CN110706199A - Z spectrum fitting method based on pseudo-Poincke decoupling type and CEST MRI quantification method - Google Patents

Abstract

The invention belongs to the field of CEST MRI, and discloses a Z spectrum fitting method based on a pseudo-Fockel cast-off line type and a CEST MRI quantification method. For any pixel in a CEST MRI image, a pseudo-Pocketsingle line type (PVP) is used for replacing Lorentz as a fitting function, so that the fitting method is suitable for fitting under the conditions of higher saturation power and higher magnetization transfer content in tissues, and the fitting robustness is improved; secondly, different B is calculated according to the five-pool Bloch simulation_1‑satThe positive and negative sides of the Z spectrum are respectively optimized and compensated during fitting of the lower Z spectrum, and the positive and negative sides of the Z spectrum acquired by experiment are respectively optimized according to the optimized and compensated coefficient to compensate different B_1‑satDirect water saturation in the lower Z spectrumAnd MT contribution; thirdly, considering the asymmetry of MT, the positive side and the negative side of the Z spectrum after optimized compensation are respectively fitted by using a PVP function to form a CEST quantitative contrast diagram, and the method is expected to be used for the quantification of pre-clinical and clinical CEST MRI.

Description

Z spectrum fitting method based on pseudo-Poincke decoupling type and CEST MRI quantification method

Technical Field

The invention belongs to the field of CEST MRI, and particularly relates to a Z spectrum fitting method based on a pseudo-Poincke decoupling type and a CEST MRI quantification method.

Background

MRI stands for magnetic resonance imaging, while CEST imaging is currently a new MRI imaging technique of great interest to indirectly detect low concentrations of metabolites and molecules of exchangeable protons with specific resonance frequencies by detecting changes in water signal caused by chemical exchange with saturated solute protons. Whereas the concentration of these pools of small solutes in biological tissue is usually only in the millimolar range, the cumulative effect of chemical exchange of water with saturated protons enables signal enhancement, with higher sensitivity, by choosing appropriate experimental parameters.

CEST allows the presence of chemical substances to be indirectly detected by reducing water signals, and various chemical substances to be accurately quantified, so that CEST information required by people can be accurately analyzed, and diseases can be accurately diagnosed. To demonstrate certain CEST effects, the Z spectrum is typically generated by acquiring a large amount of water signal intensity as a function of Radio Frequency (RF) saturation frequency shift. To achieve accurate and reliable CEST quantization, the impact of competing processes must be eliminated. Direct water saturation signal (DS), semi-solid magnetization transfer effect (MTC) and nuclear austenite effect (NOE) are several major competing effects. Meanwhile, the static magnetic field B0, saturation power (B) is applied due to these competing effects_1-sat) And other experimental parameters, rendering CEST a complex technique. One of the most commonly used quantification methods for CEST MRI is the magnetic transfer rate asymmetry (MTRasym) analysis, MTRasym is simple and easy to calculate and has been shown to correlate with the staging of tumors by APT. However, MTRasym is susceptible to several disturbances including B0 field inhomogeneity, DS and MT, and more importantly MTRasym does not distinguish the NOE effect of the upper field well from the CEST contrast.

Z spectrum fitting is an important method of distinguishing the different contributions of various sources in the Z spectrum. Theoretically, the enhancement of CEST signals in the Z spectrum depends on the size of the pool, the exchange law and the relaxation time, as evidenced by the Bloch-McConnell equation. The quantification method of the least square method Z spectrum fitting is used for solving the problems of MT, DS, saturation overflow effect and NOE effect of a plurality of pools, and a Z spectrum fitting method based on a Bloch-McConnell equation is common, but the method is complex in calculation and has strong dependence on the initial value and the boundary of a fitting parameter.

Furthermore, CEST quantification is performed on the basis of a Z spectrum fit, and depending on the shape of the Z spectrum, some other quantification methods have been proposed, including a multi-cell Lorentzian fit and Lorentzian Difference (LD). The multi-cell lorentzian fit is to set the amplitudes of the target CEST signal and NOE to 0 and to fit a lorentzian shape to where each dip in the Z spectrum. However, on the one hand, the multicell lorentz fitting requires intensive data acquisition of the Z spectra, which is time consuming to compare; on the other hand, the multi-cell Lorentzian fitting requires various fitting parameters and is sensitive to the comparison of the signal-to-noise ratio of the Z spectrum. The LD method uses the lorentz type as a reference to describe DS, followed by the difference between the fitted signal and the Z spectrum to quantify the CEST and NOE effects. LD is a relatively simple and robust quantization method, especially at lower B_1-satIn cases (. ltoreq.1. mu.T at9.4Tesla), have been shown to be effective in stroke patients. However, LD analysis overestimates CEST and NOE effects. In particular, relatively high saturation powers (B) are required for rapidly exchanging substances_1-sat) And stronger Magnetization Transfer (MT) in tissue, the Z spectrum is more gaussian than lorentz, making quantification of LD unreliable.

At present, a simple, robust and accurate quantification method does not exist in CEST MRI research. Therefore, quantification has been a major aspect of CEST MRI studies.

Disclosure of Invention

The invention aims to provide a Z spectrum fitting method based on a pseudo-Pocker offline type and a CEST MRI quantification method, which are used for solving the problem that a simple, robust and accurate quantification method is lacked in CEST MRI research in the prior art.

In order to realize the task, the invention adopts the following technical scheme:

a Z spectrum fitting method based on a pseudo-Poincke cast-off line type comprises the following steps:

step a: obtaining Z spectrum data, respectively utilizing Gaussian function G (delta omega) and Lorentz function L (delta omega) to perform pre-fitting on the Z spectrum data, and respectively obtaining Gaussian pre-fitting result Z_GuassAnd Lorentz pre-fitting results Z_Loren；

Step b: according to Z_GuassAnd Z_LorenObtaining a Lorentz proportionality coefficient alpha;

step c: establishing a pseudo-Poinck off-line model of a formula I by using the Lorentz proportionality coefficient alpha obtained in the step b, and fitting Z spectrum data according to the pseudo-Poinck off-line model to obtain a Z spectrum fitting result Z_pvp；

V (delta omega) ≈ alpha x L (delta omega) + (1-alpha) G (delta omega) formula I

Where V (Δ ω) represents a pseudo-fox offline model.

Further, step b comprises the following sub-steps:

step b 1: according to Z_GuassAnd Z_LorenCalculating the width gamma of the Gaussian half-maximum_GAnd half maximum width of lorentz Γ_L；

Step b 2: calculating the Lorentz proportionality coefficient alpha by adopting a formula II:

where Γ represents the half-maximum width of the pseudo-fox trap.

A CEST MRI quantification method, comprising:

the method comprises the steps of obtaining a CEST MRI image of an experimental object, obtaining original Z spectrum data of each pixel point in the CEST MRI image, traversing all the pixel points in the image to obtain CEST quantitative values of all the pixel points, and obtaining a CEST quantitative contrast map according to the CEST quantitative values of all the pixel points, wherein the CEST quantitative value calculation of any pixel point in the CEST MRI image comprises the following steps:

step 1: preprocessing the original Z spectrum data to obtain corrected Z spectrum data Z_exp；

Step 2: for Z obtained in step 1_expUtilizing a Lorentz function to perform pre-fitting to obtain a pre-fitting result Z_Loren；

And step 3: for Z obtained in step 1_expThe positive and negative sides of the Z spectrum are respectively optimized by adopting a formula III and a formula IV to obtain optimized Z spectrum data

Wherein the content of the first and second substances,

optimized Z spectral data representing positive half spectrum, Ratio (delta omega) representing positive half spectrum compensation factor, L_posRepresents Z_expRatio Z_LorenA low area between the maximum value of the lower-field frequency offset and the maximum value of the lower-field CEST effect ending frequency offset;

wherein the content of the first and second substances,

represents the optimized Z spectrum data of the negative half spectrum, Ratio (-delta omega) represents the compensation factor of the negative half spectrum, L_negRepresents Z_expRatio Z_LorenA lower area between the maximum value of the upper field frequency offset absolute value and the maximum value of the upper field NOE effect ending frequency offset absolute value;

and 4, step 4: for the product obtained in step 3

Blocking, inputting into any pseudo-Pock off-line model of the Z spectrum fitting method based on the pseudo-Pock off-line type for fitting to obtain fitting signal Z_{pvp_final}；

And 5: z obtained according to step 4_{pvp_final}And Z obtained in step 1_expAnd obtaining a CEST quantization value.

Further, Ratio (Δ ω) is 0.2318 × B₁+0.9502，Ratio(-Δω)＝0.4219×B₁+0.9131 wherein B₁The saturation power used in the experiment to acquire the Z spectrum is shown.

Further, step 4 comprises the following substeps:

step 4.1: will be provided with

As a first partition, will

As a second partition, will

As a third segment;

step 4.2: by blocking the first and second blocksInputting the signal into a pseudo-Buddk offline model for fitting, fitting the third block by using an LD method, and obtaining a fitting signal Z according to the fitting results of the three blocks_{pvp_final}。

Further, in step 4.1, the first fraction is 0.4ppm to 6ppm, the second fraction is-6 ppm to-0.4 ppm, and the third fraction is 0.4ppm to-0.4 ppm.

Further, the CEST quantization value in step 5 includes three calculation methods:

method (a): calculating Z_{_PVP_final}And Z_{_exp}The difference of (c) is used as CEST quantization value;

method (b): calculating Z_{_exp}Reciprocal of (a) and Z_{_PVP_final}The difference of the two inverse numbers of (c) is taken as CEST quantization value;

method (c): calculating Z_{_PVP_final}And Z_{_exp}The area between the difference and the horizontal axis is taken as the CEST quantization value.

Compared with the prior art, the invention has the following technical characteristics:

(1) the invention aims at solving the problem that when compared with a fast-switching CEST substance requiring higher-power irradiation and a higher Magnetization Transfer (MT), a Z spectrum is closer to a Gaussian form instead of Lorentz, and the invention has higher robustness by using PVP to replace the Lorentz as a fitting function.

(2) The invention proposes optimization relying on B1 to better compensate for different B_1-satThe contribution of DS and MT in time, reduces the influence of MT effect and noise caused by movement.

(3) The invention considers the asymmetry of MT, and respectively fits the data of the positive side and the negative side of the Z spectrum, and simultaneously, as DS is mainly near 0ppm of the Z spectrum, Lorentz is still used for fitting, thus improving the fitting precision of the invention.

Drawings

Fig. 1 is a flow chart of a CEST MRI quantification method;

FIG. 2 shows compensation coefficients B for the lower and upper fields of MT and DS_1-satA relationship diagram of (1);

FIG. 3(a) shows a cell model B of 5 cells_1-satFitting results of Z spectra, LD and optimized pseudo-Poincke off-line model at 0.5,1,1.5,2,2.5 and 3 μ T, respectively;

FIG. 3(B) is a view showing a model B of a 6-cell_1-satFitting results of Z spectra, LD and optimized pseudo-Poincke off-line model at 0.5,1,1.5,2,2.5 and 3 μ T, respectively;

FIG. 4(a) is an analytical solution for deriving the metric and standard of AREX from optimized pseudo-Pock off-line model fitting under 5-pool model

A comparison graph of (A);

FIG. 4(b) is a view showing a model of a 5-cellAnalytical solution for deriving AREX's metric and standard in optimizing fit of pseudo-fox off-line model

A comparison graph of (A);

FIG. 5(a) is a view T2 w;

FIG. 5(B) is B_1-satMTRasym graph at 2.4 muT

FIG. 5(c) tumor tissue at B_1-satA graph of the fitting results for the optimized pseudo-fox off-line model at 0.8 μ T, 1.2 μ T and 2.4 μ T;

FIG. 5(d) Normal tissue at B_1-satA graph of the fitting results for the optimized pseudo-fox off-line model at 0.8 μ T, 1.2 μ T and 2.4 μ T;

FIG. 6(a) is a CESTW map;

FIG. 6(b) is a graph of the optimized pseudo-Pock off-line model fit residual frequency tiles plotted with one dimension of Δ ω and all pixels in the marker row;

FIG. 7 is a graph of quantized amplitudes for optimizing fitting of a pseudo-Pockey off-line model.

The embodiments of the invention will be explained and explained in further detail with reference to the figures and the detailed description.

Detailed Description

Example 1

The embodiment discloses a Z spectrum fitting method based on a pseudo-Poincke cast-off line type, which comprises the following steps:

a Z spectrum fitting method based on a pseudo-Poincke cast-off line type comprises the following steps:

step a: obtaining Z spectrum data, respectively utilizing Gaussian function G (delta omega) and Lorentz function L (delta omega) to perform pre-fitting on the Z spectrum data, and respectively obtaining Gaussian pre-fitting result Z_GuassAnd Lorentz pre-fitting results Z_Loren；

Wherein the content of the first and second substances,

Δ ω represents the frequency offset difference at the time of pre-fitting,

ω₁representing the frequency deviation of water resonance during pre-fitting, wherein A represents the amplitude of a proton pool CEST peak during pre-fitting, omega represents the frequency deviation of the proton pool CEST peak during pre-fitting, and sigma represents the line width of the proton pool CEST peak during pre-fitting;

step b: according to Z_GuassAnd Z_LorenObtaining a Lorentz proportionality coefficient alpha;

step c: establishing a pseudo-Poinck off-line model of a formula I by using the Lorentz proportionality coefficient alpha obtained in the step b, and fitting Z spectrum data according to the pseudo-Poinck off-line model to obtain a Z spectrum fitting result Z_pvp；

V (delta omega) ≈ alpha x L (delta omega) + (1-alpha) G (delta omega) formula I

Where V (Δ ω) represents a pseudo-fox offline model.

Further, step b comprises the following sub-steps:

step b 1: according to Z_GuassAnd Z_LorenCalculating the width gamma of the Gaussian half-maximum_GAnd half maximum width of lorentz Γ_L(ii) a Wherein the content of the first and second substances,sigma represents the line width of a proton pool CEST peak when a Gaussian function is used for fitting; gamma-shaped_L2 σ, σ denotes the linewidth of the proton pool CEST peak when fitted with a lorentzian function;

step b 2: calculating the Lorentz proportionality coefficient alpha by adopting a formula II:

wherein Γ represents the half-maximum width of the pseudo-Poincke cast-off line, Γ_G ²、Γ_G ³、Γ_G ⁴And Γ_G ⁵Respectively representing the width of the Gaussian half- maximum _G2, 3, 4 and 5; gamma-shaped_L ²、Γ_L ³、Γ_L ⁴And Γ_L ⁵Respectively representing half-maximum widths Γ of lorentz_LQuadratic, cubic, quartic and quintic ofTo the power.

Example 2

The embodiment discloses a CEST MRI quantification method, which comprises the following steps:

the method comprises the steps of obtaining a CEST MRI image of an experimental object, obtaining original Z spectrum data of each pixel point in the CEST MRI image, traversing all the pixel points in the image to obtain CEST quantitative values of all the pixel points, and obtaining a CEST quantitative contrast map according to the CEST quantitative values of all the pixel points, wherein the CEST quantitative value calculation of any pixel point in the CEST MRI image comprises the following steps:

step 1: preprocessing the original Z spectrum data to obtain corrected Z spectrum data Z_exp；

Step 2: for Z obtained in step 1_expUtilizing a Lorentz function to perform pre-fitting to obtain a pre-fitting result Z_Loren；

And step 3: for Z obtained in step 1_expThe positive and negative sides of the Z spectrum are respectively optimized by adopting a formula III and a formula IV to obtain optimized Z spectrum data

Wherein the content of the first and second substances,

optimized Z spectral data representing positive half spectrum, Ratio (delta omega) representing positive half spectrum compensation factor, L_posRepresents Z_expRatio Z_LorenA low area between the maximum value of the lower-field frequency offset and the maximum value of the lower-field CEST effect ending frequency offset; | Δ ω | represents an absolute value of a frequency deviation at the time of fitting

Wherein the content of the first and second substances,

representing a negative half spectrumThe Ratio (-delta omega) represents a negative half-spectrum compensation factor, L_negRepresents Z_expRatio Z_LorenA lower area between the maximum value of the upper field frequency offset absolute value and the maximum value of the upper field NOE effect ending frequency offset absolute value;

and 4, step 4: for the product obtained in step 3Blocking, inputting the blocks into a pseudo-Pocke off-line model of any one of the Z spectrum fitting methods based on the pseudo-Pocke off-line type in the embodiment 1 for fitting to obtain a fitting signal Z_{pvp_final}；

And 5: z obtained according to step 4_{pvp_final}And Z obtained in step 1_expAnd obtaining a CEST quantization value.

Specifically, Ratio (Δ ω) is 0.2318 × B₁+0.9502，Ratio(-Δω)＝0.4219×B₁+0.9131 wherein B₁The saturation power used in the experiment to acquire the Z spectrum is shown.

Specifically, step 4 includes the following substeps:

step 4.1: will be provided withAs a first partition, will

As a second partition, will

As a third segment;

step 4.2: by blocking the first and second blocks

Inputting the signal into a pseudo-Buddk offline model for fitting, fitting the third block by using an LD method, and obtaining a fitting signal Z according to the fitting results of the three blocks_{pvp_final}。

Specifically, in step 4.1, the first partition is 0.4ppm to 6ppm, the second partition is-6 ppm to-0.4 ppm, and the third partition is 0.4ppm to-0.4 ppm.

Specifically, the CEST quantization value in step 5 includes three calculation methods:

method (a): calculating Z_{_PVP_final}And Z_{_exp}The difference of (c) is used as CEST quantization value;

method (b): calculating Z_{_exp}Reciprocal of (a) and Z_{_PVP_final}The difference of the two inverse numbers of (c) is taken as CEST quantization value;

method (c): calculating Z \u_{PVP_final}And Z_{_exp}The area between the difference and the horizontal axis is taken as the CEST quantization value.

Example 3

The embodiment discloses a CEST MRI quantification method, and the following technical features are disclosed on the basis of the embodiments 1 and 2:

acquiring a CEST MRI image of an experimental object, acquiring original Z spectrum data of each pixel point in the CEST MRI image, traversing all the pixel points in the image to acquire CEST quantitative values of all the pixel points, acquiring a CEST quantitative contrast map from the CEST quantitative values of all the pixel points,

the subject may select a subject, a test animal, and a test sample, for which the raw z-spectrum data is prepared as follows: at9.4Tesla, for B_1-satSimulations of bloch equations were performed on 5-and 6-cell models at 0.5,1,1.5,2,2.5, and 3 μ T. Wherein, 5 pond models are: water pool, amide, amine, MT and NOE (3.5ppm, 6 pool models are pool, amide, amine, MT, NOE (1.6ppm) and NOE (3.5ppm), and 6 pool models contain higher MT and NOE components and lower amide and amine components than 5 pool models for the subjects, in vivo MRI experiments were performed on a 11.7Tesla horizontal bore scanner (Bruker Bispec, Germany) using a 23mm diameter transmit-receiver volume coil_SAT2500ms) followed by a fast acquisition with relaxation enhancement (RARE) readout. The Z spectrum was collected in the range-6 ppm to 6ppm with an interval of 0.25ppm, B_1-sat0.8, 1.2 and 2.4. mu.T, respectively. Other parameters are 5500ms/11ms for TR/TE, 1mm for slice thickness, FOV＝1714mm²The matrix size is 96 × 64.

Specifically, in step 3, the maximum value of the lower-field frequency offset is-4 ppm, the maximum value of the lower-field CEST effect ending frequency offset is-6 ppm, the maximum value of the upper-field frequency offset absolute value is 4ppm, the maximum value of the upper-field NOE effect ending frequency offset absolute value is 6ppm, and if the maximum value is a 5-cell exchange model (water, amide, amino, semi-solid Macromolecules (MT) and NOE (3.5ppm)), the original Z spectrum is recorded as: z_{_5-pool}The result of the fitting using the lorentz function L (Δ ω) is recorded as: z_{_loren_5pool}4ppm to 6ppm are referred to as u _ pos, and 4ppm to-6 ppm are referred to as u _ neg.

The simulated CEST quantitative contrast map was evaluated as follows:

optimization of the pseudofleck-off-line variant AREX, because the relevant parameters are known in advance_PVPAnd comparing with an empirical solution to judge the accuracy of the quantification.

Is an empirical solution of apparent exchange-dependent relaxation (AREX), independent of T_1wNonspecific tissue parameters such as DS and semisolid MT effect, depending only on solute concentration (f)_s) Law of exchange of solute with Water (k)_sω) Transverse relaxation rate (R) of solute_2s) And irradiation power (ω)₁) The formula is as follows:

an improvement to the above formula is proposed to make AREX more specific in the presence of MT.

R_1obs≈(R_1ω+f_cR_1c)/(1+f_c)

AREX if pseudo-fox offline modification is optimized_PVPAnd the closer the empirical solution is to illustrating the more accurate the quantization.

The effect of different quantification methods was evaluated with CNR, which is defined as follows:

wherein S is_1,2Are the mean values of the two ROIs, respectively, delta_1,2Standard deviation of the two ROIs, respectively.

Analysis of Experimental results

The fitting results and in vivo quantification of the present invention are further described in conjunction with fig. 2-7.

FIG. 2 shows the compensation coefficients and B for obtaining positive and negative sides of Z spectrum_1-satAnd a flowchart for calculating the relationship. FIG. 3 is B_1-satZ spectra at 0.5,1,1.5,2,2.5 and 3 μ T, fitting results and residual spectra for the optimized pseudo-Poincare model. It can be seen that for the Z spectra simulated by the 5-pool model, the peaks of the residual spectra for the optimized pseudo-Pocke de-linear model and the LD both appear at the amide (3.5ppm), amine (2ppm) and NOE (-3.5 ppm); in the 6-cell model, the residual spectrum had a peak of NOE (-1.6ppm) in addition to a peak of the 5-cell model.

We also derived measures of AREX from optimizing the fit of the pseudo-Pock off-line model, and compared the standard analytical solution

(FIG. 4). In the comparison of the 5-pool model and the 6-pool model with the analytical solution, it can be seen that all B's for the 5-pool model_1-sat，AREX_PVPIn amide (3.5ppm) and NOE (-3.5ppm) and analytical solutions

Are relatively close. For the 6-pool model with the larger MT component, at lower B_{1_sat}(≤2μT)AREX_PVPAnd

very close but for B_{1_sat}2.5 μ T and 3 μ T AREX_PVPStill underestimate APT。

FIG. 5(a) is T₂w plot, marking the tumor region of interest and the contralateral normal tissue region. FIG. 5(B) is B_{1_sat}MTR at 2.4. mu.T_asymIn this figure, the appearance of high signals at the tumor is seen. FIGS. 5(c) and (d) are each B_{1_sat}Results of pseudo-fox cast model fitting were optimized for 0.8 μ T, 1.2 μ T, and 2.4 μ T tumor and normal tissues. It can be seen from the figure that: the drop in Z spectrum is clearly seen at 3.5ppm, 2ppm and-3.5 ppm. A drop (-1.6ppm) was also clearly seen at another point on normal tissue, but not at the tumor. Theoretically, Glu-CEST (3ppm) should be observed at high power, but not due to the broad peak in the Z spectrum. The peaks that optimized the fit of the pseudofudge deshaped model occurred at 3.5ppm, 2ppm, and-2 ppm to-5 ppm for the tumor region of interest and the contralateral region.

To visualize the quantized spectrum of all pixels, we render a frequency-stitched image with one dimension of Δ ω and all pixels in the marker line as shown in fig. 6(a) and fig. 6 (b). The abscissa is between 56 and 66, which is the location of the tumor. For tumors, the optimized pseudo-fox cast model fit values were significantly higher at 3.5ppm and 2ppm than for contralateral normal tissue.

Figure 7 gives the magnitude spectrum for the fitting of the optimised pseudo-fox off-line model. The tumor has higher APT effect than the normal tissue on the side, and our research finds that the tumor has B effect than the normal tissue_{1_sat}High NOE signals were present at 1.2 μ T and 2.4 μ T, but at B_{1_sat}At lower power of 0.8 μ T, tumors had lower NOE signals than normal tissue. At the same time, the optimized pseudo-fox-des-line model fit had a higher Glu-CEST and guanidyl-amine pattern in the tumor than in normal tissue. Optimizing the pseudo-Pockey off-line model fit provides a simple robust and also more accurate method to quantify the CEST and NOE effects.

Claims (7)

1. A Z spectrum fitting method based on a pseudo-Poincke off-line type is characterized by comprising the following steps:

step a: acquiring Z spectrum data, and respectively acquiring Z spectrum dataUtilizing a Gaussian function G (delta omega) and a Lorentzian function L (delta omega) to perform pre-fitting to respectively obtain a Gaussian pre-fitting result Z_GuassAnd Lorentz pre-fitting results Z_Loren；

Step b: according to Z_GuassAnd Z_LorenObtaining a Lorentz proportionality coefficient alpha;

step c: establishing a pseudo-Poinck off-line model of a formula I by using the Lorentz proportionality coefficient alpha obtained in the step b, and fitting Z spectrum data according to the pseudo-Poinck off-line model to obtain a Z spectrum fitting result Z_pvp；

V (delta omega) ≈ alpha x L (delta omega) + (1-alpha) G (delta omega) formula I

Where V (Δ ω) represents a pseudo-fox offline model.

2. The pseudo-fox-elimination line-based Z spectrum fitting method as recited in claim 1, wherein the step b comprises the substeps of:

step b 1: according to Z_GuassAnd Z_LorenCalculating the width gamma of the Gaussian half-maximum_GAnd half maximum width of lorentz Γ_L；

Step b 2: calculating the Lorentz proportionality coefficient alpha by adopting a formula II:

where Γ represents the half-maximum width of the pseudo-fox trap.

3. A CEST MRI quantification method, comprising:

the method comprises the steps of obtaining a CEST MRI image of an experimental object, obtaining original Z spectrum data of each pixel point in the CEST MRI image, traversing all the pixel points in the image to obtain CEST quantitative values of all the pixel points, and obtaining a CEST quantitative amplitude graph from the CEST quantitative values of all the pixel points, wherein the CEST quantitative value calculation of any pixel point in the CEST MRI image comprises the following steps:

step 1: preprocessing the original Z spectrum data to obtain corrected Z spectrum data Z_exp；

Step 2: for Z obtained in step 1_expUtilizing a Lorentz function to perform pre-fitting to obtain a pre-fitting result Z_Loren；

And step 3: for Z obtained in step 1_expThe positive and negative sides of the Z spectrum are respectively optimized by adopting a formula III and a formula IV to obtain optimized Z spectrum data

Wherein the content of the first and second substances,

optimized Z spectral data representing positive half spectrum, Ratio (delta omega) representing positive half spectrum compensation factor, L_posRepresents Z_expRatio Z_LorenA low area between the maximum value of the lower-field frequency offset and the maximum value of the lower-field CEST effect ending frequency offset;

wherein the content of the first and second substances,represents the optimized Z spectrum data of the negative half spectrum, Ratio (-delta omega) represents the compensation factor of the negative half spectrum, L_negRepresents Z_expRatio Z_LorenA lower area between the maximum value of the upper field frequency offset absolute value and the maximum value of the upper field NOE effect ending frequency offset absolute value;

and 4, step 4: for the product obtained in step 3

Blocking, inputting into the pseudo-Pock off-line model of any one of the Z spectrum fitting methods based on the pseudo-Pock off-line type according to claim 1 or 2, and fitting to obtain a fitting signal Z_{pvp_final}；

And 5: z obtained according to step 4_{pvp_final}And Z obtained in step 1_expAnd obtaining a CEST quantization value.

4. A CEST MRI quantization method as claimed in claim 3, characterized in that Ratio (Δ ω) 0.2318 xb in step 3₁+0.9502，Ratio(-Δω)＝0.4219×B₁+0.9131 wherein B₁The saturation power used in the experiment to acquire the Z spectrum is shown.

5. A CEST MRI quantification method as claimed in claim 3, characterized in that step 4 comprises the sub-steps of:

step 4.1: will be provided with

As a first partition, will

As a second partition, will

As a third segment;

step 4.2: by blocking the first and second blocks

Inputting the signal into a pseudo-Buddk offline model for fitting, fitting the third block by using an LD method, and obtaining a fitting signal Z according to the fitting results of the three blocks_{pvp_final}。

6. A CEST MRI quantification method according to claim 5, characterized in that in step 4.1 the first fraction is between 0.4ppm and 6ppm, the second fraction is between-6 ppm and-0.4 ppm and the third fraction is between 0.4ppm and-0.4 ppm.

7. A CEST MRI quantization method as claimed in claim 3, characterized in that the CEST quantization value in step 5 comprises three calculation methods:

method (a): calculating Z \u_{PVP_final}And Z_{_exp}The difference of (c) is used as CEST quantization value;

method (b): calculating Z_{_exp}Reciprocal sum Z of_{PVP_final}The difference of the two inverse numbers of (c) is taken as CEST quantization value;

method (c): calculating Z \u_{PVP_final}And Z_{_exp}The area between the difference and the horizontal axis is taken as the CEST quantization value.

Images