Image reconstruction method and system and medium

Abstract

The invention provides an image reconstruction method and system and a medium, and the method comprises the steps: obtaining k-space data collected by magnetic resonance scanning of a target object, the k-space data comprises k-space data corresponding to a plurality of collection lines, and at least two sampling points on each collection line are collected under different gradients; for each acquisition line in the plurality of acquisition lines, determining a density compensation function of the acquisition line; and generating a reconstructed image of the target object based on the density compensation function of the plurality of acquisition lines and the k-space data. According to the method, the accuracy of density compensation can be improved, so that the accuracy of image reconstruction and the uniformity of an imaging effect are improved.

Description

Technical Field

[0001] The present invention relates to the field of magnetic resonance imaging, and in particular to an image reconstruction method, system and medium. Background Art

[0002] In magnetic resonance imaging, scanning technology using non-Cartesian sampling methods (e.g., spiral sampling) has the advantages of fast imaging speed, insensitivity to motion and flow, conducive to motion artifact elimination, flexible sampling space, etc. It is widely used in the fields of coronary artery imaging and brain function imaging.

[0003] When reconstructing data collected in a non-Cartesian sampling method, it is necessary to use a density compensation function (DCF) to normalize the sampling density. The prior art usually uses the same DCF to normalize the data corresponding to multiple sampling trajectories. However, in a non-Cartesian sampling method, the acquisition line needs to be generated in cooperation with a changing magnetic field gradient. The changing magnetic field gradient will bring about an eddy current effect, resulting in different deviations between the actual trajectory of each acquisition line and the theoretical trajectory, and the degree of deviation of each acquisition line is different. Using the same DCF results in low normalization accuracy, which in turn affects the accuracy of image reconstruction.

[0004] Therefore, it is necessary to provide an image reconstruction method to eliminate or reduce the influence of the eddy current effect, thereby improving the accuracy of image reconstruction. Summary of the invention

[0005] One or more embodiments of the present specification provide an image reconstruction method, comprising: acquiring k-space data acquired by magnetic resonance scanning of a target object, the k-space data comprising k-space data corresponding to a plurality of acquisition lines, wherein at least two sampling points on each of the acquisition lines are acquired under different gradients; for each of the plurality of acquisition lines, determining a density compensation function of the acquisition line; and generating a reconstructed image of the target object based on the density compensation functions of the plurality of acquisition lines and the k-space data.

[0006] In some embodiments, determining the density compensation function of the acquisition line includes determining the density compensation function of the acquisition line using a density compensation function determination algorithm.

[0007] In some embodiments, determining the density compensation function of the acquisition line includes determining the density compensation function of the acquisition line based on the k-space trajectory amplitude of the sampling points on the acquisition line. The k-space trajectory amplitude of each sampling point is equivalent to the distance between the sampling point and the k-space center.

[0008] In some embodiments, for each of the plurality of acquisition lines, determining the density compensation function of the acquisition line includes dividing the plurality of acquisition lines into a plurality of acquisition line groups, each of the plurality of acquisition line groups including one or more acquisition lines; for each of the plurality of acquisition line groups, determining the density compensation function of at least one acquisition line in the acquisition line group; and determining the density compensation function of each acquisition line in the acquisition line group based on the density compensation function of the at least one acquisition line.

[0009] In some embodiments, dividing the plurality of acquisition lines into a plurality of acquisition line groups includes determining gradient information of a sampling point on each acquisition line; and dividing the plurality of acquisition lines into a plurality of acquisition line groups based on the gradient information of the sampling point on each acquisition line.

[0010] In some embodiments, generating a reconstructed image of the target object based on the density compensation function and k-space data of the multiple acquisition lines includes weighting the k-space data corresponding to the multiple acquisition lines based on the density compensation function of the multiple acquisition lines to obtain weighted k-space data; and generating a reconstructed image of the target object based on the weighted k-space data.

[0011] In some embodiments, each acquisition line is a spiral line.

[0012] One or more embodiments of the present specification provide an image reconstruction system, the system comprising a processor, and the processor is used to execute an image reconstruction method.

[0013] One or more embodiments of the present specification provide an image reconstruction system, the system comprising an acquisition module, a determination module and a generation module. The acquisition module is used to acquire k-space data acquired by magnetic resonance scanning of a target object. The k-space data includes k-space data corresponding to a plurality of acquisition lines, wherein at least two sampling points on each acquisition line are acquired under different gradients. The determination module is used to determine a density compensation function of an acquisition line for each of the plurality of acquisition lines. The generation module is used to generate a reconstructed image of the target object based on the density compensation functions of the plurality of acquisition lines and the k-space data.

[0014] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes an image reconstruction method.

[0015] Through the image reconstruction method described in some embodiments of this specification, a corresponding density compensation function can be set for each acquisition line. Compared with setting the same density compensation function for all acquisition lines, the differentiated setting of the density compensation function can improve the accuracy of image reconstruction and obtain a more uniform image effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0017] Figure 1 is a schematic diagram of an application scenario of an image reconstruction system according to some embodiments of this specification;

[0018] Figure 2 is an exemplary module diagram of an image reconstruction system according to some embodiments of this specification;

[0019] Figure 3 is an exemplary flow chart of an image reconstruction method according to some embodiments of this specification;

[0020] Figure 4 is an exemplary flow chart of a method for determining a density compensation function according to some embodiments of this specification;

[0021] Figure 5A is a flowchart of an exemplary process of determining a density compensation function of a target acquisition line according to some embodiments of this specification;

[0022] Figure 5B is a schematic diagram of a density compensation function according to some embodiments of the present specification;

[0023] Fig. 6A is a flowchart of an exemplary process of determining a sub-region according to some embodiments of this specification;

[0024] Figure 6B is a schematic diagram of k-space centers and sub-regions corresponding to six acquisition lines shown in some embodiments of this specification;

[0025] Figure 7 is a flowchart of an exemplary density compensation function determination process according to some embodiments of the present specification. DETAILED DESCRIPTION

[0026] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0027] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0028] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0029] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0030] Figure 1 It is a schematic diagram of an application scenario of the image reconstruction system 100 according to some embodiments of the present specification.

[0031] like Figure 1 As shown, in some embodiments, the image reconstruction system 100 may include a processing device 110 , a network 120 , a user terminal 130 , a storage device 140 , and a magnetic resonance imaging (MRI) device 150 .

[0032] The processing device 110 can be used to process data from at least one component of the image reconstruction system 100 or an external data source (e.g., a cloud data center). For example, the processing device 110 can obtain k-space data acquired by a magnetic resonance scan of a target object. The k-space data may include k-space data corresponding to a plurality of acquisition lines, wherein at least two sampling points on each acquisition line may be acquired under different gradients. For another example, the processing device 110 may determine a density compensation function of the acquisition line for each of the plurality of acquisition lines. For another example, the processing device 110 may generate a reconstructed image of the target object based on the density compensation functions of the plurality of acquisition lines and the k-space data.

[0033] In some embodiments, the processing device 110 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical operation processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, etc. or any combination thereof. In some embodiments, the processing device 110 may be a single server or a server group. In some embodiments, the processing device 110 may be local or remote. In some embodiments, the processing device 110 may be implemented on a cloud platform. By way of example only, a cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc. or any combination thereof.

[0034] The network 120 may include any suitable network capable of facilitating information and / or data exchange of the image reconstruction system 100. In some embodiments, components of the image reconstruction system 100 (e.g., the processing device 110, the user terminal 130, the storage device 140, and / or the MRI device 150) may exchange information and / or data through the network 120. For example, the processing device 110 may establish a connection with the user terminal 130 and / or the MRI device 150 through the network 120. In some embodiments, the network 120 may be any one or more of a wired network or a wireless network. In some embodiments, the network 120 may include one or more network access points. For example, the network 120 may include a wired or wireless network access point, such as a base station and / or a network switching point, through which one or more components of the image reconstruction system 100 may connect to the network 120 to exchange data and / or information.

[0035] The user terminal 130 may be a terminal device used by a user (e.g., a doctor, a nurse, etc.). In some embodiments, the user terminal 130 may include a mobile phone, a tablet, a computer, etc. In some embodiments, the user terminal 130 may include a display component (e.g., a display screen), an interactive component (e.g., a mouse, a keyboard, etc.), etc. In some embodiments, the user terminal 130 may interact with at least one component of the image reconstruction system 100 or an external data source (e.g., a cloud data center). For example, the user terminal 130 may receive a reconstructed image from the processing device 110.

[0036] The storage device 140 may be used to store data, instructions, and / or any other information. In some embodiments, the storage device 140 may store data and / or information obtained from at least one component of the image reconstruction system 100 or an external data source. For example, the storage device 140 may store k-space data, reconstructed images, etc. In some embodiments, the storage device 140 may store data and / or instructions for the processing device 110 to execute or use to complete the exemplary methods described in this specification. For example, the storage device 140 may store imaging instructions for execution by the processing device 110.

[0037] In some embodiments, the storage device 140 may include a mass storage, a removable storage, etc., or any combination thereof. In some embodiments, the storage device 140 may be implemented on a cloud platform. By way of example only, a cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc., or any combination thereof. In some embodiments, the storage device 140 may be integrated in the processing device 110 and / or the user terminal 130.

[0038] MRI device 150 can be used to scan the target object and obtain the scanning data of the target object, such as magnetic resonance signals related to the target object or a part thereof. MRI device 150 can detect multiple magnetic resonance signals by applying a magnetic resonance pulse sequence to the target object or a part thereof. In some embodiments, MRI device 150 may include, for example, a magnetic body, a gradient coil, a radio frequency coil, etc. In some embodiments, according to the type of magnetic body, MRI device 150 may be a permanent magnet magnetic resonance scanner, a superconducting electromagnetic magnetic resonance scanner, or a resistive electromagnetic magnetic resonance scanner, etc. In some embodiments, according to the strength of the magnetic field, MRI device 150 may be a high-field magnetic resonance scanner, a mid-field magnetic resonance scanner, and a low-field magnetic resonance scanner, etc. The target object may be biological or non-biological. For example, the target object may be a patient, an artificial object, etc. The target object may include a specific part, organ, tissue, and / or body part of the patient. As an example only, the scanned object may include the head, brain, neck, body, shoulder, arm, chest, heart, stomach, blood vessel, soft tissue, knee, foot, etc., or a combination thereof.

[0039] It should be noted that the above description of the image reconstruction system 100 is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various modifications or variations can be made based on the description of this specification. For example, the image reconstruction system 100 may also include one or more other components, or one or more of the components described above may be omitted. However, these changes and modifications will not deviate from the scope of this specification.

[0040] Figure 2is an exemplary module diagram of an image reconstruction system according to some embodiments of the present specification.

[0041] like Figure 2 As shown, the image reconstruction system 200 may include an acquisition module 210 , a determination module 220 , a generation module 230 and a processing module 240 .

[0042] The acquisition module 210 may be used to acquire k-space data acquired by magnetic resonance scanning of the target object. The k-space data may include k-space data corresponding to multiple acquisition lines, wherein at least two sampling points on each acquisition line may be acquired under different gradients.

[0043] The determination module 220 may be configured to determine a density compensation function of each acquisition line among the plurality of acquisition lines.

[0044] The generation module 230 may be configured to generate a reconstructed image of the target object based on the density compensation function and k-space data of the plurality of acquisition lines.

[0045] In some embodiments, the acquisition module 210 may also be used to acquire k-space trajectory amplitudes of multiple sampling points. The multiple sampling points may be located on the same target acquisition line in the k-space.

[0046] The determination module 220 may also be used to determine a density compensation function of a target acquisition line based on the k-space trajectory amplitudes of multiple sampling points.

[0047] The processing module 240 may be used to process k-space data corresponding to a target acquisition line acquired in magnetic resonance scanning based on a density compensation function.

[0048] It should be noted that the above description of the image reconstruction system 200 and its modules is only for convenience of description and does not limit the present specification to the scope of the embodiments. It is understandable that, after understanding the principle of the system, those skilled in the art may arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from the principle. In some embodiments, Figure 2 The acquisition module 210, determination module 220, generation module 230, and processing module 240 disclosed in the specification can be different modules in a system, or a module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the protection scope of this specification.

[0049] Figure 3300 is a flowchart of an exemplary image reconstruction process 300 according to some embodiments of the present specification. In some embodiments, the process 300 may be executed by the processing device 110. For example, the process 300 may be stored in a storage device (e.g., storage device 140) in the form of an instruction set (e.g., an application program). In some embodiments, the processing device 110 (e.g., Figure 2 One or more modules shown in ) can execute the instruction set and correspondingly instruct one or more components of the image reconstruction system 100 to execute the process 300.

[0050] In magnetic resonance imaging, it is necessary to collect k-space data corresponding to multiple acquisition lines to fill the k-space. For example, taking the spiral sampling technology as an example, the entire k-space can be collected by using multiple spiral lines to spiral outward from the center of the k-space (i.e., the spiral-out sampling method), and each acquisition corresponds to a spiral line. In theory, the trajectories of multiple spiral lines are rotationally symmetric. That is, the difference between the multiple spiral lines is only that the multiple spiral lines start at different rotation angles in the k-space. However, since each spiral line is generated under a changing gradient, the changing gradient will bring about an eddy current effect, resulting in different deviations between the actual trajectory of each spiral line and the theoretical trajectory, resulting in different shapes of each spiral line. If the same density compensation function is used to perform density normalization on multiple spiral lines, there will be an incompatibility between the density compensation function and the spiral line, affecting the accuracy of density normalization and subsequent image reconstruction. Therefore, it is necessary to provide an image reconstruction method for improving the matching degree between the acquisition line and the density compensation function. In some embodiments, image reconstruction can be performed by executing the following operations of process 300.

[0051] Step 310, acquiring k-space data acquired by magnetic resonance scanning of the target object. In some embodiments, step 310 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the acquisition module 210).

[0052] K-space may refer to a magnetic resonance (MR) signal space, ie, a frequency space of a Fourier transform.

[0053] K-space data may refer to data used to fill k-space. For example, k-space data may be data generated after filling MR signals (or digitized MR signals) acquired by magnetic resonance scanning into k-space. In some embodiments, k-space data may include k-space data corresponding to multiple acquisition lines. For example, k-space data may include k-space data corresponding to 4, 6, 12, 20, etc. or any number of acquisition lines.

[0054] An acquisition line (also called a k-space trajectory) may refer to a method of acquiring k-space data (or an MR signal corresponding to the k-space data) and filling it into the k-space. Exemplary acquisition lines may include Cartesian acquisition lines and non-Cartesian acquisition lines. Non-Cartesian acquisition lines may include spiral lines, radial lines, zigzag lines, etc. Accordingly, the sampling techniques for k-space data may include Cartesian sampling techniques, spiral sampling techniques, radial sampling techniques, Z sampling techniques, etc.

[0055] As an example only, each of the multiple acquisition lines may be a spiral line (eg, an Archimedean spiral line). For example, during a magnetic resonance scan acquisition process, an MRI device (eg, MRI device 150 ) may scan a target object using a spiral sampling technique to acquire multiple spiral lines.

[0056] In some embodiments, the acquisition lines may be collected at equal intervals. For example, the spacings between adjacent acquisition lines are equal. For another example, the starting angles of adjacent acquisition lines are the same. In some embodiments, the acquisition lines may be collected at non-equal intervals. For example, the trajectory of the acquisition lines may be circles, rosettes, linograms, stochastic patterns, Lissajous trajectories, variable density spirals, interleaved spirals, etc.

[0057] In some embodiments, each acquisition line may include multiple sampling points. Each sampling point in the multiple sampling points may include frequency information, phase information, etc. For example, each sampling point may be a complex number including a real part and an imaginary part, for providing frequency information, phase information, etc.

[0058] In some embodiments, at least two sampling points on each acquisition line may be acquired under different gradients. For example, each acquisition line may be generated under a changing gradient (e.g., a gradient magnetic field with a changing amplitude), and different sampling points on the acquisition line may correspond to different magnetic field gradients.

[0059] In some embodiments, the processing device 110 may directly acquire the k-space data of the target object from an MRI device (e.g., the MRI device 150). Alternatively, the processing device 110 may acquire the k-space data of the target object from a storage device (e.g., the storage device 140) storing the k-space data of the target object.

[0060] Step 320, for each acquisition line in the plurality of acquisition lines, determine a density compensation function of the acquisition line. In some embodiments, step 320 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0061] The density compensation function may refer to a function used to compensate for uneven sampling density in k-space. For example, the density compensation function of an acquisition line may include a density compensation value for each sampling point in the acquisition line. For another example, the density compensation function of an acquisition line may include the area of ​​k-space occupied by each sampling point in the acquisition line. The sampling density in k-space may reflect the density of sampling points in k-space, or the area of ​​k-space occupied by each sampling point. The sampling density of acquisition lines (e.g., at least two sampling points) acquired under different gradients is uneven in k-space. Taking non-Cartesian sampling as an example, in the central area of ​​k-space, the sampling points are denser, that is, the sampling density is high; while in the edge area of ​​k-space, the sampling points are sparser, that is, the sampling density is low. Therefore, a density compensation function is needed to compensate the acquisition line to homogenize the sampling density in k-space. It is understandable that in traditional Cartesian sampling, a density compensation function may also be used for compensation. For example, in Cartesian sampling, a density compensation function may be used to compensate the k-space data corresponding to the rising edge and / or falling edge of the gradient in the acquisition line.

[0062] In some embodiments, the processing device 110 may use a density compensation function determination algorithm to determine the density compensation function of each acquisition line in the plurality of acquisition lines. An exemplary density compensation function determination algorithm may include a Voronoi algorithm, an analytical algorithm, etc., or any combination thereof. It is understood that the density compensation function determination algorithm may also use other algorithms in the prior art.

[0063] In some embodiments, the processing device 110 may determine the density compensation function of an acquisition line among the plurality of acquisition lines based on the k-space trajectory amplitude of the sampling point on the acquisition line. The k-space trajectory amplitude of each sampling point may be equivalent to the distance between the sampling point and the k-space center. For a detailed description of determining the density compensation function of an acquisition line based on the k-space trajectory amplitude of the sampling point on the acquisition line, see Figure 4 and its related description.

[0064] In some embodiments, the processing device 110 may divide the plurality of acquisition lines into a plurality of acquisition line groups. Each of the plurality of acquisition line groups may include one or more acquisition lines. For each of the plurality of acquisition line groups, the processing device 110 may determine a density compensation function of at least one acquisition line in the acquisition line group, and determine a density compensation function of each acquisition line in the acquisition line group based on the density compensation function of the at least one acquisition line. For a detailed description of determining the density compensation function of an acquisition line based on a plurality of acquisition line groups, see Figure 7 and its related description.

[0065] Step 330, generating a reconstructed image of the target object based on the density compensation function of the plurality of acquisition lines and the k-space data. In some embodiments, step 330 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the generation module 230).

[0066] In some embodiments, the processing device 110 may perform weighted processing on the k-space data corresponding to the multiple acquisition lines based on the density compensation functions of the multiple acquisition lines to obtain weighted k-space data. The k-space data corresponding to the acquisition lines acquired under different gradients (e.g., non-Cartesian sampling methods) need to be compensated to different degrees, and the degree of compensation may be reflected by the weighting coefficient. In some embodiments, the processing device 110 may determine the weighting coefficient corresponding to the acquisition line based on the density compensation function of each acquisition line. For example, for each sampling point in each acquisition line, the processing device 110 may use the density compensation value corresponding to the sampling point in the density compensation function as the weighting coefficient of the sampling point. In some embodiments, the processing device 110 may perform weighted processing on the k-space data corresponding to the multiple acquisition lines based on the weighting coefficient corresponding to each acquisition line. For example, weighted processing on the k-space data corresponding to the multiple acquisition lines may include multiplying the k-space data corresponding to the multiple acquisition lines by their corresponding weighting coefficients (i.e., density compensation values), respectively.

[0067] In some embodiments, the processing device 110 may generate a reconstructed image of the target object based on the weighted k-space data. For example, an image reconstruction algorithm may be used to generate a reconstructed image of the target object based on the weighted k-space data. Exemplary image reconstruction algorithms may include a discrete Fourier transform algorithm, an inverse Fourier transform algorithm, a conjugate phase reconstruction algorithm, a non-uniform fast Fourier transform algorithm, a block uniform resampling algorithm, a gridding algorithm, etc., or any combination thereof. As an example only, the processing device 110 may rearrange the weighted k-space data into a Cartesian grid and generate a reconstructed image of the target object through an inverse Fourier transform algorithm.

[0068] According to some embodiments of the present specification, by determining the density compensation function corresponding to each acquisition line in the k-space data, a differentiated setting of the density compensation function can be achieved, thereby improving the accuracy of the density compensation function of each acquisition line, improving the matching degree between the acquisition line and the density compensation function, and then improving the accuracy of image reconstruction and the uniformity of imaging effects.

[0069] Figure 4 FIG. 4 is a flow chart of an exemplary density compensation function determination process 400 according to some embodiments of the present specification. In some embodiments, process 400 may be performed by processing device 110. In some embodiments, Figure 3 The density compensation function described in step 320 in FIG. 4 can be determined according to process 400 .

[0070] In magnetic resonance imaging, for acquisition lines acquired under different gradients, a density compensation function needs to be used to compensate for their sampling density in k-space. The commonly used density compensation function determination algorithm needs to collect the coordinate position of each sampling point on the acquisition line, and then accurately calculate the area occupied by each sampling point to determine the density compensation function. Since the coordinate position of all sampling points in k-space needs to be comprehensively considered, the time and equipment cost of determining the density compensation function are increased. Therefore, it is necessary to provide a density compensation function determination method for improving the efficiency of determining the density compensation function. In some embodiments, the density compensation function of the target acquisition line can be determined by executing the following operations of process 400.

[0071] In some embodiments, the target acquisition line may be Figure 3 Any one of the plurality of acquisition lines. In some embodiments, Figure 3 Each acquisition line in the plurality of acquisition lines is used as a target acquisition line to determine a density compensation function corresponding to the acquisition line through the process 400 .

[0072] like Figure 4 As shown, process 400 includes the following steps.

[0073] Step 410, obtaining k-space trajectory amplitudes of multiple sampling points. In some embodiments, step 410 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the acquisition module 210).

[0074] The multiple sampling points may be located on the same target acquisition line in the k-space. For example, multiple sampling points on the same target acquisition line may be acquired in sequence based on a spiral acquisition method. The target acquisition line may refer to an acquisition line for which a density compensation function needs to be determined.

[0075] The k-space trajectory amplitude of the sampling point may be equivalent to the distance between the sampling point and the center of the k-space. For example, the k-space trajectory amplitude of the sampling point may refer to the distance between the sampling point and the center of the k-space. As an example only, the k-space trajectory amplitude of each sampling point may be determined by formula (1): Where |k| is the k-space trajectory amplitude; k x k is the projection length of the distance between the sampling point and the center of k-space in the x-axis direction (i.e., the coordinate of the sampling point on the x-axis of k-space); y is the projection length of the distance between the sampling point and the center of k-space in the y-axis direction (i.e., the coordinate of the sampling point on the y-axis of k-space). It can be understood that as the distance between the sampling point and the center of k-space increases (i.e., the closer the sampling point is to the edge area of ​​k-space), the k-space trajectory amplitude of the sampling point increases.

[0076] Step 420, determining a density compensation function of the target acquisition line based on the k-space trajectory amplitudes of the plurality of sampling points. In some embodiments, step 420 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0077] In some embodiments, for each sampling point among the plurality of sampling points, the processing device 110 may determine at least one adjacent sampling point of the sampling point in the radial direction, and determine a density compensation value of the sampling point based on the k-space trajectory amplitude of the sampling point and the at least one adjacent sampling point. Based on the density compensation value of each sampling point of the target acquisition line, the processing device 110 may determine a density compensation function of the target acquisition line. For a detailed description of determining the density compensation function of the target acquisition line, see Figure 5A and its related description.

[0078] Step 430, based on the density compensation function, the k-space data corresponding to the target acquisition line acquired in the magnetic resonance scan is processed. In some embodiments, step 430 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the processing module 240).

[0079] For example, the k-space data includes k-space data corresponding to multiple sampling points on the target acquisition line. The processing device 110 can determine the density compensation value (also referred to as the weighting coefficient) of each sampling point based on the density compensation function. The processing device 110 can perform sampling density compensation on the k-space data corresponding to the target acquisition line by multiplying the k-space data corresponding to each sampling point by its corresponding density compensation value. For another example, the processing device 110 can perform weighted processing on the k-space data corresponding to the target acquisition line and other acquisition lines based on the density compensation function of the target acquisition line and the density compensation functions of the other acquisition lines to obtain weighted k-space data. For more information on processing k-space data, see Figure 3 Step 330 is not described again here.

[0080] In some embodiments, the processing device 110 may process the k-space data of the target object based on the density compensation function of the target acquisition line. For example, the processing device 110 may use the density compensation function of the target acquisition line as the density compensation function of one or more other acquisition lines acquired in the magnetic resonance scan, and use the density compensation function of the target acquisition line to process the k-space data corresponding to the one or more other acquisition lines.

[0081] According to some embodiments of the present specification, the density compensation function of the acquisition line can be determined based on the k-space trajectory amplitudes of multiple sampling points on the same acquisition line, thereby simplifying the process of determining the density compensation function, improving the efficiency of determining the density compensation function, and further improving the efficiency of image reconstruction.

[0082] Figure 5A FIG. 5 is a flowchart of an exemplary process 500 for determining a density compensation function of a target acquisition line according to some embodiments of the present specification. In some embodiments, the process 500 may be executed by the processing device 110. In some embodiments, Figure 4 The density compensation function described in step 420 in FIG. 5 can be determined according to process 500 .

[0083] Step 510, determining at least one adjacent sampling point of the sampling point in the radial direction among the plurality of sampling points. In some embodiments, step 510 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0084] In some embodiments, for a target acquisition line, the processing device 110 may determine a plurality of sampling points on the target acquisition line in radial order. Figure 6B , the target acquisition line may include sampling point k 1 , k 2 , k 3 , ... The processing device 110 may determine k 2 k 1The adjacent sampling points of k 1 and k 3 It is k 2 Adjacent sampling points; k 3 It is k 2 adjacent sampling points.

[0085] Step 520, determining a density compensation value of the sampling point based on the k-space trajectory amplitude of the sampling point and at least one adjacent sampling point. In some embodiments, step 520 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0086] The density compensation value may be a specific value for compensating the k-space data corresponding to the sampling point. For example, the density compensation value may be determined based on the area of ​​the sub-region occupied by the sampling point in the k-space.

[0087] In some embodiments, the processing device 110 may determine the sub-region corresponding to the sampling point in k-space based on the k-space trajectory amplitude of the sampling point and at least one adjacent sampling point. The sub-region corresponding to the sampling point in k-space may be a portion of the k-space region occupied by the sampling point. In some embodiments, the processing device 110 may divide the k-space based on the number of multiple acquisition lines acquired in the magnetic resonance scan and the k-space trajectory amplitude of multiple sampling points, thereby determining the sub-region corresponding to the sampling point in k-space. For a detailed description of determining the sub-region, see Fig. 6A and its related description.

[0088] In some embodiments, the processing device 110 may determine the density compensation value of the sampling point based on the area of ​​the sub-region. For example, the processing device 110 may use the area of ​​the sub-region corresponding to a certain sampling point as the density compensation value of the sampling point. For another example, the processing device 110 may process the area of ​​the sub-region corresponding to a certain sampling point (e.g., weighted processing, simplified processing) to obtain a processed value, and use the processed value as the density compensation value of the sampling point.

[0089] Step 530, determining a density compensation function of the target acquisition line based on the density compensation value of each sampling point of the target acquisition line. In some embodiments, step 530 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0090] For example, the processing device 110 may determine the density compensation value of each sampling point of the target acquisition line as the density compensation function of the target acquisition line. For another example, the processing device 110 may determine the correspondence between the sampling point and the density compensation value based on the density compensation value of each sampling point of the target acquisition line, and determine the correspondence as the density compensation function of the target acquisition line. Figure 5B , Figure 5Bis a schematic diagram of an exemplary density compensation function 550 according to some embodiments of the present specification. Figure 5B As shown, the density compensation function 550 is a correspondence between sampling points and density compensation values ​​determined based on the density compensation value of each sampling point of the target acquisition line, wherein the horizontal axis is the sequence number of the sampling point, and the vertical axis is the density compensation value corresponding to each sampling point.

[0091] Fig. 6A 600 is a flowchart of an exemplary sub-region determination process 600 according to some embodiments of this specification. In some embodiments, process 600 may be executed by processing device 110. In some embodiments, Figure 5A The sub-area described in step 520 may be determined according to process 500 .

[0092] Step 610, based on the number of multiple acquisition lines acquired in the magnetic resonance scan, the k-space is evenly divided into multiple initial sub-regions. In some embodiments, step 610 can be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0093] In some embodiments, the processing device 110 may evenly divide the k-space into a plurality of initial sub-regions based on the number of acquisition lines acquired in the magnetic resonance scan. For example, each acquisition line may correspond to an initial sub-region. Figure 6B As shown, the magnetic resonance scan of the target object acquires 6 acquisition lines. The 6 acquisition lines start from the center of the k-space and extend to the edge area of ​​the k-space in a spiral shape. Accordingly, the k-space is evenly divided into 6 initial sub-areas a1, b1, c1, a2, b2, and c2, and the 6 initial sub-areas are fan-shaped areas with the same area.

[0094] In some embodiments, each initial sub-region corresponds to one of the six acquisition lines. The sampling points on each acquisition line are located in the corresponding initial sub-region. For example, including sampling point k 1 , k 2 , k 3 The target acquisition line corresponds to the initial sub-region c2. In some embodiments, the separation line between two adjacent initial sub-regions can be regarded as the midline of the sampling points on the acquisition line corresponding to the two initial sub-regions.

[0095] Step 620, determining at least one concentric circle based on the k-space trajectory amplitude of the sampling point and at least one adjacent sampling point. In some embodiments, step 620 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0096] In some embodiments, a concentric circle may be determined with the center of the k-space as the center and the average value of the k-space trajectory amplitude of the sampling point and an adjacent sampling point as the radius. Figure 6B As shown, for sampling point k 2 For example, its adjacent sampling points include sampling point k 1 and k 3 . With the center k of k-space c As the center of the circle, with sampling point k 1 and k 2 The concentric circles 652 can be determined by taking the average value of the k-space trajectory amplitude of the k-space as the radius. c As the center of the circle, with sampling point k 2 and k 3 The average value of the k-space trajectory amplitude is the radius, and the concentric circle 654 can be determined. 1 The adjacent sampling points include only k 2 , at this time, only the concentric circle 652 needs to be determined. Sampling point k 3 The adjacent sampling points of only include sampling point k 2 , at this time, only the concentric circle 654 needs to be determined.

[0097] Step 630, determining a sub-region based on the multiple initial sub-regions and the at least one concentric circle. In some embodiments, step 630 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0098] In some embodiments, the processing device 110 may divide the multiple initial sub-regions into multiple sub-regions based on at least one concentric circle. Each sub-region may correspond to a sampling point. The sub-region corresponding to the sampling point may be an area surrounded by at least one concentric circle corresponding to the sampling point and the boundary of the initial sub-region. For example, the sampling point k 2 The corresponding sub-region is the sector ring region enclosed by the boundary line of the initial sub-region c2 and the concentric circles 652 and 654 (ie, the sector ring region marked with shadows in the figure). 3 The corresponding sub-region is the sector ring region surrounded by the boundary line of the initial sub-region c2, the concentric circle 654 and the k-space boundary (i.e., the sector ring region outside the sector ring region marked with shadows in the figure). 1 The corresponding sub-region is a sector-shaped region enclosed by the boundary line of the initial sub-region c2 and the concentric circle 652 (ie, the sector-shaped region marked with a shadow in the figure).

[0099] In some embodiments, the processing device 110 may determine the area of ​​the sub-region corresponding to the sampling point. For example, the processing device 110 may determine the area of ​​the sub-region corresponding to the sampling point based on the k-space trajectory amplitude of the sampling point and at least one adjacent sampling point. For another example, the processing device 110 may determine the area of ​​the sub-region corresponding to the sampling point based on at least one concentric circle corresponding to the sampling point.

[0100] like Figure 6B As shown, the sampling point k 1 The corresponding sub-region in k-space can be the sampling point k 1 The conical area where the sampling point k is located. 1 The area of ​​the sub-region in k-space can be determined by formula (2): in, is the sampling point k 1 The area of ​​the subregion in k-space, k 1 is the sampling point k 1 The k-space trajectory amplitude (i.e., the sampling point k 1 and the k-space center k c distance), k 2 is the sampling point k 2 The k-space trajectory amplitude (i.e., the sampling point k 2 With k c distance). That is, the distance between the concentric circle 652 and the center k of the k space is c distance.

[0101] Sampling point k 2 The corresponding sub-region in k-space can be the sampling point k 2 The corresponding sector ring area. Sampling point k 2 The area of ​​the sub-region in k-space can be determined by formula (3): in, is the sampling point k 2 The area of ​​the subregion in k-space, k 3 is the sampling point k 3 The k-space trajectory amplitude (i.e., the sampling point k 3 With k c distance). That is, the distance between the concentric circles 654 and the center k of the k-space is c distance.

[0102] Sampling point k 3 The corresponding sub-region in k-space can be the sampling point k 3 The corresponding sector ring area. Sampling point k 3The area of ​​the sub-region in k-space can be determined by formula (4): in, is the sampling point k 3 The area of ​​the subregion in k-space, k R is the radius of k-space.

[0103] In some embodiments, the sampling point k 3 The impact on the reconstructed image is small and can be ignored. That is, there is no need to determine the area of ​​the sector ring region corresponding to the last sampling point.

[0104] In summary, when there are n sampling points, it can be determined that each sampling point (k 1 , k 2 , k 3 , ...k n ) in the k-space. The sub-region area of ​​each sampling point in the k-space can be used as a density compensation value. In some embodiments, only the relative area between each sampling point can be considered to simplify the above formula. For example, by discarding the constant value, a generalized density compensation value determination formula (i.e., density compensation function) (5) can be obtained: in, is the sampling point k 0 Density compensation value, k + is the sampling point k 0 The k-space trajectory amplitude of the next sampling point (i.e., the sampling point k + With k c distance), k - is the sampling point k 0 The k-space trajectory amplitude of the previous sampling point (i.e., sampling point k - With k c When k 0 When it is the first sampling point, the previous sampling point can be regarded as the center of k-space (i.e., sampling point k - With k c The distance is 0). When k 0 When it is the last sampling point, the next sampling point can be regarded as the edge point of k-space. In some embodiments, the density compensation value of the last sampling point can be ignored. Because the k-space edge has little effect on the image, the error of the density compensation value of the last sampling point can be ignored.

[0105] It is understood that the above description that the magnetic resonance scan acquires 6 acquisition lines and the acquisition line includes three sampling points is only for the purpose of illustration and is not intended to limit the scope of this specification. For example, the magnetic resonance scan may also acquire other numbers of acquisition lines, such as 3, 4, 12, etc. Each acquisition line may include any other number of sampling points.

[0106] According to some embodiments of the present specification, the density compensation function of the sampling point can be determined by the amplitude of the k-space trajectory of each sampling point, thereby reducing the amount of calculation for determining the density compensation function and improving the efficiency of determining the density compensation function.

[0107] Figure 7 FIG. 7 is a flow chart of an exemplary density compensation function determination process 700 according to some embodiments of the present specification. In some embodiments, process 700 may be performed by processing device 110. In some embodiments, Figure 3 The density compensation function described in step 320 in FIG. 7 can be determined according to process 700 .

[0108] Step 710, dividing the plurality of acquisition lines into a plurality of acquisition line groups. In some embodiments, step 710 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0109] Each of the plurality of acquisition line groups may include one or more acquisition lines.

[0110] In some embodiments, the processing device 110 can divide the centrally symmetrical acquisition lines into the same acquisition line group based on the position of each acquisition line in the k-space of the multiple acquisition lines. Since the amplitude distribution of the magnetic field gradient of the centrally symmetrical acquisition lines is the same, and only the direction of the magnetic field gradient is different, the k-space trajectory amplitude of the centrally symmetrical sampling points on the multiple acquisition lines is the same, and the corresponding density compensation values ​​are also the same. Therefore, the acquisition lines of the centrally symmetrical acquisition lines can be divided into the same acquisition line group for processing. For example, Figure 6B As shown, the acquisition lines corresponding to the initial sub-areas a1 and a2 are both k c The two acquisition lines can be divided into one acquisition line group. Similarly, the acquisition lines corresponding to the initial sub-areas b1 and b2 can also be divided into the same acquisition line group; the acquisition lines corresponding to the initial sub-areas c1 and c2 can also be divided into the same acquisition line group.

[0111] In some embodiments, the processing device 110 may determine the gradient information of the sampling points on each acquisition line, and divide the multiple acquisition lines into multiple acquisition line groups based on the gradient information of the sampling points on each acquisition line. For example, the processing device 110 may predict the eddy current effect corresponding to each acquisition line based on the gradient information of the sampling points on each acquisition line; and then divide the multiple acquisition lines into multiple acquisition line groups based on the similarity of the eddy current effects corresponding to the multiple acquisition lines. Because the eddy current effect caused by the changing magnetic field gradient leads to differences in the multiple acquisition lines, the multiple acquisition lines may be divided into multiple acquisition line groups based on the similarity of the eddy current effects. For example, the processing device 110 may divide the acquisition lines whose similarity of the eddy current effects is less than a similarity threshold into the same acquisition line group.

[0112] Step 720, for each of the plurality of acquisition line groups, determine a density compensation function of at least one acquisition line in the acquisition line group. In some embodiments, step 720 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0113] In some embodiments, the processing device 110 may use any of the above methods (such as Figure 3 , Figure 4 , Figure 5A , Fig. 6A ), determine the density compensation function of at least one acquisition line in the acquisition line group, which will not be described in detail here.

[0114] Step 730, determining a density compensation function of each acquisition line in the acquisition line group based on the density compensation function of at least one acquisition line. In some embodiments, step 730 may be performed by the processing device 110 or the image reconstruction system 200 (eg, the determination module 220).

[0115] In some embodiments, the processing device 110 may use the density compensation function of at least one acquisition line as the density compensation function of each acquisition line in the acquisition line group. Figure 6B As shown, the processing device 110 may 1 , k 2 , k 3 The target acquisition line (ie, the acquisition line corresponding to the initial sub-area c2) is used to determine the density compensation function of other acquisition lines in the acquisition line group where the target acquisition line is located. As an example only, the density compensation function of the target acquisition line can be used as the density compensation function of the acquisition line corresponding to the initial sub-area c1.

[0116] According to some embodiments of the present specification, multiple acquisition lines may be grouped first, and then the density compensation function of each acquisition line in the acquisition line group may be determined, thereby simplifying the process of determining the density compensation function of the multiple acquisition lines, further reducing the amount of calculation for determining the density compensation function, and improving the efficiency of determining the density compensation function.

[0117] The present specification provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the aforementioned image reconstruction method and / or density compensation function determination method.

[0118] In some embodiments of the present specification, (1) by determining the density compensation function corresponding to each acquisition line in the k-space data, a differentiated setting of the density compensation function can be achieved, thereby improving the accuracy of the density compensation function of each acquisition line, improving the matching degree between the acquisition line and the density compensation function, and thus improving the accuracy of image reconstruction and the uniformity of image effects; (2) based on the k-space trajectory amplitude of multiple sampling points on the same acquisition line, the density compensation function of the acquisition line can be determined, thereby simplifying the process of determining the density compensation function, improving the efficiency of determining the density compensation function, and thus improving the efficiency of image reconstruction; (3) multiple acquisition lines can be grouped first, and then the density compensation function of each acquisition line in the acquisition line group can be determined, thereby simplifying the process of determining the density compensation function of multiple acquisition lines, further reducing the amount of calculation for determining the density compensation function, and improving the efficiency of determining the density compensation function.

[0119] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0120] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0121] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0122] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0123] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0124] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0125] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. An image reconstruction method, comprising: Acquire k-space data acquired by magnetic resonance scanning of the target object, wherein the k-space data includes k-space data corresponding to a plurality of acquisition lines, wherein at least two sampling points on each acquisition line are acquired under different gradients; For each acquisition line of the plurality of acquisition lines, determining a density compensation function of the acquisition line; and A reconstructed image of the target object is generated based on the density compensation functions of the plurality of acquisition lines and the k-space data.

2. The method according to claim 1, characterized in that Determining the density compensation function of the acquisition line includes: The density compensation function of the acquisition line is determined using a density compensation function determination algorithm.

3. The method according to claim 1, characterized in that Determining the density compensation function of the acquisition line includes: The density compensation function of the acquisition line is determined based on the k-space trajectory amplitudes of the sampling points on the acquisition line, wherein the k-space trajectory amplitude of each sampling point is equivalent to the distance between the sampling point and the k-space center.

4. The method according to claim 1, characterized in that The step of determining, for each acquisition line of the plurality of acquisition lines, a density compensation function of the acquisition line comprises: Dividing the plurality of acquisition lines into a plurality of acquisition line groups, each of the plurality of acquisition line groups comprising one or more acquisition lines; For each of the plurality of acquisition line groups, determining a density compensation function of at least one acquisition line in the acquisition line group; and A density compensation function of each acquisition line in the acquisition line group is determined based on the density compensation function of the at least one acquisition line.

5. The method according to claim 4, characterized in that The dividing the plurality of acquisition lines into a plurality of acquisition line groups comprises: Determining gradient information of the sampling points on each of the acquisition lines; and The plurality of acquisition lines are divided into the plurality of acquisition line groups based on the gradient information of the sampling points on each of the acquisition lines.

6. The method according to claim 1, characterized in that The generating the reconstructed image of the target object based on the density compensation function of the plurality of acquisition lines and the k-space data comprises: Based on the density compensation functions of the plurality of acquisition lines, weighted processing is performed on the k-space data corresponding to the plurality of acquisition lines to obtain weighted k-space data; and A reconstructed image of the target object is generated based on the weighted k-space data.

7. The method according to claim 1, characterized in that Each of the acquisition lines is a spiral line.

8. An image reconstruction system, characterized in that: The system comprises a processor and a storage device, wherein the storage device stores instructions for image reconstruction, and when the processor executes the instructions, the image reconstruction method according to any one of claims 1 to 7 is implemented.

9. An image reconstruction system, characterized in that: It includes an acquisition module, a determination module and a generation module; The acquisition module is used to acquire k-space data acquired by magnetic resonance scanning of the target object, wherein the k-space data includes k-space data corresponding to a plurality of acquisition lines, wherein at least two sampling points on each acquisition line are acquired under different gradients; The determination module is used to determine a density compensation function of each acquisition line among the plurality of acquisition lines; and The generating module is used to generate a reconstructed image of the target object based on the density compensation functions of the multiple acquisition lines and the k-space data.

10. A computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the image reconstruction method according to any one of claims 1 to 7.

Images