WO2021217391A1 - Rapid magnetic resonance multi-parameter imaging method and apparatus - Google Patents

Abstract

A rapid magnetic resonance multi-parameter imaging method and apparatus. The rapid magnetic resonance multi-parameter imaging method comprises: collecting N groups of data by means of a pulse sequence, and reconstructing on the basis of the N groups of data to obtain N images, wherein N is an integer greater than or equal to 1 (step 110); generating a dictionary from parameters of the pulse sequence, a spin-lattice relaxation time constant T1 and a spin-spin relaxation time constant T2 by means of a fractional order Bloch model (step 120); and matching the signal sequences of corresponding pixels on the N images with entries in the dictionary, and determining final R tissue characteristic parameter diagrams as the output of fingerprint imaging according to the matching degree, R being an integer greater than or equal to 1 (step 130).

Description

Fast magnetic resonance multi-parameter imaging method and device Technical field

The embodiments of the present application belong to the field of magnetic resonance technology, and in particular, relate to a fast magnetic resonance multi-parameter imaging method, device, terminal device, and storage medium.

Background technique

Magnetic Resonance Imaging (MRI) is a powerful medical imaging mode. It has no ionizing radiation and can provide a variety of image contrasts. It can obtain information on human anatomy, physiological functions, blood flow and metabolism. Among them, magnetic resonance fingerprint imaging (MRF) is a new rapid quantitative magnetic resonance imaging technology, which can obtain multiple characteristic parameters of tissues at the same time with one scan. Magnetic resonance fingerprint imaging technology mainly includes the use of N excitations with different repetition time (TR), echo time (TE) and flip angle (FA) pulse sequences, collecting N sets of data and reconstructing N highly undersampled images. Then use the first-order Bloch model to generate a dictionary according to the parameters TR, TE and FA of the pulse sequence, and finally match and identify the signals of the corresponding pixels on the N images with the elements in the dictionary point by point, so that multiple parameters of the organization can be obtained at the same time result.

technical problem

However, the evolution of signals in the traditional MRF process of magnetic resonance fingerprint imaging is more complicated, and the dictionary model is too simple, resulting in poor accuracy of the obtained results.

Technical solutions

One aspect of the embodiments of the present application provides a fast magnetic resonance multi-parameter imaging method, which includes:

N groups of data are collected through a pulse sequence, and N images are reconstructed based on the N groups of data; where N is an integer greater than or equal to 1;

The parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are used to generate a dictionary through a fractional Bloch model;

The signal sequence of the corresponding pixels on the N images is matched with the entries in the dictionary, and the final R tissue characteristic parameter maps are determined as the output of fingerprint imaging according to the matching degree, and R is an integer greater than or equal to 1. .

In one embodiment, the method further includes:

The dictionary is divided into K categories according to the fractional order factor, and K is an integer greater than or equal to 1.

In one embodiment, the fractional factor is a fractional factor for T1 and T2.

In an embodiment, the value range of the fractional factor is between 0 and 2.

In an embodiment, the matching the signal sequences of corresponding pixels on the N images with entries in the dictionary, and determining the final R tissue characteristic parameter maps as the output of fingerprint imaging according to the matching degree, includes:

The signal sequence composed of the corresponding pixels of the N images is matched and identified with the dictionary entries of each of the K categories, and the quantitative imaging images of the K groups of tissue characteristic parameters are obtained. Quantitative imaging images include quantitative imaging images of R tissue characteristic parameters;

For any group of quantitative imaging images in the K groups of quantitative imaging images, M tissue regions of interest are selected;

The R tissue characteristic parameters of the M tissue regions of interest of the quantitative imaging images of each group of tissue characteristic parameters are compared with the R empirical tissue characteristic parameters of the corresponding regions, and the final tissue is determined according to the deviation obtained from the comparison. The characteristic parameter map is used as the output of fingerprint imaging.

In an embodiment, the matching and identifying the signal sequence composed of the corresponding pixels of the N images with the entries in the dictionary of each category to obtain the quantitative imaging images of the K groups of tissue characteristic parameters includes:

Extract the signals of the corresponding pixels in the N images to obtain a two-dimensional signal sequence; wherein, each of the N images is composed of multiple pixels;

Matching the signal sequence with each entry of the K fractional factor category dictionary to obtain K groups of tissue characteristic parameters corresponding to each pixel position, and each group of tissue characteristic parameters includes R tissue characteristic parameters;

The tissue characteristic parameters of all pixels are converted into R quantitative imaging images.

In an embodiment, the selecting M tissue regions of interest includes:

The image area corresponding to the tissue of a single component is selected on the quantitative imaging image as the tissue area of interest.

In one embodiment, the tissue characteristic parameters of the M tissue regions of interest in the quantitative imaging images of each group of tissue characteristic parameters are matched with the R empirical tissue characteristic parameters of the corresponding regions, and the results are obtained according to the comparison. The degree of deviation determines the final R tissue characteristic parameter maps as the result of fingerprint imaging and output, including:

Sort the pixels contained in the M tissue regions of interest in each group of quantitative imaging images, and set the R empirical tissue characteristic parameters of each pixel correspondingly, and set the empirical tissue characteristic parameters of each pixel. Constitute the row vector r;

Forming a matrix J for each tissue characteristic parameter corresponding to the pixels contained in the M tissue regions of interest in the dictionary of each category based on the sorting manner of the labels;

According to the difference between each row vector of the matrix J and the row vector r, the final R tissue characteristic parameter maps are determined.

In an embodiment, the determining the final R tissue characteristic parameter maps according to the difference between each row vector of the matrix J and the row vector r includes:

Calculate the residual sum of squares of each row vector in the matrix J and the row vector r, and each residual sum of squares constitutes a column vector err of size K;

The final R tissue characteristic parameter maps are determined according to the tissue characteristic parameter corresponding to the smallest value in the column vector err.

In an embodiment, the calculating the residual sum of squares of each row vector in the matrix J and the row vector r is specifically:

Calculate the difference between the candidate tissue characteristic parameter in the matrix J and the corresponding empirical tissue characteristic parameter in the row vector r; wherein the candidate tissue characteristic parameter is the difference between the M interest in the dictionary of each category The tissue characteristic parameters corresponding to the pixels contained in the tissue area;

The difference is divided by the corresponding empirical tissue characteristic parameter and squared.

In one embodiment, the tissue characteristic parameter is T1 and/or T2.

In an embodiment, the collecting N sets of data through a pulse sequence and reconstructing to obtain N images based on the N sets of data includes:

In each excitation of the pulse sequence, different repetition time TR, echo time TE, and flip angle FA are used to collect N sets of data and reconstruct to obtain N images.

In an embodiment, the fractional Bloch model is:

M _z (t)=M _z (0)+[M ₀ -M _z (0)][1-E _β (-(t/T ₁ ) ^β )]

M _xy (t)=M _xy (0)[E _α (-(t/T ₂ ) ^α )]+M _xy (∞)

in,

Is the β-order differential operator of Riemann-Liouville in Caputo form,

Is the Riemann-Liouville α-order differential operator in Caputo form, M ₀ is the initial magnetization vector, M _z (t) is the longitudinal magnetization vector at time t, and M _xy (t) is the transverse magnetization vector at time t,

Is the β-order T ₁ relaxation time constant,

Is the α-order T ₂ relaxation time constant, E _β (-(t/T ₁ ) ^β ) is the β-order tensile Mittag-Leffler function _{of T 1 , and E α} (-(t/T ₂ ) ^α ) is T ₂ Is the α-order stretched Mittag-Leffler function, ω ₀ is the resonance frequency,

Is Riemann-Liouville's (1-α)-order integral operator, and the Mittag-Leffler function is

A second aspect of the embodiments of the present application provides a fast magnetic resonance multi-parameter imaging device, which includes:

The acquisition module is used to acquire N sets of data through a pulse sequence, and reconstruct N images based on the N sets of data; where N is an integer greater than or equal to 1;

A dictionary generation module for generating a dictionary using the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 through the fractional Bloch model;

The matching imaging module is used to match the signal sequence of the corresponding pixels on the N images with the entries in the dictionary, and determine the final R tissue characteristic parameter maps as the output of fingerprint imaging according to the matching degree, and R is greater than or equal to An integer of 1.

The third aspect of the embodiments of the present application provides a terminal device, including a memory, a processor, and computer-readable instructions stored in the memory and running on the processor, and the processor executes the computer-readable instructions. The following steps are implemented when ordering:

N groups of data are collected through a pulse sequence, and N images are reconstructed based on the N groups of data; where N is an integer greater than or equal to 1;

The parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are used to generate a dictionary through a fractional Bloch model;

The signal sequence of the corresponding pixels on the N images is matched with the entries in the dictionary, and the final R tissue characteristic parameter maps are determined as the output of fingerprint imaging according to the matching degree, and R is an integer greater than or equal to 1.

In an embodiment, the processor further implements the following steps when executing the computer-readable instructions:

The dictionary is divided into K categories according to the fractional order factor, and K is an integer greater than or equal to 1.

In an embodiment, the matching the signal sequences of corresponding pixels on the N images with entries in the dictionary, and determining the final R tissue characteristic parameter maps as the output of fingerprint imaging according to the matching degree, includes:

The signal sequence composed of the corresponding pixels of the N images is matched and identified with the dictionary entries of each of the K categories, and the quantitative imaging images of the K groups of tissue characteristic parameters are obtained. Quantitative imaging images include quantitative imaging images of R tissue characteristic parameters;

For any group of quantitative imaging images in the K groups of quantitative imaging images, M tissue regions of interest are selected;

Compare the R tissue characteristic parameters of the M tissue regions of interest in the quantitative imaging images of each group of tissue characteristic parameters with the R empirical tissue characteristic parameters of the corresponding regions, and determine the final tissue according to the matching degree obtained from the comparison The characteristic parameter map is used as the output of fingerprint imaging.

In one embodiment, the tissue characteristic parameters of the M tissue regions of interest in the quantitative imaging images of each group of tissue characteristic parameters are matched with the R empirical tissue characteristic parameters of the corresponding regions, and the results are obtained according to the comparison. The degree of deviation determines the final R tissue characteristic parameter maps as the result of fingerprint imaging and output, including:

Sort the pixels contained in the M tissue regions of interest in each group of quantitative imaging images, and set the R empirical tissue characteristic parameters of each pixel correspondingly, and set the empirical tissue characteristic parameters of each pixel. Constitute the row vector r;

Forming a matrix J for each tissue characteristic parameter corresponding to the pixels contained in the M tissue regions of interest in the dictionary of each category based on the sorting manner of the labels;

According to the difference between each row vector of the matrix J and the row vector r, the final R tissue characteristic parameter maps are determined.

In an embodiment, the determining the final R tissue characteristic parameter maps according to the difference between each row vector of the matrix J and the row vector r includes:

Calculate the residual sum of squares of each row vector in the matrix J and the row vector r, and each residual sum of squares constitutes a column vector err of size K;

The final R tissue characteristic parameter maps are determined according to the tissue characteristic parameter corresponding to the smallest value in the column vector err.

The fourth aspect of the embodiments of the present application provides a computer storage medium, the computer-readable storage medium stores computer-readable instructions, wherein the computer-readable instructions are executed by a processor to implement the following steps:

N groups of data are collected through a pulse sequence, and N images are reconstructed based on the N groups of data; where N is an integer greater than or equal to 1;

The parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are used to generate a dictionary through a fractional Bloch model;

The signal sequence of the corresponding pixels on the N images is matched with the entries in the dictionary, and the final R tissue characteristic parameter maps are determined as the output of fingerprint imaging according to the matching degree, and R is an integer greater than or equal to 1.

The fifth aspect of the embodiments of the present application provides a computer program product, which when the computer program product runs on a terminal device, causes the terminal device to execute the fast magnetic resonance multi-parameter imaging method described in any one of the above-mentioned first aspects.

Beneficial effect

In the embodiment of the present application, N sets of data are collected through a pulse sequence, and N images are reconstructed based on the N sets of data; the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin The relaxation time constant T2 is used to generate a dictionary through the fractional Bloch model; the signal sequence of the corresponding pixels on the N images is matched with the elements in the dictionary, and the final tissue characteristic parameter value is determined according to the degree of matching. For fingerprint imaging, since the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are combined to generate a dictionary, the accuracy of quantitative imaging can be improved.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative work, other drawings can be obtained from these drawings.

Fig. 1 is a schematic diagram of an application scenario provided by an embodiment of the present application;

2 is a schematic flowchart of a fast magnetic resonance multi-parameter imaging method provided by an embodiment of the present application;

Fig. 3(a) is a schematic diagram of the flip angle FA in the pulse sequence provided by an embodiment of the present application;

Figure 3(b) is a schematic diagram of the repetition time TR and the echo time TE in the pulse sequence provided by an embodiment of the present application;

FIG. 4 is a timing diagram of a pulse sequence provided by an embodiment of the present application;

FIG. 5 is a schematic flowchart of a fast magnetic resonance multi-parameter imaging method provided by an embodiment of the present application;

FIG. 6 is a schematic diagram of categorizing dictionaries according to fractional order factors according to an embodiment of the present application;

FIG. 7 is a schematic flowchart of a fast magnetic resonance multi-parameter imaging method provided by an embodiment of the present application;

FIG. 8 is a schematic diagram of a process of dividing N images into K groups of images according to an embodiment of the present application;

FIG. 9 is a schematic diagram of selecting a tissue region of interest in an image according to an embodiment of the present application;

10 is a schematic flowchart of a fast magnetic resonance multi-parameter imaging method provided by an embodiment of the present application;

FIG. 11 is a schematic diagram of sorting the labels of pixels in a tissue region of interest according to an embodiment of the present application;

FIG. 12 is a schematic diagram of the effect of this application and the effect of other methods provided by an embodiment of the application;

FIG. 13 is a schematic structural diagram of a fast magnetic resonance multi-parameter imaging device provided by an embodiment of the present application;

FIG. 14 is a schematic structural diagram of a terminal device provided by an embodiment of the present application.

Embodiments of the present invention

In the following description, for the purpose of illustration rather than limitation, specific details such as a specific system structure and technology are proposed for a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from obstructing the description of this application.

It should be understood that when used in the specification and appended claims of this application, the term "comprising" indicates the existence of the described features, wholes, steps, operations, elements and/or components, but does not exclude one or more other The existence or addition of features, wholes, steps, operations, elements, components, and/or collections thereof.

It should also be understood that the term "and/or" used in the specification and appended claims of this application refers to any combination of one or more of the items listed in the associated and all possible combinations, and includes these combinations.

As used in the description of this application and the appended claims, the term "if" can be construed as "when" or "once" or "in response to determination" or "in response to detecting ". Similarly, the phrase "if determined" or "if detected [described condition or event]" can be interpreted as meaning "once determined" or "in response to determination" or "once detected [described condition or event]" depending on the context ]" or "in response to detection of [condition or event described]".

In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

The reference to "one embodiment" or "some embodiments" described in the specification of this application means that one or more embodiments of this application include a specific feature, structure, or characteristic described in combination with the embodiment. Therefore, the sentences "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. appearing in different places in this specification are not necessarily All refer to the same embodiment, but mean "one or more but not all embodiments" unless it is specifically emphasized otherwise. The terms "including", "including", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

Magnetic resonance imaging (MRI) is a powerful medical imaging mode. It has no ionizing radiation and can provide a variety of image contrasts. It can obtain information on human anatomy, physiological functions, blood flow and metabolism. However, when traditional MRI is used for quantitative imaging, its application is limited by the scan time. In order to obtain a characteristic parameter of the tissue, it is necessary to repeat multiple scans and only change one variable. For example, the spin-echo sequence of inversion recovery is repeatedly scanned multiple times to change the inversion recovery time (TI) while keeping other scanning parameters unchanged, and the spin-lattice relaxation time constant (T1) is measured by nonlinear fitting. ; The spin echo sequence is repeatedly scanned to change the echo time while keeping other scanning parameters unchanged, and the spin-spin relaxation time constant (T2) is measured by nonlinear fitting.

Magnetic resonance fingerprint imaging (MRF) is a new fast quantitative magnetic resonance imaging technology, which can obtain multiple characteristic parameters of tissues at the same time in one scan. Magnetic resonance fingerprint imaging technology mainly includes the use of N excitations with different repetition time (TR), echo time (TE) and flip angle (FA) pulse sequences, collecting N sets of data and reconstructing N highly undersampled images. Then use the first-order Bloch model to generate a dictionary according to the parameters TR, TE and FA of the pulse sequence, and finally match and identify the signals of the corresponding pixels on the N images with the elements in the dictionary point by point, so that multiple parameters of the organization can be obtained at the same time result.

However, the fractional fingerprint imaging uses a point-by-point fractional factor, which improves the local matching accuracy but sacrifices the signal-to-noise ratio and global accuracy of the image. As a result, the result of the fractional fingerprint imaging is better than that of the first-order fingerprint. The imaging results are good, but there is still a certain gap with the classical quantitative magnetic resonance imaging MRI.

In the course of research, the inventor of the present application discovered that: the current fractional magnetic resonance fingerprint imaging only considers the matching degree between the evolution of a single point actual signal and the evolution of the dictionary analog signal, and does not make full use of the prior tissue characteristic parameters and the dictionary. The degree of match between candidate tissue characteristic parameters. In view of the shortcomings of the above technology, the present invention proposes a fast magnetic resonance multi-parameter imaging method based on fractional fingerprint quantitative imaging. N sets of data are collected through a pulse sequence, and N images are reconstructed based on the N sets of data; Parameter, spin-lattice relaxation time constant T1 and spin-spin relaxation time constant T2, a dictionary is generated through the fractional Bloch model; the signal sequence of the corresponding pixels on the N images is compared with the entries in the dictionary Matching, the final R tissue characteristic parameter maps are determined according to the matching degree as the output of fingerprint imaging, which is generated by combining the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 Dictionary, so the obtained R tissue characteristic parameter maps can improve the accuracy of quantitative imaging.

For example, the embodiments of the present application can be applied to the exemplary scenario shown in FIG. 1. Among them, the magnetic resonance device 10 and the server 20 constitute an application scenario of the above-mentioned fast magnetic resonance multi-parameter imaging method.

Specifically, the magnetic resonance device 10 obtains N sets of data according to the pulse sequence of the server 20, the data may be fingerprint image data, and sends the N sets of data to the server 20; the server 20 reconstructs and obtains N images based on the N sets of data, The parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are used to generate a dictionary through the fractional Bloch model, and the signal sequence of the corresponding pixels on the N images is compared with the dictionary The items in are matched, and the final R tissue characteristic parameter maps are determined according to the matching degree as the output of fingerprint imaging.

Among them, the above-mentioned final R tissue characteristic parameter maps are R tissue characteristic parameters that can reflect human tissues, and the above-mentioned final R tissue characteristic parameter maps can be displayed as corresponding human tissue images, for example, for the above-mentioned final R tissue The characteristic parameter map can be displayed as a corresponding human tissue image after image processing, so that the doctor can make reference observation.

Hereinafter, the fast magnetic resonance multi-parameter imaging method of the present application will be described in detail with reference to FIG. 1.

FIG. 2 is a schematic flowchart of a fast magnetic resonance multi-parameter imaging method according to an embodiment of the present application. Referring to FIG. 2, the fast magnetic resonance multi-parameter imaging method is detailed as follows:

In step 110, N sets of data are collected through a pulse sequence, and N images are reconstructed based on the N sets of data; where N is an integer greater than or equal to 1.

Exemplarily, step 110 may specifically be: in each excitation of the foregoing pulse sequence, different repetition time (Time of Repetition, TR), echo time (Time of Echo, TE), and flip angle (Flip Angle) are used. ,FA), N sets of data are collected and reconstructed to obtain N images.

Specifically, the repetition time TR, echo time TE, and flip angle FA can be adjusted. Different repetition time TR, echo time TE, and flip angle FA correspond to different pulse sequences, and then collect data according to different pulse sequences. For example, you can Is the fingerprint image data.

Figures 3(a) and 3(b) provide an exemplary embodiment of the entire repetition time TR, the echo time TE, and the flip angle FA. The flip angle FA is adjusted in Figure 3(a). In 3(b), the repetition time TR and the echo time TE are adjusted. The repetition time TR, the echo time TE, and the flip of the pulse sequence can be achieved through the methods shown in Figure 3(a) and Figure 3(b) The angle FA is adjusted, and the obtained pulse sequence is shown in FIG. 4, and N sets of data are collected according to the pulse sequence shown in FIG. 4, which may be fingerprint image data, for example.

In step 120, the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are used to generate a dictionary through a fractional Bloch model.

Among them, the relaxation time is a characteristic time of a dynamic system, and it is the time required for a certain variable of the system to change from a transient state to a certain steady state. The spin-lattice relaxation time constant T1 is the time constant for the recovery of the longitudinal magnetization, also known as the longitudinal relaxation time constant; the spin-spin relaxation time constant T2 is the time constant for the disappearance of the transverse magnetization, also known as the transverse Relaxation time constant.

Exemplarily, the parameters of the aforementioned pulse sequence may include the repetition time TR, the echo time TE, and the flip angle FA.

In this embodiment, according to the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2, the fractional Bloch model is used to generate a dictionary, so that the dictionary can be more accurate Reflects the tissue characteristic parameters, so that the subsequent imaging accuracy is higher.

In step 130, the signal sequences of corresponding pixels on the N images are matched with entries in the dictionary, and the final R tissue characteristic parameter maps are determined as the output of fingerprint imaging according to the matching degree, and R is greater than or equal to 1. Integer.

Exemplarily, the signal sequences of corresponding pixels on the N images can be matched with entries in the dictionary, and the signal sequence with the highest matching degree can be used as the final R tissue characteristic parameter maps as the output of fingerprint imaging, or according to the matching The signal sequence whose degree is greater than the threshold determines the final R tissue characteristic parameter maps as the output of fingerprint imaging, which is not limited and can be set according to actual needs.

For example, the degree of matching can be determined according to the magnitude of the difference between the signal sequence of the corresponding pixels on the N images and the entries in the dictionary. The smaller the difference, the higher the matching degree, and the larger the difference, the higher the matching degree.

In the above-mentioned fast magnetic resonance multi-parameter imaging method, N sets of data are acquired through a pulse sequence, and N images are reconstructed based on the N sets of data; the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the self The spin-spin relaxation time constant T2 is used to generate a dictionary through the fractional Bloch model; the signal sequence of the corresponding pixels on the N images is matched with the entries in the dictionary, and the final R is determined according to the matching degree The tissue characteristic parameter map is used as the output of fingerprint imaging. Since the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are combined to generate a dictionary, the final R tissue characteristic parameters The map can improve the accuracy of quantitative imaging.

FIG. 5 is a schematic flowchart of a fast magnetic resonance multi-parameter imaging method according to an embodiment of the present application. Based on the embodiment shown in FIG. 2, the fast magnetic resonance multi-parameter imaging method may further include:

In step 140, the dictionary is divided into K categories of dictionaries according to the fractional order factor, and K is an integer greater than or equal to 1.

In an embodiment, the aforementioned fractional factor may be an independent fractional factor for T1 and T2.

In an embodiment, the range of the aforementioned fractional factor may be between 0 and 2.

Exemplarily, as shown in Fig. 6, according to the parameters of the pulse sequence (repetition time TR, echo time TE and flip angle FA) and T1 and T2, the fractional Bloch equation (also known as fractional Bloch Model) generates a dictionary, and then divides the dictionary into K categories of dictionaries through each fractional factor of the fractional Bloch model, and each category of dictionaries corresponds to a signal evolution curve.

In this embodiment, the fractional order factor is used as the elastic calibration factor to classify the dictionary, and the elements in the classified dictionary are matched with the signal sequence of the corresponding pixels on the N images, and the final R tissue characteristics are determined according to the matching degree. The parameter map can improve the matching effect and further improve the accuracy of imaging.

In some embodiments, the aforementioned fractional Bloch model may specifically be:

M _z (t)=M _z (0)+[M ₀ -M _z (0)][1-E _β (-(t/T ₁ ) ^β )] (2)

M _xy (t)=M _xy (0)[E _α (-(t/T ₂ ) ^α )]+M _xy (∞) (4)

Among them, formula (1) and formula (2) correspond to the relaxation time of T1, and formula (3) and formula (4) correspond to the relaxation time of T2,

Is the β-order differential operator of Riemann-Liouville in Caputo form,

Riemann-Liouville α-order differential operator in Caputo form, M ₀ is the initial magnetization vector, M _z (t) is the longitudinal magnetization vector at time t, and M _xy (t) is the transverse magnetization vector at time t,

Is the β-order T ₁ relaxation time constant,

Is the α-order T ₂ relaxation time constant, E _β (-(t/T ₁ ) ^β ) is the β-order tensile Mittag-Leffler function _{of T 1 , and E α} (-(t/T ₂ ) ^α ) is T ₂ Is the α-order stretched Mittag-Leffler function, ω ₀ is the resonance frequency,

It is Riemann-Liouville's (1-α)-order integral operator, and when the parameter t is small, the Mittag-Leffler function can be

FIG. 7 is a schematic flowchart of a fast magnetic resonance multi-parameter imaging method provided by an embodiment of the present application. Based on the embodiment shown in FIG. 5, step 130 may specifically include:

In step 131, the signal sequence composed of the corresponding pixels of the N images is matched and identified with the dictionary entries of each of the K categories to obtain the quantitative imaging images of the K groups of tissue characteristic parameters. The group organization characteristic parameter includes R organization characteristic parameters.

In an embodiment, the signal sequence composed of the pixels corresponding to the above N images may be matched and identified with entries in the dictionary of each category to obtain quantitative imaging images of K groups of tissue characteristic parameters. That is, the above-mentioned N images can be divided into K groups of images through K categories of dictionaries.

Referring to FIG. 8, the signal sequence composed of the pixels corresponding to the reconstructed N images is matched and identified one by one with the entries of each fractional factor in the dictionary of K categories, and the quantitative imaging results of the K groups of tissue characteristic parameters can be obtained at the same time.

Exemplarily, each of the N images is composed of multiple pixels, the signals of the corresponding pixels in the N images are extracted to obtain a two-dimensional signal sequence, and then the signal sequence is combined with the dictionary of K categories. Each item is matched to obtain K groups of tissue characteristic parameters corresponding to each pixel position. Each group of tissue characteristic parameters may include R tissue characteristic parameters; the tissue characteristic parameters of all pixels are converted into R quantitative imaging images.

In step 132, for any group of quantitative imaging images in the K groups of quantitative imaging images, M tissue regions of interest are selected.

In one embodiment, for any group of quantitative imaging images in the above-mentioned K groups of quantitative imaging images, the image area corresponding to the tissue of a single component may be selected as the tissue area of interest on the quantitative imaging image. In this embodiment, the tissue area of interest is a specific tissue with a known single component. For example, the background noise area should not be included in the selection range of the tissue area of interest.

Referring to FIG. 9, a specific tissue containing four components in a quantitative imaging image is taken as an example for illustration, but it is not limited to this. For the four-component tissue in the quantitative imaging image, the tissue region of interest can be selected from the specific tissue of each component to obtain four tissue regions of interest, namely ROI1, ROI2, ROI3, and ROI4. Among them, each tissue area of interest is a tissue area of interest selected in an image area corresponding to a specific tissue of a certain component.

In this embodiment, the number of tissue regions of interest is not less than one, for example, more than three can be selected, and the number of corporate pixels contained in each tissue region of interest can be different. In addition, each tissue region of interest contains The number of pixels should be no less than 10.

In step 133, the R tissue characteristic parameters of the M tissue regions of interest of each group of quantitative imaging images are compared with the R empirical tissue characteristic parameters of the corresponding regions, and the final result is determined according to the deviation degree obtained by the comparison. The tissue characteristic parameter map is used as the output of fingerprint imaging.

Referring to FIG. 10, in an embodiment, step 133 may include the following steps:

In step 201, the pixels contained in the M tissue regions of interest in each group of quantitative imaging images are labeled and sorted, and the R empirical tissue characteristic parameters of each pixel are set correspondingly, and the pixels of each pixel are Each empirical organization characteristic parameter constitutes a row vector r.

Referring to Figure 11, for the tissue region ROI1 of interest, it can contain 12 pixels. The 12 pixels are labeled and sorted to obtain the content shown in the left image of Figure 11; among them, pixel 4, pixel 5, The pixel points 8 and the pixel points 9 are all contained in the tissue region ROI1 of interest, and the other pixels are only partially contained in the tissue region ROI1 of interest. For the tissue region ROI2 of interest, it can contain 12 pixels, and the 12 pixels are sorted by labels, and the content shown in the right figure of FIG. 11 is obtained. Among them, the pixel points 16, the pixel points 17, the pixel points 20, and the pixel points 21 are all included in the tissue region ROI1 of interest, and the other pixels are only partially included in the tissue region ROI1 of interest.

In this embodiment, the labels of the pixels in each tissue region of interest can be sorted accordingly. For example, taking four tissue regions of interest, each tissue region of interest contains 12 pixels as an example, the pixels in the tissue region of interest ROI1 are labeled from 1 to 12, and in the tissue region of interest ROI2 The numbers of the pixels are 13-24, the numbers of the pixels in the tissue region ROI3 of interest are 25-36, and the numbers of the pixels in the tissue region ROI4 of interest are 37-48.

Of course, in other embodiments, the number of pixels contained in each tissue area of interest is not limited to 12, and each tissue area of interest may contain more than 12 pixels. The above is only an exemplary description. There is no restriction on this.

After sorting the pixels contained in each tissue region of interest, R empirical tissue characteristic parameters of each pixel are correspondingly set to form a row vector r. For example, the corresponding empirical organization characteristic parameters can be formed into a row vector r according to the order of the label of each pixel.

In step 202, the candidate tissue characteristic parameters of the dictionary of each category are formed into a matrix J based on the ordering of the labels.

Based on the way of sorting the labels of the pixels in step 201, the various tissue characteristic parameters corresponding to the pixels contained in the M tissue regions of interest in the dictionary of each category are processed to form a matrix J, where the The rows represent pixel dimensions, and the columns represent K groups of tissue characteristic parameters. For ease of description, the pixel points in the dictionary of each category and the pixels contained in the M tissue regions of interest may correspond to each tissue characteristic parameter as a candidate tissue characteristic parameter.

In step 203, the final R tissue characteristic parameter maps are determined according to the difference between each row vector of the matrix J and the row vector r.

In an embodiment, the specific implementation of step 203 may be:

Step A: Calculate the residual sum of squares of each row vector in the matrix J and the row vector r, and each residual sum of squares constitutes a column vector err of size K;

Step B: Use the tissue characteristic parameters corresponding to the smallest value in the column vector err as the final R tissue characteristic parameter maps.

Among them, the residual sum of squares refers to the candidate tissue characteristic parameter in the matrix J minus the corresponding empirical tissue characteristic parameter in the vector r, divided by the empirical tissue characteristic parameter, and then squared. Step A can specifically be:

Calculating the difference between the candidate tissue characteristic parameter in the matrix J and the corresponding empirical tissue characteristic parameter in the row vector r;

The difference is divided by the corresponding empirical tissue characteristic parameter and squared.

In some embodiments, the tissue characteristic parameter is T1 and/or T2.

For the case where the tissue characteristic parameters are T1 and T2, in step A, the residual square sum err of each row of T1 and T2 are obtained respectively, and the residual square sum err of the two is averaged or added as the err of the row , Compose the err of all rows into a column vector err of size K, and the tissue characteristic parameter corresponding to the smallest value in the column vector is the final R tissue characteristic parameter map.

Figure 12 is a schematic diagram of comparing the present application, the solution in the background art (the previous technology as shown in the figure) and the traditional quantitative magnetic resonance imaging. The technology (fractional fast magnetic resonance multi-parameter imaging) is closer to the results of traditional quantitative magnetic resonance imaging.

In the above-mentioned fast magnetic resonance multi-parameter imaging method, N sets of data are acquired through a pulse sequence, and N images are reconstructed based on the N sets of data; the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the self The spin-spin relaxation time constant T2 is used to generate a dictionary through the fractional Bloch model; the signal sequence of the corresponding pixels on the N images is matched with the entries in the dictionary, and the final R is determined according to the matching degree The tissue characteristic parameter map is used as the output of fingerprint imaging. Since the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are combined to generate a dictionary, the final R tissue characteristic parameters The map can improve the accuracy of quantitative imaging.

Traditional fractional fingerprint imaging only considers the matching degree between the actual signal evolution of a single point and the dictionary analog signal evolution, and does not make full use of the matching degree between the prior tissue characteristic parameters and the candidate tissue characteristic parameters in the dictionary. In view of the shortcomings of the above technologies, this application not only considers the matching degree of signal evolution, but also makes full use of the matching degree of candidate tissue characteristic parameters related to the fractional order factor, thereby improving the accuracy of quantitative imaging.

It should be understood that the size of the sequence number of each step in the foregoing embodiment does not mean the order of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

Corresponding to the application of the above embodiment to the fast magnetic resonance multi-parameter imaging method, FIG. 13 shows a structural block diagram of the fast magnetic resonance multi-parameter imaging device provided by the embodiment of the present application. Example related parts.

Referring to FIG. 13, the fast magnetic resonance multi-parameter imaging device in the embodiment of the present application may include an acquisition module 201, a dictionary generation module 202, and a matching imaging module 203;

Wherein, the acquisition module 201 is configured to acquire N sets of data through a pulse sequence, and reconstruct N images based on the N sets of data; where N is an integer greater than or equal to 1;

The dictionary generation module 202 is used to generate a dictionary using the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 through a fractional Bloch model;

The matching imaging module 203 is configured to match the signal sequence of the corresponding pixels on the N images with the entries in the dictionary, and determine the final R tissue characteristic parameter maps as the output of fingerprint imaging according to the matching degree, and R is greater than An integer equal to 1.

Optionally, the foregoing device may further include:

The dictionary classification module is used to divide the dictionary into K categories according to the fractional order factor, where K is an integer greater than or equal to 1.

Exemplarily, the fractional factor is a fractional factor for T1 and T2.

Exemplarily, the value range of the fractional factor is between 0 and 2.

Optionally, the matching imaging module 203 may include:

The image grouping unit is used to match and recognize the signal sequence composed of the corresponding pixels of the N images with the dictionary entries of each of the K categories to obtain the quantitative imaging images of the K groups of tissue characteristic parameters, Each group of tissue characteristic parameters includes R tissue characteristic parameters;

The region of interest selection unit is configured to select M tissue regions of interest for any group of quantitative imaging images in the quantitative imaging images of the K groups of tissue characteristic parameters;

The matching imaging unit is used to compare the R tissue characteristic parameters of the M tissue regions of interest in the quantitative imaging images of each group of tissue characteristic parameters with the R empirical tissue characteristic parameters of the corresponding regions, and the results are obtained based on the comparison The degree of deviation determines the final R tissue characteristic parameter maps as the output of fingerprint imaging.

Optionally, the image grouping unit may be specifically used for:

Extract the signals of the corresponding pixels in the N images to obtain a two-dimensional signal sequence; wherein, each of the N images is composed of multiple pixels;

Matching the signal sequence with each entry of the K category dictionaries to obtain K groups of tissue characteristic parameters corresponding to each pixel position, and each group of tissue characteristic parameters includes R tissue characteristic parameters;

The tissue characteristic parameters of all pixels are converted into R quantitative imaging images.

Optionally, the above-mentioned image grouping unit may be specifically used for:

The image area corresponding to the tissue of a single component is selected on the quantitative imaging image as the tissue area of interest.

Optionally, the above-mentioned matching imaging unit may be specifically used for:

Sort the pixels contained in the M tissue regions of interest in each quantitative imaging image, and set the R empirical tissue characteristic parameter values of each pixel correspondingly, and set each empirical tissue characteristic parameter of each pixel Constitute the row vector r;

Forming a matrix J for each tissue characteristic parameter corresponding to the pixels contained in the M tissue regions of interest in the dictionary of each category based on the sorting manner of the labels;

According to the difference between each row vector of the matrix J and the row vector r, the final R tissue characteristic parameter maps are determined.

Optionally, the determining the final R tissue characteristic parameter maps according to the difference between each row vector of the matrix J and the row vector r includes:

Calculate the residual sum of squares of each row vector in the matrix J and the row vector r, and each residual sum of squares constitutes a column vector err of size K;

The tissue characteristic parameter corresponding to the smallest value in the column vector err is used as the final R tissue characteristic parameter map.

For example, the calculation of the residual sum of squares of each row vector in the matrix J and the row vector r is specifically:

Calculating the difference between the candidate tissue characteristic parameter in the matrix J and the corresponding empirical tissue characteristic parameter in the row vector r;

The difference is divided by the corresponding empirical tissue characteristic parameter and squared.

Exemplarily, the tissue characteristic parameter is T1 and/or T2.

Optionally, the collection module 201 can be specifically used for:

In each excitation of the pulse sequence, different repetition time TR, echo time TE, and flip angle FA are used to collect N sets of data and reconstruct to obtain N images.

Optionally, the fractional Bloch model is:

M _z (t)=M _z (0)+[M ₀ -M _z (0)][1-E _β (-(t/T ₁ ) ^β )]

M _xy (t)=M _xy (0)[E _α (-(t/T ₂ ) ^α )]+M _xy (∞)

in,

Is the β-order differential operator of Riemann-Liouville in Caputo form,

Is the Riemann-Liouville α-order differential operator in Caputo form, M ₀ is the initial magnetization vector, M _z (t) is the longitudinal magnetization vector at time t, and M _xy (t) is the transverse magnetization vector at time t,

Is the β-order T ₁ relaxation time constant,

Is the α-order T ₂ relaxation time constant, E _β (-(t/T ₁ ) ^β ) is the β-order tensile Mittag-Leffler function _{of T 1 , and E α} (-(t/T ₂ ) ^α ) is T ₂ Is the α-order stretched Mittag-Leffler function, ω ₀ is the resonance frequency,

Is Riemann-Liouville's (1-α)-order integral operator, and the Mittag-Leffler function is

It should be noted that the information interaction and execution process between the above-mentioned devices/units are based on the same concept as the method embodiment of this application, and its specific functions and technical effects can be found in the method embodiment section. I won't repeat it here.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above functional units and modules is used as an example. In practical applications, the above functions can be allocated to different functional units and modules as needed. Module completion, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist alone physically, or two or more units can be integrated into one unit. The above-mentioned integrated units can be hardware-based Formal realization can also be realized in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the foregoing system, reference may be made to the corresponding process in the foregoing method embodiment, which will not be repeated here.

The embodiment of the present application also provides a terminal device. Referring to FIG. 14, the terminal device 400 may include: at least one processor 410, a memory 420, and stored in the memory 420 and can be stored on the at least one processor 410. A running computer program, when the processor 410 executes the computer program, the steps in any of the foregoing method embodiments, such as steps 101 to 103 in the embodiment shown in FIG. 2, are implemented. Alternatively, when the processor 410 executes the computer program, the functions of the modules/units in the foregoing device embodiments, such as the functions of the modules 301 to 303 shown in FIG. 13, are realized.

Exemplarily, the computer program may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 420 and executed by the processor 410 to complete the application. The one or more modules/units may be a series of computer program segments capable of completing specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 400.

Those skilled in the art can understand that FIG. 14 is only an example of a terminal device, and does not constitute a limitation on the terminal device. It may include more or less components than those shown in the figure, or a combination of certain components, or different components, such as Input and output equipment, network access equipment, bus, etc.

The processor 410 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), ready-made Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.

The memory 420 may be an internal storage unit of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, and a flash memory card. (Flash Card) and so on. The memory 420 is used to store the computer program and other programs and data required by the terminal device. The memory 420 can also be used to temporarily store data that has been output or will be output.

The bus can be an Industry Standard Architecture (ISA) bus, Peripheral Component (PCI) bus, or Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus and so on. For ease of representation, the buses in the drawings of this application are not limited to only one bus or one type of bus.

The embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it realizes the above-mentioned fast magnetic resonance multi-parameter imaging method in each embodiment step.

The embodiments of the present application provide a computer program product. When the computer program product runs on a mobile terminal, the steps in each embodiment of the above-mentioned fast magnetic resonance multi-parameter imaging method are realized when the mobile terminal is executed.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the implementation of all or part of the processes in the above-mentioned embodiment methods in the present application can be accomplished by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate forms. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to the photographing device/terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), and random access memory (RAM, Random Access Memory), electric carrier signal, telecommunications signal and software distribution medium. For example, U disk, mobile hard disk, floppy disk or CD-ROM, etc. In some jurisdictions, according to legislation and patent practices, computer-readable media cannot be electrical carrier signals and telecommunication signals.

In the above-mentioned embodiments, the description of each embodiment has its own focus. For parts that are not described in detail or recorded in an embodiment, reference may be made to related descriptions of other embodiments.

A person of ordinary skill in the art may realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

In the embodiments provided in this application, it should be understood that the disclosed apparatus/network equipment and method may be implemented in other ways. For example, the device/network device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units. Or components can be combined or integrated into another system, or some features can be omitted or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that it can still implement the foregoing The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of protection of this application.

Claims (20)

1. A fast magnetic resonance multi-parameter imaging method, wherein the method includes:

   N groups of data are collected through a pulse sequence, and N images are reconstructed based on the N groups of data; where N is an integer greater than or equal to 1;

   The parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are used to generate a dictionary through a fractional Bloch model;

   The signal sequence of the corresponding pixels on the N images is matched with the entries in the dictionary, and the final R tissue characteristic parameter maps are determined as the output of fingerprint imaging according to the matching degree, and R is an integer greater than or equal to 1.
2. The fast magnetic resonance multi-parameter imaging method according to claim 1, wherein the method further comprises:

   The dictionary is divided into K categories according to the fractional order factor, and K is an integer greater than or equal to 1.
3. 3. The fast magnetic resonance multi-parameter imaging method according to claim 2, wherein the fractional factor is a fractional factor for T1 and T2.
4. The fast magnetic resonance multi-parameter imaging method according to claim 2 or 3, wherein the value range of the fractional order factor is between 0 and 2.
5. The fast magnetic resonance multi-parameter imaging method according to claim 2, wherein:

   The matching the signal sequences of the corresponding pixels on the N images with the entries in the dictionary, and determining the final R tissue characteristic parameter maps as the output of fingerprint imaging according to the matching degree, includes:

   The signal sequence composed of the corresponding pixels of the N images is matched and identified with the dictionary entries of each of the K categories, and the quantitative imaging images of the K groups of tissue characteristic parameters are obtained. Quantitative imaging images include quantitative imaging images of R tissue characteristic parameters;

   For any group of quantitative imaging images in the K groups of quantitative imaging images, M tissue regions of interest are selected;

   The R tissue characteristic parameters of the M tissue regions of interest of the quantitative imaging images of each group of tissue characteristic parameters are compared with the R empirical tissue characteristic parameters of the corresponding regions, and the final tissue is determined according to the deviation obtained from the comparison. The characteristic parameter map is used as the output of fingerprint imaging.
6. The fast magnetic resonance multi-parameter imaging method according to claim 5, wherein the signal sequence composed of the corresponding pixels of the N images is matched and identified with the entries in the dictionary of each category to obtain K groups of organization Quantitative imaging images of characteristic parameters, including:

   Extract the signals of the corresponding pixels in the N images to obtain a two-dimensional signal sequence; wherein, each of the N images is composed of multiple pixels;

   Matching the signal sequence with each entry of the K fractional factor category dictionary to obtain K groups of tissue characteristic parameters corresponding to each pixel position, and each group of tissue characteristic parameters includes R tissue characteristic parameters;

   The tissue characteristic parameters of all pixels are converted into R quantitative imaging images.
7. The fast magnetic resonance multi-parameter imaging method according to claim 5, wherein said selecting M tissue regions of interest includes:

   The image area corresponding to the tissue of a single component is selected on the image as the tissue area of interest.
8. The rapid magnetic resonance multi-parameter imaging method according to claim 5, wherein the quantitative imaging images of each group of tissue characteristic parameters are the tissue characteristic parameters of the M tissue regions of interest and the R experiences of the corresponding regions. The tissue characteristic parameters are matched, and the final R tissue characteristic parameter maps are determined as the result of fingerprint imaging according to the deviation obtained from the comparison, including:

   Sort the pixels contained in the M tissue regions of interest in each group of quantitative imaging images, and set the R empirical tissue characteristic parameters of each pixel correspondingly, and set the empirical tissue characteristic parameters of each pixel. Constitute the row vector r;

   Forming a matrix J for each tissue characteristic parameter corresponding to the pixels contained in the M tissue regions of interest in the dictionary of each category based on the sorting manner of the labels;

   According to the difference between each row vector of the matrix J and the row vector r, the final R tissue characteristic parameter maps are determined.
9. The fast magnetic resonance multi-parameter imaging method according to claim 8, wherein the determining the final R tissue characteristic parameter maps according to the difference between each row vector of the matrix J and the row vector r includes :

   Calculate the residual sum of squares of each row vector in the matrix J and the row vector r, and each residual sum of squares constitutes a column vector err of size K;

   The final R tissue characteristic parameter maps are determined according to the tissue characteristic parameter corresponding to the smallest value in the column vector err.
10. 9. The fast magnetic resonance multi-parameter imaging method according to claim 9, wherein the calculating the residual sum of squares of each row vector in the matrix J and the row vector r is specifically:

    Calculate the difference between the candidate tissue characteristic parameter in the matrix J and the corresponding empirical tissue characteristic parameter in the row vector r; wherein the candidate tissue characteristic parameter is the difference between the M interest in the dictionary of each category The tissue characteristic parameters corresponding to the pixels contained in the tissue area;

    The difference is divided by the corresponding empirical tissue characteristic parameter and squared.
11. The fast magnetic resonance multi-parameter imaging method according to any one of claims 8 to 10, wherein the tissue characteristic parameter is T1 and/or T2.
12. 8. The fast magnetic resonance multi-parameter imaging method according to claim 1, wherein the acquiring N sets of data through a pulse sequence and reconstructing the N images based on the N sets of data comprises:

    In each excitation of the pulse sequence, different repetition time TR, echo time TE, and flip angle FA are used to collect N sets of data and reconstruct to obtain N images.
13. 8. The fast magnetic resonance multi-parameter imaging method of claim 1, wherein the fractional Bloch model is:

    

    

    

    M _xy (t)=M _xy (0)[E _α (-(t/T ₂ ) ^α )]+M _xy (∞)

    in,

    

    Is the β-order differential operator of Riemann-Liouville in Caputo form,

    

    Riemann-Liouville α-order differential operator in Caputo form, M ₀ is the initial magnetization vector, M _z (t) is the longitudinal magnetization vector at time t, M _xy (t) is the transverse magnetization vector at time t, and T ₁ ^β is β Order T ₁ relaxation time constant,

    

    Is the α-order T ₂ relaxation time constant, E _β (-(t/T ₁ ) ^β ) is the β-order tensile Mittag-Leffler function _{of T 1 , and E α} (-(t/T ₂ ) ^α ) is T ₂ Is the α-order stretched Mittag-Leffler function, ω ₀ is the resonance frequency,

    

    Is Riemann-Liouville's (1-α)-order integral operator, and the Mittag-Leffler function is

    
14. A fast magnetic resonance multi-parameter imaging device, wherein the device includes:

    The acquisition module is used to acquire N sets of data through a pulse sequence, and reconstruct N images based on the N sets of data; where N is an integer greater than or equal to 1;

    A dictionary generation module for generating a dictionary using the parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 through the fractional Bloch model;

    The matching imaging module is used to match the signal sequence of the corresponding pixels on the N images with the entries in the dictionary, and determine the final R tissue characteristic parameter maps as the output of fingerprint imaging according to the matching degree, and R is greater than or equal to An integer of 1.
15. A terminal device, wherein the terminal device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and when the processor executes the computer-readable instructions To achieve the following steps:

    N groups of data are collected through a pulse sequence, and N images are reconstructed based on the N groups of data; where N is an integer greater than or equal to 1;

    The parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are used to generate a dictionary through a fractional Bloch model;

    The signal sequence of the corresponding pixels on the N images is matched with the entries in the dictionary, and the final R tissue characteristic parameter maps are determined as the output of fingerprint imaging according to the matching degree, and R is an integer greater than or equal to 1.
16. The terminal device according to claim 15, wherein the processor further implements the following steps when executing the computer-readable instruction:

    The dictionary is divided into K categories according to the fractional order factor, and K is an integer greater than or equal to 1.
17. The terminal device according to claim 16, wherein the signal sequence of the corresponding pixels on the N images is matched with entries in the dictionary, and the final R tissue characteristic parameter maps are determined as fingerprints according to the matching degree The output of the imaging, including:

    The signal sequence composed of the corresponding pixels of the N images is matched and identified with the dictionary entries of each of the K categories, and the quantitative imaging images of the K groups of tissue characteristic parameters are obtained. Quantitative imaging images include quantitative imaging images of R tissue characteristic parameters;

    For any group of quantitative imaging images in the K groups of quantitative imaging images, M tissue regions of interest are selected;

    Compare the R tissue characteristic parameters of the M tissue regions of interest in the quantitative imaging images of each group of tissue characteristic parameters with the R empirical tissue characteristic parameters of the corresponding regions, and determine the final tissue according to the matching degree obtained from the comparison The characteristic parameter map is used as the output of fingerprint imaging.
18. The terminal device according to claim 17, wherein the tissue characteristic parameters of the M tissue regions of interest of the quantitative imaging images of each group of tissue characteristic parameters are matched with the R empirical tissue characteristic parameters of the corresponding region , Determine the final R tissue characteristic parameter maps as the result of fingerprint imaging according to the deviation degree obtained from the comparison, including:

    Sort the pixels contained in the M tissue regions of interest in each group of quantitative imaging images, and set the R empirical tissue characteristic parameters of each pixel correspondingly, and set the empirical tissue characteristic parameters of each pixel. Constitute the row vector r;

    Forming a matrix J for each tissue characteristic parameter corresponding to the pixels contained in the M tissue regions of interest in the dictionary of each category based on the sorting manner of the labels;

    According to the difference between each row vector of the matrix J and the row vector r, the final R tissue characteristic parameter maps are determined.
19. The terminal device according to claim 15, wherein the determining the final R tissue characteristic parameter maps according to the difference between each row vector of the matrix J and the row vector r comprises:

    Calculate the residual sum of squares of each row vector in the matrix J and the row vector r, and each residual sum of squares constitutes a column vector err of size K;

    The final R tissue characteristic parameter maps are determined according to the tissue characteristic parameter corresponding to the smallest value in the column vector err.
20. A computer-readable storage medium, the computer-readable storage medium stores computer-readable instructions, wherein, when the computer-readable instructions are executed by a processor, the following steps are implemented:

    N groups of data are collected through a pulse sequence, and N images are reconstructed based on the N groups of data; where N is an integer greater than or equal to 1;

    The parameters of the pulse sequence, the spin-lattice relaxation time constant T1 and the spin-spin relaxation time constant T2 are used to generate a dictionary through a fractional Bloch model;

    The signal sequence of the corresponding pixels on the N images is matched with the entries in the dictionary, and the final tissue characteristic parameter map is determined according to the matching degree as the output of fingerprint imaging, and R is an integer greater than or equal to 1.

Images