System and method for magnetic resonance imaging

Abstract

The present application relates to a system and method of magnetic resonance imaging. The method includes acquiring at least two target k-space datasets by populating k-space with target magnetic resonance signals acquired by at least two coils of a magnetic resonance imaging device along a spiral trajectory. The method includes acquiring a coil sensitivity of each of the at least two coils. The method includes acquiring a point spread function corresponding to the spiral trajectory. The method includes generating a target image based on an objective function.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Application No. 17 / 304,652, filed on June 24, 2021, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates generally to systems and methods for magnetic resonance imaging (MRI), and more specifically to systems and methods for image reconstruction in magnetic resonance imaging. Background Technology

[0004] Magnetic resonance imaging (MRI) systems are widely used in medical diagnostics. MRI systems use powerful magnetic fields and radio frequency (RF) technology to generate images of the object being scanned. Parallel imaging can accelerate data acquisition in MRI by using receiver coil arrays with spatially variable sensitivity. However, the acceleration of data acquisition in parallel imaging can be limited by noise amplification during reconstruction, which can increase non-linearly with the increase of the acceleration factor associated with the degree of acceleration in parallel imaging. Therefore, it is desirable to provide a system and method that can efficiently reconstruct high-quality MRI images with a relatively high acceleration factor. Summary of the Invention

[0005] According to one aspect of this application, a magnetic resonance imaging method is provided that can be implemented on a computing device having at least one processor and at least one storage device. The method may include acquiring at least two target k-space datasets by filling target magnetic resonance signals acquired by at least two (also referred to as "a plurality of") coils of an MRI device along a helical trajectory into k-space. The method may include acquiring the coil sensitivity of each of the at least two coils. The method may include acquiring a point spread function corresponding to the helical trajectory. The method may include generating a target image based on a target function. The target function may be determined based on an operator, the at least two target k-space datasets, the at least two coil sensitivities, and the point spread function. The operator may represent a mapping relationship between the target image and the at least two target k-space datasets.

[0006] In some embodiments, the at least two target k-space datasets can be obtained by varying the density of the target magnetic resonance signal into the k-space.

[0007] In some embodiments, the method may include generating a first image based on a first set of k-space data, wherein the first set of k-space data is obtained by filling the k-space with a reference magnetic resonance signal acquired along a helical trajectory. The method may include generating a second image based on a second set of k-space data, wherein the second set of k-space data is obtained by filling the k-space with the reference magnetic resonance signal acquired along the helical trajectory. The method may include determining the point spread function based on the first image and the second image.

[0008] In some embodiments, the method may include, for each of the at least two target k-space datasets, acquiring the target magnetic resonance signal acquired by the coils of the at least two coils of the MRI device. The method may include acquiring a mask. The method may include acquiring the target k-space dataset based on the target magnetic resonance signal and the mask.

[0009] In some embodiments, the method may include setting the target image as an independent variable in the objective function.

[0010] In some embodiments, the method may include determining the operator based on the at least two coil sensitivities, the point spread function, and the mask. The method may include determining an aliasing image based on the at least two target k-space datasets, the at least two coil sensitivities, and the point spread function. The method may include determining the target function based on the operator and the aliasing image.

[0011] In some embodiments, the method may include generating a first candidate image associated with each of the at least two coils of the MRI device, based on the coil sensitivity of the coil and the target image. The method may include determining a first candidate k-space dataset based on the first candidate image, the point spread function, and the mask. The method may include generating a second candidate image associated with the coil by processing the first candidate k-space dataset based on the point spread function and the coil sensitivity of the coil. The method may include determining the operator based on at least two second candidate images associated with the at least two coils.

[0012] In some embodiments, the k-space may be three-dimensional. The first candidate image may be a three-dimensional image. The method may include generating a first processed candidate image by performing a first Fourier transform on the first candidate image along a first direction of the k-space. The method may include generating a second processed candidate image by multiplying the point spread function with the first processed candidate image. The method may include generating a third processed candidate image by performing a second Fourier transform on the second processed candidate image along a second direction of the k-space. The method may include generating a second candidate k-space dataset by performing a third Fourier transform on the third processed candidate image along a third direction of the k-space. The method may include generating the first candidate k-space dataset based on the second candidate k-space dataset and the mask, wherein the first direction, the second direction, and the third direction are orthogonal to each other.

[0013] In some embodiments, the method may include generating a first processed candidate k-space dataset by performing a first inverse Fourier transform on the first candidate k-space dataset along the third direction of the k-space. The method may include generating a second processed candidate k-space dataset by performing a second inverse Fourier transform on the first processed candidate k-space dataset along the second direction of the k-space. The method may include generating a third processed candidate k-space dataset by multiplying the second processed candidate k-space dataset by the conjugate of the point spread function. The method may include generating a third candidate image by performing a third inverse Fourier transform on the third processed candidate k-space dataset along the first direction of the k-space. The method may include generating a second candidate image by multiplying the third candidate image by the conjugate of the coil sensitivity of the coil.

[0014] In some embodiments, the k-space may be three-dimensional. The target k-space dataset may be three-dimensional. The method may include generating a first processed target k-space dataset by performing a first inverse Fourier transform on the target k-space dataset along a third direction of the k-space for each of the at least two target k-space datasets. The method may include generating a second processed target k-space dataset by performing a second inverse Fourier transform on the first processed target k-space dataset along a second direction of the k-space. The method may include generating a third processed target k-space dataset by multiplying the second processed target k-space dataset by the conjugate of the point spread function. The method may include generating a first intermediate image associated with a coil corresponding to the target k-space dataset by performing a third inverse Fourier transform on the third processed target k-space dataset along a first direction of the k-space. The method may include generating a second intermediate image associated with the coil based on the coil sensitivity of the coil and the first intermediate image. The method may include generating the aliased image based on at least two second intermediate images associated with the at least two coils.

[0015] In some embodiments, the method may include determining the target image based on the objective function and according to a conjugate gradient algorithm.

[0016] In some embodiments, the method may include obtaining the at least two target k-space datasets by filling the k-space with the target magnetic resonance signals acquired by the at least two coils of the MRI device along a random spiral trajectory.

[0017] According to another aspect of this application, a magnetic resonance imaging system is provided. The system may include at least one storage device for storing a set of instructions, and at least one processor communicating with the at least one storage device. When the stored instructions are executed, the at least one processor can cause the system to perform a method. The method may include acquiring at least two target k-space datasets by filling k-space with target magnetic resonance signals acquired by at least two coils of an MRI device along a helical trajectory. The method may include acquiring the coil sensitivity of each of the at least two coils. The method may include acquiring a point spread function corresponding to the helical trajectory. The method may include generating a target image based on a target function. The target function may be determined based on an operator, the at least two target k-space datasets, the at least two coil sensitivities, and the point spread function. The operator may represent a mapping relationship between the target image and the at least two target k-space datasets.

[0018] According to another aspect of this application, a method for magnetic resonance imaging implemented on a computing device having at least one processor and at least one storage device is provided. The method may include acquiring at least two target k-space datasets by filling target magnetic resonance signals acquired by at least two coils of an MRI device along a helical trajectory into k-space. The method may include determining aliased images associated with the at least two coils based on the at least two target k-space datasets. The method may include determining the target image based on the aliased image and a mapping relationship between the target image and the aliased image.

[0019] In some embodiments, the method may include determining an objective function based on the aliased image and the mapping relationship. The method may also include generating the objective image by iteratively solving the objective function.

[0020] In some embodiments, the method may include determining the mapping relationship based on at least two coil sensitivities, a point spread function corresponding to the spiral trajectory, and a mask corresponding to the spiral trajectory.

[0021] In some embodiments, the method may include determining at least two intermediate images corresponding to the at least two coils based on the at least two target k-space datasets, at least two coil sensitivities, and a point spread function corresponding to the helical trajectory. The method may also include generating the aliased image based on the at least two intermediate images corresponding to the at least two coils.

[0022] 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

[0023] 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:

[0024] Figure 1 These are schematic diagrams of exemplary MRI systems according to some embodiments of this application;

[0025] Figure 2 This is a schematic diagram of an exemplary MRI device 110 according to some embodiments of this application;

[0026] Figure 3This is a schematic diagram of the hardware and / or software components of an exemplary computing device 300 according to some embodiments of this application;

[0027] Figure 4 This is a schematic diagram of the hardware and / or software components of an exemplary mobile device 400 according to some embodiments of this application;

[0028] Figure 5 These are schematic diagrams of exemplary processing devices according to some embodiments of this application;

[0029] Figure 6 This is a flowchart illustrating an exemplary process for determining a target image according to some embodiments of this application;

[0030] Figure 7 This is a flowchart illustrating an exemplary process for determining an objective function according to some embodiments of this application;

[0031] Figure 8A This is a schematic diagram illustrating an exemplary Cartesian sampling pattern according to some embodiments of this application;

[0032] Figure 8B This is a schematic diagram illustrating an exemplary spiral sampling pattern according to some embodiments of this application;

[0033] Figure 9A These are schematic diagrams illustrating exemplary variable density sampling modes according to some embodiments of this application; and

[0034] Figure 9B This is a schematic diagram of an exemplary CAIPIRINHA sampling mode according to some embodiments of this application. Detailed Implementation

[0035] 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.

[0036] 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 clearly indicates otherwise. As used herein, the terms “and / or” and “at least one” include one or any and all combinations of the listed items. It should also be understood that the terms “comprising” and “including” as used herein indicate only the presence of the stated features, integers, steps, operations, components, and / or parts, but do not exclude the presence or addition of other features, integers, steps, operations, components, parts, and / or combinations thereof. Furthermore, the term “exemplary” refers to an example or illustration.

[0037] It is understood that the terms “system,” “engine,” “unit,” “module,” and / or “block” used in this application are methods of 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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”).

[0042] 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.

[0043] 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.

[0044] Furthermore, the systems and methods disclosed in this application primarily relate to image reconstruction in MRI systems. It should be understood that this is for illustrative purposes only. The systems and methods of this application can be applied to any other type of medical imaging system. In some embodiments, 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, an MRI system. A multimodal imaging system may include, for example, a computed tomography-magnetic resonance imaging (MRI-CT) system, a positron emission tomography-magnetic resonance imaging (PET-MRI) system, a single-photon emission computed tomography-magnetic resonance imaging (SPECT-MRI) system, a digital subtraction angiography-magnetic resonance imaging (DSA-MRI) system, etc.

[0045] MRI is a radiological imaging technique used to acquire images of the anatomical structures or physiological processes of an object (e.g., a patient or a body part). MRI is based on the nuclear magnetic resonance (NMR) phenomenon. In a typical MRI scan, the object is placed in a strong, static master magnetic field. Magnetic resonance signals are generated by exciting the spins of atomic nuclei in the object by applying radiofrequency pulses. After excitation, the object can emit a decaying radiofrequency signal, which can be detected by a receiving coil as a radiofrequency voltage. To distinguish received signals from different spatial locations, an additional magnetic field gradient can be superimposed on the master magnetic field, causing the field strength to vary with spatial location, thus allowing the origin of the magnetic resonance signal emitted from the object to be located. Based on gradient coding, Fourier imaging can be performed, where measurements representing the spatial frequencies of the object, called k-space, can be acquired using specific sampling trajectories. A common acquisition scheme is Cartesian sampling, and image reconstruction is performed by performing an inverse Fourier transform (e.g., fast inverse Fourier transform) on the k-space data. However, Fourier imaging has a relatively slow data acquisition speed, encoding only a limited number of k-space locations per unit time, and this process must be repeated sequentially until the entire k-space region is sampled to achieve the target spatial resolution. This low imaging speed not only increases patient discomfort but also limits spatiotemporal resolution and volumetric coverage.

[0046] Another approach to improving MRI imaging speed is to accelerate MRI data acquisition by acquiring fewer phase-encoded lines in k-space. Various parallel imaging techniques, such as Spatial Harmonic Parallel Acquisition (SMASH), Sensitivity Encoding (SENSE), and Generalized Automatic Calibration Partial Parallel Acquisition (GRAPPA), have been applied to accelerate data acquisition in MRI using receiver coil arrays with spatially varying sensitivities. A set of independent receiver channels can be used to acquire MRI signals. Receiver coils can be more sensitive to specific volumes of tissue closer to the coil, meaning they can provide an additional source of spatial information for image reconstruction. k-space data can be undersampled in phase-encoded directions (e.g., the Y-axis and Z-axis phase-encoded directions in 3D imaging) to reduce scan time. However, acceleration in parallel imaging can be limited by noise amplification during reconstruction, which can increase non-linearly with increasing acceleration factors.

[0047] Wave-Controlled Aliasing Parallel Imaging (CAPI) acquisition and reconstruction techniques involve applying sinusoidal gy and gz gradients during readout of each Kx encoding line. Uniform undersampling modes (e.g., controlled aliasing parallel accelerated imaging (CAIPIRINHA) sampling modes) can be used in wave-CAIPI acquisition and reconstruction techniques. For example, CAIPIRINHA sampling can use a unique set of k-space sampling modes to reduce aliasing artifacts (i.e., aliased pixels) in the reconstructed image. By shifting the sampling positions of alternating rows relative to the Z-axis phase encoding direction, the acquired k-space samples can be offset from normal grid-like sampling, such as... Figure 9B As shown. The acceleration factor for CAIPIRINHA sampling can be a multiple of 2, for example, 2, 4, 6, etc. By using a uniform undersampling mode, an analytical solution for determining the reconstructed image can exist, and iterative algorithms are not required to determine the reconstructed image, resulting in faster image reconstruction. Furthermore, when the acceleration factor is relatively small (e.g., less than 4), the quality of the reconstructed image can be better. However, the acceleration factor in the uniform undersampling mode is inflexible (e.g., the acceleration factor is always a multiple of 2), and when the acceleration factor is relatively high (e.g., greater than 4), the reconstructed image may have noticeable artifacts. In some embodiments, the acceleration factor can be fixed during a single MRI data acquisition.

[0048] Variable density undersampling shows promise in reducing aliasing artifacts and improving sampling efficiency. In some embodiments, variable density undersampling can allow the sampling density to be a function of the k-space location, such as... Figure 9AAs shown. This allows for flexible allocation of scan time based on signal characteristics, scan time constraints, and / or image quality suitable for a specific application. Data in the central region of k-space can correspond to structural features associated with the object being scanned, while data in the peripheral regions of k-space can correspond to detailed features associated with the object. In some embodiments, variable-density undersampling can fully sample the central region of k-space to reduce low-frequency aliasing artifacts and undersample the peripheral regions of k-space to reduce scan time, while maintaining or increasing image quality, such as spatial resolution. For example, the sampling density in the central region of k-space can be relatively high, and the sampling density in the peripheral regions of k-space can be relatively low. As used in this specification, sampling density can refer to the number (or count) of sampled data points per unit area in k-space. The acceleration factor for variable-density undersampling can be any value for different regions of k-space, such as 2, 3, 4, 5, 6, 10, etc. In some embodiments, the acceleration factor for an undersampling mode (e.g., variable-density undersampling mode, uniform undersampling mode) can be the ratio of the amount of k-space data required for fully sampled k-space to the amount of k-space data sampled according to the undersampling mode. However, existing wave-CAIPI acquisition and reconstruction techniques do not support this variable density undersampling mode.

[0049] For illustrative purposes, for 3D imaging in the Cartesian coordinate system, with ky and kz fixed in k-space, the received signal can be represented by k-space notation as equation (1):

[0050]

[0051] Where m(x, y, z) refers to the latent image (i.e., the reconstructed image); (x, y, z) refers to the coordinates of a voxel point in the latent image; k x This refers to frequency coding; k y This refers to the phase encoding in the Y-axis direction; k z This refers to phase encoding in the Z-axis direction. During each readout, in both the Y-axis and Z-axis (e.g., ... Figure 1 An additional sinusoidal wave gradient g is applied to the Y-axis and Z-axis in the coordinate system shown in 160. y and g z Then the received signal can be expressed as equation (2):

[0052]

[0053] Among them, g y and g z This refers to the additional sinusoidal wave gradient. In some embodiments, equation (2) can be rewritten more concisely as:

[0054]

[0055] Where wave[x, y, z] refers to the image acquired by applying a wave gradient; F x This refers to performing a Discrete Fourier Transform (DFT) operation along the X-axis; This refers to performing an inverse DFT operation along the X-axis; k refers to the index of the data point on the readout row in the k-space; Psf[k, y, z] refers to the point spread function (PSF) describing the wave gradient effect: each readout row of the latent image m(x, y, z) is convolved with the corresponding PSF to generate the wave image wave[x, y, z], where the PSF is a function of the spatial location (y, z). Further descriptions of the X, Y, and Z axes can be found elsewhere in this application (e.g., Figure 1-2 (and its description) were found.

[0056] One aspect of this application relates to MRI systems and methods. A processing device can acquire at least two target k-space datasets. Each target k-space dataset can correspond to a target magnetic resonance signal, which is acquired by sampling along a helical trajectory by one of at least two coils of an MRI apparatus (referred to herein as a coil). Each target k-space dataset is acquired by filling the k-space with the target magnetic resonance signal. In some embodiments, the target k-space dataset can be acquired by filling the k-space with the target magnetic resonance signal acquired along a variable-density helical trajectory or a random helical trajectory. As used herein, a variable-density helical trajectory refers to at least two helical trajectories corresponding to the target k-space dataset distributed at a variable density in the k-space. As used herein, a random helical trajectory refers to at least two helical trajectories corresponding to the target k-space dataset randomly distributed in the k-space. The processing device can acquire the coil sensitivity of each of the at least two coils. The processing device can acquire the point spread function of the corresponding helical trajectory. The processing device can generate a target image based on the target function. The target function can be determined based on an operator, at least two target k-space datasets, at least two coil sensitivities, and the point spread function. An operator can represent the mapping relationship between a target image and at least two target k-space datasets.

[0057] Therefore, this application provides a method for variable-density wave acquisition and reconstruction. As used in this specification, variable-density wave acquisition and reconstruction refers to acquiring k-space data for MRI image reconstruction by filling the k-space with magnetic resonance signals acquired along a variable-density spiral trajectory. Since the acceleration factor of variable-density undersampling can be any suitable value, the setting of the acceleration factor in the variable-density wave acquisition and reconstruction algorithm can be relatively flexible. Furthermore, when the acceleration factor is the same as that of a uniform undersampling mode (e.g., the CAIPIRINHA sampling mode) (especially when the acceleration factor is relatively high, e.g., above 6), the quality (e.g., the signal-to-noise ratio of the target image) generated based on the k-space data obtained using the variable-density undersampling mode can be relatively higher compared to the uniform undersampling mode. Since uniform undersampling does not consider the conjugate symmetry of the k-space data, and variable-density undersampling can fully sample the central region of k-space (i.e., the low-frequency region) and undersample the conjugate symmetry region, the efficiency of variable-density sampling can be relatively high, and the quality of the target image generated based on the k-space data obtained using the variable-density undersampling mode can be relatively higher compared to the uniform undersampling mode.

[0058] Furthermore, this application also provides a method for acquiring and reconstructing random waves. As used herein, acquiring and reconstructing random waves refers to acquiring k-space data for MRI image reconstruction by filling the k-space with magnetic resonance signals acquired along a random spiral trajectory. Compressed sensing techniques can be used in the reconstruction of the target image to correct random artifacts generated in the random undersampling (i.e., random spiral trajectory), which can further improve the quality of the target image.

[0059] Figure 1 This is a schematic diagram of an exemplary 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 of various ways. This is merely an example. Figure 1 As shown, MRI device 110 can be directly connected to processing device 120, as indicated by the dashed double-headed arrow connecting MRI device 110 and processing device 120, or connected via network 150. As another example, storage device 130 can be directly connected to MRI device 110, as indicated by the dashed double-headed arrow connecting MRI device 110 and storage device 130, or connected via network 150. As yet another example, terminal 140 can be directly connected to processing device 120, as indicated by the dashed double-headed arrow connecting terminal 140 and processing device 120, or connected via network 150.

[0060] MRI device 110 can be configured to scan an object (or a portion of an object) to acquire image data, such as echo signals (or magnetic resonance signals) associated with the object. For example, MRI device 110 can detect at least two echo signals by applying a sequence of MRI pulses to the object. In some embodiments, such as in combination Figure 2 The MRI 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 MRI device 110 may be a permanent magnet MRI scanner, a superconducting magnet MRI scanner, a resistive electromagnet MRI scanner, etc. In some embodiments, depending on the magnetic field strength, the MRI device 110 may be a high-field MRI scanner, a medium-field MRI scanner, a low-field MRI scanner, etc.

[0061] The object scanned by the MRI 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.

[0062] For illustrative purposes, Figure 1 It provides 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 direction of the X axis can be from the right side to the left side of the MRI device 110 when viewed from the front. Figure 1 The positive direction of the Y-axis shown can be from the bottom to the top of the MRI device 110; Figure 1 The positive direction of the Z-axis shown can be the direction in which the object moves out of the scanning channel (or aperture) of the MRI device 110.

[0063] In some embodiments, the MRI device 110 may be instructed to select an anatomical slice of the object along a slice selection direction and scan the anatomical slice to acquire at least two echo signals from the slice. During scanning, spatial encoding within the slice can be implemented using spatial encoding coils (e.g., X and Y coils) along the phase encoding direction and frequency encoding 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 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 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, and such modifications will not exceed the scope of this specification. Further description of the MRI device 110 can be found elsewhere in this specification, for example, see [link to documentation]. Figure 2 And its description.

[0064] Processing device 120 can process data and / or information obtained from MRI device 110, storage device 130, and / or terminal 140. For example, processing device 120 can acquire at least two target k-space datasets. As another example, processing device 120 can acquire information on the coil sensitivity of each of at least two coils. As another example, processing device 120 can acquire a point spread function corresponding to a helical trajectory. As another example, processing device 120 can generate a target image based on a target function. 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 MRI device 110, storage device 130, and / or terminal 140 via network 150. As another example, processing device 120 can be directly connected to MRI 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, a cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud cloud, a multi-cloud, or a combination thereof. In some embodiments, the processing device 120 may be part of the terminal 140. In some embodiments, the processing device 120 may be part of the MRI device 110.

[0065] Storage device 130 may store data, instructions, and / or any other information. In some embodiments, storage device 130 may store data acquired from MRI device 110, processing device 120, and / or terminal 140. The 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 at least two target k-space datasets acquired from an MRI device (e.g., MRI device 110). As another example, storage device 130 may store coil sensitivity information for each of at least two coils. As another example, storage device 130 may store a point spread function determined by processing device 120. As another example, storage device 130 may store a target function determined by processing device 120. 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 mass storage, removable storage, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof. Exemplary mass storage may include disks, optical disks, solid-state drives, etc. Exemplary removable storage may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tapes, etc. Exemplary volatile read-write memory 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 ROM may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), optical disk ROM (CD-ROM), and digital multifunction disk ROM, etc. In some embodiments, storage device 130 may be implemented on a cloud platform. As an example only, a cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud cloud, multi-cloud, or any combination thereof.

[0066] In some embodiments, storage device 130 may be connected to network 150 to communicate with one or more other components in MRI system 100 (e.g., processing device 120, terminal 140). One or more components of MRI 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 MRI device 110.

[0067] Terminal 140 may be connected to and / or communicate with MRI device 110, processing device 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.

[0068] Network 150 may include any suitable network that facilitates the exchange of information and / or data within the MRI system 100. In some embodiments, one or more components of the MRI system 100 (e.g., MRI 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 MRI system 100 via network 150. For example, processing device 120 and / or terminal 140 may acquire at least two target k-space datasets from MRI device 110 via network 150. As yet another example, processing device 120 and / or terminal 140 may acquire 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), etc.), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks, Wi-Fi networks, etc.), cellular networks (e.g., Long Term Evolution (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 cable networks, wired networks, fiber optic networks, telecommunications networks, the Internet, wireless local area networks (WLANs), metropolitan area networks (MANs), public switched telephone networks (PSTNs), Bluetooth networks, ZigBee networks, near field communication (NFC), etc., or 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 switching points, through which one or more components of MRI system 100 may connect to network 150 to exchange data and / or information.

[0069] This description is intended to be illustrative and not to limit 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 other and / or alternative exemplary embodiments. However, these variations and modifications do not depart from the scope of this application. In some embodiments, the MRI system 100 may include one or more other components, and / or one or more components described above may be omitted. Additionally or alternatively, two or more components of the MRI system 100 may be integrated into a single component. For example, processing device 120 may be integrated into MRI device 110. As another example, a component of the MRI system 100 may be replaced by another component capable of performing the function of that component.

[0070] Figure 2This is a schematic diagram of an exemplary MRI device 110 according to some embodiments of this application. Figure 2 The figure shows one or more components of an MRI apparatus 110. As shown, a main magnet 201 can generate a first magnetic field (or main magnetic field), which can be applied to an object (also called a body) exposed within the field. The main magnet 201 can include a resistive magnet or a superconducting magnet, both of which require a power source (not shown) to operate. Alternatively, the main magnet 201 can include a permanent magnet. The main magnet 201 can include apertures through which an object can be placed. The main magnet 201 can also control the uniformity of the generated main magnetic field. Several shimming coils can be located within the main magnet 201. The shimming coils placed in the gaps of the main magnet 201 can compensate for non-uniformity of the magnetic field of the main magnet 201. The shimming coils can be energized by a shimming power source.

[0071] Gradient coil 202 can be located within main magnet 201. Gradient coil 202 can generate a second magnetic field (or magnetic field gradient, including magnetic field gradients Gx, Gy, and Gz). The second magnetic field can 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 can change with their position in the gradient field, thereby encoding spatial information in the echo signal generated in the imaging region of the object. Gradient coil 202 may include an X coil (e.g., for generating a gradient magnetic field Gx corresponding to the X direction), a Y coil (e.g., for generating a gradient magnetic field Gy corresponding to the Y direction), and / or a Z coil (e.g., for generating a gradient magnetic 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 Maxwell coil design, and the X and Y coils may be based on a Golay coil design. The three sets of coils can generate three different magnetic fields for position encoding. Gradient coil 202 can allow spatial encoding of the echo 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 in the X, Y, or Z axis directions, respectively. Gradient coil 202 can be designed for closed-aperture MRI scanners or open-aperture MRI 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 and Y coils can be energized to generate gradient fields in the X and Y directions. As used herein, Figure 2The X-axis, Y-axis, Z-axis, X direction, Y direction, and Z direction described in the text can be compared with... Figure 1 The directions described in the text are the same or similar.

[0072] In some embodiments, the radio frequency (RF) coil 203 may be located within the main magnet 201 and function as a transmitter, receiver, or both. The RF coil 203 may be connected to an RF electronics device 209, which may be configured as 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.

[0073] In some embodiments, the radio frequency coil 203 may include one or more radio frequency transmitting coils and / or one or more radio frequency receiving coils. The radio frequency transmitting coil can transmit radio frequency pulses to the object. Under the synergistic effect of the main magnetic field, the gradient magnetic field, and the radio frequency pulses, a magnetic resonance signal related to the object can be generated according to the pulse sequence. The radio frequency receiving coil can acquire the magnetic resonance signal from the object according to the pulse sequence. In some embodiments, the radio frequency receiving coil may correspond to a channel for acquiring the magnetic resonance signal. The radio frequency receiving coil can receive magnetic resonance signals from at least two channels from the object.

[0074] When used as a transmitter, the RF coil 203 can generate an RF signal that provides a third magnetic field used to generate an echo signal related to the area 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.

[0075] When used as a receiver, the radio frequency (RF) coil can be responsible for detecting the echo signal. After excitation, the echo signal generated by the object can be sensed by the RF coil 203. Then, the receiver amplifier can receive the sensed echo signal from the RF coil 203, amplify the sensed echo signal, and provide the amplified echo signal to the analog-to-digital converter (ADC) 208. The ADC 208 can convert the echo signal from an analog signal to a digital signal. The digital echo signal can then be sent to the processing device 120 for sampling.

[0076] In some embodiments, the gradient coil 202 and the radio frequency coil 203 may be arranged circumferentially relative to the object. Those skilled in the art will understand that the main magnet 201, the gradient coil 202, and the radio frequency coil 203 may be arranged around the object in a variety of ways.

[0077] In some embodiments, the RF power amplifier 207 can amplify RF pulses (e.g., the power of the RF pulses, the voltage of the RF pulses) so that the amplified RF pulses are used to drive the RF coil 203. The RF power amplifier 207 may include a transistor-based RF power amplifier, a vacuum tube-based RF power amplifier, or any combination thereof. A transistor-based RF power amplifier may include one or more transistors. A vacuum tube-based RF power amplifier may include transistors, tetrodes, klystrons, or any combination thereof. In some embodiments, the RF power amplifier 207 may include a linear RF power amplifier or a non-linear RF power amplifier. In some embodiments, the RF power amplifier 207 may include one or more RF power amplifiers.

[0078] In some embodiments, the MRI device 110 may further include an object positioning system (not shown). The object positioning system may include an object holder and a transport device. The object may be placed on the object holder and positioned within the aperture of the main magnet 201 by the transport device.

[0079] MRI systems (e.g., MRI system 100 disclosed herein) typically acquire internal images of a patient's specific region of interest (ROI) for purposes such as diagnosis or treatment. An MRI system includes a main magnet assembly (e.g., main magnet 201) for providing a strong, uniform main magnetic field to align the 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 can 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 alignment with the main magnetic field, thereby emitting an echo signal. The echo signal is received and processed to form an MRI image. T1 relaxation can be the process of increasing / recovering the net magnetization to its initial maximum value parallel to the main magnetic field. T1 can be the time constant for longitudinal (e.g., along the main magnetic field) magnetization regeneration. T2 relaxation can be the process of decaying or dephasing of the transverse component of magnetization. T2 can be the time constant for transverse magnetization decay / dephasing.

[0080] 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 object. Therefore, in order to image a specific body part of the patient, magnetic field gradients Gx, Gy, and Gz in the x, y, and z directions (e.g., generated by gradient coil 202) can be superimposed on the uniform magnetic field. These magnetic field gradients have specific time, frequency, and phase, such that the radio frequency excitation signal excites hydrogen atoms in the desired sheet of the patient's body, and based on the position of the hydrogen atoms in the "image sheet," unique phase and frequency information is encoded in the echo signal.

[0081] 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 MRI imaging protocol being used. Protocols can be designed for one or more tissues, diseases, and / or clinical scenarios to be imaged. Protocols may include a number of pulse sequences targeting different planes and / or with different parameters. Pulse sequences may include rotating echo sequences, gradient echo sequences, diffusion sequences, inversion recovery sequences, etc., or any combination thereof. For example, rotating echo sequences may include fast rotating echo (FSE) pulse sequences, turbine rotating echo (TSE) pulse sequences, fast capture with relaxation enhancement (RARE) pulse sequences, half-Fourier acquisition single-excitation turbine rotating echo (HASTE) pulse sequences, turbine gradient rotating echo (TGSE) pulse sequences, etc., or any combination thereof. As yet another example, gradient echo sequences may include equilibrium steady-state free precession (bSSFP) pulse sequences, scrambled gradient echo (GRE) pulse sequences, echo-plane imaging (EPI) pulse sequences, steady-state free precession (SSFP), etc., or any combination thereof. The protocol may also include information regarding image contrast and / or ratio, ROI, slice thickness, imaging type (e.g., T1-weighted imaging, T2-weighted imaging, proton density-weighted imaging, etc.), T1, T2, echo type (rotational echo, fast rotational echo (FSE), fast recovery FSE, single-shot FSE, gradient echo, fast imaging with steady-state processing, etc.), flip angle, acquisition time (TA), echo time (TE), repetition time (TR), echo sequence length (ETL), number of phases, number of excitations (NEX), inversion time, bandwidth (e.g., RF receiver bandwidth, RF transmitter bandwidth, etc.), or any combination thereof. For each MRI scan, the generated echo signals can be digitized and processed to reconstruct an image according to the MRI imaging protocol used.

[0082] Figure 3This is a schematic diagram of the hardware and / or software components of an exemplary computing device 300 according to some embodiments of this application. In some embodiments, one or more components of the MRI 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 as one or more components of the computing device 300.

[0083] 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 herein. Computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform the specific functions described herein. For example, processor 310 may process image data of objects obtained from MRI device 110, storage device 130, terminal 140, and / or any other component of MRI system 100.

[0084] 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.

[0085] 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 disclosed in this application that are executed by one processor may also be executed jointly or separately 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 executed jointly or separately 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).

[0086] Storage device 320 can store data / information obtained from MRI device 110, storage device 130, terminal 140, and / or any other component of MRI 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 may include dynamic RAM (DRAM), dual date rate synchronous dynamic RAM (DDR SDRAM), static RAM (SRAM), thyristor RAM (T-RAM), and zero-capacitance RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), an optical disc ROM (CD-ROM), and a digital multifunction disk ROM, etc. In some embodiments, the storage device 320 may store one or more programs and / or instructions to perform the exemplary methods described in this application.

[0087] Input / output 330 can input and / or output signals, data, information, etc. In some embodiments, input / output 330 allows a user to interact 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 a display device, speaker, printer, projector, etc., or any combination thereof. Examples of display devices may include a liquid crystal display (LCD), a light-emitting diode (LED) based display, a flat panel display, a curved screen, a television device, a cathode ray tube (CRT), a touchscreen, etc., or any combination thereof.

[0088] 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 MRI system 100 (e.g., MRI device 110, storage device 130, and / or terminal 140). This connection can be a wired connection, a wireless connection, any other communication connection that enables data transmission and / or reception, and / or combinations thereof. Wired connections can include, for example, cables, optical fibers, telephone lines, etc., or combinations thereof. Wireless connections can include, for example, Bluetooth connections, Wi-Fi connections, WiMax connections, WLAN connections, ZigBee connections, mobile network connections (e.g., 3G, 4G, 5G, etc.), etc., or any combination thereof. In some embodiments, communication port 340 can be and / or include standardized communication ports, such as RS232, RS485, etc. In some embodiments, communication port 340 can be a specially designed communication port. For example, communication port 340 can be designed according to the Digital Imaging and Medical Communications (DICOM) protocol.

[0089] Figure 4 This is a schematic diagram of the hardware and / or software components of an exemplary mobile device 400 according to some embodiments of this application. In some embodiments, one or more components of the MRI 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.

[0090] like Figure 4 As 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) and one or more applications 480 may be loaded from storage 490 into memory 460 for execution by CPU 440. Application 480 may include a browser or any other suitable mobile application for receiving and presenting information related to the MRI system 100. User interaction with the information flow may be achieved through input / output 450 and provided via network 150 to processing device 120 and / or other components of the MRI system 100.

[0091] To implement the various modules, units, and functions described in this specification, a computer hardware platform may be used as the hardware platform for one or more components described in this specification. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of workstation or terminal. If the computer is properly programmed, it may also be used as a server.

[0092] 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 storage 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 application programs / instructions. For example, when the processing device 120 executes an application / instruction set, the module may be part of the processing device 120.

[0093] The acquisition module 510 can be configured to acquire data and / or information associated with the MRI system 100. The data and / or information associated with the MRI system 100 may include at least two target k-space datasets, the coil sensitivity of each of at least two coils, the point spread function, the target function, the target image, etc., or any combination thereof. For example, the acquisition module 510 may acquire at least two target k-space datasets. As another example, the acquisition module 510 may acquire the sensitivity of each of at least two coils. As yet another example, the acquisition module 510 may acquire the point spread function corresponding to a helical trajectory. In some embodiments, the acquisition module 510 may acquire data and / or information associated with the MRI system 100 via network 150 from one or more components of the MRI system 100 (e.g., MRI device 110, storage device 130, terminal 140).

[0094] The determination module 520 can be configured to determine data and / or information associated with the MRI system 100. In some embodiments, the determination module 520 can determine an objective function. For example, the determination module 520 can determine an operator based on at least two coil sensitivities, a point spread function, and a mask. The determination module 520 can determine an aliased image based on at least two target k-space datasets, at least two coil sensitivities, and a point spread function. The determination module 520 can determine an objective function based on an operator and an aliased image. Further description of determining the objective function can be found elsewhere in this specification (e.g., Figure 6 , 7The description of the target image can be found elsewhere in this specification. In some embodiments, the determining module 520 may generate a target image based on a target function. Further description of generating the target image can be found elsewhere in this specification (e.g., Figure 6 and 7 (and its description) were found.

[0095] Storage module 530 can be configured to store data and / or information associated with MRI system 100. For example, storage module 530 can store at least two target k-space datasets, coil sensitivity of each coil, point spread function, target function, target image, etc., or any combination thereof.

[0096] 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. Those skilled in the art can make various changes and modifications based on the description in this application. However, these changes and modifications do not depart from the scope of this application. In some embodiments, one or more modules may be combined into a single module. For example, the acquisition module 510 and the determination module 520 may be combined into a single module. In some embodiments, one or more modules may be added to or omitted from the processing device 120. For example, the storage module 530 may be omitted.

[0097] Figure 6 This is a flowchart illustrating an exemplary process for determining a 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 MRI 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 of the computing device 300 shown is, for example Figure 4 The CPU 440 of the mobile device 400 shown calls and / or executes the procedure. The operation of the procedure shown below is for illustrative purposes only. In some embodiments, the procedure 600 may be accomplished using one or more additional operations not described and / or by omitting one or more operations discussed. Additionally, Figure 6 The order of operations in process 600 shown and the following description are not intended to be limiting.

[0098] In 610, the processing device 120 (e.g., acquisition module 510) can acquire at least two target k-space datasets. The at least two target k-space datasets can be acquired by filling the k-space with target magnetic resonance signals acquired by at least two coils of an MRI device along a helical trajectory. For example, one target k-space dataset can correspond to a target magnetic resonance signal acquired by one of the coils of at least two coils of an MRI device (e.g., MRI device 110). The target k-space dataset can be acquired by filling the k-space with a target magnetic resonance signal acquired along a helical trajectory.

[0099] In some embodiments, the processing device 120 may cause an MRI apparatus to apply a magnetic resonance pulse sequence to scan an object. Exemplary magnetic resonance pulse sequences may include spin echo (SE) pulse sequences, gradient echo (GRE) pulse sequences, inversion recovery (IR) pulse sequences, multi-echo pulse sequences, etc. Exemplary objects may include a patient, a specific organ of a patient, an artificial object, etc. The MRI apparatus may include at least two coils configured to detect a target magnetic resonance signal (e.g., multiple echo signals) excited by the magnetic resonance pulse sequence during scanning. A target k-space dataset may correspond to a target magnetic resonance signal acquired by one of the at least two coils. The processing device 120 may determine each target k-space dataset based on the target magnetic resonance signal detected by the corresponding coil. For example, the processing device 120 may fill at least two regions of a k-space (e.g., a k-space matrix) with the target magnetic resonance signal to generate a target k-space dataset.

[0100] In some embodiments, the target k-space dataset may be undersampled, meaning that a portion of the multiple data points in k-space may be obtained by sampling the target magnetic resonance signal, while the remainder of the multiple data points in k-space may be obtained by assigning one or more values ​​not sampled from the target magnetic resonance signal. For illustrative purposes, the processing device 120 may assign one or more initial values ​​(e.g., zero) to the unsampled data points. In some embodiments, the target k-space dataset may include two-dimensional (2D) k-space data, 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, each target k-space dataset may be a 256×256×256 number matrix.

[0101] In some embodiments, the processing device 120 may fill the corresponding positions in k-space with the target magnetic resonance signal based on a sampling pattern or trajectory. In some embodiments, the sampling trajectory may include a Cartesian trajectory, a non-Cartesian trajectory, etc. Exemplary non-Cartesian trajectories may include spiral trajectories, radial trajectories, zigzag trajectories, propeller trajectories, and helical trajectories. In some embodiments, the sampling trajectory may include a uniform sampling trajectory, a non-uniform sampling trajectory, etc. Exemplary non-uniform sampling trajectories may include variable-density sampling trajectories and random sampling trajectories. For example, a target k-space dataset can be obtained by filling the corresponding positions in k-space with the target magnetic resonance signal obtained along a uniform spiral trajectory, a variable-density spiral trajectory, a random spiral trajectory, etc. Further description of the sampling trajectory can be found elsewhere in this specification (e.g., Figure 8A , 8B Found (9A and 9B and their descriptions).

[0102] In some embodiments, the magnetic resonance pulse sequence used to scan the object can be designed based on a sampling pattern or trajectory. For example, the target magnetic resonance signal can be acquired using wave acquisition techniques as described in other parts of this specification. In wave acquisition techniques, during each readout, an additional sinusoidal wave gradient g is applied along the Y and Z axes in coordinate system 160. y and g z The sinusoidal wave gradient effect can lead to helical trajectory sampling. For details on wave acquisition technology, please refer to "Berkin Bilgic, et al., Wave-CAIPI for highly accelerated 3D imaging, Magn Reson Med. 2015 June; 73(6):2152-2162", which is incorporated herein by reference.

[0103] In some embodiments, the processing device 120 may acquire a target k-space dataset based on a target magnetic resonance signal and a mask. For example, for each of at least two target k-space datasets, the processing device 120 may acquire a target magnetic resonance signal acquired by one of at least two coils of an MRI device. The processing device 120 may acquire a mask. Further, the processing device 120 may acquire a target k-space dataset based on the target magnetic resonance signal and the mask. In some embodiments, the mask may include a two-dimensional matrix, a three-dimensional matrix, etc. Each position in the mask may correspond to a data point in k-space. For illustrative purposes, the mask may include a binary matrix (e.g., a two-dimensional binary matrix, a three-dimensional binary matrix), where "1" indicates that the corresponding data point in k-space has been sampled, and "0" indicates that the corresponding data point in k-space has not been sampled. In some embodiments, at least two masks corresponding to at least two sampling modes or trajectories may be stored in a storage device (e.g., storage device 130) of the MRI system 100. For example, the at least two masks may include Cartesian sampling masks, spiral sampling masks, radial sampling masks, spiral sampling masks (e.g., variable-density spiral sampling masks, uniform spiral sampling masks, random spiral sampling masks), etc., or any combination thereof. The processing device 120 may determine the sampling pattern or trajectory based on actual needs (e.g., the type of MRI equipment, the quality requirements of the reconstructed image). The processing device 120 may select a mask corresponding to the determined sampling pattern or trajectory from at least two masks stored in a storage device. In some embodiments, the mask corresponding to each of the at least two target k-space datasets may be the same.

[0104] In some embodiments, processing device 120 may acquire at least two target k-space datasets from one or more components of MRI system 100 (e.g., MRI device 110, terminal 140, and / or storage device 130) or external storage devices via network 150. For example, MRI device 110 may send at least two target k-space datasets to storage device 130 or any other storage device for storage. Processing device 120 may acquire the target k-space datasets from storage device 130 or any other storage device. As another example, processing device 120 may acquire at least two target k-space datasets directly from MRI device 110.

[0105] In 620, the processing device 120 (e.g., acquisition module 510) can acquire the sensitivity of each of at least two coils.

[0106] In some embodiments, a coil may correspond to a coil sensitivity. As used herein, a coil sensitivity refers to the degree of response of a coil to receiving an input signal (e.g., a magnetic resonance signal). In some embodiments, a coil sensitivity may represent spatial brightness variations and / or phase variations introduced when an image is acquired through the coil. In some embodiments, a coil sensitivity may be a complex number, with a modulus between 0 and 1. In some embodiments, the coil sensitivity of each of at least two coils in an MRI device may be the same or different.

[0107] In some embodiments, the coil sensitivity can be determined based on a coil sensitivity algorithm. Exemplary coil sensitivity algorithms may include the sum of squares (SOS) algorithm, the signal parameter estimation algorithm based on rotational invariance techniques (ESPIRiT), etc.

[0108] In some embodiments, processing device 120 may acquire at least two coil sensitivities of at least two coils from one or more components of MRI system 100 (e.g., MRI device 110, terminal 140, and / or storage device 130) or external storage device via network 150. For example, the at least two coil sensitivities of at least two coils may be stored in storage device 130 or any other storage device. Processing device 120 may acquire the at least two coil sensitivities of at least two coils from storage device 130 or any other storage device.

[0109] In 630, the processing device 120 (e.g., acquisition module 510) can acquire the point spread function corresponding to the spiral trajectory.

[0110] In some embodiments, as described in conjunction with operation 610, the point spread function can be used to characterize wave gradient effects in wave acquisition techniques. For example, the point spread function can characterize the point (voxel or pixel) spread effect across the entire field of view (FOV) of the reconstructed image caused by a helical trajectory.

[0111] In some embodiments, the processing device 120 may generate a first image based on a first set of k-space data. The first set of k-space data may be obtained by filling the k-space with a reference magnetic resonance signal acquired along a reference trajectory. The k-space may be in a Cartesian coordinate system. In some embodiments, the reference magnetic resonance signal may be the same as the target magnetic resonance signal. In some embodiments, the reference magnetic resonance signal may be different from the target magnetic resonance signal. For example, the reference magnetic resonance signal and the target magnetic resonance signal may be MRI scan data acquired by an MRI device (e.g., MRI device 110) at different time periods during an MRI scan of an object (e.g., a patient). The reference trajectory may include a Cartesian trajectory. The processing device 120 may generate a second image based on a second set of k-space data. The second set of k-space data may be obtained by filling the k-space with a reference magnetic resonance signal acquired along a helical trajectory. In some embodiments, the processing device 120 may generate the first image and the second image based on one or more image reconstruction techniques. Exemplary reconstruction techniques may include Fourier reconstruction, inverse Fourier reconstruction, constrained image reconstruction, regularized image reconstruction in parallel MRI, compressed sensing (CS)-parallel imaging (PI) reconstruction, etc., or any combination thereof. Exemplary CS-PI reconstruction techniques may include sparse sensitivity coding (SENSE), l1-iterative self-consistent parallel imaging reconstruction (SPIRIT), CS-SENSE, CS-generalized automatic calibration partial parallel acquisition (GRAPPA), or any combination thereof.

[0112] Further, the processing device 120 can determine a point spread function based on the first image and the second image. In some embodiments, each pixel (or voxel) in the MRI image (e.g., the first image, the second image) may include phase information and amplitude information reflecting the interaction between the object and the magnetic field generated by the MRI device. The processing device 120 can determine the point spread function based on the phase information associated with the first image and the second image. In some embodiments, the processing device 120 can determine the phase difference between the first image and the second image as the point spread function. For example, the processing device 120 can determine a first phase matrix of the first image based on the values ​​of pixels (or voxels) in the first image. The values ​​in the first phase matrix of the first image may be the phase values ​​of the corresponding pixels (or voxels) in the first image. The processing device 120 can determine a second phase matrix of the second image based on the values ​​of pixels (or voxels) in the second image. The values ​​in the second phase matrix of the second image may be the phase values ​​of the corresponding pixels (or voxels) in the second image. The processing device 120 can determine the phase difference matrix between the first phase matrix of the first image and the second phase matrix of the second image and use it as the point spread function.

[0113] In 640, processing device 120 (e.g., determination module 520) can generate a target image based on a target function. For example, the target image can be determined by iteratively solving the target function. The target function can be determined based on an operator, at least two target k-space datasets, at least two coil sensitivities, and a point spread function. The operator can represent the mapping relationship between the target image and at least two target k-space datasets.

[0114] An objective function can be used to determine a target image. The target image can be set as the independent variable in the objective function. In some embodiments, the processing device 120 can determine the objective function based on an operator and an aliased image. As used herein, an aliased image refers to an image directly transformed to the image domain based on undersampled k-space data (e.g., a target k-space dataset). For example, an aliased image can be obtained by performing an inverse Fourier transform on the undersampled k-space data (e.g., the target k-space dataset). In some embodiments, an aliased image may include aliasing artifacts (i.e., aliased pixels). As used herein, a target image (also referred to as a dealiased image) refers to an image with reduced aliasing or no aliasing.

[0115] For example, processing device 120 can determine an operator based on at least two coil sensitivities, a point spread function, and a mask. Processing device 120 can determine an aliased image based on at least two target k-space datasets, at least two coil sensitivities, and a point spread function. Processing device 120 can determine an objective function based on the operator and the aliased image. Further, processing device 120 can determine a target image based on the objective function. Further descriptions of the determination of aliased images, objective functions, and target images can be found elsewhere in this specification (e.g., ...). Figure 7 (and its description) were found.

[0116] In some embodiments, the processing device 120 can acquire at least two target k-space datasets by filling the k-space with target magnetic resonance signals acquired by at least two coils of an MRI device along a helical trajectory, as described in conjunction with operation 610. The processing device 120 can determine aliased images associated with at least two coils based on the at least two target k-space datasets. For example, the processing device 120 can determine at least two intermediate images corresponding to at least two coils based on the at least two target k-space datasets, the sensitivities of at least two coils, and the point spread function of the corresponding helical trajectory. The processing device 120 can generate aliased images based on the at least two intermediate images corresponding to at least two coils. The processing device 120 can determine a target image based on the aliased image and a mapping relationship (i.e., an operator) between the target image and the aliased image. In some embodiments, the processing device 120 can determine the mapping relationship (i.e., an operator) based on the sensitivities of at least two coils, the point spread function of the corresponding helical trajectory, and a mask of the corresponding helical trajectory. In some embodiments, the processing device 120 can determine an objective function based on the aliased image and the mapping relationship. The processing device 120 can generate a target image by iteratively solving the objective function. Further descriptions of aliased images, objective functions, and objective image determination can be found elsewhere in this specification (e.g., Figure 7 (and its description) were found.

[0117] 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.

[0118] Figure 7 This is a flowchart illustrating an exemplary process for determining an objective function according to some embodiments of this application. In some embodiments, process 700 may be performed in... Figure 1 This is implemented in the MRI system 100 shown. For example, process 700 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 of the computing device 300 shown is, for example Figure 4 The CPU 440 of the mobile device 400 shown calls and / or executes the procedure. The operation of the procedure shown below is for illustrative purposes only. In some embodiments, the procedure 700 may be accomplished using one or more additional operations not described and / or by omitting one or more operations discussed. Additionally, Figure 7 The order of operations in process 700 shown and the following description are not intended to be limiting. In some embodiments, Figure 6 At least a portion of the operation 640 shown can be performed according to process 700.

[0119] In 710, the processing device 120 (e.g., determination module 520) can determine the operator based on at least two coil sensitivities, point spread function, and mask.

[0120] An operator can describe the undersampling process of k-space data (e.g., fully sampled k-space data) of a target image to generate undersampled k-space data (e.g., a target k-space dataset) for reconstructing the aliased image. For example, an operator can describe the mapping between the target image and the undersampled k-space data (e.g., the target k-space dataset). As another example, an operator can describe the mapping between the target image and the aliased image.

[0121] In some embodiments, for each of at least two coils in an MRI device, the processing device 120 may generate a first candidate image associated with the coil based on the coil sensitivity of the coil and a target image. As used herein, "coil-associated image" refers to an image generated based on magnetic resonance signals detected by the coil (e.g., at least two echo signals). For example, the processing device 120 may generate the first candidate image associated with the coil by multiplying the coil sensitivity of the coil by the target image.

[0122] Processing device 120 can determine a first candidate k-space dataset based on a first candidate image, a point spread function, and a mask. The first candidate k-space dataset may correspond to undersampled k-space data (e.g., a target k-space dataset). Taking a target image and the first candidate image as three-dimensional images as an example, processing device 120 can generate a first processed candidate image by performing a first Fourier transform on the first candidate image along a first direction in k-space (e.g., the Kx direction). Processing device 120 can generate a second processed candidate image by multiplying the point spread function with the first processed candidate image. Since the point spread function describes the wave gradient effect (i.e., each readout line in the target image is convolved with the point spread function to generate the acquired aliased image), the wave gradient effect can be reflected by multiplying the point spread function with the first processed candidate image. Processing device 120 can generate a third processed candidate image by performing a second Fourier transform on the second processed candidate image along a second direction in k-space (e.g., the Ky direction). The processing device 120 can generate a second candidate k-space dataset by performing a third Fourier transform on the third-processed candidate image along a third direction (e.g., the Kz direction) in k-space. The second candidate k-space dataset can correspond to the fully sampled k-space data corresponding to the target image. The processing device 120 can generate a first candidate k-space dataset based on the second candidate k-space dataset and a mask. For example, the processing device 120 can generate the first candidate k-space dataset by multiplying the second candidate k-space dataset by the mask. In this specification, the first-processed candidate image, the second-processed candidate image, and / or the third-processed candidate image can also be referred to as temporary data or intermediate results.

[0123] Then, the processing device 120 can transform the first candidate k-space dataset from the frequency domain to the image domain. In some embodiments, the processing device 120 can generate a second candidate image associated with the coil by processing the first candidate k-space dataset based on the point spread function and the coil sensitivity of the coil. For example, the processing device 120 can generate a first processed candidate k-space dataset by performing a first inverse Fourier transform on the first candidate k-space dataset along a third direction in k-space (e.g., the Kz direction). The processing device 120 can generate a second processed candidate k-space dataset by performing a second inverse Fourier transform on the first processed candidate k-space dataset along a second direction in k-space (e.g., the Ky direction). The processing device 120 can generate a third processed candidate k-space dataset by multiplying the second processed candidate k-space dataset by the conjugate of the point spread function. In some embodiments, the conjugate of the point spread function can be the conjugate matrix of the point spread function. For example, the conjugate matrix of a particular matrix (e.g., the point spread function) can be obtained by obtaining the complex conjugate of each element of the particular matrix (e.g., the point spread function). The wave gradient effect can be eliminated by multiplying the second processed candidate k-space dataset with the conjugate of the point spread function. The processing device 120 can generate a third candidate image by performing a third inverse Fourier transform on the third processed candidate k-space dataset along a first direction in k-space (e.g., the Kx direction). The processing device 120 can generate a second candidate image by multiplying the third candidate image with the conjugate of the coil sensitivity of the coil. In some embodiments, the conjugate of the coil sensitivity can be the conjugate matrix of the coil sensitivity. By multiplying the third candidate image with the conjugate of the coil sensitivity of the coil, the influence of the coil sensitivity of at least two coils can be eliminated. In this specification, the first processed candidate k-space dataset, the second processed candidate k-space dataset, and / or the third processed candidate k-space dataset may also be referred to as temporary data or intermediate results.

[0124] Processing device 120 can determine an operator based on at least two second candidate images associated with at least two coils. In some embodiments, processing device 120 can determine the operator by combining at least two second candidate images associated with at least two coils. For example, if the at least two second candidate images are represented as A1, A2, A3, ..., An, the operator can be determined as (A1+A2+A3, ..., +An). For illustrative purposes, memory spaces “Buffer” and “Res” can be established. “Buffer” and “Res” can be used to store the initial, intermediate, or final results of the operator. The size of “Buffer” and the size of “Res” can be the same as the size of the target image. Initial values, such as zero, can be assigned to elements in “Buffer” and elements in “Res”. Different elements in “Buffer” can be assigned the same initial value or different initial values. For example, any suitable value can be assigned to elements in “Buffer”. Different elements in “Res” can be assigned the same initial value. For example, all elements in “Res” can be assigned zero. The operator can be determined according to the following computer code:

[0125]

[0126] When the iteration or loop completes, the "Buffer" can be reset (e.g., the elements in the "Buffer" are assigned an initial value of zero, and "Res" is returned as the result). Here, Res refers to the output result after the operator operation; Nc refers to the number of coils (or count); Im refers to the image input to the operator; S... j This refers to the coil sensitivity of the j-th coil; Fx refers to the first Fourier transform operation along the Kx direction; Fy refers to the second Fourier transform operation along the Ky direction; Fz refers to the third Fourier transform operation along the Kz direction; iFx refers to the first inverse Fourier transform operation along the Kx direction; iFy refers to the second inverse Fourier transform operation along the Ky direction; iFz refers to the third inverse Fourier transform operation along the Kz direction; Psfs refers to the point spread function; conj refers to the conjugate operation; the symbol "*" refers to point-to-point multiplication of a matrix or array.

[0127] In 720, the processing device 120 (e.g., determination module 520) can determine the aliased image based on at least two target k-space datasets, at least two coil sensitivities, and point spread functions.

[0128] Taking a target k-space dataset as three-dimensional k-space data as an example, for each of at least two target k-space datasets, processing device 120 can generate a first processed target k-space dataset by performing a first inverse Fourier transform on the target k-space dataset along a third direction in k-space (e.g., the Kz direction). Processing device 120 can generate a second processed target k-space dataset by performing a second inverse Fourier transform on the first processed target k-space dataset along a second direction in k-space (e.g., the Ky direction). Processing device 120 can generate a third processed target k-space dataset by multiplying the second processed target k-space dataset by the conjugate of the point spread function. Processing device 120 can generate a first intermediate image related to the coil corresponding to the target k-space dataset by performing a third inverse Fourier transform on the third processed target k-space dataset along a first direction in k-space (e.g., the Kx direction). Processing device 120 can generate a second intermediate image related to the coil based on the coil sensitivity and the first intermediate image. For example, the processing device 120 can generate a second intermediate image associated with the coil by multiplying the first intermediate image by the conjugate of the coil sensitivity of the coil. In this specification, the first processed target k-space dataset, the second processed target k-space dataset, and / or the third processed target k-space dataset may also be referred to as temporary data or intermediate results.

[0129] Processing device 120 can generate an aliased image based on at least two second intermediate images associated with at least two coils corresponding to at least two target k-space datasets. In some embodiments, processing device 120 can generate an aliased image by combining at least two second intermediate images associated with at least two coils corresponding to at least two target k-space datasets. By way of example only, processing device 120 can determine the aliased image as the average image of at least two second intermediate images. Processing device 120 can determine the average image of at least two second intermediate images based on the pixel value of each of at least two pixels in at least two second intermediate images. For example, processing device 120 can determine the average pixel value of at least two corresponding pixels in at least two second intermediate images. As used in this specification, pixels in at least two images (e.g., at least two second intermediate images) can be considered corresponding pixels when they correspond to the same physical location of an object or the space in which the object is located. Processing device 120 can determine the average image based on at least two average pixel values.

[0130] For illustrative purposes, memory spaces "Buffer" and "b" can be created. "Buffer" and "b" can be used to store the initial, intermediate, or final result of the aliased image. The size of "Buffer" and "b" can be the same as the size of the target image. Initial values, such as zero, can be assigned to elements in "Buffer" and elements in "b". Different elements in "Buffer" can be assigned the same or different initial values. For example, any suitable value can be assigned to elements in "Buffer". Different elements in "b" can be assigned the same initial value. For example, all elements in "b" can be assigned zero. The aliased image can be determined according to the following computer code:

[0131]

[0132] When the iteration or loop completes, the "Buffer" can be reset (e.g., by assigning initial values ​​of zero to the elements of the "Buffer" and returning "b" as the result). Where y j This refers to the target k-space dataset; b refers to the aliased image; Nc refers to the number (or count) of coils; S j iFx refers to the coil sensitivity of the j-th coil; iFy refers to the first inverse Fourier transform operation along the Kx direction; iFz refers to the second inverse Fourier transform operation along the Ky direction; iFz refers to the third inverse Fourier transform operation along the Kz direction; Psfs refers to the point spread function; conj refers to the conjugate operation; the symbol "*" refers to the point-to-point multiplication of a matrix or array.

[0133] According to some embodiments of this specification, determining the operator and aliased image based on the above algorithm can improve the calculation speed of the objective function and save storage space.

[0134] In 730, the processing device 120 (e.g., determination module 520) can determine the objective function based on the operator and the aliased image.

[0135] The target image can be set as the independent variable in the objective function. For example, the processing device 120 can determine the objective function based on the operator and the aliased image according to equation (4):

[0136] optA(x)=b,(4)

[0137] Where optA(x) refers to the operator; b refers to the aliased image; and x refers to the target image.

[0138] The processing device 120 can determine the target image based on an objective function. In some embodiments, the processing device 120 can determine the target image based on an iterative algorithm. For example, the processing device 120 can determine the target image based on an objective function using a conjugate gradient algorithm.

[0139] For illustrative purposes, memory spaces "r0" and "p0" can be established. The size of "r0" and "p0" can be the same as the size of the target image. Initial values, such as zero, can be assigned to the elements in "r0" and "p0". The target image can be determined according to the following computer code:

[0140] r0 = b - optA(x)

[0141] p0=r0

[0142] k = 0

[0143] repeat:

[0144]

[0145] x k+1 =x k +α k p k

[0146] r k+1 =r k -α k optA(p k )

[0147] If r k+1 If it is small enough, exit the loop, and

[0148]

[0149] p k+1 =r k+1 +β k p k

[0150] k = k + 1

[0151] end.

[0152] When the iteration is complete, "r0" and "p0" can be reset (e.g., by assigning initial values ​​of zero to the elements of "r0" and "p0" and returning "x"). k+1 "As a result. Where x..." k+1 This refers to the target image searched in the (k+1)th step; p0 and r0 refer to the negative gradients of the operator (i.e., optA) at x = x0; α k This refers to the step size; r k This refers to the residual at the k-th search step, which means that at x k The negative gradient of the operator at point (i.e., optA); p k This refers to the search direction in the k-th step; β k It refers to p kThe step size; k refers to the number of iterations (or count); It refers to r k The conjugate transpose of; It refers to p k The conjugate transpose of .

[0153] 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 of this application. However, these changes and modifications do not depart from the scope of this application. In some embodiments, the target image may be a two-dimensional image, and the target k-space dataset may be two-dimensional k-space data. The processing device 120 may determine the objective function according to process 700 by removing operations performed along a third direction in the k-space (e.g., the Kz direction).

[0154] To better understand the establishment of the objective function, an example is used to illustrate the idea. Typically, when solving the equation Y = FSX in MRI, where Y refers to k-space data, F refers to the Fourier transform operation, S refers to the point spread function, and x refers to the target image, both the left and right sides of the equation can be multiplied by the transpose of FS (i.e., S′F′), transforming the equation into S′F′Y = S′F′FSX. Therefore, the operator determined in operation 710 can be considered as S′F′FS(X) in the equation S′F′Y = S′F′FSX, and the aliased image determined in operation 720 can be considered as S′F′Y in the equation S′F′Y = S′F′FSX.

[0155] Figure 8A This is a schematic diagram illustrating an exemplary Cartesian sampling pattern according to some embodiments of this application. Figure 8B This is a schematic diagram of an exemplary spiral sampling pattern according to some embodiments of this application.

[0156] like Figure 8A and 8B As shown, Kx refers to the frequency encoding direction, Ky refers to the Y-axis phase encoding direction, and Kz refers to the Z-axis phase encoding direction. Figure 8A As shown, the Cartesian sampling pattern includes at least two straight-line trajectories along the Kx direction. (As...) Figure 8B As shown, the spiral sampling pattern includes at least two spiral tracks along the Kx direction. The central axis of each spiral track (e.g., the central axis A of spiral track 810) is parallel to the Kx direction.

[0157] In some embodiments, if the spiral sampling pattern is a uniform spiral trajectory, then at least two spiral trajectories can be uniformly distributed in k-space. For example, the distance between the central axes of adjacent spiral trajectories can be the same. In some embodiments, if the spiral sampling pattern is a random spiral trajectory, then at least two spiral trajectories can be randomly distributed in k-space. For example, the distance between the central axes of adjacent spiral trajectories can be different. In some embodiments, if the spiral sampling pattern is a variable-density spiral trajectory, then at least two spiral trajectories can be distributed with variable density in k-space. For example, the sampling density can vary with the k-space location in the Ky and / or Kz directions. Figure 9A As shown, the sampling density in the central region of k-space can be relatively high, while the sampling density in the outer region of k-space can be relatively low.

[0158] Figure 9A This is a schematic diagram illustrating an exemplary variable density sampling pattern according to some embodiments of this application. Figure 9B This is a schematic diagram of an exemplary CAIPIRINHA sampling mode according to some embodiments of this application.

[0159] like Figure 9A and 9B As shown, Ky represents the Y-axis phase encoding direction, Kz represents the Z-axis phase encoding direction, and the white dots represent sampled data points in k-space. Figure 9A As shown, for the variable density sampling mode, the sampling density varies with the k-space location in both the Ky and Kz directions. As illustrated, the sampling density in the central region of k-space can be relatively high, while the sampling density in the peripheral region can be relatively low. For example, the central region of k-space can be fully sampled, while the peripheral region can be undersampled. In the Kx direction ( Figure 9A (Not shown in the image) The k-space can be fully sampled or undersampled. For example... Figure 9B As shown, for the CAIPIRINHA sampling mode, the k-space is undersampled in both the Ky and Kz directions. In the Kx direction ( Figure 9B (not shown in the image) can be fully sampled or undersampled in the k-space.

[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] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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 method for magnetic resonance imaging (MRI) implemented on a computing device having at least one processor and at least one storage device, characterized in that, The method includes: By filling the k-space with target magnetic resonance signals acquired by at least two coils of an MRI device along a progressive spiral trajectory, at least two target k-space datasets are obtained, each target k-space dataset corresponding to the target magnetic resonance signal acquired by one of the at least two coils of the MRI device, including: The at least two target k-space datasets are obtained by filling the k-space with the target magnetic resonance signals acquired by the at least two coils of the MRI device along random progressive spiral trajectories, wherein the distance between the central axes of adjacent random progressive spiral trajectories is different; For each of the at least two target k-space datasets Acquire the target magnetic resonance signal obtained by the coil of at least two coils of the MRI device; Get the mask; The target k-space dataset is obtained based on the target magnetic resonance signal and the mask; Obtain the coil sensitivity of each of the at least two coils; Obtaining the point spread function corresponding to the random progressive spiral trajectory includes: A first image is generated based on a first set of k-space data, wherein the first set of k-space data is obtained by filling the k-space with a reference magnetic resonance signal acquired along a reference trajectory; A second image is generated based on a second set of k-space data, wherein the second set of k-space data is obtained by filling the k-space with the reference magnetic resonance signal acquired along the random progressive spiral trajectory; The point spread function is determined based on the first image and the second image, wherein each pixel in the first image and the second image includes phase information and amplitude information reflecting the interaction between the object and the magnetic field generated by the MRI device; and A target image is generated based on an objective function, wherein the independent variable in the objective function is the target image. The objective function is determined through a process based on the operator, the at least two target k-space datasets, the at least two coil sensitivities, and the point spread function, the process including: The operator is determined based on the sensitivity of the at least two coils, the point spread function, and the mask; The aliased image is determined based on the at least two target k-space datasets, the at least two coil sensitivities, and the point spread function; The objective function is determined based on the operator and the aliased image; and The operator represents the mapping relationship between the target image and the at least two target k-space datasets.

2. The method according to claim 1, characterized in that, The at least two target k-space datasets are obtained by filling the target magnetic resonance signal into the k-space with varying density.

3. The method according to claim 1, characterized in that, The k-space is three-dimensional, the target k-space dataset is three-dimensional, and the step of determining the aliased image based on the at least two target k-space datasets, the at least two coil sensitivities, and the point spread function includes: For each of the at least two target k-space datasets A first processed target k-space dataset is generated by performing a first inverse Fourier transform on the target k-space dataset along a third direction of k-space. A second processed target k-space dataset is generated by performing a second inverse Fourier transform on the first processed target k-space dataset along the second direction of k-space. The third processed target k-space dataset is generated by multiplying the second processed target k-space dataset with the conjugate of the point spread function. A first intermediate image related to the coil corresponding to the target k-space dataset is generated by performing a third inverse Fourier transform on the third-processed target k-space dataset along a first direction in k-space. Based on the coil sensitivity and the first intermediate image, a second intermediate image related to the coil is generated; and The aliased image is generated based on at least two of the second intermediate images.

4. The method according to claim 1, characterized in that, Determining the operator based on the sensitivity of the at least two coils, the point spread function, and the mask includes: For each of the at least two coils of the MRI device Based on the coil sensitivity of the coil and the target image, a first candidate image related to the coil is generated; A first candidate k-space dataset is determined based on the first candidate image, the point spread function, and the mask. Based on the point spread function and the coil sensitivity of the coil, a second candidate image related to the coil is generated by processing the first candidate k-space dataset; The operator is determined based on at least two second candidate images associated with the at least two coils.

5. The method according to claim 4, characterized in that, The k-space is a three-dimensional space, and the first candidate image is a three-dimensional image; the step of determining the first candidate k-space dataset based on the first candidate image, the point spread function, and the mask includes: A first Fourier transform is performed on the first candidate image along a first direction in the k-space to generate a first processed candidate image; The point spread function is multiplied by the first processed candidate image to generate the second processed candidate image; A second Fourier transform is performed on the second-processed candidate image along the second direction of the k-space to generate a third-processed candidate image; A third Fourier transform is performed on the third-processed candidate image along a third direction of the k-space to generate a second candidate k-space dataset; Based on the second candidate k-space dataset and the mask, the first candidate k-space dataset is generated, wherein the first direction, the second direction, and the third direction are orthogonal to each other.

6. The method according to claim 5, characterized in that, The step of generating a second candidate image related to the coil by processing the first candidate k-space dataset based on the point spread function and the coil sensitivity includes: Perform a first inverse Fourier transform on the first candidate k-space dataset along the third direction of the k-space to generate a first processed candidate k-space dataset; A second inverse Fourier transform is performed on the first processed candidate k-space dataset along the second direction of the k-space to generate a second processed candidate k-space dataset. The candidate k-space dataset after the second processing is multiplied by the conjugate of the point spread function to generate the candidate k-space dataset after the third processing. A third inverse Fourier transform is performed on the third-processed candidate k-space dataset along the first direction of the k-space to generate a third candidate image; The third candidate image is multiplied by the conjugate of the coil sensitivity of the coil to generate the second candidate image.

7. A magnetic resonance imaging (MRI) system, characterized in that, The system includes: At least one storage device for storing a set of instructions; and At least one processor communicates with the at least one storage device, and when executing instructions stored therein, the at least one processor causes the system to perform the following operations: By filling the k-space with target magnetic resonance signals acquired by at least two coils of an MRI device along a progressive spiral trajectory, at least two target k-space datasets are obtained, each target k-space dataset corresponding to the target magnetic resonance signal acquired by one of the at least two coils of the MRI device, including: The at least two target k-space datasets are obtained by filling the k-space with the target magnetic resonance signals acquired by the at least two coils of the MRI device along random progressive spiral trajectories, wherein the distance between the central axes of adjacent random progressive spiral trajectories is different; For each of the at least two target k-space datasets Acquire the target magnetic resonance signal obtained by the coil of at least two coils of the MRI device; Get the mask; The target k-space dataset is obtained based on the target magnetic resonance signal and the mask; Obtain the coil sensitivity of each of the at least two coils; Obtaining the point spread function corresponding to the random progressive spiral trajectory includes: A first image is generated based on a first set of k-space data, wherein the first set of k-space data is obtained by filling the k-space with a reference magnetic resonance signal acquired along a reference trajectory; A second image is generated based on a second set of k-space data, wherein the second set of k-space data is obtained by filling the k-space with the reference magnetic resonance signal acquired along the random progressive spiral trajectory; The point spread function is determined based on the first image and the second image, wherein each pixel in the first image and the second image includes phase information and amplitude information reflecting the interaction between the object and the magnetic field generated by the MRI device; and A target image is generated based on an objective function, wherein the independent variable in the objective function is the target image. The objective function is determined through a process based on the operator, the at least two target k-space datasets, the at least two coil sensitivities, and the point spread function, the process including: The operator is determined based on the sensitivity of the at least two coils, the point spread function, and the mask; The aliased image is determined based on the at least two target k-space datasets, the at least two coil sensitivities, and the point spread function; The objective function is determined based on the operator and the aliased image; and The operator represents the mapping relationship between the target image and the at least two target k-space datasets.

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