A magnetic resonance quantitative imaging method

Abstract

The embodiment of the present specification provides a method for quantitative magnetic resonance imaging. The method comprises: acquiring at least two temporally continuous echo images of a scanned object, each of the echo images being obtained by reconstructing k-space data corresponding to the same echo time point acquired by multiple excitation planar echo data acquisitions; determining the water-fat transition region and phase distribution of each of the echo images; determining the water-fat quantitative distribution based on the water-fat transition region and the phase distribution of each of the echo images, and obtaining a fat quantitative map of the scanned object.

Description

[0001] Description of the case

[0002] This application is a divisional application filed for the Chinese application with the application date of May 18, 2021, application number 202110539454.6, and invention name “A Magnetic Resonance Imaging Method and System”. Technical Field

[0003] The present invention relates to the medical field, and in particular to a magnetic resonance quantitative imaging method. Background Art

[0004] Magnetic resonance imaging (MRI) is a medical imaging technique used for medical diagnosis. A magnetic resonance (MR) scanner uses a strong magnetic field, magnetic field gradients, and radio waves to generate images of the object being scanned (e.g., tissue or organs in the human body). MRI examinations can provide strong soft tissue contrast, but the scanning time is long. The long scanning time requires a high degree of cooperation from the scanned subject and may cause motion artifacts in the imaging of certain motion-sensitive areas.

[0005] Therefore, it is hoped to provide a magnetic resonance quantitative imaging method that can achieve faster imaging speed and improve imaging quality. Summary of the invention

[0006] In one aspect, the present specification provides a method for quantitative magnetic resonance imaging. The method comprises: acquiring at least two temporally continuous echo images of a scanned object, each of the echo images being obtained by reconstructing k-space data corresponding to the same echo time point acquired by multiple excitation planar echo data acquisitions; determining the water-fat transition region and phase distribution of each of the echo images; determining the water-fat quantitative distribution based on the water-fat transition region and the phase distribution of each of the echo images, and obtaining a fat quantitative map of the scanned object.

[0007] Another aspect of the present specification provides 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 magnetic resonance quantitative imaging method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] 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:

[0009] Figure 1 is a schematic diagram of a magnetic resonance imaging system according to some embodiments of the present specification;

[0010] Figure 2is an exemplary module diagram of a magnetic resonance imaging system according to some embodiments of the present specification;

[0011] Figure 3 is a schematic diagram of an exemplary EPI acquisition module according to some embodiments of this specification;

[0012] Figure 4 is an exemplary flow chart of a magnetic resonance imaging method according to some embodiments of the present specification;

[0013] Figure 5 is an exemplary schematic diagram of a pulse sequence according to some embodiments of the present specification;

[0014] Figure 6 is an exemplary schematic diagram of a space-filling trajectory according to some embodiments of the present specification;

[0015] Figure 7 is an exemplary schematic diagram of a space-filling trajectory according to other embodiments of this specification;

[0016] Figure 8 is an exemplary schematic diagram of a method for determining a weight value according to some embodiments of this specification;

[0017] Fig. 9 is an exemplary schematic diagram of a method for determining a fat quantitative map according to some embodiments of the present specification;

[0018] Fig.10 is an exemplary flow chart of a magnetic resonance imaging method according to other embodiments of the present specification. DETAILED DESCRIPTION

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification, and the relevant description is to help better understand the medical imaging method and / or system. It should be understood that the previous or subsequent operations are not necessarily performed precisely in order. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 is a schematic diagram of a magnetic resonance imaging system according to some embodiments of the present specification.

[0024] like Figure 1 As shown, the magnetic resonance imaging system 100 may include a magnetic resonance scanner 110, a processing device 120, a storage device 130, one or more terminals 140, and a network 150. The components in the magnetic resonance imaging system 100 may be connected in one or more of various ways. For example, Figure 1 As shown, the magnetic resonance scanner 110 may be connected to the processing device 120 via the network 150. As another example, the magnetic resonance scanner 110 may be directly connected to the processing device 120, such as the magnetic resonance scanner 110 and the processing device 120 may be connected as indicated by the dashed double-headed arrow. As yet another example, the storage device 130 may be directly connected to the processing device 120 ( Figure 1 As yet another example, one or more terminals 140 may be directly connected to processing device 120 (as shown by the dashed bidirectional arrows connecting terminal 140 and processing device 120) or connected through network 150.

[0025] The magnetic resonance scanner 110 can scan an object or a portion thereof located within its detection area and generate a magnetic resonance signal related to the object or a portion thereof. In the present application, the terms "object" and "subject" are used interchangeably. In some embodiments, the object may include a body, a substance, etc., or any combination thereof. In some embodiments, the object may include a specific part of the body, such as the head, chest, abdomen, etc., or any combination thereof. In some embodiments, the object may include a specific organ, such as the heart, esophagus, trachea, bronchi, stomach, gall bladder, small intestine, colon, bladder, ureter, uterus, fallopian tube, etc. The magnetic resonance scanner 110 may include a magnet assembly, a gradient coil assembly, and a radio frequency (RF) coil assembly.

[0026] The magnet assembly may generate a first magnetic field (also referred to as a main magnetic field) for polarizing the object to be scanned. The magnet assembly may include a permanent magnet, a superconducting electromagnet, a resistive electromagnet, and the like.

[0027] The gradient coil assembly can generate a second magnetic field (also referred to as a gradient magnetic field). The gradient coil assembly can include an X gradient coil, a Y gradient coil, and a Z gradient coil. The gradient coil assembly can generate one or more magnetic field gradient pulses to the main magnetic field in the X direction (Gx), the Y direction (Gy), and the Z direction (Gz) to encode the spatial information of the scanned object. In some embodiments, the X direction can be designated as a frequency encoding direction, and the Y direction can be designated as a phase encoding direction. In some embodiments, Gx can be used for frequency encoding or signal readout, which is generally referred to as a frequency encoding gradient or a readout gradient. In some embodiments, Gy can be used for phase encoding, which is generally referred to as a phase encoding gradient. In some embodiments, Gz can be used for slice selection to obtain two-dimensional k-space data. In some embodiments, Gz can be used for phase encoding to obtain three-dimensional k-space data.

[0028] The RF coil assembly may include at least two RF coils. The RF coil may include one or more RF transmitting coils and / or one or more RF receiving coils. The RF transmitting coil may transmit RF pulses to the object to be scanned. Under the synergistic effect of the main magnetic field / gradient magnetic field and the RF pulse, a magnetic resonance signal related to the object may be generated according to a pulse sequence. The RF receiving coil may acquire a magnetic resonance signal from the object according to the pulse sequence. The magnetic resonance signal may also be referred to as an echo signal. A transformation operation (e.g., Fourier transform) may be used to process the magnetic resonance signal to fill the k-space to acquire k-space data. The pulse sequence may be defined by imaging parameters and the arrangement of the imaging parameters in a time sequence. For example, the pulse sequence may be defined by one or more parameters related to time (e.g., acquisition time (TA), echo time (TE), etc.). Exemplary pulse sequences may include spin echo sequences, gradient echo sequences, diffusion sequences, inversion recovery sequences, etc., or any combination thereof. For example, the spin echo sequence may include fast spin echo (FSE), eddy current spin echo (TSE), rapid acquisition with relaxation enhancement (RARE), half Fourier acquisition single shot spin echo (HASTE), eddy current gradient spin echo (TGSE), etc., or any combination thereof. In some embodiments, the pulse sequence may include a diffusion sequence. The diffusion sequence may include at least one diffusion block associated with one or more parameters of the diffusion gradient and at least one imaging block associated with one or more scanning parameters (e.g., parameters associated with one or more encoding gradients applied in the scan). In some embodiments, the pulse sequence may include a single shot pulse sequence (e.g., single shot FSE, single shot EPI, etc.), a multiple shot pulse sequence (e.g., multiple shot FSE, multiple shot EPI, etc.) In some embodiments, for a single shot pulse sequence (e.g., single shot FSE, single shot EPI, etc.), an RF pulse may be applied once within a TR, and one or more echo signals may be sampled within a TR. For a multiple-shot pulse sequence (eg, multiple-shot FSE, multiple-shot EPI, etc.), RF pulses may be applied multiple times within a TR, and one or more echo signals may be sampled during each shot or excitation.

[0029] In some embodiments, the MRI scanner 110 may include an analog-to-digital converter (ADC) ( Figure 1 1. The analog-to-digital converter may convert the magnetic resonance signals received by one or more RF receiving coils into magnetic resonance imaging data. The analog-to-digital converter may be a direct conversion ADC, a successive approximation ADC, a ramp comparison ADC, a Wilkinson ADC, an integrating ADC, an incremental coding ADC, a pipeline ADC, a sigma-delta ADC, etc., or any combination thereof.

[0030] The processing device 120 can process data and / or information acquired and / or extracted from the magnetic resonance scanner 110, the terminal 140, the storage device 130 and / or other storage devices. For example, the processing device 120 can acquire the first imaging data and the second imaging data, and reconstruct and generate a target magnetic resonance image based on the first imaging data and the second imaging data. For another example, the processing device 120 can determine a weight value reflecting the correlation between the uncollected data and the collected data in the first imaging space based on the second imaging data fully collected in the central area of ​​the second imaging space using a preset algorithm. Then, the processing device 120 can restore the uncollected data corresponding to the partially collected first imaging data in the first imaging space based on the weight value to obtain the restored imaging data, and / or generate a magnetic resonance image based on the restored complete imaging data by image reconstruction or the like. In some embodiments, the generated magnetic resonance image can be sent to the terminal 140 and displayed on one or more display devices in the terminal 140.

[0031] In some embodiments, the processing device 120 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device 120 may be local or remote. For example, the processing device 120 may access information and / or data stored in the magnetic resonance scanner 110, the terminal 140, and / or the storage device 130 via the network 150. For another example, the processing device 120 may be directly connected to the magnetic resonance scanner 110, one or more terminals 140, and / or the storage device 130 to access the stored information and / or data. In some embodiments, the processing device 120 may be implemented on a cloud platform. As an example only, the 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 processing device 120 may be implemented on a computing device. In some embodiments, the processing device 120 may be implemented on a terminal (e.g., terminal 140). In some embodiments, the processing device 120 may be implemented on a magnetic resonance imaging device (e.g., a magnetic resonance scanner 110).

[0032] The storage device 130 may store data and / or instructions. In some embodiments, the storage device 130 may store data acquired from the terminal 140 and / or the processing device 120. For example, the storage device 130 may store the first imaging data, the second imaging data, the intermediate data generated in the magnetic resonance image reconstruction (e.g., the recovered uncollected data corresponding to the first imaging data), the target magnetic resonance image, etc. In some embodiments, the storage device 130 may store data and / or instructions, and the processing device 120 may execute or use the data and / or instructions to perform the exemplary method described in the present application. In some embodiments, the storage device 130 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. Exemplary mass storage devices may include magnetic disks, optical disks, solid-state drives, etc. Exemplary removable storage devices may include flash drives, floppy disks, optical disks, memory cards, compressed disks, tapes, etc. Exemplary volatile read-write memory may include random access memory (RAM). Exemplary RAM may include dynamic random access memory (DRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), and zero capacitance random access memory (Z-RAM), etc. Exemplary ROM may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (PEROM), electrically erasable programmable ROM (EEPROM), compact disc ROM (CD-ROM), and digital versatile disk ROM, etc. In some embodiments, the storage device 130 may be implemented on a cloud platform. By way of example only, the 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.

[0033] In some embodiments, the storage device 130 may be connected to the network 150 to communicate with one or more components of the magnetic resonance imaging system 100 (e.g., the processing device 120, one or more terminals 140, etc.). One or more components of the magnetic resonance imaging system 100 may access data or instructions stored in the storage device 130 through the network 150. In some embodiments, the storage device 130 may be directly connected to or communicate with one or more components of the magnetic resonance imaging system 100 (e.g., the processing device 120, one or more terminals 140, etc.). In some embodiments, the storage device 130 may be part of the processing device 120.

[0034] The terminal 140 may include a mobile device 140-1, a tablet computer 140-2, a laptop computer 140-3, etc., or any combination thereof. In some embodiments, the mobile device 140-1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, etc., or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a smart appliance control device, a smart monitoring device, a smart TV, a smart camera, an intercom, etc., or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart shoelace, smart glasses, a smart helmet, a smart watch, smart clothing, a smart backpack, a smart accessory, etc., or any combination thereof. In some embodiments, the smart mobile device may include a smart phone, a personal digital assistant (PDA), a gaming device, a navigation device, a point of sale (POS), etc., or any combination thereof. In some embodiments, the virtual reality device and / or the augmented reality device may include a virtual reality helmet, virtual reality glasses, virtual reality goggles, an augmented reality helmet, augmented reality glasses, augmented reality goggles, etc., or any combination thereof. For example, the virtual reality device and / or the augmented reality device may include Google Glass, Oculus Rift, Hololens, Gear VR, etc.

[0035] In some embodiments, one or more terminals 140 can remotely operate the magnetic resonance scanner 110. In some embodiments, the terminal 140 can operate the magnetic resonance scanner 110 via a wireless connection. In some embodiments, one or more terminals 140 can receive information and / or instructions input by a user, and send the received information and / or instructions to the magnetic resonance scanner 110 or the processing device 120 via the network 150. In some embodiments, one or more terminals 140 can receive data and / or information from the processing device 120. In some embodiments, one or more terminals 140 can be part of the processing device 120. In some embodiments, the terminal 140 can be omitted.

[0036] In some embodiments, one or more terminals 140 may send and / or receive information for magnetic resonance image reconstruction to the processing device 120 via a user interface. The user interface may be implemented on the terminal 140 in the form of an application for magnetic resonance image reconstruction. The user interface implemented on the one or more terminals 140 may be configured to facilitate communication between the user and the processing device 120. In some embodiments, the user may input a magnetic resonance image reconstruction request via the user interface implemented on the terminal 140. The terminal 140 may send the magnetic resonance image reconstruction request to the processing device 120 for reconstruction as described elsewhere in the present application (e.g., Figure 8and related descriptions). In some embodiments, a user may input and / or adjust scanning or imaging parameters (e.g., pulse sequences) via a user interface. In some embodiments, the user interface may facilitate presentation or display of information and / or data (e.g., signals) related to magnetic resonance imaging received from the processing device 120. For example, the information and / or data may include results generated by the processing device 120 in image reconstruction. For example, the results may include one or more images (e.g., 2D images, 3D images, etc.), one or more data graphics, one or more words, one or more numbers, one or more models for magnetic resonance image reconstruction, parameters used in such image reconstruction, etc. In some embodiments, the information and / or data may be further configured to cause the terminal 140 to display the results to the user.

[0037] The network 150 may include any suitable network that can facilitate the exchange of information and / or data of the magnetic resonance imaging system 100. In some embodiments, one or more components of the magnetic resonance imaging system 100 (e.g., the magnetic resonance scanner 110, one or more terminals 140, the processing device 120, or the storage device 130) may communicate with one or more other components of the magnetic resonance imaging system 100 to transfer information and / or data. For example, the processing device 120 may obtain magnetic resonance signals from the magnetic resonance scanner 110 via the network 150. For another example, the processing device 120 may obtain user instructions from the terminal 140 via the network 150. In some embodiments, the network 150 may be any type of wired or wireless network or a combination thereof. The network 150 may be and / or include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN), a wide area network (WAN), etc.), a wired network (e.g., Ethernet), a wireless network (e.g., an 802.11 network, a Wi-Fi network, etc.), a cellular network (e.g., a Long Term Evolution (LTE) network), a frame relay network, a virtual private network (“VPN”), a satellite network, a telephone network, a router, a hub, a switch, a server computer, and / or any combination thereof. By way of example only, the network 150 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth TM Network, ZigBee TM In some embodiments, the network 150 may include one or more network access points. For example, the network 150 may include a wired and / or wireless network access point such as a base station and / or an Internet exchange point, and one or more components of the magnetic resonance imaging system 100 may be connected to the network 150 through the wired and / or wireless access point to exchange data and / or information.

[0038] It should be noted that the above description of the magnetic resonance imaging system 100 is for illustrative purposes only and is not intended to limit the scope of the present application. For those of ordinary skill in the art, various modifications and variations can be made according to the present application. However, these variations and variations do not depart from the scope of the present application. For example, the magnetic resonance imaging system 100 may also include a display device for outputting the restored first imaging data, the finally generated magnetic resonance image, etc. For another example, the magnetic resonance scanner 110, the processing device 120 and the terminal 140 may share a storage device 130, or may have their own storage devices.

[0039] Figure 2 is an exemplary module diagram of a magnetic resonance imaging system according to some embodiments of the present specification.

[0040] like Figure 2 As shown, in some embodiments, the magnetic resonance imaging system 200 may include an EPI (Echo Planar Imaging) acquisition module 210 and an image generation module 220. In some embodiments, one or more modules in the magnetic resonance imaging system 200 may be interconnected. The connection may be wireless or wired. At least a portion of the magnetic resonance imaging system 200 may be connected to a wireless or wired network. Figure 1 The processing device 120 or the terminal 140 is shown.

[0041] The EPI acquisition module 210 can be used to acquire imaging data. For example, the EPI acquisition module 210 can acquire the first scanning data by multiple excitation echo plane data acquisition mode, and / or acquire the second scanning data by multiple excitation echo plane data acquisition mode. In this embodiment, the first scanning data and the second scanning data can refer to the data when the magnetic resonance scanner 110 scans the object or a part thereof located in its detection area when multiple excitation echo planes are acquired by the EPI acquisition module 210, that is, the acquisition data that has not yet been filled into the k-space. In some embodiments, the first scanning data is acquired using a scanning sequence set based on a first preset parameter and by multiple excitation echo plane data acquisition mode, and the first preset parameter may include a preset phase encoding gradient sequence. Wherein, in each excitation, the gradient direction and / or amplitude of the phase encoding gradient sequence changes periodically. In some embodiments, for different excitations, the gradient direction and / or amplitude value of the pre-phase of the phase encoding gradient sequence in the first preset parameter may be different, and accordingly, the gradient direction and / or amplitude value of the pre-phase corresponding to the first imaging data may be different, so that the filling position of the first imaging data in the first imaging space may be different.

[0042] In some embodiments, the EPI acquisition module 210 may be used to acquire gradient echo data after a 90 degree pulse, for example, Figure 3(a) In some embodiments, the EPI acquisition module 210 can be used to acquire spin echo data after a 90-degree to 180-degree pulse, for example, Figure 3 (b) In some embodiments, the EPI acquisition module 210 can be used to acquire stimulation echo data after a 90-degree-180-degree-180-degree pulse, for example, Figure 3 (c) In some embodiments, the EPI acquisition module 210 can be used to acquire diffusion data after a 90-degree diffusion pulse sequence, for example, Figure 3 (d) The scheme shown.

[0043] In some embodiments, the EPI acquisition module 210 may further include an acquisition unit 213 , a calculation unit 215 , and a data recovery unit 217 .

[0044] The acquisition unit 213 can be used to acquire imaging data. In some embodiments, the acquisition unit 213 can acquire first imaging data by multiple excitation plane echo data acquisition based on first preset parameters, and the first imaging data is partial acquisition data of the first imaging space. In some embodiments, the acquisition unit 213 can fill the data (for example, first scanning data) obtained by multiple excitation plane echoes into the first imaging space to obtain the first imaging data. In some embodiments, the acquisition unit 213 can acquire second imaging data by multiple excitation plane echo data acquisition based on second preset parameters when the phase encoding gradient is turned off, and the second imaging data is full acquisition data of the central area of ​​the second imaging space. In some embodiments, the acquisition unit 213 can acquire second scanning data by multiple excitation plane echoes and fill it into the central full acquisition area of ​​the second imaging space when the phase encoding gradient is turned off, and obtain the second imaging data.

[0045] The calculation unit 215 can be used for data calculation. In some embodiments, the calculation unit 215 can determine the weight value related to the uncollected data in the first imaging space based on the second imaging data using a preset algorithm, and the weight value can reflect the correlation between the collected data and the uncollected data in the first imaging space. In some embodiments, the preset algorithm may include but is not limited to an interpolation method, a low-rank method, or a machine learning algorithm, a generalized auto-calibrating partially parallel imaging (GRAPPA) algorithm, sensitivity encoding (SENSE), simultaneous imaging of spatial harmonics (SMASH) method, etc.

[0046] The data recovery unit 217 can be used to recover the uncollected data. For example, the data recovery unit 217 can determine the uncollected data in the first imaging space based on the weight value obtained by the calculation unit 215 to obtain the restored imaging data. Specifically, the data recovery unit 217 can determine the uncollected data in the first imaging space based on the weight value obtained by the calculation unit 215 to obtain the simulated data, and obtain the restored imaging data based on the simulated data and the collected data. In some embodiments, the restored imaging data may include the uncollected data (i.e., the simulated data) and the collected data determined in the k-space.

[0047] The image generation module 220 may be used to generate a magnetic resonance image. For example, the image generation module 220 may generate a magnetic resonance image based on the restored imaging data by image reconstruction or the like. In some embodiments, the magnetic resonance image may include, but is not limited to, a fat quantification map, a T2* contrast map, a T2* quantitative map, a SWI contrast map, an amplitude map, a phase map, a T1 / R1 contrast map, a T1 quantitative map, a T1 FLAIR quantitative map, a PD contrast map, a PD quantitative map, a B0 field map, a B1 field map, a WFI / QSM quantitative map, a T2 contrast map, a T2 / R2 quantitative map, a T2 FLAIR map, a DWI map, an ADC map, an EADC map, or the like, or any combination thereof.

[0048] It should be noted that the above description of the magnetic resonance imaging system 200 is for illustrative purposes only and is not intended to limit the scope of the present application. For those of ordinary skill in the art, various modifications and variations can be made according to the present application. However, these variations and variations do not depart from the scope of the present application. For example, one or more modules of the magnetic resonance imaging system 200 described above can be omitted or integrated into a single module. For another example, the magnetic resonance imaging system 200 can include one or more additional modules, for example, a storage module for data storage.

[0049] Figure 4 is an exemplary flow chart of a magnetic resonance imaging method according to some embodiments of the present specification.

[0050] Process 400 may be performed by processing device 120. For example, process 400 may be implemented as an instruction set (e.g., an application) stored in a memory external to, for example, storage device 130, a magnetic resonance imaging system (e.g., magnetic resonance imaging system 100 or magnetic resonance imaging system 200) and accessible to the magnetic resonance imaging system. Processing device 120 may execute the instruction set, and when executing the instructions, it may be configured to perform process 400. The operational diagram of process 400 presented below is illustrative. In some embodiments, the process may be completed using one or more additional operations not described and / or one or more operations not discussed. In addition, Figure 4The order in which the operations of flow 400 are illustrated and described below is not intended to be limiting.

[0051] Step 410, acquiring first imaging data by multiple excitation echo planar data acquisition. In some embodiments, step 410 may be performed by the processing device 120 or the EPI acquisition module 210 (eg, the acquisition unit 213).

[0052] Echo planar imaging (EPI) is an ultra-fast data acquisition method that uses a fast reverse gradient to generate a series of gradient echoes within a single relaxation time and phase encodes them respectively, filling them into the corresponding k-space (e.g., two-dimensional k-space, three-dimensional k-space) to achieve cross-sectional imaging. The multiple-excitation echo planar data acquisition method refers to an echo planar imaging technology that divides the original data into two or more acquisitions. In some embodiments, the data acquired after two or more excitations can be all or part of the original data.

[0053] In some embodiments, the first imaging data can be acquired by acquiring the first imaging data by multiple excitation echo-planar data acquisition methods based on the scanning sequence (e.g., the first scanning sequence) set by the first preset parameter. In some embodiments, the first preset parameter may include a preset phase encoding gradient sequence. In some embodiments, one or more phase encoding gradient sequences may be defined by one or more parameters of the phase encoding gradient, such as the amplitude of the phase encoding gradient, the gradient direction, the time of applying the phase encoding gradient, the duration of applying the phase encoding gradient, etc. In some embodiments, the phase encoding gradient amplitude and gradient direction of the phase encoding gradient sequence can be used to determine the distribution of the acquired scanning data (e.g., the first scanning data, the second scanning data) in the k-space. For example, the change in the gradient direction of two adjacent pulses of the phase encoding gradient sequence corresponds to the change in the direction of the phase encoding direction of two adjacent point data in the k-space, and the change in the amplitude value of the adjacent pulses corresponds to the change in the distance of the two adjacent point data in the phase encoding direction in the k-space. In some embodiments, partial acquisition data in the k-space can be obtained by periodically changing the direction and / or amplitude of the phase encoding gradient in the EPI acquisition module.

[0054] In some embodiments, in each excitation, the gradient direction and / or amplitude of the preset phase encoding gradient sequence may be periodically changed. In some embodiments, in each excitation, the parameters (eg, gradient direction and amplitude) of the preset phase encoding gradient sequence may be the same.

[0055] In some embodiments, the period of the preset phase coding sequence can be adjusted according to actual conditions. Figure 5In (a), PE (Phase-Encoding) indicates the phase encoding gradient direction. The first pulse in the PE direction indicates the pre-phase. The pre-phase is followed by a periodically changing phase encoding gradient pulse sequence. A pulse sequence of a period T may sequentially include two first pulses with a downward gradient, a second pulse with an upward gradient, two first pulses, and a third pulse with an upward gradient, wherein the absolute value of the amplitude of the first pulse is less than the absolute value of the amplitude of the second pulse and less than the absolute value of the amplitude of the third pulse. It can be understood that Figure 5 The phase encoding gradient sequence in (a) is only used as an example. In some alternative embodiments, the sequence can be any periodically changing sequence. For example, a set of phase encoding gradient sequences can include 3, 5, 7, or 10 cycles. The pulse sequence in each cycle can include 4 first pulses, 1 second pulse, 4 first pulses, 1 third pulse, or 5 first pulses, 2 second pulses, 1 third pulse, etc., wherein the first pulse, the second pulse, and the third pulse can be any three groups of pulses with different absolute amplitude values.

[0056] In some embodiments, the initial filling position of the k-space filling trajectory in the phase encoding direction can be changed by changing the direction and / or amplitude of the pre-phase. In some embodiments, in different excitations, the gradient direction and / or amplitude (absolute) value of the pre-phase in the first preset parameter can be different.

[0057] In some embodiments, the first preset parameters may also include at least one imaging sequence. In some embodiments, at least one imaging sequence may be defined by one or more imaging parameters and their order and / or time arrangement. Exemplary imaging parameters may include parameters related to the gradient field generated by the gradient coil (e.g., frequency encoding gradient field, etc.), parameters related to the magnetic resonance signal (e.g., echo time (TE), echo train length (ETL), spin echo type, number of phases or phase count, etc.). In some embodiments, the first preset parameters may also include an RF pulse sequence, characterized by parameters related to the RF pulses emitted by the RF coil (e.g., the frequency of the RF pulses, the bandwidth of the RF pulses, etc.). In some embodiments, the first preset parameters may also include the number of excitations, or any combination of the above-mentioned example parameters. For example, Figure 5 In (a), only a schematic diagram of the scanning sequence used for one excitation is given, and the first preset parameters may include a 90° RF pulse, a slice selection direction (SS) gradient pulse, a readout direction (RO) gradient pulse, and a phase encoding direction (PE) gradient pulse. Of course, the number of excitations may be 2, 3, 5, 6, 7, etc.

[0058] In some embodiments, the first imaging data is part of the acquired data in the first imaging space. In some embodiments, the first imaging space may be a three-dimensional k-space, for example, a three-dimensional k-space composed of readout-phase encoding-echo time. In some embodiments, the EPI acquisition module may acquire multiple sets of data by multiple excitations of echo plane data acquisition based on first preset parameters, fill the data acquired by each excitation echo plane into the first imaging space, and acquire the first imaging data, which is imaging data sampled continuously in time.

[0059] In some embodiments, a set of data obtained at each excitation covers a portion of the first imaging space, and the filling trajectory in the first imaging space changes periodically. Figure 6 The image data obtained after two excitations are shown in the figure, where the vertical axis (K y ) represents the phase encoding PE direction, the horizontal axis represents the echo time TE, the black solid points represent the acquired data, and each black solid point corresponds to Figure 5 (a) The data obtained after a trapezoidal pulse in the RO direction is applied after the PE direction is changed in the pulse sequence in the PE direction. The blank area indicates the uncollected data (i.e., the data to be recovered). Each point represents a readout line. The black solid points connected by two broken lines are the sampling trajectories of the exemplary first imaging data, which correspond to two sets of data collected by two excitation echo planes. The coordinate changes of the collected points in each set of data are Figure 5 The pulse sequence in the PE direction shown in (a) corresponds to. For example, Figure 6 The vertical distance L between the two collected points shown in Figure 5 (a) The amplitude of the gradient of the pulse sequence in the PE direction, the period T corresponds to Figure 5 (a) The period of the PE direction pulse sequence. In this embodiment, the data lines in the first imaging space are continuously distributed along the time direction, and the phase encoding gradients corresponding to the data lines filled at different echo time points are different.

[0060] Step 420, acquiring second imaging data by multiple excitation echo planar data acquisition. In some embodiments, step 420 may be performed by the processing device 120 or the EPI acquisition module 210 (eg, the acquisition unit 213).

[0061] In some embodiments, the second imaging data can be acquired by multiple excitations of echo planar data acquisition mode based on a scanning sequence set by a second preset parameter (e.g., a second scanning sequence) with the phase encoding gradient turned off. Turning off the phase encoding gradient means turning off the gradient magnetic field in the direction of the phase encoding gradient. For example, Figure 5As shown in (b), turning off the phase encoding gradient can be represented as turning off the periodic pulse sequence after the pre-phase of the pulse sequence in the PE direction. In some embodiments, after turning off the phase encoding gradient, the data collected by the EPI acquisition module collects data lines corresponding to the same phase encoding position in different two-dimensional k-spaces in the TE direction in the k-space, that is, there is no offset in the phase encoding direction. For example, Figure 7 As shown in the dotted box, the black solid dots represent the acquired data. After the phase encoding gradient is turned off, the data acquired by the EPI acquisition module is a full sampling area formed by multiple data points, where the ordinate represents the phase encoding PE direction and the abscissa represents the echo time TE.

[0062] In some embodiments, the second preset parameters may include at least one imaging sequence. In some embodiments, the at least one imaging sequence may be defined by one or more imaging parameters and their order and / or time arrangement. In some embodiments, the second preset parameters may include an RF pulse sequence characterized by parameters related to the RF pulses emitted by the RF coil (e.g., the frequency of the RF pulses, the bandwidth of the RF pulses, etc.). In some embodiments, the second preset parameters may also include the number of excitations, or any combination of the above examples. For example, Figure 5 (b) schematically shows a scanning sequence diagram used for one excitation. The scanning sequence set by the second preset parameter may include a 90° RF pulse, a slice selection direction (SS) gradient pulse, a readout direction (RO) gradient pulse, and a phase encoding direction (PE) gradient pulse. Of course, the number of excitations may be 2, 3, or 5 times, etc.

[0063] In some embodiments, the number of excitations of the first preset parameter is different from the number of excitations of the second preset parameter. For example, the number of excitations of the second preset parameter may be greater than the number of excitations of the first preset parameter. In some embodiments, the number of excitations of the second preset parameter may be determined based on the first preset parameter. In some embodiments, the number of excitations of the second preset parameter may be determined based on the coverage of the scan data collected at each excitation in the first preset parameter in the first imaging space. For example, in Figure 6 In the embodiment, at each excitation based on the first preset parameter, the collected scan data (e.g., the first scan data) longitudinally covers 6 cells of the three-dimensional k space along the PE direction in the first imaging space, and the number of excitations in the second preset parameter can be set to 3 times, or 2 times, or 4 times, or 6 times, etc. according to the number of cells 6. In some embodiments, the number of excitations of the second preset parameter can be set to a fixed value. In some embodiments, the number of excitations of the second preset parameter can be dynamically adjusted according to the actual needs of magnetic resonance imaging. For example, the number of excitations can be different values ​​for different scanning parts or objects, and this specification does not limit this.

[0064] In some embodiments, the gradient direction and / or amplitude value of the pre-phase of the second preset parameter may be different from that of the first preset parameter. In some embodiments, the gradient direction and / or amplitude value of the pre-phase in the second preset parameter may be different in different excitations.

[0065] In some embodiments, the second imaging data is the full sampling data of the central area of ​​the second imaging space (or referred to as the central full sampling data). In some embodiments, the central area can be defined as the data space near the phase encoding of 0. The full sampling data of the central area is obtained by fully sampling the original data in the central area. The central area filled with full sampling data can also be referred to as the central full sampling area. In some embodiments, the first imaging space and the second imaging space can be k-spaces of the same size, for example, a three-dimensional k-space composed of readout-phase encoding-echo time. In some embodiments, the EPI acquisition module 210 can fill the data obtained by multiple excitations of the plane echo with the phase encoding gradient turned off (such as the second scanning data) into the central full sampling area of ​​the second imaging space to obtain the second imaging data. For example, Figure 7 As shown in , after the first imaging data and the second imaging data are mapped to the same k-space (the figure only shows the two coordinate directions of the ordinate being the phase encoding direction PE and the abscissa t being the echo time TE), the black solid points that change in a broken line correspond to the data collected by multiple excitations of the echo plane when the phase encoding gradient is turned on, i.e., the first imaging data, and the black solid points that change in a horizontal straight line around the zero coordinate correspond to the data collected by multiple excitations of the echo plane after the phase encoding gradient is turned off, i.e., the second imaging data. Among them, Figure 7 The time t corresponding to each collected point may be an echo time, that is, the moment of reading data obtained at each gradient switch, including but not limited to t1, t2 and t3 shown in the figure.

[0066] The first imaging data is partially collected data, and the second imaging data is center fully collected data. By obtaining the second imaging data with center fully collected under the condition of turning off the phase encoding gradient, the correlation between the uncollected data and the collected data in the first imaging space which is the same as the second imaging space can be calculated, thereby restoring the uncollected data in the first imaging space to obtain simulated data.

[0067] Step 430, based on the second imaging data, determine the weight value associated with the uncollected data in the first imaging space using a preset algorithm. In some embodiments, step 430 may be performed by the processing device 120 or the EPI acquisition module 210 (eg, the computing unit 215).

[0068] In some embodiments, the weight value may reflect the correlation between the uncollected data and the collected data in the first imaging space, for example, the weight of the uncollected point relative to the collected point in the k-space. In some embodiments, the preset algorithm may include but is not limited to interpolation method, low-rank method, machine learning algorithm, generalized auto-calibrating partial parallel imaging (GRAPPA) algorithm, sensitivity encoding (SENSE), simultaneous imaging of spatial harmonics (SMASH) method, etc., or any combination thereof. In some embodiments, the machine learning algorithm may include a neural network learning algorithm. For example, the neural network algorithm may include but is not limited to convolutional neural networks (CNN), generative adversarial networks (GAN), etc., or any combination thereof.

[0069] In some embodiments, the EPI acquisition module 210 may calculate the weight value of the uncollected data relative to the collected data in the training mode through a preset algorithm for the second imaging data of the central fully collected area based on preset rules. The training mode may be used to determine the calculation method for obtaining the uncollected data based on the collected data. For example, the EPI acquisition module 210 may calculate the weight of the uncollected points relative to the collected points in the training mode through a neural network learning algorithm for the data of the central fully collected area of ​​the second imaging space under the training mode based on the preset training area and training mode. The training area may reflect the range of the collected data area used to calculate the uncollected data. In some embodiments, the processing device 120 may determine the preset rules based on the first imaging data. For example, the preset training area and training mode may be determined based on the distribution of the collected points in the first imaging space.

[0070] Just as an example, Figure 8 , black solid points represent acquired data, white hollow points represent unacquired data, 6 groups of acquired points with broken line changes are sampling trajectories of the first imaging data obtained by 6 excitations of echo plane, and 3 groups of acquired points with horizontal straight line changes are sampling trajectories of the second imaging data obtained by 3 excitations of echo plane when the phase encoding gradient is turned off. Among them, the training area can be Figure 8 The training mode can be the arrow combination mode of the collected points in the square training area in the rectangular dotted line frame. Figure 8The square training area and the corresponding training mode can be calculated by the neural network learning algorithm including output layer-hidden layer-output layer to obtain the weight value W of the uncollected points (i.e. the points pointed by the arrow) in the training mode based on the collected points. i , i is a positive integer, and i≤N, N is the total number of echo time points.

[0071] In some embodiments, the EPI acquisition module 210 may further optimize the calculated weight values. For example, the EPI acquisition module 210 may input the output value obtained by the neural network learning algorithm into the optimization iteration formula , and is optimized by iterative calculation. Where x is the optimization factor, which in this embodiment corresponds to the weight value W obtained by the above calculation. i ; P is the sampling matrix; is the Fourier transform factor; S is the coil sensitivity of the current channel; b is the actual collected data; R is the sparse transformation factor; α is the degree of control regularization; It expresses the square operation of the norm; argmin expresses the minimum operation.

[0072] Step 440, determining the uncollected data in the first imaging space based on the weight value to obtain the restored imaging data. In some embodiments, step 440 may be performed by the processing device 120 or the EPI acquisition module 210 (eg, the data recovery unit 217).

[0073] In some embodiments, the EPI acquisition module 210 may restore the unacquired data in the first imaging space based on the weight value and the first imaging data. Figure 8 In the example, the EPI acquisition module 210 can calculate the weight value W based on the calculated weight value W. i and the black solid collected points, and calculate the values ​​of the white hollow uncollected points in the spatial coordinates. In some embodiments, the EPI acquisition module 210 can obtain the restored imaging data based on the collected data in the first imaging space and the restored uncollected data. For example, the EPI acquisition module 210 can determine the uncollected data in the first imaging space based on the obtained weight value to obtain the simulation data, and obtain the restored imaging data based on the simulation data and the collected data. In some embodiments, the restored imaging data may include all the data in the first imaging space, that is, the collected data and the determined uncollected data (that is, the simulation data) when the phase encoding gradient is turned on.

[0074] Step 450 , generating a magnetic resonance image based on the restored imaging data. In some embodiments, step 450 may be performed by the processing device 120 or the image generation module 220 .

[0075] In some embodiments, the processing device 120 may determine a plurality of echo images based on the restored imaging data, and generate a magnetic resonance image based on at least two echo images of the plurality of echo images. In some embodiments, the processing device 120 may generate a magnetic resonance image by image processing and other methods. For example, image processing may include but is not limited to dilation method, erosion method, region growing method, level set algorithm, gradient descent method, single source shortest path algorithm, etc., or any combination thereof. In some embodiments, the processing device 120 may generate a magnetic resonance image by post-processing the echo image. In some embodiments, the post-processing operation may adopt a two-dimensional post-processing technology, such as multi-planar reconstruction technology, surface reconstruction technology, volume reconstruction technology, volume rendering technology, etc., or any combination thereof. In some embodiments, the post-processing operation may adopt a three-dimensional post-processing technology, such as three-dimensional surface reconstruction, three-dimensional volume reconstruction, volume intensity projection technology, maximum intensity projection technology, minimum intensity projection technology, average density projection technology, etc., or any combination thereof. In some embodiments, other possible post-processing technologies may include repair, rendering, filling, etc., or any combination thereof.

[0076] In some embodiments, the magnetic resonance image may include a fat quantification map. As an example only, the processing device 120 may select at least two temporally consecutive echo images from the plurality of echo images to generate a fat quantification map. For example, Fig. 9 As shown in , the processing device 120 can reconstruct a plurality of complete echo images based on the restored first imaging data, select 6 consecutive echo images from the plurality of echo images, extract the water-fat transition region based on the 6 echo images and obtain the phase distribution, determine the water-fat quantitative distribution based on the water-fat transition region and the phase distribution, and obtain a fat quantitative map.

[0077] In some embodiments, the processing device 120 may obtain an echo image based on multiple sets of data at the same echo time during multiple excitations in the restored imaging data. Figure 6 In the figure, the processing device 120 can obtain an echo image through image reconstruction based on the data corresponding to the two groups of acquired points in the same column during the two excitations in the first imaging space (the two-dimensional k-space units of the three-dimensional k-space corresponding to the same echo time point in the figure).

[0078] In a specific embodiment, the processing device 120 can calculate the global optimal solution and the local optimal solution for the signal of each voxel based on the continuous 6 frames of echo images. For example, the components of each voxel signal can be expressed as formula (1):

[0079]

[0080] Among them, TE nThe phase precession time or echo time of the signal in the corresponding echo image; S represents the pixel value of the echo signal, which is a complex value; n = 1, 2, ..., N, N is the total number of echo time points, W and F are the amplitudes of the water signal and the fat signal, respectively, and f F represents the difference between the resonant frequencies of fat and water protons; ψ represents the progression frequency, which is proportional to the main magnetic field strength; 2πf F TE n Indicates TE n The phase of the corresponding echo signal; 2πψTE n Indicates TE n To calculate the global optimal solution, we can use VARPRO and other algorithms to plan formula (1), so as to calculate S(TE n ) to perform back calculation For example, it can be planned as formula (2):

[0081]

[0082] Among them, Φ G represents the target phase accumulation of the echo signal within the ΔTE time, and ΔTE represents the echo time difference. According to the solution of formula (2), it can be deduced that: ρ = A + (Φ)S,ρ=[W,F] T . Among them, p = e i2πψΔTE =e iΦ ;S=[S(TE1),S(TE2),…,S(TE n )] T ; A(Φ)=[A1;A1;…;A N ]; A + is the pseudo-inverse matrix of A; I is the N×N identity matrix. The local optimal solution can be obtained by seeking around the global optimal solution, for example, by formula (3):

[0083]

[0084] In some embodiments, the processing device 120 can determine the global optimal solution or local optimal solution of the voxel signal based on the VARPRO (Variable Projection) algorithm, the gradient descent method, etc., and this specification does not limit this. Further, the processing device 120 can determine whether the adjacent area is a water-fat transition zone for all adjacent pixels in the echo image and mark it according to the global optimal solution and the local optimal solution. In some embodiments, for one of the solutions ρ=[W,F] T , when the absolute value of W is larger, the solution can be called P W ; When the absolute value of F is larger, the solution can be called PF Then, the image is segmented into multiple sub-regions based on the marked water-fat transition zone, and the water-fat distribution of each sub-region is determined. For example, the water-fat distribution of each sub-region can be determined by the following formulas (4) and (5):

[0085]

[0086]

[0087] Among them, C W is the objective function value of the water signal; C F is the objective function value of the water signal; j is the pixel number in the echo image; J is the total number of all pixels in the echo image; s j represents the signal value of pixel number j; k j It represents the signal value of the surrounding pixels with pixel number j; angle represents the phase operation; conj represents the conjugate operation.

[0088] The processing device 120 can re-judge the water-fat transition area based on the judgment result of each sub-region by using the region growing method, so as to obtain the final water-fat separation situation:

[0089]

[0090] Finally, the fat quantitative map is obtained, where: q represents the pixel number in the water-fat transition zone; Q is the total number of pixels in the water-fat transition zone; α q is the water-fat ratio factor of the pixel numbered q in the water-fat transition region, α q =F / W; T2* represents the time constant of exponential decay of the echo signal.

[0091] In some embodiments, the magnetic resonance image may include a contrast map and / or a quantitative map of the scanned object. For example, the magnetic resonance image may include but is not limited to a fat quantitative map, a T1 contrast map / quantitative map, a T2 contrast map / quantitative map, a proton density quantitative map, a magnetic susceptibility quantitative map, a FLAIR quantitative map / contrast map, a diffusion contrast map, a static magnetic field map, and an emission field map, or any combination thereof. In some embodiments, the EPI acquisition module 210 may obtain different types of contrast maps / quantitative maps based on its different placement positions in the magnetic resonance scanner or processing device. For example, the EPI acquisition module 210 may obtain different types of contrast maps / quantitative maps based on its different placement positions in the magnetic resonance scanner or processing device. Figure 3 In the scheme shown in (a), images of different echo times in the EPI acquisition module are used to obtain T2* contrast images, T2* quantitative images, SWI contrast images, amplitude images, phase images, T1 / R1 contrast images, T1 quantitative images, T1 FLAIR quantitative images, PD contrast images, PD quantitative images, B0 field images, B1 field images, WFI\QSM quantitative images, etc. For another example, Figure 3In the schemes shown in (a) and (b), the images of different echo times in the EPI acquisition module are used to obtain T2 contrast images, T2 / R2 quantitative images, T2 FLAIR images, etc. For another example, Figure 3 (d) The images of different echo times in the EPI acquisition module in the scheme shown in the figure obtain DWI images, ADC images, EADC images, etc.

[0092] It should be noted that the above description of process 400 is provided for illustrative purposes only and is not intended to limit the scope of the present application. For those of ordinary skill in the art, various changes and modifications can be made according to the description of the present application. However, these changes and modifications do not depart from the scope of the present application. In some embodiments, process 400 may include one or more additional operations, or one or more operations may be omitted. For example, step 410 and step 420 may be integrated into one operation. For another example, process 400 may include one or more additional operations to determine one or more magnetic resonance images.

[0093] Fig.10 is an exemplary flow chart of a magnetic resonance imaging method according to other embodiments of the present specification.

[0094] Process 1000 may be performed by processing device 120. For example, process 1000 may be implemented as an instruction set (e.g., an application) stored in, for example, a storage device 130, a memory external to a magnetic resonance imaging system (e.g., magnetic resonance imaging system 100 or magnetic resonance imaging system 200) and accessible to the magnetic resonance imaging system. Processing device 120 may execute the instruction set, and when executing the instructions, it may be configured to perform process 1000. The operational diagram of process 1000 presented below is illustrative. In some embodiments, the process may be completed using one or more additional operations not described and / or one or more operations not discussed. In addition, Fig.10 The order in which the operations of process 1000 are illustrated and described below is not intended to be limiting. Fig.10 As shown:

[0095] In step 1010, the processing device 120 may execute a first scanning sequence to obtain first imaging data, wherein the first imaging data is obtained by filling time-continuous sparse sampling points in k-space. Figure 6 As shown in , the first imaging data can be obtained by filling the scanning data represented by the black solid points with the continuous sparse sampling points at the positions corresponding to the black solid points in the k-space. In some embodiments, the first scanning sequence can be determined based on the first preset parameters. For example, Figure 5As shown in (a), the first scanning sequence may include a 90-degree RF pulse, a slice selection direction (SS) gradient pulse, a readout direction (RO) gradient pulse, and a phase encoding direction (PE) gradient pulse. In some embodiments, the processing device 120 may execute the first scanning sequence and acquire the first imaging data by multiple excitation echo planar acquisition.

[0096] In step 1020, the processing device 120 may perform a second scanning sequence to obtain second imaging data, wherein the second imaging data is obtained by completely filling the time-continuous sampling points in the central region of the k-space. Figure 6 The time-continuous sampling points in the data space near the phase encoding 0 are completely filled. In some embodiments, the second scanning sequence can be determined based on the second preset parameter. For example, Figure 5 As shown in (b), the second scanning sequence may include a 90-degree RF pulse, a slice selection direction (SS) gradient pulse, a readout direction (RO) gradient pulse, and a phase encoding direction (PE) gradient pulse. In some embodiments, the processing device 120 may execute the second scanning sequence and acquire the second imaging data by multiple excitation echo planar acquisition.

[0097] In step 1030, the processing device 120 may restore the unsampled points in the k-space corresponding to the first imaging data based on the second imaging data to obtain the restored imaging data. In some embodiments, the processing device 120 may determine the weight value associated with the uncollected data (i.e., the unsampled points) in the k-space corresponding to the first imaging data using a preset algorithm based on the second imaging data, and restore the unsampled points in the k-space corresponding to the first imaging data based on the weight value to obtain the restored imaging data.

[0098] In step 1040, the processing device 120 may generate a magnetic resonance image based on the restored imaging data. In some embodiments, the processing device 120 may determine a plurality of echo images based on the restored imaging data, and generate a magnetic resonance image based on at least two echo images of the plurality of echo images. In some embodiments, the processing device 120 may generate a magnetic resonance image by image processing or other methods. For more information on determining the restored imaging data and determining the magnetic resonance image, please refer to Figure 4 The related descriptions will not be repeated here.

[0099] It should be noted that the above description of process 1000 is provided for illustrative purposes only and is not intended to limit the scope of the present application. For those of ordinary skill in the art, various changes and modifications can be made according to the description of the present application. However, these changes and modifications do not depart from the scope of the present application.

[0100] Different from the phase encoding gradient of uniform amplitude in a single direction of the traditional EPI acquisition module, the embodiment of this specification realizes the reverse operation of the encoding position in the kt space by periodically adjusting the phase encoding gradient in the EPI acquisition module, and performs data acquisition by multiple excitations of the echo plane data acquisition method, thereby improving the speed of magnetic resonance examination, reducing the impact of motion on imaging, and providing a variety of contrast information to help improve the accuracy of lesion examination. It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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. A magnetic resonance quantitative imaging method, characterized in that: include: Acquire at least two temporally continuous echo images of the scanned object, each of the echo images being obtained by reconstructing k-space data corresponding to the same echo time point acquired by multiple excitations of echo-planar data acquisition; determining the water-fat transition region and phase distribution of each of the echo images; Determine the water-fat quantitative distribution based on the water-fat transition region and the phase distribution of each echo image to obtain a fat quantitative map of the scanned object; The echo image is obtained by: Acquire first imaging data of the scanned object by multiple excitation echo plane data acquisition, wherein the first imaging data is partial acquisition data of a first imaging space; Acquire second imaging data of the scanned object by multiple excitation echo plane data acquisition, wherein the second imaging data is full acquisition data of the central area of ​​the second imaging space, and the second imaging data is obtained by continuous sampling in the central area of ​​the k-space at different echo times; Based on the second imaging data, determining a weight value associated with the uncollected data in the first imaging space by using a preset algorithm, wherein the weight value reflects the correlation between the collected data and the uncollected data in the first imaging space; Based on the weight value, determining uncollected data in the first imaging space to obtain restored imaging data; At least two echo images of the scanned object are determined based on the restored imaging data.

2. The method according to claim 1, characterized in that Determining the water-fat transition region and phase distribution of each echo image includes: For all adjacent pixels in each of the echo images, determine whether the area corresponding to the pixel is a water-fat transition zone and mark it; The echo image is divided into a plurality of sub-regions based on the marked water-fat transition region, and the water-fat distribution of each sub-region is determined to obtain the water-fat transition region and the phase distribution of the echo image.

3. The method according to claim 2, characterized in that The step of determining the water-fat quantitative distribution based on the water-fat transition region and the phase distribution of each echo image to obtain a fat quantitative map of the scanned object includes: Based on the judgment result of the water-fat distribution of each sub-region of the echo image, the water-fat transition region is judged again by a region growing method to obtain the fat quantitative map of the scanned object.

4. The method according to claim 1, characterized in that: The first imaging data are obtained by continuous sampling at different echo times and different parts of the k-space.

5. The method according to claim 1, characterized in that The acquiring of first imaging data of the scanned object by multiple excitation echo planar data acquisition includes: Filling the first imaging space with data obtained by each excitation echo plane to obtain the first imaging data; A set of data obtained in each excitation covers a portion of the first imaging space, and a filling trajectory in the first imaging space changes periodically.

6. The method according to claim 1, characterized in that No phase encoding gradient is applied during the acquisition of the second imaging data.

7. The method according to claim 1, characterized in that The preset algorithm includes an interpolation method, a low-rank method or a machine learning algorithm.

8. 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 method according to any one of claims 1 to 7.

Images