WO2021217391A1 - Procédé et appareil d'imagerie multi-paramètres à résonance magnétique rapide - Google Patents

Procédé et appareil d'imagerie multi-paramètres à résonance magnétique rapide Download PDF

Info

Publication number
WO2021217391A1
WO2021217391A1 PCT/CN2020/087385 CN2020087385W WO2021217391A1 WO 2021217391 A1 WO2021217391 A1 WO 2021217391A1 CN 2020087385 W CN2020087385 W CN 2020087385W WO 2021217391 A1 WO2021217391 A1 WO 2021217391A1
Authority
WO
WIPO (PCT)
Prior art keywords
tissue
images
tissue characteristic
imaging
dictionary
Prior art date
Application number
PCT/CN2020/087385
Other languages
English (en)
Chinese (zh)
Inventor
王海峰
邹莉娴
梁栋
刘新
郑海荣
Original Assignee
深圳先进技术研究院
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳先进技术研究院 filed Critical 深圳先进技术研究院
Priority to PCT/CN2020/087385 priority Critical patent/WO2021217391A1/fr
Publication of WO2021217391A1 publication Critical patent/WO2021217391A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/50—NMR imaging systems based on the determination of relaxation times, e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution

Definitions

  • the 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.
  • Magnetic Resonance Imaging 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.
  • MRF magnetic resonance fingerprint imaging
  • 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.
  • 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. .
  • 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.
  • the fractional factor is a fractional factor for T1 and T2.
  • the value range of the fractional factor is between 0 and 2.
  • 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:
  • Quantitative imaging images include quantitative imaging images of R tissue characteristic parameters
  • 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.
  • 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:
  • the tissue characteristic parameters of all pixels are converted into R quantitative imaging images.
  • 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.
  • 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:
  • the final R tissue characteristic parameter maps are determined.
  • 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:
  • 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 calculating the residual sum of squares of each row vector in the matrix J and the row vector r is specifically:
  • the difference is divided by the corresponding empirical tissue characteristic parameter and squared.
  • the tissue characteristic parameter is T1 and/or T2.
  • the collecting N sets of data through a pulse sequence and reconstructing to obtain N images based on the N sets of data includes:
  • the fractional Bloch model is:
  • M z (t) M z (0)+[M 0 -M z (0)][1-E ⁇ (-(t/T 1 ) ⁇ )]
  • M xy (t) M xy (0)[E ⁇ (-(t/T 2 ) ⁇ )]+M xy ( ⁇ )
  • 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.
  • 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.
  • 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:
  • Quantitative imaging images include quantitative imaging images of R tissue characteristic parameters
  • M tissue regions of interest are selected.
  • the characteristic parameter map is used as the output of fingerprint imaging.
  • 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:
  • the final R tissue characteristic parameter maps are determined.
  • 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:
  • 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.
  • 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.
  • 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.
  • Fig. 1 is a schematic diagram of an application scenario provided by an embodiment of the present application
  • FIG. 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.
  • FIG. 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.
  • the term “if” can be construed as “when” or “once” or “in response to determination” or “in response to detecting “.
  • 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]”.
  • Magnetic resonance imaging 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.
  • traditional MRI is used for quantitative imaging, its application is limited by the scan time.
  • 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 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.
  • TR repetition time
  • TE echo time
  • FA flip angle
  • 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.
  • 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.
  • 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.
  • the embodiments of the present application can be applied to the exemplary scenario shown in FIG. 1.
  • the magnetic resonance device 10 and the server 20 constitute an application scenario of the above-mentioned fast magnetic resonance multi-parameter imaging method.
  • 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.
  • 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.
  • 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:
  • 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.
  • 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.
  • TR Time of Repetition
  • TE Time of Echo
  • Flip Angle flip angle
  • 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).
  • 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.
  • 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.
  • 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.
  • the parameters of the aforementioned pulse sequence may include the repetition time TR, the echo time TE, and the flip angle FA.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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:
  • 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.
  • the aforementioned fractional factor may be an independent fractional factor for T1 and T2.
  • the range of the aforementioned fractional factor may be between 0 and 2.
  • 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.
  • 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.
  • the aforementioned fractional Bloch model may specifically be:
  • M xy (t) M xy (0)[E ⁇ (-(t/T 2 ) ⁇ )]+M xy ( ⁇ ) (4)
  • formula (1) and formula (2) correspond to the relaxation time of T1
  • formula (3) and formula (4) correspond to the relaxation time of T2
  • 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
  • E ⁇ (-(t/T 1 ) ⁇ ) is the ⁇ -order tensile Mittag-Leffler function of T 1
  • E ⁇ (-(t/T 2 ) ⁇ ) is T 2
  • ⁇ 0 is the resonance frequency
  • It is Riemann-Liouville's (1- ⁇ )-order integral operator
  • 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:
  • 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.
  • 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.
  • 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.
  • 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.
  • step 132 for any group of quantitative imaging images in the K groups of quantitative imaging images, M tissue regions of interest are selected.
  • 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.
  • the tissue area of interest is a specific tissue with a known single component.
  • the background noise area should not be included in the selection range of the tissue area of interest.
  • 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.
  • 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.
  • 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.
  • each tissue region of interest contains The number of pixels should be no less than 10.
  • 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.
  • step 133 may include the following steps:
  • 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.
  • the tissue region ROI1 of interest 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • R empirical tissue characteristic parameters of each pixel are correspondingly set to form a row vector r.
  • the corresponding empirical organization characteristic parameters can be formed into a row vector r according to the order of the label of each pixel.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Step A can specifically be:
  • the difference is divided by the corresponding empirical tissue characteristic parameter and squared.
  • the tissue characteristic parameter is T1 and/or T2.
  • 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.
  • 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. 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.
  • 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;
  • 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.
  • 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.
  • the fractional factor is a fractional factor for T1 and T2.
  • the value range of the fractional factor is between 0 and 2.
  • 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.
  • the image grouping unit may be specifically used for:
  • the tissue characteristic parameters of all pixels are converted into R quantitative imaging images.
  • 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.
  • the above-mentioned matching imaging unit may be specifically used for:
  • the final R tissue characteristic parameter maps are determined.
  • 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:
  • the tissue characteristic parameter corresponding to the smallest value in the column vector err is used as the final R tissue characteristic parameter map.
  • the difference is divided by the corresponding empirical tissue characteristic parameter and squared.
  • the tissue characteristic parameter is T1 and/or T2.
  • the collection module 201 can be specifically used for:
  • fractional Bloch model is:
  • M z (t) M z (0)+[M 0 -M z (0)][1-E ⁇ (-(t/T 1 ) ⁇ )]
  • M xy (t) M xy (0)[E ⁇ (-(t/T 2 ) ⁇ )]+M xy ( ⁇ )
  • 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.
  • 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.
  • 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.
  • 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.
  • ISA Industry Standard Architecture
  • PCI Peripheral Component
  • EISA Extended Industry Standard Architecture
  • the bus can be divided into address bus, data bus, control bus and so on.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • ROM read-only memory
  • RAM random access memory
  • electric carrier signal telecommunications signal and software distribution medium.
  • U disk mobile hard disk, floppy disk or CD-ROM, etc.
  • computer-readable media cannot be electrical carrier signals and telecommunication signals.
  • the disclosed apparatus/network equipment and method may be implemented in other ways.
  • the device/network device embodiments described above are merely illustrative.
  • 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.
  • components can be combined or integrated into another system, or some features can be omitted or not implemented.
  • 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.

Landscapes

  • Physics & Mathematics (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

La présente invention concerne un procédé et un appareil d'imagerie multi-paramètres à résonance magnétique rapide. Le procédé d'imagerie multi-paramètre à résonance magnétique rapide comprend : la collecte de N groupes de données au moyen d'une séquence d'impulsions, et la reconstruction sur la base des N groupes de données pour obtenir N images, N étant un nombre entier supérieur ou égal à 1 (étape 110) ; la génération d'un dictionnaire à partir de paramètres de la séquence d'impulsions, d'une constante de temps de relaxation longitudinale T1 et d'une constante de temps de relaxation transversale T2 au moyen d'un modèle de Bloch d'ordre fractionnaire (étape 120) ; et la mise en correspondance des séquences de signaux de pixels correspondants sur les N images avec des entrées dans le dictionnaire, et la détermination de diagrammes de paramètres caractéristiques de tissu R finaux en tant que sortie d'imagerie d'empreinte digitale selon le degré de correspondance, R étant un nombre entier supérieur ou égal à 1 (étape
PCT/CN2020/087385 2020-04-28 2020-04-28 Procédé et appareil d'imagerie multi-paramètres à résonance magnétique rapide WO2021217391A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2020/087385 WO2021217391A1 (fr) 2020-04-28 2020-04-28 Procédé et appareil d'imagerie multi-paramètres à résonance magnétique rapide

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2020/087385 WO2021217391A1 (fr) 2020-04-28 2020-04-28 Procédé et appareil d'imagerie multi-paramètres à résonance magnétique rapide

Publications (1)

Publication Number Publication Date
WO2021217391A1 true WO2021217391A1 (fr) 2021-11-04

Family

ID=78373256

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/087385 WO2021217391A1 (fr) 2020-04-28 2020-04-28 Procédé et appareil d'imagerie multi-paramètres à résonance magnétique rapide

Country Status (1)

Country Link
WO (1) WO2021217391A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105869192A (zh) * 2016-03-28 2016-08-17 浙江大学 一种基于滑动窗的磁共振指纹识别重建技术
US20170146623A1 (en) * 2015-11-25 2017-05-25 The General Hospital Corporation Systems and Methods For Segmented Magnetic Resonance Fingerprinting Dictionary Matching
CN107194354A (zh) * 2017-05-23 2017-09-22 杭州师范大学 一种用于磁共振指纹成像的快速字典搜索方法
CN109044355A (zh) * 2018-06-28 2018-12-21 上海联影医疗科技有限公司 温度成像方法、装置、计算机设备和存储介质
CN110133554A (zh) * 2018-02-08 2019-08-16 深圳先进技术研究院 一种基于分数阶模型的磁共振指纹成像方法、装置及介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170146623A1 (en) * 2015-11-25 2017-05-25 The General Hospital Corporation Systems and Methods For Segmented Magnetic Resonance Fingerprinting Dictionary Matching
CN105869192A (zh) * 2016-03-28 2016-08-17 浙江大学 一种基于滑动窗的磁共振指纹识别重建技术
CN107194354A (zh) * 2017-05-23 2017-09-22 杭州师范大学 一种用于磁共振指纹成像的快速字典搜索方法
CN110133554A (zh) * 2018-02-08 2019-08-16 深圳先进技术研究院 一种基于分数阶模型的磁共振指纹成像方法、装置及介质
CN109044355A (zh) * 2018-06-28 2018-12-21 上海联影医疗科技有限公司 温度成像方法、装置、计算机设备和存储介质

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WANG HAIFENG; ZOU LIXIAN; YE HUIHUI; SU SHI; CHANG YUCHOU; LIU XIN; LIANG DONG: "Application of Time-Fractional Order Bloch Equation in Magnetic Resonance Fingerprinting", 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019), IEEE, 8 April 2019 (2019-04-08), pages 1704 - 1707, XP033576557, DOI: 10.1109/ISBI.2019.8759427 *

Similar Documents

Publication Publication Date Title
Kociołek et al. Does image normalization and intensity resolution impact texture classification?
CN111537931B (zh) 快速磁共振多参数成像方法和装置
CN110236543B (zh) 基于深度学习的阿尔茨海默病多分类诊断系统
Mortamet et al. Automatic quality assessment in structural brain magnetic resonance imaging
CN110082135A (zh) 设备故障识别方法、装置和终端设备
Somasundaram et al. Brain segmentation in magnetic resonance human head scans using multi-seeded region growing
Suckling et al. Removal of extracerebral tissues in dual-echo magnetic resonance images via linear scale-space features
Khademi et al. Whole volume brain extraction for multi-centre, multi-disease FLAIR MRI datasets
Zavala Bojorquez et al. Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine
CN114119546A (zh) 检测mri影像的方法及装置
Oszust et al. Blind image quality assessment of magnetic resonance images with statistics of local intensity extrema
Tabelow et al. High-resolution fMRI: Overcoming the signal-to-noise problem
US20070036409A1 (en) System and method for automatic segmentation of vessels in breast MR sequences
WO2021217391A1 (fr) Procédé et appareil d'imagerie multi-paramètres à résonance magnétique rapide
Lüth et al. Cradl: Contrastive representations for unsupervised anomaly detection and localization
Xiao et al. Automatic brain MRI segmentation scheme based on feature weighting factors selection on fuzzy c-means clustering algorithms with Gaussian smoothing
Vrooman et al. kNN-based multi-spectral MRI brain tissue classification: manual training versus automated atlas-based training
Kipele et al. Poisson noise reduction with nonlocal-pca hybrid model in medical x-ray images
CN115690409A (zh) 一种基于SEResU-Net模型的MRI脑肿瘤图像分割方法及系统
Gandhamal et al. A generalized contrast enhancement approach for knee MR images
Nagori et al. Methods and algorithms for extracting values from MRS graph for brain tumour detection
Ravi et al. Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging
Fujibayashi et al. Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease
CN112052882B (zh) 磁共振脑结构影像的分类模型构建、分类与可视化方法
CN114494014A (zh) 一种磁共振图像超分辨率重建方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20933659

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20933659

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 20/04/2023)

122 Ep: pct application non-entry in european phase

Ref document number: 20933659

Country of ref document: EP

Kind code of ref document: A1