Method and system for magnetic resonance imaging

Abstract

A method of magnetic resonance imaging (MRI) can include acquiring two or more first magnetic resonance (MR) data sets related to a region of interest (ROI) of a subject. The two or more first MR data sets are acquired based on two or more different values of a scan parameter. The method can also include determining two or more second MR data sets based on the two or more first MR data sets. Each of the two or more second MR data sets corresponds to at least two of the two or more first MR data sets. The method can also include generating two or more T1-weighted images of the ROI based on the two or more second MR data sets, each of the two or more T1-weighted images corresponding to one target time point.

Description

[0001] Priority Explanation

[0002] This disclosure claims priority to U.S. Patent Application No. 17 / 651,416, filed February 16, 2022, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] This disclosure generally relates to magnetic resonance imaging (MRI), and more specifically, to systems and methods for T1-weighted dynamic imaging. Background Technology

[0004] In T1-weighted dynamic imaging, the acquired magnetic resonance (MR) signal includes not only T1 information but also non-T1 factors such as proton density, T2* relaxation effect, and receiver coil sensitivity. These factors can introduce errors and biases into signal analysis, such as image reconstruction and physiological analysis. Therefore, there is a need to provide systems and methods for T1-weighted dynamic imaging to mitigate or eliminate the influence of non-T1 factors on T1-weighted dynamic imaging. Summary of the Invention

[0005] According to one aspect of this disclosure, a magnetic resonance imaging system may include one or more storage devices and one or more processors configured to communicate with the one or more storage devices. The one or more storage devices may include a set of instructions. When the one or more processors execute the set of instructions, the one or more processors may be instructed to perform one or more of the following operations: The one or more processors may acquire two or more first magnetic resonance datasets related to a region of interest (ROI) of a subject. The two or more first ROI datasets may be acquired based on two or more different values ​​of scanning parameters. The one or more processors may determine two or more second ROI datasets based on the two or more first ROI datasets. Each of the two or more second ROI datasets may correspond to at least two of the two or more first ROI datasets. The one or more processors may generate two or more T1-weighted images of the ROI based on the two or more second ROI datasets, each of the two or more T1-weighted images corresponding to a target time point.

[0006] According to another aspect of this disclosure, a magnetic resonance imaging method may include one or more of the following operations: One or more processors may acquire two or more first magnetic resonance datasets associated with a region of interest (ROI) of a subject. The two or more first ROI datasets may be acquired based on two or more different values ​​of scanning parameters. The one or more processors may determine two or more second ROI datasets based on the two or more first ROI datasets. Each of the two or more second ROI datasets may correspond to at least two of the two or more first ROI datasets. The one or more processors may generate two or more T1-weighted images of the ROI based on the two or more second ROI datasets, each of the two or more T1-weighted images corresponding to a target time point.

[0007] According to another aspect of this disclosure, a magnetic resonance imaging system may include an acquisition module configured to acquire two or more first magnetic resonance datasets related to a region of interest (ROI) of a subject. The two or more first ROI datasets are acquired based on two or more different values ​​of scanning parameters. The system may further include a determination module configured to determine two or more second ROI datasets based on the two or more first ROI datasets. Each of the two or more second ROI datasets corresponds to at least two of the two or more first ROI datasets. The system may further include a reconstruction module configured to generate two or more T1-weighted images of the ROI based on the two or more second ROI datasets, each of the two or more T1-weighted images corresponding to a target time point.

[0008] According to another aspect of this disclosure, a non-transitory computer-readable medium may include at least one set of instructions. The at least one set of instructions may be executed by one or more processors of a computer server. The one or more processors may acquire two or more first magnetic resonance imaging (MRI) datasets related to a region of interest (ROI) of a subject. The two or more first MRI datasets are acquired based on two or more different values ​​of scanning parameters. The one or more processors may determine two or more second MRI datasets based on the two or more first MRI datasets. Each of the two or more second MRI datasets corresponds to at least two of the two or more first MRI datasets. The one or more processors may generate two or more T1-weighted images of the ROI based on the two or more second MRI datasets, each of the two or more T1-weighted images corresponding to a target time point.

[0009] In some embodiments, at least two of the two or more first magnetic resonance datasets corresponding to each of the two or more second magnetic resonance datasets may correspond to two of two or more different values ​​of the scanning parameters.

[0010] In some embodiments, in order to determine two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets, for one of the two or more second magnetic resonance datasets, one or more processors may acquire at least one first magnetic resonance dataset associated with a first value of the scan parameter. One or more processors may acquire at least one first magnetic resonance dataset associated with a second value of the scan parameter. One or more processors may perform calculations based on at least two of the two or more first magnetic resonance datasets associated with the first and second values ​​of the scan parameter.

[0011] In some embodiments, each of the two or more first magnetic resonance datasets may be acquired based on one of two or more values ​​of the scanning parameters.

[0012] In some embodiments, the scanning parameters may include at least one of the flip angle or the repetition time.

[0013] In some embodiments, the two or more first magnetic resonance datasets may be acquired based on two or more different values ​​of the flip angle and a fixed value of the repetition time; two or more different values ​​of the repetition time and a fixed value of the flip angle; or two or more different values ​​of the flip angle and two or more different values ​​of the repetition time. The two or more first magnetic resonance datasets may be acquired such that any two adjacent first magnetic resonance datasets correspond to at least one different value of the flip angle or the repetition time.

[0014] In some embodiments, in order to determine two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets, for each of the two or more second magnetic resonance datasets, one or more processors may determine the second magnetic resonance dataset by performing a division calculation on two adjacent first magnetic resonance datasets.

[0015] In some embodiments, the target time point of one of the two or more T1-weighted images corresponding to the second magnetic resonance dataset can be specified as the average time point of the time period during which the two adjacent first magnetic resonance datasets are acquired.

[0016] In some embodiments, the two or more first magnetic resonance datasets may be acquired based on two or more different values ​​of the flip angle and a fixed value of the repetition time; or two or more different values ​​of the repetition time and a fixed value of the flip angle. At least one of the two or more first magnetic resonance datasets corresponding to a first value among the two or more values ​​of the flip angle or the repetition time may be acquired before the remainder of the two or more first magnetic resonance datasets corresponding to the remainder of the two or more values ​​of the flip angle or the repetition time.

[0017] In some embodiments, the two or more first magnetic resonance datasets may be acquired based on two or more different values ​​of the flip angle and two or more different values ​​of the repetition time. Prior to the remainder of the two or more first magnetic resonance datasets corresponding to the remainder of the two or more different values ​​of the flip angle and the remainder of the remainder of the repetition time, at least one of the two or more first magnetic resonance datasets corresponding to a first value among the two or more different values ​​of the flip angle and the first value among the two or more different values ​​of the repetition time may be acquired.

[0018] In some embodiments, in order to determine two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets, one or more processors may determine the average value of at least one of the two or more first magnetic resonance datasets. For each of the two or more second magnetic resonance datasets, the one or more processors may determine the second magnetic resonance dataset by dividing one of the remaining portions of the two or more first magnetic resonance datasets by the average value.

[0019] In some embodiments, the target time point of one of the two or more T1-weighted images corresponding to the second magnetic resonance dataset is specified as a time point within a time period during which one of the remaining portions of the two or more first magnetic resonance datasets is acquired.

[0020] In some embodiments, the target time point of one of the two or more T1-weighted images corresponding to the second magnetic resonance dataset can be specified as a time point within a time period during which one of the remaining portions of the two or more first magnetic resonance datasets is acquired.

[0021] In some embodiments, one or more processors may perform T1 mapping based on the two or more first magnetic resonance datasets.

[0022] In some embodiments, one or more processors can estimate the contrast agent concentration corresponding to each target time point based on the two or more T1-weighted images and the T1 mapping. One or more processors can then perform physiological analysis on the region of interest based on the contrast agent concentration corresponding to each target time point.

[0023] In some embodiments, one or more processors can determine the signal intensity corresponding to each target time point based on the two or more T1-weighted images. One or more processors can then perform physiological analysis on the region of interest based on the signal intensity corresponding to each target time point.

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

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

[0026] Figure 1 This is a schematic diagram of an exemplary MRI system shown according to some embodiments of the present disclosure;

[0027] Figure 2 These are schematic diagrams of exemplary MRI devices illustrated according to some embodiments of the present disclosure;

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

[0029] Figure 4 These are schematic diagrams illustrating exemplary hardware and / or software components of a mobile device that can be implemented according to some embodiments of this disclosure;

[0030] Figure 5 These are schematic diagrams of exemplary processing devices illustrated according to some embodiments of the present disclosure;

[0031] Figure 6 This is a flowchart illustrating an exemplary process for generating two or more T1-weighted images according to some embodiments of this disclosure;

[0032] Figures 7A-7C This is an exemplary schematic diagram illustrating the acquisition of two or more first magnetic resonance datasets according to some embodiments of the present disclosure;

[0033] Figures 8A-8C This is an exemplary schematic diagram illustrating the acquisition of two or more first magnetic resonance datasets according to some embodiments of the present disclosure;

[0034] Figure 9 This is a schematic diagram of exemplary intensity-time curves shown according to some embodiments of the present disclosure;

[0035] Figure 10AThese are exemplary schematic diagrams illustrating T1-weighted images generated based on a second magnetic resonance dataset, according to some embodiments of this disclosure; and

[0036] Figure 10B This is an exemplary schematic diagram showing a T1-weighted image generated based on a first magnetic resonance dataset. Detailed Implementation

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

[0038] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. As used herein, the singular form “a” is intended to include the plural form as well, unless the context clearly indicates otherwise. As used herein, the terms “and / or” and “at least one” include any and all combinations of one or more of the associated listed items. It should also be understood that the terms “comprising” and / or “including”, as used herein, specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Furthermore, the term “exemplary” is intended to refer to an example or illustration.

[0039] It should be understood that the terms “system,” “engine,” “unit,” “module,” and / or “block” used herein are an ascending order that distinguishes different levels of components, elements, parts, sections, or assemblies. However, these terms may be replaced with other expressions if the same purpose can be achieved.

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

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

[0042] The terms "pixel" and "voxel" in this application are used interchangeably to refer to elements in an image. The term "image" in this application is used to refer to images of various forms, including 2D images, 3D images, 4D images, etc.

[0043] Spatial and functional relationships between elements are described using various terms, including “connected,” “attached,” and “installed.” Unless explicitly described as “direct,” when describing a relationship between first and second elements in this disclosure, the relationship includes a direct relationship where no other intermediate elements exist between the first and second elements, and an indirect relationship where one or more intermediate elements exist between the first and second elements (spatially or functionally). Conversely, when an element is referred to as being “directly connected,” attached, or positioned to another element, no intermediate elements exist. Other terms used to describe relationships between elements should be interpreted in a similar manner (e.g., “between” vs. “directly between,” “adjacent” vs. “directly adjacent,” etc.).

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

[0045] These and other features, characteristics, functions and operating methods of related structural elements, as well as component assembly and manufacturing economics, will become more apparent from the following description of the accompanying drawings, which form part of this application specification. However, it should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of this application. It should also be understood that the drawings are not drawn to scale.

[0046] The flowcharts used in this application illustrate operations performed by a system according to some embodiments disclosed in this application. It should be clearly understood that the operations in the flowcharts may not be performed sequentially. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, one or more other operations may be added to these flowcharts. One or more operations may also be deleted from the flowcharts.

[0047] This document provides systems and components for medical imaging and / or medical treatment. In some embodiments, the medical system may include an imaging system. The imaging system may include a single-modal imaging system and / or a multimodal imaging system. A single-modal imaging system may include, for example, a magnetic resonance imaging (MRI) system. Exemplary MRI systems may include superconducting MRI systems, non-superconducting MRI systems, etc. Multimodal imaging systems may include, for example, magnetic resonance imaging (MRI-CT) systems, positron emission tomography (PET-MRI) systems, single-photon emission computed tomography (SPECT-MRI) systems, digital subtraction angiography (DSA-MRI) systems, etc. In some embodiments, the medical system may include a treatment system. Treatment systems may include treatment planning systems (TPS), image-guided radiation therapy (IGRT), etc. Image-guided radiation therapy may include treatment devices and imaging devices. Treatment devices may include linear accelerators, cyclotron accelerators, synchrotrons, etc., configured to administer radiation therapy to the subject. Treatment devices may include accelerators that can process particle types, such as photons, electrons, protons, or heavy ions. Imaging devices may include MRI scanners, CT scanners (e.g., cone-beam computed tomography (CBCT) scanners), digital radiography (DR) scanners, electronic portal imaging devices (EPID), etc.

[0048] One aspect of this disclosure relates to a system and method for T1-weighted dynamic imaging. The system and method can acquire two or more first magnetic resonance imaging (MRI) datasets associated with a region of interest (ROI) of a subject. The two or more first MRI datasets can be acquired based on two or more values ​​of scan parameters (e.g., flip angle and / or repetition time (TR)). The system and method can determine two or more second MRI datasets based on the two or more first MRI datasets. Each of the two or more second MRI datasets can be determined based on at least two of the two or more first MRI datasets corresponding to two different values ​​of the scan parameters. For example, for each of the two or more second MRI datasets, at least one first MRI dataset associated with a first value of the scan parameters can be acquired. At least one first MRI dataset associated with a second value of the scan parameters can be acquired. Computations (e.g., division calculations) can be performed based on at least two of the two or more first MRI datasets associated with the first and second values ​​of the scan parameters. The system and method can generate two or more T1-weighted images of the ROI based on the two or more second MRI datasets, each of the two or more T1-weighted images corresponding to a target time point.

[0049] In two or more first magnetic resonance datasets, in addition to T1 information, there are non-T1 factors (e.g., related to equilibrium magnetization), such as T2*, receiver coil sensitivity, echo time (TE), proton density ROI, etc., which may introduce errors and biases into signal analysis, such as image reconstruction, physiological analysis, etc.

[0050] By determining a second magnetic resonance imaging (MRI) dataset through calculations between at least two of the two or more first MRI datasets, one or more non-T1 factors from at least two of the two or more first MRI datasets can be offset. This results in the influence of one or more non-T1 factors on the two or more second MRI datasets being less significant than their influence on the two or more first MRI datasets, thereby enhancing the contrast of the T1-weighted images and making subsequent physiological analyses more accurate. Furthermore, because the interference from non-T1 factors is eliminated or mitigated in the two or more second MRI datasets, they become more sensitive to the T1 shortening effect induced by contrast agents. Therefore, lower doses of contrast agents can be used to reduce costs and minimize the potential impact of contrast agents on the human body.

[0051] Figure 1This is a schematic diagram illustrating an exemplary MRI system 100 according to some embodiments of the present disclosure. As shown, the MRI system 100 may include an MRI device 110, a processing device 120, a storage device 130, a terminal 140, and a network 150. The components of the MRI system 100 may be connected in one or more of various ways. This is merely an example. Figure 1 As shown, MRI device 110 can be directly connected to processing device 120, as indicated by the double-headed arrow in the dashed line connecting MRI device 110 and processing device 120, or connected via network 150. As another example, storage device 130 can be directly connected to MRI device 110, as indicated by the double-headed arrow in the dashed line connecting MRI device 110 and storage device 130, or connected via network 150. As yet another example, terminal 140 can be directly connected to processing device 120, as indicated by the double-headed arrow in the dashed line connecting terminal 140 and processing device 120, or connected via network 150.

[0052] MRI device 110 can be configured to scan a subject (or a portion of a subject) to acquire image data, such as echo signals (also known as magnetic resonance data or MR signals) associated with the subject. For example, MRI device 110 can detect multiple echo signals by applying a sequence of MRI pulses to the subject. In some embodiments, MRI device 110 may include, for example, a main magnet, gradient coils (or also known as spatial coding coils), radio frequency (RF) coils, etc. Figure 2 As shown. In some embodiments, depending on the type of main magnet, the MRI device 110 may be a permanent magnet MRI scanner, a superconducting electromagnetic MRI scanner, a resistive electromagnetic MRI scanner, etc. In some embodiments, depending on the strength of the magnetic field, the MRI device 110 may be a high-field MRI scanner, a medium-field MRI scanner, a low-field MRI scanner, etc.

[0053] The subject scanned by the MRI equipment 110 can be biological or non-biological. For example, the subject can include a patient, an artificial object, etc. As another example, the subject can include a specific part, organ, tissue, and / or body part of a patient. By way of example only, the subject can include the head, brain, neck, body, shoulder, arm, chest, heart, stomach, blood vessels, soft tissue, knee, foot, etc., or any combination thereof.

[0054] For the purpose of explanation, Figure 1 It can provide a coordinate system 160 including the X-axis, Y-axis and Z-axis. Figure 1 The X and Z axes shown can be horizontal, and the Y axis can be vertical. As shown in the figure, when viewed from the front of the MRI device 110, the positive X direction along the X axis can be from the right side to the left side of the MRI device 110; Figure 1 The positive Y-direction shown along the Y-axis can be from the bottom to the top of the MRI device 110; Figure 1 The positive Z-direction along the Z-axis shown can refer to the direction in which the subject is moved out of the detection area (or aperture) of the MRI device 110.

[0055] In some embodiments, the MRI device 110 can be guided along a slice selection direction to select an anatomical region (e.g., a slice or volume) of the subject and scan the anatomical region to obtain two or more echo signals from that region. During the scan, spatial encoding within the anatomical region can be achieved by spatial encoding coils (e.g., X coil, Y coil, Z coil) along the frequency encoding direction, phase encoding direction, and slice selection direction. The echo signals can be sampled, and the corresponding sampled data can be stored in a k-space matrix for image reconstruction. For illustrative purposes, the slice selection direction here can correspond to the Z direction defined in coordinate system 160 and the Kz direction in k-space; the phase encoding direction can correspond to the Y direction defined in coordinate system 160 and the Ky direction in k-space; and the frequency encoding direction (also called the readout direction) can correspond to the X direction defined in coordinate system 160 and the Kx direction in k-space. It should be noted that the slice selection direction, phase encoding direction, and frequency encoding direction can be modified as needed without departing from the scope of this disclosure. Further description of the MRI device 110 can be found in other parts of this disclosure. See also Figure 2 And its description.

[0056] Processing device 120 can process data and / or information obtained from MRI device 110, storage device 130, and / or terminal 140. For example, processing device 120 can acquire two or more first magnetic resonance datasets related to a region of interest (ROI) of a subject. The two or more first magnetic resonance datasets can be acquired based on two or more different values ​​of scanning parameters. Processing device 120 can determine two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets, each of the two or more second magnetic resonance datasets corresponding to at least two of the two or more first magnetic resonance datasets. Processing device 120 can generate two or more T1-weighted images of the ROI based on the two or more second magnetic resonance datasets, each of the two or more T1-weighted images corresponding to a target time point. In some embodiments, processing device 120 can be a single server or a group of servers. The server group can be centralized or distributed. In some embodiments, processing device 120 can be local or remote. For example, processing device 120 can access information and / or data from MRI device 110, storage device 130, and / or terminal 140 via network 150. As another example, processing device 120 can be directly connected to MRI device 110, terminal 140, and / or storage device 130 to access information and / or data. In some embodiments, processing device 120 can be implemented on a cloud platform. For example, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, etc., or combinations thereof. In some embodiments, processing device 120 may be part of terminal 140. In some embodiments, processing device 120 may be part of MRI device 110.

[0057] Storage device 130 may store data, instructions, and / or any other information. In some embodiments, storage device 130 may store data obtained from MRI device 110, processing device 120, and / or terminal 140. The aforementioned data may include image data acquired by processing device 120, algorithms and / or models for processing the image data, etc. For example, storage device 130 may store two or more T1-weighted images determined by processing device 120. In some embodiments, storage device 130 may store data and / or instructions that processing device 120 and / or terminal 140 may execute or use to perform the exemplary methods described in this disclosure. In some embodiments, storage device 130 may include mass storage, removable storage, volatile read-write storage, read-only memory (ROM), etc., or any combination thereof. Exemplary mass storage may include disks, optical disks, solid-state drives, etc. Exemplary removable storage may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tapes, etc. Exemplary volatile read-write storage may include random access memory (RAM). Exemplary RAMs may include Dynamic Random Access Memory (DRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), Static Random Access Memory (SRAM), Thyristor Random Access Memory (T-RAM), and Zero-capacitor Random Access Memory (Z-RAM), etc. Exemplary ROMs may include Mask Read-Only Memory (MROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), CompactDisc Read-Only Memory (CD-ROM), and Digital Universal Disk Read-Only Memory, etc. In some embodiments, the storage device 130 may be implemented on a cloud platform.As an example only, a cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, or any combination thereof.

[0058] In some embodiments, storage device 130 may be connected to network 150 to communicate with one or more other components in MRI system 100 (e.g., processing device 120, terminal 140). One or more components in MRI system 100 may access data or instructions stored in storage device 130 via network 150. In some embodiments, storage device 130 may be integrated into MRI device 110 or processing device 120.

[0059] Terminal 140 may connect to and communicate with MRI device 110, processing device 120, and / or storage device 130. In some embodiments, terminal 140 may include mobile device 141, tablet computer 142, laptop computer 143, etc., or any combination thereof. For example, mobile device 141 may include mobile phone, personal digital assistant (PDA), gaming device, navigation device, point of sale (POS) device, laptop computer, tablet computer, desktop computer, or as preferred, or any combination thereof. In some embodiments, terminal 140 may include input devices, output devices, etc. Input devices may include alphanumeric keys and other keys that can be input via a keyboard, touchscreen (e.g., with haptic or haptic feedback), voice input, eye-tracking input, brain monitoring system, or any other similar input mechanism. Other types of input devices may include cursor control devices, such as a mouse, trackball, or cursor arrow keys. Output devices may include a monitor, printer, etc., or any combination thereof.

[0060] Network 150 may include any suitable network that can facilitate the exchange of information and / or data between the MRI system 100 and the MRI system 100. In some embodiments, one or more components of the MRI system 100 (e.g., MRI device 110, processing device 120, storage device 130, terminal 140, etc.) may communicate information and / or data with one or more other components of the MRI system 100 via network 150. For example, processing device 120 may obtain two or more first magnetic resonance datasets related to the region of interest of a subject from MRI device 110 or storage device 130 via network 150. As another example, processing device 120 and / or terminal 140 may obtain information stored in storage device 130 via network 150. Network 150 may be and / or include public networks (e.g., the Internet), private networks (e.g., Local Area Network (LAN), Wide Area Network (WAN)), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks, Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), Frame Relay networks, Virtual Private Networks (VPNs), satellite networks, telephone networks, routers, hubs, switches, server computers, and / or any combination thereof. For example, network 150 may include wired networks, fiber optic networks, telecommunications networks, intranets, wireless local area networks (WLANs), metropolitan area networks (MANs), public switched telephone networks (PSTNs), Bluetooth, etc. TM Network, ZigBee TM A network, a Near Field Communication (NFC) network, or any combination thereof. In some embodiments, network 150 may include one or more network access points. For example, network 150 may include wired and / or wireless network access points, such as base stations and / or internet exchange points, through which one or more components of the MRI system 100 may connect to network 150 to exchange data and / or information.

[0061] The foregoing description is intended to be illustrative and not to limit the scope of this disclosure. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other features of the exemplary embodiments described herein can be combined in various ways to obtain additional and / or alternative exemplary embodiments. However, these changes and modifications do not depart from the scope of this disclosure. In some embodiments, the MRI system 100 may include one or more additional components and / or omit one or more of the aforementioned components. Additionally or alternatively, two or more components of the MRI system 100 may be integrated into a single component. For example, processing device 120 may be integrated into MRI device 110. As another example, a component of the MRI system 100 may be replaced by another component capable of performing the function of that component. Again, for example, processing device 120 and terminal 140 may be integrated into a single device.

[0062] Figure 2 This is a schematic diagram of an exemplary MRI apparatus 110 according to some embodiments of the present disclosure. As shown, a main magnet 201 can generate a first magnetic field (or main magnetic field) that can be applied to an object (also referred to as a subject) located within the first magnetic field. The main magnet 201 may include a resistive magnet or a superconducting magnet, both of which require a power source. Figure 2 (Not shown) is required for operation. Alternatively, the main magnet 201 may include a permanent magnet. The main magnet 201 can form a detection area and surround an object moving into or located within the detection area along the Z direction. The main magnet 201 can also control the uniformity of the generated main magnetic field. Several shimming coils may be present in the main magnet 201. Shimming coils placed in the gaps of the main magnet 201 can compensate for non-uniformity of the magnetic field of the main magnet 201. The shimming coils may be powered by a shimming power supply.

[0063] Gradient coil 202 may be located within main magnet 201. For example, gradient coil 202 may be located within the detection region. Gradient coil 202 may move around the detection region or an object within the detection region along the Z direction. Gradient coil 202 may be surrounded by main magnet 201 around the Z direction and closer to the object than main magnet 201. Gradient coil 202 may generate a second magnetic field (or gradient field, including gradient fields Gx, Gy, and Gz). The second magnetic field may be superimposed on the main magnetic field generated by main magnet 201 and twist the main magnetic field, such that the magnetic orientation of the protons of the object may change with its position within the gradient field, thereby encoding spatial information into an MR signal generated by the region of the imaged object. Gradient coil 202 may include an X coil (e.g., configured to generate a gradient field Gx corresponding to the X direction), a Y coil (e.g., configured to generate a gradient field Gy corresponding to the Y direction), and / or a Z coil (e.g., configured to generate a gradient field Gz corresponding to the Z direction). Figure 2(Not shown). In some embodiments, the Z coil may be designed based on a circular (Maxwell) coil, while the X and Y coils may be designed based on a saddle-shaped (Gauley) coil configuration. The three sets of coils can generate three different magnetic fields for position encoding. Gradient coil 202 can allow spatial encoding of the MR signal used for image reconstruction. Gradient coil 202 can be connected to one or more of X gradient amplifier 204, Y gradient amplifier 205, or Z gradient amplifier 206. One or more of the three amplifiers can be connected to waveform generator 216. Waveform generator 216 can generate gradient waveforms applied to X gradient amplifier 204, Y gradient amplifier 205, and / or Z gradient amplifier 206. The amplifiers can amplify the waveforms. The amplified waveforms can be applied to one of the coils in gradient coil 202 to generate magnetic fields on the x-axis, y-axis, or z-axis, respectively. Gradient coil 202 can be designed for use in closed-aperture MRI scanners or open-aperture MRI scanners. In some cases, all three sets of coils in gradient coil 202 can be energized, thereby generating three gradient fields. In some embodiments of this disclosure, the X-coil and Y-coil can be energized to generate gradient fields in the X and Y directions. As used herein, Figure 2 The X-axis, Y-axis, Z-axis, X direction, Y direction, and Z direction are related to... Figure 1 The same or similar as described in [the document].

[0064] In some embodiments, the radio frequency (RF) coil 203 may be located inside the main magnet 201 and function as a transmitter, receiver, or both. For example, the RF coil 203 may be located in a detection region. The RF coil 203 may be moved around the detection region or an object located within the detection region along the Z-direction. The RF coil 203 may be surrounded by the main magnet 201 and / or the gradient coil 202 around the Z-direction, and may be closer to the object than the gradient coil 202. The RF coil 203 may be connected to an RF electronics device 209, which may be configured or used as one or more integrated circuits (ICs) functioning as a waveform transmitter and / or waveform receiver. The RF electronics device 209 may be connected to a radio frequency power amplifier (RFPA) 207 and an analog-to-digital converter (ADC) 208.

[0065] When used as a transmitter, RF coil 203 can generate an RF signal that provides a third magnetic field for generating an MR signal associated with the region of the object being imaged. The third magnetic field can be perpendicular to the main magnetic field. Waveform generator 216 can generate RF pulses. These RF pulses can be amplified by RFPA 207, processed by RF electronics 209, and applied to RF coil 203 to generate an RF signal in response to a strong current generated by RF electronics 209 based on the amplified RF pulses.

[0066] When used as a receiver, the RF coil can be responsible for detecting the MR signal (e.g., echo). Upon excitation, the MR signal generated by the object can be sensed by the RF coil 203. The receiver amplifier can then receive the sensed MR signal from the RF coil 203, amplify the sensed MR signal, and provide the amplified MR signal to the ADC 208. The ADC 208 can convert the MR signal from an analog signal to a digital signal. The digital MR signal can then be sent to the processing device 120 for sampling.

[0067] In some embodiments, the main magnet 201, gradient coil 202, and RF coil 203 may be positioned relative to the circumference of the object in the Z direction. Those skilled in the art will understand that the main magnet 201, gradient coil 202, and RF coil 203 may be positioned around the object in various configurations.

[0068] In some embodiments, RFPA207 can amplify RF pulses (e.g., the power of the RF pulse, the voltage of the RF pulse) to generate amplified RF pulses to drive RF coil 203. RFPA207 may include a transistor-based RFPA, a vacuum tube-based RFPA, or any combination thereof. A transistor-based RFPA may include one or more transistors. A vacuum tube-based RFPA may include transistors, tetrodes, klystrons, or any combination thereof. In some embodiments, RFPA207 may include a linear RFPA or a non-linear RFPA. In some embodiments, RFPA207 may include one or more RFPAs.

[0069] In some embodiments, the MRI device 110 may also include a subject positioning system (not shown). The subject positioning system may include a subject carriage and a transport device. The subject may be placed on the subject carriage and positioned by the transport device within the main magnet 201 aperture.

[0070] MRI systems (e.g., MRI system 100 disclosed herein) are generally used to obtain internal images of specific regions of interest from a patient, which can be used for purposes such as diagnosis, treatment, or similar purposes, or a combination of both. The MRI system includes a main magnet assembly (e.g., main magnet 201) for providing a main magnetic field to align the individual magnetic moments of protons within the patient's body. In this process, protons precess around their magnetic poles at their characteristic Larmor frequency. This state can be referred to as an equilibrium state. If tissue is subjected to an additional magnetic field tuned to the Larmor frequency, the protons absorb additional energy, thereby rotating the net alignment torque of the protons. This state can be referred to as an excited state. The additional magnetic field can be provided by an RF excitation signal (e.g., an RF signal generated by RF coil 203). When the aforementioned additional magnetic field is removed, the magnetic moments of the protons rotate back to alignment with the main magnetic field, thereby emitting an echo signal. The echo signal is received and processed to form an MRI image. T1 relaxation can be a process of net magnetization increasing / returning to its initial maximum value parallel to the main magnetic field. T1 can be the time constant for longitudinal magnetization (e.g., along the main magnetic field) re-increase. T2 relaxation can be a process of decay or phase shift of the transverse component of magnetization. T2 can be the time constant of transverse magnetization decay / phase shift.

[0071] However, in reality, the main magnetic field cannot be perfectly uniform. The rotation frequency of hydrogen atoms is related to the strength of the main magnetic field. An inhomogeneous main magnetic field may cause hydrogen atoms at different locations to rotate at different frequencies. Hydrogen atoms located in areas of lower magnetic field strength may rotate more slowly, while those in areas of higher magnetic field strength may rotate more quickly. Therefore, the rotation of hydrogen atoms may be asynchronous, and the directions of their magnetization vectors may be more dispersed. The sum of these vectors may have a small amplitude, which accelerates transverse magnetization decay. The time constant for accelerating decay is T2*, which is less than T2.

[0072] If the main magnetic field is uniform across the patient's entire body, the RF excitation signal may non-selectively excite all protons in the sample. Therefore, to image a specific part of the patient's body, magnetic field gradients Gx, Gy, and Gz in the X, Y, and Z directions (e.g., generated by gradient coil 202) with specific time, frequency, and phase can be superimposed on the uniform magnetic field so that the RF excitation signal excites protons in the desired slice of the patient's body. Furthermore, unique phase and frequency information is encoded in the echo signal based on the proton's position within the "image slice." Based on this gradient encoding, Fourier imaging can be performed, where the measurement representing the spatial frequency of the subject, called k-space, can be acquired using a specific sampling trajectory. This specific sampling trajectory can include Cartesian or non-Cartesian trajectories, such as spiral trajectories, radial trajectories, etc., and image reconstruction is performed by performing an inverse Fourier transform (e.g., inverse fast Fourier transform) on the k-space data.

[0073] Typically, the portion of a patient's body to be imaged is scanned through a series of measurement cycles, where the RF excitation signal and magnetic field gradients Gx, Gy, and Gz vary according to the MRI imaging protocol being used. The protocol can be designed for one or more tissues, diseases, and / or clinical scenarios to be imaged. The protocol may include a number of pulse sequences oriented towards different planes and / or with different parameters. Pulse sequences may include spin echo sequences, gradient echo sequences, diffusion sequences, inversion recovery sequences, etc., or any combination thereof. For example, spin echo sequences may include Fast Spin Echo (FSE) pulse sequences, Turbo Spin Echo (TSE) pulse sequences, Rapid Acquisition with Relaxation Enhancement (RARE) pulse sequences, Half-Fourier Acquisition Single-Shot Turbo Spin-Echo (HASTE) pulse sequences, Turbo Gradient Spin Echo (TGSE) pulse sequences, etc., or any combination thereof. As another example, gradient echo sequences may include balanced steady-state free precession (bSSFP) pulse sequences, spoiled gradient echo (GRE) pulse sequences, echo planar imaging (EPI) pulse sequences, steady-state free precession (SSFP), or any combination thereof. For each MRI scan, the generated echo signals can be digitized and processed to reconstruct images according to the MRI imaging protocol used.

[0074] Figure 3 This is a schematic diagram illustrating exemplary hardware and / or software components of a computing device 300 according to some embodiments of the present disclosure. In some embodiments, one or more components of the MRI system 100 may be implemented on one or more components of the computing device 300. By way of example only, the processing device 120 may be implemented on one or more components of the computing device 300.

[0075] like Figure 3As shown, computing device 300 may include processor 310, memory 320, input / output (I / O) 330, and communication port 340. Processor 310 may execute computer instructions (e.g., program code) and perform the functions of processing device 120 according to the techniques described herein. Computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform the specific functions described herein. For example, processor 310 may process image data of a subject obtained from MRI device 110, storage device 130, terminal 140, and / or any other component of MRI system 100.

[0076] In some embodiments, processor 310 may include one or more hardware processors, such as microcontrollers, microprocessors, reduced instruction set computers (RISC), application-specific integrated circuits (ASICs), application-specific instruction-set processors (ASIPs), central processing units (CPUs), graphics processing units (GPUs), physical processing units (PPUs), microcontroller units, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), advanced reduced instruction set machines (ARMs), programmable logic devices (PLDs), any circuit or processor capable of performing one or more functions, or combinations thereof.

[0077] For illustrative purposes only, only one processor is described in computing device 300. However, it should be noted that computing device 300 in this disclosure may also include multiple processors. Therefore, the operations and / or method steps performed by one processor as described in this disclosure may also be performed jointly or individually by multiple processors. For example, if the processors of computing device 300 in this disclosure simultaneously execute operation A and operation B, it should be understood that operation A and operation B may also be performed jointly or separately by two or more different processors in computing device 300 (e.g., the first processor executes operation A while the second processor executes operation B, or the first and second processors jointly execute operation A and B).

[0078] The memory 320 may store data / information obtained from the MRI device 110, storage device 130, terminal 140, and / or any other component of the MRI system 100. In some embodiments, the memory 320 may include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory, or any combination thereof. For example, a mass storage device may include a hard disk, an optical disk, a solid-state drive, etc. A removable storage device may include a flash drive, a floppy disk, an optical disk, a memory card, a compact disk, a magnetic tape, etc. A volatile read-write memory may include random access memory. RAM may include dynamic RAM, double data rate synchronous dynamic RAM, static RAM, thyristor RAM, and zero-capacitance RAM, etc. ROM may include a mask ROM, a programmable ROM, an erasable programmable ROM, an electrically erasable programmable ROM, an optical disk ROM, and a digital multifunction disk read-only memory, etc. In some embodiments, the memory 320 may store one or more programs and / or instructions to perform the exemplary methods described in this disclosure.

[0079] I / O 330 can input and / or output signals, data, information, etc. In some embodiments, I / O 330 can enable user interaction with computing device 300 (e.g., processing device 120). In some embodiments, I / O 330 may include input devices and output devices. Examples of input devices may include a keyboard, mouse, touchscreen, microphone, etc., or any combination thereof. Examples of output devices may include a display device, speaker, printer, projector, etc., or any combination thereof. Examples of display devices may include a liquid crystal display (LCD), a light-emitting diode (LED) based display, a flat panel display, a curved screen, a television device, a cathode ray tube (CRT), a touchscreen screen, etc., or any combination thereof.

[0080] Communication port 340 can be connected to a network (e.g., network 150) to facilitate data communication. Communication port 340 can establish a connection between computing device 300 (e.g., processing device 120) and one or more components of MRI system 100 (e.g., MRI device 110, storage device 130, and / or terminal 140). This connection can be a wired connection, a wireless connection, any other communication connection capable of data transmission and / or reception, and / or a combination of these connections. Wired connections may include, for example, cables, optical fibers, telephone lines, etc., or combinations thereof. Wireless connections may include, for example, Bluetooth. TM Connectivity, Wi-Fi TM Connectivity, WiMax TMThe communication port 340 can be a connection such as a WLAN connection, a ZigBee connection, a mobile network connection (e.g., 3G, 4G, 5G, etc.), or any combination thereof. In some embodiments, the communication port 340 may be and / or include a standardized communication port, such as RS232, RS485, etc. In some embodiments, the communication port 340 may be a specially designed communication port. For example, the communication port 340 may be designed according to the Digital Imaging and Communications in Medicine (DICOM) protocol.

[0081] Figure 4 This is a schematic diagram illustrating exemplary hardware and / or software components of a mobile device 400 that can be implemented according to some embodiments of this disclosure. In some embodiments, one or more components of the MRI system 100 may be implemented on one or more components of the mobile device 400. By way of example only, the terminal 140 may be implemented on one or more components of the mobile device 400.

[0082] like Figure 4 As shown, the mobile device 400 may include a communication platform 410, a display 420, a GPU 430, a CPU 440, I / O 450, memory 460, and storage 490. In some embodiments, any other suitable components, including but not limited to a system bus or controller (not shown), may also be included in the mobile device 400. In some embodiments, a mobile operating system 470 (e.g., iOS) TM Android TM Windows Phone TM One or more applications 480 may be loaded from memory 490 into memory 460 for execution by CPU 440. Application 480 may include a browser or any other suitable mobile application for receiving and presenting information related to MRI system 100. User interaction with the information flow may be achieved through I / O 450 and provided via network 150 to processing device 120 and / or other components of MRI system 100.

[0083] To implement the various modules, units, and functions described in this disclosure, a computer hardware platform may be used as the hardware platform for one or more of the components described herein. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of workstation or terminal. If the computer is properly programmed, it may also be used as a server.

[0084] Figure 5This is a schematic diagram of an exemplary processing device 500 illustrated according to some embodiments of the present disclosure. In some embodiments, the processing device 120 may include an acquisition module 510, a determination module 520, and a reconstruction module 530. In some embodiments, the processing device 500 may be all or part of the hardware circuitry of the processing device 120. The processing device 500 may also be implemented as an application program or instruction set read and executed by the processing device 120. Further, the processing device 500 may be any combination of hardware circuitry and application programs / instructions. For example, when the processing device 120 is executing an application program / instruction set, a module may be part of the processing device 120.

[0085] The acquisition module 510 can acquire two or more first magnetic resonance imaging (MRI) datasets related to the subject's region of interest. The two or more first MRI datasets can be acquired based on two or more different values ​​of scanning parameters. Scanning parameters may include at least one of the flip angle or repetition time.

[0086] The determination module 520 can determine two or more second magnetic resonance datasets based on two or more first magnetic resonance datasets. Each of the two or more second magnetic resonance datasets can correspond to at least two of the two or more first magnetic resonance datasets.

[0087] The reconstruction module 530 can generate two or more T1-weighted images of the region of interest based on two or more second magnetic resonance datasets, with each of the two or more T1-weighted images corresponding to a target time point.

[0088] In some embodiments, the reconstruction module 530 may perform T1 mapping based on two or more first magnetic resonance datasets.

[0089] In some embodiments, the reconstruction module 530 can estimate the contrast agent concentration corresponding to each target time point based on the two or more T1-weighted images and the T1 mapping. The reconstruction module 530 can then perform physiological analysis on the region of interest based on the contrast agent concentration corresponding to each target time point.

[0090] In some embodiments, the reconstruction module 530 can determine the signal intensity corresponding to each target time point based on the two or more T1-weighted images. The reconstruction module 530 can then perform physiological analysis on the region of interest based on the signal intensity corresponding to each target time point.

[0091] It should be noted that the above description of the processing device 120 is for illustrative purposes only and is not intended to limit the scope of this disclosure. Those skilled in the art can make various changes and modifications based on the description in this disclosure. However, these changes and modifications do not depart from the scope of this disclosure. Two or more modules can be combined into one module, and any module can be divided into two or more units.

[0092] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. Various changes and modifications can be made by those skilled in the art based on the description of this disclosure. However, these changes and modifications do not depart from the scope of this disclosure. For example, the processing device 120 may also include a storage module ( Figure 5 (Not shown). The storage module can be configured to store data generated during any process performed by any component of the processing device 120. As another example, each component of the processing device 120 may include a storage device. Additionally or alternatively, the components of the processing device 120 may share a common storage device.

[0093] Figure 6 This is a flowchart illustrating an exemplary process 600 for generating two or more T1-weighted images according to some embodiments of the present disclosure. In some embodiments, process 600 may be... Figure 1 This is implemented in the MRI system 100 shown. For example, process 600 can be stored as instructions in storage device 130 and / or memory (e.g., memory 320, memory 490) and invoked and / or executed by processing device 120 (e.g., as shown in the image). Figure 3 The processor 310 of the computing device 300 shown, such as Figure 4 The CPU 440 of the mobile device 400 shown, or Figure 5 (One or more modules shown). The operation of the process illustrated below is for illustrative purposes only. In some embodiments, process 600 may be accomplished by one or more additional operations not described and / or without one or more of the operations discussed. Additionally, Figure 6 The order of operations in process 600 shown and described below is not restrictive.

[0094] In 610, the processing device 120 (e.g., acquisition module 510) can acquire two or more first magnetic resonance imaging (MRI) datasets related to the subject's region of interest. The two or more first MRI datasets are acquired based on two or more different values ​​of scanning parameters. Scanning parameters may include at least one of flip angle or repetition time.

[0095] In some embodiments, each of two or more first magnetic resonance datasets can be acquired by applying a pulse sequence to a region of interest (ROI). The pulse sequence may include RF excitation pulses played in the presence of a slice selection gradient to generate transverse magnetization in the ROI. In some embodiments, the RF excitation pulses may have a flip angle. In this specification, the flip angle may be a rotation of the RF excitation pulse relative to the net magnetization vector of the main magnetic field. The TR may be located between two consecutive RF excitation pulses.

[0096] In some embodiments, the pulse sequence can be a steady-state sequence. A master magnet assembly (e.g., master magnet 201) can provide a master magnetic field to align the individual magnetic moments of protons within the ROI. This state can be referred to as the equilibrium state. When the ROI is subjected to an additional magnetic field tuned to a Larmor frequency, the protons absorb additional energy, thereby rotating the net alignment torque of the protons. This state can be referred to as the excited state. The additional magnetic field can be provided by an RF excitation signal (e.g., an RF signal generated by RF coil 203). The process of returning from the excited state to the equilibrium state can be referred to as T1 relaxation. The steady-state sequence can be a pulse sequence that applies repetitive RF excitation to the protons and produces a stable, repeatable combination of equilibrium, excited, and relaxed states. Steady-state sequences can include spin echo (SE) sequences, gradient echo (GRE) sequences, magnetization preparation rapid gradient echo (MR-GRE) sequences, magnetization preparation rapid spin echo sequences, and gradient-and-spin-echo (GRASE) sequences.

[0097] Two or more first magnetic resonance datasets may include k-space data acquired by filling one or more echoes of the ROI received by two or more receiving coils (e.g., RF coil 203) of an MRI device (e.g., MRI device 110) into k-space along the sampling pattern.

[0098] In some embodiments, the ROI may include a slice or volume of a subject for 3D imaging. In some embodiments, the ROI may include one or more slices of a subject for 2D imaging.

[0099] In some embodiments, the two or more first magnetic resonance datasets may include two-dimensional (2D) k-space data, three-dimensional (3D) k-space data, four-dimensional (4D) k-space data, etc. As described herein, four-dimensional k-space data refers to a data format containing two-dimensional or three-dimensional k-space data that varies over time. By way of example only, three-dimensional k-space data may be a 256*256*256 digital matrix. In some embodiments, the two or more first magnetic resonance datasets may be undersampled, fully sampled, or oversampled.

[0100] In some embodiments, each of the two or more first magnetic resonance datasets can be obtained by performing k-space sampling once in a time-continuous manner. In some embodiments, the two or more first magnetic resonance datasets can be obtained in a time-separated manner.

[0101] In some embodiments, each of two or more first magnetic resonance imaging (MRI) datasets may be acquired based on one of two or more different values ​​of scan parameters. Each of two or more first MRI datasets may be acquired using a pulse sequence having a flip angle and a TR (transverse oscillation). In some embodiments, two or more first MRI datasets may be acquired based on two or more values ​​of flip angle and the same value of TR. For example, each of two or more first MRI datasets may be acquired based on one of two or more values ​​of flip angle and the same value of TR. In some embodiments, two or more first MRI datasets may be acquired based on two or more values ​​of TR and the same value of flip angle. For example, each of two or more first MRI datasets may be acquired based on one of two or more values ​​of TR and the same value of flip angle. In some embodiments, two or more first MRI datasets may be acquired based on two or more values ​​of flip angle and two or more values ​​of TR. For example, each of two or more first MRI datasets may be acquired based on one of two or more values ​​of flip angle and one of two or more values ​​of TR.

[0102] In some embodiments, the two or more values ​​of the flip angle can be any value. For example, for a first pulse sequence applied to the ROI, the value of the flip angle can be greater than the Ernst angle (A) corresponding to the TR of the first pulse sequence and the T1 of the ROI. E =arccos(e -TR / T1 For the second pulse sequence applied to the ROI, the flip angle value can be smaller than the TR of the second pulse sequence and the Ernst angle corresponding to T1 of the ROI. In some embodiments, two or more first magnetic resonance datasets can be used for T1-weighted dynamic imaging, and the TR used to acquire two or more first magnetic resonance datasets can be relatively short, for example, less than 500 ms.

[0103] In some embodiments, the processing device 120 may obtain two or more first magnetic resonance imaging (MRI) datasets from one or more components of the MRI system 100 (e.g., MRI device 110, terminal 140, and / or storage device 130) or from an external storage device via network 150. For example, the MRI device 110 may transfer two or more first MRI datasets to storage device 130, or any other storage device, for storage. The processing device 120 may obtain two or more first MRI datasets from storage device 130 or any other storage device. As another example, the processing device 120 may obtain two or more first MRI datasets directly from the MRI device 110.

[0104] In 620, the processing device 120 (e.g., the determination module 520) can determine two or more second magnetic resonance datasets based on two or more first magnetic resonance datasets. Each of the two or more second magnetic resonance datasets can correspond to at least two of the two or more first magnetic resonance datasets.

[0105] In some embodiments, at least two of the two or more first magnetic resonance datasets corresponding to the second magnetic resonance dataset may correspond to two different values ​​of the scanning parameters. For example, at least two of the two or more first magnetic resonance datasets corresponding to the second magnetic resonance dataset may correspond to two different values ​​of the flip angle. As another example, at least two of the two or more first magnetic resonance datasets corresponding to the second magnetic resonance dataset may correspond to two different values ​​of the TR. As yet another example, at least two of the two or more first magnetic resonance datasets corresponding to the second magnetic resonance dataset may correspond to two different values ​​of both the flip angle and the TR.

[0106] In some embodiments, for one of two or more second magnetic resonance datasets, the processing device 120 may acquire at least one first magnetic resonance dataset associated with a first value of the scan parameters. The processing device 120 may acquire at least one first magnetic resonance dataset associated with a second value of the scan parameters. The processing device 120 may perform calculations based on at least two of the two or more first magnetic resonance datasets associated with the first and second values ​​of the scan parameters. For example, the processing device 120 may perform calculations (e.g., division) between the first magnetic resonance dataset associated with the first value of the scan parameters and the first magnetic resonance dataset associated with the second value of the scan parameters to determine the second magnetic resonance dataset. As another example, the processing device 120 may determine a first average of at least two first magnetic resonance datasets associated with the first value of the scan parameters and a second average of at least two first magnetic resonance datasets associated with the second value of the scan parameters. The processing device 120 may perform calculations (e.g., division) between the first average and the second average to determine the second magnetic resonance dataset. As yet another example, the processing device 120 may determine the average of at least two first magnetic resonance datasets associated with the first value of the scan parameters. The processing device 120 can perform calculations (e.g., division) between the aforementioned average value and a first magnetic resonance dataset associated with a second value of the scanning parameters to determine a second magnetic resonance dataset. As another example, the processing device 120 can determine the average value of at least two first magnetic resonance datasets associated with a second value of the scanning parameters. The processing device 120 can perform calculations (e.g., division) between the aforementioned average value and a first magnetic resonance dataset associated with a first value of the scanning parameters to determine a second magnetic resonance dataset.

[0107] In some embodiments, two or more first magnetic resonance datasets may be acquired, such that any two adjacent first magnetic resonance datasets correspond to different values ​​of scanning parameters. For each of the two or more second magnetic resonance datasets, the processing device 120 may determine the second magnetic resonance dataset based on two adjacent first magnetic resonance datasets. For example, the processing device 120 may determine the second magnetic resonance dataset based on a calculation (e.g., division) performed on two adjacent first magnetic resonance datasets.

[0108] For example, two or more first magnetic resonance datasets can be acquired based on two or more different values ​​of the flip angle and a fixed value of the repetition time (TR), two or more different values ​​of the TR and a fixed value of the flip angle, or two or more different values ​​of the flip angle and two or more different values ​​of the TR. Two or more first magnetic resonance datasets can be acquired such that any two adjacent first magnetic resonance datasets correspond to at least one different value of the flip angle or repetition time. For each of the two or more second magnetic resonance datasets, the processing device 120 can determine the second magnetic resonance dataset by performing a calculation (e.g., division) between two adjacent first magnetic resonance datasets.

[0109] The second magnetic resonance dataset can correspond to a time point (e.g., an average time point) within the time interval of acquiring two adjacent first magnetic resonance datasets. For example, for two temporally adjacent first magnetic resonance datasets A and B, first magnetic resonance dataset A can be acquired within a time interval t1 starting at time t, and first magnetic resonance dataset B can be acquired within a time interval t2. The time point of the second magnetic resonance dataset corresponding to the two adjacent first magnetic resonance datasets A and B can be t+(t1+t2) / 2.

[0110] In some embodiments, at least one of two or more first magnetic resonance datasets corresponding to a first value among two or more values ​​of the scanning parameters may be acquired before the remainder of two or more first magnetic resonance datasets corresponding to the remainder of the remainder among two or more values ​​of the scanning parameters.

[0111] In some embodiments, the processing device 120 may determine the average value of at least one of two or more first magnetic resonance datasets corresponding to a first value among two or more values ​​of scanning parameters. For each of the two or more second magnetic resonance datasets, the processing device 120 may determine the second magnetic resonance dataset based on one of the remaining portions of the two or more first magnetic resonance datasets and the aforementioned average value. For example, the processing device 120 may determine the second magnetic resonance dataset based on a calculation (e.g., division) performed on one of the remaining portions of the two or more first magnetic resonance datasets and the aforementioned average value.

[0112] For example, two or more first magnetic resonance imaging (MRI) datasets can be acquired based on two or more different values ​​of the flip angle and a fixed value of TR, or two or more different values ​​of TR and a fixed value of the flip angle. Before the remainder of the two or more first MRI datasets corresponding to the remainder of the two or more values ​​of the flip angle or repetition time, at least one of the two or more first MRI datasets corresponding to the first value among the two or more values ​​of the flip angle or repetition time is acquired. As another example, two or more first MRI datasets can be acquired based on two or more different values ​​of the flip angle and two or more different values ​​of TR. Before the remainder of the two or more first MRI datasets corresponding to the remainder of the two or more different values ​​of the flip angle and the remainder of the two or more different values ​​of the repetition time, at least one of the two or more first MRI datasets corresponding to the first value among the two or more different values ​​of the flip angle and the first value among the two or more different values ​​of the repetition time is acquired. In both of the above examples, the processing device 120 can determine the average value of at least one of the two or more first MRI datasets. For each of the two or more second MRI datasets, the processing device 120 can determine the second MRI dataset by calculating (e.g., by division) one of the remainder of the two or more first MRI datasets and the aforementioned average value.

[0113] In some embodiments, the second magnetic resonance dataset may correspond to a time point within a time period during which one of the remaining portions of two or more first magnetic resonance datasets is acquired. For example, if the first magnetic resonance dataset is acquired based on a Cartesian trajectory, the time point corresponding to the second magnetic resonance dataset may be the time point at which the phase-encoded line (Ky=0) at the center of k-space is sampled.

[0114] In some embodiments, two or more first magnetic resonance imaging (MRI) datasets can be acquired by injecting a contrast agent into the region of interest (ROI), for example, by using dynamic contrast-enhanced (DCE) imaging. In this case, at least one of the two or more MRI datasets can be acquired before injecting the contrast agent into the ROI, and the remainder of the two or more MRI datasets can be acquired after the contrast agent injection.

[0115] Further details regarding the determination of two or more second magnetic resonance datasets can be found in the remainder of this disclosure (e.g., in combination with...). Figures 7A-8C (Description).

[0116] In 630, the processing device 120 (e.g., reconstruction module 530) can generate two or more T1-weighted images of the region of interest (ROI) based on two or more second magnetic resonance datasets, each of the two or more T1-weighted images corresponding to a target time point. In some embodiments, the T1-weighted images of the ROI may include 2D or 3D images. In some embodiments, each of the two or more T1-weighted images can be generated by reconstructing one of the two or more second magnetic resonance datasets. The target time point of the T1-weighted image may be the time point corresponding to the second magnetic resonance dataset used to generate the T1-weighted image.

[0117] In some embodiments, the processing device 120 may generate two or more T1-weighted images based on two or more second magnetic resonance datasets using any reconstruction algorithm, such as parallel imaging (PI), multi-band (MB) imaging, compressive sensing (CS), artificial intelligence (AI) reconstruction, or any combination thereof.

[0118] In some embodiments, the processing device 120 can determine the signal strength corresponding to each target time point based on two or more T1-weighted images. In some embodiments, pixel values ​​(e.g., grayscale values) can be correlated with signal strength (e.g., linear relationship), and the signal strength corresponding to each T1-weighted image can be determined based on the pixel values ​​of the T1-weighted images. For example, the processing device 120 can determine the average pixel value of all pixels in the T1-weighted image and designate this average value as the signal strength of the T1-weighted image (e.g., average signal strength). As another example, the processing device 120 can identify one or more regions in the T1-weighted image. The processing device 120 can determine the average pixel value of pixels in the aforementioned one or more regions and designate this average value as the signal strength of the T1-weighted image (e.g., average signal strength). Then, the processing device 120 can generate an intensity-time curve based on the signal strength corresponding to each T1-weighted image and the target time point.

[0119] Figure 9 This is a schematic diagram of an exemplary intensity-time curve 900 shown according to some embodiments of the present disclosure.

[0120] like Figure 9 As shown, the processing device 120 can generate two or more T1-weighted images I1-I8 corresponding to target time points t1-t8 based on process 600. The processing device 120 can determine the signal strength corresponding to each target time point based on the two or more T1-weighted images, and determine the intensity-time curve 900 based on the signal strength corresponding to the target time points t1-t8.

[0121] In some embodiments, the processing device 120 can perform physiological analysis based on the signal intensity corresponding to each target time point. For example, the processing device 120 can perform physiological analysis based on an intensity-time curve.

[0122] For physiological analysis, contrast agent uptake imaging can be performed within the ROI. In some embodiments, dynamic contrast-enhanced imaging can be applied to physiological analysis.

[0123] Disseminated vascular epithelial (DCE) imaging is an imaging technique that utilizes the vascular properties of tissues. DCE imaging can provide information about physiological tissue characteristics. In DCE imaging, a contrast agent is typically injected into the patient, and T1-weighted magnetic resonance (MRI) images are acquired before and after the injection. Due to differences in capillary permeability, vascular surface area, and blood flow velocity, the diffusion rate of the contrast agent also varies. Differences exist between different tissues, creating contrast in the images. DCE data analysis methods mainly include semi-quantitative and quantitative approaches. Semi-quantitative analysis is based on intensity-time curve analysis to obtain characteristics such as tissue perfusion, capillary surface area, capillary permeability, and extravascular extracellular space (EES). Exemplary semi-quantitative analysis parameters may include onset time, time to peak, and maximum signal intensity, which describe the shape and composition of the intensity-time curve. Quantitative analysis determines the contrast agent concentration in the region of origin (ROI) and then analyzes the characteristics of tissue perfusion, capillary surface area, capillary permeability, and extravascular extracellular space (EES). Simple quantitative parameters may include the initial area under the curve. Quantitative analysis can also fit multiple pharmacokinetic models to perform mathematical analysis and determination of intensity-time curves.

[0124] In some embodiments, the processing device 120 may perform T1 mapping based on two or more first magnetic resonance datasets. In some embodiments, if the two or more first magnetic resonance datasets are based on the flip angle (e.g., Figure 7A and Figure 8A Two or more values ​​of TR (as shown) or TR (e.g.) Figure 7B and Figure 8B If two or more values ​​(as shown) are collected, the processing device 120 can perform T1 mapping based on two or more first magnetic resonance datasets; if the two or more first magnetic resonance datasets are based on two or more values ​​of the flip angle and TR (as shown), the processing device 120 can perform T1 mapping based on two or more first magnetic resonance datasets. Figure 7C and Figure 8C If more than two values ​​are collected (as shown), then T1 mapping cannot be achieved based on more than two first magnetic resonance datasets.

[0125] In some embodiments, the processing device 120 may perform the T1 mapping using any suitable algorithm. For example, the processing device 120 may perform the T1 mapping based on the following equation (1):

[0126]

[0127] Where ρ represents the coefficient related to longitudinal magnetization; S(α, TR) represents the first magnetic resonance dataset obtained based on the flip angle α and TR; and E = e -TR / T1 .

[0128] Equation (1) can be seen as and The linear equation between them. The processing device 120 can determine the slope E and intercept ρ(1-E) based on at least two of two or more first magnetic resonance datasets, thereby determining the T1 value of each pixel or voxel and implementing T1 mapping, for example, generating a quantitative T1 map.

[0129] In some embodiments, the processing device 120 may estimate the contrast agent concentration corresponding to each target time point based on two or more T1-weighted images and T1 mappings. The processing device 120 may use any suitable algorithm to estimate the contrast agent concentration corresponding to each target time point based on two or more T1-weighted images and T1 mappings.

[0130] The processing device 120 can perform physiological analysis (e.g., DCE analysis) based on the contrast agent concentration corresponding to each target time point. For example, the processing device 120 can determine a concentration-time curve based on the contrast agent concentration corresponding to the target time point, and perform physiological analysis (e.g., DCE analysis) based on the concentration-time curve.

[0131] It should be noted that the above description of process 600 is for illustrative purposes only and is not intended to limit the scope of this disclosure. Those skilled in the art can make various changes and modifications based on the description in this disclosure. However, these changes and modifications do not depart from the scope of this application. In some embodiments, process 600 may be accomplished with one or more additional operations not described and / or without one or more operations discussed above. In some embodiments, the equations provided above are illustrative examples and may be modified in various ways. For example, one or more coefficients in the equation may be omitted, and / or the equation may further include one or more additional coefficients.

[0132] Figures 7A-7C This is an exemplary schematic diagram illustrating the acquisition of two or more first magnetic resonance datasets according to some embodiments of the present disclosure.

[0133] like Figure 7A As shown, the processing device 120 can obtain two or more first magnetic resonance datasets S a1 -S a9 S a1 -S a9 It can be retrieved in chronological order. a1 -S a9It can be obtained based on two different flip angles (α1 and α2) and the same TR. S a1 -S a9 Each of these can correspond to the same TR and one of α1 and α2. a1 -S a9 It can be acquired such that any two adjacent first magnetic resonance datasets correspond to different values ​​of the flip angle. For example, as... Figure 7A As shown, α1 and α2 can be applied alternately to obtain S. a1 -S a9 .

[0134] Processing device 120 can determine a second magnetic resonance dataset based on two adjacent first magnetic resonance datasets. For example... Figure 7A As shown, the processing device 120 can determine S a1 and S a2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' a1 S' a1 This can correspond to obtaining S a1 and S a2 Time point t within the time period a1 (For example, average time points). Processing device 120 can determine S a2 and S a3 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' a2 S' a2 This can correspond to obtaining S a2 and S a3 Time point t within the time period a2 (For example, average time points). Processing device 120 can determine S a3 and S a4 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' a3 S' a3 This can correspond to obtaining S a3 and S a4 Time point t within the time period a3 (For example, average time points). Processing device 120 can determine S a4 and S a5 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' a4 S' a4 This can correspond to obtaining S a4 and S a5 Time point t within the time period a4 (For example, average time points). Processing device 120 can determine S a5 and S a6The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' a5 S' a5 This can correspond to obtaining S a5 and S a6 Time point t within the time period a5 (For example, average time points). Processing device 120 can determine S a6 and S a7 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' a6 S' a6 This can correspond to obtaining S a6 and S a7 Time point t within the time period a6 (For example, average time points). Processing device 120 can determine S a7 and S a8 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' a7 S' a7 This can correspond to obtaining S a7 and S a8 Time point t within the time period a7 (For example, average time points). Processing device 120 can determine S a8 and S a9 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' a8 S' a8 This can correspond to obtaining S a8 and S a9 Time point t within the time period a8 (e.g., average time points).

[0135] In some embodiments, the first magnetic resonance dataset obtained based on the flip angle α and TR can be represented based on the following equation (2):

[0136]

[0137] Where S(α,TR) represents the first magnetic resonance dataset obtained based on the flip angle α and TR; E=e -TR / T1 M0 is the base signal of the ROI, which includes one or more non-T1 factors (e.g., those related to equilibrium magnetization). These one or more non-T1 factors can depend on the configuration of the MRI device 110 and the tissue characteristics of the ROI. For example, one or more non-T1 factors may include T2*, receiver coil sensitivity, echo time (TE), proton density of the ROI, etc., or any combination thereof. This is merely an example. Where C represents the sensitivity of the receiving coil. This represents the proton density.

[0138] Using the second magnetic resonance dataset S' a1 For example, the processing device 120 can determine S' according to the following equation (3). a1 :

[0139]

[0140] In some embodiments, in order to determine the second magnetic resonance dataset S' a1 The adjacent first magnetic resonance dataset S a1 and S a2 There are no restrictions on the order of calculation (e.g., the order of division). Processing device 120 can also determine S' according to the following equation (4). a1 :

[0141]

[0142] S' a2 -S' a8 It can be determined based on a method similar to equation (3) or equation (4).

[0143] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. Various changes and modifications can be made by those skilled in the art based on the description herein. However, such changes and modifications do not depart from the scope of this disclosure.

[0144] like Figure 7B As shown, the processing device 120 can obtain two or more first magnetic resonance datasets S b1 -S b9 S b1 -S b9 It can be obtained in chronological order. b1 -S b9 It can be obtained based on two different TR values ​​(TR1 and TR2) and the same flip angle α. b1 -S b9 Each of these can correspond to the same flip angle α and one of TR1 and TR2. b1 -S b9 It can be acquired such that any two adjacent first magnetic resonance datasets correspond to different values ​​of TR. For example, as Figure 7B As shown, TR1 and TR2 can be used alternately to obtain S. b1 -S b9 .

[0145] Processing device 120 can determine a second magnetic resonance dataset based on two adjacent first magnetic resonance datasets. For example... Figure 7B As shown, the processing device 120 can determine S b1 and Sb2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' b1 S' b1 This can correspond to obtaining S b1 and S b2 Time point t within the time period b1 (For example, average time points). Processing device 120 can determine S b2 and S b3 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' b2 S' b2 This can correspond to obtaining S b2 and S b3 Time point t within the time period b2 (For example, average time points). Processing device 120 can determine S b3 and S b4 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' b3 S' b3 This can correspond to obtaining S b3 and S b4 Time point t within the time period b3 (For example, average time points). Processing device 120 can determine S b4 and S b5 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' b4 S' b4 This can correspond to obtaining S b4 and S b5 Time point t within the time period b4 (For example, average time points). Processing device 120 can determine S b5 and S b6 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' b5 S' b5 This can correspond to obtaining S b5 and S b6 Time point t within the time period b5 (For example, average time points). Processing device 120 can determine S b6 and S b7 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' b6 S' b6 This can correspond to obtaining S b6 and S b7 Time point t within the time period b6 (For example, average time points). Processing device 120 can determine S b7 and S b8The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' b7 S' b7 This can correspond to obtaining S b7 and S b8 Time point t within the time period b7 (For example, average time points). Processing device 120 can determine S b8 and S b9 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' b8 S' b8 This can correspond to obtaining S b8 and S b9 Time point t within the time period b8 (e.g., average time points).

[0146] Using the second magnetic resonance dataset S' b1 For example, the processing device 120 can determine S' according to the following equation (5). b1 :

[0147]

[0148] in,

[0149] In some embodiments, in order to determine the second magnetic resonance dataset S' b1 The adjacent first magnetic resonance dataset S b1 and S b2 There are no restrictions on the order of calculation (e.g., the order of division). Processing device 120 can also determine S' according to the following equation (6). b1 :

[0150]

[0151] S' b2 -S' b8 It can be determined based on a method similar to equation (5) or equation (6).

[0152] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. Various changes and modifications can be made by those skilled in the art based on the description herein. However, such changes and modifications do not depart from the scope of this disclosure.

[0153] like Figure 7C As shown, the processing device 120 can obtain two or more first magnetic resonance datasets S c1 -S c9 S c1 -S c9 S can be obtained in chronological order.c1 -S c9 It can be obtained based on two different TR values ​​(TR1 and TR2) and two different flip angle values ​​(α1 and α2). c1 -S c9 Each of them can correspond to one of TR1 and TR2, and one of α1 and α2. c1 -S c9 It can be acquired such that any two adjacent first magnetic resonance datasets correspond to different values ​​of TR and different values ​​of the flip angle. For example, as Figure 7C As shown, in order to obtain S c1 -S c9 TR1 and TR2 can be applied alternately, as can α1 and α2. For example, as... Figure 7C As shown, S c1 It can correspond to α1 and TR1, S c2 It can correspond to α2 and TR2, and so on. For example, S c1 It can correspond to α1 and TR2, S c2 It can correspond to α2 and TR1, and so on.

[0154] Processing device 120 can determine a second magnetic resonance dataset based on two adjacent first magnetic resonance datasets. For example... Figure 7C As shown, the processing device 120 can determine S c1 and S c2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' c1 S' c1 This can correspond to obtaining S c1 and S c2 Time point t within the time period c1 (For example, average time points). Processing device 120 can determine S c2 and S c3 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' c2 S' c2 This can correspond to obtaining S c2 and S c3 Time point t within the time period c2 (For example, average time points). Processing device 120 can determine S c3 and S c4 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' c3 S' c3 This can correspond to obtaining S c3 and S c4 Time point t within the time period c3 (For example, average time points). Processing device 120 can determine Sc4 and S c5 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' c4 S' c4 This can correspond to obtaining S c4 and S c5 Time point t within the time period c4 (For example, average time points). Processing device 120 can determine S c5 and S c6 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' c5 S' c5 This can correspond to obtaining S c5 and S c6 Time point t within the time period c5 (For example, average time points). Processing device 120 can determine S c6 and S c7 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' c6 S' c6 This can correspond to obtaining S c6 and S c7 Time point t within the time period c6 (For example, average time points). Processing device 120 can determine S c7 and S c8 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' c7 S' c7 This can correspond to obtaining S c7 and S c8 Time point t within the time period c7 (For example, average time points). Processing device 120 can determine S c8 and S c9 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' c8 S' c8 This can correspond to obtaining S c8 and S c9 Time point t within the time period c8 (e.g., average time points).

[0155] Using the second magnetic resonance dataset S' c1 For example, the processing device 120 can determine S' according to the following equation (7). c1 :

[0156]

[0157] In some embodiments, in order to determine the second magnetic resonance dataset S' c1The adjacent first magnetic resonance dataset S c1 and S c2 There are no restrictions on the order of calculation (e.g., the order of division). Processing device 120 can also determine S' according to the following equation (8). c1 :

[0158]

[0159] S' c2 -S' c8 It can be determined based on a method similar to equation (7) or equation (8).

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

[0161] Figures 8A-8C This is an exemplary schematic diagram illustrating the acquisition of two or more first magnetic resonance datasets according to some embodiments of the present disclosure.

[0162] like Figure 8A As shown, the processing device 120 can obtain two or more first magnetic resonance datasets S d1 -S d9 S d1 -S d9 It can be retrieved in chronological order. d1 -S d9 It can be obtained based on two different flip angles (α1 and α2) and the same TR. S d1 -S d9 Each of these can correspond to the same TR and one of α1 and α2. S corresponds to α1. d1 It can precede S corresponding to α2 d2 -S d9 Obtain.

[0163] like Figure 8A As shown, the processing device 120 can determine S d1 and S d2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' d1 S' d1 This can correspond to obtaining S d2 Time point t within the time period d1 Processing equipment 120 can determine S d1 and S d2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' d2 S'd2 This can correspond to obtaining S d3 Time point t within the time period d2 Processing equipment 120 can determine S d1 and S d4 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' d3 S' d3 This can correspond to obtaining S d4 Time point t within the time period d3 Processing equipment 120 can determine S d1 and S d5 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' d4 S' d4 This can correspond to obtaining S d5 Time point t within the time period d4 Processing equipment 120 can determine S d1 and S d6 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' d5 S' d5 This can correspond to obtaining S d6 Time point t within the time period d5 Processing equipment 120 can determine S d1 and S d7 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' d6 S' d6 This can correspond to obtaining S d7 Time point t within the time period d6 Processing equipment 120 can determine S d1 and S d8 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' d7 S' d7 This can correspond to obtaining S d8 Time point t within the time period d7 Processing equipment 120 can determine S d1 and S d9 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' d8 S' d8 This can correspond to obtaining S d9 Time point t within the time period d8 .

[0164] Using the second magnetic resonance dataset S' d1 For example, the processing device 120 can determine S' according to the following equation (9). d1 :

[0165]

[0166] in, This represents the average value of the first magnetic resonance dataset corresponding to α1. If there is only one first magnetic resonance dataset ( Figure 8A S shown d1 If α1 corresponds to α1, then If there are two or more first magnetic resonance datasets corresponding to α1, for example, denoted as (n is an integer greater than 1). In some embodiments, in order to determine the second magnetic resonance dataset S' d1 ,right and S d2 There are no restrictions on the order of calculation of (α2,TR) (e.g., the order of division). Processing device 120 can also determine S' according to the following equation (10). d1 :

[0167]

[0168] S' d2 -S' d8 It can be determined based on a method similar to equation (9) or equation (10).

[0169] In some embodiments, the number (count) of the first magnetic resonance dataset corresponding to α1 can be one or more. In some embodiments, a first magnetic resonance dataset following the first magnetic resonance dataset corresponding to α1 can also be obtained based on two or more values ​​of the flip angle.

[0170] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. Various changes and modifications can be made by those skilled in the art based on the description herein. However, such changes and modifications do not depart from the scope of this disclosure.

[0171] like Figure 8B As shown, the processing device 120 can obtain two or more first magnetic resonance datasets S e1 -S e9 S e1 -S e9 It can be retrieved in chronological order. e1 -S e9 It can be obtained based on two different TR values ​​(TR1 and TR2) and the same flip angle α. S e1 -S e9 Each of these can correspond to the same flip angle α and one of TR1 and TR2. TR1 corresponds to S... e1 It can precede the S corresponding to TR2 e2 -S e9Obtain.

[0172] like Figure 8B As shown, the processing device 120 can determine S e1 and S e2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' e1 S' e1 This can correspond to obtaining S e2 Time point t within the time period e1 Processing equipment 120 can determine S e1 and S e2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' e2 S' e2 This can correspond to obtaining S e3 Time point t within the time period e2 Processing equipment 120 can determine S e1 and S e4 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' e3 S' e3 This can correspond to obtaining S e4 Time point t within the time period e3 Processing equipment 120 can determine S e1 and S e5 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' e4 S' e4 This can correspond to obtaining S e5 Time point t within the time period e4 Processing equipment 120 can determine S e1 and S e6 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' e5 S' e5 This can correspond to obtaining S e6 Time point t within the time period e5 Processing equipment 120 can determine S e1 and S e7 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' e6 S' e6 This can correspond to obtaining S e7 Time point t within the time period e6 Processing equipment 120 can determine S e1 and S e8 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' e7 S' e7 This can correspond to obtaining S e8Time point t within the time period e7 Processing equipment 120 can determine S e1 and S e9 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' e8 S' e8 This can correspond to obtaining S e9 Time point t within the time period e8 .

[0173] Using the second magnetic resonance dataset S' e1 For example, the processing device 120 can determine S' according to the following equation (11). e1 :

[0174]

[0175] in, This represents the average value of the first magnetic resonance dataset corresponding to TR1. If there is only one first magnetic resonance dataset ( Figure 8B S shown e1 If it corresponds to TR1, then If there are two or more first magnetic resonance datasets corresponding to TR1, for example, it is represented as

[0176] (n is an integer greater than 1).

[0177] In some embodiments, in order to determine the second magnetic resonance dataset S' e1 ,right and S e2 There are no restrictions on the order of calculation (e.g., the order of division) of (α,TR2). The processing device 120 can also determine S' based on the following equation (12). e1 :

[0178]

[0179] S' e2 -S' e8 It can be determined based on a method similar to equation (11) or equation (12).

[0180] In some embodiments, the number (count) of the first magnetic resonance dataset corresponding to TR1 can be one or more. In some embodiments, a first magnetic resonance dataset following the first magnetic resonance dataset corresponding to TR1 can also be obtained based on two or more values ​​of TR1.

[0181] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. Various changes and modifications can be made by those skilled in the art based on the description herein. However, such changes and modifications do not depart from the scope of this disclosure.

[0182] like Figure 8C As shown, the processing device 120 can obtain two or more first magnetic resonance datasets S f1 -S f9 S f1 -S f9 It can be retrieved in chronological order. f1 -S f9 It can be obtained from two different values ​​of TR (TR1 and TR2) and two different values ​​of the flip angle (α1 and α2). S f1 -S f9 Each of these can correspond to one of TR1 and TR2, and one of α1 and α2. For example, as Figure 8C As shown, in order to obtain S f1 -S f9 S corresponding to TR1 and α1 f1 It can precede S corresponding to α2 and TR2 f2 -S f9 To obtain. For example, to obtain S... f1 -S f9 S corresponding to α2 and TR2 f1 It can precede S corresponding to TR1 and α1 f2 -S f9 To obtain. For example, in order to obtain S f1 -S f9 S corresponding to TR1 and α2 f1 It can precede S corresponding to α1 and TR2 f2 -S f9 To obtain. For example, in order to obtain S f1 -S f9 S corresponding to TR2 and α1 f1 It can precede S corresponding to α2 and TR1 f2 -S f9 Obtain.

[0183] like Figure 8C As shown, the processing device 120 can determine S f1 and S f2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' f1 S' f1 This can correspond to obtaining S f2 Time point t within the time period f1 Processing equipment 120 can determine Sf1 and S f2 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' f2 S' f2 This can correspond to obtaining S f3 Time point t within the time period f2 Processing equipment 120 can determine S f1 and S f4 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' f3 S' f3 This can correspond to obtaining S f4 Time point t within the time period f3 Processing equipment 120 can determine S f1 and S f5 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' f4 S' f4 This can correspond to obtaining S f5 Time point t within the time period f4 Processing equipment 120 can determine S f1 and S f6 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' f5 S' f5 This can correspond to obtaining S f6 Time point t within the time period f5 Processing equipment 120 can determine S f1 and S f7 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' f6 S' f6 This can correspond to obtaining S f7 Time point t within the time period f6 Processing equipment 120 can determine S f1 and S f8 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' f7 S' f7 This can correspond to obtaining S f8 Time point t within the time period f7 Processing equipment 120 can determine S f1 and S f9 The calculation results (e.g., average values) are used to determine the second magnetic resonance dataset S' f8 S' f8 This can correspond to obtaining S f9 Time point t within the time period f8 .

[0184] Using the second magnetic resonance dataset S' f1 For example, the processing device 120 can determine S' according to the following equation (13). f1 :

[0185]

[0186] in, This represents the average value of the first magnetic resonance dataset corresponding to TR1 and α1. If there is only one first magnetic resonance dataset ( Figure 8C S shown f1 Corresponding to TR1 and α1, If there are two or more first magnetic resonance datasets corresponding to TR1 and α1, for example, denoted as

[0187]

[0188] (n is an integer greater than 1).

[0189] In some embodiments, in order to determine the second magnetic resonance dataset S' f1 ,right and S f2 There are no restrictions on the order of calculation (e.g., the order of division) of (α2, TR2). The processing device 120 can also determine S' based on the following equation (14). f1 :

[0190]

[0191] S' f2 -S' f8 It can be determined based on a method similar to equation (13) or equation (14).

[0192] In some embodiments, the number (count) of first magnetic resonance datasets corresponding to TR1 and α1 can be one or more. In some embodiments, a first magnetic resonance dataset following the first magnetic resonance datasets corresponding to TR1 and α1 can also be obtained based on two or more values ​​of the flip angle and two or more values ​​of TR.

[0193] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. Various changes and modifications can be made by those skilled in the art based on the description herein. However, such changes and modifications do not depart from the scope of this disclosure.

[0194] In some embodiments, such as Figures 7A-8C As shown, when two or more first magnetic resonance datasets are obtained based on injecting contrast agent into the ROI, at least one of the two or more first magnetic resonance datasets (e.g., Figure 7A S ina1 -S a3 , Figure 7B S in b1 -S b3 , Figure 7C S in c1 -S c3 , Figure 8A S in d1 -S d3 , Figure 8B S in e1 -S e3 , Figure 8C S in f1 -S f3 ) can be obtained before contrast agent injection, and the remainder of two or more first magnetic resonance datasets (e.g., Figure 7A S in a4 -S a9 , Figure 7B S in b4 -S b9 , Figure 7C S in c4 -S c9 , Figure 8A S in d4 -S d9 S e4 - Figure 8B S in e9 , Figure 8C S in f4 -S f9 It can be obtained after the contrast agent is injected.

[0195] In some embodiments, such as Figures 8A-8C As shown, before acquiring at least one of two or more first magnetic resonance imaging (MRI) datasets corresponding to the first value of two or more values ​​of the scanning parameters, the remaining portions of two or more first MRI datasets corresponding to the remaining portions of the two or more values ​​of the scanning parameters can be acquired. The first portion of the remaining portions of the two or more first MRI datasets (e.g., Figure 8A S in d2 -S d3 , Figure 8B S in e2 -S e3 , Figure 8C S in f2 -S f3 () can be acquired before the contrast agent is injected into the ROI, and the second part of the remaining portion of two or more first magnetic resonance datasets (e.g., Figure 7A S in a4 -S a9 , Figure 7B S in b4 -S b9 , Figure 7C S in c4 -S c9 , Figure 8A S in d4 -S d9 , Figure 8B S in e4 -S e9 , Figure 8C S in f4 -S f9 (It can be collected after the contrast agent is injected.)

[0196] In some embodiments, the processing device 120 may use a multi-dimensional integration (MDI) strategy to determine two or more second magnetic resonance datasets. For example, the processing device 120 may construct an L2 norm minimization problem based on the following equation (15) to determine the second magnetic resonance dataset S′:

[0197]

[0198] Where, N c This indicates the number of channels (count) receiving the echo signal; and S i This refers to one or more first magnetic resonance datasets associated with the second magnetic resonance dataset S′. That is, the second magnetic resonance dataset can be obtained by performing the calculation of formula (15) above on at least two of two or more first magnetic resonance datasets associated with the first and second values ​​of the scanning parameters. For example, if the second magnetic resonance dataset S′ is S′ defined according to equation (3) or equation (4). a1 Then S i S represents a1 (α1,TR) or S a2 (α2,TR). For example, if the second magnetic resonance dataset S′ is S′ defined according to equation (5) or equation (6). b1 Then S i S represents b2 (α,TR2) or S b1 (α,TR1). For example, if the second magnetic resonance dataset S′ is S′ defined according to equation (7) or equation (8). c1 Then S i S represents c2 (α2,TR2) or S c1 (α1,TR1). For example, if the second magnetic resonance dataset S′ is S′ defined according to equation (9) or equation (10). d1 Then S i S represents d2 (α2,TR) or As yet another example, if the second magnetic resonance dataset S′ is defined based on S′ of equation (11) or equation (12) e1 Then S i S represents e2 (α,TR2) or As yet another example, if the second magnetic resonance dataset S′ is defined based on S′ of equation (13) or equation (14) f1 Then S i S represents f2 (α2,TR2) or

[0199] The processing device 120 can determine the second magnetic resonance dataset by using, for example, least squares optimization, to solve equation (15).

[0200] For detailed information on the MDI strategy, please refer to references “Ye Y, Lyu J, Sun W, et al. A multi-dimension integration (MDI) strategy for MR T2* mapping. NMR Biomed 2021; 34(7):e4529” and / or references “Ye Y, Lyu J, Hu Y, Zhang Z, Xu J, Zhang W. Augmented T1 weighted (aT1W) contrasting dual flip angle acquisition. Proceedings 29th Scientific Meeting, International Society for Magnetic Resonance in Medicine; 2021. p.2606”, each of which is incorporated herein by reference.

[0201] In some embodiments, the processing device 120 may use other algorithms to determine more than two second magnetic resonance datasets.

[0202] In two or more first magnetic resonance imaging (MRI) datasets, in addition to T1 information, there are non-T1 factors that may introduce errors and biases into signal analysis, such as image reconstruction and physiological analysis. One or more non-T1 factors may depend on the configuration of the MRI equipment 110 and the tissue characteristics of the region of interest (ROI). For example, one or more non-T1 factors may include T2*, receiver coil sensitivity, echo time (TE), proton density of the ROI, or any combination thereof.

[0203] like Figures 7A-8CAs shown, by determining a second magnetic resonance imaging (MRI) dataset based on at least two of two or more first MRI datasets, one or more non-T1 factors (e.g., M0 in Equation (2)) of at least two of the two or more first MRI datasets can be canceled out, making the influence of one or more non-T1 factors on the two or more second MRI datasets less than its influence on the two or more first MRI datasets. This results in stronger contrast in the T1-weighted images and more accurate subsequent physiological analysis. Furthermore, since the interference of non-T1 factors is eliminated or mitigated in the two or more second MRI datasets, the two or more second MRI datasets can be more sensitive to the T1 shortening effect caused by contrast agents. Therefore, low-dose contrast agents can be used to reduce costs and the potential impact of contrast agents on the human body.

[0204] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. Various changes and modifications can be made by those skilled in the art based on the description in this application. However, these changes and modifications do not depart from the scope of this disclosure. In some embodiments, the equations provided above are illustrative examples and can be modified in various ways. For example, one or more coefficients in the equation may be omitted, and / or the equation may further include one or more additional coefficients.

[0205] Figure 10A This is illustrated according to some embodiments of the present disclosure, based on a second magnetic resonance dataset (e.g., Figure 7A S' in a1 An exemplary schematic diagram of the generated T1-weighted image 1000-1. Figure 10B This is based on a first magnetic resonance dataset (e.g., Figure 7A S in a1 or S a2 An exemplary schematic diagram of the generated T1-weighted image 1000-2. Figure 7A and Figure 7B As shown, the contrast of T1-weighted image 1000-1 is stronger than that of T1-weighted image 1000-2.

[0206] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. Various changes and modifications can be made by those skilled in the art based on the description herein. However, such changes and modifications do not depart from the scope of this disclosure.

[0207] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0208] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0209] Furthermore, those skilled in the art will understand that various aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Therefore, embodiments of this application can be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or a combination of software and hardware, all of which are generally referred to herein as “modules,” “units,” “components,” “devices,” or “systems.” Furthermore, embodiments of this application can take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied thereon.

[0210] A computer-readable signal medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. Such propagated signals can take many forms, including electromagnetic, optical, and any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable signal medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, and any combination of the above.

[0211] The computer program code for performing operations in embodiments of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​(e.g., Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc.), traditional procedural programming languages ​​(e.g., "C" programming language, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP), dynamic programming languages ​​(e.g., Python, Ruby, and Groovy), or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any type of network (including LAN or WAN), or can establish a connection with an external computer (e.g., through the network of a network service provider) or in a cloud computing environment or as a service, such as Software as a Service (SaaS).

[0212] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a purely software solution, such as an installation on an existing server or mobile device.

[0213] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, the method of the present application should not be construed as reflecting an intention that the claimed object to be scanned requires more features than expressly recited in each claim. In fact, the embodiments have fewer features than all the features of the single embodiments disclosed above.

Claims

1. A magnetic resonance imaging method implemented on a computing device having at least one processing device and at least one storage device, characterized in that, The method includes: Acquire two or more first magnetic resonance datasets related to the region of interest of the subject, wherein the two or more first magnetic resonance datasets are acquired based on two or more different values ​​of scanning parameters; Based on the two or more first magnetic resonance imaging (MRI) datasets, two or more second MRI datasets are determined, each of the two or more second MRI datasets corresponding to at least two of the two or more first MRI datasets; and Based on the two or more second magnetic resonance datasets, generate two or more T1-weighted images of the region of interest, each of the two or more T1-weighted images corresponding to a target time point; Specifically, based on the two or more first magnetic resonance datasets, two or more second magnetic resonance datasets are determined, including: For each second magnetic resonance dataset Obtain at least one first magnetic resonance dataset associated with a first value of the scanning parameters; Obtain at least one first magnetic resonance dataset associated with the second value of the scanning parameters; The second magnetic resonance dataset is obtained by performing a division calculation based on at least two of two or more first magnetic resonance datasets related to the first and second values ​​of the scanning parameters.

2. The method according to claim 1, characterized in that, At least two of the two or more first magnetic resonance datasets corresponding to each of the two or more second magnetic resonance datasets correspond to two of the two or more different values ​​of the scan parameters.

3. The method as described in claim 2, characterized in that, The determination of two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets includes: For one of the two or more second magnetic resonance datasets Obtain at least one first magnetic resonance dataset associated with a first value of the scanning parameters; Obtain at least one first magnetic resonance dataset associated with the second value of the scanning parameters; The calculation is performed based on at least two of the two or more first magnetic resonance datasets related to the first and second values ​​of the scanning parameters.

4. The method according to any one of claims 1-3, characterized in that, Each of the two or more first magnetic resonance datasets is acquired based on one of the two or more values ​​of the scan parameters.

5. The method as described in claim 3, characterized in that, The scanning parameters include at least one of the flip angle or the repetition time.

6. The method as described in claim 5, characterized in that, The two or more first magnetic resonance datasets are based on The data is collected from two or more different values ​​of the flip angle and a fixed value of the repetition time; or The repetition time is collected from two or more different values ​​and the flip angle is collected from a fixed value; or The sum of two or more different values ​​of the flip angle and two or more different values ​​of the repetition time are collected; as well as Acquire two or more first magnetic resonance datasets such that any two adjacent first magnetic resonance datasets correspond to at least one different value of the flip angle or the repetition time.

7. The method as described in claim 6, characterized in that, The determination of two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets includes: For each of the two or more second magnetic resonance datasets, the second magnetic resonance dataset is determined by performing a division calculation on two adjacent first magnetic resonance datasets.

8. The method according to claim 7, characterized in that, The target time point of one of the two or more T1-weighted images corresponding to the second magnetic resonance dataset is designated as the average time point of the time period of the two adjacent first magnetic resonance datasets.

9. The method as described in claim 5, characterized in that, The two or more first magnetic resonance datasets are based on The data is collected from two or more different values ​​of the flip angle and a fixed value of the repetition time; or The repetition time is collected from two or more different values ​​and the flip angle is collected from a fixed value. as well as Before acquiring the remainder of the two or more first magnetic resonance datasets corresponding to the remainder of the two or more values ​​of the flip angle or the repetition time, at least one of the two or more first magnetic resonance datasets corresponding to the first value of the two or more values ​​of the flip angle or the repetition time is acquired.

10. The method as described in claim 5, characterized in that, The two or more first magnetic resonance datasets were acquired based on two or more different values ​​of the flip angle and two or more different values ​​of the repetition time; and Before acquiring the remainder of the two or more first magnetic resonance datasets corresponding to the remainder of the two or more different values ​​of the flip angle and the remainder of the remainder of the two or more different values ​​of the repetition time, at least one of the two or more first magnetic resonance datasets corresponding to the first value of the two or more different values ​​of the flip angle and the first value of the two or more different values ​​of the repetition time is acquired.

11. The method as claimed in claim 9 or claim 10, characterized in that, The determination of two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets includes: Determine the average value of at least one of the two or more first magnetic resonance datasets; For each of the two or more second magnetic resonance datasets, the second magnetic resonance dataset is determined by dividing one of the remaining portions of the two or more first magnetic resonance datasets by the average value.

12. The method according to claim 11, characterized in that, The target time point of one of the two or more T1-weighted images corresponding to the second magnetic resonance dataset is designated as a time point within a time period during which one of the remaining portions of the two or more first magnetic resonance datasets are acquired.

13. The method according to claim 1, characterized in that, At least one of the two or more first magnetic resonance datasets is acquired before the contrast agent is injected into the region of interest, and the remainder of the two or more first magnetic resonance datasets is acquired after the contrast agent is injected.

14. The method of claim 1, further comprising: T1 mapping is performed based on the two or more first magnetic resonance datasets.

15. The method of claim 14, further comprising: Based on the two or more T1-weighted images and the T1 mapping, the contrast agent concentration corresponding to each target time point is estimated; as well as Physiological analysis of the region of interest is performed based on the contrast agent concentration corresponding to each target time point.

16. The method of claim 1, further comprising: Based on the two or more T1-weighted images, determine the signal strength corresponding to each target time point; as well as Physiological analysis is performed on the region of interest based on the signal strength corresponding to each target time point.

17. A magnetic resonance imaging system, comprising: At least one storage device, including a set of instructions; as well as At least one processor communicating with at least one storage device, wherein when the instruction set is executed, the at least one processor is directed to cause the system to perform operations, including: Acquire two or more first magnetic resonance imaging (MRI) datasets related to the region of interest of the subject, with the two or more first MRI datasets being acquired based on two or more different values ​​of the scanning parameters; Based on the two or more first magnetic resonance imaging (MRI) datasets, two or more second MRI datasets are determined, each of the two or more second MRI datasets corresponding to at least two of the two or more first MRI datasets; and Based on the two or more second magnetic resonance datasets, generate two or more T1-weighted images of the region of interest, each of the two or more T1-weighted images corresponding to a target time point; Specifically, based on the two or more first magnetic resonance datasets, two or more second magnetic resonance datasets are determined, including: For each second magnetic resonance dataset Obtain at least one first magnetic resonance dataset associated with a first value of the scanning parameters; Obtain at least one first magnetic resonance dataset associated with the second value of the scanning parameters; The second magnetic resonance dataset is obtained by performing a division calculation based on at least two of two or more first magnetic resonance datasets related to the first and second values ​​of the scanning parameters.

18. A magnetic resonance imaging system, comprising: The acquisition module is configured to acquire two or more first magnetic resonance datasets related to the region of interest of the subject, the two or more first magnetic resonance datasets being acquired based on two or more different values ​​of scanning parameters; The determining module is configured to determine two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets, each of the two or more second magnetic resonance datasets corresponding to at least two of the two or more first magnetic resonance datasets; wherein determining two or more second magnetic resonance datasets based on the two or more first magnetic resonance datasets includes: for each second magnetic resonance dataset, acquiring at least one first magnetic resonance dataset related to a first value of the scanning parameter; acquiring at least one first magnetic resonance dataset related to a second value of the scanning parameter; performing a division calculation based on at least two of the two or more first magnetic resonance datasets related to the first and second values ​​of the scanning parameter to obtain the second magnetic resonance dataset; and The reconstruction module is configured to generate two or more T1-weighted images of the region of interest based on the two or more second magnetic resonance datasets, each of the two or more T1-weighted images corresponding to a target time point.

19. A non-transitory computer-readable medium comprising at least one set of instructions, characterized in that, When executed by one or more processors of a computing device, the at least one set of instructions causes the computing device to perform a method, the method comprising: Acquire two or more first magnetic resonance datasets related to the region of interest of the subject, wherein the two or more first magnetic resonance datasets are acquired based on two or more different values ​​of scanning parameters; Based on the two or more first magnetic resonance imaging (MRI) datasets, two or more second MRI datasets are determined, each of the two or more second MRI datasets corresponding to at least two of the two or more first MRI datasets; wherein, determining the two or more second MRI datasets based on the two or more first MRI datasets includes: for each second MRI dataset, acquiring at least one first MRI dataset related to a first value of the scanning parameter; acquiring at least one first MRI dataset related to a second value of the scanning parameter; performing a division calculation based on at least two of the two or more first MRI datasets related to the first and second values ​​of the scanning parameter to obtain the second MRI dataset; and Based on the two or more second magnetic resonance datasets, two or more T1-weighted images of the region of interest are generated, each of the two or more T1-weighted images corresponding to a target time point.

Images