WO2021129235A1 - 三维磁共振快速参数成像方法和装置 - Google Patents

三维磁共振快速参数成像方法和装置 Download PDF

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WO2021129235A1
WO2021129235A1 PCT/CN2020/129484 CN2020129484W WO2021129235A1 WO 2021129235 A1 WO2021129235 A1 WO 2021129235A1 CN 2020129484 W CN2020129484 W CN 2020129484W WO 2021129235 A1 WO2021129235 A1 WO 2021129235A1
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parameter
image
spin
update
magnetic resonance
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French (fr)
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朱燕杰
刘元元
梁栋
刘新
郑海荣
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深圳先进技术研究院
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/5602—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution

Definitions

  • the present invention relates to the field of image processing, in particular to a method and system for rapid parametric magnetic resonance imaging.
  • Osteoarthritis is one of the most common arthritis diseases. It is the main cause of disability. It has affected hundreds of millions of people around the world and caused a huge economic burden on families and society. This disease is a degenerative disease, and with age, the incidence gradually increases.
  • the main components of articular cartilage are extracellular matrix composed of 80% water molecules, type II collagen fibers and proteoglycan molecules.
  • An important feature of osteoarthritis is the degeneration of articular cartilage. After the early diagnosis of osteoarthritis, interventions can change or even reverse the course of the disease. Therefore, clinically, a sensitive and accurate tool is urgently needed for early diagnosis of osteoarthritis, and evaluation of curative effect and prognosis.
  • Magnetic resonance parametric imaging can characterize some inherent information of the tissue, and has become an important, safe and effective diagnostic tool.
  • T2 relaxation recent studies have shown that articular cartilage degradation is caused by damage to the collagen matrix.
  • Spin-lattice relaxation in the rotating frame (T 1 ⁇ ) ) Can well reflect the change of this matrix.
  • T 1 ⁇ is more sensitive to the interaction between the protons of water molecules with restricted movement and the proteoglycan macromolecules in the extracellular matrix of articular cartilage, and the value of T 1 ⁇ varies with the protein Polysaccharides decrease and increase, so T 1 ⁇ parameter imaging has received extensive clinical attention in the diagnosis of cartilage degeneration, knee cartilage damage and other related diseases in osteoarthritis.
  • Compressed sensing theory has been widely used in magnetic resonance parametric imaging, which improves scanning efficiency while ensuring accurate parameter-weighted images and parameter values.
  • the existing three-dimensional parameter imaging time is too long. Taking T 1 ⁇ as an example, it usually takes about 15-30 minutes.
  • the present invention proposes a new high-acceleration three-dimensional under-sampling scheme ,
  • the scan time of parametric imaging is greatly shortened, and the low-rank plus sparse matrix decomposition model based on signal compensation can reconstruct high-quality parameter-weighted images from highly under-sampled under-sampled data, and further obtain accurate parameter maps.
  • the present invention proposes a new high-acceleration three-dimensional under-sampling scheme ,
  • the scan time of parametric imaging is greatly shortened, and the low-rank plus sparse matrix decomposition model based on signal compensation can reconstruct high-quality parameter-weighted images from highly under-sampled under-sampled data, and further obtain accurate parameter maps.
  • the low-rank plus sparse matrix decomposition model based on signal compensation can reconstruct high-quality parameter-weighted images from highly under-sampled under-sampled
  • the embodiment of the first aspect of the present invention taking the spin-lattice relaxation parameter T 1 ⁇ as an example , proposes a three-dimensional magnetic resonance fast parametric imaging method, which includes the following steps:
  • S1 Use the preset acquisition template to acquire the magnetic resonance parameter imaging data of the target object by under-sampling
  • S6 Determine whether the preset iteration termination conditions are met; if the preset iteration termination conditions are not met, repeat steps S4 to S6 for iteration, and if the preset iteration termination conditions are met, execute the next step;
  • the preset acquisition template adopts a three-dimensional high acceleration multiple undersampling scheme, in which the frequency encoding direction is full sampling, and the phase encoding direction is variable density undersampling. All collected points in the preset acquisition template follow spiral trajectories or radial sampling. Trajectory.
  • the image corresponding to each frequency encoding direction is reconstructed.
  • a signal compensation method is proposed based on the T 1 ⁇ parameter relaxation model to enhance the data in the TSL direction. Of low rank.
  • the compensated image sequence 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 a low-rank component (L) and a sparse component (S).
  • an embodiment of the present invention provides a three-dimensional magnetic resonance fast parametric imaging device, including:
  • the data acquisition module is used to use the preset acquisition template to acquire the magnetic resonance parameter imaging data of the target object in an under-sampling manner;
  • a full-sampling image reconstruction module configured to obtain full-sampling data of the magnetic resonance parameter imaging data in the central part of the K-space, and convert the full-sampling data to the image domain to obtain a parameter-weighted image
  • the initial parameter module is used to determine the spin lattice relaxation parameter T 1 ⁇ and the compensation coefficient according to the spin lattice relaxation model and the parameter weighted image;
  • the image update module is used to update the parameter weighted image according to the L+S model using the compensation coefficient
  • a parameter update module configured to use the updated parameter weighted image to update the spin lattice relaxation parameter T 1 ⁇ and the compensation coefficient;
  • the judgment module is used to judge whether the preset iteration termination condition is met; when the preset iteration termination condition is not met, repeatedly call the image update module and the parameter update module to update;
  • the image fitting module is configured to fit the spin-lattice relaxation parameter T 1 ⁇ image according to the parameter-weighted image and the spin-lattice relaxation model. .
  • another embodiment of the present invention provides a magnetic resonance device, including:
  • One or more processors are One or more processors;
  • Storage device for storing one or more programs
  • the one or more processors implement the imaging method as in the foregoing embodiment.
  • another embodiment of the present invention provides a storage medium containing computer-executable instructions, wherein the computer-executable instructions are used to perform imaging as in the foregoing embodiment when the computer-executable instructions are executed by a computer processor. method. .
  • the present invention designs a three-dimensional under-sampling scheme with high acceleration multiples based on the compressed sensing theory, which greatly shortens the scanning time of parametric imaging, and the low-rank plus sparse decomposition model based on signal compensation, from high under-sampling A high-quality T 1 ⁇ parameter weighted image is reconstructed from the under-sampling data, and an accurate T 1 ⁇ parameter map is further obtained, realizing fast T 1 ⁇ parameter imaging at any level and any orientation.
  • Fig. 1 shows a schematic diagram of an imaging method according to the first embodiment of the present invention
  • Figure 2 shows a schematic diagram of a collection template according to the first embodiment of the present invention
  • Fig. 3 shows another schematic diagram of the imaging method according to the first embodiment of the present invention
  • Fig. 4 shows a schematic diagram of an imaging device according to the second embodiment of the present invention.
  • Fig. 5 shows a schematic diagram of a magnetic resonance device according to the third embodiment of the present invention.
  • module or unit when a module or unit is referred to as being “on,” “connected to,” or “coupled to” another module or unit, it can be directly on the other module or unit or an intermediate module or unit that may exist, Connected or coupled to other modules or units or intermediate modules or units that may be present. In contrast, when a module or unit is referred to as being “directly on”, “directly connected to” or “directly coupled to” another module or unit, there may be no intervening modules or units.
  • the term “and/or” can include any and all combinations of one or more of the related listed items.
  • the MRI image can be generated by manipulating a virtual space called k-space.
  • k-space used herein may refer to a digital array (matrix) representing the spatial frequency in the MR image.
  • the k-space may be a 2D or 3D Fourier transform of the MR image.
  • the way of manipulating k-space, called k-space sampling, can affect the acquisition time (TA).
  • acquisition time may refer to the time to collect the signal of the entire pulse sequence.
  • the term "acquisition time” can refer to the time from when k-space is filled to the time the entire k-space data set is collected.
  • acquisition time can refer to the time from when k-space is filled to the time the entire k-space data set is collected.
  • Cartesian sampling two k-space sampling methods, Cartesian sampling and non-Cartesian sampling, are provided to manipulate k-space.
  • Cartesian sampling the k-space trajectory is a straight line
  • non-Cartesian sampling such as radiation sampling or spiral sampling
  • the k-space trajectory can be longer than the k-space trajectory in Cartesian sampling.
  • Fig. 1 shows a schematic block diagram of a three-dimensional magnetic resonance fast parametric imaging method according to an embodiment of the present invention.
  • the three-dimensional magnetic resonance fast parametric imaging method includes the following steps:
  • S1 Use the preset acquisition template to acquire the magnetic resonance parameter imaging data of the target object in an under-sampling manner
  • the present invention designs a three-dimensional high acceleration multiple under-sampling scheme based on the sparse sampling theory, in which the frequency encoding direction is fully sampled, the phase encoding direction is variable-density under-sampling, and the under-sampling template is All collected points follow a spiral trajectory or a radial sampling trajectory.
  • FIG. 2 shows the schematic and example diagrams of undersampling in the ky and kz directions.
  • One point represents a line in the kx direction, the kx direction is fully collected, and the ky and kz directions are The point represents the line to be collected, and each point has the following characteristics:
  • each point in the ring falls on a different spiral trajectory.
  • y(r) and z(r) represent the coordinates of the point (y, z) respectively, and r represents the radius of the ring where the point (y, z) is located. Its value increases exponentially from the inside to the outside, v Represents the angular velocity, Represents the azimuth angle of the spiral trajectory. In the same ring, a certain point falls on the established spiral trajectory, and the following cost function must be minimized:
  • ⁇ y, z is the azimuth angle of the point (y, z)
  • D(y, z) is the sampling density of the point (y, z)
  • its value is Constantly updated and equal to the sum of the sampling density of the selected point and a Gaussian kernel with a size of 5 ⁇ 5
  • ⁇ is a constant, different spiral trajectories, and their azimuths Is different, and meets the following law of change:
  • the sampling frequency is changing. In the central area of K-space, the sampling frequency is greater than the Nyquist frequency, which is full sampling. Therefore, this part of the fully sampled data is used to calculate Compensation factor.
  • a central area range can be set in advance, or the range of the full sampling area can be calculated according to the acquisition template.
  • the under-sampled three-dimensional T 1 ⁇ data is reconstructed, and the image in each frequency coding direction is reconstructed separately during reconstruction.
  • the initial spin-lattice relaxation parameter T 1 ⁇ and the compensation coefficient can be calculated according to the spin-lattice relaxation model and the initial parameter-weighted image.
  • the solution model is as follows:
  • ⁇ 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) (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 L of the matrix.
  • TSL spin-lock time
  • 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 the compensation coefficient
  • step S2 the central part of the K-space of the full acquisition is transformed into the image domain through Fourier transform to obtain the initial parameter weighted image, and the image is fitted according to the formula (5) T 1 ⁇ spin lattice relaxation model to estimate the initial T 1 ⁇ parameter, and get the initial value of the compensation coefficient Coef 0 according to formula (6).
  • the L+S model is used, and the specific implementation requires internal loop iteration in this step.
  • the iteration steps are as follows:
  • i represents the number of outer loops.
  • SVT( ⁇ ) represents the singular value threshold operation operator, which is defined as:
  • U and V are the matrices of left and right singular value vectors
  • V H represents the conjugate transpose of V
  • ⁇ is the diagonal matrix composed of the singular values of M
  • ⁇ ⁇ ( ⁇ ) means to keep the largest singular value in ⁇ unchanged, and all others are 0.
  • Rank(L) 1;
  • p is an element of the image matrix and v is the threshold.
  • E * represents the inverse operation of E, which is equivalent to performing inverse Fourier transformation on the k-space data of the multi-channel coil and then combining the coils to obtain an image
  • the update parameter weighted image formula is as follows:
  • C -1 ( ⁇ ) indicates that the image is divided by the compensation coefficient Coef i based on each pixel.
  • the preset termination condition can be a preset number of iterations or an iteration convergence condition. There is no specific limitation here, and all feasible iterative convergence conditions in the prior art can be used in the present invention.
  • step S3 the calculation method for updating the spin lattice relaxation parameter T 1 ⁇ and the compensation coefficient is the same as step S3, calculate T 1 ⁇ according to formula (5), estimate the initial T 1 ⁇ parameter, and calculate the compensation according to formula (6) coefficient.
  • S6 Determine whether the preset iteration termination conditions are met; if the preset iteration termination conditions are not met, repeat steps S4 to S6 for iteration, and if the preset iteration termination conditions are met, execute the next step;
  • the preset iteration termination condition may be a preset number of iterations or an iteration convergence condition, for example, the reconstruction error between two adjacent iterations is less than a preset value.
  • an iteration convergence condition for example, the reconstruction error between two adjacent iterations is less than a preset value.
  • the spiral trajectory variable density under-sampling method designed by the present invention can greatly accelerate the data scanning speed and reduce the three-dimensional T 1 ⁇ parameter imaging time.
  • the reconstruction method proposed by the present invention can More accurately reconstruct the parameter-weighted image from the highly under-collected data.
  • Fig. 4 shows a schematic block diagram according to another embodiment of the present invention.
  • the second embodiment of the present invention provides a three-dimensional magnetic resonance fast parametric imaging device, including:
  • the data acquisition module is used to use the preset acquisition template to acquire the magnetic resonance parameter imaging data of the target object in an under-collected manner;
  • a full-sampling image reconstruction module configured to obtain full-sampling data of the magnetic resonance parameter imaging data in the central part of the K-space, and convert the full-sampling data to the image domain to obtain an initial parameter-weighted image
  • the initial parameter module is used to determine the spin lattice relaxation parameter T 1 ⁇ and the compensation coefficient according to the spin lattice relaxation model and the parameter weighted image;
  • the image update module is used to update the parameter weighted image according to the L+S model using the compensation coefficient
  • a parameter update module configured to use the updated parameter weighted image to update the spin lattice relaxation parameter T 1 ⁇ and the compensation coefficient;
  • the judgment module is used to judge whether the preset iteration termination condition is met; when the preset iteration termination condition is not met, repeatedly call the image update module and the parameter update module to update;
  • the image fitting module is configured to fit the spin-lattice relaxation parameter T 1 ⁇ image according to the parameter-weighted image and the spin-lattice relaxation model.
  • Each unit in the three-dimensional magnetic resonance rapid parametric imaging device can be separately or completely combined into one or several other units to form, or some of the units can be divided into multiple smaller functionally smaller units. It can realize the same operation without affecting the realization of the technical effect of the embodiment of the present invention.
  • the above-mentioned units are divided based on logical functions. In practical applications, the function of one unit can also be realized by multiple units, or the function of multiple units can be realized by one unit.
  • the fast parametric imaging device based on three-dimensional magnetic resonance may also include other units. In practical applications, these functions may also be implemented with the assistance of other units, and may be implemented by multiple units in cooperation.
  • a general-purpose computing device such as a computer including a central processing unit (CPU), a random access storage medium (RAM), a read-only storage medium (ROM) and other processing elements and storage elements
  • CPU central processing unit
  • RAM random access storage medium
  • ROM read-only storage medium
  • the computer program may be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium, and run in it.
  • the third embodiment of the present invention provides a magnetic resonance device, including:
  • One or more processors are One or more processors;
  • Storage device for storing one or more programs
  • the one or more processors When the one or more programs are executed by the one or more processors, the one or more processors implement the three-dimensional magnetic resonance fast parametric imaging method as described in the first embodiment.
  • the device includes a processor 201, a memory 202, an input device 203, and an output device 204; the number of processors 201 in the device can be one or more.
  • one processor 201 is taken as an example; processing in the device
  • the device 201, the memory 202, the input device 203, and the output device 204 may be connected through a bus or other methods. In FIG. 5, the connection through a bus is taken as an example.
  • the memory 202 can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the three-dimensional magnetic resonance fast parametric imaging method in the embodiment of the present invention (for example, a data acquisition module). , Full sampling image reconstruction module, initial parameter module, image update module, parameter update module, judgment module, image fitting module).
  • the processor 201 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 202, that is, realizes the above-mentioned magnetic resonance parametric imaging method.
  • the memory 202 may mainly include a program storage area and a data storage area.
  • the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the terminal, and the like.
  • the memory 202 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
  • the memory 202 may further include a memory remotely provided with respect to the processor 201, and these remote memories may be connected to the device through a network. Examples of the foregoing network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • the input device 203 can be used to receive input digital or character information, and generate key signal input related to user settings and function control of the device.
  • the output device 204 may include a display device such as a display screen, for example, a display screen of a user terminal.
  • the fourth embodiment of the present invention provides a storage medium containing computer-executable instructions, which are used to execute the three-dimensional magnetic resonance fast parametric imaging method as described in the first embodiment when the computer-executable instructions are executed by a computer processor.
  • the program can be stored in a computer-readable storage medium.
  • the storage medium includes read-only Memory (Read-Only Memory, ROM), Random Access Memory (RAM), Programmable Read-only Memory (PROM), Erasable Programmable Read Only Memory, EPROM), One-time Programmable Read-Only Memory (OTPROM), Electronically-Erasable Programmable Read-Only Memory (EEPROM), CD-ROM (Compact Disc) Read-Only Memory, CD-ROM) or other optical disk storage, magnetic disk storage, tape storage, or any other computer-readable medium that can be used to carry or store data.
  • Read-Only Memory ROM
  • RAM Random Access Memory
  • PROM Programmable Read-only Memory
  • EPROM Erasable Programmable Read Only Memory
  • OTPROM One-time Programmable Read-Only Memory
  • EEPROM Electronically-Erasable Programmable Read-Only Memory
  • CD-ROM Compact Disc

Abstract

一种三维高加速倍数欠采样方案，在重建三维参数加权图像的迭代过程中，分别对每一个频率编码方向对应的图像进行重建，基于参数弛豫模型提出了一种信号补偿的方法来增强数据在TSL方向的低秩性。结合(L+S)重建模型，更新重建图像。在重建的迭代过程中，每次迭代都会根据新的重建图像和参数弛豫模型，更新磁共振参数图，并将更新后的参数图用于下一次迭代中的信号补偿，直到算法达到迭代终止条件。利用参数弛豫模型，对重建的参数加权图像进行拟合，得到最终的参数图。

Description

三维磁共振快速参数成像方法和装置 技术领域
本发明涉及图像处理领域，具体而言，涉及本发明涉及一种磁共振快速参数成像方法和系统。
背景技术
骨关节炎是最常见的一种关节炎疾病之一，是造成残疾的主要原因，已影响着全世界数百上千万人，给家庭、社会造成巨大的经济负担。这种疾病是一种退行性病变，且随着年龄的增大，发病率逐渐上升。关节软骨的主要成分是由80％水分子构成的细胞外基质、Ⅱ型胶原纤维以及蛋白多糖分子。骨关节炎的一个重要特征是关节软骨退化，早期确诊骨关节炎后，采取干预措施可改变甚至逆转疾病进程。因此，临床上急需一种敏感、精准的工具来对骨关节炎进行早期诊断，并进行疗效评价和预后评估。
磁共振参数成像(如纵向弛豫T1和横向弛豫T2等)可以表征组织的一些固有信息，已成为一种重要的、安全有效的诊断工具。除T2弛豫外，近年来的研究表明，关节软骨退化是由于胶原蛋白基质的损伤引起的，磁共振旋转坐标系下的自旋晶格弛豫(spin-lattice relaxation in the rotating frame，T 1ρ)可以很好地反映出这种基质的变化，T 1ρ对运动受限的水分子质子和关节软骨细胞外基质中的蛋白多糖大分子间的相互作用比较敏感，且T 1ρ的值随着蛋白多糖的减少而增加，因此T 1ρ参数成像已在骨关节炎中的软骨退变、膝盖软骨损伤等相关疾病诊断上受到了临床上的广泛关注。
但是，为了获得准确的参数值，成像时通常需要采集多幅不同时间点(如自旋锁定时间点，spin lock time,TSL)、回波时间(echo time,TE等)的加权图像，导致其扫描时间往往很长，这成为制约其在临床上快速发展的一大瓶颈。为了缩短扫描时间，以T 1ρ成像为例，目前的技术主要围绕以下三个方向进行开展的：
1、减少TSL的数量，这种方法由于TSL的减少导致采集的T 1ρ加权的图像数量也减少了，因此其定量的精度也降低了。2、采用快速成像序列，但由于受硬件的限制，扫描速度并不会显著提高。3、采用快速成像技术，目前商用的快速成像技术主要是并行成像技术(如敏感度编码(SENSE)、广义自动校准部分并行采集(GRAPPA)等)，但是这种方法由于受并行成像列阵线圈的限制，加速倍数越高，其成像后获得的图像的信噪比就会越低，因此采用这种方法的扫描速度通常仅能达到2-3倍。近年来基于稀疏采样理论的压缩感知技术在磁共振快速成像上得到了广泛的关注和应用。根据压缩感知的理论，只要信号是稀疏的或是压缩的，经过一个非相干的测量，利用优化方法通过求解最小化问题，就可以从高度欠采的数据中精确重建出原始信号。压缩感知理论已在磁共振参数成像上得到了大量的应用，在提高扫描效率的同时保证了准确的参数加权图像和参数值。
发明内容
现有的三维参数成像时间过长，以T 1ρ为例，通常需要15-30分钟左右，本发明正是基于上述技术问题至少之一，提出了一种新的高加速倍数的三维欠采样方案，大大缩短了参数成像的扫描时间，且基于信号补偿的低秩加稀疏矩阵分解模型，从高度欠采的欠采样数据中重建出高质量的参数加权图像，并进一步获得准确的参数图，实现了任意层面、任意方位的快速参数成像。
有鉴于此，本发明的第一方面的实施例，以自旋晶格弛豫参数T 1ρ为例 ，提出了一种三维磁共振快速参数成像方法，包括以下步骤：
S1：使用预设采集模板，以欠采样方式获取目标对象的磁共振参数成像数据；
S2：获取所述磁共振参数成像数据在K空间的中心部分的全采样数据，将所述全采样数据转换至图像域得到初始的全采低分辨率参数加权图像；
S3：根据自旋晶格弛豫模型和所述初始参数加权图像确定初始的自旋晶格弛豫参数T 1ρ和补偿系数；
S4：根据L+S模型，使用所述补偿系数对所述参数加权图像进行更新；
S5：使用所述更新后的参数加权图像更新所述自旋晶格弛豫参数T 1ρ和所述补偿系数；
S6：判断是否满足预设的迭代终止条件；若不满足预设的迭代终止条件，重复执行步骤S4至S6进行迭代，若满足预设的迭代终止条件，执行下一步骤；
S7：根据所述参数加权图像和所述自旋晶格弛豫模型拟合得到自旋晶格弛豫参数T 1ρ图像。
优选的，预设采集模板采用三维高加速倍数欠采样方案，其中，频率编码方向全采，相位编码方向变密度欠采，预设采集模板中所有被采集的点均遵循螺旋轨迹或径向采样轨迹。
在重建三维T 1ρ加权图像的迭代过程中，分别对每一个频率编码方向对应的图像进行重建，具体为：首先基于T 1ρ参数弛豫模型提出了一种信号补偿的方法来增强数据在TSL方向的低秩性。其次，将补偿后的图像序列按参数方向排成一个空间-参数矩阵，其中矩阵的每一列表示某一TSL时刻采集到的磁共振图像，然后将空间-参数矩阵(这个矩阵实际上代表着图像)分解成低秩部分(low-rank component,L)和稀疏部分(sparse component,S)。第三，结合(L+S)重建模型，并对L做奇异值阈值操作，对S做软阈值操作，得到迭代更新的L和S，通过对更新后的L和S求和，可得到更新的空间-参数矩阵(即为图像)。在重建的迭代过程中，每次迭代都会根据新重建出的T 1ρ加权图像和T 1ρ参数弛豫模型，更新T 1ρ参数图，并将更新后的T 1ρ参数图用于下一次迭代中的信号补偿，如此反复迭代，直到算法达到迭代终止条件，停止重建。最后，利用T 1ρ参数弛豫模型，对重建的参数加权图像进行拟合，得到最终的T 1ρ参数图。
另一方面，本发明的一个实施例提供一种三维磁共振快速参数成像装置，包括：
数据获取模块，用于使用预设采集模板，以欠采方式获取目标对象的磁共振参数成像数据；
全采样图像重建模块，用于获取所述磁共振参数成像数据在K空间的中心部分的全采样数据，将所述全采样数据转换至图像域得到参数加权图 像；
初始参数模块，用于根据自旋晶格弛豫模型和所述参数加权图像确定自旋晶格弛豫参数T 1ρ和补偿系数；
图像更新模块，用于根据L+S模型，使用所述补偿系数对所述参数加权图像进行更新，
参数更新模块，用于使用所述更新后的参数加权图像更新所述自旋晶格弛豫参数T 1ρ和所述补偿系数；
判断模块，用于判断是否满足预设的迭代终止条件；在不满足预设的迭代终止条件时，重复调用所述图像更新模块和所述参数更新模块进行更新；
图像拟合模块，用于根据所述参数加权图像和所述自旋晶格弛豫模型拟合得到自旋晶格弛豫参数T 1ρ图像。。
再一个方面，本发明的又一个实施例提供一种磁共振设备，包括：
一个或多个处理器；
存储装置，用于存储一个或多个程序；
当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如前述实施例中的成像方法。
再一个方面，本发明的又一个实施例提供一种包含计算机可执行指令的存储介质，其特征在于，所述计算机可执行指令在由计算机处理器执行时用于执行如前述实施例中的成像方法。。
通过以上技术方案，本发明基于压缩感知理论，设计了一种高加速倍数的三维欠采样方案，大大缩短了参数成像的扫描时间，且基于信号补偿的低秩加稀疏分解模型，从高度欠采的欠采样数据中重建出高质量的T 1ρ参数加权图像，并进一步获得准确的T 1ρ参数图，实现了任意层面、任意方位的快速T 1ρ参数成像。
附图说明
图1示出了根据本发明的实施例一的成像方法的示意图；
图2示出了根据本发明的实施例一的中的采集模板的示意图；
图3示出了根据本发明的实施例一的成像方法的另一个示意图；
图4示出了根据本发明的实施例二的成像装置的示意图；
图5示出了根据本发明的实施例三的磁共振设备的示意图。
具体实施方式
为了能够更清楚地理解本发明的上述目的、特征和优点，下面结合附图和具体实施方式对本发明进行进一步的详细描述。需要说明的是，在不冲突的情况下，本申请的实施例及实施例中的特征可以相互组合。
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是，本发明还可以采用其他不同于在此描述的方式来实施，因此，本发明的保护范围并不受下面公开的具体实施例的限制。
应当理解，当模块或单元被称为“在之上”、“连接到”或“耦合到”另一个模块或单元时，可以是直接在其他模块或单元或可以存在的中间模块或单元上、连接或耦合到其他模块或单元或可以存在的中间模块或单元。相反，当模块或单元被称为“直接在之上”、“直接连接到”或“直接耦合到”另一模块或单元时，可能不存在中间模块或单元。在本申请中，术语“和/或”可包括一个或以上相关所列条目的任何和所有组合。
本申请中所使用的术语仅用于描述特定的示例性实施例，并不限制本申请的范围。如本申请使用的单数形式“一”、“一个”及“该”可以同样包括复数形式，除非上下文明确提示例外情形。还应当理解，如在本申请说明书中，术语“包括”、“包含”仅提示存在所述特征、整体、步骤、操作、组件和/或部件，但并不排除存在或添加一个或以上其他特征、整体、步骤、操作、组件、部件和/或其组合的情况。
本申请一般涉及磁共振成像(MRI)，更具体地，涉及用于MRI中的快速成像的系统和方法。可以通过操纵被称为k空间的虚拟空间来生成MRI图像。这里使用的术语“k空间”可以指表示MR图像中的空间频率的数字阵列(矩阵)。在一些实施例中，k空间可以是MR图像的2D或3D傅里叶变换。操纵k空间的方式，被称为k空间采样，可以影响采集时间(TA)。如这里所使用的，术语“采集时间”可以指采集整个脉冲序列的信号的时间。例如，术语“采集时间”可以指从开始填充k空间到采集整个k空间 数据集的时间。传统上，提供两个k空间采样方法，笛卡尔采样和非笛卡尔采样，以操纵k空间。在笛卡尔采样中，k空间轨迹是直线，而在非笛卡尔采样中，例如辐射采样或螺旋采样，k空间轨迹可以比笛卡尔采样中的k空间轨迹更长。
实施例一
图1示出了根据本发明的一个实施例的三维磁共振快速参数成像方法的示意框图。
如图1所示，根据本发明的一个实施例的三维磁共振快速参数成像方法，包括以下步骤：
S1：使用预设采集模板，以欠采方式获取目标对象的磁共振参数成像数据；
针对三维磁共振参数成像扫描时间长特点，本发明基于稀疏采样理论，设计了一种三维高加速倍数欠采样方案，其中，频率编码方向全采，相位编码方向变密度欠采，欠采样模板中所有被采集的点均遵循螺旋轨迹或径向采样轨迹。
加速采集方案的采样模板具体参见附图2，图2中所示为ky和kz方向的欠采样示意和实例图，其中的一个点代表kx方向的一条线，kx方向全采，ky和kz方向的点表示要采集的线，且每个点具有以下特点：
在ky-kz平面，所有要采集的点分布于N个环内，环内的每个点都落在不同的螺旋轨迹线上，每条螺旋轨迹线由N个点组成，每个点来自于不同的环，若采样模板中螺旋轨迹线共Ns条，每条轨迹线上的点为N个，全采样时相位编码方向的线数分别为Ny和Nz,则总的加速倍数R＝(N y×N z)/(N s×N)，，螺旋轨迹线的定义如下：
Figure PCTCN2020129484-appb-000001
其中，y(r)，z(r)分别表示点(y,z)的坐标，r表示点(y,z)所在的环的半径，其值是呈指数形式从内到外增长的，v表示角速度，
Figure PCTCN2020129484-appb-000002
表示螺旋轨迹的方位角，在同一个环内，某个点落在既定的螺旋轨迹线上，必须满足以下代价函数最小：
Figure PCTCN2020129484-appb-000003
其中
Figure PCTCN2020129484-appb-000004
是点(y,z)经过螺旋校正后的方位角，θ y,z是点(y,z)的方位角，D(y,z)是点(y,z)的采样密度，其值是不断更新的，且等于所选的点的采样密度和一个5×5大小的高斯核的和，λ是一个常数，不同的螺旋轨迹，其方位角
Figure PCTCN2020129484-appb-000005
是不同的，且满足以下变化规律：
Figure PCTCN2020129484-appb-000006
其中
Figure PCTCN2020129484-appb-000007
表示第s条螺旋线的方位角，
Figure PCTCN2020129484-appb-000008
表示第s+1条螺旋线的方位角。
S2：获取所述磁共振参数成像数据在K空间的中心部分的全采样数据，将所述全采样数据转换至图像域得到参数加权图像；
参见附图2b的欠采样采集模板，可以看出，采样频率是变化的，在K空间的中心区域，采样频率大于奈奎斯特频率，为全采样，因此采用这部分全采样的数据来计算补偿系数。
中心区域的选择，可以预先设定一个中心区域范围，也可以根据采集模板，计算全采样区域的范围。
对欠采样的三维T 1ρ数据进行重建，重建时针对每个频率编码方向的图像分别进行重建。
S3：根据自旋晶格弛豫模型和所述参数加权图像确定自旋晶格弛豫参数T 1ρ和补偿系数；
根据前一步中重建的初始的参数加权图像，可以根据自旋晶格弛豫模型和初始的参数加权图像计算初始的自旋晶格弛豫参数T 1ρ和补偿系数。
具体来说，对欠采的三维T 1ρ数据进行重建，重建时针对每个频率编码方向的图像分别进行重建，求解模型如下：
min {X,L,S}‖S‖ 1 s.t. C(X)＝L+S,E(X)＝d,Rank(L)＝1    (4)
其中，‖·‖ 1是代表l 1范数；C(·)是一个操作算子，表示对图像进行像素级的信号补偿；X是要重建的图像序列，且其表示成大小为体素数×TSL数(T)的矩阵；L是用矩阵形式表示的图像的低秩部分，S表示图像和低秩部分L的残差；E是多通道线圈编码矩阵，其等于欠采傅里叶算子与线圈的敏感度矩阵的乘积；Rank(L)表示矩阵的L的秩。设定T 1ρ自旋晶格弛豫模型为：
M＝M 0exp(-TSL k/T 1ρ) k＝1,2,…,T          (5)
其中，M表示不同TSL下的图像强度；M 0表示不带自旋-锁脉冲 (spin-lock pulse)情况下得到的平衡图像强度；TSL k为第k个自旋-锁时间，T为自旋-锁定时间(TSL)的个数。利用公式(5)对磁共振图像中的所有像素进行非线性拟合，即可得到每个像素相应的T 1ρ值。
基于T 1ρ弛豫模型，信号补偿可具体表示为将图像中的每个像素乘以一个补偿系数，补偿系数可由下式得到：
Coef＝exp(TSL k/T 1ρ) k＝1,2,…,T,            (6)
其中Coef表示补偿系数。
步骤S2中对全采的K空间中心部分经过傅里叶变换转化到图像域，得到初始参数加权图像，根据公式(5)T 1ρ自旋晶格弛豫模型对图像进行拟合，估计初始的T 1ρ参数，并根据公式(6)得到补偿系数的初始值Coef 0。
S4：根据L+S模型，使用所述补偿系数对所述参数加权图像进行更新；
本步骤那个使用L+S模型，具体执行需要在本步骤中进行内部循环迭代，迭代步骤如下：
S41：根据所述补偿系数，对所述参数加权图像进行补偿，得到中间图像；即
Figure PCTCN2020129484-appb-000009
其中U表示补偿后的图像。
其中i表示外循环的次数。
S42：将所述中间图像分解为低秩部分L和稀疏部分S；
S43：根据奇异值阈值操作算子更新所述低秩部分L，根据软阈值操作算子更新所述稀疏部分S；
本步骤中，初始化S＝0,设定外循环次数为J,在第j＝1,2,…,J次迭代中：
(1)更新L j：
Figure PCTCN2020129484-appb-000010
其中SVT(·)表示奇异值阈值操作算子，其定义为：
SVT λ(M)＝UΛ λ(Σ)V H      (7)
其中M＝UΣV H表示奇异值分解(SVD)，U、V分别为左、右奇异值向量组成的矩阵，V H表示V的共轭转置，Σ是由M的奇异值组成的对角矩阵，Λ λ(Σ)表示保留Σ中最大的奇异值不变，其他全为0，在本发明中，只取L的最大奇异值，使得做奇异值阈值操作后L的秩Rank(L)＝1；
(2)更新S j：
Figure PCTCN2020129484-appb-000011
ST(·)是一个软阈值操作算子，定义为：
Figure PCTCN2020129484-appb-000012
其中p是图像矩阵的一个元素，v是阈值。
S44：根据更新后的低秩部分L和稀疏部分S更新所述中间图像；
更新中间图像的具体公式为：
Figure PCTCN2020129484-appb-000013
其中Ε *表示Ε的逆操作，即等于对多通道线圈k空间数据做傅里叶逆变换后再进行线圈组合，得到图像；
S45：判断是否满足预设终止条件；
S46：若不满足预设终止条件，重复执行步骤S43至步骤S45，若满足预设终止条件，根据所述补偿系数和所述中间图像，计算得到更新后的参数加权图像。
更新参数加权图像公式如下：
Figure PCTCN2020129484-appb-000014
其中C -1(·)表示将图像基于每个像素除以补偿系数Coef i。
在本步骤中，终止内部循环，得到更新后的参数加权图像。
预设的终止条件可以是预设的迭代次数，也可以是迭代收敛条件。这里不做具体限定，现有技术中可行的迭代收敛条件均可用于本发明。
S5：使用所述更新后的参数加权图像更新所述自旋晶格弛豫参数T 1ρ和所述补偿系数；
本步骤中对自旋晶格弛豫参数T 1ρ和所述补偿系数的更新的计算方法同步骤S3，根据公式(5)计算T 1ρ，估计初始的T 1ρ参数，根据公式(6)计算补偿系数。
S6：判断是否满足预设的迭代终止条件；若不满足预设的迭代终止条件，重复执行步骤S4至S6进行迭代，若满足预设的迭代终止条件，执行下一步骤；
预设的迭代终止条件可以是预设的迭代次数，也可以是迭代收敛条件，如相邻两次迭代之间的重建误差小于预设值。这里不做具体限定，现有技术中可行的迭代收敛条件均可用于本发明。
S7：根据所述参数加权图像和所述自旋晶格弛豫模型拟合得到自旋晶格弛豫参数T 1ρ图像。
与现有的商用并行成像技术相比，本发明设计的螺旋轨迹变密度欠采样方式能极大地加快数据扫描速度，减少三维T 1ρ参数成像时间，在图像重建时，本发明提出的重建方法能够更精确地从高度欠采的数据中重建出参数加权图像。
实施例二
图4示出了根据本发明的另一个实施例的示意框图。
如图4所示，本发明的第二实施例提供了一种三维磁共振快速参数成像装置，包括：
数据获取模块，用于使用预设采集模板，以欠采方式获取目标对象的磁共振参数成像数据；
全采样图像重建模块，用于获取所述磁共振参数成像数据在K空间的中心部分的全采样数据，将所述全采样数据转换至图像域得到初始的参数加权图像；
初始参数模块，用于根据自旋晶格弛豫模型和所述参数加权图像确定自旋晶格弛豫参数T 1ρ和补偿系数；
图像更新模块，用于根据L+S模型，使用所述补偿系数对所述参数加权图像进行更新，
参数更新模块，用于使用所述更新后的参数加权图像更新所述自旋晶格弛豫参数T 1ρ和所述补偿系数；
判断模块，用于判断是否满足预设的迭代终止条件；在不满足预设的迭代终止条件时，重复调用所述图像更新模块和所述参数更新模块进行更新；
图像拟合模块，用于根据所述参数加权图像和所述自旋晶格弛豫模型拟合得到自旋晶格弛豫参数T 1ρ图像。
三维磁共振快速参数成像装置中的各个单元可以分别或全部合并为一个或若干个另外的单元来构成，或者其中的某个(些)单元还可以再拆分为功能上更小的多个单元来构成，这可以实现同样的操作，而不影响本发明的实施例的技术效果的实现。上述单元是基于逻辑功能划分的，在实际应用中，一个单元的功能也可以由多个单元来实现，或者多个单元的功能由 一个单元实现。在本发明的其它实施例中，基于三维磁共振快速参数成像装置也可以包括其它单元，在实际应用中，这些功能也可以由其它单元协助实现，并且可以由多个单元协作实现。
根据本发明的另一个实施例，可以通过在包括中央处理单元(CPU)、随机存取存储介质(RAM)、只读存储介质(ROM)等处理元件和存储元件的例如计算机的通用计算设备上运行能够执行实施例二中相应方法所涉及的各步骤的计算机程序(包括程序代码)，来构造如附图4中所示的三维磁共振快速参数成像装置设备，以及来实现本发明实施例的模型训练方法。所述计算机程序可以记载于例如计算机可读记录介质上，并通过计算机可读记录介质装载于上述计算设备中，并在其中运行。
实施例三
如图5所示，本发明的实施例三提供一种磁共振设备，包括：
一个或多个处理器；
存储装置，用于存储一个或多个程序；
当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如实施例一中所述的三维磁共振快速参数成像方法。
图5中，设备包括处理器201、存储器202、输入装置203以及输出装置204；设备中处理器201的数量可以是一个或多个，图5中以一个处理器201为例；设备中的处理器201、存储器202、输入装置203以及输出装置204可以通过总线或其他方式连接，图5中以通过总线连接为例。
存储器202作为一种计算机可读存储介质，可用于存储软件程序、计算机可执行程序以及模块，如本发明实施例中的三维磁共振快速参数成像方法对应的程序指令/模块(例如，数据获取模块、全采样图像重建模块、初始参数模块、图像更新模块、参数更新模块、判断模块、图像拟合模块)。处理器201通过运行存储在存储器202中的软件程序、指令以及模块，从而执行设备的各种功能应用以及数据处理，即实现上述的磁共振参数成像方法。
存储器202可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据终端的使用所创建的数据等。此外，存储器202可以包括高速随机存取存 储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储器202可进一步包括相对于处理器201远程设置的存储器，这些远程存储器可以通过网络连接至设备。上述网络的示例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
输入装置203可用于接收输入的数字或字符信息，以及产生与设备的用户设置以及功能控制有关的键信号输入。
输出装置204可包括显示屏等显示设备，例如，用户终端的显示屏。
实施例四
本发明的实施例四提供一种包含计算机可执行指令的存储介质，所述计算机可执行指令在由计算机处理器执行时用于执行如实施例一中所述的三维磁共振快速参数成像方法。
本发明各实施例方法中的步骤可根据实际需要进行顺序调整、合并和删减。
本发明各实施例装置中的单元可根据实际需要进行合并、划分和删减。
本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质包括只读存储器(Read-Only Memory，ROM)、随机存储器(Random Access Memory，RAM)、可编程只读存储器(Programmable Read-only Memory，PROM)、可擦除可编程只读存储器(Erasable Programmable Read Only Memory，EPROM)、一次可编程只读存储器(One-time Programmable Read-Only Memory，OTPROM)、电子抹除式可复写只读存储器(Electrically-Erasable Programmable Read-Only Memory，EEPROM)、只读光盘(Compact Disc Read-Only Memory，CD-ROM)或其他光盘存储器、磁盘存储器、磁带存储器、或者能够用于携带或存储数据的计算机可读的任何其他介质。
以上结合附图详细说明了本发明的技术方案，以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何 修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (9)

  1. 一种三维磁共振快速参数成像方法，其特征在于，包括以下步骤：
    S1：使用预设采集模板，以欠采样方式获取目标对象的磁共振参数成像数据；
    S2：获取所述磁共振参数成像数据在K空间的中心部分的全采样数据，将所述全采样数据转换至图像域得到参数加权图像；
    S3：根据自旋晶格弛豫模型和所述参数加权图像确定自旋晶格弛豫参数T 1ρ和补偿系数；
    S4：根据L+S模型，使用所述补偿系数对所述参数加权图像进行更新；
    S5：使用所述更新后的参数加权图像更新所述自旋晶格弛豫参数T 1ρ和所述补偿系数；
    S6：判断是否满足预设的迭代终止条件；若不满足预设的迭代终止条件，重复执行步骤S4至S6进行迭代，若满足预设的迭代终止条件，执行下一步骤；
    S7：根据所述参数加权图像和所述自旋晶格弛豫模型拟合得到自旋晶格弛豫参数T 1ρ图像。
  2. 如权利要求1所述的成像方法，其特征在于，所述预设采集模板具有以下特征：频率编码方向为全采样，相位编码方向为变密度欠采样，被采集的点遵循螺旋轨迹或径向轨迹。
  3. 如权利要求2所述的成像方法，其特征在于，步骤S4进一步包括：
    S41：根据所述补偿系数，对所述参数加权图像进行补偿，得到中间图像；
    S42：将所述中间图像分解为低秩部分L和稀疏部分S；
    S43：根据奇异值阈值操作算子更新所述低秩部分L，根据软阈值操作算子更新所述稀疏部分S；
    S44：根据更新后的低秩部分L和稀疏部分S更新所述中间图像；
    S45：判断是否满足预设终止条件；
    S46：若不满足预设终止条件，重复执行步骤S43至步骤S45，若满 足预设终止条件，根据所述补偿系数和所述中间图像，计算得到更新后的参数加权图像。
  4. 如权利要求3所述的成像方法，其特征在于，步骤S43中，所述根据奇异值阈值操作算子更新所述低秩部分L具体为：
    Figure PCTCN2020129484-appb-100001
    其中，U表示补偿后的图像，SVT(·)表示奇异值阈值操作算子，i、j分别表示不同迭代循环中的迭代次数。
  5. 如权利要求1-4之一所述的成像方法，其特征在于，所述自旋晶格弛豫模型为：
    M＝M 0exp(-TSL k/T 1ρ) k＝1,2,…,T
    其中，M表示不同自旋锁定时间点(spin lock time,TSL)下的图像强度；M 0表示不带自旋-锁脉冲(spin-lock pulse)情况下得到的平衡图像强度，TSL k为第k个自旋-锁时间，T为自旋-锁定时间(TSL)的个数。
  6. 如权利要求5所述的成像方法，其特征在于，所述补偿系数为：
    Coef＝exp(TSL k/T 1ρ) k＝1,2,…,T,
    其中Coef表示所述补偿系数。
  7. 一种三维磁共振快速参数成像装置，其特征在于，包括：
    数据获取模块，用于使用预设采集模板，以欠采方式获取目标对象的磁共振参数成像数据；
    全采样图像重建模块，用于获取所述磁共振参数成像数据在K空间的中心部分的全采样数据，将所述全采样数据转换至图像域得到参数加权图像；
    初始参数模块，用于根据自旋晶格弛豫模型和所述参数加权图像确定自旋晶格弛豫参数T 1ρ和补偿系数；
    图像更新模块，用于根据L+S模型，使用所述补偿系数对所述参数加权图像进行更新，
    参数更新模块，用于使用所述更新后的参数加权图像更新所述自旋晶格弛豫参数T 1ρ和所述补偿系数；
    判断模块，用于判断是否满足预设的迭代终止条件；在不满足预设的迭代终止条件时，重复调用所述图像更新模块和所述参数更新模块进行更新；
    图像拟合模块，用于根据所述参数加权图像和所述自旋晶格弛豫模型拟合得到自旋晶格弛豫参数T 1ρ图像。
  8. 一种磁共振设备，其特征在于，所述磁共振设备包括：
    一个或多个处理器；
    存储装置，用于存储一个或多个程序；
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-6中任一所述的成像方法。
  9. 一种包含计算机可执行指令的存储介质，其特征在于，所述计算机可执行指令在由计算机处理器执行时用于执行如权利要求1-6中任一所述的成像方法。
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170035319A1 (en) * 2015-08-04 2017-02-09 University Of Virginia Patent Foundation Rapid 3d dynamic arterial spin labeling with a sparse model-based image reconstruction
US20170053402A1 (en) * 2014-04-30 2017-02-23 Samsung Electronics Co., Ltd. Magnetic resonance imaging device and method for generating magnetic resonance image
CN108175409A (zh) * 2018-01-05 2018-06-19 郜发宝 一种定量快速锁频磁共振成像方法
CN109658468A (zh) * 2018-12-12 2019-04-19 深圳先进技术研究院 磁共振参数成像方法、装置、设备及存储介质
CN110146836A (zh) * 2018-02-12 2019-08-20 深圳先进技术研究院 一种磁共振参数成像方法、装置、设备及存储介质
CN110161442A (zh) * 2018-02-12 2019-08-23 深圳先进技术研究院 磁共振参数成像方法、装置、医学设备及存储介质

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7777487B2 (en) * 2007-02-15 2010-08-17 Uwm Research Foundation, Inc. Methods and apparatus for joint image reconstruction and coil sensitivity estimation in parallel MRI
US9633455B1 (en) * 2015-12-10 2017-04-25 Siemens Healthcare Gmbh Dictionary-free MR parameter inference for fingerprinting reconstruction
CN107219481B (zh) * 2017-05-26 2019-09-27 深圳先进技术研究院 一种通用的三维欠采样轨迹设计方法
CN109633502B (zh) * 2018-12-03 2020-07-24 深圳先进技术研究院 磁共振快速参数成像方法及装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170053402A1 (en) * 2014-04-30 2017-02-23 Samsung Electronics Co., Ltd. Magnetic resonance imaging device and method for generating magnetic resonance image
US20170035319A1 (en) * 2015-08-04 2017-02-09 University Of Virginia Patent Foundation Rapid 3d dynamic arterial spin labeling with a sparse model-based image reconstruction
CN108175409A (zh) * 2018-01-05 2018-06-19 郜发宝 一种定量快速锁频磁共振成像方法
CN110146836A (zh) * 2018-02-12 2019-08-20 深圳先进技术研究院 一种磁共振参数成像方法、装置、设备及存储介质
CN110161442A (zh) * 2018-02-12 2019-08-23 深圳先进技术研究院 磁共振参数成像方法、装置、医学设备及存储介质
CN109658468A (zh) * 2018-12-12 2019-04-19 深圳先进技术研究院 磁共振参数成像方法、装置、设备及存储介质

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