CN114010963B

CN114010963B - System, method and computer readable storage medium for dose determination

Info

Publication number: CN114010963B
Application number: CN202111316325.7A
Authority: CN
Inventors: 刘艳芳
Original assignee: Shanghai United Imaging Healthcare Co Ltd
Current assignee: Shanghai United Imaging Healthcare Co Ltd
Priority date: 2021-11-08
Filing date: 2021-11-08
Publication date: 2023-10-20
Anticipated expiration: 2041-11-08
Also published as: CN114010963A

Abstract

The application relates to a system, a method and a computer readable storage medium for dose determination. The method includes obtaining a target radiotherapy plan of a subject, the target radiotherapy plan including sub-radiotherapy plans corresponding to gantry angles; determining a target flux map corresponding to the gantry angle based on the sub-radiotherapy plan corresponding to the gantry angle, the target flux map comprising a plurality of first sub-regions, each first sub-region corresponding to a first weight value; determining the number of sampling particles corresponding to each first sub-region based on the first weight value corresponding to each first sub-region; and determining a target dose distribution of the subject at the gantry angle according to a monte carlo algorithm based on the number of sampling particles corresponding to each first sub-region.

Description

System, method and computer readable storage medium for dose determination

Technical Field

The present application relates generally to systems and methods of radiation therapy, and more particularly to systems and methods of dose determination in radiation therapy.

Background

Radiation therapy is widely used for cancer treatment by directing ionizing radiation to tumors. Considerations for radiation therapy include that during radiation therapy, the tumor receives sufficient radiation and minimizes damage to Organs At Risk (OAR) as much as possible. Thus, accurate calculation of the dose distribution of the irradiated portion of the patient plays a decisive role in radiotherapy. The monte carlo calculation method has been widely used in the aspect of medical dose calculation, and can accurately determine the dose distribution of an irradiated part of a patient, but the monte carlo calculation method has the disadvantages of low convergence speed and long calculation time. It is therefore desirable to provide systems and methods for quickly and accurately determining a dose distribution of a patient in radiation therapy.

Disclosure of Invention

In a first aspect of the application, there is provided a method of dose determination comprising: obtaining a target radiotherapy plan of a subject, wherein the target radiotherapy plan comprises a sub-radiotherapy plan corresponding to a gantry angle; determining a target flux map corresponding to the gantry angle based on the sub-radiotherapy plan corresponding to the gantry angle, the target flux map comprising a plurality of first sub-regions, each first sub-region of the plurality of first sub-regions corresponding to a first weight value representing a target radiation intensity of the first sub-region; determining the number of sampling particles corresponding to each first sub-region based on the first weight value corresponding to each first sub-region; and determining a target dose distribution of the subject at the gantry angle according to a monte carlo algorithm based on the number of sampling particles corresponding to each first sub-region.

In some embodiments, the sub-radiotherapy plan includes sub-field information of a plurality of sub-fields at the gantry angle, and the determining the target flux map corresponding to the gantry angle based on the sub-radiotherapy plan corresponding to the gantry angle includes: determining a plurality of sub-flux maps corresponding to the plurality of sub-fields based on the sub-field information; and determining the target flux map corresponding to the rack angle by fusing the plurality of sub flux maps.

In some embodiments, the sub-field information includes a radiation time and a radiation intensity per unit time corresponding to each of the plurality of sub-fields.

In some embodiments, the method further comprises: performing outer expansion on the target flux map to determine an outer expansion area, wherein the outer expansion area comprises a plurality of second subareas; determining a second weight value corresponding to each second sub-region in the plurality of second sub-regions, wherein the second weight value represents the missed emission intensity of the second sub-region; and determining the number of sampling particles corresponding to each second sub-region based on the second weight value corresponding to each second sub-region.

In some embodiments, the determining the target dose distribution of the subject according to the monte carlo algorithm based on the number of sampling particles corresponding to each first sub-region includes: and determining the target dose distribution of the object according to a Monte Carlo algorithm based on the number of sampling particles corresponding to each first subarea and the number of sampling particles corresponding to each second subarea.

In some embodiments, the number of sample particles corresponding to the first sub-region is related to the first weight value corresponding to the first sub-region.

In some embodiments, the target radiotherapy plan comprises a plurality of sub-radiotherapy plans corresponding to a plurality of gantry angles. The method further comprises: determining a plurality of target dose distributions for the subject corresponding to the plurality of gantry angles; a total dose distribution of the subject is determined based on the plurality of target dose distributions.

In a second aspect of the application, there is also provided a method of dose determination, the method comprising: obtaining a target radiotherapy plan of a subject, wherein the target radiotherapy plan comprises a sub-radiotherapy plan corresponding to a gantry angle; determining a target flux map corresponding to the corresponding gantry angle based on the sub-radiotherapy plan; determining a sampling particle distribution corresponding to a rack angle based on the target flux map; based on the sampled particle distribution, a target dose distribution of the subject at a corresponding gantry angle is determined according to a monte carlo algorithm.

In a third aspect of the application, there is also provided a method of dose determination, the method comprising: obtaining a target radiotherapy plan of a subject, the target radiotherapy plan comprising at least one sub-radiotherapy plan corresponding to at least one gantry angle; determining the number of sampling particles corresponding to different rack angles according to the weights of the beams corresponding to the different rack angles; for each gantry angle of the at least one gantry angle, determining a target flux map corresponding to the gantry angle based on a sub-radiotherapy plan corresponding to the gantry angle, the target flux map comprising a plurality of first sub-regions; determining the number of the sampling particles corresponding to each first subarea based on the number of the sampling particles corresponding to the rack angle and a target flux map corresponding to the rack angle; determining a target dose distribution of the object under the gantry angle according to a monte carlo algorithm based on the number of sampling particles corresponding to each first sub-region; and determining a total dose distribution of the subject based on at least one target dose distribution of the subject at the at least one gantry angle and the at least one gantry angle.

In a fourth aspect of the application, a system for dose determination is provided, the system comprising at least one processor and at least one memory; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the dose determination method as described above.

In a fifth aspect of the present application, there is provided a system for dose determination, the system comprising: the acquisition module is used for acquiring a target radiotherapy plan of the object, wherein the target radiotherapy plan comprises a sub-radiotherapy plan corresponding to the angle of the rack; a flux map determining module, configured to determine a target flux map corresponding to the gantry angle based on a sub-radiotherapy plan corresponding to the gantry angle, where the target flux map includes a plurality of first sub-regions, each first sub-region of the plurality of first sub-regions corresponds to a first weight value, and the first weight value represents a target radiation intensity of the first sub-region; the sampling particle determining module is used for determining the number of sampling particles corresponding to each first subarea based on the first weight value corresponding to each first subarea; and a dose determination module for determining a target dose distribution of the subject at the gantry angle according to a monte carlo algorithm based on the number of sampling particles corresponding to each first sub-region.

In a sixth aspect of the application, a computer readable storage medium is provided, the storage medium storing computer instructions which, when executed by a processor, implement a dose determination method as described above.

Additional features of the application will be set forth in part in the description which follows. Additional features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following description and the accompanying drawings or may be learned from production or operation of the embodiments. The features of the present application may be implemented and realized by the practice or use of the methods, instrumentalities and combinations of various aspects of the specific embodiments described below.

Drawings

The application will be further described by means of exemplary embodiments. These exemplary embodiments will be described in detail with reference to the accompanying drawings. These embodiments are non-limiting exemplary embodiments in which like numerals represent similar structures throughout the several views, and in which:

FIG. 1 is a schematic diagram of an exemplary medical system shown according to some embodiments of the application;

FIG. 2 is a schematic diagram of at least a portion of an exemplary computing device upon which a medical system may be implemented, shown in accordance with some embodiments of the present application;

FIG. 3 is a schematic diagram of exemplary hardware and/or software components of an exemplary mobile device on which a terminal may be implemented, shown in accordance with some embodiments of the present application;

FIG. 4 is a block diagram of an exemplary processing device shown in accordance with some embodiments of the present application;

FIG. 5 is a flowchart of an exemplary process 500 of dose determination shown in accordance with some embodiments of the present application;

FIG. 6 is a schematic diagram of an exemplary sub-flux map and target flux map shown in accordance with some embodiments of the application; and

fig. 7 is a schematic diagram of an exemplary target flux map and flared region shown in accordance with some embodiments of the present application.

Detailed Description

In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings that are required to be used in the description of the embodiments will be briefly described below. However, it will be apparent to one skilled in the art that the present application may be practiced without these specific details. In other instances, well known methods, procedures, systems, components, and/or circuits have been described at a high-level in order to avoid unnecessarily obscuring aspects of the present application. It will be apparent to those having ordinary skill in the art that various changes can be made to the disclosed embodiments and that the general principles defined herein may be applied to other embodiments and applications without departing from the principles and scope of the application. Thus, the present application is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the claims.

The terminology used in the present application is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including" when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of other features, integers, steps, operations, elements, components, and/or groups thereof.

It is to be understood that the terms "system," "engine," "unit," "module," and/or "block" as used herein are methods for distinguishing, in ascending order, different components, elements, parts, or assemblies of different levels. However, these terms may be replaced by other expressions if the same purpose is achieved.

Generally, the terms "module," "unit," or "block" as used herein refer to logic embodied in hardware or firmware, or a set of software instructions. The modules, units, or blocks described in the present application may be implemented as software and/or hardware and may be stored in any type of non-transitory computer-readable medium or other storage device. In some embodiments, software modules/units/blocks may be compiled and linked into an executable program. It will be appreciated that software modules may be callable from other modules/units/blocks or themselves, and/or may be invoked in response to a detected event or interrupt. Software modules/units/blocks configured to execute on a computing device (e.g., processor 210 as shown in fig. 2) may be provided on a computer readable medium. Such as an optical disc, digital video disc, flash drive, magnetic disk, or any other tangible medium, or as a digital download (and may be initially stored in a compressed or installable format requiring installation, decompression, or decryption prior to execution). The software code herein may be stored in part or in whole in a memory device of a computing device executing operations and applied during operation of the computing device. The software instructions may be embedded in firmware such as EPROM. It will also be appreciated that the hardware modules/units/blocks may be included in connected logic components, such as gates and flip-flops, and/or may include programmable units, such as programmable gate arrays or processors. The modules/units/blocks or computing device functions described in this application may be implemented as software modules/units/blocks, but may be represented in hardware or firmware. In general, the modules/units/blocks described herein refer to logical modules/units/blocks, which may be combined with other modules/units/blocks or divided into sub-modules/sub-units/sub-blocks, although they are physical organizations or storage devices. The description may apply to a system, an engine, or a portion thereof.

It will be understood that when an element, engine, module or block is referred to as being "on," "connected to" or "coupled to" another element, engine, module or block, it can be directly on, connected or coupled to or in communication with the other element, engine, module or block, or intervening elements, engines, modules or blocks may be present unless the context clearly indicates otherwise. In the present application, the term "and/or" may include any one or more of the associated listed items or combinations thereof.

These and other features, characteristics, and functions of related structural elements of the present application, as well as the methods of operation and combination of parts and economies of manufacture, will become more apparent upon consideration of the following description of the drawings, all of which form a part of this specification. It is to be understood, however, that the drawings are designed solely for the purposes of illustration and description and are not intended as a definition of the limits of the application. It should be understood that the figures are not drawn to scale.

The flowcharts used by the present application illustrate the operations performed by the systems according to some embodiments of the present disclosure. It should be understood that the operations in the flow diagrams may be performed out of order. Rather, the various steps may be processed in reverse order or simultaneously. Also, one or more other operations may be added to these flowcharts. One or more operations may also be deleted from the flowchart.

Radiation therapy refers to a method of treating tumors with radiation. Due to the high beam energy, normal cells are also affected while tumor cells are killed. In order to minimize damage to normal tissue, radiation treatment planning is required. In order to determine whether a radiation treatment plan is good or bad, it is generally necessary to simulate the distribution of the dose received by each part of the human body according to the radiation treatment plan.

Conventional methods of calculating radiation dose include pencil beam algorithms, convolution algorithms, and monte carlo algorithms. The pencil beam algorithm and the convolution algorithm are based on an analysis method or an empirical method, the calculation speed is high, but due to the reasons of radiation scattering in a human body, complexity of a secondary radiation process and the like, or the influence of physical processes such as electron unbalance in radiation transmission and the like, the accuracy of calculating the radiation dose by the convolution algorithm and the pencil beam algorithm is poor, and particularly in the tissues with uneven density and near interfaces of different tissues, the calculation accuracy error can even reach 11% -32%.

The monte carlo algorithm, also called statistical simulation or random sampling, is a random simulation method that can calculate the dose distribution of the irradiated portion by using random numbers (or more commonly pseudo-random numbers). The monte carlo method obtains the distribution of the deposition energy of particles in human tissue by randomly modeling the particle-substance interactions. The monte carlo algorithm has been used in the field of radiotherapy because of its high precision, but because it needs to simulate a large number of random or pseudo-random sampling particles, the simulation time is long under the condition of certain precision, and the clinical requirement cannot be fully satisfied.

In actual clinic, the treatment plan is formulated in a short time, the Monte Carlo model is simplified in the prior art, a plurality of variance reduction algorithms are introduced, and an analysis algorithm is adopted in an electronic transportation part, so that on one hand, the randomness of the Monte Carlo algorithm cannot be maintained, and the accuracy is affected; on the other hand, the calculation time still cannot meet the clinical requirements. For example, some prior art techniques employ smoothing algorithms, which undoubtedly result in loss of accuracy, loss of feature points, and loss of accuracy of the algorithm.

The application provides a method and a system for determining Monte Carlo dose based on non-uniform sampling. In particular, a target radiotherapy plan of the subject may be acquired, the target radiotherapy plan comprising sub-radiotherapy plans corresponding to gantry angles. A target flux map corresponding to the gantry angle may be determined based on the sub-radiotherapy plan corresponding to the gantry angle, the target flux map representing a sub-radiation intensity distribution corresponding to the gantry angle. The target flux map includes a plurality of first sub-regions, each of the plurality of first sub-regions corresponding to a first weight value representing a target radiation intensity of the first sub-region. A sample particle distribution corresponding to the gantry angle may then be determined based on the target flux map. Further, a distribution of sampled particles on the target flux map may be determined from the sub-radiation intensity distribution corresponding to the gantry angle. For example, the number of sampling particles corresponding to each first sub-region may be determined based on the first weight value corresponding to each first sub-region. Finally, a target dose distribution of the subject at the gantry angle is determined according to a monte carlo algorithm based on the number of sampling particles corresponding to each first sub-region. In this embodiment, the frame angle may be a single angle or a range of angles. If the angle range is the angle range, the angle range may be discretized into a plurality of control points, each control point corresponding to a different gantry angle, so that the dose distribution at a single gantry angle is calculated according to the method in this embodiment, and the dose distribution in the final patient or in the model is determined according to the dose distribution at different gantry angles.

In the technical scheme of Monte Carlo dose determination based on non-uniform sampling, the number of sampling particles in the first subarea can be determined according to the weight corresponding to the first subarea in the target flux graph, for example, the number of sampling particles corresponding to the first subarea is in a direct proportion relation, a square relation or other functional relations with the first weight value corresponding to the first subarea. The energy deposition (i.e. radiation dose) is then calculated based on the monte carlo algorithm. Since the calculation time of the dose is proportional to the number of particles, the non-uniform sampling method can reduce the number of sampled particles, reduce the calculation time and reduce the precision loss.

FIG. 1 is a schematic diagram of an exemplary medical system shown according to some embodiments of the application. The medical system 100 may include a medical device 110, a network 120, one or more terminals 130, a processing device 140, and a storage device 150. The components in the medical system 100 may be connected in various ways. For example only, medical device 110 may be connected to processing device 140 directly (as indicated by the dashed double-headed arrow connecting medical device 110 and processing device 140) or through network 120. As yet another example, the storage device 150 may be connected to the medical device 110 directly (as indicated by the dashed double-headed arrow connecting the storage device 150 and the medical device 110) or through the network 120. As yet another example, terminal 130 may be connected to processing device 140 directly (as indicated by the dashed double-headed arrow connecting terminal 130 and processing device 140) or through network 120. As yet another example, terminal 130 may be connected to storage device 150 directly (as indicated by the dashed double-headed arrow connecting terminal 130 and storage device 150) or through network 120.

Medical device 110 may image and/or treat a subject. In some embodiments, the object may include a biological object and/or a non-biological object. For example, the object may comprise a particular part of a human body, such as a head, chest, abdomen, etc., or a combination thereof. As another example, the object may be a patient to be scanned by the medical device 110.

In some embodiments, the medical device 110 may be a medical treatment apparatus for disease diagnosis or research purposes. In some embodiments, the medical device 110 may include a single mode apparatus, such as an X-ray therapy apparatus, a Co-60 remote therapy apparatus, a medical electron accelerator, or the like. In some embodiments, the medical device 110 may be a multi-modality (e.g., bimodal) apparatus to acquire medical images related to a subject and to subject the subject with radiation therapy. For example, medical device 110 may include an Image Guided Radiation Therapy (IGRT) apparatus. For example, CT-guided radiotherapy apparatuses and MRI-guided radiotherapy apparatuses. The devices provided above are for illustrative purposes only and are not intended to limit the scope of the present application. As used herein, the term "imaging modality" or "modality" broadly refers to an imaging method or technique that collects, generates, processes, and/or analyzes imaging information of a target object.

In some embodiments, the medical device 110 may include a radiotherapy component, such as a therapy head. The treatment head is connected with the frame. In some embodiments, the treatment head may move with the movement (e.g., rotation) of the gantry. The treatment head may include a target, a therapeutic radiation source, and a collimator. The therapeutic radiation source may emit a radiation beam towards the subject. The collimator may include a primary collimator and a secondary collimator, which may include a multi-leaf collimator (MLC), a tungsten gate (jaw), and the like. Wherein the MLC may comprise a plurality of leaves. In some embodiments, the position of a plurality of leaves and/or tungsten gates (jaw) of the MLC are used to form a radiation area, i.e., the field. In some embodiments, the plurality of blades may be driven by one or more drive components (e.g., motors) to move to a particular position to change the shape of the field.

In some embodiments, the treatment head may be non-uniformly beamed. For example, in a non-uniform (flattening filter-free, FFF) mode, the intensity of the beam emitted by the treatment head exhibits a certain distribution (e.g., a gaussian distribution). In some embodiments, the treatment head may be a uniform exit beam. For example, in the leveling (flattening filter, FF) mode, the leveler can be mounted within the treatment head (e.g., above the MLC) to normalize the intensity of the beam.

In some embodiments, the medical device 110 may include a radiation therapy assist device, such as an Electronic Portal Imaging Device (EPID). The electronic portal imaging device can generate images of the subject before, during, and/or after treatment. The electron portal imaging device may include a detector for detecting radiation (e.g., X-rays, gamma rays) emitted from the therapeutic radiation source. In some embodiments, the detector may comprise one or more detection units. The detection unit may include a scintillation detector (e.g., cesium iodide detector, gadolinium oxysulfide detector), a gas detector, and the like. The detection unit may comprise a single row of detectors or a plurality of rows of detectors.

In some embodiments, the medical device 110 may also include an imaging device. The imaging device may include one or a combination of several of a computed tomography imaging device (CT), an ultrasound imaging assembly, a fluoroscopic imaging assembly, a Magnetic Resonance Imaging (MRI) device, a Single Photon Emission Computed Tomography (SPECT) device, a Positron Emission Tomography (PET) device, and the like. In some embodiments, the imaging device may include an imaging assembly, e.g., an imaging radiation source and a detector. In some embodiments, the imaging radiation source and the therapeutic radiation source may be integrated into one radiation source for imaging and/or treating a subject.

Network 120 may include any suitable network that may facilitate the exchange of information and/or data of medical system 100. In some embodiments, one or more components of the medical system 100 (e.g., the medical device 110, the terminal 130, the processing device 140, the storage device 150) may communicate information and/or data with one or more other components of the medical system 100 via the network 120. For example, processing device 140 may obtain image data from medical device 110 via network 120. As another example, processing device 140 may obtain user instructions from terminal 130 via network 120. Network 120 may be and/or include a public network (e.g., the internet), a private network (e.g., a Local Area Network (LAN), a Wide Area Network (WAN)), a wired network (e.g., a wireless local area network), an ethernet network, a wireless network (e.g., an 802.11 network, a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), a frame relay network, a virtual private network ("VPN"), a satellite network, a telephone network, a router, a hub, a switch, a server computer, and/or any combination thereof. By way of example only, network 120 may include a cable network, a wireline network, a fiber optic network, a telecommunications network, an intranet, a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), bluetooth ^TM Network, zigBee ^TM A network, a Near Field Communication (NFC) network, etc., or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, network 120 may include, for example, base stations and/or Internet exchangesA wired and/or wireless network access point, such as a point, through which one or more components of medical system 100 may connect to network 120 to exchange data and/or information.

The terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, etc., or any combination thereof. In some embodiments, the mobile device 130-1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. By way of example only, the terminal 130 may include a mobile device as shown in fig. 3. In some embodiments, the smart home devices may include smart lighting devices, smart appliance control devices, smart monitoring devices, smart televisions, smart cameras, interphones, and the like, or any combination thereof. In some embodiments, the wearable device may include bracelets, footwear, glasses, helmets, watches, clothing, backpacks, smart accessories, and the like, or any combination thereof. In some embodiments, the mobile device may include a mobile phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a point-of-sale (POS) device, a laptop computer, a tablet computer, a desktop computer, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glasses, virtual reality eyepieces, augmented reality helmet, augmented reality glasses, augmented reality eyepieces, and the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include Google Glass ^TM 、Oculus Rift ^TM 、Hololens ^TM 、Gear VR ^TM Etc. In some embodiments, one or more terminals 130 may be part of processing device 140.

The processing device 140 may process data and/or information obtained from the medical device 110, the terminal 130, and/or the storage device 150. For example, the processing device 140 may obtain a target radiotherapy plan for the subject. As another example, the processing device 140 may determine a target flux map corresponding to a gantry angle based on a sub-radiotherapy plan corresponding to the gantry angle. As yet another example, the processing device 140 may determine the number of sampling particles corresponding to a first sub-region in the target flux map based on a first weight value corresponding to the first sub-region. As yet another example, the processing device 140 may determine the target dose distribution of the subject according to a monte carlo algorithm based on the number of sample particles corresponding to the first sub-region in the target flux map. In some embodiments, the processing device 140 may be a single server or a group of servers. The server farm may be centralized or distributed. In some embodiments, the processing device 140 may be a local component or a remote component relative to one or more other components of the medical system 100. For example, processing device 140 may access information and/or data stored in medical device 110, terminal 130, and/or storage device 150 via network 120. As another example, processing device 140 may be directly connected to medical device 110, terminal 130, and/or storage device 150 to access stored information and/or data. In some embodiments, the processing device 140 may be implemented on a cloud platform. For example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, or the like, or any combination thereof. In some embodiments, processing device 140 may be implemented by a computing device 200 having one or more components as shown in fig. 2.

Storage device 150 may store data, instructions, and/or any other information. In some embodiments, the storage device 150 may store data obtained from the terminal 130 and/or the processing device 140. In some embodiments, the storage device 150 may store data and/or instructions that the processing device 140 may perform or be used to perform the exemplary methods described herein. In some embodiments, the storage device 150 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. Exemplary mass storage may include magnetic disks, optical disks, solid state drives, and the like. Exemplary removable storage may include flash drives, floppy disks, optical disks, memory cards, compact disks, tape, and the like. Exemplary volatile read-write memory can include Random Access Memory (RAM). Exemplary RAM may include Dynamic Random Access Memory (DRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), static Random Access Memory (SRAM), thyristor random access memory (T-RAM), zero capacitance random access memory (Z-RAM), and the like. Exemplary ROMs may include Mask ROM (MROM), programmable ROM (PROM), erasable Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), and digital versatile disk ROM, etc., in some embodiments, the storage device 150 may be implemented on a cloud platform. For example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, or the like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more other components of the medical system 100 (e.g., the processing device 140, the terminal 130). One or more components of the medical system 100 may access data or instructions stored in the storage device 150 via the network 120. In some embodiments, the storage device 150 may be directly connected to or in communication with one or more other components of the medical system 100 (e.g., the processing device 140, the terminal 130). In some embodiments, the storage device 150 may be part of the processing device 140.

It should be noted that the foregoing description is provided for the purpose of illustration only and is not intended to limit the scope of the present application. Many variations and modifications will be apparent to those of ordinary skill in the art, given the benefit of this disclosure. The features, structures, methods, and other features of the described exemplary embodiments of the application may be combined in various ways to obtain additional and/or alternative exemplary embodiments. However, such changes and modifications do not depart from the scope of the present application.

FIG. 2 is a schematic diagram of at least a portion of an exemplary computing device on which medical system 100 may be implemented, shown in accordance with some embodiments of the present application. As shown in fig. 2, computing device 200 may include a processor 210, memory 220, input/output (I/O) 230, and communication ports 240.

Processor 210 may execute computer instructions (e.g., program code) and perform the functions of processing device 140 in accordance with the techniques described herein. Computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform particular functions described herein. For example, the processor 210 may process data or information obtained from the medical device 110, the storage device 150, the terminal 130, and/or any other component of the medical system 100. In some embodiments, processor 210 may include one or more hardware processors, such as microcontrollers, microprocessors, reduced Instruction Set Computers (RISC), application Specific Integrated Circuits (ASICs), application specific instruction set processors (ASIPs), central Processing Units (CPUs), graphics Processing Units (GPUs), physical Processing Units (PPUs), microcontroller units, digital Signal Processors (DSPs), field Programmable Gate Arrays (FPGAs), advanced RISC Machines (ARM), programmable Logic Devices (PLDs), any circuits or processors capable of executing one or more functions, or the like, or a combination thereof.

For illustration only, only one processor is depicted in computing device 200. It should be noted, however, that the computing device 200 of the present disclosure may also include multiple processors. Thus, operations and/or method steps disclosed as being performed by one processor may also be performed by multiple processors in combination or separately. For example, if the processors of computing device 200 perform operations a and B in the present application, it should be understood that operations a and B may also be performed by two or more different processors in computing device 200 in combination or separately (e.g., a first processor performing operation a, a second processor performing operation B, or both the first and second processors performing operations a and B).

Memory 220 may store data/information obtained from medical device 110, storage device 150, terminal 130, and/or any other component of medical system 100. In some embodiments, memory 220 may include mass storage, removable storage, volatile read-write memory, read-only memory, and the like, or any combination thereof. For example, mass storage may include magnetic disks, optical disks, solid state drives, and the like. Removable memory may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Volatile read-write memory can include Random Access Memory (RAM). The RAM may include Dynamic RAM (DRAM), double rate synchronous dynamic RAM (DDR SDRAM), static RAM (SRAM), thyristor RAM (T-RAM), zero capacitor RAM (Z-RAM), and the like. ROM may include Mask ROM (MROM), programmable ROM (PROM), erasable Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. In some embodiments, memory 220 may store one or more programs and/or instructions to perform the exemplary methods described herein.

I/O230 may input and/or output signals, data, information, etc. In some embodiments, the I/O230 may enable a user to interact with the processing device 140. In some embodiments, I/O230 may include input devices and output devices. Exemplary input devices may include a keyboard, mouse, touch screen, microphone, and the like, or combinations thereof. Exemplary output devices may include a display device, speakers, printer, projector, etc., or a combination thereof. Exemplary display devices can include Liquid Crystal Displays (LCDs), light Emitting Diode (LED) based displays, flat panel displays, curved screens, television devices, cathode Ray Tubes (CRTs), touch screen screens, and the like, or combinations thereof.

Communication port 240 may be connected to a network (e.g., network 120) to facilitate data communication. The communication port 240 may establish a connection between the processing device 140 and the medical device 110, the storage device 150, and/or the terminal 130. The connection may be a wired connection, a wireless connection, any other communication connection that may enable data transmission and/or reception, and/or a combination of these connections. The wired connection may include, for example, an electrical cable, an optical cable, a telephone line, etc., or any combination thereof. The wireless connection may include, for example, bluetooth, wi-Fi, wiMax, wireless local area network, zigBee, mobile network (e.g., 3G, 4G, 5G), etc., or a combination thereof. In some embodiments, the communication port 240 may be and/or include a standardized communication port, e.g., RS232, RS485, etc. In some embodiments, the communication port 240 may be a specially designed communication port. For example, the communication port 240 may be designed according to the digital imaging and medical communications (DICOM) protocol.

FIG. 3 is an illustration of a terminal on which the present application may be implemented, as shown in some embodimentsSchematic diagram of exemplary hardware and/or software components of an exemplary mobile device. As shown in fig. 3, mobile device 300 may include a communication platform 310, a display 320, a Graphics Processing Unit (GPU) 330, a Central Processing Unit (CPU) 340, input/output (I/O) 350, memory 360, and storage 390. In some embodiments, any other suitable component, including but not limited to a system bus or controller (not shown), may also be included within mobile device 300. In some embodiments, mobile operating system 370 (e.g., iOS ^TM 、Android ^TM 、Windows Phone ^TM ) And one or more applications 380 are loaded from storage 390 into memory 360 for execution by CPU 340. The application 380 may include a browser or any other suitable mobile application for receiving and rendering information related to the medical system 100 or other information from the processing device 140. User interaction with information may be accomplished via the I/O350 and provided to the processing device 140 and/or other components of the medical system 100 via the network 120.

To implement the various modules, units, and functions thereof described herein, a computer hardware platform may be used as a hardware platform for one or more of the components described herein. A computer with user interface elements may be used as a Personal Computer (PC) or any other type of workstation or terminal device. If the computer is properly programmed, the computer can also be used as a server.

Fig. 4 is a block diagram of an exemplary processing device shown in accordance with some embodiments of the present application. The processing device 140 may include an acquisition module 410, a flux map determination module 420, a sample particle determination module 430, and a dose determination module 440.

The acquisition module 410 may acquire a target radiotherapy plan for the subject. In some embodiments, the subject may be a patient to be imaged and/or treated by a medical device (e.g., a radiotherapy device). The target radiotherapy plan is used to set how the radiation dose requirements are received by the diseased region (referred to as the target region) and/or normal tissue of the patient using the beam. The radiotherapy apparatus may treat the target zone of the subject to be treated based on the target radiotherapy plan. In some embodiments, the target radiotherapy plan may comprise at least one sub-radiotherapy plan corresponding to at least one gantry angle. In some embodiments, a component of the medical system 100 or a user (e.g., a physician) may determine a target radiation therapy plan based on relevant information of the subject (e.g., target volume shape, area, location), etc. The target radiotherapy plan may be stored in a storage device (e.g., storage device 150) of the medical system 100 from which the acquisition module 410 may acquire the target radiotherapy plan of the subject. More description about the target radiotherapy plan may be found elsewhere in the present application (e.g., step 510 in fig. 5 and descriptions thereof).

The flux map determination module 420 may determine a sub-flux map and a target flux map. In some embodiments, the flux map determination module 420 may determine a plurality of sub-flux maps corresponding to a plurality of sub-fields in a sub-radiotherapy plan corresponding to a certain gantry angle based on the sub-field information of the plurality of sub-fields. The flux map determination module 420 may determine a target flux map corresponding to the gantry angle based on the plurality of sub-flux maps. For example, the flux map determination module 420 may determine the target flux map by fusing multiple sub-flux maps. Further description of determining the sub-flux map and the target flux map may be found elsewhere in the present application (e.g., step 520 in fig. 5 and descriptions thereof).

The sample particle determination module 430 may determine, based on a first weight value corresponding to a first sub-region, a number of sample particles corresponding to the first sub-region. The number of sampling particles corresponding to the first sub-region is related to a first weight value corresponding to the first sub-region. For example, the number of sampling particles corresponding to the first sub-region is proportional, square, or other functional relationship to the first weight value corresponding to the first sub-region. In some embodiments, the sample particle determination module 430 may extrapolate the target flux map to determine an extrapolated region. The sample particle determination module 430 may determine a second weight value corresponding to a second sub-region of the outer-spread region, the second weight value representing the leakage-emission intensity of the second sub-region. The sample particle determination module 430 may determine the number of sample particles corresponding to the second sub-region based on the second weight value corresponding to the second sub-region. More description about determining the first weight value of the first sub-region and the second weight value of the second sub-region may be found elsewhere in the present application (e.g., step 530 in fig. 5 and descriptions thereof).

The dose determination module 440 may determine a target dose distribution and a total dose distribution of the subject. In some embodiments, the dose determination module 440 may determine the target dose distribution of the subject at a gantry angle according to the monte carlo algorithm based on the number of sample particles corresponding to each first sub-region in the target flux map corresponding to the gantry angle. In some embodiments, the dose determination module 440 may determine the total dose distribution of the subject based on a plurality of target dose distributions at a plurality of gantry angles in the target radiotherapy plan. Further description about determining the target dose distribution and the total dose distribution may be found elsewhere in the present application (e.g. step 540 in fig. 5 and the description thereof).

It should be noted that the above description of the processing device 140 by the present application is provided for illustrative purposes only and is not intended to limit the scope of the present application. Various changes and modifications may be made by one of ordinary skill in the art in light of the description of the application. However, such changes and modifications do not depart from the scope of the present application. For example, the processing device 140 may also include a memory module (not shown) for data storage. For example, the processing device 140 may further comprise a control module (not shown in the figures) for controlling the medical device. As another example, the flux map determination module 420 and the sample particle determination module 430 may be integrated into a single module.

Fig. 5 is a flowchart of an exemplary process 500 of dose determination shown in accordance with some embodiments of the present application. In some embodiments, at least a portion of process 500 may be performed by processing device 140 (e.g., implemented in computing device 200 shown in fig. 2). For example, process 500 may be stored in a storage device (e.g., storage device 150, memory 220, memory 390) in the form of instructions (e.g., an application program) and invoked and/or executed by processing device 140 (e.g., processor 210 shown in fig. 2, CPU 340 shown in fig. 3, or one or more modules in processing device 140 shown in fig. 4). The operation of the process shown below is for illustrative purposes only. In some embodiments, process 500 may be accomplished with one or more additional operations not described above and/or without one or more operations discussed. In addition, the order in which the operations of process 500 are illustrated in FIG. 5 and described below is not intended to be limiting.

In 510, the processing device 140 (e.g., the acquisition module 410) may acquire a target radiotherapy plan for the subject, the target radiotherapy plan including at least one sub-radiotherapy plan corresponding to at least one gantry angle.

In some embodiments, the subject may be a patient to be imaged and/or treated by a medical device (e.g., a radiotherapy device). The target radiotherapy plan is used to set how the radiation dose requirements are received by the diseased region (referred to as the target region) and/or normal tissue of the patient using the beam. The radiotherapy apparatus may treat the target zone of the subject to be treated based on the target radiotherapy plan. In some embodiments, the target radiotherapy plan may comprise at least one sub-radiotherapy plan corresponding to at least one gantry angle. The radiotherapy apparatus may deliver radiation therapy to the subject based on the sub-radiotherapy plan at least one gantry angle. As used herein, gantry angle may relate to the orientation of a radiation source (e.g., a therapeutic radiation source) with respect to an isocenter of a radiotherapy apparatus, e.g., gantry angle may be an angle between a vertical direction and a beam axis direction of a radiation beam emitted from the radiation source. For example, the therapeutic radiation source may be detachably or fixedly connected to the gantry, and the therapeutic radiation source may rotate with the gantry as the gantry rotates about a gantry axis of rotation, such that objects on the scan bed may be imaged and/or treated from different gantry angles.

In some embodiments, a sub-radiotherapy plan corresponding to a gantry angle may include sub-field information for a plurality of sub-fields at the gantry angle. The sub-fields are small fields of shaped, passable radiation. In some embodiments, a superposition of shapes of multiple sub-fields (which may be referred to as fields) may match the target shape. In some embodiments, the sub-field information may include sub-radiation intensities. The sub-radiation intensity is determined by the radiation intensity per unit time and the radiation time. The irradiation time may refer to the duration of irradiation of the target volume by a particle beam or beams generated by a radiotherapy apparatus (e.g., a therapeutic radiation source) through the sub-fields. The radiation intensity per unit time may refer to the intensity of a particle beam or beam generated by a radiotherapy apparatus (e.g., a therapeutic radiation source) that irradiates a target volume through a sub-field per unit time. The radiation intensity per unit time is determined by the dose rate of the radiotherapy apparatus and the shape of the sub-fields. In some embodiments, the radiation intensity per unit time at each point in the sub-field may be the same or different. For example, in the leveling mode, the radiation intensity per unit time of each point in the sub-field is the same. For another example, in the non-uniformizing mode, the radiation intensity per unit time is different for each point in the sub-field. The radiation intensities of the different sub-fields corresponding to the unit time can be the same or different. The number of sub-fields in the sub-radiotherapy plan may be determined according to the type of radiotherapy apparatus, the position, area, shape, etc. of the target region. For example by a radiation therapy plan optimization algorithm. The shape of the sub-fields may be achieved by the position of leaves and/or tungsten gates (JAW) in a multi-leaf collimator (MLC). When radiation therapy is performed, the variation of the blade pitch, the movement direction, the movement speed and the like in the multi-blade grating can be utilized to form the sub-fields with different shapes.

By way of example only, during the administration of radiation therapy, when the therapeutic radiation source of the radiation therapy device is fixed at a gantry angle, the blades of the beam limiter are moved to a position prescribed by the first sub-field to stop, and the radiation source may generate and emit a beam for a certain radiation time; then, the blade moves to a position specified by the second sub-field to stop, and the radiation source emits the beam again for a certain radiation time, so that the irradiation of the whole sub-field under the angle of the frame is completed. Then, the therapeutic radiation source of the radiotherapy device can continue to move to the next gantry angle and be fixed, and irradiation of the whole field under the gantry angle is completed in the above manner, so that the whole target radiotherapy plan is completed.

In some embodiments, a component of the medical system 100 or a user (e.g., a physician) may determine a target radiation therapy plan based on relevant information of the subject (e.g., target volume shape, area, location), etc. The target radiotherapy plan may be stored in a storage device (e.g., storage device 150) of the medical system 100, from which the processing device 140 may retrieve the target radiotherapy plan of the subject.

At 520, for each of the at least one gantry angles, the processing device 140 (e.g., the flux map determination module 420) may determine a target flux map corresponding to the gantry angle based on the sub-radiotherapy plan corresponding to the gantry angle. The target flux map may comprise a plurality of first sub-regions, each of the plurality of first sub-regions corresponding to a first weight value representing a target radiation intensity of the first sub-region.

In some embodiments, a target flux map may be used to characterize the flux distribution of the beam current through the beam limiter. For example, the target flux map may represent an expected radiation intensity distribution of a beam intended to be delivered to a target region of a subject in radiation therapy. In some embodiments, the processing device 140 may determine a plurality of sub-flux maps corresponding to a plurality of sub-fields based on sub-field information of the plurality of sub-fields in the sub-radiotherapy plan corresponding to a certain gantry angle. Further, the processing device 140 may determine a target flux map corresponding to the gantry angle based on the plurality of sub-flux maps. For example, the processing device 140 may determine the target flux map by fusing the plurality of sub-flux maps. In this embodiment, the sub-flux patterns and sub-radiation intensities are the same in meaning, and the two expressions can be interchanged.

Fig. 6 is a schematic diagram of an exemplary sub-flux map and target flux map shown in accordance with some embodiments of the application. The left ordinate axis and the lower abscissa axis in fig. 6 represent positions, and the right ordinate axis represents relative radiation intensities. As shown in fig. 6, A, B and C are sub-flux plots corresponding to three different sub-fields at the same gantry angle. For example, when a therapeutic radiation source of the radiotherapy device is fixed at a certain frame angle, the blade of the beam limiter moves to a position specified by the first sub-field to stop, and the radiation source can generate and emit a beam with a first radiation intensity for a first radiation time, and then corresponds to the sub-flux map A; then, the blade moves to a position specified by a second sub-field to stop, and the radiation source emits a beam with a second radiation intensity again for a second radiation time, and the sub-flux map B is corresponding to the blade; finally, the blade is moved to a position specified by the third sub-field and stopped, and the radiation source emits again a beam of the third radiation intensity for a third radiation time, at which time the sub-flux pattern C corresponds. The first radiation intensity, the second radiation intensity, and the third radiation intensity are each radiation intensities per unit time, which may be the same or different. The first irradiation time, the second irradiation time, and the third irradiation time may be the same or different.

As shown in fig. 6, the sub-flux map a, sub-flux map B, and sub-flux map C may be divided into a plurality of reference sub-regions (i.e., squares in the maps a-C), each of which may be considered to be comprised of one or more pixel points. The black reference sub-regions in the sub-flux patterns (e.g., sub-flux pattern a, sub-flux pattern B, sub-flux pattern C) represent irradiated, the corresponding sub-radiation intensities being the product of the corresponding radiation intensities (e.g., first radiation intensity, second radiation intensity, third radiation intensity) of the respective sub-fields and the irradiation time. The light gray reference sub-region represents that it is not irradiated (or missed), and the corresponding irradiation intensity is 0 or other preset value (e.g., 0.01). The shape of the sum of all black reference sub-regions in the sub-flux map (e.g., sub-flux map a, sub-flux map B, sub-flux map C) is the shape of the sub-fields (e.g., first sub-field, second sub-field, third sub-field).

As shown in fig. 6, D is a target flux map, which may include a plurality of first sub-regions, each corresponding to a reference sub-region at the same position in the sub-flux map a, the sub-flux map B, and the sub-flux map C. For example, the first sub-region D in the target flux map D corresponds to the reference sub-region a in the sub-flux map a, the reference sub-region B in the sub-flux map B, and the reference sub-region C in the sub-flux map C. The target radiation intensity (i.e., first weight value) of each first sub-region in the target flux map D may be a sum of sub-radiation intensities (e.g., product of radiation intensity per unit time and radiation time) of corresponding reference sub-regions in the sub-flux map a, sub-flux map B, and sub-flux map C. By way of example only, assume that the radiation intensity per unit time (i.e., the first radiation intensity) corresponding to the reference sub-region a in the sub-flux map a is D1 and the radiation time is T1; the radiation intensity (namely the second radiation intensity) of the unit time corresponding to the reference subarea B in the subarea diagram B is D2, and the radiation time is T2; the radiation intensity per unit time (i.e., the third radiation intensity) corresponding to the reference sub-region C in the sub-flux map C is D3 and the radiation time is T3, the target radiation intensity (i.e., the first weight value) of the first sub-region D in the target flux map D may be d1×t1+d2×t2+d3×t3.

The shade of the color of the first sub-region of the target flux map D in fig. 6 represents the level of the target radiation intensity of said first sub-region, i.e. the magnitude of the corresponding first weight value. The darker the color of a first sub-region, the higher the target radiation intensity representing said first sub-region, the greater the corresponding first weight value. The target flux map may also be represented in any other suitable form. For example, the target flux map may be represented by a two-dimensional matrix, such as target flux map 710 (square area at the bottom right in the figure) shown in fig. 7. The numbers within the first sub-region in the target flux map 710 represent the target radiation intensities (i.e., first weight values) corresponding to the first sub-region.

In 530, the processing device 140 (e.g., the sample particle determination module 430) may determine the number of sample particles corresponding to each first sub-region based on the first weight value corresponding to each first sub-region.

In some embodiments, the number of sample particles corresponding to the first sub-region is related to a first weight value corresponding to the first sub-region. For example, the number of sampling particles corresponding to the first sub-region is proportional, square, or other functional relationship to the first weight value corresponding to the first sub-region. For example, assuming that the first weight value corresponding to the first sub-region M in the target flux map is 1, the first weight value corresponding to the first sub-region N is 0.3, and the first weight value corresponding to the third sub-region P is 0, the number of sampling particles corresponding to the first sub-region M may be N, the number of sampling particles corresponding to the first sub-region N may be 0.3N, and the number of sampling particles corresponding to the first sub-region P may be 0. In some embodiments, n may range from 10 ⁶ ～10 ⁹ . For example, the value range of nMay be 10 ⁸ ～10 ⁹ 。

In some embodiments, the processing device 140 may determine a sample particle distribution corresponding to the gantry angle based on the target flux map. For example, a sub-radiation intensity distribution corresponding to the gantry angle is determined from the target flux map corresponding to the gantry angle, and a sample particle distribution on the target flux map at the corresponding angle is determined from the sub-radiation intensity distribution. The sample particle distribution may be a relative relationship of the number of sample particles in different regions on the target flux map. For the sub-radiation intensity distribution, the higher the intensity, the more the number of sampling particles in the corresponding region on the target flux map, and the lower the intensity, the fewer the number of sampling particles in the corresponding region on the target flux map.

In some embodiments, the processing device 140 may extrapolate the target flux map to determine an extrapolated region. In some embodiments, the field region (i.e., the region containing all of the irradiated first sub-regions) in the target flux map may be surrounded by a rectangular region of minimal area (e.g., region 601 in fig. 6, region 710 in fig. 7) and the rectangular region of minimal area may be flared. The flared region may represent a leaky region. The size of the flared region may be a default value for the medical system 100 or a value set by a user (e.g., physician) or one or more components (e.g., processing device 140) of the medical system 100 for different situations. For example, the size of the flared region may be manually set empirically by a user (e.g., a physician) of the medical system 100 or determined from radiation therapy information by one or more components of the medical system 100 (e.g., the processing device 140). The radiotherapy information may include a target radiotherapy plan, a model of a radiotherapy device, information of a subject (e.g., a position, an area, a shape of a target zone), and the like. In some embodiments, the flared region may be sized to be 0.2 centimeters. For example, assuming that the target flux map has dimensions of 8 cm×8 cm and the flare has dimensions of 0.2 cm, the target flux map after flare has dimensions of 8.2 cm×8.2 cm.

The processing device 140 may then determine a second weight value for each of a plurality of second sub-regions of the outer region. The second weight value may represent the leakage intensity of the second sub-region. The leaky radiation intensity may refer to the radiation intensity corresponding to the areas that are not radiated (or leaky). The leakage intensity may be set to 0 or other value. Accordingly, the second weight value may be set to 0 or other value (e.g., 0.01, 0.05). In some embodiments, the second weight value corresponding to each second sub-region of the outer region may be the same or different. The second weight value may be a default value of the medical system 100 or a value set by a user (e.g., doctor) or one or more components (e.g., processing device 140) of the medical system 100 for different situations. For example, the second weight value may be manually set by a user (e.g., a physician) of the medical system 100 according to experience, or determined by one or more components (e.g., the processing device 140) of the medical system 100 according to radiation therapy information. In some embodiments, the second weight value for each second sub-region in the flared region may all be set to 0.01. The processing device 140 may determine the number of sampling particles corresponding to each second sub-region based on the second weight value corresponding to each second sub-region. For example, as shown in fig. 7, the target flux map 710 may be subjected to outer expansion, and an outer expansion region 720 (L-shaped region in the drawing) is determined, where the outer expansion region 720 includes a plurality of second sub-regions, and each second sub-region corresponds to a second weight value of 0.01, and then the number of sampling particles corresponding to each second sub-region may be 0.01n.

In 540, the processing device 140 (e.g., the dose determination module 440) may determine a target dose distribution of the subject at the gantry angle according to a monte carlo algorithm based on the number of sample particles corresponding to each of the first sub-regions.

The dose distribution may refer to the spatial distribution of energy deposition of radiation particles, such as photons or charged particles, impinging on the object. In some embodiments, the dose profile may include a dose delivered to one or more portions of the subject and/or an absorbed dose absorbed by one or more portions of the subject.

In some embodiments, the processing device 140 may determine a target dose distribution of the subject at the gantry angle based on the number of sample particles corresponding to each first sub-region according to a monte carlo algorithm. For example, the processing device 140 may determine information for each particle emitted from the radiation source based on one or more parameters related to the radiation source. The information of the particles may include the energy of the particles, the velocity of the particles, the location of the particles, the type of particles, the charge of the particles, the source of the particles, etc., or any combination thereof. Processing device 140 may simulate the transport process for each particle based on one or more physical processes that may occur during the transport process for each particle. The one or more physical processes may include, for example, collisions of particles with atoms (or portions thereof) in a medium through which the particles are penetrating, energy changes of the particles after collisions, generation of secondary particles (e.g., electrons) after collisions, changes in the direction of movement of the particles, and the like, or any combination thereof. The processing device 140 may determine the energy deposition of the particles within each first region based on the information of the particles, the transport process of the particles, and the number of sampled particles. The processing device 140 may determine a target dose distribution of the object from the energy deposition of the particles within each first region.

In some embodiments, the processing device 140 may determine the total dose distribution of the subject based on at least one target dose distribution of the subject at least one gantry angle and the at least one gantry angle. For example, the processing device 140 may superimpose at least one target dose distribution corresponding to the at least one gantry angle to determine a total dose distribution of the subject. For example, the dose value at each point in the total dose distribution may be equal to the sum of the dose values at the respective points in the plurality of target dose distributions.

In the existing dose calculation based on the monte carlo algorithm, particles are generally uniformly distributed within a calculation range (for example, a target flux map), that is, the same number of sampling particles are placed in each first sub-region in the target flux map, then particle energy deposition in each first sub-region is calculated based on the monte carlo algorithm, and the calculated particle energy deposition is multiplied by a first weight value corresponding to the first sub-region, so that the radiation dose corresponding to the first sub-region can be obtained. In addition, in the existing dose calculation based on the monte carlo algorithm, in order to ensure the calculation accuracy, the size of the flare region is generally set to be large (for example, 3 cm). This is relatively small for a target flux map of a larger size (e.g., 20 cm x 20 cm), but for a target flux map of a smaller size (e.g., 2 cm x 2 cm), the extrapolated calculation area becomes 5 cm x 5 cm, resulting in lower computational efficiency.

In the technical scheme of Monte Carlo dose determination based on non-uniform sampling, in the process of dose calculation by utilizing a Monte Carlo algorithm, the number of sampling particles in a first subarea is determined according to the weight corresponding to the first subarea in a target flux map, for example, the number of sampling particles corresponding to the first subarea is in direct proportion to a first weight value corresponding to the first subarea. The energy deposition (i.e. the radiation dose) of the particles in the first sub-region is then calculated based on the monte carlo algorithm. Since the calculation time of the dose is proportional to the number of particles, this non-uniform sampling approach can reduce the number of sampled particles, and thus the calculation time. In addition, in dose calculation, the calculation accuracy of the region with higher radiation intensity (i.e., the first sub-region with higher first weight value) is generally more focused, and although the non-uniform sampling manner is adopted to reduce the number of sampling particles of the region with lower radiation intensity (i.e., the first sub-region with lower first weight value), that is, the calculation accuracy of the region with lower radiation intensity is reduced, the overall accuracy loss of dose calculation is not great. In addition, when the non-uniform sampling mode is adopted for dose calculation in the application, the size of the flaring region can be set to be relatively small (for example, 0.2 cm), so that the influence on calculation efficiency is small.

Compared with the traditional uniform sampling mode, the non-uniform sampling mode provided by the application can ensure that the uncertainty corresponding to each first subarea in the target flux map is relatively consistent, and improves the calculation efficiency. For the region with low particle occurrence probability (namely, the region with low first weight value), the number of particles to be calculated is small, and for the region with high particle occurrence probability (namely, the region with high first weight value), the number of particles to be calculated is large, so that the uncertainty of calculation is ensured. That is, the non-uniform sampling method of the application is adopted to calculate the dose distribution, so that the calculation accuracy similar to that of the dose distribution can be achieved by adopting the uniform sampling method, but the non-uniform sampling method of the application can obviously improve the calculation speed and the calculation efficiency.

It should be noted that the above description of the present application has been provided for illustrative purposes only and is not intended to limit the scope of the present application. Various changes and modifications may be made by one of ordinary skill in the art in light of the description of the application. However, such changes and modifications do not depart from the scope of the present application. Process 500 describes a method of determining a target dose distribution corresponding to a sub-radiation therapy plan and a total dose distribution corresponding to the target radiation therapy plan. In some embodiments, when it is desired to determine a dose distribution corresponding to each of the plurality of sub-fields at a certain gantry angle (i.e., a dose distribution under one beam irradiation), then it is desired to assign the same sample particle count to the sub-region of each sub-field corresponding to the sub-flux map having the highest radiation intensity. For example, assuming that at a certain gantry angle the radiotherapy radiation source emits a therapeutic beam under three sub-fields, respectively, each sub-field having a different sub-radiation intensity, if a dose distribution needs to be determined for each emission of the therapeutic beam (i.e. a dose distribution corresponding to each sub-flux map), then the sub-region with the highest radiation intensity in each sub-flux map (i.e. the darkest sub-region in the sub-flux map) needs to be assigned the same sample particle number (e.g. n sample particles). If a target dose distribution corresponding to the gantry angle (i.e., a dose distribution corresponding to a target flux map) needs to be determined, as described in process 500, the target flux map may be determined by fusing a plurality of sub-flux maps, and then determining the number of sample particles corresponding to each first sub-region based on a first weight value corresponding to each first sub-region in the target flux map. In some embodiments, when determining a dose distribution of the beam in the patient for a plurality of gantry angles, e.g., for at least two gantry angles, the number of sample particles corresponding to different gantry angles may be determined from the weights of the beams corresponding to different gantry angles, e.g., for the location of maximum radiation intensity in the target flux map corresponding to different gantry angles, the number of sample particles is allocated according to the relative magnitudes of the weights of the beams; then, under each gantry angle, determining a target flux map corresponding to the gantry angle based on a sub-radiotherapy plan corresponding to the gantry angle, wherein the target flux map comprises a plurality of first sub-regions; determining the number of the sampling particles corresponding to each first subarea based on the number of the sampling particles corresponding to the rack angle and a target flux map corresponding to the rack angle; and determining the target dose distribution of the object under the gantry angle according to a Monte Carlo algorithm based on the number of sampling particles corresponding to each first sub-region. The dose distribution for each gantry angle is determined, for example, using the method of process 500 described above, and then fused to determine the dose distribution in the patient. In some embodiments, when it is desired to analyze the dose distribution of the beam deposited in the patient at a single gantry angle of the plurality of gantry angles, the same number of particles is allocated for the location of maximum radiation intensity in the target flux map corresponding to the different gantry angles, for example, without regard to the weight relationship between the beams, and then the dose distribution corresponding to the single gantry angle is determined using the method of process 500 described above.

Compared with the prior art, the above embodiments of the present application may have beneficial effects including, but not limited to: (1) In the process of calculating the dose by using the Monte Carlo algorithm, a non-uniform sampling mode is adopted, and the number of sampling particles in the subarea is determined according to the weight value (i.e. the first weight value) corresponding to the subarea (i.e. the first subarea) in the target flux graph. Since the calculation time of the dose is proportional to the number of particles, the non-uniform sampling method can reduce the number of sampled particles and the calculation time. (2) The size of the flaring region can be set relatively small (e.g., 0.2 cm) by adopting the non-uniform sampling mode in the application for dose calculation, so that the influence on calculation efficiency is small. It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.

While the basic concepts have been described above, it will be apparent to those of ordinary skill in the art after reading this application that the above disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations of the application may occur to one of ordinary skill in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.

Meanwhile, the present application uses specific words to describe embodiments of the present application. For example, "one embodiment," "an embodiment," and/or "some embodiments" means a particular feature, structure, or characteristic in connection with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the application may be combined as suitable.

Furthermore, those of ordinary skill in the art will appreciate that aspects of the application are illustrated and described in the context of a number of patentable categories or conditions, including any novel and useful processes, machines, products, or materials, or any novel and useful improvements thereof. Accordingly, aspects of the present application may be implemented entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or a combination of hardware and software. The above hardware or software may be referred to as a "unit," module, "or" system. Furthermore, aspects of the present application may take the form of a computer program product embodied in one or more computer-readable media, wherein the computer-readable program code is embodied therein.

The computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, etc., or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer readable signal medium may be propagated through any suitable medium including radio, cable, fiber optic cable, RF, etc., or any combination of the foregoing.

Computer program code required for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, C#, VB.NET, python, etc., a conventional programming language such as C programming language, visualBasic, fortran2103, perl, COBOL2102, PHP, ABAP, a dynamic programming language such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer, or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the use of a network service provider's network) or provided as a service, for example, software service (SaaS).

Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of example, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the application. For example, while the implementations of the various components described above may be embodied in hardware devices, they may also be implemented as a purely software solution, e.g., an installation on an existing server or mobile device.

Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, the inventive subject matter should be provided with fewer features than the single embodiments described above.

In some embodiments, numbers expressing quantities or properties used to describe and claim certain embodiments of the application are to be understood as being modified in some instances by the term "about," approximately, "or" substantially. For example, unless otherwise indicated, "about," "approximately," or "substantially" may indicate a ±20% change in the values they describe. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations in some embodiments for use in determining the breadth of the range, in particular embodiments, the numerical values set forth herein are as precisely as possible.

All patents, patent applications, patent application publications, and other materials (e.g., articles, books, specifications, publications, records, things, and/or the like) mentioned in this application are hereby incorporated herein by reference in their entirety for all purposes except for any such document to which it pertains, any prosecution document record associated with such document, any such document inconsistent or conflicting with such document, or any such document that has a limiting effect on the broad scope of claims which are later and earlier associated with such document. For example, if there is any inconsistency or conflict between the description, definition, and/or use of a term associated with any of the incorporated materials and a term associated with the present document, the description, definition, and/or use of the term in the present document controls.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the application may be considered in keeping with the teachings of the application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly described and depicted herein.

Claims

1. A method of dose determination, the method comprising:

obtaining a target radiotherapy plan of a subject, wherein the target radiotherapy plan comprises a sub-radiotherapy plan corresponding to a gantry angle;

determining a target flux map corresponding to the gantry angle based on the sub-radiotherapy plan corresponding to the gantry angle, the target flux map comprising a plurality of first sub-regions, each first sub-region of the plurality of first sub-regions corresponding to a first weight value representing a target radiation intensity of the first sub-region;

determining the number of sampling particles corresponding to each first sub-region based on the first weight value corresponding to each first sub-region;

performing outer expansion on the target flux map to determine an outer expansion area, wherein the outer expansion area comprises a plurality of second subareas;

Determining a second weight value corresponding to each second sub-region in the plurality of second sub-regions, wherein the second weight value represents the missed emission intensity of the second sub-region;

determining the number of sampling particles corresponding to each second sub-region based on the second weight value corresponding to each second sub-region; and

and determining a target dose distribution of the object under the gantry angle according to a Monte Carlo algorithm based on the number of sampling particles corresponding to each first sub-region.

2. The method of claim 1, wherein the sub-radiotherapy plan includes sub-field information for a plurality of sub-fields at the gantry angle, the determining a target flux map for the gantry angle based on the sub-radiotherapy plan for the gantry angle, comprising:

determining a plurality of sub-flux maps corresponding to the plurality of sub-fields based on the sub-field information; and

and determining the target flux map corresponding to the rack angle by fusing the plurality of sub flux maps.

3. The method according to claim 1, wherein said determining a target dose distribution of the subject according to a monte carlo algorithm based on said number of sampled particles for each of said first sub-regions comprises:

And determining the target dose distribution of the object according to a Monte Carlo algorithm based on the number of sampling particles corresponding to each first subarea and the number of sampling particles corresponding to each second subarea.

4. The method of claim 1, wherein the number of sample particles corresponding to the first sub-region is related to the first weight value corresponding to the first sub-region.

5. The method of claim 1, wherein the target radiotherapy plan comprises a plurality of sub-radiotherapy plans corresponding to a plurality of gantry angles, the method further comprising:

determining a plurality of target dose distributions for the subject corresponding to the plurality of gantry angles; and

a total dose distribution of the subject is determined based on the plurality of target dose distributions.

6. A method of dose determination, the method comprising:

determining a target flux map corresponding to the corresponding gantry angle based on the sub-radiotherapy plan;

determining the number of sampling particles corresponding to each second sub-region based on the second weight value corresponding to each second sub-region;

determining a sampling particle distribution corresponding to a rack angle based on the target flux map; and

based on the sampled particle distribution, a target dose distribution of the subject at a corresponding gantry angle is determined according to a monte carlo algorithm.

7. A method of dose determination, the method comprising:

obtaining a target radiotherapy plan of a subject, the target radiotherapy plan comprising at least one sub-radiotherapy plan corresponding to at least one gantry angle;

determining the number of sampling particles corresponding to different rack angles according to the weights of the beams corresponding to the different rack angles;

for each of the at least one gantry angle,

determining a target flux map corresponding to the gantry angle based on a sub-radiotherapy plan corresponding to the gantry angle, the target flux map comprising a plurality of first sub-regions;

determining the number of the sampling particles corresponding to each first subarea based on the number of the sampling particles corresponding to the rack angle and a target flux map corresponding to the rack angle;

determining a target dose distribution of the object under the gantry angle according to a monte carlo algorithm based on the number of sampling particles corresponding to each first sub-region; and

a total dose distribution of the subject is determined based on at least one target dose distribution of the subject at the at least one gantry angle and the at least one gantry angle.

8. A dose determination system, the system comprising:

the acquisition module is used for acquiring a target radiotherapy plan of the object, wherein the target radiotherapy plan comprises a sub-radiotherapy plan corresponding to the angle of the rack;

a flux map determining module, configured to determine a target flux map corresponding to the gantry angle based on the sub-radiotherapy plan corresponding to the gantry angle, where the target flux map includes a plurality of first sub-regions, each first sub-region of the plurality of first sub-regions corresponding to a first weight value, and the first weight value represents a target radiation intensity of the first sub-region;

A sampling particle determination module for

a dose determining module, configured to determine a target dose distribution of the object under the gantry angle according to a monte carlo algorithm based on the number of sampling particles corresponding to each first sub-region.

9. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the method of any one of claims 1 to 7.