Three-dimensional magnetic resonance imaging method and system and storage medium

Abstract

The embodiment of the invention provides a three-dimensional magnetic resonance imaging method and system and a storage medium, and the method comprises the steps: obtaining the k-space data of a target object, and the target object comprises a plurality of layers; and reconstructing a three-dimensional magnetic resonance image of the target object based on the k-space data. Wherein the k-space data comprises a spiral data line acquired in each of a plurality of repetition times, and the spiral data line is acquired by applying an imaging sequence to the target object by using the magnetic resonance equipment and acquiring the imaging sequence in a spiral sampling manner. The imaging sequence includes applying a random phase encoding gradient to the plurality of layers in a selected layer direction such that the spiral data line acquired within each repetition time corresponds to a random one of the plurality of layers.

Description

Technical Field

[0001] This specification relates to the field of magnetic resonance imaging technology, and in particular to a three-dimensional magnetic resonance imaging method, system, and storage medium. Background Art

[0002] Cardiovascular magnetic resonance imaging plays an important role in assessing cardiac function and morphology. Real-time imaging methods can effectively capture cardiac dynamics and hold great promise for future applications. Current cardiac cine MRI techniques offer high temporal resolution, and the resulting cine images can be used to observe cardiac motion. However, achieving three-dimensional, full-heart real-time imaging remains a significant challenge.

[0003] Therefore, it is necessary to provide a three-dimensional magnetic resonance imaging method, system and storage medium that can achieve three-dimensional imaging with higher temporal resolution. Summary of the Invention

[0004] One or more embodiments of the present specification provide a three-dimensional magnetic resonance imaging method, the method comprising: acquiring k-space data of a target object, the target object comprising a plurality of layers; and reconstructing a three-dimensional magnetic resonance image of the target object based on the k-space data, wherein the k-space data comprises spiral data lines acquired within each of a plurality of repetition times, the spiral data lines being acquired by applying an imaging sequence to the target object using a magnetic resonance device and acquiring the data in a spiral sampling manner, and the imaging sequence comprising applying a random phase encoding gradient to the plurality of layers in a layer selection direction so that the spiral data lines acquired within each of the repetition times correspond to a random layer among the plurality of layers.

[0005] In some embodiments, the spiral data lines collected within each repetition time have different probabilities of corresponding to different layers.

[0006] In some embodiments, the probability that the spiral data line collected within each repetition time corresponds to each of the slices is inversely proportional to the absolute value of the spatial frequency of the slice.

[0007] In some embodiments, the spiral data line is an inward-outward spiral data line.

[0008] In some embodiments, angles of the inward-outward spiral data lines collected in adjacent repetition times among the plurality of repetition times differ by a golden angle.

[0009] In some embodiments, the three-dimensional magnetic resonance image includes multiple three-dimensional magnetic resonance images corresponding to multiple time periods, each of the time period includes a portion of the multiple repetition times, and reconstructing the three-dimensional magnetic resonance image of the target object based on the k-space data includes: for each of the time period, determining a spiral data line set from the spiral data lines collected within the multiple repetition times, the spiral data line set including the spiral data lines collected within the time period and a portion of the spiral data lines collected within its adjacent time period; based on the spiral data line set, generating the three-dimensional magnetic resonance image corresponding to the time period.

[0010] In some embodiments, the target object is a heart, and the multiple time periods are multiple heartbeat phases.

[0011] In some embodiments, the imaging sequence is a balanced steady-state free precession sequence.

[0012] One or more embodiments of the present specification provide a three-dimensional magnetic resonance imaging system, the system comprising: an acquisition module configured to acquire k-space data of a target object, the target object comprising a plurality of layers; a reconstruction module configured to reconstruct a three-dimensional magnetic resonance image of the target object based on the k-space data, wherein the k-space data comprises spiral data lines acquired within each of a plurality of repetition times, the spiral data lines being acquired by applying an imaging sequence to the target object using a magnetic resonance device and in a spiral sampling manner, and the imaging sequence comprising applying a random phase encoding gradient to the plurality of layers in a layer selection direction so that the spiral data lines acquired within each of the repetition times correspond to a random layer among the plurality of layers.

[0013] One or more embodiments of this specification provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a three-dimensional magnetic resonance imaging method as described in any one of the above embodiments.

[0014] According to the above scheme, by additionally adding a phase encoding gradient in the layer selection direction to the imaging sequence and performing this phase encoding in a random and variable density manner, reconstruction of three-dimensional multi-layer images can be achieved; at the same time, during the reconstruction process, any number of spiral data lines can be specified to be combined into one phase of three-dimensional k-space data, thereby achieving dynamic image reconstruction with high temporal resolution and effectively capturing cardiac movement. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0016] Figure 1 is a schematic diagram of an application scenario of a three-dimensional magnetic resonance imaging system according to some embodiments of this specification;

[0017] Figure 2 is an exemplary module diagram of a three-dimensional magnetic resonance imaging system according to some embodiments of this specification;

[0018] Figure 3 is an exemplary flow chart of a three-dimensional magnetic resonance imaging method according to some embodiments of the present specification;

[0019] Figure 4 is an exemplary flow chart of reconstructing a three-dimensional magnetic resonance image corresponding to a certain time period according to some embodiments of this specification;

[0020] Figure 5 is an exemplary schematic diagram of a spiral data line according to some embodiments of this specification;

[0021] Figure 6 is an exemplary schematic diagram of the gradient, zero-order moment of the gradient, and first-order moment of the gradient corresponding to the inward-outward spiral data line according to some embodiments of this specification;

[0022] Figure 7 is an exemplary schematic diagram of an imaging sequence according to some embodiments of the present specification;

[0023] Figure 8 is an exemplary schematic diagram of spiral data lines collected during multiple heartbeat phases of a heart according to some embodiments of this specification;

[0024] Figure 9 is an exemplary schematic diagram of a magnetic resonance image of a heart at multiple cardiac phases according to some embodiments of the present specification. DETAILED DESCRIPTION

[0025] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0026] It should be understood that the terms "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 terms can achieve the same purpose, the terms may be replaced by other expressions.

[0027] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0028] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0029] Figure 1 Schematic diagram of an application scenario of a three-dimensional magnetic resonance imaging system according to some embodiments of this specification. Figure 1 As shown, the application scenario 100 of the three-dimensional magnetic resonance imaging system may include a magnetic resonance device 110 , a processor 120 , a network 130 , a storage device 140 and a terminal 150 .

[0030] The magnetic resonance device 110 is configured to scan a target object (or a portion of a target object) to acquire image data, such as a magnetic resonance signal associated with the target object. For example, the magnetic resonance device 110 can acquire magnetic resonance signals by applying a magnetic resonance imaging sequence to the target object. The magnetic resonance imaging sequence can be any type of magnetic resonance imaging sequence, such as a spin echo sequence, a gradient echo sequence, a diffusion sequence, an inversion recovery sequence, etc. The target object can include a biological object (e.g., a human body, an animal, etc.), a non-biological object (e.g., a phantom), etc. In other embodiments, the target object can also include a specific part, organ, and / or tissue of a patient. For example, the target object can include the head, chest, legs, heart, etc., or any combination thereof, without limitation herein.

[0031] The processor 120 is configured to process data and / or information obtained from the magnetic resonance device 110, the storage device 140, and / or the terminal 150. For example, the processor 120 may acquire magnetic resonance signals of a target object from the magnetic resonance device 110 and fill them into k-space to generate k-space data. For another example, the processor 120 may reconstruct a three-dimensional magnetic resonance image of the target object based on the k-space data.

[0032] In some embodiments, the processor 120 can be a single server or a server group. The server group can be centralized or distributed. In some embodiments, the processor 120 can be local or remote. In some embodiments, the processor 120 can be connected to the magnetic resonance device 110, storage device 140 and / or terminal 150 via the network 130 or directly to access the information and / or data stored thereon. In some embodiments, the processor 120 can be integrated into the magnetic resonance device 110. In some embodiments, the processor 120 can be implemented on a cloud platform. By way of example only, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, or any combination thereof.

[0033] The network 130 is configured to facilitate information and / or data exchange within the 3D MRI system. In some embodiments, one or more components of the 3D MRI system (e.g., the MRI device 110, the processor 120, the storage device 140, or the terminal 150) may be connected and / or communicate with other components of the 3D MRI system via the network 130. For example, the processor 120 may acquire k-space data of a target object from the MRI device 110 via the network 130.

[0034] In some embodiments, the network 130 can be any one or more of a wired network or a wireless network. For example, the network 130 can include a cable network, a fiber optic network, a telecommunications network, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, near field communication (NFC), an intra-device bus, an intra-device line, a cable connection, or any combination thereof. The network connections between the various components can adopt one of the above methods or a variety of methods. In some embodiments, the network 130 can have various topologies, such as point-to-point, shared, centralized, or a combination of multiple topologies. In some embodiments, the network 130 can include one or more network access points. For example, the network 130 can include wired and / or wireless network access points, such as base stations and / or Internet access points, through which one or more components of the three-dimensional magnetic resonance imaging system can connect to the network 130 to exchange data and / or information.

[0035] The storage device 140 is configured to store data, instructions, and / or any other information. In some embodiments, the storage device 140 may store data obtained from the magnetic resonance device 110, the processor 120, and / or the terminal 150. In some embodiments, the storage device 140 may store data and / or instructions used by the processor 120 to execute or use the exemplary methods described herein.

[0036] In some embodiments, the storage device 140 may include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 140 may be implemented on a cloud platform.

[0037] In some embodiments, the storage device 140 may be connected to the network 130 to communicate with one or more components of the three-dimensional magnetic resonance imaging system (e.g., the magnetic resonance device 110, the processor 120, the terminal 150, etc.). One or more components of the three-dimensional magnetic resonance imaging system may access data or instructions stored in the storage device 140 via the network 130. In some embodiments, the storage device 140 may be part of the processor 120.

[0038] The terminal 150 can communicate and / or be connected with the magnetic resonance device 110, the processor 120 and / or the storage device 140. For example, the terminal 150 can display a three-dimensional magnetic resonance image (such as a three-dimensional cardiac movie image) reconstructed by the processor 120. In some embodiments, the terminal 150 can include one or any combination of a mobile device 150-1, a tablet computer 150-2, a laptop computer 150-3, or other devices with input and / or output functions. In some embodiments, the terminal 150 can remotely control the magnetic resonance device 110. In some embodiments, the terminal 150 can be part of the processor 120. In some embodiments, the terminal 150 can be integrated with the processor 120 as an operating console for the magnetic resonance device 110. In some embodiments, the terminal 150 can be omitted.

[0039] It should be noted that the application scenario 100 of the 3D MRI system is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art will readily be able to make various modifications or variations based on the description of this specification. For example, the application scenario 100 of the 3D MRI system can be implemented on other devices to achieve similar or different functionality. However, such modifications and variations do not deviate from the scope of this specification.

[0040] Figure 2 is an exemplary module diagram of a three-dimensional magnetic resonance imaging system according to some embodiments of this specification. In some embodiments, the three-dimensional magnetic resonance imaging system 200 may include an acquisition module 210 and a reconstruction module 220. In some embodiments, the three-dimensional magnetic resonance imaging system 200 may be composed of Figure 1 The processor 120 shown implements .

[0041] Acquisition module 210 is configured to acquire k-space data of a target object. The target object includes multiple slices, and the k-space data includes spiral data lines acquired during each of multiple repetition times. The spiral data lines are acquired by applying an imaging sequence to the target object using a magnetic resonance device and performing spiral sampling. The imaging sequence includes applying a random phase encoding gradient to the multiple slices in a slice selection direction, such that the spiral data lines acquired during each repetition time correspond to a random slice among the multiple slices. For more information on acquiring k-space data, see step 310 and are not further detailed here.

[0042] The reconstruction module 220 is configured to reconstruct a three-dimensional magnetic resonance image of the target object based on the k-space data. For more details on reconstructing the three-dimensional magnetic resonance image, please refer to step 320 and will not be repeated here.

[0043] It should be noted that the above description of the 3D MRI system 200 and its modules is for convenience only and does not limit this specification to the scope of the embodiments. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or form subsystems connected to other modules without departing from the principles. In some embodiments, Figure 2 The acquisition module 210 and reconstruction module 220 disclosed herein may be separate modules within a single system, or a single module may implement the functions of two or more of the aforementioned modules. For example, the modules may share a storage module, or each module may have its own storage module. Such variations are within the scope of this specification.

[0044] Figure 3 is an exemplary flow chart of a three-dimensional magnetic resonance imaging method according to some embodiments of the present specification. In some embodiments, process 300 can be executed by the processor 120 or the three-dimensional magnetic resonance imaging system 200. For example, process 300 can be stored in the storage device 140 in the form of a program or instructions, and when the processor 120 or the three-dimensional magnetic resonance imaging system 200 executes the instructions, process 300 can be implemented. The operational diagram of process 300 presented below is illustrative. In some embodiments, the process can be completed using one or more additional operations not described and / or one or more operations not discussed. In addition, Figure 3 The order in which the operations of flow 300 are illustrated and described below is not intended to be limiting.

[0045] Step 310: Acquire k-space data of the target object.

[0046] The target object refers to the object for which magnetic resonance image reconstruction is required. In some embodiments, the target object may be a part of the human body that is affected by physiological motion (such as heartbeat motion, respiratory motion), such as the heart. Through process 300, a dynamic three-dimensional magnetic resonance image (for example, a cardiac movie image) can be generated, which can show the motion of the target object over time. For more information about the target object, please refer to Figure 1 and its related descriptions.

[0047] In some embodiments, the target object includes multiple layers. A layer of the target object refers to a slice (such as a cross section) with a certain thickness in the target object.

[0048] K-space data refers to data obtained by filling k-space with magnetic resonance signals acquired during an MRI scan. In some embodiments, the k-space data includes spiral data lines acquired during each of a plurality of repetition times. The spiral data lines are acquired by applying an imaging sequence to a target subject using a magnetic resonance device and using a spiral sampling method.

[0049] An imaging sequence refers to the sequence of pulses applied to a target object during a magnetic resonance imaging scan. For example, an imaging sequence may include a spin echo sequence, a gradient echo sequence, a diffusion sequence, an inversion recovery sequence, and the like. In some embodiments, the imaging sequence is a balanced steady-state free precession (bSSFP) sequence. For example, when the target object is a heart, a bSSFP sequence can be used to acquire magnetic resonance signals. The magnetic resonance signals acquired by this sequence have a high signal-to-noise ratio, which can improve imaging quality.

[0050] Repetition time refers to the time interval between two excitation pulses during an MRI scan. During each repetition time, the MRI device applies a magnetic resonance sequence to the target object and acquires the corresponding magnetic resonance signals.

[0051] A spiral data line refers to a k-space data line obtained by spiral sampling. For example, a spiral data line may be a k-space data line spiraling outward from the center of k-space. Another example is a spiral data line spiraling inward from the periphery of k-space toward the center.

[0052] In some embodiments, the spiral data line is an inward-outward spiral data line. Figure 5 As shown, the data line trajectory of the inward-outward spiral data line can be starting from the center point O of the k-space, outward along the direction of arrow A, and then inward from the periphery of the k-space to the center along the direction of arrow B. After returning to the center point O of the k-space, it is outward along the arrow C, and then inward from the periphery of the k-space to the center along the arrow D.

[0053] The use of an inward-inward spiral data line allows the zero-order gradient moment (M0) and the first-order gradient moment (M1) to be essentially zeroed at each repetition time. The zero-order gradient moment can be expressed as ∫G(t)dt. For the X direction, the zero-order gradient moment is the time-integrated magnetic field gradient in the X direction; for the Y direction, the zero-order gradient moment is the time-integrated magnetic field gradient in the Y direction. The zero-order gradient moment is essentially zeroed at each repetition time, allowing data to be acquired directly from the center of k-space at each repetition time, without the need to apply additional gradients to zero the zero-order gradient moments in the X and Y directions.

[0054] The first-order moment of the gradient can be expressed as ∫G(t)tdt. For the X direction, the first-order moment of the gradient refers to the time integral of the area of ​​the magnetic field gradient in the X direction; for the Y direction, the first-order moment of the gradient refers to the time integral of the area of ​​the magnetic field gradient in the Y direction. The first-order moment of the gradient in each repetition time is basically zero, making the magnetic resonance scan less susceptible to the influence of the flow signal and no flow artifacts. Just as an example, Figure 6 As shown, when the inward-outward spiral data line is used, the gradient, the gradient zero-order moment and the gradient first-order moment in the X and Y directions are substantially returned to zero at the end of each repetition time.

[0055] In some embodiments, the imaging sequence includes applying random phase encoding gradients to multiple layers in a layer selection direction. The layer selection direction refers to a direction perpendicular to the layer of the target object, which corresponds to the k-space. z direction.

[0056] A random phase encoding gradient means that the size of the applied phase encoding gradient is random. For example, the sizes of the phase encoding gradients in the layer selection direction corresponding to different layers are different. When the phase encoding gradient corresponding to a certain layer is applied, the layer can be excited and imaged. In each repetition time, the phase encoding gradient corresponding to a certain layer can be randomly selected and applied in the layer selection direction. Since the phase encoding gradient applied in each repetition time is random, the layer excited and imaged in each repetition time (the layer corresponding to the collected spiral data line) is also random, so that the spiral data line collected in each repetition time corresponds to a random layer among multiple layers. In some embodiments, the processor can apply random phase encoding gradients to multiple layers in the layer selection direction by controlling the gradient coils of the magnetic resonance device.

[0057] Just as an example, Figure 7 As shown, an exemplary imaging sequence may include an excitation pulse 710 (RF), a phase encoding gradient 720 in a slice selection direction, a focusing gradient 730 (also called a zeroing gradient), and a slice selection gradient 740 .

[0058] In some embodiments, the probability that the spiral data lines collected within each repetition time correspond to different layers is different.

[0059] In some embodiments, the processor can apply a phase encoding gradient in a randomized and variable-density manner within each repetition time, such that the probability of applying the phase encoding gradient corresponding to each layer is different, thereby achieving different probabilities that the spiral data lines acquired within each repetition time correspond to different layers. As an example, if the probability of applying the phase encoding gradient corresponding to layer E is higher than that of layer F, then within each repetition time, the probability of acquiring spiral data lines corresponding to layer E is higher than that of layer F, and the number of spiral data lines acquired for layer E may ultimately be greater than that for layer F.

[0060] In some embodiments, the probability that the spiral data lines collected within each repetition time correspond to each layer is inversely proportional to the absolute value of the spatial frequency of the layer. The unit of spatial frequency is cycles per pixel width. For example, the smaller the absolute value of the spatial frequency of a layer, the greater the probability that the spiral data lines are collected within the repetition time. The spatial frequency of each layer is the coefficient obtained by uniformly interpolating the ordinal number corresponding to the layer into the spatial frequency interval. For example, if the ordinal numbers of multiple layers are 1, 2, 3, and 4, respectively, and the spatial frequency interval is [-0.5, 0.5], then the corresponding coefficients are -0.5, -0.167, 0.167, and 0.5, respectively.

[0061] In some embodiments, the processor may use the formula The probability of the spiral data line associated with the layer sequence number collected during each repetition time is calculated. Z ) is the probability of each layer corresponding to the spiral data line collected within each repetition time; k z is the spatial frequency of the layer; α is the coefficient that normalizes the sum of the probabilities of all layers appearing. In this embodiment, the coefficient α can be obtained by the formula Calculated, N z k z The total number of steps, n is an integer, and the above formula is used to make the sum of the probabilities of the spiral data lines acquired by all k-space layers equal to 1.

[0062] In some embodiments, the probability that the spiral data line collected within each repetition time corresponds to each layer can also be determined by other means. For example, the probability that the spiral data line collected within each repetition time corresponds to each layer can be a preset probability. In another example, the probability that the spiral data line collected within each repetition time corresponds to a layer near the center of the target object is greater than the probability that the spiral data line collected within each repetition time corresponds to a layer near the edge of the target object.

[0063] In some embodiments, the angles of the spiral data lines collected in adjacent repetition times among the multiple repetition times differ by a golden angle (e.g., 137.51°, 222.5°). Specifically, if the spiral data line collected in a certain repetition time is k x -k y The first angle is in the plane, and the spiral data collected in the next repetition time is at k x -k y If the angle in the plane is the second angle, then the second angle is the sum of the first angle and the golden angle, or the difference between the first angle and the golden angle.

[0064] Step 320 : reconstructing a three-dimensional magnetic resonance image of the target object based on the k-space data.

[0065] A three-dimensional magnetic resonance image refers to a magnetic resonance image obtained by stacking two-dimensional images corresponding to multiple layers of a target object.

[0066] In some embodiments, the processor can reconstruct a three-dimensional magnetic resonance image of the target object based on the k-space data using any suitable algorithm, such as a fast Fourier transform or a back-projection algorithm. In some embodiments, the processor can correct the spiral data lines corresponding to the k-space data using a gradient impulse response function, and then iteratively reconstruct the three-dimensional magnetic resonance image of the target object based on time-space constraints.

[0067] In some embodiments, a three-dimensional magnetic resonance image includes multiple three-dimensional magnetic resonance images corresponding to multiple time periods. A time period refers to a preset time period. Each time period includes a portion of the repetition time in a plurality of repetition times. For example, each time period may include 12 repetition times. There may be time overlap between adjacent time periods, or there may be no time overlap. In some embodiments, when the target object is subjected to physiological motion, the multiple time periods may be physiological motion phases (for example, cardiac phases or respiratory phases). In some embodiments, the three-dimensional magnetic resonance image corresponding to each time period can be generated based on the k-space data collected during the time period. For example, for a certain time period, the k-space data collected during the time period can be processed using an image reconstruction algorithm to obtain a three-dimensional magnetic resonance image corresponding to the time period. In some embodiments, the three-dimensional magnetic resonance image corresponding to each time period can be generated based on the k-space data collected during the repetition time contained in the time period, and the portion of the k-space data collected during the adjacent time periods of the time period. For a description of generating a three-dimensional magnetic resonance image corresponding to each time period, see Figure 4 , I will not go into details here.

[0068] In some embodiments of the present specification, based on spiral sampling, by additionally adding a phase encoding gradient in the layer selection direction to the imaging sequence, and performing this phase encoding in a random and variable density manner, combined with spiral data lines rotating at a golden angle, reconstruction of three-dimensional multi-layer images can be achieved; at the same time, during the reconstruction process, any number of spiral data lines can be specified to be combined into the three-dimensional k-space data of a phase, and the three-dimensional k-space data of each phase is unique, so as to achieve the purpose of arbitrarily adjusting the time resolution, while effectively reducing the degree of artifacts in dynamic images.

[0069] Figure 4 This is an exemplary flow chart for reconstructing a three-dimensional magnetic resonance image corresponding to a time period according to some embodiments of this specification. In some embodiments, for each time period, the processor may execute process 400 to generate a three-dimensional magnetic resonance image corresponding to the time period.

[0070] Step 410 : Determine a spiral data line set from the spiral data lines collected during multiple repetition times.

[0071] A spiral data line set refers to a data set consisting of multiple spiral data lines. In some embodiments, the spiral data line set includes the spiral data lines collected during a time period. In some embodiments, the spiral data line set includes the spiral data lines collected during a time period and a portion of the spiral data lines collected during an adjacent time period (e.g., two spiral data lines, four spiral data lines, etc.).

[0072] In some embodiments, the processor may determine a time period adjacent to the time period, select a preset number of spiral data lines from the adjacent time period and all spiral data lines collected during the current time period, and determine the selected spiral data lines as a spiral data line set. The preset number may be a system preset value, user-determined, or determined by the processor 120 based on analysis of historical reconstructed images. Adjacent time periods may include the previous time period and / or the next time period of the time period. In some embodiments, for the first time period, its adjacent time period is the next time period; for the last time period, its adjacent time period is the previous time period; and for the intermediate time period, its adjacent time period includes both the previous and next time periods.

[0073] Step 420 : Generate a three-dimensional magnetic resonance image corresponding to the time period based on the spiral data line set.

[0074] In some embodiments, the processor may reconstruct the three-dimensional magnetic resonance image corresponding to the time period based on the spiral data line set by using any suitable algorithm such as fast Fourier transform and back projection algorithm.

[0075] In some embodiments of the present specification, by sharing spiral data lines in adjacent time periods, not only can the image reconstruction quality be improved, but also only less magnetic resonance data needs to be collected in each time period to reconstruct a high-quality image.

[0076] In some embodiments, when the target object is a heart, the multiple time periods are multiple heartbeat phases. For example only, Figure 8 As shown in FIG, the target object is the heart, and phase 1, phase 2, and phase 3 are multiple heartbeat periods (i.e., multiple time periods). Each heartbeat phase includes 12 repetition times. In each repetition time, a random phase encoding gradient is applied in the layer selection direction to randomly select a layer from the 12 layers to acquire the corresponding spiral data line. In the next adjacent repetition time, the angle of the spiral data line is changed, and another layer is randomly selected to acquire the corresponding spiral data line. A total of 12 spiral data lines are acquired ( Figure 8 Each point in the image represents a spiral data line obtained. For each cardiac phase, the processor selects the spiral data lines collected in the cardiac phase and a preset number (e.g., 2 or 4) of spiral data lines collected in the adjacent cardiac phase to form a spiral data line set corresponding to the cardiac phase, and reconstructs a three-dimensional magnetic resonance image corresponding to the cardiac phase based on the spiral data line set. In some embodiments, when the spiral line data includes 4 spiral data lines of the two cardiac phases before and after each cardiac phase, full-heart 3D acquisition can be completed within a time resolution of 6.8ms×8 (54.4ms), k x -k y The intra-layer acceleration factor reaches 32.

[0077] The reconstructed magnetic resonance images of multiple phases are as follows Figure 9 As shown, it can be seen that the reconstruction results can not only accurately visualize the cardiac motion of the complete cardiac cycle with high temporal resolution, but also have excellent contrast between the myocardium and blood pool, high signal-to-noise ratio and high spatial resolution.

[0078] It should be noted that the above description of processes 300 and 400 is for illustration and purpose only and does not limit the scope of application of this specification. Those skilled in the art may, under the guidance of this specification, make various modifications and alterations to processes 300 and 400. However, such modifications and alterations remain within the scope of this specification.

[0079] One or more embodiments of this specification provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a three-dimensional magnetic resonance imaging method as described in any one of the above embodiments.

[0080] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0081] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0082] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful 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. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit 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.

[0083] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0084] In some embodiments, numbers are used to describe the quantity of components and attributes. 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 stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required characteristics 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 settings of such numerical values ​​are as accurate as possible within the feasible range.

[0085] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited in this specification is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this specification, as well as documents (currently or subsequently attached to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the content of this specification, the descriptions, definitions, and / or terminology used in this specification will control.

[0086] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not 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 described and illustrated in this specification.

Claims

1. A three-dimensional magnetic resonance imaging method, characterized in that: include: Acquiring k-space data of a target object, wherein the target object includes a plurality of layers; Based on the k-space data, a three-dimensional magnetic resonance image of the target object is reconstructed, wherein: The k-space data includes spiral data lines acquired in each repetition time of a plurality of repetition times, wherein the spiral data lines are acquired by applying an imaging sequence to the target object using a magnetic resonance device and in a spiral sampling manner; and The imaging sequence includes applying random phase encoding gradients to multiple layers in a layer selection direction, so that the spiral data lines collected within each repetition time correspond to a random layer among the multiple layers.

2. The method according to claim 1, wherein The probability that the spiral data lines collected within each repetition time correspond to different layers is different.

3. The method according to claim 2, wherein The probability that the spiral data line collected within each repetition time corresponds to each of the slices is inversely proportional to the absolute value of the spatial frequency of the slice.

4. The method according to claim 1, wherein The spiral data cable is an inward-outward spiral data cable.

5. The method according to claim 4, wherein The angles of the inward-outward spiral data lines collected in adjacent repetition times among the multiple repetition times differ by a golden angle.

6. The method according to claim 4, wherein The three-dimensional magnetic resonance image includes a plurality of three-dimensional magnetic resonance images corresponding to a plurality of time periods, each of the time periods includes a portion of the plurality of repetition times, and reconstructing the three-dimensional magnetic resonance image of the target object based on the k-space data includes: For each of the time periods, Determine a spiral data line set from the spiral data lines collected within the multiple repetition times, wherein the spiral data line set includes the spiral data lines collected within the time period and a portion of the spiral data lines collected within the adjacent time period; A three-dimensional magnetic resonance image corresponding to the time period is generated based on the spiral data line set.

7. The method according to claim 6, wherein The target object is a heart, and the multiple time periods are multiple heartbeat phases.

8. The method according to claim 7, wherein The imaging sequence is a balanced steady-state free precession sequence.

9. A three-dimensional magnetic resonance imaging system, characterized in that: include: an acquisition module configured to acquire k-space data of a target object, wherein the target object includes a plurality of layers; A reconstruction module is configured to reconstruct a three-dimensional magnetic resonance image of the target object based on the k-space data, wherein: The k-space data includes spiral data lines acquired in each repetition time of a plurality of repetition times, wherein the spiral data lines are acquired by applying an imaging sequence to the target object using a magnetic resonance device and in a spiral sampling manner; and The imaging sequence includes applying random phase encoding gradients to multiple layers in a layer selection direction, so that the spiral data lines collected within each repetition time correspond to a random layer among the multiple layers.

10. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a three-dimensional magnetic resonance imaging method according to any one of claims 1 to 8.

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