System and method for radiotherapy planning

Abstract

A computer-implemented method for radiotherapy planning is disclosed, the method comprising: performing (202) a first dose calculation for one or more scenarios included in a first of a plurality of batches, where each of the plurality of batches comprises one or more scenarios associated with radiotherapy of a patient; initiating (204), in response to completion of the first dose calculation, an optimization process for one or more scenarios included in the first batch based on an output of the first dose calculation, where the optimization process seeks to optimize one or more first parameters of a treatment plan for radiotherapy of the patient; performing (206), in parallel with the optimization process, a further dose calculation for one or more scenarios included in a further batch of the plurality of batches; and in response to completion of the additional dose calculation, updating (208) the optimization process to include the one or more scenarios included in the additional batch such that the optimization process is further based on an output of the additional dose calculation.

Description

Technical Field

[0001] Embodiments of the present invention relate to systems and methods for radiation therapy planning. More particularly, embodiments of the present invention relate to computer-implemented methods for radiation therapy planning, as well as data processing apparatus, computer programs, non-transitory computer-readable storage media, and computer program products configured to perform the methods for radiation therapy planning. Background Art

[0002] Radiation therapy (also called radiotherapy) uses ionizing radiation (such as X-rays or charged particles) to damage or destroy unhealthy cells in the human or animal body. During treatment, the ionizing radiation is formed into a beam and directed at unhealthy cells in the body, such as a tumor. The cells to be treated may be deep within the body or close to the surface, such as in the skin.

[0003] The dose distribution delivered by ionizing radiation depends on treatment parameters such as beam direction, dose per beam (or spot or beamlet), number of beams (or spots or beamlets), beamlet weighting, and exposure time. Radiation therapy planning is the process of determining a set of treatment parameter values ​​that will provide the desired treatment with minimal damage to surrounding healthy tissue.

[0004] While an "ideal" dose distribution would deliver at least the minimum required dose to unhealthy cells and zero dose to healthy cells, in reality some dose delivery to surrounding healthy tissue is unavoidable. Therefore, the goal of treatment planning is to optimize the delivered dose distribution so that unhealthy cells receive at least the minimum required dose and healthy cells receive as little dose as possible, particularly any sensitive or compromised anatomy. An optimization process is performed to determine a set of treatment parameters that will deliver the appropriate dose to the patient.

[0005] For any given treatment plan, there is typically some degree of uncertainty in one or more treatment parameters. For example, there may be uncertainty in the patient's position due to unpredictable and / or involuntary movement of the patient. Such movement may be caused by the patient breathing, coughing, burping, twitching, etc. There may also be uncertainty in other characteristics of the patient (e.g., the patient's tissue density), which are typically modeled based on patient image data.

[0006] If the optimization process is performed without taking these uncertainties into account, the resulting treatment plan may not be robust against perturbations (i.e., changes) in the treatment parameters. In other words, the resulting treatment plan may be optimized for a nominal (e.g., predicted) treatment scenario rather than for the actual treatment scenario achieved when the treatment is performed.

[0007] Uncertainty in treatment parameters can be accounted for during treatment planning using robust optimization techniques, which produce treatment plans that are robust to perturbations in one or more relevant treatment parameters. Robust optimization is performed in two stages. In the first stage, delivered dose distributions are calculated for multiple possible treatment scenarios defined by variations in one or more treatment parameters. In the second stage, an optimization process is performed that considers all calculated delivered dose distributions. Summary of the Invention

[0008] Existing robust optimization techniques provide more robust treatment plans, but are computationally intensive and therefore time consuming. It can be seen that there is a need for improved methods and systems for generating robust treatment plans.

[0009] Embodiments of the present invention seek to address this problem by providing a computer-implemented method and system for radiation therapy treatment planning, wherein dose calculations for different scenarios are performed in parallel (i.e., substantially simultaneously) with a robust optimization process. The optimization process is periodically updated to account for additional scenarios as corresponding dose calculations are completed. This reduces the time it takes to obtain a robust treatment plan.

[0010] According to a first aspect, a computer-implemented method for radiation therapy treatment planning is provided. The method includes performing a first dose calculation for one or more scenarios included in a first batch of a plurality of batches. Each batch of the plurality of batches includes one or more scenarios relevant to radiation therapy treatment of a patient. The method also includes, in response to completion of the first dose calculation, initiating an optimization process for the one or more scenarios included in the first batch based on outputs of the first dose calculation, wherein the optimization process seeks to optimize one or more first parameters of a treatment plan for the radiation therapy treatment of the patient. The method also includes performing, in parallel with the optimization process, additional dose calculations for one or more scenarios included in another batch of the plurality of batches; and, in response to completion of the additional dose calculations, updating the optimization process to include the one or more scenarios included in the another batch, such that the optimization process is further based on the outputs of the additional dose calculations.

[0011] According to a second aspect, there is provided a data processing apparatus configured to execute instructions to perform the method of the first aspect.

[0012] According to a third aspect, a data processing apparatus is provided, comprising a memory storing computer-executable instructions and a processing circuit, wherein the processing circuit (or controller circuit) is configured to execute the instructions to perform the method of the first aspect.

[0013] According to a fourth aspect, there is provided a computer program comprising instructions which, when the program is executed by a computer, cause the computer to perform the method of the first aspect.

[0014] According to a fifth aspect, there is provided a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform the method of the first aspect.

[0015] According to a sixth aspect, there is provided a computer program product comprising the computer-readable storage medium of the fifth aspect.

[0016] Thus, the present invention provides an improved method and system for radiation therapy planning.Thus, the computation time required to determine a radiation therapy plan that is robust to variations in treatment parameter values ​​due to treatment parameter uncertainties is significantly reduced.

[0017] Embodiments of the present invention may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or a combination thereof. Embodiments of the present invention may be implemented as a computer program or computer program product, i.e., a computer program tangibly embodied in a non-transitory information carrier (e.g., in a machine-readable storage device or in a propagated signal) for execution by or to control the operation of one or more hardware modules. A computer program may be in the form of a stand-alone program, a computer program portion, or more than one computer program, and may be written in any form of programming language (including compiled or interpreted languages), and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a data processing environment.

[0018] The present invention is described herein with reference to specific embodiments. Other embodiments not explicitly described herein may still fall within the scope of the claims. Unless otherwise specified, explicitly or implicitly, the steps of the methods according to embodiments of the present invention may be performed in a different order and still achieve the desired results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Exemplary embodiments will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0020] Figure 1 is a flow chart illustrating a method for radiation therapy planning using robust optimization;

[0021] Figure 2 is a flow chart illustrating a method for radiation therapy planning according to various embodiments of the present invention;

[0022] Figure 3A is a schematic diagram illustrating a method for radiation therapy planning using robust optimization;

[0023] Figure 3B is a schematic diagram illustrating a method for radiation therapy planning according to various embodiments of the present invention;

[0024] Figure 4 is a flow chart illustrating a method for radiation therapy planning according to various embodiments of the present invention;

[0025] Figure 5A is a block diagram illustrating an optimization process according to various embodiments of the present invention;

[0026] Figure 5B is a block diagram illustrating a two-stage optimization process according to various embodiments of the present invention;

[0027] Figure 6 is a schematic diagram of a radiation therapy system according to various embodiments of the present invention;

[0028] Figure 7 is a block diagram illustrating a data processing apparatus according to various embodiments of the present invention; and

[0029] Figure 8 is a schematic diagram of a radiotherapy device. DETAILED DESCRIPTION

[0030] Figure 1 is a flow chart illustrating a method 100 for radiation therapy planning using robust optimization according to the prior art.

[0031] Method 100 includes, at step 102, determining a set of scenarios for radiation treatment, wherein the scenarios differ from one another in the values ​​of one or more treatment parameters. These variations represent uncertainties in the one or more treatment parameters. The parameters may relate to, for example, the patient's position during treatment and / or the patient's tissue density. The scenarios include nominal scenarios in which the errors in all parameters are taken to be zero, and other scenarios that include one or more errors (i.e., variations) in one or more parameters.

[0032] At step 104, method 100 includes calculating a dose distribution for each scenario in the determined set of scenarios. For example, if the modality is protons (e.g., pencil beam scanning (PBS) proton therapy), the dose can be delivered to the patient via multiple monoenergetic pencil beams (referred to as points) with different lateral deflections. Thus, for a given scenario, the dose delivered to any given voxel in the patient model is determined by the dose contributions from all points to that voxel. As a second example, if the modality is photons (e.g., intensity modulated radiation therapy (IMRT-photons) or volumetric modulated radiation therapy (VMAT-photons)), the dose can be delivered to the patient via multiple beamlets that make up the radiation beam. Thus, for a given scenario, the dose delivered to any given voxel in the patient model is determined by the dose contributions from all beamlets to that voxel. The calculated dose distribution is a function of one or more treatment parameters.

[0033] At step 106 , the method includes performing an optimization process to determine a treatment plan based on all scenarios in the determined set of scenarios.

[0034] Treatment plan optimization is the process of determining a treatment plan (defined by a set of treatment parameters) that meets certain user-defined criteria. For example, a user can define a set of clinical and dosimetry goals and constraints for the treatment plan. The goals and constraints can specify maximum, minimum and / or mean radiation doses to different regions of the body. Constraints are conditions that must be met, such as a minimum dose to a region that includes a tumor. These goals are targets to be achieved. For example, a goal can be that the dose delivered to a specific region that includes healthy tissue does not exceed a maximum radiation dose, also called a reference dose. The goals and constraints can also relate to any other treatment parameters, such as point or beamlet weights, the number of points or beamlets, the range of motion of the radiotherapy device, etc.

[0035] To determine a treatment plan, the optimizer iteratively searches for a solution. For example, the optimizer may seek a set of treatment parameters that minimizes a cost function of the objective and constraints.

[0036] To achieve robust optimization, the cost function considers all scenarios. For example, the cost function can include the sum of the cost function contributions of all scenarios. The cost function of each scenario in the sum can be weighted by an importance weight, where the sum of the importance weights is equal to 1.

[0037] The solution space (i.e., the set of treatment parameters output by the optimization in step 106) is different for different planning modalities and delivery modes. For proton therapy, the solution space can be the weights of each point. For photon therapy, the solution space can be the segment shapes of the multi-leaf collimator used to shape the radiation beam and / or the weights of the beamlets that make up the radiation beam.

[0038] Thus, a robust treatment plan is defined by the set of treatment parameters output by the optimization in step 106 .

[0039] Figure 2 is a flow chart illustrating a computer-implemented method for radiation therapy planning according to various embodiments of the present invention.

[0040] A radiation therapy plan can involve any type of ionizing radiation, such as X-rays, electrons, protons, or ions. The radiation therapy can be, for example, one or more of the following: intensity-modulated radiation therapy (IMRT), volumetric modulated radiation therapy (VMAT), image-guided radiation therapy (IGRT), proton beam therapy (PBT), pencil beam scanning (PBS) proton therapy, intensity-modulated proton therapy (IMPT), and electron beam therapy (EBT). The treatment plan can be used to treat any part of the human or animal body.

[0041] The method 200 includes, at step 202, performing a first dose calculation for one or more scenarios included in a first batch of the plurality of batches. The first dose calculation is a calculation of a delivered dose distribution for each of the one or more scenarios in the first batch. Each batch in the plurality of batches includes one or more scenarios relevant to the radiation treatment of a patient. For example, each batch may be comprised of one or more scenarios relevant to the radiation treatment of a patient. Different scenarios may represent uncertainty in one or more treatment parameters that affect the dose distribution delivered by the treatment plan. These parameters are referred to herein as second parameters of the treatment plan (to distinguish them from the first parameters of the treatment plan described below). The scenarios may include variations in one or more second parameters (i.e., the scenarios may differ from each other by the values ​​of one or more second parameters).

[0042] The one or more second parameters may relate to one or more of the following parameters: patient position, beam geometry, and patient model representation.

[0043] Patient position may relate to the position of the patient on the examination table (patient bed). Due to unpredictable and / or involuntary movements of the patient (e.g., movements caused by breathing, coughing, burping, sneezing, or convulsions), uncertainty in the patient's position on the examination table may arise.

[0044] The patient position may relate to the position of the table itself, such as the table angle or the translation of the table. The beam geometry may include, for example, the beam gantry angle (with respect to Figure 8 Describing an example of a gantry.) Uncertainty in the couch position and / or gantry position may arise due to differences between the indicated position (e.g., in a treatment plan) and the position achieved by the mechanical system controlling the couch or gantry.

[0045] The patient model representation may include any parameters used to model the patient in the treatment plan. For example, the patient model representation may include electron density (e.g., of a segment of the patient model), stopping power (e.g., of a segment of the patient model), or X-ray attenuation (e.g., for a segment of the patient model) in, for example, Henry units. The patient model may be determined based on a computed tomography (CT) scan of the patient. For example, a Digital Imaging and Communications in Medicine (DICOM) image may be obtained from a CT scan of the patient. The pixel values ​​in the DICOM image may be used to derive electron density (ED) via CT-ED mapping, and mass density / stopping power may be derived by formulation. Each of these steps has some uncertainty / accuracy limits, so the final patient model representation also includes some uncertainty. This uncertainty may be captured by the scenario.

[0046] Method 200 may also include, prior to performing the first dose calculation, generating scenarios to be included in the plurality of batches. The scenarios may be generated by simulating errors in one or more second parameters relative to their nominal (error-free) values. For example, patient position uncertainty may be simulated by shifting the patient or the isocenters of the beams. Density or stopping power uncertainty may be simulated by scaling the mass density of the entire patient volume.

[0047] Method 200 may also include assigning one or more scenarios to each of the plurality of batches prior to performing the first dose calculation. The first batch may include only nominal scenarios, wherein all errors in the one or more second parameters are taken to be zero. Alternatively, the first batch may include multiple scenarios. The ability to select the batch size (e.g., based on experimental testing) provides a flexible optimization process that can be made more efficient as needed.

[0048] In response to the completion of the first dose calculation, method 200 further includes initiating an optimization process for one or more scenarios included in the first batch based on the output of the first dose calculation in step 204. In some embodiments, the optimization process may be initiated only after completing the dose calculation for the nominal scenario.

[0049] The initiated optimization process seeks to optimize one or more first parameters of a treatment plan for radiation treatment of a patient. The one or more first parameters may include any parameter that is controllable (e.g., by a treatment device) and that affects the dose delivered to the patient. For example, the one or more first parameters may include or relate to one or more of the following parameters: beam shape, beam weight, beamlet weight, spot weight, spot position, beam angle, dose histogram volume information, number of radiation beams, number of beamlets, number of spots, dose per beam, gantry angle, collimator shape (e.g., shape of a multi-leaf collimator (MLC)), number of monitoring units (MUs), and a fluence map. For example, a treatment plan for proton spot scanning may include first parameters equal to the positions and weights of various spots, while a treatment plan for photon therapy may include first parameters for control spots, wherein various control spots include gantry angle, MLC shape, and MUs.

[0050] The optimization process can include determining one or more values ​​of the one or more first parameters such that a cost function (i.e., a penalty value) is minimized. The cost function can evaluate the dose distribution based on the one or more first parameters and provide a measure of the extent to which the one or more first parameters meet one or more objectives. For example, the cost function can define a penalty for violating one or more objectives. Thus, changing the first parameters to reduce the cost function corresponds to an improvement in the treatment plan.

[0051] For example, the optimization process can correspond to the reference Figure 5A and / or Figure 5B The optimization process described herein. The cost function may correspond to cost function 502. A cost function (e.g., cost function 502) may include the sum of multiple cost functions (e.g., one for each scenario). The sum may be a weighted sum. The sum may produce a single final penalty value to be minimized.

[0052] Method 200 further includes, in step 206, performing additional dose calculations for one or more scenarios included in another batch in the plurality of batches in parallel with the optimization process. Here, "parallel" means that the optimization process and the additional dose calculations are performed concurrently, i.e., approximately simultaneously. The optimization process can be performed by processing circuitry included in a first processing unit, and the additional dose calculations can be performed by processing circuitry included in a second processing unit different from the first processing unit. For example, the first processing unit and the second processing unit can include different CPUs; different GPUs; a CPU and a GPU; different processing units of a multi-core processor; or different (separate) physical computers.

[0053] In response to the completion of the additional dose calculation, method 200 further includes, in step 208, updating the optimization process to include the one or more scenarios included in the additional batch, such that the optimization process is further based on the output of the additional dose calculation. By thus updating the optimization process, the updated optimization process seeks to optimize one or more first parameters of the treatment plan based on both the first dose calculation and the additional dose calculation. Updating the optimization process should be understood to mean that the optimization process continues, now taking into account the one or more additional scenarios in the additional batch. The values ​​of the one or more first parameters obtained by the optimization process of step 204 can be used as initial values ​​of the one or more first parameters for the updated optimization process of step 208.

[0054] Updating the optimization process to include one or more additional scenarios may include updating a cost function based on the one or more additional scenarios and executing the optimization process using the updated cost function. The cost function may consider multiple scenarios by summing the cost function contributions of the individual scenarios. The individual cost function contributions may be weighted by importance weights, where the sum of the importance weights of all cost function contributions is 1. When the optimization process is updated, the updated optimization process may include as input one or more first treatment parameters obtained from the optimization process prior to the update. This improves the efficiency of the optimization process and reduces the time spent performing the optimization.

[0055] Figure 2The method 200 may further include, for each remaining batch in the plurality of batches, iteratively (i) performing additional dose calculations for one or more scenarios included in another batch in the plurality of batches in parallel with the optimization process, and (ii) updating the optimization process to include the one or more scenarios included in the another batch, until all batches included in the plurality of batches are included in the optimization process. Thus, the optimization process is ultimately updated to consider all scenarios in the plurality of batches.

[0056] The method 200 may further include, in response to determining that all batches included in the plurality of batches are included in the optimization process and the optimization process is complete, outputting one or more first parameters for the radiation treatment of the patient to a treatment planning system (TPS). The optimization process may be considered complete when a stopping criterion is satisfied. For example, the stopping criterion may be considered satisfied when a cost function is satisfied (or when all cost functions are satisfied). Alternatively, the stopping criterion may be a defined number of optimization iterations or a defined amount of time.

[0057] The TPS can be e.g. The TPS may use one or more first parameters to determine the radiation therapy system (treatment device) (e.g., Figure 6 Treatment equipment 650 or Figure 8 Configuration of the radiotherapy device (as exemplified in FIG). Figure 2 The method 200 may also be performed by a TPS to which the one or more first parameters are output.

[0058] Figure 3A This is an example based on Figure 1 Method 100 is a schematic diagram of a method for radiation therapy planning using robust optimization, Figure 3B This is an example based on Figure 2 A schematic diagram of a method 200 of some embodiments of a method for radiation therapy planning. Figure 3A and Figure 3B The comparison demonstrates some advantages of the technology disclosed in this article.

[0059] according to Figure 3A , first perform the dose calculation for all N scenes and at time T opt Complete. The optimization process is completed in T opt Start and consider all N scenarios. Figure 3A In the example described, a dose calculation is performed on a first device (Device 1 ), and the results of the dose calculation are sent to a second device (Device 2 ) on which an optimization process is performed.

[0060] on the contrary, Figure 3BMethods according to some embodiments of the present invention are described in which the results of the dose calculation for scenario 1 are sent from device 1 to device 2 after the dose calculation for scenario 1 has been completed but before the dose calculation for scenario 2 and all further scenarios have been completed. This allows Figure 3B The optimization process of Figure 3A The optimization process starts much earlier, i.e. at T dose <T opt Each time a dose calculation for an additional scenario is completed, the results are sent from device 1 to device 2, and the optimization process executed on device 2 is updated to include the received dose calculation. As described herein, dose results may alternatively be sent to device 2 in batches of scenarios, rather than one scenario at a time.

[0061] In T opt , Figure 3B The optimization process includes dose calculation for all N scenarios, Figure 3A However, Figure 3B The optimization process of is closer to completion at this stage. Therefore, Figure 3B The optimization process of Figure 3A In other words, the techniques described herein reduce the time it takes to complete the optimization process.

[0062] Figure 4 is a flow chart illustrating a method for radiation therapy planning according to various embodiments of the present invention. The method can be applied to any radiation therapy delivery mode, such as photon therapy or pencil beam scanning (PBS) proton therapy.

[0063] The method begins with a plurality of robust scenarios for radiation treatment of a patient, and the goal is to obtain a robust treatment plan. At step 402, the method includes dividing the robust scenarios into batches, where each batch includes n scenarios or more (n is an integer equal to or greater than 1). If there are N scenarios in total, each batch may include 1 to N scenarios. Different batches may include different numbers of scenarios. The ideal batch size can be determined based on a balance between data transfer requirements (e.g., network, memory, etc.), preprocessing requirements (e.g., coordinate transformations), and the demands placed on the optimizer. For example, experimental testing can be performed to determine a set of batch sizes that provides an efficient optimization process. The ability to select a batch size based on experimental testing provides a flexible optimization process that can be made more efficient as needed. For example, if there are 100 scenarios, there may be 10 batches, each including 10 scenarios. Alternatively, there may be 5 batches, each including 20 scenarios, or 7 batches, including 4 batches of 10 scenarios and 3 batches of 20 scenarios, and so on.

[0064] The method proceeds to step 404 where a dose calculation is performed for scenario 1. Scenario 1 is a nominal scenario where all treatment parameters are taken to have their predicted values ​​(ie, the errors in the individual parameter values ​​are taken to be zero).

[0065] In step 406, the optimizer is started in the second computing device, and the dose calculation result of scenario 1 is sent to the optimizer, as shown in step 422. Here, the optimization process is initiated to optimize the treatment plan for scenario 1.

[0066] While the optimization is proceeding on the second computing device (step 416), the first computing device continues to calculate the dose for another scene. Thus, at step 408, the first computing device calculates the dose for scene i=2. At step 410, the first computing device determines whether the dose calculation has been performed for all scenes in the current batch: if not ("No"), the method returns to step 408 to calculate the dose for another scene in the current batch; if it has been performed ("Yes"), the method proceeds to step 412, at which the results of all dose calculations from the scenes in the current batch are sent to the optimizer on the second computing device. Thus, the optimization process is updated to include these dose calculations, as illustrated in steps 424 and 426.

[0067] Once the dose calculations from the current batch have been sent to the optimizer, the method proceeds to step 414, where the first computing device determines whether dose calculations have been performed for all batches of scenes identified in step 402. If not ("No"), the method returns to step 408 and calculates doses for additional scenes in the new batch. If so ("Yes"), the method proceeds to step 430, where the optimization process is awaited to be completed on the second computing device.

[0068] like Figure 4As shown, the optimization process is performed in parallel (i.e., simultaneously) with the dose calculation. While the dose calculation is being performed on the first computing device (steps 408-412), the second computing device performs the optimization process at step 416. The optimization process is performed for all dose calculations already received at the second device. Each time a dose calculation for an additional scenario (or batch of scenarios) is received (e.g., at steps 422, 424, and 426), the optimization process is updated to include it. At step 418, the second computing device determines whether the treatment plan provided by the optimizer meets the optimization criteria. If not ("No"), the optimization process continues (step 416). If it does ("Yes"), and assuming all dose calculations have been received from the first device (which is also checked by the second computing device at step 418), the optimization process ends at step 420. The optimization process can also be terminated (exited) if it is determined that the solution is infeasible, for example because the total penalty value (e.g., the summed cost function) cannot be reduced any further, while some cost functions in the sum have not yet been met. This can be determined when the difference in the total penalty value between two iterations of the optimization process is less than a tolerance (e.g., 0.001). If the maximum number of iterations is reached, the optimization process can be terminated. For example, the optimizer can be configured to iterate a maximum of 100 times. The solution can be taken as the solution obtained on the final iteration.

[0069] At step 440, the optimized treatment parameters are output by the second device, thereby defining a robust treatment plan.

[0070] will understand that about Figure 4 The described first computing device and second computing device may alternatively include different CPUs; different GPUs; a CPU and a GPU; or different processing units of a multi-core processor.

[0071] Figure 5A is a block diagram illustrating an example of an optimization process 500 for determining a parameter set (ie, one or more parameters) that defines a radiation treatment plan for a radiation treatment system according to some embodiments. The optimization process 500 may correspond to Figure 2 Step 204 or Figure 4 The optimization is performed in step 416. Figure 8 Examples of radiation therapy apparatus are described.

[0072] The optimization module 500 includes an optimizer 507 configured to receive at least a model 501, a cost function 502, and one or more parameters 503. Optionally, the optimizer 507 may also receive constraints 505 and / or a reference target 504. The optimization module 500 performs an optimization process and outputs a set of optimized parameters 511 that minimize the cost function 502. The optimized parameters 511 are the values ​​of the one or more parameters 503 input to the optimizer 507. The optimization module 500 may also output one or both of a cost function value 509 (the cost function 502 evaluated using the optimized parameters 511) and an achieved target value 513 (for comparison with the reference target 504).

[0073] The optimizer 507 uses at least one optimization algorithm, such as a simplex algorithm, a gradient-based algorithm, an interior point algorithm, or a combination thereof. Those skilled in the art will appreciate that other optimization algorithms are possible, and the present invention is not limited to any particular optimization algorithm. The optimization process may correspond to the optimization process described in European patent application publication EP3681600A1, for example.

[0074] Model 501 is a representation of the anatomical region to be treated. For radiation therapy planning, the anatomical region may include one or more anatomical regions (i.e., sub-regions), such as one or more of the following: a target region, an onset of healthy tissue region (OAR), a planning target volume, a critical structure, or a shell structure. These regions or structures may be determined or defined by performing image segmentation of a three-dimensional image or model of the anatomical region. Shell structures are structures generated to adjust the dose delivered to tissue surrounding the target, and they may be used to control dose conformality.

[0075] The cost function 502 is a mathematical formula that provides a measure of how well a parameter set 503 satisfies a reference target 504 (e.g., one or more targets). The cost function 502 evaluates the dose distribution over a region of the model 501 based on the parameter set 503. The region may be divided into voxels, and a dose is calculated for each voxel. The dose distribution may be calculated using a pencil beam algorithm, a convolution-based algorithm, and / or a Monte Carlo (MC)-based algorithm. The dose distribution reference Figure 5B This is described in more detail, and further details on how dosage may be determined are given in Training Guide, Document ID: LTGMON0530 (Elekta AB).

[0076] The cost function 502 may relate the dose distribution to a single value referred to as a cost function value 509. The cost function 502 may define a penalty for violating one or more objectives. A cost function may also be referred to as an objective function.

[0077] Cost function 502 may be a cost function for multiple scenarios (which may be referred to as a total cost function). In such a case, cost function 502 may be the sum of the contributions of each scenario (e.g., the sum may include one term for each scenario). Each term may include the cost function of the scenario multiplied by an importance weight. The sum of the importance weights of all scenarios contributing to the total cost function may be equal to 1.

[0078] Optimizer 507 evaluates cost function 502 during optimization. Optimizer 507 changes optimizable parameters 503 in order to minimize cost function 502. Alternatively, in constrained optimization, constraints 505 do not contribute to the cost function, but rather, violating constraints 505 results in rejection of the treatment plan. For example, a reference target may be an average dose to a healthy organ (e.g., the bladder) that is less than a dose value (e.g., 30 Gy), and a constraint may be a maximum dose to the patient (as a whole) that is less than another dose value (e.g., 50 Gy). The cost function may provide a mathematical formula for calculating the average dose to the bladder, and the optimizer aims to obtain a reference target (a dose to the bladder that is less than 30 Gy) that complies with the constraints. The optimized solution provided by the optimizer may include a dose to the bladder that is less than or greater than 30 Gy, but if the optimizer cannot find a solution in which the maximum dose to the patient is less than 50 Gy, no solution will be provided (e.g., no treatment plan). Constraints may in some cases be referred to as a special type of cost function that cannot be violated (so that the value of the cost function is unimportant), however, such terminology is not used herein.

[0079] One or more parameters 503 are optimizable parameters (also called decision variables or treatment parameters) for which the optimizer 507 attempts to find optimal values. The optimizable parameters 503 may relate to characteristics of the radiation to be delivered by the radiotherapy system. For example, the optimizable parameters 503 may include one or more of the following parameters: beamlet weights, beam angles, dose histogram volume information, the number of radiation beams, the dose per beam, and a dose excess (e.g., a root mean square dose excess above a reference dose). The one or more parameters 503 may be initialized to a predetermined set of values, such as a "best guess" set of values ​​or an arbitrary set of values. Alternatively, the parameters 503 may be initialized to values ​​determined in a previous step. For example, when performing multiple optimizations, the parameters may be initialized to values ​​determined in a previous optimization process (e.g., based on different reference targets 504).

[0080] Constraints 505 include one or more conditions that must be satisfied by the optimization parameters 511. Constraints 505 may include one or more hard constraints (conditions that the parameters 511 are required to satisfy). Constraints limit the set of possible solutions and can be used to define what is physically possible and / or clinically acceptable. Note that constraints 505 are an optional feature.

[0081] Reference targets 504 include targets that the optimizer is attempting to achieve. For example, reference targets may relate to dose-based targets and / or volume-based targets. Dose-based targets may include dose values ​​(in Gy), such as the maximum, minimum, or average dose for a given region of the model 501. Volume-based targets may include relative volumes (e.g., fractions / percentages of the volume of a region of the model 501) or absolute volumes. Absolute volumes may be based on physical dimensions (e.g., in mm, cm, mm, etc.). 3 、cm 3 For example, the reference target 504 may require that at least X% of the first region receive at least YGy during treatment.

[0082] In addition, the reference target 504 may include an indication of the cost function 502 to be used. The reference target 504 may also include an indication of the anatomical structure for which the target to be achieved applies, such as the region of the model 501 for which the maximum / minimum / average dose applies. The optimizer 507 will determine a set of optimized parameters 511 that results in an achieved target value 513 close to the reference target 504. Note that the reference target 504 is an optional feature.

[0083] Thus, reference target 504 is an anatomical structure-specific function that establishes a dose and / or bioresponse target, and constraints 505 are anatomical structure-specific functions that can be hard or soft. When constraints are used with targets, the constraints are hard constraints that must be satisfied. Conversely, targets are the goals that optimizer 507 attempts to achieve.

[0084] The cost function 502 , together with the model 501 , parameters 503 , constraints 505 (optional), and reference goal 504 (optional), defines the problem to be solved. The term “treatment plan goal” may collectively refer to all aspects of the reference goal 504 and / or constraints 505 .

[0085] As described above, the optimizer 507 aims to find an optimized set of parameters 511 for which the cost function 502 is minimized. The optimized parameters 511 are the output of the optimizer 507. In one example, when there is a reference target 504, the optimizer 507 minimizes the difference between the target value 513 and the reference target 504.

[0086] Optionally, the optimizer 507 outputs a cost function value 509. The cost function value 509 is the value of the cost function 502 when evaluated using the optimization parameters 511.

[0087] Optionally, the optimizer 507 outputs an achieved target value 513. The achieved target value 513 typically has the same units as the reference target so that it can be compared with the reference target 504. The achieved value 513 may be different from the cost function value 509. Unlike the cost function value 509, which may be a number that is an evaluation of a cost function, the achieved value 513 may have a physical meaning. In one example, when the reference target 504 includes a dosimetry target in grays (Gy), the achieved target value 513 also relates to the dose (i.e., it has the unit Gy and / or the same physical meaning as the reference target 504). In another example, when the reference target 504 includes a reference volume and a dose value, the achieved target value also relates to the reference volume and the dose value (i.e., it has the same physical meaning as the reference target). The reference target 504 may be based on a dose volume histogram (DVH) and may be a target for a percentage of the volume that receives a predetermined dose. The achieved target value 513 will be the achieved percentage of the volume that receives the predetermined dose. In other words, the achieved target value 513 may be compared to the reference target 504 .

[0088] The optimization process 500 can be repeated for one or more different reference targets 504. In other words, the treatment plan can be divided into a series of optimization problems, wherein each optimization problem focuses on a specific target (i.e., the reference target 504) and is optionally subject to one or more constraints. Different reference targets 504 can, for example, relate to different (or overlapping) anatomical regions (or shells or structures) in the patient's body. The output of the first optimization process 500 can contribute to the second optimization process 500. For example, the reference target 504 of the previous optimization process can be set as the constraint 505 for the subsequent optimization process. In this case, the reference targets 504 can have corresponding priorities so that the order in which the optimization problems are solved can be determined based on their corresponding priorities. Therefore, the optimization process 500 can be performed first for the reference target 504 with the highest priority, and the subsequent optimization processes can be performed in the order of decreasing priority. If the reference target 504 cannot be met, the reference target 504 can be revised and the optimization can be repeated using the revised reference target 504.

[0089] As an example, Figure 5A The optimization process 500 illustrated in FIG. 5 can be applied to intensity modulated radiation therapy (IMRT). In IMRT, one or more radiation beams are directed to a tumor, and the intensity of each beam profile is non-uniform. In this example, Figure 5AAn optimization process 500 is provided to determine the weights of the radiation beamlets (the weights corresponding to parameters 503). A cost function 502 includes a mathematical expression relating the dose distribution at a unit fluence to the beamlet weights 503. The cost function 502 incorporates a reference target 504, which sets forth the maximum dose (i.e., the reference dose) for a first region of the model 501 including the OAR. If the dose in the first region exceeds the maximum dose, a penalty is incurred in the cost function 502 (i.e., the value of the cost function 502 increases). The cost function 502 further incorporates a hard constraint 505 that the beamlet weights cannot be negative (because negative beamlet weights are impossible). The output of the optimization process 500 includes optimized beamlet weights 511.

[0090] Although the above examples apply to IMRT, note that the optimization process 500 can be applied to other radiation therapy modalities.

[0091] The optimization process 500 can be part of the first stage of the treatment planning process, in which a radiation fluence map (or intensity map) is optimized. The fluence map indicates the expected radiation dose across the region according to the radiation treatment plan. The first stage can include, for example, one or more optimization processes 500 corresponding to one or more reference targets 504.

[0092] The first phase may be followed by a second phase in which the optimized parameters 511 from the first phase are used to determine the configuration of the treatment device to be used to perform the treatment. Both the first and second phases will refer to Figure 5B Describe in more detail.

[0093] It will be understood that the technology disclosed herein is not limited to reference Figure 5B A two-stage approach is described.

[0094] Figure 5B is a block diagram illustrating a two-stage optimization process 550. Process 550 is described herein with reference to IMRT treatment planning, but it can also be applied to volumetric modulated radiation therapy (VMAT). Furthermore, it is noted that optimization process 550 can also be applied to other radiation therapy modalities.

[0095] The purpose of the optimization process 550 is to modify the IMRT beam intensity profile so that a sufficiently high dose is delivered to the tumor while reducing the dose delivered to healthy organs.

[0096] The optimization method for IMRT 550 consists of two phases. The first phase (step 551) is fluence map optimization (FMO), and the second phase (step 553) is determining the configuration of the radiotherapy system. In FMO 551, the optimal fluence map is determined. The optimal fluence map is then used to determine the configuration of the radiotherapy system in step 553.

[0097] For fluence optimization 551, each beam is split into multiple beamlets. The contribution of each beamlet to a voxel per unit fluence is then calculated. During optimization, the weights of the beamlets are adjusted to minimize a cost function. The total dose distribution is obtained by multiplying the weights by the contributions of each beamlet per unit fluence. The total dose distribution can be compared to any constraints and / or reference targets to determine the suitability of the optimized solution.

[0098] The optimization process of FMO can be referred to Figure 5A The optimization process 500 is described and will be further described below.

[0099] Optimization 551 involves minimizing a cost function f(x) by determining appropriate values ​​for parameters x based on certain constraints g. For FMO, parameters x may correspond to weights for the beamlets (parameters x may also be referred to as decision variables). The output of FMO (step 551) includes the optimized values ​​for parameters x.

[0100] The constraints g include limits. Examples of limits are a minimum dose at a voxel corresponding to a target, or a maximum dose at a voxel corresponding to an OAR, and so on.

[0101] In one example, the cost function f(x) is:

[0102] f(x)=T1+T2+…+T3, (Equation 1)

[0103] Where, for example, T1 = ∑x n ·d n , where x corresponds to the weight of the beamlet, d n represents the dose of each beamlet to the voxel at unit intensity. The voxel is obtained from the above model 501, so d n Associated with model 501. d n is a non-optimizable parameter (e.g., it may depend on the machine configuration and / or characteristics of the tissue). In one example, the dose d n It can be determined by a pencil beam algorithm, a convolution-based algorithm and / or a Monte Carlo (MC)-based algorithm. Optionally, the dose calculation uses a pencil beam algorithm or a convolution-based algorithm, which is faster than the MC-based algorithm but has reduced accuracy, i.e., results in a less realistic dose calculation. How the dose d can be determined n Additional details are in Training Guide, Document ID: LTGMON0530 (Elekta AB).

[0104] The output of stage 1 (step 551) may include a dose distribution.

[0105] In this example, the objective function f(x) is the sum of the total doses (T1, T2...T3), where each of T1, T2...T3 represents the dose on a different structure. Note that alternative formulations of the problem to be solved can be used (e.g., by defining different cost functions or constraints).

[0106] In one example, a requirement that the dose at the target be above a certain amount can be formulated as a constraint. Alternatively, such a requirement can be formulated as a reference target.

[0107] Step 551 provides an optimized set of parameters (eg, beamlet weights x) that will provide an optimal intensity map (or fluence map).

[0108] In preparation for delivery by the radiotherapy system, a further step (step 553) is required to convert the individual optimized beamlet weights into a configuration of the machine that will deliver the optimal fluence.

[0109] In step 553, the output from step 551 is converted into a configuration of the radiotherapy system. The configuration can be used by a radiotherapy device (examples of which are described herein) to deliver radiotherapy. For example, the configuration of the radiotherapy system includes a set of aperture configurations (i.e., one or more aperture configurations). The shapes and weights of the aperture configurations are selected to meet the same goals as in the first stage. The aperture configuration can be configured by a beam shaping device (e.g., Figure 8 Part 850) is implemented.

[0110] The aperture configuration may be referred to as a control point or segment. The control point and / or segment includes radiation information (e.g., energy, dose) and geometric information (e.g., gantry angle and leaf position). The shape and weight of the aperture may be determined by applying algebraic and trigonometric considerations to the arrangement of the aperture in order to achieve the optimized beamlet weights of step 551.

[0111] Alternatively, step 553 includes performing a second stage optimization process to determine an optimized aperture configuration that will achieve the optimized fluence pattern determined in step 551. The second stage optimization process may be referred to as aperture optimization or aperture refinement.

[0112] Aperture optimization can include the following:

[0113] - Receive a set of beamlet weights (eg, from step 551).

[0114] - Perform an optimization to determine the optimized aperture shape and optionally weights. The optimization can be done using e.g. Figure 5A Other optimization procedures are also possible.

[0115] When the beam shaping device includes a multi-leaf collimator (MLC), aperture optimization may include:

[0116] - Receive a set of beamlet weights from the FMO and / or fluence profile (eg, from step 551 ).

[0117] - Convert the received profile into beamlet widths (openings between leaf pairs). This produces segments.

[0118] - Optimize the weights of the resulting segments.

[0119] - Optionally, optimizing the shapes of the generated segments (using a process known as segment shape optimization).

[0120] Note that the optimization step in step 553 may include calculation of the dose distribution. The dose distribution may be calculated using a pencil beam algorithm, a convolution-based algorithm, and / or a Monte Carlo (MC)-based algorithm. Optionally, the dose calculation in step 553 uses an MC-based algorithm (which is more accurate but computationally expensive).

[0121] Additional details on aperture optimization are given in Training Guide, Document ID: LTGMON0530 (Elekta AB).

[0122] The step of determining the configuration of the radiotherapy system (step 553) is based on at least the optimized parameter set from stage one (step 551).

[0123] Although Figure 5B The example of 550 involves a two-stage optimization process for IMRT (FMO at step 551 and configuration determination at step 553), but note that alternative optimization methods such as direct machine parameter optimization (DMPO) can be used instead. In DMPO, the decision variables x correspond to parameters of the machine delivering radiation (e.g., MLC leaf positions).

[0124] Returning to the cost function 502 in the optimization process 500 , examples of cost functions include: target EUD, target penalty, secondary overdose, secondary underdose, continuous, parallel, maximum dose, overdose DVH, and underdose DVH. These cost functions are briefly described below. Each cost function provides a different calculation method.

[0125] When the cost function includes any of target penalty, parallelism, overdose DVH, and underdose DVH, the reference target 504 is a dose value and a relative volume component (eg, percentage) or an absolute volume (eg, in cc).

[0126] When the cost function includes any of target EUD, secondary overdose, continuous, maximum dose, or conformality, the reference target 504 is a dose value.

[0127] Cost functions such as continuous, parallel, quadratic overdose, overdose DVH, or maximum dose are intended to limit the dose at the anatomical structure. Cost functions such as quadratic underdose or underdose DVH are intended to increase the dose at the anatomical structure. Cost functions such as target EUD and target penalty are intended to increase the dose at the structure. Some of the above cost functions also take unitless numbers as input. The unitless number (k) is a power law exponent.

[0128] The target EUD cost function defines a structure as a target volume and expresses the probability that target cells will survive a given dose. The cost function requires a specified dose as input (i.e., the reference target of the cost function is the dose value). The specified dose in Gy is the equivalent uniform dose (EUD). EUD is a uniform dose that, if delivered at the anatomical structure, has the same clinical effect as a non-uniform dose distribution.

[0129] The target penalty cost function takes as input a prescribed dose and a minimum volume (i.e., the reference targets for the cost function are dose and relative volume). The target penalty is a quadratic penalty that starts at a threshold dose. It produces a steeper dose gradient after the target threshold is met. The target penalty is used to define the requirement that at least some portion of the total anatomical volume should receive at least the target dose.

[0130] The Quadratic Dose Excess (QO) cost function is a cost function used to limit the dose to the structure to which the dose is applied. QO can be applied to the target or the OAR. The QO cost function can be used to limit hot spots in the target. The QO cost function takes as input the maximum dose and the maximum root mean square (RMS) dose excess. The maximum dose defines the dose above which a penalty is incurred in the cost function. The maximum RMS dose excess defines the acceptable amount of violation. The maximum dose and / or the maximum RMS dose excess can be defined by the user.

[0131] The quadratic underdose cost function is a cost function applied to the target volume. The quadratic underdose function implements a quadratic penalty. The cost function takes as input the minimum dose in Gy and the maximum dose deficit in Gy (i.e., the reference target includes two dose values). The minimum dose is the minimum dose allowable in the target and represents the dose below which the penalty occurs. The maximum dose deficit is similar to the maximum RMS dose overdose in that it defines the amount of acceptable prescription violation.

[0132] The continuous cost function is often used with continuous OARs. Continuous anatomical structures are structures where a high dose would be harmful even if confined to a small volume. Examples include the spinal cord and intestine. This cost function imposes a large penalty on hotspots, even if they are small. The cost function takes as input the EUD in Gy and the power law exponent k (i.e., a reference target consisting of a dose value and a unitless number).

[0133] Parallel cost functions are commonly used for parallel OARs. Parallel structures are structures that can tolerate very high doses in a small volume while the rest of the organ is spared. Examples are the lung, parotid gland, kidney, and liver. The cost function takes as input a reference dose in Gy, the average organ damage (which is the fraction of the volume of the structure that can be sacrificed), and a power law exponent k (i.e., the reference target includes a dose value, a relative volume, and a unitless number).

[0134] The maximum dose cost function is effectively a hard barrier that can be applied to target structures or OARs. The maximum dose cost function has a penalty that takes effect whenever a voxel crosses a maximum dose threshold. The cost function takes as input the maximum dose in Gy (i.e., the reference target includes the dose value).

[0135] The overdose DVH cost function takes as input a target dose in Gy and a maximum volume (i.e., a reference target includes a dose value and a relative volume). This cost function is applied to OARs. The goal is to keep volumes that receive more than the target dose below the relative volume.

[0136] The underdose DVH cost function takes as input a target dose in Gy and a minimum volume (i.e., a reference target including a dose value and a relative volume). The cost function is applied to the target. The goal is to keep the target volume that receives less than the target dose above the minimum volume.

[0137] Additional details of the cost function are given in Training Guide, Document ID: LTGMON0530 (Elekta AB).

[0138] Figure 6 A block diagram illustrating an implementation of a radiation therapy system 600 is shown. Radiation therapy system 600 includes a computing system 610 within which a set of instructions for causing computing system 610 to perform any one or more of the methods discussed herein can be executed. Computing system 610 can implement a treatment planning system. Computing system 610 can also be referred to as a computer. Treatment planning system 610 can perform any of the methods described herein. In particular, the methods described herein can be implemented by one or more processors of treatment planning system 610.

[0139] Computing system 610 should be considered to include the set of any number of machines or machines, such as one or more computing devices, which individually or jointly execute one or more instruction sets to perform any one or more methods discussed herein. That is, hardware and / or software can be provided in a single computing device, or distributed on a plurality of computing devices in the computing system. In some implementations, one or more elements of the computing system can be connected (such as networked) to other machines such as a local area network (LAN), an intranet, an extranet or the Internet. One or more elements of the computing system can operate with the ability of a server or client machine in a client-server network environment, or operate as a peer machine in a peer-to-peer (or distributed) network environment. One or more elements of the computing system can be a personal computer (PC), a tablet computer, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a network appliance, a server, a network router, a switch or a bridge or any machine that can execute a set of instructions (sequential or otherwise), which specifies the action to be taken by the machine.

[0140] The computing system 610 includes a controller circuit 611 and a memory 613 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)). The memory 613 may include static memory (e.g., flash memory, static random access memory (SRAM), etc.) and / or auxiliary memory (e.g., a data storage device), which communicate with each other via a bus (not shown).

[0141] Controller circuit 611 represents one or more general-purpose processors, such as microprocessors, central processing units, accelerated processing units, etc. More particularly, controller circuit 611 can include complex instruction set computing (CISC) microprocessors, reduced instruction set computing (RISC) microprocessors, very long instruction word (VLIW) microprocessors, processors that implement other instruction sets, or processors that implement a combination of instruction sets. Controller circuit 611 can also include one or more special processing devices, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, etc. The one or more processors of the controller circuit can have a multi-core design. Controller circuit 611 is configured to execute the processing logic for executing the operations and steps discussed herein.

[0142] The computing system 610 may also include a network interface circuit 618. The computing system 610 may be communicatively coupled to an input device 620 and / or an output device 630 via input / output circuit 617. In some implementations, the input device 620 and / or the output device 630 may be components of the computing system 610. The input device 620 may include an alphanumeric input device (e.g., a keyboard or touch screen), a cursor control device (e.g., a mouse or touch screen), an audio device such as a microphone, and / or a tactile input device. The output device 630 may include an audio device such as a speaker, a video display unit (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), and / or a tactile output device. In some implementations, the input device 620 and the output device 630 may be provided as a single device or separate devices.

[0143] In some implementations, the computing system 610 may include image processing circuitry 619. The image processing circuitry 619 may be configured to process image data 680 (e.g., images or imaging data), such as medical images obtained from one or more imaging data sources, the treatment device 650, and / or the image acquisition device 640. The image processing circuitry 619 may be configured to process or pre-process the image data. For example, the image processing circuitry 619 may convert the received image data into a specific format, size, resolution, etc. In some implementations, the image processing circuitry 619 may be combined with the controller circuitry 611.

[0144] In some implementations, the radiation therapy system 600 may further include an image acquisition device 640 and / or a treatment device 650, such as those described herein. Figure 8 The apparatus disclosed in the examples of FIG. The image acquisition device 640 and the treatment device 650 can be provided as a single device. In some implementations, the treatment device 650 is configured to perform imaging, for example, in addition to providing treatment and / or during treatment. The treatment device 650 includes the primary radiation delivery components of the radiotherapy system, such as the beam shaping device 550.

[0145] The image acquisition device 640 may be configured to perform positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), single positron emission computed tomography (SPECT), X-ray, and the like.

[0146] Image acquisition device 640 may be configured to output image data 680 that may be accessed by computing system 610. Treatment device 650 may be configured to output treatment data 660 that may be accessed by computing system 610.

[0147] The computing system 610 can be configured to access or obtain treatment data 660, planning data 670, and / or image data 680. Treatment data 660 can be obtained from an internal data source (e.g., from memory 613) or from an external data source (e.g., treatment device 650 or an external database). Planning data 670 can be obtained from memory 613 and / or from an external source, such as a planning database. Planning data 670 can include information obtained from one or more of image acquisition device 640 and treatment device 650.

[0148] Figure 7 FIG1 is a block diagram illustrating a data processing apparatus 700 according to some embodiments of the present invention. The data processing apparatus 700 may be, for example, a computer.

[0149] The data processing device 700 includes a memory 701 storing computer executable instructions. The data processing device 700 also includes a processing circuit 702 configured to execute the instructions to perform the various methods described above (e.g. Figure 2 Method 200).

[0150] For example, about Figure 2 The steps of the described methods may be performed by computer code stored on the data processing device 700. The steps of the methods described herein may be performed in any suitable order unless a step is explicitly described as coming before or after another step and / or it is implied that a step must come before or after another step.

[0151] exist Figure 7 , processing circuitry 702 is represented by a single box. However, it will be understood that processing circuitry 702 may include processing circuitry contained in a first processing unit and processing circuitry contained in a second processing unit that is different from the first processing unit. For example, the first processing unit and the second processing unit may include different CPUs; different GPUs; a CPU and a GPU; different processing units of a multi-core processor; or different (separate) physical computers. Memory 701 may include a first memory associated with the first processing unit and a second memory associated with the second processing unit. For example, data processing apparatus 700 may include two physical computers, each of which includes a memory and a processing circuit.

[0152] The first processing unit may be configured to perform an optimization process, e.g. Figure 2 The optimization process of steps 204 and 208 of method 200, and the second processing unit can be configured to perform dose calculations, such as Figure 2 The dose calculation of steps 202 and / or 206 of method 200 may be performed. Thus, the optimization process and the dose calculation may be performed in parallel (ie, simultaneously).

[0153] The computer program and / or code for performing this method may be provided to the apparatus 700 on one or more computer-readable media, or more generally, on a computer program product. The computer-readable medium may be transient or non-transient. The one or more computer-readable media may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or a propagation medium for data transmission (e.g., for downloading code via the Internet). Alternatively, the one or more computer-readable media may take the form of one or more physical computer-readable media, such as semiconductor or solid-state memory, magnetic tape, removable computer disk, random access memory (RAM), read-only memory (ROM), hard disk, and optical disk (e.g., CD-ROM, CD-R / W, or DVD). The instructions may also reside completely or at least partially within the memory 701 and / or within the controller circuitry during their execution by the computer system, the memory 701 and the controller circuitry also constituting computer-readable storage media.

[0154] In implementation, the modules, components, and other features described herein may be implemented as discrete components or integrated into the functionality of hardware components such as an ASIC, FPGA, DSP, or similar device.

[0155] A "hardware component" is a tangible (e.g., non-transient) physical component (e.g., a set of one or more processors) that is capable of performing certain operations and may be configured or arranged in some physical manner. A hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may include a dedicated processor, such as an FPGA or ASIC. A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations.

[0156] In addition, modules and components can be implemented as firmware or functional circuits within hardware devices. Further, modules and components can be implemented in any combination of hardware devices and software components, or only in software (e.g., code stored or otherwise embodied in a machine-readable medium or transmission medium).

[0157] Figure 8 An example of a radiotherapy apparatus for use in radiation therapy is described. Those skilled in the art will appreciate that the techniques of the present invention are also applicable to other types of radiation therapy and radiation therapy systems.

[0158] Figure 8 A cross-section of a radiotherapy apparatus 800 is shown, including a radiation head 804 and a beam receiving device 806, both of which are attached to a gantry 802. The radiation head 804 includes a radiation source 807 that emits a radiation beam 822. The radiation head 804 also includes a beam shaping device 850, which controls the size and shape of the radiation field associated with the beam.

[0159] The beam receiving device 806 is configured to receive radiation emitted from the radiation head 804 in order to absorb and / or measure the radiation beam. Figure 8 In the view shown, the radiation head 804 and the beam receiving device 806 are positioned diametrically opposite each other.

[0160] The gantry 802 is rotatable and supports the radiation head 804 and the beam receiving device 806 so that they can rotate about an axis of rotation 805, which can coincide with the longitudinal axis of the patient. Figure 8 As shown, the gantry provides for rotation of the radiation head 804 and beam receiving device 806 in a plane perpendicular to the patient's longitudinal axis (e.g., the sagittal plane). Three gantry directions, XG, YG, and ZG, can be defined, where the YG direction is perpendicular to the gantry's rotation axis. The ZG direction extends from a point on the gantry corresponding to the radiation head toward the gantry's rotation axis. Thus, from the patient reference frame, the ZG direction rotates as the gantry rotates.

[0161] Figure 8 Also shown is a support surface 810 on which a subject (or patient) is supported during radiation treatment. The radiation head 804 is configured to rotate about an axis of rotation 805 so that the radiation head 804 directs radiation toward the subject from various angles around the subject in order to spread the radiation dose received by healthy tissue over a larger area of ​​healthy tissue while simultaneously accumulating a prescribed radiation dose at the target area.

[0162] The radiotherapy apparatus 800 is configured to deliver a radiation beam towards a radiation isocenter that is approximately located on the rotation axis 805 at the center of the gantry 802 regardless of the angle at which the radiation head 804 is positioned.

[0163] The rotatable gantry 802 and radiation head 804 are sized to allow for a central aperture 880. Central aperture 880 provides an opening sufficient to allow a subject to be positioned therethrough without being accidentally contacted by the radiation head 804 or other mechanical components as the gantry rotates the radiation head 804 around the subject.

[0164] like Figure 8 As shown, the radiation head 804 emits a radiation beam 822 along a beam axis 890 (or radiation axis or beam path), wherein the beam axis 890 is used to define the direction in which the radiation head emits radiation. The radiation beam 822 is incident on a beam receiving device 806, which may include at least one of a beam stop and a radiation detector. The beam receiving device 806 is attached to the gantry 802 on a side diametrically opposite the radiation head 804 to attenuate and / or detect the radiation beam after the beam passes through the subject.

[0165] For example, radiation beam axis 890 may be defined as the center or point of maximum intensity of radiation beam 822 .

[0166] The beam shaper 850 defines the spread of the radiation beam 822. The beam shaper 850 is configured to adjust the shape and / or size of the radiation field generated by the radiation source. The beam shaper 850 achieves this by defining a variable-shaped aperture (also referred to as a window or opening) to calibrate the radiation beam 822 to a selected cross-sectional shape. In this example, the beam shaper 850 can be provided by a combination of an aperture and a multi-leaf collimator (MLC). The beam shaper 850 can also be referred to as a beam modifier.

[0167] The radiation therapy device 800 can be configured to deliver radiation therapy in both coplanar and non-coplanar (also known as tilted) modes. In coplanar therapy, radiation is emitted in a plane perpendicular to the axis of rotation of the radiation head 804. In non-coplanar therapy, radiation is emitted at an angle that is not perpendicular to the axis of rotation. To deliver coplanar and non-coplanar therapy, the radiation head 804 can be moved between at least two positions: one in which radiation is emitted in a plane perpendicular to the axis of rotation (coplanar configuration) and one in which radiation is emitted in a plane that is not perpendicular to the axis of rotation (non-coplanar configuration).

[0168] In a coplanar configuration, the radiation head is positioned to rotate about an axis of rotation and within a first plane. In a non-coplanar configuration, the radiation head is tilted relative to the first plane so that the radiation field generated by the radiation head is directed at an oblique angle relative to the first plane and the axis of rotation. In a non-coplanar configuration, the radiation head is positioned to rotate in a corresponding second plane that is parallel to and displaced from the first plane. The radiation beam is emitted at an oblique angle relative to the second plane, so that the beam sweeps out a cone as the radiation head rotates.

[0169] When the radiotherapy apparatus is in coplanar mode and non-coplanar mode, the beam receiving device 806 remains in the same position relative to the rotatable gantry. Thus, the beam receiving device 806 is configured to rotate about the rotation axis in the same plane in both coplanar mode and non-coplanar mode. This can be the same plane in which the radiation head rotates.

[0170] The beam shaping device 850 is configured to reduce the spread of the radiation field in a non-coplanar configuration compared to a coplanar configuration.

[0171] Radiation therapy apparatus 800 includes a controller 840 programmed to control radiation source 807, beam receiving device 806, and gantry 802. Controller 840 may perform functions or operations such as treatment planning, treatment delivery, image acquisition, image processing, motion tracking, motion management, and / or other tasks involved in a radiation therapy procedure.

[0172] Controller 840 is programmed to control features of apparatus 800 in accordance with a radiation therapy plan to irradiate a target area of ​​the patient, also referred to as target tissue. The treatment plan includes information regarding a specific dose to be delivered to the target tissue, as well as other parameters such as beam angles, dose histogram volume information, the number of radiation beams to be used during treatment, the dose of each beam, etc. Controller 840 is programmed to control various components of apparatus 800, such as gantry 802, radiation head 804, beam receiving device 806, and support surface 810, in accordance with the treatment plan.

[0173] The hardware components of the controller 840 may include one or more computers (e.g., general-purpose computers, workstations, servers, terminals, portable / mobile devices, etc.); processors (e.g., central processing units (CPUs), graphics processing units (GPUs), microprocessors, digital signal processors (DSPs), field programmable gate arrays (FPGAs), dedicated or specially designed processors, etc.); memory / storage devices, such as memory (e.g., read-only memory (ROM), random access memory (RAM), flash memory, hard drives, optical disks, solid-state drives (SSDs), etc.); input devices (e.g., keyboards, mice, touch screens, MICS, buttons, knobs, trackballs, joysticks, handles, joysticks, etc.); output devices (e.g., displays, printers, speakers, vibration devices, etc.); circuits; printed circuit boards (PCBs); or other suitable hardware. The software components of the controller 140 may include operating device software, application software, etc.

[0174] The radiation head 804 can be connected to a head actuator 830 that is configured to actuate the radiation head 804, for example, between a coplanar configuration and one or more non-coplanar configurations. This can involve translation and rotation of the radiation head 804 relative to the gantry. In some implementations, the head actuator can include a curved track along which the radiation head 804 can move to adjust the position and angle of the radiation head 804. A controller 840 can control the configuration of the radiation head 804 via the head actuator 830.

[0175] The beam shaping device 850 includes a shaping actuator 832. The shaping actuator is configured to control the position of one or more elements in the beam shaping device 850 to shape the radiation beam 822. In some implementations, the beam shaping device 850 includes an MLC, and the shaping actuator 832 includes a means for actuating the leaves of the MLC. The beam shaping device 850 may also include an aperture, and the shaping actuator 832 may include a means for actuating the block of the aperture. The controller 840 may control the beam shaping device 850 via the shaping actuator 832.

[0176] The treatment plan may include positioning information of the beam shaping device 850. The positioning information of the beam shaping device 850 may include information indicating the configuration of one or more elements of the beam shaping device 850, such as the configuration of leaves of the MLC of the beam shaping device 850, the configuration of the aperture of the beam shaping device 850, the configuration of an opening (e.g., a window or aperture) of the MLC, etc.

[0177] Unless specifically stated otherwise, as will be apparent from the following discussion, it should be understood that throughout this specification, discussions utilizing terms such as "receiving," "determining," "comparing," "implementing," "maintaining," "identifying," "obtaining," "accessing," and the like refer to actions and processes of a computer system or similar electronic computing device that manipulates data represented as physical (electronic) quantities within the computer system's registers and memories and transforms it into other data similarly represented as physical quantities within the computer system's memories or registers or other such information storage, transmission, or display devices.

[0178] While certain embodiments have been described, these embodiments are presented by way of example only and are not intended to limit the scope of the appended claims. Those skilled in the art will appreciate that other examples may be employed in addition to these specific details. Indeed, the novel methods and apparatus described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions, and changes in form may be made to the methods and apparatus described herein.

[0179] In some cases, detailed descriptions of well-known methods and devices are omitted so as not to obscure the description with unnecessary detail. Where appropriate, the present technology may be considered to be embodied in any form of computer-readable storage (e.g., solid-state storage, magnetic disk, or optical disk) containing an appropriate set of computer instructions that will cause a processor to perform the techniques described herein.

Claims

1. A computer-implemented method for radiation therapy planning, the method comprising: performing (202) a first dose calculation for one or more scenarios included in a first batch of a plurality of batches, wherein each batch of the plurality of batches includes one or more scenarios associated with radiation treatment of a patient; In response to completion of the first dose calculation, initiating (204) an optimization process for the one or more scenarios included in the first batch based on an output of the first dose calculation, wherein the optimization process seeks to optimize one or more first parameters of a treatment plan for the radiation treatment of the patient; performing (206) additional dose calculations for one or more scenarios included in additional batches of the plurality of batches in parallel with the optimization process; and In response to completion of the additional dose calculation, the optimization process is updated (208) to include the one or more scenarios included in the additional batch, such that the optimization process is further based on the output of the additional dose calculation.

2. The method according to claim 1, further comprising: For each remaining batch in the plurality of batches, iteratively performs the steps of performing additional dose calculations in parallel with the optimization process and updating the optimization process until all of the batches included in the plurality of batches are included in the optimization process.

3. The method according to claim 2, further comprising: In response to determining that all of the batches included in the plurality of batches are included in the optimization process and the optimization process is complete, the one or more first parameters for the radiation treatment of the patient are output to a treatment planning system.

4. A method according to any one of the preceding claims, wherein The one or more first parameters include or relate to one or more of the following: beam shape, beam weight, beamlet weight, spot weight, spot position, beam angle, dose histogram volume information, number of radiation beams, number of beamlets, number of spots, dose per beam, gantry angle, collimator shape, number of monitoring units, and fluence map.

5. A method according to any one of the preceding claims, wherein The scenario includes changes in one or more second parameters of the treatment plan.

6. The method according to claim 5, wherein: The one or more second parameters relate to one or more of: patient position, beam geometry, and a patient model representation.

7. A method according to any one of the preceding claims, wherein The optimization process is performed by processing circuitry included in a first processing unit, and the further dose calculation is performed by processing circuitry included in a second processing unit different from the first processing unit.

8. A data processing device (500), configured to: A first dose calculation is performed for one or more scenes included in a first batch of the plurality of batches, wherein Each batch of the plurality of batches includes one or more scenarios associated with radiation treatment of a patient; initiating an optimization process for the one or more scenarios included in the first batch based on an output of the first dose calculation in response to completion of the first dose calculation, wherein the optimization process seeks to optimize one or more first parameters of a treatment plan for the radiation treatment of the patient; performing, in parallel with the optimization process, additional dose calculations for one or more scenes included in additional batches of the plurality of batches; and In response to completion of the further dose calculation, the optimization process is updated to include the one or more scenarios included in the further batch, such that the optimization process is further based on the output of the further dose calculation.

9. The data processing device (500) according to claim 8, further configured to perform the method according to any one of claims 2 to 7.

10. A data processing device (500), comprising: a memory (501) storing computer-executable instructions; as well as The processing circuit (502) is configured to execute the instructions to: performing a first dose calculation for one or more scenarios included in a first batch of a plurality of batches, wherein each batch of the plurality of batches includes one or more scenarios related to radiation treatment of a patient; initiating an optimization process for the one or more scenarios included in the first batch based on an output of the first dose calculation in response to completion of the first dose calculation, wherein the optimization process seeks to optimize one or more first parameters of a treatment plan for the radiation treatment of the patient; performing, in parallel with the optimization process, additional dose calculations for one or more scenes included in additional batches of the plurality of batches; and In response to completion of the further dose calculation, the optimization process is updated to include the one or more scenarios included in the further batch, such that the optimization process is further based on the output of the further dose calculation.

11. The data processing apparatus according to claim 10, wherein: The processing circuit is further configured to execute the instructions to perform the method according to any one of claims 2 to 7.

12. A computer program comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 7.

13. A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 7.

14. A computer program product comprising the non-transitory computer-readable storage medium according to claim 13.

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