, the range of values ​​can be 10 to 40. Taking the rank of 20 as an example, the first 20 columns of the second matrix are used as the representation of the time basis of the dynamic image. It can be understood that the rank of the matrix is ​​less than the column dimension of the matrix. In some embodiments, the rank of the matrix is ​​related to the temporal correlation or similarity between multiple reference images.

[0149] In some embodiments, the image representation matrix can be decomposed by singular value decomposition based on the time dimension to determine the time information of the dynamic image. For example, the image representation matrix can be expressed as LR , by performing singular value decomposition on it, we can get:

[0150] Γ LR =U∑V

[0151] Among them, U and V are an orthogonal matrix corresponding to the row dimension and an orthogonal matrix corresponding to the column dimension, respectively, and ∑ is a diagonal matrix. The orthogonal matrix corresponding to the time dimension obtained by decomposition is the first matrix corresponding to the time dimension (i.e., the matrix representing time information).

[0152] Figure 6 is an exemplary schematic diagram of the acquisition trajectory of imaging data according to some embodiments of the present specification.

[0153] Figure 6, k-space data trajectories (ie, corresponding data acquisition trajectories) of imaging data corresponding to eight consecutive times t1, t2, ..., t7, t8 are shown respectively.

[0154] like Figure 6 As shown, the k-space data trajectory of the imaging data corresponding to 8 consecutive times t1, t2 ... t7, t8 adopts a spiral type, and γ(k, t1), γ(k, t2), γ(k, t3), γ(k, t4), γ(k, t5, γ(k, t6), γ(k, t7), γ(k, t8) are the k-space central part data of the imaging data corresponding to t1, t2 ... t7, t8 respectively. The adopted spiral type is a spiral type with decreasing density from the center to the outside, that is, the density of the k-space data trajectory in the central part of the k-space is greater than the density of the k-space data trajectory in the surrounding part of the k-space.

[0155] In some embodiments, among the multiple times, the imaging data acquisition trajectory or k-space data trajectory corresponding to at least two adjacent times has a rotation angle. In some embodiments, the acquisition trajectory or k-space data trajectory corresponding to some adjacent times among the multiple times (for example, t4...t8 among t1, t2...t7, t8) may have a rotation angle. In some embodiments, each of the multiple times may have a rotation angle with the acquisition trajectory or k-space data trajectory corresponding to its adjacent time. Figure 6 As shown, in the k-space data trajectory of the imaging data corresponding to the eight consecutive times t1, t2 ... t7, t8, each time has a rotation angle with the acquisition trajectory or k-space data trajectory corresponding to the adjacent time. The rotation angle can be determined according to actual needs or experience, for example, it can be rotated 137.5° or 111° around the k-space.

[0156] In some embodiments, based on the rotation angle of the acquisition trajectory of imaging data corresponding to adjacent times or the k-space data trajectory, it can be achieved that imaging data corresponding to different times can cover data at different spatial positions, making the k-space data distribution more uniform. Figure 6 As shown, if imaging data corresponding to different times (such as k-space data lines of multiple k-spaces corresponding to multiple times) are filled into one k-space, a k-space data set with uniform spatial position coverage can be obtained.

[0157] It should be noted that the above Figure 6 The acquisition trajectory or k-space data trajectory in is only for example and explanation, and does not limit the scope of application of the present application. For those skilled in the art, various modifications and changes can be made to the preset weight curve and its determination method under the guidance of the present application. However, these modifications and changes are still within the scope of the present application.

[0158] The embodiment of the present specification also provides a device, including a processor, the processor is used to execute the aforementioned magnetic resonance (MR) imaging method. The magnetic resonance (MR) imaging method may include: acquiring imaging data of an object acquired by an MR imaging device within a scanning time; determining multiple reference images corresponding to multiple times within the scanning time based on at least part of the imaging data; determining time information of a dynamic image corresponding to the object within the scanning time based on the multiple reference images; determining spatial information of the dynamic image based on the time information and the imaging data; and reconstructing the dynamic image based on the time information and the spatial information.

[0159] The basic concepts have been described above, and it will be apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be given only as an example and not as a limitation. Various changes, improvements, and modifications may occur and are contemplated by those skilled in the art, although not expressly stated herein. Such changes, improvements, and modifications are intended to be suggested by the present disclosure and fall within the spirit and scope of the exemplary embodiments of the present disclosure.

[0160] In addition, specific terms have been used to describe the various embodiments of the present disclosure. For example, the terms "one embodiment", "an embodiment" and / or "some embodiments" mean that a specific feature, structure or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present disclosure. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "an embodiment" or "an alternative embodiment" in various parts of this specification may not all refer to the same embodiment. In addition, specific features, structures or characteristics may be appropriately combined in one or more embodiments of the present disclosure.

[0161] In addition, those skilled in the art will appreciate that various aspects of the disclosure may be illustrated and described herein in any of several patentable classes or contexts, including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. In addition, various aspects of the disclosure may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied thereon.

[0162] Computer readable signal media may include a propagated data signal having a computer readable program code implemented therein (e.g., in baseband or as part of a carrier wave). Such propagated signals may take any form in various forms, including electromagnetic, optical, etc., or any suitable combination thereof. Computer readable signal media may be any computer readable medium that is not a computer readable storage medium and can convey, propagate, or transmit a program for execution by an instruction execution system, device, or equipment or in conjunction with it. The program code implemented on a computer readable signal medium may be transmitted using any appropriate medium, including wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0163] The computer program code for performing the operation of various aspects of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages, such as "C" programming language, VisualBasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages, such as Python, Ruby and Groovy, or other programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, a remote computer can be connected to the user's computer through any type of network (including a local area network (LAN) or a wide area network (WAN)), or a connection to an external computer can be made (e.g., using an Internet service provider through the Internet) or in a cloud computing environment or provided as a service (such as software as a service (SaaS)).

[0164] Furthermore, the order in which the processing elements or sequences, or the recitation using numbers, letters or other reference numbers are not intended to limit the claimed processes and methods to any order, except as may be indicated in the claims. Although the above disclosure discusses what are currently considered to be various useful embodiments of the present disclosure throughout the various examples, it should be understood that such details are for this purpose only, and the appended claims are not limited to the disclosed embodiments, but are instead intended to cover modifications and equivalent arrangements that fall within the spirit and scope of the disclosed embodiments. For example, although the implementation of the various components described above can be implemented in a hardware device, it can also be implemented as a software-only solution - for example, installed on an existing server or mobile device.

[0165] Similarly, it should be appreciated that in the above description of the embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure to aid in understanding one or more of the various inventive embodiments. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, inventive embodiments exist in fewer features than all of the features of a single previously disclosed embodiment.

[0166] In some embodiments, the numbers expressing the quantities or properties used to describe and claim certain embodiments of the present application should be understood to be modified by the terms "approximately", "approximately" or "substantially" in some instances. For example, "approximately", "approximately" or "substantially" may indicate a variation of ±20% of the value described, unless otherwise stated. Therefore, in some embodiments, the numerical parameters set forth in the written description and the appended claims are approximate values ​​that may vary depending on the desired properties sought to be obtained by a particular embodiment. In some embodiments, these numerical parameters should be interpreted according to the number of reported significant figures and using ordinary inclusion techniques. Although the numerical ranges and parameters setting forth the broad range of some embodiments of the present application are approximate, the numerical values ​​set forth in the specific examples are reported in a manner that is as precise as practical.

[0167] Each patent, patent application, patent application publication, and other material (such as articles, books, specifications, publications, documents, articles, etc.) cited herein is hereby incorporated by reference in its entirety for all purposes, except for any prosecution document history associated with the aforementioned material, material in the aforementioned material that is inconsistent or conflicting with this document, or material in the aforementioned material that may have a limiting effect on the maximum scope of the claims now or later associated with this document. By way of example, in the event of any inconsistency or conflict between the description, definition, and / or use of a term associated with any incorporated material and the description, definition, and / or use of the term associated with this document, the description, definition, and / or use of the term in this document shall control.

[0168] Finally, it should be understood that the embodiments of the application disclosed herein are explanations of the principles of the embodiments of the present application. Other modifications that can be adopted may fall within the scope of the present application. Thus, as an example and not a limitation, alternative configurations of the embodiments of the present application may be utilized according to the teachings herein. Therefore, the embodiments of the present application are not limited to as precisely shown and described.

Claims

1. A magnetic resonance (MR) imaging method, comprising: Acquiring imaging data of the object acquired by an MR imaging device during a scanning time; Determining, based on at least a portion of the imaging data, a plurality of reference images corresponding to a plurality of times within the scanning time; Determining time information of a dynamic image corresponding to the object within the scanning time based on the multiple reference images includes: Determine an image representation matrix based on the multiple reference images and the multiple time sequences corresponding to the multiple reference images, the image representation matrix comprising a spatial dimension and a time dimension, the spatial dimension corresponding to the spatial position in the reference image, and the time dimension representing the multiple times corresponding to the multiple reference images; and Determining the time information based on the image representation matrix and the time dimension; Determining spatial information of the dynamic image based on the time information and the imaging data includes: determining an objective function based on the time information and the imaging data; and Solving the objective function value through multiple iterations to determine the spatial information, the objective function includes a consistency term and a regularization term, the consistency term is used to constrain the difference between the imaging data and the estimated imaging data determined based on the estimated spatial information generated in each iteration and the time information, and the regularization term is used to constrain the estimated spatial information generated in each iteration; The dynamic image is reconstructed based on the time information and the spatial information.

2. The method according to claim 1, wherein determining, based on at least part of the imaging data, a plurality of reference images corresponding to a plurality of times within the scanning time comprises: Acquire first imaging data having a frequency lower than a frequency threshold in the imaging data; as well as The multiple reference images are reconstructed based on the second imaging data respectively corresponding to the multiple times in the first imaging data.

3. The method according to claim 1, wherein determining, based on at least part of the imaging data, a plurality of reference images corresponding to a plurality of times within the scanning time comprises: Acquire first imaging data located in a central region of the k-space among the imaging data; Based on the multiple times in the first imaging data, divide the phases to obtain multiple second imaging data corresponding to the multiple phases; as well as The plurality of reference images are reconstructed based on the plurality of second imaging data.

4. The method according to claim 1, wherein the acquisition trajectory of the imaging data corresponding to at least one of the multiple times comprises: The acquisition density of imaging data with a frequency lower than a frequency threshold is greater than the acquisition density of imaging data with a frequency higher than the frequency threshold.

5. The method as described in claim 4, wherein there is a rotation angle between the acquisition trajectories of the imaging data corresponding to at least two adjacent times among the multiple times.

6. The method according to claim 1, wherein reconstructing the dynamic image based on the time information and the spatial information comprises: Based on the time information and the spatial information, obtaining a low-rank representation corresponding to the dynamic image; Based on the low-rank representation, the dynamic image is determined.

7. A magnetic resonance (MR) imaging system comprising: An acquisition module, for acquiring imaging data of the object acquired by the MR imaging device within a scanning time; A reference image determination module, configured to determine a plurality of reference images corresponding to a plurality of times within the scanning time based on at least a portion of the imaging data; A time information determination module, used to determine the time information of the dynamic image corresponding to the object within the scanning time based on the multiple reference images, including: Determine an image representation matrix based on the multiple reference images and the multiple time sequences corresponding to the multiple reference images, the image representation matrix comprising a spatial dimension and a time dimension, the spatial dimension corresponding to the spatial position in the reference image, and the time dimension representing the multiple times corresponding to the multiple reference images; and Determining the time information based on the image representation matrix and the time dimension; A spatial information determination module, used to determine the spatial information of the dynamic image based on the time information and the imaging data, comprising: determining an objective function based on the time information and the imaging data; and Solving the objective function value through multiple iterations to determine the spatial information, the objective function includes a consistency term and a regularization term, the consistency term is used to constrain the difference between the imaging data and the estimated imaging data determined based on the estimated spatial information generated in each iteration and the time information, and the regularization term is used to constrain the estimated spatial information generated in each iteration; An image reconstruction module is used to reconstruct the dynamic image based on the time information and the spatial information.

8. A magnetic resonance (MR) imaging apparatus, comprising at least one processor and at least one storage device, wherein the storage device is used to store instructions, and when the at least one processor executes the instructions, the method according to any one of claims 1 to 6 is implemented.

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