Method and related device for achieving inter-room beam dosimetric equivalence in multi-treatment room units

Abstract

This invention discloses a matching method and related equipment for achieving beam dosimetric equivalence between multiple treatment chamber units, belonging to the field of proton and heavy ion radiotherapy technology. The method includes: acquiring beam physical characteristic parameters of each treatment chamber unit under multiple single-energy beam conditions covering the clinical treatment energy range; defining a reference benchmark; and determining a matching parameter set for the treatment unit to be matched, using the beam physical characteristic parameters of the reference benchmark as the target, so that its beam characteristics are adjusted to be overall equivalent to the reference benchmark in the multi-dimensional characteristic space; the matching parameter set includes physical additional module parameters and dose correction parameters. Through a hierarchical method of matching single-energy beams one by one, multi-energy comprehensive iterative optimization, and unified matching parameter output, dosimetrically equivalent treatment can be performed between multiple treatment chamber units without re-planning the treatment. This improves the cross-device versatility, treatment efficiency, and safety of multi-source multi-chamber proton radiotherapy systems, making proton therapy readily accessible.

Description

[0001] This application is a divisional application of Chinese application filed on February 5, 2026, with application number 2026101636591 and invention title "Matching method and related equipment for achieving beam dosimetric equivalence between multiple treatment room units". Technical Field

[0002] This invention relates to the field of proton and heavy ion radiotherapy technology, and in particular to a matching method and related equipment for achieving dosimetric equivalence of treatment flow between treatment chambers in a multi-source, multi-chamber radiotherapy system. Background Technology

[0003] Proton and heavy ion radiotherapy, with their unique physical dose distribution advantages, have become important techniques in the field of tumor radiotherapy. Clinical implementation of particle therapy usually requires multiple sessions, with treatment cycles lasting several weeks, and the formulation and execution of treatment plans are highly dependent on the beam characteristics of specific radiotherapy unit units.

[0004] In actual clinical practice, patients may need to switch to different proton or heavy ion therapy units during their treatment cycle due to various factors such as planned equipment maintenance, sudden malfunctions, performance parameter drift, dynamic allocation of treatment load, and adjustments to clinical scheduling. However, the beam characteristics of different treatment unit units directly determine the accuracy of dose delivery. Even treatment unit units of the same model and configuration may have different beam physical characteristics due to manufacturing and installation deviations, changes in system stability during long-term operation, and drift in beam control parameters. These differences can directly lead to dose deviations exceeding clinically acceptable limits when the same treatment plan is executed on different treatment units, thus affecting treatment efficacy.

[0005] Currently, there are significant limitations in addressing the differences in beam characteristics between multiple treatment chamber units. On the one hand, existing technologies are mostly applicable to architectures where multiple treatment chambers share the same beam generation and transmission subsystem. In such architectures, the beam source of each treatment chamber is consistent with the core modulation unit, resulting in high consistency of beam parameters and eliminating the need for systematic matching. However, for radiotherapy systems consisting of multiple independently operating treatment chamber units, i.e., multi-source multi-chamber radiotherapy systems, where at least two treatment chamber units have independent particle accelerators or beam modulation systems (corresponding to the beam generation or transmission subsystems respectively) and do not share the beam generation and transmission subsystems, the differences in beam parameters are more significant. Existing technologies lack targeted beam matching solutions and cannot meet the clinical needs of cross-unit treatment.

[0006] On the other hand, existing beam adjustment methods heavily rely on the operator's experience, requiring repeated manual measurements and trial-and-error parameter adjustments to achieve approximate matching of some parameters. This is not only cumbersome and time-consuming, but also makes it difficult to guarantee the repeatability and long-term stability of the matching results. Furthermore, existing methods typically only match specific energies, depths, or a few parameters, which can easily lead to dosimetric deviations under non-reference conditions, making it difficult to achieve consistency in overall dosimetric characteristics.

[0007] Furthermore, when switching between different treatment units, existing protocols often require re-modeling the beam, verifying the dosimetry, and even re-developing the treatment plan. This not only increases the clinical workload but may also lead to treatment delays, limiting the universality and interchangeability of treatment plans across different treatment units. Summary of the Invention

[0008] To address the aforementioned issues, the present invention aims to provide a matching method and related equipment for achieving beam dosimetric equivalence between multiple treatment chamber units in a multi-source, multi-chamber radiotherapy system. This method employs a hierarchical approach, involving individual beam matching, multi-energy integrated iterative optimization, and unified matching parameter output, to accurately quantify the differences in beam characteristics between multiple independent treatment chamber units. It also jointly determines physical add-on modules and dose correction parameters, enabling cross-unit dosimetric equivalence treatment without requiring a revised treatment plan. This significantly improves the equipment versatility, treatment efficiency, and clinical safety of radiotherapy systems.

[0009] In a first aspect, the present invention provides a matching method for achieving beam dosimetric equivalence between multiple treatment chamber units, applied to a radiotherapy system comprising at least two independently operating proton or heavy ion treatment chamber units, the method comprising:

[0010] Acquire beam physical characteristic parameters of each treatment room unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone;

[0011] Define a reference baseline in the plurality of treatment room units;

[0012] For multiple monoenergetic beams of the treatment room unit to be matched, with the beam physical characteristic parameters of the reference reference as the target, a set of exclusive physical additional module parameters and dose correction parameters are generated for each monoenergetic beam as the initial matching parameter set corresponding to that monoenergetic beam;

[0013] Based on the distribution characteristics and consistency constraints of multiple initial matching parameter sets in the energy dimension, comprehensive optimization across the energy dimension is performed to generate unified physical add-on module configuration parameters applicable to the treatment room unit to be matched throughout the entire clinical energy range, as well as dose correction parameters that work synergistically with the unified physical add-on module configuration parameters.

[0014] The matching parameter set is configured such that, across the entire clinical energy range, the adjusted beam physical characteristics of the treatment chamber unit to be matched are dosimetrically equivalent to the reference reference in the multidimensional beam characteristic space.

[0015] Optionally, the step of generating a dedicated set of physical add-on module parameters and dose correction parameters for each monoenergetic beam includes:

[0016] Based on particle transport calculation methods, a mapping model is established from physical add-on module parameters and dose correction parameters to the adjusted beam characteristics.

[0017] Among the multiple monoenergetic beams of the reference benchmark, the monoenergetic beam with the smallest overall difference in the multidimensional beam characteristic space with the current monoenergetic beam of the treatment room unit to be matched is selected based on the comprehensive difference metric value, and is used as the target reference benchmark for the monoenergetic beam.

[0018] The parameters of the physical add-on module and the dose correction parameters are adjusted by an iterative optimization algorithm until the comprehensive difference between the predicted beam characteristics and the target reference meets the preset clinically acceptable error range, and this is recorded as the initial matching parameter set of the monoenergetic beam.

[0019] Optionally, the mapping model is constructed in at least one of the following ways:

[0020] Based on the Monte Carlo particle transport simulation algorithm, it is obtained by training or fitting through simulated calculation samples;

[0021] It is constructed based on the analytical theoretical formula of beam transmission;

[0022] The model is obtained by training a machine learning algorithm based on existing beam measurement data.

[0023] Preferably, the input parameters of the mapping model include at least: the number and thickness combination of the range modulator, the geometric parameters of the ridge filter, and the nominal energy of the monoenergetic beam to be matched; the output parameters include at least: the predicted beam range, the Bragg peak width, and the beam spot size.

[0024] Preferably, the comprehensive optimization includes an iterative optimization process, which uses an objective function to uniformly constrain the matching results under multiple energy conditions. The objective function is configured to characterize the overall equivalence of all monoenergetic beams of the treatment chamber unit to be matched relative to the reference benchmark in the multidimensional beam characteristic space under the unified matching parameter set configuration. When the objective function reaches the optimal or convergence condition, the corresponding unified matching parameter set is determined as the optimal matching parameter set between the treatment chamber unit to be matched and the reference benchmark.

[0025] Preferably, in the comprehensive optimization, different weighting coefficients are assigned to each monoenergetic beam; the allocation of the weighting coefficients is determined based on at least one of the following strategies:

[0026] The average contribution ratio of each beam energy to the tumor target dose is allocated based on the typical treatment plan library established based on the aforementioned reference benchmark.

[0027] The energy of each beam is allocated based on the statistical frequency of its use in historical treatment data.

[0028] Preferably, the reference datum is determined by any of the following methods:

[0029] At least one treatment chamber unit is selected from the plurality of treatment chamber units, and the beam physical characteristic parameters obtained by that treatment chamber unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone are used as the reference benchmark; or,

[0030] Based on the beam physics characteristics obtained by multiple treatment chamber units with independent beam systems under multiple monoenergetic beam conditions covering the clinical treatment energy zone, a virtual reference benchmark is constructed through comprehensive calculation to characterize the target equivalent beam of the multi-treatment chamber unit system.

[0031] Preferably, the step of constructing a virtual reference benchmark through comprehensive calculation includes:

[0032] For the same beam physical characteristic parameter of the multiple treatment room units, calculate its statistical average value, and use the set of statistical average values ​​of each parameter to constitute the beam physical characteristic parameter of the reference benchmark; or,

[0033] With the goal of minimizing the overall difficulty or expected difference in matching all treatment room units to be matched, a set of beam physical characteristic parameters is calculated in reverse optimization and used as the reference benchmark.

[0034] Preferably, the method further includes:

[0035] The determined set of matching parameters is applied to the actual operation of the treatment room unit to be matched;

[0036] Under the same dose testing conditions and treatment plan conditions, dosimetric verification is performed. By comparing the dosimetric indicators obtained by the treatment room unit to be matched with the reference baseline when performing the same treatment plan, it is determined whether the difference between the two is within the preset clinically acceptable range.

[0037] When the dosimetric verification results do not meet the preset clinically acceptable range, a feedback mechanism is triggered, returning to the comprehensive optimization steps of matching parameter sets.

[0038] Preferably, the physical add-on module includes at least one of a range adjuster and a ridge filter; the dose correction parameters include at least one of an output factor for achieving dose output consistency between different treatment chamber units and a physical distance between the dose detection point and the accelerator head for adjusting the beam spot dosimetry response.

[0039] Secondly, the present invention provides a matching device for achieving beam dosimetric equivalence between multiple treatment chamber units, applied in a radiotherapy system comprising at least two independently operating proton or heavy ion treatment chamber units, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the following steps when executing the computer program:

[0040] Acquire beam physical characteristic parameters of each treatment room unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone;

[0041] Define a reference baseline in the plurality of treatment room units;

[0042] For multiple monoenergetic beams of the treatment room unit to be matched, with the beam physical characteristic parameters of the reference reference as the target, a set of exclusive physical additional module parameters and dose correction parameters are generated for each monoenergetic beam as the initial matching parameter set corresponding to that monoenergetic beam;

[0043] Based on the distribution characteristics and consistency constraints of multiple initial matching parameter sets in the energy dimension, comprehensive optimization across the energy dimension is performed to generate unified physical add-on module configuration parameters applicable to the treatment room unit to be matched throughout the entire clinical energy range, as well as dose correction parameters that work synergistically with the unified physical add-on module configuration parameters.

[0044] The matching parameter set is configured such that, across the entire clinical energy range, the adjusted beam physical characteristics of the treatment chamber unit to be matched are dosimetrically equivalent to the reference reference in the multidimensional beam characteristic space.

[0045] Thirdly, the present invention provides a multi-source, multi-chamber radiotherapy system, comprising:

[0046] At least two independently operating treatment room units, each with its own independent beam generation and transmission subsystem;

[0047] as well as,

[0048] The matching device for achieving beam dosimetric equivalence between multiple treatment room units as described in the embodiments of the present invention;

[0049] The device is configured to uniformly match the beam characteristics between the at least two treatment chamber units, so that different treatment chamber units are dosimetrically interchangeable when performing the same treatment plan.

[0050] Fourthly, the present invention provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of any of the methods described in the embodiments of the present invention or the functions of the apparatus described in the embodiments of the present invention.

[0051] Fifthly, the present invention provides a computer-readable storage medium that stores computer instructions, wherein when a computer reads the computer instructions, the computer executes the steps of the method described in any one of the embodiments of the present invention.

[0052] Compared with the prior art, the beneficial effects of the present invention include at least the following: for the treatment system architecture of multi-chamber independent operation, a beam matching method is systematically proposed, filling the gap of lack of corresponding technical solutions under this architecture. Its essence lies in realizing the characteristic conversion between different beam sources, rather than relying on the natural physical consistency of traditional co-beam systems, laying a technical foundation for achieving clinical dosimetric equivalence between heterogeneous treatment units.

[0053] Abandoning the traditional approach of coarse matching based on a few representative energies, this method decomposes the therapeutic beam into multiple monoenergetic beams covering the clinical energy range, comprehensively considering multidimensional characteristic parameters such as depth dose distribution, lateral dose distribution, beam spot size, range, and absolute dose. By independently analyzing and initially matching each monoenergetic beam, the precision of the matching and the coverage of the entire energy range are significantly improved.

[0054] This invention combines parameter adjustment of physical add-on modules (such as range adjusters and ridge filters) with mapping correction of dose correction parameters (such as output factor and detector distance). Through the synergistic optimization of hardware adjustment and software correction, the beam range, Bragg peak morphology, beam spot size, and absolute dose are simultaneously controlled, effectively avoiding the local optima that may be trapped in by single parameter adjustment, and achieving a more comprehensive and stable beam characteristic matching.

[0055] Overcoming the limitations of independent matching for single-energy beams, this paper proposes minimizing the comprehensive differences among all single-energy beams in the beam characteristic space as the global optimization objective. Through iterative optimization, a unified set of physical add-on module configuration parameters applicable to the entire clinical energy range and their corresponding dose correction parameter mapping relationships are derived. This parameter system can be directly integrated into the treatment control system, supporting the rapid recall of matching parameters based on beam conditions during actual clinical operation, significantly improving the efficiency of engineering applications.

[0056] Through the above systematic matching and optimization, the present invention enables different independent treatment room units to achieve dosimetric equivalence within a clinically acceptable error range. This means that the treatment plan for the same patient does not need to be re-formulated, optimized and verified due to the change of treatment room unit, and can be executed safely and reliably. This provides key technical support for patients to receive treatment across treatment room units and significantly improves the clinical flexibility, safety and overall operational efficiency of multi-source multi-room particle radiotherapy systems. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of a matching method for achieving beam dosimetric equivalence between multiple treatment room units according to an embodiment of the present invention;

[0058] Figure 2 This is a flowchart illustrating the matching method for achieving beam dosimetric equivalence between multiple treatment room units according to an embodiment of the present invention.

[0059] Figure 3 This is a schematic diagram of a multi-source, multi-chamber radiotherapy system according to an embodiment of the present invention. Detailed Implementation

[0060] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided to make the invention more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore repeated descriptions of them will be omitted.

[0061] The terms used to express position and direction in this invention are illustrated with reference to the accompanying drawings, but changes can be made as needed, and all such changes are included within the scope of protection of this invention.

[0062] Example 1, refer to Appendix Figure 1 and attached Figure 2 This embodiment provides a matching method for achieving beam dosimetric equivalence between multiple treatment chamber units, applied to a radiotherapy system comprising at least two independently operating proton or heavy ion treatment chamber units. The method includes:

[0063] Acquire beam physical characteristic parameters of each treatment room unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone;

[0064] A reference benchmark is defined in the plurality of treatment room units, and the beam physical characteristic parameters of the reference benchmark are used as the target reference to determine a matching parameter set for at least one treatment room unit to be matched, which is used to match the beam physical characteristics of the treatment room unit to be matched to the reference benchmark, so that the difference in beam characteristics between the two treatment rooms after matching meets the preset clinically acceptable error range.

[0065] The matching parameter set includes physical add-on module parameters and dose correction parameters; when the beam physical characteristic parameters of the treatment room unit to be matched are consistent with those of the reference reference, the matching parameter set is empty.

[0066] The matching parameter set is configured such that, across the entire clinical energy range, through a unified physical add-on module configuration and dose correction, the adjusted beam physical characteristics of the treatment chamber unit to be matched are made dosimetrically equivalent to the reference benchmark in the multidimensional beam characteristic space.

[0067] Here, consistency means that, without applying any matching parameter set for active configuration adjustment, the difference in beam physical characteristics between the treatment room unit to be matched and the reference benchmark has met the preset clinically acceptable error range.

[0068] Obtaining the beam physical characteristic parameters includes:

[0069] For each treatment chamber unit, or treatment unit, beam measurement data is acquired under multiple monoenergetic beam conditions covering the clinical treatment energy zone. The beam measurement data is processed using unified measurement conditions, measurement procedures, and data analysis methods across all treatment chamber units to extract beam physics characteristic parameters used to characterize the beam physics and dosimetry behavior.

[0070] The beam physical characteristic parameters together constitute a multidimensional beam characteristic space for describing the beam behavior of the corresponding treatment chamber unit, so as to ensure that the beam characteristic parameters of different treatment chamber units are comparable in the same characteristic space.

[0071] The beam physical characteristic parameters include at least one or more of the following parameters: integral depth dose distribution, range, Bragg peak correlation characteristics, beam spot size and / or transverse dose distribution.

[0072] Uniform measurement conditions include using the same measurement equipment and the same accelerator beam output settings;

[0073] For parameters that need to be analyzed and calculated, the same set of code and the same hyperparameter settings are used for extraction;

[0074] The beam physical characteristic parameters include at least one or more of the following parameters: integral depth dose distribution, range, Bragg peak correlation characteristics, beam spot size, and transverse dose distribution.

[0075] In application, for multi-source, multi-chamber radiotherapy systems, at least two independently operating treatment chamber units (denoted as treatment chamber unit j and treatment chamber unit k, see reference for details) are required. Figure 3 As shown, Figure 3 There are three treatment chamber units: the leftmost unit is treatment chamber unit j, and the middle unit is treatment chamber unit k. Both treatment chamber units j and k have particle accelerators and beam transport subsystems for generating the beam. Standardized beam measurements will be conducted first.

[0076] Multiple monoenergetic beam conditions were set according to the requirements of covering the clinical treatment energy zone. Beam irradiation tests were conducted on each treatment room unit to collect raw measurement data reflecting the physical characteristics of the beam. The core data included the integrated depth dose curve (IDD), beam spot size at different spatial locations, and absolute dose information.

[0077] To avoid measurement deviations affecting subsequent matching accuracy, the measurement process for all treatment room units strictly follows a unified standard: using the same measurement equipment, maintaining the same measurement conditions (such as consistent detector distance from the accelerator center), and adopting the same accelerator beam output settings (such as uniform dose MU values) to ensure the comparability of raw data.

[0078] Based on the collected raw measurement data, parameters that can accurately characterize the core properties of the beam are further extracted:

[0079] The extracted beam physical characteristic parameters include at least one or more of the following: integral depth dose distribution, range, Bragg peak correlation characteristics (such as peak position, peak width, and peak height), beam spot size, and transverse dose distribution.

[0080] For parameters such as the Bragg peak width that need to be obtained through analysis and calculation, the same set of code and the same hyperparameter settings are used to avoid distortion of feature parameters due to differences in calculation methods, and to ensure that the feature parameter extraction logic of different treatment room units is completely consistent.

[0081] One of the treatment chamber units (e.g., treatment chamber unit k) can be used as a reference benchmark, and its beam physical characteristic parameters can be used as a target reference to establish a dedicated set of beam matching parameters for the other treatment chamber units to be matched (e.g., treatment chamber unit j).

[0082] This matching parameter set includes physical add-on module parameters and dose correction parameters, designed to characterize and realize the convertible relationship of beam physical characteristics between treatment chamber units. Specifically, it includes physical add-on module parameters (such as range adjuster combination, ridge filter configuration) and dose correction parameters (such as output factor, physical distance between dose detection point and accelerator head), used to adjust the beam characteristics of the treatment chamber units to be matched.

[0083] The matching parameter set is configured such that, across the entire clinical energy range, through a unified physical add-on module configuration and dose correction, the adjusted beam physical characteristics of the treatment chamber unit to be matched are made dosimetrically equivalent to the reference benchmark in the multidimensional beam characteristic space.

[0084] When the difference in beam characteristics between the treatment unit to be matched and the reference baseline meets the preset clinically acceptable error range without any active adjustment, it can be determined that the beam physical parameters between the two units are clinically consistent. At this time, the corresponding matching parameter set is empty, and no additional adjustment parameters need to be configured.

[0085] By applying this matching parameter set, the beam physics characteristics of the treatment room unit to be matched can be precisely matched to the level of the reference benchmark, ultimately supporting dosimetric equivalence when different treatment room units perform the same clinical treatment plan.

[0086] The effects of the above technical solution are as follows:

[0087] For radiotherapy systems consisting of multiple independently operating proton / heavy ion therapy chamber units, this embodiment provides a systematic beam matching solution, which solves the shortcomings of existing technologies that are only applicable to multi-treatment chamber shared beam systems and lack multi-energy / multi-parameter matching schemes across independent units, and achieves precise unification of beam dosimetric characteristics between different independent treatment chamber units.

[0088] It covers multiple dimensions of beam physical characteristics, such as integral depth dose distribution, range, Bragg peak correlation characteristics, beam spot size, and lateral dose distribution. It fully considers the physical coupling relationship between each parameter, avoiding dose deviation under non-reference conditions caused by existing technologies that only target a few parameters or specific energy matching, and ensuring the overall consistency of beam characteristics across the entire clinical energy range.

[0089] By applying the matching parameter set, the difference in beam characteristics between the treatment unit to be matched and the reference benchmark is made to meet the clinically acceptable error range. Without the need to redo the treatment plan, remodel, or repeatedly verify the dosimetry, patients can receive continuous treatment across treatment units, effectively avoiding treatment delays, reducing clinical workload, and improving the equipment utilization and operational efficiency of the radiotherapy system.

[0090] It supports cross-unit treatment switching in various scenarios such as treatment room unit fault backup, dynamic allocation of treatment load, and clinical scheduling adjustment. When the beam parameters of the treatment room units are naturally consistent, the matching parameter set is automatically empty and no additional adjustment is required. Through a closed-loop dose verification mechanism, it further ensures the dosimetric equivalence of cross-unit treatment, minimizes the treatment risks caused by equipment differences, and ensures patient treatment safety.

[0091] The reference benchmark is determined in any of the following ways:

[0092] At least one treatment chamber unit is selected from the plurality of treatment chamber units, and the beam physical characteristic parameters obtained by that treatment chamber unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone are used as the reference benchmark; or,

[0093] Based on the beam physics characteristics obtained by multiple treatment chamber units with independent beam systems under multiple monoenergetic beam conditions covering the clinical treatment energy zone, a virtual reference benchmark is constructed through comprehensive calculation to characterize the target equivalent beam of the multi-treatment chamber unit system.

[0094] In one possible implementation, the beam physical characteristic parameters obtained by the treatment room units, which are based on multiple independent beam systems, under multiple monoenergetic beam conditions covering the clinical treatment energy zone, are used to construct a virtual reference benchmark through comprehensive calculation, including:

[0095] For the same beam physical characteristic parameter of the multiple treatment units, calculate its statistical average value, and use the set of statistical average values ​​of each parameter to form the beam physical characteristic parameter of the reference benchmark; the statistical average value is one of arithmetic mean, geometric mean or weighted average.

[0096] From multiple treatment units within a radiotherapy system, one unit is selected as a reference treatment unit. The beam physics characteristics of this unit (obtained through the standardized measurement and extraction process described above, including integral depth dose distribution, range, Bragg peak correlation characteristics, beam spot size, and lateral dose distribution) are directly used as a unified reference benchmark for the system. This method requires no additional calculations, directly targeting the actual beam characteristics of existing units, and is suitable for scenarios with stable performance and mature clinical applications of treatment units.

[0097] When it is necessary to avoid the deviation of a single unit characteristic, a virtual reference benchmark is generated by combining the parameters of multiple units using the statistical averaging method. The specific steps are as follows:

[0098] Collect beam physical characteristic parameters of all treatment units through a standardized process to ensure that parameters of the same type are comparable and to eliminate outliers that exceed the preset range in advance to avoid interfering with the results;

[0099] For each beam physical characteristic parameter, the statistical average of that dimension parameter is calculated separately for all treatment units. The statistical method can be one of arithmetic mean, geometric mean, or weighted mean (the weighted mean can be combined with unit stability and application frequency to assign weights).

[0100] The statistical average values ​​of the beam physical characteristic parameters of each dimension are integrated into a complete parameter set, which serves as a reference benchmark. This benchmark characterizes the average level of the multi-unit beam characteristics and provides a neutral target for the system.

[0101] For each beam physics characteristic parameter (such as range, Bragg peak position, beam spot size, absolute dose, etc.), a separate statistical average calculation is performed across treatment units. The specific logic is as follows:

[0102] The beam physical characteristic parameters of all treatment units are classified by dimension to form several parameter sets of the same dimension, such as the range parameter set of all units and the Bragg peak width parameter set of all units.

[0103] For each set of parameters in the same dimension, a statistical average is calculated using one of the following methods: arithmetic mean, geometric mean, or weighted mean. The weighted mean can be weighted based on factors such as the clinical application duration of the treatment unit and parameter stability, making the mean more closely reflect actual clinical needs. Abnormal parameter values ​​exceeding a preset normal fluctuation range can be removed before calculation to ensure that the statistical average reflects the true level of beam characteristics for most treatment units.

[0104] The effects of the above technical solution are as follows:

[0105] The baseline setting method can be selected according to the actual system situation. Single-unit selection is suitable for scenarios that pursue high efficiency and have mature baseline units, while the statistical averaging method is suitable for scenarios that need to avoid the bias of a single unit and pursue system balance, covering different clinical operation and maintenance needs. Regardless of the method, it is based on the beam physical characteristic parameters extracted by standardized measurement to ensure that the baseline parameters are accurate and traceable, providing a stable and reliable target reference for subsequent beam matching and avoiding matching deviations caused by baseline distortion.

[0106] The virtual reference generated by the comprehensive calculation of multi-unit parameters can offset the inherent deviation of the equipment and accidental measurement deviation of a single unit, and better match the overall beam characteristics of the system, avoiding the multi-unit matching imbalance caused by the extreme characteristics of a single reference unit.

[0107] Compared to selecting a single treatment unit as a reference benchmark, a virtual benchmark constructed based on the statistical average of multiple units can offset the inherent bias and accidental measurement bias of a single unit, better reflect the overall beam characteristics of the system, and improve the fairness and accuracy of multi-unit matching.

[0108] In another possible implementation, the beam physical characteristic parameters obtained by the treatment room units with multiple independent beam systems under multiple monoenergetic beam conditions covering the clinical treatment energy zone are used to construct a virtual reference benchmark through comprehensive calculation, including:

[0109] With the goal of minimizing the overall difficulty or expected difference in matching all treatment units to be matched, a set of beam physical characteristic parameters is calculated in reverse optimization and used as the reference benchmark.

[0110] The overall difficulty or expected difference is quantified by estimating the range of physical add-on module parameters that need to be adjusted when each treatment unit to be matched with a candidate reference benchmark, or by the expected comprehensive difference metric.

[0111] When it is necessary to reduce the overall difficulty of beam matching across the entire system and improve matching efficiency, the goal is to minimize the overall difficulty or expected difference in matching all treatment units to be matched. A virtual reference baseline is generated through reverse optimization calculation. The specific steps are as follows:

[0112] To clarify the quantification method for overall difficulty or expected differences, two paths can be chosen: First, clarify the quantification standard for overall difficulty or expected differences, and achieve this by adopting one of the following two paths: One is to estimate the range of physical auxiliary module parameters that need to be adjusted when each treatment unit to be matched is matched with the candidate reference benchmark (such as the adjustment range of the range adjuster, the change in the thickness of the spinal filter). The smaller the adjustment range, the lower the matching difficulty; the other is based on a preset formula. The expected comprehensive difference metric value of the beam physical characteristic parameters after each unit matches with the candidate reference is calculated. The smaller the metric value, the smaller the expected difference. Here, Δ is the comprehensive difference metric value of the beam physical characteristic parameters, w_k is the preset weight coefficient of the beam characteristic in the k-th dimension, ΔP_k is the difference value of the beam physical characteristic parameters in the k-th dimension, and P_k0 is the beam physical characteristic parameter value of the reference reference in the k-th dimension.

[0113] With the goal of minimizing the sum of quantitative indicators of all treatment units to be matched, an inverse optimization function is constructed, with the constraint that the beam physical characteristic parameters of the candidate reference benchmark must be within a clinically reasonable range (such as the range and beam spot size meeting clinical treatment requirements).

[0114] The objective function is solved by iterative optimization algorithm to obtain a set of optimal beam physics characteristic parameters. This set of parameters is the reference benchmark generated by inverse optimization, which can minimize the overall difficulty of matching the whole system or minimize the expected difference.

[0115] Verify whether the matching difficulty / expected difference of each unit corresponding to the benchmark parameter group is within the clinically acceptable range, and ensure that the benchmark takes into account both global optimization and individual adaptability.

[0116] The effects of the above technical solution are as follows:

[0117] By locking the optimal baseline through reverse optimization, the adjustment range of physical additional module parameters of each unit to be matched can be significantly reduced, the number of iterations can be reduced, equipment adjustment losses and clinical operation and maintenance time can be reduced, and the overall efficiency of beam matching between multiple treatment units can be greatly improved.

[0118] With the goal of minimizing the overall difficulty / difference of the entire system, and with the addition of clinical parameter constraints, the generated benchmark can balance the matching needs of each unit, avoid excessive adjustment pressure on a single unit, and adapt to the actual treatment scenario, ensuring that the beam characteristics after matching meet clinical requirements.

[0119] By adjusting the range through physical add-on modules and quantifying the matching difficulty and differences through comprehensive difference metrics, combined with clear optimization algorithms and constraints, the entire benchmark generation process is transparent and traceable, and the results are stable and reproducible, avoiding interference from subjective experience.

[0120] The generated benchmark can minimize the overall difference after the entire system is matched, improve the consistency of the beam characteristics of each treatment unit from the source, provide solid support for the equivalent execution of cross-unit treatment plans, and minimize the multi-unit matching imbalance caused by equipment differences.

[0121] By minimizing the overall matching difficulty / expected difference, the parameter adjustment range of each unit to be matched can be narrowed, the number of iterations can be reduced, equipment adjustment losses and clinical operation and maintenance time can be reduced, and the overall efficiency and consistency of beam matching of the whole system can be improved.

[0122] In another possible implementation, the beam physical characteristic parameters obtained by the treatment room units with multiple independent beam systems under multiple monoenergetic beam conditions covering the clinical treatment energy zone are used to construct a virtual reference benchmark through comprehensive calculation, including:

[0123] It has a library of pre-stored standard beam models or historical typical beam physical characteristic parameter sets as candidate benchmarks;

[0124] Calculate the degree of similarity between the beam physics characteristic parameters of the multiple treatment units and the comprehensive similarity between them and the candidate benchmarks in the candidate benchmark library;

[0125] The reference benchmark is generated by selecting the candidate benchmark with the highest overall similarity, or by fusing multiple candidate benchmarks with high similarity.

[0126] The reference benchmark is generated by comprehensively calculating the beam physical characteristic parameters of multiple treatment units. The beam physical characteristic parameters of the generated reference benchmark are different from the original beam physical characteristic parameters of any one of the multiple treatment units.

[0127] When applied, a candidate benchmark library can be pre-built and stored. The library contains a variety of standard beam models (such as ideal beam models that conform to clinical treatment guidelines) or historical typical beam physical characteristic parameter sets (such as high-quality beam parameters that have been validated in previous multi-center clinical trials). All candidate benchmarks have been clinically validated to ensure that they are suitable for actual treatment scenarios.

[0128] Beam physics characteristic parameters extracted from multiple treatment units using a standardized process are collected. For each candidate benchmark in the database, the overall similarity between the benchmark and the beam physics characteristic parameters of all treatment units is calculated. The calculation is based on a pre-defined formula for a comprehensive difference metric. The smaller the Δ value, the higher the degree of proximity, and vice versa, comprehensively covering core dimensions such as range, Bragg peak characteristics, and beam size.

[0129] The reference benchmark is generated using one of the following two methods: First, the candidate benchmark with the highest overall similarity is directly selected as the unified reference benchmark for the system; second, multiple candidate benchmarks with high overall similarity are selected and integrated through weighted fusion, parameter complementarity, and other methods to generate a new reference benchmark.

[0130] After generating the reference baseline, ensure that its beam physical characteristic parameters are different from the original beam physical characteristic parameters of any of the multiple treatment units, so as to avoid the reference baseline becoming a replica of a single unit and to ensure the adaptability and neutrality of the reference baseline to the entire system.

[0131] The effects of the above technical solution are as follows:

[0132] The standard models and historical parameters pre-set in the candidate benchmark library have all been clinically validated. The reference benchmarks generated based on this do not require additional clinical rationality verification and can be directly connected to the subsequent beam matching process, which greatly reduces the time and manpower costs of benchmark validation.

[0133] The generated baseline is different from the original parameters of any treatment unit, which not only eliminates the dependence on a single unit, but also balances the differences in beam characteristics among multiple units, avoiding global matching imbalance caused by the baseline bias towards a certain unit, and adapting to systems with large differences in the characteristics of each unit.

[0134] Without the need for complex reverse optimization or statistical modeling, benchmarks can be generated simply by quantifying and filtering / merging the degree of proximity. The process is simple, transparent, and reproducible, and the candidate benchmark library can be pre-configured, eliminating the need for real-time calculations and improving the efficiency of benchmark construction.

[0135] The candidate benchmark library uses standardized models and historical typical parameters. Compared with benchmarks generated in real time, it is less affected by accidental measurement deviations and unit performance fluctuations, and the benchmark parameters are more stable, providing a reliable target for subsequent cross-unit beam matching.

[0136] In one possible implementation, determining the matching parameter set includes:

[0137] Based on the aforementioned beam physical characteristic parameters, within the same multidimensional beam characteristic space, the systematic differences between the reference benchmark and the treatment room unit to be matched in multiple beam characteristic dimensions are analyzed.

[0138] Using the beam physical characteristic parameters of the reference benchmark as the target reference, for the multiple monoenergetic beams of the treatment room unit to be matched, a set of dedicated physical additional module parameters and dose correction parameters are generated for each monoenergetic beam in an independent calculation manner, which serves as the initial matching parameter set corresponding to the monoenergetic beam; wherein, the initial matching parameter set is configured to: directionally adjust the beam behavior of the treatment room unit to be matched in multiple beam characteristic dimensions, so as to achieve preliminary equivalence of its beam characteristics with the reference benchmark.

[0139] The analysis examines the systematic differences between the reference benchmark and the treatment room unit to be matched across multiple beam characteristic dimensions; including:

[0140] For each monoenergetic beam covering the clinical treatment energy zone, the characteristic deviation between the reference benchmark and the treatment room unit to be matched is quantified in multiple preset beam characteristic dimensions to obtain the corresponding multidimensional difference value set.

[0141] Based on the set of multidimensional difference values, a comprehensive difference metric is constructed to characterize the overall deviation of the monoenergetic beam within the multidimensional beam characteristic space. The comprehensive difference metric is used to characterize the overall equivalent deviation level of the monoenergetic beam relative to the reference benchmark.

[0142] The beam physics model can be constructed based on the Monte Carlo particle transport algorithm; multiple preset dimensions include range, Bragg peak position, Bragg peak width, beam spot size, absolute dose, and consistency of lateral dose distribution.

[0143] To obtain high-precision beam characteristic parameters, Monte Carlo simulation based on the Geant4 toolkit can be used. First, the three-dimensional geometry of the treatment head is accurately reconstructed, including key components such as the range adjuster, ridge filter, and collimator, and a standard water phantom is configured to ensure that the model geometry is consistent with the actual treatment equipment. At the physical process level, key physical behaviors such as the electromagnetic interaction and nuclear reactions between protons (or heavy ions) and materials are fully simulated to accurately characterize the beam transmission and energy deposition processes in complex media.

[0144] During simulation, detailed configuration parameters for both the reference reference and the treatment chamber unit to be matched are input for a specific monoenergetic beam (defined by the initial proton energy). These parameters include at least: the initial proton energy, the thickness of each adjustment plate in the range modulator, and the specific model and installation location of the ridge filter. After the simulation runs, a high-precision integrated depth-dose curve is generated by statistically analyzing the energy deposition data of the detectors in the phantom. Then, core beam physics characteristics such as range and Bragg peak half-width at half-maximum (FWHM) are automatically extracted from this curve. By comparing the two sets of simulation outputs from the reference reference and the unit to be matched, the differences between them in six preset dimensions—range, Bragg peak position, Bragg peak width, beam spot size, absolute dose, and lateral dose distribution consistency—can be precisely quantified. These differences are then fused using a preset weighted algorithm to form a comprehensive difference metric characterizing the overall difference level of the monoenergetic beam.

[0145] To meet the extremely high computational efficiency requirements of scenarios (such as rapid analysis of multi-energy batches and real-time scheduling pre-assessment), a fast analytical calculation model based on the beam envelope equation can be adopted. This model focuses on the physical processes that have the most significant impact on the final dose distribution (such as beam transmission and range determination mechanisms), and reasonably simplifies secondary interactions, thereby reducing the single-shot characteristic calculation time to the millisecond level while ensuring the accuracy of key parameter calculations (such as range error <1 mm).

[0146] When applying the model, the basic configuration parameters of the reference benchmark and the unit to be matched are input separately. The model can quickly output the predicted values ​​of the feature parameters of the two units across multiple preset dimensions and their differences, and also generate a comprehensive difference metric, providing real-time and efficient data support for subsequent matching decisions.

[0147] The effects of the above technical solution are as follows:

[0148] The Monte Carlo simulation implementation ensures high fidelity of the extracted beam physical characteristic parameters by fully reproducing the device geometry and core physical processes. Its output error is small, making it particularly suitable for scenarios with extremely high matching accuracy requirements (such as intracranial tumor treatment), and providing a reliable data foundation for generating high-quality initial matching parameters.

[0149] The rapid analytical model implementation achieves millisecond-level computation speed with acceptable accuracy loss through algorithm optimization. It is perfectly suited for application scenarios that require rapid differential analysis of a large number of monoenergetic beams or real-time scheduling of treatment resources. It solves the core pain point of traditional methods being too time-consuming to compute in complex models and failing to meet the requirements of clinical efficiency.

[0150] The aforementioned model not only compares data on a single dimension but also systematically quantifies differences across multiple core dosimetric dimensions, including range, Bragg peak morphology, beam spot size, absolute dose, and lateral distribution consistency. This multi-dimensional comprehensive evaluation mechanism fundamentally avoids the one-sidedness of matching caused by focusing only on a single parameter (such as output dose), ensuring that the calculation of subsequent initial matching parameters is based on a comprehensive and objective understanding of the differences. This lays a solid data foundation for ultimately achieving the matching goal of clinical dosimetric equivalence.

[0151] Based on the comprehensive difference metric calculated independently for each monoenergetic beam using the aforementioned model, the system can tailor its own initial matching parameters for each energy point. This one-beam-one-scheme strategy ensures that the matching parameter calculation process for each energy point is independent, effectively avoiding parameter interference problems that may occur between different energy beams due to physical coupling. The direct effect is that the characteristics of each monoenergetic beam can independently meet the clinical matching requirements with the reference benchmark, thereby improving the consistency and reliability of beam characteristic matching across the entire clinical energy range at the unit level.

[0152] In one possible implementation, based on the set of multidimensional difference values, a comprehensive difference metric is constructed by weighted averaging to characterize the overall deviation of the monoenergetic beam within the multidimensional beam characteristic space.

[0153] The overall difference metric for monoenergetic beams is calculated using the following formula:

[0154]

[0155] Δ is the comprehensive difference measure of the beam physical characteristic parameters, w_k is the preset weight coefficient of the beam characteristic in the kth dimension, ΔP_k is the difference value of the beam physical characteristic parameters in the kth dimension, and P_k0 is the beam physical characteristic parameter value of the reference benchmark in the kth dimension.

[0156] The preset weight coefficients for each dimension are determined based on at least one of the following criteria:

[0157] Clinical dosimetry priority criteria;

[0158] Based on parameter sensitivity.

[0159] In one possible implementation, based on the clinical dosimetry priority, the sum of the weight coefficients of the first type of dimension, which is directly related to the dose accuracy of the tumor target area, is set to be higher than the sum of the weight coefficients of the second type of dimension, which is related to the dose distribution or beam morphology of the non-target area.

[0160] The first type of dimension includes at least range, Bragg peak position, and absolute dose; the second type of dimension includes at least beam spot size and lateral dose distribution consistency.

[0161] The sum of the weight coefficients of the first type of dimension is not less than 0.6.

[0162] In another possible implementation, based on the parameter sensitivity, a relatively higher weighting coefficient is assigned to the dimensions of the beam physical characteristic parameters that are more difficult to adjust or whose deviations have a more significant impact on clinical dose distribution.

[0163] The weighting coefficients for the range dimension and the Bragg peak position dimension are no less than the weighting coefficients for the beam spot size dimension.

[0164] The weighting coefficients are also adaptively fine-tuned according to specific clinical treatment scenarios, and the fine-tuning is based on a preset scenario-weight correction mapping relationship.

[0165] The effects of the above technical solution are as follows:

[0166] By prioritizing clinical dosimetry, the weight of target dose-related dimensions is locked at no less than 0.6, ensuring that the comprehensive difference metric value prioritizes the parameter deviations that play a decisive role in the treatment effect, avoiding secondary dimensions from interfering with the core matching target, making the beam matching results more in line with clinical dosimetry requirements, and ensuring accurate delivery of target dose.

[0167] The basic weights are set by combining clinical priority and parameter sensitivity to eliminate subjective experience bias. At the same time, it supports fine-tuning based on specific clinical scenarios, which not only adapts to the treatment needs of different tumor types and sites, but also ensures the standardization of weight adjustment (based on preset mapping relationship), thereby improving the universality and clinical adaptability of the method.

[0168] The formula normalizes the difference values ​​of each dimension through P_k0 (reference unit baseline parameter), which solves the problem that parameters of different dimensions such as range, beam size, and absolute dose cannot be directly integrated. This enables the comprehensive difference measurement value to objectively reflect the relative size of the differences in each dimension, providing a precise and unified judgment standard for iterative optimization.

[0169] The comprehensive difference metric serves as a core indicator for initial matching parameter selection, iterative convergence, and multi-energy comprehensive optimization. It achieves unified indicators for the entire process of beam difference analysis, parameter optimization, and optimization verification, ensuring smooth connection between each link and strengthening the closed-loop nature and rigor of the technical solution.

[0170] By prioritizing key sensitive dimensions such as beam range and Bragg peak position, the deviation of core parameters is precisely controlled, while taking into account the dose distribution in non-target areas and the optimization of beam morphology. This minimizes the risks of insufficient target dose and damage to normal tissues caused by beam differences, providing a safety guarantee for patients undergoing treatment across treatment room units.

[0171] In one possible implementation, the generation of a dedicated set of physical add-on module parameters and dose correction parameters for each monoenergetic beam includes:

[0172] Based on particle transport calculation methods, a mapping model is established from physical add-on module parameters and dose correction parameters to the adjusted beam characteristics.

[0173] Among the multiple monoenergetic beams of the reference benchmark, the monoenergetic beam with the smallest overall difference in the multidimensional beam characteristic space with the current monoenergetic beam of the treatment room unit to be matched is selected based on the comprehensive difference metric value, and is used as the target reference benchmark for the monoenergetic beam.

[0174] In the parameter search space, the physical auxiliary module parameters and dose correction parameters used to adjust the current monoenergetic beam of the treatment room unit to be matched are adjusted by an iterative optimization algorithm.

[0175] In each iteration, the adjusted beam characteristics are predicted using the mapping model, and a comprehensive difference metric between the adjusted beam characteristics and the beam physical characteristic parameters of the target reference is calculated.

[0176] When the comprehensive difference metric value meets the preset clinically acceptable error range, the iteration stops, and the parameter combination at this time is recorded as the initial matching parameters of the monoenergetic beam.

[0177] The physical add-on module parameters include the number of stages of the range regulator and the equivalent water thickness of each stage, as well as the thickness of the ridge filter; the dose correction parameters include the output factor correction coefficient and the physical distance correction between the dose detection point and the accelerator head.

[0178] In addition to range modulators, other physical structures or devices with equivalent regulating effects can be used, such as range modulators made of different materials or geometries; energy modulation structures with equivalent Bragg peak broadening functions; and adjustable or multi-level combined physical regulation modules.

[0179] For example, when calculating the initial matching parameters, the adjustment step size of the range adjuster is constrained to be no greater than 0.2 mm, the thickness gradient of the ridge filter is constrained to be no greater than 0.1 mm, the correction accuracy of the output factor is constrained to be no less than ±1%, and the calibration error of the physical distance is constrained to be no greater than ±0.1 mm.

[0180] In one possible implementation, the mapping model is constructed in at least one of the following ways:

[0181] Based on the Monte Carlo particle transport simulation algorithm, it is obtained by training or fitting through simulated calculation samples;

[0182] It is constructed based on the analytical theoretical formula of beam transmission;

[0183] The model is obtained by training a machine learning algorithm based on existing beam measurement data.

[0184] The input parameters of the mapping model include at least: the number and thickness combination of the range modulator, the geometric parameters of the ridge filter, and the nominal energy of the monoenergetic beam to be matched; the output parameters include at least: the predicted beam range, the Bragg peak width, and the beam spot size.

[0185] In practical applications, a quantitative correlation model (i.e., a mapping model) is constructed based on particle transport calculation methods, linking physical add-on module parameters and dose correction parameters to the adjusted beam characteristics. The physical add-on module parameters encompass the combination scheme of the range adjuster and the selection and configuration parameters of the ridge filter. The dose correction parameters cover the output factor corresponding to the same absolute dose and the physical distance between the dose detection / calculation point and the accelerator head when the beam spot size is consistent. This model can accurately predict the trend of beam physical characteristics changes under different parameter combinations, providing a reliable computational basis for subsequent parameter iterations.

[0186] For the i-th monoenergetic beam b in treatment room unit j j,i Using the monoenergetic beam physical characteristic parameters of treatment chamber unit k as a reference database, b is calculated. j,i The beam with the smallest comprehensive difference metric value among all monoenergetic beams in the database is selected as the initial matching beam, and the physical characteristic parameters of this beam are used as the target reference benchmark to ensure that subsequent parameter adjustments have a clear and reasonable optimization direction.

[0187] Within a predefined parameter search space, an iterative optimization algorithm is used to adjust the current monoenergetic beam b used to regulate the treatment room unit j. j,i Physical add-on module parameters and dose correction parameters: In each iteration, the adjusted parameters are input into the mapping model to predict the corresponding beam physical characteristics; the comprehensive difference metric between the predicted beam physical characteristics and the target reference benchmark is calculated to determine whether it meets the preset clinically acceptable error range.

[0188] When the generated initial matching parameter set is generated and the comprehensive difference metric obtained from the iterative calculation meets the clinically acceptable error range, the iteration is immediately stopped. The physical add-on module parameters and dose correction parameters at this time are combined and recorded as the monoenergetic beam b of treatment room unit j. j,i A dedicated initial matching parameter set. Repeat the above process to generate a corresponding initial matching parameter set for each monoenergetic beam in treatment chamber unit j, achieving independent and precise matching of the monoenergetic beams.

[0189] Particle transport computation is a numerical calculation method that describes the motion, interaction, and energy deposition of charged particles (protons, heavy ions) in a medium. Its core is to accurately simulate the entire process of the beam being drawn from the accelerator, modulated by treatment head components (range adjuster, ridge filter, etc.), and finally forming a dose distribution in the phantom / patient body by solving the particle transport equation.

[0190] In this embodiment, the particle transport calculation method can be any of the following, including:

[0191] Monte Carlo particle transport algorithm: This is a numerical simulation method based on statistical sampling. Its core is to track the random trajectories of a large number of particles and obtain macroscopic beam physics parameters through statistical averaging. The algorithm fully incorporates the electromagnetic interactions (such as ionization and scattering) and nuclear reactions (such as nuclear fragmentation and inelastic scattering) between particles and the atoms in the medium, accurately reproducing the transport patterns of particles in range adjusters, ridge filters, collimators, and phantoms. In practical applications, mature toolkits such as Geant4 and FLUKA can be used, eliminating the need to write physical process code from scratch and allowing direct access to pre-set high-precision physical databases.

[0192] Analytical Particle Transport Algorithm: This is a simplified calculation method based on the beam envelope equation and dose deposition theory. Its core principle is to ignore the randomness of particle motion and derive the relationship between beam characteristics and control parameters through theoretical formulas. The beam is treated as a particle cluster with statistically averaged characteristics, and the beam envelope equation describes the cross-sectional changes of the cluster during transmission (corresponding to beam spot size). Using Bragg peak formation theory, a quantitative relationship is established between the range adjuster thickness and the beam range and Bragg peak position. Experimental fitting coefficients are introduced to correct theoretical deviations, ensuring that core parameters (such as range error <1mm) meet clinical requirements while achieving millisecond-level rapid calculations.

[0193] The mapping model is a numerical mapping model that takes physical add-on module parameters and dose correction parameters as inputs and adjusted beam characteristic parameters as outputs. The input parameters include the range adjuster thickness combination, ridge filter model, output factor, and detector point distance. The output parameters include beam physical characteristic parameters such as range, Bragg peak position, Bragg peak width, beam spot size, absolute dose, and lateral dose distribution consistency.

[0194] The effects of the above technical solution are as follows:

[0195] Using the beam physics characteristics of treatment unit k as a reference database, the beam closest to the beam to be matched is selected as the benchmark, avoiding adjustment deviations caused by blindly setting optimization targets and ensuring that the parameter adjustment direction is highly consistent with the clinical dosimetric equivalence requirements. A dedicated initial matching parameter set is generated for each monoenergetic beam, and the parameter tuning process for each beam is independent, effectively solving the problem of parameter coupling between beams of different energies. This ensures that the characteristics of each monoenergetic beam meet the clinical matching requirements with the reference unit, improving matching consistency across the entire energy range. The mapping model covers parameters of core physical add-on modules such as the range adjuster and ridge filter, as well as key dose correction parameters such as the output factor and probe distance, accurately predicting the impact of parameter changes on beam characteristics. Combined with an iterative optimization algorithm, it can quickly converge to a parameter combination that meets clinical error requirements, significantly improving beam matching accuracy. The specific types of physical add-on modules and dose correction parameters are clearly defined; they are all conventional control parameters for proton / heavy ion therapy systems, requiring no additional hardware modifications and can be directly interfaced with the parameter control modules of existing treatment unit units, reducing the difficulty of clinical implementation.

[0196] In one possible implementation, determining the matching parameter set includes:

[0197] For the treatment room unit to be matched, under multiple monoenergetic beam conditions covering the clinical treatment energy zone, a set of initial matching parameters is obtained respectively.

[0198] Based on the distribution characteristics and consistency constraints of the multiple initial matching parameter sets in the energy dimension, the initial matching parameter sets are comprehensively optimized across the energy dimension.

[0199] Through the comprehensive optimization, uniform physical add-on module configuration parameters applicable to the treatment room unit to be matched across the entire clinical energy range are generated, along with dose correction parameters that work in conjunction with the uniform physical add-on module configuration parameters, to achieve overall beam dosimetric equivalence of the treatment room unit to be matched relative to the reference reference.

[0200] The comprehensive optimization includes an iterative optimization process, which uses an objective function to uniformly constrain the matching results under multiple energy conditions. The objective function is configured to characterize the overall equivalence of all monoenergetic beams of the treatment chamber unit to be matched relative to the reference benchmark in the multidimensional beam characteristic space under the unified matching parameter set configuration. When the objective function reaches the optimal or convergence condition, the corresponding unified matching parameter set is determined as the optimal matching parameter set between the treatment chamber unit to be matched and the reference benchmark.

[0201] The overall beam dosimetric equivalence is quantified by calculating a global comprehensive metric of the differences in physical characteristic parameters of each monoenergetic beam, with minimizing this metric as the specific optimization objective. This comprehensive metric can employ different forms of difference evaluation functions, including but not limited to: weighted average difference; maximum difference minimization; constraint optimization where all differences are less than a preset threshold; and similarity indices based on statistical characteristics. Different evaluation methods can all serve as equivalent alternatives to the optimization objective function.

[0202] The physical add-on module includes at least one of a range modulator and a ridge filter; the dose correction parameters include at least one of an output factor for achieving dose output consistency between different treatment chamber units and a physical distance between the dose detection point and the accelerator head for adjusting the beam spot dosimetric response.

[0203] The comprehensive optimization adopts a joint optimization strategy, taking the differences in beam geometry and energy characteristics in physical space, as well as the differences in dose distribution in dosimetric characteristics space, as joint optimization objectives. At the same time, engineering implementation constraints of physical additional modules are introduced in the comprehensive optimization process. These engineering implementation constraints include at least physical size constraints, material property constraints, and / or manufacturability constraints to ensure that the obtained unified matching parameter set is engineering-realizable.

[0204] The physical spatial differences include beam spot size deviation and physical distance deviation between the dose detection point and the accelerator head; the dosimetric spatial differences include range deviation, Bragg peak position / width deviation, and absolute dose deviation.

[0205] The feasibility constraints of the project include: the adjustment step size of the range adjuster is ≤0.2mm, the thickness gradient of the ridge filter is ≤0.1mm, and the total adjustment range of the physical additional modules does not exceed the mechanical limits of the equipment.

[0206] The iterative optimization process is not limited to specific algorithm implementations and may employ optimization strategies including, but not limited to, the following: least squares method or its variations; gradient descent optimization methods; intelligent optimization algorithms such as genetic algorithms and particle swarm optimization; and parameter inversion methods based on machine learning or data-driven approaches. Different optimization algorithms only affect computational efficiency or convergence path, but all can obtain beam matching results that meet clinical error requirements.

[0207] In the comprehensive optimization, different second weighting coefficients are assigned to each monoenergetic beam; the assignment of the second weighting coefficients is determined according to at least one of the following strategies:

[0208] The average contribution ratio of each beam energy to the tumor target dose is allocated based on the typical treatment plan library established based on the aforementioned reference benchmark.

[0209] The energy of each beam is allocated based on the statistical frequency of its use in historical treatment data.

[0210] As a specific implementation method, a weighting coefficient of not less than 0.5 is assigned to the single-energy beam within the preset commonly used energy range of the tumor target area, and a weighting coefficient of not more than 0.5 is assigned to the single-energy beam outside the range. The commonly used energy range of the tumor target area is a sub-range within the range of 70MeV to 230MeV.

[0211] The physical add-on module parameters include the thickness combination of each adjustment plate in the range modulator, and the model or core geometric parameters of the ridge filter. Different configuration strategies can be adopted for beams with energies below or equal to a preset threshold (e.g., 100 MeV): a single-stage range modulator combined with a thinner ridge filter is preferred; for beams above this threshold, a multi-stage range modulator combined with a thicker ridge filter is preferred.

[0212] In another possible implementation, an initial matching is performed on a portion of representative energies, and then expanded to the full energy range, or a phased, multi-level matching strategy is adopted; or different weights and priorities are set according to the importance of different physical properties, prioritizing the accuracy of important physical properties, and discarding the accuracy of some relatively unimportant physical properties when necessary.

[0213] In practice, an initial matching parameter set can be independently generated for each monoenergetic beam in the treatment room unit j to be matched, ensuring that each beam, after individual adjustment, meets the clinical matching requirements with the beam corresponding to the reference benchmark k. Subsequently, the initial parameters of all monoenergetic beams are integrated to form a physical add-on module parameter set and a dose correction parameter set, and then globally fused and optimized. The specific steps are as follows:

[0214] Using the beam physical characteristic parameters of each monoenergetic beam in treatment chamber unit j as target reference values, global fusion of multiple beam parameters is achieved through iterative calculation and optimization. The core optimization objective is to minimize the global comprehensive metric, that is, to minimize the overall difference between the adjusted beam physical characteristic parameters of each monoenergetic beam in treatment chamber unit j and the original physical characteristic parameters corresponding to the reference benchmark k under a unified matching parameter configuration. This optimization simultaneously considers the dual sub-objectives of physical space and dosimetric space, forming a global and sub-objective synergistic optimization system to ensure that the results balance spatial accuracy and dosimetric equivalence.

[0215] The differences are precisely divided into two categories: physical spatial differences focus on the beam spatial morphology and measurement calibration accuracy, mainly including beam spot size deviation and physical distance deviation between the dose detection point and the accelerator head; dosimetric spatial differences focus on core treatment dose parameters, mainly including range deviation, Bragg peak position / width deviation and absolute dose deviation, to achieve refined management and control of the differences.

[0216] Iterative optimization is initiated within a preset parameter space. In each iteration, the current unified physical add-on module configuration parameters and dose correction parameter mapping relationship are applied to each original monoenergetic beam in treatment room unit k. The difference between the adjusted beam physical characteristic parameters and the target reference value is calculated, thereby obtaining the global comprehensive metric value. The parameters are continuously iterated and adjusted until the global comprehensive difference reaches the minimum value or converges to the preset threshold. The corresponding parameter configuration at this point is the optimal beam matching parameter.

[0217] To ensure the feasibility of the optimization results in actual clinical equipment, engineering constraints were applied concurrently during the iteration process, including: a range adjuster adjustment step size of ≤0.2mm to ensure range adjustment accuracy; a ridge filter thickness gradient of ≤0.1mm to maintain beam morphology stability; and the total adjustment range of the physical add-on modules not exceeding the mechanical limits of the equipment to prevent parameters from exceeding the actual operating capabilities of the equipment. These constraints ensured the compatibility of the optimization results with the equipment hardware.

[0218] The effects of the above technical solution are as follows:

[0219] By eliminating the limitations of independent parameters for single-energy beams, a unified parameter configuration and mapping relationship is formed through comprehensive optimization. This can cover the beam adjustment needs of the entire clinical energy range of the treatment room unit to be matched, eliminating the need to configure parameters separately for different energy beams. This greatly improves the universality and clinical applicability of the parameters, making it suitable for multi-energy clinical treatment scenarios.

[0220] By taking the differences in physical space and dosimetric space as a joint objective, it ensures the consistency of spatial parameters such as beam spot size and detection distance, while precisely controlling the deviation of core dose parameters such as range, Bragg peak characteristics, and absolute dose, so that the matching results fully meet the dual requirements of clinical treatment for dose accuracy and beam morphology stability.

[0221] By clearly defining the precision and range constraints of mechanical adjustments, the problem of insufficient compatibility between theoretical optimization and clinical equipment is avoided, thus ensuring that the optimal parameters can be directly applied to actual treatment room units and reducing the difficulty of implementation and promotion.

[0222] With the minimum global comprehensive metric as the core objective, combined with the convergence threshold judgment criteria, the optimization process is ensured to converge in an orderly manner. The resulting unified parameters can minimize the overall beam difference across the entire energy range, which significantly improves the stability and consistency of beam matching across treatment room units compared to the configuration of dispersed parameters.

[0223] In one possible implementation, the method further includes:

[0224] The determined set of matching parameters is applied to the actual operation of the treatment room unit to be matched;

[0225] Under the same dose testing conditions and treatment plan conditions, dosimetric verification is performed. By comparing the dosimetric indicators obtained by the treatment room unit to be matched with the reference baseline when performing the same treatment plan, it is determined whether the difference between the two is within the preset clinically acceptable range.

[0226] When the dosimetric verification results do not meet the preset clinically acceptable range, a feedback mechanism is triggered, returning to the comprehensive optimization steps of matching parameter sets.

[0227] In one possible implementation, the dose verification includes offline verification and online verification:

[0228] For example, the offline verification requires the use of a standard phantom to perform the verification plan, with a gamma pass rate of not less than 95% for the dose distribution, an absolute dose deviation of not more than ±2%, and a beam spot size deviation of not more than ±0.3 mm;

[0229] The online verification requires real-time monitoring of beam parameters during clinical treatment. When a deviation is detected that exceeds a preset threshold, an alarm is triggered or automatic adjustment is performed.

[0230] For example, the clinically acceptable error range includes: a range deviation of no more than 0.3 mm and an absolute dose deviation of no more than ±3%.

[0231] In one possible implementation, if the verification fails, the system automatically identifies the energy points and feature parameter dimensions with the most significant deviations, and assigns higher optimization weights to these energy points or parameter dimensions in subsequent optimization iterations.

[0232] Offline validation proactively identifies static parameter deviations, while online validation manages dynamic treatment risks in real time. This dual approach, combined with clear quantitative indicators, ensures that dosage accuracy in cross-treatment unit therapy meets clinical requirements, minimizing the risks of insufficient target dose and damage to normal tissues. When validation fails, the system automatically identifies core deviation points and assigns them higher optimization weights, avoiding blind iterations, significantly reducing the time cost of secondary optimization, and improving the specificity of optimization results, ensuring rapid convergence to clinically acceptable parameter configurations. Clear quantitative thresholds for gamma pass rate, dose deviation, and range deviation replace traditional vague experience-based judgments, ensuring consistent evaluation standards for validation results from different operators and treatment batches, improving the repeatability and stability of matching results.

[0233] When the radiotherapy system includes three or more treatment room units, one of them is selected as the global reference benchmark, and the remaining units are treatment room units to be matched and the matching method is executed respectively; each treatment room unit to be matched can indirectly achieve equivalent execution of treatment plans between them based on the matching result with the global reference benchmark.

[0234] All units to be matched are based on the same global reference unit, and their beam characteristics are aligned to a unified standard, indirectly achieving equivalent execution of treatment plans between them. This breaks the limitation of treatment plans being tied to a single device, allowing patients to flexibly switch between any equivalent treatment room units according to their needs such as treatment load, equipment failure, and scheduling adjustments, adapting to diverse clinical scenarios. Using a single global reference unit avoids the problem of cumulative deviations caused by inconsistent references when matching multiple units pairwise, ensuring that the beam characteristics of all treatment room units follow the same standard calibration, improving the consistency and accuracy of beam matching across the entire system, guaranteeing dosimetric equivalence of cross-unit treatment from the source, and reducing clinical treatment risks.

[0235] Example 2: This example provides a matching device for achieving beam dosimetric equivalence between multiple treatment chamber units, applied to a radiotherapy system comprising at least two independently operating proton or heavy ion treatment chamber units. The device includes a memory and a processor. The memory stores a computer program, and the processor executes the following steps when executing the computer program:

[0236] Acquire beam physical characteristic parameters of each treatment room unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone;

[0237] A reference benchmark is defined in the plurality of treatment room units, and the beam physical characteristic parameters of the reference benchmark are used as the target reference to determine a matching parameter set for at least one treatment room unit to be matched, which is used to match the beam physical characteristics of the treatment room unit to be matched to the reference benchmark, so that the difference in beam characteristics between the two treatment rooms after matching meets the preset clinically acceptable error range.

[0238] The matching parameter set includes physical add-on module parameters and dose correction parameters; when the beam physical characteristic parameters of the treatment room unit to be matched are consistent with those of the reference reference, the matching parameter set is empty.

[0239] The matching parameter set is configured such that, across the entire clinical energy range, through a unified physical add-on module configuration and dose correction, the adjusted beam physical characteristics of the treatment chamber unit to be matched are made dosimetrically equivalent to the reference benchmark in the multidimensional beam characteristic space.

[0240] The physical add-on module includes at least one of a range adjuster and a ridge filter; the dose correction parameters include at least one of an output factor for achieving dose output consistency between different treatment chamber units and a physical distance between a dose detection point and the accelerator head, i.e., the accelerator treatment head, for adjusting the beam spot dosimetric response.

[0241] The reference benchmark is determined in any of the following ways:

[0242] At least one treatment chamber unit is selected from the plurality of treatment chamber units, and the beam physical characteristic parameters obtained by that treatment chamber unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone are used as the reference benchmark; or,

[0243] Based on the beam physics characteristics obtained by multiple treatment chamber units with independent beam systems under multiple monoenergetic beam conditions covering the clinical treatment energy zone, a virtual reference benchmark is constructed through comprehensive calculation to characterize the target equivalent beam of the multi-treatment chamber unit system.

[0244] When the processor executes the computer program, it obtains the beam physical characteristic parameters by performing the following steps:

[0245] For each treatment room unit, beam measurement data is acquired under multiple monoenergetic beam conditions covering the clinical treatment energy zone. The beam measurement data is processed using unified measurement conditions, measurement procedures, and data analysis methods across all treatment room units to extract beam physics characteristic parameters used to characterize the beam physics and dosimetry behavior.

[0246] The beam physical characteristic parameters together constitute a multidimensional beam characteristic space for describing the beam behavior of the corresponding treatment chamber unit, so as to ensure that the beam characteristic parameters of different treatment chamber units are comparable in the same characteristic space.

[0247] The beam physical characteristic parameters include at least one or more of the following parameters: integral depth dose distribution, range, Bragg peak correlation characteristics, beam spot size, and transverse dose distribution.

[0248] When the processor executes the computer program, it determines the matching parameter set by performing the following steps:

[0249] Based on the aforementioned beam physical characteristic parameters, within the same multidimensional beam characteristic space, the systematic differences between the reference benchmark and the treatment room unit to be matched in multiple beam characteristic dimensions are analyzed.

[0250] Using the beam physical characteristic parameters of the reference benchmark as the target reference, for multiple monoenergetic beams of the treatment room unit to be matched, a set of exclusive physical additional module parameters and dose correction parameters are generated for each monoenergetic beam as the initial matching parameter set corresponding to that monoenergetic beam;

[0251] The initial matching parameter set is configured to directionally adjust the beam behavior of the treatment room unit to be matched in multiple beam characteristic dimensions to achieve preliminary equivalence of its beam characteristics with the reference benchmark.

[0252] When the processor executes the computer program, it analyzes the systematic differences between the reference benchmark and the treatment room unit to be matched in multiple beam characteristic dimensions by performing the following steps:

[0253] Based on particle transport theory, a beam physics model is constructed to characterize the original beam physics state of each treatment chamber unit. The input parameters of the beam physics model include at least the initial beam energy, energy dispersion, spatial position distribution, and beam spot characteristic parameters.

[0254] For each monoenergetic beam covering the clinical treatment energy zone, the characteristic deviation between the reference benchmark and the treatment room unit to be matched is quantified in multiple preset beam characteristic dimensions to obtain the corresponding multidimensional difference value set.

[0255] Based on the set of multidimensional difference values, a comprehensive difference metric is constructed to characterize the overall deviation of the monoenergetic beam within the multidimensional beam characteristic space. The comprehensive difference metric is used to characterize the overall equivalent deviation level of the monoenergetic beam relative to the reference benchmark.

[0256] When the processor executes the computer program, it generates a set of dedicated physical add-on module parameters and dose correction parameters for each monoenergetic beam by performing the following steps:

[0257] Based on particle transport calculation methods, a mapping model is established from physical add-on module parameters and dose correction parameters to the adjusted beam characteristics.

[0258] Among the multiple monoenergetic beams of the reference benchmark, the monoenergetic beam with the smallest overall difference in the multidimensional beam characteristic space with the current monoenergetic beam of the treatment room unit to be matched is selected based on the comprehensive difference metric value, and is used as the target reference benchmark for the monoenergetic beam.

[0259] In the parameter search space, the physical auxiliary module parameters and dose correction parameters used to adjust the current monoenergetic beam of the treatment room unit to be matched are adjusted by an iterative optimization algorithm.

[0260] In each iteration, the adjusted beam characteristics are predicted using the mapping model, and a comprehensive difference metric between the adjusted beam characteristics and the beam physical characteristic parameters of the target reference is calculated.

[0261] When the comprehensive difference metric value meets the preset clinically acceptable error range, the iteration stops, and the parameter combination at this time is recorded as the initial matching parameter set of the monoenergetic beam.

[0262] When the processor executes the computer program, it also determines the matching parameter set by performing the following steps:

[0263] For the treatment room unit to be matched, under multiple monoenergetic beam conditions covering the clinical treatment energy zone, a set of initial matching parameters is obtained respectively.

[0264] Based on the distribution characteristics and consistency constraints of the multiple initial matching parameter sets in the energy dimension, the initial matching parameter sets are comprehensively optimized across the energy dimension.

[0265] Through the comprehensive optimization, uniform physical add-on module configuration parameters applicable to the treatment room unit to be matched across the entire clinical energy range are generated, along with dose correction parameters that work in conjunction with the uniform physical add-on module configuration parameters, to achieve overall beam dosimetric equivalence of the treatment room unit to be matched relative to the reference reference.

[0266] The comprehensive optimization includes an iterative optimization process, which uses an objective function to uniformly constrain the matching results under multiple energy conditions. The objective function is configured to characterize the overall equivalence of all monoenergetic beams of the treatment chamber unit to be matched relative to the reference benchmark in the multidimensional beam characteristic space under the unified matching parameter set configuration. When the objective function reaches the optimal or convergence condition, the corresponding unified matching parameter set is determined as the optimal matching parameter set between the treatment chamber unit to be matched and the reference benchmark.

[0267] The comprehensive optimization adopts a joint optimization strategy, taking the differences in beam geometry and energy characteristics in physical space, as well as the differences in dose distribution in dosimetric characteristics space, as joint optimization objectives. At the same time, engineering implementation constraints of physical additional modules are introduced in the comprehensive optimization process. These engineering implementation constraints include at least physical size constraints, material property constraints, and / or manufacturability constraints to ensure that the obtained unified matching parameter set is engineering-realizable.

[0268] When the processor executes the computer program, it also performs the following steps:

[0269] The determined set of matching parameters is applied to the actual operation of the treatment room unit to be matched;

[0270] Under the same dose testing conditions and treatment plan conditions, dosimetric verification is performed. By comparing the dosimetric indicators obtained by the treatment room unit to be matched with the reference baseline when performing the same treatment plan, it is determined whether the difference between the two is within the preset clinically acceptable range.

[0271] When the dosimetric verification results do not meet the preset clinically acceptable range, a feedback mechanism is triggered, returning to the comprehensive optimization steps of matching parameter sets.

[0272] The principle and effect of the above technical solution are the same as those of the method described in Example 1, and will not be repeated here.

[0273] This invention also provides a multi-source, multi-chamber radiotherapy system, comprising:

[0274] At least two independently operating treatment room units, each with its own independent beam generation and transmission subsystem;

[0275] as well as,

[0276] The device for achieving beam matching between multiple treatment room units as described in Example 2;

[0277] The device is configured to uniformly match the beam characteristics between the at least two treatment chamber units, so that different treatment chamber units are dosimetrically interchangeable when performing the same treatment plan.

[0278] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in Embodiment 1 of this invention or the function of the device described in Embodiment 2 of this invention.

[0279] This invention also provides a computer-readable storage medium for storing a computer program. When the computer program is executed, it implements the steps of the method in Embodiment 1 of this invention. The specific implementation method is consistent with the implementation method and the technical effects achieved in the above method embodiments, and some contents will not be repeated.

[0280] In this invention, a readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The program product can take the form of any combination of one or more readable media. A readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0281] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on a user computing device, partially on an associated device, as a standalone software package, partially on a user computing device and 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).

[0282] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the invention without departing from the principles and spirit of the invention, and all such changes should fall within the protection scope of the claims of the present invention.

Claims

1. A matching method for achieving beam dosimetric equivalence between multiple treatment room units, characterized in that, The method, applied to a radiotherapy system comprising at least two independently operating proton or heavy ion therapy chamber units, includes: Acquire beam physical characteristic parameters of each treatment room unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone; Define a reference baseline in the plurality of treatment room units; For multiple monoenergetic beams of the treatment room unit to be matched, with the beam physical characteristic parameters of the reference reference as the target, a set of exclusive physical additional module parameters and dose correction parameters are generated for each monoenergetic beam as the initial matching parameter set corresponding to that monoenergetic beam; Based on the distribution characteristics and consistency constraints of multiple initial matching parameter sets in the energy dimension, comprehensive optimization across the energy dimension is performed to generate unified physical add-on module configuration parameters applicable to the treatment room unit to be matched throughout the entire clinical energy range, as well as dose correction parameters that work synergistically with the unified physical add-on module configuration parameters. The matching parameter set is configured such that, across the entire clinical energy range, the adjusted beam physical characteristics of the treatment chamber unit to be matched are dosimetrically equivalent to the reference reference in the multidimensional beam characteristic space.

2. The method according to claim 1, characterized in that, The process involves generating a dedicated set of physical add-on module parameters and dose correction parameters for each monoenergetic beam, including: Based on particle transport calculation methods, a mapping model is established from physical add-on module parameters and dose correction parameters to the adjusted beam characteristics. Among the multiple monoenergetic beams of the reference benchmark, the monoenergetic beam with the smallest overall difference in the multidimensional beam characteristic space with the current monoenergetic beam of the treatment room unit to be matched is selected based on the comprehensive difference metric value, and is used as the target reference benchmark for the monoenergetic beam. The parameters of the physical add-on module and the dose correction parameters are adjusted by an iterative optimization algorithm until the comprehensive difference between the predicted beam characteristics and the target reference meets the preset clinically acceptable error range, and this is recorded as the initial matching parameter set of the monoenergetic beam.

3. The method according to claim 2, characterized in that, The mapping model is constructed in at least one of the following ways: Based on the Monte Carlo particle transport simulation algorithm, it is obtained by training or fitting through simulated calculation samples; It is constructed based on the analytical theoretical formula of beam transmission; The model is obtained by training a machine learning algorithm based on existing beam measurement data.

4. The method according to claim 2, characterized in that, The input parameters of the mapping model include at least: the number and thickness combination of the range modulator, the geometric parameters of the ridge filter, and the nominal energy of the monoenergetic beam to be matched; the output parameters include at least: the predicted beam range, the Bragg peak width, and the beam spot size.

5. The method according to claim 1, characterized in that, The comprehensive optimization includes an iterative optimization process, which uses an objective function to uniformly constrain the matching results under multiple energy conditions. The objective function is configured to characterize the overall equivalence of all monoenergetic beams of the treatment room unit to be matched relative to the reference benchmark in the multidimensional beam characteristic space under a unified matching parameter set configuration. When the objective function reaches the optimal or convergence condition, the corresponding unified matching parameter set is determined as the optimal matching parameter set between the treatment room unit to be matched and the reference benchmark.

6. The method according to claim 1, characterized in that, In the comprehensive optimization, different weighting coefficients are assigned to each monoenergetic beam; the allocation of these weighting coefficients is determined based on at least one of the following strategies: The average contribution ratio of each beam energy to the tumor target dose is allocated based on the typical treatment plan library established based on the aforementioned reference benchmark. The energy of each beam is allocated based on the statistical frequency of its use in historical treatment data.

7. The method according to claim 1, characterized in that, The reference benchmark is determined in any of the following ways: At least one treatment chamber unit is selected from the plurality of treatment chamber units, and the beam physical characteristic parameters obtained by that treatment chamber unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone are used as the reference benchmark; or, Based on the beam physics characteristics obtained by multiple treatment chamber units with independent beam systems under multiple monoenergetic beam conditions covering the clinical treatment energy zone, a virtual reference benchmark is constructed through comprehensive calculation to characterize the target equivalent beam of the multi-treatment chamber unit system.

8. The method according to claim 7, characterized in that, The construction of a virtual reference benchmark through comprehensive calculation includes: For the same beam physical characteristic parameter of the multiple treatment room units, calculate its statistical average value, and use the set of statistical average values ​​of each parameter to constitute the beam physical characteristic parameter of the reference benchmark; or, With the goal of minimizing the overall difficulty or expected difference in matching all treatment room units to be matched, a set of beam physical characteristic parameters is calculated in reverse optimization and used as the reference benchmark.

9. The method according to claim 1, characterized in that, Also includes: The determined set of matching parameters is applied to the actual operation of the treatment room unit to be matched; Under the same dose testing conditions and treatment plan conditions, dosimetric verification is performed. By comparing the dosimetric indicators obtained by the treatment room unit to be matched with the reference baseline when performing the same treatment plan, it is determined whether the difference between the two is within the preset clinically acceptable range. When the dosimetric verification results do not meet the preset clinically acceptable range, a feedback mechanism is triggered, returning to the comprehensive optimization steps of matching parameter sets.

10. The method according to claim 1, characterized in that, The physical add-on module includes at least one of a range modulator and a ridge filter; the dose correction parameters include at least one of an output factor for achieving dose output consistency between different treatment chamber units and a physical distance between the dose detection point and the accelerator head for adjusting the beam spot dosimetric response.

11. A matching device for achieving beam dosimetric equivalence between multiple treatment room units, characterized in that, An application in a radiotherapy system comprising at least two independently operating proton or heavy ion therapy chamber units, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: Acquire beam physical characteristic parameters of each treatment room unit under multiple monoenergetic beam conditions covering the clinical treatment energy zone; Define a reference baseline in the plurality of treatment room units; For multiple monoenergetic beams of the treatment room unit to be matched, with the beam physical characteristic parameters of the reference reference as the target, a set of exclusive physical additional module parameters and dose correction parameters are generated for each monoenergetic beam as the initial matching parameter set corresponding to that monoenergetic beam; Based on the distribution characteristics and consistency constraints of multiple initial matching parameter sets in the energy dimension, comprehensive optimization across the energy dimension is performed to generate unified physical add-on module configuration parameters applicable to the treatment room unit to be matched throughout the entire clinical energy range, as well as dose correction parameters that work synergistically with the unified physical add-on module configuration parameters. The matching parameter set is configured such that, across the entire clinical energy range, the adjusted beam physical characteristics of the treatment chamber unit to be matched are dosimetrically equivalent to the reference reference in the multidimensional beam characteristic space.

12. A multi-source, multi-chamber radiotherapy system, characterized in that, include: At least two independently operating treatment room units, each with its own independent beam generation and transmission subsystem; as well as, The matching device for achieving beam dosimetric equivalence between multiple treatment chamber units as described in claim 11; The device is configured to uniformly match the beam characteristics between the at least two treatment chamber units, so that different treatment chamber units are dosimetrically interchangeable when performing the same treatment plan.

13. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method according to any one of claims 1-10.

14. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions, and when the computer reads the computer instructions, the computer implements the steps of the method according to any one of claims 1-10.

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