CN110133554A - A kind of magnetic resonance fingerprint imaging method, apparatus and medium based on fractional model - Google Patents
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Abstract
The present invention relates to magnetic resonance parameters image reconstructions, propose a kind of magnetic resonance fingerprint quantitative parameter imaging method based on fractional order Bloch model, quantitative parameter imaging accuracy can be improved, and reduce the sweep time of quantitative parameter imaging.Its technical solution is broadly divided into: acquiring data using different repetition times, echo time and flip angle in the excitation each time of pulse train and reconstruction obtains image；Dictionary is generated based on fractional order Bloch model；Multiparameter imaging is obtained using pattern-recognition.The beneficial effects of the practice of the present invention mainly has: improving the sample rate and imaging accuracy of the imaging of magnetic resonance fingerprint quantitative parameter；While improving quantitative parameter imaging accuracy, additional data and sweep time are not increased.
Description
Technical field
The present invention relates to mr imaging technique field, in particular to a kind of magnetic resonance fingerprint imaging method.
Background technique
Magnetic resonance imager is the image that bodily tissue and structure are generated using magnetic field and radio wave pulses.Compared to
Traditional magnetic resonance imaging, magnetic resonance fingerprint imaging (Magnetic Resonance Fingerprinting, MRF) are surveyed every time
Measure available more information.As a kind of new method for obtaining multi-parameter quantitative imaging quickly, simultaneously, magnetic resonance fingerprint at
As technology mainly comprises the steps that 1, in the excitation each time of pulse train using different repetition time (Time
OfRepetition, TR), echo time (Time of Echo, TE) and flip angle (Flip Angle, FA), and with repeatedly swashing
Hair helical trajectory (multi-interleaf spiral), which acquires data and rebuilds, obtains the image sequence of lack sampling；2, according to arteries and veins
The parameter (TR, TE and FA) for rushing sequence calculates dictionary based on classical single order Bloch (Bloch) model；3, it will rebuild
Image sequence and dictionary in the point-by-point match cognization of element, multi-parameter quantitative imaging result can be obtained simultaneously.
Traditional quantitative magnetic resonance imaging passes through classical Bloch model (model of description magnetic resonance physical process) to image
Signal on each location of pixels carries out nonlinear fitting and obtains the characterisitic parameter of tissue, such as: longitudinal relaxation time (T1), it is horizontal
To relaxation time (T2) etc..However, the imaging method error is big, quantitative parameter imaging results are also undesirable.
Summary of the invention
It is big to solve error that the technical problem to be solved in the present invention is to provide a kind of new magnetic resonance fingerprint imaging methods
Problem.
In order to solve the above-mentioned technical problem, the present invention discloses a kind of magnetic resonance fingerprint imaging method, technical side first
Case is implemented:
A kind of the step of magnetic resonance fingerprint imaging method, the magnetic resonance fingerprint imaging method includes:
Step S1, in the excitation each time of pulse train, using different repetition times, echo time, flip angle, and
Data acquisition is carried out using the non-cartesian trajectories in the space K；
Step S2 is generated with calculating is simulated based on the Bloch model of fractional model and is based on fractional order Bloch mould
The dictionary of type；
Step S3 rebuilds magnetic resonance image, and the signal of respective element in image is compared with the dictionary, finally
Obtain multi-parameter quantitative imaging result.
Preferably, the step S2 is specifically, according to the parameter sets of the pulse train of use, with fractional order Bloch mould
Pattern is quasi- to be calculated, and the dictionary is generated.
Preferably, in the step S3, the method for rebuilding magnetic resonance image includes: non-uniform Fast Fourier transformation, odd
Different value decomposes one of Inverse Projection and low-rank alternating direction Multiplier Algorithm.
Preferably, in the step S3, by element maximum inner product method in the point-by-point time series of reconstruction image and dictionary
Match cognization one by one obtains multi-parameter quantitative imaging result.
Preferably, the variable in the dictionary includes longitudinal relaxation time and lateral relaxation time；
In the step S3, multi-parameter quantitative imaging result is quantitative tissue characteristics parametric imaging, including longitudinal relaxation
Time parameter imaging and lateral relaxation time parametric imaging.
Preferably, longitudinal relaxation is expressed as with fractional order Bloch model:
Mz(t)=Mz(0)+[M0-Mz(0)][1-Eβ(-(t/T1)β)]
Preferably, transverse relaxation is expressed as with fractional order Bloch model:
Mxy(t)=Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞),
Preferably, it is assumed that when the value of τ is lesser, meter Ta-Lie Fule function be can be approximated to be
ThenOr/and
The invention also discloses a kind of computer-readable medium, which has the journey being stored therein
Sequence, the program execute the magnetic resonance fingerprint imaging method for computer.
The invention also discloses a kind of for using the magnetic resonance image processing unit of the magnetic resonance fingerprint imaging method,
Including data acquisition module, dictionary generation module, imaging results generation module；
The data acquisition module is used in the excitation each time of pulse train, using different repetition times, echo
Time, flip angle, and data acquisition is carried out using the non-cartesian trajectories in the space K；
The dictionary generation module is calculated by being simulated based on the Bloch model of fractional model, is generated and is based on score
The dictionary of rank Bloch model；
The imaging results generation module for rebuilding magnetic resonance image, and by the signal of respective element in image with it is described
Dictionary is compared, final to obtain multi-parameter quantitative imaging result.
Preferably, the dictionary generation module is according to the parameter sets of the pulse train of use, with fractional order Bloch mould
Pattern is quasi- to be calculated, and the dictionary is generated.
The imaging results generation module is converted using non-uniform Fast Fourier, singular value decomposition Inverse Projection and low-rank
One of alternating direction Multiplier Algorithm generates and rebuilds magnetic resonance image.
Preferably, the imaging results generation module is maximum by element in the point-by-point time series of reconstruction image and dictionary
Law of Inner Product match cognization one by one obtains multi-parameter quantitative imaging result.
Preferably, the multi-parameter quantitative imaging result that the imaging results generation module generates is that quantitative tissue characteristics are joined
Number imaging, including longitudinal relaxation time parametric imaging and lateral relaxation time parametric imaging.
Preferably, the dictionary generation module passes through the fractional order Bloch model of longitudinal relaxation or/and transverse relaxation
Fractional order Bloch model generates the model；
The fractional order Bloch model of longitudinal relaxation are as follows:
Mz(t)=Mz(0) ten [M0-Mz(0)][1-Eβ(-(t/T1)β)]
The fractional order Bloch model of transverse relaxation are as follows:
Mxy(t)=Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞),
The beneficial effects of the practice of the present invention has:
1, the present invention improves the sample rate and imaging accuracy of magnetic resonance fingerprint quantitative parameter imaging；
2, the present invention does not increase additional data and sweep time while improving quantitative parameter imaging accuracy.
Detailed description of the invention
Technical solution for a better understanding of the invention, can refer to it is following, for being carried out to the prior art or embodiment
The attached drawing of explanation.These attached drawings will carry out brief displaying to section Example or prior art related products or method.This
The essential information of a little attached drawings is as follows:
Fig. 1 is in one embodiment, and the flip angle of used pulse train changes；
Fig. 2 is the repetition time of used pulse train and echo time variation in one embodiment；
Fig. 3 is magnetic resonance fingerprint imaging pulse sequence timing diagram in one embodiment；
Fig. 4 is longitudinal relaxation time parametric imaging result figure in one embodiment；
Fig. 5 is lateral relaxation time parametric imaging result figure in one embodiment.
Specific embodiment
Present technical solution in the embodiment of the present invention or beneficial effect make further expansion description, it is clear that are retouched
The embodiment stated is only some embodiments of the invention, and and not all.
It should be pointed out that the proposition of the invention, primarily to solving in MR image reconstruction field, accordingly
Problem of the existing technology, so the invention is especially suitable for the subdivision field, but not meaning the invention
The applicable range of technical solution institute it is therefore limited, those skilled in the art can be as needed, under field of magnetic resonance imaging
Various concrete application occasions reasonably implemented.
A kind of the step of magnetic resonance fingerprint imaging method, the imaging method includes:
Step S1, in the excitation each time of pulse train, using different repetition times, echo time, flip angle, and
Data acquisition is carried out using the non-cartesian trajectories in the space K；
Step S2 is generated with calculating is simulated based on the Bloch model of fractional model and is based on fractional order Bloch mould
The dictionary of type；
Step S3 rebuilds magnetic resonance image, and the signal of respective element in image is compared with the dictionary, finally
Obtain multi-parameter quantitative imaging result.
Wherein, the setting of pulse train can refer to FIG. 1 to FIG. 3.
The present invention substitutes classical single order Bloch (Bloch) model using more accurate fractional order Bloch model to build
The dictionary of vertical fractional order Bloch model recycles the dictionary element of generation to carry out pattern-recognition match cognization, obtains quantitative
Tissue characteristics parametric imaging.The method increase the dictionary accuracy that magnetic resonance fingerprint imaging is established, and reduce mode
The error of identification, so that the quantitative parameter of magnetic resonance fingerprint imaging closer to goldstandard is imaged.
In a preferred embodiment, the step S2 is used specifically, according to the parameter sets of the pulse train of use
Fractional order Bloch modeling calculates, and generates the dictionary.
In a preferred embodiment, in the step S3, the method for rebuilding magnetic resonance image includes: non-homogeneous quick
Fourier transform, one of singular value decomposition Inverse Projection and low-rank alternating direction Multiplier Algorithm.
In a preferred embodiment, in the step S3, by member in the point-by-point time series of reconstruction image and dictionary
Element maximum inner product method match cognization one by one, obtains multi-parameter quantitative imaging result.
In a preferred embodiment, the variable in the dictionary includes longitudinal relaxation time and lateral relaxation time；
In the step S3, multi-parameter quantitative imaging result is quantitative tissue characteristics parametric imaging, including longitudinal relaxation
Time parameter imaging and lateral relaxation time parametric imaging.
In a preferred embodiment, longitudinal relaxation is expressed as with fractional order Bloch model:
Mz(t)=Mz(0)+[M0-Mz(0)][1-Eβ(-(t/T1)β)]
In a preferred embodiment, transverse relaxation is expressed as with fractional order Bloch model:
Mxy(t)=Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞),
In above-mentioned formula, the meaning of each character are as follows:
The β rank differential operator of the Riemann-Liouville of Caputo form
M0: initial magnetization vector
Mz(t): t moment longitudinal magnetization vector
Mxy(t): t moment transverse magnetization vector
β rank T1Relaxation
α rank T2Relaxation
Eβ(-(t/T1)β): T1β rank stretch Mittag-Leffler function
Eα(-(t/T2)α): T2α rank stretch Mittag-Leffler function
ω0: resonant frequency
(1- α) rank integral operator of Riemann-Liouville
Caputo and Riemann-Liouville formula can refer to by the high heart, Liu Xingwen, the written " fractional order of Shao's bodyguard spring
The chaos of dynamic system, control and synchronization " or other relevant documents and materials.
Mittag-Leffler (meter Ta-Lie Fule) correlation function can refer to " the complex function opinion for being relied by minister in ancient times's Chinese and being write
(revised and enlarged edition) " or other relevant documents and materials.
In a preferred embodiment, particularly assume when the value of τ is lesser, meter Ta-Lie Fule approximation to function
For
ThenOr/and
The invention also discloses a kind of computer-readable medium, which has the journey being stored therein
Sequence, the program execute the magnetic resonance fingerprint imaging method for computer.
The invention also discloses a kind of for using the magnetic resonance image processing unit of the magnetic resonance fingerprint imaging method,
Including data acquisition module, dictionary generation module, imaging results generation module；
The data acquisition module is used in the excitation each time of pulse train, using different repetition times, echo
Time, flip angle, and data acquisition is carried out using the non-cartesian trajectories in the space K；
The dictionary generation module is calculated by being simulated based on the Bloch model of fractional model, is generated and is based on score
The dictionary of rank Bloch model；
The imaging results generation module for rebuilding magnetic resonance image, and by the signal of respective element in image with it is described
Dictionary is compared, final to obtain multi-parameter quantitative imaging result.
In a preferred embodiment, the dictionary generation module is used according to the parameter sets of the pulse train of use
Fractional order Bloch modeling calculates, and generates the dictionary.
The imaging results generation module is converted using non-uniform Fast Fourier, singular value decomposition Inverse Projection and low-rank
One of alternating direction Multiplier Algorithm generates and rebuilds magnetic resonance image.
In a preferred embodiment, the imaging results generation module is by the point-by-point time series and word of reconstruction image
Element maximum inner product method match cognization one by one, obtains multi-parameter quantitative imaging result in allusion quotation.
In a preferred embodiment, the multi-parameter quantitative imaging result that the imaging results generation module generates is fixed
The tissue characteristics parametric imaging of amount, including longitudinal relaxation time parametric imaging and lateral relaxation time parametric imaging.
In a preferred embodiment, the dictionary generation module passes through the fractional order Bloch model of longitudinal relaxation
Or/and the fractional order Bloch model of transverse relaxation generates the model；
The fractional order Bloch model of longitudinal relaxation are as follows:
Mz(t)=Mz(0)+[M0-Mz(0)][1-Eβ(-(t/T1)β)]
The fractional order Bloch model of transverse relaxation are as follows:
Mxy(t)=Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞),
Carrying out simulation using method of the invention can be obtained Fig. 4 and Fig. 5, and show the mean difference of each image
(avg.diff.△).Wherein Fig. 4 is to generate dictionary using conventional method and the method for the present invention respectively, and eventually by singular value
The longitudinal relaxation time parametric imaging (T1mapping) that decomposition backprojection and low-rank alternating direction Multiplier Algorithm obtain.Fig. 5 is
Dictionary is generated using conventional method and the method for the present invention respectively, and eventually by singular value decomposition backprojection and low-rank alternating side
The lateral relaxation time parametric imaging (T2mapping) obtained to Multiplier Algorithm.
Magnetic resonance fingerprint imaging of the reference mean square deviation of image and comparison reference picture than classical single order Bloch model
Reduced, while not increasing additional data and sweep time.Experiment shows fractional order Bloch mould of the invention at present
Quantitative parameter imaging of the T1mapping and T2mapping that type generates than original single order Bloch model improves 50% or more
Accuracy.
Finally it should be pointed out that embodiment cited hereinabove, is more typical, preferred embodiment of the invention, only
For being described in detail, explaining technical solution of the present invention, in order to reader's understanding, the protection scope being not intended to limit the invention
Or application.Therefore, within the spirit and principles in the present invention any modification, equivalent replacement, improvement and so on and obtain
Technical solution should be all included within protection scope of the present invention.
Claims (14)
1. a kind of magnetic resonance fingerprint imaging method based on fractional model, it is characterised in that:
The step of magnetic resonance fingerprint imaging method includes:
Step S1 using different repetition times, echo time, flip angle, and is utilized in the excitation each time of pulse train
Non-cartesian trajectories in the space K carry out data acquisition；
Step S2 is generated with calculating is simulated based on the Bloch model of fractional model based on fractional order Bloch model
Dictionary；
Step S3 rebuilds magnetic resonance image, and the signal of respective element in image is compared with the dictionary, final to obtain
Multi-parameter quantitative imaging result.
2. magnetic resonance fingerprint imaging method according to claim 1, it is characterised in that:
The step S2 is calculated specifically, according to the parameter sets of the pulse train of use with fractional order Bloch modeling,
Generate the dictionary.
3. magnetic resonance fingerprint imaging method according to claim 2, it is characterised in that:
In the step S3, the method for rebuilding magnetic resonance image includes: non-uniform Fast Fourier transformation, and singular value decomposition is counter to throw
One of shadow method and low-rank alternating direction Multiplier Algorithm.
4. magnetic resonance fingerprint imaging method according to claim 3, it is characterised in that:
In the step S3, the point-by-point time series of reconstruction image is matched into knowledge with element in dictionary with maximum inner product method one by one
Not, multi-parameter quantitative imaging result is obtained.
5. according to any magnetic resonance fingerprint imaging method of claim 4, it is characterised in that:
Variable in the dictionary includes longitudinal relaxation time and lateral relaxation time；
In the step S3, multi-parameter quantitative imaging result is quantitative tissue characteristics parametric imaging, including longitudinal relaxation time
Parametric imaging and lateral relaxation time parametric imaging.
6. magnetic resonance fingerprint imaging method according to claim 5, it is characterised in that:
Longitudinal relaxation is expressed as with fractional order Bloch model:
Mz(t)=Mz(0)+[M0-Mz(0)][1-Eβ(-(t/T1)β)]
7. magnetic resonance fingerprint imaging method according to claim 6, it is characterised in that:
Transverse relaxation is expressed as with fractional order Bloch model:
Mxy(t)=Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞),
8. magnetic resonance fingerprint imaging method according to claim 7, it is characterised in that:
It assume that meter Ta-Lie Fule approximation to function is
ThenOr/and
9. a kind of computer-readable medium, which has the program being stored therein, and the program is for calculating
Magnetic resonance fingerprint imaging method described in any one of machine perform claim requirement 1~8.
10. a kind of magnetic resonance image for using magnetic resonance fingerprint imaging method described in any one of claim 1~8 is handled
Device, it is characterised in that:
Including data acquisition module, dictionary generation module, imaging results generation module；
The data acquisition module be used in the excitation each time of pulse train, using the different repetition times, the echo time,
Flip angle, and data acquisition is carried out using the non-cartesian trajectories in the space K；
The dictionary generation module is calculated by being simulated based on the Bloch model of fractional model, is generated and is based on fractional order cloth
The dictionary of the conspicuous model in Lip river；
The imaging results generation module is for rebuilding magnetic resonance image, and by the signal of respective element in image and the dictionary
It is compared, it is final to obtain multi-parameter quantitative imaging result.
11. device according to claim 10, it is characterised in that:
The dictionary generation module is calculated according to the parameter sets of the pulse train of use with fractional order Bloch modeling,
Generate the dictionary；
The imaging results generation module is converted using non-uniform Fast Fourier, singular value decomposition Inverse Projection and low-rank alternating
One of direction Multiplier Algorithm generates and rebuilds magnetic resonance image.
12. device according to claim 10, it is characterised in that:
The imaging results generation module by element in the point-by-point time series of reconstruction image and dictionary with maximum inner product method one by one
Match cognization obtains multi-parameter quantitative imaging result.
13. device according to claim 10, it is characterised in that:
The multi-parameter quantitative imaging result that the imaging results generation module generates is quantitative tissue characteristics parametric imaging, including
Longitudinal relaxation time parametric imaging and lateral relaxation time parametric imaging.
14. device according to claim 10, it is characterised in that:
The dictionary generation module passes through the fractional order Bloch model of longitudinal relaxation or/and the fractional order Bloch of transverse relaxation
Model generates the model；
The fractional order Bloch model of longitudinal relaxation are as follows:
Mz(t)=Mz(0)+[M0-Mz(0)][1-Eβ(-(t/T1)β)]
The fractional order Bloch model of transverse relaxation are as follows:
Mxy(t)=Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞)。
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| WO2021217391A1 (en) * | 2020-04-28 | 2021-11-04 | 深圳先进技术研究院 | Rapid magnetic resonance multi-parameter imaging method and apparatus |
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| RICHARD L. MAGIN: "《Fractional order analysis of Sephadex gel structures: NMR measurements reflecting anomalous diffusion》", 《COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION》 * |
| 黄敏: "《基于SVD的快速组匹配磁共振指纹新方法》", 《生物医学工程研究》 * |
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| WO2021217391A1 (en) * | 2020-04-28 | 2021-11-04 | 深圳先进技术研究院 | Rapid magnetic resonance multi-parameter imaging method and apparatus |
| CN111537931B (en) * | 2020-04-28 | 2022-05-17 | 深圳先进技术研究院 | Rapid magnetic resonance multi-parameter imaging method and device |
| WO2023093842A1 (en) * | 2021-11-29 | 2023-06-01 | 浙江大学 | Method for multi-parametric quantitative imaging of liver |
| CN115561690A (en) * | 2022-09-23 | 2023-01-03 | 深圳市联影高端医疗装备创新研究院 | Magnetic resonance data processing method and device and computer equipment |
| CN115561690B (en) * | 2022-09-23 | 2023-09-26 | 深圳市联影高端医疗装备创新研究院 | Magnetic resonance data processing method and device and computer equipment |
| CN117310574A (en) * | 2023-11-28 | 2023-12-29 | 华中科技大学 | Method for acquiring magnetic field conversion matrix, external magnetic field measurement method and system |
| CN117310574B (en) * | 2023-11-28 | 2024-02-13 | 华中科技大学 | Method for acquiring magnetic field conversion matrix, external magnetic field measurement method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2019153654A1 (en) | 2019-08-15 |
| CN110133554B (en) | 2021-04-30 |
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