Generate multiple possible treatment plans for multi-criteria optimization

Abstract

A method for generating a plurality of possible 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 generating a plurality of possible treatment plans (12) for multi-criteria optimization (MCO) based on a set of optimization functions, at least one of which depends on the distribution of the product of the radiation dose and linear energy transfer (LET) in a specified region of the patient, resulting in a plurality of treatment plans.

Description

Technical Field

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

Background Art

[0002] In ion beam therapy, a beam of ions (e.g., protons or heavier ions) is directed towards a target volume. The target volume can represent, for example, a cancer tumor. These particles penetrate the tissue and deliver a dose of energy to induce cell death. An advantage of ion beam therapy is the presence of a prominent peak in the dose distribution known as the Bragg peak. The Bragg peak is a peak in dose delivery that occurs at a specific depth, after which the dose delivery rapidly decreases. 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 dose reduction distally cannot be controlled by the same sharp decrease as in the case of ion therapy.

[0003] The depth of the Bragg peak in a 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 enables the delivery of a very localized dose to a well-controlled position within the patient's body. The dose delivered by a specific combination of kinetic energy and lateral deflection of the beam is called a spot. The number of particles delivered in a spot is generally called the spot weight. By providing spots at many different positions in three-dimensional space, the target volume can be covered with a desired dose distribution. The kinetic energy of the spots is often, but not necessarily, distributed for many discrete energies. A group of spots having the same kinetic energy but different lateral deflections is often called an energy layer. This procedure is called active scanning ion beam therapy, also known as pencil beam scanning.

[0004] A plan for how to deliver spots is made in a treatment planning system, whereby a treatment plan is obtained. The treatment planning system determines the energy layers to be used as well as the distribution and weights of spots therein, but the treatment planning system does not deliver ion beams. The delivery of ion beams is performed by a radiation delivery system provided with the treatment plan.

[0005] The determination of a treatment plan can be done in many different ways. One way is to use multi-criteria optimization (MCO). In MCO, a plurality of possible treatment plans are generated and each of them is optimized in a different way. Then the plan operator can linearly combine these possible treatment plans in real time, where the combination is generated and visualized in real time together with performance metrics.

[0006] Giantsoudi Drosoula et al. published "Linear Energy Transfer-Guided Optimization in Intensity Modulated Proton Therapy: Feasibility Study and Clinical Potential", INTERNATIONAL JOURNAL OF RADIATION: ONCOLOGY BIOLOGY PHYSICS, PERGAMON PRESS, USA, vol. 87, no. 1, June 19, 2013, pages 216-222. However, any improvement in the method of generating treatment plans is very helpful.

Summary of the Invention

[0007] One objective is a method for improving optimization and a method for improving MCO with respect to the relative biological effectiveness ratio (RBE).

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

[0009] At least one of the optimization functions may be specified as an objective.

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

[0011] At least one of the optimization functions may be specified as a constraint.

[0012] The constraint may be that at least one optimization function value must not exceed a specified value, or that at least one optimization function value must not be lower than a specified value.

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

[0014] According to a second aspect, a treatment planning system for generating a plurality of possible treatment plans is provided, each treatment plan specifying a distribution of radiation to a target volume. The treatment planning system includes a processor and a memory storing instructions that, when executed by the processor, cause the treatment planning system to perform a process of generating a plurality of possible treatment plans (12) for multi-criteria optimization (MCO) based on a set of optimization functions, where at least one of the optimization functions depends on the distribution of the product of the radiation dose and the linear energy transfer, i.e., LET, in a specified region of the patient, whereby a plurality of treatment plans are obtained.

[0015] At least one optimization function may be specified as an objective.

[0016] The objective 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 must not exceed a specified value, or must not be lower than a specified value.

[0019] According to a third aspect, there is provided a computer program for generating a plurality of possible treatment plans, each treatment plan specifying the distribution of radiation to a target volume. The computer program, when run on a treatment planning system, causes the treatment planning system to perform a multi-criteria optimization based on a set of optimization functions, where at least one optimization function depends on the distribution of the product of the radiation dose and the linear energy transfer, i.e., LET, in a specified region of the patient, thereby obtaining a plurality of treatment plans. The computer program code includes steps for performing this.

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

[0021] In general, all terms used in the claims should be construed according to their ordinary meaning in the art, unless specifically defined otherwise herein. All references to "a / an / the element, apparatus, component, means, step, etc." should be construed as referring to at least one example of the element, apparatus, component, means, step, etc., unless specifically stated otherwise. The steps of any method disclosed herein need not be performed in the exact order disclosed, unless specifically stated.

[0022] Next, embodiments and implementations will be described by way of example with reference to the accompanying drawings.

Brief Description of the Drawings

[0023]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Modes for Carrying Out the Invention

[0024] Next, the aspects of the present disclosure will be more fully described below with reference to the accompanying drawings showing specific embodiments of the present invention. However, these aspects can be embodied in many different forms and should not be construed as limiting. On the contrary, these embodiments are provided as examples to make the present disclosure thorough and complete and to fully convey the scope of all aspects of the present invention to those skilled in the art. Like numbers refer to like elements throughout this specification.

[0025] FIG. 1 is a schematic diagram illustrating an environment to which the embodiments shown in this specification can be applied. The treatment planning system 1 determines the distribution of radiation for radiotherapy. This is transmitted to the radiation delivery system 2 as treatment plan 12. Based on the treatment plan, the radiation delivery system 2 generates a beam 7 to deliver radiation to the target volume 3 of the patient while avoiding radiation to the risk organ 5.

[0026] The radiation delivery system 2 may be, for example, an ion beam system for pencil beam scanning. In such a case, the radiation is delivered at a number of spots, where each spot has a position in a three-dimensional coordinate system. The position of the maximum dose (Bragg peak) of the spots along the depth direction, i.e., the z-axis, is controlled by the kinetic energy of the ions, and the higher the energy, the deeper the position where the maximum dose occurs. Further, the lateral position is controlled using an electromagnet for deflecting the beam 7. In this way, a dose distribution can be achieved that three-dimensionally covers the target volume 3 while keeping the radiation to the risk organ 5 low.

[0027] Alternatively, the radiation delivery system 2 is based on tomotherapy, where the radiation is delivered during a circular or helical movement around the patient.

[0028] Other types of radiation delivery systems are also possible.

[0029] FIG. 2 is a schematic graph illustrating dose delivery using the radiation delivery system 2 of FIG. 1 with ions, particularly the Bragg peak. The horizontal axis represents the depth in water, and 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 appears immediately before the sharp decrease in dose.

[0030] When radiation is delivered to a patient, ions produce different biological effects in terms of cells being destroyed compared to photons of the same absorbed dose (measured in grays). To be able to compare the dose levels from ionizing radiation and photon radiation, it is often necessary to convert the ion dose into a photon dose distribution that gives an equivalent biological effect in the patient. The ratio of this dose equivalent to photons and the physical absorbed dose from ionizing radiation is called the relative biological effectiveness ratio (RBE). Thus, the conversion between the physical absorbed dose and the dose equivalent to photons is performed using an RBE model. There are several available RBE models, ranging from the simplest one with only a constant conversion factor to very complex ones determined by, for example, the type of ion, ion energy, and target element composition.

[0031] The most common RBE model used for photon-ion therapy is the constant coefficient model, where, for example, RBE = 1.1 is often used for protons. However, it is well known that the RBE can be substantially higher in some parts of the dose distribution, which may cause undesirable effects. In the case of ion beam therapy, it has been found that high linear energy transfer (LET), which is the derivative of the absorbed dose with respect to distance (i.e., the derivative of curve 20 in FIG. 2), has a strong correlation with high RBE. Due to this effect, it is desirable to control the LET distribution in the patient when a constant coefficient RBE model is used.

[0032] According to the embodiments presented herein, multi-criteria optimization (MCO) is performed, where the optimization function depends on the distribution of the product of the radiation dose and LET (denoted herein as D×LET) in a specified region of the patient, or the optimization function uses another RBE model that is compared to the RBE model used for other objectives and / or constraints of the MCO problem. This will be explained in more detail below.

[0033] FIG. 3 is a schematic diagram illustrating the functional modules of the treatment planning system of FIG. 1. This module is implemented using software instructions such as a computer program executed in the treatment planning system 1. Alternatively or additionally, this module is 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 circuitry.

[0034] The optimization module 10 is a module that performs optimization to reach a possible treatment plan. As will be described in more detail below, several different treatment plans can be determined by the optimization module 10 for multi-criteria optimization (MCO). Each treatment plan is optimized based on one or more optimization functions. Each treatment plan can be restricted by constraints that define that the optimization function needs to have a value that exceeds a specific level or is less than a specific level. Alternatively or additionally, each treatment plan has an objective that defines that the optimization function needs to have a value that is as high as possible or as low as possible.

[0035] The optimization module outputs a plurality of treatment plans, each of which is optimized based on a specific set of one or more optimization functions as constraints and / or objectives. The plurality of plans includes a single objective plan, which is optimized for a single optimization function. Further, the plurality of plans can include additional plans in which a plurality of optimization functions are combined.

[0036] Since many optimization functions are contradictory to each other, the MCO module 11 is used to find a balance among the optimization functions. The MCO module 11 enables a planning operator (i.e., the user of the treatment planning system) to generate a treatment plan that is navigated based on multiple treatment plans using multi-criteria optimization. The planning operator can freely adjust any one or more priorities of the treatment plan. When the treatment plan is adjusted, for example, using slider controls in a graphical user interface (GUI), the navigated treatment plan is regenerated as the concave first-order combination of multiple treatment plans.

[0037] Calculate some performance indicators of the navigated treatment plan and present them to the planning operator in real time. Examples of performance indicators include dose distribution (2D and / or 3D) and graphical display of numerical values, as well as binary indicators regarding whether specific clinical goals are met or not.

[0038] This enables the planning operator to adjust the weights of different possible treatment plans and evaluate different treatment plans in real time. When the planning operator is satisfied with the performance indicators, the navigated treatment plan is used as the basis for treatment by the radiation delivery system.

[0039] Figure 4 is a flowchart illustrating an embodiment of a method for generating a plurality of possible treatment plans performed in the treatment planning system of Figure 1. Each treatment plan specifies the distribution of radiation to the target volume. This method can be performed in the optimization module 10 of the treatment planning system 1.

[0040] In step 40 of generating a plan, the treatment planning system generates a plurality of possible plans. The plurality of plans are intended for use in 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 (D×LET) of the radiation dose and LET in the specified area of the patient. This step results in a plurality of treatment plans.

[0041] The specified region may be any suitable region defined in three dimensions, such as a target volume, a risk organ, and sub-regions thereof.

[0042] The optimization is based on a set of optimization functions by repeatedly adjusting a set of optimization variables. The configuration of the optimization variables determines the value of the optimization function.

[0043] In one embodiment, at least one optimization function is specified as an objective. This objective may be to minimize or maximize at least one optimization function value.

[0044] In one embodiment, at least one optimization function is specified as a constraint. For example, this constraint may be that at least one optimization function value must not exceed a specified value, or that at least one optimization function value must not be lower than a specified value.

[0045] In step 42 of providing a treatment plan for any MCO, after performing the optimization, the treatment plan is provided to the MCO module 11, and at that point, the plan operator can navigate the plan as described above. In other words, step 40 corresponds to an optimization stage (optimized at least partially for D×LET) for generating a plurality of plans provided to the MCO module in this step (step 42). These plans can then be linearly combined in real time by the operator of the MCO module.

[0046] By including an optimization function based on the D×LET distribution, the optimization for optimizing based on D×LET has already been performed. Since the D×LET distribution is closely related to the RBE, this greatly improves the plan with respect to the RBE.

[0047] FIG. 5 is a schematic diagram illustrating the components of the treatment planning system 1 of FIG. 1 according to one embodiment. The processor 60 can be provided using one or more arbitrary combinations of a suitable central processing unit (CPU), a multiprocessor, a microcontroller, a digital signal processor (DSP), an application-specific integrated circuit, etc., which are stored in the memory 64 and can thus be a computer program product. The processor 60 can be configured to execute the method described with reference to FIG. 4 above.

[0048] The memory 64 may be any combination of a random access memory (RAM) and a read-only memory (ROM). The memory 64 includes, for example, any one or combination of a magnetic memory, an optical memory, a solid-state memory, or even a persistent storage device that may be a remotely mounted memory.

[0049] A data memory 66 is also provided for reading and / or storing data during the execution of software instructions in the processor 60.

[0050] The treatment planning system 1 further includes an I / O interface 62 for communication with other external entities. Optionally, the I / O interface 62 also includes a user interface.

[0051] Other components of the treatment planning system 1 are omitted so as not to obscure the concepts shown herein.

[0052] FIG. 6 shows an example of a computer program product including computer-readable means. The computer-readable means can store a computer program 91, which can cause a processor to execute the methods according to the embodiments described herein. In this example, the computer program product is an optical disc such as a CD (compact disc), a DVD (digital versatile disc), or a Blu-ray disc. As described above, a computer program product such as the computer program product 64 of FIG. 5 can also be embodied in the memory of the device. The computer program 91 is schematically shown here as a track on the depicted optical disc, but the computer program can 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.

[0053] Next, embodiments will be listed below together with Roman numerals.

[0054] i. A method for verifying the quality of a treatment plan, wherein the radiotherapy plan specifies the distribution of radiation and thereby delivers radiation to a planned target volume, the method being performed by a quality assurance device and obtaining a treatment plan calculated in a treatment planning system and a corresponding first dose that is the predicted dose accumulated in a patient using the treatment plan; starting to calculate a secondary dose that is the dose accumulated by the treatment plan using a secondary dose calculation algorithm; repeatedly calculating a confidence interval of a comparative statistical measurement by comparing the first dose and the secondary dose over a defined geometric volume; and interrupting the calculation of the secondary dose when the confidence interval is better than at least one predefined criterion (in which case the treatment plan is considered to have passed quality assurance). A method comprising.

[0055] ii. The method according to embodiment i, wherein each voxel in the secondary dose contains an estimated value of the current confidence interval for the voxel.

[0056] iii. The method according to embodiment i or ii, wherein the defined geometric volume is the planned target volume.

[0057] iv. The method according to embodiment i or ii, wherein the defined geometric volume is the risk organ.

[0058] v. The method according to embodiment i or ii, wherein the defined geometric volume includes the planned target volume and the risk organ.

[0059] vi. The method according to any one of the preceding embodiments, wherein the step of calculating the confidence interval of the comparative statistical measurement is based on the confidence interval available for the voxel in the secondary dose and the spread of the possible secondary dose.

[0060] vii. The method according to embodiment vi, wherein the comparative statistical measurement is based on calculating a similarity by accumulating difference measurements between corresponding voxels at the first dose and the second dose.

[0061] viii. The method according to embodiment vi, wherein the comparative statistical measurement is based on calculating a similarity by finding the difference between a third value and a fourth value, where the third value is obtained by accumulating the dose values of the first dose in all voxels within the defined geometric volume, and the fourth value is obtained by accumulating the dose values of the second dose in all voxels of the defined geometric volume.

[0062] ix. A quality assurance device for verifying the quality of a treatment plan, wherein the treatment plan specifies a radiation distribution and thereby delivers radiation to a planned target volume, and the quality assurance device includes a processor, and when executed by the processor, causes the quality assurance device to Obtaining a treatment plan calculated in a treatment planning system and a corresponding first dose that is a predicted dose accumulated in a patient using the treatment plan Starting to calculate a secondary dose that is the dose accumulated by a treatment plan using a secondary dose calculation algorithm Repeatedly calculating a confidence interval of a comparative statistical measurement by comparing the first dose and the secondary dose over a defined geometric volume, and Interrupting the calculation of the secondary dose when the confidence interval is better than at least one predefined criterion (in which case the treatment plan is considered to pass quality assurance) A memory storing instructions to cause the above to be performed, and A quality assurance device comprising the same

[0063] x. Each voxel in the secondary dose includes an estimated value of the current confidence interval for the voxel. The quality assurance device according to embodiment ix

[0064] xi. The defined geometric volume is a planned target volume. The quality assurance device according to embodiment ix or x

[0065] xii. The defined geometric volume is an organ at risk. The quality assurance device according to embodiment ix or x

[0066] xiii. The defined geometric volume includes a planned target volume and an organ at risk. The quality assurance device according to embodiment ix or x

[0067] xiv. A computer program for verifying the quality of a treatment plan, wherein the treatment plan specifies a radiation distribution and thereby delivers radiation to a planned target volume, and the computer program, when run on a quality assurance device, causes the quality assurance device to Obtain a treatment plan calculated in a treatment planning system and a corresponding first dose that is a predicted dose accumulated in a patient using the treatment plan Start calculating a secondary dose that is the dose accumulated by a treatment plan using a secondary dose calculation algorithm Repeatedly calculating a confidence interval of a comparative statistical measure by comparing a first dose and a secondary dose over a defined geometric volume, and Interrupting the calculation of the secondary dose when the confidence interval is better than at least one predefined criterion (in which case the treatment plan is considered to pass quality assurance). A computer program comprising computer program code for causing the above to be performed.

[0068] xv. A computer program product comprising the computer program according to embodiment xiv and computer-readable means having the computer program stored therein.

[0069] Aspects of the present disclosure have been described above mainly with reference to several embodiments. However, as will be readily understood by those skilled in the art, other embodiments other than those disclosed above are equally possible within the scope of the invention as defined by the appended claims. Accordingly, while 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 not limiting, and the true scope and spirit are indicated by the following claims.

Claims

1. A method for generating a plurality of possible treatment plans (12), each treatment plan specifying a distribution of radiation to a target volume (3), said method being performed by a treatment planning system (1); generating (40) a plurality of possible treatment plans (12) for multi-criteria optimization, or MCO, based on a set of optimization functions, where at least one optimization function depends on the distribution of radiation dose products and linear energy transfer, or LET, in a specified region of the patient, thereby resulting in a plurality of treatment plans; A method comprising:

2. The method of claim 1 , wherein the at least one optimization function is specified as an objective.

3. The method of claim 2 , wherein the objective is to minimize or maximize the at least one optimization function value.

4. The method of claim 1 , wherein the at least one optimization function is specified as a constraint.

5. The method of claim 4 , wherein the constraint is that the at least one optimization function value must not exceed a specified value or that the at least one optimization function value must not be lower than a specified value.

6. providing (42) said plurality of treatment plans to an MCO module (11); 10. The method of any one of the preceding claims, further comprising:

7. A treatment planning system (1) for generating a plurality of possible treatment plans (12), each treatment plan specifying a distribution of radiation to a target volume (3), said treatment planning system comprising: a processor (60); When executed by the processor, the treatment planning system (1) generating a plurality of possible treatment plans (12) for multi-criteria optimization, or MCO, based on a set of optimization functions (40), at least one of which depends on the distribution of radiation dose products and linear energy transfer, or LET, in a specified region of the patient, thereby resulting in a plurality of treatment plans; a memory (64) storing instructions (67) for causing A treatment planning system comprising:

8. The treatment planning system (1) of claim 7, wherein said at least one optimization function is specified as an objective.

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

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

11. 11. The treatment planning system (1) of claim 10, wherein the constraint is that the at least one optimization function value must not exceed a specified value or that the at least one optimization function value must not be lower than a specified value.

12. When executed by the processor, the treatment planning system (1) providing said plurality of treatment plans to an MCO module (11); 12. The treatment planning system (1) of any one of claims 7 to 11, further comprising instructions (67) to:

13. A computer program (67, 91) for generating a plurality of possible treatment plans (12), each treatment plan specifying a distribution of radiation to a target volume (3), said computer program, when run on a treatment planning system (1), causing said treatment planning system (1) to: generating a plurality of possible treatment plans (12) for multi-criteria optimization, or MCO, based on a set of optimization functions (40), at least one of which depends on the distribution of radiation dose products and linear energy transfer, or LET, in a specified region of the patient, thereby resulting in a plurality of treatment plans; a computer program comprising computer program code for causing a computer to perform the following:

14. A computer program product (64, 90) comprising a computer program according to claim 13 and computer readable means on which said computer program is stored.

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