Method for determining a treatment plan for radiation treatment

Abstract

Disclosed is a computer-implemented method for determining a treatment plan for a radiotherapy treatment including a dose distribution, the method comprising the steps of: (a) determining a plurality of clinical objectives, wherein the plurality of clinical objectives are associated with a dose distribution, (b) determining a priority for each of the clinical objectives based at least in part on user input, (c) automatically determining a subset and ordering of scripts from among the plurality of scripts based on the plurality of clinical objectives and their respective priority, wherein each script is configured to, when executed, adjust one or more optimization objectives and / or adjust weights for optimization and / or provide and / or adjust an optimization structure, (d) generating a dose distribution,wherein generating the dose distribution comprises executing the subset of scripts in the determined order, wherein executing the subset of scripts comprises, for each of the scripts, obtaining at least one of: an optimization structure, optimization objectives, and weights for the optimization, and performing an optimization based thereon.

Description

FIELD OF THE INVENTIONThe present invention relates to a computer-implemented method for determining a treatment plan including dose distribution, a data processing system, a computer program product and a computer-readable medium.BACKGROUND ARTIrradiation with ionizing radiation, for example in the context of radiotherapy, generally presupposes that how the irradiation is carried out is planned in advance. This can be referred to as radiation therapy planning.Planning can result in a treatment plan that can include, among other things, a spatial dose distribution.Generally, a treatment plan and dose distributions can be determined using an optimization method.In conventional radiotherapy treatment planning, treatment designers have the task of ensuring that certain clinical goals, which are often predetermined by clinicians, are achieved. Conventional methods of radiation therapy planning can be used. Examples of these methods are manually setting and adjusting weights and / or constraints and / or manually creating optimization structures and / or goals.To achieve clinical goals, complex manual changes to the global optimization problem sometimes need to be made. These changes make it possible to direct the optimization algorithm in a specific direction, for example by formulating local indications as to where the problem is to be locally constrained or relaxed. This is already difficult as such and involves a considerable amount of trial and error.In addition, conventional optimization algorithms operate with tradeoffs such that changing parameters to reach one target typically affects reaching another target. This may be unacceptable in some cases, and results in even more trial and error and / or complex user interaction being required to achieve acceptable results.As a result, unacceptable delays may occur and the method is cumbersome and prone to errors.The present invention aims to provide a method, a data processing system, a computer program product and a computer readable medium that enable at least some of the above mentioned challenges to be overcome.The invention can be used to provide information which can be used, for example, in conjunction with an image-driven radiotherapy system such as VERO® and ExacTrac® both products of Brainlab AG.Aspects of the present invention, examples and exemplary steps and embodiments thereof are disclosed below. Various exemplary embodiments of the invention can be combined according to the invention wherever technically expedient and possible.EXEMPLARY BRIEF DESCRIPTION OF THE INVENTIONA brief description of the specific features of the present invention will be given below, which is not to be understood as limiting the invention only to the features described in this section or a combination of the features.The invention provides a method, a data processing system, a computer program product and a computer readable medium according to the independent claims. Preferred embodiments are set out in the dependent claims.The present disclosure provides, among other things, a computer-implemented method for determining a treatment plan for radiotherapy treatment including dose distribution, the method comprising the steps of: (a) determining a plurality of clinical goals, the plurality of clinical goals being associated with dose distribution, (b) determining a priority for each of the clinical goals based at least in part on user input, (c) automatically determining a subset and order of scripts from a plurality of scripts based on the plurality of clinical goals and their respective priority, each script being configured to, when executed, adjust one or more optimization goals and / or adjust weights for the optimization and / or provide and / or adjust an optimization structure, (d) generating a dose distribution, wherein generating the dose distribution comprises executing the subset of scripts in the determined order, wherein executing the subset of scripts comprises, for each of the scripts, obtaining at least one of: an optimization structure, optimization goals, and weights for the optimization, and performing optimization based thereon.GENERAL DESCRIPTION OF THE INVENTIONIn this section, a description of the general features of the present invention is provided by presenting possible embodiments of the invention.The present disclosure provides a computer-implemented method for determining a treatment plan for radiotherapy treatment including dose distribution, the method comprising the steps of: (a) determining a plurality of clinical goals, the plurality of clinical goals associated with dose distribution, (b) determining a priority for each of the clinical goals based at least in part on user input, (c) automatically determining a subset and order of scripts among a plurality of scripts based on the plurality of clinical goals and their respective priority, each script configured to, when executed, adjust one or more optimization goals and / or adjust weights for optimization and / or provide and / or adjust an optimization structure, (d) generating a dose distribution, wherein generating the dose distribution comprises executing the subset of scripts in the determined order, wherein executing the subset of scripts comprises, for each of the scripts, obtaining at least one of: an optimization structure, optimization goals, and weights for the optimization, and performing optimization based thereon.In other words, the present disclosure enables automatic determination of a dose distribution that is part of a radiotherapy plan without requiring user intervention that exceeds the user input used to determine the priority of clinical goals. To do this, the available information, i.e., clinical goals and their priorities, is used as input to automatically select a plurality of scripts and automatically determine an order in which the scripts are to be executed. The scripts are executed in this order, thereby sequentially generating and / or adjusting optimization structures, weights, and / or targets. The creation and / or adjustment performed by executing each of the scripts aims to achieve a clinical goal for which the script was selected.Each of the scripts can provide a dose distribution that can be adjusted by the subsequent script. When execution of the scripts is ended, e.g., after execution of the last script of the subset of scripts, or when an abort is triggered, the results of the last executed script may be the final results. For example, the dose distribution may be the final dose distribution and / or the optimization structure(s) and / or weights and / or targets may be the final optimization structure(s), weights and targets.The method of the present disclosure therefore allows for a significant reduction in trial and error. If there is a solution to the problem, the method will provide this solution efficiently and reliably. Moreover, due to the modular approach with separate prioritized destinations and correspondingly selected scripts, the method provides a basis to avoid tradeoffs that may not be necessary or desirable and / or to avoid violation of higher-ranking destinations when attempting to achieve low-ranking destinations. An example utilizing this advantage will be discussed in detail below, and certain results may be disabled.The method of the present disclosure enables script-controlled generation and / or adjustment of optimization structures, optimization goals, and / or weights for optimizations (also referred to herein as optimization weights or weights) that enable a prioritized list of clinical goals to be achieved iteratively without requiring user interaction. The method is particularly relevant for scenarios in which "intermediate solutions" as often achieved with the existing methods due to tradeoffs are unacceptable because it provides an automated way of investigating solutions that do not fall within the "intermediate ranges", e.g., investigating the more extreme ranges, despite the need to join certain trade-offs.Therefore, the present disclosure provides a method that addresses at least some of the challenges described above.According to the present disclosure, a treatment plan for radiotherapy treatment includes dose distribution. The term dose distribution may refer to a spatial dose distribution. The dose distribution can be, for example, in the form of a dose gradient.The treatment plan can be stored, for example as a reference for setting operating parameters for the operation of a radiation source.The majority of clinical goals are associated with dose distribution. The clinical goals may represent, for example, goals that are to be fulfilled by the dose distribution that is determined within the framework of the determination of the treatment plan. The clinical goals may relate, for example, to attributes of a dose or a dose distribution, e.g. to target values and / or upper and / or lower threshold values of a dose in one or more regions and / or a dose gradient in or between regions. Examples of clinical goals related to dose distribution are given below.Determining the clinical goals may be based in part on user input, e.g., a user may define and / or select a clinical goal. Alternatively or additionally, the clinical goals may also be determined based on other sources. These other sources may include a data store in which clinical targets associated with, for example, certain types of radiotherapy treatments, e.g., to irradiate a particular anatomical structure, may be stored. Determining a clinical target may include automatically determining the clinical targets associated with that type for the type of radiotherapy plan in question.The priority of clinical goals is determined based at least in part on user input, in accordance with the present disclosure. For example, a user may interact with a user interface to set and / or change a priority for one or more clinical goals, i.e., to increase or decrease, e.g., to change the priority over a previous or default setting. Optionally, other factors besides user inputs may be taken into account in determining the priority of clinical goals. For example, for certain types of radiotherapy, there may be default settings or previous settings for the priorities of the clinical goals that may be retrieved and used unless otherwise specified by the user input.The method may optionally include the user input to override all default or previous settings to set and / or change a priority. Optionally, some priorities may be excluded from this principle, e.g. the setting and / or change by the user may be allowed only within predetermined ranges or not at all for the excluded priorities.The determining of the priority based at least in part on the inputs of the user may be in the form of an order of the destinations with respect to their priorities or, in other words, in the form of a priority-based ranking of the destinations. Alternatively or additionally to specifying the order of the priorities, the user may also select a category for each of the destinations that specifies its priority. Examples of these are categories such as "must not be violated", "very high", "high", "medium", "low", "very low". Optionally, a certain prioritization can also take place automatically and then checked and possibly processed by the user.As mentioned above, according to the present disclosure, a subset and order of scripts from a plurality of scripts is automatically determined based on the plurality of clinical goals and their respective priority.The term "subset of scripts" refers to two or more scripts selected from a set of scripts that includes two or more, in particular three or more, scripts. The order of the scripts refers to the order in which the scripts are to be executed.The scripts and the order of the scripts can be set so that the destinations are reached in descending order of their priority, i.e., the destination with the highest priority is reached first.In an example, automatically determining may include automatically looking up a subset and the order of scripts associated with the plurality of clinical goals and their priorities. Alternatively or additionally, a rule-based selection of the subset of scripts and their order can be carried out. The manner in which the automatic determination is carried out may depend on the respective case, e.g. on the number of possible scenarios which are to be taken into account by the automatic determination. Examples are given below.The term script should be interpreted widely. It may refer to a series of instructions for defining a low-level optimization problem. In particular, it can define and / or generate optimization structures, dose objectives, volume objectives and / or corresponding weights. These may be subject to iterative changes during a script run. During a script run, for example, an optimization volume, e.g., an auxiliary volume, can be extended by a target volume until a specific criterion is fulfilled.As discussed above, each script is configured to, when executed, adjust one or more optimization objectives and / or weights for optimization and / or provide and / or adjust an optimization structure.Creating and / or adapting an optimization structure may include creating and / or adapting one or more optimization structures, e.g. a main target area, a transition area adjoining the main target area, an area connected to a risk structure and / or a transition area adjoining the area connected to the risk structure or the like. An optimization structure according to the present disclosure, in particular the final optimization structure, may also comprise one or more optimization structures, e.g. a main target area, a transition area adjacent to the main target area, an area connected to a risk structure, and / or a transition area adjacent to the area connected to the risk structure, or the like.Adjusting the targets may include changing a target value. A target value may be, for example, a target dose value, an upper or lower threshold value for a dose value, or the like.Adjusting weights may mean that the weights and / or constraints are changed in an optimization function. The weights are used to balance targets and trade-offs. For example, weights may be used to define the penalty of deviations from the targets.As mentioned above, the method of the present disclosure includes generating a dose distribution, wherein generating the dose distribution includes executing the subset of scripts in the specified order, wherein executing the subset of scripts includes, for each of the scripts, obtaining an optimization structure and / or optimization goals and / or weights for the optimization and performing optimization based thereon.This means that, for example, optimization structures and / or optimization goals and / or weights for the optimization can be iteratively adjusted by executing the scripts. Accordingly, the dose distribution can also be iteratively adjusted by performing an optimization on the basis of the iteratively adjusted optimization structures, goals and / or weights.When an optimization is carried out, a dose distribution is obtained. Accordingly, a dose distribution is determined for each script being executed.All dose distributions except for the dose distribution obtained by the last executed script, which is referred to as final dose distribution in the present disclosure, are considered as intermediate dose distributions.Unless otherwise specified, the term "executed script" in the present disclosure refers to a successfully executed script.The treatment plan can in particular contain only the final dose distribution. That is, the intermediate dose distribution may be omitted from the treatment plan. If the final dose distribution is determined after executing all scripts of the subset of scripts, i.e. after successfully executing all scripts, it can be automatically included in the treatment plan.If the final dose distribution was determined before the execution of all scripts of the subset of scripts, i.e. if the determination of the dose distribution was ended or discontinued before the execution of all scripts, the user can first be prompted to confirm the inclusion of the final dose distribution in the treatment plan. For this purpose, information about the termination of the determination of the dose distribution can be output to the user. Further details for terminating the determination are given below.As mentioned above, once all scripts have been executed (successfully) or the execution of the scripts has been aborted, a final dose distribution can be obtained that is generated by performing the optimization with the final optimization structures, goals, and weights obtained by executing the most recently executed script.The term "final" in the context of the final optimization structure, goals of optimization, weights and dose distribution may refer to the optimization structure, goals, weights and dose distribution as determined by successfully executing all scripts of the subset of scripts or by executing the last script that was successfully executed before execution aborts.With respect to the final optimization structure, this may include one or more optimization sub-structures, e.g., a final main target area and / or one or more final auxiliary structures, e.g., auxiliary volume.Accordingly, in particular, successfully executing all scripts of the subset of scripts can result in a final optimization structure, final optimization goals, and final weights, as well as a final dose distribution that can be achieved through optimization using the final optimization structure, goals, and weights.At least the final dose distribution and optionally information about the final optimization structure, the final optimization objectives and the final weights may be included in the treatment plan. That is, the treatment plan may include at least the final dose distribution.As seen above, the optimization is based on the optimization structure and / or the optimization goals and / or weights. This may mean that the optimization structure and / or the optimization goals and / or the weights are used as input for an optimization algorithm.The optimization of the present disclosure, as such and itself, may be any optimization method known in the art for automatically determining a dose distribution. It may, for example, use any known optimization algorithm known in the art.According to the present disclosure, at least a part of an optimization target and / or a weight for the optimization and / or an optimization structure obtained by executing a script or a sequence of scripts may be locked, so that the locked part cannot be changed by executing subsequent scripts.In other words, some results achieved when executing the method, in particular some results achieved by executing the scripts, can be blocked. The blocked results can then no longer be changed by subsequent steps. In particular, the results obtained by executing one or more scripts may be locked so that the clinical goal associated with the one or more scripts cannot be violated in subsequent steps, e.g., subsequent steps that aim to achieve lower priority goals.The scripts and the order of the scripts can be set so that the destinations are reached in descending order of their priority, i.e., the destination with the highest priority is reached first. In this way, by disabling the results as described above, one can prevent targets already achieved from being adversely affected by subsequent steps.The blocking of results can have the advantage that certain parameters of certain trade-offs can be excluded in subsequent steps or can be subject to trade-offs in subsequent steps only to a limited extent. In this way, some overall optimization results are available that could not otherwise be achieved due to tradeoffs made in the course of optimization. Since the claims provide for executing a particular set of scripts in a particular order, which in turn is determined based on the priority of clinical goals, this means that it is possible to indirectly control where tradeoffs are acceptable or not.According to the present disclosure, if it determines that a dose distribution cannot be determined without changing a locked optimization goal and / or a locked weight for the optimization and / or a locked optimization structure, e.g., from the beginning or after executing a script, the method may abort the generation of the dose distribution and optionally initiate a notification to the user about failure to determine a dose distribution and / or about possible reasons and / or solutions for failure to determine a dose distribution. It should be noted that failure to determine a dose distribution in the present disclosure is to be understood in particular as failure to determine a dose distribution that meets all requirements, e.g. clinical goals, restrictions or the like.As discussed above, disabling results may prevent the method from making certain tradeoffs. Although this ensures that the clinical goals of higher rank are not violated, it can also lead to situations in which no solution can be found, i.e. no distribution can be determined that meets all requirements. Instead of making tradeoffs in this case, the generation of the dose distribution may be discontinued. The method thus does not provide a dose distribution that opaquely violates the targets. The user may be informed of the fault and possible reasons and / or solutions for the fault. In response, the user may make changes to the user-defined input parameters, e.g., clinical goals and / or priorities. Instead of aborting the generation of the dose distribution, the generation of the dose distribution may also be continued despite a violation, optionally after confirmation by a user. For example, there may be rules that specify in which cases continuation is permitted despite a violation and / or rules that specify in which cases continuation requires confirmation by the user despite a violation. In either case, the information about the violation may be stored, e.g. for analyses, checks and / or decisions regarding the usability of the results.According to the present disclosure, each script may be executed limited by at least a portion of the results of earlier executed scripts. In particular, an optimization carried out during the execution of the script can be restricted by at least some of the results of the scripts executed previously.That is, the method may include using or assuming results from previous scripts as constraints for subsequently executed scripts. This is an example of the at least partial blocking of results.According to the present disclosure, generating the dose distribution may be an iterative process comprising at least a first level of iterations, each iteration associated with exactly one of the clinical goals, and the iterations performed in the order of priority of the clinical goals.That is, the method may iteratively proceed from the clinical goal with the highest priority to the clinical goal with the lowest priority, each iteration aiming at achieving the clinical goal to which it is associated, such that the clinical goals are iteratively achieved. For example, when performing the locking described above, the method may include locking at least some of the results of one iteration and then proceeding to the next iteration, which may include, for example, transmitting some of the locked results as restrictions.These first level iterations of the iterations may also be considered highest level iterations.According to the present disclosure, the iterative process may include a second level of iterations associated with the scripts, each iteration of the second level of iterations associated with exactly one of the scripts, wherein each iteration of the first level of iterations may be associated with one or more iterations of the second level of iterations.That is, at a lower level than the first level of iterations, a second level of iterations may be provided, wherein an iteration of the first level may include one or more iterations of the second level. For example, if a first level iteration is associated with a target associated with multiple scripts, the first level of iterations may be associated with a number of second level iterations corresponding to the number of multiple scripts.The second level iterations of the iterations may be considered middle level iterations.According to the present disclosure, each iteration of the second level of iterations may include performing an optimization process or a sequence of optimization processes.That is, at a lower level than the second level of iterations, a third level of iterations may be provided, wherein an iteration of the second level of iterations may include one or more iterations of the third level. An iteration at the third level may be associated with a script or step that associates scripts with each other.The third level iterations of the iterations may be considered low level iterations.The levels of the iterations are also referred to as iteration levels in the present disclosure.The above-described iterative scheme, in particular at multiple levels, enables consideration of a plurality of possible scenarios, e.g. the combination and prioritization of clinical goals. Thanks to the iterative scheme, a modular approach can be chosen to achieve clinical goals. If new scripts become available or existing scripts are changed, they can be easily integrated due to the iterative and modular nature of the method. Thus, the method allows greater flexibility with respect to the type of problem that can be solved and with respect to the modification and improvement of the method steps performed to create a treatment plan.According to the present disclosure, generating the dose distribution may be a hierarchical process in which, in particular, each iteration of the first level of iterations may be higher in rank than the subsequent iterations of the first level of iterations and / or in which, among the iterations of the second level of iterations associated with one of the iterations of the first level of iterations, each iteration may be higher in rank than the subsequent iterations.A hierarchical process may be a process in which some steps and / or results are higher in rank than other steps and / or results. For example, some results may be invariable, as described above in connection with disabling results. The hierarchy may be derived from the priorities of the clinical goals.The hierarchical process can be implemented in particular by the above-described iteration scheme. Within each iteration level, the rank of the iterations may decrease from one iteration to the next, i.e., the first iteration has the highest rank, the last iteration has the lowest.For example, it can be ensured that some compromise can be avoided if they would violate higher-ranking targets.According to the present disclosure, the clinical goals may comprise at least one of the following features: high dose coverage of a main target area for the irradiation, steep dose gradient in the direction of the areas adjoining the main target area for the irradiation, low upper dose limits in the areas adjoining the main target area for the irradiation, high lower dose limits in the main target area for the irradiation, high dose homogeneity, in particular in the main target area for the irradiation, upper dose limits for an area defined as a risk structure, e.g. at least a part of an organ such as the spinal cord.The main target area for irradiation may be the area to be irradiated, for example, for treatment. Regions adjoining this region can, for example, partially or completely enclose the main target region.The goal of high dose coverage of the main target area may mean that it is an optimization goal to increase dose coverage as much as possible. Alternatively or additionally, it may mean that the coverage exceeds a predetermined relative or absolute threshold value. This target can be given high priority when it is important, for example, that the entire area of the main target area is irradiated appropriately.The goal of a steep dose gradient in the direction of the regions adjoining the main target region can mean that it is an optimization goal to increase the gradient of the dose gradient as far as possible. Alternatively or additionally, it can mean that the gradient exceeds a predefined threshold value. This target can be given high priority when, for example, it is important that areas around the main target area receive little irradiation. For example, if sensitive anatomical structures are near the main target area, it may be important that the dose falls steeply toward these structures.Similarly, the low upper dose threshold target may have high priority in areas adjacent to a / the main target area for irradiation, e.g., where it is important that these areas do not receive too much irradiation, e.g., sensitive anatomical structures.Targets relating to areas adjacent to the main target area can also be treated with high priority if this is to create an optimisation structure which can also comprise an auxiliary structure as will be explained in more detail below.The target of high dose lower limits in a / the main target area for the irradiation may have high priority, for example, when it is important that the main target area does not receive too little irradiation.The goal of high dose homogeneity, especially in the main target area for irradiation, may have a high priority, for example when it is important that there are no hot spots and / or no sub-irradiated sites.The target of dose upper limits for an area defined as a risk structure, e.g. at least a part of an organ such as the spinal cord, may have high priority if excessive irradiation of the risk structure may result in significant damage to the risk structure. The risk structure may be located in an area adjoining the main target area, but does not have to be.According to the present disclosure, the optimization objectives may include one or more constraints, in particular for one or more ranges at least one of: a lowest allowed dose value, a highest allowed dose value, a lowest allowed dose gradient, a lowest allowed dose homogeneity value, a highest allowed temporal dose variation value, a target dose value, a target dose gradient value, a target dose homogeneity value, a target dose location variation value, and a target dose time variation value.It is noted that the dose gradient, dose homogeneity and dose variation over time can be quantified in any manner suitable for the particular application.Quantification of the homogeneity can be effected, for example, by evaluating dose / volume points (DVH points) which correspond to the target volume. Such points may correspond to or quantify the minimum and maximum dose for the target volume. A quotient of these points (e.g., normalized to the prescribed dose) can be used to quantify homogeneity with a single number.Dose gradients, etc., can be quantified by dose / volume assessment of artificial volumes around the target volume. For example, the volume receiving half of the prescribed dose may be quantified. Alternatively, DVH points for a ring structure enclosing, e.g., the target volume may be calculated in a predetermined size. Alternatively, the distance between the dose isolines can also be used for quantifying a dose gradient.According to the present disclosure, the optimization structure may comprise at least one / the main target area for the irradiation and optionally auxiliary structures, e.g. around the main target area in which irradiation is not required but may be allowed to reach the optimization targets, and / or auxiliary structures defining and / or surrounding an / the area defined as a risk structure, e.g. at least part of an organ such as the spinal cord.An auxiliary structure in which irradiation is not required but can be permitted to achieve optimization goals can comprise, for example, a transition region with one or more shells around the main target region. Within these shells, there may be constraints between the constraints in the respective adjacent regions, such that they allow for, for example, control of dose decay at the edges of the main target region. Such auxiliary structures may, for example, enable to meet all constraints (e.g., lower dose limits and homogeneity requirements) in the main target area without sacrificing constraints in adjacent areas, e.g., constraints for a low upper dose.A further example: auxiliary structures defining and / or surrounding an area defined as a risk structure, e.g. at least a part of an organ such as the spinal cord, may be provided to enable milder and thus easier to maintain restrictions in most areas outside the main target area, while simultaneously protecting critical areas from irradiation. Similar to the main target area and the shell-like auxiliary structures described above, the auxiliary structures defining the area defined as the risk structure may also comprise a main area surrounded by shells, e.g. shells with less severe constraints than the main area.According to the present disclosure, executing scripts may include at least one of the following elements: adjusting a / the main target area for irradiation, generating auxiliary structures, for example auxiliary volumes, around a / the main target area for irradiation, adjusting auxiliary structures, for example auxiliary volumes, around a / the main target area for irradiation, adjusting constraints within a / the main target area for irradiation and / or within auxiliary structures, for example auxiliary volumes, around a / the main target area for irradiation, and / or generating and / or adjusting auxiliary structures defining a / the area defined as a risk structure, for example, at least part of an organ such as the spinal cord, and / or adjusting constraints within auxiliary structures defining an area / areas defined as a risk structure, e.g. at least a part of an organ such as the spinal cord.The adjustment of the main target area for the irradiation can mean, for example, that the size and / or shape of the main target area is adjusted. The adaptation may be limited, for example, by corresponding restrictions.Generating auxiliary structures may mean, for example, that the size, shape and position of an auxiliary structure and optionally restrictions for the dose distribution within the auxiliary structure are defined. Adapting an auxiliary structure may mean that the size, shape and / or position of the auxiliary structure is changed.For example, a script may be provided that specifies that a transition region is to be created, wherein the transition region comprises one or more shells and adjoins the main target region and surrounds the latter at least partially. The script may also determine a shell thickness for each of the shells and dose constraints in each shell.Similarly, a script may be provided that generates an optimization structure representing a risk structure and optionally an optimization structure in the form of a transition region around the risk structure.As another example, a script may be provided that generates an adjusted main target area with an adjusted shape that takes into account a risk structure, e.g., a compromised organ.Another example is a script that increases the size of the main target area, e.g., the main target volume, to improve homogeneity of dose distribution. Firstly, plan optimization for the main target volume can be carried out. If the dose is too heterogeneous, the script may apply a ring volume around the target volume and set a dose / target for that ring that is slightly lower than the prescribed dose for the target volume.The script can then perform a plan optimization involving the newly added ring (a larger volume will typically result in a more homogeneous dose, especially for small target volumes). This could also be carried out iteratively.Steps a) to d) described above do not require user intervention.This means that the treatment plan can be determined fully automatically at least after the priorities have been established. This, of course, does not exclude that the user may optionally intervene to adjust / adjust clinical goals and / or priorities in response to the above-described steps of aborting, creating the dose distribution, and notifying the user of the failure to create a dose distribution.Optionally, all steps of the method of the present disclosure can be carried out by a data processing system, in particular fully automatically. In particular, predetermined values can be automatically accessed even if they have been entered by a user at some time.The invention also provides a data processing system configured to carry out the method of the present disclosure, i.e. one or more, in particular all, steps of the method of the present disclosure. In particular, the data processing system may comprise one or more processors configured to perform one or more, in particular all, steps of the method of the present disclosure.The invention also provides a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the present disclosure, i.e. one or more, in particular all, steps of the method of the present disclosure.The invention also provides a computer readable medium containing instructions which, when executed by a computer, cause the computer to carry out the method of the present disclosure, i.e., one or more, in particular all, of the steps of the method of the present disclosure.For example, the invention does not include an invasive step which represents a considerable physical intervention in the body, which requires professional medical skills to be performed, and which poses a considerable health risk even when performed with the required professional care and skills, and in particular does not include such a step. For example, the invention does not include the step of positioning a medical implant to attach it to an anatomical structure, or the step of attaching the medical implant to the anatomical structure, or the step of preparing the anatomical structure for attachment of the medical implant thereto. In particular, the invention does not comprise any surgical or therapeutic measures. Rather, the invention is directed to providing results that can be used to determine settings for a device that provides a beam, where the results and / or settings enable compliance with certain spatial dosing constraints. For this reason alone, no surgical or therapeutic activity and in particular no surgical or therapeutic step is required or implied in the execution of the invention.The features and advantages described above in connection with the method apply similarly to the data processing system, computer program product, and computer readable medium of the present disclosure.DEFINITIONSIn this section, definitions are offered for certain terms used in this disclosure, which are also part of the present disclosure.Computer-implemented methodThe method according to the invention is, for example, a computer-implemented method. For example, all steps or only some of the steps (i.e. less than the total number of steps) of the method according to the invention can be executed by a computer (e.g. at least one computer). An embodiment of the computer-implemented method is a use of the computer for carrying out a data processing method. An embodiment of the computer-implemented method is a method related to the operation of the computer such that the computer is operated to perform one, more, or all steps of the method.The computer comprises, for example, at least one processor and, for example, at least one memory, in order to (technically) process the data, for example electronically and / or optically. The processor consists, for example, of a substance or composition which is a semiconductor, for example an at least partially n- and / or p-doped semiconductor, for example at least one II, III, IV, V, VI semiconductor material, for example (doped) silicon and / or gallium arsenide. The described calculation or determination steps are carried out, for example, by a computer. Determination steps or calculation steps are, for example, steps for determining data within the scope of the technical method, for example within the scope of a program. A computer is, for example, any type of data processing device, for example an electronic data processing device. A computer may be a device generally envisioned as such, e.g., desktop PCs, notebooks, netbooks, etc., but may also be any programmable device, such as a mobile phone or embedded processor. A computer may comprise, for example, a system (network) of "sub-computers", each sub-computer representing a separate computer. The term "computer" also includes a cloud computer, for example a cloud server. The term "cloud computer" includes a cloud computer system, for example including a system of at least one cloud computer and, for example, a plurality of cloud computers operatively connected to each other, such as a server farm. Such a cloud computer is preferably connected to a wide area network such as the world wide web (WWW) and is located in a so-called cloud of computers, all of which are connected to the world wide web. Such infrastructure is used for cloud computing, which describes computing, software, data access and storage services where the end user does not need to know the physical location and / or configuration of the computer providing a particular service. The term "cloud" is used in this context as a metapher for the Internet (world wide web), for example. For example, the cloud provides computer infrastructure as a service (IaaS). The cloud computer may function as a virtual host for an operating system and / or a data processing application used to perform the method of the invention. The cloud computer is, for example, an elastic computing cloud (EC2) offered by Amazon Web Services™. A computer has, for example, interfaces for receiving or outputting data and / or performing an analog-to-digital conversion. The data are, for example, data which represent physical properties and / or which are generated from technical signals. The technical signals are generated, for example, with the aid of (technical) detection devices (such as devices for detecting marking devices) and / or (technical) analysis devices (such as devices for carrying out (medical) imaging methods), wherein the technical signals are, for example, electrical or optical signals. The technical signals represent, for example, the data received or output by the computer. The computer is preferably operatively coupled to a display device which enables the information output by the computer to be displayed, for example to a user. An example of a display device is a virtual reality device or an augmented reality device (also referred to as virtual reality glasses or augmented reality glasses) that can be used as "glasses" for navigating. A specific example of such augmented reality glasses is Google Glass (a trademark of Google, Inc.). An augmented reality device or a virtual reality device may be used to input information into the computer by user interaction as well as display information that the computer outputs. Another example of a display device would be a standard computer monitor, e.g., comprising a liquid crystal display operatively coupled to the computer for receiving display control data from the computer and generating signals used to display image information content on the display device. A specific embodiment of such a computer monitor is a digital light box. An example of such a digital light box is Buzz® a product of Brainlab AG. The monitor may also be the monitor of a portable, e.g., portable device, such as a smartphone, a personal digital assistant, or a digital media player.The invention also relates to a program which, when executed on a computer, causes the computer to execute one or more or all of the method steps described herein, and / or to a program storage medium on which the program is stored (in particular in non-transitory form), and / or to a computer containing the program storage medium, and / or to a (physical, e.g. electrical, e.g. technical generated) signal wave, e.g. a digital signal wave carrying information representing the program, e.g. the aforementioned program, comprising e.g. code means suitable for executing one or all of the method steps described herein.Within the scope of the invention, computer program elements may be embodied by hardware and / or software (including firmware, resident software, microcode, etc.). Within the scope of the invention, computer program elements may take the form of a computer program product which may be embodied by a computer-usable, e.g. computer-readable, data storage medium containing computer-usable, e.g. computer-readable program instructions, "code" or a "computer program" embodied in said data storage medium for use on or in connection with the instruction-executing system. Such a system may be a computer; a computer may be a data processing device comprising means for executing the computer program elements and / or the program according to the invention, for example a data processing device comprising a digital processor (central processing unit or CPU) executing the computer program elements and optionally a volatile memory (for example a random access memory or RAM) for storing data used for and / or generated by the execution of the computer program elements. Within the scope of the present invention, a computer-usable, e.g., computer-readable, storage medium may be any storage medium that can contain, store, communicate, propagate, or transport the program for use on or in connection with the instruction-executing system, apparatus, or device. The computer usable, e.g., computer readable, data storage medium may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or a broadcast medium such as the Internet. The computer usable or computer readable medium could even be, for example, paper or other suitable medium upon which the program is printed, as the program could be electronically captured, for example, by optically scanning the paper or other suitable medium, and then compiled, interpreted, or otherwise processed in a suitable manner. The data storage medium is preferably a non-volatile data storage medium. The computer program product and the software and / or hardware described here form the various means for carrying out the functions of the invention in the exemplary embodiments. The computer and / or the data processing device may, for example, contain a guidance information device which comprises means for outputting guidance information. The guidance information can be output to a user, for example, visually by a visual display means (e.g. a monitor and / or a lamp) and / or acoustically by an acoustic display means (e.g. a loudspeaker and / or a digital voice output device) and / or tactilely by a tactile display means (e.g. a vibrating element or a vibrating element incorporated into an instrument). For the purposes of this document, a computer is a technical computer which comprises, for example, technical, for example material components, for example mechanical and / or electronic components. Any device mentioned as such in this document is a technical and, for example, tangible device.Treatment JetThe present disclosure relates to the field of determining a dose distribution for planning the manner in which radiation is to be applied by a treatment beam. The treatment beam treats body parts to be treated, which will be referred to as "treatment body parts" hereinafter. These body parts are, for example, parts of the patient's body, i.e. anatomical body parts.The present disclosure relates to the field of medicine and, for example, to aspects in connection with beams, such as beams, for the treatment of body parts of a patient, which are therefore also referred to as treatment beams. A treatment beam treats body parts to be treated, which will be referred to as "treatment body parts" hereinafter. These body parts are, for example, parts of the patient's body, i.e. anatomical body parts. For example, ionizing radiation is used for the treatment. The treatment beam comprises or consists, for example, of ionizing radiation. The ionizing radiation comprises or consists of particles (e.g. subatomic particles or ions) or electromagnetic waves that are sufficiently energy-rich to dissolve electrons from atoms or molecules and thus ionize them. Examples of such ionizing radiation are X-rays, high-energy particles (high-energy particle beams) and / or ionizing radiation emanating from a radioactive element. The treatment radiation, for example the treatment beam, is used, for example, in radiotherapy or radiotherapy, for example in the field of oncology. In particular in the treatment of cancer, parts of the body having a pathological structure or tissue such as a tumor are treated with ionizing radiation. The tumor is then an example of a treated body part.The treatment beam is preferably controlled to pass through the body part to be treated. However, the treatment beam may have adverse effects on body parts outside the body part to be treated. These body parts are referred to herein as "outer body parts". As a rule, a treatment beam must penetrate the outer body parts in order to reach and penetrate the body part to be treated.In this connection, reference is also made to the following web pages: http: / / www.elekta.com / healthcare_us_selecta_vmat.php and http: / / www.varian.com / us / oncology / treatments / treatment_techniques / rapidarc.BRIEF DESCRIPTION OF THE DRAWINGSThe invention will now be described with reference to the accompanying figures, which provide background explanations and illustrate specific embodiments of the invention. However, the scope of the invention is not limited to the specific features disclosed in connection with the figures, wherein FIG. 1 schematically illustrates a method according to the present disclosure; FIG. 2 schematically illustrates the iterative and hierarchical character of a method according to the present disclosure; FIG. 3 schematically illustrates a method according to the present disclosure; FIGS. 4 aand 4 bschematically illustrate example scripts according to the present disclosure; and FIG. 5 is a schematic illustration of a system according to the present disclosure.DESCRIPTION OF THE EMBODIMENTSFIG. 1 illustrates example steps of a computer-implemented method for determining a treatment plan for radiotherapy including dose distribution according to the present disclosure.In step S 11, a plurality of clinical targets are determined.For example, one or more clinical targets may be determined, i.e., selected, based on user input indicating the clinical targets. Alternatively or additionally, one or more clinical targets may be automatically retrieved, e.g., depending on the radiotherapy application currently running. Of course, other methods for determining clinical goals are also conceivable.The plurality of clinical goals are associated with a dose distribution, i.e. they represent goals that are to be achieved by the dose distribution to be determined within the framework of the treatment plan. The clinical goals may relate, for example, to attributes of a dose or a dose distribution, e.g. to target values and / or upper and / or lower threshold values of a dose in one or more regions and / or a dose gradient in or between regions.The clinical goals are ultimately presented in an optimization problem to be solved to obtain dose distribution. This is clear in the following steps.In step S 12, a priority is determined for each of the clinical goals based at least in part on a user input. For example, a user may specify their respective priority for the destinations determined in step S 11. This may be simply in the form of indicating an order of the destinations with respect to their priorities or, in other words, a priority-based ranking of the destinations. Alternatively or additionally to specifying the order of the priorities, the user may also select a category for each destination that specifies its priority. Examples of these are categories such as "must not be violated", "very high", "high", "medium", "low", "very low". Optionally, a part of the prioritization can also take place automatically and then be checked and possibly processed by the user.Thus, in step S 12, user inputs made, for example, by a clinician and / or a technical expert at a particular point in time can be used to obtain detailed instructions on properties of the dose distribution that are considered relevant in the present case. For example, if the physician deemed a very steep dose decay outside the main target area to be particularly important for irradiation, e.g., to keep the irradiated area very narrow, then he may give the target of maximizing the dose decay a high priority, e.g., higher than the target of high dose coverage up to the edges of the main target area.In step S 13, a subset and the order of the scripts are automatically determined from a plurality of scripts. The determination is based on the plurality of clinical goals and their respective priority. Each script is configured to, when executed, adjust one or more optimization objectives and / or weights for optimization and / or provide and / or adjust an optimization structure.Each clinical goal may be associated with one or more scripts that aim to achieve the clinical goal. These scripts may include optimization techniques. By executing a script, the optimization problem is manipulated, which is subsequently solved to obtain the dose distribution.Scripts may, for example, change an optimization goal, e.g., may change constraints and / or target values for the dose. Scripts may also change weights for optimization, e.g., they may reduce or increase penalty if certain constraints or target values are not met. Scripts can also provide and / or adapt an optimization structure. For example, auxiliary structures may be generated in the form of auxiliary volumes that are not part of the main target range, and constraints and / or target values and / or weights may be set for each auxiliary volume. Such optimization structures may help to achieve some optimization goals in the main goal area, possibly the drawback of other areas, without directly resulting in a penalty. Such optimization structures may also help protect sensitive areas outside of the main target areas, e.g., by higher penalty than other areas for certain target injuries.In addition, executing each script may include optimization that yields dose distribution. If the script has generated and / or adjusted an optimization structure, goals, and / or weights, the generated and / or adjusted optimization structure, goals, and weights may be used for the optimization. If no changes are made to the predetermined optimization structures, goals, and / or weights, the predetermined optimization structures, goals, and / or weights may be used for the optimization. Thus, a mixture of added, adjusted, and predetermined optimization structures, targets, and / or weights may serve as input for the optimization.A script may execute not only one of the above-described steps but any combination thereof. Examples of scripts are given below.From the above it can be seen that the selection of the appropriate scripts depends on the clinical goals. In addition, the order in which scripts are appropriately executed depends on the priorities of the clinical goals. For example, the creation of a protective optimization structure as described above may be performed before other steps when the protection of a particular area has a higher priority.In some cases, it may be helpful to lock certain results so that executing subsequent scripts does not alter these results. This is explained in more detail below. As an example, once the protective optimization structure has been created, the presence of the structure and at least some of the associated constraints may be disabled. In this way, it is possible to avoid optimization goals with lower priority being counteracted within the scope of a compromise of the protective effect of the optimization structure.The automatic selection of scripts and the order in which they are executed can take various forms. The selection may be rule-based or based on a lookup function that allows a particular selection and ordering to be retrieved for any selection and prioritization of clinical goals, as the case may be.In step S 14, a dose distribution is generated. This step includes at least step S14a in which the subset of scripts is executed in the specified order. Step S 14 aincludes, for each of the scripts, step S 14 a- 1 in which at least one optimization structure, optimization goals, and weights for the optimization are determined, and step S 14 a- 2 in which optimization based thereon is performed.In step S 14 a, reference is made to the explanations relating to the preceding steps, which clarify that optimization structures, optimization goals and / or weights are generated and / or adapted by executing the scripts. Since the generating and / or adjusting is performed for each script, the optimization structures, optimization goals, and / or weights may be iteratively adjusted. Successfully executing all scripts can result in a final optimization structure, optimization goals, and final weights.The optimization structure obtained for each script, including the final optimization structure, may comprise one or more optimization (sub)structures, e.g. a main target area and / or one or more auxiliary structures, e.g. auxiliary volume.In step S 14 a- 2, after the specification of an optimization structure that possibly contains auxiliary structures, optimization goals and weights, an optimization is carried out on the basis of this structure. The optimization results in a dose distribution.Steps S14a-1 and S14a-2 are performed for each of the scripts, so that the dose distribution obtained by performing optimization based on the respective optimization structure, the targets, and the weights can be iteratively adjusted.For completeness, it should be noted that generating and / or adjusting the optimization structure, goals, and / or weights may also include solving optimization problems. By carrying out certain optimizations separately from one another (in the form of separate scripts) and in a predefined sequence, better control of the tradeoffs that are met and, in particular, the trade-offs that are not met is possible.FIG. 1 also shows optional step S 15. This step illustrates that optionally at least a portion of the results obtained by executing a script may be locked so that the locked portion cannot be changed by executing subsequent scripts. The blocking can be carried out multiple times in different phases of the method, which is not shown individually in FIG. 1, however, but rather is summarized as step S 15.FIG. 1 also shows optional step S 16, in which, after determining that a dose distribution cannot be determined without changing a blocked optimization target and / or a blocked weighting for the optimization and / or a blocked optimization structure, e.g. from the beginning or after executing a script, the generation of the dose distribution is discontinued and optionally the information of a user about an error in the determination of a dose distribution and / or about possible reasons and / or solutions for an error in the determination of a dose distribution is initiated.All steps of the method can be carried out without user input, in particular fully automatically. Optionally, all steps may be performed fully automatically unless an error occurs. In this case, the user may be prompted to select or confirm the next steps.FIG. 2 schematically illustrates the iterative and hierarchical character of a method according to the present disclosure.This method of the present disclosure may use a prioritized list of clinical goals as input, for example two or three, and aim to reach the goals one after the other in the order of their priority, in particular while maintaining the clinical goals previously reached. This first optimization stage, also referred to as first iteration level, comprises a number of iterations corresponding to the number of clinical goals, i.e. there is one iteration per clinical goal.At a second, lower optimization level or iteration level, the method of the present disclosure provides multiple scripts that are executed in a particular order, each script corresponding to an iteration of the second iteration level. Each script may define a scheme of optimization interaction steps. These steps may alter optimization structures, optimization goals (e.g., dose and / or volume goals), and / or optimization weights, e.g., goal weights. One or more scripts may be associated with a clinical goal, i.e., an iteration at the first optimization level.It should be noted that scripts can be created in advance based on the development of expert knowledge and explorative tests. Some of these scripts are described in more detail below.On a third, even lower, optimization level or iteration level, optimizations are iteratively carried out within the scope of executing scripts, wherein, for example, each script can contain one or more such optimizations.The second and third planes may cooperate, for example, as follows. A script may aim to adapt the dose constraints and / or optimization structures. At the third level, goals such as dose constraints and optimization structures can be iteratively adjusted. For example, for each second level iteration, an optimization problem may be formulated with a target function and the target function minimized. Such a target function penalizes the deviation from the targets. The second and third levels may thus enable the investigation of the possible search space, the presentation of clinical goals in a practicable manner (it may be difficult to provide a goal function representative of complex goals) and / or the avoidance of the low level optimization getting stuck at local minima.FIG. 3 schematically illustrates a method of the present disclosure.In this example, the clinical constraints and clinical goals A, B, C and their priorities are determined (at least in part) based on user input, e.g., a clinician's input.On the left side, the destinations A, B, and C are prioritized in descending order. For this prioritization, scripts 1 and 3 are automatically selected and executed in this order.In the middle, the destinations B, A and C are prioritized in descending order. For this prioritization, script 4 is automatically selected and executed.On the right side, the destinations C, B and A are prioritized in descending order. The scripts are selected based on the prioritization. In this prioritization, scripts 2 and 3 are automatically selected and executed in this order.The order in which the scripts are executed is not shown in the drawing for simplicity.In this example, script 1 adjusts a target related to clinical target A, script 2 creates an optimization structure referred to as "volume i" in the drawing, and script 3 creates another optimization structure referred to as "volume ii" in the drawing. Script 4 adjusts the weights.The scripts thus generate and / or adapt an optimization structure, goals, and weights.executing each script also includes optimization to obtain dose distribution. An optimization function OFV may be used and the optimization may be performed in consideration of the optimization structure(s), the goals and / or the weights obtained when executing the script, so as to obtain a dose distribution after executing each script. For the three different prioritization scenarios, different dose distributions ("dose 1", "dose 2", "dose 3") can be determined. After executing the last script, a final dose distribution is obtained as output.Two examples of scripts are explained in more detail below with reference to FIGS. 4 aand 4 b.A first script can relate to the generation of auxiliary structures, in particular a transition region around a main target region, which is also referred to below as a high dose target volume.The script may include the step of determining a transition region target thickness I t of a transition region included in the low dose target volume and adjacent to the high dose target volume based on a predetermined low dose constraint C HL of a high dose target volume and a low dose constraint C LU of a low dose target volume surrounding and adjacent to the high dose target volume. The script may further include the step of determining, based on the thickness I t a number n of shells to be created to form the transition region and creating n shells S i where i=0 to n-1. The script may further determine, for each of the shells, a shell-specific upper dose limit Csu(i) based at least on the lower dose limit C HL of the target high dose volume and the upper dose limit C LU of the target low dose volume, wherein the upper dose limit of at least one of the shells is higher than the upper dose limit C LU of the target low dose volume, and wherein the shell-specific upper dose limit Csu(i) increases from the outermost shell S n-1 to the innermost shell S 0. The script may further comprise the step of generating a dose distribution by means of an optimization algorithm, the optimization algorithm being restricted by the predetermined lower dose limit C HL in the high dose target volume, the upper dose limit C LU in the low dose target volume with the exception of the transition region and the respective shell-specific upper dose limit Csu(i) for each of the shells in the transition region.In other words: With the aid of this script, a transition region, which is also referred to as build region or expansion volume, can be automatically generated at the interface between the high dose target volume, which can be the main target volume described above and the low dose target volume, with modified dose restrictions. The transition area can consist of shells, which can be considered as auxiliary volumes or auxiliary objects. The transition region and / or its shells may be considered examples of optimization structures as mentioned in the present disclosure. To some extent, the transition region extends the target high dose volume to the target low dose volume.The script described above facilitates optimization in effectively shaping the dose distribution at the interface between the low dose and high dose target volumes, particularly so as to meet the constraints.This is schematically shown in FIG. 4 a. The low dose target volume 10, the high dose target volume 11 and a plurality of shells 12 are shown. The remaining volume of the low dose target volume (outside the target area) is marked 10a.Also shown are the predetermined low dose limit C HL of the target high dose volume and a, for example, predetermined high dose limit C LU of the target low dose volume. Also shown are a predetermined upper dose limit C HU of the target high dose volume and a predetermined lower dose limit C LL of the target low dose volume. Also shown is the respective shell specific upper dose limit Csu(i) for each of the shells in the transition region. It can be seen that the restriction increases towards the target volume at high dose. In particular, in the present disclosure, the innermost shell has an upper dose constraint that corresponds to the upper dose constraint of the target high dose volume.An exemplary dose distribution 13 as it results from an optimization based on the above optimization structure and objectives is also illustrated.Details about the concept of such a transition region as an auxiliary structure can be found in the international patent application PCT / EP2022 / 063077 ("METHOD FOR DETERMINING A TREATMENT PLAN INCLUDING A DOSE DISTRIBUTION"). In particular, the script described above may perform the method as shown in FIG. 1 and described in connection with this FIG. 1.A second example script may relate to adjusting the main target area to account for risk organs. When executing the second script, an optimized scheduling target volume is obtained. The script is shown in Figure 4b.The script may perform the step of providing an initial coverage volume 118 for a target planned volume 116 to be irradiated at a prescribed dose during irradiation treatment. The script may also perform the step of providing at least one constraint to a risk organ 120, the at least one constraint indicating an allowed dose deposited in at least a portion or subvolume of the risk organ. In a further step, when an initial irradiation plan is used and / or according to the initial irradiation plan, an organ dose is calculated which is deposited in the at least one part or partial volume of the risk organ. The script further includes the step of determining the extent of injury to the at least one constraint based on the comparison of the at least one constraint and the calculated organ dose. The script also includes the step of calculating a reduced coverage volume for the planning target volume based on the determined extent of the violation. The script further includes the step of creating a virtual planning object 122 by changing, e.g., enlarging or reducing, a volume of the risk organ such that an overlap area 124 of the virtual planning object with the planning target volume corresponds to the reduction coverage volume. This step may include determining the overlap region. The script may further include the step of generating an optimized planning target volume 132 to be irradiated during the irradiation treatment based on and / or by reducing the initial coverage volume of the planning target volume based on and / or by removing at least a portion of the overlap area from the planning target volume. With the script described above, an optimum compromise can be found between a biologically effective dose deposited in at least a partial volume of the planning target volume, e.g. corresponding to the optimized planning target volume, and the at least one restriction and / or the preservation of the risk organ.For the purpose of illustration, the figure also shows a radius 123 and an outer surface, a contour, a circumference and / or a periphery 125 of the virtual planning object 122.Details on the concept of optimized planning of target volumes taking into account an organ at risk can be found in the international patent application PCT / EP2018 / 055207 ("IRRADIATION TREATMENT PLANNING BASED ON TARGET COVERAGE REDUCTION"). In particular, the script described above may perform the method shown in FIG. 1 and described in conjunction with this FIG. 1.Other scripts are conceivable as detailed in the claims and throughout the specification.Referring now to FIG. 4, there is shown a schematic diagram of a data processing system 1 in accordance with the present disclosure. The data processing system may comprise at least processing means 2 and storage means 3, which may comprise a temporary memory, e.g. RAM, and / or a permanent memory, e.g. ROM. Moreover, the data processing system may optionally comprise one or more communication interfaces 4 for receiving and transmitting data via one or more data connections 5. The data processing system may include, for example, one or more computers.Although the invention has been illustrated and described in detail in the drawings and the foregoing description, these drawings and descriptions are to be considered as exemplary and not limiting. The invention is not limited to the disclosed embodiments. In view of the foregoing description and drawings, it will be apparent to those skilled in the art that various modifications may be made within the scope of the invention as defined in the claims.References included in the specificationThis list of documents cited by the applicant has been produced in an automated manner and is only included for the better information of the reader. The list is not part of the German patent application or utility model application. The DPMA does not take any adhesion for any faults or omissions.Patent Literature citedEP 2022 / 063077

[0164] EP 2018 / 055207

[0168] Cited Non-Patent Literaturehttp: / / www.eleta.com / healthcare_us_selecta_vmat.php and http: / / www.varian.com / us / oncology / treatments / treatment_techniques / rapidarc

[0115]

Claims

A computer-implemented method for determining a treatment plan for radiotherapy treatment including dose distribution, the method comprising the steps of: a) determining (S11) a plurality of clinical goals, the plurality of clinical goals associated with dose distribution; b) determining (S12) a priority for each of the clinical goals based at least in part on user input; c) automatically determining (S13) a subset and order of scripts from a plurality of scripts based on the plurality of clinical goals and their respective priority, each script being configured to, when executed, adjust one or more optimization goals and / or adjust weights for optimization and / or provide and / or adjust an optimization structure; and d) generating (S14) a dose distribution, wherein generating the dose distribution comprises executing (S14a) the subset of scripts in the set order, wherein executing the subset of scripts comprises, for each of the scripts, obtaining (S14a-1) an optimization structure, optimization goals, and / or weights for the optimization, and performing an optimization (S14a-2) based thereon.The method according to claim 1, wherein at least a part of an optimization target and / or a weight for the optimization and / or an optimization structure obtained by executing a script or a sequence of scripts is locked (S15), so that the locked part cannot be changed by executing subsequent scripts.Method according to claim 1 or 2, wherein the method initiates the generation of the dose distribution upon a detection that a dose distribution cannot be determined without changing a blocked optimization target and / or a blocked weighting for the optimization and / or a blocked optimization structure, e.g. from the beginning or after executing a script (S16), and optionally informing a user about an error in the determination of a dose distribution and / or about possible reasons and / or solutions for an error in the determination of a dose distribution.Method according to one of the preceding claims, wherein each script is executed restricted by at least a part of the results of the previously executed scripts, in particular wherein an optimization which is carried out during the execution of the script is carried out restricted by the at least a part of the results of the previously executed scripts.The method according to any of the preceding claims, wherein the generating of the dose distribution is an iterative process comprising at least a first level of iterations, each iteration being associated with exactly one of the clinical goals, and the iterations being performed in the order of priority of the clinical goals.The method of claim 5, wherein the iterative process comprises a second level of iterations associated with the scripts, wherein each iteration of the second level of iterations is associated with exactly one of the scripts, in particular wherein each iteration of the first level of iterations is associated with one or more iterations of the second level of iterations.The method of claim 6, wherein each iteration of the second level of iterations comprises performing an optimization process or a sequence of optimization processes.Method according to any one of the preceding claims, wherein the generation of the dose distribution is a hierarchical process, in particular wherein each iteration of the first level of iterations is of higher rank than the subsequent iterations of the first level of iterations and / or wherein, among the iterations of the second level of iterations associated with one of the iterations of the first level of iterations, each iteration is of higher rank than the subsequent iterations.The method according to any of the preceding claims, wherein the clinical targets comprise at least one of the following features: high dose coverage of a main target area for irradiation, steep dose gradient towards areas adjoining the main target area for irradiation, low upper dose limits in areas adjoining the main target area for irradiation, high lower dose limits in the main target area for irradiation, high dose homogeneity, in particular in the main target area for irradiation, upper dose limits for an area defined as risk structure, e.g. at least a part of an organ such as the spinal cord.The method according to any of the preceding claims, wherein the optimization objectives comprise one or more constraints, in particular for one or more ranges, at least one of: a lowest allowed dose value, a highest allowed dose value, a lowest allowed dose gradient, a lowest allowed dose homogeneity value, a highest allowed temporal dose variation value, a target dose value, a target dose gradient value, a target dose homogeneity value, a target dose time variation value; and / or wherein the optimization structure comprises at least one main target area for the irradiation and optionally comprises auxiliary structures, e.g. around the main target area in which irradiation is not required but may be allowed to reach the optimization targets, and / or auxiliary structures defining an area / areas defined as a risk structure, e.g. at least a part of an organ such as the spinal cord.The method according to any of the preceding claims, wherein executing scripts may comprise at least one of the following: adjusting a / the main target area for the irradiation, generating auxiliary structures, e.g. auxiliary volumes, around a / the main target area for the irradiation, adjusting auxiliary structures, e.g. auxiliary volumes, around a / the main target area for the irradiation, adjusting restrictions within a / the main target area for the irradiation and / or within auxiliary structures, e.g. auxiliary volumes, around a / the main target area for the irradiation, generating and / or adjusting one or more auxiliary structures defining an area defined as a risk structure, e.g. at least a part of an organ such as the spinal cord, and / or adapting constraints within one or more auxiliary structures defining an area / areas defined as a risk structure, e.g. at least part of an organ such as the spinal cord.The method of any preceding claim, wherein steps a) to d) do not require user intervention.A data processing system (1) configured to perform the method of any preceding claim.A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of any one of claims 1 to 12.A computer readable medium containing instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 12.

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