A method, system and device for automatically determining field information

Abstract

An embodiment of this specification provides a method, a system, and a device for automatically determining field information. The method includes: obtaining a candidate field angle range; determining a collimator angle and field locking information based on the candidate field angle range; and determining target field information based on the collimator angle and the field locking information.

Description

Technical Field

[0001] This specification relates to the field of medical technology, and particularly to a method and system for automatically determining beam field information. Background Art

[0002] Radiation therapy is a method for treating diseases such as malignant tumors. Before radiation therapy, it is usually necessary to determine a treatment plan for achieving the treatment goal so that the irradiation field of the radiotherapy device reasonably covers the target area. In the prior art, the limitation of the beam angle and field locking is usually achieved manually by a physician, and the rare automatic methods can only determine or optimize the beam angle. However, for some complex cases (for example, radical breast cases including the clavicle), simply optimizing the beam angle not only cannot achieve the protection of the lungs or heart, but also requires the user to make modifications to optimize the treatment plan, affecting the realization of the one-stop workflow. Therefore, it is desirable to provide a method and system for automatically determining beam field information to improve the quality and production efficiency of the treatment plan. Summary of the Invention

[0003] One aspect of this specification provides a method for automatically determining beam field information. The method includes: obtaining a candidate beam angle range; determining a collimator angle and field locking information based on the candidate beam angle range; and determining target beam field information based on the collimator angle and the field locking information.

[0004] In some embodiments, the obtaining of the candidate beam angle range includes: determining a corresponding historical case based on the current case information; and determining the candidate beam angle range based on the historical case.

[0005] In some embodiments, the case information includes the tumor type and / or the delineation information of the region of interest; the determining of the corresponding historical case based on the current case information includes: determining the corresponding historical case based on the correlation between the current case information and the historical case information.

[0006] In some embodiments, the method further includes: adjusting the candidate beam angle range based on a user instruction.

[0007] In some embodiments, the determining of the collimator angle and the field locking information based on the candidate beam angle range includes: determining the collimator angle by geometric optimization based on the candidate beam angle range; and determining the field locking information based on the candidate beam angle range by using a field locking information determination model, where the field locking information determination model includes a trained machine learning model.

[0008] In some embodiments, determining the collimator angle and the field locking information based on the candidate beam angle range includes: determining the collimator angle and the field locking information based on the candidate beam angle range by using a beam information determination model, where the beam information determination model includes a trained machine learning model.

[0009] In some embodiments, determining the target beam information based on the collimator angle and the field locking information includes: determining at least one candidate beam with the collimator angle and the field locking information based on the candidate beam angle range; and determining the target beam information based on the at least one candidate beam by using a multi-stage optimization method optimized based on the fluence map.

[0010] In some embodiments, the method further includes: determining a radiotherapy plan based on the target beam information.

[0011] Another aspect of this specification provides a system for automatically determining beam information. The system includes: an acquisition module for acquiring a candidate beam angle range; a first determination module for determining the collimator angle and the field locking information based on the candidate beam angle range; and a second determination module for determining the target beam information based on the collimator angle and the field locking information.

[0012] Another aspect of this specification provides a device for automatically determining beam information. The device includes: at least one storage medium storing computer instructions; and at least one processor for executing the computer instructions to implement the method for automatically determining beam information as described above.

[0013] Another aspect of this specification provides a computer-readable storage medium storing computer instructions, and when a computer reads the computer instructions, the computer executes the method for automatically determining beam information as described above.

[0014] The method for automatically determining beam information provided by the embodiments of this specification can determine the candidate beam angle range by using the correlation between the current case information and the historical case information, and automatically generate the target beam information with the collimator angle and the field locking information based on the candidate beam angle range. This can reduce the impact of the beam information on the treatment plan effect, improve the quality of the treatment plan, and can also shorten the time for making the treatment plan, and thus can be integrated into the online automatic planning process. Description of the Drawings

[0015] This specification will be further described by way of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, where:

[0016] Figure 1It is a schematic diagram of the application scenario of an exemplary beam information determination system shown in some embodiments of this specification;

[0017] Figure 2 It is a block diagram of an exemplary beam information determination system shown in some embodiments of this specification;

[0018] Figure 3 It is a flowchart of an exemplary beam information determination method shown in some embodiments of this specification;

[0019] Figure 4 It is a flowchart for determining an exemplary candidate beam angle range shown in some embodiments of this specification;

[0020] Figure 5 It is a flowchart of an exemplary beam locking information determination method shown in some embodiments of this specification;

[0021] Figure 6 It is a flowchart of an exemplary beam information determination method shown in some other embodiments of this specification;

[0022] Figure 7 It is a schematic diagram of an exemplary irradiation field projection shown in some embodiments of this specification. Detailed implementation manners

[0023] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0024] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0025] Generally, the terms "module", "unit", or "block" as used herein refer to logic embodied in hardware or firmware, or a collection of software instructions. The modules, units, or blocks described herein can be implemented as software and / or hardware and can be stored in any type of non-transitory computer-readable medium or another storage device. In some embodiments, software modules / units / blocks can be compiled and linked into an executable program. It should be understood that software modules can call from other modules / units / blocks or from themselves, and / or can be called in response to detected events or interrupts. Software modules / units / blocks configured to execute on a computing device can be provided on a computer-readable medium (e.g., a compact disc, a digital video disc, a flash drive, a magnetic disk, or any other tangible medium), or as a digital download (initially stored in a compressed or installable format that requires installation, decompression, or decryption before execution). The software code herein can be stored in part or in whole in the storage device of the computing device performing the operations and applied in the operations of the computing device. Software instructions can be embedded in firmware, such as an EPROM. It should also be understood that hardware modules / units / blocks can include connected logic components, such as gates and flip-flops, and / or can include programmable units, such as programmable gate arrays or processors. The modules / units / blocks or computing device functions described herein can be implemented as software modules / units / blocks, but can be represented by hardware or firmware. Generally, the modules / units / blocks described herein refer to logical modules / units / blocks, which can be combined with other modules / units / blocks or divided into sub-modules / sub-units / sub-blocks, although they are physically organized or storage devices. This description can apply to a system, an engine, or a part thereof.

[0026] It will be understood that, unless the context clearly dictates otherwise, when a unit, engine, module, or block is referred to as being "on", "connected" or "coupled to" another unit, engine, module, or block, it can be directly on, connected to, or coupled or in communication with the other unit, engine, module, or block, or there may be intervening units, engines, modules, or blocks. In this specification, the term "and / or" can include any one or more of the related listed items or a combination thereof.

[0027] In this specification, the terms "radiation therapy", "radiotherapy", and "treatment" can be used interchangeably to refer to treating a patient. The terms "target object", "patient", "treatment area", "tumor" can be used interchangeably to refer to the object and / or area being treated. The terms "area", "target area", "location", and "treatment area" can be used interchangeably to refer to the location of the treatment area shown in an image or the actual location of the treatment area within or on the patient's body. The term "image" can refer to a 2D image, a 3D image, or a 4D image.

[0028] As shown in this specification and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0029] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. The relevant descriptions are to help better understand the medical imaging method and / or system. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0030] In the treatment planning of radiotherapy (e.g., in inverse intensity-modulated radiation therapy (IMRT)), usually, a physicist (e.g., a doctor) sets the beam angle and field locking method (field locking information) based on their own experience and the positional relationship between the target area and the organs. However, in relatively complex cases (e.g., breast cases) and / or in the scenario of novice treatment planning, the method based on manual experience is likely to lead to unreasonable beam angles and field locking methods, thus affecting the quality of the treatment plan. Therefore, in order to minimize the impact of beam angles and field locking methods on the quality of the treatment plan as much as possible, it is often improved through a trial-and-error method, which increases the complexity of treatment plan making to a certain extent. In some cases, the beam angle can be automatically optimized. However, for complex cases (e.g., radical breast cases including the clavicle), simply automatically optimizing the beam angle (excluding field locking information) not only fails to achieve the protective effect on the lungs and heart but also requires the user to make changes based on the automatic field arrangement to optimize the treatment plan, affecting the realization of a one-stop workflow.

[0031] Based on this, embodiments of the present specification provide a method and system for automatically determining beam information. The corresponding historical case can be determined based on the current case information, the candidate beam angle range can be determined based on the historical case, the collimator angle and the beam locking information can be determined based on the candidate beam angle range, and the candidate beam with the beam locking information and the collimator angle can be further determined. Accordingly, the target beam information can be determined by automatically optimizing the candidate beam. The process involves big data of beam arrangement information (such as the beam arrangement information corresponding to historical cases), the geometric relationship between the target area and organs, and a machine learning model to automatically determine the beam locking information and / or the collimator angle, etc. This can not only reduce the impact of beam information on the treatment plan effect, improve the quality of the treatment plan, but also shorten the time for making the treatment plan, and thus can be integrated into the online automatic planning process to improve the production efficiency of the treatment plan.

[0032] Figure 1 It is a schematic diagram of the application scenario of an exemplary beam information determination system shown according to some embodiments of the present specification.

[0033] As Figure 1 shown, in some embodiments, the beam information determination system 100 may include a medical device 110, a processing device 120, a terminal device 130, a storage device 140, and a network 150. In some embodiments, each component in the beam information determination system 100 may be connected to each other through the network 150 or directly connected without passing through the network 150. For example, the medical device 110 and the terminal device 130 may be connected through the network 150. Again, for example, the medical device 110 and the processing device 120 may be connected through the network 150 or directly connected. Again, for example, the processing device 120 and the terminal device 130 may be connected through the network 150 or directly connected.

[0034] The medical device 110 may collect images of the target and / or perform a treatment plan on the target. For example, the medical device 110 may perform radiotherapy on a lesion area (also referred to as a target area) such as a tumor of the target. Again, for example, the medical device 110 may image the target, obtain the current image, and perform radiotherapy based on the current image. In some embodiments, the target may be biological or non-biological. For example, the target may include a patient, an artificial object, etc. In some embodiments, the target may include a specific part of the body, such as the head, chest, abdomen, etc. or any combination thereof. In some embodiments, the target may include a specific organ, such as the heart, esophagus, trachea, bronchus, stomach, gallbladder, small intestine, colon, bladder, ureter, uterus, fallopian tube, etc. or any combination thereof. In some embodiments, the target may include a region of interest (ROI), such as a tumor, a nodule, a critical organ, etc.

[0035] In some embodiments, the medical device 110 may include one or more medical devices. In some embodiments, one of the one or more medical devices may be used for imaging and treatment simultaneously. In some embodiments, the imaging and treatment processes may be completed by different medical devices.

[0036] In some embodiments, the medical device 110 may include a radiotherapy device, and the radiotherapy device may perform radiotherapy on at least a part of a target. In some embodiments, the radiotherapy device may include a single-modal device, for example, an X-ray therapy device, a Co-60 teletherapy device, a medical electron accelerator, etc. In some embodiments, the radiotherapy device may include a multi-modal (e.g., dual-modal) device. In some embodiments, the multi-modal device may acquire medical images related to at least a part of the target and perform radiotherapy on at least a part of the target. For example, the radiotherapy device may include an Image Guided Radiation Therapy (IGRT) device (e.g., a CT-guided radiotherapy device, an MRI-guided radiotherapy device). In some embodiments, the radiotherapy device may further include an IMRT device. The IMRT device may make the radiation dose more accurate by adjusting (or controlling) the intensity of the radiation according to the shape of the target area.

[0037] In some embodiments, the medical device 110 may include a fixed part and a rotating part. The rotating part is mounted on the fixed part, and the rotating part can rotate around a central axis so as to perform radiotherapy on a patient at different angles. One side of the rotating part may include or be equipped with a treatment head (e.g., treatment head 113), and the treatment head can generate high-energy beams to perform radiotherapy on a target on a medical bed (e.g., medical bed 115). The beams may include electrons, photons, or any other type of radiation. In some embodiments, the medical device 110 may include a radiotherapy device with homologous dual beams. The treatment head can generate low-energy X-rays for imaging the patient, and use the obtained patient images for image-guided radiotherapy of the patient. When imaging with low-energy X-rays, the treatment head can emit cone-beam X-rays, and an imaging device (e.g., electronic portal imaging devices (EPID)) on the other side of the rotating part receives the X-rays passing through the patient to form a projection image at this angle. When the treatment head irradiates at different angles, projection images at multiple angles can be formed.

[0038] In some embodiments, the treatment head may include a collimator for beam shaping. The collimator can rotate around a rotation axis to enable the treatment head to form various desired beam shapes (e.g., shapes close to the target area). In some embodiments, the collimator may include a primary collimator and a secondary collimator.

[0039] The primary collimator is a fixed collimator with conical holes. On the one hand, it can be used to determine the maximum irradiation field range that the accelerator can provide, and on the other hand, it is used to block the primary radiation generated by the radiation source outside the maximum radiation field range. For example, a multi-leaf collimator (MLC).

[0040] The multi-leaf collimator (also known as multi-leaf grating or multi-leaf aperture) consists of two sets of closely arranged blades. Each blade is strip-shaped and is driven by a small motor. It can achieve passing through multiple closely arranged blades to form an area for the radiation source to radiate, that is, the irradiation field. The area blocked by the blades of the multi-leaf collimator will not be radiated, so as to accurately project the required radiation dose to the patient's treatment target area while maximizing the protection of surrounding normal tissues.

[0041] The secondary collimator is a device for secondary collimation of the beam. For example, the secondary collimator can be composed of two pairs of upper and lower openable and closable rectangular collimators (also known as upper and lower apertures or tungsten gates). By the opening and closing movement of the two pairs of upper and lower rectangular collimators, a square or rectangular irradiation field can be formed.

[0042] In some embodiments, the medical device 110 may include an imaging device. For example, the imaging device may include one or a combination of an X-ray device, a computed tomography imaging device (CT), a three-dimensional (3D) CT, a four-dimensional (4D) CT, an ultrasonic imaging component, a fluoroscopic imaging component, a magnetic resonance imaging (MRI) device, a single photon emission computed tomography (SPECT) device, a positron emission tomography (PET) device, etc. The imaging devices provided above are only for illustrative purposes and are not intended to limit the scope of the present application.

[0043] The processing device 120 can process the data and / or information involved in the radiation field information determination system 100. For example, the processing device 120 can obtain the current image of the target from the medical device 110, determine the case information of the current image, and determine the corresponding historical cases based on the case information, and then determine the candidate radiation field angle range. For another example, the processing device 120 can receive a user instruction from the terminal device 130 and adjust the candidate radiation field angle range based on the user instruction.

[0044] In some embodiments, the processing device 120 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device 120 may be local or remote. For example, the processing device 120 may access information and / or data from the medical device 110, the terminal device 130, and / or the storage device 140 via the network 150. Alternatively, the processing device 120 may be directly connected to the medical device 110, the terminal device 130, and / or the storage device 140 to access information and / or data. In some embodiments, the processing device 120 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, etc., or any combination thereof.

[0045] The terminal device 130 may interact with other components in the radiation field information determination system 100 via the network 150. For example, the terminal device 130 may send one or more control instructions to the medical device 110 via the network 150 to control the medical device 110 to perform radiotherapy on a target object according to the instructions. In some embodiments, the terminal device 130 may include a mobile device 131, a tablet computer 132, a laptop computer 133, etc., or any combination thereof. In some embodiments, the mobile device 131 may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, etc., or any combination thereof.

[0046] In some embodiments, the terminal device 130 may be a part of the processing device 120. In some embodiments, the terminal device 130 may be integrated with the processing device 120 to form an operation console of the medical device 110. For example, a user / operator (e.g., a doctor or a nurse) of the radiation field information determination system 100 may control the operation of the medical device 110 via this operation console, such as performing radiotherapy on a target.

[0047] The storage device 140 may store data (e.g., historical cases), instructions, and / or any other information. In some embodiments, the storage device 140 may store data obtained from the medical device 110, the processing device 120, and / or the terminal device 130. For example, the storage device 140 may store scan data of a target and / or historical treatment data (e.g., beam arrangement information corresponding to a historical case) obtained from the medical device 110. In some embodiments, the storage device 140 may store data and / or instructions that the processing device 120 may execute or use to perform the exemplary methods described in this specification.

[0048] In some embodiments, the storage device 140 may include one or a combination of a mass storage, a removable storage, a volatile read-write memory, a read-only memory (ROM), etc. The mass storage may include a magnetic disk, an optical disk, a solid state drive, a removable storage, etc. The removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a ZIP disk, a magnetic tape, etc. The volatile read-write memory may include a random access memory (RAM). The ROM may include a mask read-only memory (MROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), a digital versatile disc, etc. In some embodiments, the storage device 140 may be implemented by the cloud platform described in this specification.

[0049] The network 150 may include any suitable network capable of facilitating the exchange of information and / or data of the radiation field information determination system 100. In some embodiments, one or more components of the radiation field information determination system 100 (e.g., the medical device 110, the processing device 120, the terminal device 130, the storage device 140) may exchange information and / or data with one or more components of the radiation field information determination system 100 via the network 150.

[0050] In some embodiments, the network 150 may include a combination of one or more of a public network (e.g., the Internet), a private network (e.g., a local area network (LAN), a wide area network (WAN), etc.), a wired network (e.g., Ethernet), a wireless network (e.g., an 802.11 network, a wireless Wi-Fi network, etc.), 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 server computer, etc. In some embodiments, the network 150 may include one or more network access points. For example, the network 150 may include a wired and / or wireless network access point, e.g., a base station and / or an Internet exchange point, through which one or more components of the radiation field information determination system 100 may connect to the network 150 to exchange data and / or information.

[0051] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various changes and modifications can be made under the guidance of the content of this specification. The features, structures, methods, and other features of the exemplary embodiments described in this specification can be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the medical device 110, the processing device 120, and the terminal device 130 can share a storage device 140, or they can each have their own storage devices. However, these changes and modifications do not depart from the scope of this specification.

[0052] Figure 2 is a block diagram of a module of an exemplary beam field information determination system shown in some embodiments of this specification.

[0053] As Figure 2 shown, in some embodiments, the beam field information determination system 200 may include an acquisition module 210, a first determination module 220, and a second determination module 230. In some embodiments, one or more modules in the beam field information determination system 200 may be interconnected. The connection can be wireless or wired. At least a part of the beam field information determination system 200 may be implemented on the processing device 120 as shown in Figure 1 shown.

[0054] The acquisition module 210 can be used to acquire a candidate beam field angle range. In some embodiments, the acquisition module 210 may determine a corresponding historical case based on the current case information; determine the candidate beam field angle range based on the historical case. In some embodiments, the acquisition module 210 may determine a corresponding historical case based on the correlation between the current case information and the historical case information. In some embodiments, the case information may include the tumor type and / or the delineation information of the region of interest. In some embodiments, the acquisition module 210 may adjust the candidate beam field angle range based on a user instruction.

[0055] The first determination module 220 can be used to determine the collimator angle and the field locking information. In some embodiments, the first determination module 220 may determine the collimator angle through geometric optimization based on the candidate beam field angle range. In some embodiments, the first determination module 220 may determine the field locking information based on the candidate beam field angle range by using a field locking information determination model, and the field locking information determination model includes a trained machine learning model. In some embodiments, the first determination module 220 may determine the collimator angle and the field locking information based on the candidate beam field angle range by using a beam field information determination model, and the beam field information determination model includes a trained machine learning model.

[0056] The second determination module 230 may be used to determine target beam information. In some embodiments, the second determination module 230 may determine the target beam information based on the collimator angle and the beam locking information. In some embodiments, the second determination module 230 may determine at least one candidate beam with the collimator angle and the beam locking information based on the candidate beam angle range; and determine the target beam information by using a multi-stage optimization method based on fluence map optimization for the at least one candidate beam. In some embodiments, the second determination module 230 may also be used to determine a radiotherapy plan based on the target beam information.

[0057] It should be noted that the above description of the beam information determination system 200 is for illustrative purposes only and is not intended to limit the scope of this specification. Various variations and modifications can be made by those of ordinary skill in the art according to this specification. However, these changes and modifications do not depart from the scope of this specification. For example, one or more modules of the above beam information determination system 200 may be omitted or integrated into a single module. Also, for example, the beam information determination system 200 may include one or more additional modules, such as a storage module for data storage, etc.

[0058] Figure 3 is a flowchart of an exemplary beam information determination method shown according to some embodiments of this specification. In some embodiments, process 300 may be executed by the beam information determination system 100 (e.g., the processing device 120) or the beam information determination system 200. For example, process 300 may be stored in a storage device (e.g., the storage device 140, the storage unit of the system) in the form of a program or instruction, and when the processing device 120 or the beam information determination system 200 executes the instruction, process 300 may be implemented. The operation schematic diagram of process 300 presented below is illustrative. In some embodiments, one or more additional operations not described and / or one or more operations not discussed may be used to complete the process. Additionally, Figure 3 the order of the operations of process 300 shown and described below is not restrictive.

[0059] Step 310, obtain a candidate beam angle range. In some embodiments, step 310 may be executed by the processing device 120 or the acquisition module 210.

[0060] The candidate beam angle range may refer to an angle candidate pool for determining the beam angles corresponding to a treatment plan. In some embodiments, the candidate angle range may be any interval within [0°, 360°] (e.g., [0°, 90°], [0°, 180°], [5°, 10°], [0°, 45°], etc.).

[0061] In some embodiments, a corresponding historical case may be determined based on the current case information, and a candidate beam angle range may be determined based on the historical case. In some embodiments, a corresponding historical case may be determined based on the correlation between the current case information and the historical case information. For example, the processing device 120 may retrieve from a database (e.g., the storage device 140) a historical case that is similar to or the same as the current case information, and determine the candidate beam angle range based on the beam angle, collimator angle, beam locking information, etc. (beam layout information) corresponding to the historical case. For more details, reference may be made to Figure 4 its related description, which will not be elaborated here.

[0062] In some embodiments, the candidate beam angle range may be adjusted based on a user instruction. For example, the candidate beam angle range may be adjusted based on parameters related to the candidate beam angle range input by the user through the terminal device 130. In some embodiments, the parameters may include, but are not limited to, the maximum / minimum interval, angle interval, number of angles, etc. or any combination thereof. The maximum / minimum interval may refer to the range interval that the candidate beam angle range needs to satisfy (e.g., within the range of [0°, 180°]). The angle interval may refer to the difference between adjacent beam angles when determining the beam angles from the candidate beam angle range. For example, if the candidate beam angle range is [0°, 10°] and the angle interval is 1, the candidate beam angles may be determined as 0°, 1°, 2°, 3°, 4°, …, 10° based on the candidate beam angle range. The number of angles may refer to the number of candidate beam angles that can be determined within the candidate beam angle range.

[0063] Step 320, determine the collimator angle and beam locking information based on the candidate beam angle range. In some embodiments, step 320 may be executed by the processing device 120 or the first determination module 220.

[0064] The collimator angle may reflect or affect the cross-sectional shape of the beam (e.g., Figure 7 the white square shown).

[0065] In some embodiments, the collimator angle may be determined by geometric optimization based on the candidate beam angle range. In some embodiments, geometric optimization may refer to the optimization of geometric shapes. In some embodiments, geometric optimization may refer to the optimization of the geometric shapes of the projection of the region of interest and / or the irradiation field.

[0066] In some embodiments, the region of interest may include a target area and / or an organ at risk. The target area may refer to the area where radiotherapy is required for the target (e.g., the tumor area). The organ at risk (OAR) may refer to important organs or tissues involved in the irradiation field during radiotherapy. Since the radiosensitivity of these organs is high (low tolerance dose), radiation damage to them will seriously affect the patient's life or quality of life, thus directly affecting the design and implementation of the irradiation plan (i.e., the radiotherapy plan).

[0067] In some embodiments, geometric optimization may include constraint optimization. For example, constraint optimization may be constrained by the maximum angular variation range of the collimator (e.g., -90° to 90°). For another example, constraint optimization may be constrained by the number of leaves of the multi-leaf collimator and the movement direction of the leaves. In some embodiments, geometric optimization may include the optimization of the cost function. For example, the optimization of the cost function defined based on the projection of the region of interest.

[0068] In some embodiments, for each candidate beam angle in the candidate beam angle range, the collimator angle may be determined by geometric optimization based on the projection of the region of interest in the beam eyes view (BEV) corresponding to that beam angle. For example, for each candidate beam angle in the candidate beam angle range, the collimator angle may be determined by geometric optimization based on the projection of the region of interest in the BEV plane corresponding to that beam angle and based on the optimization parameters. In some embodiments, the optimization parameters may include the movement direction of the collimator leaves, the number of leaves, the area of the target area corresponding to the irradiation field, the area of the non-target area, the "geometric degree of freedom" of the beam, etc., or any combination thereof. For example, through geometric optimization, the ratio of the coverage of the irradiation field on the non-target area to the coverage on the target area may be adjusted to be greater than a preset threshold (e.g., 85%, or 90%, 95%, etc.), and the corresponding collimator angle at this time may be determined as the final collimator angle.

[0069] The field locking information may reflect the tungsten gate locking position of the collimator (e.g., Figure 7 the positions of X1, X2, Y1, Y2 shown in

[0070] In some embodiments, based on the candidate beam angle range, the field locking information may be determined using the field locking information determination model. In some embodiments, for each candidate beam angle in the candidate beam angle range, the field locking information may be determined using the field locking information determination model based on the projection of the region of interest and the beam projection corresponding to that angle. In some embodiments, the input of the field locking information determination model may be the projection of the region of interest and the beam projection corresponding to the candidate beam angle, and the output may be the field locking information corresponding to the candidate beam angle. For more content about the field locking information determination model, please refer to Figure 5And its related descriptions are not elaborated here.

[0071] In some embodiments, based on a candidate beam angle range, a collimator angle and beam locking information can be determined together using a beam information determination model. In some embodiments, for each candidate beam angle in the candidate beam angle range, based on the projection of the region of interest and the beam projection corresponding to this angle, a collimator angle and beam locking information can be determined using the beam information determination model. In some embodiments, the input of the beam information determination model can be the projection of the region of interest and the beam projection corresponding to the candidate beam angle, and the output can be the collimator angle and beam locking information corresponding to the candidate beam angle. More content about the beam information determination model can be found in Figure 6 And its related descriptions are not elaborated here.

[0072] Step 330: Determine target beam information based on the collimator angle and beam locking information. In some embodiments, step 330 can be executed by the processing device 120 or the second determination module 230.

[0073] Beam information can refer to the relevant parameters of the irradiation field used to achieve the treatment purpose during radiotherapy (for example, beam angle, collimator angle, beam locking information, etc. or any combination thereof).

[0074] In some embodiments, based on a candidate beam angle range, at least one candidate beam with a collimator angle and beam locking information can be determined, and using a multi-stage optimization method based on fluence map optimization (FMO), the at least one candidate beam can be optimized to determine the target beam information.

[0075] A fluence map can represent the expected intensity distribution of the beams planned to be delivered to the target volume of the target area during radiotherapy.

[0076] FMO can refer to determining a "best" set of beam intensities that meet the imposed constraints (such as dose constraints) through an iterative optimization method. For example, through FMO, the radiation dose of the treatment plan can be delivered to the irradiated target area (such as the target volume PTV, tumor area, etc.), while not exceeding the maximum tolerable dose of the area to be avoided (such as organs at risk).

[0077] In some embodiments, FMO can include any feasible iterative optimization method such as the alternating direction method of multipliers, the Chambolle - Pock algorithm, or the accelerated proximal gradient method, etc., and this application does not limit this.

[0078] Multi-stage optimization based on FMO can refer to optimizing the beam in two or more stages based on fluence map optimization. For example, the multi-stage optimization process can include: the first stage of class greedy method sparsity, and the second stage of simplex local optimization for optimization.

[0079] In some embodiments, for each candidate beam angle in the candidate beam angle range, a corresponding candidate beam (candidate irradiation field) can be determined based on the candidate beam angle, collimator angle, and beam locking information, and multi-stage optimization based on FMO can be performed on the candidate beam to determine the final target beam information.

[0080] In some embodiments, a beam combination that meets a preset condition can be determined as the target beam information based on multi-stage optimization based on FMO. For example, for each candidate beam angle in the candidate beam angle range, multi-stage beam optimization based on FMO can be performed based on its corresponding candidate beam, and an optimal beam parameter combination (for example, an optimal combination of beam angle, collimator angle, and beam locking information) that meets the range limit of the number of beams can be selected as the target beam information. The optimal beam parameter combination can include a beam parameter combination that enables the radiation dose of the treatment plan to be delivered to the irradiation target area while avoiding irradiating the organs at risk. For example, a beam parameter combination that minimizes the irradiation of normal tissues on the basis that the coverage of the target area by the irradiation field meets the clinical requirements.

[0081] In some embodiments, a radiotherapy plan can be determined based on the target beam information. For example, the beam angle, collimator angle, beam locking information, etc. in the treatment plan can be set based on the target beam information to determine the radiotherapy plan for the target object. For another example, based on the determined target beam information, for the initially preset target beam information lacking some beam information (for example, lacking the collimator angle, etc.), the required beam information (for example, increasing the collimator angle) can be optimized separately to determine the radiotherapy plan. In some embodiments, the radiotherapy plan can be determined automatically or semi-automatically. For example, the processing device 120 can automatically determine the radiotherapy plan based on the target beam information. For another example, the processing device 120 can determine the radiotherapy plan according to the beam optimization settings set by medical staff based on the determined target beam information.

[0082] It should be noted that the above description of process 300 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those of ordinary skill in the art can make various changes and modifications according to the description of this specification. However, these changes and modifications do not depart from the scope of this specification. In some embodiments, process 300 may include one or more additional operations, or one or more of the above operations may be omitted. For example, process 300 may include one or more additional operations for automatically adjusting beam information.

[0083] Figure 4It is a flowchart for determining an exemplary candidate beam angle range shown in some embodiments of this specification. In some embodiments, process 400 may be executed by beam information determination system 100 (e.g., processing device 120) or beam information determination system 200 (e.g., acquisition module 210). For example, process 400 may be stored in a storage device (e.g., storage device 140, the storage unit of the system) in the form of a program or instructions. When processing device 120 or beam information determination system 200 executes the instructions, process 400 may be implemented. The operation schematic diagram of process 400 presented below is illustrative. In some embodiments, the process may be completed using one or more additional operations not described and / or one or more operations not discussed. Additionally, Figure 4 the order of the operations of process 400 shown and described below is not restrictive.

[0084] Step 410, obtain a target image.

[0085] The target image may refer to a medical image containing the target area of the treatment object. In some embodiments, the target image may include 2D images, 3D images, or 4D images. In some embodiments, the target image may include CT images, MRI images, PET images, X-ray images, ultrasound images, radiotherapy beam images, etc. or any combination thereof.

[0086] In some embodiments, the target image may be obtained from a medical device (e.g., medical device 110). In some embodiments, the target image may be obtained from a storage device (e.g., storage device 140), or a database, or a medical system. For example, processing device 120 may search and obtain the corresponding target image from the medical system through network 150 based on the patient's personal information, medical record information, etc. In some embodiments, the target image may be obtained through other means and / or from other channels (e.g., provided by the patient himself), and this specification does not limit this.

[0087] Step 420, determine the case information corresponding to the target image.

[0088] In some embodiments, the case information may include the tumor type and / or the delineation information of the region of interest. Among them, the region of interest includes the target area and the organs at risk.

[0089] In some embodiments, the tumor type may include the tissue origin of the tumor (e.g., epithelial-derived tumor, mesenchymal-derived tumor, neurogenic tumor, lymphatic-derived tumor, etc.), the nature of the tumor (e.g., benign tumor, borderline tumor, and malignant tumor), the growth pattern of the tumor (e.g., carcinoma in situ, invasive carcinoma, and metastatic carcinoma), the extent of invasion of the tumor (e.g., early-stage cancer, middle-stage cancer, and advanced-stage cancer), the degree of malignancy of the tumor (e.g., low-grade malignant tumor, medium-grade malignant tumor, and high-grade malignant tumor), the corresponding anatomical location of the tumor (e.g., breast tumor, lung tumor, uterine tumor, gastric tumor, etc.), etc. or any combination thereof.

[0090] The delineation of the region of interest may refer to the annotation of the target area and / or the organs at risk (e.g., outlining the regions where the target area and / or the organs at risk are located in the target image). Accordingly, the delineation information may reflect the position of the target area and / or the organs at risk, the size of the target area and / or the organs at risk, the relative positional relationship between the target area and the organs at risk, etc. or any combination thereof.

[0091] In some embodiments, the case information corresponding to the target image may be determined by any reasonable method, and this specification does not limit it. For example, the processing device 120 may determine the corresponding case information based on the tumor type recognized by the doctor according to the target image and the delineation of the region of interest in the target image. Another example is that the processing device 120 may automatically extract the case information in the target image through a trained machine learning model.

[0092] Step 430, determine the corresponding historical case.

[0093] In some embodiments, the corresponding historical case may be determined based on the case information. In some embodiments, the corresponding historical case may be determined based on the correlation between the case information of the target image (i.e., the current case information) and the historical case information. The correlation may reflect the similarity degree between the historical case information and the current case information.

[0094] In some embodiments, the corresponding historical case may be determined by searching in the database based on the case information of the target image. For example, the processing device 120 may retrieve in the database (e.g., the storage device 140 or the medical system, etc.) based on the case information of the target image, a historical case with a tumor type similar to or the same as the current case, or a historical case with delineation information of the region of interest similar to or the same as the current case, or a historical case with both the tumor type and the delineation information of the region of interest similar to or the same as the current case.

[0095] Step 440, determine the candidate beam angle range.

[0096] In some embodiments, a candidate gantry angle range may be determined based on a treatment plan of historical cases. For example, a candidate gantry angle range corresponding to a target image may be determined through analysis based on the gantry angles of the treatment plans corresponding to the determined historical cases.

[0097] In some embodiments, a candidate gantry angle range may be determined by a trained machine learning model. For example, scanned images corresponding to various different cases and their corresponding gantry information in historical radiotherapy data may be used as training samples to train a corresponding model. Correspondingly, during application, a target image may be input into the trained machine learning model to determine a candidate gantry angle range.

[0098] Merely as an example, if the tumor type of the current patient is breast tumor, historical cases related to breast tumors may be retrieved from a database, the gantry angles corresponding to the retrieved historical breast tumor cases may be obtained, and a candidate gantry angle range may be determined based on the analysis of the gantry angles.

[0099] In some embodiments, a candidate gantry angle range may be adjusted based on a user instruction.

[0100] It should be noted that the above description of process 400 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those of ordinary skill in the art may make various changes and modifications according to the description of this specification. However, these changes and modifications do not depart from the scope of this specification.

[0101] Figure 5 is a flowchart of an exemplary gantry information determination method shown according to some embodiments of this specification. In some embodiments, process 500 may be executed by a gantry information determination system 100 (e.g., a processing device 120) or a gantry information determination system 200 (e.g., a first determination module 220). For example, process 500 may be stored in a storage device (e.g., a storage device 140, a storage unit of the system) in the form of a program or an instruction. When the processing device 120 or the gantry information determination system 200 executes the instruction, process 500 may be implemented. The operation schematic diagram of process 500 presented below is illustrative. In some embodiments, the process may be completed using one or more additional operations not described and / or one or more operations not discussed. Additionally, Figure 5 the order of the operations of process 500 shown and described below is not restrictive.

[0102] Step 513, obtain a gantry projection.

[0103] The beam's-eye view (BEV) projection can reflect the geometric information of the irradiation field (e.g., geometric shape, etc.). In some embodiments, the BEV projection can include the projection of the candidate beam angles in the BEV plane. In some embodiments, for each candidate beam angle in the candidate beam angle range, the corresponding BEV projection can be determined.

[0104] Step 515: Obtain the projection of the region of interest.

[0105] In some embodiments, the region of interest may include the target area and the organs at risk. Accordingly, the projection of the region of interest can reflect the geometric information of the target area and the organs at risk. In some embodiments, the projection of the region of interest can include the projections of the target area and the organs at risk in the BEV plane.

[0106] In some embodiments, the projection of the region of interest can be obtained based on medical images. In some embodiments, the projection of the region of interest can be obtained by delineating the medical images. For example, the projection of the region of interest can be determined by delineating the tumor area and the organs at risk in the medical images.

[0107] Step 520: Input the BEV projection and the projection of the region of interest into the beam locking information determination model.

[0108] In some embodiments, for each candidate beam angle in the candidate beam angle range, the projection of the region of interest and the BEV projection corresponding to the beam angle can be input into the beam locking information determination model to determine the beam locking information.

[0109] In some embodiments, the beam locking information determination model can include a trained machine learning model. For example, the machine learning model can include a Convolutional Neural Network (CNN), a fully connected neural network, a Recurrent Neural Network (RNN), etc.

[0110] Step 530: Determine the beam locking information. [[ID=XX]] [[ID=XX]]

[0111] In some embodiments, the beam locking information determination model can analyze and process the input BEV projection and the projection of the region of interest to output the beam locking information. For example, the beam locking information determination model can analyze and process the projection of the region of interest and the BEV projection corresponding to each candidate beam angle to output the beam locking information corresponding to the candidate beam angle.

[0112] In some embodiments, as shown in step 540, the beam locking information determination model can be trained based on the first training sample.

[0113] In some embodiments, the first training sample may include multiple sets of sample data groups composed of sample field-of-view projections, sample regions of interest projections, and corresponding field-locking information. For example, the field-of-view projection, region-of-interest projection, and field-locking information corresponding to a sample case may constitute a sample data group.

[0114] In some embodiments, the first training sample may be determined based on historical radiotherapy data. In some embodiments, treatment data corresponding to multiple different types of radiotherapy cases may be obtained as the first training sample. For example, historical radiotherapy data (such as treatment plans) of various types of sample cases such as breast tumors, lung tumors, uterine tumors, gastric tumors, epithelial-derived tumors, mesenchymal-derived tumors, neurogenic tumors, and lymphatic-derived tumors may be obtained as the first training sample.

[0115] In some embodiments, the sample field-of-view projection and the sample region-of-interest projection corresponding to the sample case may be used as the input for model training, and the field-locking information corresponding to the field-of-view may be used as the label to train the first initial model to obtain a trained field-locking information determination model. In some embodiments, the field-locking information determination model may be obtained through any reasonable and feasible training method, and this specification does not limit this.

[0116] It should be noted that the above description of process 500 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those of ordinary skill in the art can make various changes and modifications according to the description of this specification. However, these changes and modifications do not depart from the scope of this specification.

[0117] Figure 6 is a flowchart of an exemplary field-of-view information determination method according to some other embodiments of this specification. In some embodiments, process 600 may be executed by the field-of-view information determination system 100 (such as the processing device 120) or the field-of-view information determination system 200. For example, process 600 may be stored in a storage device (such as the storage device 140, the storage unit of the system) in the form of a program or instruction. When the processing device 120 or the field-of-view information determination system 200 executes the instruction, process 600 may be implemented. The operation schematic diagram of process 600 presented below is illustrative. In some embodiments, the process may be completed using one or more additional operations not described and / or one or more operations not discussed. Additionally, Figure 6 the order of the operations of process 600 shown and described below is not restrictive.

[0118] Step 610, obtain a candidate field-of-view angle range.

[0119] In some embodiments, a corresponding historical case may be determined based on the current case information, and a candidate gantry angle range may be determined based on the historical case. In some embodiments, the candidate gantry angle range may be adjusted based on a user instruction. For more details, reference may be made to Figure 3 and / or Figure 4 its related descriptions, which will not be elaborated herein.

[0120] Step 613: Obtain a gantry projection.

[0121] In some embodiments, the gantry projection may include the projection of the candidate gantry angle on the BEV plane. In some embodiments, for each candidate gantry angle in the candidate gantry angle range, a corresponding gantry projection may be determined.

[0122] Step 615: Obtain a projection of the region of interest.

[0123] In some embodiments, the region of interest may include a target area and an organ-at-risk area. Correspondingly, the projection of the region of interest may reflect the geometric information of the target area and the organ-at-risk. In some embodiments, the projection of the region of interest may include the projections of the target area and the organ-at-risk on the BEV plane.

[0124] For more details about the gantry projection and the projection of the region of interest, reference may be made to Figure 5 (e.g., Step 513 and Step 515) and its related descriptions, which will not be elaborated herein.

[0125] Step 620: Input the gantry projection and the projection of the region of interest into a gantry information determination model.

[0126] In some embodiments, the gantry information determination model may include a trained machine learning model (e.g., CNN, fully connected neural network, RNN, etc.). In some embodiments, for each candidate gantry angle in the candidate gantry angle range, the projection of the region of interest and the gantry projection corresponding to this gantry angle may be input into the gantry information determination model to determine the collimator angle and the field locking information.

[0127] Step 630: Determine the collimator angle and the field locking information.

[0128] In some embodiments, the gantry information determination model may analyze and process the input gantry projection and the projection of the region of interest, and output the collimator angle and the field locking information. For example, the gantry information determination model may analyze and process the projection of the region of interest and the gantry projection corresponding to each candidate gantry angle, and output the collimator angle and the field locking information corresponding to this candidate gantry angle.

[0129] In some embodiments, for each candidate beam angle in the candidate beam angle range, a candidate beam with collimator angle and beam locking information can be determined, and multi-stage optimization based on FMO can be performed on the candidate beam to determine the target beam information.

[0130] In some embodiments, as shown in step 640, the beam information determination model can be trained and obtained based on the second training sample.

[0131] In some embodiments, the second training sample can include multiple groups of sample data sets composed of sample beam projections, sample region of interest projections, and corresponding collimator angles and beam locking information. For example, the region of interest projection, beam projection, collimator angle, and beam locking information corresponding to a sample case can form a sample data set.

[0132] In some embodiments, the second training sample can be determined based on historical radiotherapy data. In some embodiments, treatment data corresponding to multiple different types of radiotherapy cases can be obtained as the second training sample. For example, historical radiotherapy data (such as treatment plans) of various types of sample cases such as breast tumors, lung tumors, uterine tumors, gastric tumors, carcinoma in situ, invasive carcinoma, and metastatic carcinoma can be obtained as the second training sample.

[0133] In some embodiments, the first training sample and the second training sample can be the same or different sample cases. In some embodiments, the first training sample and the second training sample can be the same or different radiotherapy data.

[0134] In some embodiments, the sample beam projection and the sample region of interest projection corresponding to the sample case can be used as the input for model training, and the collimator angle and beam locking information corresponding to the beam can be used as the label to train the second initial model to obtain a trained beam information determination model. In some embodiments, the beam information determination model can be trained and obtained by any reasonable and feasible method, and this specification does not limit this.

[0135] In some embodiments, the beam locking information determination model and the beam information determination model can be trained and obtained by the same or different training methods. In some embodiments, the beam locking information determination model and the beam information determination model can be trained simultaneously, jointly, or separately.

[0136] It should be noted that the above description of process 600 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those of ordinary skill in the art can make various changes and modifications according to the description of this specification. However, these changes and modifications do not depart from the scope of this specification.

[0137] On the other hand, this specification provides a method for determining a radiotherapy plan, including: determining a corresponding historical case based on the current case information, and determining a candidate gantry angle range based on the historical case; determining a collimator angle and field locking information based on the candidate gantry angle range; determining target field information based on the collimator angle and field locking information; and determining a radiotherapy plan based on the target field information.

[0138] In the method and / or system for automatically determining field information provided in the embodiments of this specification, (1) by automatically determining a candidate gantry angle range based on the correlation between the current case information and the historical medical record information, the complexity of treatment plan making can be reduced, and the occurrence of errors or unreasonable phenomena caused by manual setting can be avoided; (2) by using a machine learning model to automatically determine the collimator angle and field locking information based on the candidate gantry angle range, the formulation efficiency and accuracy of field information can be improved; (3) by automatically determining the target field information based on the collimator angle and field locking information, the influence of field information on the treatment plan effect can be reduced, and thus it can be integrated into the online automatic planning process, which helps to achieve a one-stop workflow; (4) by determining a radiotherapy plan based on the target field information, the production time of the radiotherapy plan can be shortened, and the quality of the treatment plan can be improved.

[0139] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the possible beneficial effects may be any one or several combinations of the above, or any other possible beneficial effects that can be obtained.

[0140] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are proposed in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0141] At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment" or "one embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0142] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences, the use of numerical and alphabetical characters, or the use of other names described in this specification are not used to limit the order of the processes and methods in this specification. Although some currently useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details are for illustrative purposes only. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0143] Similarly, it should be noted that, in order to simplify the presentation of the disclosure in this specification and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this specification, sometimes multiple features are grouped into one embodiment, drawing, or description thereof. However, this method of disclosure does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the individual embodiments disclosed above.

[0144] In some embodiments, numbers are used to describe the components and the quantity of attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximate", or "substantially" in some examples. Unless otherwise stated, "about", "approximate", or "substantially" indicate that the stated numbers allow a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and such approximate values can change according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are as precise as possible within the feasible range.

[0145] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. This excludes the application history documents that are inconsistent with or conflict with the content of this specification, and also excludes the documents (currently or subsequently appended to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the attached materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0146] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly presented and described in this specification.

Claims

1. A method for automatically determining field information, characterized in that, The method includes: Obtaining a candidate beam angle range; Based on the candidate beam angle range, determining a collimator angle and field locking information, including: For each candidate beam angle in the candidate beam angle range, based on the projection of the region of interest and the beam projection corresponding to the candidate beam angle, using a field locking information determination model or a beam information determination model to determine the field locking information corresponding to the candidate beam angle; Based on the collimator angle and the field locking information, determining target beam information.

2. The method according to claim 1, wherein The obtaining of the candidate beam angle range includes: Determining a corresponding historical case based on the current case information; Determining the candidate beam angle range based on the historical case 3. The method according to claim 2, wherein The case information includes the tumor type and / or the delineation information of the region of interest; 4. The method according to claim 2, characterized in that The determining of the corresponding historical case based on the current case information includes: Determining the corresponding historical case based on correlation between the current case information and the historical case information.

5. The method according to claim 1, characterized in that, The method further includes: Adjusting the candidate beam angle range based on user instructions.

6. The method according to claim 5, wherein 7. The method according to claim 1, characterized in that, Using the field locking information determination model determines the field locking information corresponding candidate beam angle, and determining the collimator angle based on the candidate beam angle range includes:

8. The method according to claim 7, wherein For each candidate beam angle in candidate beam angle range, based on the projection of the region of interest in the beam direction view corresponding candidate beam angle, determining collimator angle by geometric optimization.

9. The method according to claim 1, characterized in that, The input of the field locking information determination model is the projection of the region of interest and the beam projection corresponding candidate beam angle, and the output is the field locking information corresponding candidate beam angle.

10. The method according to claim 1, characterized in that, Based on the candidate beam angle range, using beam information determination model to determine the collimator angle and the field locking information. The input of the beam information determination model is the projection of the region of interest and beam projection corresponding candidate beam angle output collimator angle and field locking information corresponding candidate beam angle. The field locking information determination model includes a trained machine learning model, and the beam information determination model includes trained machine learning model.

11. The method according to claim 1, wherein The determining of the target beam information based on the collimator angle and the field locking information includes:

12. A system for automatically determining beam information, characterized in that, Based on the candidate beam angle range determining at least one candidate beam with the collimator angle and the field locking information Using multi-stage optimization method based fluence map optimization optimizing the at least one candidate beam two or more stages determining the target beam information. The method further includes: Determining a radiotherapy plan based on the target beam information The system includes: An obtaining module for obtaining a candidate beam angle range; A first determination module for determining a collimator angle and field locking information based on the candidate beam angle range, including: For each candidate beam angle in the candidate beam angle range, based on the projection region of interest and beam projection corresponding candidate beam angle, using field locking information determination model or beam information determination model to determine the field locking information corresponding candidate beam angle A second determination module, configured to determine target beam information based on the collimator angle and the field locking information.

13. An apparatus for automatically determining field information, characterized in that, The apparatus includes: At least one storage medium storing computer instructions; At least one processor that executes the computer instructions to implement the method according to any one of claims 1 to 11.

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