Pencil beam dose distribution calculation method and device and electronic equipment

Abstract

The invention provides a pencil beam dose distribution calculation method. The pencil beam dose distribution calculation method comprises the following steps: acquiring human body image data; and calculating the dose distribution of each pencil beam based on the target model and the human body image data. Wherein the target model is composed of a first calculation mode and a second calculation mode, the first calculation mode is used for restraining the central area of the pencil beam to be a sharp and symmetrical peak, and the second calculation mode is used for restraining the dose attenuation rate, away from the central area, of the pencil beam; wherein the dose attenuation rate of the dose distribution, far away from the central area, of the pencil beam is lower than the dose attenuation rate, far away from the central area, in the pencil beam generated based on the target mode; wherein the target mode is a dose distribution model established based on a Gaussian function.

Description

Technical Field

[0001] The present disclosure relates to the field of medical radiation, and more particularly, to a pencil beam dose distribution calculation method, apparatus, and electronic device. Background Art

[0002] With the development of medical technology, particle therapy, as an important means of radiotherapy, is widely used in the treatment of major diseases such as tumors. The pencil beam dose distribution modeling, as a key module in the particle therapy planning system, is directly related to the accuracy of the treatment dose and the treatment effect. In the prior art, for the modeling method of the pencil beam, a Gaussian function is usually used for simulation, and the dose distribution characteristics are approximately described by superimposing multiple Gaussian functions. However, the method of superimposing multiple Gaussian functions increases the complexity of the model, resulting in a large amount of convolution operations during the dose calculation, which affects the real-time performance of the treatment plan generation and optimization; in addition, the Gaussian function itself has insufficient accuracy for the dose distribution, which is likely to cause dose distribution errors. Summary of the Invention

[0003] In view of this, the present disclosure provides a pencil beam dose distribution calculation method and an electronic device.

[0004] One aspect of the present disclosure provides a pencil beam dose distribution calculation method, including: obtaining human body image data; calculating the dose distribution of each pencil beam based on the target model and the human body image data. Wherein, the target model is composed of a first calculation method and a second calculation method, the first calculation method is used to constrain the central region of the pencil beam to be a sharp and symmetric peak, and the second calculation method is used to constrain the dose attenuation rate of the pencil beam away from the central region; wherein, the dose attenuation rate of the dose distribution of the pencil beam away from the central region is lower than that of the pencil beam generated based on the target method away from the central region; wherein, the target method is a dose distribution model established based on the Gaussian function.

[0005] According to an embodiment of the present disclosure, the first calculation method is generated according to the Gaussian function.

[0006] According to an embodiment of the present disclosure, the second calculation method is generated according to the Lorentz function.

[0007] According to an embodiment of the present disclosure, the first calculation method is determined based on the following operations: obtaining the first depth dose curve corresponding to the first function; taking the product of the preset first weight, the first function, and the first depth dose curve as the first calculation method; wherein, the first function is the Gaussian function.

[0008] According to an embodiment of the present disclosure, the second calculation method is determined based on the following operations: obtaining a second depth dose curve corresponding to a second function; taking the product of a preset second weight, the second function, and the second depth dose curve as the second calculation method; wherein, the second function is generated according to a Lorentz function.

[0009] According to an embodiment of the present disclosure, the second function is a convolution of the product of a Gaussian function and a Lorentz function.

[0010] According to an embodiment of the present disclosure, the parameters of the Gaussian function in the second function are determined by the covariance matrix determined in the first calculation method.

[0011] According to an embodiment of the present disclosure, the dose attenuation rate of the dose distribution of the pencil beam away from the central region matches the Lorentz attenuation characteristic.

[0012] Another aspect of the present disclosure provides a pencil beam dose distribution calculation device, including: a first acquisition module that acquires human body image data; a first calculation module that calculates the dose distribution of each pencil beam based on a target model and the human body image data; wherein, the target model is composed of a first calculation method and a second calculation method, the first calculation method is used to constrain the central region of the pencil beam to be a sharp and symmetric peak, and the second calculation method is used to constrain the dose attenuation rate of the pencil beam away from the central region; wherein, the dose attenuation rate of the dose distribution of the pencil beam away from the central region is lower than that of the pencil beam generated based on the target method away from the central region; wherein, the target method is a dose distribution model established based on a Gaussian function.

[0013] Another aspect of the present 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, and the instructions are executed by the at least one processor to enable the at least one processor to execute the pencil beam dose distribution calculation method of any one of the foregoing embodiments.

[0014] Another aspect of the present disclosure provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the pencil beam dose distribution calculation method according to any one of the foregoing embodiments.

[0015] Another aspect of the present disclosure provides a computer program product, including computer programs / instructions, characterized in that when the computer programs / instructions are executed by a processor, the operations of the pencil beam dose distribution calculation method of any one of the foregoing embodiments are implemented.

[0016] According to an embodiment of the present disclosure, the pencil beam dose distribution calculation method provided by the present disclosure has at least one of the following beneficial effects: By adopting the convolution combination of the Gaussian function and the Lorentz function, a simplified and accurate transverse dose distribution model is established. Compared with the traditional triple-Gaussian model, the required computational amount is significantly reduced. The introduction of the Lorentz function component effectively compensates for the deficiency of the traditional Gaussian model in dose modeling in the region far from the beam center, and can more accurately fit the long-tail dose characteristics of the real particle beam obtained by Monte Carlo simulation. It can be seamlessly integrated into the existing analytical dose calculation process, and has good adaptability to particle types, is applicable to proton, carbon ion and other heavy ion therapies, and has broad applicability and promotion potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, the above and other objects, features and advantages of the present disclosure will become clearer. In the drawings:

[0018] Figure 1 Schematically shows a flowchart of a pencil beam dose distribution calculation method according to an embodiment of the present disclosure;

[0019] Figure 2 Schematically shows a flowchart of generating a first calculation method in the pencil beam dose distribution calculation method according to an embodiment of the present disclosure;

[0020] Figure 3 Schematically shows a flowchart of generating a second calculation method in the pencil beam dose distribution calculation method according to an embodiment of the present disclosure;

[0021] Figure 4 Schematically shows a comparison diagram of the pencil beam dose distribution calculation method according to an embodiment of the present disclosure and other methods;

[0022] Figure 5 Schematically shows a block diagram of a pencil beam dose distribution calculation device according to an embodiment of the present disclosure; and

[0023] Figure 6 Schematically shows a block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the purpose of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present disclosure.

[0025] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted to have a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0027] In the case of using expressions such as "at least one of A, B, and C, etc.", generally, it should be interpreted according to the meaning that those of ordinary skill in the art usually understand this expression (for example, "a system having at least one of A, B, and C" should include, but is not limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0028] In the embodiments of the present disclosure, in terms of the collection, update, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the involved data (for example, including but not limited to user personal information), all comply with the provisions of relevant laws and regulations, are used for legal purposes, and do not violate public order and good customs. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and to safeguard the security of user personal information, network security, and national security.

[0029] The embodiments of the present disclosure provide a method for calculating a pencil beam dose distribution, including obtaining human body image data; calculating the dose distribution of each pencil beam based on a target model and the human body image data. Among them, the target model is composed of a first calculation method and a second calculation method. The first calculation method is used to constrain the central region of the pencil beam to be a sharp and symmetric peak, and the second calculation method is used to constrain the dose attenuation rate of the pencil beam away from the central region; among them, the dose attenuation rate of the dose distribution of the pencil beam away from the central region is lower than that of the pencil beam generated based on the target method away from the central region; among them, the target method is a dose distribution model established based on the Gaussian function.

[0030] Figure 1 The flowchart of the method for calculating a pencil beam dose distribution according to an embodiment of the present disclosure is schematically shown.

[0031] As Figure 1 shown, the method for calculating a pencil beam dose distribution may at least include operations S110 to S120.

[0032] In operation S110, human body image data is acquired. The human body image data may include three-dimensional CT images or MRI images of the region to be treated, etc., which are used to provide tissue density distribution information. Based on this information, physical modeling of the propagation path and energy deposition process of particles in the medium can be performed. In addition, the human body image data may further include high-resolution structure information obtained through multimodal imaging fusion processing to improve the modeling accuracy of complex tissue interfaces and density inhomogeneous regions, and to achieve a more accurate description of the particle beam propagation environment.

[0033] In operation S120, based on the target model and the human body image data, the dose distribution of each pencil beam is calculated. The target model models the lateral expansion characteristics of the pencil beam dose distribution, and at the same time combines the physical characteristics of different tissues in the human body image data to generate the three-dimensional dose distribution result of each beam of particles in the target area. By superimposing multiple pencil beams, the overall dose distribution map in the entire treatment area can be obtained, providing a basis for the optimization of subsequent treatment plans.

[0034] According to an embodiment of the present disclosure, the target model is composed of a first calculation method and a second calculation method. The first calculation method is used to constrain the central region of the pencil beam to be a sharp and symmetric peak, and the second calculation method is used to constrain the dose attenuation rate of the pencil beam away from the central region. Among them, the first calculation method is used to describe the central dose peak formed by the main scattering of the particle beam, and the second calculation method models the far-field dose tailing phenomenon caused by nuclear scattering to completely characterize the lateral dose characteristics of the pencil beam after propagation in the medium. Through parametric modeling, the above two calculation methods can flexibly adjust parameters according to actual measurement data, so as to adapt to different particle types, energy levels and clinical indications, and have high generality.

[0035] Main scattering refers to the small-angle scattering of particles when passing through human tissues, affected by tiny particles such as electrons. The resulting energy deposition is mainly concentrated in the central region of the particle path, forming a sharp peak in the dose distribution. Main scattering has a significant impact on the dose distribution in the central region, showing strong symmetry and concentration. Nuclear scattering refers to the large-angle scattering caused by the interaction of particles with atomic nuclei. Although the occurrence probability is lower than that of main scattering, its influence range is wider, resulting in a significant long-tail trailing phenomenon in the dose distribution away from the central region. Nuclear scattering is the main source of far-field dose expansion and affects the peripheral dose attenuation characteristics of the pencil beam. To accurately reflect the above physical mechanisms, different mathematical functions are used to perform piecewise modeling of the main scattering and nuclear scattering dose distributions respectively.

[0036] Specifically, the first calculation method uses a specific mathematical model to describe the dose distribution in the center of the pencil beam based on the physical characteristics of the main scattering. The model ensures that the dose presents a sharp and symmetrical peak shape in the center of the beam, which facilitates the precise control of the main dose deposition in the target area. The parameters of the first calculation method can be obtained by fitting and optimizing with Monte Carlo simulation data or measured data to ensure the consistency between theoretical modeling and actual observation results.

[0037] Specifically, the second calculation method constructs a corresponding mathematical representation method for the far-zone dose tailing effect caused by nuclear scattering, so that the dose shows a slower attenuation trend when it is away from the central area, thereby improving the accuracy of the overall dose modeling, especially playing an important role in the dose prediction of the edge area.

[0038] According to an embodiment of the present disclosure, the dose attenuation rate of the dose distribution of the pencil beam away from the central region is lower than the dose attenuation rate of the pencil beam generated based on the target mode. For example, the target mode is a dose distribution model established based on a Gaussian function.

[0039] In traditional methods, a single Gaussian function or a superposition of multiple Gaussian functions is usually used to simulate the lateral dose distribution characteristics of a pencil beam. Taking carbon ion therapy as an example, a common practice is to use three Gaussian functions for superposition, where the first Gaussian function fits the central dose distribution caused by the main scattering, and the second and third Gaussian functions are used to fit the far-zone dose tailing caused by nuclear scattering. By adjusting the standard deviation and weight of each Gaussian function, an attempt is made to approximate the dose expansion phenomenon actually observed as a whole. However, this superposition mode not only makes the parameter optimization process cumbersome, but also is limited by the physical characteristics of the Gaussian distribution itself, making it difficult to truly characterize the far-zone dose extension phenomenon, affecting the model's extrapolation ability.

[0040] However, due to the exponential decline of the Gaussian function itself, the dose value drops sharply with the increase of the lateral distance when it is far away from the central area, which leads to certain limitations of the traditional model in dealing with the long-tail dose characteristics generated by nuclear scattering, especially in the large dose gradient area or the edge of the target area, which is prone to underestimation of dose. In addition, the superposition of multiple Gaussians increases the computational complexity and has a certain impact on the optimization of real-time treatment plans.

[0041] In contrast, the embodiments of the present disclosure adopt a first calculation method and a second calculation method to model the main scattering and nuclear scattering effects respectively. The second calculation method improves the far-field dose distribution characteristics caused by nuclear scattering, such that the dose attenuation rate of the pencil beam away from the central region is lower than the dose attenuation rate established by the Gaussian function in the traditional target method. Specifically, in this embodiment, by introducing a modeling method that more conforms to the actual physical phenomenon of long-tail attenuation characteristics, the decline rate of the far-field dose is slowed down, such that a reasonable dose coverage can still be maintained at a relatively long distance, avoiding the problem of dose underestimation caused by rapid attenuation in the traditional method.

[0042] On the one hand, during the treatment plan optimization process, the accuracy of dose distribution fitting is improved. Especially in the region of the target edge or the normal tissue interface, the dose gradient transition is more natural, reducing potential treatment errors. On the other hand, the optimization of far-field dose control helps to improve the protection ability for surrounding healthy tissues, further enhancing the safety and effectiveness of the treatment.

[0043] In addition, since the embodiments of the present disclosure adopt a target model that is only modeled based on two transverse dose calculation methods, compared with the traditional method that requires superposition fitting of multiple Gaussian functions, the number of function convolution and integral operations in the calculation process is significantly reduced, lowering the overall calculation complexity. Especially during the treatment plan optimization process, a large number of pencil beams need to be repeatedly calculated for dose and cumulatively summed. In this embodiment, by reducing the number of operation units, a significant improvement in the generation speed of the dose distribution is achieved.

[0044] According to the embodiments of the present disclosure, the first calculation method is generated based on the Gaussian function. The Gaussian function has good symmetry and sharpness, and can effectively characterize the dose distribution characteristics of the central region generated under the action of the main scattering of the particle beam. Specifically, the Gaussian function has a maximum value at the center. As the transverse distance increases, the dose value decreases rapidly in an exponential manner, thereby forming a dose peak with the beam central axis as the axis of symmetry. By applying the Gaussian function as the first calculation method, it can be ensured that a highly concentrated dose deposition is formed within the central region of the target area of the pencil beam, meeting the requirement of precise irradiation of the treatment target.

[0045] In addition, the mathematical characteristics of the Gaussian function are convenient for analytical processing and rapid calculation, which is beneficial to reducing the operation complexity during the dose calculation process and improving the efficiency of treatment plan generation. Using the Gaussian function as the basis for central region modeling can take into account the optimal allocation of computing resources while ensuring dose accuracy, and is suitable for particle treatment planning systems with high real-time requirements.

[0046] Figure 2 Schematically shows a flowchart for generating the first calculation method in the pencil beam dose distribution calculation method according to the embodiments of the present disclosure.

[0047] As Figure 2 shown, on the basis of the foregoing embodiments, the generation of the first calculation method may include operations S210 to S220.

[0048] In operation S210, obtain the first depth dose curve corresponding to the first function. The first depth dose curve, the first depth dose curve (Integrated Depth Dose Curve, abbreviated as IDD curve) is used to describe the energy deposition of the particle beam at different depth positions along the propagation path.

[0049] This curve reflects the dose intensity change law at different depths under the condition of a unit initial particle number, usually showing the characteristic of first rising and then falling, which is closely related to the energy loss mechanism of the particles. By introducing the first depth dose curve, the energy distribution characteristics of the particles in the longitudinal direction (i.e., the depth direction) can be accurately reflected in the dose modeling.

[0050] The acquisition method of the first depth dose curve may include two ways: experimental measurement and simulation calculation. Experimental measurement is usually carried out in a standard water phantom. By placing the detector at different depth positions and recording the dose deposition values of the particle beam at each depth, the depth dose change curve can be drawn. Equipment such as ionization chamber detectors, scintillator detectors or solid track detectors can be used during the measurement process to ensure the accuracy and repeatability of the dose data.

[0051] In addition to experimental measurement, the Monte Carlo simulation method can also be used to simulate the propagation path and energy deposition process of particles in the tissue equivalent medium according to the particle transport theory, and obtain high-precision depth dose curve data. Monte Carlo simulation can consider various physical processes (such as energy loss, scattering, nuclear reactions, etc.) and is applicable to complex treatment conditions that are difficult to measure directly.

[0052] The obtained first depth dose curve data usually needs to be normalized (such as normalizing to 1 at the maximum dose point), and can be interpolated, smoothed or parameter-fitted according to the actual output characteristics of different energy layers of the treatment device to adapt to the dose modeling requirements of the treatment planning system.

[0053] The first depth dose curve D(z) is usually expressed as a function of depth z, that is:

[0054]

[0055] where f(z) is the dose intensity function measured or calculated according to the particle energy and medium characteristics. The first depth dose curve can be normalized so that D(zmax) = 1 at the maximum dose depth point.

[0056] According to the embodiments of the present disclosure, the first function may be a Gaussian function. The Gaussian function has the following standard form:

[0057]

[0058] Among them, r represents the radial distance from the beam center axis, A is the amplitude coefficient, and σ is the standard deviation, which controls the width of the distribution. This function takes the maximum value at r = 0, and as r increases, the dose value decays rapidly according to an exponential law.

[0059] In operation S220, the product of the preset first weight, the first function, and the first depth dose curve is used as the first calculation method.

[0060] The first weight is used to globally adjust the amplitude of the result after multiplying the first function and the first depth dose curve, so as to control the intensity of the pencil beam dose distribution. The preset first weight is essentially a scaling factor, which is used to normalize or adjust the intensity of the calculated dose distribution according to different treatment requirements, so as to meet the dose coverage and limitation requirements in a specific treatment plan.

[0061] The first weight can be obtained based on the particle beam source intensity, the energy layer parameters, and the dose specification preset in the treatment plan. For example, according to the target area prescription dose requirement in the treatment planning system, combined with the standard output data of each energy layer, the corresponding weight value can be determined by looking up the table in the existing dose calibration database, or calculated through a simple proportional conversion formula, so as to ensure the consistency and controllability of different pencil beams during dose accumulation.

[0062] The following gives a method for obtaining the first weight. Assume that the first weight is w1, then

[0063]

[0064] where D target is the standard dose required for the target area, and D model is the dose calculated by the unweighted model. This weight can be preset or obtained by looking up the table through the treatment planning system to meet the dose adjustment requirements under different target areas or treatment conditions.

[0065] Specifically, the first calculation method F1 can be:

[0066]

[0067] Among them, the subscript G indicates that this parameter or function is set for the first function (Gaussian function) and its corresponding dose distribution characteristics, used to distinguish other functions or dose components that may appear later, and the subscript i represents the specific lateral position index, that is, at the radial distance r from the pencil beam central axis iThe corresponding dose calculation point. The lateral distance r can be a set of discretely sampled points for dose calculation and distribution reconstruction across the entire pencil beam cross-section. ω G represents a preset first weight, used to adjust the overall amplitude of the dose distribution in the central region of the pencil beam to match the dose specification set in the treatment planning system. IDD G represents the normalization value of the first depth dose curve, reflecting the relative dose intensity of particle beam energy deposition at a specific depth position. G(r i ,σ G ) represents a Gaussian function, describing the lateral dose distribution at a distance r from the central axis of the pencil beam i . σ G is the standard deviation parameter, controlling the width of the Gaussian distribution.

[0068] The parameters of the Gaussian function (such as the standard deviation σ G ) are determined by experimental measurement or Monte Carlo simulation to match the main scattering characteristics under specific particle energy and medium conditions.

[0069] According to an embodiment of the present disclosure, the second calculation method is generated based on the Lorentz function. Specifically, the second calculation method establishes a lateral dose distribution model based on the Lorentz function to describe the dose spreading characteristics of the particle beam when it is far from the central region. The standard mathematical expression of the Lorentz function is:

[0070]

[0071] where L(r) represents the dose value at a distance r from the central axis of the pencil beam, B is the amplitude constant, and γ is the full width at half maximum parameter, controlling the distribution width. The Lorentz function reaches its maximum value at r = 0, and as r increases, the dose value decreases according to the slow decay law of 1 / r 2 , showing a longer far-field tail than the Gaussian distribution.

[0072] By using the Lorentz function to model the second calculation method, the dose distribution characteristics in the far region of the pencil beam caused by physical mechanisms such as nuclear scattering can be more accurately simulated, making up for the deficiency of the traditional Gaussian model in far-region dose fitting. Compared with the Gaussian function, the Lorentz function has a slower decay characteristic, which can more realistically reflect the dose spreading phenomenon of the particle beam in the large lateral distance region. In addition, the Lorentz function has a simple structure and a clear analytical form, which is conducive to rapid numerical calculation and dose modeling in the treatment planning system, taking into account both accuracy and operation efficiency.

[0073] Figure 3 Schematically shows a flowchart for generating the second calculation method in the pencil beam dose distribution calculation method according to an embodiment of the present disclosure.

[0074] As shown Figure 3 in the figure, on the basis of the foregoing embodiments, the generation of the second calculation method may include operations S310 to S320.

[0075] In operation S310, a second depth dose curve corresponding to the second function is obtained. The second depth dose curve is used to describe the longitudinal energy deposition characteristics of the particle beam matching the second function, and its acquisition method is similar to that of the first depth dose curve, and reference may be made to the foregoing description of the first depth dose curve. Specifically, the second depth dose curve can be obtained by experimental measurement in a standard medium (such as a water phantom) or by using the Monte Carlo simulation method. According to different particle energy layers, scattering conditions, and equipment parameters, the second depth dose curve can be normalized to meet the dose modeling requirements of the treatment planning system.

[0076] According to an embodiment of the present disclosure, the second function is generated according to the Lorentz function. Specifically, the second function is the convolution of the Gaussian function and the Lorentz function. That is, the second function can be expressed as:

[0077]

[0078] where V(r,σ,γ) represents the dose distribution function generated by the convolution of the Gaussian function standard deviation σ and the Lorentz function full width at half maximum γ parameters at the radial position r. r represents the current radial distance of interest, that is, the lateral distance from the central axis of the pencil beam; σ represents the standard deviation parameter of the Gaussian function, which is used to control the expansion width of the dose distribution in the central region. The smaller the standard deviation, the sharper the distribution; γ represents the full width at half maximum parameter of the Lorentz function, which controls the broadening degree of the dose distribution in the far region. The larger the full width at half maximum, the more obvious the far region expansion; r′ represents the integration variable, which represents the intermediate radial position introduced during the convolution process; G(r′,σ) represents the Gaussian function, which is the lateral dose distribution function at the position r′ with a standard deviation of σ; L(r−r′,γ) is the Lorentz function, which represents the lateral dose distribution function at the position r−r′ with a full width at half maximum of γ.

[0079] By performing convolution processing after the product of the Gaussian function and the Lorentz function, the dose expansion characteristics of the particle beam in different lateral regions can be more accurately characterized, retaining both the dose concentration in the central region and reflecting the dose broadening phenomenon in the far region, thereby improving the accuracy of the lateral dose modeling.

[0080] In operation S320, the product of the preset second weight, the second function, and the second depth dose curve is used as the second calculation method. Specifically, the second calculation method F2 can be expressed as:

[0081]

[0082] The subscript v indicates that the parameter or function is set for the second function and its corresponding dose distribution characteristics, used to distinguish other functions or dose components. The subscript i represents the specific lateral position index, that is, the dose calculation point corresponding to a radial distance r from the central axis of the pencil beam. i ω v represents a preset second weight, IDD v represents the normalization value of the first depth dose curve. The subscript i represents the specific lateral position index, that is, the dose calculation point corresponding to a radial distance r from the central axis of the pencil beam. i

[0083] According to an embodiment of the present disclosure, the parameters of the Gaussian function in the second function are determined by the covariance matrix determined by the first calculation method. Specifically, by statistically analyzing the main scattering Gaussian distribution described by the first calculation method, the covariance matrix of its lateral dose distribution (denoted as Σ G ) is extracted. Based on the main diagonal elements of the covariance matrix, the standard deviation parameter of the Gaussian component in the second function is obtained by taking the square root. Through this mathematical derivation process, the standard deviation parameter of the Gaussian component in the second function can accurately inherit the main scattering physical diffusion characteristics reflected by the first function, thus ensuring that the Voigt function model has continuous and physically consistent dose modeling characteristics between the central region and the far region of the dose distribution.

[0084] According to an embodiment of the present disclosure, combining the foregoing content, the dose d i (that is, the mathematical formula of the target model) of any dose network can be expressed as:

[0085]

[0086] According to an embodiment of the present disclosure, the dose attenuation rate of the dose distribution in the region far from the center of the pencil beam matches the Lorentz attenuation characteristics. Specifically, when far from the beam center, the dose distribution of the particle beam shows a relatively slow attenuation characteristic. This attenuation characteristic is very similar to the attenuation mode of the Lorentz function, and the Lorentz function can describe the slow attenuation behavior in the region far from the center. Especially at large distances, the dose does not disappear quickly like the Gaussian distribution, but maintains a relatively gentle attenuation trend. The scattering and energy loss processes of the particle beam can be well reflected by the method in the foregoing embodiments. For example, in carbon ion therapy, the halo dose caused by nuclear scattering contributes about 5% of the relative dose at |r| = 20 mm, while the predicted value of the three-Gaussian model is less than 1%.

[0087] Figure 4 Schematically shows a comparison diagram of the pencil beam dose distribution calculation method according to an embodiment of the present disclosure with other methods.

[0088] As​Figure 4 As shown, in the central region (near r = 0), both the three-Gaussian model and the hybrid model can well fit the Monte Carlo data. In the region far from the center (large r), the three-Gaussian model (segmented dotted line) rapidly decays to a very low value, significantly deviating from the Monte Carlo real data. While the pencil beam dose distribution calculation method (dot-dashed line) of the embodiment of the present disclosure decays more gently and is closer to the Monte Carlo real data, especially in the range of r = 10 to 30 mm.

[0089] It can be seen that the hybrid model (Gaussian-Lorentz convolution model) in the present technical solution shows a better fitting effect in the modeling of the transverse dose distribution, especially in the region far from the center (large transverse coordinate values). When the absolute value of the transverse coordinate is relatively large (for example, |r| > 10 mm), due to its exponential decay characteristic, the dose value of the traditional three-Gaussian model rapidly decreases and is significantly lower than the Monte Carlo simulation result. While the hybrid model in the present disclosure can more accurately follow the decay trend of the Monte Carlo data, showing the long-tail dose distribution characteristic brought by the Lorentz component, and the fitting accuracy is significantly better than that of the three-Gaussian model.

[0090] By introducing the Lorentz component in the Voigt function, the present technical solution can more accurately simulate the long-tail dose distribution characteristic caused by nuclear scattering when the particle beam is far from the central region. Compared with simply using the Gaussian function for modeling, adopting a convolution model with Lorentz characteristics can provide more accurate dose fitting in the beam edge region, effectively improving the modeling accuracy of the halo dose in the treatment plan, thereby enhancing the reliability of dose control around the treatment target area and the treatment effect.

[0091] In addition, the present technical solution can be seamlessly integrated into the existing analytical dose calculation process without significantly modifying the basic architecture of the treatment planning system. Whether it is protons, carbon ions or other particle types, the present technical solution can be adapted, with wide applicability and promotion, and can be quickly applied to existing particle therapy equipment and software systems, shortening the clinical transformation cycle and improving the overall system upgrade efficiency.

[0092] Figure 5 A block diagram of a pencil beam dose distribution calculation device according to an embodiment of the present disclosure is schematically shown.

[0093] As Figure 5 shown, the pencil beam dose distribution calculation device 500 may include a first acquisition module 510 and a first calculation module 520.

[0094] The first acquisition module 510 is used to acquire human body image data. In some embodiments, the first acquisition module 510 may be used to perform the operation S110 in the above-mentioned pencil beam dose distribution calculation method, which will not be elaborated here.

[0095] The first calculation module 520 is configured to calculate the dose distribution of each pencil beam based on the target model and the human body image data. The target model is composed of a first calculation method and a second calculation method. The first calculation method is used to constrain the central region of the pencil beam to be a sharp and symmetric peak, and the second calculation method is used to constrain the dose attenuation rate of the pencil beam away from the central region. The dose attenuation rate of the dose distribution of the pencil beam away from the central region is lower than that of the pencil beam generated based on the target method away from the central region. The target method is a dose distribution model established based on the Gaussian function. In some embodiments, the first calculation module 520 may be configured to perform the operation S120 in the above pencil beam dose distribution calculation method, which will not be elaborated here.

[0096] According to embodiments of the present disclosure, any plurality of, or at least part of the functions of any of the modules, sub-modules, units, and sub-units can be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present disclosure can be split into multiple modules for implementation. Any one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present disclosure can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or can be implemented by any other reasonable manner of integrating or packaging circuits in hardware or firmware, or in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present disclosure can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions can be executed.

[0097] For example, any number of the first acquisition module 510 and the first calculation module 520 may be combined and implemented in one module / unit / sub-unit, or any one of the modules / units / sub-units may be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units may be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the first acquisition module 510 and the first calculation module 520 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on a substrate, a system in a package, an application specific integrated circuit (ASIC), or any other reasonable manner of integrating or packaging circuits, etc., implemented by hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the first acquisition module 510 and the first calculation module 520 may be at least partially implemented as a computer program module, and when the computer program module is run, it can execute the corresponding functions.

[0098] It should be noted that the data processing system part in the embodiments of the present disclosure corresponds to the data processing method part in the embodiments of the present disclosure. For the description of the data processing system part, please refer to the data processing method part specifically, and details will not be repeated here.

[0099] Figure 6 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 6 The shown electronic device is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0100] As Figure 6 shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to the program stored in the read only memory (ROM) 602 or the program loaded from the storage part 608 into the random access memory (RAM) 603. The processor 601 may include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 601 may also include on-board memory for caching purposes. The processor 601 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.

[0101] In the RAM 603, various programs and data required for the operation of the electronic device 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing the programs in the ROM 602 and / or the RAM 603. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to the embodiments of the present disclosure by executing the programs stored in the one or more memories.

[0102] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, and the input / output (I / O) interface 605 is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0103] According to an embodiment of the present disclosure, the method flow according to the embodiments of the present disclosure may be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable storage medium, and the computer program includes program codes for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system according to the embodiments of the present disclosure are executed. According to an embodiment of the present disclosure, the above-described systems, devices, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0104] The present disclosure also provides a computer-readable storage medium, which may be included in the device / device / system described in the above embodiments; or may exist separately without being assembled into the device / device / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0105] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include but is 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 of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, device, or device.

[0106] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include one or more memories other than the above-described ROM 602 and / or RAM 603 and / or ROM 602 and RAM 603.

[0107] The embodiments of the present disclosure also include a computer program product, which includes a computer program, and the computer program includes program code for executing the method provided by the embodiments of the present disclosure. When the computer program product runs on an electronic device, the program code is used to cause the electronic device to implement the control method provided by the embodiments of the present disclosure.

[0108] When the computer program is executed by the processor 601, the above functions defined in the system / device of the embodiments of the present disclosure are executed. According to an embodiment of the present disclosure, the above-described systems, devices, modules, units, etc. may be implemented by computer program modules.

[0109] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and be downloaded and installed through the communication part 609, and / or be installed from the removable medium 611. The program code included in the computer program may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above. According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure may be written in any combination of one or more programming languages. Specifically, these computing programs may be implemented using high-level procedures and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combinations of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions. Those skilled in the art can 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 or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0111] The embodiments of the present disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments have been described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present disclosure.

Claims

1. A method for calculating the dose distribution of a pencil beam, comprising: Obtaining human body image data; Calculating the dose distribution of each pencil beam based on a target model and the human body image data; Wherein, the target model is composed of a first calculation method and a second calculation method, the first calculation method is used to constrain the central region of the pencil beam to be a sharp and symmetric peak, and the second calculation method is used to constrain the dose attenuation rate of the pencil beam away from the central region; Wherein, the dose attenuation rate of the dose distribution of the pencil beam away from the central region is lower than that of the pencil beam generated based on the target method away from the central region; Wherein, the target method is a dose distribution model established based on a Gaussian function.

2. The method according to claim 1, characterized in that, The first calculation method is generated according to a Gaussian function.

3. The method according to claim 1, characterized in that, The second calculation method is generated according to a Lorentz function.

4. The method according to claim 2, wherein The first calculation method is determined based on the following operations: Obtaining a first depth dose curve corresponding to a first function; Taking the product of a preset first weight, the first function, and the first depth dose curve as the first calculation method; Wherein, the first function is a Gaussian function.

5. The method according to claim 3, characterized in that, The second calculation method is determined based on the following operations: Obtaining a second depth dose curve corresponding to a second function; Taking the product of a preset second weight, the second function, and the second depth dose curve as the second calculation method; Wherein, the second function is generated according to a Lorentz function.

6. The method according to claim 5, wherein The second function is a convolution of a Gaussian function and a Lorentz function.

7. The method according to claim 6, characterized in that, The parameters of the Gaussian function in the second function are determined by the covariance matrix determined in the first calculation method.

8. The method according to claim 1, wherein The dose attenuation rate of the dose distribution of the pencil beam away from the central region matches the Lorentz attenuation characteristic.

9. A device for calculating the dose distribution of a pencil beam, comprising: A first acquisition module for acquiring human body image data; A first calculation module for calculating the dose distribution of each pencil beam based on a target model and the human body image data; Wherein, the target model is composed of a first calculation method and a second calculation method, the first calculation method is used to constrain the central region of the pencil beam to be a sharp and symmetric peak, and the second calculation method is used to constrain the dose attenuation rate of the pencil beam away from the central region; Wherein, the dose attenuation rate of the dose distribution of the pencil beam away from the central region is lower than that of the pencil beam generated based on the target method away from the central region; Wherein, the target method is a dose distribution model established based on a Gaussian function.

10. An electronic device, comprising: One or more processors; A memory for storing one or more computer programs, Characterized in 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 8.

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