Positron emission tomography dose monitoring method, apparatus and device

Abstract

The application provides a positron emission tomography dose monitoring method, device and equipment, which can be applied to the fields of physics and computers. The method comprises the following steps: determining the density of different elements in each voxel based on the dual-energy CT image data of an irradiated object, so as to obtain element density distribution data at the voxel level; the dual-energy CT image comprises at least two groups of CT images obtained under different effective energy conditions; constructing a material model according to the element density distribution data, wherein the material model is used to quantify the distribution of each element in the voxel and the nuclear reaction characteristics; performing Monte Carlo simulation based on the material model, obtaining positron emitting radionuclide distribution data by simulating the nuclear reaction process of heavy ions in the irradiated object; and predicting positron emission tomography activity distribution data according to the positron emitting radionuclide distribution data; and the positron emission tomography activity distribution data is used to be compared with a measured positron emission tomography image, so as to verify or correct the treatment parameters.

Description

Technical Field

[0001] This application relates to the fields of physics and computer science, specifically to the application of physics and computer technology in the field of radiomedicine, and more specifically to a positron emission tomography dose monitoring method, apparatus, and equipment. Background Technology

[0002] Heavy ion radiotherapy, with its prominent Bragg peak and high biological efficacy, holds significant value in precise tumor radiotherapy. However, the actual range and dose distribution of heavy ions within the patient's body are highly dependent on the physical and chemical composition of the tissues. Range deviations can lead to under-irradiation of the tumor or over-irradiation of normal tissues. Therefore, achieving accurate and reliable online or near-online dose monitoring during heavy ion therapy is one of the key technical challenges in the field.

[0003] PET imaging-based dose monitoring is a widely used method for validating heavy ion therapy in clinical practice and research. This method detects positron-emitting radionuclides (such as...) produced by the nuclear reaction between heavy ions and human tissue. 11 C 15 O、 13 (N, etc.), and compare the activity distribution measured by PET with the predicted distribution obtained by Monte Carlo simulation to verify the range and dose distribution. Existing dose monitoring methods based on PET imaging cannot accurately reflect the true elemental composition of tissues and voxel-level elemental differences, leading to deviations in subsequent simulations and predictions, and affecting the accuracy of range and dose distribution judgments. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for positron emission tomography dose monitoring.

[0005] According to a first aspect of this application, a positron emission tomography (PET) dose monitoring method is provided, comprising: determining the density of different elements in each voxel based on dual-energy CT image data of the irradiated object to obtain voxel-level elemental density distribution data; the dual-energy CT images include at least two sets of CT images obtained under different effective energy conditions; constructing a material model based on the elemental density distribution data, the material model being used to quantify the distribution of each element in the voxel and the nuclear reaction characteristics; performing Monte Carlo simulation based on the material model to obtain positron-emitting radionuclide distribution data by simulating the nuclear reaction process of heavy ions in the irradiated object; predicting positron emission tomography activity distribution data based on the positron-emitting radionuclide distribution data; and comparing the positron emission tomography activity distribution data with measured positron emission tomography images to verify or correct treatment parameters.

[0006] According to an embodiment of this application, determining the density of different elements in each voxel based on dual-energy CT image data of the irradiated object to obtain voxel-level element density distribution data includes: preprocessing the dual-energy CT image data; inputting the preprocessed dual-energy CT image data into a preset element decomposition model so that the element decomposition model can calculate the density of each element in each voxel to obtain voxel-level element density distribution data; the element decomposition model is any one of a physics-driven model, a statistical learning model, or a machine learning model.

[0007] According to an embodiment of this application, a material model is constructed based on element density distribution data, including: for each voxel, representing the voxel as a combination of multiple element units according to the element density distribution of the voxel; assigning corresponding nuclear reaction parameters to each element unit to construct a nuclear reaction modeling unit, wherein the nuclear reaction modeling unit is composed of nuclear reaction modeling units of multiple elements in parallel, and each nuclear reaction modeling unit corresponds to the nuclear reaction cross-sectional parameters, reaction channel type and positron radioactivity of the element.

[0008] According to an embodiment of this application, Monte Carlo simulation is performed based on a material model to obtain positron-emitting radionuclide distribution data by simulating the nuclear reaction process of heavy ions in the irradiated object. This includes: simulating the heavy ion transport process according to preset heavy ion transport parameters; calculating the probability information of the nuclear reaction between heavy ions and the voxel for each interaction event occurring within the voxel by calling the material model; and simulating the positron-emitting radionuclide generation process based on the probability information to obtain positron-emitting radionuclide distribution data.

[0009] According to an embodiment of this application, the method further includes: for each interaction event occurring within a voxel, calling the nuclear reaction modeling unit corresponding to the element in the material model according to the element type within the voxel, and calculating the probability of a nuclear reaction between heavy ions and the element based on the nuclear reaction modeling unit; and simulating the generation process of the positron-emitting radionuclides corresponding to each element according to the probability of a nuclear reaction between each element, thereby obtaining the positron-emitting radionuclide distribution data of each voxel.

[0010] According to embodiments of this application, the method further includes: grouping each element into nuclear reaction sensitivity groups based on the interfacial characteristics of different elements in heavy-ion nuclear reactions; calculating the type and probability of each element reacting with heavy ions for multiple elements in each sensitivity group; counting the number of radionuclides generated by each element according to the reaction type and calculating the contribution information of each element to the positron emission tomography image based on the number of radionuclides.

[0011] According to an embodiment of this application, the method further includes: assessing the difference between the actually measured positron emission tomography (PET) image and the predicted PET activity distribution data; and correcting treatment parameters if the difference exceeds a preset threshold; the treatment parameters include at least one of range parameters, dose distribution parameters, and nuclear reaction parameters.

[0012] According to an embodiment of this application, spatial registration is performed between the actually measured positron emission tomography (PET) image and the predicted PET activity distribution data, and the difference between the two is evaluated. This includes: weighting the voxel-level activity signal in the PET signal based on contribution information to obtain weighted signal components corresponding to different elements; comparing the weighted signal components with the PET activity distribution data to calculate the PET signal deviation distribution.

[0013] According to a second aspect of this application, a positron emission tomography (PET) dose monitoring device is provided, comprising: a determination module for determining the density of different elements in each voxel based on dual-energy CT image data of the irradiated object, thereby obtaining voxel-level elemental density distribution data; the dual-energy CT images include at least two sets of CT images obtained under different effective energy conditions; a construction module for constructing a material model based on the elemental density distribution data, the material model being used to quantify the distribution of each element in the voxel and the nuclear reaction characteristics; a simulation module for performing Monte Carlo simulation based on the material model, obtaining positron-emitting radionuclide distribution data by simulating the nuclear reaction process of heavy ions in the irradiated object; and a prediction module for predicting positron emission tomography activity distribution data based on the positron-emitting radionuclide distribution data; the positron emission tomography activity distribution data being used to compare with measured positron emission tomography images to verify or correct treatment parameters.

[0014] According to a third aspect of this application, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method.

[0015] According to a fourth aspect of this application, a computer-readable storage medium is provided that stores a computer program or instructions thereon, characterized in that the computer program or instructions, when executed by a processor, implement the steps of the method described above. Attached Figure Description

[0016] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0017] Figure 1A schematic diagram illustrating the system architecture of a positron emission tomography dose monitoring method according to an embodiment of this application is shown.

[0018] Figure 2 A flowchart illustrating positron emission tomography dose monitoring according to an embodiment of this application is shown schematically.

[0019] Figure 3 This illustration schematically shows a flowchart of constructing a material model based on elemental density distribution data according to an embodiment of this application;

[0020] Figure 4 This illustration schematically shows a flowchart of obtaining positron-emitting radionuclide distribution data through Monte Carlo simulation based on a material model according to an embodiment of this application.

[0021] Figure 5 A flowchart illustrating the contribution information of each element to a positron emission tomography (PET) image is shown schematically according to an embodiment of this application.

[0022] Figure 6 A flowchart illustrating the correction of treatment parameters based on positron emission tomography (PET) radionuclide distribution data according to an embodiment of this application is shown schematically.

[0023] Figure 7 A schematic diagram illustrating the structure of a positron emission tomography dose monitoring device according to an embodiment of this application is shown.

[0024] Figure 8 A block diagram of an electronic device for a positron emission tomography dose monitoring method according to an embodiment of this application is shown schematically. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of a feature, step, operation, and / or component, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0027] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0028] In the description of this application, it should be understood that the terms "longitudinal", "length", "circumferential", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the subsystem or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0029] Throughout the accompanying drawings, identical elements are represented by the same or similar reference numerals. Conventional structures or configurations have been omitted where they may cause confusion in understanding this application. Furthermore, the shapes, dimensions, and positional relationships of the components in the drawings do not reflect actual size, scale, or actual positional relationships. Additionally, any reference symbols placed within parentheses should not be construed as limiting.

[0030] Similarly, to simplify this application and aid in understanding one or more of the various disclosed aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. The use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicates that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0031] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0032] In the technical solution of this application, the collection, storage, use, processing, transmission, provision, disclosure, and application of user personal information all comply with relevant laws and regulations, necessary confidentiality measures have been taken, and they do not violate public order and good morals. In the technical solution of this application, user authorization or consent has been obtained before acquiring or collecting user personal information.

[0033] This application provides a positron emission tomography dose monitoring method, apparatus, and device. Before introducing the technical solution provided by this application, the relevant technologies involved in this application will be described first.

[0034] Dual-energy CT scans simultaneously emit two different types of X-rays and use the differences in the absorption characteristics of materials to high- and low-energy photons to estimate the composition ratio of tissue components.

[0035] Positron emission tomography (PET) is a tomographic image that reflects the metabolic activity of tissues by injecting a tracer labeled with a short-lived radionuclide to detect gamma photons produced by the annihilation of positrons and electrons.

[0036] During a PET scan, when a positron and an electron annihilate each other, a pair of gamma photons with the same energy but opposite directions are generated. The detector receives these gamma photons and converts them into electrical signals, which constitute the positron emission tomography (PET) signal. The PET signal contains information such as the arrival time, location, and energy of the gamma photons, and is the raw data for PET imaging.

[0037] Positron emission tomography (PET) images are visualizations obtained through a series of complex processing and reconstructions of PET signals. These processing methods include signal filtering, correction (such as random coincidence correction and scattering correction), and the application of image reconstruction algorithms (such as filtered back projection algorithms and iterative reconstruction algorithms), ultimately converting the raw signal data into two-dimensional or three-dimensional images that reflect the metabolic state of internal tissues in the human body.

[0038] Existing PET dose monitoring methods are typically based on single-energy CT images. They convert CT values ​​into parameters of a limited number of tissues or equivalent materials using look-up tables (LUTs) or equivalent material mapping methods, and then input these parameters into a Monte Carlo program to simulate nuclear reactions and PET signals. However, this type of method has the following drawbacks:

[0039] 1. Single-energy CT cannot accurately reflect the true elemental composition of tissues. Different elements (such as carbon, oxygen, nitrogen, calcium, etc.) have significantly different cross-sectional characteristics in heavy-ion nuclear reactions.

[0040] 2. Equivalent material modeling can mask voxel-level elemental differences, leading to errors in PET nuclide generation prediction;

[0041] 3. Existing methods are difficult to introduce element-level physical constraints in the PET signal inversion process, which limits the accuracy of range and dose correction.

[0042] Therefore, there is an urgent need for a new PET dose monitoring technology that can introduce more accurate tissue element information while maintaining compatibility with existing PET imaging procedures, thereby improving the accuracy and reliability of heavy ion therapy dose monitoring.

[0043] Figure 1 A schematic diagram of the system architecture of a positron emission tomography dose monitoring method according to an embodiment of this application is shown.

[0044] like Figure 1 As shown, application scenario 100 according to this embodiment may include terminal devices 101, 102, and 103, network 104, and server 105. Network 104 is used as a medium to provide a communication link between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0045] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as physics simulation applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0046] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0047] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using terminal devices 101, 102, and 103 (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0048] It should be noted that the positron emission tomography (PET) dose monitoring method provided in this application embodiment can generally be executed by server 105. Correspondingly, the PET dose monitoring device provided in this application embodiment can generally be located in server 105. The PET dose monitoring method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the PET dose monitoring device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0049] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0050] The following will be based on Figure 1 The described scene, through Figures 2-6 The positron emission tomography dose monitoring method of the disclosed embodiments is described in detail.

[0051] Figure 2 A flowchart illustrating positron emission tomography dose monitoring according to an embodiment of this application is shown schematically.

[0052] like Figure 2 As shown, the positron emission tomography dose monitoring method of this embodiment includes operations S210 to S240.

[0053] In operation S210, the density of different elements in each voxel is determined based on the dual-energy CT image data of the irradiated object, and voxel-level element density distribution data is obtained.

[0054] In some embodiments, dual-energy CT images include at least two sets of CT images obtained under different effective energy conditions. A dual-energy CT system can be used to scan an irradiated subject to obtain at least two sets of CT images under different effective energy conditions. By analyzing the attenuation differences of X-rays at different energies, dual-energy CT can more accurately distinguish tissue types and elemental composition.

[0055] A suitable element decomposition model can be selected based on the actual situation. The element decomposition model can be used to calculate the different element densities in each voxel in the dual-energy CT image, and voxel-level element density distribution data can be obtained. The density distribution data of each voxel can include, for example, the type and concentration of the element.

[0056] In operation S220, a material model is constructed based on element density distribution data. The material model is used to quantify the distribution of each element in the voxel and the characteristics of nuclear reactions.

[0057] In some embodiments, relevant characteristic data of different elements (carbon, oxygen, nitrogen, calcium, etc.) in heavy-ion nuclear reactions, such as nuclear reaction cross-sections and reaction products, can be obtained through experimental measurements, theoretical calculations, or by consulting relevant nuclear physics databases. A material model is then constructed based on the elemental density distribution data and the nuclear reaction characteristics of the elements, where different elements are treated as independent nuclear reaction pathways within the material model.

[0058] In S230 operation, Monte Carlo simulations were performed based on the material model to obtain positron-emitting radionuclide distribution data by simulating the nuclear reaction process of heavy ions in the irradiated object.

[0059] In some embodiments, the transport process of heavy ions in the irradiated object can be simulated using the Monte Carlo method based on a material model (such as collisions between ions and tissue atoms, energy deposition, nuclear reactions (such as fragmentation), and the generation and distribution of positron-emitting radionuclides). The location, type, and information of the generated positron-emitting radionuclides for each nuclear reaction are recorded. After the simulation, the simulation results are statistically analyzed, and the distribution data of positron-emitting radionuclides are determined based on their generation location and quantity.

[0060] During the S240 operation, positron emission tomography (PET) activity distribution data are predicted based on positron emission tomography (PET) radionuclide distribution data.

[0061] In some embodiments, positron-emitting radionuclides decay over time. The positron emission tomography (PET) activity distribution data for each voxel can be calculated based on the PET nuclide distribution data. This allows for the prediction of the PET activity distribution of the entire irradiated subject based on the PET activity data from multiple voxels. The predicted PET activity distribution data is then compared with measured PET images to verify or correct treatment parameters.

[0062] For example, the predicted PET activity data can be compared and analyzed with the measured PET image data. Based on the results of the comparison and analysis, it can be determined whether the treatment parameters are accurate or have deviations. Then, the treatment parameters can be corrected based on the magnitude and distribution of the deviations. Treatment parameters may include range parameters, dose parameters, nuclear reaction parameters, etc.

[0063] This application's embodiments determine voxel-level elemental density distribution using dual-energy CT image data, enabling a more accurate understanding of the elemental composition within the irradiated object. Different elements exhibit varying nuclear reaction characteristics with heavy ions; accurate elemental information provides a reliable foundation for subsequent simulations and predictions, thereby improving the accuracy of dose monitoring. Monte Carlo simulations based on material models can realistically simulate the nuclear reaction process of heavy ions in the irradiated object, more accurately predicting positron emission tomography (PET) activity distribution and providing more precise data support for dose monitoring.

[0064] Comparing the predicted positron emission tomography (PET) activity distribution data with the measured images can quantify the differences between the two, providing a clear basis for adjusting treatment parameters, enabling timely detection and adjustment of deviations in treatment parameters, improving treatment efficacy, and reducing damage to normal tissues.

[0065] According to one embodiment of this application, determining the density of different elements in each voxel based on dual-energy CT image data of an irradiated object to obtain voxel-level elemental density distribution data includes: preprocessing the dual-energy CT image data; and inputting the preprocessed dual-energy CT image data into a preset elemental decomposition model so that the elemental decomposition model can calculate the density of each element in each voxel to obtain voxel-level elemental density distribution data. The elemental decomposition model can be any one of a physics-driven model, a statistical learning model, or a machine learning model.

[0066] In some embodiments, preprocessing may include, for example, denoising, image registration, normalization, and voxel alignment. For instance, filtering algorithms or deep learning denoising models can be used to reduce image noise and improve the signal-to-noise ratio. If motion artifacts exist in dual-energy CT images, rigid or non-rigid registration algorithms can be used to align the two sets of images to ensure voxel spatial correspondence. CT values ​​in dual-energy CT images can be converted into linear attenuation coefficients to eliminate the influence of device differences on subsequent decomposition.

[0067] In some embodiments, preprocessed dual-energy CT image data can be input into a preset element decomposition model. The element decomposition model calculates the density of different elements in each voxel based on voxel information in the image and using pre-defined algorithms or trained model parameters. For example, when the element decomposition model is a physics-driven model, element density can be calculated by solving a system of linear equations based on the physical equations of X-ray attenuation. When the element decomposition model is a statistical learning model, 3D U-Net or ResNet algorithms can be used for element density calculation.

[0068] Figure 3 The flowchart illustrating the construction of a material model based on elemental density distribution data according to an embodiment of this application is shown in the illustration.

[0069] like Figure 3 As shown, this embodiment constructs a material model based on element density distribution data, including operations S310~S320.

[0070] In operation S310, for each voxel, the voxel is represented as a combination of multiple element units based on the element density distribution of the voxel.

[0071] In operation S320, corresponding nuclear reaction parameters are assigned to each element unit to construct a nuclear reaction modeling unit. The nuclear reaction modeling unit is composed of nuclear reaction modeling units of multiple elements in parallel. Each nuclear reaction modeling unit corresponds to the nuclear reaction cross section parameters, reaction channel type and positron radioactivity of the element.

[0072] In some embodiments, when a heavy particle beam passes through a voxel, it reacts with each element in the voxel simultaneously, rather than reacting with only a single "equivalent material". For each voxel, based on its elemental density distribution data, the components contained within the voxel and their relative abundances are analyzed.

[0073] Based on the relative abundance of elements, a voxel is represented as a combination of multiple elemental units. For example, if a voxel contains elements A and B, and element A accounts for 70% of the density while element B accounts for 30%, then this voxel can be approximated as being composed of 70% element A units and 30% element B units. Here, an elemental unit can be understood as the smallest constituent unit with the properties of a single element, used as the foundation for constructing material models.

[0074] Each element unit is assigned corresponding nuclear reaction parameters. These parameters include nuclear reaction cross-section parameters, which describe the probability of a specific element undergoing a certain nuclear reaction. Different nuclear reactions (such as neutron capture, fission, etc.) correspond to different cross-section parameters, and the cross-section parameters are usually related to factors such as the energy of the incident particle; reaction pathway type, which refers to the different reaction paths that an element may follow when undergoing a nuclear reaction. For example, some elements may undergo (n, γ) reactions (neutron capture reactions) or (n, f) reactions (fission reactions), etc.; and positron radioactivity. For some nuclear reaction processes that produce positrons, it is necessary to determine the positron radioactivity characteristics of the element unit, such as the probability of positron emission and energy distribution.

[0075] A nuclear reaction modeling unit is composed of parallel nuclear reaction modeling units of multiple elements. That is, for a nuclear reaction modeling unit corresponding to a voxel containing multiple elements, it simultaneously contains multiple nuclear reaction modeling sub-units corresponding to different elements. These sub-units work in parallel, simulating the nuclear reaction process according to their respective assigned nuclear reaction parameters. For example, when simulating the interaction between a neutron and a voxel, the neutron may simultaneously undergo different types of nuclear reactions with elements A and B within the voxel. The corresponding two nuclear reaction modeling sub-units will independently calculate the probability of the neutron reacting with its respective element, the reaction products, and other information.

[0076] For example, the carbon density in a voxel (1 mm³) oxygen density The voxel contains carbon units and oxygen units, where the carbon unit has the following density: nuclear reaction cross section (like (cross section), positron generation probability (like (Probability of generation). Oxygen unit: density nuclear reaction cross section σ O (like (cross section), positron generation probability (like (The generation probability). In this voxel, all element units exist in parallel, together forming a voxel-level material structure. Each element's nuclear reaction modeling unit corresponds to its own nuclear reaction cross-sectional parameters, reaction channel type, and positron-emitting radionuclide generation probability.

[0077] This application's embodiments, by representing voxels as combinations of multiple elemental units, more accurately describe the distribution of elements within a material, thus more realistically reflecting the material's physical properties. Assigning detailed nuclear reaction trees to each elemental domain and constructing parallel nuclear reaction modeling units enables the model to simultaneously handle different nuclear reaction processes of multiple elements, effectively improving the efficiency of nuclear reaction simulation.

[0078] Figure 4 The flowchart illustrating the process of obtaining positron-emitting radionuclide distribution data through Monte Carlo simulation based on a material model according to an embodiment of this application is shown.

[0079] like Figure 4 As shown, this embodiment uses a material model to perform Monte Carlo simulation to obtain positron-emitting radionuclide distribution data, including operations S410~S430.

[0080] When operating S410, the heavy ion transport process is simulated according to the preset heavy ion transport parameters.

[0081] When operating S420, for each interaction event occurring within a voxel, the material model is invoked to calculate the probability information of a nuclear reaction between heavy ions and that voxel.

[0082] By operating S430, the generation process of positron-emitting radionuclides is simulated based on probability information to obtain positron-emitting radionuclide distribution data.

[0083] In some embodiments, during heavy ion transport, for the interaction events of particles within each voxel, the probability of heavy ions reacting with each element is independently calculated based on the nuclear reaction modeling unit corresponding to different elements within that voxel; and based on the nuclear reaction probability, the generation process of positron-emitting radionuclides corresponding to each element is simulated, thereby obtaining the voxel-level positron-emitting radionuclide generation distribution.

[0084] Heavy ion transport parameters can include, for example, the type of heavy ion (such as carbon ions, oxygen ions, etc.), initial energy, incident direction, beam intensity, and other transport parameters. These parameters affect the trajectory of heavy ions and energy deposition in the irradiated object.

[0085] The Monte Carlo method is used to track the motion of heavy ions. Starting from the incident point of the heavy ion, its motion direction and energy loss in the next small step are randomly determined according to a certain probability distribution based on its initial energy and direction. During transport, the heavy ion will undergo various interactions with atoms in the material, such as elastic scattering and inelastic scattering, and each interaction will change the motion state of the heavy ion. The above process is repeated until the energy of the heavy ion decreases to a certain level (e.g., below a certain threshold) or leaves the irradiated object region, thus completing the simulation of the transport process of a single heavy ion.

[0086] When a heavy ion is transported to a voxel location, a pre-built material model is invoked for any interaction events occurring within that voxel. The material model calculates the probability of a nuclear reaction between the heavy ion and different elements within that voxel, based on the nuclear reaction parameters (such as nuclear reaction cross-sectional parameters) of each elemental unit within the voxel, combined with information such as the current energy of the heavy ion.

[0087] For example, if a voxel contains elements A and B, the material model will calculate the probability values ​​of specific nuclear reactions (such as fission reactions, capture reactions, etc.) between heavy ions and elements A and B, respectively. This probability information is the basis for subsequent simulations of positron-emitting radionuclide generation.

[0088] Based on the calculated nuclear reaction probability information, a random number generation method is used to determine whether a nuclear reaction will occur and what type of nuclear reaction will occur. If a nuclear reaction occurs, the products generated are further determined based on the type of nuclear reaction and the reaction pathway, including possible positron-emitting radionuclides.

[0089] For each generated positron-emitting radionuclide, its location information within the irradiated object is recorded. Through simulation and statistics of a large number of heavy ions, the number of positron-emitting radionuclides generated in each voxel is gradually accumulated, ultimately obtaining the distribution data of positron-emitting radionuclides within the irradiated object.

[0090] The embodiments of this application determine the interaction between heavy ions and the elements of each voxel through material models and Monte Carlo simulations, so as to provide accurate information on the generation and distribution of positron-emitting radionuclides. This more realistically and accurately reflects the actual situation of heavy ions initiating nuclear reactions in irradiated objects to generate positron-emitting radionuclides, and provides reliable data support for subsequent research and applications.

[0091] According to one embodiment of this application, for each interaction event occurring within a voxel, the nuclear reaction modeling unit corresponding to the element in the material model is called according to the element type within the voxel, and the probability of a nuclear reaction between heavy ions and the element is calculated based on the nuclear reaction modeling unit; the generation process of the positron-emitting radionuclides corresponding to each element is simulated according to the probability of a nuclear reaction between each element, and the positron-emitting radionuclide distribution data of each voxel is obtained.

[0092] According to one embodiment of this application, when a heavy ion is transported to a voxel location, an interaction event is determined to have occurred within that voxel. Based on the types of elements contained within the voxel, the corresponding nuclear reaction modeling unit in the material model is invoked. Each nuclear reaction modeling unit calculates the probability of a specific nuclear reaction between the heavy ion and the element based on its assigned nuclear reaction parameters (such as nuclear reaction cross-sectional parameters) and the current energy of the heavy ion. For example, if the voxel contains elements A and B, the probability values ​​of a certain nuclear reaction (such as fission reaction) between the heavy ion and elements A and B are calculated respectively. Based on the calculated probabilities of nuclear reactions for each element, a random number generation method is used to determine whether a nuclear reaction occurs and what type of nuclear reaction occurs. For each element, a random number is generated; if the random number is less than the corresponding nuclear reaction probability, a nuclear reaction is determined to have occurred. If a nuclear reaction occurs, the generated products are further determined based on the type and reaction pathway of the nuclear reaction, including potentially generated positron-emitting radionuclides. For example, some nuclear reactions may generate radioactive isotopes with positron emission characteristics. For each generated positron-emitting radionuclide, its position within the irradiated object is recorded, i.e., its voxel location. Through simulation and statistical analysis of a large number of heavy ions, the number of positron-emitting radionuclides generated in each voxel is gradually accumulated. The number of positron-emitting radionuclides generated in each voxel is statistically analyzed and compiled to obtain the final positron-emitting radionuclide distribution data for each voxel.

[0093] This application's embodiments calculate the probability of nuclear reactions between heavy ions and each element by calling the corresponding nuclear reaction modeling unit based on the element type within the voxel, fully considering the unique nuclear reaction characteristics of different elements. Precise calculations for each element avoid errors that might arise from using a uniform model, thus more accurately reflecting the actual interaction between heavy ions and matter. Simulating the generation process of positron-emitting radionuclides based on the accurate nuclear reaction probabilities of each element makes the quantity and distribution of generated nuclides more consistent with real physical processes, providing a reliable foundation for subsequent nuclide generation simulations, and thereby improving the accuracy of the entire simulation process in predicting the distribution of positron-emitting radionuclides.

[0094] The irradiated object is divided into voxels, and simulations are performed separately for different elements within each voxel. This fully considers the non-uniformity of elemental composition at the microscale of the irradiated object, thereby enabling independent simulation of nuclear reactions and nuclide formation for each element within each voxel, and obtaining more refined positron-emitting radionuclide distribution data.

[0095] Figure 5 A flowchart illustrating the contribution information of each element to a positron emission tomography (PET) image is shown schematically according to an embodiment of this application.

[0096] like Figure 5 As shown, this embodiment calculates the contribution information of each element to the positron emission tomography (PET) image, including operations S510 to S530.

[0097] In S510, elements are grouped according to their interfacial characteristics in heavy-ion nuclear reactions to assess their nuclear reaction sensitivity.

[0098] In some embodiments, the heavy-ion nuclear reaction cross section is a physical quantity describing the probability of a specific nuclear reaction between an incident heavy ion and a target nucleus; its magnitude directly reflects the sensitivity of an element to nuclear reactions. For example, when light heavy ions such as carbon-12 and oxygen-16 undergo nuclear reactions with carbon and oxygen elements in biological tissues, they generate radioactive nuclides that emit positrons (such as...). , These nuclides are the main source of PET signals.

[0099] Based on the cross-sectional characteristics of different elements in heavy-ion nuclear reactions, elements can be divided into a high-sensitivity group (such as carbon and oxygen, which have large nuclear reaction cross sections and high positron-emitting nuclide yields), a medium-sensitivity group (such as nitrogen), and a low-sensitivity group (such as calcium).

[0100] In operation S520, for multiple elements in each sensitivity group, the type and probability of each element reacting with heavy ions are calculated.

[0101] In some embodiments, for each element in a sensitivity group, a suitable theoretical model and calculation method are selected to calculate the type and probability of the element reacting with heavy ions. The models and / or calculation methods used for different sensitivity groups may be different. By calculating for each element in each sensitivity group, the possible reaction types (such as elastic scattering, inelastic scattering, fission, fusion, etc.) between the element and heavy ions are obtained, as well as the probability of each reaction type occurring.

[0102] During operation S530, the number of radionuclides generated by each element is counted according to the reaction type, and the contribution information of each element to the positron emission tomography (PET) image is calculated based on the number of radionuclides.

[0103] In some embodiments, based on the calculated type and probability of each element reacting with heavy ions, and combined with the heavy ion incident flux (the number of heavy ions incident on a unit area per unit time), the number of radionuclides generated by each element after the reaction is counted. For example, if the probability of element B reacting with heavy ions to generate radionuclide C is 20%, and the heavy ion incident flux is... If the number of atoms of element B per unit area is Then, the number of radioactive nuclides C generated by element B per unit time is .

[0104] Based on the statistically obtained number of radionuclides generated by each element and their contribution mechanisms to the PET signal, the contribution information of each element to the PET signal is calculated. This contribution information can be expressed using relative intensity, percentage, or other suitable metrics. For example, calculations show that the total contribution of the radionuclides generated by element D to the PET signal accounts for 15% of the total contribution from all elements.

[0105] This application fully considers the differences in cross-sectional characteristics of different elements in heavy-ion nuclear reactions. By classifying elements through sensitivity grouping, it can more accurately describe the interaction process between each element and heavy ions, avoiding simulation errors caused by neglecting differences in elemental characteristics, thereby improving the accuracy of nuclear reaction type and probability calculations. Based on accurate reaction type and probability calculations, the number of radionuclides generated by each element can be more accurately counted. The number of radionuclides is an important factor affecting the intensity of PET signals. Accurate statistical results provide a reliable basis for subsequent calculations of PET signal contributions, thus improving the accuracy of PET signal prediction in the entire simulation process.

[0106] Grouping elements according to their nuclear reaction sensitivities allows for similar computational methods and parameter settings within each group, reducing variables and complexity in the computation process. Compared to performing complex calculations on each element individually, grouped computation significantly improves computational efficiency and shortens computation time, especially when dealing with large numbers of elements and complex nuclear reaction systems. Furthermore, since the calculations between different sensitivity groups are relatively independent, this approach has good parallel computing potential. Multi-core processors or distributed computing systems can be used to simultaneously calculate different groups, further accelerating computation speed, improving the utilization efficiency of computing resources, and meeting the demand for rapid solutions to large-scale simulation problems.

[0107] Figure 6 A flowchart illustrating the correction of treatment parameters based on positron emission tomography (PET) radionuclide distribution data according to an embodiment of this application is shown.

[0108] like Figure 6 As shown, the correction of treatment parameters based on positron emission tomography (PET) radionuclide distribution data in this embodiment includes operations S610~S620.

[0109] In operation S610, the actual measured positron emission tomography (PET) images are spatially registered with the predicted PET activity distribution data, and the differences between the two are evaluated.

[0110] In some embodiments, a PET scanner can be used to scan the target object to obtain the actual measured positron emission tomography (PET) signal (hereinafter referred to as the PET measurement signal). The PET measurement signal reflects the distribution of photons generated by positron annihilation within the research object, and can intuitively present the distribution location and intensity information of radionuclides within the body. The PET measurement signal can be considered as a superposition of multiple positron-emitting radionuclides generated by nuclear reactions between heavy ions and different elements within the voxel. Due to significant differences in the nuclear reaction cross-section, reaction pathway, and product nuclides among different elements, their contribution to the PET signal also varies significantly.

[0111] Based on the characteristics of the PET measurement signals and the predicted positron emission tomography (PET) activity distribution data, a suitable spatial registration method can be selected. Common registration methods include image grayscale-based methods (such as mutual information and cross-correlation methods) and feature-based methods (such as point feature, line feature, and area feature registration). Mutual information methods measure the similarity between two images by calculating their mutual information values, thereby finding the optimal registration transformation parameters. Feature-based registration methods first extract feature points or structures from the images and then match these features to achieve image registration. Using the selected registration method, the actual PET image and the predicted activity distribution data are spatially registered to achieve optimal alignment in spatial location, enabling accurate subsequent assessment of their differences.

[0112] Difference assessment metrics can include, for example, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Based on these defined metrics, the difference between the actual PET image and the predicted activity distribution data can be calculated. By comparing the calculated difference value with a preset threshold, it can be determined whether the difference exceeds the allowable range. The preset threshold can be determined based on specific clinical needs, treatment precision requirements, and previous research experience.

[0113] When operating S620, if the difference exceeds a preset threshold, the treatment parameters are corrected.

[0114] In some embodiments, the treatment parameters include at least one of range parameters, dose distribution parameters, and nuclear reaction parameters. When the difference exceeds a preset threshold, the cause of the difference is analyzed in detail. Possible causes include inaccurate range parameters leading to deviations in the deposition location of radionuclides in the body, unreasonable dose distribution parameters resulting in excessively high or low local doses, and errors in nuclear reaction parameters affecting the generation and decay processes of radionuclides.

[0115] Based on the results of the difference analysis, the corresponding treatment parameters can be corrected. If the difference is mainly due to inaccurate range parameters, the range parameters can be adjusted to ensure that the radionuclide reaches the target area more accurately. If the problem lies with the dose distribution parameters, the dose distribution can be re-optimized to ensure that the target area receives sufficient dose irradiation while minimizing damage to surrounding normal tissues. For differences caused by errors in nuclear reaction parameters, the relevant parameters in the nuclear reaction model need to be corrected to improve the accuracy of radionuclide generation and decay prediction. The corrected treatment parameters will be used for subsequent treatment plan adjustments and implementation.

[0116] This application's embodiments, by registering and evaluating the differences between actual PET images and predicted activity distribution data, can promptly detect deviations during treatment. Correcting treatment parameters ensures that the radionuclide reaches the target tissue more accurately, achieving more precise localization and treatment, effectively improving treatment efficacy and safety.

[0117] Different patients have different anatomical structures, physiological functions, and radionuclide metabolism. The protocol proposed in this application can be personalized to adjust treatment parameters based on actual measurement results, better adapting to individual patient differences, improving the targeting and effectiveness of treatment, and providing patients with higher quality treatment services.

[0118] According to one embodiment of this application, spatial registration is performed between the actually measured positron emission tomography (PET) signal and the predicted PET activity distribution data, and the difference between the two is evaluated. This includes: weighting the voxel-level activity signal in the PET signal based on contribution information to obtain weighted signal components corresponding to different elements; comparing the weighted signal components with the PET activity distribution data to calculate the PET signal deviation distribution.

[0119] In some embodiments, contribution information can reflect the relative contribution weights of different elements within each voxel in the generation of positron-emitting radionuclides under heavy ion irradiation. The weight reflects the relative importance of the element in the generation of the positron signal. For example, if element A has a high density and high efficiency in generating positron-emitting radionuclides in a voxel, then element A will have a larger relative contribution weight in that voxel.

[0120] Activity signals reflect the intensity of photons generated by positron annihilation within the studied object and are related to the distribution and concentration of radionuclides. Voxel-level activity signals in PET measurements can be weighted based on the contribution information of each element. For example, the activity signal of each voxel can be assigned according to the weights of different elements to obtain weighted PET signal components corresponding to different elements. For instance, if the activity signal of a voxel is 100, with element A having a weight of 0.6 and element B having a weight of 0.4, then the weighted PET signal component corresponding to element A is 60, and that of element B is 40.

[0121] The obtained weighted PET signal components are compared with positron emission tomography (PET) activity distribution data. The PET signal deviation distribution is obtained by calculating the difference or relative error between the two. This deviation distribution reflects the degree and spatial distribution of the difference between the actual measured signal and the theoretically predicted signal. For example, at some voxel locations, the weighted PET signal components may be higher than the predicted signal, while at other locations they may be lower.

[0122] Figure 7 A schematic block diagram of a positron emission tomography dose monitoring device according to an embodiment of this application is shown.

[0123] like Figure 7 As shown, the positron emission tomography dose monitoring device 700 of this embodiment includes a determination module 710, a construction module 720, a simulation module 730, and a prediction module 740.

[0124] The determination module 710 is used to determine the density of different elements in each voxel based on the dual-energy CT image data of the irradiated object, thereby obtaining voxel-level element density distribution data; the dual-energy CT images include at least two sets of CT images obtained under different effective energy conditions. In one embodiment, the determination module 710 can be used to perform the operation S210 described above, which will not be repeated here.

[0125] The construction module 720 is used to construct a material model based on elemental density distribution data. The material model is used to quantify the distribution of each element and the nuclear reaction characteristics in the voxel. In one embodiment, the construction module 720 can be used to perform the operation S220 described above, which will not be repeated here.

[0126] The simulation module 730 is used to perform Monte Carlo simulations based on a material model, obtaining positron-emitting radionuclide distribution data by simulating the nuclear reaction process of heavy ions in the irradiated object. In one embodiment, the simulation module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0127] The prediction module 740 is used to predict positron emission tomography (PET) activity distribution data based on positron emission tomography (PET) radionuclide distribution data. The PET activity distribution data is used to compare with measured PET images to verify or correct treatment parameters. In one embodiment, the prediction module 740 can be used to perform the operation S240 described above, which will not be repeated here.

[0128] According to embodiments of this application, any plurality of modules among the determining module 710, building module 720, simulation module 730, and prediction module 740 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this application, at least one of the determining module 710, building module 720, simulation module 730, and prediction module 740 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the determining module 710, building module 720, simulation module 730, and prediction module 740 can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0129] Figure 8 A block diagram of an electronic device for a positron emission tomography dose monitoring method according to an embodiment of this application is shown schematically.

[0130] like Figure 8 As shown, an electronic device 800 according to an embodiment of this application includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage portion 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0131] RAM 803 stores various programs and data required for the operation of electronic device 800. Processor 801, ROM 802, and RAM 803 are interconnected via bus 804. Processor 801 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 802 and / or RAM 803. It should be noted that the program may also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in one or more memories.

[0132] According to embodiments of this application, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to a bus 804. The electronic device 800 may also include one or more of the following components connected to the input / output (I / O) interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output (I / O) interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0133] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0134] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0135] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, and these substitutions and modifications should all be included within the protection scope of this application.

Claims

1. A positron emission tomography (PET) dose monitoring method, characterized in that, include: The density of different elements in each voxel is determined based on the dual-energy CT image data of the irradiated object, and voxel-level element density distribution data is obtained; the dual-energy CT images include at least two sets of CT images obtained under different effective energy conditions; A material model is constructed based on the element density distribution data. The material model is used to quantify the distribution of each element in the voxel and the nuclear reaction characteristics. Monte Carlo simulations were performed based on the material model to obtain positron-emitting radionuclide distribution data by simulating the nuclear reaction process of heavy ions in the irradiated object. Based on the positron emission tomography (PET) radionuclide distribution data, positron emission tomography (PET) activity distribution data are predicted; the PET activity distribution data is used to compare with the measured PET images to verify or correct treatment parameters.

2. The method according to claim 1, characterized in that, The density of different elements in each voxel is determined based on the dual-energy CT image data of the irradiated object to obtain voxel-level elemental density distribution data, including: The dual-energy CT image data is preprocessed; The preprocessed dual-energy CT image data is input into a preset element decomposition model so that the element decomposition model can calculate the density of each element in each voxel and obtain voxel-level element density distribution data. The element decomposition model can be any one of a physics-driven model, a statistical learning model, or a machine learning model.

3. The method according to claim 1, characterized in that, The construction of the material model based on the elemental density distribution data includes: For each voxel, the voxel is represented as a combination of multiple element units according to the element density distribution of the voxel; Each element unit is assigned corresponding nuclear reaction parameters to construct a nuclear reaction modeling unit. The nuclear reaction modeling unit is composed of nuclear reaction modeling units of multiple elements in parallel. Each nuclear reaction modeling unit corresponds to the nuclear reaction cross section parameters, reaction channel type and positron radioactivity of the element.

4. The method according to claim 1, characterized in that, The Monte Carlo simulation based on the material model, by simulating the nuclear reaction process of heavy ions in the irradiated object, obtains positron-emitting radionuclide distribution data, including: The heavy ion transport process is simulated based on preset heavy ion transport parameters; For each interaction event occurring within a voxel, the material model is invoked to calculate the probability information of a nuclear reaction between heavy ions and that voxel; The generation process of positron-emitting radionuclides is simulated based on the probability information to obtain positron-emitting radionuclide distribution data.

5. The method according to claim 3 or 4, characterized in that, The method further includes: For each interaction event occurring within a voxel, the nuclear reaction modeling unit corresponding to that element in the material model is called according to the element type within the voxel, so as to calculate the probability of a nuclear reaction between heavy ions and that element based on the nuclear reaction modeling unit. Based on the probability of nuclear reactions of each element, the generation process of the corresponding positron-emitting radionuclides is simulated to obtain the positron-emitting radionuclide distribution data of each voxel.

6. The method according to claim 4, characterized in that, The method further includes: Based on the interfacial characteristics of different elements in heavy-ion nuclear reactions, the elements are grouped according to their nuclear reaction sensitivity. For each element in a sensitivity group, calculate the type and probability of each element reacting with heavy ions; The number of radionuclides generated by each element is counted according to the reaction type, and the contribution information of each element to the positron emission tomography (PET) image is calculated based on the number of radionuclides.

7. The method according to claim 1, characterized in that, The method further includes: Evaluate the difference between the actual measured positron emission tomography (PET) images and the predicted PET activity distribution data; If the difference exceeds a preset threshold, the treatment parameters should be adjusted. The treatment parameters include at least one of the following: range parameters, dose distribution parameters, and nuclear reaction parameters.

8. The method according to claim 7, characterized in that, The step of spatially registering the actually measured positron emission tomography (PET) images with the predicted PET activity distribution data and evaluating the differences between the two includes: Based on the contribution information, the voxel-level activity signal in the positron emission tomography signal is weighted and decomposed to obtain the weighted signal components corresponding to different elements. The weighted signal components are compared with the positron emission tomography (PET) activity distribution data to calculate the PET signal deviation distribution.

9. A positron emission tomography (PET) dose monitoring device, characterized in that, include: The determination module is used to determine the density of different elements in each voxel based on the dual-energy CT image data of the irradiated object, and obtain voxel-level element density distribution data; the dual-energy CT images include at least two sets of CT images obtained under different effective energy conditions; A construction module is used to construct a material model based on the element density distribution data. The material model is used to quantify the distribution of each element in the voxel and the nuclear reaction characteristics. The simulation module is used to perform Monte Carlo simulations based on the material model, and obtain positron-emitting radionuclide distribution data by simulating the nuclear reaction process of heavy ions in the irradiated object. The prediction module is used to predict positron emission tomography (PET) activity distribution data based on the positron emission tomography (PET) radionuclide distribution data; the PET activity distribution data is used to compare with the measured PET images to verify or correct treatment parameters.

10. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

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