WO2023108423A1 - Magnetic resonance quantitative imaging method based on image structure and physical relaxation prior - Google Patents

Abstract

A magnetic resonance quantitative imaging method and apparatus based on an image structure and a physical relaxation prior, a device, and a storage medium thereof. The method comprises: extracting, on the basis of a block matching method, image blocks having a structural similarity, and constructing a low-rank structure tensor according to the image blocks (110); using a signal physical relaxation prior to construct a low-rank parameter tensor for a weighted image (120); and establishing, by means of the low-rank structure tensor and the low-rank parameter tensor, an image reconstruction model and a solving method based on a low-rank tensor (130). The method can greatly increase the data scanning speed and shorten the quantitative imaging time, and during image reconstruction, the reconstruction method can still accurately reconstruct a parameter weighted image from highly undersampled data at a higher acceleration factor.

Description

MRI Quantitative Imaging Method Based on Image Structure and Physical Relaxation Prior technical field

The present invention relates to magnetic resonance parameter quantitative imaging, in particular to a magnetic resonance quantitative imaging method, device, equipment and storage medium based on image structure and physical relaxation prior.

Background technique

Magnetic resonance parameter quantitative imaging is a technique to quantify some specific physiological parameters of tissues. Compared with conventional magnetic resonance imaging, this technique can provide a large amount of tissue-specific information other than morphological assessment, and it is an emerging method of disease diagnosis and evaluation in recent years. important method of imaging. Depending on the quantitative technique, when performing quantitative measurements on tissues, multiple data sets with different quantitative control parameters are usually collected first, from which a parameter-weighted image is reconstructed, and finally a parametric map is fitted from the image by a suitable parametric model. However, the acquisition of multiple data sets can multiply imaging time, leading to increased motion artifacts, high RF power deposition, and patient discomfort. Sensitivity to motion during scanning and shifting during scanning can make quantification difficult, greatly reducing The clinical application value of this method. Therefore, it has important theoretical research and clinical application value to speed up the imaging speed, improve the imaging efficiency, and realize the quantitative imaging method of magnetic resonance parameters that can be used clinically.

In order to shorten the scanning time, the current technology is mainly carried out around the following three directions: First, reduce the number of TSL, this method reduces the number of T _1ρ- weighted images acquired due to the reduction of TSL, so its quantitative accuracy is also reduced. Reduced. Second, a fast imaging sequence is used, but due to hardware limitations, the scanning speed will not be significantly increased. Finally, using fast imaging technology, the current commercial fast imaging technology is mainly parallel imaging technology (such as sensitivity encoding (SENSE), generalized automatic calibration partial parallel acquisition (GRAPPA), etc.), but this method is limited by the parallel imaging array coil The higher the acceleration factor, the lower the signal-to-noise ratio of the image obtained after imaging, so the scanning speed of this method can usually only reach 2-3 times. In recent years, compressive sensing technology based on sparse sampling theory has been widely concerned and applied in fast magnetic resonance imaging. According to the theory of compressed sensing, as long as the signal is sparse or compressible, after an incoherent measurement, the original signal can be accurately reconstructed from the highly undersampled data by solving the minimization problem using an optimization method.

Compressed sensing theory has been widely used in quantitative imaging of magnetic resonance parameters, which can ensure accurate parameter-weighted images and parameter values while improving scanning efficiency. For the space-parameter matrix composed of weighted image sequences, a reconstruction method based on low-rank and sparse constraints is proposed in the existing literature; or the use of sparse regularization effectively improves the imaging speed of bone and joint quantitative imaging ^[5] ; or for space- The parameter matrix imposes local low-rank constraints to further improve the reconstruction performance; however, the vectorization or matrix processing of the data in the reconstruction process of the above methods destroys the original spatial structure of the data. On this basis, some technologies use high-order tensors to better explore the correlation of high-dimensional image data and maximize the preservation of the original structure of the data. For example, the low-rank tensor reconstruction method based on image domain structure similarity and K-space domain structure similarity proposed in the existing literature, but it only studies the low-rank property driven by structural similarity, and ignores the signal physics Relaxation-related low-rank properties ^[9,10] .

Existing fast imaging methods based on compressed sensing theory mostly study the correlation of image data under the framework of two-dimensional matrix. In the process of image reconstruction, high-dimensional data needs to be vectorized or matrixed. For high-dimensional images of three-dimensional and above For data, this operation will destroy the inherent structure of the original image signal and cover up the redundant information that originally existed in high-dimensional data.

Contents of the invention

In view of the above defects or deficiencies in the prior art, it is desired to provide a magnetic resonance quantitative imaging method, device, equipment and storage medium based on image structure and physical relaxation prior.

In the first aspect, the embodiment of the present application provides a magnetic resonance quantitative imaging method based on image structure and physical relaxation prior, the method comprising:

Extract image blocks with structural similarity based on the block matching method, and construct a low-rank structure tensor according to the image blocks;

Construct a low-rank parameter tensor for the weighted image using the physical relaxation prior of the signal;

The low-rank tensor-based image reconstruction model and solution method are established through the low-rank structure tensor and low-rank parameter tensor.

In one of the embodiments, the extraction of image blocks with structural similarity based on the block matching method includes:

为T _1ρ加权图像，其中N _x、N _y、N _TSL分别为沿频率编码、相位编码方向的像素点个数、以及TSL的数量； definition

is the T _1ρ weighted image, where N _x , N _y , and N _TSL are the number of pixels along the frequency encoding and phase encoding directions, and the number of TSLs;

其中

Search for all image blocks in the neighborhood r×r

in

The similarity between image patches is measured by the normalized _L2- number distance, which is defined as:

当

的距离小于阈值λ _m时，则定义图像块

和B _i为结构相似性的图像块。

when

When the distance is less than the threshold λ _m , the image block is defined

and B _i are image blocks of structural similarity.

的距离小于阈值λ _m之后，该方法还包括： In one of the embodiments, when the

After the distance is less than the threshold λ _m , the method also includes:

The number of similar image patches to be searched is limited to N _patch , and all similar image patches are formed into a matrix with a size of b ² ×N _patch , where b is the size of the image patch (b×b).

In one of the embodiments, the constructing the low-rank structure tensor according to the image block includes:

Extract similar image blocks at a given pixel point i in all T _1ρ weighted images;

设定P _i为相似图像块提取算子，则

Construct a third-rank tensor with low-rank properties from all similar image patches

Set P _i as the similar image block extraction operator, then

In one of the embodiments, the use of signal physical relaxation prior to construct a low-rank parameter tensor for the weighted image includes:

对加权图像中的每一像素点进行逐点的拟合；其中，TSL表示自旋锁定时间，M表示经过TSL时间后的信号，M ₀为一常量，表示不施加准备脉冲时的基准信号； According to the principle of T _1ρ quantitative imaging, based on the formula

Carry out point-by-point fitting to each pixel in the weighted image; wherein, TSL represents the spin-lock time, M represents the signal after the TSL time, and M ₀ is a constant, representing the reference signal when the preparation pulse is not applied;

Carry out cluster analysis on the pixels in the image according to the estimated tissue T _1ρ quantitative value, so that the pixels in each cluster group belong to the same type of tissue;

For the clustered pixels, along the TSL direction, the pixels at the same position in different T _1ρ weighted images form a low-rank Hankel matrix;

Hankel matrices are stacked to form a third-order parameter tensor with low-rank properties.

In one of the embodiments, the low-rank structure tensor and low-rank parameter tensor are used to establish a low-rank tensor-based image reconstruction model and solution method, including:

Establish a low-rank tensor-based image reconstruction model based on the low-rank structure tensor and low-rank parameter tensor

和

分别为结构张量和参数张量； Among them, where E is a multi-channel encoding operator, which is equal to the product of the coil sensitivity matrix and the undersampled Fourier transform operator, X is the image to be reconstructed, Y is the collected K-space data, ||·| | _F represents the Frobenius norm, ||·|| _* represents the nuclear norm, λ _{1, i} and λ _{2, j} are regularization parameters,

and

are structure tensor and parameter tensor respectively;

The optimization problem of image reconstruction model is solved iteratively by low-rank tensor decomposition and alternating direction multiplier method.

In the second aspect, the embodiment of the present application also provides a magnetic resonance quantitative imaging device based on image structure similarity and physical relaxation prior, the device includes:

A low-rank structure tensor unit for extracting image blocks with structural similarity based on the block matching method, and constructing a low-rank structure tensor according to the image blocks;

The low-rank parameter tensor unit is used to construct a low-rank parameter tensor for the weighted image by using the physical relaxation prior of the signal;

A solving unit is established for establishing a low-rank tensor-based image reconstruction model and a solving method through the low-rank structure tensor and the low-rank parameter tensor.

In the third aspect, the embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the program, it implements the The method described in any one of the descriptions of the examples.

In a fourth aspect, the embodiment of the present application also provides a computer device, a computer-readable storage medium, on which a computer program is stored, and the computer program is used for: when the computer program is executed by a processor, the computer program according to the present application is implemented. The method described in any one of the descriptions of the examples.

Beneficial effects of the present invention:

The magnetic resonance quantitative imaging method based on image structure similarity and physical relaxation prior provided by the present invention greatly accelerates the data scanning speed and reduces the quantitative imaging time. However, it is still possible to accurately reconstruct parameter-weighted images from highly undersampled data.

Description of drawings

Other characteristics, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

FIG. 1 shows a schematic flow diagram of a magnetic resonance quantitative imaging method based on image structure similarity and physical relaxation prior provided by an embodiment of the present application;

FIG. 2 shows an exemplary structural block diagram of a magnetic resonance quantitative imaging device 200 based on image structure similarity and physical relaxation prior according to an embodiment of the present application;

Fig. 3 shows a schematic structural diagram of a computer system suitable for implementing a terminal device according to an embodiment of the present application.

Detailed ways

In order to make the above objects, features and advantages of the present invention more comprehensible, specific implementations of the present invention will be described in detail below in conjunction with the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention can be implemented in many other ways different from those described here, and those skilled in the art can make similar improvements without departing from the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

In describing the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", " Back", "Left", "Right", "Vertical", "Horizontal", "Top", "Bottom", "Inner", "Outer", "Clockwise", "Counterclockwise", "Axial" , "radial", "circumferential" and other indicated orientations or positional relationships are based on the orientations or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying the referred device or Elements must have certain orientations, be constructed and operate in certain orientations, and therefore should not be construed as limitations on the invention.

In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

In the present invention, unless otherwise clearly specified and limited, terms such as "installation", "connection", "connection" and "fixation" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection , or integrated; it may be mechanically connected or electrically connected; it may be directly connected or indirectly connected through an intermediary, and it may be the internal communication of two components or the interaction relationship between two components, unless otherwise specified limit. Those of ordinary skill in the art can understand the specific meanings of the above terms in the present invention according to specific situations.

In the present invention, unless otherwise clearly specified and limited, the first feature may be in direct contact with the first feature or the first and second feature may be in direct contact with the second feature through an intermediary. touch. Moreover, "above", "above" and "above" the first feature on the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. "Below", "beneath" and "beneath" the first feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is less horizontally than the second feature.

It should be noted that when an element is referred to as being “fixed on” or “disposed on” another element, it may be directly on the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or intervening elements may also be present. As used herein, the terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions are for the purpose of illustration only and are not intended to represent the only embodiments.

Please refer to FIG. 1 , which shows a schematic flowchart of a magnetic resonance quantitative imaging method based on image structure similarity and physical relaxation prior provided by an embodiment of the present application.

As shown in Figure 1, the method includes:

Step 110, extracting image blocks with structural similarity based on the block matching method, and constructing a low-rank structure tensor according to the image blocks;

Step 120, using the physical relaxation prior of the signal to construct a low-rank parameter tensor for the weighted image;

Step 130, establishing a low-rank tensor-based image reconstruction model and solution method through the low-rank structure tensor and low-rank parameter tensor.

Using the above-mentioned technical solution, the data scanning speed is greatly accelerated, and the quantitative imaging time is reduced. During image reconstruction, the reconstruction method proposed by the present invention can still accurately reconstruct a parameter-weighted image from highly under-collected data at a relatively high acceleration multiple. .

为T _1ρ加权图像，其中N _x、N _y、N _TSL分别为沿频率编码、相位编码方向的像素点个数、以及TSL的数量；搜索邻域r×r范围内的所有图像块

其中

通过归一化的L ₂数距离来度量图像块之间的相似度，该距离定义为：

当

的距离小于阈值λ _m时，则定义图像块

和B _i为结构相似性的图像块。限定搜索的相似图像块的数量为N _patch，将所有的相似图像块组成一个大小为b ²×N _patch的矩阵，其中b为图像块的大小(b×b)。 In some embodiments, the block matching method in the present invention extracts image blocks with structural similarity, including: defining

is T _1ρ weighted image, where N _x , N _y , and N _TSL are the number of pixels along the direction of frequency encoding and phase encoding, and the number of TSL; search for all image blocks in the neighborhood r×r

in

The similarity between image patches is measured by the normalized _L2- number distance, which is defined as:

when

When the distance is less than the threshold λ _m , the image block is defined

and B _i are image blocks of structural similarity. The number of similar image patches to be searched is limited to N _patch , and all similar image patches are formed into a matrix with a size of b ² ×N _patch , where b is the size of the image patch (b×b).

为T _1ρ加权图像，其中N _x、N _y、N _TSL分别为沿频率编码、相位编码方向的像素点个数、以及TSL的数量，对于像素点i处的大小为b×b的参考块B _i，搜索其邻域r×r范围内的所有图像块

其中

通过归一化的L ₂数距离来度量图像块之间的相似度，该距离定义为：

当该公式中的距离小于阈值λ _m时，即认为图像块

和B _i是相似的。限定搜索到 的相似图像块的数量为N _patch，则所有的相似图像块可组成一个大小为b ²×N _patch的矩阵。 Specifically, the present invention extracts image blocks with structural similarity based on the block matching method, and the specific method is as follows: define

is the T _1ρ weighted image, where N _x , N _y , and N _TSL are the number of pixels along the frequency encoding and phase encoding directions, and the number of TSLs, respectively. For a reference block B of size b×b at pixel i _i , search for all image blocks within the range of its neighborhood r×r

in

The similarity between image patches is measured by the normalized _L2- number distance, which is defined as:

When the distance in this formula is less than the threshold λ _m , the image block is considered

and B _i are similar. The number of searched similar image blocks is limited to N _patch , then all similar image blocks can form a matrix with a size of b ² ×N _patch .

设定P _i为相似图像块提取算子，则

In some embodiments, constructing a low-rank structure tensor according to image blocks in the present invention includes: extracting similar image blocks at a given pixel point i in all T _1ρ weighted images; constructing a low-rank tensor according to all similar image blocks third-order tensor of quantity properties

Set P _i as the similar image block extraction operator, then

其中TSL表示自旋锁定时间，M表示经过TSL时间后的信号，M ₀为一常量，表示不施加准备脉冲时的基准信号。因此，T _1ρ加权图像数据在TSL方向具有较高的冗余，利用这一弛豫先验信息，将所有T _1ρ加权图像中既定像素点i处的相似图像块提取出来，即可构成一个具有低秩特性的三阶张量

定义P _i为相似图像块提取算子，则

Specifically, since the T _1ρ weighted image decays exponentially in the TSL direction, its relaxation law is as follows:

Among them, TSL represents the spin-locking time, M represents the signal after the TSL time has elapsed, and M ₀ is a constant, representing the reference signal when no preparation pulse is applied. Therefore, T _1ρ weighted image data has high redundancy in the TSL direction. Using this relaxation prior information, we can extract similar image blocks at a given pixel point i in all T _1ρ weighted images to form a Third-rank tensors with low-rank properties

Define P _i as the similar image block extraction operator, then

型对加权图像中的每一像素点进行逐点的拟合；其中，TSL表示自旋锁定时间，M表示经过TSL时间后的信号，M ₀为一常量，表示不施加准备脉冲时的基准信号；根据预估的组织T _1ρ定量值对图像中的像素点进行聚类分析，使得每一聚类群组中的像素点属于同一类组织；对聚类后的像素点，沿TSL方向将不同T _1ρ加权图像中相同位置的像素构成一个低秩的Hankel矩阵；将Hankel矩阵以堆叠方式形成具有低秩特性的三阶参数张量。 In some embodiments, using signal physical relaxation priors in the present invention to construct low-rank parameter tensors for weighted images includes: according to the principle of T _1ρ quantitative imaging, based on the formula

The type fits each pixel in the weighted image point by point; among them, TSL represents the spin-lock time, M represents the signal after the TSL time, and M ₀ is a constant, representing the reference signal when no preparation pulse is applied ; Carry out cluster analysis on the pixels in the image according to the estimated tissue T _1ρ quantitative value, so that the pixels in each cluster group belong to the same type of tissue; the clustered pixels will be different along the TSL direction The pixels at the same position in the T _1ρ weighted image form a low-rank Hankel matrix; the Hankel matrix is stacked to form a third-order parameter tensor with low-rank characteristics.

中的弛豫模型对加权图像中的每一像素点进 行逐点的拟合，即可得到估计的组织T _1ρ定量值。由于同一类组织的T _1ρ定量值是相等的，因此可根据预估的组织T _1ρ定量值对图像中的像素点进行聚类分析，本发明采用直方图分析法对图像像素点进行聚类，使得每一聚类群组中的像素点属于同一类组织。 Specifically, according to the principle of T _1ρ quantitative imaging, based on the formula

The relaxation model in is fitted point-by-point to each pixel in the weighted image, and the estimated tissue T _1ρ quantitative value can be obtained. Since the _T1ρ quantitative values of the same type of tissue are equal, the pixels in the image can be clustered and analyzed according to the estimated tissue _T1ρ quantitative values. The present invention uses a histogram analysis method to cluster the image pixels, Make the pixels in each clustering group belong to the same type of organization.

The present invention utilizes the physical relaxation prior of the signal to form a low-rank Hankel matrix with pixels at the same position in different T _1ρ weighted images along the TSL direction, and the matrix is H[I(r)], where r represents a pixel point The position of , I(r) represents the pixel at position r in the weighted image, then there is

为第j个组构成的参数张量，

为参数张量构建操作算子，则有

Where I(r,TSL _K ) represents the pixel at position r in the TSL _Kth image, and K is defined as the smallest integer greater than or equal to N _TSL /2. For each pixel belonging to the same type of organization, construct a low-rank Hankel matrix along the TSL direction according to the above formula, and finally form a third-order parameter tensor with low-rank characteristics by stacking these Hankel matrices, let N _g be the clustering group The number of , N _tissue is the number of pixels belonging to the same type of tissue in each group,

is the parameter tensor formed by the jth group,

Build operators for parameter tensors, then have

In some embodiments, the low-rank structure tensor and low-rank parameter tensor in the present invention are used to establish a low-rank tensor-based image reconstruction model and solution method, including: according to the low-rank structure tensor and low-rank Establishing an Image Reconstruction Model Based on Low-Rank Tensors Using Parameter Tensor

和

分别为结构张量和参数张量；通过低秩张量分解和交替方向乘子法对图像重建模型的优化问题进行迭代求解。 Among them, where E is a multi-channel encoding operator, which is equal to the product of the coil sensitivity matrix and the undersampled Fourier transform operator, X is the image to be reconstructed, Y is the collected K-space data, ||·| | _F represents the Frobenius norm, ||·|| _* represents the nuclear norm, λ _{1, i} and λ _{2, j} are regularization parameters,

and

They are structure tensor and parameter tensor respectively; the optimization problem of image reconstruction model is solved iteratively by low-rank tensor decomposition and alternating direction multiplier method.

Specifically, the present invention combines low-rank structure tensors and parameter tensors to establish an image reconstruction model based on low-rank tensors. The model is as follows:

和

分别为结构张量和参数张量，引入拉格朗日乘子后，上述公式可转化为： Among them, E is the multi-channel encoding operation operator, which is equal to the product of the coil sensitivity matrix and the undersampling Fourier transform operator, X is the image to be reconstructed, Y is the collected K-space data, ||·|| _F represents the Frobenius norm, ||·|| _* represents the nuclear norm, λ _{1, i} and λ _{2, j} are regularization parameters, respectively,

and

are the structure tensor and the parameter tensor respectively. After introducing the Lagrange multiplier, the above formula can be transformed into:

Among them, μ ₁ and μ ₂ penalty term factors, α _1,i and α _2,j are Lagrangian multipliers, the formula is equivalent to:

The present invention uses Alternating Direction Method of Multipliers (ADMM) on the basis of low-rank tensor decomposition (HOSVD decomposition) to iteratively solve the optimization problem of the formula.

In the present invention, since the image signal decays exponentially in the parameter direction, its weighted image sequence has high redundancy in the direction of time of spin lock (TSL), and this relaxation prior information is used , the present invention extracts image blocks with structural similarity based on a block matching method, thereby constructing a structure tensor.

Based on the physical relaxation prior of the signal, a Hankel matrix with low-rank characteristics can be constructed for each pixel in the weighted image along the _TSL direction. Based on the physical relaxation model, a T _1ρ parameter map is estimated, and the pixel points of the image are clustered and analyzed according to the T _1ρ quantitative value in the parameter map, and the clustered groups are constructed using the Hankel matrix parameter tensor.

Combine structure tensor and parameter tensor to build an image reconstruction model based on low-rank tensor, and use low-rank tensor decomposition (HOSVD decomposition) to design an image reconstruction solution method on the basis of low-rank tensor.

Further, referring to FIG. 2 , FIG. 2 shows an exemplary structural block diagram of a magnetic resonance quantitative imaging apparatus 200 based on image structure similarity and physical relaxation prior according to an embodiment of the present application.

As shown in Figure 2, the device includes:

A low-rank structure tensor unit 210, configured to extract image blocks with structural similarity based on the block matching method, and construct a low-rank structure tensor according to the image blocks;

The low-rank parameter tensor unit 220 is configured to construct a low-rank parameter tensor for the weighted image by using the physical relaxation prior of the signal;

A solving unit 230 is established, which is used to establish a low-rank tensor-based image reconstruction model and a solving method through the low-rank structure tensor and the low-rank parameter tensor.

It should be understood that the units or modules recorded in the device 200 correspond to the steps in the method described with reference to FIG. 1 . Therefore, the operations and features described above for the method are also applicable to the device 200 and the units contained therein, and will not be repeated here. The apparatus 200 may be pre-implemented in the browser of the electronic device or other security applications, and may also be loaded into the browser of the electronic device or its security applications by means of downloading or the like. The corresponding units in the apparatus 200 may cooperate with the units in the electronic device to implement the solutions of the embodiments of the present application.

Referring now to FIG. 3 , it shows a schematic structural diagram of a computer system 300 suitable for implementing a terminal device or a server according to an embodiment of the present application.

As shown in FIG. 3 , a computer system 300 includes a central processing unit (CPU) 301 that can operate according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random-access memory (RAM) 303 Instead, various appropriate actions and processes are performed. In the RAM 303, various programs and data required for the operation of the system 300 are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. An input/output (I/O) interface 305 is also connected to the bus 304 .

The following components are connected to the I/O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 308 including a hard disk, etc. and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 310 as necessary so that a computer program read therefrom is installed into the storage section 308 as necessary.

In particular, according to an embodiment of the present disclosure, the process described above with reference to FIG. 1 may be implemented as a computer software program. For example, embodiments of the present disclosure include a method of quantitative magnetic resonance imaging based on image structure similarity and physical relaxation priors, comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising a method for Program code for executing the method in FIG. 1 . In such an embodiment, the computer program may be downloaded and installed from a network via communication portion 309 and/or installed from removable media 311 .

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of code that includes one or more logical functions for implementing specified executable instructions. It should also be noted that, 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 they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The units or modules involved in the embodiments described in the present application may be implemented by means of software or by means of hardware. The described units or modules may also be set in a processor. For example, it may be described as: a processor includes a first sub-region generating unit, a second sub-region generating unit, and a display region generating unit. Wherein, the names of these units or modules do not constitute limitations on the units or modules themselves in some cases, for example, the display area generation unit can also be described as "used to generate The cell of the display area of the text".

As another aspect, the present application also provides a computer-readable storage medium, which may be the computer-readable storage medium contained in the aforementioned devices in the above-mentioned embodiments; computer-readable storage media stored in the device. The computer-readable storage medium stores one or more programs, and the aforementioned programs are used by one or more processors to execute the text generation method applied to transparent window envelopes described in this application.

The above description is only a preferred embodiment of the present application and an illustration of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solution formed by the specific combination of the above-mentioned technical features, and should also cover the technical solutions formed by the above-mentioned technical features or without departing from the aforementioned inventive concept. Other technical solutions formed by any combination of equivalent features. For example, a technical solution formed by replacing the above-mentioned features with technical features with similar functions disclosed in (but not limited to) this application.

Claims (10)

A magnetic resonance quantitative imaging method based on image structure and physical relaxation prior, characterized in that the method comprises: Extract image blocks with structural similarity based on the block matching method, and construct a low-rank structure tensor according to the image blocks; Construct a low-rank parameter tensor for the weighted image using the physical relaxation prior of the signal; The low-rank tensor-based image reconstruction model and solution method are established through the low-rank structure tensor and low-rank parameter tensor. The magnetic resonance quantitative imaging method based on image structure and physical relaxation prior according to claim 1, wherein the extraction of image blocks with structural similarity based on the block matching method comprises: definition

is the T _1ρ weighted image, where N _x , N _y , and N _TSL are the number of pixels along the frequency encoding and phase encoding directions, and the number of TSLs; Search for all image blocks in the neighborhood r×r

in

The similarity between image patches is measured by the normalized _L2- number distance, which is defined as:

when

When the distance is less than the threshold λ _m , the image block is defined

and B _i are image blocks of structural similarity. The magnetic resonance quantitative imaging method based on image structure and physical relaxation prior according to claim 2, wherein said when

After the distance is less than the threshold λ _m , the method also includes: The number of similar image patches to be searched is limited to N _patch , and all similar image patches are formed into a matrix with a size of b ² ×N _patch , where b is the size of the image patch (b×b). The magnetic resonance quantitative imaging method based on image structure and physical relaxation prior according to claim 3, wherein said constructing a low-rank structure tensor according to an image block comprises: Extract similar image blocks at a given pixel point i in all T _1ρ weighted images; Construct a third-rank tensor with low-rank properties from all similar image patches

Set P _i as the similar image block extraction operator, then

The magnetic resonance quantitative imaging method based on image structure and physical relaxation prior according to claim 4, wherein said utilizing signal physical relaxation prior to constructing a low-rank parameter tensor for weighted images comprises: According to the principle of T _1ρ quantitative imaging, based on the formula

Carry out point-by-point fitting for each pixel in the weighted image; where, TSL represents the spin-lock time, M represents the signal after the TSL time, and M ₀ is a constant, representing the reference when the T _1ρ preparation pulse is not applied Signal; Carry out cluster analysis on the pixels in the image according to the estimated tissue T _1ρ quantitative value, so that the pixels in each cluster group belong to the same type of tissue; For the clustered pixels, along the TSL direction, the pixels at the same position in different T _1ρ weighted images form a low-rank Hankel matrix; Hankel matrices are stacked to form a third-order parameter tensor with low-rank properties. The magnetic resonance quantitative imaging method based on image structure and physical relaxation prior according to claim 5, wherein the low-rank tensor is used to establish the low-rank tensor-based Image reconstruction models and solution methods, including: Establish a low-rank tensor-based image reconstruction model based on the low-rank structure tensor and low-rank parameter tensor

Among them, where E is a multi-channel encoding operator, which is equal to the product of the coil sensitivity matrix and the undersampled Fourier transform operator, X is the image to be reconstructed, Y is the collected K-space data, ||·| | _F represents the Frobenius norm, ||·|| _* represents the nuclear norm, λ _{1, i} and λ _{2, j} are regularization parameters,

and

are structure tensor and parameter tensor respectively; The optimization problem of image reconstruction model is solved iteratively by low-rank tensor decomposition and alternating direction multiplier method. A magnetic resonance quantitative imaging device based on image structure and physical relaxation prior, characterized in that the device includes: A low-rank structure tensor unit for extracting image blocks with structural similarity based on the block matching method, and constructing a low-rank structure tensor according to the image blocks; The low-rank parameter tensor unit is used to construct a low-rank parameter tensor for the weighted image by using the physical relaxation prior of the signal; A solving unit is established for establishing a low-rank tensor-based image reconstruction model and a solving method through the low-rank structure tensor and the low-rank parameter tensor. The magnetic resonance quantitative imaging device based on image structure and physical relaxation prior according to claim 7, wherein the extraction of image blocks with structural similarity based on the block matching method includes: definition

is the T _1ρ weighted image, where N _x , N _y , and N _TSL are the number of pixels along the frequency encoding and phase encoding directions, and the number of TSLs; Search for all image blocks in the neighborhood r×r

in

The similarity between image patches is measured by the normalized _L2- number distance, which is defined as:

when

When the distance is less than the threshold λ _m , the image block is defined

and B _i are image blocks of structural similarity. A computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, characterized in that, when the processor executes the program, it implements any of claims 1-6 described method. A computer-readable storage medium having stored thereon a computer program for: When the computer program is executed by the processor, the method according to any one of claims 1-6 is implemented.

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