CN110133554B - Magnetic resonance fingerprint imaging method, device and medium based on fractional order model - Google Patents

Magnetic resonance fingerprint imaging method, device and medium based on fractional order model Download PDF

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CN110133554B
CN110133554B CN201810127489.7A CN201810127489A CN110133554B CN 110133554 B CN110133554 B CN 110133554B CN 201810127489 A CN201810127489 A CN 201810127489A CN 110133554 B CN110133554 B CN 110133554B
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王海峰
梁栋
邹莉娴
刘新
郑海荣
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Abstract

The invention relates to magnetic resonance parameter image reconstruction, and provides a magnetic resonance fingerprint quantitative parameter imaging method based on a fractional order Bloch model, which can improve the quantitative parameter imaging accuracy and reduce the scanning time of quantitative parameter imaging. The technical scheme mainly comprises the following steps: acquiring data by adopting different repetition time, echo time and flip angle in each excitation of the pulse sequence and reconstructing to obtain an image; generating a dictionary based on the fractional-order Bloch model; a multi-parameter image is obtained using pattern recognition. The beneficial effects of the implementation of the invention are mainly as follows: the sampling speed and the imaging accuracy of the magnetic resonance fingerprint quantitative parameter imaging are improved; the quantitative parameter imaging accuracy is improved, and meanwhile, extra data and scanning time are not increased.

Description

Magnetic resonance fingerprint imaging method, device and medium based on fractional order model
Technical Field
The invention relates to the technical field of magnetic resonance imaging, in particular to a magnetic resonance fingerprint imaging method.
Background
Magnetic resonance imagers use magnetic field and radio wave pulses to generate images of body tissues and structures. Magnetic Resonance Fingerprinting (MRF) can acquire more information per measurement than conventional MRF. As a new method for rapidly and simultaneously obtaining multi-parameter quantitative imaging, the magnetic resonance fingerprint imaging technology mainly comprises the following steps: 1. adopting different repetition Time (TR), Echo Time (TE) and Flip Angle (FA) in each excitation of the pulse sequence, acquiring data by using a multi-excitation spiral track (multi-inter spiral) and reconstructing to obtain an undersampled image sequence; 2. calculating a dictionary based on a classical first-order Bloch (Bloch) model according to parameters (TR, TE and FA) of the pulse sequence; 3. and matching and identifying the reconstructed image sequence and elements in the dictionary point by point, so that multi-parameter quantitative imaging results can be obtained simultaneously.
Conventional quantitative magnetic resonance imaging obtains characteristic parameters of tissue by nonlinear fitting of a classical bloch model (a model describing the magnetic resonance physical process) to signals at each pixel position of an image, such as: longitudinal relaxation time (T1), transverse relaxation time (T2), etc. However, the imaging method has large error, and the quantitative parameter imaging result is not ideal.
Disclosure of Invention
The invention aims to provide a new magnetic resonance fingerprint imaging method to solve the problem of large error.
In order to solve the above technical problem, the present invention firstly discloses a magnetic resonance fingerprint imaging method, which is implemented as follows:
a magnetic resonance fingerprinting method, the steps of which comprise:
step S1, in each excitation of the pulse sequence, different repetition time, echo time and flip angle are adopted, and data acquisition is carried out by utilizing a non-Cartesian track in a K space;
step S2, simulating calculation by using a Bloch model based on the fractional order model, and generating a dictionary based on the fractional order Bloch model;
and step S3, reconstructing a magnetic resonance image, and comparing signals of corresponding elements in the image with the dictionary to finally obtain a multi-parameter quantitative imaging result.
Preferably, in step S2, the dictionary is generated by using a fractional-order bloch model simulation calculation according to the parameter set of the adopted pulse sequence.
Preferably, in step S3, the method for reconstructing a magnetic resonance image includes: non-uniform fast Fourier transform, singular value decomposition back projection method and low rank alternating direction multiplier algorithm.
Preferably, in step S3, the point-by-point time series of the reconstructed image and the elements in the dictionary are matched and identified by maximum inner product method, so as to obtain a multi-parameter quantitative imaging result.
Preferably, the variables in the dictionary include longitudinal relaxation times and transverse relaxation times;
in step S3, the multi-parameter quantitative imaging result is quantitative tissue characteristic parameter imaging, including longitudinal relaxation time parameter imaging and transverse relaxation time parameter imaging.
Preferably, the longitudinal relaxation is expressed in a fractional order bloch model as:
Figure BDA0001573940960000021
Mz(t)＝Mz(0)+[M0-Mz(0)][1-Eβ(-(t/T1)β)]
preferably, the transverse relaxation is expressed in a fractional order bloch model as:
Figure BDA0001573940960000022
Mxy(t)＝Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞)，
preferably, the Mitta-Leffer function can be approximated as if the value of τ is small
Figure BDA0001573940960000023
Then
Figure BDA0001573940960000031
Or/and
Figure BDA0001573940960000032
the invention also discloses a computer readable medium having a program stored therein for a computer to perform the magnetic resonance fingerprint imaging method.
The invention also discloses a magnetic resonance image processing device for using the magnetic resonance fingerprint imaging method, which comprises a data acquisition module, a dictionary generation module and an imaging result generation module;
the data acquisition module is used for acquiring data by adopting different repetition time, echo time and flip angle and utilizing a non-Cartesian track in a K space in each excitation of a pulse sequence;
the dictionary generation module generates a dictionary based on a fractional-order Bloch model by simulating calculation based on the fractional-order Bloch model;
the imaging result generation module is used for reconstructing a magnetic resonance image, comparing signals of corresponding elements in the image with the dictionary and finally obtaining a multi-parameter quantitative imaging result.
Preferably, the dictionary generation module generates the dictionary by using a fractional-order bloch model simulation calculation according to a parameter set of the adopted pulse sequence.
The imaging result generation module generates a reconstructed magnetic resonance image by adopting one of non-uniform fast Fourier transform, singular value decomposition back projection method and low-rank alternating direction multiplier algorithm.
Preferably, the imaging result generation module performs matching recognition on the point-by-point time sequence of the reconstructed image and elements in the dictionary by using a maximum inner product method, so as to obtain a multi-parameter quantitative imaging result.
Preferably, the multi-parameter quantitative imaging result generated by the imaging result generation module is quantitative tissue characteristic parameter imaging including longitudinal relaxation time parameter imaging and transverse relaxation time parameter imaging.
Preferably, the dictionary generation module generates the model by a longitudinally relaxed fractional order bloch model or/and a transversely relaxed fractional order bloch model;
the fractional bloch model of longitudinal relaxation is:
Figure BDA0001573940960000033
Mz(t)＝Mz(0) ten [ M ]0-Mz(0)][1-Eβ(-(t/T1)β)]
The fractional bloch model of transverse relaxation is:
Figure BDA0001573940960000041
Mxy(t)＝Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞)，
the beneficial effects of the implementation of the invention are as follows:
1. the invention improves the sampling speed and the imaging accuracy of the magnetic resonance fingerprint quantitative parameter imaging;
2. the invention improves the imaging accuracy of quantitative parameters without adding extra data and scanning time.
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For a better understanding of the technical aspects of the invention, reference is made to the following drawings, which illustrate the prior art or embodiments. These drawings will briefly illustrate some embodiments or products or methods related to the prior art. The basic information for these figures is as follows:
FIG. 1 is a diagram of the flip angle variation of the pulse sequence employed in one embodiment;
FIG. 2 is a graph of the repetition time and echo time variation of the pulse sequence employed in one embodiment;
FIG. 3 is a timing diagram of a magnetic resonance fingerprint imaging pulse sequence in one embodiment;
FIG. 4 is a graph of longitudinal relaxation time parameter imaging results in one embodiment;
fig. 5 is a graph of transverse relaxation time parameter imaging results in one embodiment.
Detailed Description
Now, the technical solutions or advantages of the embodiments of the present invention will be further described, and it is obvious that the described embodiments are only some implementations of the present invention, and not all implementations.
It should be noted that the present invention is proposed to solve the problems in the related art in the field of magnetic resonance image reconstruction, so that the present invention is particularly suitable for this subdivision field, but it is not intended that the applicable scope of the technical solution of the present invention is limited, and those skilled in the art can reasonably implement the present invention in various specific application fields in the field of magnetic resonance imaging according to needs.
A magnetic resonance fingerprint imaging method, the steps of the imaging method comprising:
step S1, in each excitation of the pulse sequence, different repetition time, echo time and flip angle are adopted, and data acquisition is carried out by utilizing a non-Cartesian track in a K space;
step S2, simulating calculation by using a Bloch model based on the fractional order model, and generating a dictionary based on the fractional order Bloch model;
and step S3, reconstructing a magnetic resonance image, and comparing signals of corresponding elements in the image with the dictionary to finally obtain a multi-parameter quantitative imaging result.
The setting of the pulse sequence can refer to fig. 1 to 3.
According to the method, a dictionary of the fractional-order Bloch model is established by using a more accurate fractional-order Bloch model to replace a classical first-order Bloch (Bloch) model, and then pattern recognition matching recognition is performed by using generated dictionary elements to obtain quantitative tissue characteristic parameter imaging. The method improves the accuracy of the dictionary established by the magnetic resonance fingerprint imaging, reduces the error of pattern recognition, and enables the magnetic resonance fingerprint imaging to be closer to the quantitative parameter imaging of the gold standard.
In a preferred embodiment, the step S2 is specifically to generate the dictionary by using a fractional-order bloch model simulation calculation according to the parameter set of the adopted pulse sequence.
In a preferred embodiment, in step S3, the method for reconstructing a magnetic resonance image includes: non-uniform fast Fourier transform, singular value decomposition back projection method and low rank alternating direction multiplier algorithm.
In a preferred embodiment, in step S3, the point-by-point time series of the reconstructed image is matched and identified with elements in the dictionary by using a maximum inner product method, and a multi-parameter quantitative imaging result is obtained.
In a preferred embodiment, the variables in the dictionary include longitudinal relaxation times and transverse relaxation times;
in step S3, the multi-parameter quantitative imaging result is quantitative tissue characteristic parameter imaging, including longitudinal relaxation time parameter imaging and transverse relaxation time parameter imaging.
In a preferred embodiment, the longitudinal relaxation is expressed in a fractional order bloch model as:
Figure BDA0001573940960000051
Mz(t)＝Mz(0)+[M0-Mz(0)][1-Eβ(-(t/T1)β)]
in a preferred embodiment, the transverse relaxation is expressed in a fractional order bloch model as:
Figure BDA0001573940960000061
Mxy(t)＝Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞)，
in the above formula, the meaning of each character is:
Figure BDA0001573940960000062
beta order differential operator of Riemann-Liouville in Caputo form
M0: initial magnetization vector
Mz(t): longitudinal magnetization vector at time t
Mxy(t): transverse magnetization vector at time t
Figure BDA0001573940960000063
Beta order T1Relaxation of
Figure BDA0001573940960000064
Alpha order T2Relaxation of
Eβ(-(t/T1)β)：T1Beta order stretch Mittag-Leffler function of
Eα(-(t/T2)α)：T2Alpha-order stretching Mittag-Leffler function of
ω0: resonant frequency
Figure BDA0001573940960000065
(1-alpha) order integral operator of Riemann-Liouville
The Caputo and Riemann-Liouville equations may be found in chaos, control and synchronization of fractional dynamics systems, known by Highson, Liuxing, Shishao spring, or other relevant literature.
Mittag-Leffler correlation function can be referred to the "Complex function theory (extended order) written by Qinghai-lysine or other relevant literature data.
In a preferred embodiment, it is particularly assumed that at small values of τ, the Mitta-Leffer function is approximated by
Figure BDA0001573940960000066
Then
Figure BDA0001573940960000067
Or/and
Figure BDA0001573940960000068
the invention also discloses a computer readable medium having a program stored therein for a computer to perform the magnetic resonance fingerprint imaging method.
The invention also discloses a magnetic resonance image processing device for using the magnetic resonance fingerprint imaging method, which comprises a data acquisition module, a dictionary generation module and an imaging result generation module;
the data acquisition module is used for acquiring data by adopting different repetition time, echo time and flip angle and utilizing a non-Cartesian track in a K space in each excitation of a pulse sequence;
the dictionary generation module generates a dictionary based on a fractional-order Bloch model by simulating calculation based on the fractional-order Bloch model;
the imaging result generation module is used for reconstructing a magnetic resonance image, comparing signals of corresponding elements in the image with the dictionary and finally obtaining a multi-parameter quantitative imaging result.
In a preferred embodiment, the dictionary generation module generates the dictionary using a fractional-order bloch model simulation calculation based on a set of parameters of the pulse sequence employed.
The imaging result generation module generates a reconstructed magnetic resonance image by adopting one of non-uniform fast Fourier transform, singular value decomposition back projection method and low-rank alternating direction multiplier algorithm.
In a preferred embodiment, the imaging result generation module performs strip-by-strip matching recognition on the point-by-point time sequence of the reconstructed image and elements in the dictionary by using a maximum inner product method to obtain a multi-parameter quantitative imaging result.
In a preferred embodiment, the multi-parameter quantitative imaging results generated by the imaging result generation module are quantitative tissue property parameter imaging including longitudinal relaxation time parameter imaging and transverse relaxation time parameter imaging.
In a preferred embodiment, the dictionary generation module generates the model by a fractional-order bloch model of longitudinal relaxation or/and a fractional-order bloch model of transverse relaxation;
the fractional bloch model of longitudinal relaxation is:
Figure BDA0001573940960000071
Mz(t)＝Mz(0)+[M0-Mz(0)][1-Eβ(-(t/T1)β)]
the fractional bloch model of transverse relaxation is:
Figure BDA0001573940960000072
Mxy(t)＝Mxy(0)[Eα(-(t/T2)α)]+Mxy(∞)，
simulations using the method of the present invention resulted in fig. 4 and 5, and the average difference (avg. Fig. 4 shows longitudinal relaxation time parameter imaging (T1mapping) obtained by generating dictionaries by using a conventional method and the method of the present invention, respectively, and finally by using a singular value decomposition back projection method and a low rank alternating direction multiplier algorithm. Fig. 5 is a transverse relaxation time parametric imaging (T2mapping) obtained by generating dictionaries using the conventional method and the method of the present invention, respectively, and finally by the singular value decomposition back projection method and the low rank alternating direction multiplier algorithm.
The reference mean variance of the image and the contrast reference image is reduced compared with the magnetic resonance fingerprint imaging of the classical first-order bloch model, and the extra data and scanning time are not increased. The present experiment shows that the T1mapping and the T2mapping generated by the fractional order Bloch model of the invention can improve the accuracy by more than 50% compared with the quantitative parameter imaging of the original first order Bloch model.
Finally, it should be noted that the above-mentioned embodiments are typical and preferred embodiments of the present invention, and are only used for explaining and explaining the technical solutions of the present invention in detail, so as to facilitate the reader's understanding, and are not used to limit the protection scope or application of the present invention. Therefore, any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be covered within the protection scope of the present invention.

Claims (14)

1. A magnetic resonance fingerprint imaging method based on a fractional order model is characterized in that:
the steps of the magnetic resonance fingerprint imaging method comprise:
step S1, in each excitation of the pulse sequence, different repetition time, echo time and flip angle are adopted, and data acquisition is carried out by utilizing a non-Cartesian track in a K space;
step S2, simulating calculation by using a Bloch model based on the fractional order model, and generating a dictionary based on the fractional order Bloch model;
and step S3, reconstructing a magnetic resonance image, and comparing signals of corresponding elements in the image with the dictionary to finally obtain a multi-parameter quantitative imaging result.
2. A magnetic resonance fingerprint imaging method as set forth in claim 1, characterized in that:
the step S2 is specifically to generate the dictionary by using a fractional-order bloch model simulation calculation according to the parameter set of the adopted pulse sequence.
3. A magnetic resonance fingerprint imaging method as set forth in claim 2, characterized in that:
in step S3, the method for reconstructing a magnetic resonance image includes: non-uniform fast Fourier transform, singular value decomposition back projection method and low rank alternating direction multiplier algorithm.
4. A magnetic resonance fingerprint imaging method as set forth in claim 3, characterized in that:
in the step S3, the point-by-point time series of the reconstructed image and the elements in the dictionary are matched and identified by maximum inner product method to obtain a multi-parameter quantitative imaging result.
5. A magnetic resonance fingerprint imaging method as set forth in any one of claims 4, wherein:
variables in the dictionary include longitudinal relaxation time and transverse relaxation time;
in step S3, the multi-parameter quantitative imaging result is quantitative tissue characteristic parameter imaging, including longitudinal relaxation time parameter imaging and transverse relaxation time parameter imaging.
6. The magnetic resonance fingerprint imaging method of claim 5, wherein:
the longitudinal relaxation is expressed in a fractional bloch model as:
Figure DEST_PATH_IMAGE002
。
7. the magnetic resonance fingerprint imaging method of claim 6, wherein:
the transverse relaxation is expressed in a fractional bloch model as:
Figure DEST_PATH_IMAGE004
。
8. the magnetic resonance fingerprint imaging method of claim 7, wherein:
it can be assumed that the Mitta-Leffer function is approximated as
Figure DEST_PATH_IMAGE006
，
Then
Figure DEST_PATH_IMAGE008
Or/and
Figure DEST_PATH_IMAGE010
。
9. a computer readable medium having a program stored therein for a computer to perform the magnetic resonance fingerprint imaging method of any one of claims 1 to 8.
10. A magnetic resonance image processing apparatus for using the magnetic resonance fingerprint imaging method according to any one of claims 1 to 8, characterized by:
the system comprises a data acquisition module, a dictionary generation module and an imaging result generation module;
the data acquisition module is used for acquiring data by adopting different repetition time, echo time and flip angle and utilizing a non-Cartesian track in a K space in each excitation of a pulse sequence;
the dictionary generation module generates a dictionary based on a fractional-order Bloch model by simulating calculation based on the fractional-order Bloch model;
the imaging result generation module is used for reconstructing a magnetic resonance image, comparing signals of corresponding elements in the image with the dictionary and finally obtaining a multi-parameter quantitative imaging result.
11. The apparatus of claim 10, wherein:
the dictionary generation module generates the dictionary by using a fractional-order Bloch model for simulation calculation according to the parameter set of the adopted pulse sequence;
the imaging result generation module generates a reconstructed magnetic resonance image by adopting one of non-uniform fast Fourier transform, singular value decomposition back projection method and low-rank alternating direction multiplier algorithm.
12. The apparatus of claim 10, wherein:
and the imaging result generation module performs matching identification on the point-by-point time sequence of the reconstructed image and elements in the dictionary by using a maximum inner product method to obtain a multi-parameter quantitative imaging result.
13. The apparatus of claim 10, wherein:
the multi-parameter quantitative imaging result generated by the imaging result generation module is quantitative tissue characteristic parameter imaging, including longitudinal relaxation time parameter imaging and transverse relaxation time parameter imaging.
14. The apparatus of claim 10, wherein:
the dictionary generation module generates the model through a longitudinally relaxed fractional order Bloch model or/and a transversely relaxed fractional order Bloch model;
the fractional bloch model of longitudinal relaxation is:
Figure 133892DEST_PATH_IMAGE002
；
the fractional bloch model of transverse relaxation is:
Figure DEST_PATH_IMAGE011
。
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