Quantitative measurement system and method for magnetic resonance imaging

Abstract

The present specification provides a method and system for magnetic resonance imaging, the method comprising: acquiring at least two signals of a subject, the at least two signals being generated using a magnetic resonance imaging device, each of the at least two signals corresponding to a set of values ​​in at least two signal dimensions, the at least two signal dimensions being signal dimensions for signal acquisition using the magnetic resonance imaging device; determining a main signal dimension associated with the signal representation of the subject in the at least two signal dimensions; determining a first value and a second value associated with the signal representation of the subject based on the at least two signals, wherein the first value represents the signal representation along a first direction of the main signal dimension, the second value represents the signal representation along a second direction of the main signal dimension, and the first direction is opposite to the second direction; and determining a quantitative parameter value of the subject based on the first value and the second value.

Description

[0001] Cross-references

[0002] This application claims priority to U.S. application No. 18 / 300,827, filed on April 14, 2023, and the contents of the above priority application are incorporated by reference into this application. Technical Field

[0003] The present disclosure relates generally to magnetic resonance imaging, and more particularly to quantitative measurement methods and systems for magnetic resonance imaging. Background Art

[0004] Magnetic resonance imaging systems utilize powerful magnetic fields and radio frequency (RF) technology and are widely used in medical diagnosis and / or treatment. When a subject is subjected to a magnetic resonance scan, after a magnetic resonance pulse sequence is applied to the subject, at least two coil units of the magnetic resonance imaging device can detect at least two echo signals representing at least two echoes. In some cases, one or more quantitative parameters of the subject can be determined based on the echo signals, such as longitudinal relaxation time (T1), transverse relaxation time (T2 or ), transverse relaxation rate (R2 or Quantitative parameters can reflect physiological characteristics of a subject and can be used for disease diagnosis. Therefore, it is desirable to provide a system and method for performing quantitative measurements in magnetic resonance imaging to determine quantitative parameters of a subject. Summary of the Invention

[0005] According to one aspect of the present specification, a system for magnetic resonance imaging can be provided. The system can include at least one storage device including an instruction set for quantitative measurement in magnetic resonance imaging, and at least one processor in communication with the at least one storage device. When executing the instruction set, the at least one processor can be configured to instruct the system to perform one or more of the following operations. The system can acquire at least two (or more) signals of a subject, the at least two signals being generated using a magnetic resonance imaging device, each of the at least two signals corresponding to a set of values ​​in at least two signal dimensions, the at least two signal dimensions being signal dimensions for signal acquisition using the magnetic resonance imaging device; a main signal dimension associated with the signal representation of the subject can be determined in the at least two signal dimensions; a first value and a second value associated with the signal representation of the subject can be determined based on the at least two signals, wherein the first value represents a signal representation along a first direction of the main signal dimension, the second value represents a signal representation along a second direction of the main signal dimension, and the first direction is opposite to the second direction; and a quantitative parameter value of the subject can be determined based on the first value and the second value.

[0006] In some embodiments, if the primary signal dimension is echo time, the first direction is a direction of time lapse and the second direction is a direction of time reversal.

[0007] In some embodiments, in order to determine the quantitative parameter value of the subject based on the first value and the second value, the system can determine a first initial value of the quantitative parameter of the subject based on the first value; determine a second initial value of the quantitative parameter of the subject based on the second value; and determine the quantitative parameter value of the subject based on the first initial value and the second initial value.

[0008] In some embodiments, in order to determine a first initial value of a quantitative parameter of a subject based on a first value, the system may obtain a first relationship related to the signal representation and the quantitative parameter along a first direction of the main signal dimension; and determine the first initial value of the quantitative parameter of the subject based on the first value of the signal representation and the first relationship.

[0009] In some embodiments, in order to determine a second initial value of the quantitative parameter of the subject based on the second value, the system can obtain a second relationship related to the signal representation and the quantitative parameter along a second direction of the main signal dimension; and determine the second initial value of the quantitative parameter of the subject based on the second value of the signal representation and the second relationship.

[0010] In some embodiments, the system may determine a signal-to-noise parameter value indicating a signal level of the at least two signals relative to a noise level of the at least two signals based on the first value and the second value.

[0011] In some embodiments, the subject is one of at least two physical points of an object, and the system may perform one or more of the following operations: the system may generate a parameter map related to a quantitative parameter of the object based on the quantitative parameter value of each physical point of the object; determine, for each reference signal representation of at least one reference signal representation, a first signal-to-noise parameter value indicating a signal level of the reference signal representation relative to a noise level of the reference signal representation; and determine, based on the first signal-to-noise parameter value of each reference signal representation, a second signal-to-noise parameter value indicating a signal level of the parameter map relative to a noise level of the parameter map.

[0012] In some embodiments, the quantitative parameter of the subject may be a transverse relaxation time, and to determine a value of the quantitative parameter of the subject based on the first initial value and the second initial value, the system may perform the following operations: the system may determine a first inverse of the first initial value and a second inverse of the second initial value; and determine a value of the transverse relaxation time based on the first inverse and the second inverse.

[0013] In some embodiments, the quantitative parameter of the subject may be a transverse relaxivity, and the system may assign an average of the first initial value and the second initial value as the value of the transverse relaxivity.

[0014] In some embodiments, a system may determine a first value of a signal representation of a subject by performing the following operations: the system may determine at least one secondary signal dimension in at least two signal dimensions; for each of at least one value in the at least one secondary signal dimension, determine at least one initial value of the signal representation along a first direction of the primary signal dimension based on a portion of at least two signals corresponding to the value in the at least one secondary signal dimension; and determine the first value of the signal representation based on at least a portion of the at least one initial value of the signal representation.

[0015] In some embodiments, a system may determine a first value of a signal representation of a subject by performing the following operations: the system may determine at least one secondary signal dimension among the at least two signal dimensions; obtain an optimization function for the signal representation, the optimization function including a primary signal dimension along a first direction and the at least one secondary signal dimension; and determine the first value of the signal representation of the subject by inputting the at least two signals into the optimization function.

[0016] In some embodiments, in order to determine a first value of the signal representation of the subject by inputting at least two signals into an optimization function, for at least one value of at least one secondary signal dimension, the system can determine at least one pair of signals in the at least two signals corresponding to the value on at least one secondary signal dimension, wherein each pair of the at least one pair of signals corresponds to a different value on the primary signal dimension; and determine the first value of the signal representation of the subject by inputting at least one pair of signals into the optimization function.

[0017] In some embodiments, to acquire at least two signals from a subject, the system may cause a magnetic resonance imaging device to apply a magnetic resonance pulse sequence to the subject. The system may cause the magnetic resonance imaging device to apply the magnetic resonance pulse sequence to the subject; detect at least two echo signals using at least one coil unit of the magnetic resonance imaging device; and determine the at least two signals from the subject based on the at least two echo signals.

[0018] In some embodiments, the subject may be a physical point of an object. In order to determine at least two signals of the subject based on at least two echo signals, the system may reconstruct at least two images including image data of the physical point based on the at least two echo signals; and designate the image data corresponding to the physical point in the at least two images as at least two signals of the physical point.

[0019] In some embodiments, the subject may be a physical point of an object, and the signal representation may be the change in signal intensity of the physical point with scanning parameters, and the quantitative parameters of the physical point include at least one of longitudinal relaxation time, transverse relaxation time, transverse relaxation rate, apparent diffusion coefficient (ADC), field distribution, signal-to-noise ratio index, or signal ratio index.

[0020] In some embodiments, the primary signal dimension may be echo time, and the quantitative parameter may be at least one of transverse relaxation time, transverse relaxation decay, or field distribution. Alternatively, the primary signal dimension may be T2 preparation duration, and the quantitative parameter may be transverse relaxation time. Alternatively, the primary signal dimension may be T1p preparation duration, and the quantitative parameter may be longitudinal relaxation time in a rotating coordinate system. Alternatively, the primary signal dimension may be inversion time, and the quantitative parameter may be longitudinal relaxation time. Alternatively, the primary signal dimension may be b-value, and the quantitative parameter may be ADC.

[0021] In some embodiments, the signal representation may be represented by a complex number, the complex number including a phase component and an amplitude component, and the quantitative parameter value may be determined based on at least one of the phase component or the amplitude component of the complex number. The signal representation may also be represented by a real number, and the quantitative parameter value may be determined based on the real number.

[0022] In some embodiments, at least two signal dimensions may include echo time (TE), unit repetition time (TR), inversion recovery time (TI), b value, T1ρ-preparation duration, T2-preparation duration, number of repeated scans (number of scan repetitions), velocity encoding value, number of radio frequency (RF) channels, flip angle, RF center frequency or at least two of the RF receiving coil units.

[0023] According to another aspect of the present specification, a method for magnetic resonance imaging may be provided. The method may include acquiring at least two signals of a subject, the at least two signals being generated using a magnetic resonance imaging device, each of the at least two signals corresponding to a set of values ​​in at least two signal dimensions, the at least two signal dimensions being signal dimensions for signal acquisition using the magnetic resonance imaging device; determining a primary signal dimension associated with a signal representation of the subject in the at least two signal dimensions; determining a first value and a second value associated with the signal representation of the subject based on the at least two signals, wherein the first value represents a signal representation along a first direction of the primary signal dimension, the second value represents a signal representation along a second direction of the primary signal dimension, and the first direction is opposite to the second direction; and determining a quantitative parameter value of the subject based on the first value and the second value.

[0024] According to another aspect of the present specification, a non-transitory computer-readable medium may be provided. The non-transitory computer-readable medium may include at least one set of instructions for magnetic resonance imaging. 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 may include acquiring at least two signals of a subject, the at least two signals being generated using a magnetic resonance imaging device, each of the at least two signals corresponding to a set of values ​​in at least two signal dimensions, the at least two signal dimensions being signal dimensions for signal acquisition using the magnetic resonance imaging device; determining a primary signal dimension associated with a signal representation of the subject in the at least two signal dimensions; determining a first value and a second value associated with the signal representation of the subject based on the at least two signals, wherein the first value represents a signal representation along a first direction of the primary signal dimension, the second value represents a signal representation along a second direction of the primary signal dimension, and the first direction is opposite to the second direction; and determining a quantitative parameter value of the subject based on the first value and the second value.

[0025] Some additional features of this specification may be explained in the following description. Some additional features of this specification will be apparent to those skilled in the art from a study of the following description and accompanying drawings, or from the production or operation of the embodiments. Features of this specification may be realized and achieved by practicing or using various aspects of the methods, tools, and combinations described in the detailed examples discussed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0027] Figure 1 is a schematic diagram of an exemplary magnetic resonance imaging system according to some embodiments of the present specification.

[0028] Figure 2 is a block diagram of an exemplary magnetic resonance scanner according to some embodiments of the present specification.

[0029] Figure 3 is a block diagram of an exemplary processing device 120 according to some embodiments of the present specification.

[0030] Figure 4 is a flow chart of an exemplary process 400 for performing quantitative measurements in magnetic resonance imaging according to some embodiments of the present specification;

[0031] Figure 5 is a flow chart of an exemplary process 500 of determining a first value represented by a signal according to some embodiments of the present specification;

[0032] Figure 6 is a flow chart of an exemplary process 600 of determining a first value represented by a signal according to some embodiments of the present specification;

[0033] Figure 7 is a flow chart of an exemplary process 700 for determining a quantitative parameter value of a subject based on a first value and a second value according to some embodiments of the present specification;

[0034] Figure 8A and Figure 8B According to some embodiments of the present invention, a subject is scanned by magnetic resonance imaging. and Schematic diagram of exemplary quantitative measurement results;

[0035] Figures 9A-9C is an exemplary patient's brain according to some embodiments of this specification. Schematic diagrams of Figures 900A-900C;

[0036] Figures 9D-9F is an exemplary patient's brain according to some embodiments of this specification. Schematic diagrams of Figures 900D-900F;

[0037] Figure 10A and 10B is an exemplary patient's brain according to some embodiments of this specification. Schematic diagrams of Figures 1000A and 1000B;

[0038] Figure 10C and 10D is an exemplary patient's brain according to some embodiments of this specification. Schematic diagrams of Figures 1000C and 1000D. DETAILED DESCRIPTION

[0039] In order to more clearly illustrate the technical solutions of the embodiments of this specification, a brief introduction to the drawings required for use in the description of the embodiments will be given below. However, it should be understood by those skilled in the art that this specification can be implemented without these details. In other cases, in order to avoid unnecessarily obscuring various aspects of this specification, well-known methods, processes, systems, components and / or circuits have been described at a higher level. It is obvious to those skilled in the art that various changes can be made to the disclosed embodiments, and the general principles defined in this specification can be applied to other embodiments and application scenarios without departing from the principles and scope of this specification. Therefore, this specification is not limited to the embodiments shown, but is in the broadest scope consistent with the scope of the patent application.

[0040] The terms used in this specification are for the purpose of describing specific example embodiments only and are not intended to be limiting. As used in this specification, the singular forms "a," "an," and "the" may also include the plural forms unless the context clearly indicates an exception. It should also be understood that the terms "comprises" and "includes" as used in this specification merely indicate the presence of the stated features, integers, steps, operations, components, and / or parts, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, and / or combinations thereof.

[0041] It is understood that the terms "system," "device," "unit," "module," and / or "block" used in this specification are a method of distinguishing different components, elements, parts, portions, or assemblies at different levels in ascending order. However, these terms may be replaced by other expressions if the same purpose can be achieved.

[0042] 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 may be implemented as software and / or hardware and may be stored in any type of non-transitory computer-readable medium or other storage device. In some embodiments, software modules / units / blocks may be compiled and linked into an executable program. It will be appreciated that software modules may be called from other modules / units / blocks or themselves, and / or may be called in response to detected events or interrupts. Software modules / units / blocks configured for execution on a computing device may be provided on a computer-readable medium (e.g., a compact disc, digital video disc, flash drive, disk, or any other tangible medium), or as a digital download (and may initially be stored in a compressed or installable format that requires installation, decompression, or decryption prior to execution). The software code herein may be stored in part or in whole in a storage device of the computing device performing the operation and applied in the operation of the computing device. The software instructions may be embedded in firmware, such as an EPROM. It should also be understood that hardware modules / units / blocks may be included in connected logical components (e.g., gates and triggers), and / or may include programmable units (e.g., programmable gate arrays or processors). The modules / units / blocks or computing device functions described in this specification may be implemented as software modules / units / blocks, but may also be represented by hardware or firmware. Generally, the modules / units / blocks described herein refer to logical modules / units / blocks, which may be combined with other modules / units / blocks or divided into sub-modules / sub-units / sub-blocks, although they are physical organizations or storage devices. The content may be applicable to systems, devices, or parts thereof.

[0043] It will be understood that when a unit, device, module or block is referred to as being "on," "connected to" or "coupled to" another unit, device, module or block, it may be directly on, connected to or coupled to, or in communication with, another unit, device, module or block, or an intermediate unit, device, module or block may be present, unless the context clearly indicates otherwise. In this specification, the term "and / or" may include any one or more of the relevant listed items or a combination thereof. The terms "pixel" and "voxel" in this specification may be used interchangeably to refer to elements in an image. The anatomical structures displayed in an image of a subject (e.g., a patient) may correspond to actual anatomical structures present in or on the subject's body. For example, a body part displayed in an image may correspond to an actual body part present in or on the subject's body, and a feature point in the image may correspond to an actual physical point present in or on the subject's body. For ease of description, the anatomical structures displayed in an image and their corresponding actual anatomical structures may be used interchangeably. For example, the subject's chest refers to the subject's actual chest or a region representing the chest in the subject's image. The term "image" in this specification is used to refer to various forms of images, including two-dimensional images, three-dimensional images, four-dimensional images, etc.

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

[0045] This specification uses flowcharts to illustrate operations performed by systems according to some embodiments disclosed herein. It should be clearly understood that the operations in the flowcharts may not be performed in order. Instead, various steps may be performed in reverse order or simultaneously. Furthermore, one or more additional operations may be added to these flowcharts, or one or more operations may be deleted from these flowcharts.

[0046] In addition, although the systems and methods disclosed in this specification are primarily described with respect to performing quantitative measurements in a magnetic resonance imaging system, it should be understood that this is for illustrative purposes only. The systems and methods of this specification can be applied to any other type of medical imaging system. In some embodiments, the imaging system may include a single-modality imaging system and / or a multi-modality imaging system. A single-modality imaging system may include a magnetic resonance imaging system, etc. A multi-modality imaging system may include a computed tomography-magnetic resonance imaging (MRI-CT) system, a positron emission tomography-magnetic resonance imaging (PET-MRI) system, a single-photon emission computed tomography-magnetic resonance imaging (SPECT-MRI) system, a digital subtraction angiography-magnetic resonance imaging (DSA-MRI) system, etc.

[0047] As used herein, performing quantitative measurements in magnetic resonance imaging refers to determining one or more quantitative parameters of a subject based on data collected using magnetic resonance imaging techniques.

[0048] Recently, a multidimensional integration (MDI) technology has been used to determine the quantitative parameter values ​​of a subject. For example, since the quantitative parameters of a subject can be related to the subject signal representation obtained or determined during the examination or imaging of the subject, the MDI technology based on the subject signal representation can be used to determine the quantitative parameters of the subject. Specifically, using magnetic resonance imaging technology, the value of the subject signal representation can be determined based on at least two subject signals generated using a magnetic resonance imaging device. Each of the at least two signals can correspond to a set of values ​​on at least two signal dimensions of the signal obtained using the magnetic resonance imaging device. Specifically, the signal dimension can include a main signal dimension related to the signal representation and at least one secondary signal dimension outside the main signal dimension. The MDI technology can jointly process different signal dimensions of the signal, including the main signal dimension and at least one secondary signal dimension. In addition, the quantitative parameter values ​​of the subject can be determined based on the signal representation values ​​of the subject.

[0049] However, conventionally, the value of the signal representation of a subject is determined along only one direction of the primary signal dimension of at least two signal dimensions (e.g., the direction of signal attenuation with increasing echo time). Therefore, the value of a quantitative parameter of a subject is determined based on the value of the signal representation of the subject along one direction of the primary signal dimension. Due to the presence of noise, this method of determining the signal representation may result in inherent computational bias in the quantitative parameter determined based on signals with a medium or relatively low signal-to-noise ratio (SNR), thereby reducing accuracy. Therefore, it is desirable to provide systems and methods for determining quantitative parameters with higher accuracy.

[0050] One aspect of the present disclosure relates to a system and method for determining a quantitative parameter value of a subject during magnetic resonance imaging. The system can acquire at least two signals of the subject, the at least two signals being generated using a magnetic resonance imaging device, each of the at least two signals corresponding to a set of values ​​in at least two signal dimensions, the at least two signal dimensions being signal dimensions for signal acquisition using the magnetic resonance imaging device; determine a primary signal dimension associated with a signal representation of the subject in the at least two signal dimensions; determine a first value and a second value associated with the signal representation of the subject based on the at least two signals, wherein the first value represents a signal representation along a first direction of the primary signal dimension, the second value represents a signal representation along a second direction of the primary signal dimension, and the first direction is opposite to the second direction; and determine a quantitative parameter value of the subject based on the first value and the second value.

[0051] Compared with traditional quantitative measurement methods using MDI technology, the methods and systems of the present specification determine the quantitative parameter values ​​of the subject based on the first value and the second value of the signal representation representing the first direction and the second direction along the main signal dimension, which can improve the accuracy, stability and reliability of the determined quantitative parameter values.

[0052] Figure 1 FIG is a schematic diagram of an exemplary magnetic resonance imaging system according to some embodiments of the present specification. Figure 1 As shown, the magnetic resonance imaging system 100 may include an MRI scanner 110, a processing device 120, a storage device 130, one or more terminals 140, and a network 150. In some embodiments, the MRI scanner 110, the processing device 120, the storage device 130, and / or the terminals 140 may be interconnected and / or communicate with each other via wireless connections, wired connections, or a combination thereof. The connections between the components of the MRI system 100 may be variable. For example, the MRI scanner 110 may be connected to the processing device 120 via the network 150. As another example, the MRI scanner 110 may be directly connected to the processing device 120.

[0053] The magnetic resonance scanner 110 can be configured to scan a subject (or a portion of a subject) to acquire image data, such as echo signals (or magnetic resonance signals) associated with the subject. For example, the magnetic resonance scanner 110 can detect at least two echo signals by applying a magnetic resonance pulse sequence to the subject. In some embodiments, the magnetic resonance scanner 110 can include, for example, a magnet, a gradient coil, a radio frequency coil, etc. In some embodiments, depending on the type of magnet, the magnetic resonance scanner 110 can be a permanent magnet magnetic resonance scanner, a superconducting electromagnetic magnetic resonance scanner, or a resistive electromagnetic magnetic resonance scanner, etc. In some embodiments, depending on the magnetic field strength, the magnetic resonance scanner 110 can be a high-field magnetic resonance scanner, a medium-field magnetic resonance scanner, a low-field magnetic resonance scanner, etc.

[0054] The subject can be a living or non-living thing. For example, the subject can include a patient, an artificial object, etc. For example, the subject can include a specific part, organ, tissue, and / or physical point of a patient. For example, the subject can include the head, brain, neck, torso, shoulder, arm, chest, heart, stomach, blood vessels, soft tissue, knee, foot, or the like, or a combination thereof.

[0055] The processing device 120 may process data and / or information acquired from the magnetic resonance scanner 110, the storage device 130, and / or the terminal 140. For example, the processing device 120 may generate a magnetic resonance image by processing image data (e.g., echo signals) collected by the magnetic resonance scanner 110. For another example, the processing device 120 may determine a quantitative parameter value of a subject based on the image data (e.g., echo signals) collected by the magnetic resonance scanner 110.

[0056] In some embodiments, the processing device 120 can be a single server or a server group. The server group can be centralized or distributed. In some embodiments, the processing device 120 can be local or remote. For illustrative purposes only, only one processing device 120 is described in the medical system 100. However, it should be noted that the medical system 100 in this specification may also include at least two processors. Therefore, the operations and / or method steps performed by one processing device 120 as described in this specification may also be performed jointly or separately by at least two processing devices. For example, if in the content of this specification, the processing device 120 of the medical system 100 executes process A and process B simultaneously, it should be understood that process A and process B may also be executed jointly or separately by two or at least two different processing devices in the medical system 100 (for example, the first processing device executes process A, the second processing device executes process B, or the first and second processing devices execute process A and process B jointly).

[0057] Storage device 130 can store data, instructions, and / or any other information. In some embodiments, storage device 130 can store data acquired from magnetic resonance scanner 110, processing device 120, and / or terminal 140. In some embodiments, storage device 130 can store data and / or instructions that processing device 120 can execute or use to execute the exemplary methods described herein. In some embodiments, storage device 130 can include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), or the like, or a combination thereof. In some embodiments, storage device 130 can be implemented on a cloud platform.

[0058] In some embodiments, the storage device 130 may be connected to a network 150 to communicate with one or more other components of the magnetic resonance imaging system 100 (e.g., the magnetic resonance scanner 110, the processing device 120, and / or the terminal 140). One or more components of the magnetic resonance imaging system 100 may access data or instructions stored in the storage device 130 via the network 150. In some embodiments, the storage device 130 may be part of the processing device 120 or the terminal 140.

[0059] The terminal 140 can be configured to enable user interaction between the user and the magnetic resonance imaging system 100. For example, the terminal 140 can receive instructions issued by the user to cause the magnetic resonance scanner 110 to scan a target. For another example, the terminal 140 can receive processing results (e.g., quantitative parameter values ​​of a subject) from the processing device 120 and display the processing results to the user. In some embodiments, the terminal 140 can be connected to and / or communicate with the magnetic resonance scanner 110, the processing device 120, and / or the storage device 130. In some embodiments, the terminal 140 can include a mobile device 140-1, a tablet computer 140-2, a laptop computer 140-3, or similar devices, or a combination thereof. In some embodiments, the terminal 140 can include an input device, an output device, etc. In some embodiments, the terminal 140 can be part of the processing device 120 or the magnetic resonance scanner 110.

[0060] The network 150 may include any suitable network that can facilitate the exchange of information and / or data among the magnetic resonance imaging system 100. In some embodiments, one or more components of the magnetic resonance imaging system 100 (e.g., the magnetic resonance scanner 110, the processing device 120, the storage device 130, the terminal 140, etc.) may communicate information and / or data with one or more other components of the magnetic resonance imaging system 100 via the network 150. For example, the processing device 120 may obtain image data (e.g., echo signals) from the magnetic resonance scanner 110 via the network 150. For another example, the processing device 120 may obtain user instructions from the terminal 140 via the network 150.

[0061] The above description is intended to illustrate the problem and not to limit the scope of this specification. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and characteristics of the exemplary embodiments described in this specification can be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the storage device 130 can be a data storage including a cloud computing platform, such as a public cloud, a private cloud, a community cloud, and a hybrid cloud. In some embodiments, the processing device 120 can be integrated into the magnetic resonance scanner 110. However, such changes and modifications do not depart from the scope of this specification.

[0062] Figure 2 FIG is a block diagram of an exemplary magnetic resonance scanner according to some embodiments of the present specification. Figure 2 As shown, the magnetic resonance scanner 110 may include a magnet 220 , a gradient coil 230 , a radio frequency coil 240 , and a pulse sequence module 250 .

[0063] The magnet 220 may generate a static magnetic field while scanning at least a portion of the body 210. The magnet 220 may be of various types, including, for example, a permanent magnet, a superconducting electromagnet, a resistive electromagnet, and the like.

[0064] The gradient coils 230 can provide magnetic field gradients for the main magnetic field in the X direction, Y direction, and / or Z direction. As described in this specification, the X direction, Y direction, and Z direction can represent the X-axis, Y-axis, and Z-axis in a coordinate system. For example, the Z-axis can be along the axis of the magnet 220, the X-axis and Z-axis can form a horizontal plane, and the X-axis and Y-axis can form a vertical plane. In some embodiments, the gradient coils 230 can include an X-direction coil for providing a magnetic field gradient for the main magnetic field in the X direction, a Y-direction coil for providing a magnetic field gradient for the main magnetic field in the Y direction, and / or a Z-direction coil for providing a magnetic field gradient for the main magnetic field in the Z direction. In some embodiments, the X-direction coil, the Y-direction coil, and / or the Z-direction coil can be of various shapes or configurations.

[0065] The RF coil 240 can transmit RF pulse signals to the subject 210 under examination and / or receive echo signals from the subject 210 under examination. In some embodiments, the RF coil 240 may include a transmitting coil and a receiving coil. The transmitting coil can emit signals (e.g., RF pulses) to excite the cell nuclei in the subject 210 to generate resonance. The receiving coil can receive the echo signals emitted from the subject 210. In some embodiments, the RF transmitting coil and the RF receiving coil can be integrated into the same coil. In some embodiments, the RF coil 240 can be of various types, such as orthogonal detection (QD) orthogonal coils, phased array coils, specific element spectrum coils, etc. In some embodiments, the RF coil 240 can be a phased array coil, including at least two coil units (also called RF receiving coil units), each of which can independently detect echo signals.

[0066] In some embodiments, the radio frequency coil 240 can be used to detect signals generated by a magnetic resonance pulse sequence (also known as a magnetic resonance imaging protocol). There are many types of magnetic resonance pulse sequences, such as spin echo (SE) pulse sequences, gradient focused echo (GRE) pulse sequences, inversion recovery (IR) pulse sequences, multi-echo magnetic resonance pulse sequences, T1p pretreatment pulse sequences, T2 pretreatment pulse sequences, and diffusion-weighted imaging (DWI) pulse sequences. The multi-echo magnetic resonance pulse sequence used in this specification may refer to a pulse sequence that generates (or detects) at least two echo signals after each excitation pulse. A T1p preparation pulse sequence may refer to a pulse sequence that includes a T1p-weighted magnetization preparation pulse (also known as a spin-locked pulse). A T2 preparation pulse sequence may refer to a pulse sequence that includes a T2 preparation pulse. A DWI pulse sequence may refer to a pulse sequence (typically a spin echo sequence) that is applied in a pulse sequence, such as a 180-degree pulse, with a pair of diffusion-sensitization gradients before and after the pulse.

[0067] In some embodiments, an MRI pulse sequence can be defined by one or more parameters, including, for example, the type of MRI pulse sequence, the time at which the MRI pulse sequence is applied, the duration of the MRI pulse sequence, parameters related to the RF pulses in the MRI pulse sequence, the count (or number) of RF pulses in the MRI pulse sequence, the unit repetition time (TR), the number of repetitions, the inversion time (TI), the b-value, the T1p preparation duration, the T2 preparation duration, the echo sequence length, the echo interval, the velocity encoding (VENC) value, the count (or number) of average values, and the like. As used herein, TR may refer to the time interval between two repeated and consecutive RF pulses in an MRI pulse sequence (e.g., the time interval between two consecutive excitation RF pulses in an SE pulse sequence, the time interval between two consecutive 180° inversion pulses in an IR pulse sequence). The repetition number may refer to the count (or number) of repetitions in the MRI pulse sequence. The TI may refer to the time interval between a 180° inversion pulse and the subsequent 90° excitation pulse in an IR pulse sequence. The b-value may refer to a factor reflecting the strength and timing of the diffusion sensitization gradient in a DWI pulse sequence. The T1p preparation duration may refer to the duration of a spin-locking pulse in a T1p preparation pulse sequence. The T2-preparation duration may refer to the duration of a T2 preparation pulse in a T2 preparation pulse sequence.

[0068] In some embodiments, the RF coil 240 can detect (or receive) one or more echo signals corresponding to one or more echoes excited by a magnetic resonance pulse sequence. In some embodiments, the echo signal (or echo) can be defined by one or more parameters, such as the echo signal type (spin echo, fast spin echo (FSE), fast recovery FSE, single shot FSE, gradient echo, steady-state prepulse fast imaging), echo time (TE), echo signal strength, coil unit (e.g., represented by the coil unit identification (ID) or serial number), and the number of repetitions of detecting the echo signal (e.g., represented by a repetition sequence number). TE refers to the time between the application of the excitation RF pulse and the peak of the echo excited by the excitation RF pulse.

[0069] The pulse sequence module 250 can be configured to define parameters and schedules associated with the MRI scanner 110 before and / or during a scan of the subject 210. In some embodiments, the parameters associated with the MRI scanner 110 can include one or more parameters associated with the MRI pulse sequence applied by the MRI scanner 110 (e.g., type of MRI pulse sequence, TR, number of repetitions, TI, etc.), one or more parameters associated with the gradient fields or RF fields generated by the gradient coils 230 (e.g., RF center frequency, flip angle, etc.), one or more parameters associated with the echo signals detected by the RF coils 240 (e.g., TE, spin echo type) as described elsewhere herein, or the like, or any combination thereof. In some embodiments, the parameters associated with the MRI scanner 110 can include one or more other imaging parameters, such as RF channel count (or number), image contrast and / or ratio, slice thickness, imaging type (e.g., T1-weighted imaging, T2-weighted imaging, proton density-weighted imaging, etc.), field of view (FOV) of the MRI scanner 110, eccentricity frequency offset of the MRI scanner 110, or the like, or any combination thereof.

[0070] In some embodiments, the pulse sequence module 250 can be connected to and / or communicate with the processing device 120. For example, prior to an MRI scan procedure, the processing device 120 can design and / or determine at least a portion of the parameters and schedule associated with the MRI scanner 110 based on clinical needs or a scan protocol, and transmit the parameters and schedule to the pulse sequence module 250. During the MRI scan procedure, the MRI scanner 110 can scan the subject 210 based on the parameters and schedule defined by the pulse sequence module 250. For example, the MRI scanner 110 can apply a MRI pulse sequence having specific parameters associated with the MRI pulse sequence defined by the pulse sequence module 250, and the RF coil 240 can receive echo signals based on specific parameters associated with the echo signals defined by the pulse sequence module 250. In some embodiments, the echo signals and data generated based on the echo signals (e.g., image data or K-space data) can be defined by the parameters associated with the MRI scanner 110, and the echo signals are acquired using the MRI scanner 110 under these parameters. For example, parameters related to the magnetic resonance scanner 110 that acquires the echo signal can be considered as at least two signal dimensions of the echo signal and the data generated based on the echo signal. More description of signal dimensions can be found elsewhere in this specification. See, for example, Figure 4 and related descriptions, Figure 4 and related instructions.

[0071] The description of the magnetic resonance scanner 110 provided above is intended to be illustrative and not to limit the scope of this specification. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and characteristics of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the pulse sequence module 250 may be integrated into the processing device 120. However, such variations and modifications do not depart from the scope of this specification.

[0072] Figure 3 is a block diagram of an exemplary processing device 120 according to some embodiments of the present specification.

[0073] like Figure 3 As shown, the processing device 120 may include an acquisition module 302 and a determination module 304 .

[0074] The acquisition module 302 can be configured to acquire information related to the magnetic resonance imaging system 100. For example, the acquisition module 202 can acquire at least two signals of a subject. The at least two signals can be generated using a magnetic resonance imaging device, and each of the at least two signals can correspond to a set of values ​​in at least two signal dimensions of the signals acquired using the magnetic resonance imaging device. Further description of acquiring at least two signals of a subject can be found elsewhere in this specification, see, for example, Figure 4 Step 410 and related instructions in .

[0075] The determination module 304 can be configured to determine a primary signal dimension associated with the signal representation of the subject in at least two signal dimensions. As described herein, a signal representation can refer to a representative value or attribute value of a subject's signal. The signal representation of the subject can reflect one or more physiological or physical characteristics of the subject, which can provide a basis for medical diagnosis and / or treatment. Further description of determining the primary signal dimension can be found elsewhere in this specification. See, for example, Figure 4 Step 420 and related instructions in .

[0076] The determination module 304 may also be configured to determine a first value and a second value related to the signal representation of the subject based on the at least two signals. The first value may represent the signal representation along a first direction of the main signal dimension, and the second value may represent the signal representation along a second direction of the main signal dimension, and the first direction may be opposite to the second direction. Further description of the determination of the first value and the second value can be found elsewhere in this specification. See, for example, Figure 4 Step 430 and related instructions in .

[0077] The determination module 304 can also be configured to determine a quantitative parameter value of the subject based on the first value and the second value. The primary signal dimension and the signal representation can be associated with the quantitative parameter. More description of determining the quantitative parameter value of the subject can be found elsewhere in this specification. See, for example, Figure 4 Step 440 and related instructions in .

[0078] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those skilled in the art, various changes and modifications can be made based on the description of this specification. However, these changes and modifications do not depart from the scope of this specification. In some embodiments, any module can be divided into two or at least two units. For example, the acquisition module 302 can be divided into two units, configured to obtain different data. In some embodiments, the processing device 120 may include one or more additional modules, for example, a storage module (not shown) for storing data.

[0079] Figure 4 FIG. 4 is a flow chart of an exemplary process 400 for performing quantitative measurements in magnetic resonance imaging according to some embodiments of the present specification. In some embodiments, the process 400 may be performed by the magnetic resonance imaging system 100. For example, the process 400 may be implemented as an instruction set (e.g., an application) stored in a storage device (e.g., the storage device 130). In some embodiments, the processing device 120 (e.g., Figure 3 The one or more modules shown in FIG400 can execute the instruction set and be instructed to execute the process 400 accordingly. The steps of the process shown below are for illustration purposes only. In some embodiments, the process 400 can be completed by one or more additional operations not described and / or without one or more steps discussed. In addition, Figure 4 The order in which the operations of process 400 are illustrated and described below is not intended to be limiting.

[0080] At step 410 , the processing device 120 (eg, the acquisition module 302 ) may acquire at least two signals of the subject.

[0081] As used herein, a subject may refer to any biological or non-biological subject, such as a patient (or a portion of a patient) or an artificial object (e.g., a phantom). In some embodiments, a subject may refer to a physical point on an object (e.g., a patient, an organ or tissue of a patient, or an animal). For ease of explanation, a patient is described as an exemplary subject in the following description.

[0082] In some embodiments, at least two signals may be generated using a magnetic resonance imaging device (e.g., magnetic resonance scanner 110), for example, by applying a multi-echo pulse sequence to a subject. As described herein, the subject's signals may convey information about one or more attributes or characteristics of the subject. For example, the subject's signals may include or include image data or K-space data related to the subject.

[0083] In some embodiments, the processing device 120 can cause the magnetic resonance imaging device to apply a magnetic resonance pulse sequence (e.g., an SE pulse sequence, a GRE pulse sequence, an IR pulse sequence, a multi-echo pulse sequence, a T1ρ preparation pulse sequence, a T2 preparation pulse sequence, a DWI pulse sequence as described elsewhere in this specification) to scan the subject.

[0084] In some embodiments, the processing device 120 may cause a magnetic resonance imaging device to apply a magnetic resonance pulse sequence to a patient. The magnetic resonance imaging device may include at least two coil units that can detect at least two echo signals generated by the magnetic resonance pulse sequence during scanning. The processing device 120 may further determine at least two signals based on the echo signals detected by the coil units. For example, the processing device 120 may fill the echo signals into at least two regions of K-space (e.g., a K-space matrix) to generate at least two sets of K-space data, where the at least two sets of K-space data can be designated as signals of the subject. As another example, the subject can be a physical point in the patient. The processing device 120 may reconstruct at least two images based on the at least two echo signals. Each image may include image data of the physical point (e.g., a pixel with a specific pixel value, a voxel with a specific voxel value). The processing device 120 may then designate the image data of the physical point in the image as the signal of the physical point. As another example, the echo signals may form at least two echo chains based on the trajectory of K-space. The at least two echo chains can be designated as the signal of the subject.

[0085] In some embodiments, the echo signal may be a complex signal or a real signal, and the subject signal determined based on the echo signal may be a complex signal or a real signal. By way of example only, the subject may be a physical point on a patient. Each reconstructed image may include complex pixel values ​​or real pixel values ​​for a pixel corresponding to the physical point. The complex pixel value or real pixel value of the physical point in each image may be designated as one of the subject signals.

[0086] In some embodiments, at least two signals of the subject may be determined in advance by the processing device 120 or other computing device and stored in a storage device (e.g., storage device 130) of the magnetic resonance imaging system 100 or an external source. The processing device 120 may obtain the signals from the storage device 130 or the external source.

[0087] In some embodiments, each signal of a subject may correspond to a set of values ​​in at least two signal dimensions of a signal acquired using a magnetic resonance imaging device. As described herein, a signal dimension of a signal may refer to a parameter describing an instance of determining or acquiring the signal using a magnetic resonance imaging device. In some embodiments, the at least two signal dimensions may include echo time (TE), unit repetition time (TR), inversion recovery time (TI), b-value, T1p-preparation duration, T2-preparation duration, number of repeated scans, velocity encoding value, number of radio frequency (RF) channels, flip angle, RF center frequency, RF receive coil unit, etc., or any combination thereof. By way of example only, a signal A of a subject may be a pixel value of a physical point in an image, where the image may be reconstructed based on echo signals detected by a magnetic resonance imaging device during a scan of the subject. The signal dimensions corresponding to signal A may include, for example, one or more parameters associated with the magnetic resonance imaging device during a scan of the subject, one or more parameters associated with determining signal A based on a corresponding image (e.g., coordinates of a corresponding pixel in the image), etc. Exemplary parameters associated with the magnetic resonance imaging apparatus during a scan may include one or more parameters associated with the magnetic resonance pulse sequence applied during the scan (e.g., TE, TR, TI, b-value, T1ρ-preparation duration, T2-preparation duration, velocity encoding value, number of repetition scans), one or more parameters associated with the gradient field or radio frequency field used during the scan (e.g., flip angle, radio frequency center frequency), and one or more other imaging parameters of the magnetic resonance imaging apparatus (e.g., radio frequency channel count (or number), coil unit), or the like, or any combination thereof. Further description of parameters associated with the magnetic resonance imaging apparatus during a scan of a subject may be found elsewhere in this specification. See, for example, Figure 2 and related instructions.

[0088] For ease of illustration, Table 1 below provides the patient's physical point P r An example of at least two signals of P can be obtained using a magnetic resonance imaging device having m coil units. r signal. The magnetic resonance imaging device can be made to apply a multi-echo pulse sequence comprising two repeated scans to the patient. In each repeated scan, each coil unit can detect n echo signals, corresponding to n echoes occurring sequentially at different TEs (denoted as TE1, TE2, ... and TEn). Wherein m and n are integers. In some embodiments, at least two echoes can occur sequentially within substantially the same time interval (denoted as ΔTE) between consecutive echoes. The echo signals detected by each coil unit can be used to reconstruct a series of images, wherein each image may include images corresponding to the physical point P. r The pixel value of the corresponding pixel (for simplicity, referred to as P rThe pixel value of the image P r The pixel value can be specified as P r In some embodiments, the image P r The pixel value can reflect the physical point P r MRI signal intensity.

[0089] Table 1 Examples of at least two signals at a physical point Pr

[0090]

[0091] in, to Pointer r At least two signals, Refers to the P in the image reconstructed based on the nth echo signal detected by the mth coil unit in repeated scan 1 r The pixel value of Refers to the P in the image reconstructed based on the nth echo signal detected by the mth coil unit in repeated scan 2 r The pixel value of .

[0092] Table 1 P r Each signal may correspond to a set of values ​​in at least two signal dimensions of a signal acquired using a magnetic resonance imaging device. Exemplary signal dimensions may include echo time, coil unit, number of repetitions, etc., or any combination thereof. For example, Can correspond to TE in the echo time dimension n , m in the coil unit dimension, and 1 in the repetition scan number dimension. It should be noted that the examples shown in Table 1 are for illustrative purposes only and are not intended to limit the scope of this specification. For example, a multi-echo pulse sequence applied to a patient may include only one repetition scan and / or more than one flip angle.

[0093] At step 420 , the processing device 120 (eg, the determination module 304 ) may determine a dominant signal dimension associated with the signal representation of the subject among the at least two signal dimensions.

[0094] As described herein, a signal representation may refer to a representative value or attribute value of a subject's signal. A subject's signal representation may reflect one or more physiological or physical characteristics of the subject and may provide a basis for medical diagnosis and / or treatment.

[0095] If the signal dimension and the signal representation have a certain mathematical correlation (e.g., exponential correlation, linear correlation, or any other mathematical correlation), the signal dimension can be considered to be correlated with the signal representation. r Described as an exemplary subject and provided below is P rPlease refer back to the example in Table 1. The magnetic resonance imaging device can use a multi-echo pulse sequence to scan the patient. r The signal representation can be P r The change in signal strength at P during the time interval ΔTE between consecutive echoes is denoted as ΔS1(r). r The signal strength at P r In some embodiments, the signal representation ΔS1r may be related to the echo time as shown in the following formula (1) or (2):

[0096]

[0097] as well as

[0098]

[0099] in, and T2(r) refers to P r The transverse relaxation time is γ, the gyromagnetic ratio is γ, and ΔB(r) is the local magnetic field distribution at Pr. In this case, the main signal dimension can be the echo time related to ΔS1(r).

[0100] As another example, a magnetic resonance imaging apparatus may be caused to scan a patient using a T2 preparation pulse sequence comprising at least two T2 preparation pulses having different T2 preparation durations. r The signal representation can be P r Signal strength at time interval The change during this period is expressed as ΔS2. It can refer to the time difference between two T2 preparation durations corresponding to two consecutive T2 preparation pulses in a T2 preparation pulse sequence. As shown in the following formula (3), the signal representation ΔS2 can be related to the T2 preparation duration:

[0101]

[0102] Where T2(r) refers to P r In this case, the main signal dimension may be the T2 preparation duration related to ΔS2(r).

[0103] As another example, a magnetic resonance imaging apparatus may be caused to scan a patient using a T1p preparation pulse sequence, the sequence comprising at least two T1p weighted magnetization preparation pulses having different T1p preparation durations. r The signal representation can be P rThe change in signal intensity at the time interval Δτ can be expressed as ΔS3. Δτ can refer to the time difference between two T1ρ preparation durations corresponding to two consecutive T1ρ-weighted magnetization preparation pulses in the T1ρ preparation pulse sequence. The signal representation ΔS3 can be related to the T1ρ preparation duration as shown in the following formula (4):

[0104]

[0105] Among them, T 1ρ (r) refers to P r The longitudinal relaxation time in the rotating coordinate system. In this case, the main signal dimension may be the T1ρ preparation duration related to ΔS3(r).

[0106] As another example, a magnetic resonance imaging device may scan a patient using an IR pulse sequence comprising at least two excitation pulses at different TIs. r The signal representation can be P r The change in signal intensity at the IR pulse sequence during ΔTI is represented by ΔS4(r). ΔTI may refer to the time difference between two TIs corresponding to two consecutive excitation pulses in the IR pulse sequence. In some embodiments, the signal representation ΔS4(r) may be related to the inversion time as shown in the following formula (5):

[0107]

[0108] Where T1(r) refers to P r In this case, the main signal dimension can be the reversal time related to ΔS4(r).

[0109] As another example, a magnetic resonance imaging device may apply a DWI pulse sequence to a patient, which includes multiple pairs of diffusion sensitization gradients with different b values. r The signal representation can be P r The signal intensity at the point where the image is taken as a function of Δb is expressed as ΔS5. Δb can refer to the difference between the two b-values ​​corresponding to two consecutive pairs of diffusion sensitization gradients in a DWI pulse sequence. The signal representation ΔS5 can be related to the b-value as shown in the following formula (6):

[0110]

[0111] Where ADC(r) refers to P r In this case, the main signal dimension can be the b-value related to ΔS5(r).

[0112] In some embodiments, two or more of the at least two signal dimensions may be associated with the signal representation. One of the two or more signal dimensions may be selected as the primary signal dimension. This selection may be performed automatically by the processing device 120 or based on user instructions.

[0113] At step 430 , the processing device 120 (eg, the determination module 304 ) may determine a first value and a second value related to the signal representation of the subject based on the at least two signals.

[0114] The first value may represent a signal representation along a first direction of the primary signal dimension. The second value may represent a signal representation along a second direction of the primary signal dimension. The first direction may be opposite to the second direction. For example, if the primary signal dimension is echo time, the first direction may be a direction in which the echo time increases (i.e., a direction in which time passes), and the second direction may be a direction in which the echo time decreases (i.e., a direction in which time regresses). As another example, if the primary signal dimension is T2 preparation duration, the first direction may be a sequential direction in which the T2 preparation duration increases sequentially, and the second direction may be a sequential direction in which the T2 preparation duration decreases sequentially.

[0115] In some embodiments, processing device 120 may determine at least one secondary signal dimension in at least two signal dimensions. For at least one value in the at least one secondary signal dimension, processing device 120 may determine an initial value of the signal representation along a first direction of the primary signal dimension (denoted as initial value V1) based on a portion of the at least two signals corresponding to the value in the at least one secondary signal dimension. Furthermore, processing device 120 may determine a first value of the signal representation based on at least a portion of the at least one initial value V1 of the signal representation.

[0116] In some embodiments, processing device 120 may obtain an optimization function (also referred to as a first optimization function) for the signal representation. The optimization function may include the primary signal dimension along a first direction. Further, processing device 120 may determine a first value of the signal representation of the subject by inputting at least two signals into the optimization function.

[0117] More details about determining the first value represented by the signal can be found in other parts of this specification (e.g., Figure 5 and Figure 6 and its description).

[0118] In some embodiments, the determination of the second value of the signal representation can be performed in a manner similar to the determination of the first value of the signal representation. For example, for at least one value in at least one secondary signal dimension, the processing device 120 can determine an initial value of the signal representation in a second direction along the primary signal dimension (expressed as initial value V2) based on a portion of at least two signals corresponding to the value in at least one secondary signal dimension. In addition, the processing device 120 can determine the second value of the signal representation based on at least a portion of the at least one initial value V2 of the signal representation. As another example, the processing device 120 can obtain a second optimization function that includes the signal representation of the primary signal dimension along the second direction. In addition, the processing device 120 can determine the second value of the signal representation of the subject by inputting at least two signals into the second optimization function.

[0119] At step 440 , the processing device 120 (eg, the determination module 304 ) may determine a quantitative parameter value of the subject based on the first value and the second value.

[0120] The primary signal dimension and the signal representation can be correlated with a quantitative parameter. In some embodiments, if the quantitative parameter and the signal dimension have a certain correlation, for example, a correlation that can be represented or described by a mathematical relationship (e.g., an exponential correlation, a linear correlation, or any other mathematical correlation), then the quantitative parameter can be considered to be correlated with the signal dimension. Similarly, if the quantitative parameter and the signal representation have a certain correlation, then the quantitative parameter can be considered to be correlated with the signal representation.

[0121] In some embodiments, the subject may be a physical point of the object, and the signal representation may be a change in the signal intensity of the physical point as a function of the scan parameters. Exemplary quantitative parameters of the physical point may include longitudinal relaxation time, transverse relaxation time (T2 or ), transverse relaxation rate (R2 or ), apparent diffusion coefficient (ADC), field distribution, longitudinal relaxation time in a rotating coordinate system, signal-to-noise ratio index or signal ratio index, or similar parameters, or any combination thereof.

[0122] In some embodiments, the primary signal dimension may be the echo time, and the quantitative parameter may be the transverse relaxation time, field distribution, or similar parameter, or any combination thereof. Alternatively, the primary signal dimension may be the T2 preparation duration, and the quantitative parameter may be the transverse relaxation time. Alternatively, the primary signal dimension may be the T1p preparation duration, and the quantitative parameter may be the longitudinal relaxation time in a rotating coordinate system. Alternatively, the primary signal dimension may be the inversion time, and the quantitative parameter may be the longitudinal relaxation time. Alternatively, the primary signal dimension may be the b-value, and the quantitative parameter may be the ADC.

[0123] In some embodiments, the processing device 120 may determine a first initial value of a quantitative parameter of the subject based on the first value. The processing device 120 may also determine a second initial value of the quantitative parameter of the subject based on the second value. In addition, the processing device 120 may also determine the quantitative parameter value of the subject based on the first initial value and the second initial value of the quantitative parameter. For more information on determining the quantitative parameter value of the subject, please refer to other parts of this specification (for example, Figure 7 and its description).

[0124] In some embodiments, the processing device 120 may determine a signal-to-noise parameter value indicating a signal level of the at least two signals relative to a noise level of the at least two signals based on the first value and the second value. For example, the processing device 120 may determine the product of the first value and the second value as the signal-to-noise parameter value of the at least two signals.

[0125] In some embodiments, the subject may be one of at least two physical points of the object. The processing device 120 may execute process 400 for each physical point of the object to determine the corresponding value of the quantitative parameter. The quantitative parameter value of the physical point may reflect one or more physiological characteristics or physical characteristics of different parts of the patient, and thus may be used for disease diagnosis. In addition, the processing device 120 may also generate a parameter map (e.g., T2 map, T2* map, R2 map, R2* map, etc.) related to the quantitative parameter of the object based on the quantitative parameter values ​​of each physical point of the object. The parameter map may be used for medical diagnosis. As described in this specification, a parameter map related to the quantitative parameter of the object may be a map showing the quantitative parameter value of the subject at the element position (e.g., pixel position, voxel position) of the parameter map. Each element position may correspond to a physical point of the subject.

[0126] In some embodiments, the processing device 120 may further perform a noise analysis on the parameter map to determine a noise level of the parameter map. For example, to perform the noise analysis, at least one reference signal representation may be determined. The at least one reference signal representation may be related to the signal representation.

[0127] For each of the at least one reference signal representation, the processing device 120 may determine a first signal-to-noise parameter value indicating a signal level of the reference signal representation relative to a noise level of the reference signal representation. Furthermore, the processing device 120 may determine a second signal-to-noise parameter value indicating a signal level of the parameter map relative to a noise level of the parameter map based on the first signal-to-noise parameter value for each reference signal representation.

[0128] As an example only, at least one reference signal representation may include a first reference signal representation and a second reference signal representation. The signal representation may be a ratio of the first reference signal representation to the second reference signal representation. Taking formula (9) as an example, the first reference signal representation may be the denominator of formula (9) and the second reference signal representation may be the numerator of formula (9). The processing device 120 may expand the function representing the first reference signal representation to obtain an expanded expression corresponding to the first reference signal representation, wherein the expanded expression includes a noise-containing term and a signal-containing term. The processing device 120 may extract the noise-containing term from the expanded expression corresponding to the first reference signal representation. In addition, the processing device 120 may determine a first signal-to-noise parameter value for the first reference signal representation based on the noise-containing term and at least two signals of the subject. The first signal-to-noise parameter value for the second reference signal representation may be determined in a manner similar to determining the first signal-to-noise parameter value for the first reference signal representation. In addition, the processing device 120 may determine a first initial value and a second initial value of a third signal-to-noise parameter indicating a signal level of the signal representation relative to a noise level of the signal representation based on the first signal-to-noise parameter values ​​of the first reference signal representation and the second reference signal representation. The first initial value of the third signal-to-noise parameter may correspond to a first direction of the primary signal dimension, and the second initial value of the third signal-to-noise parameter may correspond to a second direction of the primary signal dimension. Finally, processing device 120 may determine a second signal-to-noise parameter value of the parameter map based on the first initial value and the second initial value of the third signal-to-noise parameter.

[0129] According to some embodiments of the present specification, the noise level of a parameter map (e.g., the second signal-to-noise parameter value) and / or the noise level of a signal representation (e.g., the first and second initial values ​​of a third signal-to-noise parameter) can be determined based on the noise level of at least one reference signal representation (e.g., the first signal-to-noise parameter value of each reference signal representation). Because the noise level of at least one reference signal representation is easier to determine, the method disclosed herein for performing noise analysis on a parameter map and a signal representation is more efficient and can save computing resources compared to methods that directly determine the noise level of the parameter map and the noise level of the signal representation.

[0130] It should be noted that the above description of process 400 is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art may make various changes and modifications based on the description of this specification. However, such changes and modifications do not depart from the scope of this specification.

[0131] In some embodiments, after executing step 420 , the processing device 120 may directly execute steps 430 and 440 to determine a first value and a second value represented by the signal, and then determine a quantitative parameter value based on the first value and the second value.

[0132] Alternatively, after executing step 420, the processing device 120 may determine whether it is necessary to determine the first value and the second value of the signal representation. For example, the processing device 120 (e.g., the determination module 304) may determine, based on the signal representation of the subject, a signal-to-noise parameter value indicating the signal level of the at least two signals relative to the noise level of the at least two signals. In some embodiments, the noise parameter may be used to analyze the noise of the signal representation and, accordingly, may indicate the signal level relative to the noise level of the at least two signals. For example, the signal-to-noise parameter value may be within a certain range, such as [0, 1]. If the signal-to-noise parameter value is equal to or close to 1 (e.g., the signal-to-noise parameter value is greater than 0.95, 0.99, etc.), the signal may be considered to be pure noise that does not include or includes very little non-noise components. If the signal-to-noise parameter value is equal to or close to 0 (e.g., the signal-to-noise parameter value is less than 0.05, 0.01, etc.), the signal may be considered to be a pure signal that does not include or almost does not include noise. In some embodiments, if the signal-to-noise parameter value is less than a preset value, such as 0.1, the signal may be considered to include a non-noise signal.

[0133] The processing device 120 (e.g., determination module 304) can determine whether the signal-to-noise parameter value is greater than a preset threshold. If the signal-to-noise parameter value is greater than the preset threshold, the processing device 120 can determine that the signal is noise or that the signal has a low signal level (or high noise level), and needs to determine the first and second values. Therefore, if the signal-to-noise parameter value is greater than the preset threshold, steps 430 and 440 can be performed. If the signal-to-noise parameter value is less than the preset threshold, the processing device 120 can determine that the signal includes a non-noise signal or has a high signal level (or low noise level), and the processing device 120 can directly determine the quantitative parameter value of the subject based on only one of the first value and the second value represented by the signal according to the traditional MDI method. If the noise level of the signal is relatively low, the quantitative parameter determined by the traditional MDI method has ideal accuracy. By determining the signal-to-noise parameter value of the signal and adaptively selecting the traditional MDI method based on the signal-to-noise parameter value, the amount of calculation in the quantitative measurement can be reduced and the determination efficiency can be improved.

[0134] It should be noted that the above description of the signal-to-noise ratio parameter is for illustrative purposes only and is not restrictive. In some alternative embodiments, if the signal-to-noise ratio parameter is equal to 1, the signal can be regarded as a pure signal, and if the signal-to-noise ratio parameter is equal to 0, the signal can be regarded as pure noise.

[0135] In some embodiments, the processing device 120 may determine a reference value for the signal representation. In some embodiments, the reference value for the signal representation may be either the first value or the second value described in step 430. The processing device 120 may determine an updated value for the updated signal representation of the subject by adding signal interference to the at least two signals. The updated signal representation may correspond to a signal representation in the same direction of the primary signal dimension (e.g., the first direction or the second direction described in step 430). Furthermore, the processing device 120 may determine a signal-to-noise parameter value based on the signal representation of the subject and the updated signal representation.

[0136] Signal interference as used herein refers to noise with a known signal variation level, which is intentionally added to the signal of the subject in order to generate an updated signal representation that is different from the signal representation. Adding signal interference to the signal as described herein refers broadly to combining the signal interference and the signal in any suitable manner, for example, by adding the signal interference to the signal, multiplying the signal interference by the signal, or a similar manner, or any combination thereof. The signal interference may have the same dimensions as the signal (in the same units) or may be a dimensionless factor. The updated signal representation may be viewed as a signal representation of a combination of at least two signals and a signal interference.

[0137] In some embodiments, the processing device 120 may determine the signal-to-noise parameter value based on a relationship function of the difference between the reference value of the measured signal representation and the updated value of the updated signal representation of the subject. The relationship function may include one or more processing operations, such as linear operations (e.g., addition, subtraction, multiplication, division), nonlinear operations (e.g., exponential functions, power functions, logarithmic functions), or the like, or any combination thereof, performed on the signal representation and the updated signal representation. The signal-to-noise parameter value may be determined by inputting the reference value of the signal representation and the updated value of the updated signal representation into the relationship function.

[0138] Figure 5 FIG. 5 is a flow chart of an exemplary process 500 for determining a first value represented by a signal according to some embodiments of the present specification. In some embodiments, the process 500 may be performed by the magnetic resonance imaging system 100. For example, the process 500 may be implemented as an instruction set (e.g., an application) stored in a storage device (e.g., the storage device 130). In some embodiments, the processing device 120 (e.g., Figure 3 The one or more modules shown in FIG5 can execute the instruction set and can be instructed to execute the process 500 accordingly. The operations of the process shown below are for illustration purposes only. In some embodiments, the process 500 can be completed by one or more additional operations not described and / or without one or more operations discussed. In addition, Figure 5The order in which the operations of process 500 are shown and described below is not intended to be limiting. In some embodiments, one or more operations of process 500 may be performed to implement the Figure 4 At least a portion of step 430 is related.

[0139] At step 510 , the processing device 120 (eg, the determination module 304 ) may determine at least one secondary signal dimension among the at least two signal dimensions.

[0140] At least one secondary signal dimension may include any signal dimension of the main signal other than the main signal dimension. In some embodiments, each secondary signal dimension may be independent of (or related to) the signal representation. In some embodiments, at least one secondary signal dimension may include all or part of the signal dimension of the main signal other than the main signal dimension. In some embodiments, if the signal dimension is independent of the signal representation and has two or at least two values ​​on the signal dimension, then the signal dimension may be determined as a secondary signal dimension. For example, referring again to the example in Table 1, at least one secondary signal dimension may include two secondary signal dimensions, namely the coil unit dimension and the repeated scan number dimension, and these two dimensions are independent of ΔS1(r) in the above formula (1). If the multi-echo pulse sequence used for the patient includes only one repeated scan (i.e., there is only one value in the repeated scan number), then the repeated scan number may not be designated as a secondary signal dimension.

[0141] Step 520, for each of at least one value in at least one secondary signal dimension, the processing device 120 (e.g., the determination module 304) can determine at least one initial value of the signal representation along the first direction of the primary signal dimension based on a portion of at least two signals corresponding to the value in at least one secondary signal dimension.

[0142] For the purpose of explanation, the following will refer to Figure 4 The example in Table 1 is described. Assume that the signal to be determined is represented by ΔS1r (i.e., the patient's physical point P r =Change in signal strength during ΔTE). As described above, the primary signal dimension associated with ΔS1r can be the echo time, and the at least one secondary signal dimension can include the coil unit and the number of scan repetitions. In some embodiments, for at least one coil unit in each scan repetition (i.e., for at least one value in the coil unit dimension), the processing device 120 can determine at least one initial value of the signal representation associated with the echo time along a first direction of the primary signal dimension.

[0143] For example only, the first direction may be a direction in which the echo time increases (ie, time elapses). For coil 1 in repeated scan 1, the processing device 120 may calculate the P value corresponding to coil 1 and repeated scan 1 based on the P value corresponding to coil 1 and repeated scan 1. rThe signal shown in Table 1 to Determine the physical point P r The at least one initial value represented by the signal. The at least one initial value may include and in It means in TE i and TE i-1 The time interval between the echo time and the TE i-1 Add to TE i ), relative to P r The signal strength change of coil 1 in repeated scan 1 at . Can be based on and OK. For example, Can be equal to Similarly, the processing device 120 can determine the physical point P for each other coil unit in Repeat Scan 1 and each coil unit in Repeat Scan 2. r In this way, the physical point P can be determined r In some embodiments, the processing device 120 can determine at least one initial value of the signal representation for a portion of the coil units and / or a portion of the repeated scan. In this way, the P that needs to be determined r The number of initial values ​​represented by the signal can be less than 2m*(n-1).

[0144] It should be noted that the above description of the examples in Table 1 is for illustrative purposes only and is not intended to limit the scope of this specification. A person skilled in the art may make various changes and modifications based on the description of this specification. However, such changes and modifications do not depart from the scope of this specification. For example, the processing device 120 may determine an initial value (represented by ) of the signal representation for coil 1 in repeated scan 1. ). Can be equal to and Alternatively, as described elsewhere in this specification (e.g. Figure 6 and related instructions), the processing device 120 can be and Input optimization function (e.g., formula (7)) to determine In some embodiments, the physical point P r At least one sub-signal dimension of the signal is illustrative. r At least one sub-signal dimension of the signal may include only one of the coil unit and the number of repetition scans. Additionally or alternatively, P rThe at least one secondary signal dimension of the signal may further comprise one or more other secondary signal dimensions, for example one or more imaging parameters of a magnetic resonance imaging device.

[0145] At step 530 , the processing device 120 (eg, the determination module 304 ) may determine a first value of the signal representation based on at least a portion of the at least one initial value of the signal representation.

[0146] In some embodiments, the first value of the signal representation may be the sum, average, or median of at least a portion of the at least one initial value of the signal representation. In some embodiments, the entire value of the at least one initial value of the signal representation determined in step 520 may be used to determine the first value of the signal representation of the subject. Alternatively, a portion of the at least one initial value of the signal representation determined in step 520 may be used to determine the first value of the signal representation of the subject. Using the example shown in Table 1 as an example, processing device 120 may determine the first value of the signal representation based on the initial values ​​of the signal representation corresponding to coils 1 through m-1, for example, if coil m has some operational fault.

[0147] Figure 6 FIG. 6 is a flow chart of an exemplary process 600 for determining a first value represented by a signal according to some embodiments of the present specification. In some embodiments, the process 600 may be performed by the magnetic resonance imaging system 100. For example, the process 600 may be implemented as an instruction set (e.g., an application) stored in a storage device (e.g., the storage device 130). In some embodiments, the processing device 120 (e.g., Figure 3 The one or more modules shown in FIG600 can execute the instruction set and can be instructed to execute the process 600 accordingly. The operations of the process shown below are for illustration purposes only. In some embodiments, the process 600 can be completed by one or more additional operations not described and / or without one or more operations discussed. In addition, Figure 6 The order in which the operations of process 600 are shown and described below is not intended to be limiting. In some embodiments, one or more operations of process 600 may be performed to implement the Figure 4 At least a portion of step 430 is related.

[0148] At step 610 , the processing device 120 (eg, the determination module 304 ) may determine at least one secondary signal dimension in at least two signal dimensions.

[0149] In some embodiments, step 610 may be performed using a method similar to Figure 5 The process 500 is performed in the manner of step 510.

[0150] In 620 , the processing device 120 (eg, the determination module 304 ) may obtain an optimization function for a signal representation, the optimization function comprising a primary signal dimension along a first direction and at least one secondary signal dimension.

[0151] For ease of explanation, assume that the subject is a physical point on the object, and the signal to be determined is represented by ΔS1(r) (i.e., the change in signal strength at the physical point with echo time). As described above, the primary signal dimension associated with ΔS1(r) can be the echo time, and at least one secondary signal dimension can include the coil unit. If the first direction is the direction of increasing echo time, then the optimization function can be the following formula (7):

[0152]

[0153] Among them, argmin ΔS1(r)+ The first value of the signal representation along the direction of increasing echo time (ie, the patient physical point P increases with the echo time ΔTE) is represented by r The signal strength attenuation degree is shown in Figure 2. i represents the echo number, j represents the coil unit number, and N represents the echo number. e Refers to the count of echo time, N c Refers to the count of coil units, S i+1,j (t) and S i,j (r) refers to a pair of signals corresponding to two consecutive echoes i and i+1 detected by one coil unit j.

[0154] If the first direction is a direction in which the echo time decreases, the optimization function may be the following formula (8):

[0155]

[0156] Among them, argmin ΔS1(r)- represents the first value of the signal representation along the decreasing direction of the echo time (ie, as the echo time decreases ΔTE, the patient's physical point P r the degree of signal strength enhancement at the specified location).

[0157] It should be noted that the above equations (7) and (8) are for illustrative purposes only and are not intended to limit the scope of this specification. In some embodiments, the signal at the physical point may have one or more other secondary signal dimensions, which may be incorporated into equations (7) and (8), for example, in a manner similar to that used for coil units.

[0158] At step 630 , the processing device 120 (eg, the determination module 304 ) may determine a first value represented by the subject signal by inputting the at least two signals into an optimization function.

[0159] The processing device 120 may input the signal corresponding to the physical point into Formula (7) (or Formula (8)) and solve Formula (7) (or Formula (8)) to determine ΔS1(r). For example, ΔS1(r) may be determined by solving Formula (7) + The value of can be described by formula (9) as follows:

[0160]

[0161] Determine ΔS1(r) by solving equation (8) - The value of can be referred to the following formula (10):

[0162]

[0163] In some embodiments, the processing device 120 may use a least squares method, a neural network model, a support vector machine (SVM), or similar methods, or any combination thereof to solve the optimization function.

[0164] In some embodiments, for at least one value in at least one secondary signal dimension, the processing device 120 may determine at least one pair of signals corresponding to the value in at least one secondary signal dimension in the at least two signals. Each pair of at least one pair of signals may correspond to a different value in the primary signal dimension. For example, at a physical point P r For example, for coil 1 in repeated scan 1, the processing device 120 may determine (n-1) pairs of signals corresponding to the continuous echo signals detected by coil 1 in repeated scan 1, for example, the first pair and Second pair and wait.

[0165] Furthermore, the processing device 120 may determine the first value of the signal representation of the subject by inputting at least one pair of signals into the optimization function. For example, at least one pair of signals may be input into formula (7) (or formula (8)) to determine ΔS1(r).

[0166] In processes 500 and 600, the signal representation of the subject is determined by jointly processing signals of different signal dimensions (including a primary signal dimension and at least one secondary signal dimension). Compared with independently processing signals of different signal dimensions, the efficiency and / or accuracy of signal representation determination can be improved. For example, in process 500, one or more initial signal representations can be determined for each coil unit (i.e., each value in the secondary signal dimension). The signal representation of the subject can be determined based on the initial signal representations of all coil units of the magnetic resonance imaging device. For example, the signal representation of the subject can be the average value of the initial signal representations of all coil units of the magnetic resonance imaging device. In addition, in some embodiments, the initial value of the signal representation can be determined based on a comparison between the signals detected by different coil units. For example, as described above, P r The signal indicates that the initial value can be equal to This can reduce the impact of coil performance (e.g., sensitivity distribution, signal-to-noise ratio (SNR)) on the signal representation, thereby improving the accuracy of the determined signal representation. As another example, in process 600, determining the first value of the subject signal representation using an optimization function that includes and processes a primary signal dimension and at least one secondary signal dimension can improve computational efficiency and reduce processing time.

[0167] Figure 7 FIG. 7 is a flow chart of an exemplary process 700 for determining a quantitative parameter value of a subject based on a first value and a second value according to some embodiments of the present specification. In some embodiments, the process 700 may be performed by the magnetic resonance imaging system 100. For example, the process 700 may be implemented as an instruction set (e.g., an application) stored in a storage device (e.g., the storage device 130). In some embodiments, the processing device 120 (e.g., Figure 3 The one or more modules shown in FIG. 7A and FIG. 7B may execute the instruction set and may be instructed to execute process 700 accordingly. The operations of the process shown below are for illustration purposes only. In some embodiments, process 700 may be completed by one or more additional operations not described and / or without one or more of the operations discussed. In addition, Figure 7 The order in which the operations of process 700 are illustrated and described below is not intended to be limiting.

[0168] In some embodiments, one or more operations of process 700 may be performed to implement Figure 4 At least a portion of step 440 is related.

[0169] In some embodiments, as described elsewhere in this specification, the signal representation and primary signal dimension may be associated with a quantitative parameter. Each value in at least one secondary signal dimension may not be associated with a quantitative parameter.

[0170] For ease of explanation, Figure 4The patient's physical point P is described in r Described as an example object and provided below r Determination of exemplary quantitative parameters. For example, P r The signal representation can be ΔS1(r), and the quantitative parameters can include and / or ΔB(r), where, according to formula (1), and ΔB(r) are related to the echo time (i.e., the main signal dimension relative to ΔS1(r)). For example, P r The signal representation of can be ΔS2(r), and the quantitative parameter can be T2(r), where according to formula (3), T2(r) is related to the T2 preparation duration (i.e., the main signal dimension relative to ΔS2(r)). For another example, P r The signal representation of can be ΔS3(r), and the quantitative parameter can be T1ρr, wherein, according to formula (4), T1ρr is related to the T1ρ preparation duration (i.e., the main signal dimension relative to ΔS3(r)). For another example, P r The signal representation of can be ΔS4(r), and the quantitative parameter can be T1(r), where, according to formula (5), T1(r) is related to the inversion time (i.e., the main signal dimension relative to ΔS4(r)). For another example, P r The signal representation of may be ΔS5(r) and the quantitative parameter may be ADC(r), wherein ADC(r) is related to the b-value (ie, the dominant signal dimension relative to ΔS5(r)) according to formula (6).

[0171] In some embodiments, the signal representation of the subject may be a processing result in K-space. A quantitative parameter may be any parameter related to the processing result in K-space. In some embodiments, the quantitative parameter may be data in K-space. Alternatively, the quantitative parameter may be data in the image domain, wherein the quantitative parameter value may be determined during image reconstruction. For example, image reconstruction may be achieved by determining one or more quantitative parameters in the image domain from the signal representation in K-space.

[0172] At step 710 , the processing device 120 (eg, the determination module 304 ) may determine a first initial value of a quantitative parameter of the subject based on the first value.

[0173] In some embodiments, the processing device 120 can obtain a first relationship related to the signal representation and the quantitative parameter along the first direction of the main signal dimension. The processing device 120 can determine the first initial value of the quantitative parameter of the subject based on the first value of the signal representation and the first relationship. For example, the first relationship can be described in the form of a correlation function, such as any one of formulas (1) to (6). The processing device 120 can determine the first initial value of the quantitative parameter by solving the correlation function. As another example, the first relationship can be presented in the form of a table or a curve, in which different signal representations and their corresponding first initial values ​​of the quantitative parameters are recorded. The processing device 120 can determine the first initial value of the quantitative parameter by querying the table or the curve.

[0174] In some embodiments, the signal representation of the subject can be represented by a complex number, the complex number including a phase component and an amplitude component. The first initial value of the quantitative parameter can be determined based on at least one of the phase component or the amplitude component of the complex number. Alternatively, the signal representation can be represented by a real number, and the first initial value of the quantitative parameter can be determined based on the real number. r For example, if the first direction is the direction of increasing echo time, and the first value represented by the signal is the above ΔS1(r) + , we can determine P r Department The first initial value (expressed as ) and / or P r Department The first initial value (expressed as ),in Pointer r The transverse relaxation rate is usually considered to be determined based on the value of the signal representation of the subject in the same direction along the main signal dimension. and The values ​​of are reciprocals of each other, that is, and are reciprocal of each other, the steps described in step 720 and are reciprocals of each other. If ΔS1r + is a real number, then we can use ΔS1(r) + Sure and If ΔS1(r) + is a complex number, then it can be based on ΔS1r + The amplitude component of and For example, based on ΔS1(r) + Sure and Formulas (11) and (12) are as follows:

[0175]

[0176] as well as

[0177]

[0178] At step 720 , the processing device 120 (eg, the determination module 304 ) may determine a second initial value of the quantitative parameter based on the second value.

[0179] In some embodiments, the processing device 120 may obtain a second relationship between the signal representation and the quantitative parameter along a second direction of the primary signal dimension. Furthermore, the processing device 120 may determine a second initial value of the quantitative parameter of the subject based on the second value of the signal representation and the second relationship. In some embodiments, the second initial value of the quantitative parameter may be determined in a manner similar to the determination of the first initial value of the quantitative parameter. For example, at a physical point P r For example, if the second direction is the direction of decreasing echo time, and the second value represented by the signal is the above-mentioned ΔS1r-, then P can be determined. r Department The second initial value (expressed as ) and / or P r Department The second initial value (expressed as ). If ΔS1(r) - is a real number, then and Can be determined based on ΔS1r-. If ΔS1(r) - is a complex number, then it can be based on ΔS1(r) - The amplitude component of and For example, based on ΔS1(r) - Sure and Formula (13) and formula (14) are as follows:

[0180]

[0181] as well as

[0182]

[0183] At step 730 , the processing device 120 (eg, the determination module 304 ) may determine a quantitative parameter value of the subject based on the first initial value and the second initial value.

[0184] Since one of the first initial value and the second initial value is usually smaller than the true value of the quantitative parameter, and the other of the first initial value and the second initial value is usually larger than the true value of the quantitative parameter, the defects of the first initial value and the second initial value can be compensated by combining the first initial value and the second initial value. Therefore, in some embodiments, the processing device 120 can determine the quantitative parameter value of the subject by combining the first initial value and the second initial value. In some embodiments, the quantitative parameter of the subject is the transverse relaxation rate. The processing device 120 may set the first initial value and the second initial value The average value of is designated as the transverse relaxation rate For example only, The value can be based on and Determine according to the following formula (15):

[0185]

[0186] In some embodiments, the quantitative parameter of the subject is the transverse relaxation time The processing device 120 may determine a first initial value The first reciprocal and second initial value The second reciprocal of , and the transverse relaxation time is determined based on the first and second reciprocals For example only, The value can be based on and Determine according to the following formula (16):

[0187]

[0188] In some embodiments, the value of T2 may be determined similarly to The value of R2 can be determined in a similar way to The value is determined by the method.

[0189] Compared with the first initial value and the second initial value of the quantitative parameter, the quantitative parameter value determined by combining the first initial value and the second initial value can be closer to the actual value of the quantitative parameter and have higher accuracy. In some embodiments, as the SNR decreases, the impact of noise on the first initial value and the second initial value becomes greater and greater, that is, the difference between the first initial value and the true value of the quantitative parameter and the difference between the second initial value and the true value of the quantitative parameter become larger and larger, and the accuracy of the first initial value and the second initial value gradually decreases. According to some embodiments of the present specification, when the SNR is relatively low, the quantitative parameter value determined by combining the first initial value and the second initial value can have relatively high accuracy.

[0190] For example, Figure 8A and Figure 8B According to some embodiments of the present invention, a subject is scanned by magnetic resonance imaging. and Schematic diagram of exemplary quantitative measurement results. Figure 8A As shown, The true value is 25Hz. As the SNR decreases, The first initial value MDI+ gradually deviates from the true value and is greater than the true value. As the SNR decreases, The second initial value MDI- gradually deviates from the true value and is less than the true value. The value MDI new is substantially equal to the true value. It can be seen that the defects of the first initial value MDI+ and the second initial value MDI- are compensated by combining the first initial value MDI+ and the second initial value MDI-. The average value of the first initial value MDI+ and the second initial value MDI- (ie, The value MDI new) is substantially equal to the true value. Therefore, compared with the first initial value MDI+ and the second initial value MDI-, The value of MDI new can have higher accuracy, especially when the SNR is relatively low.

[0191] like Figure 8B As shown, The actual value is 40ms. As the SNR decreases, The first initial value MDI+ gradually deviates from the true value and is smaller than the true value. As the SNR decreases, The second initial value MDI- gradually deviates from the true value and is greater than the true value. The new value MDI new is substantially equal to the true value. It can be seen that since the defects of the first initial value MDI+ and the second initial value MDI- are compensated by combining the first initial value MDI+ and the second initial value MDI-, The new value MDI new is substantially equal to the true value. Therefore, compared with the first initial value MDI+ and the second initial value MDI-, The new value MDI new may have higher accuracy, especially when the SNR is relatively low.

[0192] As described elsewhere in this specification, according to conventional quantitative measurement methods using MDI technology, a quantitative parameter value of a subject is determined based solely on the value of the signal representation of the subject along one direction of the primary signal dimension, and the accuracy of the determined quantitative parameter value is limited. According to some embodiments of this specification, a quantitative parameter value of a subject can be determined based on first and second values ​​of the signal representation along the first and second directions of the primary signal dimension. This can combine the first and second initial values ​​to compensate for the deficiencies of the first and second initial values, thereby improving the accuracy, stability, and reliability of the quantitative parameter value determination.

[0193] Example

[0194] Figures 9A-9C is an exemplary patient's brain according to some embodiments of this specification. Schematic diagrams of Figures 900A-900C.

[0195] like Figures 9A-9C As shown, Each of the graphs 900A-900C may include at least two pixels representing at least two physical points of the brain. In Figure 900A, the pixel value corresponding to each physical point shows the physical point The first initial value of , wherein the first initial value is determined by executing step 710 disclosed in this specification. In Figure 900B, the pixel value corresponding to each physical point shows the value of the physical point. The second initial value of , wherein the second initial value is determined by executing step 720 disclosed in this specification. In FIG900C, the pixel value corresponding to each physical point shows the value determined by executing step 730. The value, that is, the pixel value corresponding to the physical point in 900C is the average of the first initial value of the physical point in 900A and the second initial value of the physical point in 900B.

[0196] It can be seen that with Comparing Figures 900A and 900B, There are fewer noise points in Figure 900C, which indicates that R2* in Figure 900C Value Ratio More precisely in Figures 900A and 900B. In addition, due to The background pixels in the image are pure noise. Background pixels in Figure 900A The value is extremely positive, so Figure 900A has a pure white background; Background pixels in Figure 900B The value is extremely negative, so Figure 900B has a solid black background; The R2* value of the background pixel in Figure 900C can be 0, which is consistent with Figure 8A The quantitative measurement results are consistent with those in .

[0197] Figures 9D-9F is an exemplary patient's brain according to some embodiments of this specification. Schematic diagrams of Figures 900D-900F.

[0198] like Figures 9D-9F As shown, Each of the graphs 900A-900C may include at least two pixels representing at least two physical points of the brain. In Figure 900D, the pixel value corresponding to each physical point shows the physical point The first initial value of , wherein the first initial value is determined by executing step 710 disclosed in this specification. In Figure 900E, the pixel value corresponding to each physical point shows the value of the physical point. The second initial value of , wherein the second initial value is determined by executing step 720 disclosed in this specification. In FIG900F, the pixel value corresponding to each physical point shows the value determined by executing step 730. The value, that is, the pixel value corresponding to the physical point in 900F is a new value obtained by combining the first initial value of the physical point in 900D and the second initial value of the physical point in 900E.

[0199] It can be seen that with Comparing Figures 900D and 900E, Figure 900F has less noise. Compared with Figure 900F, In Figures 900D and 900E There are more noise points in the area with relatively large values ​​and in the background. This indicates Figure 900C Figures 900D and 900E are more precise.

[0200] Figure 10A and 10B is an exemplary patient's brain according to some embodiments of this specification. Schematic diagrams of Figures 1000A and 1000B.

[0201] like Figure 10A and 10B As shown, each The map may include at least two pixels representing at least two physical points of the brain. The pixel value of each pixel in the image can be based on the corresponding physical point The calculated value is determined. The magnetic resonance imaging device is guided to apply a multi-echo pulse sequence to the patient's brain, and at least two echoes are sequentially generated at different TEs. Each coil unit of the magnetic resonance imaging device detects at least two echo signals corresponding to the echoes. For each coil unit, at least two brain images corresponding to the at least two echoes are obtained by image reconstruction based on the corresponding echo signals. In Figure 1000A, the images of different coil units corresponding to the same echo can be combined into one echo image, for example, using the Sum of Square (SOS) algorithm or the Adaptive Coil Combination (ACC) algorithm. Based on the echo images corresponding to different echoes, a data fitting algorithm is used to determine Pixel values ​​for pixels in Figure 1000A. The pixel values ​​of the pixels in Figure 1000B are determined by executing the method disclosed in this specification (e.g., process 400).

[0202] like Figure 10A and 10B As shown, Graphs 1000A and 1000B have different signal-to-noise ratios. Compared with Figure 1000A,

[0203] Figure 1000B has fewer white spots and is smoother, showing a higher SNR. This shows that the method disclosed in this specification can be used to generate images with higher quality and higher signal-to-noise ratio. picture.

[0204] Figure 10C and 10D is an exemplary patient's brain according to some embodiments of this specification. Schematic diagrams of Figures 1000C and 1000D.

[0205] like Figure 10C and 10D As shown, each The map may include at least two pixels representing at least two physical points of the brain. The calculated value determines each The pixel value of each pixel in the image. The generation method of Figure 1000C is the same as Figure 1000A is generated in a similar manner. Figure 1000D is generated in the same way as Figure 1000B is generated in a similar manner. Figure 10C and 10D As shown, Figures 1000C and 1000D have different signal-to-noise ratios. Compared with Figure 1000C, The white spots in Figure 1000D are significantly fewer and smoother, showing a higher SNR. This shows that the method disclosed in this specification can be used to generate images with higher quality and higher signal-to-noise ratio. picture.

[0206] It should be noted that 9A to 10D The above examples shown in the foregoing are for illustrative purposes only and are not intended to limit the scope of this specification. For those skilled in the art, various changes and modifications can be made based on the description of this specification. However, these changes and modifications do not depart from the scope of this specification.

[0207] It will be apparent to those skilled in the art that various changes and modifications may be made to this specification without departing from the spirit and scope of this specification. In this manner, this specification is intended to include such modifications and variations as fall within the scope of the appended claims and their equivalents.

[0208] The basic concepts have been described above. It will be apparent to those skilled in the art after reading this application that the above disclosures are provided for illustrative purposes only and do not constitute limitations on this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and revisions to this specification. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0209] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that the mention of "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification does not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0210] Furthermore, it will be understood by those skilled in the art that various aspects of this specification may be illustrated and described in terms of a number of patentable categories or situations, including any new and useful process, machine, product, or combination of substances, or any new and useful improvement thereof. Accordingly, various aspects of this specification may be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of software and hardware, which may be generally referred to in this specification as "modules," "units," "components," "devices," or "systems." Furthermore, various aspects of this specification may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code.

[0211] A computer-readable signal medium may include a propagated data signal embodying computer program code, for example, in baseband or as part of a carrier wave. Such propagated signals may be in a variety of forms, including electromagnetic, optical, or any suitable combination. A computer-readable signal medium may be any computer-readable medium other than a computer-readable storage medium that can be coupled to an instruction execution system, device, or apparatus to communicate, propagate, or transfer a program for use. Program code on a computer-readable signal medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or any combination of the foregoing.

[0212] The computer program code for performing the various aspects of the operation of this specification can be written in any combination of one or more programming languages, including subject-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python or similar languages. These languages ​​include subject-oriented programming languages ​​(e.g., Java, Scala, Smallalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python or similar languages), traditional program programming languages ​​(e.g., C programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP), dynamic programming languages ​​(e.g., Python, Ruby and Groovy) or other programming languages. The program code can be run entirely on a user's computer, or run on a user's computer as an independent software package, or run partially on a user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., by using a network service provider's network) or in a cloud computing environment or provided as a service, such as software as a service (SaaS).

[0213] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some embodiments of the invention that are currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a pure software solution, for example, installation on an existing server or mobile device.

[0214] Similarly, it should be noted that, in order to simplify the presentation of this specification and thereby facilitate understanding of one or more of the inventive embodiments, the foregoing descriptions of the embodiments of this specification sometimes combine various features into a single embodiment, figure, or description thereof. However, this approach should not be interpreted as reflecting an intention that the claimed object material to be scanned requires more features than expressly recited in each claim. In practice, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0215] In some embodiments, numbers representing quantities or properties used to describe and claim some embodiments of this specification should be understood as being modified by the terms "approximately," "approximately," or "substantially" in certain circumstances. For example, unless otherwise specified, "approximately," "approximately," or "substantially" can represent a certain variation of the value being described (e.g., ±1%, ±5%, ±10%, or ±20%). Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can vary based on the desired characteristics of individual embodiments. In some embodiments, numerical parameters should take into account the specified number of significant digits and adopt a general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, such numerical values ​​are set as accurately as possible within the feasible range. In some embodiments, the classification conditions used for classification or determination are provided for illustration and are modified according to different circumstances. For example, the classification condition of "value greater than a threshold" can further include or exclude the condition of "probability value equal to a threshold."

Claims

1. A quantitative measurement system for magnetic resonance imaging, comprising at least one storage device comprising a set of instructions for quantitative measurements in magnetic resonance imaging; and at least one processor in communication with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to direct the system to: acquiring at least two signals of a subject, the at least two signals being generated using a magnetic resonance imaging device, each of the at least two signals corresponding to a set of values ​​in at least two signal dimensions, the at least two signal dimensions being signal dimensions for signal acquisition using the magnetic resonance imaging device; determining a dominant signal dimension associated with the signal representation of the subject among the at least two signal dimensions; Based on the at least two signals, a first value and a second value related to the signal representation of the subject are determined, wherein The first value represents a signal representation along a first direction of the main signal dimension, the second value represents a signal representation along a second direction of the main signal dimension, and the first direction is opposite to the second direction; determining a first initial value of a quantitative parameter of the subject based on the first value; determining a second initial value of a quantitative parameter of the subject based on the second value; as well as Based on the first initial value and the second initial value, a quantitative parameter value of the subject is determined.

2. The system according to claim 1, wherein If the main signal dimension is echo time, the first direction is the direction of time passage, and the second direction is the direction of time reversal.

3. The system according to claim 1, wherein: Determining a first initial value of the quantitative parameter of the subject based on the first value includes: Acquiring a first relationship between the signal representation and the quantitative parameter along the first direction of the primary signal dimension; and determining the first initial value of the quantitative parameter of the subject based on the first value represented by the signal and the first relationship; or Determining a second initial value of the quantitative parameter of the subject based on the second value includes: Acquiring a second relationship between the signal representation and the quantitative parameter along the second direction of the main signal dimension; and The second initial value of the quantitative parameter of the subject is determined based on the second value represented by the signal and the second relationship.

4. The system according to claim 1, wherein: The quantitative parameter of the subject is transverse relaxation time, and determining the value of the quantitative parameter of the subject based on the first initial value and the second initial value includes: determining a first inverse of the first initial value and a second inverse of the second initial value; and Based on the first reciprocal and the second reciprocal, a value of the transverse relaxation time is determined.

5. The system according to claim 1, wherein: The quantitative parameter of the subject is a transverse relaxation rate, and determining the value of the quantitative parameter of the subject based on the first initial value and the second initial value includes: An average value of the first initial value and the second initial value is designated as the value of the transverse relaxation rate.

6. The system according to claim 1, wherein: The operations further include: Based on the first value and the second value, a signal-to-noise parameter value is determined that indicates a signal level of the at least two signals relative to a noise level of the at least two signals.

7. The system according to claim 1, wherein: The subject is one physical point of at least two physical points of the object, and the operations further include: generating a parameter map related to the quantitative parameter of the object based on the quantitative parameter value of each physical point of the object; determining, for each reference signal representation of at least one reference signal representation, a first signal-to-noise parameter value indicative of a signal level of the reference signal representation relative to a noise level of the reference signal representation; and Based on the first signal-to-noise parameter value represented by each reference signal, a second signal-to-noise parameter value is determined that is indicative of a signal level of the parametric map relative to a noise level of the parametric map.

8. A method for quantitative measurement of magnetic resonance imaging, the method being implemented on a computing device having at least one storage device and at least one processor, the method comprising: acquiring at least two signals of a subject, the at least two signals being generated using a magnetic resonance imaging device, each of the at least two signals corresponding to a set of values ​​in at least two signal dimensions, the at least two signal dimensions being signal dimensions for signal acquisition using the magnetic resonance imaging device; determining a dominant signal dimension associated with the signal representation of the subject among the at least two signal dimensions; determining a first value and a second value related to the signal representation of the subject based on the at least two signals, wherein the first value represents the signal representation along a first direction of the primary signal dimension and the second value represents the signal representation along a second direction of the primary signal dimension, and wherein the first direction is opposite to the second direction; determining a first initial value of a quantitative parameter of the subject based on the first value; determining a second initial value of a quantitative parameter of the subject based on the second value; and Based on the first initial value and the second initial value, a quantitative parameter value of the subject is determined.

9. A non-transitory computer readable medium comprising at least one set of instructions for quantitative measurement of magnetic resonance imaging, wherein: When executed by one or more processors of a computing device, at least one set of instructions causes the computing device to perform a method comprising: acquiring at least two signals of a subject, the at least two signals being generated using a magnetic resonance imaging device, each of the at least two signals corresponding to a set of values ​​in at least two signal dimensions, the at least two signal dimensions being signal dimensions for signal acquisition using the magnetic resonance imaging device; determining a dominant signal dimension associated with the signal representation of the subject among the at least two signal dimensions; determining a first value and a second value related to the signal representation of the subject based on the at least two signals, wherein the first value represents the signal representation along a first direction of the primary signal dimension and the second value represents the signal representation along a second direction of the primary signal dimension, and wherein the first direction is opposite to the second direction; determining a first initial value of a quantitative parameter of the subject based on the first value; determining a second initial value of a quantitative parameter of the subject based on the second value; and Based on the first initial value and the second initial value, a quantitative parameter value of the subject is determined.

Images