Generate multiple potential treatment plans for multi-criteria optimization

Abstract

A method for generating a plurality of potential treatment plans, each treatment plan specifying a distribution of radiation to a target volume, is provided, the method being performed by a treatment planning system and comprising the steps of generating a plurality of potential treatment plans (12) for performing a multi-criteria optimization (MCO) based on a set of optimization functions, at least one of the optimization functions depending on the distribution of the product of the radiation dose and the linear energy transfer (LET) in a specified region of the patient, thereby generating the plurality of treatment plans.

Description

Technical Field

[0001] The present disclosure relates to the field of treatment planning for distributing radiation to a target volume and, in particular, in this context to multi-criteria optimization. Background Art

[0002] In ion beam therapy, an ion beam (e.g., protons or heavier ions) is directed toward a target volume. The target volume may, for example, represent a cancerous tumor. The particles penetrate the tissue and deliver a dose of energy to induce cell death. An advantage of ion beam therapy is that there is a significant peak in the dose distribution, called the Bragg peak. The Bragg peak is a peak in dose delivery that occurs at a certain depth, after which the dose delivery decreases rapidly. This can be compared to electron beam therapy or X-ray (photon) therapy, where the maximum dose always occurs at a shallow depth, and the decline in distal dose cannot be controlled by the same sharp decline as in ion therapy.

[0003] The depth of the Bragg peak in the patient can be controlled by adjusting the kinetic energy of the particles. The lateral position can be controlled using electromagnets to deflect the focused beam. This allows a highly localized dose to be delivered at a well-controlled location in the patient. The dose delivered from a certain combination of kinetic energy and lateral deflection of the beam is called a spot. The number of particles delivered to the spot is often referred to as the spot weight. By providing spots at many different locations in three-dimensional space, the target volume can be covered with the desired dose distribution. The kinetic energy of the spot is usually but not necessarily distributed over several discrete energies. A group of spots with the same kinetic energy but different lateral deflections are often referred to as energy layers. This process is called active scanning ion beam therapy, also known as pencil beam scanning.

[0004] Planning of how the spots should be delivered is performed in the treatment planning system, resulting in a treatment plan. The treatment planning system determines the energy layers to be used and the distribution and weights of the spots therein, but the treatment planning system does not deliver the ion beam. The delivery of the ion beam is completed by the radiation delivery system that provides the treatment plan.

[0005] The determination of the treatment plan can be done in many different ways. One way is to use multi-criteria optimization (MCO). In MCO, multiple potential treatment plans are generated, each of which is optimized in a different way. The planning operator can then linearly combine these potential treatment plans in real time, where the combination is generated in real time along with performance indicators and visualized.

[0006] Giantsoudi Drosoula et al. have published: “Linear Energy Transfer-Guided Optimization in Intensity Modulated Proton Therapy: Feasibility Study and Clinical Potential”, International Journal of Radiation: Physics of Tumor Biology, Pergamon Press, USA, Vol. 87, No. 1, June 19, 2013, pp. 216-222. However, any improvement in the way treatment plans are generated would be of great benefit. Summary of the invention

[0007] One goal is to improve how to optimize to improve MCO in relation to relative bioeffectiveness (RBE).

[0008] According to a first aspect, a method for generating a plurality of potential treatment plans, each treatment plan specifying a distribution of radiation to a target volume, is provided, the method being performed by a treatment planning system and comprising the steps of generating a plurality of potential treatment plans for multi-criteria optimization (MCO) based on a set of optimization functions (12). At least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified region of a patient, thereby generating the plurality of treatment plans.

[0009] At least one optimization function may be specified as a target.

[0010] The goal may be to minimize or maximize at least one optimization function value.

[0011] At least one optimization function may be specified as a constraint.

[0012] The constraint may be that at least one optimization function value cannot exceed a specified value, or at least one optimization function value cannot fall below a specified value.

[0013] The method may further include the step of providing the plurality of treatment plans to the MCO module.

[0014] According to a second aspect, a treatment planning system for generating a plurality of potential treatment plans is provided, each treatment plan specifying a distribution of radiation to a target volume. The treatment planning system comprises: a processor; and a memory storing instructions which, when executed by the processor, cause the treatment planning system to: generate a plurality of potential treatment plans (12) for multi-criteria optimization (MCO) based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified region of a patient, thereby generating the plurality of treatment plans.

[0015] At least one optimization function may be specified as a target.

[0016] The goal may be to minimize or maximize at least one optimization function value.

[0017] At least one optimization function may be specified as a constraint.

[0018] The constraint may be that at least one optimization function value cannot exceed a specified value, or at least one optimization function value cannot fall below a specified value.

[0019] According to a third aspect, there is provided a computer program for generating a plurality of potential treatment plans, each treatment plan specifying a distribution of radiation to a target volume. The computer program comprises computer program code which, when executed in a treatment planning system, causes the treatment planning system to: perform a multi-criteria optimization based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer, LET, in a specified region of a patient, thereby generating the plurality of treatment plans.

[0020] According to a fourth aspect, there is provided a computer program product comprising the computer program according to the third aspect and a computer readable device having the computer program stored thereon.

[0021] Generally, all terms used in the claims should be interpreted according to their ordinary meaning in the technical field, unless otherwise explicitly defined herein. Unless otherwise explicitly stated, all references to "a / an / the element, device, component, means, step, etc." should be openly interpreted as referring to at least one instance of an element, device, component, means, step, etc. Unless explicitly stated, the steps of any method disclosed herein do not have to be performed in the exact order disclosed. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Various aspects and embodiments will now be described by way of example with reference to the accompanying drawings, in which:

[0023] Figure 1 is a schematic diagram illustrating an environment in which embodiments presented herein may be applied;

[0024] Figure 2 is a diagram illustrating the use of ion dose delivery and in particular the use of Figure 1 Schematic diagram of the Bragg peak of a radiation delivery system;

[0025] Figure 3 It is a graphic Figure 1 A schematic diagram of the functional modules of the treatment planning system;

[0026] Figure 4 is a flow chart illustrating an embodiment of a method for generating a plurality of potential treatment plans in Figure 1 Implementation in the treatment planning system;

[0027] Figure 5 is a diagram illustrating a method according to an embodiment Figure 1 a schematic diagram of components of a treatment planning system; and

[0028] Figure 6 An example of a computer program product comprising computer readable means is shown. DETAILED DESCRIPTION

[0029] Various aspects of the disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which certain embodiments of the invention are shown. However, these aspects may be embodied in many different forms and should not be construed as limiting; rather, these embodiments are provided by way of example so that the disclosure will be thorough and complete and the scope of all aspects of the invention will be fully conveyed to those skilled in the art. The same numbers refer to the same elements throughout the description.

[0030] Figure 1 is a schematic diagram illustrating an environment in which the embodiments presented herein may be applied. A treatment planning system 1 determines the distributed radiation for radiotherapy. This is communicated to a radiation delivery system 2 as a treatment plan 12. Based on the treatment plan, the radiation delivery system 2 generates a beam 7 for providing radiation to a target volume 3 of a patient while avoiding radiation to an organ at risk 5.

[0031] The radiation delivery system 2 can be, for example, an ion beam system for pencil beam scanning. In this case, radiation is provided in a large number of spots, each of which has a position in a three-dimensional coordinate system. The position of the dose maximum (Bragg peak) in the depth direction of the spot (i.e. along the z axis) is controlled by the kinetic energy of the ions; higher energies result in deeper positions of the dose maximum. In addition, electromagnets are used to control the lateral position to deflect the beam 7. In this way, a dose distribution that covers the target volume 3 in three dimensions can be achieved while keeping the radiation to the organs at risk 5 at a low level.

[0032] Alternatively, the radiation delivery system 2 is based on tomotherapy, where radiation is delivered during circular or helical movements around the patient.

[0033] Other types of radiation delivery systems are possible.

[0034] Figure 2 is a diagram showing the use of ions, and in particular the use of Figure 1Schematic diagram of dose delivery of the Bragg peak of the radiation delivery system 2. The horizontal axis represents the depth in water, while the vertical axis represents the dose. Curve 20 shows the dose distribution with respect to depth. The Bragg peak 22 is the peak of the curve, which occurs just before the dose drops off.

[0035] When delivering radiation to a patient, ions result in different biological effects in terms of destroyed cells compared to photons with the same amount of absorbed dose (measured in grays). In order to be able to compare dose levels of ionizing radiation with dose levels of photon radiation, the ion dose is usually transformed into a photon dose distribution, providing an equivalent biological effect in the patient. The ratio between this photon equivalent dose and the physically absorbed dose of the ionizing radiation is called the relative biological effectiveness (RBE). Therefore, the transformation between the physically absorbed dose and the photon equivalent dose is performed using an RBE model. There are several RBE models available, ranging from the simplest model that is just a constant scaling factor to very complex models that depend on, for example, the ion type, the ion energy, and the target elemental composition.

[0036] The most common RBE model for photoion therapy is the constant factor model, where, for example, RBE = 1.1 is commonly used for protons. However, it is well known that RBE can be significantly higher in parts of the dose distribution, leading to adverse effects. It has been found that the distance derivative of the absorbed dose (i.e., Figure 2 The high linear energy transfer (LET) of the curve 20 in Figure 20 is strongly correlated with the high RBE used for ion beam therapy. Due to this effect, it is desirable to control the distribution of LET in the patient when using the constant factor RBE model.

[0037] According to the embodiments presented herein, a multi-criteria optimization (MCO) is performed, wherein the optimization function depends on the distribution of the product of radiation dose and LET (denoted herein as DxLET) in a specified region of the patient, or wherein the optimization function uses an alternative RBE model compared to the RBE model for other objectives and / or constraints of the MCO problem. This will be described in more detail below.

[0038] Figure 3 It is a graphic Figure 1 Schematic diagram of functional modules of the treatment planning system of FIG. The modules are implemented using software instructions such as a computer program executed in the treatment planning system 1. Alternatively or additionally, the modules are implemented using hardware such as any one or more of an ASIC (application specific integrated circuit), an FPGA (field programmable gate array), or discrete logic circuits.

[0039] The optimization module 10 is a module that performs optimization to arrive at a potential treatment plan. As explained in more detail below, the optimization module 10 can determine several different treatment plans for a multi-criteria optimization (MCO). Each treatment plan is optimized based on one or more optimization functions. Each treatment plan may be subject to constraints that define that the optimization function needs to have a value above a certain level or below a certain level. Alternatively or additionally, each treatment plan has a target that defines that the optimization function needs to have a value that is as high or as low as possible.

[0040] The optimization module outputs a plurality of treatment plans, each of which is optimized based on a certain set of one or more optimization functions as constraints and / or objectives. The plurality of plans includes a single target plan optimized for a single optimization function. In addition, the plurality of plans may include additional plans in which a plurality of optimization functions are combined.

[0041] Because many optimization functions conflict with each other, the MCO module 11 is used to find a balance between the optimization functions. The MCO module 11 allows a planning operator (i.e., a user of the treatment planning system) to generate a navigated treatment plan based on multiple treatment plans using multi-criteria optimization. The planning operator can freely adjust the priority of any one or more of the treatment plans. When the adjustment of the treatment plan is completed, for example using a slider control in a graphical user interface (GUI), the navigated treatment plan is regenerated as a concave linear combination of the multiple treatment plans.

[0042] Several performance indicators of the navigated treatment plan are calculated and presented to the planning operator in real time. Performance indicators may include: graphical representations of dose distribution (in 2D and / or 3D) and numerical values; and binary indicators, such as whether specific clinical goals are met.

[0043] This allows the planning operator to adjust the weights of different potential treatment plans and evaluate the different treatment plans in real time.When the planning operator is satisfied with the performance metrics, the navigated treatment plan is used as the basis for treatment by the radiation delivery system.

[0044] Figure 4 is a flow chart illustrating an embodiment of a method for generating a plurality of potential treatment plans in Figure 1 Each treatment plan specifies the distribution of radiation to the target volume. The method can be performed in an optimization module 10 of the treatment planning system 1.

[0045] In a generate plan step 40, the treatment planning system generates a plurality of potential plans. The plurality of plans are intended for use with the MCO (as described above). The plurality of plans are generated based on a set of optimization functions. At least one optimization function depends on the distribution of the product of radiation dose and LET (DxLET) in a specified region of the patient. The result of this step is a plurality of treatment plans.

[0046] The designated area may be any suitable area defined in three dimensions, such as a target volume, an organ at risk, any sub-area of ​​these, or the like.

[0047] The optimization is based on a set of optimization functions by iteratively adjusting a set of optimization variables. The configuration of the optimization variables determines the value of the optimization function.

[0048] In one embodiment, at least one optimization function is specified as a goal. The goal may be to minimize or maximize at least one optimization function value.

[0049] In one embodiment, at least one optimization function is specified as a constraint. For example, the constraint may be that at least one optimization function value cannot exceed a specified value, or at least one optimization function value cannot be lower than a specified value.

[0050] In the optional step of providing treatment plans to MCO, after the optimization is completed, the treatment plans are provided to the MCO module 11, at which point the planning operator can navigate the plans as described above. In other words, step 40 corresponds to the optimization phase of generating multiple plans (at least partially optimized on DxLET), in which the plans are provided to the MCO module (step 42). These plans can then be linearly combined in real time by the operator of the MCO module.

[0051] By including an optimization function based on the DxLET distribution, the optimization has been performed to optimize based on DxLET. This greatly improves the plan related to RBE because the DxLET distribution is closely related to RBE.

[0052] Figure 5 is a diagram illustrating a method according to an embodiment Figure 1 Schematic diagram of components of the treatment planning system 1 of FIG. The processor 60 is provided using any combination of one or more of a suitable central processing unit (CPU), a multiprocessor, a microcontroller, a digital signal processor (DSP), an application specific integrated circuit, etc., capable of executing software instructions 67 stored in a memory 64 (which may thus be a computer program product). The processor 60 may be configured to perform the above-referenced Figure 4 Describe the method.

[0053] The memory 64 may be any combination of random access memory (RAM) and read-only memory (ROM). The memory 64 also includes permanent storage, which may be any one or combination of magnetic memory, optical memory, solid-state memory, or even remotely mounted memory, for example.

[0054] A data memory 66 is also provided for reading and / or storing data during execution of software instructions in the processor 60 .

[0055] The treatment planning system 1 further comprises an I / O interface 62 for communicating with external entities. Optionally, the I / O interface 62 further comprises a user interface.

[0056] Other components of the treatment planning system 1 are omitted so as not to obscure the concepts presented herein.

[0057] Figure 6 An example of a computer program product comprising a computer readable device is shown. On the computer readable device, a computer program 91 may be stored, which computer program may cause a processor to perform a method according to the embodiments described herein. In this example, the computer program product is an optical disc, such as a CD (Compact Disc) or a DVD (Digital Versatile Disc) or a Blu-ray Disc. As explained above, the computer program product may also be embodied in a memory of a device, such as Figure 5 Computer program product 64. Although the computer program 91 is schematically shown here as tracks on the depicted optical disc, the computer program may be stored in any manner suitable for a computer program product, such as a removable solid-state memory, for example a Universal Serial Bus (USB) drive.

[0058] Following this is a collection of embodiments with Roman numerals.

[0059] i. A method for checking the quality of a treatment plan, wherein the treatment plan specifies the distribution of radiation so as to provide radiation to a planning target volume, the method being performed by a quality assurance device and comprising the steps of:

[0060] obtaining a treatment plan and a corresponding first dose, the treatment plan having been calculated in a treatment planning system, the first dose being a predicted dose that will be placed in the patient using the treatment plan;

[0061] initiating calculation of a second dose using a second dose calculation algorithm, the second dose being a dose placed by the treatment plan;

[0062] iteratively calculating a confidence interval for the comparison statistical measurement by comparing the first dose and the second dose over a defined geometric volume; and

[0063] When the confidence interval is better than at least one predefined criterion, the calculation of the second dose is interrupted, in which case the treatment plan is considered to have passed quality assurance.

[0064] ii. The method of embodiment i, wherein each voxel in the second dose includes an estimate of a current confidence interval for the voxel.

[0065] iii. The method according to embodiment i or ii, wherein the defined geometric volume is a planning target volume.

[0066] iv. The method of embodiment i or ii, wherein the defined geometric volume is an organ at risk.

[0067] v. The method according to embodiment i or ii, wherein the defined geometric volume covers the planning target volume and the organ at risk.

[0068] vi. The method according to any of the preceding embodiments, wherein the step of calculating confidence intervals for the comparison statistic measure is based on available confidence intervals for voxels in the second dose and on the allocation of possible second doses.

[0069] vii. The method of embodiment vi, wherein comparing the statistical measures is based on calculating the similarity by accumulating difference measures between corresponding voxels in the first dose and the second dose.

[0070] viii. A method according to embodiment vi, wherein comparing statistical measurement results is based on calculating similarity by finding the difference between a third value and a fourth value, wherein the third value is obtained by accumulating dose values ​​of the first dose in all voxels of the defined geometric volume, and the fourth value is obtained by accumulating dose values ​​of the second dose in all voxels of the defined geometric volume.

[0071] ix. A quality assurance device for checking the quality of a treatment plan, wherein the treatment plan specifies the distribution of radiation so as to provide radiation to a planning target volume, the quality assurance device comprising:

[0072] Processor; and

[0073] a memory storing instructions that, when executed by a processor, cause the quality assurance device to:

[0074] obtaining a treatment plan and a corresponding first dose, the treatment plan having been calculated in a treatment planning system, the first dose being a predicted dose to be placed in a patient using the treatment plan;

[0075] initiating calculation of a second dose using a second dose calculation algorithm, the second dose being a dose placed by the treatment plan;

[0076] iteratively calculating a confidence interval for the comparison statistical measurement by comparing the first dose and the second dose over a defined geometric volume; and

[0077] When the confidence interval is better than at least one predefined criterion, the calculation of the second dose is interrupted, in which case the treatment plan is considered to have passed quality assurance.

[0078] x. The quality assurance apparatus of embodiment ix, wherein each voxel in the second dose includes an estimate of a current confidence interval for the voxel.

[0079] xi. The quality assurance apparatus of embodiment ix or x, wherein the defined geometric volume is a planning target volume.

[0080] xii. The quality assurance device of embodiment ix or x, wherein the defined geometric volume is an organ at risk.

[0081] xiii. The quality assurance apparatus of embodiment ix or x, wherein the defined geometric volume covers the planning target volume and the organ at risk.

[0082] xiv. A computer program for checking the quality of a treatment plan, wherein the treatment plan specifies a distribution of radiation so as to provide radiation to a planning target volume, the computer program comprising computer program code which, when run on a quality assurance device, causes the quality assurance device to:

[0083] obtaining a treatment plan and a corresponding first dose, the treatment plan having been calculated in a treatment planning system, the first dose being a predicted dose to be placed in a patient using the treatment plan;

[0084] initiating calculation of a second dose using a second dose calculation algorithm, the second dose being a dose placed by the treatment plan;

[0085] iteratively calculating a confidence interval for the comparison statistical measurement by comparing the first dose and the second dose over a defined geometric volume; and

[0086] When the confidence interval is better than at least one predefined criterion, the calculation of the second dose is interrupted, in which case the treatment plan is considered to have passed quality assurance.

[0087] xv. A computer program product, comprising the computer program according to embodiment xiv and a computer readable device having the computer program stored thereon.

[0088] Various aspects of the present disclosure are described above mainly with reference to several embodiments. However, as those skilled in the art will readily appreciate, within the scope of the present invention as defined by the appended patent claims, other embodiments other than the embodiments disclosed above are equally possible. Therefore, although various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for illustrative purposes and are not intended to be limiting, and their true scope and spirit are indicated by the following claims.

Claims

1. A method for generating a plurality of potential treatment plans (12), each treatment plan specifying a distribution of radiation to a target volume (3), the method being performed by a treatment planning system (1) and comprising the following steps: A plurality of potential treatment plans (12) for multi-criteria optimization (MCO) are generated (40) based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified region of the patient, thereby producing a plurality of treatment plans. The method according to claim 1 , wherein the at least one optimization function is specified as a target.

3. The method according to claim 2, wherein the goal is to minimize or maximize the at least one optimization function value. The method of claim 1 , wherein the at least one optimization function is specified as a constraint. 5 . The method according to claim 4 , wherein the constraint is that the at least one optimization function value cannot exceed a specified value, or the at least one optimization function value cannot be lower than a specified value.

6. The method according to any one of claims 1 to 5, further comprising the following steps: The plurality of treatment plans are provided (42) to the MCO module (11).

7. A treatment planning system (1) for generating a plurality of potential treatment plans (12), each treatment plan specifying a distribution of radiation to a target volume (3), the treatment planning system comprising: Processor (60); as well as a memory (64) storing instructions (67) which, when executed by the processor, cause the treatment planning system (1) to: A plurality of potential treatment plans (12) for multi-criteria optimization (MCO) are generated (40) based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified region of the patient, thereby producing a plurality of treatment plans.

8. The treatment planning system (1) according to claim 7, wherein the at least one optimization function is specified as a target.

9. The treatment planning system (1) according to claim 8, wherein the goal is to minimize or maximize the at least one optimization function value.

10. The treatment planning system (1) according to claim 7, wherein the at least one optimization function is specified as a constraint.

11. The treatment planning system (1) according to claim 10, wherein the constraint is that the at least one optimization function value cannot exceed a specified value, or the at least one optimization function value cannot fall below a specified value.

12. The treatment planning system (1) according to any one of claims 7 to 11, further comprising instructions (67) which, when executed by the processor, cause the treatment planning system (1) to: The plurality of treatment plans are provided to the MCO module (11).

13. A computer program product (64, 90) comprising a computer program for generating a plurality of potential treatment plans (12), each treatment plan specifying a distribution of radiation to a target volume (3), and a computer readable device having the computer program stored thereon, the computer program comprising computer program code which, when executed on a treatment planning system (1), causes the treatment planning system (1) to: A plurality of potential treatment plans (12) for multi-criteria optimization (MCO) are generated (40) based on a set of optimization functions, wherein at least one optimization function depends on the distribution of the product of radiation dose and linear energy transfer (LET) in a specified region of the patient, thereby producing a plurality of treatment plans.

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