WO2021217509A1 - Spin-lattice relaxation imaging method and system in magnetic resonance rotating coordinate system - Google Patents

Abstract

Disclosed are a spin-lattice relaxation imaging method and system in a magnetic resonance rotating coordinate system. The method comprises: with regard to different spin-locking times, configuring same as spin-lattice relaxation weighted image data in a rotating coordinate system that acquires a target image in two separate instances, and configuring a recovery time between two consecutive acquisitions, so as to obtain spin-lattice relaxation imaging data in a two-dimensional multilayer rotating coordinate system; acquiring low-resolution image data for reconstructing K-space center data and estimating a multi-channel coil sensitivity matrix; and on the basis of the low-resolution image data, reconstructing the acquired spin-lattice relaxation imaging data in the two-dimensional multilayer rotating coordinate system, and fitting a final spin-lattice relaxation parameter diagram in the rotating coordinate system. The present invention can achieve spin-lattice relaxation quantitative imaging in a rapid magnetic resonance rotating coordinate system having multiple layers and a high signal-to-noise ratio.

Description

Spin lattice relaxation imaging method and system under magnetic resonance rotating coordinate system Technical field

The present invention relates to the technical field of magnetic resonance parametric imaging, and more specifically, to a spin-lattice relaxation imaging method and system under a magnetic resonance rotating coordinate system.

Background technique

Magnetic resonance parametric imaging (such as longitudinal relaxation T ₁ and transverse relaxation T _2, etc.) can characterize some inherent information of tissues, and has become an important, safe and effective diagnostic tool. In addition to T ₂ relaxation, in recent years, a new parameter relaxation-spin-lattice relaxation in the rotating frame (T _1ρ ) in the rotating frame of magnetic resonance has received more and more attention from researchers. extensive attention. T _1ρ imaging is an imaging method that explores the interaction of molecules in slow motion to cause relaxation, and has been used in the examination of many diseases. Some studies believe that T _1ρ imaging can reflect molecular activity information, which cannot be achieved by _{traditional T 1} relaxation and T _{2 relaxation.} T _1ρ imaging locks the effective magnetic field in the horizontal axis direction to avoid disordered and spontaneous energy transfer between macromolecules by transverse relaxation, so that the magnetization or spin becomes orderly. This technology can evaluate hydrogen atoms and large molecules in free water. The low-frequency flow between molecules reflects the density of cells and detects changes in the metabolism and biochemical information of water-containing tissues at the molecular level. Therefore, it can provide screening and early warning information for early lesions and mild lesions before the morphological changes of the tissues, and provide a reliable basis for early detection and early treatment. In terms of brain applications, current studies have shown that T _1ρ has important application value in the preoperative grading of brain tumors, the research of progressive disease, Alzheimer's disease and Parkinson's disease.

T _1ρ imaging uses a resonant and continuous spin-locked pulse to force the transverse magnetization vector to remain in the direction of the transverse magnetization vector for relaxation. At this time, the transverse magnetization vector relaxes in a new way. The spin-locked magnetization vector is based on T _{The 1ρ} time constant relaxes the spin lattice in the rotating coordinate system. With the continuous increase of spin-locking times (TSL), T _1ρ weighted signals of different intensities are collected, and a certain signal relaxation model is used to fit the signals to obtain the T _1ρ diagram. The T _{1ρ quantitative imaging sequence is usually realized by adding a T 1ρ} preparation pulse before the conventional fast spin echo or gradient echo sequence, and acquires images _{with different T 1ρ} weights by changing the spin lock time. The traditional two-dimensional T _1ρ quantitative imaging technology _{can only acquire one layer of images after each T 1ρ} preparation pulse. The overall acquisition time is the time to acquire an image × the number of TSL × the number of acquisition layers. There are many layers and the overall scanning time is too long. For example, when scanning the whole brain, about 20 layers need to be scanned, and the scanning time exceeds 60 minutes, which severely restricts its clinical application. In the published _{research work on the quantification of brain T 1ρ} , usually only one layer is collected. Three-dimensional T _1ρ quantitative imaging faces similar problems, and the scanning time usually exceeds 30 minutes. Because the existing T _1ρ quantitative imaging time is too long, the resolution and coverage of the image are limited.

In order to shorten the scanning time, the prior art is mainly carried out in the following three directions: 1) Reduce the number of _{TSLs. This method reduces the number of T 1ρ-} weighted images collected due to the reduction of TSL, so it is quantitative The accuracy is also reduced. 2) Adopt fast imaging sequence. Due to the limitation of hardware, the scanning speed will not be significantly improved in this way. 3) Adopt fast imaging technology. The current commercial fast imaging technology is mainly parallel imaging technology (such as sensitivity coding (SENSE), generalized automatic calibration part parallel acquisition (GRAPPA), etc.), but this method is affected by parallel imaging array lines. The limit of the circle, the higher the acceleration factor, the lower the signal-to-noise ratio of the image obtained after imaging, so the scanning speed using this method can usually only reach 2-3 times.

Summary of the invention

The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art, and provide a spin-lattice relaxation imaging method and system in a magnetic resonance rotating coordinate system, which is a new method of rapid and wide-range magnetic resonance T _1ρ quantitative imaging. _{The technical solution can realize fast T 1ρ} quantitative imaging with multiple layers and high signal-to-noise ratio.

According to the first aspect of the present invention, a spin-lattice relaxation imaging method in a magnetic resonance rotating coordinate system is provided. The method includes the following steps:

For different spin-locking times, configure the spin-lattice relaxation weighted image data in the rotating coordinate system of the target image to be collected twice, and set the recovery time between two consecutive acquisitions to obtain a two-dimensional multi-layer rotating coordinate system Down-spin lattice relaxation imaging data;

Acquire low-resolution image data for reconstruction of K-space center data and estimation of multi-channel coil sensitivity matrix;

Based on the low-resolution image data, the acquired spin-lattice relaxation imaging data in the two-dimensional multilayer rotating coordinate system is reconstructed, and the final spin-lattice relaxation parameter map in the rotating coordinate system is fitted. .

In one embodiment, the spin-lattice relaxation imaging data in a two-dimensional multilayer rotating coordinate system is obtained according to the following steps:

The last 90-degree pulse in the spin-lattice relaxation preparation pulse in the rotating coordinate system during the first acquisition is applied along the -x axis, and the longitudinal magnetization vector acquired for the first time is expressed as:

M ₁ (TSL)=M ₀ +(M _init e ^-TSL/T1ρ -M ₀ )e ^-Trec/T1 ;

The last 90-degree pulse in the spin-lattice relaxation preparation pulse in the rotating coordinate system during the second acquisition is applied along the x-axis direction, and the longitudinal magnetization vector acquired in the second acquisition is expressed as:

M ₂ (TSL)=M ₀ +(-M _init e ^-TSL/T1ρ -M ₀ )e ^-Trec/T1 ;

Subtract the longitudinal magnetization vector acquired for the first time with the longitudinal magnetization vector acquired for the second time to obtain the spin lattice parameter relaxation model in the rotating coordinate system:

M(TSL)=Ae ^-TSL/T1ρ

Among them, M _init is the longitudinal magnetization vector before the spin-lattice relaxation preparation pulse is applied in the rotating coordinate system, M ₀ is the longitudinal magnetization vector in the equilibrium state, T1 is the time constant of longitudinal relaxation, and TSL represents the spin lock Time, T _1ρ is the spin-lattice relaxation time in the rotating coordinate system, M(TSL)=M ₁ (TSL)-M ₂ (TSL), A=2M _init e ^-Trec/T1 , Trec is the first time Recovery time between acquisition and second acquisition.

In one embodiment, in the process of acquiring the spin-lattice relaxation weighted image data in the rotating coordinate system of the target image, the frequency encoding direction is fully collected, and in the phase encoding direction, the center part of the K-space adopts a uniform density under-sampling method. , And the area outside the center of K-space adopts variable density under-sampling, and the sampling density decreases as the distance from the center of K-space increases.

In one embodiment, reconstructing the acquired spin-lattice relaxation imaging data in the two-dimensional multilayer rotating coordinate system includes the following sub-steps:

Use the collected low-resolution images to reconstruct the central part of the K-space data;

Estimate the sensitivity matrix of the multi-channel coil using the reconstructed K-space center data;

The spin lattice relaxation weighted image under the rotating coordinate system of each layer is reconstructed separately, and the solution model is expressed as:

min _{X,L,S} ‖S‖ ₁ st C(X)=L+S, E(X)=d, Rank(L)=1

Among them, ‖·‖ ₁ represents the l ₁ norm, C(·) is an operation operator, which represents the pixel-level signal compensation of the image; X is the image sequence to be reconstructed, and L is the image in the form of a matrix. The low-rank part, 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-picked Fourier operator and the multi-channel coil sensitivity matrix, Rank(L) represents the matrix L The rank of, d represents the under-collected K-space data.

In an embodiment, the pixel-level signal compensation for the image is represented as multiplying each pixel in the image by a compensation coefficient.

In an embodiment, the compensation coefficient is expressed as:

Coef=exp(TSL _k /T _1ρ ), k=1, 2,..., T

Among them, Coef represents the compensation coefficient, TSL _k is the k-th spin lock time, and T is the number of the spin lock time TSL.

According to a second aspect of the present invention, a spin-lattice relaxation imaging system in a magnetic resonance rotating coordinate system is provided. The system includes:

Icon image acquisition unit: For different spin locking times, configure the spin lattice relaxation weighted image data in the rotating coordinate system of the target image acquired in two times and set the recovery time between two consecutive acquisitions, Obtain spin-lattice relaxation imaging data in a two-dimensional multilayer rotating coordinate system;

Low-resolution image acquisition unit: used to acquire low-resolution image data used to reconstruct the K-space center data and estimate the sensitivity matrix of the multi-channel coil;

Image reconstruction unit: used to reconstruct the spin-lattice relaxation imaging data in the collected two-dimensional multi-layer rotating coordinate system in the rotating coordinate system based on the low-resolution image data, and fitting the final rotating coordinate The spin lattice relaxation parameter diagram under the system.

Compared with the prior art, the present invention has the advantage that, in view of the existing T _1ρ quantitative imaging time being too long, which limits the resolution and coverage of the image, the present invention provides a two-dimensional T _{1ρ capable of rapid multi-layer scanning.} Quantitative imaging program. At the same time, in order to improve image acquisition efficiency and reduce scanning time, a two-dimensional under-sampling method with high acceleration multiples is proposed, and a low-rank and sparse decomposition model based on parallel imaging and signal compensation is used to reconstruct high quality from highly under-sampled under-sampled data. the parameter _T 1ρ weighted images to obtain a more accurate _T 1ρ FIG parameters, and by combining quantitative calculation undersampled variable density ultimately multidimensional, high SNR _T 1ρ rapid quantitative imaging.

Through the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, other features and advantages of the present invention will become clear.

Description of the drawings

The drawings incorporated in the specification and constituting a part of the specification illustrate the embodiments of the present invention, and together with the description are used to explain the principle of the present invention.

FIG. 1 is a flowchart of a spin-lattice relaxation imaging method in a magnetic resonance rotating coordinate system according to an embodiment of the present invention;

2 is a schematic diagram _{of a two-dimensional multilayer T 1ρ} quantitative imaging sequence according to an embodiment of the present invention;

Fig. 3 is a schematic diagram of under-sampling according to an embodiment of the present invention;

In the figure, Acquisition 1-first acquisition; Acquisition 2-second acquisition; Recovery time-recovery time; Slice-layer.

Detailed ways

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that unless specifically stated otherwise, the relative arrangement of components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention.

The following description of at least one exemplary embodiment is actually only illustrative, and in no way serves as any limitation to the present invention and its application or use.

The technologies, methods, and equipment known to those of ordinary skill in the relevant fields may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be regarded as part of the specification.

In all examples shown and discussed herein, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiment may have different values.

It should be noted that similar reference numerals and letters indicate similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further discussed in the subsequent drawings.

_{The present invention addresses the problems of} traditional two-dimensional magnetic resonance T 1ρ quantitative imaging T _{1ρ that} can only acquire one image after preparing the pulse and the scanning time is long, and provides a two-dimensional T _1ρ quantitative imaging scheme capable of multi-layer scanning and two-dimensional high acceleration multiples Under-sampling scheme, and based on signal compensation low-rank plus sparse decomposition model, from highly under-sampled under-sampling data to reconstruct high-quality T _1ρ parameter weighted image, so as to obtain a more accurate T _1ρ parameter map.

Specifically, referring to FIG. 1, the spin-lattice relaxation imaging method in the magnetic resonance rotating coordinate system according to the embodiment of the present invention includes the following steps:

In step S1000, for different spin locking times, it is configured to acquire T _1ρ weighted image data of the _{target image in two times to obtain a T 1ρ} parameter relaxation model.

Figure 2 is a schematic diagram of a two-dimensional multilayer T _1ρ quantitative imaging sequence, in which Figure 2 (a) is a schematic diagram of a two-dimensional multilayer T _1ρ quantitative imaging sequence, taking two target images as an example, Figure 2 (b) is the first _T 1ρ acquisition times a schematic preparation pulses, FIG. 2 (c) is the second acquisition schematically _T 1ρ preparation pulses. In the embodiment of FIG. 2, the image acquired twice, the first time _T 1ρ preparation pulses collected in the last 90 ° pulse in -x-axis direction, and when the preparation pulse _T 1ρ second acquisition last 90 °The pulse is applied along the x-axis direction, and the recovery time Trec is set after each acquisition (for example, it can be set to 1ms, 5ms, 10ms, etc.) to restore the magnetization vector to a steady state.

Specifically, the longitudinal magnetization vector acquired for the first time is expressed as:

M ₁ (TSL)=M ₀ +(M _init e ^-TSL/T1ρ -M ₀ )e ^-Trec/T1 (1)

The longitudinal magnetization vector collected for the second time is expressed as:

M ₂ (TSL)=M ₀ +(-M _init e ^-TSL/T1ρ -M ₀ )e ^-Trec/T1 (2)

Wherein, M _init is applied before the longitudinal magnetization vector preparation pulse time _T 1ρ, M ₀ is the equilibrium longitudinal magnetization vector, T ₁ is the longitudinal relaxation time constant, TSL represents spin lock time, _T 1ρ is a rotating coordinate system The relaxation time of the spin lattice under.

After subtracting formula (1) from formula (2), we get:

M(TSL)=2M _init e ^-Trec/T1 e ^-TSL/T1ρ (3)

Among them, M(TSL)=M ₁ (TSL)-M ₂ (TSL), let A=2M _init e ^-Trec/T1 , since Trec is a fixed value, so A is a constant, formula (3) can be equivalent to :

M(TSL)=Ae ^-TSL/T1ρ (4)

It can be seen from formula (4) that by collecting T _1ρ weighted images of different spin-locked (TSL), after fitting, a quantitative T _1ρ parameter value can be obtained.

In the embodiment of the present invention, when performing two-dimensional multilayer T _1ρ quantitative imaging, a recovery time is introduced, and the acquisition is divided into two times, and the T _1ρ preparation pulses of the two acquisitions are different.

In step S2000, in the image acquisition process, a sampling method of full sampling in the frequency encoding direction and variable density under sampling in the phase encoding direction is adopted to obtain two-dimensional multi-layer T _1ρ imaging data.

Optionally, in order to speed up the imaging speed, the present invention adopts a sampling method of full sampling in the frequency encoding direction and variable density under sampling in the phase encoding direction in image acquisition.

Undersampling Specifically, K _x and K _y direction, see Fig. 3 and K _y and K _t is a schematic directions, wherein the direction of the whole mining K _x, K _y -K _t in a plane, with conventional compression based on sparse sampling perception The theoretical under-sampling method is different. In the embodiment of the present invention, the central part of the K-space adopts the uniform under-sampling method, while the area outside the K-space center adopts the variable-density under-sampling method, and the sampling density is reduced according to the distance from the center of the K-space, for example , The higher sampling density is used when the distance is short, and the lower sampling density is used when the distance is short. FIG. 3 (a) is a K _x and K _y direction of the matrix size of 256 × 256 sub-sampling is a schematic, FIG. 3 (b) K _y and K _t is the orientation matrix size of 256 × 5 undersampled schematically.

According to the theory of compressed sensing, as long as the signal is sparse or compressed, after an incoherent measurement, using optimization methods to solve the minimization problem, the original signal can be accurately reconstructed from the highly under-collected data. Therefore, using the two-dimensional multi-layer T _1ρ imaging sequence of step S1000 and the under-sampling method of step S2000, the T _{1ρ weighted image data of different TSLs are} collected in two times (the scanning parameters of the first acquisition and the second acquisition are exactly the same ) While improving the scanning efficiency, it ensures accurate parameter weighted images and parameter values.

In the embodiment of the present invention, the variable-density under-sampling method can not only improve the scanning efficiency, but also ensure the quality of the reconstructed image.

Step S3000, collecting low-resolution image data for reconstruction of K-space center data and estimating the sensitivity matrix of the multi-channel coil.

In this step, a low-resolution data is collected for subsequent reconstruction of the K-space center data and estimation of the multi-channel coil sensitivity matrix. The method for estimating the multi-channel coil sensitivity matrix can use the existing technology. This will not be repeated here.

In step S4000, the two-dimensional multilayer T _1ρ imaging data is reconstructed to obtain a reconstructed T _1ρ parameter weighted image.

In this step, the under-collected two-dimensional multilayer T _1ρ imaging data can be reconstructed in combination with the existing technology.

For example, in conjunction with the prior art signal compensation-based low-rank plus sparse reconstruction model (SCOPE), 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.), the _{reconstruction of the under-collected two-dimensional multi-layer T 1ρ} imaging data includes the following steps:

Step S141, firstly, using the VCC-GRAPPA method and the low-resolution image to reconstruct the central part of the K-space data.

The VCC-GRAPPA method can refer to the existing literature ("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, using the reconstructed K-space central part data to estimate the sensitivity matrix of the multi-channel coil.

For this step, please refer to the literature ("sensitivity encoding for fast MRI", Magn Reson Med 1999; 42(5):952-962, Pruessmann KP, Weiger M, etc.).

In step S143, on the basis of step S141 and step S142, the T _1ρ weighted image of each layer is reconstructed separately, and the solution model is expressed as:

min _{X,L,S} ‖S‖ ₁ st C(X)=L+S, E(X)=d, Rank(L)=1 (5)

Among them, ‖·‖ ₁ represents the l ₁ norm; C(·) is an operation operator that represents pixel-level signal compensation for the image; X is the image sequence to be reconstructed, and its size is expressed as the number of voxels × A matrix of TSL numbers (T); L is the low-rank part of the image expressed 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 under-picked Fourier operator The product of the sensitivity matrix of the coil; Rank(L) represents the rank of the matrix L, and d represents the under-collected K-space data.

Based on the T _1ρ relaxation model of formula (4), signal compensation can be specifically expressed as multiplying each pixel in the image by a compensation coefficient, which can be obtained by the following formula:

Coef=exp(TSL _k /T _1ρ ), k=1, 2,..., T (6)

Where Coef represents the compensation coefficient, TSL _k is the k-th spin-lock time, and T is the number of spin-lock time (TSL).

In one embodiment, the solution process of formula (5) includes the following steps:

Step S151: Transform the K-space center data reconstructed by VCC-GRAPPA into the image domain through Fourier transform, fit the image according to the T _1ρ relaxation model of formula (4), and estimate the initial T _1ρ parameters, And according to formula (6), the initial value of the compensation coefficient Coef ^{0 is obtained} ;

Step S152, set the loop as i=1, 2,..., in the i-th iteration, execute:

Step S152-1 compensates the image according to the compensation coefficient, that is

Where U represents the compensated image;

Step S152-2, initialize S=0, set the number of outer loops to J, in the j=1, 2,..., Jth iteration, execute:

a), update L _j :

Among them, SVT(·) represents the singular value threshold operation operator, which is defined as:

SVT _λ (M)=UΛ _λ (Σ)V ^H (7)

Where M=UΣV ^H represents the singular value decomposition (SVD), U and V are the matrices of left and right singular value vectors, V ^H represents the conjugate transpose of V, and Σ is the diagonal matrix composed of the singular values of M , Λ _λ (Σ) means to keep the largest singular value in Σ 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 the rank of L after the singular value threshold operation is performed. )=1;

b), update S _j :

ST(·) is a soft threshold operator, defined as:

Where p is an element of the image matrix and v is the threshold.

c). Update data fidelity items:

Where E ^* represents the inverse operation of E, which is equivalent to performing inverse Fourier transformation on the multi-channel coil K-space data and then combining the coils to obtain an image;

d), update the image X ⁱ :

Where C ^-1 (·) means that the image is divided by the compensation coefficient Coef ⁱ based on each pixel;

e), terminate the inner loop iteration

Step S152-3, according to the X ⁱ obtained in step S152-2, combined with the parameter relaxation model in formula (4), update

And update the compensation coefficient

Step S152-4: When the algorithm reaches the iteration termination condition (for example, the number of iterations is greater than the maximum number of iterations or the reconstruction error between two adjacent iterations is less than a preset value), the loop iteration is terminated, and the final reconstruction parameter weight is obtained Image X.

In step S5000, according to the reconstructed T _1ρ parameter weighted image and the T _1ρ parameter relaxation model, non-linear fitting is performed on all pixels in the image to fit the final T _1ρ parameter map.

In the above-mentioned embodiment, when reconstructing a two-dimensional T _1ρ weighted image, first use VCC-GRAPPA to reconstruct the center part of K space to remove curling artifacts caused by under-sampling of the center of K space, and then low-rank addition based on signal compensation Sparse decomposition model, iteratively reconstruct the T _1ρ weighted image of each layer. Among them, the reconstruction process of low rank plus sparse decomposition based on signal compensation is as follows: firstly _{, the method of signal compensation is adopted based on the T 1ρ} parameter relaxation model to enhance the low rank of the data in the TSL direction. Then, the image sequence is arranged into a space-parameter matrix according to the parameter direction, where each column of the matrix represents the magnetic resonance image collected at a certain TSL time, and then the space-parameter matrix (this matrix actually represents the image) is decomposed into Low-rank component (L) and sparse component (S). Next, combine (L+S) to rebuild the model, perform singular value threshold operation on L, and perform soft threshold operation on S to get the iteratively updated L and S. By summing the updated L and S, the updated can be obtained The space-parameter matrix (that is, the image). In the iterative process of image reconstruction, each iteration will update the T _{1ρ parameter map according to the newly reconstructed T 1ρ} weighted image and the T _1ρ parameter relaxation model, and use the updated T _1ρ parameter map in the next iteration The signal compensation is repeated in this way, until the algorithm reaches the iterative termination condition, and the reconstruction is stopped. Finally, the T _1ρ parameter relaxation model is used to fit the reconstructed parameter-weighted image to obtain the final T _1ρ parameter map.

In summary, compared with the existing two-dimensional T _1ρ quantitative imaging technology, the present invention can realize multi-layer T _1ρ quantitative imaging, and the designed variable density under-sampling method can greatly accelerate the data scanning speed and reduce the T 1ρ quantitative _imaging. The imaging time, during image reconstruction, the present invention can accurately reconstruct a parameter-weighted image from highly under-collected data, and further improve the image signal-to-noise ratio through quantitative calculation.

The present invention may be a system, a method and/or a computer program product. The computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling 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 instructions used by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical 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 (non-exhaustive list) of computer-readable storage media include: 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 disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon The protruding structure in the hole card or the groove, and any suitable combination of the above. The computer-readable storage medium used here is not interpreted as the instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded 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, optical fiber 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 the computer-readable storage medium in each computing/processing device .

The computer program instructions used to perform the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or in one or more programming languages. Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages. Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server implement. In the case of a remote computer, the remote computer can 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 it can be connected to an external computer (for example, using an Internet service provider to connect to the user's computer) connect). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions. The computer-readable program instructions are executed to implement various aspects of the present invention.

Here, various aspects of the present invention are described with reference to flowcharts and/or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each block of the flowcharts and/or block diagrams, and combinations of blocks in the flowcharts and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner. Thus, the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.

It is also possible to load computer-readable program instructions on a computer, other programmable data processing device, or other equipment, so that a series of operation steps are executed on the computer, other programmable data processing device, or other equipment to produce a computer-implemented process , So that the instructions executed on the computer, other programmable data processing apparatus, or other equipment realize the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.

The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions, and operations of the system, method, and computer program product according to multiple embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function. Executable instructions. In some alternative implementations, the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation through hardware, implementation through software, and implementation through a combination of software and hardware are all equivalent.

The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Without departing from the scope and spirit of the illustrated embodiments, many modifications and changes are obvious to those of ordinary skill in the art. The choice of terms used herein is intended to best explain the principles, practical applications, or technical improvements in the market of the various embodiments, or to enable other ordinary skilled in the art to understand the various embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims (10)

1. A spin-lattice relaxation imaging method in a magnetic resonance rotating coordinate system includes the following steps:

   For different spin-locking times, configure the spin-lattice relaxation weighted image data in the rotating coordinate system of the target image to be collected twice and set the recovery time between two consecutive acquisitions to obtain a two-dimensional multi-layer rotating coordinate system Down-spin lattice relaxation imaging data;

   Acquire low-resolution image data for reconstruction of K-space center data and estimation of multi-channel coil sensitivity matrix;

   Based on the low-resolution image data, the acquired spin-lattice relaxation imaging data in the two-dimensional multilayer rotating coordinate system is reconstructed, and the final spin-lattice relaxation parameter map in the rotating coordinate system is fitted. .
2. The spin-lattice relaxation imaging method in a magnetic resonance rotating coordinate system according to claim 1, wherein the spin-lattice relaxation imaging data in a two-dimensional multilayer rotating coordinate system is obtained according to the following steps:

   The last 90-degree pulse in the spin-lattice relaxation preparation pulse in the rotating coordinate system during the first acquisition is applied along the -x axis, and the longitudinal magnetization vector acquired for the first time is expressed as:

   M ₁ (TSL)=M ₀ +(M _init e ^-TSL/T1ρ -M ₀ )e ^-Trec/T1 ;

   The last 90-degree pulse in the spin-lattice relaxation preparation pulse in the rotating coordinate system during the second acquisition is applied along the x-axis direction, and the longitudinal magnetization vector acquired in the second acquisition is expressed as:

   M ₂ (TSL)=M ₀ +(-M _init e ^-TSL/T1ρ -M ₀ )e ^-Trec/T1 ;

   Subtract the longitudinal magnetization vector acquired for the first time with the longitudinal magnetization vector acquired for the second time to obtain the spin lattice parameter relaxation model in the rotating coordinate system:

   M(TSL)=Ae-TSL/T1ρ

   Among them, M _init is the longitudinal magnetization vector before the spin-lattice relaxation preparation pulse is applied in the rotating coordinate system, M ₀ is the longitudinal magnetization vector in the equilibrium state, T1 is the time constant of longitudinal relaxation, and TSL represents the spin lock Time, T _1ρ is the spin-lattice relaxation time in the rotating coordinate system, M(TSL)=M ₁ (TSL)-M ₂ (TSL), A=2M _init e ^-Trec/T1 , Trec is the first time Recovery time between acquisition and second acquisition.
3. The spin-lattice relaxation imaging method in the magnetic resonance rotating coordinate system according to claim 1, wherein in the process of acquiring the spin-lattice relaxation weighted image data in the rotating coordinate system of the target image, the frequency encoding direction For full sampling, in the phase encoding direction, the center of K-space adopts uniform density under-sampling, while the area outside the center of K-space adopts variable-density under-sampling, and the sampling density decreases as the distance from the center of K-space increases.
4. The spin-lattice relaxation imaging method in a magnetic resonance rotating coordinate system according to claim 1, wherein reconstructing the acquired spin-lattice relaxation imaging data in a two-dimensional multilayer rotating coordinate system includes the following Substep:

   Use the collected low-resolution images to reconstruct the central part of the K-space data;

   Estimate the sensitivity matrix of the multi-channel coil using the reconstructed K-space center data;

   The spin lattice relaxation weighted image under the rotating coordinate system of each layer is reconstructed separately, and the solution model is expressed as:

   min _{X,L,S} ‖S‖ ₁ stC(X)=L+S, E(X)=d, Rank(L)=1

   Among them, ‖·‖ ₁ represents the l ₁ norm, C(·) is an operation operator, which represents the pixel-level signal compensation of the image; X is the image sequence to be reconstructed, and L is the image in the form of a matrix. The low-rank part, 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-picked Fourier operator and the multi-channel coil sensitivity matrix, Rank(L) represents the matrix L The rank of, d represents the under-collected K-space data.
5. 4. The spin-lattice relaxation imaging method in a magnetic resonance rotating coordinate system according to claim 4, wherein said performing pixel-level signal compensation on the image is expressed as multiplying each pixel in the image by a compensation coefficient.
6. The spin-lattice relaxation imaging method in a magnetic resonance rotating coordinate system according to claim 5, wherein the compensation coefficient is expressed as:

   Coef=exp(TSL _k /T _1ρ ), k=1, 2,..., T

   Among them, Coef represents the compensation coefficient, TSL _k is the k-th spin lock time, and T is the number of the spin lock time TSL.
7. A spin-lattice relaxation imaging system in a magnetic resonance rotating coordinate system, including:

   Icon image acquisition unit: For different spin locking times, configure the spin lattice relaxation weighted image data in the rotating coordinate system of the target image acquired in two times and set the recovery time between two consecutive acquisitions, Obtain spin-lattice relaxation imaging data in a two-dimensional multilayer rotating coordinate system;

   Low-resolution image acquisition unit: used to acquire low-resolution image data used to reconstruct the K-space center data and estimate the sensitivity matrix of the multi-channel coil;

   Image reconstruction unit: used to reconstruct the spin-lattice relaxation imaging data in the collected two-dimensional multi-layer rotating coordinate system in the rotating coordinate system based on the low-resolution image data, and fitting the final rotating coordinate The spin lattice relaxation parameter diagram under the system.
8. The spin-lattice relaxation imaging system in a magnetic resonance rotating coordinate system according to claim 7, wherein the spin-lattice relaxation imaging data in a two-dimensional multilayer rotating coordinate system is obtained according to the following steps:

   The last 90-degree pulse in the spin-lattice relaxation preparation pulse in the rotating coordinate system during the first acquisition is applied along the -x axis, and the longitudinal magnetization vector acquired for the first time is expressed as:

   M ₁ (TSL)=M ₀ +(M _init e ^-TSL/T1ρ -M ₀ )e ^-Trec/T1 ;

   The last 90-degree pulse in the spin-lattice relaxation preparation pulse in the rotating coordinate system during the second acquisition is applied along the x-axis direction, and the longitudinal magnetization vector acquired in the second acquisition is expressed as:

   M ₂ (TSL)=M ₀ +(-M _init e ^-TSL/T1ρ -M ₀ )e ^-Trec/T1 ;

   Subtract the longitudinal magnetization vector acquired for the first time with the longitudinal magnetization vector acquired for the second time to obtain the spin lattice parameter relaxation model in the rotating coordinate system:

   M(TSL)=Ae ^-TSL/T1ρ

   Among them, M _init is the longitudinal magnetization vector before the spin-lattice relaxation preparation pulse is applied, M ₀ is the longitudinal magnetization vector in the equilibrium state, T1 is the time constant of longitudinal relaxation, TSL is the spin lock time, and T _1ρ is Spin lattice relaxation time in a rotating coordinate system, M(TSL)=M ₁ (TSL)-M ₂ (TSL), A=2M _init e ^-Trec/T1 , Trec is the first acquisition and the second Recovery time between acquisitions.
9. The spin-lattice relaxation imaging system in the magnetic resonance rotating coordinate system according to claim 7, wherein in the process of acquiring the spin-lattice relaxation weighted image data in the rotating coordinate system of the target image, the frequency encoding direction For full sampling, in the phase encoding direction, the center of K-space adopts uniform density under-sampling, while the area outside the center of K-space adopts variable-density under-sampling, and the sampling density decreases as the distance from the center of K-space increases.
10. A computer-readable storage medium on which a computer program is stored, where the program is executed by a processor to achieve the following operations:

    For different spin-locking times, configure the spin-lattice relaxation weighted image data in the rotating coordinate system of the target image to be collected twice and set the recovery time between two consecutive acquisitions to obtain a two-dimensional multi-layer rotating coordinate system Down-spin lattice relaxation imaging data;

    Acquire low-resolution image data for reconstruction of K-space center data and estimation of multi-channel coil sensitivity matrix;

    Based on the low-resolution image data, the acquired spin-lattice relaxation imaging data in the two-dimensional multilayer rotating coordinate system is reconstructed, and the final spin-lattice relaxation parameter map in the rotating coordinate system is fitted. .

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