CEST fat artifact removal method, medium, apparatus, and magnetic resonance imaging apparatus

Abstract

This invention discloses a CEST fat artifact removal method, medium, apparatus, and magnetic resonance imaging apparatus, and belongs to the field of magnetic resonance technology. This invention incorporates a multi-peak model of fat into a simplified water-magnetization transfer (MT) two-pool Bloch-McConnell equation model and iteratively solves iteratively to simultaneously fit the multi-peak signals of water, magnetization transfer, and fat, obtaining a reference signal of the CEST effect free from direct fat artifacts. Finally, a normalization method removes the indirect influence of the fat signal on the CEST signal. The fat-inclusive Bloch-McConnell equation adopted in this invention more accurately reflects the composition of the acquired signal, enabling the acquisition of a more accurate CEST reference signal. This invention can remove fat artifacts from the CEST signal without adjusting the acquisition sequence, thereby improving the accuracy of CEST imaging.

Description

[Technical Field]

[0001] This invention belongs to the field of magnetic resonance imaging, and more particularly to the removal of fat artifacts and the extraction of contrast in chemical exchange saturation transfer imaging. [Background technology]

[0002] Chemical exchange saturation transfer (CEST) imaging in magnetic resonance provides molecular-level information and can reflect various pathological changes. Currently, CEST is applied to a considerable extent in the brain, for example, to detect and grade gliomas through the protein and metabolite information provided by CEST. However, when CEST is applied to the body, especially fat-rich tissues such as mammary gland, it is strongly affected by fat artifacts. The analysis method for CEST usually involves taking the difference between the reference signal and the labeled signal, and this difference reflects the CEST effect of the target group. Magnetization transfer ratio asymmetry (MTR) is commonly used in CEST analysis. asym The analysis uses a reference signal where the resonance frequency of the target group is at a frequency position symmetric with respect to the resonance frequency of water. This method assumes that the signal at the symmetric frequency position does not contain the CEST effect and that other effects are the same as those of the labeled signal, and that the difference between the two is the CEST signal. However, in tissues containing fat, the resonance frequency range of fat usually overlaps with the frequency range of the reference signal, and the fat signal is not symmetric with respect to the resonance frequency of water. This is the MTR asym This means that the influence of fat cannot be eliminated during analysis, resulting in fat artifacts in the results. Furthermore, magnetization transfer (MT) is also affected by MTR. asym This will have some influence on the analysis.

[0003] Current fat artifact removal methods can be broadly divided into (1) methods based on acquisition sequence design and (2) methods based on post-processing analysis. Acquisition sequence design methods include fat suppression sequences, water excitation sequences, and CEST-Dixon water-fat separation methods. These methods require additional sequence design, which is disadvantageous for widespread clinical application. Fat suppression sequences have a limited fat suppression effect, and fat signals may remain. Water excitation sequences increase the energy absorbed by the human body, limiting their acquisition speed and range of application. CEST-Dixon water-fat separation methods require twice the data for analysis, significantly extending the acquisition time. Post-processing analysis methods are mainly fitting analysis methods based on the normalization of CEST signals. Specifically, the fitting methods used are mainly multi-pool Lorentz fitting, but analysis can also be performed using single-pool Lorentz difference methods and extrapolated magnetization transfer signal fitting methods. Single-pool Lorentz difference methods and extrapolated magnetization transfer signal fitting methods do not incorporate fat signals into the model, which can cause artifacts in the processing results. The multi-pool Lorentz fitting method assumes that low-power saturation reaches a steady state, but this assumption may not be met in actual applications. Furthermore, this method has many fitting parameters and requires a large amount of data for fitting, leading to extended data acquisition time and reduced robustness. [Overview of the project]

[0004] The main objective of the present invention is to overcome the shortcomings of existing methods and provide a more complete and robust CEST lipid artifact removal method based on differential analysis with fitted magnetization transfer and lipid signals (DIGITAL), which has a wide range of applicability and is post-processed. This method incorporates the lipid signal into a model based on the Bloch-McConnell equation, obtains a reference signal through fitting, and finally performs normalization to remove the lipid artifact.

[0005] To achieve the above-mentioned objectives, the present invention employs several technical solutions as described below. In a first aspect, the present invention provides a CEST fat artifact removal method based on magnetization transfer (MT) and fat signal fitting difference. This method performs fat artifact removal and signal extraction processing on each voxel in the raw data collected by CEST (Chemical Exchange Saturation Transfer), and the processing process for each voxel includes the following. Step 1: Linear interpolation correction of the principal magnetic field frequency B0 field offset is performed on the raw data collected by CEST to obtain the corrected Z spectrum corresponding to the target voxel. Step 2: Using data containing only MT in the corrected Z spectrum, and data containing the direct saturation effects of water and fat, as fitting samples, the model parameters in the water-MT-fat three-pool Bloch-McConnell equation model are fitted within the given upper and lower limits. The water-MT-fat three-pool Bloch-McConnell equation model is obtained by incorporating a multi-peak model of fat into the water-MT two-pool Bloch-McConnell equation. Step 3: Substitute the model parameters obtained from the fitting into the water-MT-fat trippool Bloch-McConnell equation model to generate CEST reference signals for each frequency point in the corrected Z spectrum. Subtract the actual acquired signals in the corrected Z spectrum from the reference signals corresponding to each frequency point to obtain the CEST effect values ​​for the corresponding frequency points in the CEST effect curve. Step 4: Normalize the CEST effect curve by dividing it by (1-FF). Here, FF is the fat percentage obtained from the fitting. In the normalized CEST effect curve, the value at the frequency of interest is the CEST effect value after removing fat artifact interference.

[0006] Preferably, the water-MT-fat triploop Bloch-McConnell equation model is as follows: JPEG2026517420000002.jpg250170

[0007] Preferably, the longitudinal relaxation rate R of the MT 1m and the resonance frequency of the fat peak are both input into the model with preset values as preconditions.

[0008] Preferably, the applied RF pulse intensity ω1 is calculated from a separately collected RF magnetic field intensity map and then input into the model as a precondition.

[0009] Preferably, in step 2, the data containing only MT are the data points with frequencies in the range of 80 to 6 ppm in the corrected Z spectrum. The data containing the direct saturation effect of water are the data points with frequencies in the range of 1.5 to -1.5 ppm in the corrected Z spectrum. The data containing the direct saturation effect of fat are the data points with frequencies in the range of -1 to -6 ppm in the corrected Z spectrum.

[0010] Preferably, in step 2, the data as the fitting sample are the data with frequencies in the ranges of 80 to 10 ppm and 0.75 to -6 ppm in the corrected Z spectrum.

[0011] Preferably, in step 2, the fitting waiting parameters T 1w ,T 2w ,T 2m ,M 0m ,k wm ,FF,T 1f ,T 2f ,ΔB0,CL,ndb,nmidb during fitting have upper bounds of [1.50, 0.100, 5e -6 , 0.4500, 40, 1, 0.400, 0.150, 64, 18, 5.5, 2.5] respectively, and lower bounds of [1.35, 0.015, 1e -6 , 0.0001, 20, 0, 0.250, 0.025, -64, 17, 1, 0] respectively, and the initial fitting values are [1.35, 0.015, 1e -6,0.0001,20,1,0.400,0.025,0,17,1,0.

[0012] Preferably, in the step 2, the RF pulse absorption rate of the MT group JPEG2026517420000003.jpg430, where JPEG2026517420000004.jpg44 is a linear function and includes a Gaussian type, a Lorentz type, and a super Lorentz type. In breast data, the super Lorentz type is suitable for the linear function.

[0013] Preferably, in the step 2, data within the range of 80 to 10 ppm is set with a higher weight during fitting, and the weight is twice the weight of data within the range of 0.75 to -6 ppm.

[0014] Preferably, in the step 4, the frequency of interest is 3.5 ppm, and the CEST effect value at 3.5 ppm in the normalized CEST effect curve is the APT # value.

[0015] As a second aspect, the present invention provides a computer-readable storage medium. A computer program is stored in the storage medium, and when the computer program is executed by a processor, the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference described in any one of the above methods is realized.

[0016] As a third aspect, the present invention provides a computer electronic device including a processor and a storage medium. A computer program is stored in the storage medium, and when the computer program is executed by a processor, the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference described in any one of the above methods is realized.

[0017] As a fourth aspect, the present invention provides a magnetic resonance imaging apparatus for removing CEST fat artifacts. This apparatus includes a magnetic resonance scanner and a control unit. The magnetic resonance scanner is used to acquire a CEST image through magnetic resonance CEST imaging. The control unit can acquire the CEST image, and a computer program is stored in the control unit. When the computer program is executed, it realizes the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference described in any one of the above methods, and is used to output the CEST effect value with the fat artifact removed for each voxel.

[0018] The present invention has the following beneficial effects. The present invention incorporates a multi-peak model of fat into the Bloch-McConnell equation for iterative solution, and simultaneously fits the multi-peak signals of water, magnetization transfer, and fat, so that the Bloch-McConnell equation containing fat can more truly reflect the composition of the collected signals, thereby obtaining a more accurate CEST reference signal. Without adjusting the acquisition sequence, the removal of fat artifacts in the CEST signal can be realized, and the accuracy of CEST imaging can be improved.

Brief Description of the Drawings

[0019] [Figure 1] FIG. 1 is a schematic diagram of the specific implementation flow of the present invention. [Figure 2] FIG. 2 is the data curve collected in the example and the fitted data curve. [Figure 3] FIG. 3 is the dynamic enhanced image collected in the example and the APT# image obtained by the present method. [Figure 4] FIG. 4 is the fat fraction image obtained by fitting using the method proposed by the present invention in the example.

Modes for Carrying Out the Invention

[0020] The present invention will be further described below with reference to specific embodiments and their drawings. This invention provides a post-processing-based, broadly applicable, more complete, and robust CEST lipid artifact removal method based on differential analysis with fitted magnetization transfer and lipid signals (DIGITAL). The invention incorporates seven resonance peak signals of lipid into a simplified water-MT two-pool Bloch-McConnell equation model and fits the water, MT, and lipid signals to obtain a reference signal of the CEST effect free from direct lipid artifacts. Finally, the indirect influence of the lipid signal on the CEST signal is removed by a normalization method.

[0021] The core of this method lies in rationally incorporating fat into a simplified water-MT two-pool Bloch-McConnell equation model to construct a water-MT-fat three-pool Bloch-McConnell equation model. Simultaneously, rational simplifications are applied to this model to reduce the computational resource requirements of this method, thereby broadening its applicability. The water-MT-fat three-pool Bloch-McConnell equation of this method will be further explained below. The simplified water-MT two-pool (water pool w, MT pool m) Bloch-McConnell equation currently used in the field can be expressed as follows: JPEG2026517420000005.jpg90170

[0022] JPEG2026517420000006.jpg45170

[0023] JPEG2026517420000007.jpg252170

[0024] JPEG2026517420000008.jpg244170

[0025] JPEG2026517420000009.jpg201170

[0026] Therefore, based on the theoretical considerations described above, the specific implementation process of the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference in the present invention can be described as follows. Each voxel in the raw data collected by CEST underwent fat artifact removal and signal extraction processing, and the processing steps for each voxel included the following: Step 1: Linear interpolation correction of the principal magnetic field frequency B0 field offset is performed on the raw data collected by CEST to obtain the corrected Z spectrum corresponding to the target voxel. Step 2: Using data containing only MT in the corrected Z spectrum, and data containing the direct saturation effects of water and fat, as fitting samples, the model parameters in the water-MT-fat three-pool Bloch-McConnell equation model are fitted within the given upper and lower limits. The water-MT-fat three-pool Bloch-McConnell equation model described above is given by equation (5), and will not be described again here. Furthermore, in the process of extracting data points from the corrected Z spectrum described above to use as a fitting sample, the specific frequency ranges to select must be determined based on the frequency ranges in which the three effects exist. Generally, data containing only MT are data points in the corrected Z spectrum with frequencies in the range of 80 to 6 ppm. Data containing the direct saturation effect of water are data points in the corrected Z spectrum with frequencies in the range of 1.5 to -1.5 ppm. Data containing the direct saturation effect of fat are data points in the corrected Z spectrum with frequencies in the range of -1 to -6 ppm. Theoretically, data points within these three frequency ranges in the corrected Z spectrum can be extracted and used as a fitting sample. However, considering the fitting effect, it is desirable that the frequencies of the above data points do not fall around the final frequency of interest. For example, if the frequency of interest is 3.5 ppm, it is preferable not to use data points around 3.5 ppm as a fitting sample. Therefore, when setting data to be used as a fitting sample, priority can be given to selecting data in the corrected Z spectrum with frequencies in the ranges of 80 to 10 ppm and 0.75 to -6 ppm. Step 3: Substitute the model parameters obtained by fitting into the water-MT-fat three-pool Bloch-McConnell equation model to generate CEST reference signals for each frequency point in the corrected Z spectrum. Subtract the actual acquired signals in the corrected Z spectrum from the reference signals corresponding to each frequency point to obtain the CEST effect values ​​for the corresponding frequency points in the CEST effect curve. Step 4: Normalize the CEST effect curve by dividing it by (1-FF). Here, FF is the fat percentage obtained by fitting. In the normalized CEST effect curve, the value at the frequency of interest is the CEST effect value after removing the interference of fat artifacts. While the selection of the frequency of interest is arbitrary, in research in this field, the resonance frequency of a group exhibiting the CEST effect is typically selected as the frequency of interest, and the signal intensity at that frequency is reflected. For example, if the frequency of interest is 3.5 ppm, the CEST effect value calculated using this method is APT # (Amide proton transfer signal from digital analysis, obtained using the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference) is the value of the amide proton transfer signal after removing fat artifacts. Other frequencies that reflect the CEST effect can also be selected as frequencies of interest, and this method does not limit the selection of frequencies of interest. The present invention further illustrates the method proposed by this invention through non-limiting examples. [Examples]

[0027] In this embodiment, the specific implementation process of the DIGITAL method is shown in Figure 1. The method proposed by the present invention first requires the collection of CEST image data, and then the DIGITAL method is performed for each voxel in the collected CEST image data. That is, steps 1 to 4 are performed sequentially to remove fat artifacts in each voxel and obtain APT # The value is calculated. The specific implementation process for Steps 1 through 4 is described in detail below. Step 1: Linear interpolation correction for the principal magnetic field frequency B0 field offset is performed on the raw data collected by CEST to obtain the corrected Z spectrum. Step 2: Data containing only MT in the Z spectrum, and data containing the direct saturation effects of water and fat, are used as fitting samples. The pre-defined water-MT-fat three-pool Bloch-McConnell equation model is used to perform fitting according to the given upper and lower limits. The fitting model is used to calculate the reference signal of the Z spectrum. As described above, the water-MT-fat three-pool Bloch-McConnell equation model used in this method is as follows: JPEG2026517420000010.jpg253152

[0028] JPEG2026517420000011.jpg227170 Step 4: The CEST effect curve calculated in Step 3 is renormalized by dividing it by (1-FF) obtained from fitting its voxels. This removes the scaling effect of the CEST effect due to the fat signal, and a corrected CEST effect curve is obtained. In this curve, the data at a frequency of 3.5 ppm is APT # This will be the effect value. Of course, when calculating the reference curve in the steps above, it is not necessary to calculate the complete reference curve. By calculating the value at the frequency of 3.5 ppm on the reference curve and subtracting the value shown by the Z spectral curve after correction of the main magnetic field B0 at the 3.5 ppm position, the CEST effect value at 3.5 ppm can be obtained. Dividing this CEST effect value by (1-FF) gives the corrected CEST effect value, and this corrected CEST effect value is used for APT # This can be used as an effect value. After performing the operations described in steps 1 to 4 above on all voxels in the CEST image, each voxel will have one APT # Corresponds to the value. These APT # If we establish a one-to-one correspondence between the values ​​and each voxel in the CEST image, we can create a single APT # An image can be obtained. The above-described digital method was tested on one breast tumor patient to further demonstrate its technical effectiveness. In this example, the imaging sequence for CEST data acquisition includes a CEST saturation module and a high-speed spin echo acquisition module. The CEST saturation module contains 10 Gaussian waveform saturation pulses with a duration of 100 ms, an effective amplitude of 1 μT, and an interval of 10 ms between saturation pulses. It should be noted that this method does not require fat suppression during the CEST imaging process, and therefore does not require the addition of a fat suppression module during CEST acquisition. Furthermore, separate main magnetic field B0 maps and RF magnetic field B1 maps are also acquired and used for B0 correction and parameter fitting.

[0029] JPEG2026517420000012.jpg42170 The experimental data obtained in this example is shown in Figure 3, and the APT in the figure # In the image, high-signal regions in the dynamically enhanced image have corresponding high-signal regions (black arrows in Figure 3). The color bar on the right is APT. # Only images are shown. Figure 4 shows the fat content percentage diagram obtained by fitting in this experiment, and a clear change in fat content can be observed. APT in Figure 3. # Comparing the figure with the fat content diagram, APT is observed in glandular regions with low fat content and glandular regions with high fat content. # There was no clear difference in the effect values, suggesting that the fat artifacts had already been removed.

[0030] Similarly, based on the same inventive concept, another preferred embodiment of the present invention provides a computer electronic device corresponding to the CEST fat artifact removal method based on magnetization transfer and fat signal fitting differences provided in the above-described embodiment. This device includes a memory device and a processor. The aforementioned storage device is used to store computer programs. The processor is used to implement the CEST fat artifact removal method based on the magnetization transfer and fat signal fitting difference described above when executing the computer program.

[0031] Similarly, based on the same inventive concept, another preferred embodiment of the present invention provides a computer-readable storage medium corresponding to the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference provided in the above-described embodiment. The storage medium stores a computer program, and when the computer program is executed by a processor, the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference described above is realized. To make it clear, the aforementioned storage media and devices can include Random Access Memory (RAM) and Non-Volatile Memory (NVM), such as at least one disk storage device. At the same time, the storage media can also be various media capable of storing program code, such as USB memory, removable hard disks, magnetic disks, or optical disks. Of course, with the widespread application of cloud servers, the aforementioned software programs are also installed on cloud platforms to provide corresponding services, so computer-readable storage media are not limited to local hardware formats. To make it clear, the aforementioned processors can be general-purpose processors, including Central Processing Units (CPUs) and Network Processors (NPs). They can also be Digital Signal Processing (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0032] It is necessary to further explain that, for the convenience and brevity of explanation, engineers in the field can refer to the corresponding processes in the embodiments of the aforementioned methods for the specific working processes of the apparatus described above, and will not repeat them here. In each embodiment provided by this application, the division of steps or modules in the apparatus and methods is merely a kind of logical functional division, and in actual implementation, other division methods may be possible, for example, multiple modules or steps may be combined or integrated, and a single module or step may also be divided. Furthermore, the logical instructions in the aforementioned storage device can be implemented in the form of a software function unit and, if sold or used as an independent product, can be stored on a single computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present invention, or a contribution to the prior art, or a portion of the technical solution, can be embodied in the form of a computer software product, which is stored on a single storage medium and includes a plurality of instructions for a single computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention.

[0033] Similarly, based on the same inventive concept, another preferred embodiment of the present invention provides a magnetic resonance imaging apparatus corresponding to the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference provided in the above-described embodiment. This apparatus includes a magnetic resonance scanner and a control unit. The aforementioned magnetic resonance scanner is used to acquire CEST images through magnetic resonance CEST imaging. The control unit is capable of acquiring the CEST image and has a computer program stored within it. When the computer program is executed, it is used to implement the CEST fat artifact removal method based on the magnetization transfer and fat signal fitting difference described above, and to output the CEST effect value with the fat artifact removed for each voxel. It should be noted that the magnetic resonance imaging apparatus can be any magnetic resonance scanner capable of implementing a parallel imaging method, its structure belongs to the prior art, and mature commercially available products can be used; specific model numbers are not limited. Furthermore, in addition to the computer program mentioned above, the control unit of the magnetic resonance imaging apparatus should also be equipped with the imaging sequence and other software programs necessary to realize CEST imaging.

[0034] Each module and function according to the above-described embodiments of the present invention can be completed by a circuit, other hardware, or executable program code, as long as it can realize the corresponding function. If code is used, the code can be stored in a memory device and executed by the corresponding element in a computing device. The realization of the present invention is not limited to any particular combination of hardware and software. The hardware models used in the present invention can be commercially available products and can be selected based on actual user needs. Of course, the magnetic resonance CEST imaging sequence and apparatus also require other necessary hardware or software combinations, which will not be repeated here.

[0035] The embodiments described above are merely embodiments of the present invention and do not limit the invention. Any modifications and improvements made to the invention that do not depart from the spirit and scope of the invention are included in the invention and are protected within the scope of the appended claims if they are applicable directly or indirectly to other relevant art.

Claims

1. A CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference, This method performs fat artifact removal and signal extraction on each voxel in the raw data collected by CEST, and the processing process for each voxel includes the following: Step 1: Linear interpolation correction of the main magnetic field frequency B0 field offset is performed on the raw data collected by CEST to obtain the corrected Z spectrum corresponding to the target voxel. Step 2: Using data containing only MT in the corrected Z spectrum, and data containing the direct saturation effects of water and fat as fitting samples, the model parameters in the water-MT-fat three-pool Bloch-McConnell equation model are fitted within the given upper and lower limits. The water-MT-fat three-pool Bloch-McConnell equation model is obtained by incorporating a multi-peak model of fat into the water-MT two-pool Bloch-McConnell equation. Step 3: Substitute the model parameters obtained from fitting into the water-MT-fat triploat Bloch-McConnell equation model to generate CEST reference signals for each frequency point in the corrected Z spectrum. Subtract the actual acquired signals in the corrected Z spectrum from the reference signals corresponding to each frequency point to obtain the CEST effect values ​​for the corresponding frequency points in the CEST effect curve. Step 4: The CEST effect curve is normalized by dividing it by (1-FF), where FF is the fat percentage obtained by fitting, and the value at the frequency of interest in the normalized CEST effect curve is the CEST effect value after removing fat artifact interference. This is a CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference.

2. The aforementioned water-MT-fat triploat Bloch-McConnell equation model is as follows:

3. Preferably, the longitudinal relaxation speed R of the MT. １ｍ The resonance frequencies of the fat peaks are both input into the model as preconditions, with pre-set values. Preferably, the applied RF pulse intensity ω １ The CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference according to claim 2, characterized in that the difference is calculated from a separately collected RF magnetic field strength map and then input into the model as a precondition.

4. Preferably, in step 2, data containing only MT are data points with frequencies in the corrected Z spectrum within the range of 80 to 6 ppm, data containing the direct saturation effect of water are data points with frequencies in the corrected Z spectrum within the range of 1.5 to -1.5 ppm, and data containing the direct saturation effect of fat are data points with frequencies in the corrected Z spectrum within the range of -1 to -6 ppm. Preferably, the data used as fitting samples are data in which the frequencies in the corrected Z spectrum are in the range of 80 to 10 ppm and 0.75 to -6 ppm, characterized in that the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference according to claim 1.

5. Preferably, in the step 2, the fitting parameter T in the model １ｗ , T ２ｗ , T ２ｍ , M ０ｍ , k ｗｍ , FF, T １ｆ , T ２ｆ , ΔB ０ , CL, ndb, nmidb, the upper bounds during fitting are respectively [1.50, 0.100, 5e －６ , 0.4500, 40, 1, 0.400, 0.150, 64, 18, 5.5, 2.5], the lower bounds are respectively [1.35, 0.015, 1e －６ , 0.0001, 20, 0, 0.250, 0.025, -64, 17, 1, 0], and the initial values of fitting are respectively [1.35, 0.015, 1e －６ , 0.0001, 20, 1, 0.400, 0.025, 0, 17, 1, 0]. Preferably, in step 2, data in the range of 80 to 10 ppm are given a higher weight during fitting, and this weight is twice that of data in the range of 0.75 to -6 ppm, characterized in that the CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference according to claim 2.

6.

7. In step 4, the frequency of interest is 3.5 ppm, and the CEST effect value at 3.5 ppm in the normalized CEST effect curve is APT ＃ The CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference according to claim 1, characterized in that the value is a value.

8. A computer-readable storage medium, A computer-readable storage medium, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, a CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference described in any one of claims 1 to 7 is realized.

9. A computer electronic device, including a processor and a storage medium, The computer electronic device is characterized in that a computer program is stored in the storage medium, and when the computer program is executed by a processor, a CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference described in any one of claims 1 to 7 is realized.

10. A magnetic resonance imaging apparatus for removing CEST fat artifacts, comprising a magnetic resonance scanner and a control unit, A magnetic resonance imaging apparatus for removing CEST fat artifacts, characterized in that the magnetic resonance scanner is used to acquire a CEST image through magnetic resonance CEST imaging, the control unit is capable of acquiring the CEST image and stores a computer program, and when the computer program is executed, it is used to implement a CEST fat artifact removal method based on magnetization transfer and fat signal fitting difference as described in any one of claims 1 to 7, and to output a CEST effect value with fat artifacts removed for each voxel.

Images