Method and device for generating mismatched image based on CEST image and structural magnetic resonance image

Abstract

The invention discloses a mismatched image generation method and device based on a CEST image and a structural magnetic resonance image, and belongs to the technical field of magnetic resonance. According to the method, a mismatched image is obtained by using CEST data and corresponding structural magnetic resonance data. According to the method, various mismatched image data can be obtained by utilizing various CEST effect quantitative index images and various structural magnetic resonance data, the mismatched images can serve as new images capable of being quantitatively analyzed, iconography information for assisting clinical diagnosis is provided, and therefore the application of CEST imaging quantitative analysis is expanded.

Description

Technical Field

[0001] The present invention belongs to the field of magnetic resonance imaging, and particularly relates to the generation of mismatched images based on Chemical Exchange Saturation Transfer (CEST) images and structural magnetic resonance images. Background Art

[0002] Chemical Exchange Saturation Transfer (CEST) imaging is a new type of magnetic resonance technology that has been used to detect metabolites such as proteins in biological tissues and can reflect physiological information at the molecular level. Common detectable 1 1H includes amide protons (proteins, polypeptides), amine protons (creatine), hydroxyl protons (glucose), etc. Based on the detection of the above different metabolites, CEST imaging has been widely used in the detection and grading of tumors, epilepsy diagnosis, and osteoarticular disease diagnosis. In CEST quantitative analysis, the most commonly used index is Magnetization Transfer Ratio asymmetry (MTR asym ), also known as Chemical Exchange Saturation Transfer Ratio (CESTR). The commonly used Amide Proton Transfer (APT) imaging uses the value of MTR asym at the water frequency offset of +3.5 ppm to construct an image called the APTw image, but it is interfered by the Nuclear Overhauser Effect (NOE) and the magnetization transfer (MT) effect of the solid-state pool. Based on MTR asym , the Chemical Exchange Saturation Transfer Ratio normalized with the reference value (CESTR nr ), the inverse magnetization transfer ratio (MTR Rex ), and the apparent exchange-dependent relaxation (AREX) are derived. The CEST quantitative image of a specific substance obtained based on fitting algorithms (such as Lorentz fitting, EMR, NEMR, etc.) is also a quantization index of the CEST effect.

[0003] Conventionally used structural magnetic resonance images include T1-weighted, T2-weighted images before and after contrast enhancement, and fluid-attenuated inversion recovery (FLAIR) images, etc., which have the characteristic of reflecting the tissue relaxation time distribution, while the CEST effect quantification index images have the characteristic of reflecting the concentration distribution of specific metabolites in tissues. These two types of images are not ideal in some clinical applications.

[0004] Based on this, it is necessary to propose a method for generating mismatched images based on CEST images and structural magnetic resonance images to expand the CEST quantitative analysis method. Summary of the Invention

[0005] The object of the present invention is to provide a method and device for generating mismatched images based on CEST images and structural magnetic resonance images.

[0006] To achieve the above object, the specific technical solutions adopted by the present invention are as follows:

[0007] In a first aspect, the present invention provides a method for generating mismatched images based on CEST images and structural magnetic resonance images, which includes:

[0008] Obtaining a CEST source image, a CEST effect quantification index image, and a structural magnetic resonance image for an imaging target;

[0009] Registering the structural magnetic resonance image onto the CEST source image with a pre-delineated region of interest, and calculating the mean value of the registered structural magnetic resonance image within the region of interest, and dividing the registered structural magnetic resonance image by the mean value to obtain a normalized structural magnetic resonance image;

[0010] Dividing the CEST effect quantification index image by the normalized structural magnetic resonance image to obtain a quotient image;

[0011] Subtracting the quotient image from the CEST effect quantification index image to obtain a mismatched image.

[0012] As a preference of the above first aspect, the CEST effect quantification index image is a CESTR, CESTR nr , MTR Rex or AREX image, or a CEST quantitative image obtained by Lorentz, EMR, or NEMR fitting algorithms.

[0013] As a preference of the above first aspect, the structural magnetic resonance image is a T1-weighted image, a T2-weighted image, and a FLAIR image before and after contrast enhancement.

[0014] As a preference of the first aspect above, the CEST effect quantification index image is a CESTR, CESTR nr , MTR Rex or AREX image, and their respective calculation methods are as follows: Obtain the CEST image data of the imaging target at different frequency offsets, and extract the original Z-spectrum of each voxel therefrom for main magnetic field B0 frequency offset correction to obtain the corrected Z-spectrum; For each voxel, a frequency point deviating from 0 ppm in the corrected Z-spectrum or the fitted Z-spectrum of the corrected Z-spectrum is used as a specific frequency point, and magnetization transfer ratio asymmetry analysis (MTR asym ) is used to obtain the MTR asym value at this specific frequency point. The MTR asym values of all voxels constitute the CESTR image; The value of 0 ppm in the denominator of the original asymmetry analysis is changed to the value of the frequency point symmetric to the specific frequency point about 0 ppm to obtain a new MTR asym value. The new MTR asym values of all voxels constitute the CESTR nr image; Subtract the inverse of the value of the contralateral frequency point normalized by the value at 0 ppm from the inverse of the value of the specific frequency point normalized by the value at 0 ppm to obtain a difference. The above differences of all voxels constitute the MTR Rex image; Divide the MTR Rex image by the quantitative T1 image of water to obtain the AREX image.

[0015] As a preference of the first aspect above, the specific frequency point is +3.5 ppm, and the corresponding CESTR image is the APTw image.

[0016] In a second aspect, the present invention provides a quantitative analysis system based on a mismatch image, which includes:

[0017] A mismatch image acquisition module for obtaining the mismatch image of the imaging target according to the generation method described in any one of the above first aspect solutions;

[0018] An image analysis module for calculating the signal mean of the region of interest in the mismatch image, and combining with a threshold optimized in advance for the target disease, and judging and outputting the grading or typing result of the target disease by the threshold method.

[0019] As a preference of the second aspect above, the target disease is glioblastoma, medulloblastoma, abdominal neuroblastoma.

[0020] In a third aspect, the present invention provides a computer program product, including a computer program / instructions, which when executed by a processor, can implement the mismatch image generation method based on the CEST image and the structural magnetic resonance image described in any one of the above first aspect solutions.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it can implement the method for generating a mismatched image based on CEST images and structural magnetic resonance images as described in any one of the above first aspects.

[0022] In a fifth aspect, the present invention provides a computer electronic device, which includes a memory and a processor;

[0023] The memory is used to store a computer program;

[0024] The processor is used to, when executing the computer program, implement the method for generating a mismatched image based on CEST images and structural magnetic resonance images as described in any one of the above first aspects.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] In the prior art, the CEST effect quantification index images obtained by using Z-spectrum calculation or fitting algorithms are not satisfactory in some applications. However, the method provided by the present invention incorporates structural magnetic resonance images to generate a new contrast image different from both. The present invention uses CEST data to obtain one or more CEST effect quantification index images, registers the corresponding structural magnetic resonance images to the CEST source image, performs ROI delineation, takes the mean value of the structural magnetic resonance images within the ROI and normalizes it, and then calculates the quotient image and the mismatched image using the CEST effect quantification index image and the registered structural magnetic resonance image, providing a new quantitatively analyzable image, thereby expanding the CEST imaging quantitative analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] This specification will further illustrate by way of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive.

[0028] Figure 1 It is a schematic diagram of the specific implementation process of the present invention;

[0029] Figure 2 It is the T2-weighted image, FLAIR image, APTw image, and the calculated mismatched image of a medulloblastoma patient in one embodiment.

[0030] Figure 3 It is the FLAIR image, APTw image, quotient image, and the calculated APTw-FLAIR mismatched image of two medulloblastoma patients with different molecular subtypes in one embodiment.

[0031] Figure 4The ROC curve for differentiating Group 4 subtype from other subtypes using the mean value within the ROI of the APTw map, APTw-T2 weighted mismatch map, and APTw-FLAIR mismatch map in one embodiment. Detailed implementation

[0032] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] As shown in this specification and the claims, unless the context clearly indicates an exceptional situation, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0034] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the operations before or after do not necessarily need to be executed precisely in sequence. On the contrary, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0035] The present invention provides a method for generating a mismatch image based on magnetic resonance CEST imaging and structural and magnetic resonance mismatch images. This method generates a mismatch image by performing operations between the CEST effect quantification index image and the structural magnetic resonance image. Such a mismatch image can be used as a new quantitatively analyzable image to provide auxiliary support for disease diagnosis. The present invention will be further elaborated and described below with reference to the accompanying drawings and specific implementation manners.

[0036] The method for generating a mismatch image based on CEST images and structural magnetic resonance images of the present invention is specifically implemented as follows:

[0037] 1) Obtain the CEST source image, CEST effect quantification index image, and structural magnetic resonance image for the imaging target.

[0038] 2) Register the structural magnetic resonance image to the CEST source image with the pre-delineated region of interest, and calculate the mean value of the registered structural magnetic resonance image within the region of interest (ROI). Divide the registered structural magnetic resonance image by the mean value to obtain a normalized structural magnetic resonance image.

[0039] 3) Divide the CEST effect quantification index image by the normalized structural magnetic resonance image to obtain a quotient image.

[0040] 4) Subtract the quotient image from the CEST effect quantification index image to obtain a mismatch image.

[0041] It should be noted that for the acquisition of the CEST source image, CEST effect quantification index image, and structural magnetic resonance image of the imaging target, it can be to read offline the already acquired and processed image data, or to acquire and process these image data online through relevant devices, and there is no limitation on this.

[0042] The CEST effect quantification index image adopted by the present invention can be a CESTR image, or it can be a CESTR nr , MTR Rex and AREX (requiring additional acquisition of quantitative T1 map), etc., multiple CEST effect quantification index images. There is no method limitation for obtaining the CEST quantitative index map from CEST imaging data. In addition, the CEST effect quantification index image can also be a quantitative image of the content of certain metabolites obtained using algorithms such as Lorentzian fitting, EMR, and NEMR. The structural magnetic resonance image adopted by the present invention can be T1-weighted images, T2-weighted images, and FLAIR images before and after contrast enhancement.

[0043] It should be noted that the CEST source image for delineating the ROI generally uses an unsaturated frame, or it can also use the CEST image frame corresponding to the proton of interest, such as the 3.5 ppm CEST image frame of amide protons. The ROI region on the CEST source image needs to be delimited according to actual needs, and for different diseases, the corresponding ROI region can be delineated according to expert experience.

[0044] It should be noted that the addition, subtraction, multiplication, and division operations between two images are actually to perform operations on the pixel values at the same positions in the two images, and the operation results are recorded at the corresponding pixels of the result image.

[0045] As Figure 1 shown, in a preferred embodiment of the present invention, exemplarily using a CESTR image as the CEST effect quantification index image and a T2-weighted image as the structural magnetic resonance image, the implementation process of the above-mentioned method for generating a mismatch image based on magnetic resonance CEST images and structural magnetic resonance images is specifically shown:

[0046] 1) Obtain the CEST image sequence data of the imaging target at different frequency offsets, extract the original Z-spectrum (i.e., CEST spectrum) of each voxel therefrom and perform main magnetic field B0 frequency offset correction to obtain the corrected Z-spectrum;

[0047] 2) Based on the corrected Z-spectrum of each voxel, one frequency point deviating from 0 ppm is used as a specific frequency point, and the magnetization transfer ratio (MTR) at the specific frequency point is obtained using magnetization transfer ratio asymmetry analysis. asym The MTR asym values of all voxels constitute the chemical exchange saturation transfer ratio (CESTR) image;

[0048] 3) Outline the region of interest (ROI) on the CEST source image with reference to the T2-weighted image;

[0049] 4) Using the CEST source image as a template, register the T2-weighted image to the CEST source image with the ROI outlined, and then calculate the mean signal within the ROI in the registered T2-weighted image;

[0050] 5) Normalize the registered T2-weighted image with the above-mentioned mean signal, that is, divide the registered T2-weighted image by the above-mentioned mean signal to obtain the normalized T2-weighted image;

[0051] 6) Divide the CESTR image obtained by MTR asym analysis by the normalized T2-weighted image to obtain a quotient image;

[0052] 7) Subtract the quotient image from the CESTR image obtained by MTR asym analysis to obtain a mismatch image.

[0053] It should be noted that extracting the Z-spectrum from the CEST image data is a conventional technical means. The CEST imaging sequence can be run to collect the CEST image data at different frequency offsets (also referred to as CEST source images in the present invention). The CEST image data at different frequency offsets are combined. The values of the same voxel in different CEST image data can be combined in sequence according to the corresponding frequency offset to construct the Z-spectrum corresponding to this voxel. This Z-spectrum can be directly used after main magnetic field B0 frequency offset correction, or a fitting algorithm can be used to obtain a fitted Z-spectrum for subsequent operations. The CEST effect quantification index image in the present invention is an image recording the CEST effect quantification index values at different voxels, and its specific types include but are not limited to CESTR, CESTR nr , MTR Rex , AREX, and the CEST quantitative image obtained by the fitting algorithm.

[0054] In addition, it should be noted that the MTR in the present invention asymAnalyze the corresponding specific frequency points without determining specific frequency values. Theoretically, any frequency point deviating from 0 ppm is acceptable, but frequency points with different offsets correspond to different CEST effect quantification values. In the embodiments of the present invention, it is preferably to use a frequency offset of +3.5 ppm as the specific frequency point, and the corresponding CEST R image obtained therefrom is the APTw image. When registering the T1-weighted, T2-weighted, and FLAIR structural magnetic resonance images of the present invention, the registration method used is limited to the rigid registration type.

[0055] The mismatched image obtained by the present invention is a new quantifiable analysis image. Different mismatched images are generated based on different CEST quantitative maps and different structural magnetic resonance images, which can be used for the imaging-assisted diagnosis of different diseases. Taking the APTw-T2 weighted mismatched image as an example, the mismatched image can be used for the high-grade and low-grade classification of gliomas, the genotype classification of medulloblastomas, and the risk classification of abdominal neuroblastomas, etc. When specifically performing assisted diagnosis, the signal mean value of the ROI in the mismatched image can be calculated, and this signal mean value can reflect the classification or typing of the target disease. In practical applications, a threshold can be determined through the ROC analysis of a large number of sample data, and then combined with the signal mean value of the ROI in the mismatched image and the determined threshold, the classification or typing of the target disease can be determined by the threshold method.

[0056] Therefore, based on the same inventive concept, the present invention can also provide a quantitative analysis system based on the mismatched image, which includes:

[0057] A mismatched image acquisition module, configured to obtain the mismatched image of the imaging target according to the foregoing method for generating a mismatched image based on a CEST image and a structural magnetic resonance image;

[0058] An image analysis module, configured to calculate the signal mean value of the region of interest in the mismatched image, and combine a threshold optimized in advance for the target disease, and judge and output the classification or typing result of the target disease by the threshold method.

[0059] The target diseases applicable to the present invention are gliomas, medulloblastomas, abdominal neuroblastomas, etc. The threshold for each disease can be determined by analyzing a large number of sample data of this disease.

[0060] It should be noted that the foregoing method for generating a mismatched image based on a CEST image and a structural magnetic resonance image can essentially be implemented in the form of a computer program.

[0061] Therefore, based on the same inventive concept, the present invention also provides a computer electronic device corresponding to the method for generating a mismatched image based on a CEST image and a structural magnetic resonance image provided in the foregoing embodiment, which includes a memory and a processor;

[0062] The memory is used to store a computer program;

[0063] The processor is used to implement the method for generating a mismatched image based on a CEST image and a structural magnetic resonance image as described above when executing the computer program;

[0064] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.

[0065] Therefore, based on the same inventive concept, the present invention provides a computer-readable storage medium corresponding to the method for generating a mismatched image based on a CEST image and a structural magnetic resonance image. A computer program is stored on the storage medium, and when the computer program is executed by a processor, it can implement the method for generating a mismatched image based on a CEST image and a structural magnetic resonance image as described above.

[0066] Therefore, based on the same inventive concept, the present invention provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, it can implement the method for generating a mismatched image based on a CEST image and a structural magnetic resonance image as described above.

[0067] It can be understood that the above storage medium may include a random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory. At the same time, the storage medium may also be various media such as a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc that can store program codes.

[0068] It can be understood that the above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0069] In addition, it should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system can refer to the corresponding process in the foregoing method embodiments, and will not be elaborated herein. In the embodiments provided in the present application, the division of steps or modules in the system and method is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules or steps can be combined or integrated together, and a module or step can also be split.

[0070] In addition, it should be noted that the above computer program product, storage medium, and computer electronic device can be integrated on the data processing terminal of the CEST imaging device itself, or an additional data processing device can be separately provided to implement.

[0071] The following shows the specific implementation and technical effects of the present invention through embodiments.

[0072] Embodiment

[0073] In this embodiment, the above CEST effect quantification index image is subjected to magnetization transfer ratio asymmetry analysis using the Z-spectrum after main magnetic field B0 frequency offset correction, and the value at +3.5 ppm is taken to form the APTw image. The structural magnetic resonance images used are T2-weighted images and FLAIR images. Specifically, the generation steps of the mismatch image in this example are as follows:

[0074] 1) Obtain the CEST image sequence data of the imaging target at different frequency offsets, extract the original Z-spectrum of each voxel therefrom, and then for each voxel, perform main magnetic field B0 frequency offset correction on the obtained original Z-spectrum to obtain the corrected Z-spectrum;

[0075] 2) Perform magnetization transfer ratio asymmetry analysis on the corrected Z-spectrum of each voxel, and take the MTR asym value at the specific frequency point +3.5 ppm. The MTR of all voxelsasym The values constitute the APTw image;

[0076] 3) Using the T2-weighted image and FLAIR image as reference images, the ROI was drawn on the +3.5 ppm frame of the CEST source image;

[0077] 4) Using the +3.5 ppm CEST source image as a template, the T2-weighted image and FLAIR image were registered to the template, and the mean signal value within the ROI of the registered T2-weighted image and FLAIR image was calculated respectively;

[0078] 5) Normalize the registered T2-weighted and FLAIR images using the calculated signal mean within each ROI. For the registered T2-weighted image, divide it by the signal mean within the ROI in the registered T2-weighted image to obtain the normalized T2-weighted image; for the registered FLAIR image, divide it by the signal mean within the ROI in the registered FLAIR image to obtain the normalized FLAIR image.

[0079] 6) Dividing the APTw image by the normalized T2-weighted image and the normalized FLAIR image, respectively, to obtain two quotient images, namely, the APTw / T2-weighted quotient image and the APTw / FLAIR quotient image;

[0080] 7) Subtracting the two quotient images from the APTw image to obtain two mismatch images. The APTw / T2-weighted quotient image is subtracted from the APTw image to obtain the APTw-T2-weighted mismatch image; and the APTw / FLAIR quotient image is subtracted from the APTw image to obtain the APTw-FLAIR mismatch image.

[0081] In this example, CEST data were collected from 46 participants, and APTw-T2-weighted mismatched images and APTw-FLAIR mismatched images were obtained according to 1) to 7) above. The 46 participants included 5 WNT, 13 SHH, 9 Group 3, and 19 Group 4 medulloblastoma patients.

[0082] like Figure 3As shown, the APTw, FLAIR, quotient, and APTw-FLAIR mismatch images of patients with two different subtypes (SHH, Group4) of medulloblastoma are presented. Based on the APTw-FLAIR mismatch images, the mean value of the mismatch images within the original ROI is used for ROC analysis. Taking the differentiation between Group4 subtype and other subtypes as an example, 46 participants are divided into Group4 group and non-Group4 group. The mean values within the ROI of the APTw-FLAIR mismatch images of the two groups of patients are used for ROC analysis to obtain the threshold and calculate the AUC. If the AUC is greater than 0.5, patients with a mean value within the ROI of the APTw-FLAIR mismatch image greater than the threshold are judged as Group4 group, and those less than the threshold are judged as non-Group4 group; if the AUC is less than 0.5, patients with a mean value within the ROI of the APTw-FLAIR mismatch image less than the threshold are judged as Group4 group, and those greater than the threshold are judged as non-Group4 group.

[0083] Similarly, the APTw-T2 weighted mismatch image can be calculated from the APTw image and the T2 weighted image. Using the mean value within the ROI of the APTw-T2 weighted mismatch images of 46 participants for ROC analysis, a similar classification process can be carried out. As Figure 4 shown, the ROC curves for differentiating Group4 group and other groups using the mean values within the ROI of the APTw, APTw-T2 weighted mismatch, and APTw-FLAIR mismatch images are presented. The results show that the effects of using the APTw-FLAIR mismatch image (AUC = 0.77) and the APTw-T2 weighted mismatch image (AUC = 0.67) are significantly better than using the APTw image (AUC = 0.56).

[0084] It should be noted that, in order to simplify the description disclosed in this specification and thus help in the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this specification, sometimes multiple features are merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the subject of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0085] It should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification can be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for generating mismatched images based on CEST images and structural magnetic resonance images, characterized in that, Comprising: Obtaining a CEST source image, a CEST effect quantification index image, and a structural magnetic resonance image for an imaging target; Registering the structural magnetic resonance image onto the CEST source image with a pre-delineated region of interest, and calculating the mean value of the registered structural magnetic resonance image within the region of interest, and dividing the registered structural magnetic resonance image by the mean value to obtain a normalized structural magnetic resonance image; Dividing the CEST effect quantification index image by the normalized structural magnetic resonance image to obtain a quotient image; Subtracting the quotient image from the CEST effect quantification index image to obtain a mismatch image.

2. The method for generating a mismatched image based on CEST images and structural magnetic resonance images according to claim 1, wherein The CEST effect quantitative index image is CESTR, CESTR nr 、MTR Rex , AREX or CEST quantitative images obtained by Lorentz, EMR, and NEMR fitting algorithms.

3. The method for generating a mismatched image based on CEST images and structural magnetic resonance images according to claim 1, wherein The structural magnetic resonance image is a T1-weighted image, a T2-weighted image, and a FLAIR image before and after contrast enhancement.

4. The method for generating a mismatched image based on CEST images and structural magnetic resonance images according to claim 1, wherein The CEST effect quantification index image is a CESTR image, and its calculation method is as follows: Obtain the CEST image data of the imaging target at different frequency offsets, and extract the original Z-spectrum of each voxel therefrom for main magnetic field B0 frequency offset correction to obtain the corrected Z-spectrum; for each voxel, a frequency point deviating from 0 ppm in the corrected Z-spectrum or the fitted Z-spectrum of the corrected Z-spectrum is used as a specific frequency point, and the magnetization transfer ratio (MTR) at this specific frequency point is obtained by magnetization transfer ratio asymmetry analysis. asym The values of MTR asym for all voxels constitute the CESTR image.

5. The method for generating a mismatched image based on CEST images and structural magnetic resonance images according to claim 4, wherein The specific frequency point is +3.5 ppm, and the corresponding CESTR image is an APTw image.

6. A quantitative analysis system based on mismatched images, characterized in that, Comprising: A mismatch image acquisition module for obtaining a mismatch image of an imaging target according to the generation method described in any one of claims 1 to 5; An image analysis module for calculating the signal mean value of the region of interest in the mismatch image, and combining a threshold optimized in advance for the target disease, and judging and outputting the grading or typing result of the target disease by the threshold method.

7. The quantitative analysis system based on mismatched images according to claim 6, characterized in that The target disease is glioma, medulloblastoma, and abdominal neuroblastoma.

8. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, the mismatch image generation method based on CEST images and structural magnetic resonance images described in any one of claims 1 to 7 can be implemented.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is executed by a processor, the mismatch image generation method based on CEST images and structural magnetic resonance images described in any one of claims 1 to 7 is implemented.

10. A computer electronic device, characterized in that, Comprising a memory and a processor; The memory is used for storing a computer program; The processor is used for implementing the mismatch image generation method based on CEST images and structural magnetic resonance images described in any one of claims 1 to 7 when executing the computer program.

Images