CN114533102A - Method for investigating individual-level general metabolic abnormality by using general SUV image - Google Patents
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Abstract
The invention discloses a method for investigating individual-level whole body metabolic abnormalities by using a whole body SUV image, which comprises the following steps: acquiring 18F-FDG PET/CT scanning data of different sampling areas of different subjects to obtain images with standard uptake values SUV of the different subjects, and selecting the sampling areas through the images; the subjects include healthy controls, lung cancer, subjects 30 days after discharge of Covid-19, subjects with unexplained gastrointestinal bleeding; constructing individual connection networks of different subjects, and obtaining the metabolic abnormal degree of each sampling area of the different subjects through the individual connection networks of the different subjects; and performing control group homogeneity analysis, lung cancer group heterogeneity analysis, group-to-individual network analysis and individual network-to-single organ analysis according to the metabolic abnormality degree of each sampling region of different subjects. The methods of the invention can potentially identify systemic metabolic abnormalities.
Description
Technical Field
The invention belongs to the technical field of medical image processing, and particularly relates to a method for investigating individual-level whole body metabolic abnormalities by using a whole body SUV image.
Background
Human metabolic homeostasis depends on complex neuronal, vascular and humoral mechanisms at the systemic level. Simultaneous nonlinear interactions between organs form different physiological networks. Many systemic diseases are due to or associated with interference of physiological interactions between organs. Although established methods exist for studying this interference at the organ level, further development of general methods sufficient for quantification at the system level presents challenges.
To date, most research on these topics has used non-imaging tools. Thiele et al developed a metabolic network reconstruction method that uses organ-specific information from literature and omics data. Data sources including 20 organs, 6 individual organs, 6 blood cell types and 13 biological fluid compartments. Barajas-marti ienez et al describe physiological networks based on anthropometry, fasting blood tests and other vital signs. They conclude that the specific structural characteristics of the network will change throughout the life of the human being and may provide an indication of health. Cui et al rebuilds the global mammalian metabolic network in tissues and cell types and attempts to link organ-to-organ metabolite transport. Bashan et al and Bartsch et al developed a framework to detect interactions between different systems and determine a physiological network that exhibits interactions between network topology and function. None of the above methods can be quantified at the system level.
Disclosure of Invention
The invention provides a method for investigating individual-level whole body metabolic abnormalities by using a whole body SUV image, aiming at the problem that the existing metabolic abnormality analysis method cannot be quantified on a system level.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of investigating systemic metabolic abnormalities at the individual level using a systemic SUV image, comprising:
and 3, performing control group homogeneity analysis, lung cancer group heterogeneity analysis, group-to-individual network analysis and individual network-to-single organ analysis according to the metabolic abnormality degree of each sampling region of different subjects.
Further, the step 1 comprises:
step 1.1: firstly, carrying out CT scanning to carry out attenuation correction, and then carrying out 18F-FDG PET/CT acquisition; reconstructing the scan data into a voxel size-fixed matrix using a 3D ordered subset expectation-maximization algorithm; performing attenuation and scatter correction using the CT-based attenuation correction map; then, converting the reconstructed moving images into images with standard uptake values SUV by normalizing the injection dosage and the weight;
step 1.2: for each scan, a sampled region of interest of all organs of interest is delineated on the SUV image; the sampling region includes the whole brain, blood, left ventricle, lung, liver, pancreas, spleen, left/right kidney, muscle and spine; performing brain cell division by using statistical parameter mapping, extracting a plane containing the brain in a reconstructed image into a new volume, and performing spatial normalization in a Montreal neurological institute space; the normalized image was smoothed by a gaussian filter and then divided into regions defined by AAL2 maps, of which brainstem, whole cerebellum, cerebrospinal fluid, whole white matter, caudate nucleus, putamen and frontal cortex were selected as new sampling regions, with a total of 18 sampling regions analyzed per scan along with the depicted organ.
Further, the step 2 comprises:
step 2.1, constructing a reference metabolic network refNET from the health control group, wherein the reference metabolic network is obtained by calculating Pearson correlation coefficients between SUVs of each region pair and adopts a covariance network structure;
step 2.2: adding a patient to the healthy control group to form a new group so as to construct a new structural covariance network, wherein the network is marked as a perturbation network ptbNET;
step 2.3, calculating the difference between the disturbance network ptbNET and the reference network refNET as a residual network resNET; setting a threshold of 0.3 to eliminate weak correlation; a Z-score map of the remaining network resNET is obtained:
where N is the total number of topics in the new group, the remaining networks essentially represent an abnormal level of connectivity, each network consisting of 153 edges connecting 18 regions, each edge exhibiting a different degree of metabolic variation; to quantify the degree of anomaly, the intensity STR of each region anomaly is defined:
where m is the area index number and,
is a set of the data to be transmitted,
m-18 is the number of zones, ZCCmiRepresenting the correlation coefficient of the Z-fraction graph between the region m and its neighboring regions i, the total number of neighboring nodes being equal to m-1.
Further, in step 3, the control group homogeneity analysis is performed as follows:
subjects in each healthy control group were subjected to individual network analysis, intra-group similarity was measured by averaging the Z-fractional Pearson correlation coefficient between any pair of networks, and then re-sampling was performed to test subject selection repeatability.
Further, in step 3, the heterogeneity analysis of the lung cancer group is performed as follows:
the intensity of the personal network of each lung cancer patient was compared to the intensity in the reference network and the similarity between the lung cancer patient networks was calculated by measuring the average of the Pearson correlation coefficients between the subjects between the paired Z-scores on all 153 edges.
Further, in step 3, the network analysis at the group and individual level is performed as follows:
establishing group-level metabolic networks for the patient group and the healthy control group, respectively, establishing two group-level metabolic networks by calculating Pearson correlations of pairs of brain regions of all subjects in the two groups, normalized differences between the two group-level metabolic networks being considered as group-level difference networks Diffgroup，i：
Where i represents the edge, patNET represents the group-level metabolic network of the patient group, refNET represents the group-level metabolic network of the healthy control group;
constructing an average residual network Diff by averaging the Z-scores for each edge across all patient networksindividual，i：
Where j is the patient index number, NpIs the total number of patients;
calculating average residual error network Diffindividual，iAnd group-level differentiated network Diffgroup，iPearson correlation coefficient therebetween.
Further, in step 3, the personal network and single organ analysis is performed as follows:
separate connection networks were constructed for one subject 30 days after Covid-19 discharge and one subject with unexplained gastrointestinal bleeding, and the change in SUV was quantified based on the network strength of the organ.
Compared with the prior art, the invention has the following beneficial effects:
the invention can use the whole body PET/CT SUV image of the subject and the healthy control group to construct the individual metabolic abnormality network. The method provided by the invention can be used for investigating the systemic metabolic abnormality at an individual level and characterizing the molecular connectivity of glucose metabolism at the individual level, which cannot be solved by the current analysis method. The methods of the present invention can potentially identify systemic metabolic abnormalities that cannot be derived by traditional population-level methods due to the large heterogeneity between lung cancer groups. Furthermore, the present invention can systematically investigate how the brain and other organs interact in healthy and diseased conditions.
Drawings
FIG. 1 is a basic flow chart of a method for investigating metabolic abnormalities of the whole body at an individual level using a whole body SUV image according to an embodiment of the present invention;
FIG. 2 is a diagram of a target region for 2D sampling according to an embodiment of the present invention;
FIG. 3 is an overall framework diagram of an individual metabolic network obtained by an embodiment of the present invention;
FIG. 4 is a box plot of the individual metabolic network connection strength of the lungs of the healthy control group and the lung cancer group and the corresponding SUV values in the two groups of lungs according to the embodiment of the present invention;
FIG. 5 is a graph showing the individual metabolic connectivity of a subject 30 days after discharge of Covid-19 and a subject with unexplained gastrointestinal bleeding, in accordance with an embodiment of the present invention;
fig. 6 is a graph of the correlation between the organ | Δ SUV | and the network strength calculated by the network of fig. 4 according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in the present invention, we propose a framework that can construct individual metabolic abnormality networks using whole-body PET/CT SUV images of subjects and healthy controls. Implementation details are first demonstrated, and then verified. Finally, an example application was demonstrated and its performance compared to traditional group-level connectivity and single organ uptake analysis.
Specifically, as shown in fig. 1, a method for investigating a systemic metabolic abnormality at an individual level using a systemic SUV image, includes:
step S101, data acquisition and processing: acquiring 18F-FDG PET/CT scanning data of different sampling areas of different subjects to obtain images with standard uptake values SUV of the different subjects, and selecting the sampling areas through the images; the subjects include a healthy control group consisting of subjects without any disease record, a lung cancer group consisting of subjects diagnosed with lung cancer and having different lesion sites, subjects 30 days after discharge of Covid-19, subjects with unexplained gastrointestinal bleeding;
step S102, constructing individual connection networks of different subjects, and obtaining the metabolic abnormal degree of each sampling area of the different subjects through the individual connection networks of the different subjects;
step S103, performing control group homogeneity analysis, lung cancer group heterogeneity analysis, group-to-individual network analysis and individual network-to-single organ analysis according to the metabolic abnormality degree of each sampling region of different subjects.
Specifically, the present embodiment includes a total of 36 18F-FDG PET/CT scans. Of the subjects, twenty-four were young healthy subjects without any disease records; ten cases are diagnosed as lung cancer, and the lesion sites are different; one was performed 30 days after Covid-19 discharge; one is gastrointestinal bleeding of unknown cause. The demographics of these studies are listed in table 1.
TABLE 1 demographics of the subjects
In the data acquisition and processing process of step S101, all scans were obtained on the uexplor PET/CT scanner in the national hospital of the province of china, hannan. These studies were approved by the local ethics committee. Written consent was obtained for each subject prior to scanning. The scanning process and data format are as follows. A CT scan is first performed for attenuation correction. A 60 minute list mode PET acquisition was then initiated in which 18F-FDG was injected intravenously from the lower extremities (see table 1 for injected dose). To obtain the SUV images, the scan data within 50-60 minutes is reconstructed to a voxel size of 3.125 × 3.125 × 2.866mm using a 3D Ordered Subset Expectation Maximization (OSEM) algorithm on the workstation3192 × 192 × 80 matrix. The reconstruction applies 3 iterations, 28 subsets and 2mm gaussian post smoothing. Attenuation and scatter correction is performed using a CT-based attenuation correction map. Reconstructed live images (in Bq/cc) were then converted to images with Standard Uptake Values (SUV) by normalizing the injected dose and weight.
For each scan, a region of interest (ROI) of all organs of interest is manually delineated on the SUV image. The target area of sampling is shown in fig. 2, and there are 11 sampling areas including the whole brain, blood, left ventricle, lung, liver, pancreas, spleen, left/right kidney, muscle and spine, where the brain refers to the whole brain, and the lung refers to the left or right lung where the lesion is located, excluding the lesion itself. Brain cell division was further performed using statistical parameter mapping (SPM 12). Planes containing the brain in the reconstructed image were extracted as new volumes and the FDG-PET template was spatially normalized in the Montreal Neurological Institute (MNI) space. The normalized image was smoothed with an 8mm FWHM gaussian filter and then divided into 94 regions defined by the AAL2 map. Sub-regions were further extracted from the brain according to the AAL2 map defined in the context. In this example, only brainstem, whole Cerebellum (CER), cerebrospinal fluid (CSF), whole White Matter (WM), caudate nucleus, putamen and frontal cortex (SF) were selected for subsequent analysis. Together with the depicted organ, a total of 18 sample regions were analyzed per scan. Network analysis was performed using Brain Connectivity Toolbox. All statistical analyses were performed using the statistical and machine learning toolbox in Matlab R2018 b.
In step S102, in order to construct an individual connection network, we adopt a method for brain anatomical MRI. The overall frame is shown in fig. 3. The basic idea is to analyze the interference of the individual sample in the overall control sample set to obtain the abnormal information of the individual sample. First, a reference metabolic network refNET obtained by calculating partial Pearson correlation coefficients (age, gender as covariate) between SUVs of each region pair was constructed from a healthy control group (24 healthy subjects), using a covariance network structure. The nodes of the network are target areas, and the connection edges are the correlation coefficients among the nodes, wherein the larger the correlation coefficient is, the stronger the connection is. Typically, the constructed reference network has common characteristics of all control samples. We then added one patient to the healthy control group, forming a new group of 25 subjects, to construct a new structural covariance network, which was labeled as the perturbation network ptbNET. Next, the difference between the perturbation network ptbNET and the reference network refNET is calculated as the remaining network resNET. The threshold 0.3 is set to eliminate weak correlation that may come from noise. A Z-score plot for resNET was obtained:
where N is the total number of topics in the new group, i.e. the total number of samples. The remaining networks essentially represent an exceptional level of connectivity, each network consisting of 153 edges connecting 18 regions. Each edge exhibited a different degree of metabolic change and resulted in a deviation from normal in the control group. Under normal conditions, the body network is structurally stable and connected. When the metabolic index changes in a subject due to disease, the network links change accordingly. To quantify the degree of anomaly, we define the intensity STR of each region anomaly:
where m is the area index number and,
is a set of the parameters that,
m-18 is the number of regions. ZCCmiRepresenting the correlation coefficient of the Z-fraction map between region m and its neighboring region i. The total number of neighboring nodes equals m-1.
In the data and statistical analysis of step S103, we first investigated the overall consistency of healthy control groups by measuring intra-group similarity. Second, heterogeneity of individual networks of lung cancer patients was demonstrated. Third, the metrics of the individual-level network are compared to the metrics of the team-level network. Finally, the ability of a single network to reveal single organ abnormalities is tested by measuring the correlation between the SUV and the network strength. Details of how these investigations are carried out are listed below.
(1) Control homogeneity analysis
A separate network analysis was performed for each healthy control group of subjects. Similarity within a group is measured by averaging the correlation coefficient of the Z-score between any pair of networks. We then run a resampling procedure to test the repeatability of the theme selection. The idea is that two random populations from the same population should not be different from each other. There were a total of 30 normal samples, from which we randomly drawn 24 samples (4/5 out of 30) as controls. The sampling process was repeated 20 times. We constructed refNET from each randomly sampled group using the proposed method. Repeatability between these refnets is quantified by averaging the correlation coefficients of the Z-scores between any pair of networks. We further performed a resampling test on the sensitivity of the control group sample size. The idea is to investigate the minimum number of normal subjects required to construct the control group. We randomly selected 10,15,18,20 and 24 samples (20 times each) from the above 30 normal subjects as a control group. A new resNET was then calculated for each patient in the lung cancer group using the proposed method and compared to the resNET in context.
(2) Analysis of group heterogeneity of Lung cancers
The strength of the personal network of each patient is compared to the strength in the reference network. Similarity between patient networks was calculated by measuring the average of the inter-subject Pearson correlation coefficients between paired Z scores over all 153 edges.
(3) Group and individual level network analysis
We constructed group-level metabolic networks for patients and healthy controls, respectively. Two group-level metabolic networks (both structural covariance networks) were constructed by calculating Pearson correlations for pairs of brain regions for all subjects in the two groups. The normalized difference between two group-level metabolic networks is considered to be a group-level difference network Diffgroup，i。
Where i represents the edge, patNET represents the group-level metabolic network of the patient group, and refNET represents the group-level metabolic network of the healthy control group.
We also construct the mean residual network Diff by averaging the Z-scores at each edge across all patient networksindividual，i：
Where j is the patient index number, NpIs the total number of patients.
Calculating average residual error network Diffindividual，iGroup-level differentiated network Diffgroup，iPearson correlation coefficient therebetween.
(4) Personal network and single organ analysis
We constructed a single connection network for a subject discharged from Covid-19 and a patient with digestive tract bleeding. The variation of the SUV is quantified in terms of the network intensity of the organ to reveal the ability of the proposed method to reveal organ level abnormalities.
Specifically, the analysis results are as follows:
(1) control homogeneity analysis
Similarity within a group is measured by averaging the correlation coefficient of the Z-score between any pair of networks. The similarity coefficient was 0.921 ± 0.133, indicating that the inter-subject variability between the control groups was low. A resampling process was performed to quantify the repeatability between these refnets by averaging the Z-fractional correlation coefficients (0.872 ± 0.152) between any pair of networks. This indicates that the method is robust to the selection of the control object. We further performed a resampling test on the sensitivity of the control group sample size. For sample sizes 10,15,18,20,22, and 24, the overall mean similarity relative to sample size 30 was 0.58 ± 0.18,0.70 ± 0.157,0.79 ± 0.142,0.89 ± 0.126,0.92 ± 0.124, and 0.97 ± 0.102, respectively. This indicates that as the number of subjects increases, the mean similarity increases and the corresponding variance decreases. The more samples in the control group, the more stable the proposed method. The data for 24 healthy subjects used in this example is sufficient to construct the control group.
(2) Analysis of group heterogeneity of Lung cancers
The intensity of each individual patient network was significantly different from that in the reference network (P <0.01, Bonferroni corrected 153 edges). However, the similarity between patient networks was low, with an average inter-subject Pearson correlation coefficient between paired Z scores for all 153 edges of 0.196 ± 0.182. Despite significant organ-wide heterogeneity among individual patients, the connections that make up the lungs were far more abnormal than the others (FIG. 4, where A is a box plot of individual network connection strengths across the lungs for the control (i.e., healthy control) and disease (i.e., lung cancer) groups B are the corresponding SUV values (50-60 minutes) in both groups of lungs, and connection strengths from a single network appear to be more able to separate the control and disease groups). The overall burden of structural deviation or the number of significant changes per patient margin reflects the severity of the abnormality.
(3) Network analysis at the personal and community level
The Pearson correlation coefficient between the average residual network Diff _ individual and the group-level difference network Diff _ group obtained by the proposed method is 0.78. This suggests that each subject collectively results in differences in the glucose metabolism levels of the panel, although the heterogeneity of the lung cancer group demonstrates a high degree of heterogeneity between subjects.
(4) Personal network and single organ analysis
We constructed a separate connection network for a subject 30 days after discharge from Covid-19 and a patient with unexplained gastrointestinal bleeding, as shown in fig. 5. On the left is the connection matrix for all organs of interest and on the right is the network connection, where the darker lines indicate stronger connections between nodes and the degree of black indicates the strength of a given node. Fig. 5 reveals metabolic connection information between organs. For subjects discharged from Covid-19, the lung, which is the abnormal center, had the strongest joint strength, especially for the left ventricle and brain (B in fig. 5). As shown in fig. 6, intensity was significantly correlated with organ Δ SUV (R0.973 and R0.893, P <0.05), indicating how much the SUV deviated from the control group mean. These show that a single network can reveal metabolic abnormalities at the network and organ level.
In summary, we propose a framework to analyze whole-body PET data using network principles, providing a platform to determine metabolic dysfunctions at the systemic level, which is not possible with current analyses. Subtle deviations in metabolic connectivity can be revealed and are highly correlated with SUV measurements at the organ level. The present invention can potentially identify systemic metabolic abnormalities that cannot be derived by traditional population-level methods due to the large heterogeneity between disease groups. This heterogeneity may be due to differences in disease expression or altered system function. In other words, the proposed method is complementary to the conventional method from the network point of view. It should be noted that the proposed method does not provide a true metabolic connection network for scanning, but a perturbation network for the reference network, which reflects the variation between normal and disease samples on the system level. Whole-body SUV PET is typically available for conventional PET imaging, although a set of reference data sets is required. Another method to deduce metabolic networks at the individual level is to use dynamic PET, where the time-activity curves of organs are correlated at the subject level. However, the regional dynamics carry information of non-specific tracer binding and delivery, which may hide the specific interaction of the tracer with its target.
We constructed individual metabolic networks based on PET/CT SUV images. However, our method can be adapted to use other functional parameters, such as net metabolic rate, blood flow, phosphorylation rate when dynamic scans are available. Also, it can be readily applied to non-FDG tracers, such as tracers that visualize neurotransmitters, to reveal abnormalities in brain organ interactions at the subject level.
The invention can use the whole body PET/CT SUV image of the subject and the healthy control group to construct the individual metabolic abnormality network. The method provided by the invention can be used for investigating the systemic metabolic abnormality at an individual level and characterizing the molecular connectivity of glucose metabolism at the individual level, which cannot be solved by the current analysis method. The methods of the present invention can potentially identify systemic metabolic abnormalities that cannot be derived by traditional population-level methods due to the large heterogeneity between lung cancer groups. Furthermore, the present invention can systematically investigate how the brain and other organs interact in healthy and diseased conditions.
The above shows only the preferred embodiments of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (7)
1. A method for investigating systemic metabolic abnormalities at an individual level using a systemic SUV image, comprising:
step 1, data acquisition and processing: acquiring 18F-FDG PET/CT scanning data of different sampling areas of different subjects to obtain images with standard uptake values SUV of the different subjects, and selecting the sampling areas through the images; the subjects included a healthy control group consisting of subjects without any disease record, a lung cancer group consisting of subjects diagnosed with lung cancer with different lesions, subjects 30 days after discharge of Covid-19, subjects with unexplained gastrointestinal bleeding;
step 2, constructing individual connection networks of different subjects, and obtaining the metabolic abnormal degree of each sampling area of the different subjects through the individual connection networks of the different subjects;
and 3, performing control group homogeneity analysis, lung cancer group heterogeneity analysis, group-to-individual network analysis and individual network-to-single organ analysis according to the metabolic abnormality degree of each sampling region of different subjects.
2. The method for investigating individual-level general metabolic abnormalities using a general SUV image according to claim 1, wherein said step 1 comprises:
step 1.1: firstly, carrying out CT scanning to carry out attenuation correction, and then carrying out 18F-FDG PET/CT acquisition; reconstructing the scan data into a voxel size-fixed matrix using a 3D ordered subset expectation-maximization algorithm; performing attenuation and scatter correction using the CT-based attenuation correction map; then, converting the reconstructed moving images into images with standard uptake values SUV by normalizing the injection dosage and the weight;
step 1.2: for each scan, a sampled region of interest of all organs of interest is delineated on the SUV image; the sampling region includes the whole brain, blood, left ventricle, lung, liver, pancreas, spleen, left/right kidney, muscle and spine; performing brain cell division by using statistical parameter mapping, extracting a plane containing the brain in a reconstructed image into a new volume, and performing spatial normalization in a Montreal neurological institute space; the normalized image was smoothed by a gaussian filter and then divided into regions defined by AAL2 maps, of which brainstem, whole cerebellum, cerebrospinal fluid, whole white matter, caudate nucleus, putamen and frontal cortex were selected as new sampling regions, with a total of 18 sampling regions analyzed per scan along with the depicted organ.
3. The method for investigating individual-level general metabolic abnormalities using a general SUV image according to claim 2, wherein said step 2 comprises:
step 2.1, constructing a reference metabolic network refNET from the health control group, wherein the reference metabolic network is obtained by calculating Pearson correlation coefficients between SUVs of each region pair and adopts a covariance network structure;
step 2.2: adding a patient to the healthy control group to form a new group so as to construct a new structural covariance network, wherein the network is marked as a perturbation network ptbNET;
step 2.3, calculating the difference between the disturbance network ptbNET and the reference network refNET as a residual network resNET; setting a threshold of 0.3 to eliminate weak correlation; a Z-score map of the remaining network resNET is obtained:
where N is the total number of topics in the new group, the remaining networks essentially represent an abnormal level of connectivity, each network consisting of 153 edges connecting 18 regions, each edge exhibiting a different degree of metabolic variation; to quantify the degree of anomaly, the intensity STR of each region anomaly is defined:
where m is the area index number and,
is a set of the data to be transmitted,
m-18 is the number of zones, ZCCmiRepresenting the correlation coefficient of the Z-fraction graph between the region m and its neighboring regions i, the total number of neighboring nodes being equal to m-1.
4. The method for investigating individual-level general metabolic abnormalities using whole-body SUV images as claimed in claim 3, wherein in said step 3, the control homogeneity analysis is performed as follows:
subjects in each healthy control group were subjected to individual network analysis, intra-group similarity was measured by averaging the Z-fractional Pearson correlation coefficient between any pair of networks, and then re-sampling was performed to test subject selection repeatability.
5. The method for investigating individual-level general metabolic abnormalities using whole-body SUV images as claimed in claim 3, wherein in said step 3, lung cancer group heterogeneity analysis is performed as follows:
the intensity of the personal network of each lung cancer patient was compared to the intensity in the reference network and the similarity between the lung cancer patient networks was calculated by measuring the average of the Pearson correlation coefficients between the subjects between the paired Z-scores on all 153 edges.
6. The method for investigating metabolic abnormalities of the whole body at an individual level using whole body SUV images as set forth in claim 3, wherein in step 3, the group-to-individual level network analysis is performed as follows:
establishing group-level metabolic networks for the patient group and the healthy control group, respectively, establishing two group-level metabolic networks by calculating Pearson correlations of pairs of brain regions of all subjects in the two groups, normalization between the two group-level metabolic networksDifferentiated networks Diffgroup，i：
Where i represents the edge, patNET represents the group-level metabolic network of the patient group, refNET represents the group-level metabolic network of the healthy control group;
constructing an average residual network Diff by averaging the Z-scores for each edge across all patient networksindividual，i：
Where j is the patient index number, NpIs the total number of patients;
calculating average residual error network Diffindividual，iAnd group-level differentiated network Diffgroup，iPearson correlation coefficient therebetween.
7. The method for investigating metabolic abnormalities of the whole body at an individual level using whole body SUV images as claimed in claim 3, wherein in said step 3, the personal network and single organ analysis is performed as follows:
separate connection networks were constructed for one subject 30 days after Covid-19 discharge and one subject with unexplained gastrointestinal bleeding, and the change in SUV was quantified based on the network strength of the organ.
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