CN113567901B - Spin lattice relaxation imaging method and system under magnetic resonance rotation coordinate system - Google Patents
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Abstract
The invention discloses a spin lattice relaxation imaging method and a spin lattice relaxation imaging system under a magnetic resonance rotation coordinate system. The method comprises the following steps: for different spin locking time, the method is configured to acquire spin lattice relaxation weighted image data under a rotation coordinate system of a target image twice and set recovery time between two continuous acquisitions to obtain spin lattice relaxation imaging data under a two-dimensional multilayer rotation coordinate system; collecting low-resolution image data for reconstructing K-space center data and estimating a multi-channel coil sensitivity matrix; reconstructing the acquired spin lattice relaxation imaging data under the two-dimensional multilayer rotation coordinate system based on the low-resolution image data, and fitting a spin lattice relaxation parameter diagram under the final rotation coordinate system. The invention can realize spin lattice relaxation quantitative imaging under a multi-layer high signal-to-noise ratio rapid magnetic resonance rotation 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 rotation coordinate system.
Background
Magnetic resonance parametric imaging (such as longitudinal relaxation T 1 and transverse relaxation T 2) can characterize some of the intrinsic information of tissue and has become an important, safe and effective diagnostic tool. In addition to T 2 relaxation, spin lattice relaxation in a new parametric relaxation-magnetic resonance rotational coordinate system (spin-lattice relaxation in the rotating frame, T 1ρ) has received increasing attention from researchers in recent years. T 1ρ imaging, an imaging method that induces relaxation by exploring molecular interactions in slow motion, has been used in the examination of a variety of diseases. It is thought that T 1ρ imaging reflects molecular activity information that is not achieved by conventional T 1 relaxation and T 2 relaxation. T 1ρ imaging can evaluate low-frequency flow between hydrogen atoms in free water and macromolecules, reflect the density of cells and detect the change of metabolism and biochemical information of water-containing tissues on a molecular level by locking an effective magnetic field in the transverse axis direction, avoiding unordered and spontaneous energy transfer between macromolecules by transverse relaxation and enabling magnetization or spin to become ordered. Therefore, screening and early warning information can be provided for early lesions and mild lesions before morphological changes of tissues, and reliable basis is provided for early discovery and early treatment. In the aspect of brain application, the current research shows that the T 1ρ has important application value in the research of the advanced lesion Alzheimer disease and the parkinsonism before brain tumor operation.
T 1ρ imaging relaxes by forcing the transverse magnetization vector to remain in the direction of the transverse magnetization vector through a resonant and continuous spin-lock pulse, where the transverse magnetization vector relaxes in a new manner, and the spin-locked magnetization vector relaxes the spin lattice in the rotating coordinate system according to the T 1ρ time constant. Along with the continuous increase of spin-locking Time (TSL), T 1ρ weighted signals with different intensities are collected, and a certain signal relaxation model is used for fitting the signals, so that a T 1ρ diagram can be obtained. The T 1ρ quantitative imaging sequence is typically implemented by adding T 1ρ preparation pulses before a conventional fast spin echo or gradient echo sequence, by varying the spin lock time to acquire images with different T 1ρ weights. The traditional two-dimensional T 1ρ quantitative imaging technology can only acquire one layer of image after each T 1ρ preparation pulse, the total acquisition time is the time for acquiring one image multiplied by the number of TSL multiplied by the number of acquisition layers, and when the two-dimensional T 1ρ quantitative imaging technology is used for covering a large range, the total scanning time is overlong due to the fact that the number of acquisition layers is large. For example, when scanning the whole brain, about 20 layers need to be scanned, and the scanning time exceeds 60 minutes, which severely restricts the clinical application of the whole brain. In the studies of the quantification of brain T 1ρ that have been published, only 1 layer is usually collected. Three-dimensional T 1ρ quantitative imaging faces similar problems, with scan times typically exceeding 30 minutes. The resolution and coverage of the image are limited because the existing T 1ρ quantitative imaging time is too long.
In order to shorten the scanning time, the prior art has been developed mainly around the following three directions: 1) The number of TSLs is reduced, and in this way, the number of images weighted by T 1ρ acquired due to the reduction of TSLs is also reduced, and thus the accuracy of quantification is also reduced. 2) By adopting a rapid imaging sequence, the scanning speed is not improved significantly due to the limitation of hardware. 3) The current commercial rapid imaging technology is mainly parallel imaging technology (such as sensitivity encoding (SENSE), generalized automatic calibration partial parallel acquisition (GRAPPA) and the like), but the higher the acceleration multiple, the lower the signal-to-noise ratio of an image obtained after imaging is due to the limitation of parallel imaging array coils, so the scanning speed of the method can only reach 2-3 times.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a spin lattice relaxation imaging method and a spin lattice relaxation imaging system under a magnetic resonance rotation coordinate system, is a novel technical scheme of rapid large-range coverage magnetic resonance T 1ρ quantitative imaging, and can realize rapid T 1ρ quantitative imaging with multiple layers and high signal to noise ratio.
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 time, the method is configured to acquire spin lattice relaxation weighted image data under a rotation coordinate system of a target image twice and set recovery time between two continuous acquisitions to obtain spin lattice relaxation imaging data under a two-dimensional multilayer rotation coordinate system;
collecting low-resolution image data for reconstructing K-space center data and estimating a multi-channel coil sensitivity matrix;
reconstructing the acquired spin lattice relaxation imaging data under the two-dimensional multilayer rotation coordinate system based on the low-resolution image data, and fitting a spin lattice relaxation parameter diagram under the final rotation coordinate system.
In one embodiment, spin lattice relaxation imaging data in a two-dimensional multi-layer rotational coordinate system is obtained according to the following steps:
The last 90-degree pulse of the spin lattice relaxation preparation pulse 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；
The last 90-degree pulse of the spin lattice relaxation preparation pulse in the rotating coordinate system at the time of the second acquisition is applied along the x-axis direction, and 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 rotation coordinate system:
M(TSL)＝Ae-TSL/T1ρ
Where M init is a longitudinal magnetization vector before a spin lattice relaxation preparation pulse in a rotation coordinate system is applied, M 0 is a longitudinal magnetization vector in an equilibrium state, T1 is a time constant of longitudinal relaxation, TSL represents a spin lock time, T 1ρ is a spin lattice relaxation time in a rotation coordinate system, and M (TSL) =m 1(TSL)-M2(TSL),A＝2Minite-Trec/T1, trec is a recovery time between the first acquisition and the second acquisition.
In one embodiment, during the process of acquiring spin lattice relaxation weighted image data of a target image under a rotating coordinate system, the frequency encoding direction is fully acquired, in the phase encoding direction, a central part of the K space adopts a uniform density undersampling mode, and a region beyond the center of the K space adopts a variable density undersampling mode, and the sampling density decreases with increasing distance from the center of the K space.
In one embodiment, reconstructing the acquired spin lattice relaxation imaging data in the two-dimensional multi-layer rotational coordinate system comprises the sub-steps of:
reconstructing the data of the central part of the K space by using the acquired low-resolution image;
estimating a sensitivity matrix of the multichannel coil by using the reconstructed K space center part data;
Reconstructing spin lattice relaxation weighted images of each layer under a rotation coordinate system, wherein a solving model is expressed as follows:
min{X,L,S}‖S‖1s.t.C(X)＝L+S,E(X)＝d,Rank(L)＝1
Wherein II 1 is the norm denoted by l 1, C (-) is an operator representing pixel-level signal compensation of the image; is an image sequence to be reconstructed, L is a low Rank part of an image represented in a matrix form, S represents a residual of the image and the low Rank part L, E is a multi-channel coil coding matrix equal to a product of an undersampled fourier operator and a multi-channel coil sensitivity matrix, rank (L) represents a Rank of the matrix L, and d represents undersampled 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 coefficient is expressed as:
Coef＝exp(TSLk/T1ρ)，k＝1，2，…，T
Where Coef represents the compensation coefficient, TSL k is the kth spin-lock time, and T is the number of spin-lock times TSL.
According to a second aspect of the present invention there is provided a spin lattice relaxation imaging system in a magnetic resonance rotational coordinate system. The system comprises:
icon image acquisition unit: the method comprises the steps of for different spin locking time, configuring spin lattice relaxation weighted image data under a rotation coordinate system of a target image acquired twice, setting recovery time between two continuous acquisitions, and obtaining spin lattice relaxation imaging data under a two-dimensional multilayer rotation coordinate system;
a low resolution image acquisition unit: the method comprises the steps of acquiring low-resolution image data for reconstructing K space center data and estimating a multichannel coil sensitivity matrix;
an image reconstruction unit: and reconstructing the acquired spin lattice relaxation imaging data under the two-dimensional multilayer rotation coordinate system under the rotation coordinate system based on the low-resolution image data, and fitting a spin lattice relaxation parameter diagram under the final rotation coordinate system.
Compared with the prior art, the invention has the advantages that aiming at the defects that the prior quantitative imaging time of T 1ρ is overlong and the resolution and coverage range of an image are limited, the invention provides a two-dimensional quantitative imaging scheme of T 1ρ which can be rapidly scanned in multiple layers. Meanwhile, in order to improve the image acquisition efficiency and reduce the scanning time, a two-dimensional undersampling mode with high acceleration multiple is provided, a high-quality T 1ρ parameter weighted image is reconstructed from undersampled data with high undersampling based on a low-rank sparse decomposition model with parallel imaging and signal compensation, a more accurate T 1ρ parameter map is obtained, and multi-layer and high-signal-to-noise-ratio rapid T 1ρ quantitative imaging is finally realized by combining quantitative calculation and variable-density undersampling.
Other features of the present invention and its advantages will become apparent from the following detailed description of exemplary embodiments of the invention, 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 chart of a spin lattice relaxation imaging method in a magnetic resonance rotational coordinate system according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of a two-dimensional multi-layer T 1ρ quantitative imaging sequence according to one embodiment of the invention;
FIG. 3 is a schematic diagram of undersampling according to one embodiment of the invention;
In the drawing, 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, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one 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 specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Aiming at the problems that only one image can be acquired after pulse preparation of the traditional two-dimensional magnetic resonance T 1ρ quantitative imaging T 1ρ and the scanning time is long, the invention provides a two-dimensional T 1ρ quantitative imaging scheme and a two-dimensional high acceleration multiple undersampling scheme which can carry out multi-layer scanning, and reconstructs a high-quality T 1ρ parameter weighted image from undersampled data which are highly undersampled based on a low-rank and sparse decomposition model of signal compensation, thereby obtaining a more accurate T 1ρ parameter map.
Specifically, referring to fig. 1, the spin lattice relaxation imaging method under a magnetic resonance rotation coordinate system according to an embodiment of the present invention includes the following steps:
Step S1000, for different spin locking times, is configured to acquire T 1ρ weighted image data of the target image in two times, and obtain a T 1ρ parameter relaxation model.
Fig. 2 is a schematic diagram of a two-dimensional multi-layer T 1ρ quantitative imaging sequence, where fig. 2 (a) is a schematic diagram of a two-dimensional multi-layer T 1ρ quantitative imaging sequence, taking two target images as an example, fig. 2 (b) is a schematic diagram of a first acquired T 1ρ preparation pulse, and fig. 2 (c) is a schematic diagram of a second acquired T 1ρ preparation pulse. In the embodiment of fig. 2, the image is acquired in two times, with the last 90 ° pulse of the T 1ρ preparation pulses applied in the-x-axis direction at the first acquisition and the last 90 ° pulse of the T 1ρ preparation pulses applied in the x-axis direction at the second acquisition, with a recovery time Trec (which may be set to 1ms, 5ms, 10ms, etc. as needed) after each acquisition, so that the magnetization vector recovers to steady state.
Specifically, the longitudinal magnetization vector of the first acquisition is expressed 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)
Where M init is the longitudinal magnetization vector before the application of the preparation pulse of T 1ρ, M 0 is the longitudinal magnetization vector in equilibrium, T 1 is the time constant for longitudinal relaxation, TSL is the spin lock time, and T 1ρ is the spin lattice relaxation time in the rotating coordinate system.
Subtracting the formula (2) from the formula (1) to obtain:
M(TSL)＝2Minite-Trec/T1e-TSL/T1ρ (3)
Where M (TSL) =m 1(TSL)-M2 (TSL), let a=2m inite-Trec/T1, since Trec is a fixed value, a is a constant, equation (3) can be equivalently:
M(TSL)＝Ae-TSL/T1ρ (4)
As can be seen from equation (4), the quantitative T 1ρ parameter values can be obtained by acquiring T 1ρ weighted images of different spin locks (TSLs) and fitting.
In the embodiment of the invention, a recovery time is introduced when two-dimensional multi-layer T 1ρ quantitative imaging is carried out, and the two acquisitions are divided into two preparation pulses of T 1ρ, wherein the two acquisitions are different.
And step S2000, in the image acquisition process, acquiring two-dimensional multilayer T 1ρ imaging data by adopting a sampling mode of full acquisition in the frequency coding direction and undersampling in the phase coding direction in a variable density mode.
Optionally, in order to accelerate the imaging speed, the invention adopts a sampling mode of full sampling in the frequency coding direction and undersampling in the phase coding direction in the image acquisition.
Specifically, referring to the undersampling schematic of the directions K x and K y and the directions K y and K t in fig. 3, the direction K x is fully sampled, and in the plane K y-Kt, unlike the traditional compressive sensing theory undersampling method based on sparse sampling, in the embodiment of the present invention, the central portion of the K space adopts a uniform undersampling method, and the area beyond the center of the K space adopts a variable density undersampling method, and the sampling density decreases according to the distance from the center of the K space, for example, a higher sampling density is adopted when the distance is near, and a lower sampling density is adopted when the distance is near. Fig. 3 (a) is an undersampling pattern of 256×256 in the direction of K x and K y, and fig. 3 (b) is an undersampling pattern of 256×5 in the direction of K y and K t.
According to the theory of compressed sensing, as long as the signals are sparse or compressed, through an incoherent measurement, the original signals can be accurately reconstructed from highly undersampled data by solving a minimization problem through an optimization method. Therefore, by using the two-dimensional multi-layer T 1ρ imaging sequence of step S1000 and the undersampling method of step S2000, T 1ρ weighted image data of different TSLs are acquired twice (wherein the scan parameters of the first acquisition and the second acquisition are identical), while improving the scan efficiency, accurate parameter weighted images and parameter values are ensured.
In the embodiment of the invention, the quality of the reconstructed image can be ensured while the scanning efficiency is improved by a variable density undersampling mode.
Step S3000, collecting low-resolution image data for reconstructing K space center data and estimating a multichannel coil sensitivity matrix.
In this step, a low-resolution data is collected for subsequent reconstruction of K-space center data and estimation of a multi-channel coil sensitivity matrix, wherein the method for estimating a multi-channel coil sensitivity matrix may employ a scheme of the prior art, which is not described herein.
And S4000, reconstructing the two-dimensional multi-layer T 1ρ imaging data to obtain a reconstructed T 1ρ parameter weighted image.
In this step, undersampled two-dimensional multi-layer T 1ρ imaging data may be reconstructed in combination with the prior art.
For example, in connection with the low rank plus sparse reconstruction model (SCOPE) based on signal compensation of the prior art, see literature ("signal compensation for low-rank plus sparse decomposition",Phys Med Biol 2018;63(18):185009,Zhu Y,Liu Y,Ying L,Peng X, et al), reconstructing undersampled two-dimensional multi-layer T 1ρ imaging data includes the steps of:
step S141, firstly, reconstructing the data of the central part of the K space by using the VCC-GRAPPA method and the low-resolution image.
Wherein the VCC-GRAPPA process is described in the prior art ("Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils",Physic in Medicine and Biology,2019,64,14NT01(10pp),DOI:10.1088/1361-6560/ab274d)
Step S142, estimating a sensitivity matrix of the multi-channel coil by using the reconstructed K space center part data.
This step can be referred to in the literature ("SENSITIVITY ENCODING FOR FAST MRI", magn Reson Med 1999;42 (5): 952-962,Pruessmann KP,Weiger M, etc.).
Step S143, based on step S141 and step S142, reconstructing T 1ρ weighted images of each layer, and solving a model to represent:
min{X,L,S}‖S‖1 s.t.C(X)＝L+S,E(X)＝d,Rank(L)＝1 (5)
Wherein II 1 is the norm representing l 1; c (·) is an operator representing pixel-level signal compensation of the image; is a sequence of images to be reconstructed and is represented as a matrix of size number of voxels x TSL number (T); l is a low rank part of the image represented in a matrix form, S represents a residual of the image and the low rank part L; e is a multi-channel coil encoding matrix equal to the product of the undersampled Fourier operator and the sensitivity matrix of the coil; rank (L) represents the Rank of matrix L, and d represents undersampled K-space data.
Based on the T 1ρ relaxation model of equation (4), signal compensation can be expressed specifically as multiplying each pixel in the image by a compensation coefficient, which can be obtained by:
Coef＝exp(TSLk/T1ρ)，k＝1，2，…，T (6)
Where Coef represents the compensation coefficient, TSL k is the kth spin-lock time, and T is the number of spin-lock Times (TSLs).
In one embodiment, the solution process of equation (5) includes the steps of:
Step S151, performing Fourier transformation on the K space center data reconstructed by VCC-GRAPPA, converting the K space center data into an image domain, fitting an image according to a T 1ρ relaxation model in a formula (4), estimating an initial T 1ρ parameter, and obtaining an initial value Coef 0 of a compensation coefficient according to a formula (6);
step S152 sets the loop to i=1, 2 …, and in the i-th iteration, performs:
step S152-1 compensates the image according to the compensation coefficient, i.e Wherein U represents the compensated image;
Step S152-2, initializing s=0, setting the number of outer loops to J, and executing, in the j=1, 2, …, J iterations:
a) Updating Where SVT (-) represents a singular value threshold operator, defined as:
SVTλ(M)＝UΛλ(Σ)VH (7)
Wherein m=uΣv H represents Singular Value Decomposition (SVD), U, V is a matrix composed of left and right singular value vectors, V H represents a conjugate transpose of V, Σ is a diagonal matrix composed of singular values of M, Λ λ (Σ) represents that the largest singular value in Σ is kept unchanged, and the others are all 0, in the embodiment of the present invention, only the largest singular value of L is taken, so that Rank (L) =1 of L after singular value threshold operation is performed;
b) Update S j: ST (·) is a soft threshold operator defined as:
Where p is an element of the image matrix and v is a threshold.
C) Update data fidelity item: Wherein E * represents the inverse operation of E, namely, the inverse Fourier transform is performed on the K space data of the multi-channel coil, and then the coil combination is performed to obtain an image;
d) Updating images Wherein C -1 (·) represents dividing the image by the compensation coefficient Coef i on a per pixel basis;
e) Terminating internal loop iteration
Step S152-3, according to X i obtained in step S152-2, updating in combination with the parameter relaxation model in formula (4)And updating the compensation coefficient
In step S152-4, when the algorithm reaches the iteration termination condition (for example, when the iteration number is greater than the maximum iteration number or the reconstruction error between two adjacent iterations is smaller than the preset value), the loop iteration is terminated, and the finally reconstructed parameter weighted image X is obtained.
And S5000, performing nonlinear fitting on all pixels in the image according to the reconstructed T 1ρ parameter weighted image and the T 1ρ parameter relaxation model, and fitting to obtain a final T 1ρ parameter map.
In the above embodiment, when reconstructing a two-dimensional T 1ρ weighted image, firstly, reconstructing a K space center portion by using VCC-GRAPPA, removing a roll-over artifact caused by undersampling of the K space center, and then iteratively reconstructing a T 1ρ weighted image of each layer based on a low-rank sparse decomposition model of signal compensation. The low-rank plus sparse decomposition reconstruction process based on signal compensation is as follows: firstly, a signal compensation method is adopted to enhance the low rank property of data in the TSL direction based on a T 1ρ parameter relaxation model. The image sequence is then arranged in a parametric direction into a space-parameter matrix, wherein each column of the matrix represents a magnetic resonance image acquired at a certain TSL instant, and the space-parameter matrix (this matrix in fact represents the image) is then decomposed into a low-rank component (L) and a sparse component (S). Next, combining the (l+s) reconstruction model, performing singular value threshold operation on L, performing soft threshold operation on S, obtaining iteratively updated L and S, and summing the updated L and S to obtain an updated space-parameter matrix (i.e., an image). In the iterative process of image reconstruction, each iteration updates a T 1ρ parameter map according to the newly reconstructed T 1ρ weighted image and the T 1ρ parameter relaxation model, and uses the updated T 1ρ parameter map for signal compensation in the next iteration, and the iteration is repeated until the algorithm reaches an iteration termination condition, and the reconstruction is stopped. And finally, fitting the reconstructed parameter weighted image by using a T 1ρ parameter relaxation model to obtain a final T 1ρ parameter map.
In summary, compared with the existing two-dimensional T 1ρ quantitative imaging technology, the invention can realize multi-layer T 1ρ quantitative imaging, and the designed variable density undersampling mode can greatly accelerate the data scanning speed, reduce the T 1ρ quantitative imaging time, and can accurately reconstruct a parameter weighted image from highly undersampled data during image reconstruction, thereby further improving 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 thereon for causing a processor to implement aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage 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: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through 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 over 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 transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface 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.
Computer program instructions for carrying out operations of the present invention may be assembly 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 be executed 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected 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 electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.
Various 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 having the instructions stored therein includes 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 flowcharts 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, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or 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 various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements 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 (8)
1. A spin lattice relaxation imaging method in a magnetic resonance rotational coordinate system, comprising the steps of:
For different spin locking time, the method is configured to acquire spin lattice relaxation weighted image data under a rotation coordinate system of a target image twice and set recovery time between two continuous acquisitions to obtain spin lattice relaxation imaging data under a two-dimensional multilayer rotation coordinate system;
collecting low-resolution image data for reconstructing K-space center data and estimating a multi-channel coil sensitivity matrix;
Reconstructing the collected spin lattice relaxation imaging data under the two-dimensional multilayer rotation coordinate system based on the low-resolution image data, and fitting a spin lattice relaxation parameter diagram under a final rotation coordinate system;
wherein spin lattice relaxation imaging data in the two-dimensional multi-layer rotational coordinate system is obtained according to the steps of:
The last 90-degree pulse of the spin lattice relaxation preparation pulse 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；
the last 90-degree pulse of the spin lattice relaxation preparation pulse in the rotating coordinate system at the time of the second acquisition is applied along the x-axis direction, and 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 rotation coordinate system:
M(TSL)＝Ae-TSL/T1ρ
Where M init is a longitudinal magnetization vector before a spin lattice relaxation preparation pulse in a rotation coordinate system is applied, M 0 is a longitudinal magnetization vector in an equilibrium state, T1 is a time constant of longitudinal relaxation, TSL represents a spin lock time, T 1ρ is a spin lattice relaxation time in a rotation coordinate system, and M (TSL) =m 1(TSL)-M2(TSL),A＝2Minite-Trec/T1, trec is a recovery time between the first acquisition and the second acquisition.
2. The spin lattice relaxation imaging method of claim 1, wherein in the process of acquiring spin lattice relaxation weighted image data of a rotation coordinate system of a target image, a frequency encoding direction is fully acquired, a central portion of a K space adopts a uniform density undersampling mode in a phase encoding direction, a region outside a K space center adopts a variable density undersampling mode, and a sampling density decreases with an increase in distance from the K space center.
3. 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-layer rotational coordinate system comprises the sub-steps of:
reconstructing the data of the central part of the K space by using the acquired low-resolution image;
estimating a sensitivity matrix of the multichannel coil by using the reconstructed K space center part data;
Reconstructing spin lattice relaxation weighted images of each layer under a rotation coordinate system, wherein a solving model is expressed as follows:
min{x,L,S}||S||1 s.t.C(X)＝L+S，E(X)＝d，Rank(L)＝1
Wherein, |·| 1 is the norm representing l 1, C (·) is an operator representing pixel-level signal compensation of the image; x 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 multichannel coil coding matrix, which is equal to the product of the undersampled Fourier operator and the multichannel coil sensitivity matrix, rank (L) represents the Rank of matrix L, and d represents the undersampled K-space data.
4. A method of spin lattice relaxation imaging in the magnetic resonance rotational coordinate system as claimed in claim 3, wherein said pixel level signal compensation of the image is represented by multiplying each pixel in the image by a compensation coefficient.
5. The method of spin lattice relaxation imaging in a magnetic resonance rotational coordinate system of claim 4, wherein the compensation factor is represented as:
Coef＝exp(TSLk/T1ρ)，k＝1，2，…，T
Where Coef represents the compensation coefficient, TSL k is the kth spin-lock time, and T is the number of spin-lock times TSL.
6. A spin lattice relaxation imaging system in a magnetic resonance rotational coordinate system, comprising:
icon image acquisition unit: the method comprises the steps of for different spin locking time, configuring spin lattice relaxation weighted image data under a rotation coordinate system of a target image acquired twice, setting recovery time between two continuous acquisitions, and obtaining spin lattice relaxation imaging data under a two-dimensional multilayer rotation coordinate system;
a low resolution image acquisition unit: the method comprises the steps of acquiring low-resolution image data for reconstructing K space center data and estimating a multichannel coil sensitivity matrix;
an image reconstruction unit: the method comprises the steps of reconstructing spin lattice relaxation imaging data under a two-dimensional multilayer rotation coordinate system under the collected rotation coordinate system based on the low-resolution image data, and fitting a spin lattice relaxation parameter diagram under a final rotation coordinate system;
wherein spin lattice relaxation imaging data in the two-dimensional multi-layer rotational coordinate system is obtained according to the steps of:
The last 90-degree pulse of the spin lattice relaxation preparation pulse 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；
the last 90-degree pulse of the spin lattice relaxation preparation pulse in the rotating coordinate system at the time of the second acquisition is applied along the x-axis direction, and 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 rotation coordinate system:
M(TSL)＝Ae-TSL/T1ρ
Where M init is a longitudinal magnetization vector before the application of the spin lattice relaxation preparation pulse, M 0 is a longitudinal magnetization vector in an equilibrium state, T1 is a time constant of longitudinal relaxation, TSL represents a spin lock time, T 1ρ is a spin lattice relaxation time in a rotation coordinate system, M (TSL) =m1 (TSL) -M2 (TSL), a=2m inite-Trec/T1, trec is a recovery time between the first acquisition and the second acquisition.
7. The system of claim 6, wherein the frequency encoding direction is fully acquired during acquisition of the spin lattice relaxation weighted image data in the rotational coordinate system of the target image, the central portion of the K-space is in a uniform density undersampling mode and the region outside the K-space is in a variable density undersampling mode in the phase encoding direction, and the sampling density decreases with increasing distance from the K-space center.
8. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor performs the operations of:
For different spin locking time, the method is configured to acquire spin lattice relaxation weighted image data under a rotation coordinate system of a target image twice and set recovery time between two continuous acquisitions to obtain spin lattice relaxation imaging data under a two-dimensional multilayer rotation coordinate system;
collecting low-resolution image data for reconstructing K-space center data and estimating a multi-channel coil sensitivity matrix;
Reconstructing the collected spin lattice relaxation imaging data under the two-dimensional multilayer rotation coordinate system based on the low-resolution image data, and fitting a spin lattice relaxation parameter diagram under a final rotation coordinate system;
wherein spin lattice relaxation imaging data in the two-dimensional multi-layer rotational coordinate system is obtained according to the steps of:
The last 90-degree pulse of the spin lattice relaxation preparation pulse 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；
the last 90-degree pulse of the spin lattice relaxation preparation pulse in the rotating coordinate system at the time of the second acquisition is applied along the x-axis direction, and 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 rotation coordinate system:
M(TSL)＝Ae-TSL/T1ρ
Where M init is a longitudinal magnetization vector before the application of the spin lattice relaxation preparation pulse, M 0 is a longitudinal magnetization vector in an equilibrium state, T1 is a time constant of longitudinal relaxation, TSL represents a spin lock time, T 1ρ is a spin lattice relaxation time in a rotation coordinate system, and M (TSL) =m 1(TSL)-M2(TSL),A＝2Minite-Trec/T1, trec is a recovery time between the first acquisition and the second acquisition.
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