Method and apparatus for performing irradiation time optimization for intensity modulated proton therapy

Abstract

A computer-implemented method of determining a resulting treatment plan for a proton radiotherapy system based on a given dose-volume constraint, wherein the resulting treatment plan is optimized for treatment time. The method comprises: accessing the dose-volume constraint and range information, wherein the range information indicates an acceptable deviation from the dose-volume constraint; accessing, based on the proton radiotherapy system, machine configuration information comprising a plurality of machine parameters, the plurality of machine parameters defining a maximum resolution achievable by the proton radiotherapy system when irradiating a patient; iteratively adjusting the plurality of machine parameters to a value that reduces the maximum resolution, and simulating a plurality of candidate treatment plans to generate a plurality of treatment plan results, wherein each treatment plan result comprises a respective treatment time and a respective plan quality; selecting the resulting treatment plan with the shortest treatment time. The present disclosure provides a solution to reduce irradiation time during treatment delivery without sacrificing dose distribution or treatment plan quality.

Description

[0001] This application is a divisional application of the patent application with application number 201910924605.2, application date 27 September 2019, and invention title “Method and apparatus for performing fluence time optimization for intensity modulated proton therapy”. TECHNICAL FIELD

[0002] The present specification generally relates to the field of radiation therapy, and more specifically to optimizing performance of a therapy system while maintaining acceptable planning quality when performing radiation therapy treatment planning. BACKGROUND

[0003] Radiation therapy treatment planning typically employs medical imaging, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and the like. Generally, a series of two-dimensional patient images, each image representing a two-dimensional cross-sectional“slice” of the patient’s anatomy, are used to reconstruct a three-dimensional representation of a volume of interest (VOI) or structures of interest from the patient’s anatomy.

[0004] The VOI typically includes one or more organs of interest, which often include a planning target volume (PTV), such as a malignant growth or an organ including malignant tissue targeted by the radiation therapy; a relatively healthy organ at risk (OAR) near the malignant growth that is at risk of exposure to the radiation therapy; or a larger portion of the patient’s anatomy that includes a combination of one or more PTVs and one or more OARs. The goal of radiation therapy treatment planning is often to irradiate the PTV as closely as possible to a prescribed dose while minimizing irradiation of nearby OARs.

[0005] The resulting radiation therapy treatment plan is used during a medical procedure to selectively expose precise areas of the body, such as malignant tumors, to specific doses of radiation to destroy the undesirable tissue. In developing a patient-specific radiation therapy treatment plan, information is often extracted from the three-dimensional model to determine parameters of one or more PTVs and one or more OARs, such as shape, volume, location, and orientation.

[0006] Proton therapy is a form of external-beam radiation therapy characterized by the use of a proton beam to irradiate diseased tissue. Generally, radiation therapy involves directing a beam of high-energy protons, photons, or electrons (“therapy radiation”) into a target volume (e.g., a tumor or lesion). The primary advantage of proton therapy over other conventional therapies, such as X-ray or neutron radiation therapy, is that proton radiation can be limited by depth, and thus can avoid exposure to unintended radiation, or at least to non-target cells beyond a target computational region.

[0007] The mainstream implementation of proton therapy uses single-energy pencil beams at different energy levels that are spot-scanned at one or more depth layers of these single-energy pencil beams over the target region. By superimposing several proton beams of different energies, the Bragg peak can be spread uniformly to cover the target volume. This enables proton radiation delivery to be more precisely located relative to other types of external beam radiation therapy. During a proton therapy treatment, a particle accelerator such as a cyclotron or synchrotron is used to generate a proton beam from an internal ion source, for example, located at the center of the particle accelerator. Prior to the final delivery of the radiation to the target volume in the treatment room through a radiation delivery device (typically through a radiation nozzle) at the end of a beam line segment, the protons in the beam are accelerated (via an electric field generated), then the accelerated proton beam is "extracted" and magnetically guided through a series of interconnected tubes called a beam line, often through multiple chambers, rooms, and even floors of a building.

[0008] Since the volume targeted by the radiation therapy (e.g., an organ or body region) is typically located below the surface of the skin and / or extends along three dimensions, and since proton therapy, like all radiation therapy, can be harmful to intervening tissue in the subject's body between the target region and the beam emitter, it is critical to correctly calculate and apply the correct amount and location of the dose to avoid exposing areas of the subject's body outside of the specific region targeted to receive the radiation.

[0009] Prior to treating a patient with radiation, a treatment plan specific to that patient is developed. This plan uses simulation and optimization based on past experience to define various aspects of the therapy. For example, for intensity modulated radiation therapy (IMRT), the plan can specify the appropriate beam type and the appropriate beam energy. Other parts of the plan can specify, for example, the angle of the beam relative to the patient, the beam shape, the location of a bolus and a shield, etc. Generally, the goal of the treatment plan is to deliver enough radiation to the target volume while minimizing the exposure of surrounding healthy tissue to radiation.

[0010] In IMRT, the planner's goal is to find the best solution for multiple clinical objectives, which can be self-contradictory in the sense that an improvement to one objective can adversely affect the achievement of another objective. For example, a treatment plan that leaves the liver unexposed to a radiation dose can result in the stomach being exposed to too much radiation. These trade-offs result in an iterative process in which the planner creates different plans to find one that best achieves the desired results. Further, treatment planning software can be used to find the best plan that takes into account all of the clinical objectives and dose measurement criteria.

[0011] In proton therapy, it is desirable to have a short irradiation time. During therapy to certain organs, especially the lungs, the patient has to hold his breath to prevent the tumor to move in or out of the proton beam. Therefore, lung cancer or liver cancer is often treated with breath-hold techniques to minimize the interaction of the moving target. Thus, delivering the required dose as fast as possible limits the time the patient has to hold his breath.

[0012] One of the drawbacks of conventional commercially available treatment planning systems is as follows: While they can be optimized, for example, according to certain dose volume constraints for target volumes and organs at risk or according to planning robustness, taking inter- or intra-fraction positional inaccuracies into account, generally, conventional proton therapy systems do not take the time behavior of the beam application into account during optimization. That is, treatment planning is currently optimized for planning quality, for example, according to given dose volume constraints for target volumes and organs at risk. More specifically, conventional treatment planning systems for proton therapy do not optimize for time by taking certain machine specific criteria into account that have a significant impact on the irradiation time, related to the characteristics of the proton therapy delivery system. In other words, conventional treatment planning systems cannot optimize for delivery system machine specific limitations, which can result in prolonged irradiation times and an increased likelihood of interlocks occurring. SUMMARY

[0013] According to embodiments of the present application, a method is provided that performs a time-based optimization of an intensity modulated proton therapy system during the treatment planning process, in particular by taking certain beam characteristics and configuration (or calibration) criteria into account that are related to the physical constraints of the machine delivering the proton therapy. In other words, embodiments according to the present application create a treatment plan that is optimized for performance efficiency (using certain radiotherapy beam and machine specific parameters) while at the same time delivering a clinically acceptable planning quality (for the dose distribution).

[0014] In one embodiment, a computer-implemented method of determining a resulting treatment plan for a proton radiation therapy system based on a given dose volume constraint is disclosed, wherein the resulting treatment plan is optimized for a treatment time. The method comprises accessing the dose volume constraint and range information, wherein the range information indicates an acceptable deviation from the dose volume constraint. Based on the proton radiation therapy system, the method further comprises accessing machine configuration information comprising a plurality of machine parameters defining a maximum resolution achievable by the proton radiation therapy system when irradiating a patient. Further, the method comprises iteratively adjusting the plurality of machine parameters to generate a plurality of candidate treatment plans, wherein the iteratively adjusting comprises adjusting the plurality of machine parameters to values that decrease the maximum resolution. Subsequently, the method comprises simulating the plurality of candidate treatment plans for the proton radiation therapy system to generate a plurality of treatment plan results, wherein each treatment plan result comprises a respective treatment time and a respective plan quality. Finally, the method comprises selecting the resulting treatment plan from the plurality of candidate treatment plans, wherein the resulting treatment plan yields a treatment plan result comprising a shortest treatment time and an acceptable plan quality with respect to the dose volume constraint. Embodiments further comprise a computer system implemented to perform the method as described above.

[0015] Thus, embodiments according to the present application specifically improve the field of radiation treatment planning and generally the field of radiation therapy. In IMRT, beam intensities vary across each treatment region (target volume) of a patient. Instead of treating a patient with a relatively large and uniform beam, a patient can be treated with many smaller beams (e.g., pencil beams or sub-beams), each of which can have its own intensity and can be delivered from different angles (which can be referred to as beam geometry) to irradiate a spot. Due to the many possible beam geometries, beam numbers, and beam intensity ranges, there is effectively an infinite number of possible treatment plans, and thus, it is beyond the capacity of humans to always and efficiently generate and evaluate high quality treatment plans and requires the use of computer systems, particularly in view of the time constraints associated with treating diseases such as cancer with radiation therapy, and particularly in view of the large number of patients undergoing or needing to undergo radiation therapy during any given time period.

[0016] Further, performing multi-directional optimization is optimizing the treatment plan by considering complex machine and beam specific parameters (e.g. minimum number of monitor units per spot, energy layer spacing, spot size, and spot spacing, etc.) to improve efficiency beyond the capability of a human and requiring the use of a computing system quality without sacrificing the quality of the plan. Embodiments in accordance with the present application allow for the generation of an effective treatment plan with low treatment delivery time, which limits the possibility of irregular or inaccurate treatment delivery due to patient movement. Furthermore, embodiments in accordance with the present application help improve the functionality of the computing system by improving system reliability and availability, which is caused by a lower likelihood of interlock occurrence.

[0017] The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the application will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A block diagram illustrating an example of a computing system on which embodiments described herein can be implemented is shown.

[0019] Figure 2 An embodiment of a knowledge-based planning system incorporating a combination of dosimetric criteria and certain delivery system characteristics for generating a radiation therapy plan is illustrated in accordance with an embodiment of the present application.

[0020] Figure 3A A data flow diagram of a process that can be implemented to create a treatment plan taking certain physical degrees of freedom into account is illustrated in accordance with an embodiment of the present application.

[0021] Figure 3B A data flow diagram of a process that can be implemented to select a resulting treatment plan from several candidate treatment plans generated by varying machine specific parameters is illustrated in accordance with an embodiment of the present application.

[0022] Figure 4 A manner in which the resulting energies and the steps between them can be optimized is illustrated in accordance with an embodiment of the present application.

[0023] Figure 5A And Figure 5B An exemplary grid in the x-y plane on which proton therapy is delivered is illustrated in accordance with an embodiment of the present application.

[0024] Figure 6 A table illustrating a plurality of candidate treatment plans and their results in accordance with an embodiment of the present application, wherein each cell of the table represents a radiotherapy machine model implemented by the optimization engine to determine the radiation time and plan quality of the corresponding treatment plan.

[0025] Figure 7 is a high-level software flow diagram illustrating a way of using machine-specific parameters in a treatment planning system to determine a most efficient treatment plan according to embodiments of the application.

[0026] Figure 8 is a flow diagram depicting another exemplary process flow for determining a resulting treatment plan for a proton radiotherapy system based on a given dose volume constraint according to embodiments of the application, wherein the final plan is optimized for treatment time. DETAILED DESCRIPTION

[0027] Reference will now be made in detail to various embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. While the disclosure will be described in conjunction with these embodiments, it is understood that they are not intended to limit the disclosure to these embodiments. On the contrary, the disclosure is intended to cover alternatives, modifications, and equivalents, which can be included within the spirit and scope of the disclosure as defined by the appended claims. Furthermore, in the following detailed description of embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be recognized that the present disclosure can be practiced without resort to the

[0028] Some portions of the following detailed description are presented in terms of procedures, logic blocks, processing, and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In this application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those utilizing physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in a computing system. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as transactions, bits, values, elements, symbols, characters, samples, pixels, or the like.

[0029] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the present disclosure, discussions utilizing terms such as "accessing," "adjusting," "simulating," "selecting," "loading," and "using" or the like, refer to the action and processes of a computing system, or similar electronic computing device or processor (e.g., computing system 100 of FIG. 1) that manipulates and transforms data represented as physical quantities (e.g., electronic) Figure 1 Figure 8 ​The computer executable instructions may be stored on a computer readable storage medium, which can include any device or apparatus that stores such instructions. One or more components of system may each be a computer-readable storage medium storing computer executable instructions. A computer readable storage medium can be, but is not limited to, ROM, RAM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can accessed in a computer readable storage medium.

[0030] Portions of the detailed description that follow are presented in terms of methods. Although steps and sequences are disclosed herein in the description of the methods (e.g., flowcharts) in which the steps are presented, these steps and sequences are exemplary. Embodiments are well suited to performing various other steps or variations of the steps recited in the flowcharts of the figures herein, and in different sequences than presented, and are Figure 8 performed by, a computer system or similar electronic computing device manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[0031] The embodiments described herein can be discussed in the general context of computer-executable instructions residing on some form of computer- readable storage medium such as program modules. By way of example, and not limitation, computer-readable storage media can comprise non-transitory computer-readable storage media, and communication media. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of steps and methods described herein are not restricted to any particular sequence or combination of steps, and any combination of steps or sequences from the various steps disclosed can be employed.

[0032] Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory or other memory technology, compact disc ROM (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can accessed to retrieve that information.

[0033] Communication media can embody computer executable instructions, data structures, and program modules, and includes any information delivery media. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. Combinations of the any of the above can also be included within the scope of computer readable media.

[0034] The present disclosure provides a solution to the inherent challenge of reducing irradiation time during treatment delivery without sacrificing the dose distribution (or treatment planning quality). Specifically, various embodiments of the present disclosure provide a method that performs time-based optimization of a proton therapy system during a treatment planning process of the proton therapy system, specifically by taking into account the characteristics of the proton delivery system (e.g., certain beam characteristics and configuration / calibration guidelines related to delivering proton therapy) in the optimization. In other words, embodiments according to the present disclosure create a treatment plan that is optimized for performance efficiency (using certain radiotherapy beam and machine specific parameters) while delivering clinically acceptable planning quality (for dose distribution) at the same time.

[0035] Conventional commercially available radiotherapy planning systems can be optimized according to given dose volume constraints for planning target volumes (PTVs) and organs at risk (OARs) and according to planning robustness (taking inter-fraction or intra-fraction positional imprecision into account). However, conventional treatment planning systems do not take into account certain physical constraints or characteristics of the delivery system (e.g., certain beam characteristics or machine specific operating parameters and their limitations) in the underlying data considered in their optimization execution. As such, even though a conventional treatment planning system can produce a clinically acceptable treatment plan, the formulated treatment plan cannot utilize the full system or machine functionality to obtain the treatment delivered as planned in the most efficient or most reliable manner. In other words, because certain delivery system specific parameters (e.g., related to physical constraints of the beam machine) are fixed (and cannot be varied during treatment planning or when trying to establish an optimal treatment plan), the treatment plan formulated by a conventional treatment planning system, while acceptable, cannot be optimized in terms of time and thus is inefficient. Thus, even though the optimized treatment plan in a conventional system passes the criteria of planning quality and treatment delivery time, the radiotherapy beam machine cannot be applied during delivery, or the treatment plan can not be planned for optimal delivery efficiency as required by the planning goals during treatment planning due to limitations specific to beam machine functionality (or machine specific planning parameters) that are not taken into account by currently available commercial treatment planning systems.

[0036] As such, embodiments of the present invention take into account and adjust certain delivery system specific parameters required for planning quality of delivery, such as beam energy, spot position, overall minimum spot intensity, and spot lateral spread (or spot size), when formulating a performance optimized treatment plan. This allows embodiments of the present invention to perform multi-directional optimization and deliver a treatment plan that delivers clinically acceptable planning quality (for dose distribution) at the shortest irradiation time period. By contrast, parameters associated with certain physical constraints of conventional treatment delivery systems (e.g., beam energy, spot position, spot lateral spread, etc.) are fixed and cannot be optimized when formulating a treatment plan.

[0037] In one embodiment, the present invention includes a software-based optimization engine that converts delivery system knowledge into a treatment plan. The optimization engine provides an acceptable treatment plan quality (dose distribution) for the treatment plan while optimizing for plan delivery efficiency (especially delivery time for all fields of the treatment plan) and overall delivery performance to improve system reliability and availability. Optimization efficiency (by taking into account the specific behavior, thresholds, and limitations of the radiation therapy beam machine) also reduces the likelihood of interlocks occurring. Thus, embodiments of the present invention are able to deliver a treatment plan with the full capabilities of the treatment delivery system (TDS) with the highest possible efficiency. The optimization engine allows users of the TDS to purposefully utilize the radiotherapy delivery system and treatment planning system by finding the best outcome for individual treatments (with respect to efficiency impacting variables related to treatment delivery).

[0038] Embodiments of the present invention are advantageous in that they allow for optimal use of machine capabilities, which reduces the occurrence of interlocks, which can be managed and directly influenced during treatment planning. With fewer interlocks, the delivery system is able to keep up with the resulting treatment plan, and machine downtime is avoided.

[0039] Further, the optimization engine of the present invention ensures sufficient treatment plan quality and customizes treatment delivery to utilize the maximum system capabilities of the TDS (including time behavior and reliability). Thus, embodiments of the present invention minimize overall irradiation time while maintaining the restrictions that guarantee accurate beam position and dose application. Because the treatment time is shorter, embodiments of the present invention support advanced breath hold treatment planning. In other words, with shorter treatment times, the patient can not need to hold their breath for as long, allowing for accurate delivery of the treatment while minimizing patient movement. Still further, because the radiation time is shorter, machine load / wear is reduced.

[0040] Additionally, optimization for certain delivery system specific parameters (e.g., beam energy, spot position, overall minimum spot intensity, and spot lateral spread (or spot size)) results in a more robust treatment plan. For example, varying spot positions and spot lateral spread can allow for the use of fewer spots to apply an acceptable dose. Optimization over other physical degrees of freedom can also result in better results for dose measurement criteria because of the larger set of qualitative degrees of freedom available to the optimization engine. Specifically, it results in improved dose homogeneity for target structures (e.g., tumors).

[0041] Designing a more reliable system by considering certain machine degrees of freedom (e.g., beam energy, spot position, overall minimum spot intensity, and spot lateral spread) also prevents the designer of the treatment plan from having to redesign the treatment plan in the event that a previous treatment plan fails (e.g., due to interlocks). By providing increased degrees of freedom in the optimization process and better utilizing hardware capabilities, embodiments of the present invention can reduce delivery time, machine interlock probability, and machine maintenance without significant impact on primary dose measurement goals.

[0042] Figure 1 A block diagram of an example of a computing system 100 upon which embodiments described herein can be implemented is shown. In its most basic configuration, a system 100 includes at least one processing unit 102 and memory 104. The most basic configuration is illustrated in Figure 1 by dashed line 106. System 100 can also have additional features and / or functionality. For example, system 100 can also include additional storage such as removable storage and / or non-removable storage including, but not limited to, magnetic disks, optical disks, or tape. Such additional storage is illustrated in Figure 1 by removable storage 108 and non-removable storage 120. System 100 can also contain one or more communication connections 122 allowing the device to communicate with other devices using a logical connection to one or more remote computers.

[0043] System 100 can also include one or more input devices 124 such as keyboard, mouse, pen, voice input device, touch input device, etc. One or more output devices 126 such as a display device, speakers, printer, etc. can also be included.

[0044] As will be further explained below, embodiments in accordance with the present invention utilize an optimization engine 218. In Figure 1 the example, memory 104 includes computer readable instructions, data structures, program modules, etc. associated with optimization engine 218. However, optimization engine 218 can reside in any of the computer storage media used by system 100, or can be distributed across some combination of computer storage media, or can be distributed across some combination of networked computers.

[0045] The optimization engine 218 is programmed to optimize for performance efficiency while also considering treatment planning quality and customizing treatment delivery to take advantage of the maximum system capabilities of the TDS, including temporal behavior and reliability. In conventional treatment planning systems, machine-specific physical degrees of freedom (e.g., number of available beam energies and steps between them, spot positions, spot lateral spread, etc.) are fixed parameters. The optimization engine 218 allows the user to optimize for efficiency by varying the machine-related physical degrees of freedom. In contrast, conventional treatment planning systems use fixed parameters for the physical degrees of freedom (e.g., number of available beam energies and steps between them, spot positions, spot lateral spread, etc.), which can be formulated through trial and error.

[0046] Figure 2 An embodiment of a knowledge-based planning system 200 that incorporates a combination of dosimetric criteria and certain delivery system characteristics for generating a radiation treatment plan is illustrated in accordance with an embodiment of the present application. In the example of FIG. 1, the system 200 includes a knowledge base 202 and a set of treatment planning tools 210. The knowledge base 202 includes patient records 204 (e.g., radiation treatment plans), treatment types 206, statistical models 208, and other dosimetric criteria for delivering an effective treatment plan. In accordance with an embodiment of the present application, the knowledge base 202 can also include certain delivery system characteristics 238 (e.g., degrees of freedom that can be varied, including number of available energies and steps between them, spot positions, spot intensities, and spot lateral spread). Figure 2

[0047] Figure 2 The set of treatment planning tools 210 in the example of FIG. 1 includes a current patient record 212, a treatment type 214, a medical image processing module 216, an optimizer 218, a dose distribution module 220, and a final radiation treatment plan 222.

[0048] The set of treatment planning tools 210 searches the knowledge base 202 (through the patient records 204) for a previous patient record similar to the current patient record 212. The statistical models 208 can be used to compare the predicted outcome of the current patient record 212 to the statistical patient. Using the current patient record 212, the selected treatment type 206, the selected statistical model 208, and the delivery system characteristics 238, the set of tools 210 generates a radiation treatment plan 222 using the optimization engine 218 to optimize for several (possibly conflicting) objectives (e.g., temporal versus dose distribution appropriateness). A radiation treatment plan formulated in this manner (e.g., the treatment plan 222) can be referred to as a balanced plan.

[0049] ​More specifically, based on past clinical experience, there can be most commonly used treatment types when a patient exhibits certain diagnoses, stages, age, weight, gender, comorbidities, etc. By selecting the treatment type that the planner used in the past for a similar patient, the treatment type 214 can be selected. The medical image processing module 216 uses the medical images in the current patient record 212 to provide automatic contours and automatic segmentation of two-dimensional cross-sectional slides (e.g., from computed tomography or magnetic resonance imaging) to form 3D images. The dose distribution map is computed by the dose distribution module 220.

[0050] A combination of objectives that can be applied by the optimization engine 218 to determine the dose distribution can be searched in the knowledge base 202. For example, a mean organ at risk dose volume histogram, a mean population organ at risk dose volume histogram, and a mean organ at risk objective can be selected from the knowledge base 202. In embodiments according to the present application, the optimization engine 218 can optimize with respect to certain delivery system specific parameters (from delivery system characteristics 238), such as the number of available energies and the steps between them, the spot positions, the spot intensities, and the spot lateral extensions. As mentioned above, since a larger set of qualitative degrees of freedom is available to the optimization engine, optimizing on other physical degrees of freedom can also result in a better outcome of the dose prescription criteria or clinical objectives. In particular, it results in an improved dose homogeneity of the target structure (e.g., the tumor). Further, it also results in an optimization time, i.e., an acceptable dose quality can be delivered in the fastest and most efficient way.

[0051] In contrast, in conventional therapy systems, the only criterion considered is the static property of the final dose distribution, which actually determines the quality of the plan. However, embodiments of the present application also consider the temporal aspect of the delivered dose distribution. In other words, embodiments of the present application enable the delivery of an acceptable quality of a treatment plan in the most time-efficient way.

[0052] Figure 3A Fig. illustrates a process 300 according to embodiments of the present application, which can be implemented to create a treatment plan that takes certain physical degrees of freedom into account. The process 300 can be implemented as computer readable instructions stored in a computer usable medium and executed on a computing system of a system 100 as Figure 1 illustrated in Fig. 1.

[0053] Figure 3AThe clinical objectives 320 include (as computer-readable data) a clinical objective or a set of clinical objectives. Generally, a clinical objective is a factor related to the outcome of the treatment. The clinical objectives provide leeway for compromise between the competing objectives of delivering a dose to a target volume (e.g., diseased tissue) while minimizing the dose to surrounding (e.g., healthy) tissue. The clinical objectives 320 can also include acceptable ranges of deviation from those objectives, and still produce an acceptable quality of planning.

[0054] The clinical objectives 320 are used to guide the formulation of a radiation treatment plan, which describes, among other parameters, the type of radiation to be used, the orientation of the radiation therapy beams to be directed at the patient at multiple beam stations, the collimating shape of the beams, and the dose to be delivered at each station. The clinical objectives can also define constraints or objectives for quality metrics, such as minimum and maximum amounts of dose and average dose to particular tissue volumes (referred to as regions of interest or ROIs), dose homogeneity, target volume dose distribution, organ at risk dose distribution, other normal tissue dose distribution, other spatial dose distribution, and other acceptable ranges of deviation.

[0055] Given the patient's anatomical details 310 (also available in the knowledge base 202) and the clinical objectives 320, the treatment planning system can optimize against dose prescription criteria. For example, the dose prescription criteria can specify a minimum amount of radiation to be applied to a planning target volume (PTV) and a maximum amount of radiation to be applied to an organ at risk (OAR). In other words, the dose prescription criteria can be thought of as a dose volume planning or dose distribution planning, which determines how the dose is distributed over the three-dimensional space being treated.

[0056] Embodiments of the present invention include an optimization engine 218 that delivers an acceptable quality of treatment planning (for a given dose prescription criteria), while optimizing against the delivery system characteristics 330 (e.g., various physical degrees of freedom associated with the treatment delivery system) to formulate a treatment plan 340. As mentioned above, conventional treatment systems can only optimize against the dose prescription criteria. In contrast, embodiments of the present invention allow the user to optimize for efficiency by varying the machine-related physical degrees of freedom (e.g., the number of available beam energies and the steps between them, the spot positions, the spot lateral extensions, etc.).

[0057] In one embodiment, the treatment planning software can receive as input machine configurations such as maximum and minimum ranges for each of various machine-specific parameters, e.g., maximum and minimum depths of a target structure for determining inter-layer spacing, maximum and minimum number of monitor units (MUs) per spot that a beam machine can deliver, maximum and minimum spot size (or spot lateral extent), and maximum and minimum width of a target structure for determining spot positioning. Given the ranges for each of the parameters, the optimization engine 218 can perform simulations and employ some sophisticated optimization algorithms to deliver an optimal treatment plan.

[0058] In one embodiment, the range information provided to the treatment planning software can be an acceptable deviation from a dose volume constraint. For example, the PVT coverage can be between 95% to 107%, and the maximum dose is less than 112%.

[0059] In one embodiment, the optimization engine 218 of the treatment planning software can run simulations, for example, to determine various irradiation times associated with selected sets of machine-specific parameters. The optimization engine 218 can be programmed to determine ways to vary the machine-specific parameters in order to solve for the shortest irradiation time while maintaining an acceptable planning quality. In other words, the optimization engine 218 can be programmed to analyze intermediate results and use them to converge more quickly toward a solution that produces the lowest irradiation time. In one embodiment, the optimization engine 218 can be programmed with one or several different optimization algorithms that allow the engine to converge to the most efficient solution without sacrificing planning quality.

[0060] In one embodiment of the present invention, based on a proton radiotherapy system, machine configuration information is accessed that includes various machine parameters. The machine parameters may, for example, define a maximum resolution that the proton radiotherapy system can achieve in irradiating a patient. In one embodiment, an optimization engine can be configured to iteratively adjust the various machine parameters to generate one or more candidate treatment plans. For example, the machine parameters can be iteratively adjusted to a value that reduces the maximum resolution. Subsequently, the various candidate treatment plans that are generated can be simulated in order to determine a respective treatment time and a respective planning quality associated with each of the treatment plans. The optimization engine can then be programmed to select a candidate treatment plan that can produce an acceptable planning quality and the shortest possible treatment time.

[0061] Figure 3B A data flow diagram of a process that can be implemented to select a resulting treatment plan from a plurality of candidate treatment plans generated by varying machine-specific parameters is illustrated in accordance with an embodiment of the present invention.

[0062] The various degrees of freedom 352 and the acceptable high and low ranges for each parameter 354 are used to generate a set of candidate treatment plans 356. Each candidate treatment plan can have different values (or degrees of freedom) for each of the machine specific parameters, but within a particular candidate treatment plan, the values can remain constant. For example, three different candidate treatment plans can be generated, where each of the candidate treatment plans can have different values for the minimum number of MUs per spot, e.g., 3, 5, and 7, respectively. Subsequently, the simulator 362 of the optimization engine generates treatment plan results 386 for one or more of the candidate treatment plans. In one embodiment, the optimization engine can be able to converge to the most efficient solution without having to simulate each of the candidate treatment plans. Given the dosimetry criteria 370, the treatment plan selector 380 then selects the most time-efficient treatment plan 382 from the set of results.

[0063] For example, embodiments of the present application allow the overall minimum spot intensity (measured in monitor units or MUs) to be considered through the optimization process. In conventional systems, the overall minimum spot intensity is fixed based on the beam machine. Embodiments of the present application allow the minimum number of MUs per spot to vary in order to optimize for time constraints. For example, in certain parts of the body, it is beneficial to place a spot that delivers only the minimum number of MUs. However, in other parts, it is not beneficial in terms of the dosimetry criteria to place many low intensity spots versus placing fewer, higher intensity spots. In other words, for certain parts of the body, it can be faster and more efficient to receive fewer, high intensity spots versus several, low intensity spots. For certain parts of the body, the treatment delivery system can use fewer spots, each using more MUs, which sacrifices resolution while maintaining treatment plan quality allows the system to be more efficient.

[0064] By allowing the minimum number of MUs per spot to vary, the optimization engine 218 can optimize the treatment plan by delivering fewer, higher intensity spots on the patient (if permitted) versus delivering several, lower intensity different spots. Further, by reducing the total number of spots, the optimization engine can formulate a treatment plan where the irradiation time is faster and the dose uniformity of the target structure (e.g., tumor) is improved. For example, referring to the example provided above, if the minimum number of monitor units (MUs) per spot is specified to be 5, with a range of ±3, the optimization engine can generate several candidate treatment plans where the minimum number of MUs per spot varies between 2 MUs to 8 MUs between the various candidate treatment plans. After simulating the various treatment plans, the optimization engine can select the treatment plan that generates an acceptable plan quality with the fastest treatment time.

[0065] Figure 4Figures illustrate ways in which the number of energies used and the step between them can be optimized according to embodiments of the application.

[0066] In IMRT, beam intensities vary between each treatment region (target volume) of a patient. Rather than treating a patient with a relatively large and uniform beam, a patient can be treated with many smaller beams (e.g., pencil beams or sub-beams), each of which can have its own intensity and can be delivered from different angles (which can be referred to as beam geometry). Due to the many possible beam geometries, number of beams, and range of beam intensities, there can effectively be an infinite number of possible treatment plans. As mentioned above, in formulating a treatment plan, embodiments of the application use the optimization engine 218 to select the most performance efficient treatment plan without sacrificing plan quality.

[0067] One of the beam machine specific parameters that can be optimized according to embodiments of the application is the number of energies used and the spacing or distance between them. Energy levels correspond to the depth within a treatment region that a beam can reach. For example, a 180 MeV beam is able to irradiate a PTV at a greater depth than a 150 MeV beam. In conventional systems, the energy difference between adjacent / contiguous layers (such as a fixed number of layers or a fixed layer spacing) is fixed before any optimization using a certain plan (which is the difference between contiguous energies, e.g., 3 MeV between each layer).

[0068] For example, in one embodiment of the application, the number of energies used and the difference between contiguous energies can be considered an additional parameter that can vary between various candidate treatment plans. In other words, individual candidate treatment plans can differ between energy levels at which a treatment field is delivered. As mentioned above, proton therapy can be limited by depth, thus, exposure to unintended radiation can be avoided, or at least limited to non-target cells beyond the target computational region. As mentioned above, in conventional treatment systems, the energy layer spacing (difference between contiguous energy layers) is a fixed parameter and is associated with a fixed distance between each layer. For example, in a conventional treatment system, if the minimum depth of a target structure is 100 MeV and the maximum depth of the target structure is 200 MeV, where the layer spacing is fixed at 5 MeV, then a treatment delivery system can deliver treatment between 100 MeV and 200 MeV in increments of 5 MeV (e.g., at 105 MeV, 110 MeV, 115 MeV, etc.).

[0069] In contrast, embodiments of the present application allow the optimization engine to determine the number of energy levels (which need to be added between the maximum depth and minimum depth of the target structure) and the difference between the consecutive energy levels (in delivering the therapy field). For example, the therapy planning software can receive a minimum therapy depth and a maximum therapy depth (based on the patient). Alternatively, the therapy planning software can also receive an acceptable energy range. As shown in Figure 4 the PTV can be between 220 MeV and 182 MeV. Knowing the minimum depth and maximum depth (or acceptable energy range) and the minimum and maximum number of energy layers it can deposit, the therapy planning software can generate several candidate therapy plans with different depths and number of energy layers. Subsequently, the optimization engine can simulate one or more of the various candidate therapy plans and automatically perform optimization to determine the optimal number of layers and spacing therebetween. For example, for the PTV shown in Figure 4 the optimization engine 218 can determine the optimal number of layers to be 4 with a spacing of 210 MeV, 205 MeV, 195 MeV, and 190 MeV. Thus, in contrast to conventional therapy planning systems, embodiments of the present application are not limited to a fixed number of layers or a fixed spacing between the layers (which can be very time consuming). The number of layers and the distance between consecutive layers can vary. The optimization engine will typically choose the number of layers and layer spacing to minimize the irradiation time while maintaining the constraints that ensure accurate beam position and dose application.

[0070] To determine the optimal number of layers and spacing therebetween, the optimization engine can need to perform various simulations on multiple candidate therapy plans with different energy levels. For example, the optimization engine can start from 182 MeV and vary the energy level by a constant value or a multiple of that value (e.g., by increments of 5 MeV or multiples of 5 MeV (across multiple candidate therapy plans)) in order to determine the optimal number of layers and spacing. It can not be necessary to simulate every possible therapy plan as the optimization algorithm is configured to converge towards the optimal solution after a certain number of simulations.

[0071] Embodiments of the present application are also configured to vary the spot position and spot lateral extent (spot size) on various candidate therapy plans when optimizing for efficiency. As previously mentioned, in conventional systems, the spot size and grid size on which therapy is delivered are typically fixed. However, embodiments of the present application allow for flexibility and higher efficiency by varying the spot size and spot position within an acceptable range of multiple candidate therapy plans. For example, given the shape of the target (and associated position constraints), the optimization engine can determine the optimal spot size and spot position. As mentioned above, these parameters allow the therapy planning system to sacrifice maximum resolution for increased efficiency. In other words, the radiation therapy system can iteratively adjust the spot positioning and spot lateral extent to find the optimal combination of resolution and efficiency.

[0072] Figure 5A and Figure 5B FIG. illustrates an exemplary grid in the x-y plane within the energy levels on which proton therapy is delivered, according to embodiments of the present application. As Figure 5B seen in FIG. 1, the spot lateral expansion is greater than Figure 5A the spot lateral expansion in FIG. 2. Since the spot size and spot pitch parameters are related, the larger the spot size, the narrower the pitch required between spots. Embodiments of the present application allow the spot size and spot pitch to be set at optimal levels so that the required dose can be delivered in the fastest amount of time possible. For example, in one particular treatment, instead of delivering proton therapy at two spots 510 and 520 (as shown in FIG. 1), the treatment planning software can determine that it is equally effective and more efficient to simply direct the dose energy to a single larger spot 530. In this example, the irradiation time can be minimized, for example, because the optimization engine allows for fewer larger sized spots to be irradiated instead of several smaller spots. Figure 5A

[0073] In one aspect of the present application, the spot positions can be varied across multiple candidate treatment plans so that the spots do not need to conform to a fixed grid. In conventional treatment systems, the spots need to conform to a fixed grid because the spot size and spot pitch parameters are fixed. Embodiments of the present application allow the spot lateral expansion and spot pitch parameters to be varied freely (within an acceptable range) across multiple candidate treatment plans, and therefore, the spots do not need to conform to a fixed grid. For example, as seen in FIG. 3, the spots 585 do not conform to a fixed grid. Figure 5A

[0074] Figure 6 is a table illustrating multiple candidate treatment plans and their results, according to embodiments of the present application, where each cell of the table represents a radiation machine model implemented by the optimization engine to determine the radiation time and plan quality for the corresponding treatment plan.

[0075] As shown in the table of FIG. 4, the minimum MUs per spot and energy layer pitch are adjusted to formulate multiple candidate treatment plans (for three fields, field 1, field 2, and field 3). In the table shown in FIG. 4, each of the cells indicated in the table includes the results of a simulation associated with the corresponding treatment plan. For example, each cell presents the duration (in seconds) taken by the treatment delivery system to perform the corresponding simulation associated with the corresponding settings of the energy layer pitch and minimum MUs per spot. Figure 6 Figure 6 As shown in the table of FIG. 4, the minimum MUs per spot and energy layer pitch are adjusted to formulate multiple candidate treatment plans (for three fields, field 1, field 2, and field 3). In the table shown in FIG. 4, each of the cells indicated in the table includes the results of a simulation associated with the corresponding treatment plan. For example, each cell presents the duration (in seconds) taken by the treatment delivery system to perform the corresponding simulation associated with the corresponding settings of the energy layer pitch and minimum MUs per spot.

[0076] As shown in the table of FIG. 4, the minimum MUs per spot and energy layer pitch are adjusted to formulate multiple candidate treatment plans (for three fields, field 1, field 2, and field 3). In the table shown in FIG. 4, each of the cells indicated in the table includes the results of a simulation associated with the corresponding treatment plan. For example, each cell presents the duration (in seconds) taken by the treatment delivery system to perform the corresponding simulation associated with the corresponding settings of the energy layer pitch and minimum MUs per spot. Figure 6 ​​​As shown, grid 630 displays time-based results for all associated candidate treatment plans that satisfy all treatment planning quality criteria (e.g., PTV coverage, maximum dose, etc.). One or more planning quality criteria for other candidate treatment plans fail. Still further, among these criteria, grid 640 displays results for all treatment plans that can be performed in the shortest time period while maintaining acceptable planning quality. In this example, treatment plan 640 can be the output of optimization engine 218.

[0077] Likewise, for all degrees of freedom, optimization engine 218 will determine treatment times and corresponding planning quality for various candidate treatment plans by varying machine parameters within acceptable ranges to perform various simulations. In most instances, optimization engine can not need to perform every possible simulation, but can use optimization algorithms to converge to the most efficient solution (with acceptable planning quality).

[0078] As discussed in connection with Figure 3B , the various degrees of freedom 352 and acceptable high and low ranges for each parameter 354 are used to generate a set of candidate treatment plans 356. For example, in the table of Figure 6 , a candidate treatment plan is generated for each combination of minimum MUs (e.g., 3 MUs, 5 MUs, 8 MUs, and 11 MUs) and energy layer spacing (3 MeV, 5 MeV, 8 MeV, and 11 MeV) for each spot. Each candidate treatment plan can have a different value for each machine-specific parameter (or degree of freedom), but that value remains constant within a particular candidate treatment plan.

[0079] For example, returning to the table in Figure 6 , a candidate treatment plan can be generated that uses a minimum of 3 MUs for each spot with an energy layer spacing of 3 MeV, i.e., the MUs and energy layer spacing for each spot remain constant within that particular candidate treatment plan. Simulator 362 of optimization engine then generates treatment planning results 386 for one or more candidate treatment plans. As mentioned above, in one embodiment, optimization engine can be able to converge to the most efficient solution without simulating every one of the candidate treatment plans. In other words, optimization engine can not need to generate results for every possible combination of minimum MUs and energy layer spacing for each spot during treatment planning.

[0080] In view of dose measurement criteria 370, treatment plan selector 380 then selects the most time-efficient treatment plan 382 from the set of results. In the example of Figure 6 , result plan 640 is determined to be the most time-efficient treatment plan from the set of results.

[0081] In one embodiment, the treatment planning software may include a graphical user interface (GUI) that calculates and displays field irradiation times during optimization. Furthermore, the GUI may allow the user to set goals and priorities for the delivery time of each treatment field. Furthermore, the GUI may allow the user to vary field-specific machine parameters within the optimization interface, such as the lateral spread of the spot, the minimum number of MUs per spot, and the like.

[0082] Figure 7 is a high-level software flow diagram illustrating the manner in which machine-specific parameters are used in a treatment planning system to determine the most efficient treatment plan, in accordance with an embodiment of the present invention.

[0083] The treatment planning software system receives the machine parameters from the machine parameter database 772 into the beam data service module 732 and into the service layer 730. The machine parameters are then transmitted to the business layer 722, where the control system 712 performs calculations using the machine parameters for various treatment plans. The irradiation data determined by the control system calculation module 712 is transmitted to the application layer 702. Within the application layer, the treatment planning system 704 uses the irradiation data to determine the treatment time using module 708. Once the treatment time calculation script 708 determines the treatment time, the treatment planning system can select the most time-efficient treatment while maintaining acceptable plan quality.

[0084] Figure 8 is a flow chart depicting another exemplary process flow 800 for determining a resulting treatment plan for a proton radiation therapy system based on given dose-volume constraints, wherein the resulting plan is optimized for treatment time, according to an embodiment of the present invention.

[0085] At step 802, dose volume constraints and range information are accessed, wherein the range information indicates acceptable deviations from the dose volume constraints.As mentioned above, minimum and maximum ranges of machine specific parameters may also be provided to the treatment planning software to perform optimization.

[0086] At step 804, machine configuration information is accessed for the proton radiation therapy system, including a plurality of machine parameters that define the maximum resolution achievable by the proton radiation therapy system when irradiating a patient. In other words, the machine parameters can be varied to achieve the maximum possible resolution when irradiating the patient; however, for efficiency, the same parameters can also be varied to deliver treatment to the patient in the shortest amount of time by trading off maximum resolution.

[0087] At step 806, the optimization engine of the treatment planning software iteratively adjusts the plurality of machine parameters to generate a plurality of candidate treatment plans, wherein the iterative adjustment includes adjusting the plurality of machine parameters to values ​​that reduce the maximum resolution. For example, reducing the maximum resolution may result in higher performance.

[0088] At step 808, the optimization engine simulates a plurality of candidate treatment plans for the proton radiation therapy system to generate a plurality of treatment plan results, where each treatment plan result includes a respective treatment time and a respective plan quality. As shown, for each treatment plan simulated, a time value can be obtained to indicate an amount of time required to deliver the respective treatment. Figure 6

[0089] Finally, at step 810, the optimization engine selects a resulting treatment plan from the plurality of candidate treatment plans, where the resulting treatment plan yields a treatment plan result that includes a shortest treatment time among the plurality of treatment plan results and an acceptable plan quality with respect to the dose volume constraints.

[0090] Thus, embodiments in accordance with the present application have been described. In addition to IMRT, these embodiments can also be used to plan different types of external beam radiation therapy, which include, for example, image guided radiation therapy (IGRT), RapidArc TM therapy, stereotactic body radiation therapy (SBRT), and stereotactic ablative radiation therapy (SABR).

[0091] Still further, embodiments of the present application perform multi-directional optimization that optimizes treatment plans for efficiency by taking into account complex machine and beam specific parameters (e.g., minimum applied monitor units per spot, energy layer spacing, spot size, spot spacing, etc.) without sacrificing plan quality, i.e., performing such multi-directional optimization is beyond the capability of humans and requires the use of a computing system. Embodiments in accordance with the present application allow for the generation of efficient treatment plans with low treatment delivery times that limit the likelihood of irregular or inaccurate treatment delivery due to patient movement. By optimizing for efficiency and time-based constraints, embodiments in accordance with the present application help improve the functionality of a computing system as they improve system reliability and availability.

[0092] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.​

Claims

1. A computer-implemented method of determining a resulting treatment plan for a proton radiation therapy system, the method comprising: accessing a dose-volume constraint for the proton radiation therapy system; accessing machine configuration information for the proton radiation therapy system including a plurality of machine parameters that can be changed, wherein the plurality of machine parameters are associated with beam characteristics of a beam machine of the proton radiation therapy system, and wherein the machine configuration information includes a range for each machine parameter of the plurality of machine parameters; iteratively adjusting the plurality of machine parameters to generate a plurality of candidate treatment plans; simulating the plurality of candidate treatment plans for the proton radiation therapy system to generate a plurality of treatment plan results, wherein each treatment plan result includes a respective treatment time and a respective plan quality, and wherein the simulating includes performing one or more simulations using values selected for each machine parameter of the plurality of machine parameters within a respective range; selecting the resulting treatment plan from the plurality of candidate treatment plans, wherein the resulting treatment plan produces a treatment plan result that includes a shortest treatment time of the plurality of treatment plan results and an acceptable plan quality with respect to the dose-volume constraint.

2. The method of claim 1, wherein accessing the dose volume constraint comprises: accessing range information for the dose-volume constraint, wherein the range information indicates an acceptable deviation from the dose-volume constraint.

3. The method of claim 1, wherein the plurality of machine parameters can be changed such that the proton radiation therapy system achieves a maximum resolution when irradiating a patient, and wherein the iterative adjustment comprises: adjusting the plurality of machine parameters to values that reduce the maximum resolution.

4. The method of claim 1, wherein the plurality of machine parameters includes a minimum monitoring units (MUs) per spot.

5. The method of claim 4, wherein the plurality of machine parameters further includes a number of available energies and a step between the available energies, wherein both the number of available energies and the step between the available energies can be changed.

6. The method of claim 5, wherein an energy level of the available energies corresponds to a depth within a treatment region associated with a resulting treatment plan that a beam produced by the beam machine can reach.

7. The method of claim 5, wherein the plurality of machine parameters further includes a spot lateral spread.

8. The method of claim 5, wherein the plurality of machine parameters further comprises a spot lateral extent, wherein changing the spot lateral extent comprises: changing a spot size associated with a beam produced by the beam machine.

9. The method of claim 7, wherein the plurality of machine parameters further includes a spot positioning.

10. The method of claim 7, wherein the plurality of machine parameters further comprise a spot positioning, wherein changing the spot positioning comprises: changing a spacing between spots associated with a beam produced by the beam machine.

11. The method of claim 1, wherein the acceptable plan quality is defined as a plan quality of the dose-volume constraint that is within a range, wherein the range includes an acceptable deviation from the dose-volume constraint.

12. The method of claim 1, further comprising: loading the resulting treatment plan into the proton radiation therapy system.

13. The method of claim 1, wherein accessing the dose-volume constraint comprises accessing range information for the dose-volume constraint, wherein the range information for the dose-volume constraint indicates an acceptable deviation from the dose-volume constraint, wherein the range information for the dose-volume constraint comprises: a PTV coverage range between a lower percent and an upper percent; and a maximum dose below a floor percent.

14. The method of claim 1, wherein the proton radiotherapy system is associated with external beam therapy selected from the group consisting of intensity modulated radiation therapy (IMRT), image guided radiation therapy (IGRT), RapidArc TM radiation therapy, stereotactic body radiation therapy (SBRT), and stereotactic ablative radiation therapy (SABR).

15. A computer system comprising a processor coupled to a bus and a memory coupled to the bus, wherein the memory is programmed with instructions that, when executed by the processor, cause the computer system to implement a method of determining a resulting treatment plan for a proton radiotherapy system, wherein the method comprises: accessing a dose-volume constraint for the proton radiotherapy system; accessing machine configuration information for the proton radiotherapy system comprising a plurality of machine parameters that can be changed, wherein the plurality of machine parameters are associated with configuration criteria of a machine of the proton radiotherapy system, and wherein the machine configuration information comprises a range for each machine parameter of the plurality of machine parameters; iteratively adjusting the plurality of machine parameters to generate a plurality of candidate treatment plans; simulating the plurality of candidate treatment plans for the proton radiotherapy system to generate a plurality of treatment plan results, wherein each treatment plan result comprises a respective treatment time and a respective plan quality, and wherein the simulating comprises performing one or more simulations using values for each machine parameter of the plurality of machine parameters selected within a respective range; selecting the resulting treatment plan from the plurality of candidate treatment plans, wherein the resulting treatment plan produces a treatment plan result comprising a shortest treatment time of the plurality of treatment plan results and an acceptable plan quality with respect to the dose-volume constraint.

16. The system of claim 15, wherein accessing the dose volume constraint comprises: accessing range information for the dose-volume constraint, wherein the range information indicates an acceptable deviation from the dose-volume constraint.

17. The system of claim 15, wherein the plurality of machine parameters can be changed such that the proton radiotherapy system achieves a maximum resolution when irradiating a patient, and wherein the iterative adjustment comprises: adjusting the plurality of machine parameters to values that reduce the maximum resolution.

18. The system of claim 15, wherein the acceptable plan quality is defined as a plan quality of the dose-volume constraint within a range, wherein the range comprises an acceptable deviation from the dose-volume constraint.

19. A computer-implemented method of determining a treatment plan for proton radiotherapy, the treatment plan optimized for treatment time, the method comprising: accessing information defining a patient anatomy, dose specification criteria, and delivery system characteristics, wherein the dose specification criteria comprise a dose-volume constraint and range information, wherein the range information indicates an acceptable deviation from the dose-volume constraint; Based on a proton radiation therapy system, accessing delivery system characteristics including a plurality of machine parameters that configure the proton radiation therapy system to achieve a maximum resolution, wherein the plurality of machine parameters are associated with calibration standards of a beam machine of the proton radiation therapy system, and wherein the delivery system characteristics include a range for each machine parameter of the plurality of machine parameters; iteratively adjusting the plurality of machine parameters to generate a plurality of candidate treatment plans; simulating the plurality of candidate treatment plans for the proton radiation therapy system to generate a plurality of treatment plan results, wherein each treatment plan result includes a respective treatment time and a respective plan quality, wherein the simulating includes: performing one or more simulations using values selected for each machine parameter of the plurality of machine parameters within a respective range; and selecting the treatment plan from the plurality of candidate treatment plans, wherein the treatment plan produces a treatment plan result that includes: a shortest treatment time of the plurality of treatment plan results and an acceptable plan quality with respect to the dose volume constraints.

20. The method of claim 19, wherein the iterative adjustment comprises: adjusting the plurality of machine parameters to values that reduce the maximum resolution achievable by the proton radiation therapy system.

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