Carbon ion dose distribution calculation method and system in lattice radiotherapy

Abstract

The present disclosure provides a method for calculating carbon ion dose distribution in lattice radiotherapy, comprising: obtaining image data of a target, the image data being generated based on a target object according to a target ray; generating a plurality of candidate points according to the image data, the candidate points representing the radiation positions of the carbon ion beams; repeating the following steps until a preset condition is met to obtain the target carbon ion dose distribution: adjusting each candidate point and the corresponding carbon ion beam parameters according to the target parameters and the physical characteristics of the carbon ions; in response to meeting the stop condition, calculating the target carbon ion lattice dose distribution according to the position distribution of each adjusted candidate point and the corresponding carbon ion beam parameters.

Description

Technical Field

[0001] This disclosure relates to the field of radiotherapy, and more specifically, to a method and system for calculating carbon ion dose distribution in lattice radiotherapy. Background Technology

[0002] Radiotherapy is one of the core treatment methods for malignant tumors. In recent years, heavy ion radiotherapy, represented by carbon ions, has attracted much attention due to its unique physical and biological advantages. When a carbon ion beam penetrates biological tissue, it forms a sharp dose peak, known as the Bragg peak, at the end of its range. This allows the majority of the energy to be precisely deposited within the tumor target area, while significantly reducing damage to normal tissue in front of and behind the tumor. Furthermore, carbon ion beams have a high linear energy transfer density (LET) in the peak region, resulting in a higher relative biological effect (RBE), and exhibiting a stronger killing effect on some hypoxic or resistant tumors that are insensitive to conventional photon radiotherapy.

[0003] To further improve the therapeutic gain ratio, the concept of lattice radiotherapy has been introduced into carbon ion therapy. This technique deliberately creates a highly non-uniform dose distribution within the tumor target area, consisting of several discrete high-dose points (peaks) and surrounding low-dose areas (valleys). This dosing pattern aims to utilize the "bystander effect" and stimulate the immune response of the tumor microenvironment. Tumor cells are sensitized by low-dose radiation in the valleys, and precisely killed by high-dose radiation in the peaks, thereby achieving tumor control comparable to or even better than uniform irradiation while potentially reducing overall toxicity to normal tissues. However, existing technologies still have limitations in planning carbon ion lattice radiotherapy. Fixed geometric arrays are difficult to conform to irregular tumor boundaries, potentially leading to insufficient coverage of some tumor areas or excessive radiation doses to adjacent organs at risk. Manual delineation is not only time-consuming and inefficient but also highly dependent on the operator's experience, making it difficult to guarantee optimal planning and repeatability. Summary of the Invention

[0004] In view of this, the present disclosure provides a method and system for calculating carbon ion dose distribution in lattice radiotherapy.

[0005] One aspect of this disclosure provides a carbon ion lattice dose prediction method, comprising: acquiring image data of a target, the image data being generated based on a target ray and a target object; generating multiple candidate points based on the image data, the candidate points representing the emission positions of a carbon ion beam; repeating the following steps until preset conditions are met to obtain a target carbon ion dose distribution; adjusting each candidate point and the corresponding carbon ion beam parameters based on target parameters and the physical properties of carbon ions; and generating a target carbon ion dose distribution based on the adjusted position distribution of each candidate point and the corresponding carbon ion beam parameters in response to a stopping condition being met.

[0006] According to embodiments of this disclosure, the physical properties of carbon ions include at least one of the following: Bragg peak parameters of carbon ions, inverted dose distribution characteristics of carbon ions, energy transfer linear density of carbon ions, transverse scattering parameters of carbon ions, relative biological effects of carbon ions, energy spectrum controllability of carbon ions, local ion density saturation of carbon ions, and bystander effect parameters of carbon ions.

[0007] According to embodiments of this disclosure, generating candidate points includes: selecting the tumor target area and the organs at risk to be avoided; calculating the spatial relationship between the tumor target area and the organs at risk; and setting the positional distribution of each candidate point and the carbon ion beam parameters corresponding to each candidate point based on the spatial relationship.

[0008] According to embodiments of this disclosure, calculating the spatial relationship between the tumor target area and the organs at risk includes: calculating the inward range of the tumor target area and the outward range of the organs at risk based on a polygon offset algorithm.

[0009] According to embodiments of this disclosure, candidate points include first candidate points, and setting the positional distribution of each candidate point includes: finding an approximate axis of symmetry of the tumor target region according to a reflection symmetry detection algorithm; constructing multiple grids according to the approximate axis of symmetry and the size of the tumor target region; and arranging the first candidate points on the vertices of each grid.

[0010] According to embodiments of this disclosure, the candidate points further include second candidate points. Setting the positional distribution of each candidate point also includes: adding at least one second candidate point within a preset range of the first candidate point based on the size and shape of the grid; the carbon ion beam parameters corresponding to the second candidate point are used to compensate the carbon ion beam parameters corresponding to the first candidate point.

[0011] According to embodiments of this disclosure, adjusting each candidate point and the corresponding carbon ion beam parameters based on target parameters and the physical properties of carbon ions includes: calculating the carbon ion lattice dose distribution based on the position of the candidate point and the corresponding carbon ion beam parameters; evaluating the carbon ion lattice dose distribution; and adjusting each candidate point and the corresponding carbon ion beam parameters based on the evaluation results.

[0012] According to embodiments of this disclosure, calculating the dose distribution of a carbon ion lattice includes at least one of the following: determining the dose depth distribution of each candidate point based on the Bragg peak parameters of carbon ions; calculating the biological dose weighting result based on the energy transfer linear density of carbon ions and / or the relative biological effect; calculating the diffusion range of the dose in the surrounding voxels based on the transverse scattering parameters of carbon ions; and weighting and superimposing the dose distributions formed at the same candidate point in multiple energy layers based on the controllability of the energy spectrum of carbon ions, the local ion density saturation of carbon ions, and the bystander effect parameters of carbon ions.

[0013] According to embodiments of this disclosure, adjusting each candidate point and the corresponding carbon ion beam parameters includes adjusting the position of the candidate points, and / or increasing the number of candidate points, and / or decreasing the number of candidate points, and / or adjusting the dose weight of the carbon ion beam corresponding to the candidate points; wherein, adjusting the position of the candidate points, and / or increasing the number of candidate points, and / or decreasing the number of candidate points, so that under the adjusted position distribution of each candidate point, the valley-to-peak ratio, the ratio of lattice volume to tumor target volume, and the weighted biological dose of carbon ions are less than 5 Gy in volume that meet preset requirements.

[0014] Another aspect of this disclosure provides a carbon ion dose distribution calculation system for lattice radiotherapy, comprising: a first acquisition module for acquiring image data of a target, the image data being generated based on a target ray and a target object; a first generation module for generating multiple candidate points based on the image data, the candidate points representing the radiation positions of the carbon ion beam; and an iteration module for repeating the following steps until a stopping condition is met to obtain the target carbon ion dose distribution: adjusting each candidate point and the corresponding carbon ion beam parameters based on target parameters and the physical properties of carbon ions; and generating the target carbon ion dose distribution based on the adjusted position distribution of each candidate point and the corresponding carbon ion beam parameters in response to meeting the stopping condition.

[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 carbon ion lattice dose prediction 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 carbon ion lattice dose prediction 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 carbon ion lattice dose prediction method of any of the foregoing embodiments. Attached Figure Description

[0018] 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:

[0019] Figure 1 A flowchart illustrating a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure is shown schematically.

[0020] Figure 2 This schematically illustrates a flowchart of setting candidate points and corresponding carbon ion beam parameters in a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure;

[0021] Figure 3 This schematically illustrates another flowchart of setting candidate points and corresponding carbon ion beam parameters in a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure;

[0022] Figure 4 This schematically illustrates a flowchart of setting a first candidate point in a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure;

[0023] Figure 5 This schematically illustrates a flowchart of the method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure, in which parameters corresponding to the first candidate point are compensated.

[0024] Figure 6 This schematically illustrates a flowchart of adjusting candidate points and corresponding carbon ion beam parameters in a carbon ion dose distribution calculation method for lattice radiotherapy according to an embodiment of the present disclosure;

[0025] Figure 7 Another flowchart illustrating a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure is shown schematically.

[0026] Figure 8 A block diagram schematically illustrates a carbon ion dose distribution calculation system in lattice radiotherapy according to embodiments of the present disclosure; and

[0027] Figure 9 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

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.).

[0032] 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.

[0033] The embodiments of this disclosure provide a method for calculating carbon ion dose distribution in lattice radiotherapy, comprising: acquiring image data of a target, the image data being generated based on a target ray and a target object; generating multiple candidate points based on the image data, the candidate points representing the radiation positions of the carbon ion beam; repeating the following steps until a preset condition is met to obtain the target carbon ion dose distribution; adjusting each candidate point and the corresponding carbon ion beam parameters based on target parameters and the physical properties of carbon ions; and generating the target carbon ion dose distribution based on the adjusted position distribution of each candidate point and the corresponding carbon ion beam parameters in response to meeting a stopping condition.

[0034] Figure 1 A flowchart illustrating a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure is shown.

[0035] like Figure 1 As shown, the method for calculating the carbon ion dose distribution in lattice radiotherapy may include at least operations S110~S150.

[0036] In operation S110, image data of the target is acquired. This image data is generated based on the target radiation and the target object. The image data is a digital representation carrying information about the internal structure and density of the target object, providing an anatomical basis for subsequent dose calculations. The target object typically refers to the organism receiving radiation therapy, such as a patient. One implementation involves reading a set of image files conforming to the Medical Digital Imaging and Communication (DICOM) standard from a Medical Image Archive and Communication System (PACS) or local storage. This image set contains at least a series of computed tomography (CT) images and may optionally include images from other modalities such as magnetic resonance imaging (MRI) or positron emission tomography (PET), as well as a set of structures of the tumor target area and organs at risk delineated by the clinician.

[0037] For example, a CT image sequence file (e.g., a .dcm format file) of a patient with a head and neck tumor can be read. This sequence file contains image data of multiple axial slices from top to bottom, with the voxel value of each image reflecting the tissue electron density information at the corresponding location. Simultaneously, a radiotherapy structure set file (RT StructureSet) registered with this CT image can be read, from which the clinically defined total tumor volume (GTV) and the three-dimensional contour coordinates of organs at risk (OARs) to be protected, such as the brainstem and spinal cord, can be extracted.

[0038] For example, CT volume data (including pixel spacing and slice thickness) in the treatment position can be used and a structure set can be loaded; or cone-beam CT can be used and rigidly registered with a reference CT for the day's planned correction.

[0039] In operation S120, multiple candidate points are generated based on image data. These candidate points characterize the radiation position of the carbon ion beam. A candidate point is an initial set of coordinates defined in three-dimensional space to characterize the incident position of the carbon ion pencil beam. Each candidate point is a basic operational unit in the subsequent dose optimization process, and the initial spatial distribution of this set constitutes the preliminary morphology of the high-dose "peak" in lattice radiotherapy. For example, based on the patient's anatomical information, an initial scan point distribution is automatically generated within the treatment area. This generation process can arrange a set of three-dimensional coordinate points within a preset volume of interest, according to specific geometric rules or a random algorithm. These points serve as the starting point for the optimization algorithm, and their initial arrangement aims to initially form spatially separated high-dose centers.

[0040] For example, within the volume enclosed by the three-dimensional contour of the GTV obtained by operation S110, an initial three-dimensional lattice is generated. The generation of this lattice can be independent of any pre-drawn lattice target area, but can directly generate a series of discrete coordinate points within a certain range of the geometric center of the GTV according to a preset initial spacing (e.g., 20 mm horizontally and 20 mm vertically) to form an initial set of candidate points.

[0041] Execute operations S130 to S140 until the stop condition is met.

[0042] In operation S130, based on the target parameters and the physical properties of carbon ions, the candidate points and their corresponding carbon ion beam parameters are adjusted. Target parameters refer to a series of pre-defined dosimetric or geometric constraints and optimization objectives to achieve clinical treatment goals. Carbon ion beam parameters refer to the physical quantities associated with each candidate point that determine its dose contribution, such as beam energy, particle weight, or number. Operation S130 continuously modifies the positions of candidate points and their corresponding beam parameters through a feedback loop. In each loop, a temporary dose distribution is calculated based on the current set of candidate points, compared with the pre-defined target parameters, and, based on the comparison results, an optimization algorithm is applied to adjust the positions of one or more candidate points or their beam parameters, aiming to make the dose distribution calculated in the next loop closer to clinical requirements.

[0043] For example, suppose a target parameter is "to create a peak-to-trough dose ratio within the tumor region, while the radiation dose to a nearby organ at risk is below a certain threshold." In one iteration, if the calculation finds that the dose distribution generated by the current set of candidate points causes the dose to the organ at risk to exceed the limit, the optimization algorithm might slightly shift the positions of the candidate points closest to the organ at risk away from it. Alternatively, if the dose level in the dose "trough" is found to be too high, the algorithm might reduce the carbon ion beam weights corresponding to all candidate points to lower the overall dose in the trough.

[0044] In operation S140, in response to the satisfaction of the stopping condition, a target carbon ion dose distribution is generated based on the adjusted position distribution of each candidate point and the corresponding carbon ion beam parameters. The stopping condition is a set of logical judgment criteria used to terminate the iterative optimization process. The target carbon ion dose distribution is the final three-dimensional dose distribution map that meets clinical requirements, calculated from the finally determined set of candidate points and their corresponding carbon ion beam parameters after the iterative optimization process converges. When one or more states in the iteration process satisfy the preset stopping condition, the optimization loop terminates. Using the optimal candidate point position distribution determined in the last iteration and the corresponding carbon ion beam parameters, the final three-dimensional dose distribution for clinical application of carbon ion lattice radiotherapy is calculated using a precise dose calculation model.

[0045] For example, if the improvement in the comprehensive evaluation function value composed of the target parameters is less than 0.1% in five consecutive iterations, a stopping condition is triggered. At this point, the system locks the final set of candidate points (containing the x, y, z coordinates of each point) and its corresponding carbon ion beam parameters (containing the energy layer, weight, and other information of each point), calls the carbon ion dose calculation engine, performs the final dose calculation on the patient's CT image data, and generates a three-dimensional dose matrix.

[0046] The stopping conditions include: reaching a preset maximum number of iterations; or, key target parameters (e.g., dose to organs at risk, target coverage, etc.) meeting clinical requirements and showing no significant improvement in subsequent iterations; or, the comprehensive objective function value used to evaluate the quality of dose distribution converging to a stable value; or, a combination of the above conditions.

[0047] The determination of stopping conditions in operations S130 and S140 constitutes a closed-loop iterative optimization process. By repeatedly executing the "adjust-calculate-evaluate" cycle, the optimal scan point layout and parameter combination are explored step by step and automatically. Each iteration aims to make the current dose solution closer to the preset clinical goal until an acceptable or optimal solution that satisfies all constraints is found.

[0048] Specifically, starting from the initial candidate point set generated by operation S120, a loop is entered. Inside the loop, operation S130 is executed to adjust the points and weights; subsequently, a temporary dose is calculated and the stopping condition defined in operation S140 is evaluated. If not, the loop returns to the beginning and adjustments are continued based on the evaluation results of the previous round; if the condition is met, the loop is exited, and the subsequent steps of operation S140 are executed to generate the final dose file.

[0049] According to embodiments of this disclosure, by directly generating and iteratively optimizing candidate points characterizing the radiation location of the carbon ion beam, the steps required in traditional processes, such as manually delineating the lattice target area or relying on fixed geometric templates, are replaced. This achieves a high degree of automation and intelligence in carbon ion lattice radiotherapy planning. It enables the distribution of high-dose peaks to more flexibly adapt to irregular tumor shapes and actively avoid surrounding organs at risk. Thus, without the need for pre-setting lattice geometry, it directly generates individualized scan point plans that meet complex clinical needs, improving the quality and efficiency of treatment planning.

[0050] Based on the foregoing embodiments, the physical properties of carbon ions include at least one of the following: the Bragg peak parameter of carbon ions, the inverted dose distribution characteristics of carbon ions, the energy transfer linear density of carbon ions, the transverse scattering parameter of carbon ions, the relative biological effects of carbon ions, the controllability of the energy spectrum of carbon ions, the local ion density saturation of carbon ions, and the bystander effect parameter of carbon ions.

[0051] The physical properties of carbon ions refer to a series of quantitative model parameters describing the transport, energy deposition, and biological effects of carbon ion beams in a medium. These properties are the fundamental physical basis that distinguishes carbon ion radiotherapy from traditional photon radiotherapy.

[0052] When iteratively adjusting each candidate point and the corresponding carbon ion beam parameters, the dose calculation and evaluation process integrates the physical properties of at least one carbon ion as the core input into the dose calculation engine. This enables the temporary dose distribution generated in each iteration to highly realistically simulate the actual physical behavior and biological effects of the carbon ion beam in the patient's heterogeneous tissues, thereby providing more accurate and biologically meaningful feedback to the optimization algorithm to guide the adjustment direction and magnitude of the candidate points.

[0053] Specifically, the Bragg peak parameters of carbon ions refer to characteristic parameters describing the energy deposition depth distribution of a carbon ion beam in a medium, including peak position (range), peak height, full width at half maximum (FWHM), and the dose ratio between the plateau and peak areas. When adjusting the carbon ion beam parameters corresponding to candidate points, the carbon ion beam energy with specific Bragg peak parameters is precisely selected based on the target depth to ensure that the high-dose energy deposition region accurately covers the predetermined depth of the tumor target area. For example, to configure the dose for a candidate point located 15 cm deep in a phantom, a beam energy with a Bragg peak position parameter exactly equal to the equivalent depth of 15 cm of water would be selected.

[0054] The inverted dose distribution characteristic of carbon ions refers to the physical phenomenon where a carbon ion beam deposits a lower dose along its initial path (plateau) into the medium, but rapidly releases most of its energy near the end of its range, forming a dose peak. Optimization algorithms utilize this characteristic to prioritize beam paths that minimize the risk to upstream organs at risk when adjusting candidate locations, thereby naturally reducing the radiation dose to normal tissues.

[0055] Linear transfer density (LET) of carbon ions: refers to the energy lost per unit length of a charged particle's track. Its value is directly proportional to the square of the particle's charge and inversely proportional to the square of its velocity. The LET value of carbon ions reaches its maximum in the Bragg peak region. During the adjustment process, the system calculates and evaluates the three-dimensional distribution of LET to ensure high LET coverage within the tumor target area, thereby enhancing the killing effect on hypoxic or radiation-resistant tumor cells. For example, if an iteration shows that the LET value of a peak region generated by a candidate point is lower than a preset biological effect threshold, the algorithm may fine-tune its energy to change the LET spectrum.

[0056] The lateral scattering parameter of carbon ions describes the degree of lateral dispersion of a carbon ion beam due to Coulomb scattering as it penetrates a medium. Because of their larger mass, carbon ions scatter much less laterally than protons, resulting in a sharper dose penumbra. When adjusting candidate point positions, the lateral broadening of the dose pencil beam is calculated based on an accurate lateral scattering model. This is crucial for ensuring a steep dose gradient between the "peak" and "valley" regions in lattice radiotherapy. For example, to maintain a sufficiently low dose in the valley region, the algorithm ensures that the lateral dose distribution of adjacent candidate points does not overlap excessively in the valley region.

[0057] The relative biological effect (RBE) of carbon ions refers to the ratio of the absorbed dose of a reference beam (usually X-rays) to the absorbed dose of the beam being tested, under conditions that produce the same biological endpoint (such as cell viability). The RBE of carbon ions is not a constant value but a complex function that varies with factors such as LET (Left-Effect Transmission), dose, and tissue type. During the adjustment process, specific RBE models (such as microdose kinetic models or local effect models) are used to convert the physical dose distribution into a biologically equivalent dose distribution for evaluation and optimization, in order to more accurately predict clinical efficacy. For example, the optimization objective function is directly targeted at the RBE-weighted dose, ensuring that biological damage to organs at risk is strictly limited below the tolerance threshold.

[0058] Controllability of the carbon ion energy spectrum: This refers to the ability of a therapeutic device to provide a series of discrete and precisely tunable initial energies for the carbon ion beam. When adjusting the carbon ion beam parameters corresponding to candidate points, one or more energy layers and their corresponding weights are selected from the device's available energy list for each candidate point to form a depth-controlled dose deposition at a single candidate point location. For example, a single energy layer can be selected for a candidate point to form the sharpest dose peak, or multiple energy layers can be selected and stacked to form a miniature broadened Bragg peak (SOBP) to cover a slightly thicker target.

[0059] Local ion density saturation of carbon ions, also known as the "overkill effect," refers to the extremely high density of ionization events in high LET regions, leading to multiple hits on biological targets (such as DNA) that exceed the threshold for single-hit lethality. This means that increasing the dose no longer leads to a proportional increase in biological effect. The RBE model already includes corrections for this effect. When adjusting the weights of candidate sites, this effect is considered to avoid over-distributing particle numbers in regions that have reached saturation, thereby distributing the dose more effectively to other unsaturated regions and maximizing the overall biological effect.

[0060] The bystander effect parameter of carbon ions describes the killing or damaging effect of irradiated cells on neighboring unirradiated cells by releasing signaling molecules. In dose assessment, biological models incorporating the bystander effect can be introduced to assess indirect damage to cells within low-dose "troughs." During adjustment, this effect can be utilized to appropriately adjust the dose in the troughs while ensuring sufficient peak dose, aiming to synergistically enhance control over the entire tumor region through the bystander effect.

[0061] According to embodiments of this disclosure, by deeply integrating the unique physical and biological properties of carbon ions into the dose calculation and optimization loop, the final dose distribution is not only physically accurate but also biologically effective. This meticulous consideration of each property ensures that every decision in the optimization process—whether moving a candidate point or adjusting its energy and weights—is based on a profound understanding of the interaction between carbon ions and biological tissues. This allows for a more complete utilization of the advantages of carbon ion radiotherapy, namely, achieving a higher biological killing effect within the tumor target area while more precisely controlling biological damage to surrounding normal tissues, thereby fundamentally improving the therapeutic gain ratio of lattice radiotherapy.

[0062] Figure 2 The flowchart illustrating the setting of candidate points and corresponding carbon ion beam parameters in a carbon ion dose distribution calculation method in lattice radiotherapy according to an embodiment of the present disclosure is shown.

[0063] like Figure 2 As shown, based on the aforementioned embodiments, operation S120 may include operations S210 to S230.

[0064] In operation S210, the tumor target area and the organs at risk to be avoided are selected to generate candidate points. The tumor target area is the region where high-dose irradiation is desired, while the organs at risk are normal tissues or organs that require strict protection to limit their irradiation dose. Through this operation, one or more three-dimensional volumes will be identified as the generation region of high-dose "peaks" and one or more three-dimensional volumes as dose avoidance regions.

[0065] For example, in a plan to treat pancreatic cancer, the treating physician selects the "total pancreatic tumor volume" as the tumor target area from a list of structures through a user interface, and simultaneously selects the "spinal cord," "left kidney," and "duodenum" as organs at risk to be avoided.

[0066] In operation S220, the spatial relationship between the tumor target region and organs at risk is calculated. Spatial relationship refers to a quantitative description of the relative position, distance, overlap, or adjacency between the tumor target region and one or more organs at risk in three-dimensional space. Using geometric algorithms, the structure selected in operation S210 is analyzed to generate one or more sets of data to characterize the geometric risk between the target region and organs at risk. The results of this analysis directly guide the generation strategy for initial candidate points. One implementation involves calculating the shortest distance from each point within the target region to the surface of the nearest organ at risk, forming a distance field.

[0067] For example, starting with the surface of the "duodenum," the Euclidean distances from all voxel points within the "total pancreatic tumor volume" to that surface are calculated, thus generating a three-dimensional distance map. In this distance map, areas within the total tumor volume less than 5 mm from the duodenal surface are marked as high-risk areas, while areas greater than 20 mm are marked as low-risk areas.

[0068] In operation S230, the positional distribution of each candidate point and the corresponding carbon ion beam parameters are set according to the spatial relationship. Using the spatial relationship information calculated in operation S220, the initial positional distribution of candidate points is intelligently generated. This setting process is not a uniform or random point arrangement, but rather a differentiated point arrangement based on geometric risk, aiming to naturally make the initial point set tend towards a better solution.

[0069] Specifically, based on the calculated spatial relationships, a non-uniform initial candidate point distribution is generated within the tumor target region. In regions farther from organs at risk, the initial density of candidate points can be higher; while in regions adjacent to organs at risk, the initial density is lower, or even zero, to establish a protection margin for organs at risk before optimization begins. Each candidate point can be assigned a uniform set of initial carbon ion beam parameters, such as the same initial weights and an initial energy corresponding to the depth at the center of the target region.

[0070] For example, based on the distance map generated above, initial candidate points are generated at 15-millimeter intervals within the low-risk zone of "total pancreatic tumor volume" (greater than 20 mm from the duodenum); while no initial candidate points are generated within the high-risk zone (less than 5 mm from the duodenum). All generated candidate points are uniformly assigned an initial weight value of 1.0 and an initial energy corresponding to 100 MeV per nucleon.

[0071] According to embodiments of this disclosure, by incorporating consideration of the spatial relationship between the tumor target region and organs at risk during the initial candidate point generation stage, a guided setting of the initial distribution of candidate points is achieved. This "biased" initialization strategy ensures that the optimization process begins with pre-considering the avoidance of organs at risk, preventing iteration from starting from a completely random or uniform state that could lead to high doses to organs at risk. This significantly accelerates the convergence speed of subsequent iterative optimization processes, improves the overall efficiency of dose distribution calculation, and enhances the quality of the final plan.

[0072] Figure 3 This schematically illustrates another flowchart of setting candidate points and corresponding carbon ion beam parameters in a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure.

[0073] like Figure 3 As shown, based on the aforementioned embodiments, operation S220 may include operation S310.

[0074] In operation S310, the shrinkage range of the tumor target region and the expansion range of the organs at risk are calculated based on the polygon offset algorithm. The polygon offset algorithm is a calculation method for isometric shrinkage or expansion of two-dimensional or three-dimensional geometric contours. The shrinkage range of the tumor target region and the expansion range of the organs at risk refer to the new geometric volumes generated after the original geometric contours are shrunk or expanded by this algorithm. By generating two new virtual geometric volumes, namely the shrunk tumor target region and the expanded organs at risk, a "safe region" that allows the generation of candidate points and a "avoidance region" that prohibits the generation of candidate points are explicitly defined. The boundaries of these two newly generated volumes together constitute a quantitative definition of the spatial positional relationship between the original tumor target region and the organs at risk, providing clear geometric constraints for the initial placement of subsequent candidate points.

[0075] For example, in a case of liver cancer, the selected tumor target region is the "total liver tumor volume," and the adjacent organ at risk is the "stomach." A polygon offset algorithm is used to shrink the contour of each slice representing the "total liver tumor volume" by 5 mm, generating a new, smaller shrinking target region. Simultaneously, the contour representing the "stomach" is expanded by 3 mm, generating a larger, expanded organ at risk volume. Thus, subsequent candidate point generation operations are strictly limited to within the shrinking target region volume and must be located outside the expanded organ at risk volume.

[0076] According to embodiments of this disclosure, by employing a polygon offset algorithm, the calculation of complex spatial proximity relationships is transformed into the generation of well-defined geometric entities (inward-shrinking target area and outward-expanding organs at risk), thereby establishing a clear and unambiguous candidate point generation constraint boundary. This is not only computationally efficient but also allows for the direct application of clinical experience regarding safety boundaries (e.g., setting the margins of the planned target area or planned risk volume) to the candidate point initialization stage. This ensures that the initial candidate point location distribution naturally meets the most basic safety distance requirements, further improving the efficiency of subsequent optimization processes and the safety of the final plan.

[0077] Figure 4 The flowchart illustrating the setting of a first candidate point in a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure is shown.

[0078] like Figure 4 As shown, based on the aforementioned embodiments, the candidate point includes a first candidate point, and operation S230 may include operations S410 to S430.

[0079] In operation S410, the approximate axis of symmetry of the tumor target region is found using a reflection symmetry detection algorithm. The reflection symmetry detection algorithm is a computational method that analyzes three-dimensional geometry to determine its optimal plane or axis of symmetry. The approximate axis of symmetry is the spatial straight line found by the algorithm that maximizes the degree of coincidence between the tumor target region and itself after rotation or mirroring; it represents the main geometric orientation of the tumor target region. Operation S410 can be understood as establishing an intrinsic reference coordinate system for the tumor target region that matches its own shape. By processing the three-dimensional voxel data of the tumor target region, its most significant stretching direction or principal axis direction is automatically identified, providing an objective and reproducible reference direction for subsequent construction of a regular mesh.

[0080] For example, for a soft tissue sarcoma in the thigh region that has an irregular ellipsoidal shape, a reflection symmetry detection algorithm is run. This algorithm calculates the symmetry score of different directional axes, and finally determines the straight line along the femur direction as the approximate symmetry axis of the tumor target area, and outputs the direction vector of this axis and the coordinates of the center point.

[0081] In operation S420, multiple meshes are constructed based on the approximate axis of symmetry and the size of the tumor target region. A mesh, in this context, refers to a discrete set of points arranged in three-dimensional space according to a predetermined rule, its overall layout aligned with the approximate axis of symmetry found in operation S410. The size of the tumor target region determines the spatial extent of the mesh and the spacing between mesh points. This can also be understood as generating a three-dimensional lattice along the determined approximate axis of symmetry and orthogonal directions perpendicular to that axis. The arrangement of this lattice is ordered and regular, its orientation conforming to the main shape of the tumor target region, and its extent defined by the boundaries of the tumor target region.

[0082] For example, taking the aforementioned soft tissue sarcoma as an example, its approximate axis of symmetry is the Z-axis. Based on the tumor target area's length of 120 mm along the Z-axis and its maximum diameter of 80 mm in a plane perpendicular to the Z-axis (XY plane), a three-dimensional mesh is constructed. The mesh spacing along the Z-axis is set to 20 mm, and the meshes are arranged in squares with a spacing of 20 mm in the XY plane, thus generating a regular three-dimensional mesh lattice that spans the entire tumor target area.

[0083] In operation S430, first candidate points are placed on each grid vertex. The first candidate points are the core set of points that constitute the initial dose "peak" location. Placing the first candidate points on each grid vertex means using the coordinates of the grid lattice constructed in operation S420 as the initial three-dimensional spatial position of the first candidate points. Specifically, each regular grid vertex generated in the previous step is transformed into an initial candidate point. In some implementations, these vertices are further filtered, retaining only those vertices located within the safe area defined in previous steps (such as operation S310) as valid first candidate points.

[0084] For example, the coordinates of each vertex of the aforementioned 20-millimeter-spaced 3D mesh are initialized as a first candidate point. Before deployment, each vertex is checked; if a vertex is located outside the tumor target area after a 5-millimeter inward adjustment, or within an organ at risk after a 3-millimeter outward adjustment, that vertex is discarded and not generated as a first candidate point. Ultimately, a regular, lattice-like initial distribution of first candidate points is formed within the core safety region of the tumor target area.

[0085] According to embodiments of this disclosure, by introducing a method for constructing a regular grid based on the geometric features (approximate axis of symmetry) of the tumor target region itself, an objective and structured scheme is provided for the initial arrangement of the first candidate points. This ensures that the distribution of the initial candidate points is no longer random, but matches the macroscopic morphology of the tumor, forming a regular initial lattice structure. This structured starting point provides a superior initial solution for subsequent iterative optimization, helping to improve the stability and convergence efficiency of the optimization process, thereby generating a more reasonable and predictable dose distribution.

[0086] Figure 5 The flowchart illustrating the compensation of parameters corresponding to the first candidate point in the carbon ion dose distribution calculation method in lattice radiotherapy according to an embodiment of the present disclosure is shown.

[0087] like Figure 5 As shown, based on the aforementioned embodiments, the candidate point also includes a second candidate point, and operation S230 may also include operations S510 to S520.

[0088] In operation S510, at least one second candidate point is added within a preset range of the first candidate point, based on the size and shape of the mesh. A second candidate point is an auxiliary candidate point spatially closely related to a first candidate point. The preset range refers to a local area defined in three-dimensional space centered on a first candidate point. The size and shape of the mesh here refer to the geometric parameters of the regular lattice formed by the first candidate points, such as the point spacing and arrangement.

[0089] Specifically, based on the macroscopic, regular skeleton grid formed by the first candidate points, a set of local, fine-grained satellite points are added to each skeleton point (or a portion of the skeleton points). The locations of the added second candidate points are not arbitrary, but are arranged within a specific local space around them, based on the geometric characteristics of the first candidate point grid. This is intended to provide additional degrees of freedom for subsequent dose fine-tuning.

[0090] For example, suppose the first candidate points form a cubic grid with a spacing of 20 mm. Then, a preset range can be defined as a sphere with a radius of 5 mm centered on each first candidate point. Within this sphere, based on the cubic shape of the grid, a second candidate point is added 2 mm away from each of the first candidate points along the positive and negative directions of the three orthogonal axes X, Y, and Z. This results in a total of six second candidate points being added around each first candidate point, forming a tiny local point cluster structure.

[0091] In operation S520, the carbon ion beam parameters corresponding to the second candidate point are used to compensate for the carbon ion beam parameters corresponding to the first candidate point. Compensating for the carbon ion beam parameters corresponding to the first candidate point refers to the dose distribution generated by the second candidate point. Its purpose is not to independently form a new dose peak, but rather to correct, supplement, or optimize the dose distribution morphology generated by the associated first candidate point. Therefore, the setting of the carbon ion beam parameters (such as energy and weight) of the second candidate point is coupled with the parameters of the first candidate point. Specifically, a specific initial carbon ion beam parameter is assigned to the newly added second candidate point to enable it to play a local shaping role in the main dose peak. The first candidate point is usually assigned a higher weight to form the high-dose "peak" core in lattice radiotherapy; while the second candidate point is assigned a significantly lower weight, and its dose contribution is mainly used to adjust the dose gradient, plateau uniformity, or transition morphology to the "valley" region of the "peak".

[0092] For example, relating to the previous example, the first candidate point located at a grid vertex is assigned a relative weight of 1.0 to produce the main dose peak. The six surrounding second candidate points are each assigned a relative weight of 0.05. When the dose contributions from these points are superimposed, the sharp Gaussian dose peak originally generated by a single first candidate point is compensated into a smaller, flatter-topped, and slightly larger high-dose plateau. This compensation improves the dose uniformity within a single lattice "peak."

[0093] According to embodiments of this disclosure, by introducing second candidate points for compensation on top of a regular first candidate point grid, the ability to finely shape the local dose distribution is greatly enhanced. This method decouples the macroscopic lattice layout (determined by the first candidate points) from the microscopic dose peak morphology (determined jointly by the first and second candidate points), enabling fine adjustment of the shape of individual high-dose peaks while maintaining the overall lattice structure. This provides a more structurally superior initial solution that is closer to clinical ideals for subsequent iterative optimization, contributing to the generation of a final dose distribution with more uniform dose within the peak region and steeper peak-valley transitions.

[0094] Figure 6 The flowchart illustrating the adjustment of candidate points and corresponding carbon ion beam parameters in a carbon ion dose distribution calculation method in lattice radiotherapy according to an embodiment of the present disclosure is shown.

[0095] like Figure 6 As shown, based on the aforementioned embodiments, operation S130 may include operations S610 to S630.

[0096] In operation S610, the carbon ion lattice dose distribution is calculated based on the location of the candidate points and the corresponding carbon ion beam parameters. The carbon ion lattice dose distribution is a numerical representation of the three-dimensional dose deposition generated by all current candidate points and their corresponding carbon ion beam parameters on the patient's anatomical imaging data. Specifically, the candidate point parameter scheme for the current iteration (including the location, energy, and weight of each point) is transformed into a specific dose distribution map that can be evaluated spatially.

[0097] For example, assuming there are 100 candidate points, each with a defined spatial coordinate, energy layer, and particle weight, for each candidate point, calculate its dose contribution in the patient's CT voxel grid, and then linearly superimpose the dose contributions of all 100 points on each voxel to finally obtain an overall three-dimensional dose distribution map covering the entire calculation range.

[0098] In operation S620, the carbon ion lattice dose distribution is evaluated. Specifically, the carbon ion lattice dose distribution calculated in operation S610 is compared with preset clinical goals and constraints to quantify the merits of the current dosing regimen.

[0099] For example, the three-dimensional dose distribution calculated in the previous step is registered with the pre-drawn contours of the tumor target area and organs at risk (such as the spinal cord). Subsequently, multiple clinical evaluation parameters are automatically calculated and recorded, such as the highest dose received by the spinal cord, the average of the peak dose within the tumor target area, and the average of the dose trough.

[0100] In operation S630, the candidate points and their corresponding carbon ion beam parameters are adjusted based on the evaluation results. Adjusting the candidate points and their corresponding carbon ion beam parameters means that, based on the deviation between the current dose distribution and the clinical target identified in the evaluation step, the optimization algorithm automatically decides to modify the position of one or more candidate points or their associated carbon ion beam parameters in order to obtain a better dose distribution in the next iteration.

[0101] For example, the evaluation revealed that the peak dose to the spinal cord exceeded clinical limits. Based on this evaluation, the optimization algorithm identified several candidate points that contributed most to the spinal cord dose. As an adjustment, the algorithm decided to shift the positions of these candidate points by 1 mm away from the spinal cord, or reduce their corresponding carbon ion beam weights by 10%. The adjusted set of new candidate point parameters will be used in the next round of iterative calculations.

[0102] According to embodiments of this disclosure, by constructing an iterative optimization closed loop of "computation-evaluation-adjustment", the complex, multi-objective dose distribution optimization problem is transformed into an automated, step-by-step solution process. This can systematically and collaboratively address the mutually constraining clinical needs of high-dose coverage of the tumor target area and protection of organs at risk. Through continuous feedback and correction, the location distribution and parameter combinations of candidate points are guided to converge toward an optimal solution that satisfies all clinical constraints, thereby improving the automation level of dose calculation and the quality of the final plan.

[0103] Based on the foregoing embodiments, operation S140 may include at least one of a first operation, a second operation, a third operation, and a fourth operation.

[0104] The first step involves determining the dose depth distribution for each candidate point based on the Bragg peak parameters of carbon ions. Dose depth distribution refers to a one-dimensional profile of dose deposition formed in a medium by a single-energy carbon ion beam along its incident direction. Based on the carbon ion beam energy selected for the candidate point, the system retrieves pre-stored or analytically calculated Bragg peak parameters (e.g., range, peak position, and full width at half maximum (FWHM) corresponding to a specific energy) to determine the physical dose received by each voxel along the beam axis.

[0105] For example, for a candidate point where a dose peak needs to be projected to a depth of 12 cm underwater, an energy of 250 MeV per nucleon is assigned. Based on the Bragg peak parameters at this energy, the calculation engine generates a one-dimensional depth-dose curve, which shows a low plateau in the dose range of 0 to 11.5 cm depth, a sharp peak at 12 cm, and then a rapid drop.

[0106] The second step involves calculating a bio-dose-weighted result based on the carbon ion linear energy density and / or relative biological effect. The bio-dose-weighted result refers to a dose value that better reflects the clinical lethality, obtained by transforming the physically absorbed dose through a model that considers biological effects. While calculating the physical dose for each voxel, the linear energy density (LET) value at that point is also calculated, and the physical dose is multiplied by the corresponding RBE value at that point according to a predetermined relative biological effect (RBE) model (e.g., a micro-dose kinetic model) to obtain the bioequivalent dose.

[0107] For example, in the aforementioned case, a voxel located in the Bragg peak region at a depth of 12 cm has a physical dose of 2 Gy, but the LET value at this location is very high, and the RBE model calculates its RBE value to be 3.2. Therefore, the bio-dose weighted result for this voxel is 2 Gy multiplied by 3.2, which is 6.4 Gy (relative bio-effect weighted). In the incident plate region, however, the physical dose is 0.6 Gy, the LET value is low, and the RBE value is 1.2, resulting in a bio-dose weighted result of 0.72 Gy (relative bio-effect weighted).

[0108] The third step involves calculating the diffusion range of the dose in the surrounding voxels based on the transverse scattering parameters of the carbon ions. The diffusion range refers to the region where the dose distribution deviates from the ideal incident line and diffuses laterally due to multiple Coulomb scatterings of the carbon ions in the medium. A transverse scattering model (e.g., a Gaussian model) is used, the width (standard deviation) of which is determined by the energy and penetration depth of the carbon ions. Using this model, the dose along the central axis is distributed to the surrounding off-axis voxels according to a specific transverse profile function.

[0109] For example, for the aforementioned 250 MeV per nucleon beam, at a depth of 12 cm, the lateral scattering parameters might define a Gaussian distribution with a standard deviation of 1.5 mm. After determining the dose of a voxel at that depth on the central axis, the computational engine calculates the dose of its neighboring voxels based on this Gaussian function. The voxel at a distance of 1.5 mm from the central axis receives approximately 60.7% of the peak dose.

[0110] The fourth step involves weighted superposition of dose distributions generated at the same candidate point across multiple energy layers, based on the controllability of the carbon ion energy spectrum, the local ion density saturation of carbon ions, and the bystander effect parameters of carbon ions. Weighted superposition refers to linearly summing multiple independent three-dimensional dose distributions generated at the same candidate point by carbon ion beams with different initial energies (energy layers) according to preset weighting coefficients to form a composite dose distribution. By combining Bragg peaks at different depths, a dose deposition with a specific shape and range in the depth direction is generated for a candidate point. Specifically, using the controllability of the energy spectrum, multiple energy layers and corresponding weights are configured for a single candidate point. When calculating the biological dose of each energy layer, a correction for local ion density saturation (i.e., the "overkill" effect) can be introduced to avoid overestimating the biological effect in high LET regions. Furthermore, a bystander effect model can be introduced to provide additional compensation for the biological effect in low-dose regions. Finally, the biologically corrected dose distributions from all energy layers are superimposed.

[0111] For example, to cover a small target region from 11.5 cm to 12.5 cm in depth at a candidate site, three energy layers were configured with Bragg peaks at 11.5, 12.0, and 12.5 cm, respectively, and assigned weights of 0.8, 1.0, and 0.8. The three-dimensional bioequivalent dose distributions of each of the three energy layers were calculated (saturation effects were considered in the calculations), and then the corresponding voxels of these three-dimensional dose matrices were summed to form the final composite dose contribution for the candidate site.

[0112] According to embodiments of this disclosure, a high-fidelity dosing calculation model is constructed by incorporating various key physical and biological properties of carbon ions into each stage of dosing calculation. This model can not only accurately simulate the energy deposition of a carbon ion beam in three-dimensional space but also accurately predict its biological effects, ensuring that the dose distribution used in each iterative evaluation closely approximates actual clinical conditions. This computational precision is the fundamental guarantee for subsequent optimization and adjustments to arrive at a safe and effective treatment plan.

[0113] Figure 7 Another flowchart illustrating a method for calculating carbon ion dose distribution in lattice radiotherapy according to an embodiment of the present disclosure is shown schematically.

[0114] like Figure 7 As shown, based on the aforementioned embodiments, operation S130 may include operation S710.

[0115] In operation S710, the positions of the candidate points are adjusted, and / or the number of candidate points is increased, and / or the number of candidate points is decreased, and / or the dose weighting of the carbon ion beam corresponding to the candidate points is adjusted. Specifically, adjusting the positions of the candidate points, and / or increasing or decreasing the number of candidate points, ensures that the adjusted distribution of candidate point positions results in a valley-to-peak ratio, a lattice volume to tumor target volume ratio, and a volume where the weighted biological dose of the carbon ions is less than 5 Gy, all meeting preset requirements.

[0116] Peak-to-valley ratio (VVR), the ratio of the average dose in the high-dose region (peak region) to the average dose in the low-dose region (valley region) of a crystal lattice, is a key indicator for evaluating the quality of lattice radiotherapy. Lattice volume refers to the total volume comprised of the high-dose peak regions generated by all candidate points. The volume with a weighted biological dose less than 5 Gy refers to the volume within the tumor target area where the bioequivalent dose is below a basic effective threshold (e.g., 5 Gy); this region can be considered a treatment cold spot. Dose weight is a coefficient assigned to each candidate point that determines its dose contribution.

[0117] This adjustment is a multi-dimensional process, encompassing not only adjustments to the dose magnitude (through dose weights) but, more importantly, adjustments to the spatial distribution of the dose (by changing the position and number of candidate points). The optimization algorithm uses preset dosimetric indicators (such as peak-to-valley ratio and lattice volume ratio) as optimization targets and employs targeted adjustment strategies that can most effectively improve these indicators.

[0118] For example, in an evaluation step of a certain iteration, if it is found that the peak-to-trough ratio of the current dose distribution is only 3:1 (the preset target is greater than 5:1), and there is a region with a volume of 2 cubic centimeters at the edge of the tumor target area, whose weighted biological dose is less than 5 Gy (the preset requirement is that this volume is less than 0.5 cubic centimeters), then the following adjustments can be made:

[0119] Position Adjustment: To improve the valley-to-peak ratio, two candidate points that are too close in space and cause elevated dose in the valley area are identified, and the "position adjustment" operation is performed to increase the distance between them by 2 mm in the mutually repulsive direction.

[0120] Adding candidate points: To eliminate low-dose cold zones, the algorithm performs an "add candidate point" operation at the geometric center of the 2 cubic centimeter region, generating a new candidate point and assigning it an initial weight.

[0121] In another scenario, if the evaluation finds that the ratio of the lattice volume to the tumor target volume is too small, the algorithm may "add" new candidate points in the gaps of the current lattice. Conversely, if a candidate point is too close to an organ at risk, causing its dose contribution to be more harmful than beneficial, the algorithm may perform a "candidate point reduction" operation to remove it. At the same time, if the evaluation finds that the overall peak dose is too low, the algorithm will also perform a "dose weight adjustment" operation on all or some candidate points, uniformly increasing their weights by 5%.

[0122] According to embodiments of this disclosure, by providing multi-dimensional and multi-strategy adjustment methods, including adjusting position, increasing or decreasing quantity, and adjusting weights, the optimization process becomes highly flexible and targeted. This method effectively decouples the spatial morphology optimization of dose distribution (through position and quantity adjustments) from the optimization of dose amplitude (through weight adjustments). Based on different dosimetric assessment results, the most suitable adjustment tool can be invoked, thereby more efficiently and accurately solving specific problems encountered during the optimization process, such as insufficient peak-to-valley ratio, target cold spots, or uneven lattice coverage, ultimately guiding the dose distribution to rapidly converge to the optimal solution that meets all clinical requirements.

[0123] Figure 8 A block diagram of a carbon ion dose distribution calculation system in lattice radiotherapy according to an embodiment of the present disclosure is illustrated.

[0124] like Figure 8 As shown, the carbon ion dose distribution calculation system 800 in lattice radiotherapy may include a first acquisition module 810, a first generation module 820, and an iteration module 830.

[0125] The first acquisition module 810 is used to acquire image data of the target, which is generated based on the target ray and the target object. In some embodiments, the first acquisition module 810 can be used to perform operation S110 in the above-described method for calculating the carbon ion dose distribution in lattice radiotherapy, which will not be described in detail here.

[0126] The first generation module 820 is used to generate multiple candidate points based on the image data, wherein the candidate points represent the radiation positions of the carbon ion beam. In some embodiments, the first generation module 820 can be used to perform operation S120 in the above-described method for calculating the carbon ion dose distribution in lattice radiotherapy, which will not be elaborated here.

[0127] The iteration module 830 is used to repeatedly execute the following steps until a preset condition is met to obtain the target carbon ion dose distribution: adjusting each candidate point and the corresponding carbon ion beam parameters according to the target parameters and the physical properties of carbon ions; and generating the target carbon ion dose distribution according to the adjusted position distribution of each candidate point and the corresponding carbon ion beam parameters in response to the stopping condition. In some embodiments, the iteration module 830 can be used to execute operation S130 in the above-described carbon ion dose distribution calculation method in lattice radiotherapy, which will not be elaborated here.

[0128] 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.

[0129] For example, any plurality of the first acquisition module 810, the first generation module 820, and the iteration module 830 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 part of the functionality of one or more of these modules / units / subunits can be combined with at least part of the functionality 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 810, the first generation module 820, and the iteration module 830 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the first acquisition module 810, the first generation module 820, and the iteration module 830 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0130] 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.

[0131] Figure 9 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 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0132] like Figure 9 As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 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 901 may also include onboard memory for caching purposes. The processor 901 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.

[0133] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 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.

[0134] According to embodiments of this disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0135] 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 909, and / or installed from removable medium 911. When the computer program is executed by processor 901, 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.

[0136] 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.

[0137] 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.

[0138] For example, according to embodiments of this disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0139] 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 carbon ion dose distribution calculation method in lattice radiotherapy provided in the embodiments of this disclosure.

[0140] When the computer program is executed by the processor 901, 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.

[0141] 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 909, and / or installed from removable medium 911. 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).

[0142] 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.

[0143] 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 carbon ion dose distribution in lattice radiotherapy, comprising: Acquire image data of the target, which is generated based on the target ray and the target object; Based on the image data, multiple candidate points are generated, and the candidate points represent the emission positions of the carbon ion beam; Repeat the following steps until the preset conditions are met to obtain the target carbon ion dose distribution: Based on the target parameters and the physical properties of carbon ions, adjust the parameters of each candidate point and the corresponding carbon ion beam. In response to the satisfaction of the stopping condition, the target carbon ion dose distribution is generated based on the adjusted position distribution of each candidate point and the carbon ion beam parameters corresponding to each candidate point. The physical properties of the carbon ion include at least one of the following: the Bragg peak parameter of the carbon ion, the inverted dose distribution characteristics of the carbon ion, the energy transfer linear density of the carbon ion, the transverse scattering parameter of the carbon ion, the relative biological effect of the carbon ion, the energy spectrum controllability of the carbon ion, the local ion density saturation of the carbon ion, and the bystander effect parameter of the carbon ion. The step of adjusting each candidate point and the corresponding carbon ion beam parameters based on the target parameters and the physical properties of carbon ions includes: The carbon ion lattice dose distribution is calculated based on the position of the candidate point and the carbon ion beam parameters corresponding to the candidate point. The dose distribution of the carbon ion lattice was evaluated; Adjust the parameters of each candidate point and the corresponding carbon ion beam based on the evaluation results. The calculation of the carbon ion lattice dose distribution includes: determining the dose depth distribution of each candidate point based on the Bragg peak parameters of the carbon ions.

2. The method according to claim 1, characterized in that, The generation of multiple candidate points includes: Select the tumor target area and avoidable organs to generate candidate points; Calculate the spatial relationship between the tumor target area and the organ at risk; Based on the spatial relationship, the positional distribution of each candidate point and the corresponding carbon ion beam parameters are set.

3. The method according to claim 2, characterized in that, The calculation of the spatial relationship between the tumor target area and the organ at risk includes: Based on the polygon offset algorithm, the inward shrinkage range of the tumor target area and the outward expansion range of the organs at risk are calculated.

4. The method according to claim 2, characterized in that, The candidate points include a first candidate point, and setting the position distribution of each candidate point includes: The approximate axis of symmetry of the tumor target region is found using a reflection symmetry detection algorithm; Based on the approximate axis of symmetry and the size of the tumor target region, multiple grids are constructed; Arrange the first candidate points on each of the aforementioned grid vertices.

5. The method according to claim 4, characterized in that, The candidate points further include second candidate points, and the setting of the position distribution of each candidate point further includes: Based on the size and shape of the grid, at least one second candidate point is added within a preset range of the first candidate point; The carbon ion beam parameters corresponding to the second candidate point are used to compensate for the carbon ion beam parameters corresponding to the first candidate point.

6. The method according to claim 1, characterized in that, The calculation of the carbon ion lattice dose distribution further includes at least one of the following: The biodosage weighted result is calculated based on the energy transfer linear density of the carbon ions and / or the relative biological effects. The diffusion range of the dose in the surrounding voxel is calculated based on the transverse scattering parameters of the carbon ions. Based on the controllability of the energy spectrum of the carbon ions, the local ion density saturation of the carbon ions, and the bystander effect parameters of the carbon ions, the dose distributions formed by the same candidate point at multiple energy levels are weighted and superimposed.

7. The method according to claim 6, characterized in that, The adjustment of each candidate point and the corresponding carbon ion beam parameters includes adjusting the position of the candidate point, and / or increasing the number of candidate points, and / or decreasing the number of candidate points, and / or adjusting the dose weight of the carbon ion beam corresponding to the candidate point. Specifically, the positions of the candidate points are adjusted, and / or the number of candidate points is increased, and / or the number of candidate points is decreased, so that the valley-to-peak ratio, the ratio of lattice volume to tumor target volume, and the volume of the weighted biological dose of carbon ions less than 5 Gy meet preset requirements under the adjusted position distribution of each candidate point.

8. A carbon ion dose distribution calculation system for lattice radiotherapy, comprising: The first acquisition module is used to acquire image data of the target, the image data being generated based on the target ray and the target object; The first generation module is used to generate multiple candidate points based on the image data, wherein the candidate points represent the emission positions of the carbon ion beam; The iterative module is used to repeat the following steps until the stopping condition is met to obtain the target carbon ion dose distribution: Based on the target parameters and the physical properties of carbon ions, adjust the parameters of each candidate point and the corresponding carbon ion beam. In response to the satisfaction of the stopping condition, the target carbon ion dose distribution is generated based on the adjusted position distribution of each candidate point and the carbon ion beam parameters corresponding to each candidate point. The physical properties of the carbon ion include at least one of the following: the Bragg peak parameter of the carbon ion, the inverted dose distribution characteristics of the carbon ion, the energy transfer linear density of the carbon ion, the transverse scattering parameter of the carbon ion, the relative biological effect of the carbon ion, the energy spectrum controllability of the carbon ion, the local ion density saturation of the carbon ion, and the bystander effect parameter of the carbon ion. The step of adjusting each candidate point and the corresponding carbon ion beam parameters based on the target parameters and the physical properties of carbon ions includes: The carbon ion lattice dose distribution is calculated based on the position of the candidate point and the carbon ion beam parameters corresponding to the candidate point. The dose distribution of the carbon ion lattice was evaluated; Adjust the parameters of each candidate point and the corresponding carbon ion beam based on the evaluation results. The calculation of the carbon ion lattice dose distribution includes: determining the dose depth distribution of each candidate point based on the Bragg peak parameters of the carbon ions.

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