Pen-shaped beam dose calculation method, device and electronic equipment

Abstract

The present disclosure provides a pencil beam dose calculation method, comprising: acquiring human body image data; determining a plurality of particle types for treatment; calculating an initial pencil beam dose distribution corresponding to each particle type; determining a target action layer of each dose distribution in the human body image data based on the particle type, wherein the target action layer positions corresponding to different particle types are different; adjusting each initial dose distribution based on the target action layer corresponding to each particle type to obtain a particle pencil beam dose distribution corresponding to each particle type; superimposing the particle pencil beam dose distributions corresponding to each particle type to obtain a target pencil beam dose distribution; wherein the dose proportion of the particle pencil beam dose distribution corresponding to the same particle type in the target action layer is greater than the dose proportion of the particle pencil beam dose of the particle type in other parts of the human body image data.

Description

Technical Field

[0001] This disclosure relates to the field of medical radiation, and more specifically, to a pencil beam dose calculation method, apparatus, and electronic device. Background Technology

[0002] Heavy ion or proton therapy, as an advanced radiotherapy method, has been widely used in the treatment of various malignant tumors due to its excellent dose deposition characteristics and high biological effect enhancement ratio. To achieve precise dose control and improve treatment efficacy, pencil beam dosimetry is currently widely used to plan and optimize the treatment dose distribution.

[0003] In existing technologies, pencil beam dosing calculation methods typically perform dose simulation and treatment planning for a single particle type. These methods usually predict and optimize the distribution of particle dose within patient tissues through a dose convolution model based on predefined particle physics characteristics.

[0004] However, existing treatment plans do not provide a more suitable and comprehensive approach for dealing with multi-particle situations. Summary of the Invention

[0005] In view of this, the present disclosure provides a pencil beam dose calculation method, apparatus, electronic device, medium, and program product.

[0006] One aspect of this disclosure provides a pencil beam dose calculation method, comprising: acquiring human image data; determining multiple particle types for treatment; calculating an initial pencil beam dose distribution corresponding to each particle type; determining the target action layer of each dose distribution in the human image data based on the particle type, wherein the target action layer positions are different for different particle types; adjusting each initial dose distribution based on the target action layer corresponding to each particle type to obtain a particle pencil beam dose distribution corresponding to each particle type; superimposing the particle pencil beam dose distributions corresponding to each particle type to obtain a target pencil beam dose distribution; wherein the dose proportion of the particle pencil beam dose distribution corresponding to the same particle type in the target action layer is greater than the dose proportion of the particle pencil beam dose of that particle type in other parts of the human image data.

[0007] According to embodiments of this disclosure, determining the target action layer of each dose distribution in human image data includes: determining the target action layer based on particle type and depth information in human image data, wherein the target action layer corresponding to different particle types has different depth ranges in human image data.

[0008] According to embodiments of this disclosure, determining the target layer of each dose distribution in human image data includes: determining the target layer of each dose distribution in human image data based on the energy deposition characteristics of each particle type and / or the Bragg peak position.

[0009] According to embodiments of this disclosure, the particle type is a proton, helium ion, carbon ion, oxygen ion, neon ion, hydrogen nucleus, silicon ion, or iron ion; the target interaction layer corresponding to a proton is a proton interaction layer; the target interaction layer corresponding to a helium ion is a helium ion interaction layer; the target interaction layer corresponding to a carbon ion is a carbon ion interaction layer; the target interaction layer corresponding to an oxygen ion is an oxygen ion interaction layer; the target interaction layer corresponding to a neon ion is a neon ion interaction layer; the target interaction layer corresponding to a hydrogen nucleus is a hydrogen nucleus interaction layer; the target interaction layer corresponding to a silicon ion is a silicon ion interaction layer; and the target interaction layer corresponding to an iron ion is an iron ion interaction layer.

[0010] According to embodiments of this disclosure, the average distance between the helium ion interaction layer and the epidermis in the human image data is the first distance; the average distance between the carbon ion interaction layer and the epidermis in the human image data is the second distance; the average distance between the proton interaction layer and the epidermis in the human image data is the third distance; the average distance between the oxygen ion interaction layer and the epidermis in the human image data is the fourth distance; the average distance between the neon ion interaction layer and the epidermis in the human image data is the fifth distance; the average distance between the hydrogen nucleus interaction layer and the epidermis in the human image data is the sixth distance; the average distance between the silicon ion interaction layer and the epidermis in the human image data is the seventh distance; and the average distance between the iron ion interaction layer and the epidermis in the human image data is the eighth distance. The distances, ordered from smallest to largest, are: sixth distance, third distance, first distance, second distance, fourth distance, fifth distance, seventh distance, and eighth distance.

[0011] According to embodiments of this disclosure, calculating the initial pencil beam dose distribution corresponding to each particle type includes: matching the dose calculation model corresponding to each particle type based on the mass number, proton number, initial energy range, scattering angle parameter and linear energy transfer parameter of each particle type; and calculating the initial pencil beam dose distribution corresponding to each particle type based on the dose calculation model.

[0012] According to embodiments of this disclosure, the dose calculation model is a double Gaussian model, a triple Gaussian model, or a mixed model; wherein, the mixed model is constructed by a Gaussian function and a Vogt function.

[0013] According to embodiments of this disclosure, before calculating the initial pencil beam dose distribution corresponding to each particle type, the method further includes: calculating a ray tracing path based on a preset ray tracing algorithm; calculating the initial pencil beam dose distribution corresponding to each particle type includes: calculating the initial pencil beam dose distribution corresponding to each particle type based on the ray tracing path.

[0014] Another aspect of this disclosure provides a pencil beam dose calculation device, comprising: a first acquisition module for acquiring human image data; a first determination module for determining multiple particle types for treatment; a first calculation module for calculating the initial pencil beam dose distribution corresponding to each particle type; a second determination module for determining the target layer of each dose distribution in the human image data based on the particle type, wherein the target layer positions corresponding to different particle types are different; an adjustment module for adjusting each initial dose distribution based on the target layer corresponding to each particle type to obtain the particle pencil beam dose distribution corresponding to each particle type; and a superposition module for superimposing the particle pencil beam dose distributions corresponding to each particle type to obtain the target pencil beam dose distribution; wherein the dose proportion of the particle pencil beam dose distribution corresponding to the same particle type in the target layer is greater than the dose proportion of the particle pencil beam dose of that particle type in other parts of the human image data.

[0015] Another aspect of this disclosure provides an electronic device including: at least one processor; and a memory 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 pencil beam dose calculation method of any of the foregoing embodiments.

[0016] Another aspect of this disclosure provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform a pencil beam dose calculation method according to any of the foregoing embodiments.

[0017] Another aspect of this disclosure provides a computer program product, including a computer program / instructions, characterized in that the computer program / instructions, when executed by a processor, implement the operation of the pencil beam dose calculation method of any of the foregoing embodiments.

[0018] The pencil beam dose calculation method disclosed herein has at least one of the following beneficial effects: By introducing a multi-particle adaptation mechanism based on particle physics characteristics and combining it with the spatial distribution requirements of the target layer, the dose distribution of each particle type is dynamically adjusted, achieving precise control of particle dose in different depth regions. Through unified ray tracing path reuse and dynamic configuration of the dose convolution model, the dose calculation efficiency is significantly improved, system resource consumption is reduced, and the flexibility and adaptability of multi-particle therapy schemes are enhanced. The technical solution disclosed herein can effectively improve the spatial selectivity of therapeutic dose, optimize target area dose coverage, reduce radiation exposure to normal tissues, and improve the safety and efficacy of treatment, demonstrating strong technological advancement and practical application value. Attached Figure Description

[0019] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0020] Figure 1 A flowchart illustrating a pencil beam dose calculation method according to an embodiment of the present disclosure is shown schematically.

[0021] Figure 2 Another flowchart illustrating a pencil beam dose calculation method according to an embodiment of the present disclosure is shown schematically;

[0022] Figure 3 Another flowchart illustrating a pencil beam dose calculation method according to an embodiment of the present disclosure is shown schematically;

[0023] Figure 4 A flowchart illustrating the calculation of the initial pencil beam dose distribution in a pencil beam dose calculation method according to an embodiment of the present disclosure is shown.

[0024] Figure 5 Another flowchart illustrating a pencil beam dose calculation method according to an embodiment of the present disclosure is shown schematically;

[0025] Figure 6 A block diagram of a pencil beam dosing calculation device according to an embodiment of the present disclosure is schematically shown; and

[0026] Figure 7 A block diagram of an electronic device suitable for implementing the methods described above, according to embodiments of the present disclosure, is illustrated schematically. Detailed Implementation

[0027] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0028] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0029] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0030] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0031] In the embodiments disclosed herein, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0032] Embodiments of this disclosure provide a pencil beam dose calculation method, including: acquiring human image data; determining multiple particle types for treatment; calculating the initial pencil beam dose distribution corresponding to each particle type; determining the target action layer of each dose distribution in the human image data based on the particle type, wherein the target action layer positions are different for different particle types; adjusting each initial dose distribution based on the target action layer corresponding to each particle type to obtain the particle pencil beam dose distribution corresponding to each particle type; superimposing the particle pencil beam dose distributions corresponding to each particle type to obtain the target pencil beam dose distribution; wherein the dose proportion of the particle pencil beam dose distribution corresponding to the same particle type in the target action layer is greater than the dose proportion of the particle pencil beam dose of that particle type in other parts of the human image data.

[0033] Figure 1 A flowchart illustrating a pencil beam dose calculation method according to an embodiment of the present disclosure is shown schematically.

[0034] like Figure 1 As shown, the pencil beam dose calculation method may include at least operations S110 to S160.

[0035] In operation S110, human imaging data is acquired. Specifically, human imaging data can include medical imaging data such as CT images, MRI images, and PET images. Each data package contains the density distribution, tissue type classification, and depth location information of the patient's body tissues or organs in three-dimensional space. Human imaging data is essentially spatial data, capable of characterizing the physical properties of each voxel at different spatial locations, thereby supporting treatment planning based on spatial layering. For example, soft tissue and bone tissue can be distinguished based on density information in CT images, thus providing a spatial basis for determining the subsequent particle dose application layer.

[0036] In operation S120, multiple particle types for treatment are determined. Specifically, to adapt to the treatment needs of different depth regions and improve the accuracy of dose distribution and the controllability of biological effects, multiple particle types with different physical properties are selected and combined for application, thereby achieving a more rational dose allocation in space. For example, highly penetrating particles and low-penetrating particles can be combined for irradiation of different regions to balance dose coverage and tissue protection across the entire treatment range. By selecting multiple particles, the limitations of single-particle therapy in terms of dose distribution uniformity and biological effect control can be overcome.

[0037] In operation S130, the initial pencil beam dose distribution corresponding to each particle type is calculated. Specifically, for each particle type, based on its known basic physical parameters and treatment plan requirements, the initial pencil beam dose distribution is calculated according to the corresponding dose calculation method. The initial pencil beam dose distribution refers to the basic dose deposition characteristics formed along the propagation path of the particle beam under homogeneous medium conditions before adaptive modulation, including lateral dose spread characteristics and longitudinal energy deposition characteristics.

[0038] During the calculation, a preset dose distribution model, such as a standard Gaussian convolution model or other suitable dose extension model, can be used to obtain the dose distribution curves of particles in the propagation direction and the transverse direction, based on parameters such as particle incident energy, beam size, and tissue density information. This provides basic data for subsequent dose adjustment based on the target layer.

[0039] In operation S140, based on particle type, the target action layer of each dose distribution in the human imaging data is determined, and the target action layer positions are different for different particle types. Specifically, a corresponding target action layer is assigned to each particle type, and different particle types mainly exert their dose effect in different spatial regions, thereby achieving spatial stratification of particle dose within human tissues.

[0040] In operation S150, based on the target layer corresponding to each particle type, the initial dose distributions are adjusted to obtain the particle pencil beam dose distribution for each particle type. Specifically, the dose proportion of the particle pencil beam dose distribution corresponding to the same particle type within the target layer is greater than the dose proportion of that particle type's particle pencil beam dose in other parts of the human imaging data. Specifically, the initial dose distributions for each particle type are adjusted to maximize the dose contribution of each particle type within its corresponding target layer and reduce its dose contribution in other regions, thereby improving the spatial selectivity of the particle dose distribution. Through these adjustments, the treatment efficiency of each particle type in its respective responsible area can be effectively improved, while suppressing dose leakage to irrelevant areas and further reducing unnecessary radiation to normal tissues.

[0041] Specifically, adjusting the initial dose distribution can include: performing regional weighting or normalization on the initial dose distribution based on the target action layer depth range corresponding to each particle type.

[0042] For the dose distribution of the pencil beam within the target layer, an enhancement weighting strategy is employed to increase the dose value within that range; for regions beyond the target layer, a suppression weighting strategy is used to reduce the dose contribution in the corresponding region. The weighting factor can be set according to parameters such as particle penetration characteristics, target layer center depth, and layer thickness to form a spatial dose distribution adjustment curve that meets treatment requirements.

[0043] For example, for carbon ions targeting a depth of 8–20 cm, the dose value in the initial dose distribution for the 8–20 cm segment can be multiplied by an enhancement factor (e.g., 1.1–1.3 times), while the dose in the 0–8 cm and beyond 20 cm is multiplied by an inhibition factor (e.g., 0.5–0.8 times) to ensure that the carbon ion dose is mainly concentrated in the mid-to-deep region. Similarly, for oxygen ions targeting a depth of 12–25 cm, the dose distribution can be weighted and amplified within this depth range, while the dose contribution from shallow and ultra-deep layers is weakened.

[0044] Furthermore, dose adjustments can be refined by incorporating biological effect parameters of particle type (such as relative biological effect ratio, RBE) to further optimize the biological dose distribution while meeting the requirements of physical dose spatial distribution, thereby improving the precision of the biological effect of treatment.

[0045] In operation S160, the particle pencil beam dose distributions corresponding to each particle type are superimposed to obtain the target pencil beam dose distribution. Specifically, the adjusted particle pencil beam dose distributions for each particle type are superimposed to form an overall treatment dose plan that comprehensively considers the multi-particle layering characteristics and spatial dose distribution characteristics, so as to achieve precise coverage of the tumor area and reduce the dose burden on normal tissues. By superimposing multi-particle dose distributions, the flexibility and controllability of the dose distribution can be further improved, so that the treatment plan can meet the needs of sufficient dose in the tumor area while maximizing the protection of surrounding normal tissues, thus achieving comprehensive optimization of treatment efficacy and safety.

[0046] Figure 2 Another flowchart illustrating a pencil beam dose calculation method according to an embodiment of the present disclosure is shown schematically.

[0047] like Figure 2 As shown, based on the foregoing embodiments, S140 may include operation S210.

[0048] In operation S210, the target action layer is determined based on the particle type and depth information in the human image data. The target action layer corresponding to different particle types has different depth ranges in the human image data.

[0049] Specifically, based on the penetration characteristics and energy deposition patterns of each particle type, combined with tissue depth information recorded in human imaging data, different target action layers can be defined. Different particles correspond to different penetration depths and deposition characteristics, thus adapting to different treatment depth regions, thereby achieving a reasonable match between the treatment particles and the target area depth.

[0050] For example, the particle type can be a proton, helium ion, carbon ion, oxygen ion, neon ion, hydrogen nucleus, silicon ion, or iron ion. Specifically, the target interaction layer corresponding to a proton is the proton interaction layer; the target interaction layer corresponding to a helium ion is the helium ion interaction layer; the target interaction layer corresponding to a carbon ion is the carbon ion interaction layer; the target interaction layer corresponding to an oxygen ion is the oxygen ion interaction layer; the target interaction layer corresponding to a neon ion is the neon ion interaction layer; the target interaction layer corresponding to a hydrogen nucleus is the hydrogen nucleus interaction layer; the target interaction layer corresponding to a silicon ion is the silicon ion interaction layer; and the target interaction layer corresponding to an iron ion is the iron ion interaction layer.

[0051] Specifically, the arrangement of different particle action layers within the human body is determined by comprehensively considering the range of each particle within the tissue, the rate of energy loss, and the tissue's response characteristics.

[0052] For example, hydrogen nuclei (i.e., hydrogen ions) have the smallest mass number and charge number, and therefore limited penetrating ability. They are suitable for treating very superficial areas and can be used to treat tumors near the epidermis or in the superficial layer.

[0053] For example, protons have moderate penetrating power and energy deposition properties, which can be used for the treatment of superficial to middle-layer tissues, such as superficial organs and some middle and deep tissues.

[0054] For example, helium ions have slightly stronger penetrating power than protons, while having a smaller scattering effect, allowing them to cover intermediate tissue layers, making them particularly suitable for treating solid tumors of moderate depth.

[0055] For example, carbon ions have high mass and energy deposition efficiency, and a large penetration depth, making them suitable for treating deep tumors, with significantly enhanced biological effects.

[0056] For example, oxygen ions penetrate deeper than carbon ions and provide higher LET values ​​in deep tissues, which can be used to treat deep, refractory tumors that require large-dose deposition.

[0057] For example, neon ions further enhance penetration depth and biological effects, and can be used to treat very deep or radioresistant tumors.

[0058] For example, silicon ions have higher mass number and energy, and maintain good penetration and dose concentration characteristics in ultra-deep tissues, making them suitable for the treatment of tumors in ultra-deep locations.

[0059] For example, iron ions, as the class of heavy ions with the largest mass number, penetrate the deepest and can be used for treatment of extremely deep tissues, such as the deep pelvic cavity and the posterior part of the pancreas, which are difficult to reach.

[0060] According to embodiments of this disclosure, the average distance between the helium ion interaction layer and the epidermis in the human image data is the first distance; the average distance between the carbon ion interaction layer and the epidermis in the human image data is the second distance; the average distance between the proton interaction layer and the epidermis in the human image data is the third distance; the average distance between the oxygen ion interaction layer and the epidermis in the human image data is the fourth distance; the average distance between the neon ion interaction layer and the epidermis in the human image data is the fifth distance; the average distance between the hydrogen nucleus interaction layer and the epidermis in the human image data is the sixth distance; the average distance between the silicon ion interaction layer and the epidermis in the human image data is the seventh distance; and the average distance between the iron ion interaction layer and the epidermis in the human image data is the eighth distance. The distances, ordered from smallest to largest, are: sixth distance, third distance, first distance, second distance, fourth distance, fifth distance, seventh distance, and eighth distance.

[0061] Specifically, the different target layers for different particles are determined based on their penetrating power, energy deposition patterns, and applicable treatment depths within the tissue. Superficial penetrating particles are mainly distributed in superficial to mid-superficial tissues, intermediate penetrating particles in mid-tissues, and deep penetrating particles in deep to very deep tissues. This layered arrangement based on particle physics allows each particle to exert its optimal therapeutic effect at its most suitable depth, while simultaneously optimizing dose distribution and controlling biological effects.

[0062] For example, in one specific embodiment, the hydrogen nucleus interaction layer can be set as a region with an average depth of 0–6 cm, starting from the epidermis in human imaging data; the proton interaction layer can be set as a region with a depth of 0–8 cm; the helium ion interaction layer can be set as a region with a depth of 5–12 cm; the carbon ion interaction layer can be set as a region with a depth of 8–20 cm; the oxygen ion interaction layer can be set as a region with a depth of 12–25 cm; the neon ion interaction layer can be set as a region with a depth of 15–30 cm; the silicon ion interaction layer can be set as a region with a depth of 20–30+ cm; and the iron ion interaction layer can be set as a region with a depth of 30+ cm.

[0063] By dividing the target action layer into different depth ranges as described above, the dose deposition of each particle in the treatment plan can be concentrated in its most suitable depth region, thereby enhancing the accuracy of target area dose coverage, while reducing radiation exposure of non-target tissues, and improving treatment safety and efficacy.

[0064] Figure 3 Another flowchart illustrating a pencil beam dose calculation method according to an embodiment of the present disclosure is shown schematically.

[0065] like Figure 3 As shown, based on the foregoing embodiments, S140 may include operation S310.

[0066] In operation S310, based on the energy deposition characteristics of each particle type and / or the Bragg peak position, the target action layer of each dose distribution in the human imaging data is determined.

[0067] Specifically, when particles propagate through human tissue, their energy deposition characteristics manifest as a distribution curve of energy change along the propagation path. Different particles exhibit varying energy deposition characteristics due to differences in mass, charge, and initial energy. The Bragg peak position refers to the depth at which the particle's energy deposition rises sharply to its peak value in the terminal region of propagation, representing the area where the particle's energy release is most concentrated. By analyzing the energy deposition distribution and Bragg peak position of each particle, corresponding target action layers can be defined within the treatment space for different particles, achieving precise matching between particle characteristics and spatial dosage requirements.

[0068] For example, protons exhibit low energy deposition initially, followed by a rapid rise at the terminal region, with their Bragg peak typically appearing at a depth of 6–8 cm, making them suitable for treating superficial to mid-superficial tissues. Carbon ions show a slight increase in energy deposition initially, forming a distinct Bragg peak at the terminal region, located in the 8–20 cm depth range, suitable for treating mid-to-deep tumors. Oxygen ions have even deeper Bragg peaks, reaching 12–25 cm, suitable for treating deep solid tumors. For neon, silicon, and iron ions, due to their increased particle mass, their Bragg peak positions shift sequentially to later regions, enabling them to cover deeper tissue areas, such as ultra-deep regions exceeding 30 cm. By utilizing the Bragg peak depth and energy deposition characteristics of each particle, the spatial action layer configuration for each particle type in the treatment plan can be effectively determined, thereby improving the concentration of the treatment dose on the target area and the safety of treatment.

[0069] By incorporating particle energy deposition characteristics and Bragg peak positions as criteria for determining the target layer, the correlation between physical parameters and spatial dose distribution during particle therapy can be highlighted. This approach enables treatment planning to not only rely on the spatial structure of the image but also to intelligently adapt based on the physical deposition characteristics of the particles themselves, thereby enhancing the accuracy of the technical solution.

[0070] Figure 4 A flowchart illustrating the calculation of the initial pencil beam dose distribution in a pencil beam dose calculation method according to an embodiment of the present disclosure is shown.

[0071] like Figure 4 As shown, based on the aforementioned embodiments, S130 may include operations S410 to S420.

[0072] In operation S410, based on the mass number, proton number, initial energy range, scattering angle parameter, and linear energy transfer parameter of each particle type, the corresponding dose calculation model for each particle type is matched.

[0073] Specifically, the energy deposition behavior of different particle types during their propagation in tissues is influenced by their fundamental physical properties, such as mass number (A), proton number (Z), initial energy range, scattering angle variation within the tissue, and linear energy transfer (LET) characteristics. Based on these physical properties, different dose calculation models can be pre-constructed to describe the dose deposition distribution, scattering spread, and energy loss patterns of particles within a unit path length. During the treatment planning phase, the system automatically matches the dose calculation model best suited to the characteristics of the particle type based on the aforementioned parameters, thereby improving the accuracy and reliability of dose distribution prediction.

[0074] For example, for protons, a Gaussian broadening convolution model specific to protons can be selected to describe their dose characteristics, which involve a small scattering angle and narrow energy distribution in tissues. For carbon ions, a carbon-specific multi-group energy deposition model can be matched to fully reflect the large energy deposition density and moderate lateral scattering behavior of carbon ions in tissues. For heavy ions such as oxygen and silicon ions, multi-level, multi-energy-segment convolution models can be selected to reflect their complex energy loss and scattering characteristics. By automatically selecting the dose calculation model based on the particle physics characteristics, more accurate dose prediction for each particle type in the treatment plan can be ensured, thus improving treatment efficacy.

[0075] In operation S420, based on the dose calculation model, the initial pencil beam dose distribution corresponding to each particle type is calculated.

[0076] Specifically, the initial pencil beam dose distribution refers to the ideal dose distribution of particles during their initial propagation from the accelerator exit to the target area, before modulation and adaptive correction. Based on the aforementioned matched dose calculation model, dose distribution calculations can be performed for each particle type under different incident energies and beam conditions to obtain its preliminary spatial dose spread characteristics. The initial pencil beam dose distribution typically characterizes the particle beam diameter, the location of the energy deposition center, and the shape of the dose decline curve, providing fundamental data for subsequent treatment plan optimization. From another perspective, the initial pencil beam dose distribution can be understood as the expected pencil beam dose distribution for treatment using a specific particle alone.

[0077] In other embodiments, different configuration files can be saved in advance according to different particle types, and the initial pencil beam dose distribution corresponding to each particle type can be calculated according to the configuration files and the corresponding dose calculation model.

[0078] Specifically, for each particle type, a configuration file containing information such as the initial energy range of the particle, beam divergence angle, energy spread characteristics, and dose deposition parameters can be preset. The configuration file stores standard data on the particle's unique physical properties and dose behavior. When performing dose calculations, the treatment system can call the corresponding configuration file based on the particle type and combine it with the matching dose calculation model to quickly complete the calculation process of the initial pencil beam dose distribution, improving system processing speed and calculation consistency.

[0079] For example, in proton therapy, a pre-defined proton profile can be used, containing parameters such as an energy range of 50–250 MeV, a beam scattering angle of 1–3 degrees, and energy deposition curve parameters. In carbon ion therapy, a carbon ion profile can be used, containing parameters such as an energy range of 100–430 MeV / u, a scattering angle of 0.5–2 degrees, and LET variation curve parameters. By calling the corresponding particle profiles and dose calculation models, the initial pencil beam dose distribution calculation for different therapeutic particles can be completed quickly, supporting dynamic switching and efficient generation of treatment plans.

[0080] According to embodiments of this disclosure, the dose calculation model is a double-Gaussian model, a triple-Gaussian model, or a mixed model.

[0081] The double-Gaussian model refers to the use of the superposition of two Gaussian functions with different standard deviations and relative weights to simulate the lateral dose spread characteristics of a particle pencil beam. By using double-Gaussian superposition, the main scattering and small-angle secondary scattering components of particles can be well described, making it suitable for basic calculations of conventional pencil beam dose distribution.

[0082] The triple-Gaussian model introduces a third Gaussian term on top of the double-Gaussian model to further refine the scattering behavior at different scales. By superimposing the triple Gaussian term, the fitting accuracy of the lateral dose distribution can be improved, especially in the presence of multi-level scattering sources, and it can more accurately reflect the dose spread characteristics.

[0083] A hybrid model refers to a combined model of Gaussian functions and other complex functions introduced into dose calculations to further optimize the dose distribution fitting in regions far from the center of the pencil beam. This hybrid model can more accurately describe the main scattering and large-angle scattering characteristics generated during particle beam propagation, improving the physical fitting accuracy of the dose distribution in the center and far-tail regions. Specifically, it can be implemented by combining the Gaussian distribution with other characteristic distribution functions (such as the Vogt function) to construct the hybrid model.

[0084] Figure 5 Another flowchart illustrating a pencil beam dose calculation method according to an embodiment of the present disclosure is shown schematically.

[0085] like Figure 5 As shown, based on the aforementioned embodiments, the pencil beam dose calculation method may include operations S510 and S520.

[0086] When operating the S510, the ray tracing path is calculated based on the preset ray tracing algorithm.

[0087] Specifically, ray tracing algorithms are used to simulate the propagation path of particle beams in different tissue media based on patient image data, obtaining information on particle positions at different tissue interfaces along the propagation direction, propagation medium properties (such as density and composition), and local energy loss. Ray tracing methods such as ray stepping, volume sampling, or adaptive step size changes based on medium properties can be used to establish efficient and detailed particle propagation path records for subsequent multi-particle dose distribution calculations.

[0088] For example, a weighted ray stepping method based on CT image data can be used to progressively advance through voxels in a patient image, recording density changes, energy deposition ratios, and refraction effects within each path length to form a dataset of precise particle propagation trajectories within human tissue.

[0089] Operation S130 may include operation S520, in which the initial pencil beam dose distribution corresponding to each particle type is calculated based on the ray tracing path.

[0090] Specifically, based on the completed ray tracing path data, the dose convolution weight allocation on the ray trajectory can be dynamically adjusted for each particle type, taking into account its physical characteristics such as mass number, proton number, initial energy, and linear energy transfer parameter (LET). For example, according to the changes in energy deposition efficiency of particles in different tissue density regions, the corresponding dose distribution model parameters (such as Gaussian kernel standard deviation and Vogt kernel broadening parameter) can be dynamically adjusted to achieve personalized dose convolution calculations for different particle characteristics and local tissue environments.

[0091] For example, on the same ray tracing path, for protons, the initial dose distribution can be calculated by matching a standard Gaussian convolution kernel with a lower LET value (approximately 0.5–2 keV / μm); while for carbon ions, due to their higher LET value (approximately 30–80 keV / μm), the weight of the Vogt convolution kernel can be dynamically increased during dose calculation, making the dose distribution decrease more gently in the far tail region, while adjusting the ratio of main scattering to nuclear scattering to optimize dose deposition characteristics.

[0092] In the above implementation, since the geometric trajectories of the propagation paths of various particle types in the same patient image data are identical, the differences lie only in the physical properties of the particles (such as initial energy, scattering characteristics, and linear energy transfer values) and dose convolution kernel parameters. Therefore, a basic particle propagation path dataset can be generated through a unified one-time ray tracing. In traditional methods, ray tracing in pencil beam algorithms consumes the majority of the computation time, including: traversing each voxel in the 3D CT image data; calculating path length and medium thickness; simulating energy deposition, etc. Each additional ray tracing step is equivalent to repeatedly traversing the entire treatment area, resulting in an exponential increase in time consumption. However, in this scheme, when calculating the dose for each particle type, it is only necessary to adjust the convolution kernel and perform dose integration based on the existing ray trajectory and corresponding particle characteristic parameters, without needing to perform path tracing again.

[0093] Figure 6 A block diagram of a pencil beam dose calculation device according to an embodiment of the present disclosure is shown schematically.

[0094] like Figure 6 As shown, the pencil beam dose calculation device 600 may include a first acquisition module 610, a first determination module 620, a first calculation module 630, a second determination module 650, an adjustment module 650, and an overlay module 660.

[0095] The first acquisition module 610 is used to acquire human image data. In some embodiments, the first acquisition module 610 can be used to perform operation S110 in the pencil beam dose calculation method described above, which will not be elaborated here.

[0096] The first determining module 620 is used to calculate the initial pencil beam dose distribution corresponding to each particle type. In some embodiments, the first determining module 620 can be used to perform operation S120 in the pencil beam dose calculation method described above, which will not be elaborated here.

[0097] The first calculation module 620 is used to determine the target layer of each dose distribution in the human image data based on the particle type. The target layer positions are different for different particle types. In some embodiments, the first calculation module 620 can be used to perform operation S130 in the pencil beam dose calculation method described above, which will not be elaborated here.

[0098] The second determining module 620 is used to adjust the initial dose distributions based on the target action layer corresponding to each particle type, thereby obtaining the particle pencil beam dose distribution corresponding to each particle type. In some embodiments, the second determining module 620 can be used to perform operation S140 in the pencil beam dose calculation method described above, which will not be elaborated here.

[0099] The adjustment module 620 is used to adjust the initial dose distributions based on the target action layer corresponding to each particle type, thereby obtaining the particle pencil beam dose distribution corresponding to each particle type. In some embodiments, the adjustment module 620 can be used to perform operation S150 in the pencil beam dose calculation method described above, which will not be elaborated here.

[0100] The superposition module 660 is used to superimpose the particle pencil beam dose distributions corresponding to each particle type to obtain the target pencil beam dose distribution. In some embodiments, the superposition module 660 can be used to perform operation S160 in the pencil beam dose calculation method described above, which will not be elaborated here.

[0101] Among them, the proportion of particle pencil beam dose distribution of the same particle type in the target layer is greater than the proportion of particle pencil beam dose of that particle type in other parts of the human image data.

[0102] Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure, or at least part of the functions of any one or more of them, can be implemented in one module. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be implemented by dividing them into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure 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-Chip, a System-on-a-Substrate, a System-on-Package, an Application-Specific Integrated Circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0103] For example, any and more of the first acquisition module 610, first determination module 620, first calculation module 630, second determination module 650, adjustment module 650, and superposition module 660 can be combined into one module / unit / subunit, or any one of these modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least some of the functions of one or more of these modules / units / subunits can be combined with at least some of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of this disclosure, at least one of the first acquisition module 610, the first determination module 620, the first calculation module 630, the second determination module 650, the adjustment module 650, and the superposition module 660 can be at least partially implemented as hardware circuits, such as field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits (ASICs), or any other reasonable means of integrating or packaging circuits, 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 first acquisition module 610, the first determination module 620, the first calculation module 630, the second determination module 650, the adjustment module 650, and the superposition module 660 can be at least partially implemented as computer program modules, which can perform corresponding functions when the computer program module is run.

[0104] It should be noted that the data processing system part in the embodiments of this disclosure corresponds to the data processing method part in the embodiments of this disclosure. The specific description of the data processing system part is referred to in the data processing method part, and will not be repeated here.

[0105] Figure 7 A block diagram of an electronic device suitable for implementing the methods described above, according to embodiments of the present disclosure, is illustrated schematically. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0106] like Figure 7As shown, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 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 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0107] RAM 703 stores various programs and data required for the operation of electronic device 700. Processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Processor 701 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0108] According to embodiments of this disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the input / output (I / O) interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0109] According to embodiments of this disclosure, the method flow according to embodiments of this disclosure can be implemented as a computer software program. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by processor 701, it performs the functions defined in the system of embodiments of this disclosure. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0110] This disclosure 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 that, when executed, implement the method according to the embodiments of this disclosure.

[0111] According to embodiments of this disclosure, 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 disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0112] For example, according to embodiments of this disclosure, a computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0113] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of this disclosure. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the control methods provided in the embodiments of this disclosure.

[0114] When the computer program is executed by the processor 701, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0115] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices or magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via communication section 709, and / or installed from removable medium 711. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof. According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of this disclosure may be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0117] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for calculating pencil beam dose, characterized in that, include: Acquire human body image data; Identify multiple particle types for treatment; Calculate the ray tracing path based on the preset ray tracing algorithm; Based on the ray tracing path, calculate the initial pencil beam dose distribution corresponding to each particle type; Based on the particle type, the target action layer of each dose distribution in the human image data is determined, and the target action layer positions are different for different particle types; Based on the target action layer corresponding to each of the particle types, the initial dose distributions are adjusted to obtain the particle pencil beam dose distributions corresponding to each of the particle types. The target pencil beam dose distribution is obtained by superimposing the particle pencil beam dose distributions corresponding to each of the aforementioned particle types. Among them, the proportion of the particle pencil beam dose distribution of the same particle type in the target action layer is greater than the proportion of the particle pencil beam dose of that particle type in other parts of the human image data. Determining the distribution of each dose in the target layer of the human imaging data includes: Based on the energy deposition characteristics of each particle type and / or the Bragg peak position, the target action layer of each dose distribution in the human imaging data is determined.

2. The method according to claim 1, characterized in that, Determining the distribution of each dose in the target layer of the human imaging data includes: The target action layer is determined based on the particle type and the depth information in the human body image data. The depth range of the target action layer corresponding to different particle types in the human body image data is different.

3. The method according to claim 1, characterized in that, The particle type is a proton, helium ion, carbon ion, oxygen ion, neon ion, hydrogen nucleus, silicon ion, or iron ion; The target interaction layer corresponding to the proton is the proton interaction layer; The target interaction layer corresponding to the helium ion is a helium ion interaction layer; The target interaction layer corresponding to the carbon ions is a carbon ion interaction layer; The target interaction layer corresponding to the oxygen ions is an oxygen ion interaction layer; The target interaction layer corresponding to the neon ions is a neon ion interaction layer; The target interaction layer corresponding to the hydrogen nucleus is the hydrogen nucleus interaction layer; The target interaction layer corresponding to the silicon ions is a silicon ion interaction layer; The target layer corresponding to the iron ions is an iron ion interaction layer.

4. The method according to claim 3, characterized in that, The average distance between the helium ion interaction layer and the epidermis in the human image data is the first distance; the average distance between the carbon ion interaction layer and the epidermis in the human image data is the second distance; the average distance between the proton interaction layer and the epidermis in the human image data is the third distance; the average distance between the oxygen ion interaction layer and the epidermis in the human image data is the fourth distance; the average distance between the neon ion interaction layer and the epidermis in the human image data is the fifth distance; the average distance between the hydrogen nucleus interaction layer and the epidermis in the human image data is the sixth distance; the average distance between the silicon ion interaction layer and the epidermis in the human image data is the seventh distance; and the average distance between the iron ion interaction layer and the epidermis in the human image data is the eighth distance. The distances, arranged from smallest to largest, are: the sixth distance, the third distance, the first distance, the second distance, the fourth distance, the fifth distance, the seventh distance, and the eighth distance.

5. The method according to claim 1, characterized in that, The calculation of the initial pencil beam dose distribution corresponding to each particle type includes: Based on the mass number, proton number, initial energy range, scattering angle parameter, and linear energy transfer parameter of each particle type, a dose calculation model corresponding to each particle type is matched. Based on the dose calculation model, the initial pencil beam dose distribution corresponding to each particle type is calculated.

6. The method according to claim 5, characterized in that, The dose calculation model is a double Gaussian model, a triple Gaussian model, or a mixed model; The hybrid model is constructed from Gaussian and Vogt functions.

7. A pencil-beam dose calculation device, characterized in that, include: The first acquisition module is used to acquire human image data; The first determining module is used to determine the multiple particle types to be used for treatment; The first calculation module is used to calculate the ray tracing path based on a preset ray tracing algorithm, and to calculate the initial pencil beam dose distribution corresponding to each particle type based on the ray tracing path. The second determining module is used to determine the target action layer of each dose distribution in the human image data based on the particle type, and the target action layer positions are different for different particle types; The adjustment module is used to adjust each initial dose distribution based on the target action layer corresponding to each of the particle types, so as to obtain the particle pencil beam dose distribution corresponding to each of the particle types. The superposition module is used to superimpose the particle pencil beam dose distributions corresponding to each of the particle types to obtain the target pencil beam dose distribution. Among them, the proportion of the particle pencil beam dose distribution corresponding to the same particle type in the target action layer is greater than the proportion of the particle pencil beam dose of that particle type in other parts of the human image data. Determining the distribution of each dose in the target layer of the human imaging data includes: Based on the energy deposition characteristics of each particle type and / or the Bragg peak position, the target action layer of each dose distribution in the human imaging data is determined.

8. 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 method according to any one of claims 1 to 6.

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