CN113567901A - Spin lattice relaxation imaging method and system under magnetic resonance rotating coordinate system - Google Patents
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Abstract
The invention discloses a spin lattice relaxation imaging method and system under a magnetic resonance rotating coordinate system. The method comprises the following steps: for different spin locking times, acquiring spin lattice relaxation weighted image data of a target image under a rotating coordinate system in two times, and setting recovery time between two continuous acquisitions to obtain spin lattice relaxation imaging data under a two-dimensional multilayer rotating coordinate system; acquiring low-resolution image data for reconstructing K space central data and estimating a multi-channel coil sensitivity matrix; and reconstructing the acquired spin lattice relaxation imaging data under the two-dimensional multilayer rotating coordinate system based on the low-resolution image data, and fitting a final spin lattice relaxation parameter map under the rotating coordinate system. The invention can realize multi-layer and high-signal-to-noise ratio spin lattice relaxation quantitative imaging under a rapid magnetic resonance rotating coordinate system.
Description
Technical Field
The invention relates to the technical field of magnetic resonance parameter imaging, in particular to a spin lattice relaxation imaging method and system under a magnetic resonance rotating coordinate system.
Background
Magnetic resonance parametric imaging (e.g. longitudinal relaxation T)1And transverse relaxation T2Etc.) can characterize some inherent information of the tissue, has become an important, safe and effective diagnostic tool. Except for T2In addition to relaxation, a new parametric relaxation in the rotating magnetic resonance coordinate system has been developed in recent years, namely spin-lattice relaxation (T-relaxation) in the rotating frame1ρ) Are receiving increasing attention from researchers. T is1ρImaging is an imaging method that induces relaxation by exploring molecular interactions in slow motion and has been used in the examination of a variety of diseases. There is a study that T1ρThe imaging can reflect molecular activity information, is conventional T1Relaxation and T2Relaxation cannot be achieved. T is1ρThe imaging method has the advantages that the effective magnetic field in the direction of the transverse axis is locked, the disordered and spontaneous energy transfer of transverse relaxation among macromolecules is avoided, the magnetization or spin is enabled to be ordered, the low-frequency flow between hydrogen atoms and the macromolecules in free water can be evaluated by the technology, the density degree of cells is reflected, and the change of the metabolism and biochemical information of the water-containing tissues is detected on the molecular level. Therefore, screening and early warning information can be provided for early lesions and mild lesions before morphological change of tissues, and reliable basis is provided for early discovery and early treatment. In brain applications, studies have shown that T is now available1ρHas important application value in the research of the Alzheimer disease and the Parkinson disease of the progressive lesion before the brain tumor operation.
T1ρThe imaging is relaxed by forcing the transverse magnetization vector to remain in the transverse magnetization vector direction by a resonant and continuous spin-lock pulse, where the transverse magnetization vector is relaxed in a new way, the spin-locked magnetization vector being dependent on T1ρThe time constant is the spin lattice relaxation in a rotating coordinate system. Absence of spin-locking Times (TSL)Increased, different strength T1ρThe weighted signal is collected, and T is obtained by fitting the signal with a certain signal relaxation model1ρFigure (a). T is1ρQuantitative imaging sequences usually incorporate T prior to the conventional fast spin echo or gradient echo sequence1ρPreparation pulse implementation with different T acquisition by varying spin-lock time1ρA weighted image. Conventional two-dimensional T1ρQuantitative imaging technique at each T1ρOnly one layer of image can be acquired after the preparation pulse, the total acquisition time is the time for acquiring one image multiplied by the number of TSLs multiplied by the number of acquisition layers, and when the large-range coverage is realized, the total scanning time is too long due to more acquisition layers. For example, in the case of whole brain scan, about 20 layers of the image are required to be scanned, and the scanning time is more than 60 minutes, which severely restricts the clinical application of the image. The brain T has been published1ρIn quantitative research work, only 1 layer is usually collected. Three-dimensional T1ρQuantitative imaging faces similar problems, with scan times typically exceeding 30 minutes. Due to the existing T1ρThe quantitative imaging time is too long, and the resolution and the coverage range of the image are limited.
In order to shorten the scanning time, the prior art mainly develops around the following three directions: 1) reducing the number of TSLs in a manner that results in T of acquisition due to the reduction of TSLs1ρThe number of weighted images is also reduced and thus the accuracy of their quantification is also reduced. 2) The fast imaging sequence is used, which is limited by hardware and the scanning speed is not increased significantly. 3) The current commercial fast imaging technology is mainly parallel imaging technology (such as sensitivity encoding (SENSE), generalized auto-calibration partial parallel acquisition (GRAPPA), etc.), but the higher the acceleration multiple is, the lower the signal-to-noise ratio of the image obtained after imaging is, because of the limitation of parallel imaging array coils, the scanning speed by adopting the method is usually only 2-3 times.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned drawbacks of the prior art and providing a method and system for spin-lattice relaxation imaging in a magnetic resonance rotational coordinate system, which is fastMagnetic resonance T with wide coverage1ρThe new technical scheme of quantitative imaging can realize multi-level and high signal-to-noise ratio quick T1ρAnd (6) quantitative imaging.
According to a first aspect of the present invention, there is provided a method of spin lattice relaxation imaging in a magnetic resonance rotational coordinate system. The method comprises the following steps:
for different spin locking times, acquiring spin lattice relaxation weighted image data of a target image under a rotating coordinate system in two times, and setting recovery time between two continuous acquisitions to obtain spin lattice relaxation imaging data under a two-dimensional multilayer rotating coordinate system;
acquiring low-resolution image data for reconstructing K space central data and estimating a multi-channel coil sensitivity matrix;
and reconstructing the acquired spin lattice relaxation imaging data under the two-dimensional multilayer rotating coordinate system based on the low-resolution image data, and fitting a final spin lattice relaxation parameter map under the rotating coordinate system.
In one embodiment, spin lattice relaxation imaging data in a two-dimensional multilayer rotational coordinate system is obtained according to the following steps:
the last 90-degree pulse in the spin lattice relaxation preparation pulses in the rotating coordinate system at the time of the first acquisition is applied along the-x axis direction, and the longitudinal magnetization vector of the first acquisition is expressed as:
M1(TSL)＝M0+(Minite-TSL/T1ρ-M0)e-Trec/T1；
and applying the last 90-degree pulse in the spin lattice relaxation preparation pulse in the rotating coordinate system during the second acquisition along the direction of the x axis, wherein the longitudinal magnetization vector of the second acquisition is expressed as:
M2(TSL)＝M0+(-Minite-TSL/T1ρ-M0)e-Trec/T1；
subtracting the longitudinal magnetization vector acquired for the first time from the longitudinal magnetization vector acquired for the second time to obtain a spin lattice parameter relaxation model under a rotating coordinate system:
M(TSL)＝Ae-TSL/T1ρ
wherein M isinitIs the longitudinal magnetization vector, M, before the moment of applying the spin-lattice relaxation preparation pulse in the rotating coordinate system0Is the longitudinal magnetization vector of the equilibrium state, T1 is the time constant of the longitudinal relaxation, TSL denotes the spin-lock time, T1ρIs the spin-lattice relaxation time in a rotating coordinate system, M (TSL) ═ M1(TSL)-M2(TSL)，A＝2Minite-Trec/T1And Trec is the recovery time between the first and second acquisitions.
In one embodiment, in the process of acquiring the spin lattice relaxation weighted image data under the rotating coordinate system of the target image, the frequency encoding direction is fully acquired, in the phase encoding direction, the central part of the K space adopts a uniform density undersampling mode, the area outside the center of the K space adopts a variable density undersampling mode, and the sampling density is reduced along with the distance from the center of the K space.
In one embodiment, reconstructing the acquired spin lattice relaxation imaging data in the two-dimensional multi-slice rotational coordinate system comprises the sub-steps of:
reconstructing central part data of the K space by using the acquired low-resolution images;
estimating a sensitivity matrix of the multi-channel coil by using the reconstructed data of the central part of the K space;
and respectively reconstructing the spin lattice relaxation weighted images under the rotating coordinate system of each layer, wherein the solution model is expressed as:
min{X,L,S}‖S‖1s.t.C(X)＝L+S,E(X)＝d,Rank(L)＝1
wherein |1Is represented by1Norm, C (·) is an operator, which represents the pixel-level signal compensation of the image; is the image sequence to be reconstructed, L is the low rank part of the image represented in matrix form, S represents the residual of the image and the low rank part L, E is the multi-channel coil coding matrix, which is equal to the product of the under-sampled fourier operator and the multi-channel coil sensitivity matrix, rank (L) represents the rank of the matrix L, d represents the under-sampled K-space data.
In one embodiment, the pixel-level signal compensation of the image is represented by multiplying each pixel in the image by a compensation coefficient.
In one embodiment, the compensation factor is expressed as:
Coef＝exp(TSLk/T1ρ)，k＝1，2，…，T
where Coef denotes the compensation factor, TSLkIs the kth spin-lock time, T is the number of spin-lock times TSL.
According to a second aspect of the invention, there is provided a spin-lattice relaxation imaging system in a magnetic resonance rotational coordinate system. The system comprises:
an icon image acquisition unit: the system comprises a rotating coordinate system, a two-dimensional multi-layer rotating coordinate system and a data acquisition unit, wherein the rotating coordinate system is used for acquiring a target image of a target object in two times, and acquiring a spinning lattice relaxation weighted image data of the target object in the two-dimensional multi-layer rotating coordinate system;
a low resolution image acquisition unit: for acquiring low resolution image data for reconstructing K-space center data and estimating a multi-channel coil sensitivity matrix;
an image reconstruction unit: and reconstructing the spin lattice relaxation imaging data under the two-dimensional multilayer rotating coordinate system under the acquired rotating coordinate system based on the low-resolution image data, and fitting a final spin lattice relaxation parameter map under the rotating coordinate system.
Compared with the prior art, the invention has the advantages of aiming at the prior T1ρThe invention provides a two-dimensional T capable of realizing rapid multi-layer scanning, and overcomes the defects of long quantitative imaging time and limitation on image resolution and coverage1ρA quantitative imaging protocol. Meanwhile, in order to improve the image acquisition efficiency, reduce the scanning time, provide a two-dimensional undersampling mode with high acceleration multiple, and reconstruct high-quality T from highly undersampled data based on a low-rank sparse decomposition model with parallel imaging and signal compensation1ρThe parameter weighted image obtains more accurate T1ρParameter diagramFinally, the multi-level and high signal-to-noise ratio fast T is realized by combining quantitative calculation and variable density undersampling1ρAnd (6) quantitative imaging.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram of a method of spin lattice relaxation imaging in a magnetic resonance rotational coordinate system according to one embodiment of the present invention;
FIG. 2 is a two-dimensional multilayer T according to one embodiment of the present invention1ρA schematic of a quantitative imaging sequence;
FIG. 3 is a schematic diagram of undersampling according to one embodiment of the present invention;
in the figure, Acquisition 1-first Acquisition; acquisition 2-second Acquisition; recovery time-Recovery time; slice-layer.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The invention aims at the traditional two-dimensional magnetic resonance T1ρQuantitative imaging T1ρOnly one image can be acquired after preparing pulse, the scanning time is long, and a two-dimensional T capable of multi-layer scanning is provided1ρA quantitative imaging scheme and a two-dimensional high-acceleration multiple undersampling scheme, and a high-quality T is reconstructed from highly undersampled data based on a signal compensation low-rank sparse decomposition model1ρThe image is weighted parametrically, thereby obtaining more accurate T1ρA parameter map.
Specifically, referring to fig. 1, the method for spin lattice relaxation imaging in a magnetic resonance rotating coordinate system according to an embodiment of the present invention includes the following steps:
step S1000, for different spin locking times, the method is configured to acquire T of the target image twice1ρWeighting the image data to obtain T1ρA parametric relaxation model.
FIG. 2 is a two-dimensional multilayer T1ρSchematic diagram of quantitative imaging sequence, in which FIG. 2(a) is a two-dimensional multilayer T1ρQuantitative imaging sequence schematic, taking the example of acquiring two target images, FIG. 2(b) is the first acquired T1ρPreparation pulse schematic, FIG. 2(c) is T of second acquisition1ρReady pulse schematic. In the embodiment of fig. 2, the images are acquired in two passes, T at the time of the first acquisition1ρThe last 90 of the preparation pulses is applied along the-x axis, and T is acquired for the second time1ρThe last 90-degree pulse in the preparation pulses is applied along the x-axis direction, and a recovery time Trec is set (for example, 1ms, 5ms, 10ms, etc., as needed) after each acquisition, so that the magnetization vector is recovered to a steady state.
Specifically, the longitudinal magnetization vector acquired for the first time is represented as:
M1(TSL)＝M0+(Minite-TSL/T1ρ-M0)e-Trec/T1 (1)
the longitudinal magnetization vector of the second acquisition is expressed as:
M2(TSL)＝M0+(-Minite-TSL/T1ρ-M0)e-Trec/T1 (2)
wherein M isinitIs applying T1ρPreparing the longitudinal magnetization vector, M, before the pulse time0Is the longitudinal magnetization vector of the equilibrium state, T1Is the time constant of the longitudinal relaxation, TSL denotes the spin-lock time, T1ρIs the spin lattice relaxation time in a rotating coordinate system.
Subtracting the formula (2) from the formula (1) to obtain:
M(TSL)＝2Minite-Trec/T1e-TSL/T1ρ (3)
wherein M (TSL) ═ M1(TSL)-M2(TSL) where A is 2Minite-Trec/T1Since Trec is a fixed value, a is a constant, and equation (3) can be equivalently:
M(TSL)＝Ae-TSL/T1ρ (4)
as can be seen from equation (4), T is obtained by acquiring different spin locks (TSL)1ρWeighting the image, fitting to obtain quantitative T1ρThe parameter values.
In the embodiment of the invention, two-dimensional multilayer T is carried out1ρIn quantitative imaging, a recovery time is introduced, and two acquisitions are divided, the T of the two acquisitions1ρThe preparation pulses are different.
S2000, in the image acquisition process, a sampling mode of full sampling in the frequency coding direction and variable density undersampling in the phase coding direction is adopted to obtain a two-dimensional multilayer T1ρImaging data.
Optionally, in order to increase the imaging speed, the invention adopts a sampling mode of full sampling in the frequency encoding direction and undersampling in the phase encoding direction.
In particular, see K of FIG. 3xAnd KyDirection and KyAnd KtUndersampled representation of direction, where KxDirection is taken all over, and at Ky-KtPlane, with conventional basesThe compression sensing theory undersampling modes of sparse sampling are different, the central part of a K space in the embodiment of the invention adopts a uniform undersampling mode, the region outside the center of the K space adopts a variable density undersampling mode, and the sampling density is reduced according to the distance from the central part of the K space, for example, a higher sampling density is adopted when the distance is close, and a lower sampling density is adopted when the distance is close. FIG. 3(a) is KxAnd KyUndersampling of the directional matrix size 256 × 256, K in FIG. 3(b)yAnd KtUndersampling of the direction matrix size 256 × 5 is illustrative.
According to the theory of compressed sensing, as long as signals are sparse or compressed, the original signals can be accurately reconstructed from highly undersampled data by solving a minimization problem through an incoherent measurement and an optimization method. Thus, the two-dimensional multilayer T of step S1000 is utilized1ρThe imaging sequence and the undersampling mode of step S2000 acquire T of different TSL in two times1ρWeighting the image data (where the scan parameters of the first and second acquisitions are identical) ensures accurate parameter-weighted image and parameter values while improving scan efficiency.
In the embodiment of the invention, the scanning efficiency is improved and the quality of the reconstructed image can be ensured by a variable density undersampling mode.
And S3000, acquiring low-resolution image data for reconstructing K space central data and estimating a multi-channel coil sensitivity matrix.
In this step, a low-resolution data is collected for subsequent reconstruction of K-space central data and estimation of the multi-channel coil sensitivity matrix, wherein the estimation method of the multi-channel coil sensitivity matrix can adopt the scheme of the prior art and is not described herein again.
Step S4000, two-dimensional multilayer T1ρReconstructing the imaging data to obtain reconstructed T1ρThe image is parametrically weighted.
In this step, the existing technology can be combined to the two-dimensional multilayer T which is undersampled1ρThe imaging data is reconstructed.
E.g. in conjunction with prior artThe signal compensation-based low rank sparse reconstruction model (SCOPE) of the art, see the literature ("signal compensation for low-rank plus sparse decomposition", Phys Med Biol 2018; 63(18):185009, Zhu Y, Liu Y, Ying L, Peng X, etc.), for two-dimensional multi-layered T-layers under-harvested1ρThe reconstruction of the imaging data comprises the following steps:
in step S141, first, the VCC-GRAPPA method and the low-resolution image are used to reconstruct the data of the center portion of the K space.
Wherein the VCC-GRAPPA method can be referred to the existing literature ("Improving GRAPPA recovery using joint non-linear kernel mapped and phase-connected viral lipids", physical in Medicine and Biology, 2019, 64, 14NT01(10pp), DOI:10.1088/1361-
And step S142, estimating a sensitivity matrix of the multi-channel coil by using the reconstructed central part data of the K space.
This step can be referred to in the literature ("sensitivity encoding for fast MRI", Magn Reson Med 1999; 42(5): 952-.
Step S143, based on step S141 and step S142, of determining T for each layer1ρThe weighted images are respectively reconstructed, and the solving model is expressed as:
min{X,L,S}‖S‖1 s.t.C(X)＝L+S,E(X)＝d,Rank(L)＝1 (5)
wherein |1Is represented by1A norm; c (-) is an operator, which represents the pixel-level signal compensation of the image; is the sequence of images to be reconstructed and is represented as a matrix of size voxel number x TSL number (T); l is a low rank portion of the image represented in matrix form, S represents a residual of the image and the low rank portion L; e is a multi-channel coil encoding matrix, which is equal to the product of the under-sampled Fourier operator and the sensitivity matrix of the coil; rank (L) represents the rank of matrix L and d represents the undersampled K-space data.
T based on formula (4)1ρThe relaxation model, signal compensation, may specifically be expressed as multiplying each pixel in the image by a compensation factor, which may be given by:
Coef＝exp(TSLk/T1ρ)，k＝1，2，…，T (6)
where Coef denotes the compensation factor, TSLkIs the k-th spin-lock time, and T is the number of spin-lock Times (TSL).
In one embodiment, the solution process of equation (5) includes the following steps:
step S151, transforming the K space center data reconstructed by VCC-GRAPPA into an image domain through Fourier transformation, and converting the K space center data into T according to a formula (4)1ρFitting the image by a relaxation model, estimating the initial T1ρParameters and obtaining an initial value Coef of the compensation coefficient according to formula (6)0；
In step S152, a loop is set to i equal to 1,2 …, and in the ith iteration:
step S152-1 compensates the image according to the compensation coefficient, i.e. compensating
Wherein U represents the compensated image;
step S152-2, where the initialization S is 0, the number of outer loops is set to J, and in J- th iterations 1,2, …, the following steps are performed:
a) update the data
Wherein SVT (-) represents a singular value threshold operator defined as:
SVTλ(M)＝UΛλ(Σ)VH (7)
wherein M is U ∑ VHRepresenting Singular Value Decomposition (SVD), U, V being a matrix of left and right singular value vectors, VHRepresenting the conjugate transpose of V, ∑ being a diagonal matrix composed of singular values of M, Λλ(Σ) means that the maximum singular value in Σ is kept unchanged, and the others are all 0, in the embodiment of the present invention, only the maximum singular value of L is taken, so that the rank (L) of L becomes 1 after the singular value threshold operation is performed;
b) update Sj：
ST (-) is a soft threshold operator defined as:
where p is an element of the image matrix and v is a threshold.
c) Updating data fidelity items:
wherein E*Representing the inverse operation of E, namely performing inverse Fourier transform on the K space data of the multi-channel coil and then performing coil combination to obtain an image;
d) updating the image
Wherein C is-1Denotes dividing the image by the compensation coefficient Coef on a per pixel basisi；
e) Terminating the inner loop iteration
Step S152-3, obtaining X according to step S152-2iUpdating in combination with the parametric relaxation model of equation (4)
And updating the compensation coefficient
And step S152-4, when the algorithm reaches an iteration termination condition (for example, the iteration number is greater than the maximum iteration number or the reconstruction error between two adjacent iterations is less than a preset value), terminating the loop iteration to obtain a finally reconstructed parameter weighted image X.
Step S5000, according to the reconstructed T1ρParametric weighted image sum T1ρA parameter relaxation model, which performs nonlinear fitting on all pixels in the image to obtain the final T1ρA parameter map.
In the above embodiment, two-dimensional T is reconstructed1ρWhen weighting the image, firstly reconstructing the central part of the K space by using VCC-GRAPPARemoving convolution artifacts caused by undersampling of the K space center, and then iteratively reconstructing T of each layer based on a signal compensation low-rank sparse decomposition model1ρThe image is weighted. The low-rank sparse decomposition reconstruction process based on signal compensation comprises the following steps: based first on T1ρThe parametric relaxation model adopts a signal compensation method to enhance the low rank property of data in the TSL direction. The image sequence is then parametrically ordered into a spatial-parametric matrix, where each column of the matrix represents a magnetic resonance image acquired at a certain TSL instant, and the spatial-parametric matrix, which in practice represents an image, is then decomposed into a low-rank component (L) and a sparse component (S). And combining the (L + S) reconstruction model, performing singular value threshold operation on L, performing soft threshold operation on S to obtain iteratively updated L and S, and summing the updated L and S to obtain an updated space-parameter matrix (namely an image). In the iterative process of image reconstruction, each iteration is based on newly reconstructed T1ρWeighted image sum T1ρUpdating T of the parametric relaxation model1ρA parameter map, and updating the updated T1ρAnd (4) the parameter graph is used for signal compensation in the next iteration, and the iteration is repeated until the algorithm reaches the iteration termination condition, and the reconstruction is stopped. Finally, using T1ρThe parameter relaxation model fits the reconstructed parameter weighted image to obtain the final T1ρA parameter map.
In summary, compared with the existing two-dimensional T1ρCompared with the quantitative imaging technology, the invention can realize multilayer T1ρQuantitative imaging, and the designed variable density undersampling mode can greatly accelerate the data scanning speed and reduce T1ρThe method can accurately reconstruct a parameter weighted image from the highly undersampled data during image reconstruction, and further improves the signal-to-noise ratio of the image through quantitative calculation.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (10)
1. A method of spin lattice relaxation imaging in a magnetic resonance rotational coordinate system, comprising the steps of:
for different spin locking times, acquiring spin lattice relaxation weighted image data of a target image under a rotating coordinate system in two times, and setting recovery time between two continuous acquisitions to obtain spin lattice relaxation imaging data under a two-dimensional multilayer rotating coordinate system;
acquiring low-resolution image data for reconstructing K space central data and estimating a multi-channel coil sensitivity matrix;
and reconstructing the acquired spin lattice relaxation imaging data under the two-dimensional multilayer rotating coordinate system based on the low-resolution image data, and fitting a final spin lattice relaxation parameter map under the rotating coordinate system.
2. The method of spin lattice relaxation imaging in a magnetic resonance rotating coordinate system of claim 1, wherein the spin lattice relaxation imaging data in a two-dimensional multi-slice rotating coordinate system is obtained according to the following steps:
the last 90-degree pulse in the spin lattice relaxation preparation pulses in the rotating coordinate system at the time of the first acquisition is applied along the-x axis direction, and the longitudinal magnetization vector of the first acquisition is expressed as:
M1(TSL)＝M0+(Minite-TSL/T1ρ-M0)e-Trec/T1；
and applying the last 90-degree pulse in the spin lattice relaxation preparation pulse in the rotating coordinate system during the second acquisition along the direction of the x axis, wherein the longitudinal magnetization vector of the second acquisition is expressed as:
M2(TSL)＝M0+(-Minite-TSL/T1ρ-M0)e-Trec/T1；
subtracting the longitudinal magnetization vector acquired for the first time from the longitudinal magnetization vector acquired for the second time to obtain a spin lattice parameter relaxation model under a rotating coordinate system:
M(TSL)＝Ae-TSL/T1ρ
wherein M isinitIs the longitudinal magnetization vector, M, before the moment of applying the spin-lattice relaxation preparation pulse in the rotating coordinate system0Is the longitudinal magnetization vector of the equilibrium state, T1 is the time constant of the longitudinal relaxation, TSL denotes the spin-lock time, T1ρIs the spin-lattice relaxation time in a rotating coordinate system, M (TSL) ═ M1(TSL)-M2(TSL)，A＝2Minite-Trec/T1And Trec is the recovery time between the first and second acquisitions.
3. The method according to claim 1, wherein in acquiring the spin lattice relaxation weighted image data in the rotational coordinate system of the target image, the frequency encoding direction is fully acquired, the central portion of K-space is under-sampled with a uniform density in the phase encoding direction, the region other than the center of K-space is under-sampled with a variable density, and the sampling density decreases with increasing distance from the center of K-space.
4. The method of spin lattice relaxation imaging in a magnetic resonance rotational coordinate system of claim 1, wherein reconstructing the acquired spin lattice relaxation imaging data in a two-dimensional multi-slice rotational coordinate system comprises the sub-steps of:
reconstructing central part data of the K space by using the acquired low-resolution images;
estimating a sensitivity matrix of the multi-channel coil by using the reconstructed data of the central part of the K space;
and respectively reconstructing the spin lattice relaxation weighted images under the rotating coordinate system of each layer, wherein the solution model is expressed as:
min{X,L,S}‖S‖1 s.t.C(X)＝L+S,E(X)＝d,Rank(L)＝1
wherein |1Is represented by1Norm, C (·) is an operator, which represents the pixel-level signal compensation of the image; is the image sequence to be reconstructed, L is the low rank part of the image represented in matrix form, S represents the residual of the image and the low rank part L, E is the multi-channel coil coding matrix, which is equal to the product of the under-sampled fourier operator and the multi-channel coil sensitivity matrix, rank (L) represents the rank of the matrix L, d represents the under-sampled K-space data.
5. The method of spin lattice relaxation imaging in a magnetic resonance rotating coordinate system as claimed in claim 4, wherein said performing of signal compensation of image at pixel level is represented by multiplying each pixel in image by a compensation coefficient.
6. The method of spin lattice relaxation imaging in a magnetic resonance rotational coordinate system of claim 5, wherein the compensation coefficient is expressed as:
Coef＝exp(TSLk/T1ρ)，k＝1，2，…，T
where Coef denotes the compensation factor, TSLkIs the kth spin-lock time, T is the number of spin-lock times TSL.
7. A spin lattice relaxation imaging system in a magnetic resonance rotational coordinate system, comprising:
an icon image acquisition unit: the system comprises a rotating coordinate system, a two-dimensional multi-layer rotating coordinate system and a data acquisition unit, wherein the rotating coordinate system is used for acquiring a target image of a target object in two times, and acquiring a spinning lattice relaxation weighted image data of the target object in the two-dimensional multi-layer rotating coordinate system;
a low resolution image acquisition unit: for acquiring low resolution image data for reconstructing K-space center data and estimating a multi-channel coil sensitivity matrix;
an image reconstruction unit: and reconstructing the spin lattice relaxation imaging data under the two-dimensional multilayer rotating coordinate system under the acquired rotating coordinate system based on the low-resolution image data, and fitting a final spin lattice relaxation parameter map under the rotating coordinate system.
8. The system of claim 7, wherein the spin-lattice relaxation imaging data in the two-dimensional multi-slice rotational coordinate system is obtained according to the following steps:
the last 90-degree pulse in the spin lattice relaxation preparation pulses in the rotating coordinate system at the time of the first acquisition is applied along the-x axis direction, and the longitudinal magnetization vector of the first acquisition is expressed as:
M1(TSL)＝M0+(Minite-TSL/T1ρ-M0)e-Trec/T1；
and applying the last 90-degree pulse in the spin lattice relaxation preparation pulse in the rotating coordinate system during the second acquisition along the direction of the x axis, wherein the longitudinal magnetization vector of the second acquisition is expressed as:
M2(TSL)＝M0+(-Minite-TSL/T1ρ-M0)e-Trec/T1；
subtracting the longitudinal magnetization vector acquired for the first time from the longitudinal magnetization vector acquired for the second time to obtain a spin lattice parameter relaxation model under a rotating coordinate system:
M(TSL)＝Ae-TSL/T1ρ
wherein M isinitIs the longitudinal magnetization vector, M, before the moment of applying the spin-lattice relaxation preparation pulse0Is the longitudinal magnetization vector of the equilibrium state, T1 is the time constant of the longitudinal relaxation, TSL denotes the spin-lock time, T1ρIs the spin-lattice relaxation time in a rotating coordinate system, M (TSL) ═ M1(TSL)-M2(TSL)，A＝2Minite-Trec/T1And Trec is the recovery time between the first and second acquisitions.
9. The system according to claim 7, wherein in acquiring the spin lattice relaxation weighted image data in the rotational coordinate system of the target image, the frequency encoding direction is fully acquired, the central portion of K-space is under-sampled with a uniform density in the phase encoding direction, the region other than the center of K-space is under-sampled with a variable density, and the sampling density decreases with increasing distance from the center of K-space.
10. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, performs the operations of:
for different spin locking times, acquiring spin lattice relaxation weighted image data of a target image under a rotating coordinate system in two times, and setting recovery time between two continuous acquisitions to obtain spin lattice relaxation imaging data under a two-dimensional multilayer rotating coordinate system;
acquiring low-resolution image data for reconstructing K space central data and estimating a multi-channel coil sensitivity matrix;
and reconstructing the acquired spin lattice relaxation imaging data under the two-dimensional multilayer rotating coordinate system based on the low-resolution image data, and fitting a final spin lattice relaxation parameter map under the rotating coordinate system.
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