Method, computer program product, and computer system for particle-based radiation therapy treatment planning

Abstract

A method for radiotherapy treatment planning for particle-based arc therapy, comprising the following steps: · determining (S11; S21) an initial set of candidate energy layers; · performing calculations on the initial set, · based on the results of the calculations, determining (S12; S23) at least one additional energy layer to produce an extended set of candidate layers, wherein the at least one additional energy layer involves an energy level that was not previously used; · adding at least one additional energy (S14; S24) to the initial set to produce an extended set of candidate energy layers; · optimizing (S16; S26) the radiotherapy treatment plan based on the extended set of candidate layers.

Description

Technical Field

[0001] The present invention relates to a system and method for particle-based radiation therapy treatment planning and delivery, and in particular to the planning of arc treatments. Background Art

[0002] In particle-based radiotherapy treatment, the patient is irradiated with charged particles (e.g., protons). These particles are controlled so that they deposit most of their energy in the patient at a specific depth, thereby covering the entire target. At the same time, some energy will be deposited along the path toward the target. Each particle will deposit most of its energy toward the end of its path, the so-called Bragg peak. The depth of the Bragg peak in the patient's body can be controlled by adjusting the kinetic energy of the particles. Electromagnets can be used to control the lateral position of the Bragg peak to deflect the focused beam. This allows a highly localized dose to be delivered at a well-controlled position in the patient's body. The dose delivered based on a specific combination of kinetic energy and lateral deflection of the beam is called a spot. The number of particles delivered to the spot is generally referred to as the spot weight. By providing a spot at many different positions in three-dimensional space, the target volume can be completely covered by the desired dose distribution. This process is called active scanning ion beam therapy, also known as pencil beam scanning.

[0003] The kinetic energy of the spot is usually but not necessarily distributed over multiple discrete energies. A group of spots with the same kinetic energy but different lateral deflections are usually called energy layers. In order to cover the required volume in the patient, different energy layers are defined so that the particles of a specific energy layer deposit their energy at a specific depth in the patient. These energy layers are selected so that the Bragg peaks will be distributed over the volume to be treated.

[0004] The spot weights for all energy layers are determined in the treatment planning system by optimization, and these spot weights are iteratively varied to achieve desired targets or constraints related to dose levels or other relevant quantities. The situation in which the spot weights are allowed to vary freely across the beam is called intensity modulated proton therapy (IMPT).

[0005] Proton arc therapy involves radiating from a series of a large number of beams at different angles, for example 10 or more, 50 or more, or 100 or more angles, each beam having a plurality of energy layers, for example up to 20 energy layers. Adjustment between each energy layer and between each angle takes time. The arc can be delivered as a plurality of separate beams or by a continuously moving arc. In the latter case, the beam will act as a control point, as is known from proton arc therapy. In the following, if the arc is continuous, the term beam, beam direction or beam angle may likewise be replaced by the term control point. The term energy layer in this document refers to a specific energy level in a specific beam. If the same energy level is used in two beams, this will be considered to constitute two separate energy layers.

[0006] The selection of energy layers, including the selection of beam angles, is critical for the entire arc to achieve a high quality plan. A larger number of energy layers generally achieves a higher plan quality, but also increases the delivery time to the patient, thereby increasing the incurred plan cost in terms of risk of small patient movements, patient discomfort during treatment, and clinical efficiency and economic considerations. It is always desirable to reduce the delivery time while still maintaining a high quality plan to ensure effective treatment of the patient. Therefore, treatment planning should strive to select energy layers to achieve a high quality plan with a limited set of energy layers.

[0007] Published U.S. Patent Application No. 2019 / 0091488 proposes an iterative method for proton arc therapy, including control point resampling, energy layer redistribution, energy layer filtering, and energy layer resampling in each iteration. Each control point corresponds to a beam angle. A small number of control points are defined, each of which has multiple energy layers, and the plan is optimized. After optimization, a control point is randomly selected, split and replaced by a pair of adjacent sub-control points, which are positioned, for example, 5 degrees to either side of the initial control point. Before optimizing the plan again, the energy layer of the initial control point is copied to the two sub-control points or split between the sub-control points. Since the sub-control points are positioned at a smaller angle to the initial control point, it is assumed that the same energy level should be used. This can be repeated multiple times so that the sub-control points can also be divided and replaced by two adjacent control points, which have all or a subset of the same energy layer. Spots and / or energy layers with low weights can then be filtered out by conventional spot or energy layer filtering methods.

[0008] Co-pending European application No. 18165472.4 discloses a treatment planning method for particle-based arc therapy, starting from a set of energy layers, wherein, as part of an optimization step, the number of energy layers included in the plan can be reduced by including in the optimization problem a penalty designed to limit the number of energy layers. This allows optimizing the number of energy layers and their positions in the same operation as optimizing the treatment plan. The method only selects between energy layers already present in the set of energy layers and does not foresee the addition of energy layers. Summary of the invention

[0009] It is an object of the present invention to provide a set of energy slices on which arc radiation therapy treatment planning is based, which will provide a high quality treatment plan while also minimizing the required computational effort.

[0010] According to the invention, this object is achieved by a method of radiotherapy treatment planning for creating a plan for delivering at least one particle-based arc to a patient using a device arranged to deliver radiation in the form of charged particles from a plurality of different directions, the method comprising the following steps:

[0011] a. determine an initial set of candidate energy layers, each energy layer involving a specific energy level at a specific beam angle,

[0012] b. Perform calculations on the initial set,

[0013] c. based on the result of said calculation, determining at least one additional energy layer to produce an extended set of candidate layers, wherein the at least one additional energy layer relates to an energy level not previously used in any energy layer,

[0014] d. adding at least one additional energy to the initial set to produce an expanded set of candidate energy layers,

[0015] e. Optimizing radiation therapy treatment plans based on the expanded set of candidate layers.

[0016] Therefore, according to the present invention, the candidate set of energy layers can be expanded by adding energy layers that will make a significant contribution to the generated plan. Which energy layers to add is determined based on the results of plan optimization or dose calculation. For example, energy layers can be added in areas where a large amount of energy is deposited, where another energy layer seems to be useful. Alternatively or additionally, energy layers can be added to produce spots in areas lacking coverage to improve coverage.

[0017] Importantly, energy levels not previously used by any energy layer may be used so that the added energy layer or layers will have energy levels not previously used by any energy layer. Energy layers with previously unused energy levels may be added to existing beam angles or for newly added beam angles.

[0018] In one embodiment, the calculation involves a dose calculation and determining at least one additional energy layer is based on the result of the dose calculation, e.g. identifying an area where the Bragg peak density is low and determining the additional energy layer to provide a Bragg peak in that area. This involves limited computational effort and in particular enables the addition of new energy layers without first performing an optimization.

[0019] In a preferred embodiment, the calculation involves a treatment plan optimization based on an optimization problem, and determining at least one additional energy layer is based on the resulting treatment plan. This optimization requires more computing power, but also provides a better basis for determining new energy layers in the best possible way.

[0020] In a preferred embodiment, the energy layer expansion method is combined with a method step for discarding energy layers. One such method involves optimizing the plan using an optimization problem formulation designed to produce a suitable dose distribution so that the plan will only use a subset of beam angles and a subset of energy layers from a set of beam angles and a set of energy layers, respectively. This can be done using a greedy or reverse greedy method, both of which start with a candidate set of energy layers. In the reverse greedy method, the complete candidate set is included from the beginning, and some layers are discarded according to the scores based on the spot weights and / or gradients of each layer. After the first optimization, the layers to be discarded are determined. For example, n layers with the lowest scores are discarded, n is an integer, such as 10% of the candidate set. Alternatively, all layers with scores below a threshold can be discarded. Then, the optimization is repeated and the m layers with the lowest spot weight sum are discarded, m is an integer equal to t or different from n. This can be repeated again until a specific number of energy layers are retained. A combination of greedy and reverse greedy methods can be used by first generating a subset using a greedy method and then refining the subset using a reverse greedy method.

[0021] In the greedy method, a subset based on the candidate set is first optimized. A score based on the gradient point is determined. Based on the score, layers in the candidate set that are not included in the subset can be added to the subset, and a new optimization can be performed. For example, a fixed number of layers can be added, or all layers with scores above a certain threshold can be added. This can be repeated until a specific number of energy layers is reached. In the greedy and reverse greedy methods, for example, the optimization can be repeated until a predetermined number of energy layers or a portion of the candidate set is retained, or until all retained energy layers have a spot weight sum above a threshold. The energy layer discarding step in the reverse greedy method and the energy layer including step in the greedy method can be limited to acting on the entire arc or one or more subsets of the arc, each subset including one or more beams.

[0022] In a preferred embodiment, the optimization is penalized by being designed to limit the number of energy slices. This will ensure that the best energy slices, i.e. the energy slices with the highest efficiency in contributing to the dose, are retained, while energy slices that produce low doses are discarded.

[0023] The penalty may be defined as one or more constraints or terms in the objective function used in the optimization, or a combination of both.

[0024] The penalty can be expressed in terms of the number of energy slices. Since changing between different energy slices is very time consuming, minimizing the number of energy slices used can shorten the planned delivery time. Alternatively, the penalty can be expressed in terms of the number of energy slice changes between adjacent angles. For example, this will enable energy slices to be maintained when changing beam angles, thereby saving time. It is also possible to express the penalty in terms of the number of energy slices and the number of energy slice changes.

[0025] The penalty may be defined to act on the entire arc or one or more subsets of the arc, each subset comprising one or more beams.

[0026] The present invention also relates to a computer program product for controlling a radiotherapy device, the computer program product preferably being stored on a carrier such as a non-transitory storage device, the computer program product comprising computer readable code means which, when run in a processor of the radiotherapy device, will cause the device to perform the above-mentioned method.

[0027] The invention further relates to a radiation therapy treatment planning device comprising a processor and a program memory storing a computer program product according to above, the computer program product being arranged to run in the processor for controlling the radiation therapy treatment planning device. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be described in more detail below by way of example and with reference to the accompanying drawings, in which:

[0029] Figure 1 is a flow chart of a first embodiment of a method according to the present invention;

[0030] Figure 2 is a flow chart of a second embodiment of a method according to the present invention;

[0031] Figure 3 A system in which the invention may be implemented is schematically shown. DETAILED DESCRIPTION

[0032] The method according to the invention is based on a candidate set of energy strata, which can be extended according to certain criteria.

[0033] This method can be used, for example, Figure 1is shown in its simplest form. In a first step S11, an initial set of energy layers is obtained. As described above, an energy layer is an energy level associated with a specific beam angle. In this embodiment, the initial set should preferably be small enough so that the initial dose calculation or optimization can be completed within a reasonable time frame. The set can, for example, consist of one or several beam angles, each with several energy layers. Next, in step S12, calculations, such as treatment plan optimization, are performed based on the initial set. In step S13, the calculation results are used to determine at least one additional energy layer, which is in a new beam angle or in a beam angle already included in the initial set. Then, in step S14, the initial set of energy layers is extended by adding one or more energy layers from step S13 to create an extended set. Energy layers can be added to existing beam angles and / or one or more new beam angles can be selected. This can be repeated one or more times by treating the extended set as the initial set, expanding it again and returning to step S11.

[0034] In one embodiment, the calculation in step S12 involves treatment plan optimization. In that case, information from the generated treatment plan can be used to determine additional energy layers in step S13. In other embodiments, step S12 instead involves simpler calculations, such as dose calculations for each energy layer in the initial set. In step S13 in the latter case, additional one or more energy layers are determined based on the generated doses calculated for each energy layer. This will provide less information than treatment plan optimization, but still provide information such as Bragg peak positions that can be used to determine additional energy layers. For example, areas where the Bragg peak density is low can be identified, and additional energy layers can be determined so that new Bragg peaks will be created in these areas, thereby ensuring a suitable Bragg peak density across the target.

[0035] Step S15 is a decision step to decide whether another extension of the set of energy layers should be performed. If yes, return to step S12 to perform the calculation of step S12 on the extended set. If not, optimization is performed based on the extended set in step S16 to obtain an arc treatment plan. Step S17 is an optional step in which spots with low spot weights are filtered out. Spot filtering can be performed to improve treatment efficiency, but also because the machine may have physical limitations on the size of the spot weight that the machine can deliver. Typically, the spots are also classified, that is, the order in which the spots will be irradiated is determined to minimize the time required. Spot filtering and spot classification are known processes that can be performed at any suitable point in the process.

[0036] In all embodiments, a selection should be performed in step S13 and in the following step S23 to add energy slices that are likely to be useful for the plan, ie, energy slices that will contribute significantly to the total dose. In practice, several different criteria may be applied to achieve this.

[0037] For example, new energy layers can be added near new energy layers that produce high spot weights in the generated treatment plan. Alternatively or additionally, the geometry of the Bragg peak position can be taken into account, so that new energy layers that produce new Bragg peaks are added to areas where the Bragg peak density is low. Another criterion may be that if the amount of radiation from one beam angle or a group of adjacent beam angles is very high, new energy levels should preferably be added near those beam angles in the form of new beam angles, each beam angle having multiple energy layers. New energy layers can also be added near energy layers with high weights in the same beam to increase the energy level density within the beam. Beam angles and energy layers can be selected based on those that make the most useful contribution to the total dose, regardless of the beam angles and energy levels of existing energy layers, in a sense that new energy layers can be added in exactly the way that is deemed appropriate, and new energy levels that were not originally used may be used. This means that for existing beam angles, one or more energy layers can be added where they are deemed useful, without regard to existing energy layers in that beam or other beams. For a new beam angle, an energy layer will be selected as the optimal energy layer for this new beam angle without considering the energy layers of any existing beam angles at all.

[0038] After the desired number of iterations, the resulting expanded set is used in the optimization step for the treatment plan.

[0039] There may be a predetermined candidate set of possible energy layers including possible beam angles that may be selected for addition to the initial set of energy layers, or new beam angles and associated energy layers may be freely defined.

[0040] Preferably, the method also includes a way of reducing the number of energy layers to ensure that energy layers that are not important for the plan are discarded. This usually means removing energy layers that contribute very little to the total dose, which reduces the computational workload and minimizes the impact on the quality of the plan. This can be done by a simple method, such as energy layer filtering, in which energy layers with low sums of spot weights are filtered out. According to a preferred embodiment, this is achieved by an optimization problem that includes a penalty designed to reduce the number of energy layers.

[0041] Figure 2 An embodiment is shown in which a series of expansions and reductions of the set of energy layers is performed iteratively.

[0042] In a first step S21, a potential set of energy layers is defined. For pencil beam scanning treatment methods, a set of spots for each layer is also defined. This may involve obtaining an arc trajectory and specifying multiple beam angles for the arc and the energy layers associated with each beam angle.

[0043] In the next step S22, an optimization is performed, wherein the treatment plan is optimized based on the potential set of energy slices defined in step S21. In the optimization problem, one or more penalties are set for the number of energy slices and / or the number of energy slice changes and / or the number of beams. In the following, the singular form of "penalty" is used, but it should be understood that multiple penalties can also be applied together. More details on this step are provided below. This optimization will produce a reduced set of energy slices, which can be used as Figure 1 The initial set of energy layers in the method, or for creating an extended set as described below.

[0044] In a next step, the reduced set of energy layers from step S22 is expanded to create an extended set. In step S23, the results of the treatment plan optimization in step S22 are used to determine at least one additional energy layer in a new beam angle or in a beam angle already included in the initial set. The initial set of energy layers is then expanded in step S24 by adding one or more energy layers from step S23 to create an extended set. Energy layers may be added to existing beam angles and / or one or more new beam angles may be selected.

[0045] Step S25 is a decision step to decide whether another expansion of the set of energy layers should be performed. If so, then return to step S22 and perform the optimization of step S22 using the expanded set as the new potential set. If not, then in step S26, an optimization is performed based on the expanded set and using the same type of optimization problem discussed in step S22 to obtain an arc treatment plan. Step S27 is an optional step in which energy layers with low spot weights are filtered out in a conventional manner. For Figure 1 The process of spot filtering and spot classification can be performed at any suitable point in the process.

[0046] In steps S22 and S26, optimization is performed using an optimization problem that includes a penalty designed to reduce the number of energy layers. The penalty can be defined as a constraint that sets an absolute limit, or as a term in an objective function that reflects the desire to limit the number of energy layers as much as possible. The penalty can be defined as limiting the number of energy layers, limiting the number of energy layer changes during treatment, or defined in any other suitable manner that ensures that the optimization algorithm will systematically shorten the time it must take to change between energy layers during treatment delivery. In step S22, the optimization can start with an initial set of complete beam angles and energy layers so that the optimization process will reduce the number of beam angles and energy layers by removing the beam angles and energy layers that contribute the least to the plan. When an energy layer is removed, the spot weight of the energy layer can be assigned to the remaining energy layers so that the disturbance caused by removing the energy layer to the solution is minimized. How to do this is well known in the art and may involve constructing a three-dimensional Delaunay triangulation of the Bragg peaks of the remaining layers and assigning the weight of each spot to be removed according to the position of the spot in the triangulation.

[0047] Alternatively, the optimization may start with no beam angles and energy layers, and the optimization process will continue to add those beam angles and energy layers that make the most useful contribution to the plan. In both cases, the beam angles and energy layers considered are those from the set defined in step S21. Some more specific examples of these two optimization methods are discussed below.

[0048] The penalty can be set to act on the entire arc, limiting the number of energy layers for each arc to a maximum value. Alternatively, the penalty can be set to act on each angle separately to set a maximum number of energy layers for each beam. As a third option, sub-arcs can be defined, each of which contains a subset of the arc, and the penalty can be set to limit the number of energy layers for each sub-arc. The same applies if the penalty is set according to an objective function. A sub-arc can include any suitable number of consecutive beams (for example, three or five), but a sub-arc can also include only one beam. In particular, if the penalty is specified to limit the number of energy layer changes, it will be appropriate to set the penalty for the entire arc or a sub-arc including more than one angle. This is because in this case, by using the last energy layer in one beam as the first energy layer in the next beam, this penalty can avoid changing the energy layer between two beams.

[0049] The energy layer selection penalty can be expressed mathematically by introducing the function y: m →R n , which converts the spot weight vector x∈R mA measure of the degree to which each energy layer is used at each angle (defined across all energy layers and angles). i ,....,y n can preferably be defined such that if the combination of energy layer and angle at index i has any spot with a positive weight, then y i (x) is positive, otherwise it is zero. One possible formulation of the component function y is the subvector x associated with a certain energy level and angle i The norm of ||x i ||. The penalty for the maximum number of energy layers per arc can be expressed in this notation as card(y)≤b, where the cardinality operator card(·) refers to the number of non-zero entries of the vector. Constraints on sub-arcs or single angles are similarly formulated with respect to sub-vectors of y. This penalty will be referred to as the cardinality penalty hereinafter. The penalty term in the optimization function is based on a continuous approximation method.

[0050] Such an approach would start with a complete set of beams and energy layers and remove some of them during the optimization process. The cardinality operator is here approximated by a continuous function that removes the combinatorial aspect of the problem. The resulting approximation problem can be solved by some continuous optimization method, such as interior point methods or sequential quadratic programming. One possibility to transform a problem with cardinality penalty into a continuous problem is to formulate the cardinality operator as per card(y) = ∑ i s(y i ), where s is evaluated to zero for non-positive numbers and 1 otherwise, and then the exact function s is replaced by a continuous but approximate step function. Examples of functions that can be used to approximate s are logistic functions or error functions, or just positive partial functions. The method can be applied in steps, performing multiple optimizations and improving the accuracy of the step function approximation between optimizations until the result is acceptable taking into account the cardinality penalty. If the cardinality penalty is formulated as a constraint, it means that this constraint must be satisfied. If the cardinality penalty is formulated as a term in the objective function, then that term is minimized.

[0051] The objective function and constraints in the optimization problem should be able to be optimized over different quantities, the most important of which are dose- and LET-related quantities, such as LET and dose-weighted LET. If taken into account in the optimization, LET (Linear Energy Transfer) can be focused more effectively inside the target using a proton arc than in normal IMPT.

[0052] At any point in the optimization process, typically at the end, as indicated by steps S17 and S27, spot filtering can be applied to filter out spots that are assigned low weights. Preferably, the weights of these filtered spots are reallocated to other spots that remain in the plan. Several methods of doing this are well known in the art, and these methods will not be discussed in any detail here.

[0053] If the number of spots in the initial concentration of the energy layer is too large to calculate and store the doses of all the spots, it can be pre-processed to remove some energy levels before starting the process according to the present invention. This can be done in any suitable way. For example, for two adjacent beam angles with the same or similar energy levels, some energy levels can be removed from each beam angle so that those energy levels removed from one beam angle remain in the other beam angle, and vice versa. The pre-processing method can remove energy layers in a uniform manner until the candidate set is small enough to allow numerical calculations. It can also be based on certain rules, such as a preference for far-side layers over near-side layers. If an approximate and inexpensive technique for calculating the Bragg peak position from the spot position and energy is available, the pre-processing can also be based on the Bragg peak position. For such a method, the pre-processing can remove energy layers in a way that attempts to keep the Bragg peaks evenly distributed over the target volume.

[0054] Figure 3 is an overview of a system for radiotherapy treatment imaging and / or planning, the system being adapted to deliver radiation to a patient from different directions. It will be appreciated that such a system may be designed in any suitable manner, and Figure 3 The designs shown in are examples only. The patient 11 is located on a treatment couch 13. The system includes a treatment unit 10 having a radiation source 15 mounted in a gantry 17 for emitting radiation toward the patient located on the treatment couch 13. Typically, the couch 13 and the gantry 17 are movable relative to each other in multiple dimensions to provide radiation to the patient as flexibly and correctly as possible. In particular, the gantry is capable of rotating around the couch, either between specific angles or for a full 360° rotation. Alternatively, the gantry may be fixed and the couch carrying the patient may rotate. Another alternative is to place the patient in a seated position on a chair. Arc therapy in this case may be performed by changing the beam direction or rotating the chair. These parts are well known to technicians. The system also includes a computer 21, which may be used for radiotherapy treatment planning and / or for controlling radiotherapy treatment. For example, it will be understood that the computer 21 may be a separate unit that is not connected to the imaging unit.

[0055] The computer 21 comprises a processor 23, a data memory 24 and a program memory 26. Preferably, there are also one or more user input devices 28, 29 in the form of a keyboard, a mouse, a joystick, a voice recognition device or any other available user input device. The user input device may also be arranged to receive data from an external memory unit.

[0056] The data memory 24 includes clinical data and / or other information for obtaining a treatment plan, including a set of clinical objectives for the plan. The data memory 24 also includes one or more dose maps for one or more patients to be used in the treatment plan according to an embodiment of the present invention. The program memory 26 stores a computer program known per se, which is arranged to be used for treatment plan optimization. The program memory 26 also stores a computer program, which is arranged to cause the computer to control the treatment of the patient according to the present invention.

[0057] It should be understood that the data memory 24 and program memory 26 are only schematically shown and discussed. There may be multiple data memory units, each storing one or more different types of data, or there may be one data memory that stores all data in a suitably structured manner, and the same is true for the program memory. One or more memories may also be stored on other computers. For example, the computer may be arranged to perform only one of the methods, while another computer is present to perform the optimization.

[0058] To further reduce damage to surrounding tissue, the optimization method described can be combined with a method in which the plan is set for similar arcs delivered over different treatment fractions delivered on different days. The different arcs include different beam angles so that the target is covered in the same way, but the way through the patient to the tumor is different for different fractions.

[0059] Radiation therapy using charged particles such as protons can optimize highly accurate plans. This in turn means that small errors, such as errors in the setup, may have a large impact on the delivered dose. Therefore, the optimization method used according to the present invention should preferably be a robust optimization method. This becomes even more important when the number of energy layers is reduced. The method should be robust to different uncertainties to ensure that the plan quality is maintained even when there are uncertainties in the delivery (such as range and setup uncertainties). As an alternative to robust optimization methods, methods that aim to create a uniform dose for each angle (compare SFUD (single field uniform dose) in normal active scan plan optimization) or for each group of angles (each sub-arc) would be suitable. If a lower number of energy layers is desired for each angle, the most feasible solution is to achieve a uniform dose for each sub-arc.

[0060] The methods described can be used for all types of active scanning, including spot scanning, raster scanning, and line scanning. The implementation of line scanning in conjunction with proton arc therapy will be beneficial in terms of delivery time. These methods are applicable to planning treatments for humans, as well as treatments for small animals (e.g., mice) for preclinical trials. The methods described are equally applicable to other light ions besides protons, such as helium, carbon, and oxygen, as long as the different biological effects of different ion types are considered in the biological (RBE) model included in the optimization process.

Claims

1. A method of radiotherapy treatment planning, the method for creating a plan for delivering at least one particle-based arc to a patient using a device arranged to deliver radiation in the form of charged particles from a plurality of different directions, the method comprising the steps of: a. determining (S11; S21) an initial set of candidate energy layers, each layer involving a specific energy level at a specific beam angle, b. performing calculations on the initial set, c. based on the result of said calculation, determining (S12; S23) at least one additional energy layer to generate an extended set of candidate layers, wherein said at least one additional energy layer relates to energy levels not previously used in any of said energy layers, d. adding (S14; S24) said at least one additional energy layer to said initial set to produce an extended set of candidate energy layers, e. Optimizing ( S16 ; S26 ) a radiotherapy treatment plan based on the expanded set of candidate layers.

2. The method according to claim 1, wherein: The calculating involves a dose calculation and determining at least one additional energy layer is based on a result of the dose calculation.

3. The method according to claim 2, wherein: An area where the Bragg peak density is low is identified and the additional energy layer is determined to provide the Bragg peak in this area.

4. The method according to claim 1, wherein: The calculating involves optimization of a treatment plan based on an optimization problem, and determining at least one additional energy layer is based on the generated treatment plan.

5. The method according to claim 4, wherein: The optimization (S22; S26) is performed based on an optimization problem formulation designed to produce a suitable dose distribution so that the plan will use only a subset of beam angles and / or a subset of energy slices from the set of beam angles and the set of energy slices, respectively.

6. The method according to claim 5, wherein: A potential set of energy layers is provided, and the optimization includes a first optimization process based on the entire potential set and a subsequent step in which some energy layers are discarded from the potential set based on their spot weights and / or gradients to create a reduced potential set and a second optimization process is performed based on the reduced potential set (S26).

7. The method according to claim 5, wherein: A potential set of energy layers is provided, and the optimization includes a first optimization process based on a first subset of the potential set and a subsequent step, in which some energy layers in the potential set that are not included in the subset are added to the subset based on the calculation of the gradient of their spot weights relative to the optimization target to create an extended subset, and a second optimization process is performed based on the extended subset.

8. The method according to claim 5, wherein: The optimization is designed to limit the number of energy layers.

9. The method according to claim 8, wherein: The limitation of the energy horizon is achieved by a penalty which is implemented as a constraint or term in the objective function used in the optimization.

10. The method according to claim 9, wherein: The limitation of the energy strata is achieved by a penalty, which is expressed in terms of the number of energy strata or the number of energy strata changes.

11. A computer program product for controlling a radiation therapy treatment planning apparatus, the computer program product comprising computer readable code means which, when executed in a processor of a radiation therapy treatment planning apparatus, will cause the apparatus to perform a method according to any one of claims 1 to 10.

12. The computer program product according to claim 11, wherein: The computer program product is stored on a carrier.

13. The computer program product of claim 12, wherein: The carrier is a non-transitory storage device.

14. A radiotherapy treatment planning device (10), comprising a processor (23) and a program memory (26), the program memory storing a computer program product according to any one of claims 11 to 13, the computer program product being arranged to run in the processor to control the radiotherapy treatment planning device.

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