PET / MRI-based A beta-PET semi-quantitative analysis method and application thereof

Abstract

The invention discloses a PET / MRI (positron emission tomography / magnetic resonance imaging)-based A beta-PET semi-quantitative analysis method, which comprises the following steps of: acquiring a plurality of PET / MRI brain image samples and a plurality of PET / CT (computed tomography) brain image samples, and determining a reference area to calculate a first standard uptake value of the plurality of PET / MRI brain image samples and a second standard uptake value of the plurality of PET / CT brain image samples; performing linear regression analysis on the plurality of PET / MRI brain image samples and the plurality of PET / CT brain image samples to obtain calibration standard uptake values of the plurality of PET / MRI brain image samples; according to the method, the target area is determined, the Centiloid value of the PET / MRI brain image sample of the subject is calculated on the basis of the calibration standard uptake value to serve as the A beta-PET semi-quantitative analysis result, the problem that the evaluation of the A beta deposition level of the PET / MRI brain image is inaccurate is solved, and the accuracy of A beta-PET semi-quantitative analysis based on various PET / MRI scanning systems is improved.

Description

Technical Field

[0001] This application belongs to the technical field of biomolecular detection. Specifically, it relates to a semi - quantitative analysis method of Aβ - PET based on PET / MRI and its uses. Background Art

[0002] Amyloid - β (Aβ) - positron emission tomography (PET) has been widely used in the diagnosis and treatment of Alzheimer's disease (AD). Commonly used Aβ - PET tracers verified by pathological criteria include fluorine 18 F]bethaben ( 18 F - FBB), fluorine 18 F]betapir, and fluorine 18 F]metofenin, etc.; in addition to visual assessment, the standardized uptake value ratio (SUVR) is usually used for quantitative analysis of Aβ deposition. However, the critical value of SUVR calculated for differentiating AD is affected by various factors, including different selections of Aβ tracers, PET scanners, and target regions or reference regions. Evidence shows that the combined SUVR critical values for differentiating AD patients from normal control groups obtained using different processing methods or Aβ - PET tracers can vary from 1.10 to 1.70, unable to provide a unified reference value.

[0003] In this context, a new evaluation method, Centiloid (CL), has been proposed to objectively quantify PET imaging using different Aβ tracers, and its measurement results are expressed in a common unit, thus unifying the critical values for the pathology, diagnosis, and prognosis of Aβ deposition in multi - centers. This method was initially designed for PET using 11 C - PiB and can convert the SUVR of multiple tracers into standard CL units through linear transformation. Currently, standard equations for quantifying the SUVR of multiple Aβ tracers (including 18 F - FBB) into CL units have been established in the PET research field, thereby calculating the CL value, which has strong practicality in clinical applications and can help physicians break through the limitations of tracer use to evaluate the abnormal conditions of patients' Aβ deposition levels under the same dimension.

[0004] However, these CL - based standard equations have only been studied and verified for the analysis of Aβ images obtained using positron emission tomography / computed tomography (PET / CT), and their applicability on positron emission tomography / magnetic resonance imaging (PET / MRI) has not been studied, nor has it been clinically applied.

[0005] Therefore, a method capable of effectively unifying the quantitative analysis of Aβ deposition levels based on PET / MRI is needed. Summary of the Invention

[0006] To solve the problems existing in the prior art, the purpose of this application is to provide a semi - quantitative analysis method of Aβ - PET based on PET / MRI, obtain the standardized uptake values (SUVs) of multiple PET / MRI brain image samples and multiple PET / CT brain image samples, use the linear regression analysis method to establish a functional relationship between the known SUVs of PET / CT in the selected reference region and the SUVs of PET / MRI in the selected reference region, so as to calibrate the SUVR of PET / MRI using the SUVs of the reference region, calculate the critical value of the CL value of PET / MRI through the calibrated SUVR, thereby eliminating the difference in the CL critical values between the semi - quantitative analysis of Aβ - PET using PET / CT and the semi - quantitative analysis of Aβ - PET using PET / MRI, and providing a reliable unified solution for the semi - quantitative analysis of Aβ - PET using different PET / MRI devices.

[0007] Specifically, this application relates to the following aspects:

[0008] 1. A semi - quantitative analysis method of Aβ - PET based on PET / MRI, including: obtaining multiple PET / MRI brain image samples, determining a reference region to calculate the first standardized uptake value of each PET / MRI brain image sample in the multiple PET / MRI brain image samples; obtaining multiple PET / CT brain image samples, calculating the second standardized uptake value of each PET / CT brain image sample in the multiple PET / CT brain image samples according to the reference region; performing linear regression analysis on the multiple PET / MRI brain image samples and the multiple PET / CT brain image samples to obtain the calibrated standardized uptake values of the multiple PET / MRI brain image samples, wherein the first standardized uptake values of the multiple PET / MRI brain image samples are explanatory variables, and the second standardized uptake values of the multiple PET / CT brain image samples are response variables; determining a target region to calculate the calibrated standardized uptake value ratio of the PET / MRI brain image sample of the subject based on the calibrated standardized uptake value, and calculating the Centiloid value of the PET / MRI brain image sample of the subject using the calibrated standardized uptake value ratio as the semi - quantitative analysis result of the Aβ - PET of the PET / MRI brain image sample of the subject.

[0009] 2. The semi - quantitative analysis method of Aβ - PET based on PET / MRI according to item 1, wherein obtaining multiple PET / MRI brain image samples includes: injecting a PET / MRI tracer into the target population; using a PET / MR device to collect the PET / MRI images of the target population and performing PET reconstruction and MRI attenuation correction respectively; and fusing the PET image and the MRI image of the target population to obtain multiple PET / MRI brain image samples.

[0010] 3. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to item 2, wherein the PET / MRI tracer is fluorine 18 F] beta benzene.

[0011] 4. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to item 1, wherein the reference brain region is the VOI region of the whole cerebellum, and the target region is the ROI extracted from the whole brain according to the standard global cortical target region; wherein the standard global cortical target region includes the frontal cortex, the temporal cortex, the parietal cortex, the precuneus cortex and / or the insular cortex.

[0012] 5. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to item 1, wherein performing linear regression analysis on multiple PET / MRI brain image samples and multiple PET / CT brain image samples includes: sorting multiple PET / MRI brain image samples in ascending order based on the magnitude of the first standardized uptake value, and sorting multiple PET / CT brain image samples in ascending order based on the magnitude of the second standardized uptake value; performing head-to-head linear regression analysis on the first standardized uptake value and the second standardized uptake value according to the ascending order sorting results, and fitting a linear regression equation to determine the linear relationship between the first standardized uptake value and the second standardized uptake value.

[0013] 6. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to item 1, wherein the calibrated standardized uptake value is obtained by the following formula:

[0014] calWC suv =αWC suv +β

[0015] wherein, WC suv is the first standardized uptake value, calWC suv is the calibrated standardized uptake value, and α and β are obtained by linear regression fitting of the first standardized uptake value and the second standardized uptake value.

[0016] 7. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to item 6, wherein determining the calibrated standardized uptake value ratio of the PET / MRI brain image sample of the subject based on the calibrated standardized uptake value is obtained by the following formula:

[0017] calCTX suvr =CTX suv / calWC suv

[0018] wherein, CTX suv is the standardized uptake value of the PET / MRI brain image sample of the patient calculated for determining the target region, and calCTX suvr is the calibrated standardized uptake value ratio.

[0019] 8. The PET / MRI-based Aβ-PET semi-quantitative analysis method according to item 7, wherein the Centiloid value of the PET / MRI brain image sample of the subject is calculated by using the calibrated standardized uptake ratio and obtained through the following formula:

[0020] Centiloid value=icalCTX suvr +k

[0021] wherein, the Centiloid value is the Centiloid value, i = 153.4, and k = -154.9.

[0022] 9. A PET / MRI-based Aβ-PET semi-quantitative analysis device, comprising: a sample variable acquisition unit, which acquires a plurality of PET / MRI brain image samples, determines a reference region to calculate the first standardized uptake value of each PET / MRI brain image sample among the plurality of PET / MRI brain image samples; acquires a plurality of PET / CT brain image samples, and calculates the second standardized uptake value of each PET / CT brain image sample among the plurality of PET / CT brain image samples according to the reference region; a linear regression analysis unit, which performs a linear regression analysis on the plurality of PET / MRI brain image samples and the plurality of PET / CT brain image samples to obtain the calibrated standardized uptake values of the plurality of PET / MRI brain image samples, wherein the first standardized uptake values of the plurality of PET / MRI brain image samples are explanatory variables, and the second standardized uptake values of the plurality of PET / CT brain image samples are response variables; a target quantitative analysis unit, which determines a target region to calculate the calibrated standardized uptake ratio of the PET / MRI brain image sample of the subject based on the calibrated standardized uptake value, and calculates the Centiloid value of the PET / MRI brain image sample of the patient by using the calibrated standardized uptake ratio, as the Aβ-PET semi-quantitative analysis result of the PET / MRI brain image sample of the patient.

[0023] 10. An electronic device, comprising: a processor; and a memory, in which computer program instructions are stored, and when the computer program instructions are run by the processor, the processor is caused to execute the PET / MRI-based Aβ-PET semi-quantitative analysis method according to any one of items 1-8.

[0024] 11. A computer program product, comprising computer program instructions, and when the computer program instructions are run by the processor, the processor is caused to execute the PET / MRI-based Aβ-PET semi-quantitative analysis method according to any one of items 1-8.

[0025] 12. A computer-readable storage medium storing computer program instructions that, when run by a processor, cause the processor to execute the Aβ-PET semi-quantitative analysis method based on PET / MRI according to any one of items 1-8.

[0026] The Aβ-PET semi-quantitative analysis device based on PET / MRI in this application determines a reference region and uses two brain image samples obtained through PET / CT and PET / MRI respectively to obtain the linear relationship required to calibrate the Aβ-PET semi-quantitative analysis critical value between the two devices. Thus, the SUVR on PET / MRI is converted into a unified CL unit using a standard equation, solving the problem of inaccurate evaluation of the Aβ deposition level based on SUVR for PET / MRI. In this way, this application can provide a device that migrates the CL value calculation method based on PET / CT to PET / MRI images, enabling clinical semi-quantitative analysis of Aβ to break through the detection instrument limitations and accurately evaluate the abnormal Aβ deposition level and diagnose AD. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The flowchart of the Aβ-PET semi-quantitative analysis method based on PET / MRI according to an embodiment of this application is illustrated.

[0028] Figure 2 The flowchart of obtaining the Aβ-PET semi-quantitative analysis result of a subject by the Aβ-PET semi-quantitative analysis method based on PET / MRI according to an embodiment of this application is illustrated.

[0029] Figure 3A The schematic diagram of the Aβ-PET brain image of a subject with positive Aβ deposition is illustrated.

[0030] Figure 3B The schematic diagram of the Aβ-PET brain image of a subject with negative Aβ deposition is illustrated.

[0031] Figure 3C The schematic diagram of the difference level of the Aβ semi-quantitative analysis results of PET / MRI and PET / CT before calibration is illustrated.

[0032] Figure 4A The schematic diagram of the visual evaluation comparison of the Aβ semi-quantitative analysis results of PET / MRI and PET / CT after calibration is illustrated.

[0033] Figure 4B The schematic diagram of the difference level of the Aβ semi-quantitative analysis results of PET / MRI before calibration, PET / MRI after calibration, and PET / CT is illustrated.

[0034] Figure 5ASchematic diagram showing the linear regression analysis results of the Aβ-PET semi-quantitative analysis method based on PET / MRI according to an embodiment of the present application.

[0035] Figure 5B Schematic diagram showing the performance of the Aβ-PET semi-quantitative analysis method based on PET / MRI according to an embodiment of the present application and the obtained Aβ-PET semi-quantitative analysis critical value.

[0036] Figure 6 Block diagram showing the Aβ-PET semi-quantitative analysis device based on PET / MRI according to an embodiment of the present application.

[0037] Figure 7 Block diagram showing the electronic device according to an embodiment of the present application. Detailed implementation manners

[0038] The present application will be further described below in conjunction with embodiments. It should be understood that the embodiments are only used to further illustrate and explain the present application, and are not used to limit the present application.

[0039] Unless otherwise defined, the technical and scientific terms in this specification have the same meaning as commonly understood by those skilled in the art in this field. Although methods and materials similar or identical to those described herein can be applied in experiments or practical applications, the materials and methods are still described below. In case of conflict, this specification, including its definitions, shall prevail. Additionally, the materials, methods, and examples are for illustrative purposes only and are not restrictive. The present application will be further described below in conjunction with specific embodiments, but is not used to limit the scope of the present application.

[0040] Application overview

[0041] As described above, the differences between the two imaging instruments, PET / CT and PET / MRI, will cause deviations when using SUV to determine the Aβ deposition critical value of the detector. Compared with the traditional PET / CT, PET / MRI is significantly different. It can perform attenuation correction without CT acquisition and has a longer axial field of view. There are obvious differences between PET / MRI and PET / CT in the quantitative evaluation of the brain or the whole body using the same tracer. Related studies have shown that compared with PET / CT, PET / MRI will systematically underestimate the SUV value. For example, the SUV of the cerebellar region in PET / MRI scans is about 30% lower than that in the same region in PET / CT scans. In addition, for the use of fluorine 18In the images obtained from the PET / CT and PET / MRI scans of flutemetamol, there are often differences, even significant differences, between the visual assessment results of professionals and the assessment results based on SUVR values. These differences pose a severe challenge to the unified quantification of Aβ deposition based on PET imaging, making it difficult to ensure the compatibility of the two imaging modalities, which leads to inaccurate semi-quantitative assessment of PET / MRI and causes difficulties in the screening of cognitively impaired populations, especially AD screening, and longitudinal Aβ studies.

[0042] Based on the current lack of differential calibration of the Aβ deposition critical value based on SUVR and the unification of the CL value calculation methods between the two imaging methods of PET / CT and PET / MRI, the present application proposes an Aβ-PET semi-quantitative analysis method based on PET / MRI, which includes obtaining multiple PET / MRI brain image samples, determining a reference region to calculate the first standardized uptake value of each PET / MRI brain image sample in the multiple PET / MRI brain image samples; obtaining multiple PET / CT brain image samples, and calculating the second standardized uptake value of each PET / CT brain image sample in the multiple PET / CT brain image samples according to the reference region; obtaining multiple PET / MRI brain image samples includes injecting a PET / MRI tracer into the target population, and the tracer can be selected as flutemetamol. 18 Using the PET / MR device to collect the PET / MRI images of the target population and respectively perform PET reconstruction and MRI attenuation correction, and fusing the PET image and the MRI image of the target population to obtain multiple PET / MRI brain image samples. In this way, two brain image samples with low local errors are obtained for accurately fitting the SUV differences between the two imaging methods.

[0043] Furthermore, the first standardized uptake value and the second standardized uptake value are sorted in ascending order based on the size of the SUV value of the reference region, and a head-to-head linear regression analysis is performed on the first standardized uptake value and the second standardized uptake value according to the ascending order result to fit a linear regression equation to determine the linear relationship between the first standardized uptake value and the second standardized uptake value, so as to obtain the calibrated standardized uptake value of multiple PET / MRI brain image samples. The first standardized uptake value of multiple PET / MRI brain image samples is the explanatory variable, and the second standardized uptake value of multiple PET / CT brain image samples is the response variable; the reference brain region can be selected as the whole cerebellar VOI region. In this way, through the linear fitting and conversion of the SUV values between the two imaging methods under the reference region, a reliable calculation paradigm for the SUVR of PET / MRI imaging is obtained, and the unknown PET / MRI samples are calibrated using standard PET / CT samples.

[0044] Finally, determine the target region to calculate the calibration standardized uptake value ratio of the patient's PET / MRI brain image sample based on the calibration standardized uptake value, and calculate the Centiloid value of the patient's PET / MRI brain image sample using the calibration standardized uptake value ratio as the Aβ-PET semi-quantitative analysis result of the patient's PET / MRI brain image sample. The target region can be selected as the ROI extracted from the whole brain according to the standard global cortical target regions, such as the frontal cortex, temporal cortex, parietal cortex, precuneus cortex, and / or insular cortex; obtain the CL value of the subject based on PET / MRI using the conversion relationship between SUVR and SUV under a given target region and further, the conversion relationship between CL and SUVR.

[0045] In this way, the Aβ-PET semi-quantitative analysis device based on PET / MRI of the present application can perform semi-quantitative analysis on Aβ-PET using different tracers and different imaging instruments to determine the critical value, improving the applicable range of Aβ-PET evaluation, and facilitating the application and development of the PET / MRI technology with high sensitivity, specificity, and higher safety in the field of cognitive rehabilitation.

[0046] After introducing the basic principle of the present application, various non-limiting embodiments of the present application will be specifically introduced with reference to the accompanying drawings.

[0047] Exemplary Method

[0048] Figure 1 Illustrated is the Aβ-PET semi-quantitative analysis method based on PET / MRI according to an embodiment of the present application.

[0049] As Figure 1 shown, the Aβ-PET semi-quantitative analysis method based on PET / MRI according to an embodiment of the present application includes the following steps.

[0050] Step S110: Obtain multiple PET / MRI brain image samples, determine a reference region to calculate the first standardized uptake value of each PET / MRI brain image sample among the multiple PET / MRI brain image samples; obtain multiple PET / CT brain image samples, and calculate the second standardized uptake value of each PET / CT brain image sample among the multiple PET / CT brain image samples according to the reference region. Among them, the multiple PET / CT brain image samples can be from a public database or directly obtained through a PET / CT scanner; in some embodiments, a tracer is injected into the target population and imaged by a PET / CT scanner, and any feasible image reconstruction method can be used. Operations such as low-dose CT transmission can also be performed before the PET scan to correct attenuation. Among them, the multiple PET / MRI brain image samples can be from a public database or obtained through an integrated PET / MRI system; in some embodiments, a tracer is injected into the target population, and the PET / MRI images of the target population are collected by a PET / MR device and PET reconstruction and MRI attenuation correction are performed separately, and the PET image and the MRI image of the target population are fused to obtain multiple PET / MRI brain image samples. PET reconstruction, MRI attenuation correction, and multimodal image fusion can be any feasible methods selected by those skilled in the art according to the actual situation.

[0051] It can be understood that the multiple PET / MRI brain image samples can come from the same PET / MRI system or different PET / MRI systems; the multiple PET / MRI brain image samples and the multiple PET / CT brain image samples can come from the same target population or different target populations. In this way, the linear relationship obtained by fitting the SUV values of the multiple PET / MRI brain image samples and the multiple PET / CT brain image samples can have higher robustness, making the method not restricted by specific imaging devices, specific detection populations, or uncertain factors in practical applications. The tracer can be fluorine 18 F] beta-amyloid ([[''END]] 18 F-FBB), fluorine 18 F] beta-pyridine, or fluorine 18 F] metofenol, etc., preferably 18 F-FBB, which has a longer half-life and shows good gray-white matter contrast in PET images, thus having high sensitivity and specificity, and helping to more clearly show the distribution of Aβ deposition plaques in the brain.

[0052] In particular, for the Aβ-PET semi-quantitative analysis method based on PET / MRI according to the embodiments of the present application, the selected reference region is the whole cerebellum volume of interest (VOI) region. The entire cerebellum region in the brain imaging sample is selected as an overall reference region to calculate the SUV value. The cerebellum has less Aβ deposition, lower metabolic activity and is relatively more stable than other regions of the brain. Selecting the whole cerebellum VOI region, such as the gray or white matter region of the entire cerebellum, can be used as a normal control to evaluate the pathological changes in other brain regions, reducing the degree of variation among multiple PET / MRI brain imaging samples, multiple PET / CT brain imaging samples, and the above two types of samples, so as to improve the accuracy of evaluating pathological changes using the SUVR of other regions of the brain, such as the target region mentioned in the subsequent content.

[0053] For the selection of the reference region, candidate regions well-known to those skilled in the art, such as the whole gray matter region, the whole cerebellum + brainstem, and the pons, can also be considered. The purpose of selecting the whole gray matter is to maximize the utilization rate of the brain gray matter and reduce the white matter region with non-specific cumulative PiB. However, the size of the VOI region based on the whole gray matter is relatively small, excluding the possible Aβ variability information of the white matter in the cerebellar peduncle, and its advantages may not be well extended on PET / CT or PET / MRI; when calculating the SUV of the reference region, all normalization methods handle the brainstem less well than the gray matter. Therefore, the Aβ-PET semi-quantitative analysis method based on PET / MRI according to the embodiments of the present application preferably uses the whole cerebellum as the reference region, so that the SUV acquisition process and the subsequent calibration process based on different scanning instruments can achieve more accurate quantitative results on a more standard basis, improving the reliability of the method.

[0054] Step S120: Perform a linear regression analysis on multiple PET / MRI brain imaging samples and multiple PET / CT brain imaging samples to obtain the calibrated standardized uptake values of the multiple PET / MRI brain imaging samples. Among them, the first standardized uptake value of the multiple PET / MRI brain imaging samples is the explanatory variable, and the second standardized uptake value of the multiple PET / CT brain imaging samples is the response variable. Here, the SUV value of the PET / MRI brain imaging sample is used as the explanatory variable to analyze its linear relationship with the SUV value of the PET / CT brain imaging sample. The purpose is to calibrate the inaccurate SUVR values of these PET / CT brain imaging samples.

[0055] As described above, the SUVR-based semi-quantitative PET / MRI-Aβ analysis may result in fluctuations in the Aβ critical value due to various factors such as the instrument used, the tracer, or the image segmentation method, leading to inconsistent analysis results and non-uniform analysis methods, making it not generally applicable; using the CL method of PET / CT to evaluate the Aβ critical value of PET / MRI samples also has the problem of inaccuracy. Therefore, the purpose of selecting the SUV values of multiple PET / CT brain image samples in the reference region in the Aβ-PET semi-quantitative analysis method based on PET / MRI according to the embodiments of the present application is to use them as the standard reference values for Aβ-PET semi-quantitative analysis, and use these standard reference values to determine the adjustment parameters, that is, the calibration coefficients, for the non-standard or uncertain SUV values of PET / MRI, so as to help those skilled in the art obtain the calibrated PET / MRI-SUVR values, enabling the unified measurement CL method to be applicable to the PET / MRI system and produce accurate Aβ critical value results.

[0056] Therefore, the stable whole cerebellum is selected as the reference region, and the SUV values of multiple PET / CT brain image samples in the whole cerebellum are used as the standard response variables to adjust the SUV values of multiple PET / MRI brain image samples in the whole cerebellum through linear regression analysis, and accurate PET / MRI-SUV values can be obtained. After the regression analysis is completed, the linear relationship between the SUVs of the two brain image samples can be obtained. Since both the Aβ-SUVR semi-quantitative analysis paradigm and the CL analysis paradigm based on PET / CT are verified standard paradigms, the linear relationship obtained above can be used to correct the paradigm so that it can be used for the Aβ semi-quantitative analysis of PET / CT.

[0057] In the Aβ-PET semi-quantitative analysis method based on PET / MRI according to the preferred embodiment of the present application, the linear regression analysis of multiple PET / MRI brain image samples and multiple PET / CT brain image samples includes: sorting the multiple PET / MRI brain image samples in ascending order based on the magnitude of the first standardized uptake value, and sorting the multiple PET / CT brain image samples in ascending order based on the magnitude of the second standardized uptake value; performing a head-to-head linear regression analysis on the first standardized uptake value and the second standardized uptake value according to the ascending order sorting results, and fitting a linear regression equation to determine the linear relationship between the first standardized uptake value and the second standardized uptake value, and the linear relationship is:

[0058] refWC suv =αWC suv +β

[0059] (Formula 1)

[0060] Where WC suv is the first standardized uptake value, refWC suvis the second standardized uptake value, where α and β are the slope and intercept obtained through regression fitting respectively, and serve as the calibration coefficients for the standardized uptake value of PET / MRI in the reference region. According to the preferred implementation of linear regression, the value range of α is 1.146 - 1.583 (95% CI), and its further preferred value is α = 1.365; according to the preferred implementation of linear regression, the value range of β is -0.7522 - 0.2311 (95% CI), and its further preferred value is β = -0.2605. It can be understood that by performing the same sorting on two brain image samples, such as ascending sorting (or other sorting methods), to conduct head-to-head linear regression analysis, the characteristic that the SUV values of multiple PET / CT brain image samples are already clinically valid control data is utilized to determine the clinically valid value range with high confidence for the SUV values of multiple PET / MRI brain image samples, thereby obtaining calibration coefficients with high confidence.

[0061] Step S130: Determine the target region to calculate the calibration standardized uptake value ratio of the PET / MRI brain image sample of the subject based on the calibration standardized uptake value, and calculate the Centiloid value of the PET / MRI brain image sample of the subject using the calibration standardized uptake value ratio as the Aβ-PET semi-quantitative analysis result of the PET / MRI brain image sample of the subject. It can be seen that this step, as Figure 2 shown, can be further divided into steps S1301 - S1303: Step S1301: Adjust the first standardized uptake value of the PET / MRI brain image sample of the subject based on the calibration coefficient used to fit the calibration standardized uptake value to obtain the calibration standardized uptake value of the reference region. In order to accurately cover the possible locations where Aβ deposits in the brain, according to the Aβ-PET semi-quantitative analysis method based on PET / MRI in the embodiments of the present application, the target region can be set as the region of interest (ROI) extracted from the whole brain by any feasible method according to the standard global cortical target region, where the standard global cortical target region can include the possible brain regions of lesions. Preferably, the standard global cortical target region can include one or more of the frontal cortex, temporal cortex, parietal cortex, precuneus cortex, and / or insular cortex; it can be understood that the target region can also include or be any other possible ROI.

[0062] Here, after obtaining the calibration coefficient for adjusting the standardized uptake value based on the reference region through step S120, the SUV calibration of the new PET / MRI brain image sample can be performed to facilitate the semi-quantitative analysis of the Aβ information it contains. Specifically, the preferred interval of the calibration coefficient can be used, or further, the preferred value can be used to adjust the SUV value of the reference region from the PET / MRI brain image sample of the subject to obtain the corresponding SUVR value. The adjustment process is shown in the following formula:

[0063] calWC suv = αWC suv + β

[0064] (Formula 2)

[0065] Wherein, WC suv is the first standardized uptake value of the PET / MRI brain image sample of the subject after determining the reference region, calWC suv is the adjusted calibrated standardized uptake value. α and β are obtained by linear regression fitting of the above-mentioned first standardized uptake value and the second standardized uptake value. As described above, the value range of α is 1.146 to 1.583 (95% CI), and its further preferred value is α = 1.365; according to the preferred implementation of linear regression, the value range of β is -0.7522 to 0.2311 (95% CI), and its further preferred value is β = -0.2605. The verification process of the preferred scheme of the above calibration coefficients will be given in the example part.

[0066] That is, the reference region SUV of the PET / MRI brain image sample of the subject is adjusted to the reference region SUV of the theoretically PET / CT brain image sample of the subject through the calibration coefficients obtained by linear regression. The SUVR of the PET / MRI brain image sample calculated using the new SUV value has the same or very close credibility as the SUVR of the PET / CT brain image sample, correcting the problem that the SUVR of the existing PET / MRI brain image sample is difficult to accurately evaluate the Aβ deposition critical value due to the unevenness of the imaging instrument categories.

[0067] Step S1302, determine the target region, and obtain the calibration standardized uptake value ratio of the target region through the calibration standardized uptake value of the reference region of the PET / MRI brain image sample of the subject and the calibration standardized uptake value of the target region. Those skilled in the art can understand that the SUVR of Aβ-PET (such as PET / CT, PET / MRI involved in this application, etc.) in the target region can be obtained through the correlation calculation between the SUV of the target region and the SUV of the reference region after obtaining them:

[0068] calCTX suvr = CTX suv / calWC suv

[0069] (Formula 3)

[0070] Wherein, CTX suv is the SUV value of the PET / MRI brain image sample of the subject after determining the target region, calCTX suvrFor its corresponding SUVR value. The SUVR is calculated by dividing the average SUV of the ROI extracted from the whole brain in the target region, such as the ROI extracted from the whole brain according to the standard global cortical target regions (such as the prefrontal lobe, parietal lobe, temporal lobe, precuneus, etc. mentioned above), by the reference region, such as the average SUV of the whole cerebellar gray matter mentioned above. In the case of determining the scanning instrument, the Aβ deposition in the cerebral cortex can be accurately evaluated; the SUV of the reference region, such as the average SUV, is used as the background level. It can be understood that the SUV of the PET / MRI (and PET / CT) brain image sample for determining the reference region mentioned in this application can be the average SUV of the reference region or other specific SUVs, and the SUV of the PET / MRI brain image sample for determining the target region can also be the average SUV of the target region or other SUVs. Those skilled in the art can select the SUV value according to the actual situation and obtain the calibrated SUVR value according to the Aβ-PET semi-quantitative analysis method of the preferred embodiment of this application.

[0071] Step S1303, calculating the Centiloid value of the PET / MRI brain image sample of the subject by using the calibrated standardized uptake ratio of the target region of the PET / MRI brain image sample of the subject. The calculation process is represented by the following formula:

[0072] Centiloid value = icalCTX suvr +k

[0073] (Formula 4)

[0074] Wherein, the Centiloid value is the Centiloid value of the PET / MRI brain image sample of the subject, i = 153.4, k = -154.9; it can be understood that the values of i and k have been proven to be preferred values in the PET / CT research, and the verification of their preferred application on PET / MRI will be given in the embodiment part. The Centiloid value is a linear scale index ranging from 0 to 100, where 0 represents the brain of a young and healthy individual with almost no Aβ deposition, and 100 represents the brain of a typical AD patient with extremely high Aβ deposition. The Centiloid value has a cutoff value within its range. The Centiloid value of the subject can be compared with the cutoff to distinguish whether the Aβ deposition level in the subject's brain is negative or positive. The purpose of the analysis method described in this application is to provide accurate calculation of the Centiloid value for the subjects scanned by the PET / MRI system, which can standardize the results of different tracers and scanning instruments, so as to help the subjects intervene in cognitive function in a timely manner, or help researchers better understand the relationship between Aβ deposition and cognitive function according to the pathological conditions of the subjects, and provide a basis for early intervention.

[0075] Embodiment

[0076] This application generally and / or specifically describes the materials and test methods used in the experiments. For raw materials or instruments whose manufacturers are not specified, they are all conventional raw material products or instruments that can be obtained through commercial purchases.

[0077] Example 1 Acquisition and Statistical Analysis of Brain Imaging Samples

[0078] Four independent cohorts consisting of a total of 133 patients were used: Group A, including: 6 patients who underwent head-to-head 18 F-FBB PET / CT + PET / MRI scans at Huashan Hospital Affiliated to Fudan University in Shanghai, China; Group B: 48 patients who underwent 18 F-FBB PET / CT scans at Huashan Hospital; Group C: 79 patients who underwent 18 F-FBB PET / MRI scans at Huashan Hospital; and Group D: 10 patients who underwent 18 F-FBB PET / MRI scans at Xuanwu Hospital in Beijing to verify the effectiveness of the analysis method described in this application. 3 / 6 patients in Group A, 28 / 48 patients in Group B, 44 / 79 patients in Group C, and 4 / 10 patients in Group D were diagnosed with AD, all showing positive core clinical symptoms and visual assessment of Aβ imaging; other patients had other types of cognitive impairments, including Lewy body dementia, posterior cortical atrophy, and multiple system atrophy, and their visual assessments of Aβ imaging were all negative. The exclusion criteria for enrollment included a history of stroke, major medical diseases, recent cancer diagnosis, and substance use disorders. Each patient also underwent MMSE and MoCA assessments verified by experienced neurologists in the cognitive disorder clinic. In addition, 35 patients on the website of the Global Alzheimer's Association Interactive Network (GAAIN) were used as a reference (ref) group. These patients included attention deficit disorder and other types of cognitive impairments and underwent 18 F-FBB PET / CT scan examinations.

[0079] The head-to-head scan protocol for patients in Group A was as follows: First, each patient was injected with 300 MBq (±10%) of 18 F-FBB (the 18 precursor of the F-FBB product was provided by Beijing Xiantong International Pharmaceutical Co., Ltd.), and in the PET / CT system uMi780 (United Imaging Healthcare, Shanghai, China), it was performed in three-dimensional (3D) mode 18F-FBB PET / CT scans were performed, and PET / CT data were acquired from the time window of 70 - 90 minutes after injection. Within 90 - 110 minutes after injection, PET / MRI scans were performed and images were acquired on the integrated PET / MRI system uPMR 790 (United Imaging Healthcare, Shanghai, China). The three-dimensional Dixon sequence MR technique was used for attenuation correction, and the OSEM algorithm was used for PET image reconstruction.

[0080] Each patient in group B was injected with 300 MBq (±10%) of 18 F-FBB (the precursor of the 18 F-FBB product was provided by Beijing Xiantong International Medical Technology Co., Ltd.), and F-FBB PET / CT scans were performed in the three-dimensional (3D) mode on the Biograph mCT Flow PET / CT scanner (Siemens Healthcare, Erlangen, Germany) at Huashan Hospital. Low-dose CT transmission was performed before the PET scan to correct for attenuation, and the OSEM 3D method was used for image reconstruction after the scan; in addition, separate T1 MRI scans were also performed using a Skyra 3-T scanner (Siemens). 18 F-FBB PET / CT scans were performed, and low-dose CT transmission was performed before the PET scan to correct for attenuation. The OSEM 3D method was used for image reconstruction after the scan; in addition, separate T1 MRI scans were also performed using a Skyra 3-T scanner (Siemens).

[0081] The PET / MRI imaging process for patients in group C and group D was the same as that in group A.

[0082] Three experienced nuclear medicine physicians visually evaluated (VR) the PET imaging of all patients using a predefined binary (Aβ positive or negative) system, and they did not pay extra attention to any clinical information of the patients. The VR method for Aβ-PET was as follows: when the cortical activity in one or more brain lobes was equal to or exceeded the white matter activity, the imaging result would be classified as positive, and a gray and rainbow color scale was used to represent it.

[0083] ANOVA, Kruskal-Wallis tests, and χ 2 tests were used to compare the demographic information, clinical characteristics, and VR results of Aβ-PET in group A, group B, group C, group D, and the ref group; all statistical analyses were performed using SPSS V25.0 software (SPSS Inc., Chicago, IL, USA), and a P value less than 0.05 was considered statistically significant. The results are shown in the following table:

[0084] Table 1 Patient demographic information and clinical characteristics

[0085]

[0086] Among them, a represents ANOVA and Kruskal-Wallis tests were performed, and b represents χ 2Examination. There were no significant differences in age, gender, MMSE score, MoCA score, and VR positive rate among the five groups.

[0087] The differences in SUVR and CL critical values between PET / CT and PET / MRI in Example 2 prove

[0088] Select two representative patients from the cohort of Example 1: Patient 1: MMSE = 27, Aβ positive, female, 71 years old, whose Aβ-PET imaging is as Figure 3A shown; Patient 2: MMSE = 27, Aβ negative, male, 61 years old, whose Aβ-PET imaging is as Figure 3B shown; and select the 18 F-FBB PET / CT and 18 F-FBB PET / MRI scan samples of all six patients in Group A. Using the whole cerebellum (hereinafter referred to as WC) as the reference region, directly perform Aβ semi-quantitative analysis using Formula 4, and find that there are significant differences in the Aβ semi-quantitative analysis results between PET / CT and PET / MRI on the ROI (hereinafter referred to as CTX) extracted from the whole brain according to the standard global cortical target region, including the Meta-ROI, that is, the frontal cortex, temporal cortex, parietal cortex, precuneus cortex, and insular cortex, as Figure 3C shown. In particular, the SUVR value of PET / MRI is significantly higher than that of PET / CT, and the P value of almost all samples < 0.05.

[0089] Continue to compare the Aβ semi-quantitative analysis results between Group B and Group C. Figure 4ADisplays a VR intuitive comparison of the results of group B patients who received PET / CT and group C patients who received PET / MRI. In group B, the calculated mean (± variance) of the target region SUVR (hereinafter referred to as CTXsuvr) for Aβ negative / positive patients was 1.023 ± 0.104 and 1.479 ± 0.203, respectively. The Aβ negative / positive critical value of CTXsuvr was 1.140, and the corresponding CL critical value was 20, with a sensitivity and specificity of 96.43% and 90.00%, respectively. Compared with the recognized standard (CL critical value range of 11.8 - 19.2), the difference between the Aβ deposition critical value in group B and the standard was within the range of ±10%, with high credibility. In group C, the calculated mean (± variance) of CTXsuvr for Aβ negative / positive patients was 1.146 ± 0.100 and 1.743 ± 0.254, respectively. The critical value of CTXsuvr was 1.401, and the corresponding CL critical value was 60.0, with a sensitivity and specificity of 93.18% and 97.14%, respectively. In contrast, the difference between the Aβ deposition critical value and the recognized standard was much greater than ±10%. In addition, in group C, the mean ± variance of the combined CTXsuvr for Aβ negative and Aβ positive patients was significantly higher than the corresponding value in group B (P value < 0.001), as Figure 4B shown.

[0090] Thus, it is proved that the calculation method of the CL critical value that is feasible in the Aβ semi - quantitative analysis of PET / CT in group B will cause inaccurate analysis results when used in the samples of PET / MRI in group C, and CTXsuvr will also increase significantly.

[0091] Example 3 Calibration of SUVR and CL critical value of PET / MRI

[0092] According to the head - to - head linear regression analysis provided by the analysis method described in this application, determine the SUV of the whole cerebellum reference region (hereinafter referred to as PET / MRI - WC SUV ) of group C patients in Example 1, using the WC SUV of the ref group (hereinafter referred to as referenceWC SUV ) as the standard, and obtain the calibrated WC SUV of the PET / MRI imaging data of group C patients (hereinafter referred to as calWC SUV ) through linear regression. The regression equation was determined as:

[0093] referenceWCsuv = 1.365xPET / MRI - WCsuv - 0.2605, as Figure 5A shown.

[0094] Therefore, it can be concluded that the calibration 18The process of the equation for semi - quantitative analysis of Aβ deposition in F - FBB PET / MRI is as follows:

[0095] calWCsuv = 1.365xPET / MRI - WCsuv - 0.2605;

[0096] In this way, the calibrated CTXsuvr (hereinafter referred to as calCTXsuvr) of the PET / MRI imaging data of group C patients can be further calculated:

[0097] calCTXsuvr = PET / MRI - CTXsuv / (1.365xPET / MRI - WCsuv - 0.2605),

[0098] Thereby, the calibrated CL value (hereinafter referred to as calCL) is calculated:

[0099] calCL = 153.4xPET / MRI - CTXsuv / (1.365xPET / MRI - WCsuv - 0.2605) - 154.9.

[0100] After the SUV of the PET / MRI in group C is calibrated, the VR of CTX is as Figure 4A shown. After calibration, the mean ± variance of calCTXsuvr of Aβ - negative / positive patients in group C are 0.988 ± 0.102 and 1.594 ± 0.257 respectively. Then, the Aβ critical values of calCTXsuvr and calCL of the PET / MRI imaging data of group C patients are 1.132 and 19 respectively, with a sensitivity of 100% and a specificity of 91.43%, as Figure 5B shown. Compared with the recognized standard, the difference in the Aβ deposition critical value is within the range of ±10%, with high credibility. In addition, in group C, there is no significant difference (P value > 0.05) in the mean ± variance of calCTXsuvr between Aβ - negative and Aβ - positive patients compared with the corresponding values in group B, as Figure 4B shown.

[0101] That is, after calibrating the SUV of the PET / MRI image reference region of group C patients by the linear regression method, the accurate CL critical value for evaluating Aβ deposition is obtained. This is because the overly high SUVR of group C patients is adjusted to the normal level, initially proving the effectiveness of the analysis method provided in this application when processing PET / MRI imaging data.

[0102] Example 4 Further verification of the calibration paradigm of SUVR and CL critical values for PET / MRI

[0103] For the calibration formula obtained in Example 3, in an independent group D 18Its reliability in determining the critical value of Aβ deposition was further verified in the F-FBB PET / MRI cohort. Specifically, the calibrated SUVR and CL values of the F-FBB PET / MRI imaging data of the patients in group D were calculated, and the Aβ critical values of the corresponding calCTXsuvr and calCL were obtained. The results showed that the mean ± variance of calCTXsuvr of the negative / positive patients in group D were 0.91 ± 0.064 and 1.237 ± 0.064, respectively, and the Aβ deposition critical values of calCTXsuvr and calCL were 1.090 and 12, respectively, with a sensitivity and specificity of 100%. Compared with the recognized standard, the difference in the CL critical value was within the range of ±10%. 18

[0104] Thus, it can be proved that the calibration paradigm proposed by the analysis method described in the present application shows a high consistency with the recognized standard, and it can also be proved that the analysis method described in the present application can also be used for the Aβ-PET semi-quantitative analysis of different types of PET / MRI scanning systems.

[0105] According to the Aβ-PET semi-quantitative analysis method based on PET / MRI according to the embodiments of the present application, it can analyze the Aβ deposition level of an individual through the following devices, electronic devices, computer program products, and / or computer-readable storage media. Therefore, it can be understood that the following devices, electronic devices, computer program products, and / or computer-readable storage media are all in the form of the use of the Aβ-PET semi-quantitative analysis method based on PET / MRI according to the embodiments of the present application for Aβ-PET semi-quantitative analysis in the field of biomolecular detection.

[0106] Exemplary device

[0107] Figure 6 The block diagram of the Aβ-PET semi-quantitative analysis device based on PET / MRI according to the embodiments of the present application is illustrated.

[0108] As Figure 6 shown, the Aβ-PET semi-quantitative analysis device 200 based on PET / MRI according to the embodiments of the present application includes:

[0109] ​The sample variable acquisition unit 210 acquires multiple PET / MRI brain image samples, determines a reference region to calculate a first standardized uptake value for each PET / MRI brain image sample among the multiple PET / MRI brain image samples; acquires multiple PET / CT brain image samples, and calculates a second standardized uptake value for each PET / CT brain image sample among the multiple PET / CT brain image samples according to the reference region; the linear regression analysis unit 220 performs a linear regression analysis on the multiple PET / MRI brain image samples and the multiple PET / CT brain image samples to obtain a calibrated standardized uptake value for the multiple PET / MRI brain image samples, wherein the first standardized uptake value of the multiple PET / MRI brain image samples is an explanatory variable, and the second standardized uptake value of the multiple PET / CT brain image samples is a response variable; the target quantitative analysis unit 230 determines a target region to calculate a calibrated standardized uptake value ratio of the PET / MRI brain image sample of the subject based on the calibrated standardized uptake value, and calculates the Centiloid value of the PET / MRI brain image sample of the patient by using the calibrated standardized uptake value ratio as the Aβ-PET semi-quantitative analysis result of the PET / MRI brain image sample of the patient.

[0110] Here, those skilled in the art can understand that the specific functions and operations of each unit and module in the above-mentioned Aβ-PET semi-quantitative analysis device 200 based on PET / MRI have been introduced in detail in the description of the Figures 1 to 5B Aβ-PET semi-quantitative analysis method based on PET / MRI, and therefore, the repeated description thereof will be omitted.

[0111] As described above, the Aβ-PET semi-quantitative analysis device 200 based on PET / MRI according to the embodiments of the present application can be implemented in various terminal devices, such as a server for storing variables used in linear regression analysis, etc. In some examples, the Aβ-PET semi-quantitative analysis device 200 based on PET / MRI according to the embodiments of the present application can be integrated into the terminal device as a software module and / or a hardware module. For example, the Aβ-PET semi-quantitative analysis device 200 based on PET / MRI can be a software module in the operating system of the terminal device, or can be an application program developed for the terminal device; of course, the Aβ-PET semi-quantitative analysis device 200 based on PET / MRI can also be one of the many hardware modules of the terminal device.

[0112] Alternatively, in some other examples, the Aβ-PET semi-quantitative analysis device 200 based on PET / MRI and the terminal device can also be separate devices, and the Aβ-PET semi-quantitative analysis device 200 based on PET / MRI can be connected to the terminal device through a wired and / or wireless network and transmit interaction information in accordance with a predefined data format.

[0113] Exemplary electronic device

[0114] Next, with reference to Figure 7 an electronic device according to an embodiment of the present application will be described.

[0115] Figure 7 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0116] As Figure 7 shown, the electronic device 10 includes one or more processors 11 and a memory 12.

[0117] The processor 13 may be a central processing unit (CPU) or other form of processing unit having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0118] The memory 12 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may run the program instructions to implement the Aβ-PET semi-quantitative analysis method based on PET / MRI according to various embodiments of the present application described above and / or other desired functions. Various contents such as PET / CT brain image samples, PET / MRI brain image samples, Centiloid values, etc. may also be stored in the computer-readable storage medium.

[0119] In one example, the electronic device 10 may further include: an input device 13 and an output device 14, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0120] The input device 13 may include, for example, a keyboard, a mouse, and the like.

[0121] The output device 14 may output various information to the outside, including calibration coefficients obtained by linear regression, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0122] Of course, for simplicity, Figure 7Only some of the components in the electronic device 10 related to this application are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device 10 may further include any other appropriate components.

[0123] Exemplary computer program product and computer-readable storage medium

[0124] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions that, when run by a processor, cause the processor to execute the steps in the PET / MRI-based Aβ-PET semi-quantitative analysis method according to various embodiments of the present application described in the "Exemplary Method" section of this specification.

[0125] The computer program product can be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0126] In addition, an embodiment of the present application may also be a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are run by a processor, the processor is caused to execute the steps in the PET / MRI-based Aβ-PET semi-quantitative analysis method according to various embodiments of the present application described in the "Exemplary Method" section of this specification.

[0127] The computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0128] The basic principles of the present application have been described in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not limitations. It cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. Additionally, the specific details disclosed above are only for illustrative and easy-to-understand purposes and not limitations. These details do not limit the present application to necessarily implement using the above specific details.

[0129] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present application are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended terms meaning "including but not limited to" and can be used interchangeably with each other. The word "or" and "and" used herein refer to the word "and / or" and can be used interchangeably with each other, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with each other.

[0130] It should also be noted that in the devices, equipment, and methods of the present application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0131] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0132] The above description has been given for purposes of illustration and description. Additionally, this description does not intend to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.

Claims

1. Aβ-PET semi-quantitative analysis method based on PET / MRI, comprising: Obtaining a plurality of PET / MRI brain image samples, determining a reference region to calculate the first standardized uptake value of each PET / MRI brain image sample in the plurality of PET / MRI brain image samples; obtaining a plurality of PET / CT brain image samples, and calculating the second standardized uptake value of each PET / CT brain image sample in the plurality of PET / CT brain image samples according to the reference region; Performing linear regression analysis on the plurality of PET / MRI brain image samples and the plurality of PET / CT brain image samples to obtain the calibrated standardized uptake values of the plurality of PET / MRI brain image samples, wherein the first standardized uptake values of the plurality of PET / MRI brain image samples are explanatory variables, and the second standardized uptake values of the plurality of PET / CT brain image samples are response variables; Determining a target region to calculate the calibrated standardized uptake value ratio of the PET / MRI brain image sample of the subject based on the calibrated standardized uptake value, and calculating the Centiloid value of the PET / MRI brain image sample of the subject by using the calibrated standardized uptake value ratio, as the Aβ-PET semi-quantitative analysis result of the PET / MRI brain image sample of the subject.

2. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to claim 1, wherein The obtaining a plurality of PET / MRI brain image samples includes: Injecting a PET / MRI tracer into the target population; Using a PET / MR device to collect PET / MRI images of the target population and performing PET reconstruction and MRI attenuation correction respectively; and Fusing the PET image and the MRI image of the target population to obtain the plurality of PET / MRI brain image samples.

3. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to claim 2, wherein The PET / MRI tracer is fludeoxyglucose 18 F]beta-fluorobenzene.

4. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to claim 1, wherein The reference brain region is the VOI region of the whole cerebellum, and the target region is the ROI extracted from the whole brain according to the standard global cortical target region; Wherein, the standard global cortical target region includes the frontal cortex, the temporal cortex, the parietal cortex, the precuneus cortex and / or the insular cortex.

5. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to claim 1, wherein Performing linear regression analysis on the plurality of PET / MRI brain image samples and the plurality of PET / CT brain image samples includes: Sorting the plurality of PET / MRI brain image samples in ascending order based on the magnitude of the first standardized uptake value, and sorting the plurality of PET / CT brain image samples in ascending order based on the magnitude of the second standardized uptake value; Performing head-to-head linear regression analysis on the first standardized uptake value and the second standardized uptake value according to the ascending order sorting result, and fitting a linear regression equation to determine the linear relationship between the first standardized uptake value and the second standardized uptake value.

6. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to claim 1, wherein, the calibrated standardized uptake value is obtained by the following formula: calWC suv = αWC suv + β Among them, WC suv is the first standard uptake value, calWC suv is the calibrated standard uptake value, and α and β are obtained by linear regression fitting of the first standard uptake value and the second standard uptake value.

7. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to claim 6, wherein, the determination of the target region to calculate the calibrated standardized uptake value ratio of the PET / MRI brain image sample of the subject based on the calibrated standardized uptake value is obtained by the following formula: calCTX suvr = CTX suv / calWC suv Among them, CTX suv is the standardized uptake value of the patient's PET / MRI brain image sample calculated to determine the target area, and calCTX suvr is the calibration standardized uptake value ratio.

8. The Aβ-PET semi-quantitative analysis method based on PET / MRI according to claim 7, wherein, the calculation of the Centiloid value of the PET / MRI brain image sample of the subject by using the calibrated standardized uptake value ratio is obtained by the following formula: Centiloid value=icalCTX suvr +k wherein, Centiloid value is the Centiloid value, i = 153.4, k = -154.

9.

9. The Aβ-PET semi-quantitative analysis device based on PET / MRI, comprising: a sample variable acquisition unit, which acquires a plurality of PET / MRI brain image samples, determines a reference region to calculate the first standardized uptake value of each PET / MRI brain image sample in the plurality of PET / MRI brain image samples; acquires a plurality of PET / CT brain image samples, and calculates the second standardized uptake value of each PET / CT brain image sample in the plurality of PET / CT brain image samples according to the reference region; a linear regression analysis unit, which performs a linear regression analysis on the plurality of PET / MRI brain image samples and the plurality of PET / CT brain image samples to obtain the calibrated standardized uptake value of the plurality of PET / MRI brain image samples, wherein the first standardized uptake value of the plurality of PET / MRI brain image samples is an explanatory variable, and the second standardized uptake value of the plurality of PET / CT brain image samples is a response variable; a target quantitative analysis unit, which determines a target region to calculate the calibrated standardized uptake value ratio of the PET / MRI brain image sample of the subject based on the calibrated standardized uptake value, and calculates the Centiloid value of the PET / MRI brain image sample of the patient by using the calibrated standardized uptake value ratio as the Aβ-PET semi-quantitative analysis result of the PET / MRI brain image sample of the patient.

10. An electronic device, comprising: a processor; and a memory, in which computer program instructions are stored, and when the computer program instructions are run by the processor, the processor executes the Aβ-PET semi-quantitative analysis method according to any one of claims 1-8.

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