Whole body organ metabolism network analysis method, device and equipment and storage medium

Abstract

The invention relates to a whole body organ metabolism network analysis method and device, equipment and a storage medium. The method comprises the steps that a PET image of a subject is collected, an SUV image, a Ki image and TAC data are obtained according to the PET image, and physiological information, clinical pathology information and daily habit information of the subject are collected; performing metabolism feature extraction based on the PET image, the SUV image and the Ki image corresponding to the region of interest, performing curve fitting on the TAC data of each organ by using the PET image, and representing metabolism disturbance under different organs by using a fitting residual error; constructing a whole body organ metabolism network according to the physiological information, the clinical pathological information, the daily habit information, the metabolic characteristics and the metabolic disturbance, and performing whole body organ metabolism analysis on the subject through the whole body organ metabolism network. According to the method, individualized differences are avoided, the metabolic association among organs of the whole body of a specific disease can be captured more comprehensively, and early diagnosis, accurate evaluation and individualized treatment of clinical diseases are facilitated.

Description

Technical Field

[0001] The present application belongs to the field of medical imaging technology, and in particular relates to a method, device, equipment and storage medium for analyzing a whole-body organ metabolic network. Background Art

[0002] There are complex interactions between the organs of the human body, which create different physiological states. A healthy human body can maintain "homeostasis" balance and normal circulation metabolism in the body through dynamic multi-organ system interactions; however, inflammation caused by infection or tissue damage may cause homeostasis interruption, leading to the occurrence of diseases such as tumors, diabetes, autoimmune diseases and cardiovascular diseases. Different diseases usually have complex causes and clinical manifestations, involving not only multiple physiological processes and metabolic pathways, but also interactions and influences between physiological metabolism, making the diagnosis and treatment of diseases more complicated.

[0003] At present, various medical imaging methods such as CT (Computed Tomography), MR (Magnetic Resonance Imaging), and functional metabolic imaging technologies such as PET (positron emission tomography) and SPECT (single photon emission computed tomography) are commonly used in clinical practice to comprehensively evaluate the progression of the disease and guide the formulation of clinical diagnosis and treatment plans. In recent years, with the emergence and development of whole-body PET scanners, real-time and synchronous observation of the metabolism of whole-body systems and organs has become a reality. For example, based on 18F-FDG dynamic PET, real-time monitoring and evaluation of the glucose metabolism process of whole-body organs can be achieved. However, most of the current research remains at the imaging level for disease diagnosis and treatment and response evaluation, and the interaction and metabolic association of whole-body organs are still in the exploratory stage.

[0004] Tao Sun et al. published an article in the European Journal of Nuclear Medicine and Molecular Imaging in 2022 titled “Identifying the individual metabolic abnormalities from a systemic perspective using whole Based on the whole-body 18F-FDG SUV (Standard Uptake Value) images of the subjects and the normal control database, an "individual metabolic abnormality network framework" was proposed. The framework established a standard network through the normal control database, and then input the clinical SUV images of the subjects for analysis, so as to capture the deviations between the patients and the healthy control group at the organ level. By drawing the organ connection map and observing the changed network edge strength, the abnormal metabolic connections between organs are revealed.

[0005] However, the existing metabolic network is limited to analyzing individual metabolic abnormalities, which is prone to individual metabolic deviations, and it is not convenient to extend the metabolic network conclusions obtained from the analysis to the diagnosis, treatment and evaluation of clinical diseases. At the same time, the existing technology only analyzes the SUV image data of subjects and healthy controls. It has been clinically proven that dynamic PET multi-parameter images can provide better tumor characterization and higher sensitivity and specificity, but it has not yet been included in the study. Therefore, the existing technology urgently needs to be updated based on experimental data. In addition, the existing technology only focuses on the imaging data of the subjects, and the basic physiological information and clinical pathological information of the patients are not included in the analysis scope of the metabolic network, resulting in the metabolic analysis being not comprehensive and accurate. Summary of the invention

[0006] The present application provides a whole-body organ metabolic network analysis method, device, equipment and storage medium, aiming to solve at least one of the above-mentioned technical problems in the prior art to a certain extent.

[0007] In order to solve the above problems, this application provides the following technical solutions:

[0008] A whole-body organ metabolic network analysis method, comprising:

[0009] Acquire PET images of the subject, obtain SUV images, Ki images and TAC data based on the PET images, and collect physiological information, clinical pathological information and daily habit information of the subject;

[0010] Perform region of interest analysis on the subject's organs according to the disease type, select the region of interest related to the disease type, and extract metabolic features based on the PET image, SUV image and Ki image corresponding to the region of interest, and use the PET image to perform curve fitting on the TAC data of each organ, and use the fitting residual to characterize the metabolic disturbance of each organ;

[0011] A whole-body organ metabolic network is constructed based on the physiological information, clinical pathological information, daily habit information, metabolic characteristics and metabolic disturbances. The metabolic associations between various organs under specific diseases are mined through the whole-body organ metabolic network, and whole-body organ metabolic analysis is performed on the subjects.

[0012] The technical solution adopted in the embodiment of the present application also includes: acquiring a PET image of the subject, and obtaining a SUV image, a Ki image, and TAC data according to the PET image, specifically:

[0013] Whole-body PET imaging of the subject was performed using a 2-meter PET device to collect static PET images and dynamic PET images of the subject, and SUV images, Ki images, and TAC data were obtained through the static PET images and dynamic PET images.

[0014] The technical solution adopted by the embodiment of the present application also includes: the collection of physiological information, clinical pathological information and daily habit information of the subject, specifically:

[0015] The physiological information includes gender, age, weight, height and BMI information, the clinical pathological information includes cytology, histology and serology medical examination information, and the daily habit information includes smoking history and secondhand smoke environment information.

[0016] The technical solution adopted in the embodiment of the present application also includes: performing region of interest analysis on the organs of the subject according to the disease type, and before selecting the region of interest related to the disease type, it also includes:

[0017] A CT image of a subject is acquired based on a CT device, the subject's systemic organs are segmented using the CT image, and the segmentation mask of the systemic organs is adapted to the PET image.

[0018] The technical solution adopted in the embodiment of the present application also includes: performing region of interest analysis on the organs of the subject according to the disease type, selecting the region of interest related to the disease type, and extracting metabolic features based on the PET image, SUV image and Ki image corresponding to the region of interest, and using the PET image to perform curve fitting on the TAC data of each organ, and using the fitting residual to characterize the metabolic disturbance of each organ, specifically:

[0019] A region of interest analysis is performed on the segmented whole body organs according to the disease type, and the region of interest related to the disease type is selected. Metabolic features are extracted based on the segmentation mask of the region of interest and the adapted PET image, SUV image and Ki image. The extracted metabolic features include mean SUV, max SUV, mean Ki and max Ki. The dynamic PET image is used to perform curve fitting on the TAC data of each organ, and the fitting residual is used to characterize the metabolic disturbance in different organs.

[0020] The technical solution adopted in the embodiment of the present application also includes: constructing a whole body organ metabolic network based on physiological information, clinical pathological information, daily habit information, metabolic characteristics and metabolic disturbances, mining the metabolic associations between various organs under specific diseases through the whole body organ metabolic network, and performing whole body organ metabolic analysis on the subject, including:

[0021] The whole body organ metabolic network includes an individual abnormal metabolic network and a group abnormal metabolic network. The individual abnormal metabolic network is constructed by: establishing an individual reference network based on the metabolic characteristics of N healthy control groups, adding a subject to the healthy control group to form an individual subject network; extracting individual difference values ​​between the individual subject network and the reference network, performing a Z-score standardization operation on the individual difference values, and performing a thresholding process on the individual difference values ​​after the standardization operation, and selecting individual difference values ​​that are less than and greater than a set confidence interval as significant abnormal metabolism in the individual subject network.

[0022] The technical solution adopted by the embodiment of the present application also includes: constructing a whole body organ metabolic network based on physiological information, clinical pathological information, daily habit information and extracted metabolic features, mining the metabolic associations between various organs under specific diseases through the whole body organ metabolic network, and performing whole body organ metabolic analysis on the subject, and also includes:

[0023] The method for constructing the reference network of the abnormal metabolic network of the group includes: establishing a group reference network based on the metabolic characteristics of N healthy control groups, and establishing a group subject network based on the metabolic characteristics of the entire disease group; extracting the group difference value between the group subject network and the group reference network, and analyzing the abnormal metabolic association between organs / systems according to the group difference value; the abnormal metabolic association analysis process includes: classifying the main organs in the whole body organs, and dividing the whole body organs into nervous system, respiratory system, circulatory system, digestive system, endocrine system, immune system, urinary system and motor system according to physiological characteristics, drawing the association map between organs / systems according to the main organ classification results and the system division results, based on the association map, analyzing the correlation and its significance between organs / systems according to the association map, and quantifying and visualizing the organ metabolic association of the group.

[0024] Another technical solution adopted by the embodiment of the present application is: a whole body organ metabolic network analysis device, comprising:

[0025] Data acquisition module: used to acquire PET images of subjects, obtain SUV images, Ki images and TAC data based on the PET images, and collect physiological information, clinical pathological information and daily habit information of subjects;

[0026] Feature extraction module: used to analyze the regions of interest of the subject's organs according to the disease type, select the regions of interest related to the disease type, and extract metabolic features based on the PET images, SUV images and Ki images corresponding to the regions of interest, and use the PET images to perform curve fitting on the TAC data of each organ, and use the fitting residuals to characterize the metabolic disturbances in different organs;

[0027] Metabolic analysis module: used to construct a whole-body organ metabolic network based on the physiological information, clinical pathological information, daily habit information, metabolic characteristics and metabolic disturbances, and to mine the metabolic associations between organs under specific diseases through the whole-body organ metabolic network to perform whole-body organ metabolic analysis on the subjects.

[0028] Another technical solution adopted by the embodiment of the present application is: a device, the device includes a processor and a memory coupled to the processor, wherein:

[0029] The memory stores program instructions for implementing the whole body organ metabolic network analysis method;

[0030] The processor is used to execute the program instructions stored in the memory to control the whole-body organ metabolic network analysis method.

[0031] Another technical solution adopted by the embodiment of the present application is: a storage medium storing program instructions executable by a processor, wherein the program instructions are used to execute the whole-body organ metabolic network analysis method.

[0032] Compared with the prior art, the beneficial effects produced by the embodiments of the present application are as follows: the whole body organ metabolic network analysis method, device, equipment and storage medium of the embodiments of the present application combine the clinical imaging information, physiological information and pathological information of the subject to construct a whole body organ metabolic network, and comprehensively analyze the interactions between multiple organs or multiple systems under the occurrence of the disease through the whole body organ metabolic network of individuals and groups, so as to more accurately characterize the metabolic state of whole body organs under specific diseases, avoid individual differences, and more comprehensively capture the metabolic associations between whole body organs of specific diseases, so as to more accurately characterize the impact of the disease on whole body organs or systems, and more comprehensively analyze the interactions between multiple organs in clinical diseases and promote the understanding of their causal relationships, thereby realizing the quantitative and visual analysis of whole body organ metabolic associations, which is helpful for the early diagnosis, accurate evaluation and personalized treatment of clinical diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of a whole body organ metabolic network analysis method according to an embodiment of the present application;

[0034] Figure 2 A schematic diagram of constructing an individual abnormal metabolic network according to an embodiment of the present application;

[0035] Figure 3 A schematic diagram of constructing a population abnormal metabolic network according to an embodiment of the present application;

[0036] Figure 4 Schematic diagram of abnormal metabolic associations between organs in different smoking groups in lung cancer patients, where (a) is the abnormal metabolic association analysis result of non-smokers, (b) is the abnormal metabolic association analysis result of light smokers, and (c) is the abnormal metabolic association analysis result of heavy smokers;

[0037] Figure 5 This is a schematic diagram of the structure of a whole body organ metabolic network analysis device according to an embodiment of the present application;

[0038] Figure 6 A schematic diagram of the device structure of an embodiment of the present application;

[0039] Figure 7 A schematic diagram of the structure of a storage medium according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] The terms "first", "second", "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second", "third" can expressly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In the embodiments of this application, all directional indications (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the steps or units listed, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0042] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0043] Specifically, see Figure 1 , is a flow chart of the whole body organ metabolic network analysis method of the present application embodiment. The whole body organ metabolic network analysis method of the present application embodiment comprises the following steps:

[0044] S100: using a PET device to collect PET images of the subject, and obtaining SUV images, Ki (dynamic parameter) images, and TAC (time activity curve) data according to the PET images;

[0045] In this step, in order to acquire a complete whole-body PET image of the subject, the embodiment of the present application utilizes 2-meter PET equipment such as uEXPLORER to perform whole-body PET imaging on the subject, acquires static PET images and dynamic PET images of the subject, and obtains SUV images, Ki images and TAC data through static PET images and dynamic PET images. When performing whole-body organ metabolic network analysis, the whole-body organ metabolic network is constructed by combining SUV images, Ki images and TAC data to more comprehensively and accurately reflect the metabolic status of whole-body organs.

[0046] S110: Collect the subjects’ physiological information, clinical pathological information, and daily habit information;

[0047] In this step, the physiological information collected includes gender, age, weight, height, BMI (Body Mass Index) and other information. The clinical pathological information collected includes medical examination information such as cytology, histology and serology. The daily habit information of the subjects is collected according to specific diseases. For example, for lung cancer patients, it is necessary to collect their smoking history and secondhand smoke environment information.

[0048] Furthermore, when conducting whole-body organ metabolic network analysis, the subject group can also be grouped according to the subjects' physiological information, clinical pathological information, and daily habit information, and the metabolic parameters of various tracer drugs, abnormal associations between organs / systems, etc. can be analyzed. Through group analysis, a more comprehensive preference or association of a specific disease with respect to certain specific physiological information, clinical pathological information, and daily habit information can be obtained.

[0049] S120: acquiring a CT image of the subject based on a CT device, segmenting the subject's whole body organs using the CT image, and adapting the segmentation mask of the whole body organs to the PET image;

[0050] In this step, the CT image is used to segment the whole body organs of the subject, and the segmentation mask of the whole body organs is adapted to the PET image, so as to facilitate the subsequent metabolic network analysis.

[0051] S130: performing region of interest analysis on the segmented organs according to the disease type, selecting the region of interest related to the disease type, extracting metabolic features based on the adapted PET image, SUV image, and Ki image corresponding to the region of interest, and performing curve fitting on the TAC data of each organ using the dynamic PET image, and using the fitting residual to characterize the metabolic disturbance of different organs;

[0052] In this step, since different diseases correspond to different regions of interest, the embodiment of the present application selects corresponding regions of interest for analysis for different disease types. For example, for lung cancer patients, it is necessary to select the thyroid gland, esophagus, spinal cord, sternum, ribs, lungs, heart, liver, spleen, adrenal glands, kidneys, pancreas and muscles from the whole body organs as regions of interest for feature extraction, and for the brain region, the insula, nucleus accumbens, anterior cingulate gyrus, amygdala, thalamus, hippocampus, anterior superior temporal gyrus and other regions related to smoking addiction and lung cancer are selected as regions of interest for feature extraction.

[0053] After selecting the regions of interest related to different disease types, metabolic features are extracted based on the segmentation mask of the region of interest and the adapted PET image, SUV image, and Ki image. The extracted metabolic features include but are not limited to meanSUV, max SUV, mean Ki, max Ki, etc. At the same time, the TAC data of each organ are curve fitted using multi-frame dynamic PET images, and the residuals of the fitting are used to characterize the metabolic disturbances in different organs, and then the metabolic associations between various organs are constructed using multiple feature dimensions.

[0054] S140: Construct a whole-body organ metabolic network based on physiological information, clinical pathological information, daily habit information, and extracted metabolic features and metabolic perturbations. Use the whole-body organ metabolic network to mine the metabolic associations between organs under specific diseases and conduct whole-body organ metabolic analysis on the subjects.

[0055] In this step, feature extraction is performed by combining clinical imaging data such as SUV images, Ki images, and TAC data, and a whole-body organ metabolic network is constructed by comprehensively considering the subjects' physiological information, pathological information, and daily habit information. The metabolic associations between various organs under specific diseases are explored, thereby achieving quantitative characterization and visual analysis of specific disease groups, and more comprehensively and accurately analyzing the development laws of clinical diseases, which is conducive to the early diagnosis and treatment evaluation of various diseases.

[0056] Specifically, the whole body organ metabolic network includes individual abnormal metabolic network and group abnormal metabolic network, such as Figure 2 and Figure 3 As shown, Figure 2 This is a schematic diagram of constructing an individual abnormal metabolic network according to an embodiment of the present application. Figure 3 Schematic diagram of the construction of the abnormal metabolic network of the group of the present application embodiment. The individual abnormal metabolic network construction method specifically includes: first, an individual reference network (RefNet) is established based on the metabolic characteristics of N healthy control groups, and then a subject is added to the healthy control group to form an individual test network (Individual_PatNet); the individual difference values ​​of the individual test network and the individual reference network are extracted, and the individual difference values ​​are standardized by Z-score, that is, the individual difference value data is converted into a standard normal distribution with a mean of 0 and a standard deviation of 1, so that the extracted metabolic features can be compared and processed on the same scale; then the individual difference values ​​after the standardized operation are thresholded, and the individual difference values ​​less than and greater than the set confidence interval are selected as the significant abnormal metabolism in the individual test network. Among them, the confidence interval is set to 95%, that is, the difference values ​​less than 1.96 and greater than 1.96 are selected as the significant abnormal metabolism in the individual test network.

[0057] Furthermore, in order to avoid the impact of individual differences, it is necessary to conduct group abnormal metabolic network analysis while conducting individual abnormal metabolic network analysis. The specific method of constructing the reference network of group abnormal metabolic network is as follows: Similarly, a group reference network is established based on the metabolic characteristics of N healthy control groups, and a group subject network (Group_PatNet) is established based on the metabolic characteristics of the entire disease group. The group difference values ​​between the group subject network and the group reference network are extracted, and the abnormal metabolic associations between organs / systems are analyzed based on the group difference values; the specific analysis process includes: classifying the main organs such as lungs, heart, and kidneys in the whole body organs, and dividing the whole body organs into eight systems such as the nervous system, respiratory system, circulatory system, digestive system, endocrine system, immune system, urinary system, and motor system according to physiological characteristics, drawing the association map between organs / systems based on the organ classification results and system division results, and analyzing the correlation and significance between organs / systems based on the association map to quantify and visualize the organ metabolic associations of the group.

[0058] It can be understood that the construction of the whole-body organ metabolic network is not limited to the whole-body organ metabolic network of individuals and groups, but can also be extended to different groups. For example, the whole-body organ metabolic analysis of people with different diseases in different regions such as Asia, Europe, and America can be observed, and the group organ metabolic network analysis can be conducted in a cross-group manner.

[0059] In order to verify the feasibility and effectiveness of the embodiments of the present application, the following embodiments use abnormal metabolic association networks of different smoking populations in lung cancer patients to conduct experiments. Figure 4 The figure shows the abnormal metabolic associations between organs in different smoking groups in lung cancer patients, where (a) is the abnormal metabolic association analysis result of non-smokers, (b) is the abnormal metabolic association analysis result of light smokers, and (c) is the abnormal metabolic association analysis result of heavy smokers. In the figure, 1 to 20 represent the regions of interest selected in the experiment, which are: 1-thyroid, 2-esophagus, 3-spinal cord, 4-sternum, 5-ribs, 6-lungs, 7-heart, 8-liver, 9-spleen, 10-adrenal glands, 11-kidneys, 12-pancreas, 13-muscles, 14-insula, 15-nucleus accumbens, 16-anterior cingulate gyrus, 17-amygdala, 18-thalamus, 19-hippocampus and 20-anterior superior temporal gyrus, where 1-13 are body organs and 14-20 are brain structures. The lower triangle of the matrix illustrates the specific differences in the Pearson correlation coefficients between lung cancer patients and healthy controls with different smoking intensities, while the upper triangle graphically displays the positive / negative correlations and significance marks corresponding to the specific differences. Through analysis, it was found that with the increase in smoking intensity, the abnormal metabolic association between the lungs and other body organs gradually changed from positive correlation to negative correlation, which means that smoking exacerbates the metabolic reversal between the lungs and multiple organs. When the lung metabolism of heavy smokers increases, the metabolism of other organs weakens instead. At the same time, in the abnormal metabolic associations between brain structure and body organs or brain structure, a positive correlation trend gradually emerges with the increase in smoking intensity, that is, smoking enhances the metabolic synchronization effect within the brain region and between the brain and other organs. When the metabolism of the brain structure of heavy smokers increases, the metabolism of other body organs or other brain structures also increases significantly. The experimental results demonstrate the feasibility and effectiveness of the present invention.

[0060] Based on the above, the whole-body organ metabolic network analysis method of the embodiment of the present application combines the clinical imaging information, physiological information and pathological information of the subjects to construct a whole-body organ metabolic network, and comprehensively analyzes the interactions between multiple organs or multiple systems under the occurrence of the disease through the whole-body organ metabolic network of individuals and groups, so as to more accurately characterize the metabolic state of whole-body organs under specific diseases, avoid individual differences, and more comprehensively capture the metabolic associations between whole-body organs of specific diseases, so as to more accurately characterize the impact of the disease on whole-body organs or systems, and more comprehensively analyze the interactions between multiple organs in clinical diseases and promote the understanding of their causal relationships, thereby realizing the quantitative and visual analysis of the metabolic associations of whole-body organs, which is helpful for the early diagnosis, accurate evaluation and personalized treatment of clinical diseases.

[0061] See also Figure 5 , is a schematic diagram of the structure of the whole body organ metabolic network analysis device according to an embodiment of the present application. The whole body organ metabolic network analysis method device 40 according to an embodiment of the present application comprises:

[0062] Data acquisition module 41: used to acquire PET images of the subject, obtain SUV images, Ki images and TAC data according to the PET images, and collect physiological information, clinical pathological information and daily habit information of the subject;

[0063] Feature extraction module 42: used to perform region of interest analysis on the whole body organs of the subject according to the disease type, select the region of interest related to the disease type, and extract metabolic features based on the PET image, SUV image and Ki image corresponding to the region of interest, and use the PET image to perform curve fitting on the TAC data of each organ, and use the fitting residual to characterize the metabolic disturbance of each organ;

[0064] Metabolic analysis module 43: used to construct a whole-body organ metabolic network based on the physiological information, clinical pathological information, daily habit information, metabolic characteristics and metabolic disturbances, and to mine the metabolic associations between various organs under specific diseases through the whole-body organ metabolic network to perform whole-body organ metabolic analysis on the subject.

[0065] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0066] The device provided in the embodiment of the present application can be applied in the aforementioned method embodiment. For details, please refer to the description of the aforementioned method embodiment, which will not be repeated here.

[0067] See also Figure 6 , is a schematic diagram of the device structure of an embodiment of the present application. The device 50 includes:

[0068] A memory 51 storing executable program instructions;

[0069] A processor 52 connected to the memory 51;

[0070] The processor 52 is used to call the executable program instructions stored in the memory 51 and perform the following steps: acquiring a PET image of the subject, obtaining an SUV image, a Ki image and TAC data based on the PET image, and collecting the subject's physiological information, clinical pathological information and daily habit information; performing a region of interest analysis on the subject's systemic organs according to the disease type, selecting a region of interest related to the disease type, and extracting metabolic features based on the PET image, SUV image and Ki image corresponding to the region of interest, and using the PET image to perform curve fitting on the TAC data of each organ, and using the fitting residual to characterize the metabolic disturbance of each organ; constructing a systemic organ metabolic network based on the physiological information, clinical pathological information, daily habit information, metabolic features and metabolic disturbance, and using the systemic organ metabolic network to mine the metabolic associations between various organs under specific diseases, and performing systemic organ metabolic analysis on the subject.

[0071] The processor 52 may also be referred to as a CPU (Central Processing Unit). The processor 52 may be an integrated circuit chip having signal processing capabilities. The processor 52 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0072] See also Figure 7, is a schematic diagram of the structure of the storage medium of the embodiment of the present application. The storage medium of the embodiment of the present application stores a program instruction 61 that can implement the following steps: collect PET images of the subject, obtain SUV images, Ki images and TAC data according to the PET images, and collect the subject's physiological information, clinical pathological information and daily habit information; perform region of interest analysis on the subject's whole body organs according to the disease type, select the region of interest related to the disease type, and extract metabolic features based on the PET images, SUV images and Ki images corresponding to the region of interest, and use the PET images to curve fit the TAC data of each organ, and use the fitted residuals to characterize the metabolic disturbance under each organ; construct a whole body organ metabolic network based on the physiological information, clinical pathological information, daily habit information, metabolic features and metabolic disturbances, and mine the metabolic associations between various organs under specific diseases through the whole body organ metabolic network, and perform whole body organ metabolic analysis on the subject. Among them, the program instruction 61 can be stored in the above storage medium in the form of a software product, including a number of instructions to enable a device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the various implementation methods of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program instructions, or terminal devices such as computers, servers, mobile phones, tablets, etc. Among them, the server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0073] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0074] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the description and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A whole body organ metabolic network analysis method, characterized in that: include: Acquire PET images of the subject, obtain SUV images, Ki images and TAC data based on the PET images, and collect physiological information, clinical pathological information and daily habit information of the subject; Perform region of interest analysis on the subject's organs according to the disease type, select the region of interest related to the disease type, and extract metabolic features based on the PET image, SUV image and Ki image corresponding to the region of interest, and use the PET image to perform curve fitting on the TAC data of each organ, and use the fitting residual to characterize the metabolic disturbance of each organ; A whole-body organ metabolic network is constructed based on the physiological information, clinical pathological information, daily habit information, metabolic characteristics and metabolic disturbances. The metabolic associations between various organs under specific diseases are mined through the whole-body organ metabolic network, and whole-body organ metabolic analysis is performed on the subjects.

2. The whole body organ metabolic network analysis method according to claim 1, characterized in that: The collecting of the PET image of the subject and obtaining the SUV image, Ki image and TAC data according to the PET image are specifically as follows: Whole-body PET imaging of the subject was performed using a 2-meter PET device to collect static PET images and dynamic PET images of the subject, and SUV images, Ki images, and TAC data were obtained through the static PET images and dynamic PET images.

3. The whole body organ metabolic network analysis method according to claim 2, characterized in that: The collection of the subject's physiological information, clinical pathological information, and daily habit information is specifically as follows: The physiological information includes gender, age, weight, height and BMI information, the clinical pathological information includes cytology, histology and serology medical examination information, and the daily habit information includes smoking history and secondhand smoke environment information.

4. The whole body organ metabolic network analysis method according to any one of claims 1 to 3, characterized in that: The performing of region of interest analysis on the organs of the subject according to the disease type, before selecting the region of interest related to the disease type, also includes: A CT image of a subject is acquired based on a CT device, the subject's systemic organs are segmented using the CT image, and the segmentation mask of the systemic organs is adapted to the PET image.

5. The whole body organ metabolic network analysis method according to claim 4, characterized in that: The method includes performing region of interest analysis on the organs of the subject according to the disease type, selecting a region of interest related to the disease type, extracting metabolic features based on the PET image, SUV image and Ki image corresponding to the region of interest, and performing curve fitting on the TAC data of each organ using the PET image, and using the fitting residual to characterize the metabolic disturbance of each organ, specifically: A region of interest analysis is performed on the segmented whole body organs according to the disease type, and the region of interest related to the disease type is selected. Metabolic features are extracted based on the segmentation mask of the region of interest and the adapted PET image, SUV image, and Ki image. The extracted metabolic features include mean SUV, max SUV, mean Ki, and max Ki. The dynamic PET image is used to perform curve fitting on the TAC data of each organ, and the fitting residual is used to characterize the metabolic disturbance in different organs.

6. The whole body organ metabolic network analysis method according to claim 5, characterized in that: The whole body organ metabolic network is constructed based on physiological information, clinical pathological information, daily habit information, metabolic characteristics and metabolic disturbances, and the metabolic associations between various organs under specific diseases are mined through the whole body organ metabolic network to perform whole body organ metabolic analysis on the subject, including: The whole body organ metabolic network includes an individual abnormal metabolic network and a group abnormal metabolic network. The individual abnormal metabolic network is constructed by: establishing an individual reference network based on the metabolic characteristics of N healthy control groups, adding a subject to the healthy control group to form an individual subject network; extracting individual difference values ​​between the individual subject network and the reference network, performing a Z-score standardization operation on the individual difference values, and performing a thresholding process on the individual difference values ​​after the standardization operation, and selecting individual difference values ​​that are less than and greater than a set confidence interval as significant abnormal metabolism in the individual subject network.

7. The whole body organ metabolic network analysis method according to claim 6, characterized in that: The whole body organ metabolic network is constructed based on physiological information, clinical pathological information, daily habit information, metabolic characteristics and metabolic disturbances, and the metabolic associations between various organs under specific diseases are mined through the whole body organ metabolic network to perform whole body organ metabolic analysis on the subject, and also includes: The method for constructing the reference network of the abnormal metabolic network of the group includes: establishing a group reference network based on the metabolic characteristics of N healthy control groups, and establishing a group subject network based on the metabolic characteristics of the entire disease group; extracting the group difference value between the group subject network and the group reference network, and analyzing the abnormal metabolic association between organs / systems according to the group difference value; the abnormal metabolic association analysis process includes: classifying the main organs in the whole body organs, and dividing the whole body organs into nervous system, respiratory system, circulatory system, digestive system, endocrine system, immune system, urinary system and motor system according to physiological characteristics, drawing the association map between organs / systems according to the main organ classification results and the system division results, analyzing the correlation and significance between organs / systems according to the association map, and quantifying and visualizing the organ metabolic association of the group.

8. A whole body organ metabolic network analysis device, characterized in that: include: Data acquisition module: used to acquire PET images of subjects, obtain SUV images, Ki images and TAC data based on the PET images, and collect physiological information, clinical pathological information and daily habit information of subjects; Feature extraction module: used to analyze the regions of interest of the subject's organs according to the disease type, select the regions of interest related to the disease type, and extract metabolic features based on the PET images, SUV images and Ki images corresponding to the regions of interest, and use the PET images to perform curve fitting on the TAC data of each organ, and use the fitting residuals to characterize the metabolic disturbances in different organs; Metabolic analysis module: used to construct a whole-body organ metabolic network based on the physiological information, clinical pathological information, daily habit information, metabolic characteristics and metabolic disturbances, and to mine the metabolic associations between organs under specific diseases through the whole-body organ metabolic network to perform whole-body organ metabolic analysis on the subjects.

9. A device, characterized in that: The device comprises a processor and a memory coupled to the processor, wherein: The memory stores program instructions for implementing the whole body organ metabolic network analysis method according to any one of claims 1 to 7; The processor is used to execute the program instructions stored in the memory to control the whole-body organ metabolic network analysis method.

10. A storage medium, characterized in that: The method stores program instructions executable by a processor, wherein the program instructions are used to execute the whole-body organ metabolic network analysis method according to any one of claims 1 to 7.

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