System and method for characterizing radiation dose distribution in radiotherapy treatment plan

Abstract

A method for a system for characterizing a radiation dose distribution in a radiotherapy treatment plan is disclosed. The radiotherapy treatment plan is used to treat an anatomical structure with a radiotherapy device. The system includes a processor and a user interface. The processor is configured to: receive data obtained from a radiation simulation at the anatomical structure under a nominal condition and one or more artificially applied uncertainty conditions; generating a plurality of dose distributions including dose-volume histograms (DVHs) using the received data, each DVH representing a tissue volume of the anatomical structure receiving a respective radiation dose, the DVHs including a nominal DVH corresponding to a nominal condition, and one or more uncertain DVHs corresponding to one or more artificially applied uncertain conditions; and determining a dose distribution characteristic based on statistical characteristics of the generated DVH. The user interface is configured to display the determined dose distribution characteristic to a user.

Description

[0001] This application is a divisional application of the patent application with international application number PCT / CN2020 / 120355 filed on October 12, 2020, application number 202080106067.7 entering the Chinese national phase, and titled “Evaluation and Presentation of the Robustness of Treatment Plans”. Technical Field

[0002] This document relates generally to dose calculations in radiation therapy treatment systems and, more particularly, to systems and methods for evaluating the robustness of radiation therapy treatment plans. Background Art

[0003] Radiation therapy (or "radiotherapy") has been used to treat cancer or other diseases in mammalian tissue. A radiotherapy technique is provided that uses a linear accelerator (also known as a "linac") to irradiate a target area with high-energy particles (e.g., electrons, high-energy photons, etc.). MR-linac is a radiotherapy system that combines linac radiotherapy with diagnostic-grade magnetic resonance imaging (MRI). In another example, radiotherapy can be provided using a heavy charged particle accelerator (e.g., protons, carbon ions, etc.). The goal of radiotherapy is to maximize the radiation dose to the target tissue (e.g., a tumor or other abnormal tissue) while minimizing damage to surrounding healthy tissues such as "organs at risk" (OARs). The physician prescribes a predetermined amount of radiation dose to the target (tumor or other abnormal tissue) and clinical dose limits for the surrounding organs, similar to the prescription of a drug. Typically, ionizing radiation in the form of a collimated beam is directed to the patient from an external radiation source. The radiation beam can be precisely controlled to ensure dose delivery.

[0004] A range of diagnostic or therapeutic energy levels may be delivered using specified or selectable beam energies. Ionizing radiation in the form of a collimated beam may be directed at the patient from an external radiation source. Modulation of the radiation beam may be provided by one or more attenuators or collimators such as a multi-leaf collimator (MLC) and jaws. The intensity and shape of the radiation beam may be adjusted by a collimator to avoid damaging healthy tissue adjacent to the target tissue, for example by conforming the projected beam to the contours of the target tissue.

[0005] Treatment planning is a process that involves determining the radiation therapy parameters used to achieve the treatment goals within constraints. Examples of radiation therapy parameters include radiation beam angles, dose intensity levels, dose distributions, etc. The radiation dose can be calculated using a dose calculation algorithm. The outcome of the treatment planning process is a radiation therapy treatment plan (also called a "treatment plan" or simply a "plan"). A treatment plan can be developed using a treatment planning system (TPS). A treatment plan is customized for each patient before radiation therapy can be delivered to the patient. In order to create a plan, an image of the target tumor must be provided using one or more medical imaging techniques, such as images from X-rays, computed tomography (CT), magnetic resonance imaging (MR), positron emission tomography (PET), single photon emission computed tomography (SPECT), or ultrasound.

[0006] Ideally, the dose determined according to the treatment plan should be equal to the dose received by the patient. However, uncertainties in the treatment plan may lead to deviations of the delivered dose from the calculated dose. Treatment uncertainty may come from different sources. The quality of a radiotherapy treatment plan strongly depends on the robustness of the treatment plan to various sources of uncertainty. Careful assessment of plan robustness can help optimize the treatment plan design before delivery. Summary of the invention

[0007] Design of a treatment plan may include using images of the patient's anatomy to identify target structures (e.g., target tumors) and surrounding tissues near the target structures (e.g., OARs), delineating the target to receive the prescribed radiation dose, and similarly delineating nearby tissues such as OARs that are damaged by the radiation therapy. Treatment plans may be developed using software known as treatment planning systems (TPSs). For example, automated tools such as those provided by Elekta AB of Sweden may be used. ) to help identify or delineate target tumors and organs at risk. Optimization techniques can then be used to create a treatment plan based on clinical and dosimetry goals and constraints (e.g., maximum, minimum, or average radiation dose to tumors and OARs).

[0008] The therapeutic ratio is an important consideration in the design of a treatment plan, which represents a balance between the possibility of target control and complications or damage to nearby normal tissues such as OARs. The therapeutic ratio may be affected by the distribution of radiation doses throughout the target and nearby tissues. When designing a treatment plan, the planner attempts to comply with various treatment goals or constraints and consider their respective importance to formulate a clinically acceptable treatment plan. The treatment plan may include parameters that specify the direction, cross-sectional shape, and intensity of one or more radiation beams. In some examples, the treatment plan may include dose "divisions" so that a series of radiation treatments (e.g., 30 to 45 daily portions) can be provided over a predetermined period of time, wherein each treatment includes a specific portion of the total prescribed dose. Once a treatment plan is generated, the treatment plan can be executed by positioning the patient in a treatment machine and delivering the prescribed radiation therapy guided by the optimized plan parameters.

[0009] Radiation therapy, also known as proton therapy, can be provided by using particles such as protons. Compared to other forms of radiation therapy (e.g., X-rays), proton therapy can advantageously improve the therapeutic ratio, provide excellent dose distribution with minimal exit dose, and have fewer complications or side effects on normal tissues such as OARs than other forms of radiation such as X-rays in at least some patients. For tumors that require high radiation doses, the radiation dose can be increased to achieve the desired local control. Therefore, the quality of life of patients during and after proton therapy treatment can be improved.

[0010] There are different options for designing a proton therapy treatment. In an example, a proton beam is used to deliver a uniform dose to a target. In another example, multiple beams may be used, each delivering a uniform dose to a different portion of the target. Such an approach is more flexible in sparing organs at risk, especially where the target is partially wrapped around such an organ. In some examples, each beam may deliver an optimized non-uniform dose distribution to the target, a technique known as intensity modulated proton therapy. The doses delivered by all beams are then combined to produce the desired uniform dose.

[0011] When designing a proton therapy plan, it is necessary to accurately determine the limited range of the proton beam. This range is limited to the position where the dose has been reduced to a certain percentage (e.g., 80% or 90%) of the maximum dose, such as in the distal dose drop. To this end, the proton stopping power can be determined for the patient's anatomical structure. A conversion algorithm is used to determine the stopping energy based on the CT scan, taking into account the typical composition of human tissue. Throughout the treatment field, the range of the proton beam is usually different. In the case of a uniform dose, the beam is given a range sufficient to reach the distal surface of the target volume at each position. Intensity modulated beams may also stop within the target volume. Unlike beams that pass through homogeneous matter, these ranges cannot always be clearly defined as a single average range. If the anatomical structure is inhomogeneous, protons entering the patient at the same energy and at the same position may end up having different ranges because differences in scattering can result in different trajectories through the anatomical structure.

[0012] A challenge in proton therapy is the uncertainty in the proton beam range. The end of the range is where the beam is characterized by its sharpest dose gradient. Therefore, undershoot of the proton range can cause the distal edge of the tumor to not receive the expected dose. The sources of uncertainty in the proton range may include systematic uncertainty and random uncertainty. Systematic uncertainty may affect most of the tissues traversed by a typical beam and affect the entire treatment process. Random uncertainty can be attributed to the reproducibility of the beam and the patient setup. Examples of sources of uncertainty can include, for example, CT Hounsfield unit (HU) conversion uncertainty of the stopping energy (which may be related to patient size, scanning technology or reconstruction algorithm), stopping energy measurement or calculation uncertainty, CT artifacts, uncertainty in the formation of the proton beam, uncertainty in the determination of the radiological thickness of the bolus / compensator material and accessories, for example, due to differences in the positioning and position of the patient at the time of treatment compared to the patient's positioning and position at the time of treatment simulation and differences between the fractions (inter-fractional) patient setup errors, errors in reproducing the patient's positioning, organ motion and anatomical changes (e.g., tumor regression or growth during the treatment process), etc.

[0013] Radiation therapy such as proton therapy is based on a treatment plan carefully designed for an individual patient. For treatment planning, a CT scan is obtained with the patient in the same position as that used during treatment. Then, based on the physician's prescription, a treatment planning system is used to design a proton beam that delivers a dose distribution that provides a good trade-off between target coverage and excess of organs at risk. For a target, the goal is usually to create a high and uniform dose volume. In the case of OARs, dose tolerance and the importance of the mean dose or maximum dose vary depending on the type of organ.

[0014] Treatment plans need to be robust to range uncertainties including systematic and random fractionated patient setup errors to ensure that the target volume receives a tumor lethal dose and that the OAR dose remains below the complication threshold. Robustness assessment of treatment plans against setup and range uncertainties can aid the treatment planning process and may impact clinical decision making. In order to evaluate a treatment plan, a dose distribution can be calculated based on evaluating the treatment plan under nominal conditions and one or more uncertainty conditions that deviate from the nominal conditions, respectively. Such a dose distribution can be calculated separately for the target structure and surrounding tissues (e.g., one or more OARs). The clinical user can then determine the quality and robustness of the treatment plan based on the dose distribution of the target and / or the dose distribution of the surrounding tissues.

[0015] The dose distribution can be represented by a dose volume histogram (DVH). Conventionally, the dose distribution under different conditions (e.g., nominal conditions and uncertainty conditions) for different structures (e.g., targets and OARs) is graphically presented on a two-dimensional (2D) DVH map. Viewing and interpreting the dose distribution from such a DVH map can be challenging because the DVH of one structure may often overlap with the DVH of another structure, making it difficult to distinguish dose distributions between different structures. In addition, for a particular structure, the superposition of DVH clusters for different scenarios (e.g., nominal conditions and uncertainty conditions) does not provide easily understandable information about the robustness of the treatment plan, and there are almost no qualitative or quantitative indicators of robustness. For at least the reasons described above, the inventors have recognized that there is an unmet need for improving the assessment of the robustness of treatment plans and for more effectively presenting such information.

[0016] This document discusses systems and methods for evaluating and presenting the robustness of a radiotherapy treatment plan used in radiotherapy. An exemplary system includes: a processor that generates a dose distribution at an anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions in a radiation simulation according to a radiotherapy treatment plan under evaluation, determines a dose distribution characteristic of the anatomical structure using the received dose distribution, and generates a robustness indicator for the radiotherapy treatment plan. The dose distribution can be determined at a target structure and one or more structures at risk (e.g., OARs) and graphically presented in a three-dimensional (3D) dose-volume-structure space. An output circuit can output the dose distribution characteristic or robustness indicator to a user or a treatment planning system.

[0017] Various examples discussed herein can improve the process of evaluating the robustness of treatment plans and presenting the robustness of treatment plans to users. Compared with conventional 2D DVH maps, 3D DVH maps as discussed according to various examples in this document better organize and present information on dose distributions (e.g., DVHs) for different simulation scenarios (e.g., nominal conditions and multiple artificially imposed uncertainty conditions) at different structures (e.g., target structures and one or more adjacent OARs). DVHs for different structures are unfolded along the "structure" axis in the 3D dose-volume-structure space. This can prevent or substantially reduce the chance of overlapping DVHs, thereby allowing clinical users to more accurately and effectively identify the differences between dose distributions of different structures under different simulation scenarios. According to some examples, clinical users are provided with graphical control tools to manipulate 3D DVH maps to improve viewing experience, control flexibility, and more comprehensive evaluation of treatment plan robustness. This document further describes various robustness indicators representing, for example, statistical features of DVHs, which can be presented on 3D DVH maps. Robustness indicators provide more concise and meaningful information about the robustness of the treatment plan under evaluation. As a result, treatment plans can be developed more efficiently, clinician time and effort can be reduced, and an enhanced user experience and more accurate assessment of plan robustness can be achieved. By implementing an improved treatment plan evaluation process as discussed herein, individualized radiotherapy and patient treatment outcomes can be improved, and overall cost savings associated with radiotherapy treatment planning can be achieved.

[0018] The above is intended to provide an overview of the subject matter of this patent application. The above is not intended to provide an exclusive or exhaustive explanation of the present invention. The specific implementation is included to provide further information about this patent application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are not necessarily drawn to scale, and in the drawings, like reference numerals describe substantially similar components throughout the several views. Like reference numerals with different letter suffixes represent different instances of substantially similar components. The accompanying drawings generally illustrate various examples discussed in this document by way of example and not by way of limitation.

[0020] Figure 1 An exemplary radiation therapy system is shown.

[0021] Figure 2A An exemplary radiation therapy system that can provide a therapy beam is shown.

[0022] Figure 2B An exemplary combined system including a computed tomography (CT) imaging system and a radiation therapy system is shown.

[0023] Figure 3A partial cross-sectional view of an exemplary combined system including a magnetic resonance (MR) imaging system and a radiation therapy system is shown.

[0024] Figure 4 An example of a particle therapy system configured to provide proton therapy is shown.

[0025] Figure 5 It is a diagram showing the radiation dose depth (depth-dose distribution) of various types of particles in human tissue.

[0026] Figure 6 is a graph showing the spread of the Bragg peak (SOBP).

[0027] Figure 7 An example of an actively scanning proton beam delivery system is shown.

[0028] Figure 8 is a diagram showing a conventional two-dimensional DVH graph.

[0029] Fig. 9 is a diagram showing an example of a three-dimensional DVH map spanning in dose-volume-structure space.

[0030] Fig.10 is a diagram showing an example of a 3D DVH map in the case of user-controlled display of dose distribution characteristics.

[0031] Fig.11 is a flow chart illustrating an example of a method for evaluating the robustness of a radiation therapy treatment plan.

[0032] Fig.12 A block diagram is generally shown of an example machine on which any one or more of the techniques (eg, methodologies) discussed herein may be performed. DETAILED DESCRIPTION

[0033] In the following detailed description, reference is made to the accompanying drawings, which form a part of this document and are shown by way of illustrated specific examples in which the present disclosure may be practiced. These examples, also referred to herein as "examples," are described in sufficient detail to enable those skilled in the art to practice the present disclosure, and it should be understood that the examples may be combined or other examples may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present disclosure. Therefore, the following detailed description should not be considered to have a limiting meaning, and the scope of the present disclosure is limited by the attached aspects and their equivalents.

[0034] Figure 1An exemplary radiation therapy system 100 for providing radiation therapy to a patient is shown. The radiation therapy system 100 includes, among other components, a data processing device 112. The data processing device 112 may be connected to a network 120. The network 120 may be connected to the Internet 122. The network 120 may connect the data processing device 112 with one or more of: a database 124, a hospital database 126, an oncology information system (OIS) 128, a radiation therapy device 130, an image acquisition device 132, a display device 134, and a user interface 136. The data processing device 112 may be configured to generate a radiation therapy treatment plan 142 to be used by the radiation therapy device 130.

[0035] The data processing device 112 may include a memory 116, a processor 114, and a communication interface 118. The memory 116 may store computer executable instructions, such as a radiation therapy treatment plan 142 (e.g., an original treatment plan, an adjusted treatment plan, etc.), an operating system 143, a software program 144, and any other computer executable instructions to be executed by the processor 114. The memory 116 may additionally store data, such as medical images 146, patient data 145, and other data required to implement the radiation therapy treatment plan 142.

[0036] The software program 144 may include one or more software packages that, when executed by a machine such as the processor 114, may perform specific image processing and generate a radiation therapy treatment plan 142. In an example, the software program 144 may convert a medical image in one format (e.g., MRI) to another format (e.g., CT) by generating a synthetic image such as a pseudo CT image. For example, the software program 144 may include an image processing program to train a prediction model for converting a medical image from a medical image 146 of one modality (e.g., an MR image) to a synthetic image of a different modality (e.g., a pseudo CT image); alternatively, the trained prediction model may convert a CT image to an MR image. In another example, the software program 144 may register a patient image (e.g., a CT image or an MR image) with a dose distribution (also represented as an image) of the patient so that corresponding image voxels and dose voxels are appropriately associated through a network. In yet another example, the software program 144 may replace a function of a patient image such as a signed distance function or a processed version of the image that emphasizes some aspect of the image information. Such a function may emphasize edges or differences in voxel texture, or any other structural aspect useful for neural network learning. The software program 144 may replace a function of the dose distribution that emphasizes some aspect of the dose information. Such a function may emphasize steep gradients around the target or any other structural aspect useful for neural network learning.

[0037] In an example, the software program 144 can generate projection images for a two-dimensional (2D) and / or 3D CT or MR image set depicting an anatomical structure (e.g., one or more targets and one or more OARs), the projection images representing different views of the anatomical structure from the perspective of a treatment gantry of a radiotherapy device. For example, the software program 144 can process a CT or MR image set and create a stack of projection images depicting different views of the anatomical structure depicted in the CT or MR image from various perspectives of the gantry of the radiotherapy device. In particular, one projection image can represent a view of the anatomical structure from 0 degrees of the gantry, a second projection image can represent a view of the anatomical structure from 45 degrees of the gantry, and a third projection image can represent a view of the anatomical structure from 90 degrees of the gantry. The degree can be the direction of the beam relative to a particular axis of the anatomical structure depicted in the CT or MR image. The axis can remain the same for each beam of different degrees.

[0038] In an example, the software program 144 can generate a graphical aperture image representation of the MLC leaf position at various gantry angles. These graphical aperture images are also referred to as aperture images. In particular, the software program 144 can receive a set of control points that are used to control the radiotherapy device to produce a shaped radiotherapy beam. The control points can represent other machine parameters such as beam intensity, gantry angle relative to the patient position, and leaf position of the MLC. Based on these control points, a graphical image can be generated to graphically represent the beam shape and intensity output by the MLC and jaws at a specific gantry angle. The software program 144 can align the graphical image of the aperture at a specific gantry angle with the corresponding projection image generated at that angle. The image is aligned with the projection and scaled so that the projection image pixels are aligned with the corresponding aperture image pixels.

[0039] The software program 144 may include treatment planning software. The treatment planning software, when executed, for example, by a treatment planning system (TPS), may generate a radiation therapy treatment plan 142. In an example, execution of the treatment planning software may generate a graphical aperture image representation of MLC leaf positions at a given gantry angle for an anatomical projection image representing a view of the anatomical structure from a given gantry angle.

[0040] As depicted, the software program 144 may include a beam model. The beam model is represented by various characteristics of a radiation beam that accompanies the introduction of a wide radiation field specific to the treatment machine and exits the radiation machine and is applied to the patient. Using an appropriately determined beam model, machine parameters or control points for a given type of machine may be calculated, and the radiation machine may output a beam from the MLC that achieves the same or similar estimated graphical aperture image representation of the MLC leaf positions and the same or similar intensity. The treatment planning software, when executed, may output an image representing an estimated image of the beam shape and intensity for a given gantry angle and for a given projection image of the gantry at that angle, and the functionality may calculate control points for a given radiotherapy device to achieve that beam shape and intensity.

[0041] The beam model may be represented by a function of one or more beam model types that characterize various features of one or more radiation modes, such as photons or electrons. Different beam models may differ in the number and / or configuration of radiation sources. Therefore, beam model parameters (e.g., the size, position, energy spectrum, or fluence distribution of the radiation source) may vary for different beam model types. By way of example and not limitation, beam model parameters may include: the size and position of one or more photon sources within a radiation machine, the maximum energy or average energy of a photon spectrum of photons emitted from the radiation machine, a factor describing the shape of a photon spectrum emitted from the radiation machine, the size and position of one or more electron sources within a radiation machine, the maximum energy or average energy of an electron spectrum emitted from the radiation machine, a factor describing the shape of an electron spectrum, or one or more numbers describing how radiation (e.g., electrons or photons) emitted by a radiation machine may vary off-axis, etc.

[0042] In addition to the memory 116 storing the software program 144, the software program 144 may additionally or alternatively be stored on a removable computer medium, such as a hard drive, a computer disk, a CD-ROM, a DVD, an HD, a Blu-ray DVD, a USB flash drive, an SD card, a memory stick or any other suitable medium; and the software program 144 may be executed by the processor 114 when the software program 144 is downloaded to the data processing device 112.

[0043] The processor 114 may be communicatively coupled to the memory 116, and the processor 114 may be configured to execute computer executable instructions stored in the memory 116. The processor 114 may send the medical image 146 to the memory 116 or receive the medical image 146 from the memory 116. For example, the processor 114 may receive the medical image 146 from the image acquisition device 132 via the communication interface 118 and the network 120 to store the medical image 146 in the memory 116. The processor 114 may also send the medical image 146 stored in the memory 116 via the communication interface 118 and the network 120 to store the medical image 146 in the database 124 or the hospital database 126.

[0044] The processor 114 can generate a beam model for a specific radiation machine (with a specific collimator type and / or energy level). The generated beam model can be stored in the software program 144. In an example, the beam model can be presented to the user, such as displayed on the display device 134. Other information can be presented to the user (e.g., displayed on the display device 134), such as a report containing beam model parameters, geometric information, dose calculation settings, and fitting results, which shows both the measured dose distribution and the dose distribution calculated based on the beam model. Fitting results. In an example, the beam model can be fed to the TPS for clinical treatment planning.

[0045] In some examples, the processor 114 may validate the bundle model after it is generated, before deploying the bundle model to the TPS for clinical use. Validation may include importing the bundle model into the TPS to execute treatment planning software (e.g., Elekta AB manufactured by Stockholm, Sweden). A treatment planning system) is used to calculate a dose distribution in a virtual phantom (e.g., a water phantom). The algorithm used to calculate the dose distribution in the virtual phantom may include a Monte Carlo dose algorithm, such as an XVMC algorithm. The calculated dose distribution may be compared with measured beam characteristics from a target radiation machine (e.g., a linac) to determine whether the calculated dose meets a dosimetry verification criterion (also called a delivery criterion), such as one or more dose measurements falling within a tolerance range (±x%) relative to the measured dose measurements.

[0046] The processor 114 may include a dose engine configured to calculate dose metrics or dose statistics using a beam model. Various algorithms may be used to calculate dose. In an example, the dose engine may use a Monte Carlo algorithm or a folded cone convolution technique (CCC) algorithm (which may be implemented as a software package stored in the software program 144) to calculate dose metrics or dose statistics. Examples of dose engines may include a voxel Monte Carlo (VMC) dose engine, an X-ray voxel Monte Carlo (XVMC) dose engine, or a GPU Monte Carlo dose (GPUMCD).

[0047] The processor 114 may include at least a portion of a treatment planning system (TPS) configured to execute treatment planning software (as part of the software program 144) and generate a radiation therapy treatment plan 142 using the beam model, the medical image 146, and the patient data 145. The medical image 146 may include information such as imaging data associated with a patient's anatomical region, organ, or volume of interest segmentation data. The patient data 145 may include information such as: functional organ modeling data (e.g., serial organs vs. parallel organs, appropriate dose response models, etc.); radiation dose data (e.g., DVH information); or other clinical information about the patient and treatment (e.g., other surgeries, chemotherapy, previous radiation therapy, etc.).

[0048] In some examples, the processor 114 can analyze the robustness of the candidate treatment plan. The processor 114 can calculate the dose distribution at one or more anatomical structures during a radiation simulation using the candidate treatment plan. The dose distribution can be calculated separately for a target structure to receive radiation therapy and one or more nearby endangered structures (e.g., OARs) to avoid radiation therapy. For a specific anatomical structure, the dose distribution can be calculated under different simulation scenarios corresponding to, for example, nominal conditions and one or more artificially imposed uncertainty conditions, wherein the one or more artificially imposed uncertainty conditions represent corresponding deviations from the nominal conditions. The processor 114 can use the received dose distribution to determine the dose distribution characteristics for each of the anatomical structures (e.g., target structures or OARs) separately. In some examples, the dose distribution can be represented by a dose volume histogram (DVH). The processor 114 can use the DVH to generate the dose distribution characteristics of the anatomical structure. By way of example and not limitation, and as described below with reference to Fig. 9As further discussed, dose distribution characteristics may include: boundary DVHs, a range of DVHs due to applied uncertainty conditions, a DVH band that graphically represents the range of DVHs, extreme scenario DVHs that represent uncertainty DVHs that deviate significantly from a nominal DVH based on a particular dosimetry standard, identification of out-of-range DVHs that fall outside of a tolerance margin based on the dosimetry standard, an out-of-range sub-band that graphically represents the range of the out-of-range DVHs, a count of out-of-range DVHs, or a number (e.g., a percentage) relative to the total number of uncertainty DVHs, etc.

[0049] The processor 114 may generate a robustness indicator for a candidate treatment plan based on the dose distribution characteristics. The dose distribution characteristics or robustness indicators may be stored in the memory 116 and may be accessed by a user or a treatment planning system. In an example, the dose distribution characteristics or robustness indicators may be presented to a user, such as displayed on a display device 134. In an example, a three-dimensional (3D) DVH map may be generated and displayed on the display device 134. In some examples, the user interface 136 may include one or more user controls (e.g., on-screen control elements, or a touch screen capable of finger touch and navigation control), which enable the user to manipulate the display of the DVH map to more effectively reveal the differences in dose distribution characteristics between different structures. The dose distribution characteristics or robustness indicators may be provided to the TPS in addition or alternatively. For example, if the dose distribution characteristics or robustness indicators meet a specific robustness criterion indicating that the treatment plan under evaluation is robust, the treatment plan may be stored in the memory 116 (as a radiation therapy treatment plan 142) and deployed in the patient's radiation therapy treatment. However, if the treatment plan does not meet certain robustness criteria, the user can reject the treatment plan or modify the treatment plan, such as increasing the treatment margin or changing the radiation direction. In an example, a suggestion for accepting, rejecting, or modifying the treatment plan can be automatically generated and displayed on the display device 134 to prompt the user for input. Fig. 9 Examples of DVH plots and various dose distribution characteristics are discussed.

[0050] In some examples, the processor 114 may utilize the software program 144 to generate intermediate data, such as, for example, updated parameters to be used by a machine learning model such as a neural network model; or to generate an intermediate 2D or 3D image, which may then be subsequently stored in the memory 116. The processor 114 may then subsequently transmit the executable radiation therapy treatment plan 142 to the radiation therapy device 130 via the communication interface 118 to the network 120, where the radiation therapy plan may be used to treat the patient with radiation. In addition, the processor 114 may execute the software program 144 to implement functions such as image conversion, image segmentation, deep learning, neural networks, and artificial intelligence. For example, the processor 114 may execute a software program 144 that trains or contours medical images; such a software program 144, when executed, may train a boundary detector or utilize a shape dictionary.

[0051] The processor 114 may be a processing device including: one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc. More specifically, the processor 114 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that implements other instruction sets, or a processor that implements a combination of instruction sets. The processor 114 may also be implemented by one or more special-purpose processing devices such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), etc. As will be appreciated by those skilled in the art, in some examples, the processor 114 may be a special-purpose processor rather than a general-purpose processor. The processor 114 may include one or more known processing devices, such as those from Intel. TM Pentium TM 、Core TM , Xeon TM or series of microprocessors from AMD TM Made by Turion TM , Athlon TM 、Sempron TM 、Opteron TM FX TM 、Phenom TM series of microprocessors, or any of a variety of processors manufactured by Sun Microsystems. Processor 114 may also include a graphics processing unit, such as a graphics processing unit from Nvidia TM Manufactured series, by Intel TM GMA, Iris TM series or by AMD TM Made by Radeon TM Processor 114 may also include an accelerated processing unit, such as an Intel TM Xeon Phi manufactured TM The disclosed examples are not limited to any type of processor that is otherwise configured to meet the computational requirements of identifying, analyzing, maintaining, generating and / or providing large amounts of data or manipulating such data to perform the methods disclosed herein. In addition, the term "processor" may include more than one processor (e.g., a multi-core design or multiple processors each having a multi-core design). The processor 114 may execute sequences of computer program instructions stored in the memory 116 to perform various operations, processes, and methods that will be described in more detail below.

[0052] The memory 116 may store medical images 146. In some examples, the medical images 146 may include one or more MR images (e.g., 2D MRI, 3D MRI, 2D flow MRI, four-dimensional (4D) MRI, 4D volume MRI, 4D imaging MRI, etc.), functional MR images (e.g., fMRI, DCE-MRI, diffusion MRI), CT images (e.g., 2D CT, cone beam CT, 3D CT, 4D CT), ultrasound images (e.g., 2D ultrasound, 3D ultrasound, 4D ultrasound), one or more projection images representing views of anatomical structures depicted in MRI, synthetic CT (pseudo-CT) and / or CT images at different angles of the gantry relative to the patient axis, PET images, X-ray images, fluoroscopic images, radiotherapy portal images, SPECT images, computer-generated synthetic images (e.g., pseudo-CT images), aperture images, graphical aperture image representations of MLC leaf positions at different gantry angles, etc. In addition, the medical images 146 may also include medical image data, such as training images and ground truth images, contour images, and dose images. In an example, the medical image 146 may be received from the image acquisition device 132. Thus, the image acquisition device 132 may include an MRI imaging device, a CT imaging device, a PET imaging device, an ultrasound imaging device, a fluoroscopy device, a SPECT imaging device, an integrated linac and MRI imaging device, or other medical imaging device for obtaining medical images of a patient. The medical image 146 may be received and stored in any data type or any format type that the data processing device 112 may use to perform operations consistent with the disclosed examples.

[0053] The memory 116 may be a non-transitory computer-readable medium such as a read-only memory (ROM), a phase-change random access memory (PRAM), a static random access memory (SRAM), a flash memory, a random access memory (RAM), a dynamic random access memory (DRAM) such as a synchronous DRAM (SDRAM), an electrically erasable programmable read-only memory (EEPROM), a static memory (e.g., flash memory, a flash disk, a static random access memory), and other types of random access memory, a cache memory, a register, a CD-ROM, a DVD or other optical storage device, a cassette, other magnetic storage devices, or any other non-transitory medium that can be used to store information including (e.g., stored in any format) images, data, or computer executable instructions that can be accessed by the processor 114 or any other type of computer device. The computer program instructions can be accessed by the processor 114, read from the ROM or any other suitable memory location, and loaded into the RAM for execution by the processor 114. For example, the memory 116 may store one or more software applications. The software applications stored in the memory 116 may include, for example, an operating system 143 for common computer systems and for software-controlled devices. In addition, the memory 116 may store an entire software application or only a portion of a software application that may be executed by the processor 114. For example, the memory 116 may store one or more radiation therapy treatment plans 142.

[0054] The data processing device 112 can communicate with the network 120 via the communication interface 118, which can be communicatively coupled to the processor 114 and the memory 116. The communication interface 118 can provide a communication connection between the data processing device 112 and components of the radiation therapy system 100 (e.g., allowing data to be exchanged with external devices). For example, in some examples, the communication interface 118 can have appropriate interface circuitry to connect to the user interface 136, which can be a hardware keyboard, keypad, or touch screen through which a user can enter information into the radiation therapy system 100.

[0055] The communication interface 118 may include, for example, a network adapter, a cable connector, a serial connector, a USB connector, a parallel connector, a high-speed data transmission adapter (e.g., such as optical fiber, USB 3.0, thunderbolt, etc.), a wireless network adapter (e.g., such as a WiFi adapter), a telecommunication adapter (e.g., 3G, 4G / LTE, etc.), etc. The communication interface 118 may include one or more digital and / or analog communication devices that allow the data processing device 112 to communicate with other machines and devices, such as remotely located components, via the network 120.

[0056] The network 120 may provide the functionality of a local area network (LAN), a wireless network, a cloud computing environment (e.g., software as a service, platform as a service, infrastructure as a service, etc.), a client-server, a wide area network (WAN), etc. For example, the network 120 may be a LAN or a WAN, which may include other systems S1 (138), S2 (140), and S3 (141). The systems S1, S2, and S3 may be the same as the data processing device 112, or may be different systems. In some examples, one or more of the systems in the network 120 may form a distributed computing / simulation environment that collaboratively performs the examples described herein. In some examples, one or more of the systems S1, S2, and S3 may include a CT scanner that obtains a CT image (e.g., a medical image 146). In addition, the network 120 may be connected to the Internet 122 to communicate with servers and clients that are remotely residing on the Internet.

[0057] Thus, the network 120 may allow data transfer between the data processing device 112 and many different other systems and devices, such as the OIS 128, the radiation therapy device 130, and the image acquisition device 132. In addition, data generated by the OIS 128 and / or the image acquisition device 132 may be stored in the memory 116, the database 124, and / or the hospital database 126. Data may be transmitted / received via the network 120 through the communication interface 118 for access by the processor 114 as desired.

[0058] The data processing device 112 can communicate with the database 124 through the network 120 to send / receive a variety of different types of data stored on the database 124. For example, the database 124 can store machine data associated with the radiation therapy device 130, the image acquisition device 132, or other machines related to radiotherapy. The machine data information may include control points such as radiation beam size, arc placement, duration of beam on and off, machine parameters, segmentation, MLC configuration, gantry speed, MRI pulse sequence, etc. The database 124 can be a storage device and can be equipped with an appropriate database management software program. Those skilled in the art will understand that the database 124 may include multiple devices located in a central or distributed manner.

[0059] In some examples, database 124 may include a processor-readable storage medium (not shown). Although the processor-readable storage medium in the example may be a single medium, the term "processor-readable storage medium" should be deemed to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) storing one or more sets of computer-executable instructions or data. The term "processor-readable storage medium" should also be deemed to include any medium capable of storing or encoding instruction sets for execution by a processor and causing the processor to execute any one or more of the methods of the present disclosure. Therefore, the term "processor-readable storage medium" should be deemed to include, but is not limited to, solid-state memories, optical media, and magnetic media. For example, a processor-readable storage medium may be one or more volatile, non-transient, or non-volatile tangible computer-readable media.

[0060] The processor 114 may communicate with the database 124 to read images into the memory 116 or store images from the memory 116 to the database 124. For example, the database 124 may be configured to store a plurality of images (e.g., 3D MRI, 4D MRI, 2D MRI slice images, CT images, 2D fluoroscopic images, X-ray images, raw data from MR scans or CT scans, Digital Imaging and Communications in Medicine (DICOM) data, projection images, graphic aperture images, etc.) received by the database 124 from the image acquisition device 132. The database 124 may store data to be used by the processor 114 when executing the software program 144 or when creating the radiation therapy treatment plan 142. The data processing device 112 can receive imaging data, such as medical images 146 (e.g., 2D MRI slice images, CT images, 2D fluoroscopic images, X-ray images, 3D MR images, 4D MR images, projection images, graphic aperture images, etc.) from a database 124, a radiotherapy device 130 (e.g., MR-linac) and / or an image acquisition device 132 to generate a treatment plan 142.

[0061] In an example, the radiotherapy system 100 may include an image acquisition device 132 that is capable of acquiring medical images of a patient (e.g., MR images, 3D MRI, 2D flow MRI, 4D volume MRI, CT images, cone beam CT, PET images, functional MR images (e.g., fMRI, DCE-MRI, and diffusion MRI), X-ray images, fluoroscopic images, ultrasound images, radiotherapy portal images, SPECT images, etc.). The image acquisition device 132 may, for example, be an MRI imaging device, a CT imaging device, a PET imaging device, an ultrasound device, a fluoroscopic device, a SPECT imaging device, or any other suitable medical imaging device for obtaining one or more medical images of a patient. The images acquired by the image acquisition device 132 may be stored in the database 124 as imaging data and / or test data. By way of example, the images acquired by the image acquisition device 132 may also be stored in the memory 116 by the data processing device 112 as medical images 146.

[0062] In an example, for example, the image acquisition device 132 can be integrated into a single device with the radiation therapy device 130. For example, an MR imaging device can be combined with a linear accelerator to form a system called an "MR-linac". Such an MR-linac can be used, for example, to determine the location of a target organ or target tumor in a patient's body to accurately direct the radiation therapy to a predetermined target according to the radiation therapy treatment plan 142.

[0063] The image acquisition device 132 can be configured to acquire one or more images of the patient's anatomical structure for a region of interest (e.g., a target organ, a target tumor, or both). Each image, typically a 2D image or slice, can include one or more parameters (e.g., 2D slice thickness, orientation, and position, etc.). In an example, the image acquisition device 132 can acquire 2D slices of any orientation. For example, the orientation of the 2D slice can include a sagittal orientation, a coronal orientation, or an axial orientation. The processor 114 can adjust one or more parameters such as the thickness and / or orientation of the 2D slice to include the target organ and / or the target tumor. In an example, the 2D slice can be determined based on information such as a 3D MRI volume. For example, in the case of using a radiotherapy device 130, such a 2D slice can be acquired by the image acquisition device 132 in "real time" while the patient is receiving radiotherapy treatment, where "real time" means acquiring data at least in milliseconds or less.

[0064] The data processing device 112 can generate and store a radiation therapy treatment plan 142 for one or more patients. The radiation therapy treatment plan 142 can provide information about the specific radiation dose to be applied to each patient. The radiation therapy treatment plan 142 can also include other radiotherapy information such as control points, including beam angles, gantry angles, beam intensities, dose-histogram-volume (DVH) information, the number of radiation beams used during the therapy, the dose of each beam, etc.

[0065] Processor 114 may be configured to perform a treatment planning operation using a software program 144 such as treatment planning software (e.g., manufactured by Elekta AB of Sweden). ) to generate a radiation therapy treatment plan 142. To generate the radiation therapy treatment plan 142, the processor 114 can communicate with an image acquisition device 132 (e.g., a CT device, an MRI device, a PET device, an X-ray device, an ultrasound device, etc.) to access images of the patient and depict a target such as a tumor. In some examples, it may be necessary to depict one or more OARs such as healthy tissue surrounding a tumor or close to a tumor. Therefore, when the OAR is close to the target tumor, segmentation of the OAR can be performed. In addition, if the target tumor is close to the OAR (e.g., the prostate is close to the bladder and rectum), by segmenting the OAR relative to the tumor, the radiation therapy system 100 can not only study the dose distribution in the target, but also study the dose distribution in the OAR.

[0066] In order to depict the target organ or target tumor relative to the OAR, medical images such as MR images, CT images, PET images, fMR images, X-ray images, ultrasound images, radiotherapy field images, SPECT images, etc. of the patient undergoing radiotherapy can be non-invasively obtained by the image acquisition device 132 to reveal the internal structure of the body part. Based on the information from the medical image, the 3D structure of the relevant anatomical part can be obtained. In addition, during the treatment planning process, many parameters can be considered to achieve a balance between effective treatment of the target tumor (for example, so that the target tumor receives sufficient radiation dose for effective treatment) and low radiation to the OAR (for example, the OAR receives the lowest possible radiation dose). Other parameters that can be considered include the location of the target organ and the target tumor, the location of the OAR, and the movement of the target relative to the OAR. For example, the 3D structure can be obtained by outlining the outline of the target in each 2D layer or slice of the MRI or CT image or outlining the outline of the OAR and combining the outlines of each 2D layer or slice. It can be manually (for example, by a physician, dosimetrist or medical staff using a program such as Elekta AB manufactured by Sweden). ) or automatically (e.g., using a program such as the Atlas-based automatic segmentation software ABAS manufactured by Elekta AB of Sweden TM) Generate a contour. In some examples, the 3D structure of the target tumor or OAR can be automatically generated by the treatment planning software.

[0067] After the target tumor and (one or more) OARs have been located and delineated, the dosimetrist, physician, or medical staff can determine the radiation dose to be applied to the target tumor and any maximum amount of dose that can be received by the OARs close to the tumor (e.g., left and right parotid glands, optic nerves, eyes, lenses, inner ears, spinal cord, brainstem, etc.). After the radiation dose is determined for each anatomical structure (e.g., target tumor, OAR), a process known as inverse planning can be performed to determine one or more treatment planning parameters that will achieve the desired radiation dose distribution. Examples of treatment planning parameters include volume delineation parameters (e.g., which define target volumes, contour-sensitive structures, etc.), margins around target tumors and OARs, beam angle selection, collimator settings, and beam on time. During the inverse planning process, the physician can define dose constraint parameters that set limits on how much radiation the OARs can receive (e.g., define full dose to tumor targets and zero dose to any OAR; define 95% of the dose to target tumors; define spinal cord, brainstem, and optic nerve structures to receive ≤45Gy, ≤55Gy, and <54Gy, respectively). The results of the inverse planning may constitute a radiation therapy treatment plan 142 that may be stored in the memory 116 or the database 124. Some of these treatment parameters may be related. For example, adjusting one parameter (e.g., weightings for different objectives, such as increasing the dose to the target tumor) in an attempt to change the treatment plan may affect at least one other parameter, which in turn may result in the development of a different treatment plan. Thus, the data processing device 112 may generate a customized radiation therapy treatment plan 142 having these parameters so that the radiation therapy device 130 provides the radiation therapy treatment to the patient.

[0068] In addition, the radiation therapy system 100 may include a display device 134 and a user interface 136. The display device 134 may include one or more display screens that display medical images, interface information, treatment plan parameters (e.g., projection images, graphic aperture images, contours, doses, beam angles, etc.), treatment plans, targets, positioning targets, and / or tracking targets, or any related information to the user. The user interface 136 may be a keyboard, a keypad, a touch screen, or any type of device that a user can use to input information to the radiation therapy system 100. Alternatively, the display device 134 and the user interface 136 may be integrated into a device such as a tablet computer.

[0069] In addition, any and all components of the radiotherapy system 100 can be implemented as virtual machines (e.g., VMWare, Hyper-V, etc.). For example, a virtual machine can be software that acts as hardware. Therefore, the virtual machine can include at least one or more virtual processors, one or more virtual memories, and one or more virtual communication interfaces that act together as hardware. For example, the data processing device 112, OIS128, and image acquisition device 132 can be implemented as virtual machines. In view of the processing power, memory, and computing power available, the entire radiotherapy system 100 can be implemented as a virtual machine.

[0070] Figure 2A An exemplary radiotherapy device 202 is shown, which may include a radiation source (e.g., an x-ray source or linac), a table 216, an imaging detector 214, and a radiotherapy output 204. The radiotherapy device 202 may be configured to emit a radiation beam 208 to provide treatment to a patient. The radiotherapy output 204 may include one or more attenuators or collimators, such as MLCs. The patient may be positioned in the area 212 and supported by the table 216 to receive a radiotherapy dose according to a radiotherapy treatment plan. The radiotherapy output 204 may be mounted or attached to a gantry 206 or other mechanical support. When the table 216 is inserted into the treatment area, one or more chassis motors (not shown) may rotate the gantry 206 and the radiotherapy output 204 around the table 216. In an example, when the table 216 is inserted into the treatment area, the gantry 206 may rotate continuously around the table 216. In another example, when the table 216 is inserted into the treatment area, the gantry 206 may rotate to a predetermined position. For example, the gantry 206 can be configured to rotate the therapy output 204 about an axis ("A"). The table 216 can be independently moved to other positions around the patient, for example, being movable in a transverse direction ("T"), being movable in a lateral direction ("L"), or being rotatable about one or more other axes, such as about a transverse axis (shown as "R"). A controller communicatively connected to one or more actuators (not shown) can control the movement or rotation of the table 216 so as to appropriately position the patient within or outside of the radiation beam 208 according to the radiation therapy treatment plan. Both the table 216 and the gantry 206 can be moved independently of each other in multiple degrees of freedom, which allows the patient to be positioned so that the radiation beam 208 can target the tumor. The MLC can be integrated with the gantry 206 to deliver a radiation beam 208 of a specific shape.

[0071] Figure 2AThe coordinate system shown (including axes A, T, and L) can have an origin located at an isocenter 210. The isocenter can be defined as the location where the central axis of the radiation beam 208 intersects the origin of the coordinate axes so as to deliver a prescribed radiation dose to a location on or within a patient. Alternatively, the isocenter 210 can be defined as the location where the central axis of the radiation beam 208 intersects the patient for various rotational positions of the radiation therapy output 204 positioned by the gantry 206 about axis A. As discussed herein, the gantry angle corresponds to the position of the gantry 206 relative to axis A, although any other axis or combination of axes can be referenced and used to determine the gantry angle.

[0072] The linac system can have an imaging detector 214 preferably opposite to the radiation therapy output 204. In an example, the imaging detector 214 can be located within the field of the therapy beam 208. The imaging detector 214 can remain aligned with the therapy beam 208. The imaging detector 214 can rotate around the rotation axis as the gantry 206 rotates. In an example, the imaging detector 214 can be a flat panel detector (e.g., a direct detector or a scintillator detector). In this way, the imaging detector 214 can monitor the therapy beam 208, or generate an image of the patient's anatomical structure. The control circuit system of the radiotherapy device 202 can be integrated into the system 100 or remote from the system 100.

[0073] In the illustrative example, one or more of the table 216, the therapy output 204, or the gantry 206 can be automatically positioned, and the therapy output 204 can establish the therapy beam 208 according to the specified dose for a particular therapy delivery instance. The therapy delivery sequence can be specified according to the radiation therapy treatment plan (e.g., using one or more different orientations or positions of the gantry 206, the table 216, or the therapy output 204). The therapy delivery can occur sequentially, but can be staggered in a desired treatment site (e.g., at the isocenter 210) on or in the patient. Thus, a prescribed dose of radiation therapy can be delivered to the treatment site while reducing or avoiding damage to tissue near the treatment site.

[0074] Figure 2B An exemplary radiation therapy apparatus 202 is shown that combines a radiation system (e.g., a linac) and a CT imaging system. The radiation therapy output 204 may include an MLC (not shown). The CT imaging system may include an imaging X-ray source 218 that provides X-ray energy, for example, in the kiloelectronvolt (keV) energy range. The imaging X-ray source 218 may provide a fan and / or cone beam 208 directed toward an imaging detector 222, such as a flat panel detector. The radiation therapy apparatus 202 may be similar to that described with respect to Figure 2AThe depicted system, for example, includes a radiation therapy output 204, a gantry 206, an examination table 216, and another imaging detector 214 (eg, a flat panel detector). An X-ray source 218 may provide a relatively low energy diagnostic X-ray beam for imaging.

[0075] like Figure 2B As shown, the radiation therapy output 204 and the x-ray source 218 can be mounted on the same rotating rotating mechanism, rotated 90 degrees apart from each other. In some examples, two or more x-ray sources can be mounted along the periphery of the rotating mechanism, so that each x-ray source has its own detector arrangement to provide diagnostic imaging at multiple angles simultaneously. Similarly, multiple radiation therapy outputs 204 can be provided.

[0076] Figure 3 An exemplary radiation therapy system 300 that combines a radiation system (e.g., linac) and a nuclear MR imaging system is shown, and is also referred to as an MR-linac system. The system 300 may include a viewing table 216, an image acquisition device 320, and a radiation delivery device 330. The system 300 may deliver radiation therapy to a patient according to a radiation therapy treatment plan (e.g., a treatment plan 142 generated and stored in the memory 116). In some examples, the image acquisition device 320 may correspond to Figure 1 An image acquisition device 132 can acquire an image of a first modality (for example, an MR image) or a destination image of a second modality (for example, a CT image).

[0077] The examination table 216 can support the patient during the treatment session. In some implementations, the examination table 216 can move along a horizontal translation axis (labeled "I") so that the examination table 216 can move a patient lying on the examination table 216 into and / or out of the system 300. The examination table 216 can also rotate about a central vertical rotation axis that is transverse to the translation axis. To allow such movement or rotation, the examination table 216 can have a motor (not shown) that enables the examination table 216 to move. A controller (not shown) can control these movements or rotations to appropriately position the patient according to the treatment plan.

[0078] In some examples, the image acquisition device 320 may include an MR imaging machine that can acquire 2D or 3D MR images of the patient before, during, and / or after a treatment session. The image acquisition device 320 may include a magnet 321 for generating a main magnetic field for magnetic resonance imaging. The magnetic field lines generated by the operation of the magnet 321 may extend substantially parallel to the central translation axis "I". The magnet 321 may include one or more coils whose axes extend parallel to the translation axis "I". In some examples, one or more coils in the magnet 321 may be spaced apart so that the central window 323 of the magnet 321 is free of coils. In other examples, the coils in the magnet 321 may be thin enough or have a reduced density so that the coils are substantially transparent to radiation of a wavelength generated by the radiotherapy device 330. In some examples, the image acquisition device 320 may also include one or more shielding coils that may generate magnetic fields of approximately equal amplitude and opposite polarity outside the magnet 321 to eliminate or reduce any magnetic field outside the magnet 321. As described below, the radiation source 331 of the radiotherapy device 330 can be positioned in a region where the magnetic field is at least cancelled to first order or reduced.

[0079] The image acquisition device 320 may also include two gradient coils 325 and 326, which may generate a gradient magnetic field superimposed on the main magnetic field. The coils 325 and 326 may generate a gradient in the magnetic field generated, which allows protons to be spatially encoded so that the position of the protons can be determined. The gradient coils 325 and 326 may be positioned around a common central axis with the magnet 321 and may be displaced along the central axis. The displacement may create a gap or window between the coils 325 and 326. In an example where the magnet 321 includes a central window 323 between the coils, the two windows may be aligned with each other.

[0080] In some examples, the image acquisition device 320 may be an imaging device other than MRI, for example, X-ray, CT, CBCT, spiral CT, PET, SPECT, optical tomography, fluorescence imaging, ultrasound imaging, radiotherapy portal imaging device, etc. As one of ordinary skill in the art will appreciate, the above description of the image acquisition device 320 relates to certain examples and is not intended to be limiting.

[0081] The radiotherapy device 330 may include a radiation source 331 (e.g., an x-ray source or linac) and a collimator such as an MLC 332. The collimator is a beam limiting device that can help shape the radiation beam coming out of the machine and can limit the maximum field size of the beam. The MLC 332 can be used to shape, direct, or modulate the intensity of the radiotherapy beam to a specified target site in the patient. The MLC 332 may include a metal collimator plate (also referred to as an MLC blade) that slides into position to form a desired field shape. The radiotherapy device 330 may be mounted on a chassis 335. When the examination table 216 is inserted into the treatment area, one or more chassis motors (not shown) can rotate the chassis 335 around the examination table 216. In an example, when the examination table 216 is inserted into the treatment area, the chassis 335 can rotate continuously around the examination table 216. The chassis 335 may also have an attached radiation detector (not shown) that is preferably positioned opposite the radiation source 331 and with the rotation axis of the chassis 335 positioned between the radiation source 331 and the detector. In addition, the device 330 may include a control circuit system (not shown) for controlling, for example, one or more of the examination table 216, the image acquisition device 320, and the radiation therapy device 330. The control circuit system of the radiation therapy device 330 may be integrated within the system 300 or remote from the system 300.

[0082] During a radiation therapy treatment session, a patient may be positioned on the viewing table 216. The system 300 may then move the viewing table 216 into a treatment area defined by the magnet 321 and coils 325, 326, and chassis 335. The control circuitry may then control the radiation source 331, MLC 332, and chassis motors to deliver radiation to the patient through the window between coils 325 and 326 according to the radiation therapy treatment plan.

[0083] FIG. 2A to FIG. 2B and Figure 3 The radiation therapy output configurations shown in the drawings - e.g., a configuration in which the radiation therapy output can rotate about a central axis (e.g., axis "A") - are for purposes of illustration and not limitation. Other radiation therapy output configurations may be used. For example, the radiation therapy output may be mounted to a robotic arm or manipulator having multiple degrees of freedom. In yet another example, the therapy output may be fixed (e.g., located in an area laterally separated from the patient), and a platform supporting the patient may be used to align the radiation therapy isocenter with a designated target site within the patient.

[0084] Figure 4An example of a particle therapy system 400 configured to provide a proton therapy beam is shown. The particle therapy system 400 includes an ion source 401 , an injector 403 , an accelerator 405 , an energy selector 407 , a plurality of bending magnets 409 , a plurality of scanning magnets 411 , and a nozzle 413 .

[0085] Ion source 401, such as a synchrotron (not shown), can be configured to provide a particle (e.g., proton) stream. The particle stream is transmitted to an injector 403, which uses the Coulomb force to provide an initial acceleration for the charged particles. The particles are further accelerated to about 70% of the speed of light by an accelerator 405. The acceleration provides energy to the particles, which determines the depth to which the particles can travel in the tissue. An energy selector 407 (e.g., a range scatterer) can be used to select the energy of the protons to be given to the patient. In an embodiment referred to as passive scattering, an optional range modulator 408 (e.g., also referred to as a ridge filter or range modulation wheel) can be used to widen the beam to accommodate the tumor. After selecting the energy, a set of bending magnets 409 can be used to transmit the proton stream to a radiotherapy treatment room in a hospital. In addition, a scanning magnet 411 (e.g., an xy magnet) is used to diffuse the proton beam into an accurate image of the tumor shape or to track an accurate image of the tumor shape. The nozzle 413 or components of the nozzle 413 (eg, collimation devices) may be used to further shape the proton beam. In various embodiments, the particle stream may consist of carbon ions, muons, or positively charged ions.

[0086] Figure 5 is a diagram showing the depth of radiation dose (depth-dose distribution) of various types of particles in human tissue. By way of example, the relative depth of penetration of photons (e.g., X-rays) relative to protons, relative to carbon ions into human tissue (e.g., including any radiation dose provided at a certain distance below the surface, including secondary radiation or scattering) is shown. Each radiation dose is shown relative to the peak dose of a proton beam having a single energy that has been set to 100%.

[0087] The dose delivered by a monoenergetic (e.g., single energy) proton beam can be matched to the target in depth. Since protons are massive and charged particles, they directly ionize atomic electrons. In each interaction, the proton loses only a small amount of its energy. Most of the protons travel in an almost straight line through the body until they have lost all of their energy. The depth at which the proton comes to rest depends on its initial energy. Most of the proton's energy is deposited just before the end of its range, which results in a peak in the depth dose distribution - called the Bragg peak. As Figure 5As shown, the proton beam indicates a flat region starting at about 25%, which gradually increases until a depth of about 10 cm in the tissue, at 15 cm it rapidly increases to the Bragg peak, and then advantageously drops to zero over a short distance. No additional dose is delivered at the end of the Bragg peak.

[0088] The photon beam (e.g., labeled X-rays) indicates an initial buildup due to electron scattering (e.g., the primary method by which X-rays deliver dose to tissue is by transferring energy to electrons in the tissue). This is followed by an exponential decay that continues beyond the distal edge of the target, which is at a depth of approximately 15 cm in the figure. The X-ray beam has an entry (skin) dose that is set to match that of the proton beam. Using normalization (e.g., scaling) at a depth of 15 cm, the dose caused by the x-rays is 40% of the dose provided by the proton beam, while the x-ray beam has a peak dose greater than 95% ("near" 100%) at a depth of approximately 3 cm. If the X-ray data were renormalized to achieve 100% dose at 15 cm, the peak dose at a depth of approximately 3 cm, at a location where dose is not desired (e.g., before the target), would be approximately 240%. Thus, with x-rays, a significant amount of dose is delivered before the target and a significant amount of dose is delivered after the target. A proton beam can deliver the same dose to a target while delivering less than half the integrated dose of an X-ray beam.

[0089] The monoenergetic carbon beam shows a flat region at a lower injection dose than the proton beam. The carbon beam has a sharp Bragg peak that drops off more steeply than the proton beam, but the carbon beam has a tail several centimeters behind the desired target (e.g., called a "spatulation tail" where some of the carbon nuclei fragment into helium ions), with an additional dose of about 10% or less. The carbon ion beam has undesirable injection and skin doses compared to the proton beam, but the carbon ion beam has a non-negligible dose delivered over the target.

[0090] Figure 6is a graph showing the Spreading of the Bragg Peak (SOBP), which represents the relative depth dose curve for a set of proton beam combinations of different initial energies, each of which has some energy divergence (e.g., variable absorption of energy in tissue). The desired result of having a uniform dose for a target of a specific thickness. As shown, the target is shown to have a proximal depth of approximately 10 cm, a distal depth of approximately 13 cm, and a target thickness of approximately 3 cm. Within the target, the dose is fairly uniform (with an average value normalized at 100%). The graph does not start at a depth of 0 cm, and the injection (skin) dose is not explicitly shown, but the characteristic of the injection area of ​​the proton beam is a relatively flat depth dose curve. Typically, the injection (skin) dose will be approximately 70% of the target dose (e.g., shown at the rightmost edge of the x-axis). SOBP can be obtained using a variety of methods, including using a scattered proton beam (variable absorption) in which energy modulation is performed using various devices (e.g., a static ridge filter or a dynamic range modulation wheel), or by selecting a large number of monoenergetic proton beams that do not undergo scattering.

[0091] Figure 7 A diagrammatic representation of an actively scanned proton beam delivery system is shown. As shown, a single layer of pencil beam scan is being delivered, wherein a beam spot grid is delineated on the patient in conjunction with an outline of the cross-sectional area to which particles are to be delivered. A specified amount of energy in the energy of the incoming monoenergetic proton beam is shifted by a range shifter (e.g., Figure 7 The particles are absorbed by a range shifter plate (in which is a range shifter plate) to obtain a beamlet with the desired energy to achieve a specific depth for the Bragg peak in the patient to treat a specified layer. A magnetic scanner has the ability to deflect particles in both vertical and horizontal directions. The strength of the magnetic field can be adjusted to control the deflection in a direction perpendicular to the magnetic field and the incoming beamlet. The rate at which the magnetic field strength can be adjusted determines the rate at which scanning can be performed. For example, the strength of the proton beamlet combined with the scan rate determines how much dose can be delivered to a specific area in a specific amount of time (e.g., in Figure 7 The magnetic field strengths can theoretically be adjusted independently of each other (similar to the Spin Master developed by Toronto, Canada). TM The children's toy "Etch a " mode; where the variable pencil beam intensity is not available in children's toys). The most common scanning scheme is to scan quickly in one direction and more slowly in a vertical direction in a raster manner, similar to early television control methods (e.g., cathode ray tubes (CRTs) using electrons rather than protons), but arbitrary patterns can be scanned (similar to the toys mentioned earlier). Delivery of different beam spots is achieved by increasing the scanning magnetic field strength and suppressing the pencil beam intensity between increments.

[0092] As mentioned above, treatment plans need to be robust to range uncertainties including systematic and random patient setup errors to ensure that the target volume receives a tumor lethal dose and that the OAR dose remains below the complication threshold. Robustness assessment of treatment plans against setup and range uncertainties can aid the treatment planning process and may influence clinical decision making.

[0093] The robustness of a radiotherapy treatment plan can be evaluated in a radiation simulation process including multiple simulation scenarios. The simulation scenario can include a dose simulation under nominal conditions and a dose simulation under one or more artificially imposed uncertainty conditions that deviate from the nominal conditions. The simulation scenario corresponding to the uncertainty condition is also referred to as a deviation scenario. A dose distribution can be generated for each simulation scenario using the treatment plan under evaluation. An example of a dose distribution is a dose volume histogram (DVH), which relates the radiation dose to the volume of an anatomical structure. For a specific anatomical structure, a DVH cluster can be calculated under different simulation scenarios (e.g., nominal conditions and one or more uncertainty conditions). For example, a DVH cluster can be calculated for a target structure to receive radiotherapy, and one or more separate DVH clusters can be calculated for one or more endangered structures (e.g., one or more OARs) to avoid radiotherapy.

[0094] Artificially imposed uncertainty conditions may include range uncertainty, patient setup error, or a combination thereof. In an example, the nominal condition includes the nominal position of the anatomical structure, and a deviation scenario may be simulated for treatment under a system setup error represented by one or more position deviations from the nominal position of the anatomical structure. The position deviation may include deviations in one or more directions of + / -X, + / -Y, or + / -Z. In another example, the nominal condition includes a nominal CT Hounsfield unit to proton stopping energy (HU-SP) conversion. A deviation scenario may be simulated for treatment under a Hounsfield unit (HU) range uncertainty represented by one or more percentile deviations from the nominal HU-SP conversion. In yet another example, a deviation scenario may be simulated for treatment under a combination of a system setup error and HU uncertainty. For example, with six different system setup errors (e.g., a setup error of 3 mm in each of the +X, -X, +Y, -Y, +Z, and -Z directions) and two different HU range uncertainty values ​​(e.g., 3% and -3%), twelve deviation scenarios can be simulated and dose distributions such as DVH can be calculated for each of these deviation scenarios.

[0095] The DVH can be used to assess the robustness of the radiation therapy treatment plan used to generate the DVH. Figure 8A conventional two-dimensional (2D) DVH map 800 is shown, which includes DVH clusters generated for different structures under various simulation scenarios using data (e.g., CT scans) generated by a therapy planning system according to a treatment plan to be evaluated. The 2D DVH map 800 can be displayed on a user interface such as the display device 134 of the system 100. In this example, a DVH cluster 810 (also referred to as "target DVH") for a target structure and three other DVH clusters 820, 830, and 840 for three different structures at risk OAR-1, OAR-2, and OAR-3 are shown in the 2D DVH map 800, respectively. Each cluster includes a corresponding nominal DVH (e.g., 811, 821, 831, and 841) corresponding to a nominal condition without artificially introduced errors or uncertainties, and a number of uncertainty DVHs (also referred to as deviation DVHs, e.g., 812, 822, 832, and 842) corresponding to artificially imposed uncertainty conditions such as range uncertainty, patient setup errors, or combinations thereof. By way of example, an uncertainty DVH may be created at a specific deviation from a nominal position in one or more of the + / -X, + / -Y, or + / -Z directions, a HU uncertainty value, or a combination of deviation from the nominal position and a HU uncertainty value, as discussed above.

[0096] Each DVH represents the volume of a structure that receives a dose greater than or equal to a particular dose or dose range. The x-axis of the DVH graph represents dose bins, each dose bin representing a dose range in gray (Gy) or centigray (cGy). The y-axis of the DVH graph represents the volume of a structure, or a relative volume represented by a percentage of the total volume of the structure under consideration that receives a dose greater than or equal to the corresponding dose bin. The volume of a structure can be determined by the total number of voxels (e.g., from a CT scan) characterized by a specified dose range for the organ under consideration. In the case of small bin sizes, the accumulated DVH takes on the appearance of a smooth line graph, such as Figure 8 As shown. The line is always tilted and starts from the upper left to the lower right. For structures that receive a very uniform dose (e.g., 100% of the volume in the volume receives exactly 10 Gy), the DVH will be displayed as a horizontal line at the top of the graph at 100% of the volume plotted vertically with a vertical drop at 10 Gy on the horizontal axis. Ideally, the dose received by the target structure will cover 100% of the target volume (i.e., the dose completely washes the target), while the dose received by the OAR will account for 0% of the OAR volume (i.e., the dose does not wash the OAR).

[0097] Clinical users may find it challenging to view and interpret dose distributions using the 2D DVH map 800, which includes DVH clusters corresponding to multiple structures, each structure including DVHs under multiple simulation scenarios (e.g., nominal conditions and multiple uncertainty conditions). For example, DVHs for different structures may overlap with each other, making it difficult to distinguish and determine the differences between the DVHs for different structures. For a particular structure, the superposition of DVH clusters under different scenarios (e.g., nominal conditions and uncertainty conditions) does not provide easily understandable information about the robustness of the treatment plan, and there are almost no qualitative or quantitative indicators of robustness. Clinical users also lack the flexibility to manipulate the DVH map or DVH clusters for better data visualization and interpretation.

[0098] Fig. 9 An example of a three-dimensional (3D) DVH map 900 that is an improvement over the conventional 2D DVH map 800 is shown. Compared to the 2D DVH map 800, the 3D DVH map 900 includes a "structure" dimension that represents different anatomical structures involved in the dose simulation, such as the target structure and one or more OARs. Instead of superimposing DVH information of different structures (e.g., DVH clusters 810, 820, 830, and 840) on the same 2D volume dose plane, Fig. 9 In the 3D Dose-Volume-Structure space, DVH information of different structures can be stacked along the "Structure" axis. Expanding DVH information along the "Structure" axis can improve the visualization and identification of dose distribution differences between different structures and across different simulation scenarios, thereby improving the assessment of treatment plan robustness.

[0099] As shown in the 3D DVH map 900, another improvement to the conventional 2D DVH map 800 includes dose distribution characteristics generated and presented on the DVH map. For example, instead of or in addition to the DVH clusters (e.g., 810, 820, 830, and 840) of the original DVH curve, the 3D DVH map shows additional DVH characteristics of the target structure 910 and DVH characteristics of OAR-1 920, OAR-2 930, and OAR-3 940. Fig. 9 As shown, the DVH characteristics of the target structure 910 may include a nominal DVH 811 (solid line) and one or more statistical features generated from the DVH cluster 810. Similarly, the DVH characteristics 920, 930, and 940 may each include a corresponding nominal DVH 821, 831, or 841 (solid line) and one or more statistical features generated from the DVH clusters 820, 830, or 840, respectively. Based on the DVH characteristics, the processor 114 may generate a robustness indicator for the treatment plan under evaluation.

[0100] Examples of DVH characteristics and robustness indicators generated therefrom are discussed below. In some examples, a composite robustness indicator may be generated using a combination of two or more different DVH characteristics as discussed herein.

[0101] DVH tape

[0102] The DVH band represents the DVH range due to the uncertainty conditions involved in the dose simulation. Using the target structure as an example, the DVH characteristics of the target structure 910 include a DVH band 912 bounded by a first boundary DVH 912A and a second boundary DVH 912B. The first boundary DVH 912A represents the highest received dose across the volume range of the target structure. The second boundary DVH 912B represents the lowest received dose across the volume range of the target structure. The first boundary DVH and the second boundary DVH can be identified from the DVH cluster 810. For example, the first boundary DVH 912A can be identified as the rightmost DVH relative to the nominal DVH 811, which indicates that the dose across all volumes of the target (X-axis from 0% to 100%) is higher than the nominal DVH. The second boundary DVH 912B can be identified as the leftmost DVH relative to the nominal DVH 811, which indicates that the dose across all volumes of the target is lower than the nominal DVH.

[0103] Alternatively, instead of selecting from DVH cluster 810, a first boundary DVH or a second boundary DVH may be calculated using DVH cluster 810. In an example, a first boundary DVH 912A may be determined by taking the maximum dose of all DVHs across cluster 810 at each volume level from 0% to 100%. Similarly, a second boundary DVH 912B may be determined by taking the minimum dose of all DVHs across cluster 810 at each volume level from 0% to 100%. The boundary DVHs 912A and 912B thus determined are the boundaries that enclose all DVHs in cluster 810.

[0104] The DVH range can be graphically represented by a DVH band bounded by a first boundary DVH 912A and a second boundary DVH 912B. The boundary DVHs and the DVH bands enclosed therein can be similarly determined, such as boundary DVHs 932A and 932B for OAR-2, as shown in FIG. Fig. 9 shown.

[0105] The DVH band, the first boundary DVH 912A, or the second boundary DVH 912B may be used to generate a robustness indicator for the treatment plan under evaluation. In an example, the “width” of the DVH band, such as the deviation Δ from the first boundary DVH, may be used. B To calculate the robustness index. In another example, the deviation Δ between the first boundary DVH and the nominal DVH can be used.B1 Or the deviation of the second boundary DVH from the nominal DVH Δ B2 To calculate robustness indicators. Deviation (such as Δ B , Δ B1 or Δ B2 ) can be measured at a specific volume (e.g. 90%), or taken as the maximum deviation across the entire volume range of 0% to 100%. Narrower DVH bands Δ B or narrow deviation Δ B1 or Δ B2 A high treatment plan robustness may be indicated. In some examples, the robustness indicator may be determined as the DVH bandwidth Δ of the target structure. B (or Δ B1 or Δ B2 ) and the DVH bandwidth Δ of one or more OARs B (or Δ B1 or Δ B2 ) is a combination (e.g., a weighted sum) of . If the robustness indicator satisfies a condition, such as falling below a threshold, the processor 114 may determine that the treatment plan is robust.

[0106] Extreme Scene DVH

[0107] For a particular anatomical structure, an extreme scenario DVH may be generated using a DVH cluster created for that anatomical structure under different simulation scenarios. Using target structure 910 as an example, extreme scenario DVH 913 (dashed line) represents the DVH identified from DVH cluster 810 that most significantly deviates from the dosimetry criterion. Extreme scenario DVH 913 is also referred to as a worst-case scenario DVH. The dosimetry criterion may be defined using a reference volume 916 (e.g., 95%), a reference radiation dose 917 (e.g., 4200 cGy), or a combination of a reference volume 916 and a reference radiation dose 917.

[0108] In an example, an extreme scenario DVH 913 may be determined as a DVH identified from the DVH cluster 810 that corresponds to a minimum radiation dose received by a reference volume 916 (e.g., 95%) of the target structure. In another example, an extreme scenario DVH 913 may be determined as a DVH corresponding to a minimum tissue volume of a target structure that receives a reference radiation dose 917 (e.g., 4200 cGy). For example, a clinical user may define a dosimetry criterion as 95% of the volume of the target structure receiving a dose level equal to or greater than 4200 cGy. The clinical user may then perform a dose simulation including 20 deviation scenarios with different uncertainty conditions. Accordingly, the processor 141 may generate 21 DVHs, including one nominal DVH corresponding to the nominal condition and 20 uncertainty DVHs corresponding to the 20 deviation scenarios. For the 95% reference volume 916, assume that the doses received by 95% of the target structure volume under different deviation scenarios are as follows: 4100 cGy (deviation scenario is 3 mm setup error and 3% HU range uncertainty in the +X direction); 4120 cGy (deviation scenario is 3 mm setup error and -3% HU range uncertainty in the -X direction); 4300 cGy (deviation scenario is 3 mm setup error and -3% HU range uncertainty in the +Y direction); 4328 cGy (deviation scenario is 3 mm setup error and 3% HU range uncertainty in the -Y direction); and 4428 cGy (deviation scenario is 3 mm setup error and 3% HU range uncertainty in the +Z direction), etc. The extreme scenario DVH 913 can then be identified as the DVH in the cluster 810 having the lowest dose received by 95% of the volume, which in this case is the DVH corresponding to 4100 cGy at the 95% volume marker. The lowest dose received by 95% of the volume was always present in the 21 scenario cases, regardless of whether the result passed the dosimetry criteria. In the example, the clinical user can view the extreme scenarios to evaluate the robustness of the treatment plan in terms of the dose received at the target structure without having to view each and every simulation scenario (e.g., the DVH in cluster 810).

[0109] For a structure at risk, such as OAR-2, corresponding to the DVH cluster 830, an extreme scenario DVH 933 (dashed line) may be determined as the DVH in the DVH cluster 830 corresponding to the maximum radiation dose received by a reference tissue volume 936 (e.g., 70%) of OAR-2. In another example, the extreme scenario DVH may be determined as the DVH corresponding to the maximum tissue volume of OAR-2 that receives a reference dose level 937 (e.g., 1000 cGy).

[0110] The extreme scenario DVH can be used to generate a robustness indicator for the treatment plan under evaluation. In an example, the deviation Δ from the extreme scenario DVH to a dosimetry standard such as a reference volume 916 or a reference radiation dose 917 can be used. WS To determine the robustness index. Smaller Δ WS Indicating that the robustness of the treatment plan is high. In some examples, the Δ WS and one or more Δ corresponding to one or more OARs WS The robustness index is calculated by combining (eg, weighted sum) the treatment plans. If the robustness index satisfies a condition such as falling below a threshold, the processor 114 may determine that the treatment plan is robust.

[0111] Over range DVH

[0112] The DVH characteristics for each of the structures may include identification of one or more DVHs in the corresponding DVH cluster that fail to meet the dosimetry criteria, e.g., fall outside a tolerance margin relative to the nominal DVH. The identified DVHs are referred to as "out-of-range DVHs." In contrast to the extreme scenario DVHs, which are the DVHs that most significantly deviate from the dosimetry criteria, the "out-of-range DVHs" include all those DVHs in the DVH cluster of a particular structure that fall outside a particular range according to the dosimetry criteria. Similar to the discussion above with reference to the extreme scenario DVHs, the dosimetry criteria may be defined using a reference volume 916 (e.g., 95%), a reference radiation dose 917 (e.g., 4200 cGy), or a combination of a reference volume 916 and a reference radiation dose 917.

[0113] The out-of-range DVHs for the target structure include those DVHs in the DVH cluster 810 that cause the radiation dose at the reference tissue volume to fall below the reference radiation dose by a tolerance margin. For example, for a 95% reference volume and a reference radiation dose of 4200 cGy, it is assumed that the dose received by 95% of the target structure volume under nominal conditions (corresponding to the nominal DVH 811) is 4350 cGy, and the dose received by 95% of the volume under different deviation conditions (corresponding to the deviation scenario DVH 812) is 4100 cGy, 4120 cGy, 4150, 4200, 4300 cGy, 4320 cGy, 4380 cGy, 4400 cGy, etc. Then, for a tolerance margin of 20 cGy, only those deviation scenarios having a dose level less than 4180 cGy at the 95% volume mark (dose level of the nominal DVH of 4200 cGy, less than a margin of 20 cGy) are counted as "out-of-range DVH".

[0114] For structures at risk such as OAR-2, out-of-range DVHs may similarly be identified as those DVHs in DVH cluster 830 whose radiation dose at a reference tissue volume (eg, 95%) exceeds the reference radiation dose by a tolerance margin.

[0115] The out-of-range DVHs identified for a structure (target structure or OAR) may be graphically represented by sub-bands covering the identified out-of-range DVHs, such as out-of-range sub-band 914 within the DVH band bounded by 912A and 912B for the target structure or out-of-range sub-band 934 within the DVH band bounded by 932A and 932B for OAR-2, as shown in FIG. Fig. 9 shown.

[0116] In some examples, a count of out-of-range DVHs or a number (eg, a percentage) relative to the total number of uncertainty DVHs may be determined and presented on DVH graph 900. For example, Fig. 9 It shows that 28% of the uncertainty DVHs generated for the target structure were identified as out-of-range DVHs, 32% of the uncertainty DVHs generated for OAR-1 were identified as out-of-range DVHs, 15% of the uncertainty DVHs generated for OAR-2 were identified as out-of-range DVHs, and 4% of the uncertainty DVHs generated for OAR-3 were identified as out-of-range DVHs.

[0117] like Fig. 9 As shown, for the target structure, the over-range DVH and the corresponding over-range sub-band 914 and the extreme scenario DVH 913 are below the nominal DVH 811 (or to the left of the nominal DVH 811). This is because for the target structure, it is generally expected that the target structure receives the full radiation dose for effective radiotherapy. Therefore, any simulation scenario with a lower radiation dose at the target structure (e.g., those DVHs within the extreme scenario DVH 913 and the over-range sub-band 914) is considered a "failed DVH". In contrast, for an OAR such as OAR-2, the over-range DVH and the corresponding over-range sub-band 934 and the extreme scenario DVH 933 are above the nominal DVH 831 (or to the right of the nominal DVH 831). This is because for the OAR, it is expected that the OAR receives less radiation dose or no radiation dose to avoid radiation. Therefore, any simulated scenario with a higher radiation dose at the OAR (eg, those DVHs within the extreme scenario DVH 933 and the out-of-range sub-band 934) is considered a "failed DVH."

[0118] The identified over-range DVH or over-range sub-band can be used to generate a robustness indicator for the treatment plan under evaluation. In an example, the width of the over-range sub-band can be used to calculate the robustness indicator. A narrower sub-band indicates that the robustness of the treatment plan is higher. In an example, the robustness indicator can be represented by an over-range DVH percentage number. A smaller over-range DVH percentage number indicates that the robustness of the treatment plan is higher. In some examples, the robustness indicator can be calculated using a combination (e.g., a weighted sum) of the following: (1) the over-range sub-band width or over-range DVH percentage number of the target structure, and (2) the over-range sub-band width or over-range DVH percentage number of one or more OARs. If the robustness indicator meets a condition such as falling below a threshold, the processor 114 can determine that the treatment plan is robust.

[0119] The DVH graph 900 may be displayed on a user interface such as the display device 134 of the system 100. Figure 1 As discussed, the user interface 136 may include one or more user controls (e.g., on-screen control elements, or a touch screen enabling finger touch and navigation control) that allow a user to manipulate the display of the DVH map 900 to more effectively reveal differences in dose distribution characteristics between different structures. Fig.10 An example of a user-controlled display of a dose distribution characteristic on a 3D DVH map 1000 is shown. In an example, via the user interface 136, a user can use a pointing device or finger taps on a touch screen to position a cursor at a particular area of ​​the 3D DVH map (e.g., a DVH band, an extreme scene DVH, or an out-of-range DVH sub-band, etc.). In response, more information about the dose distribution characteristic can be automatically displayed, for example, in a message box 1010. For example, Fig.10 As shown, when the cursor hovers over the extreme scenario DVH 913 of the target structure, information about the deviation scenario and the dose values ​​received by a specific percentage of the volume of the target structure can be automatically displayed. The display of information can be updated by moving the cursor along the extreme scenario DVH 913, or moving to different areas in the DVH characteristics of the target structure 910 (e.g., the nominal DVH 811, the boundary DVHs 912A and 912B or the DVH band therebetween, or the over-range DVH sub-band 914), or moving to areas in the DVH characteristics of different structures (e.g., 920 for OAR-1, 930 for OAR-2, and 940 for OAR-3).

[0120] Fig.111 is a flow chart illustrating an exemplary method 1100 for evaluating the robustness of a radiotherapy treatment plan for use in radiotherapy. The treatment plan needs to be robust to a range of uncertainties (e.g., systematic and random patient setup errors) to ensure that the target volume receives a tumor lethal dose and that the OAR dose remains below a complication threshold. Evaluating the robustness of the treatment plan against uncertainty can aid in the treatment planning process and may impact clinical decision making. In an example, the method 1100 can be implemented in and performed by a radiotherapy system 100, which can include a radiation machine that provides radiotherapy to a subject according to the treatment plan. For example, at least a portion of the method 1100 can be machine-readable instructions executable by a processor 114.

[0121] The robustness of the treatment plan can be evaluated in a radiation simulation process including multiple simulation scenarios. At 1110, a dose distribution at an anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions can be generated, for example, by using a processor 114 according to the treatment plan under evaluation. The artificially imposed uncertainty conditions representing conditions that deviate from the nominal conditions may include range uncertainty, patient setting errors, or a combination thereof. The simulation scenario corresponding to the uncertainty condition is also referred to as a deviation scenario. In an example, a deviation scenario can be simulated for treatment under a system setting error, and the system setting error is represented by a specific deviation from the nominal position corresponding to the nominal condition in one or more directions of + / -X, + / -Y or + / -Z directions. In another example, a treatment simulation scenario under a specific percentage of the Hounsfield unit (HU) range uncertainty can be performed. In yet another example, a treatment simulation scenario under a combination of system setting errors and HU uncertainty can be performed.

[0122] An example of a dose distribution is a dose volume histogram (DVH). The DVH at the anatomical structure may be calculated for different simulation scenarios, such as a nominal DVH under nominal conditions (ie, no artificially introduced errors or uncertainties) and a number of uncertainty DVHs under corresponding artificially imposed uncertainty conditions.

[0123] In an example, multiple anatomical structures may be analyzed in a dose simulation to assess the robustness of a treatment plan. Examples of anatomical structures may include a target structure to receive radiation therapy, and / or one or more structures at risk (e.g., one or more OARs) to be avoided for radiation therapy. Thus, in some examples, a first DVH cluster (including a nominal DVH and one or more uncertainty DVHs) may be generated for the target structure, and additional one or more DVH clusters may be generated for one or more OARs, respectively, as described above with reference to Figure 8 discussed.

[0124] At 1120, a dose distribution characteristic may be determined using a dose distribution generated for the anatomical structure, such as a DVH. The dose distribution characteristic may include one or more statistical features generated from the DVH for different simulation scenarios. Fig. 9 As discussed, examples of dose distribution characteristics generated from DVHs for anatomical structures may include: boundary DVHs, a range of DVHs due to applied uncertainty conditions, a DVH band that graphically represents the range of DVHs, an extreme scenario DVH (therefore also referred to as a worst-case scenario DVH) that represents the uncertainty DVH that most significantly deviates from the dosimetry standard, identification of out-of-range DVHs that fall outside the tolerance margin relative to the dosimetry standard, an out-of-range sub-band that graphically represents the range of the out-of-range DVHs, a count of out-of-range DVHs, or a number (e.g., a percentage) relative to the total number of uncertainty DVHs, and the like.

[0125] At 1130, a robustness indicator for the treatment plan under evaluation may be generated based on one or more of the dose distribution characteristics, such as DVH characteristics. In an example, the robustness indicator may be calculated using a DVH bandwidth, a deviation of a boundary DVH from a nominal DVH, a deviation of an extreme scenario DVH from a reference volume or reference radiation dose, an out-of-range sub-band width, or an out-of-range DVH percentage number, etc. In some cases, a composite robustness indicator may be generated using two or more different DVH characteristics as discussed herein. In some examples, the robustness indicator may be calculated using a combination (e.g., a weighted sum) of a DVH characteristic of a target structure and a DVH characteristic of one or more OARs.

[0126] At 1140, dose distribution characteristics (eg, DVH characteristics of the target structure and one or more OARs) or robustness indicators may be provided to a user or a treatment planning system (TPS). Fig. 9 As shown, dose distribution characteristics for different structures can be stacked on the 3D volume-dose-structure space along the "structure" axis. Expanding the DVH information along the "structure" axis can improve the visualization of differences in dose distributions at different structures in different simulation scenarios, thereby improving the assessment of the robustness of the treatment plan. DVH characteristics such as DVH bands and over-range DVH sub-bands, among other characteristics, are graphical representations of DVH curve statistics, which can further simplify the data interpretation and clinical decision making process. In some examples, upon receiving user input from a user interface (e.g., user interface 136), additional information about the dose distribution characteristics can be presented in a graphical form such as a graphical form. Fig.10 The user interface of the message box shown in .

[0127] The dose distribution characteristic or robustness indicator can be provided to the treatment planning system. For example, if the dose distribution characteristic or robustness indicator meets a specific robustness criterion indicating that the treatment plan under evaluation is robust, the treatment plan can be deployed in the radiation therapy treatment of the patient. However, if the treatment plan does not meet the specific robustness criterion, the user can reject the treatment plan, or modify the treatment plan such as increasing the treatment margin or changing the radiation direction. In an example, a suggestion for accepting, rejecting, or modifying the treatment plan can be automatically generated and displayed on the display device 134, prompting the user for input.

[0128] Fig.12 A block diagram of an example of a machine 1200 is shown, on which one or more of the methods discussed herein can be implemented. In one or more examples, one or more items of a data processing device 112 can be implemented by the machine 1200. In an alternative example, the machine 1200 operates as an independent device, or can be connected (e.g., networked) to other machines. In one or more examples, the data processing device 112 may include one or more of the items of the machine 1200. In a networked deployment, the machine 1200 can operate in the capacity of a server or client machine in a server-client network environment, or operate as a peer machine in a peer (or distributed) network environment. The machine can be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a network device, a network router, a switch or a bridge, or any machine capable of (sequentially or otherwise) executing instructions that specify actions to be taken by the machine. Further, while a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0129] The example machine 1200 includes a processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit, circuitry such as one or more transistors, resistors, capacitors, inductors, diodes, logic gates, multiplexers, buffers, modulators, demodulators, radios (e.g., transmitting or receiving radios or transceivers), sensors 1221 (e.g., transducers that convert one form of energy (e.g., light, heat, electricity, mechanical or other energy) to another form of energy), etc. or a combination thereof), a main memory 1204 and a static memory 1206, which communicate with each other via a bus 1208. The machine 1200 (e.g., a computer system) may also include a video display unit 1210 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The machine 1200 also includes an alphanumeric input device 1212 (e.g., a keyboard), a user interface (UI) navigation device 1214 (e.g., a mouse), a disk drive or mass storage unit 1216, a signal generating device 1218 (e.g., a speaker), and a network interface device 1220.

[0130] The disk drive unit 1216 includes a machine-readable medium 1222 on which is stored one or more sets of instructions and data structures (e.g., software) 1224 that embody or are utilized by any one or more of the methods or functions described herein. During execution of the instructions 1224 by the machine 1200, the instructions 1224 may also reside, in whole or in part, within the main memory 1204 and / or within the processor 1202, the main memory 1204 and the processor 1202 also constituting machine-readable media.

[0131] As shown, the machine 1200 includes an output controller 1228. The output controller 1228 manages the flow of data to and from the machine 1200. The output controller 1228 is sometimes referred to as a device controller, where software that interacts directly with the output controller 1228 is referred to as a device driver.

[0132] Although the machine-readable medium 1222 is shown as a single medium in the example, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) storing one or more instructions or data structures. The term "machine-readable medium" should also be considered to include any tangible medium capable of storing, encoding or carrying instructions or capable of storing, encoding or carrying data structures that are executed by a machine and cause the machine to perform any one or more of the methods of the present invention, which data structures are utilized by or associated with such instructions. Therefore, the term "machine-readable medium" should be considered to include, but are not limited to, solid-state memory and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, which includes, by way of example: semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0133] Instructions 1224 may also be sent or received over a communication network 1226 using transmission media. Instructions 1224 may be sent using a network interface device 1220 and any of a number of well-known transmission protocols (e.g., HTTP). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet, a mobile telephone network, a plain old telephone (POTS) network, and wireless data networks (e.g., WiFi and WiMax networks). The term "transmission medium" shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by a machine, and includes digital or analog communication signals or other intangible media that facilitates communication of such software.

[0134] As used herein, "communicatively coupled between" means that entities on either of the couplings must communicate through an item between them, and the entities cannot communicate with each other without communicating through the item.

[0135] A further description of various non-limiting exemplary embodiments is provided below. The exemplary embodiments described below may be practiced in conjunction with one or more other aspects or exemplary embodiments. That is, exemplary embodiments of the present invention, such as those described below, may be implemented, practiced or utilized in any combination (e.g., any combination that is suitable, practicable and / or feasible), and are not limited to those combinations described herein and / or included in the appended claims.

[0136] Example 1 is a system for evaluating the robustness of a radiotherapy treatment plan used by a radiotherapy device to treat a patient. The system includes a processor configured to: generate a dose distribution at an anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions in a radiation simulation according to the radiotherapy treatment plan; determine a dose distribution characteristic of the anatomical structure using the dose distribution; and generate a robustness indicator of the radiotherapy treatment plan based on the dose distribution characteristic. The system includes a memory configured to store a dose distribution characteristic or a robustness indicator that can be accessed by a user or a treatment planning system.

[0137] In Example 2, the subject matter of Example 1 optionally includes an anatomical structure, which may include a target structure to receive radiation therapy and at least one structure at risk to avoid radiation therapy. The processor may be configured to: determine a first dose distribution characteristic using a first dose distribution at the target structure, and determine a second dose distribution characteristic using a second dose distribution at the at least one structure at risk; and generate a three-dimensional (3D) graphical representation of the first dose distribution characteristic and the second dose distribution characteristic in a dose-volume-structure space to be displayed on a display.

[0138] In Example 3, the subject matter of any one or more of Examples 1 to 2 optionally includes a dose distribution, which may include a dose-volume histogram (DVH), each DVH representing a tissue volume of an anatomical structure that receives a corresponding radiation dose, the DVH including: a nominal DVH corresponding to a nominal condition; and one or more uncertainty DVHs corresponding to one or more artificially imposed uncertainty conditions using the dose distribution.

[0139] In Example 4, the subject matter of Example 3 optionally includes a dose distribution characteristic, which may include (1) a first boundary DVH corresponding to a lowest dose within a volumetric range of the anatomical structure and (2) a second boundary DVH corresponding to a highest dose within the volumetric range of the anatomical structure, the first boundary DVH and the second boundary DVH defining a DVH range. The processor circuit may be configured to generate a DVH band to be displayed on the display that graphically represents the DVH range.

[0140] In Example 5, the subject matter of Example 4 optionally includes a processor, which can be configured to generate a robustness indicator for the radiotherapy treatment plan based on at least one of: a first deviation of the first boundary DVH from the nominal DVH; a second deviation of the second boundary DVH from the nominal DVH; or a third deviation of the second boundary DVH from the first boundary DVH.

[0141] In Example 6, the subject matter of any one or more of Examples 3 to 5 optionally includes a processor that can be configured to: determine a dose distribution characteristic, including identifying an extreme scenario DVH from one or more uncertainty DVHs based on a dosimetry criterion including at least one of a reference tissue volume or a reference radiation dose; and generate a graphical representation of the extreme scenario DVH to be displayed on a display.

[0142] In Example 7, the subject matter of Example 6 can optionally include a processor that can be configured to generate a robustness indicator for the radiation therapy treatment plan based on deviations of the extreme scenario DVH from a dosimetry criterion.

[0143] In Example 8, the subject matter of any one or more of Examples 6 to 7 optionally includes a processor, which can be configured to: generate a first set of DVHs for a target structure to receive radiation therapy, the first set of DVHs including a first nominal DVH corresponding to a nominal condition and one or more first uncertainty DVHs corresponding to one or more artificially imposed uncertainty conditions; and identify a first extreme scenario DVH from the first set of DVHs, the first extreme scenario DVH having at least one of: (1) a minimum radiation dose received by a first reference tissue volume of the target structure, or (2) a minimum tissue volume of the target structure that receives the first reference radiation dose.

[0144] In Example 9, the subject matter of any one or more of Examples 6 to 8 optionally includes a processor, which can be configured to: generate a second set of DVHs for the endangered structure to be protected from radiation therapy, the second set of DVHs including a second nominal DVH corresponding to a nominal condition and one or more second uncertainty DVHs corresponding to one or more artificially imposed uncertainty conditions; and identify a second extreme scenario DVH from the second uncertainty DVHs, the second extreme scenario DVH having at least one of: (1) a maximum radiation dose received by a second reference tissue volume of the endangered structure, or (2) a maximum tissue volume of the endangered structure that receives the second reference radiation dose.

[0145] In Example 10, the subject matter of any one or more of Examples 3 to 9 optionally includes a processor that can be configured to: determine a dose distribution characteristic, including identifying one or more out-of-range DVHs that fall outside a tolerance margin from one or more uncertainty DVHs based on a dosimetry criterion including at least one of a reference tissue volume or a reference radiation dose; and generate an out-of-range DVH sub-band to be displayed on a display that graphically represents the identified one or more out-of-range DVHs.

[0146] In Example 11, the subject matter of Example 10 can optionally include a processor that can be configured to generate a robustness indicator for the radiation therapy treatment plan using the count of out-of-range DVHs relative to the count of one or more uncertainty DVHs.

[0147] In Example 12, the subject matter of any one or more of Examples 10 to 11 optionally includes a processor, which can be configured to: generate a first set of DVHs for a target structure to receive radiation therapy, the first set of DVHs including a first nominal DVH corresponding to a nominal condition and one or more first uncertainty DVHs corresponding to one or more artificially imposed uncertainty conditions; and identify one or more out-of-range DVHs from the first uncertainty DVHs, each out-of-range DVH having a radiation dose at a reference tissue volume that is lower than a first reference radiation dose by a first tolerance margin.

[0148] In Example 13, the subject matter of any one or more of Examples 10 to 12 optionally includes a processor, which can be configured to: generate a second set of DVHs for endangered structures to be avoided from radiation therapy, the second set of DVHs including a second nominal DVH corresponding to a nominal condition and one or more second uncertainty DVHs corresponding to one or more artificially imposed uncertainty conditions; and identify one or more out-of-range DVHs from the second uncertainty DVHs, each out-of-range DVH having a radiation dose at a reference tissue volume that is higher than a second reference radiation dose by a second tolerance margin.

[0149] In Example 14, the subject matter of any one or more of Examples 1 to 13 optionally includes radiotherapy, which may include proton therapy, and wherein, in the simulated proton therapy, a dose distribution is determined for the anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions.

[0150] In Example 15, the subject matter of Example 14 optionally includes one or more artificially imposed uncertainty conditions, which may include deviations from nominal conditions, the deviations representing one or more of range uncertainty or patient setup errors associated with the proton beam.

[0151] In Example 16, the subject matter of any one or more of Examples 1 to 15 may optionally include the processor, which may be further configured to modify the radiation therapy treatment plan or generate a recommendation to modify the radiation therapy treatment plan based on the robustness indicator of the radiation therapy treatment plan.

[0152] Example 17 is a method for evaluating the robustness of a radiotherapy treatment plan used by a radiotherapy device to treat a patient. The method includes the steps of: generating, via a processor, a dose distribution at an anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions using the radiotherapy treatment plan; determining a dose distribution characteristic of the anatomical structure using the dose distribution; generating a robustness indicator of the radiotherapy treatment plan based on the dose distribution characteristic; and providing the dose distribution characteristic or the robustness indicator to a user or a treatment planning system.

[0153] In Example 18, the subject matter of Example 17 optionally includes an anatomical structure, which may include a target structure to receive radiation therapy and at least one structure at risk to avoid radiation therapy. The method includes the steps of determining a first dose distribution characteristic using a first dose distribution at the target structure, and determining a second dose distribution characteristic using a second dose distribution at the at least one structure at risk; and generating a three-dimensional (3D) graphical representation of the first dose distribution characteristic and the second dose distribution characteristic in a dose-volume-structure space.

[0154] In Example 19, the subject matter of any one or more of Examples 17 to 18 optionally includes a dose distribution, which may include a dose-volume histogram (DVH), each DVH representing a tissue volume of an anatomical structure that receives a corresponding radiation dose. The DVH may include: a nominal DVH corresponding to a nominal condition; and one or more uncertainty DVHs corresponding to one or more artificially imposed uncertainty conditions using the dose distribution.

[0155] In Example 20, the subject matter of Example 19 optionally includes a dose distribution characteristic, which may include (1) a first boundary DVH corresponding to a lowest dose within a volumetric range of the anatomical structure and (2) a second boundary DVH corresponding to a highest dose within the volumetric range of the anatomical structure, wherein: a robustness indicator for generating a radiotherapy treatment plan is based on a DVH range defined by the first boundary DVH and the second boundary DVH; and providing the dose distribution characteristic or the robustness indicator includes displaying a DVH band that graphically represents the DVH range.

[0156] In Example 21, the subject matter of any one or more of Examples 19 to 20 optionally includes the following steps: identifying an extreme scenario DVH that meets dosimetry criteria relative to a nominal DVH from one or more uncertainty DVHs; and generating a robustness indicator for a radiotherapy treatment plan based on deviations of the extreme scenario DVH from the nominal DVH; wherein providing a dose distribution characteristic or a robustness indicator includes displaying a graphical representation of the extreme scenario DVH.

[0157] In Example 22, the subject matter of any one or more of Examples 19 to 21 optionally includes the following steps: identifying one or more out-of-range DVHs that fall outside a tolerance margin relative to a nominal DVH from one or more uncertainty DVHs; and generating a robustness indicator for a radiotherapy treatment plan based on a count of the out-of-range DVHs relative to the count of the one or more uncertainty DVHs; wherein providing a dose distribution characteristic or a robustness indicator includes displaying an out-of-range DVH sub-band that graphically represents the identified one or more out-of-range DVHs.

[0158] In Example 23, the subject matter of any one or more of Examples 17 to 22 optionally includes modifying the radiation therapy treatment plan or generating a recommendation to modify the radiation therapy treatment plan based on the robustness indicator of the radiation therapy treatment plan.

[0159] In Example 24, the subject matter of any one or more of Examples 17 to 23 optionally includes radiotherapy, which may include proton therapy, and wherein the one or more artificially imposed uncertainty conditions may include deviations from nominal conditions, the deviations representing one or more of range uncertainty or patient setup errors associated with the proton beam.

[0160] Additional Notes

[0161] The above detailed description includes reference to the accompanying drawings, which form a part of the detailed description. The accompanying drawings show specific examples by way of illustration rather than by way of limitation, in which the present disclosure can be put into practice. These examples are also referred to as "examples" in this article. These examples may include elements other than those shown or described. However, the inventors have also contemplated providing examples of those elements that are only shown or described. In addition, with respect to a specific example (or one or more aspects thereof) or with respect to other examples (or one or more aspects thereof) shown or described in this article, the inventors have also contemplated using examples of any combination or permutation (or one or more aspects thereof) of those elements shown or described.

[0162] All publications, patents, and patent documents referenced in this document are incorporated herein by reference in their entirety, as if individually incorporated by reference. If there are inconsistent usages between this document and those documents incorporated by reference, the usage in the incorporated reference or references should be considered supplementary to that of this document; for conflicting inconsistencies, the usage in this document controls.

[0163] In this document, when introducing elements of various aspects of the present disclosure or examples thereof, the terms "a", "an", "the", and "said" are used, as is common in patent literature, to include one or more than one or more of the elements, independent of any other instances or uses of "at least one" or "one or more". In this document, the term "or" is used to represent a non-exclusive, or, such that "A or B" includes "A but not B", "B but not A", and "A and B" unless otherwise specified.

[0164] In the appended aspects, the terms "including" and "in which" are used as the plain-English equivalents of the respective terms "comprising" and "wherein." Further, in the following aspects, the terms "comprising," "including," and "having" are intended to be open-ended, meaning that there may be additional elements in addition to the listed elements, such that anything following such a term (e.g., comprising, including, having) in an aspect is still considered to fall within the scope of that aspect. Further, in the following aspects, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

[0165] The examples of the present disclosure can be implemented with computer executable instructions. Computer executable instructions (e.g., software code) can be organized into one or more computer executable components or modules. Any number of such components or modules and any organization of such components or modules can be used to implement the various aspects of the present disclosure. For example, the various aspects of the present disclosure are not limited to the specific computer executable instructions or specific components or modules shown in the drawings and described in this article. Other examples of the present disclosure may include different computer executable instructions or components with more or less functions than those shown and described herein.

[0166] The method examples described herein (e.g., operations and functions) may be at least partially machine or computer implemented (e.g., implemented as software code or instructions). Some examples may include a computer-readable medium or machine-readable medium encoded with instructions that can be operated to configure an electronic device to perform the method as described in the above examples. The implementation of such a method may include software code such as microcode, assembly language code, high-level language code, etc. (e.g., "source code"). Such software code may include computer-readable instructions (e.g., "objects" or "executable code") for performing various methods. The software code may form part of a computer program product. The software implementation of the examples described herein may be provided via an article having codes or instructions stored thereon, or via a method of operating a communication interface to send data via a communication interface (e.g., wirelessly, via the Internet, via satellite communications, etc.).

[0167] In addition, the software code may be tangibly stored on one or more volatile or non-volatile computer-readable storage media during execution or at other times. These computer-readable storage media may include any mechanism for storing information in a form that can be accessed by a machine (e.g., a computing device, an electronic system, etc.), such as, but not limited to, a floppy disk, a hard disk, a removable disk, any form of disk storage media, a CD-ROM, a magneto-optical disk, a removable optical disk (e.g., a compact disk and a digital video disk), a flash memory device, a cassette, a memory card or stick (e.g., a secure digital card), a RAM (e.g., a CMOS RAM, etc.), a recordable / non-recordable medium (e.g., a read-only memory (ROM)), an EPROM, an EEPROM, or any type of medium suitable for storing electronic instructions, etc. Such a computer-readable storage medium is coupled to the computer system bus so that it can be accessed by the processor and other parts of the OIS.

[0168] In an example, the computer readable storage medium may have encoded a data structure for a treatment plan, wherein the treatment plan may be adaptive. The data structure for the computer readable storage medium may be in at least one of a Digital Imaging and Communications in Medicine (DICOM) format, an extended DICOM format, an XML format, and the like. DICOM is an international communication standard that defines a format for transmitting data related to medical images between various types of medical equipment. DICOM RT refers to a communication standard specific to radiation therapy.

[0169] In various examples of the present disclosure, the methods of creating components or modules can be implemented in software, hardware, or a combination thereof. For example, the methods provided by the various examples of the present disclosure can be implemented in software using standard programming languages ​​such as, for example, C, C++, Java, Python, etc., and combinations thereof. As used herein, the terms "software" and "firmware" are interchangeable and include any computer program stored in a memory for execution by a computer.

[0170] A communication interface includes any mechanism that interfaces with any of a hardwired medium, a wireless medium, an optical medium, etc. to communicate with another device, such as a memory bus interface, a processor bus interface, an Internet connection, a disk controller, etc. The communication interface may be configured by providing configuration parameters and / or sending signals to prepare the communication interface to provide data signals describing the software content. The communication interface may be accessed via one or more commands or signals sent to the communication interface.

[0171] The present disclosure also relates to a system for performing the operations herein. The system can be specially constructed for the desired purpose, or can include a general-purpose computer selectively activated or reconfigured by a computer program stored in a computer. Unless otherwise specified, the operation or execution order of the operations in the examples of the present disclosure shown and described herein is not necessary. That is, unless otherwise specified, operations can be performed in any order, and compared with those operations disclosed herein, the examples of the present disclosure may include other operations or fewer operations. For example, it is contemplated that before another operation, simultaneously with another operation, or after another operation, performing or performing a specific operation falls within the scope of the various aspects of the present disclosure.

[0172] The embodiments of the present disclosure disclose but are not limited to the following schemes:

[0173] Solution 1. A system for evaluating the robustness of a radiation therapy treatment plan used by a radiation therapy device to treat a patient, the system comprising:

[0174] A processor configured to:

[0175] generating, in a radiation simulation according to the radiotherapy treatment plan, a dose distribution at the anatomical structure under nominal conditions and under one or more artificially imposed uncertainty conditions;

[0176] determining a dose distribution characteristic of the anatomical structure using the dose distribution; and

[0177] generating a robustness indicator of the radiotherapy treatment plan based on the dose distribution characteristics; and

[0178] A memory configured to store the dose distribution characteristic or the robustness indicator accessible by a user or a treatment planning system.

[0179] Embodiment 2. The system of embodiment 1, wherein the anatomical structure comprises a target structure to receive radiation therapy and at least one structure at risk to avoid radiation therapy, and wherein the processor is configured to:

[0180] determining a first dose distribution characteristic using the first dose distribution at the target structure, and determining a second dose distribution characteristic using the second dose distribution at the at least one structure at risk; and

[0181] A three-dimensional (3D) graphical representation of the first dose distribution characteristic and the second dose distribution characteristic in a dose-volume-structure space is generated to be displayed on a display.

[0182] Solution 3. A system according to Solution 1 or 2, wherein the dose distribution comprises a dose-volume histogram (DVH), each DVH representing a tissue volume of the anatomical structure that receives a corresponding radiation dose, and the DVH comprises:

[0183] a nominal DVH corresponding to the nominal condition; and

[0184] One or more uncertainty DVHs of the dose distribution corresponding to the one or more artificially imposed uncertainty conditions are used.

[0185] Option 4. The system according to Option 3, wherein the dose distribution characteristic includes (1) a first boundary DVH corresponding to a lowest dose within a volume range of the anatomical structure and (2) a second boundary DVH corresponding to a highest dose within the volume range of the anatomical structure, the first boundary DVH and the second boundary DVH defining a DVH range; and

[0186] Wherein the processor circuit is configured to generate a DVH strip to be displayed on a display that graphically represents the DVH range.

[0187] Embodiment 5. The system of embodiment 4, wherein the processor is configured to generate a robustness indicator of the radiotherapy treatment plan based on at least one of the following:

[0188] a first deviation of the first boundary DVH from the nominal DVH;

[0189] a second deviation of the second boundary DVH from the nominal DVH; or

[0190] A third deviation of the second boundary DVH from the first boundary DVH.

[0191] Solution 6. A system according to any one of Solution 3 to Solution 5, wherein the processor is configured to:

[0192] determining the dose distribution characteristic, including identifying an extreme scenario DVH from the one or more uncertainty DVHs based on a dosimetry criterion including at least one of a reference tissue volume or a reference radiation dose; and

[0193] A graphical representation of the extreme scenario DVH is generated to be displayed on a display.

[0194] Option 7. A system according to Option 6, wherein the processor is configured to generate a robustness indicator for the radiotherapy treatment plan based on the deviation of the extreme scenario DVH from the dosimetry standard.

[0195] Embodiment 8. The system according to embodiment 6, wherein the processor is configured to:

[0196] generating a first set of DVHs for a target structure to receive radiation therapy, the first set of DVHs comprising a first nominal DVH corresponding to the nominal condition and one or more first uncertainty DVHs corresponding to the one or more artificially imposed uncertainty conditions; and

[0197] A first extreme scenario DVH is identified from the first set of DVHs, the first extreme scenario DVH having at least one of: (1) a minimum radiation dose received by a first reference tissue volume of the target structure, or (2) a minimum tissue volume of the target structure that receives the first reference radiation dose.

[0198] Embodiment 9. A system according to any one of embodiments 6 to 8, wherein the processor is configured to:

[0199] generating a second set of DVHs for the endangered structure to be avoided from radiation therapy, the second set of DVHs comprising a second nominal DVH corresponding to the nominal condition and one or more second uncertainty DVHs corresponding to the one or more artificially imposed uncertainty conditions; and

[0200] A second extreme scenario DVH is identified from the second uncertainty DVH, the second extreme scenario DVH having at least one of: (1) a maximum radiation dose received by a second reference tissue volume of the endangered structure, or (2) a maximum tissue volume of the endangered structure that receives the second reference radiation dose.

[0201] Embodiment 10. A system according to any one of embodiments 3 to 9, wherein the processor is configured to:

[0202] determining the dose distribution characteristic, including identifying one or more out-of-range DVHs from the one or more uncertainty DVHs that fall outside of a tolerance margin based on a dosimetry criterion including at least one of a reference tissue volume or a reference radiation dose; and

[0203] An out-of-range DVH subband graphically representing the identified one or more out-of-range DVHs is generated to be displayed on a display.

[0204] Embodiment 11. The system of Embodiment 10, wherein the processor is configured to generate a robustness indicator for the radiotherapy treatment plan using the count of the out-of-range DVH relative to the count of the one or more uncertainty DVHs.

[0205] Embodiment 12. The system according to embodiment 10, wherein the processor is configured to:

[0206] generating a first set of DVHs for a target structure to receive radiation therapy, the first set of DVHs comprising a first nominal DVH corresponding to the nominal condition and one or more first uncertainty DVHs corresponding to the one or more artificially imposed uncertainty conditions; and

[0207] One or more out-of-range DVHs are identified from the first uncertainty DVHs, each out-of-range DVH having a radiation dose at a reference tissue volume that is lower than a first reference radiation dose by a first tolerance margin.

[0208] Embodiment 13. A system according to any one of embodiments 10 to 12, wherein the processor is configured to:

[0209] generating a second set of DVHs for the endangered structure to be avoided from radiation therapy, the second set of DVHs comprising a second nominal DVH corresponding to the nominal condition and one or more second uncertainty DVHs corresponding to the one or more artificially imposed uncertainty conditions; and

[0210] One or more out-of-range DVHs are identified from the second uncertainty DVHs, each out-of-range DVH having a radiation dose at a reference tissue volume that is greater than a second reference radiation dose by a second tolerance margin.

[0211] Option 14. A system according to any one of Option 1 to Option 10, wherein the radiation therapy includes proton therapy, and wherein, in simulated proton therapy, the dose distribution is determined for the anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions.

[0212] Option 15. A system according to Option 14, wherein the one or more artificially imposed uncertainty conditions include deviations from the nominal conditions, the deviations representing one or more of range uncertainty or patient setup errors associated with the proton beam.

[0213] Option 16. A system according to any one of Option 1 to Option 15, wherein the processor is further configured to modify the radiotherapy treatment plan or generate a recommendation to modify the radiotherapy treatment plan based on a robustness indicator of the radiotherapy treatment plan.

[0214] 17. A method for evaluating the robustness of a radiation therapy treatment plan used by a radiation therapy device to treat a patient, the method comprising:

[0215] generating, via a processor, a dose distribution at the anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions using the radiation therapy treatment plan;

[0216] determining a dose distribution characteristic of the anatomical structure using the dose distribution;

[0217] generating a robustness indicator of the radiotherapy treatment plan based on the dose distribution characteristics; and

[0218] The dose distribution characteristic or the robustness indicator is provided to a user or a treatment planning system.

[0219] Embodiment 18. The method of embodiment 17, wherein the anatomical structure includes a target structure to receive radiation therapy and at least one structure at risk to avoid radiation therapy, the method comprising:

[0220] determining a first dose distribution characteristic using the first dose distribution at the target structure, and determining a second dose distribution characteristic using the second dose distribution at the at least one structure at risk; and

[0221] A three-dimensional (3D) graphical representation of the first dose distribution characteristic and the second dose distribution characteristic in a dose-volume-structure space is generated.

[0222] Embodiment 19. The method according to embodiment 17 or 18, wherein the dose distribution comprises a dose-volume histogram (DVH), each DVH representing a tissue volume of the anatomical structure receiving a corresponding radiation dose, and the DVH comprises:

[0223] a nominal DVH corresponding to the nominal condition; and

[0224] One or more uncertainty DVHs of the dose distribution corresponding to the one or more artificially imposed uncertainty conditions are used.

[0225] Embodiment 20. The method of embodiment 19, wherein the dose distribution characteristic comprises (1) a first boundary DVH corresponding to a lowest dose within a volume range of the anatomical structure and (2) a second boundary DVH corresponding to a highest dose within a volume range of the anatomical structure, wherein:

[0226] Generating a robustness indicator for the radiotherapy treatment plan is based on a DVH range defined by the first boundary DVH and the second boundary DVH; and

[0227] Providing the dose distribution characteristic or the robustness indicator includes displaying a DVH band graphically representing the DVH range.

[0228] Scheme 21. The method according to Scheme 19 or 20, comprising:

[0229] identifying, from the one or more uncertainty DVHs, an extreme scenario DVH that meets dosimetry criteria relative to the nominal DVH; and

[0230] generating a robustness indicator of the radiotherapy treatment plan based on a deviation of the extreme scenario DVH from the nominal DVH;

[0231] Wherein providing the dose distribution characteristic or the robustness indicator comprises displaying a graphical representation of the extreme scenario DVH.

[0232] A method according to any one of Aspects 19 to 21, comprising:

[0233] identifying, from the one or more uncertainty DVHs, one or more out-of-range DVHs that fall outside of a tolerance margin relative to the nominal DVH; and

[0234] generating a robustness indicator for the radiation therapy treatment plan based on the count of the out-of-range DVHs relative to the count of the one or more uncertainty DVHs;

[0235] Wherein providing the dose distribution characteristic or the robustness indicator comprises displaying an out-of-range DVH sub-band graphically representing the identified one or more out-of-range DVHs.

[0236] Embodiment 23. The method of any one of Embodiments 17 to 22, comprising modifying the radiotherapy treatment plan or generating a recommendation to modify the radiotherapy treatment plan based on a robustness indicator of the radiotherapy treatment plan.

[0237] Option 24. A method according to any one of Option 17 to Option 23, wherein the radiotherapy includes proton therapy, and wherein the one or more artificially imposed uncertainty conditions include deviations from the nominal conditions, the deviations representing one or more of range uncertainty or patient setup errors associated with the proton beam.

[0238] In view of the above, it will be seen that several objects of the present disclosure are achieved and other advantageous results are obtained. Having described various aspects of the present disclosure in detail, it will be apparent that modifications and variations are feasible without departing from the scope of the various aspects of the present disclosure as defined in the attached various aspects. Since various changes can be made in the above constructions, products and methods without departing from the scope of the various aspects of the present disclosure, it is intended that all contents contained in the above description and shown in the accompanying drawings should be interpreted as illustrative, and not in a restrictive sense.

[0239] The above description is intended to be illustrative, rather than restrictive. For example, the above examples (or one or more aspects thereof) can be used in combination with each other. In addition, without departing from the scope of the present disclosure, many modifications can be made to adapt specific situations or materials to the teachings of the present disclosure. Although the size and type of the material and coating described herein are intended to limit the parameters of the present disclosure, they are by no means restrictive, but exemplary examples. After reviewing the above description, many other examples will be apparent to those skilled in the art. Therefore, the scope of the present disclosure should be determined with reference to the full range of equivalents to which each of the attached aspects and these aspects are assigned.

[0240] In addition, in the above specific embodiments, various features can be combined together to simplify the present disclosure. This should not be interpreted as an unexpected disclosure feature being necessary for any aspect. Instead, the subject matter of the present invention may be less than all the features of a specific disclosed example. Therefore, the following aspects are thus incorporated into the specific embodiments, with each aspect acting as a separate example alone. The scope of the present disclosure should be determined with reference to the attached various aspects and the full range of equivalents to which these aspects are assigned. In addition, the following limitations of various aspects are not written in a device plus function format, and are not intended to be interpreted based on the sixth paragraph of 35 U.S.C. §112, unless and until the limitation of such aspects explicitly uses the phrase "device for...", "for" is followed by a functional statement without further structure.

[0241] The Abstract is provided to comply with 37 CFR §1.72(b), to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the aspects.

Claims

1. A system for characterizing radiation dose distribution in a radiotherapy treatment plan for treating an anatomical structure using a radiotherapy device, the system comprising: A processor configured to: receiving data obtained from a radiation simulation at the anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions; generating a plurality of dose distributions using the received data, the plurality of dose distributions comprising dose-volume histograms DVH, each DVH representing a tissue volume of the anatomical structure receiving a corresponding radiation dose, the DVHs comprising a nominal DVH corresponding to the nominal condition and one or more uncertainty DVHs corresponding to the one or more artificially imposed uncertainty conditions; as well as determining dose distribution characteristics based on statistical characteristics of the generated DVH; and A user interface is configured to display the determined dose distribution characteristics to a user.

2. The system according to claim 1, wherein: The statistical characteristics of the generated DVH include at least one of the following: a first boundary DVH corresponding to a lowest dose within a range of a tissue volume of the anatomical structure; or A second boundary DVH corresponds to a highest dose within the range of the tissue volume of the anatomical structure.

3. The system according to claim 2, wherein: The statistical features of the generated DVH include a DVH band defined by the first boundary DVH and the second boundary DVH.

4. The system according to claim 2, wherein: The statistical characteristics of the generated DVH include a deviation of the first boundary DVH or the second boundary DVH from the nominal DVH.

5. The system according to claim 1, wherein: The statistical features of the generated DVH include an extreme scenario DVH based on a dosimetry criterion including at least one of a reference tissue volume or a reference radiation dose.

6. The system according to claim 1, wherein: The statistical features of the generated DVHs include one or more out-of-range DVHs identified from the generated DVHs, the one or more out-of-range DVHs falling outside of a tolerance margin based on a dosimetry criterion including at least one of a reference tissue volume or a reference radiation dose.

7. The system according to claim 6, wherein: The generated statistical features of the DVHs include a count of the one or more out-of-range DVHs.

8. The system according to claim 6, wherein: The statistical characteristic of the generated DVH includes a ratio of a count of the one or more out-of-range DVHs to a total count of the generated DVHs.

9. The system according to claim 6, wherein: The statistical features of the generated DVHs include out-of-range DVH sub-bands that define a range for the one or more out-of-range DVHs.

10. The system according to claim 1, wherein: The processor is configured to generate a robustness indicator for the radiation therapy treatment plan based on the dose distribution characteristic, and Wherein, the user interface is configured to display the robustness indicator to the user.

11. The system according to claim 1, wherein: The anatomical structure comprises at least two different structures, Wherein, the processor is configured to: determining a corresponding dose distribution characteristic for each of the at least two different structures based on statistical characteristics of the DVH generated from radiation simulation data obtained from the corresponding structure; and A three-dimensional graphical representation of the dose distribution characteristic is generated in a dose-volume-structure space for display on the user interface.

12. The system according to claim 11, wherein: The at least two different structures include a target structure to receive radiation therapy and at least one endangered structure to be avoided from the radiation therapy.

13. A method for characterizing a radiation dose distribution in a radiotherapy treatment plan for treating an anatomical structure using a radiotherapy device, the method comprising: receiving data obtained from a simulation of radiation at the anatomical structure under nominal conditions and one or more artificially imposed uncertainty conditions; generating a plurality of dose distributions using the received data, the plurality of dose distributions comprising dose-volume histograms DVH, each DVH representing a tissue volume of the anatomical structure receiving a corresponding radiation dose, the DVHs comprising a nominal DVH corresponding to the nominal condition and one or more uncertainty DVHs corresponding to the one or more artificially imposed uncertainty conditions; determining a dose distribution characteristic based on statistical characteristics of the generated DVH; as well as The determined dose distribution characteristics are displayed on a user interface.

14. The method according to claim 13, wherein: The statistical features of the generated DVH include at least one of the following: a first boundary DVH corresponding to the lowest dose within the range of the tissue volume of the anatomical structure, a second boundary DVH corresponding to the highest dose within the range of the tissue volume of the anatomical structure, or a DVH band defined by the first boundary DVH and the second boundary DVH.

15. The method according to claim 13, wherein: The statistical features of the generated DVH include an extreme scenario DVH based on a dosimetry criterion including at least one of a reference tissue volume or a reference radiation dose.

16. The method according to claim 13, wherein: The statistical features of the generated DVHs include one or more out-of-range DVHs identified from the generated DVHs, the one or more out-of-range DVHs falling outside of a tolerance margin based on a dosimetry criterion including at least one of a reference tissue volume or a reference radiation dose.

17. The method according to claim 16, wherein: The statistical feature of the generated DVH includes a count of the one or more out-of-range DVHs, or a ratio of the count of the one or more out-of-range DVHs to a total count of the generated DVHs.

18. The method according to claim 16, wherein: The statistical features of the generated DVHs include out-of-range DVH sub-bands that define a range for the one or more out-of-range DVHs.

19. The method of claim 13, further comprising generating a robustness indicator for the radiation therapy treatment plan based on the dose distribution characteristic, and displaying the robustness indicator on the user interface.

20. The method according to claim 13, wherein: The anatomical structure comprises at least two different structures, and the method comprises: determining a corresponding dose distribution characteristic for each of the at least two different structures based on statistical characteristics of the DVH generated from radiation simulation data obtained from the corresponding structure; and A three-dimensional graphical representation of the dose distribution characteristic in a dose-volume-structure space is displayed on the user interface.

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