Multi-standard processing plan optimization using LET cost function

Abstract

Systems and techniques may be applicable to radiotherapy treatment plans. The technique may include determining a set of optimization functions having an initial optimization goal, the set of optimization functions including at least one optimization function according to LET and at least one optimization function for selecting a dose. The techniques may include, for example, using processing circuitry, utilizing patient information, generating a processing plan via automatic multi-standard optimization of a set of optimization functions while preserving an initial optimization goal. In some examples, a processing plan may be output (e.g., stored or displayed).

Description

Background Art

[0001] Radiation therapy, or "radiotherapy", can be used to treat cancer or other diseases in mammalian (e.g., human and animal) tissues. One such radiotherapy technique is called "gamma knife", by which a patient is irradiated using a number of low-intensity gamma rays that converge at a targeted region (e.g., a tumor) with higher intensity and high precision. In another example, a linear accelerator ("linac") is used to provide radiotherapy, whereby the targeted region is irradiated with high-energy particles (e.g., electrons, high-energy photons, etc.). In another example, a heavy charged particle accelerator (e.g., protons, carbon ions, etc.) is used to provide radiotherapy. The position and dose of the radiation beam are precisely controlled so as to deliver a prescribed dose of radiation to the targeted region. The radiation beam is also typically controlled to reduce or minimize damage to surrounding healthy tissue, such as the surrounding healthy tissue that can be referred to as the "(one or more) organs at risk" (OAR). The radiation can be referred to as "prescribed" because, generally speaking, a doctor orders that a predetermined dose of radiation be delivered to the targeted region (e.g., a tumor). Brief Description of the Drawings

[0002] In the drawings, which are not necessarily to scale, like numerals may describe similar components in different views. Like numerals with different letter suffixes may represent different instances of similar components. The drawings illustrate, by way of example (but not by way of limitation), various examples discussed in this document.

[0003] Figure 1 An optimized block diagram according to an example is illustrated.

[0004] Figure 2 A multi-criteria optimization block diagram according to an example is illustrated.

[0005] Figure 3 An example user interface for optimization according to an example is illustrated.

[0006] Figure 4 A flowchart showing a technique for radiotherapy treatment planning according to an example is illustrated.

[0007] Figure 5 An example of a system according to an example is generally illustrated, which may include, for example, a particle therapy system controller.

[0008] Figure 6 An example of a radiotherapy system according to an example is generally illustrated, which may include, for example, a particle handling system and an imaging acquisition device.

[0009] Figure 7A particle handling system according to an example is generally illustrated, which may include a radiotherapy output configured to provide a proton therapy beam.

[0010] Figure 8 The radiation dose depth in human tissue for various types of particles according to an example is generally illustrated.

[0011] Figure 9 An extended Bragg peak according to an example is generally illustrated.

[0012] Figure 10 Pencil beam scanning of an irregularly shaped volume from a distal edge to a proximal edge according to an example is generally illustrated.

[0013] Figure 11 A schematic diagram of an active scanning proton beam delivery system according to an example is generally illustrated. Detailed Description

[0014] As discussed above, radiotherapy or "RT" is used to treat cancer or other diseases in mammalian (e.g., human and animal) tissues. Generally, ionizing radiation in the form of a collimated beam is directed from an external radiation source towards a patient. The modulation of the radiation beam can be provided by one or more attenuators or collimators (e.g., multi-leaf collimators). The intensity and shape of the radiation beam can be adjusted by collimation so that the projected beam conforms to the contour of the target tissue, thereby avoiding damage to healthy tissue (e.g., OAR) near the target tissue.

[0015] In one approach, radiotherapy can be provided by using particles (e.g., protons instead of electrons). This is generally referred to as proton therapy. A significant known advantage of proton therapy is that it provides a superior dose distribution with very little exit dose compared to other forms of radiotherapy (e.g., X-ray therapy). Due to the very little exit dose, the dose received by organs at risk (OAR) is significantly reduced. Further advantages include a lower dose per treatment, thereby reducing the risk of side effects and enabling an improvement in the quality of life during and after proton therapy treatment.

[0016] Compared with lower LET (linear energy transfer) values, higher LET values may result in increased cell damage in proton radiotherapy when the dose distributions are similar. Incorporating LET into proton treatment plan optimization can increase the killing of cancer cells when increasing the LET value in the target structure, while reducing the damage to healthy tissue structures when decreasing the LET value. However, there is no clinical protocol specifically specifying LET prescriptions yet. Currently, treatment plan goals mainly rely on dose distribution characteristics.

[0017] In the case of adding LET to the optimization, the treatment planning problem includes an additional set of parameters. This increases the time that the treatment planner needs to spend on adjusting the treatment plan to achieve the desired LET behavior while maintaining the existing dose objectives.

[0018] The treatment planning disclosed herein includes automatic multi-criteria optimization for dose objectives and constraints. The systems and techniques described herein address the planning efficiency problem by incorporating LET into the optimization process and may include automatic multi-criteria optimization options. The improvement in workflow efficiency may include selecting LET objectives or constraints with loose initial prescription values and automatically improving the LET values through multi-criteria optimization without compromising the selected dose or LET objectives or constraints. In some examples, instead of or in addition to directly using LET, one or more LET surrogates (e.g., quantities based on D x LET) may be used.

[0019] In some examples of existing dose cost functions (e.g., cost functions based on dose volume histogram (DVH) points, equivalent uniform dose (EUD) cost functions, etc.), the specified cost functions applying LET or LET surrogates can be reused to achieve objectives or constraints. In other examples, cost functions newly designed for LET purposes (e.g., inverse serial cost function, etc.) may be used. Due to the increased sensitivity of the dose and LET distributions in proton therapy to uncertainties (e.g., geometric shape, material, and other uncertainties may lead to incorrect dose and LET distributions), in some examples, robust optimization of the LET or LET surrogate cost function can be performed simultaneously on the basis of existing dose robust optimization. The dose, LET, or LET surrogate cost function can be selected as multi-criteria or not separately. To further improve treatment planning efficiency, any of the above selections or settings can be stored in a template that can be applied to other patients.

[0020] In treatment planning, in some examples, dose-based optimization (e.g., using a cost function of dose) is used to create a plan. The dose distribution is used as the main metric optimized by the radiation oncologist during treatment planning. LET can be used as a further metric for optimization by the radiation oncologist. Since LET as a metric has not been well established, the optimization process for LET may be limited to changes that do not affect dose optimization. The present systems and techniques are capable of optimizing LET without negatively affecting the optimized dose. This can be achieved by using multi-criteria optimization (MCO).

[0021] The systems and techniques described herein can be used to automatically improve treatment plans by generating treatment plans that include a set of optimization functions with initial optimization objectives. In some examples, at least one optimization function can be based on linear energy transfer (LET), or at least one optimization function can be used to automatically improve the optimization objective while preserving one or more initial optimization objectives.

[0022] Figure 1 An optimization block diagram 100 according to an example is illustrated. The optimization block diagram 100 includes an optimization library that receives real-world model inputs and outputs results. The optimization library includes a mathematical model and one or more cost functions to define the problem. An optimizer is used to solve the problem and outputs a solution as the result.

[0023] When using optimization to solve real-world problems, one challenge is formulating the optimization problem, and another challenge is how to interpret the solution. The real-world problem to be solved by this optimization library can be related to intensity-modulated particle therapy (IMPT), the purpose of which is to better control tumors with fewer side effects. In other examples, the optimization can be related to proton arc therapy.

[0024] Given an optimized real-world model, the optimization library of the optimization block diagram 100 uses problem components to convert it into a mathematical model. The treatment objective can be described by one or more objective functions and constraints, such as based on the dose deposited in the patient's body. The objective and constraints are referred to as cost functions. The cost functions and the mathematical model can be referred to as the problem to be solved.

[0025] As the problem is generated, the optimization library can now call an optimizer to obtain the best result. The optimization library can include multiple optimizers, such as a constraint-based conjugated gradient (CG) optimizer. Constraint-based means that non-target objectives are satisfied before the constraints are satisfied. After optimization, a result with a non-negative sub-beam weight map can be output.

[0026] Due to the higher energy concentration deposited, particles with a higher linear energy transfer (LET) are more likely to have a biological damaging effect on a given volume of tissue. LET optimization allows the user to control the LET distribution to avoid high-LET regions in organs at risk, or to maximize the LET of the target while maintaining a clinically acceptable dose distribution. The LET optimization function can be used with Proton Arc or Proton PBS delivery modes. In some examples, the product of dose and LET (LET x D) can be used in the optimization instead of using direct LET optimization.

[0027] In an example, a biodosimetric quantity b can be used in a cost function according to Equation 1 below:

[0028] b = ∑c × Li × Di

[0029] Equation 1

[0030] In Equation 1, Di and Li represent the dose and LET in a single voxel. The quantity b is calculated for each cost function and summed over all voxels relevant to the cost function being calculated. The value of the constant c can be specified by the user. In some examples, c can be specific to the cost function. The quantity b can be interpreted as a biological extra dose attributed to high LET. In some examples, c is not determined and depends on the tissue type and prescription dose level. In the case of an optimization workflow, an exact knowledge of the value of c is not required for its use, and this parameter is mainly understood as a way to change the relative weight (by which the contribution of a voxel at a given dose and LET level to the overall cost function is reflected).

[0031] In some examples, a LET optimization cost function can use a dose optimization cost function. For example, any cost function available for dose optimization can be used, optionally, in addition to the target EUD or target penalty and conformality. Dose optimization can be used to optimize the distribution of LET or LET x D. The LET cost function can be calculated in a similar way to the dose cost function, and in the case of LET or LET x D, the input can be a 3D voxelized LET distribution instead of a dose distribution. To suppress noise in the LET distribution or reduce the workload of the optimizer, a treatment planning system (TPS) and dose threshold settings can be used to calculate the LET cost function. In the optimization, voxels with a deposited dose below the threshold can be ignored.

[0032] In an example, a target sequence cost function (e.g., a cost function that is not a dose optimization cost function) can be used. The target sequence cost function can mathematically be the inverse function of a sequence cost function. This function can be defined according to Equation 2 below:

[0033]

[0034] In Equation 2, x represents the LET or LET x D in a specific voxel, X0 represents the prescribed LET or LET x D, and N represents the power-law exponent. This cost function can be used to increase the LET in the target area.

[0035] Dose and LET optimization can be either robust or non-robust. Robust optimization can include multiple dose distributions, while non-robust optimization can include a single dose distribution. In non-robust optimization, each beam in a given plan can be considered as a single, explicitly specified point within a single patient model. Thus, each beam is associated with a single dose distribution, and there is a single total planned dose distribution equal to the sum of the individual beam dose distributions. In a non-robust approach, the overall value of the cost function for a given plan can be determined entirely by the single total dose distribution.

[0036] Robust optimization can improve this functionality by considering one or more of various sources of dose variation (such as patient setup errors, density or material definition uncertainties, patient geometry changes, calculation parameters, etc.). The optimization framework can eliminate the assumption that each beam within a given plan exists only at a single point in space and within a single patient model. In a robust approach, each beam in a given plan can be associated with multiple dose distributions (optionally associated with LET or LET x D distributions if LET is required). To account for multiple possible total planned dose distributions or LET distributions, the cost function for the robust approach can be redefined.

[0037] Figure 2 A multi-criteria optimization block diagram 200 according to an example is illustrated. The multi-criteria optimization block diagram 200 includes steps for implementing multi-criteria optimization. After conventional optimization converges, multi-criteria options can be considered. When the multi-criteria options are not used, the optimization can terminate. When used, a factor can be constructed to increase the Lagrange multiplier for the multi-criteria on the cost function, such as reducing an isoconstraint. After constructing the factor to increase the Lagrange multiplier, one or more additional optimization iterations may occur, limited by determining whether the objective function has been corrupted. When corrupted, the multi-criteria optimization can terminate, ending the optimization. When not corrupted, the objective function can return to a previous step to construct a new factor to increase the Lagrange multiplier.

[0038] In the constrained mode, the optional parameters for the OAR or target constraint cost function are multi-criteria. The treatment planning system (TPS) can view this cost function as a secondary objective attached to the regular constraint role. For example, after regular constraint optimization, when the primary objectives are well satisfied, the TPS can use a multi-criteria method to further minimize these multi-criteria cost functions while maintaining each primary criterion being satisfied. This process can continue until no further improvement can be made or until a specified time has elapsed. When all objectives can be achieved, multi-criteria optimization can be used. When no higher-priority criteria can be satisfied, multi-criteria optimization can be avoided. When multiple multi-criteria cost functions are selected, the TPS can build factors for each cost function based on their respective conditions while increasing their weights in an additional optimization process.

[0039] In the constrained optimization mode, the optimizer attempts to increase the dose in the target structure to the required coverage while keeping the dose in the OAR below a defined level. When the constraints are strictly satisfied, the objective can obtain an optimal result. In some examples, when loose constraints are set, the system can minimize the dose to the OAR as much as possible while maintaining the prescribed dose to the target. One or more cost functions can be used to increase or decrease LET or LET x D. When optimizing, for the target cost function, generally the aim is to increase the isoconstraint, while for the OAR cost function, the general aim is to decrease the isoconstraint.

[0040] In some examples, the available cost functions can include quadratic overdosage, quadratic underdosage, sequential, parallel, conformality, overdosage DVH, underdosage DVH, maximum dose, target sequence, etc. For proton PBS planning or proton arc planning, optimization of the distribution of one or more of dose, LET, or LET x D can be used. The objectives of LET and LET x D optimization are similar, both aiming to avoid high values of LET in critical structures within or near the target volume while limiting the degradation of the optimal physical or biological dose distribution. When LET is selected, the system can optimize the LET of the dose average. To reduce the computational effort in optimization and remove regions of low importance, a dose threshold can be set so that LET is only considered in regions where the dose is higher than the dose threshold.

[0041] Figure 3 An example user interface for optimization according to an example is illustrated. The example user interface can include a first state 300 when LET is selected for optimization or a second state 302 when LET x D is selected for optimization. The first state 300 includes an indication for typing in the dose threshold for LET calculation. The second state 302 includes an indication for typing in a constant c (e.g., refer to Figure 1Equation 1) as described above. Both state 300 and state 302 include indications for typing equivalent uniform LET, typing power-law exponent, selecting multi-criteria optimization, or closing the example user interface.

[0042] The target sequence cost function can be used for LET and LET x D optimization. This cost function may not be used for optimizing the dose distribution. The target series cost function can be used to increase the LET or LET x D in the structural volume to a specific equivalent uniform LET or equivalent uniform LET x D value (according to the first state 300 or the second state 302 respectively).

[0043] When the type of the target sequence cost function is LET, as in the first state 300, the following parameters are available:

[0044]

[0045] When the type of the target sequence cost function is LET x D, as in the second state 302, the following parameters are available:

[0046]

[0047] In LET or LET x D (e.g., for the first state 300 or the second state 302), the following parameters are available:

[0048]

[0049]

[0050] Figure 4 A flowchart of a technique 400 for radiotherapy treatment planning according to an example is illustrated. Technique 400 can be implemented using a processing circuit. Technique 400 includes an operation 402 for receiving patient information corresponding to a patient. Technique 400 includes an operation 404 for receiving a selection to optimize the linear energy transfer (LET) during the treatment planning.

[0051] Technique 400 includes an operation 406 for determining (e.g., in response to receiving the selection in operation 404) a set of optimization functions having an initial optimization goal, the set of optimization functions including at least one optimization function based on LET and at least one optimization function for selecting a dose. At least one optimization function based on LET may include using an LET surrogate. At least one optimization function based on LET may include a modified version of an existing dose cost function. In some examples, the initial optimization goal may include a goal of increasing the LET in a target or a target sub-region of the patient or a goal of reducing the LET in an organ at risk or a sub-region of an organ at risk.

[0052] Technique 400 includes operation 408 for generating a treatment plan while preserving an initial optimization goal via automated multi-criteria optimization of an optimization function set using patient information. In some examples, the treatment plan may include using proton arc therapy. In other examples, the treatment plan may include using intensity modulated protontherapy (IMPT). Operation 408 may include generating a robust optimization treatment plan, e.g., at least one beam of the robust optimization treatment plan is associated with a plurality of dose distributions. Technique 400 includes operation 410 for outputting the treatment plan.

[0053] Figure 5 An example of a system 500 according to an example is generally illustrated, which system 500 may include, for example, a particle therapy system controller. System 500 may include a database or a hospital database. The particle therapy system controller may include a processor, a communication interface, or a memory. The memory may include treatment planning software, an operating system, or a delivery controller. The delivery controller may include a sub-beam module for determining or planning spot delivery (e.g., using a spot delivery module) or segment delivery (e.g., using a segment delivery module).

[0054] In an example, the spot delivery module or the sub-beam module may be configured to plan the size of the sub-beam, the position of the target or the spot, etc. The sub-beam module may be used to determine the order of delivering the sub-beams, e.g., in a spiral pattern as described herein. The order of the delivery module may communicate with the treatment planning software to plan the delivery of the sub-beams. For example, the treatment planning software may be used to determine or plan the gantry angle, the gantry speed, the sub-beam size, the spiral pattern (e.g., clockwise or counterclockwise), the angular range of a particular spiral pattern (e.g., every ten degrees of gantry rotation), etc.

[0055] The processor may implement the plan via the communication interface or other means, e.g., by communicating with components for implementing the plan (e.g., control devices or components such as those described below with reference to Figure 7 In an example, the communication interface may be used to retrieve stored information from the database or the hospital database (e.g., patient information, past surgical information of the patient or other patients, surgical instructions, information about a particular device or component, etc.).

[0056] Figure 6 An example of a radiotherapy system 600 according to an example is generally illustrated, which radiotherapy system 600 may include, for example, a particle processing system and an imaging acquisition device. The particle processing system includes an ion source, an accelerator, and a scanning magnet, each of which will be described below with respect to Figure 7A more detailed description will be given. The particle handling system includes a gantry and a table, where the gantry can be mounted on the table, fixed to the table, or stable relative to the table. The table can carry a patient. The gantry can be a rotating gantry and can rotate relative to the table (e.g., rotate around the table) or relative to the patient (and the table or a part of the table can rotate with the gantry).

[0057] The particle handling system can communicate with a treatment control system, which can be used to control the operation of the particle handling system. The treatment control system can communicate with an imaging acquisition device (e.g., for receiving images taken by the imaging acquisition device or an imaging database) or a tumor information system. The tumor information system can provide, for example, treatment plan details received from a treatment planning system to the treatment control system. The treatment control system can use the treatment plan to control the particle handling system (e.g., activate the gantry, ion source, accelerator, scanning magnet, particle beam, etc.). The treatment control system can include, for example, sub-beam intensity control, sub-beam energy control, scanning magnet control, table control, gantry control, etc. In an example, the sub-beam intensity control and sub-beam energy control can be used to activate a sub-beam of a specific size or target a specific location. The scanning magnet control can be used to deliver the sub-beam according to the treatment plan, for example, in a helical pattern. The gantry can be rotated using the gantry control or the table control.

[0058] The treatment planning software can include components (e.g., sub-beam delivery and sorting module) with separate controls such as sub-beam sorting for spots or line segments, as described in more detail above for Figure 5 The treatment planning software can access the imaging database to retrieve images or store information. When the treatment plan is completed, the treatment planning software can send the plan to the tumor information system for communication with the treatment control system.

[0059] Figure 7 An example of a particle handling system 700 is illustrated, which can include a radiotherapy output configured to provide a proton therapy beam. The particle handling system 700 includes an ion source 701, an injector 703, an accelerator 705, an energy selector 707, a plurality of bending magnets 709, a plurality of scanning magnets 711, and a snout 713.

[0060] An ion source 701 (e.g., a synchrotron (not shown)) can be configured to provide a particle beam (e.g., protons). The particle beam is transported to an injector 703, which uses Coulomb force to provide an initial acceleration to the charged particles. The particles are further accelerated through an accelerator 705 to about 10% of the speed of light. The acceleration provides energy to the particles, and the energy determines the depth at which the particles travel within the tissue. An energy selector 707 (e.g., range scattering) can be used to select the energy of the protons to be delivered to the patient. In an example called passive scattering, an optional range modulator 708 (e.g., also called a ridge filter or range modulation wheel) can be used to broaden the beam to fit the tumor. After the energy is selected, a set of bending magnets 709 can be used to transport the proton beam to the radiotherapy treatment room of the hospital. Further, a scanning magnet 711 (e.g., an x-y magnet) is used to spread the proton beam to an exact image of the tumor shape or to track an exact image of the tumor shape. A nozzle 713 is used to further shape the proton beam. In some examples, the particle beam can consist of carbon ions, mesons, or positively charged ions.

[0061] Figure 8 An illustration of the comparison of the depth of radiation dose of various types of particles in human tissue is provided. As shown, the relative depth of penetration of photons (e.g., X-rays) into human tissue compared to protons and carbon ions is provided (e.g., including any radiation dose provided at a distance below the surface, including secondary radiation or scattering). Each radiation dose is shown relative to the peak dose of a proton beam having a single energy that has been set to 100%.

[0062] A monoenergetic (e.g., single energy) proton beam indicates a plateau region that starts at about 25%, which gradually increases until a depth of nearly 10 cm in the tissue, rapidly increases to a Bragg peak at 15 cm, and then favorably drops to zero within a short distance. No additional dose is delivered at the end of the Bragg peak.

[0063] A photon beam (e.g., labeled as X-ray) indicates an initial buildup due to electron scattering (e.g., the main way X-rays deliver dose to tissue is by transferring energy to electrons in the tissue). Subsequently, it decreases exponentially and continues past the distal edge of the target, which is at approximately 15 cm depth in the figure. The entrance (skin) dose of the X-ray beam is set to match the entrance (skin) dose of the proton beam. Normalization (e.g., scaling) is performed at 15 cm depth. Since the dose of the X-ray is 40% of the dose provided by the proton beam, and the X-ray beam has a peak dose greater than 95% (close to 100%) at approximately 3 cm depth. If the X-ray data is renormalized to achieve 100% dose at 15 cm, the peak dose at approximately 3 cm depth in the location where no dose is needed (e.g., before the target) will be close to 240%. Therefore, when using X-rays, a significant amount of dose is delivered before the target, and a considerable amount of dose is also delivered past the target.

[0064] The monoenergetic carbon beam shows a plateau region lower than the proton beam at the entrance dose. The carbon beam has a sharper Bragg peak than the proton beam and drops more steeply, but the carbon beam has a tail (e.g., called a "spallation tail" where some carbon nuclei break into helium ions), which has an additional dose of close to 10% or less, several centimeters past the desired target. Compared with the proton beam, the carbon ion beam has an undesired entrance and skin dose, but the carbon ion beam has a significant dose delivered past the target.

[0065] Figure 9 A diagram of a spread-out Bragg peak (SOBP) is provided. The SOBP shows the relative depth dose curve of a combination of proton beam sets with various initial energies, and each of the proton beam sets in the combination has some spread in energy (e.g., the absorption rate of energy in tissue is variable). A uniform dose is applied to a target of a specific thickness, which is the desired result. As shown in the figure, the target shown has a proximal depth of approximately 10 cm, a distal depth of approximately 13 cm, and a target thickness of approximately 3 cm. Inside the target, the dose is quite uniform (averaged and normalized to 100%). The figure does not start from 0 cm depth and does not explicitly show the entrance (skin) dose, but the nature of the proton beam entrance region is a relatively flat depth dose curve. Generally, the entrance (skin) dose will be close to 70% of the target dose (e.g., shown at a location on the x-axis away from the right edge). The SOBP can be obtained using various methods, including using scattered proton beams and modulating the energy (variable absorption) using various devices (e.g., a static ridge filter or a dynamic range modulation wheel), or by selecting several monoenergetic proton beams that do not undergo scattering.

[0066] Figure 10Illustrations are provided of pencil beam scanning of an irregular shaped volume from a distal edge (e.g., bottom) to a proximal edge (e.g., top). As shown, the irregular shaped tumor volume is irradiated by proton layers. For example, a first time snapshot 1002 shows the first layer of protons being delivered, while a later time snapshot 1004 shows that most of the layers have been delivered. Each layer has its own cross-sectional area to which protons of the same energy are delivered. The total radiation dose is provided in the form of a layer-by-layer sub-beam set. Each layer can have a different energy. The most common way to specify and deliver a sub-beam set to a cross-sectional area is to define a sub-beam with a constant diameter ("spot size") and deliver it to selected grid points on each layer. While the main dose from the sub-beam is delivered to the target layer, a significant amount of dose is delivered along the path to the target layer. The dose from the sub-beam defined for the distal layer to the proximal layer is taken into account in the specification of the sub-beam defined for the proximal layer. The ability to individually specify the number of particles (e.g., meter set) of a given sub-beam ensures that each part of the irradiated volume receives the required dose.

[0067] Figure 11 Illustrations are provided of a graphical representation of a typical active scanning proton beam delivery system. As shown, a pencil beam scan of a single layer is being delivered, and a spot grid and the outline of the cross-sectional area to which the particles are to be delivered are depicted on the patient. The incident monoenergetic proton sub-beam has a specified amount of energy absorbed by a range shifter (e.g., a range shifting plate in Figure 11 ), resulting in a sub-beam with the required energy to bring the Bragg peak of the patient to a certain depth to treat the specified layer. A magnetic scanner, which has the ability to deflect particles in the vertical and horizontal directions. The magnetic field strength can be adjusted to control the deflection in a direction perpendicular to the magnetic field and the incident sub-beam. The rate of adjustment of the magnetic field strength determines the rate at which the scan occurs. For example, the intensity of the proton sub-beam combined with the scan rate determines how much dose can be delivered to a specific area (e.g., Figure 11 a "spot") within a specific amount of time (e.g., particles / unit area). In theory, the magnetic field strengths can be adjusted independently of each other (in a manner similar to the children's toy "Etch a " provided by Spin MasterTM of Toronto, Canada; the pencil beam intensity is a variable not present in the children's toy). The most common scan pattern is to scan quickly in one direction and slower in a raster pattern in the perpendicular direction, similar to the control of early televisions (e.g., a cathode ray tube (CRT), which uses electrons instead of protons), but any pattern can be scanned (similar to the toy mentioned earlier). The delivery of different spots is achieved by increasing the scan magnetic field strength and limiting the pencil beam intensity between increments.

[0068] The foregoing detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings illustrate, by way of example, specific embodiments in which the invention may be practiced. These embodiments may include elements other than those shown or described. However, the inventors also contemplate embodiments that include only the elements shown or described. In addition, the inventors also contemplate embodiments using any combination or arrangement of the elements shown or described (or one or more aspects thereof), whether with respect to a particular embodiment (or one or more aspects thereof) or to other embodiments shown or described herein (or one or more aspects thereof).

[0069] If there is any inconsistency in the usage between this document and any document incorporated by reference, the usage of this document shall prevail.

[0070] In this document, the use of the term "a" or "an", as is common in patent documents, includes one or more than one, independent of any other instance or the usage of "at least one" or "one or more". In this document, unless otherwise indicated, the term "or" is used to mean a non-exclusive or, e.g., "A or B" includes "A but not B", "B but not A", and "A and B". In this document, the terms "including" and "in which" are used as plain English equivalents of the terms "comprising" and "wherein", respectively. Further, in the appended claims, the terms "including" and "comprising" are open-ended, i.e., a system, apparatus, article, composition, formulation, or method that includes elements other than those listed after such terms in the claims is still considered to fall within the scope of that claim. Further, in the appended claims, the terms "first", "second", "third", etc. are used merely as labels and are not intended to impose numerical requirements on their objects.

[0071] The method examples described herein may be implemented at least in part by a machine or computer. Some examples may include a computer-readable medium or machine-readable medium encoded with operable instructions to configure an electronic device to perform the method described in the above examples. The implementation of such methods may include code (e.g., microcode, assembly language code, high-level language code, etc.). Such code may include computer-readable instructions for performing various methods. The code may form part of a computer program product. In addition, in an example, the code may be tangibly stored on one or more volatile, non-temporary or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media may include, but are not limited to, hard disks, removable disks, removable optical disks (e.g., compact disks and digital video disks), tapes, memory cards or memory sticks, random access memory (RAM), read-only memory (ROM), etc.

[0072] The above description is intended to illustrate rather than limit. For example, the above examples (or one or more aspects thereof) can be used in combination with each other. For example, a person of ordinary skill in the art can use other examples after reading the above description. An abstract is provided in accordance with 37CFR§1.72(b) to allow readers to quickly determine the nature of the technical disclosure. It should be understood at the time of submission that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the above detailed description, various features can be combined together to simplify the disclosure. This should not be interpreted as an intention that the disclosed features that are not claimed for protection are necessary for any claim. On the contrary, the subject matter of the invention may be less than all the features of a specific disclosed example. Therefore, the attached claims are hereby incorporated into the detailed description as examples, wherein each claim itself is a separate example, and it is expected that these examples can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the attached claims and the full scope of the equivalent schemes assigned to these claims.

[0073] Example 1 is a method for radiotherapy treatment planning, the method comprising: receiving patient information corresponding to a patient; determining an optimization function set having an initial optimization objective, the optimization function set comprising at least one optimization function based on linear energy transfer density (LET) and at least one optimization function for selecting a dose; using a processing circuit, utilizing patient information, to generate a treatment plan while retaining the initial optimization objective through automatic multi-criteria optimization of the optimization function set.

[0074] In Example 2, the subject matter of Example 1 includes, wherein the treatment plan includes using proton arc therapy.

[0075] In Example 3, the subject matter of Examples 1-2 includes, wherein the treatment plan includes using intensity modulated proton therapy (IMPT).

[0076] In Example 4, the subject matter of Examples 1 to 3 includes, wherein generating a treatment plan includes generating a robust optimization treatment plan, and wherein at least one beam of the robust optimization treatment plan is associated with multiple dose distributions, multiple LET distributions, or multiple LET surrogate distributions.

[0077] In Example 5, the subject matter of Examples 1 to 4 includes, wherein at least one optimization function based on LET includes using a LET surrogate.

[0078] In Example 6, the subject matter of Examples 1 to 5 includes, wherein at least one optimization function based on LET is a modified version of an existing dose cost function.

[0079] In Example 7, the subject matter of Examples 1 to 6 includes, wherein an initial optimization goal includes a goal of increasing the LET in a patient target or a target sub-region.

[0080] In Example 8, the subject matter of Examples 1 to 7 includes, wherein an initial optimization goal includes a goal of reducing the LET in an organ at risk or a sub-region of an organ at risk.

[0081] Example 9 is at least one machine-readable medium including instructions for radiotherapy treatment planning, which when executed by a processing circuit cause the processing circuit to perform the following operations: receive patient information corresponding to a patient; determine a set of optimization functions having an initial optimization goal, the set of optimization functions including at least one optimization function based on linear energy transfer (LET) and at least one optimization function for selecting a dose; generate a treatment plan using the patient information via automatic multi-criteria optimization of the set of optimization functions while retaining the initial optimization goal; and output the treatment plan.

[0082] In Example 10, the subject matter of Example 9 includes, wherein the treatment plan includes using proton arc therapy.

[0083] In Example 11, the subject matter of Examples 9 - 10 includes, wherein the treatment plan includes using intensity-modulated proton therapy (IMPT).

[0084] In Example 12, the subject matter of Examples 9 to 11 includes, wherein, to generate the treatment plan, the operations cause the processing circuit to generate a robust optimization treatment plan, and wherein at least one beam of the robust optimization treatment plan is associated with multiple dose distributions, multiple LET distributions, or multiple LET surrogate distributions.

[0085] In Example 13, the subject matter of Examples 9 to 12 includes, wherein at least one optimization function based on LET includes using a LET surrogate.

[0086] In Example 14, the subject matter of Examples 9 to 13 includes, wherein at least one optimization function based on LET is a modified version of an existing dose cost function.

[0087] In Example 15, the subject matter of Examples 9 to 14 includes, wherein the initial optimization objective includes an objective of increasing the LET of a patient target or a sub-region of the target.

[0088] In Example 16, the subject matter of Examples 9 to 15 includes, wherein the initial optimization objective includes an objective of reducing the LET of an organ at risk or a sub-region of the organ at risk.

[0089] Example 17 is a system for radiotherapy treatment planning, the system comprising: a processing circuit; a memory including instructions which, when executed by the processing circuit, cause the processing circuit to: receive patient information corresponding to a patient; determine a set of optimization functions having an initial optimization objective, the set of optimization functions including at least one optimization function based on linear energy transfer (LET) and at least one optimization function for selecting a dose; generate a treatment plan while preserving the initial optimization objective via automatic multi-criteria optimization of the set of optimization functions using the patient information; and output the treatment plan.

[0090] In Example 18, the subject matter of Example 17 includes, wherein the treatment plan includes using proton arc therapy.

[0091] In Example 19, the subject matter of Examples 17 to 18 includes, wherein the treatment plan includes using intensity-modulated proton therapy (IMPT).

[0092] In Example 20, the subject matter of Examples 17 to 19 includes, wherein the processing circuit is further caused to receive a selection of optimizing the LET during the treatment plan, and wherein, in response to receiving the selection, determine the set of optimization functions.

[0093] Example 21 is at least one machine-readable medium including instructions which, when executed by a processing circuit, cause the processing circuit to operate to implement any one of Examples 1 to 20.

[0094] Example 22 is a device including means for implementing any one of Examples 1 to 20.

[0095] Example 23 is a system implementing any one of Examples 1 to 20.

[0096] Example 24 is a method implementing any one of Examples 1 - 20.

Claims

1. A method for radiotherapy treatment planning, the method comprising: Receiving patient information corresponding to a patient; Determining an optimization function set having an initial optimization goal, the optimization function set including at least one optimization function based on linear energy transfer (LET) and at least one optimization function for selecting a dose; Using a processing circuit, and generating a treatment plan while preserving the initial optimization goal via automatic multi-criteria optimization of the optimization function set using the patient information; And Outputting the treatment plan.

2. The method according to claim 1, wherein, The treatment plan includes using proton arc therapy.

3. The method according to claim 1, wherein, The treatment plan includes using intensity modulated proton therapy (IMPT).

4. The method according to claim 1, wherein Generating the treatment plan includes generating a robust optimization treatment plan, and wherein at least one beam of the robust optimization treatment plan is associated with multiple dose distributions, multiple LET distributions, or multiple LET surrogate distributions.

5. The method according to claim 1, wherein At least one optimization function based on LET includes using an LET surrogate.

6. The method according to claim 1, wherein, At least one optimization function based on LET is a modified version of an existing dose cost function.

7. The method according to any one of claims 1 to 6, wherein, The initial optimization goal includes a goal of increasing LET in a target or a sub-region of a target of a patient.

8. The method according to any one of claims 1 to 6, wherein The initial optimization goal includes a goal of reducing LET in an organ at risk or a sub-region of an organ at risk.

9. At least one machine-readable medium, including instructions for radiotherapy treatment planning, which when executed by a processing circuit, cause the processing circuit to perform operations to: Receive patient information corresponding to a patient; Determine an optimization function set having an initial optimization goal, the optimization function set including at least one optimization function based on linear energy transfer (LET) and at least one optimization function for selecting a dose; Generate a treatment plan while preserving the initial optimization goal via automatic multi-criteria optimization of the optimization function set using the patient information; and Output the treatment plan.

10. The at least one machine-readable medium according to claim 9, wherein, The treatment plan includes using proton arc therapy.

11. The at least one machine-readable medium according to claim 9, wherein, The treatment plan includes using intensity modulated proton therapy (IMPT).

12. The at least one machine-readable medium according to claim 9, wherein, To generate the treatment plan, the operations cause the processing circuit to generate a robust optimization treatment plan, and wherein at least one beam of the robust optimization treatment plan is associated with multiple dose distributions, multiple LET distributions, or multiple LET surrogate distributions.

13. The at least one machine-readable medium according to claim 9, wherein, At least one optimization function based on LET includes using an LET surrogate.

14. The at least one machine-readable medium according to claim 9, wherein, At least one optimization function based on LET is a modified version of an existing dose cost function.

15. The at least one machine-readable medium according to any one of claims 9 to 14, wherein, The initial optimization goal includes a goal of increasing LET in a target or a sub-region of a target of a patient.

16. The at least one machine-readable medium according to any one of claims 9 to 14, wherein The initial optimization goal includes a goal of reducing LET in an organ at risk or a sub-region of an organ at risk.

17. A system for radiotherapy treatment planning, the system comprising: A processing circuit; A memory, the memory including instructions which when executed by the processing circuit cause the processing circuit to: Receive patient information corresponding to a patient; Determine an optimization function set having an initial optimization goal, the optimization function set including at least one optimization function based on linear energy transfer (LET) and at least one optimization function for selecting a dose; Using the patient information, automatic multi-criteria optimization via the set of optimization functions generates a treatment plan while preserving the initial optimization goal; and outputting the treatment plan.

18. The system according to claim 17, wherein The treatment plan includes using proton arc therapy.

19. The system according to claim 17, wherein, The treatment plan includes using intensity-modulated proton therapy (IMPT).

20. The system according to any one of claims 17 to 19, wherein Also causes the processing circuit to receive a selection of optimized LET during the treatment planning, and wherein the set of optimization functions is determined in response to receiving the selection.

Images