System and method for magnetic resonance imaging

Abstract

A method for magnetic resonance imaging (MRI) can include acquiring imaging signals related to a region of interest (ROI) of a subject. The method can also include selecting a portion of the imaging signals as auxiliary signals associated with at least one temporal dimension of the ROI. The method can also include generating at least one target image associated with the at least one temporal dimension of the ROI based on the imaging signals and the auxiliary signals.

Description

[0001] Cross-references

[0002] This application claims priority to the following applications:

[0003] U.S. application number 17 / 649,360, filed on January 29, 2022;

[0004] The contents of the above application are incorporated herein by reference. Technical Field

[0005] This application relates generally to medical imaging, and more particularly to systems and methods for magnetic resonance imaging (MRI). Background Technology

[0006] Multitasking techniques enable the acquisition of multidimensional MRI data (e.g., information related to various physiological movements, relaxation, etc.) within a single MRI scan. In some cases, navigation signals configured to determine temporal information for image reconstruction and image signals configured to determine spatial information for image reconstruction are acquired; however, this leads to increased imaging time and reduced imaging efficiency. Therefore, it is necessary to provide efficient systems and methods for multitasking techniques. Summary of the Invention

[0007] According to one aspect of this application, a system for magnetic resonance imaging (MRI) may include one or more storage devices and one or more processors configured to communicate with the one or more storage devices. The one or more storage devices may include a set of instructions. When the one or more processors execute the set of instructions, they may direct the one or more processors to perform one or more of the following operations: The one or more processors may acquire imaging signals associated with a region of interest (ROI) of an object. The one or more processors may select a portion of the imaging signals as an auxiliary signal associated with at least one temporal dimension of the ROI. The one or more processors may determine temporal information related to at least one temporal dimension of the ROI based on the auxiliary signal. The one or more processors may generate at least one target image based on the imaging signals and the auxiliary signal, the target image being associated with the at least one temporal dimension of the ROI.

[0008] According to another aspect of this application, a method for magnetic resonance imaging (MRI) may include one or more of the following operations: One or more processors may acquire imaging signals associated with a region of interest (ROI) of a subject. One or more processors may select a portion of the imaging signals as an auxiliary signal associated with at least one temporal dimension of the ROI. One or more processors may determine temporal information related to at least one temporal dimension of the ROI based on the auxiliary signal. One or more processors may generate at least one target image based on the imaging signals and the auxiliary signal, the target image being associated with the at least one temporal dimension of the ROI.

[0009] According to another aspect of this application, a system for magnetic resonance imaging (MRI) may include an acquisition module configured to acquire imaging signals associated with a region of interest (ROI) of a subject. The system may further include a determination module configured to select a portion of the imaging signals as auxiliary signals associated with at least one temporal dimension of the ROI. The system may also include a reconstruction module configured to generate at least one target image based on the imaging signals and the auxiliary signals, the target image being associated with the at least one temporal dimension of the ROI.

[0010] According to another aspect of this application, a non-transitory computer-readable medium may include at least one set of instructions. The at least one set of instructions may be executed by one or more processors of a computer server. One or more processors may acquire imaging signals associated with a region of interest (ROI) of an object. One or more processors may select a portion of the imaging signals as auxiliary signals associated with at least one temporal dimension of the ROI. One or more processors may determine temporal information related to at least one temporal dimension of the ROI based on the auxiliary signals. One or more processors may generate at least one target image based on the imaging signals and the auxiliary signals, the target image being associated with the at least one temporal dimension of the ROI.

[0011] In some embodiments, the auxiliary signal includes a portion of the imaging signal sampled in the central region of the k-space.

[0012] In some embodiments, the central region of the k-space includes the k-space centerline along the layer selection direction of the k-space.

[0013] In some embodiments, the imaging signal is acquired based on at least two k-space trajectories, each of which passes through the central region.

[0014] In some embodiments, in order to select the portion of the imaging signal as the auxiliary signal associated with the region of interest, one or more processors may acquire at least two datasets, each dataset including a portion of the at least two k-space trajectories. Within each dataset, one or more processors may select the imaging signal sampled in the central region as an auxiliary subset. The one or more processors may designate the at least two auxiliary subsets as the auxiliary signal.

[0015] In some embodiments, for each dataset, the k-space trajectories in the dataset can be sampled continuously.

[0016] In some embodiments, for each of the at least two datasets, the selected imaging signal in the corresponding auxiliary subset corresponds to a different sampling location in the central region.

[0017] In some embodiments, the imaging signal is acquired in at least two parallel k-space sheets arranged along the selected layer direction of k-space.

[0018] In some embodiments, the k-space trajectory in each of the at least two parallel layers passes through the center of the layer. The central region passes through the center of the at least two parallel layers along the selected layer direction.

[0019] In some embodiments, for each of the datasets, the k-space trajectory is a radial trajectory and corresponds to the same angle, wherein each of the radial trajectories originates from one of the at least two parallel sheets.

[0020] In some embodiments, in order to generate the at least one target image based on the imaging signal and the auxiliary signal, the target image is associated with at least one temporal dimension of the region of interest. One or more processors may determine temporal information related to the at least one temporal dimension of the region of interest based on the auxiliary signal. One or more processors may determine spatial information related to at least one spatial dimension of the region of interest based on the temporal information and the imaging signal. One or more processors may generate the at least one target image of the region of interest based on the temporal information and the spatial information.

[0021] In some embodiments, the time information includes at least one time basis function associated with the at least one time dimension, and the spatial information includes at least one spatial basis function associated with the at least one spatial dimension.

[0022] In some embodiments, the at least one time dimension includes at least one of cardiac motion, respiratory motion, T1 relaxation, T2 relaxation, chemical exchange saturation transfer (CEST), contrast agent dynamics, T1p contrast, molecular diffusion, or duration.

[0023] In some embodiments, in order to determine the spatial information related to at least one spatial dimension of the region of interest based on the temporal information and the imaging signal, one or more processors may construct an objective function based on the imaging signal and the temporal information. The one or more processors may determine the estimated spatial information. The one or more processors may determine the estimated imaging data based on the estimated spatial information and the temporal information. The one or more processors may determine the difference between the imaging signal and the estimated imaging data. The one or more processors may solve the objective function based on the difference to determine the spatial information.

[0024] Some of the additional features of this application will be described in the following description. These additional features will be apparent to those skilled in the art from the study of the following description and the accompanying drawings, or from an understanding of the production or operation of the embodiments. The features of this application can be implemented and achieved through the practice or use of methods, means, and combinations thereof relating to the specific embodiments described below. Attached Figure Description

[0025] This application will be further described through exemplary embodiments. These exemplary embodiments will be described in detail with reference to the accompanying drawings. The drawings are not drawn to scale. These embodiments are non-limiting exemplary embodiments, in which the same numbers in the figures denote similar structures, wherein:

[0026] Figure 1 These are schematic diagrams of exemplary magnetic resonance imaging systems according to some embodiments of this application;

[0027] Figure 2 These are schematic diagrams of exemplary magnetic resonance imaging scanning devices according to some embodiments of this application;

[0028] Figure 3 These are schematic diagrams of exemplary hardware and / or software components of a computing device according to some embodiments of this application;

[0029] Figure 4 These are schematic diagrams of exemplary hardware and / or software components of a mobile device according to some embodiments of this application;

[0030] Figure 5 These are schematic diagrams of exemplary processing devices according to some embodiments of this application;

[0031] Figure 6 This is a flowchart illustrating an exemplary process for generating at least one target image according to some embodiments of this application.

[0032] Figure 7 This is a schematic diagram of an exemplary sampling mode of an imaging signal according to some embodiments of this application;

[0033] Figure 8 This is a schematic diagram of an exemplary sampling sequence of imaging signals according to some embodiments of this application;

[0034] Figure 9 This is an exemplary schematic diagram illustrating contrast agent-related respiratory movements and signal intensity changes according to some embodiments of this application;

[0035] Figures 10A-10F These are exemplary schematic diagrams of target images shown according to some embodiments of this application; and

[0036] Figures 11A-11F This is an exemplary schematic diagram of a target image shown according to some embodiments of this application. Detailed Implementation

[0037] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. However, those skilled in the art should understand that this application can be implemented without these details. In other instances, to avoid unnecessarily obscuring various aspects of this application, well-known methods, processes, systems, components, and / or circuits have been described at a higher level. It will be apparent to those skilled in the art that various changes can be made to the disclosed embodiments, and the general principles defined in this application can be applied to other embodiments and application scenarios without departing from the principles and scope of this application. Therefore, this application is not limited to the embodiments shown, but conforms to the broadest scope consistent with the scope of the claims.

[0038] The terminology used in this application is for the purpose of describing particular exemplary embodiments only and is not restrictive. The singular forms “a,” “an,” and “the” used herein may also include the plural forms unless the context explicitly indicates otherwise. As used herein, the terms “and / or” and “at least one” include any and all combinations of one or more of the associated listed items. It should be further understood that when the terms “comprising,” “including,” “including,” and / or “comprising” are used in this application, the presence of the stated features, integers, steps, operations, elements, and / or components is specified, but the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof is not excluded. Furthermore, the term “exemplary” indicates an example or illustration.

[0039] It should be understood that the terms “system,” “engine,” “unit,” “module,” and / or “block” used in this application are methods for distinguishing different components, elements, parts, sections, or assemblies at different levels in ascending order. However, these terms may be replaced by other expressions if the same purpose can be achieved.

[0040] Generally, the terms "module," "unit," or "block" as used herein refer to logic embodied in hardware or firmware, or a collection of software instructions. The modules, units, or blocks described herein can be implemented as software and / or hardware and can be stored on any type of non-transitory computer-readable medium or other storage device. In some embodiments, software modules / units / blocks can be compiled and linked into an executable program. It should be understood that software modules can be invoked from other modules / units / blocks or from themselves, and / or can be invoked in response to detected events or interrupts. Software modules / units / blocks configured for execution on a computing device can be provided on computer-readable media, such as optical discs, digital video discs, flash drives, disks, or any other tangible media, or as digital downloads (and may initially be stored in a compressed or installable format, requiring installation, decompression, or decryption before execution). The software code herein can be stored, in part or in whole, in the storage device of the computing device performing the operation and applied in the operation of the computing device. Software instructions can be embedded in firmware, for example, an erasable programmable read-only memory. It should also be understood that hardware modules / units / blocks may be included in connected logical components, such as gates and flip-flops, and / or may include programmable units, such as programmable gate arrays or processors. The modules / units / blocks or computing device functions described herein may be implemented as software modules / units / blocks, but can be represented in hardware or firmware. Typically, the modules / units / blocks described herein refer to logical modules / units / blocks that can be combined with other modules / units / blocks or divided into submodules / subunits / subblocks, although they are physical organization or storage devices. This description may apply to a system, an engine, or a part thereof.

[0041] It should be understood that although the terms "first," "second," "third," etc., may be used herein to describe various elements, the various elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element without departing from the scope of the exemplary embodiments of this application.

[0042] The terms "pixel" and "voxel" are used interchangeably in this application to refer to elements in an image. The term "image" is used to refer to images of various forms, including two-dimensional images, three-dimensional images, four-dimensional images, etc.

[0043] Various terms are used to describe spatial and functional relationships between elements, including “connection,” “attachment,” and “installation.” Unless explicitly described as “direct” when describing a relationship between the first and second elements in this application, the relationship includes a direct relationship where no other intervening element exists between the first and second elements, and may also include an indirect relationship where one or more intervening elements exist between the first and second elements (spatially or functionally). Conversely, when an element is referred to as being “directly connected,” attached, or positioned to another element, no intervening element exists. Other words used to describe relationships between elements should be interpreted in a similar manner (e.g., “between both” vs. “directly between both,” “adjacent” vs. “directly adjacent”).

[0044] These and other features, characteristics, functions and operating methods of related structural elements, as well as component assembly and manufacturing economics, will become more apparent from the following description of the accompanying drawings, which form part of this application specification. However, it should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of this application. It should also be understood that the drawings are not drawn to scale.

[0045] The flowcharts used in this application illustrate the operations performed by a system according to some embodiments disclosed in this application. It should be clearly understood that the operations in the flowcharts may not be implemented sequentially. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, one or more other operations may be added to these flowcharts. One or more operations may also be deleted from the flowcharts.

[0046] This application provides systems and components for medical imaging and / or medical treatment. In some embodiments, the medical system may include an imaging system. The imaging system may include a single-modal imaging system and / or a multimodal imaging system. A single-modal imaging system may include, for example, a magnetic resonance imaging (MRI) system. Exemplary MRI systems may include superconducting MRI systems, non-superconducting MRI systems, etc. Multimodal imaging systems may include, for example, computed tomography-magnetic resonance imaging (MRI-CT) systems, positron emission tomography-magnetic resonance imaging (PET-MRI) systems, single-photon emission computed tomography-magnetic resonance imaging (SPECT-MRI) systems, digital subtraction angiography-magnetic resonance imaging (DSA-MRI) systems, etc. In some embodiments, the medical system may include a treatment system. The treatment system may include a treatment planning system (TPS), image-guided radiotherapy (IGRT), etc. Image-guided radiotherapy (IGRT) may include a treatment device and an imaging device. The treatment device may include a linear accelerator, a cyclotron accelerator, a synchrotron, etc., configured to perform radiotherapy on a subject. Processing equipment may include accelerators for various particle types, such as photons, electrons, protons, or heavy ions. Imaging equipment may include magnetic resonance scanners, computed tomography scanners (e.g., cone-beam computed tomography (CBCT) scanners), digital radiography (DR) scanners, electronic portal imaging devices (EPID), and the like.

[0047] One aspect of this application relates to systems and methods for magnetic resonance imaging, and more specifically, to systems and methods for multi-task techniques in magnetic resonance imaging.

[0048] Imaging signals can be acquired based on at least two k-space trajectories, each tracing through a central region of a three-dimensional k-space, for example, along a k-space centerline along a selected layer direction (e.g., Kx = Ky = 0 in three-dimensional k-space). These systems and methods can acquire at least two datasets, each comprising a portion of at least two consecutively sampled k-space trajectories. In each of the at least two datasets, the system and method can select imaging signals sampled in the central region as auxiliary subsets. The system and method can designate at least two auxiliary subsets as auxiliary signals. For each of the at least two datasets, selected imaging signals from the corresponding auxiliary subsets can be sampled at different locations within the central region, such that the distribution of the selected imaging signals in k-space covers the central region. The system and method can determine temporal information related to at least one temporal dimension of the region of interest based on the auxiliary signals. The system and method can determine spatial information related to at least one spatial dimension of the region of interest based on the auxiliary signals and the imaging signals. The system and method can generate at least one target image of the region of interest based on the temporal and spatial information.

[0049] In some cases, auxiliary signals can be acquired by repeatedly sampling the same subset of k-space (e.g., the same location or the same region) at the same sampling frequency. For example, the auxiliary signal may correspond to one or more identical k-space lines (e.g., the k-space center line where Ky = Kz = 0 in three-dimensional k-space), and the auxiliary signal can be acquired by repeatedly sampling the k-space lines at the same sampling frequency.

[0050] In this application, each of the at least two auxiliary subsets can correspond to the same region in k-space, for example, the k-space centerline. The time interval of the auxiliary subsets can correspond to the sampling frequency, compared to acquiring auxiliary signals by repeatedly sampling the k-space centerline at a sampling frequency. Therefore, the auxiliary subsets selected from the imaging data can be used as auxiliary signals to determine the temporal information of the multi-tasking technique.

[0051] In this application, auxiliary signals can be extracted from the imaging signal instead of acquiring auxiliary signals by performing additional sampling, which reduces imaging time and improves the efficiency of magnetic resonance imaging.

[0052] Figure 1 This is a schematic diagram of an exemplary magnetic resonance imaging (MRI) system according to some embodiments of this application. As shown, the MRI system 100 may include an MRI device 110, a processing device 120, a storage device 130, a terminal 140, and a network 150. The components of the MRI system 100 may be connected in one or more ways. This is merely an example. Figure 1 As shown, the magnetic resonance imaging (MRI) device 110 can be directly connected to the processing device 120, as indicated by the dashed double-headed arrow connecting the MRI device 110 and the processing device 120, or connected via network 150. As another example, the storage device 130 can be directly connected to the MRI device 110, as indicated by the dashed double-headed arrow connecting the MRI device 110 and the storage device 130, or connected via network 150. As yet another example, the terminal 140 can be directly connected to the processing device 120, as indicated by the dashed double-headed arrow connecting the terminal 140 and the processing device 120, or connected via network 150.

[0053] Magnetic resonance imaging device 110 can be configured to scan an object (or a portion of an object) to acquire image data, such as echo signals associated with the object (also known as magnetic resonance (MR) data or magnetic resonance signals). For example, magnetic resonance imaging device 110 can detect at least two echo signals by applying a magnetic resonance imaging pulse sequence to the object. In some embodiments, such as in combination Figure 2The magnetic resonance imaging device 110 may include, for example, a main magnet, gradient coils (or also called spatial coding coils), radio frequency (RF) coils, etc. In some embodiments, depending on the type of main magnet, the magnetic resonance imaging device 110 may be a permanent magnet magnetic resonance imaging scanner, a superconducting magnet magnetic resonance imaging scanner, a resistive electromagnet magnetic resonance imaging scanner, etc. In some embodiments, depending on the magnetic field strength, the magnetic resonance imaging device 110 may be a high-field magnetic resonance imaging scanner, a medium-field magnetic resonance imaging scanner, a low-field magnetic resonance imaging scanner, etc.

[0054] The object scanned by the magnetic resonance imaging device 110 can be biological or non-biological. For example, the object may include a patient, a man-made object, etc. As another example, the object may include a specific part, organ, tissue, and / or body part of a patient. By way of example only, the object may include the head, brain, neck, body, shoulder, arm, chest, heart, stomach, blood vessels, soft tissue, knee, foot, etc., or any combination thereof.

[0055] For illustrative purposes, Figure 1 It can provide a coordinate system 160 including the X-axis, Y-axis and Z-axis. Figure 1 The X and Z axes shown can be horizontal, and the Y axis can be vertical. As shown in the figure, the positive X direction along the X axis can be viewed from the direction facing the front of the magnetic resonance imaging device 110, from the right side to the left side of the magnetic resonance imaging device 110; Figure 1 The positive Y-direction of the Y-axis shown can be the direction from the bottom to the top of the magnetic resonance imaging device 110; Figure 1 The positive Z-direction along the Z-axis shown can refer to the direction in which the object moves out of the scanning channel (or scanning aperture) of the magnetic resonance imaging device 110.

[0056] In some embodiments, the magnetic resonance imaging device 110 may be instructed to select an anatomical region (e.g., sheet or volume) of an object along a slice selection path and scan the anatomical region to acquire at least two echo signals from the anatomical region. During scanning, spatial encoding within the anatomical region can be implemented using spatial encoding coils (e.g., X coil, Y coil, Z coil) along the frequency encoding direction, phase encoding direction, and slice selection direction. The echo signals may be sampled, and the corresponding sampled data may be stored in a k-space matrix for image reconstruction. For illustrative purposes, the slice selection direction in this application may correspond to the Z direction defined by coordinate system 160 and the Kz direction in k-space; the phase encoding direction may correspond to the Y direction defined by coordinate system 160 and the Ky direction in k-space; and the frequency encoding direction (also referred to as the readout direction) may correspond to the X direction defined by coordinate system 160 and the Kx direction in k-space. It should be noted that the slice selection direction, phase encoding direction, and frequency encoding direction may be modified as needed without departing from the scope of this application. Further description of the magnetic resonance imaging device 110 can be found elsewhere in this application. For example, see... Figure 2 And its description.

[0057] Processing device 120 can process data and / or information acquired from magnetic resonance imaging device 110, storage device 130, and / or terminal 140. For example, processing device 120 can acquire imaging signals associated with a region of interest (ROI) of an object. Processing device 120 can select a portion of the imaging signal as an auxiliary signal associated with the ROI. Processing device 120 can determine temporal information associated with at least one temporal dimension of the ROI based on the auxiliary signal. Processing device 120 can determine spatial information associated with at least one spatial dimension of the ROI based on the auxiliary signal and the imaging signal. Processing device 120 can generate at least one target image of the ROI based on the temporal and spatial information. In some embodiments, processing device 120 can be a single server or a group of servers. The server group can be centralized or distributed. In some embodiments, processing device 120 can be local or remote. For example, processing device 120 can access information and / or data from magnetic resonance imaging device 110, storage device 130, and / or terminal 140 via network 150. As another example, processing device 120 can be directly connected to magnetic resonance imaging device 110, terminal 140, and / or storage device 130 to access information and / or data. In some embodiments, processing device 120 can be implemented on a cloud platform. For example, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud cloud, multi-cloud, etc., or combinations thereof. In some embodiments, processing device 120 may be part of terminal 140. In some embodiments, processing device 120 may be part of magnetic resonance imaging device 110.

[0058] Storage device 130 may store data, instructions, and / or any other information. In some embodiments, storage device 130 may store data obtained from magnetic resonance imaging device 110, processing device 120, and / or terminal 140. Data may include image data acquired by processing device 120, algorithms and / or models for processing the image data, etc. For example, storage device 130 may store imaging signals acquired from a magnetic resonance imaging device (e.g., magnetic resonance imaging device 110). As another example, storage device 130 may store information on the coil sensitivity of each of at least two coils. As yet another example, storage device 130 may store a target image determined by processing device 120. In some embodiments, storage device 130 may store data and / or instructions that processing device 120 and / or terminal 140 may execute or use to perform the exemplary methods described herein. In some embodiments, storage device 130 may include a mass storage device, removable memory, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof. Exemplary mass storage devices may include disks, optical disks, solid-state drives, etc. Exemplary removable storage devices may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tapes, etc. Exemplary volatile read-write storage devices may include random access memory (RAM). Exemplary RAM may include dynamic random access memory (DRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), and zero-capacitance random access memory (Z-RAM), etc. Exemplary read-only memory may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), optical disc read-only memory (CD-ROM), and digital multifunction disk read-only memory, etc. In some embodiments, storage device 130 may be implemented on a cloud platform. By way of example only, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud cloud, multi-cloud, etc., or any combination thereof.

[0059] In some embodiments, storage device 130 may be connected to network 150 to communicate with one or more other components of magnetic resonance imaging system 100 (e.g., processing device 120, terminal 140). One or more components of magnetic resonance imaging system 100 may access data or instructions stored in storage device 130 via network 150. In some embodiments, storage device 130 may be integrated into magnetic resonance imaging device 110 or processing device 120.

[0060] Terminal 140 may be connected to and communicate with magnetic resonance imaging equipment 110, processing equipment 120, and / or storage device 130. In some embodiments, terminal 140 may include mobile device 141, tablet computer 142, laptop computer 143, etc., or any combination thereof. For example, mobile device 141 may include mobile phone, personal digital assistant (PDA), gaming device, navigation device, point-of-sale (POS) device, laptop computer, tablet computer, desktop computer, etc., or any combination thereof. In some embodiments, terminal 140 may include input devices, output devices, etc. Input devices may include alphanumeric keys and other keys that can be input via a keyboard, touchscreen (e.g., with haptic or haptic feedback), voice input, eye-tracking input, brain monitoring system, or any other similar input mechanism. Other types of input devices may include cursor control devices, such as a mouse, trackball, or cursor arrow keys. Output devices may include a display, printer, etc., or any combination thereof.

[0061] Network 150 may include any suitable network that facilitates the exchange of information and / or data between the magnetic resonance imaging system 100. In some embodiments, one or more components of the magnetic resonance imaging system 100 (e.g., magnetic resonance imaging device 110, processing device 120, storage device 130, terminal 140, etc.) may communicate information and / or data with one or more other components of the magnetic resonance imaging system 100 via network 150. For example, processing device 120 may acquire magnetic resonance data from magnetic resonance imaging device 110 via network 150. As another example, processing device 120 and / or terminal 140 may obtain information stored in storage device 130 via network 150. Network 150 may be and / or include public networks (e.g., the Internet), private networks (e.g., local area networks (LANs), wide area networks (WANs)), wired networks (e.g., Ethernet), wireless networks (e.g., 802.11 networks, Wi-Fi networks), cellular networks (e.g., LTE networks), Frame Relay networks, virtual private networks (VPNs), satellite networks, telephone networks, routers, hubs, switches, server computers, and / or any combination thereof. For example, network 150 may include wired networks, fiber optic networks, telecommunications networks, intranets, wireless local area networks (WLANs), metropolitan area networks (MANs), public switched telephone networks (PSTNs), Bluetooth networks, ZigBee networks, near field communication (NFC) networks, and any combination thereof. In some embodiments, network 150 may include one or more network access points. For example, network 150 may include wired and / or wireless network access points, such as base stations and / or internet exchange points, through which one or more components of magnetic resonance imaging system 100 may connect to network 150 to exchange data and / or information.

[0062] This description is intended to be illustrative, not limiting, of the scope of this application. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other features of the exemplary embodiments described herein can be combined in various ways to obtain additional and / or alternative exemplary embodiments. However, these variations and modifications do not depart from the scope of this application. In some embodiments, the magnetic resonance imaging system 100 may include one or more additional components, and / or one or more of the aforementioned components may be omitted. Alternatively or optionally, two or more components of the magnetic resonance imaging system 100 may be integrated into a single component. For example, the processing device 120 may be integrated into the magnetic resonance imaging device 110. As another example, a component of the magnetic resonance imaging system 100 may be replaced by another component capable of performing the function of that component. As yet another example, the processing device 120 and the terminal 140 may be integrated into a single device.

[0063] Figure 2 This is a schematic diagram of an exemplary magnetic resonance imaging scanning apparatus according to some embodiments of this application. As shown, a main magnet 201 can generate a first magnetic field (or main magnetic field), which can be applied to an object (also referred to as the target) located within the first magnetic field. The main magnet 201 may include a resistive magnet or a superconducting magnet, both of which require a power source. Figure 2 (Not shown in the image) to perform the operation. Alternatively, the main magnet 201 may include a permanent magnet. The main magnet 201 can form a detection area and move around an object moving within or positioned within the detection area along the Z direction. The main magnet 201 can also control the uniformity of the generated main magnetic field. Some shimming coils may be located in the main magnet 201. The shimming coils placed in the gaps of the main magnet 201 can compensate for the non-uniformity of the magnetic field of the main magnet 201. The shimming coils can be energized by a shimming power supply.

[0064] Gradient coil 202 may be located within main magnet 201. For example, gradient coil 202 may be located within the detection region. Gradient coil 202 may move around or be positioned within the detection region along the Z-direction around an object within the detection region. Gradient coil 202 may be surrounded by main magnet 201 along the Z-direction and closer to the object than main magnet 201. Gradient coil 202 may generate a second magnetic field (or gradient field, including gradient fields Gx, Gy, and Gz). The second magnetic field may be superimposed on the main magnetic field generated by main magnet 201 and distort the main magnetic field so that the magnetic orientation of the protons of the object may vary with their position within the gradient field, thereby encoding spatial information into a magnetic resonance signal generated by the region of the imaged object. Gradient coil 202 may include an X coil (e.g., configured to generate a gradient field Gx corresponding to the X-direction), a Y coil (e.g., configured to generate a gradient field Gy corresponding to the Y-direction), and / or a Z coil (e.g., configured to generate a gradient field Gz corresponding to the Z-direction). Figure 2 (Not shown in the image). In some embodiments, the Z coil may be based on a circular (Maxwell) coil design, while the X and Y coils may be based on a saddle-shaped (Goryley) coil configuration design. The three sets of coils can generate three different magnetic fields for position encoding. Gradient coil 202 can allow spatial encoding of the magnetic resonance signal for image reconstruction. Gradient coil 202 can be connected to one or more of the X gradient amplifier 204, Y gradient amplifier 205, or Z gradient amplifier 206. One or more of the three amplifiers can be connected to waveform generator 216. Waveform generator 216 can generate gradient waveforms applied to X gradient amplifier 204, Y gradient amplifier 205, and / or Z gradient amplifier 206. The amplifiers can amplify the waveforms. The amplified waveforms can be applied to one of the coils in gradient coil 202 to generate magnetic fields along the X, Y, or Z axes, respectively. Gradient coil 202 can be designed for closed-aperture or open-aperture magnetic resonance imaging scanners. In some cases, all three sets of coils in gradient coil 202 can be energized, thereby generating three gradient fields. In some embodiments of this application, the X coil and Y coil can be energized to generate gradient fields in the X and Y directions. As used herein, Figure 2 The description of the X-axis, Y-axis, Z-axis, X direction, Y direction, and Z direction is consistent with... Figure 1 The same or similar as described in the text.

[0065] In some embodiments, the radio frequency (RF) coil 203 may be located within the main magnet 201 and used as a transmitter, receiver, or both. For example, the RF coil 203 may be located in a detection region. The RF coil 203 may be moved around the detection region or positioned within an object in the detection region along the Z-direction. The RF coil 203 may be surrounded by the main magnet 201 and / or the gradient coil 202 around the object, and may be closer to the object than the gradient coil 202. The RF coil 203 may be connected to an RF electronics device 209, which may be configured or used as one or more integrated circuits (ICs) serving as waveform transmitters and / or waveform receivers. The RF electronics device 209 may be connected to an RF power amplifier (RFPA) 207 and an analog-to-digital converter (ADC) 208.

[0066] When used as a transmitter, the RF coil 203 can generate an RF signal that provides a third magnetic field for generating a magnetic resonance signal associated with the region of the imaged object. The third magnetic field can be perpendicular to the main magnetic field. The waveform generator 216 can generate RF pulses. These RF pulses can be amplified by the RF power amplifier 207, processed by the RF electronics 209, and applied to the RF coil 203 to generate an RF signal in response to a strong current generated by the RF electronics 209 based on the amplified RF pulses.

[0067] When used as a receiver, the radio frequency (RF) coil can be responsible for detecting magnetic resonance signals (e.g., echoes). After excitation, the magnetic resonance signal generated by the object can be sensed by the RF coil 203. The receiver amplifier can then receive the sensed magnetic resonance signal from the RF coil 203, amplify the sensed magnetic resonance signal, and provide the amplified magnetic resonance signal to the analog-to-digital converter (ADC) 208. The ADC 208 can convert the magnetic resonance signal from an analog signal to a digital signal. The digital magnetic resonance signal can then be sent to the processing device 140 for sampling.

[0068] In some embodiments, the main magnet coil 201, gradient coil 202, and radio frequency coil 203 can be circumferentially positioned relative to the object in the Z direction. Those skilled in the art will understand that the main magnet 201, gradient coil 202, and radio frequency coil 203 can be located in various configurations around the object.

[0069] In some embodiments, the RF power amplifier 207 can amplify the RF pulse (e.g., the power of the RF pulse, the voltage of the RF pulse) to generate amplified RF pulses to drive the RF coil 203.

[0070] Magnetic resonance imaging (MRI) systems (e.g., MRI system 100 disclosed herein) are typically used to acquire internal images of a specific region of interest (ROI) from a patient, which may be used for diagnostic, therapeutic, or other purposes, or a combination thereof. The MRI system includes a master magnet assembly (e.g., master magnet 201) for providing a strong, uniform master magnetic field to align the individual magnetic moments of hydrogen atoms within the patient's body. In this process, hydrogen atoms oscillate around their magnetic poles at their characteristic Larmor frequencies. If tissue is subjected to an additional magnetic field tuned to the Larmor frequency, the hydrogen atoms absorb additional energy, thereby rotating the net alignment torque of the hydrogen atoms. The additional magnetic field may be provided by a radio frequency excitation signal (e.g., a radio frequency signal generated by radio frequency coil 203). When the additional magnetic field is removed, the magnetic moments of the hydrogen atoms rotate back to their alignment with the master magnetic field, thus emitting an echo signal. The echo signal is received and processed to form an MRI image. T1 relaxation can be the process of net magnetization increasing / recovering to its initial maximum value parallel to the master magnetic field. T1 can be the time constant for longitudinal magnetization regrowth (e.g., along the master magnetic field). T2 relaxation can be the process of transverse component decay or dephase of magnetization. T2 can be the time constant of transverse magnetization decay / dephase.

[0071] If the main magnetic field is uniformly distributed throughout the patient's body, the radio frequency excitation signal may non-selectively excite all hydrogen atoms in the sample. Therefore, to image a specific part of the patient's body, magnetic field gradients Gx, Gy, and Gz in the X, Y, and Z directions (e.g., generated by gradient coil 202) with specific timing, frequency, and phase can be superimposed on the uniform magnetic field. This allows the radio frequency excitation signal to excite hydrogen atoms in the desired slice of the patient's body. Based on the position of the hydrogen atoms in the "image slice," unique phase and frequency information is encoded in the echo signal. Fourier imaging can be performed based on gradient encoding, where measurements representing the spatial frequency of the object can be obtained using specific sampling trajectories, referred to as k-space. These specific sampling trajectories can include Cartesian or non-Cartesian trajectories, such as spiral trajectories, radial trajectories, etc., and image reconstruction is performed by applying an inverse Fourier transform (e.g., inverse fast Fourier transform) to the k-space data.

[0072] Typically, the patient's body is scanned through a series of measurement cycles to visualize the area to be imaged, where the radiofrequency excitation signal and magnetic field gradients Gx, Gy, and Gz vary according to the imaging protocol being used for magnetic resonance imaging. The protocol can be designed for one or more tissues, diseases, and / or clinical scenarios. The protocol may include a specific number of pulse sequences in different planes and / or with different parameters. Pulse sequences may include spin echo sequences, gradient echo sequences, diffusion sequences, inversion recovery sequences, etc., or any combination thereof. For example, spin echo sequences may include fast spin echo (FSE) pulse sequences, turbine spin echo (TSE) pulse sequences, fast capture with relaxation enhancement (RARE) pulse sequences, half-Fourier capture single-excitation turbine spin echo (HASTE) pulse sequences, turbine gradient spin echo (TGSE) pulse sequences, etc., or any combination thereof. As another example, gradient echo sequences may include equilibrium steady-state free precession (bSSFP) pulse sequences, destructive gradient echo (GRE) pulse sequences, echo-plane imaging (EPI) pulse sequences, steady-state free precession (SSFP), etc., or any combination thereof. For each MRI scan, the generated echo signal can be digitized and processed to reconstruct an image according to the imaging protocol used in the MRI.

[0073] Figure 3 This is a schematic diagram of exemplary hardware and / or software components of a computing device according to some embodiments of this application. In some embodiments, one or more components of the magnetic resonance imaging system 100 may be implemented on one or more components of the computing device 300. By way of example only, the processing device 120 and / or the terminal 140 may each be implemented by one or more components of the computing device 300.

[0074] like Figure 3 As shown, computing device 300 may include processor 310, storage device 320, input / output (I / O) 330, and communication port 340. Processor 310 may execute computer instructions (e.g., program code) and perform the functions of processing device 120 according to the techniques described in this application. Computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform the specific functions described in this application. For example, processor 310 may process image data of objects obtained from magnetic resonance imaging device 110, storage device 130, terminal 140, and / or any other component of magnetic resonance imaging system 100.

[0075] In some embodiments, processor 310 may include one or more hardware processors, such as microcontrollers, microprocessors, reduced instruction set computers (RISC), application-specific integrated circuits (ASICs), application-specific instruction set processors (ASIPs), central processing units (CPUs), graphics processing units (GPUs), physical processing units (PPUs), microcontroller units, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), advanced RISC machines (ARMs), programmable logic devices (PLDs), any circuit or processor capable of performing one or more functions, or combinations thereof.

[0076] For illustrative purposes only, only one processor is described in computing device 300. However, it should be noted that computing device 300 in this application may also include multiple processors. Therefore, the operations and / or method steps performed by one processor as described in this application may also be performed jointly or individually by multiple processors. For example, if, in this application, the processors of computing device 300 simultaneously execute operation A and operation B, it should be understood that operation A and operation B may also be performed jointly or individually by two or more different processors in computing device 300 (e.g., the first processor executes operation A, the second processor executes operation B, or the first and second processors jointly execute operation A and B).

[0077] Storage device 320 can store data / information obtained from magnetic resonance imaging device 110, storage device 130, terminal 140, and / or any other component of magnetic resonance imaging system 100. In some embodiments, storage device 320 may include mass storage devices, removable storage devices, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof. For example, mass storage devices may include disks, optical disks, solid-state drives, etc. Removable storage devices may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tapes, etc. Volatile read-write memory may include random access memory (RAM). Random access memory may include dynamic random access memory (DRAM), dual data rate synchronous dynamic random access memory (DDRSDRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), and zero-capacitance random access memory (Z-RAM), etc. Read-only memory may include mask ROM (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), optical disc read-only memory (CD-ROM), and digital multifunction disk read-only memory, etc. In some embodiments, storage device 320 may store one or more programs and / or instructions to perform the exemplary methods described in this application.

[0078] Input / output 330 can input and / or output signals, data, information, etc. In some embodiments, input / output 330 can enable user interaction with computing device 300 (e.g., processing device 120). In some embodiments, input / output 330 may include input devices and output devices. Examples of input devices may include a keyboard, mouse, touchscreen, microphone, etc., or any combination thereof. Examples of output devices may include display devices, speakers, printers, projectors, etc., or any combination thereof. Examples of display devices may include liquid crystal displays (LCDs), light-emitting diode (LED) based displays, flat panel displays, curved screens, television equipment, cathode ray tubes (CRTs), touchscreens, etc., or any combination thereof.

[0079] Communication port 340 can be connected to a network (e.g., network 150) to facilitate data communication. Communication port 340 can establish a connection between computing device 300 (e.g., processing device 120) and one or more components of magnetic resonance imaging system 100 (e.g., magnetic resonance imaging device 110, storage device 130, and / or terminal 140). This connection can be a wired connection, a wireless connection, any other communication connection capable of data transmission and / or reception, and / or a combination of these connections. Wired connections may include, for example, cables, optical fibers, telephone lines, etc., or combinations thereof. Wireless connections may include, for example, Bluetooth networks, Wi-Fi networks, WiMax networks, WLAN networks, ZigBee networks, mobile networks (e.g., 3G, 4G, 5G, etc.), etc., or any combination thereof. In some embodiments, communication port 340 may be and / or include standardized communication ports, such as RS232, RS485, etc. In some embodiments, communication port 340 may be a specially designed communication port. For example, communication port 340 may be designed according to the Digital Imaging and Medical Communications (DICOM) protocol.

[0080] Figure 4 These are schematic diagrams of exemplary hardware and / or software components of a mobile device according to some embodiments of this application. In some embodiments, one or more components of the magnetic resonance imaging system 100 may be implemented on one or more components of the mobile device 400. By way of example only, the terminal 140 may be implemented on one or more components of the mobile device 400.

[0081] like Figure 4As shown, the mobile device 400 may include a communication platform 410, a display 420, a graphics processing unit (GPU) 430, a central processing unit (CPU) 440, an input / output 450, memory 460, and a storage 490. In some embodiments, any other suitable components, including but not limited to a system bus or controller (not shown), may also be included in the mobile device 400. In some embodiments, a mobile operating system 470 (e.g., iOS, Android, Windows Phone, etc.) and one or more applications 480 may be loaded from storage 490 into memory 460 for execution by the CPU 440. Application 480 may include a browser or any other suitable mobile application for receiving and presenting information related to the magnetic resonance imaging system 100. User interaction with the information flow may be implemented via input / output 450 and provided via network 150 to processing device 120 and / or other components of the magnetic resonance imaging system 100.

[0082] To implement the various modules, units, and functions described in this application, a computer hardware platform may be used as the hardware platform for one or more of the components described herein. A computer with user interface elements may be used to implement a personal computer (PC) or another type of workstation or terminal device. However, if the computer is properly programmed, it may also act as a server.

[0083] Figure 5 This is a schematic diagram of an exemplary processing device according to some embodiments of this application. In some embodiments, the processing device 120 may include an acquisition module 510, a determination module 520, and a reconstruction module 530. In some embodiments, the modules may be all or part of the hardware circuitry of the processing device 120. These modules may also be implemented as an application program or instruction set read and executed by the processing device 120. Furthermore, the modules may be any combination of hardware circuitry and applications / instructions. For example, when the processing device 120 is executing an application / instruction set, the module may be part of the processing device 120.

[0084] The acquisition module 510 can be configured to acquire imaging signals related to the region of interest (ROI) of the object.

[0085] The determining module 520 can be configured to select a portion of the imaging signal as an auxiliary signal associated with at least one time dimension of the region of interest.

[0086] The reconstruction module 530 can be configured to determine temporal information related to at least one time dimension of the region of interest based on auxiliary signals.

[0087] The reconstruction module 530 can also be configured to determine spatial information related to at least one spatial dimension of the region of interest based on temporal information and imaging signals.

[0088] The reconstruction module 530 can also be configured to generate at least one target image of the region of interest based on temporal and spatial information.

[0089] It should be noted that the above description of the processing device 120 is for illustrative purposes only and is not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description of this application. However, these changes and modifications do not depart from the scope of this application. Two or more modules can be combined into one module, and any module can be split into two or more units. For example, the acquisition module 510 and the determination module 520 can be combined into a single module. As another example, the reconstruction module 530 can be divided into three units. The first unit can be configured to determine temporal information related to at least one temporal dimension of the region of interest based on auxiliary signals. The second unit can be configured to determine spatial information related to at least one spatial dimension of the region of interest based on auxiliary signals and imaging signals. The third unit can be configured to generate at least one target image of the region of interest based on the temporal and spatial information.

[0090] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description herein. However, these changes and modifications do not depart from the scope of this application. For example, the processing device 120 may also include a storage module ( Figure 5 (Not shown in the image). The storage module may be configured to store data generated during any processing performed by any component of the processor in the processing device 120. As another example, each component of the processing device 120 may include a storage device. Alternatively or optionally, the components of the processing device 120 may share a common storage device.

[0091] Figure 6 This is a flowchart illustrating an exemplary process for generating at least one target image according to some embodiments of this application. In some embodiments, process 600 may be performed in... Figure 1 This is implemented in the magnetic resonance imaging system 100 shown. For example, process 600 can be stored as instructions in storage device 130 and / or memory (e.g., memory 320, memory 490) and processed by processing device 120 (e.g., such as...). Figure 3 The processor 310 and / or of the computing device 300 shown Figure 5 The procedure 600 may be invoked and / or executed by one or more modules shown. The operation of the procedures illustrated below is for illustrative purposes only. In some embodiments, procedure 600 may be accomplished by one or more additional operations not described and / or one or more operations not discussed. Furthermore, as... Figure 6 The order of operations of process 600 shown and described below is not intended to be restrictive.

[0092] In 610, the processing device 120 (e.g., acquisition module 510) can acquire imaging signals related to the region of interest (ROI) of the object.

[0093] In some embodiments, an imaging signal can be acquired by applying a pulse sequence to the region of interest. The imaging signal can be k-space data obtained by filling k-space (e.g., 3D k-space) with magnetic resonance data (e.g., one or more echoes from the region of interest) received by at least two receiving coils (e.g., radio frequency coil 203) of a magnetic resonance imaging device (e.g., magnetic resonance imaging device 110) along a sampling pattern. Magnetic resonance data can be generated based on the pulse sequence. In some embodiments, the region of interest can include a sheet or volume of the scanned object for 3D imaging.

[0094] The imaging signal may include high spatial resolution image data associated with at least one spatial variation dimension (also referred to as spatial dimension) of the region of interest of the object. Exemplary spatial variation dimensions may relate to layer selection orientation, phase encoding orientation, frequency encoding orientation, etc., or any combination thereof. In some embodiments, the imaging signal may be used to determine spatial information including at least one spatial basis function associated with at least one spatial variation dimension of the region of interest, which will be described in detail in conjunction with operation 640.

[0095] In some embodiments, the imaging signal may include three-dimensional (3D) k-space data, four-dimensional (4D) k-space data, etc. As used herein, four-dimensional k-space data refers to a data format containing three-dimensional k-space data that varies over time. By way of example only, a three-dimensional imaging signal may be a 256×256×256 digital matrix.

[0096] In some embodiments, the imaging signal may be undersampled, fully sampled, or oversampled along at least one of the slice selection direction, phase encoding direction, and readout direction. In some embodiments, as used herein, the slice selection direction may correspond to the Kz direction in k-space and the direction derived from... Figure 1 The Z direction is defined by the coordinate system 160 in the coordinate system; the phase encoding direction can correspond to the Ky direction in k-space and the direction defined by the coordinate system 160 in ... Figure 1 The Y direction is defined by the coordinate system 160 in the coordinate system; and the readout direction can correspond to the Kx direction in k-space and the direction defined by the coordinate system 160 in ... Figure 1 The X direction is defined in coordinate system 160.

[0097] In some embodiments, the sampling pattern may include at least two acquisition tracks (also referred to as k-space tracks). Exemplary acquisition tracks may include helical tracks, radial tracks, oscillating tracks, etc., or any combination thereof. In some embodiments, one of the at least two acquisition tracks may correspond to one or more echoes; for example, one or more echoes may fill the k-space along one of the at least two acquisition tracks. By way of example only, one echo may fill the k-space along one acquisition track.

[0098] In some embodiments, imaging signals can be acquired based on at least two k-space trajectories, each tracing through a central region of k-space along a layer-selection direction. In some embodiments, the central region can be a k-space centerline (e.g., a line where Kx = Ky = 0) in three-dimensional k-space. In some embodiments, the central region can be determined based on practical needs or experience. For example, a k-space region (e.g., a spherical region in three-dimensional k-space) with frequency values ​​ranging from 0 to a threshold (e.g., 120 Hz, 130 Hz, etc.) centered at the k-space centerline can be defined as the central region. In some embodiments, a cylindrical region in three-dimensional k-space centered at the k-space centerline can be defined as the central region.

[0099] In some embodiments, imaging signals can be acquired based on at least two k-space trajectories, each k-space trajectory intersecting the k-space centerline along the selected layer direction in k-space.

[0100] In some embodiments, one or more intersection points may exist between the k-space trajectory and the k-space centerline. By way of example only, one or more echoes may fill the k-space along the k-space trajectory to generate corresponding imaging signals. The imaging signal sampled at the k-space centerline may be referred to as the intersection point between the k-space trajectory and the k-space centerline.

[0101] In some embodiments, imaging signals can be acquired based on at least one k-space trajectory parallel to the Kx-Ky plane of k-space and / or at least one k-space trajectory at an angle to the Kx-Ky plane.

[0102] In some embodiments, processing device 120 may acquire imaging signals from one or more components of magnetic resonance imaging system 100 or external storage devices (e.g., magnetic resonance imaging device 110, terminal 140, and / or storage device 130) via network 150. For example, magnetic resonance imaging device 110 may send imaging signals to storage device 130 or any other storage device for storage. Processing device 120 may acquire imaging signals from storage device 130 or any other storage device. As another example, processing device 120 may acquire imaging signals directly from magnetic resonance imaging device 110.

[0103] In some embodiments, the pulse sequence may include a spin echo (SE) sequence, a gradient echo sequence, a diffusion sequence, an inversion recovery (IR) sequence, or any combination thereof. For example, a spin echo sequence may include a fast spin echo (FSE) pulse sequence, a turbine spin echo (TSE) pulse sequence, a fast capture with relaxation enhancement (rare) pulse sequence, a half-Fourier capture single-excitation turbine spin echo (HASTE) pulse sequence, a turbine gradient spin echo (TGSE) pulse sequence, or any combination thereof. As another example, a gradient echo sequence may include a equilibrium steady-state free precession (bSSFP) pulse sequence, a destructive gradient echo (GRE) pulse sequence, an echo plane imaging (EPI) pulse sequence, a steady-state free precession (SSFP) pulse sequence, or any combination thereof.

[0104] In 620, the processing device 120 (e.g., the determination module 520) can select a portion of the imaging signal as an auxiliary signal (also known as a navigation signal or training signal) associated with the region of interest. In this application, the auxiliary signal can be extracted from the imaging signal without performing additional sampling to obtain the auxiliary signal configured to determine the temporal information of image reconstruction, thereby achieving the technical effect of reducing imaging time and improving imaging efficiency.

[0105] In some embodiments, the auxiliary signal may include the portion of the imaging signal sampled in the central region of the k-space. In some embodiments, for each of at least two k-space trajectories, the processing device 120 may select one or more imaging signals sampled in the central region. The imaging signals selected from the at least two k-space trajectories may form the auxiliary signal.

[0106] In some embodiments, the auxiliary signal may include the portion of the imaging signal sampled in the k-space centerline. In some embodiments, for each of at least two k-space trajectories, the processing device 120 may select one or more imaging signals (one or more intersections between the k-space trajectory and the k-space centerline), each selected imaging signal being sampled at a location in the k-space centerline.

[0107] In some embodiments, the processing device 120 may obtain at least two datasets, each dataset comprising a portion of a plurality of k-space trajectories. In each of the at least two datasets, the processing device 120 may select an imaging signal sampled in the central region as an auxiliary subset. For example, in each of the at least two datasets, the processing device 120 may select an imaging signal sampled along the k-space centerline as an auxiliary line. The processing device 120 may designate at least two auxiliary subsets as auxiliary signals.

[0108] In some embodiments, the number (or count) of k-space trajectories in each of at least two datasets may be the same or different.

[0109] In some embodiments, k-space trajectories may be sampled sequentially in each of at least two datasets to acquire at least two datasets in chronological order. For example, one of the at least two datasets may be acquired after or before the other of the at least two datasets.

[0110] In some embodiments, for each of at least two datasets, the selected imaging signals in the corresponding auxiliary subsets may correspond to different locations in the central region, such that the distribution of the selected imaging signals in k-space can cover the central region. For example, for each of at least two datasets, the selected imaging signals in the corresponding auxiliary subsets may correspond to different locations in the k-space centerline, such that the distribution of the selected imaging signals in k-space can cover the k-space centerline.

[0111] In some embodiments, the auxiliary signal may include high temporal resolution data relating to at least one temporal variation dimension (also referred to as a time dimension) of the region of interest of the object, which can be used to implement multitasking techniques. Exemplary time-varying dimensions may relate to cardiac motion, respiratory motion, T1 relaxation, T2 relaxation, chemical exchange saturation transfer (CEST), contrast agent kinetics, T1p contrast, molecular diffusion, elapsed time, etc., or any combination thereof. It should be noted that exemplary time-varying dimensions are provided merely for illustration and not for limitation. At least one time-varying dimension may include any dimension reflecting the temporal variation characteristics or dynamic information of the object. In some embodiments, the auxiliary signal may be used to estimate temporal information including at least one time basis function associated with at least one time-varying dimension, which will be described in detail in conjunction with operation 630.

[0112] In some cases, auxiliary signals can be acquired by repeatedly sampling the same subset of k-space (e.g., the same location or the same region) at the same sampling frequency. For example, the auxiliary signal may correspond to one or more identical k-space lines (e.g., the k-space center line where Ky = Kz = 0 in three-dimensional k-space), and the auxiliary signal can be acquired by repeatedly sampling the k-space lines at the same sampling frequency.

[0113] In this application, each of the at least two auxiliary subsets can correspond to the same region in k-space, for example, the central region of k-space. Furthermore, trajectories in each of the at least two datasets are sampled continuously over a period of time, such that the at least two auxiliary subsets have temporal resolution. The auxiliary subsets extracted from the imaging signal can approximate data obtained by repeatedly sampling the same k-space subsets. Therefore, the auxiliary subsets selected from the imaging signal can be used as auxiliary signals to determine the temporal information of multi-tasking techniques.

[0114] For example, in conventional data acquisition for multitasking techniques, even if the imaging signal includes data sampled in the central region of a 3D k-space, the portion of the imaging signal sampled in the central region of the 3D k-space is not used as an auxiliary signal for determining temporal information. Instead, an auxiliary signal is obtained by repeatedly sampling the central region of the k-space. In this application, the imaging signal sampled in the central region of the 3D k-space not only participates in the determination of spatial information but also serves as an auxiliary signal in the determination of temporal information. Therefore, in this application, the additional operation of repeatedly sampling the same subset of k-space to obtain an auxiliary signal can be omitted, which can reduce the time and improve the efficiency of multitasking techniques.

[0115] If an echo is generated within a repetition time (TR) and fills the k-space along a k-space trajectory, the time interval of the auxiliary subset can be TR*Ln (where Ln is the number (or count) of k-space trajectories in the dataset). Therefore, the auxiliary signals selected from the imaging signal (comprising at least two auxiliary subsets) can have temporal resolution, which can be used for determining temporal information in multi-task reconstruction. The time interval of the auxiliary subsets can correspond to the sampling frequency, compared to acquiring auxiliary signals by repeatedly sampling the same subset of k-space at the sampling frequency.

[0116] In some embodiments, the time interval of the auxiliary subset can be shorter than an interval threshold, resulting in high temporal resolution for the auxiliary signal. The interval threshold can be a default value, manually determined by the user, or determined by the processing device 120 based on data analysis. For example, the interval threshold can be determined based on at least one temporal variation dimension to be analyzed. As an example only, the temporal variation dimension may relate to the respiratory motion of an object, and the object's respiratory cycle may be close to 0.75 seconds. To obtain dynamic information related to the object's respiratory motion, the time interval of the auxiliary subset may need to be shorter than the 0.75-second interval threshold. As another example, the interval threshold can be determined based on practical needs (e.g., accuracy requirements), experience, data models, etc.

[0117] In some embodiments, the time interval of the auxiliary subset can be determined by adjusting the acquisition of the imaging signal, such as the length of the TR, the number (or count) of echoes sampled in a TR, the number (or count) of k-space trajectories in each of multiple datasets, or any combination thereof.

[0118] In 630, the processing device 120 (e.g., reconstruction module 530) can determine time information related to at least one time dimension of the region of interest based on the auxiliary signal.

[0119] In some embodiments, a target image of a region of interest having multiple dimensions (e.g., at least one spatial variation dimension and at least one temporal variation dimension) can be represented by a multidimensional tensor. For example, the target image can be represented as an (N+1)-dimensional image tensor (or array), where a first tensor dimension can measure at least one spatial variation dimension, and each of the other N tensor dimensions can measure a temporal variation dimension. N is a positive integer equal to the number of temporal variation dimensions.

[0120] Low-rank tensor image models can be used to resolve multiple overlapping dynamics (e.g., at least one temporal variation dimension). For example, according to a low-rank tensor image model, a target image can be represented by the product of a core tensor and (N+1) basis matrices. The core tensor controls the interactions between the (N+1) basis matrices. The (N+1) basis matrices can include spatial factor matrices and N temporal factor matrices. The spatial factor matrices can include one or more spatial basis functions associated with at least one spatial variation dimension of the region of interest. Each of the N temporal factor matrices (or matrices) can correspond to one of at least one temporal variation dimension and includes one or more temporal basis functions associated with the corresponding temporal variation dimension. To generate the target image, it may be necessary to determine at least one spatial basis function, at least one temporal basis function, and the core tensor based on auxiliary signals and imaging signals.

[0121] In some embodiments, the time information may include one or more time basis functions associated with at least one time-varying dimension. For example, one or more time basis functions may include one or more cardiac time basis functions associated with cardiac motion in the region of interest, one or more respiratory time basis functions associated with respiratory motion in the region of interest, one or more T1 recovery time basis functions associated with T1 relaxation in the region of interest, or any combination thereof. The time basis functions associated with the time-varying dimension can reflect dynamic information along the time-varying dimension and include high temporal resolution information.

[0122] In some embodiments, the processing device 120 may determine one or more time basis functions and core tensors for one or more time-varying dimensions based on auxiliary signals obtained in operation 620.

[0123] As shown in operation 620, the auxiliary signal may include multiple auxiliary lines corresponding to the same k-space subset (e.g., the central region) but with different sampling times. Therefore, the auxiliary signal may correspond to a partially encoded image related to one or more temporal dimensions of the region of interest. Thus, the auxiliary signal may include temporal information about the region of interest, and temporal information can be extracted from the auxiliary signal.

[0124] For example, processing device 120 may construct a first optimization function (also called a first objective function) associated with the undersampled auxiliary data (e.g., the auxiliary signal obtained in operation 620), a low-rank tensor representing the fully sampled auxiliary signal to be determined, and a matrix corresponding to each time-varying dimension. The matrix corresponding to the time-varying dimension may include rows measuring the time-varying dimension and columns measuring other time-varying dimensions. Processing device 120 may determine the low-rank tensor representing the fully sampled auxiliary signal by solving the first optimization function.

[0125] Based on the low-rank tensor, the processing device 120 can determine at least one time basis function for each time-varying dimension and the core tensor. The low-rank tensor can be decomposed into a partially encoded spatial factor matrix, a core tensor, and one or more time basis matrices. For example, the processing device 120 can utilize an explicit strategy to recover one or more time basis functions and a core tensor based on the low-rank tensor according to a singular value decomposition (SVD) algorithm or a higher-order singular value decomposition (HOSVD) algorithm.

[0126] In 640, the processing device 120 (e.g., reconstruction module 530) can determine spatial information related to at least one spatial dimension of the region of interest based on temporal information and imaging signals.

[0127] In some embodiments, spatial information may include a spatial factor matrix, which includes one or more spatial basis functions associated with at least one spatial variation dimension of the region of interest.

[0128] Spatial basis functions can include high spatial resolution information along spatially varying dimensions. For example, a spatial basis function can reflect the relationship between pixel information of a region of interest in the image domain and spatial information of the region of interest in the physical domain. In some embodiments, a spatial basis function can be represented as a base image including high spatial resolution information. Different spatial basis functions can be represented as base images including different high spatial resolution information.

[0129] In some embodiments, the processing device 120 may construct a second optimization function (also referred to as a second objective function) associated with at least one spatial basis function. In some embodiments, the second optimization function may include imaging signals and temporal information. The processing device 120 may further determine spatial information by solving the second optimization function.

[0130] In some embodiments, the processing device 120 can determine estimated spatial information. The processing device 120 can determine estimated imaging data based on the estimated spatial and temporal information. The processing device 120 can determine the differences between at least two imaging signals and the estimated imaging data. The processing device 120 can solve a second optimization function based on the differences to determine the spatial information.

[0131] In some embodiments, the second optimization function can be solved by minimizing the difference to obtain satisfactory conditions, such as the difference being less than a threshold. In some embodiments, the second optimization function can be solved using multiple iterations. For example, in the current iteration, in response to determining that the difference is less than the threshold, the estimated spatial information corresponding to the current iteration can be used as the spatial information of the region of interest. In response to determining that the difference is not less than the threshold, new estimated spatial information can be obtained to initiate a new iteration.

[0132] In some embodiments, the second optimization function may include a comparison term configured to limit the difference between at least two imaging signals and estimated imaging data.

[0133] In some embodiments, the second optimization function may further include a regularization term configured to constrain the estimated spatial information. The regularization term may be configured to stabilize the estimated spatial information. For example, the regularization term may minimize fluctuations in the estimated spatial information obtained over multiple iterations, thereby leading to more accurate final spatial information. In some embodiments, the regularization term may be determined based on the estimated spatial information. For example, the L1 norm of the coefficients determined by performing a wavelet transform on the estimated spatial information may be used as the regularization term. As another example, the total change in the spatial dimension of the estimated spatial information may be used as the regularization term. As yet another example, other regularization algorithms, such as Bayesian algorithms, may also be used to obtain the regularization term. In some embodiments, the regularization term may be omitted.

[0134] As an example only, the processing device 120 can determine the spatial information of the region of interest according to the second optimization function shown in equation (1), as follows:

[0135]

[0136] in, U represents the optimal spatial factor matrix (e.g., spatial information to be determined) of the region of interest determined by solving equation (1). xLet represent the estimated spatial factor matrix of the region of interest (e.g., estimated spatial information), d represent the imaging signal, Ω represent the undersampling operator corresponding to the imaging signal, F represent the Fourier transform operator, S represent the coil sensitivity map corresponding to the region of interest, Φ represent the product of the core tensor and one or more time factor matrices, and R(U x ) represents the regularization term, which is a constraint term related to the spatial factor matrix of the region of interest (which may be omitted under certain conditions). In some embodiments, the comparison term in equation (1) may include:

[0137] In some embodiments, the processing device 120 may be based on the formula Ω(FSU) in equation (1). x Φ) Determine the estimated imaging data.

[0138] In some embodiments, the coil sensitivity diagram may refer to the coil sensitivity of at least two receiving coils of the magnetic resonance imaging device 110.

[0139] In some embodiments, the processing device 120 (e.g., acquisition module 510) may acquire the coil sensitivity of each of at least two receiving coils. In some embodiments, the receiving coil may correspond to the coil sensitivity. As used herein, the coil sensitivity of a receiving coil refers to the degree of response of the receiving coil to receiving an input signal (e.g., a magnetic resonance signal). In some embodiments, the coil sensitivity of a receiving coil may represent the spatial brightness variation and / or phase variation introduced when the receiving coil acquires an image. In some embodiments, the coil sensitivity may be a complex number, and the modulus of the complex number may be between 0 and 1. In some embodiments, the coil sensitivity of each of the at least two receiving coils in a magnetic resonance imaging device may be the same or different.

[0140] In some embodiments, the coil sensitivity of the receiving coil can be determined based on a coil sensitivity algorithm. Exemplary coil sensitivity algorithms may include a sum-of-squares (SOS) algorithm, an algorithm for estimating signal parameters using rotation-invariant techniques (ESPIRiT), etc.

[0141] In some embodiments, processing device 120 may acquire coil sensitivity maps from one or more components of magnetic resonance imaging system 100 or external storage device (e.g., magnetic resonance imaging device 110, terminal 140, and / or storage device 130) via network 150. For example, at least two coil sensitivities of at least two receiving coils may be stored in storage device 130 or any other storage device. Processing device 120 may obtain at least two coil sensitivities of at least two receiving coils from storage device 130 or any other storage device.

[0142] In 650, the processing device 120 (e.g., reconstruction module 530) can generate one or more target images of the region of interest based on temporal and spatial information.

[0143] The target image of the region of interest (ROI) can be a static image and / or a dynamic image of the ROI. In some embodiments, the static image may correspond to a specific motion phase of the ROI. For example, the static image may include a two-dimensional or three-dimensional image of the ROI corresponding to a specific cardiac phase or a specific respiratory phase. The dynamic image may reflect the dynamic information of the ROI along at least one time-varying dimension. In some embodiments, the dynamic image may include a series of two-dimensional or three-dimensional images that vary over time, such as at least two two-dimensional or three-dimensional images of the ROI corresponding to at least two motion phases of the object. For example, the dynamic image may reflect cardiac motion of a cardiac sheet or volume during the cardiac cycle and include at least two images of a cardiac sheet or volume corresponding to at least two cardiac phases during the cardiac cycle.

[0144] In some embodiments, the processing device 120 can generate three-dimensional static images corresponding to at least two regions of interest in at least one time dimension, and further generate four-dimensional dynamic images corresponding to at least one region of interest in at least one time dimension by combining the at least two three-dimensional static images of the regions of interest.

[0145] In some embodiments, the processing device 120 can generate at least two two-dimensional static images for each slice of the region of interest. The at least two two-dimensional static images can correspond to at least one time dimension. The processing device 120 can also generate a three-dimensional dynamic image of the region of interest corresponding to at least one time dimension by combining the at least two two-dimensional static images of the slice of the region of interest.

[0146] In some embodiments, the processing device 120 can generate a dynamic image corresponding to at least one time dimension for each slice of the region of interest, and further generate a three-dimensional dynamic image of the region of interest by combining the dynamic images of the slices of the region of interest.

[0147] As described above, a target image with a region of interest (ROI) having multiple dimensions can be represented by a multidimensional tensor, which can be determined based on the spatial information of the ROI (e.g., a spatial factor matrix including one or more spatial basis functions), the temporal information of the ROI (e.g., one or more temporal factor matrices including one or more temporal basis functions), and a core tensor. For example, when the core tensor, spatial information, and temporal information are known, the processing device 120 can generate a target image with a ROI having multiple temporally varying dimensions by determining the product between one or more temporal factor matrices, spatial factor matrices, and the core tensor, where one or more temporal factor matrices include one or more temporal basis functions, the spatial factor matrix includes one or more spatial basis functions, and the core tensor can control the interaction between the temporal factor matrices and the spatial factor matrices. For example, by determining the product of spatial information, temporal information, and the core tensor, the processing device 120 can generate the target image by multiplying the spatial information, temporal information, and the core tensor.

[0148] In some embodiments, the processing device 120 can generate a target image of a region of interest corresponding to a specific time-varying dimension based on a time factor matrix corresponding to a specific time-varying dimension and a spatial factor matrix including at least one spatial basis function. For example, the processing device 120 can generate a dynamic image of the region of interest by determining the product of a spatial factor matrix including spatial basis functions of the region of interest, a time factor matrix including time basis functions related to cardiac motion, and a core tensor controlling the interaction between the spatial factor matrix and the time factor matrix. As another example, the processing device 120 can further extract a static image of the region of interest corresponding to a specific cardiac phase from the dynamic image of the region of interest.

[0149] It should be noted that the above description of process 600 is provided for illustrative purposes only and is not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description in this application. However, these changes and modifications do not depart from the scope of this application. In some embodiments, process 600 can be accomplished by one or more additional operations not described and / or without one or more of the aforementioned operations. In some embodiments, the equations provided above are illustrative examples and can be modified in various ways. For example, one or more coefficients in the equation can be omitted, and / or the equation can also include one or more additional coefficients.

[0150] Figure 7 This is a schematic diagram of an exemplary sampling mode of imaging signals according to some embodiments of this application. Figure 7In the diagram, Kx, Ky, and Kz correspond to the readout direction, phase encoding direction, and layer selection direction in the k-space, respectively, and correspond to... Figure 1 The X-axis, Y-axis, and Z-axis in the diagram.

[0151] In some embodiments, imaging signals can be acquired at multiple parallel layers arranged along the selected layer direction.

[0152] In some embodiments, a 3D stack acquisition can be used to acquire imaging signals. In some embodiments, a layer-selective gradient can be applied to the region of interest along the layer-selective direction to divide the 3D k-space into at least two parallel regions (e.g., a Kx-Ky plane) along the layer-selective direction, which enables Cartesian encoding along the layer-selective direction.

[0153] In some embodiments, data sampling can be performed in each of at least two parallel layers along at least two k-space trajectories, each k-space trajectory passing through the center of a single layer. The central region of k-space can pass through the centers of at least two parallel k-space layers. For example, if the central region of k-space is a k-space centerline, then the center of a single layer can be a point in the layer where Kx = Ky = 0. As another example, if the central region of k-space is a cylindrical region centered on a k-space centerline, then the center of a single layer can be a circular region centered on the point in the layer where Kx = Ky = 0.

[0154] In some embodiments, at least two parallel layers may correspond to the same or different k-space trajectories. For example, data sampling may be performed in each of at least two parallel layers along at least two radial trajectories, each radial trajectory having a specific angle and passing through the center of a single layer. The angle and number (or count) of the radial trajectories in each of the at least two parallel layers may be the same.

[0155] For example only, such as Figure 7 As shown, imaging signals can be acquired in parallel layers 701-706 arranged at different locations along the layer selection direction. Within layers 701-706, imaging signals can be acquired along radial k-space trajectories, such as radial trajectories 711-715 in layer 701. Cartesian encoding can be performed in the layer selection direction (e.g., the Kz direction), and the imaging signal can be obtained within each individual layer along a radial trajectory passing through the center (Kx = Ky = 0) (e.g., 731 in layer 701). Figure 7 As shown, layer 701 can correspond to five radial tracks 711-715. Each of layers 702-706 can also correspond to five radial tracks, each having the same angle as radial tracks 711-715.

[0156] A dataset may include one or more radial trajectories for each slice. These radial trajectories may be sampled consecutively, but the order is not limited. In some embodiments, the sampling order of the radial trajectories in the dataset may be sequential along the positive z-direction or the negative z-direction. For example, along the negative z-direction, a radial trajectory in slice 701 is sampled first, then a radial trajectory in slice 702, and so on, until a radial trajectory in slice 706 is sampled last. As another example, along the positive z-direction, a radial trajectory in slice 706 is sampled first, then a radial trajectory in slice 705, and so on, until a radial trajectory in slice 701 is sampled last. In some embodiments, the sampling order of the radial trajectories in the dataset may be from the central slice to the periphery of k-space. For example, first sample a radial trajectory in layer 703, then sample a trajectory in layer 704, then sample a radial trajectory in layer 702, then sample a radial trajectory in layer 705, then sample a radial trajectory in layer 701, and finally sample a radial trajectory in layer 706.

[0157] In some embodiments, the sampling order of radial trajectories in different datasets may be the same or different. For example, the sampling order of radial trajectories in the first dataset may be sequential along the positive z-direction or the negative z-direction, while the sampling order of radial trajectories in the second dataset may be from the central sheet to the periphery of k-space.

[0158] In some embodiments, the radial trajectories in the dataset may be the same or different. For example, with Figure 7 For example, the dataset can include radial trajectory 711 in layer 701 and radial trajectories with the same angle as radial trajectory 711 in layers 702-706.

[0159] In some embodiments, within the dataset, the processing device 120 may select imaging signals sampled at the center of each of the sheets 701-706 as an auxiliary subset. In some embodiments, the selected imaging signals are sampled at the centers of different sheets such that the distribution of the selected imaging signals can cover the central region of k-space, such as the k-space centerline 721.

[0160] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description herein. However, these changes and modifications do not depart from the scope of this application.

[0161] Figure 8 This is a schematic diagram of an exemplary sampling sequence of imaging signals according to some embodiments of this application. Figure 8Kx, Ky, and Kz in the text correspond to Figure 7 Kx, Ky, and Kz in the equation.

[0162] like Figure 8 As shown, a three-dimensional stack can be used to acquire imaging signals. This can be achieved from n parallel layers P1-P n Acquiring imaging signals. Within each slice, imaging signals can be acquired along multiple radial trajectories (also called spokes) that pass through the center of the slice (Kx = Ky = 0).

[0163] like Figure 8 As shown, in the process of acquiring imaging signals, firstly, the layers P1-P can be sequentially processed in any order. n The first spokes corresponding to the first angle are sampled to form the first dataset. Then, the layers P1-P can be sampled in any order. n The second spokes corresponding to the second angle in the dataset are sampled to form the second dataset, and so on.

[0164] In some embodiments, any value can be varied between each sampled spoke angle, such as 105.44 degrees, 111.25 degrees, and 180 degrees / n. 总 (The total number of spokes in the lamellae). In some embodiments, the change in spoke angle each time can be the same or different. For example, as... Figure 8 As shown, the angle difference between the first and second spokes can be α1, and the angle difference between the second and third spokes can be α2, where α1 can be the same as or different from α2.

[0165] like Figure 8 As shown, for a dataset, with slice P1-P n The spokes corresponding to a specific angle can form a plane in k-space that is parallel to the layer selection direction and passes through the k-space centerline 801. Imaging signals sampled in the k-space centerline 801 can be selected from the dataset (e.g., Figure 8 Signals 811-813 in the data are used to form an auxiliary subset (e.g., auxiliary line data along the selected layer direction).

[0166] If an echo is generated in a TR, and the echo fills the k-space along the spokes, then according to Figure 8 The time interval between the multiple auxiliary lines obtained can be TR×n (n is the number of layers P1-P). n The quantity (or count) of auxiliary signals selected from the imaging signal. Therefore, the auxiliary signals (including multiple auxiliary subsets) can have temporal resolution, which can be used to determine temporal information in multi-task reconstruction. Compared to the case where auxiliary signals are collected by repeatedly sampling the k-space centerline at a sampling frequency, the time interval of the auxiliary subsets can correspond to the sampling frequency.

[0167] In some embodiments, the time interval of the auxiliary subset can be shorter than an interval threshold, resulting in high temporal resolution for the auxiliary signal. The interval threshold can be a default value, manually determined by the user, or determined by the processing device 120 based on data analysis. For example, the interval threshold can be determined based on at least one temporal variation dimension to be analyzed. As an example only, the temporal variation dimension may be related to the respiratory motion of an object, and the object's respiratory cycle may be close to 0.75 seconds. To obtain dynamic information related to the object's respiratory motion, the time interval of the auxiliary lines may need to be shorter than the 0.75-second interval threshold. As another example, the interval threshold can be determined based on practical needs (e.g., accuracy requirements), experience, data models, etc.

[0168] In some embodiments, the time interval of the auxiliary subset can be determined by adjusting the acquisition parameters of the imaging signal, such as the length of the TR, the number (or count) of echoes sampled in a TR, the number (or count) of layers in the k-space (the number of phase coding steps along the layer selection direction), or any combination thereof.

[0169] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description herein. However, these changes and modifications do not depart from the scope of this application.

[0170] Figure 9 This is an exemplary schematic diagram illustrating respiratory movements and signal intensity changes associated with contrast agents, according to some embodiments of this application.

[0171] For example, in dynamic contrast-enhanced (DCE) multitasking imaging of the abdomen during free breathing, there are two time dimensions: respiratory motion and contrast agent dynamics.

[0172] like Figure 9 As shown, the horizontal axis represents time. Figure 9 In the diagram, color depth indicates the intensity of the magnetic resonance signal affected by the contrast agent. The darker the color, the stronger the magnetic resonance signal. Curve 901 represents the abdominal movement curve over time, reflecting the range of abdominal movement.

[0173] Assuming a respiratory cycle of approximately 0.75 seconds, the time interval of the auxiliary subset obtained from process 600 can reach approximately 200-300 milliseconds, which is sufficient to capture the dynamic changes of respiratory motion and contrast agent over time.

[0174] Figures 10A-10F This is an exemplary schematic diagram of a target image shown according to some embodiments of this application.

[0175] In some embodiments, the processing device 120 may use operation 620 of process 600 to extract a portion of the imaging signal as an auxiliary signal, and then use multitasking techniques to reconstruct the imaging signal and the auxiliary signal to obtain a real-time target image with high temporal resolution, for example... Figures 10A-10F The target image in the image. For example... Figures 10A-10F As shown, each target image corresponds to a 0.1-second acquisition time. Users (e.g., doctors, technicians, etc.) can observe changes in organ shape caused by body movements (e.g., respiratory movements, heart movements) through real-time images. For example, users (e.g., doctors, technicians, etc.) can... Figures 10A-10F The target image can be used to observe changes in the shape of the kidney 1001 caused by respiratory movements. For example, in image-guided radiotherapy, high-resolution real-time imaging results can be generated, allowing doctors to observe morphological changes in the treated organ or surrounding normal organs during radiotherapy and then adjust the radiotherapy plan.

[0176] Figures 11A-11F This is an exemplary schematic diagram of a target image shown according to some embodiments of this application.

[0177] In some embodiments, the processing device 120 can process real-time images (e.g., based on at least one time dimension, such as respiratory phase and contrast agent dynamics (e.g., changes in magnetic resonance signal intensity affected by contrast agent) as two dimensions. Figures 10A-10F The image in the image is filled into the matrix. The processing device 120 can process the matrix, for example, by performing interpolation, filling, etc., and then select the image corresponding to the breathing time, for example, Figure 11A-11F The image in the image.

[0178] like Figure 11A-11F As shown, these images correspond to the same respiratory phase. Each image corresponds to a 5-second acquisition time. After fixing the respiratory phase, the change in magnetic resonance signal intensity affected by the contrast agent over time can be reflected in... Figure 11A-11F In the image. For example, in Figure 11A-11F In the study, the different brightness levels of liver 1002 reflect the changes in magnetic resonance signal intensity over time, and further reflect the changes in contrast agent concentration over time.

[0179] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description herein. However, these changes and modifications do not depart from the scope of this application.

[0180] In some embodiments, the processing device 120 may use an alternative method to determine time information without requiring additional sampling of the k-space.

[0181] In some embodiments, the processing device 120 may extract a portion of at least two imaging signals. The processing device 120 may determine at least two reference images based on the portions of the at least two imaging signals that meet specific frequency requirements, or the portions that meet specific spatial location requirements in k-space, or the portions determined by other algorithmic rules.

[0182] In some embodiments, a reference image can be reconstructed based on the portions of at least two imaging signals whose frequencies are below a frequency threshold. The frequency threshold can be determined based on practical needs or experience, for example, 120 Hz or 130 Hz, or, as another example, 0.08 Hz or 0.09 Hz. In some embodiments, the frequency threshold can be determined based on the magnetic resonance frequency range corresponding to the scanned object. For example, a threshold corresponding to the low-frequency portion of the frequency range can be determined as the frequency threshold.

[0183] In some embodiments, a reference image can be reconstructed based on a portion of at least two imaging signals located in a central region of k-space. The central region can be determined based on practical needs or experience. For example, the central region can be a spatial region with a frequency corresponding to a frequency of 0 to 120 Hz in spatial coordinates, with the center point of k-space as the origin. It is understood that the frequency corresponding to the central region of k-space is below a frequency threshold.

[0184] In some embodiments, selected portions of at least two imaging signals may correspond to multiple acquisitions. A reference image can be reconstructed based on portions of at least two imaging signals corresponding to a single acquisition.

[0185] In some embodiments, imaging signals can be acquired based on at least two helical trajectories, the density of which decreases from the center of the k-space outwards, i.e., the k-space data trajectory density at the center of the k-space is greater than the k-space data trajectory density in the surrounding part of the k-space.

[0186] In some embodiments, in at least two spiral trajectories, at least two spiral trajectories corresponding to adjacent times have a rotation angle.

[0187] In some embodiments, the processing device 120 may determine an image representation matrix based on a plurality of reference images and a plurality of time series corresponding to the plurality of reference images. The image representation matrix may include a spatial dimension and a temporal dimension. The spatial dimension may correspond to a spatial location in the reference images, and the temporal dimension may represent the plurality of reference images corresponding to a plurality of acquisition times.

[0188] In some embodiments, a reference image can be represented as a vector, and based on multiple time series corresponding to multiple reference images, multiple vectors corresponding to multiple reference images can be sequentially arranged to obtain a corresponding image representation matrix.

[0189] In some embodiments, a reference image may be represented as a row vector, and multiple row vectors corresponding to multiple reference images may be arranged sequentially along the column direction based on a time series to obtain an image representation matrix. In some embodiments, a reference image may be represented as a column vector, and multiple column vectors corresponding to multiple reference images may be arranged sequentially along the row direction based on a time series to obtain an image representation matrix.

[0190] A reference image can comprise m×n pixels, where m and n are integers. Each pixel includes pixel information (e.g., spatial location, pixel value, etc.). In some embodiments, a reference image comprising m×n pixels can be represented as a vector comprising m×n elements, where each element corresponds to pixel information (e.g., spatial location of the pixel). The image representation matrix can include spatial location information (e.g., spatial location of pixels) and temporal information (e.g., scan time corresponding to the multiple reference images) from multiple reference images.

[0191] It is understandable that, based on the arrangement of the vector representation and image representation matrix corresponding to the reference images, the row and column dimensions of the image matrix correspond to the spatial and temporal dimensions of multiple reference images, respectively. The spatial dimension corresponds to the spatial location information in the reference images, and the temporal dimension represents the temporal information of the multiple reference images, that is, the multiple acquisition times corresponding to the multiple reference images.

[0192] In some embodiments, the processing device 120 may determine time information based on an image representation matrix and a time dimension.

[0193] As described above, the image representation matrix can include spatial information (e.g., spatial location of pixels) and temporal information (e.g., multiple acquisition times corresponding to the multiple reference images) from multiple reference images. The row and column dimensions of the image matrix correspond to the spatial and temporal dimensions of the multiple reference images, respectively.

[0194] In some embodiments, the image representation matrix can be decomposed 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 spatial dimension (i.e., a matrix representing the spatial information of the reference image). In some embodiments, the first matrix obtained by decomposition (i.e., the matrix representing the time information of the reference image) may include time information of multiple reference images (e.g., multiple acquisition times corresponding to multiple reference images), and the second matrix (i.e., the matrix representing the spatial information of the reference image) may include spatial information of multiple reference images (e.g., the spatial positions of pixels in the reference images).

[0195] In some embodiments, the temporal information of the dynamic image can be determined based on the first matrix. In some embodiments, the time base of the dynamic image can be determined based on the first matrix, and the time base of the dynamic image can be used as the temporal information of the dynamic image.

[0196] In some embodiments, the rank of the first matrix can be determined, and a portion of the first matrix can be determined based on its rank as a representation of the time base of the dynamic image. In some embodiments, the rank of the matrix can be obtained by solving the matrix. For example, elementary transformations, Gaussian elimination, and other algorithms can be used to obtain the rank. In some embodiments, the range of the rank of the matrix can be set empirically or according to actual needs. For example, the range can be 10-40. Taking rank 20 as an example, the first 20 columns of the second matrix are used as a representation of the time base of the dynamic image. It is 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.

[0197] In some embodiments, the matrix decomposition of the image representation matrix can be performed by performing singular value decomposition on the image representation matrix based on the time dimension to determine the temporal information of the dynamic image. For example, by performing singular value decomposition, the image representation matrix can be represented based on orthogonal matrices corresponding to the row dimension, orthogonal matrices corresponding to the column dimension, and diagonal matrices. The orthogonal matrix corresponding to the time dimension obtained by decomposition can be used as a first matrix corresponding to the time dimension (i.e., the matrix representing the time information).

[0198] Compared to the above processing, in process 600, the auxiliary signal extracted from the imaging signal approximates the data obtained by repeatedly sampling the same k-space subset and can be used to directly determine temporal information without the need to generate a reference image. In some embodiments, the processing device 120 can obtain imaging signals related to the region of interest (ROI) of the object (e.g., such as...). Figure 6 The process 600 is shown in operation 610. The processing device 120 can select a portion of the imaging signal as an auxiliary signal associated with at least one time dimension of the region of interest (e.g., as shown in operation 610). Figure 6 (As shown in operation 620 of process 600). Processing device 120 can generate at least one target image associated with at least one time dimension of the region of interest based on imaging signals and auxiliary signals.

[0199] The auxiliary signal selected from the imaging signal may include at least two auxiliary subsets, each corresponding to the same region (identical subset) in k-space. The at least two auxiliary subsets of the auxiliary signal may have temporal resolution so that the auxiliary signal can reflect the magnetic resonance signal variations in the region of interest in at least one time dimension, thereby enabling the auxiliary signal to be used to generate at least one magnetic resonance image in at least one time dimension. Since the auxiliary signal is obtained from the imaging signal, rather than performing additional k-space sampling outside of acquiring the imaging signal, this reduces imaging time.

[0200] For illustrative purposes, as described above, multi-task reconstruction using auxiliary signals can be taken as an example. It should be noted that those skilled in the art will recognize that the auxiliary signals described herein can be applied to other similar situations, such as other magnetic resonance reconstruction algorithms for generating at least one magnetic resonance image in at least one time dimension.

[0201] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0202] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0203] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may manifest as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0204] A computer-readable signal medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. Such propagated signals can take many forms, including electromagnetic, optical, and any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable signal medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, and any combination of the above.

[0205] The computer program code that performs the operations of this application may 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, or similar languages; traditional procedural programming languages ​​such as the "C" programming language, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code may run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (e.g., through the network of an internet service provider) or in a cloud computing environment, or provided as a service, such as a software service (SaaS).

[0206] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a purely software solution, such as an installation on an existing server or mobile device.

[0207] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, the methods described in this application should not be construed as reflecting an intention that the subject of the claimed invention requires more features than expressly recited in each claim. On the contrary, the subject of the invention should possess fewer features than in any single embodiment described above.

Claims

1. A magnetic resonance imaging method, implemented on a machine including at least one processing device and at least one storage device, the method comprising: Acquire imaging signals related to the region of interest of the object; A portion of the imaging signal is selected as an auxiliary signal, and the auxiliary signal is associated with at least one time dimension of the region of interest; as well as Based on the imaging signal and the auxiliary signal, at least one target image is generated, the target image being associated with at least one time dimension of the region of interest.

2. The method according to claim 1, characterized in that, The auxiliary signal includes a portion of the imaging signal sampled in the central region of the k-space.

3. The method according to claim 2, characterized in that, The central region of the k-space includes the k-space centerline along the layer selection direction of the k-space.

4. The method according to claim 2, characterized in that, The imaging signal is acquired based on at least two k-space trajectories, each of which passes through the central region.

5. The method according to claim 4, characterized in that, Selecting a portion of the imaging signal as the auxiliary signal associated with the region of interest includes: Obtain at least two datasets, each dataset comprising a portion of the at least two k-space trajectories; In each of the datasets, the imaging signals sampled in the central region are selected as an auxiliary subset; and The at least two auxiliary subsets are designated as the auxiliary signals.

6. The method according to claim 5, characterized in that, The imaging signal is acquired in at least two parallel k-space layers, which are arranged along the selected layer direction of k-space.

7. The method according to claim 6, characterized in that, The k-space trajectory in each of the at least two parallel k-space layers passes through the center of the layer, and the central region passes through the center of the at least two parallel k-space layers along the selected layer direction.

8. The method according to claim 7, characterized in that, For each of the datasets, The k-space trajectory mentioned therein is a radial trajectory and corresponds to the same angle. Each of the radial trajectories originates from one of the at least two parallel k-space sheets.

9. A magnetic resonance imaging system, comprising: The acquisition module is configured to acquire imaging signals related to the region of interest of the object; The determining module is configured to select a portion of the imaging signal as an auxiliary signal, the auxiliary signal being associated with at least one time dimension of the region of interest; as well as The reconstruction module is configured to generate at least one target image based on the imaging signal and the auxiliary signal, the target image being associated with at least one temporal dimension of the region of interest.

10. A magnetic resonance imaging device, characterized in that, The device includes: At least one storage medium that stores computer instructions; At least one processor executes the computer instructions to implement the method of any one of claims 1 to 8.

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