A method and system for determining dose distribution

Abstract

The embodiments of this specification disclose a method and system for determining a dose distribution. The method includes: obtaining treatment-related data of a target object; determining the dose distribution based on the treatment-related data of the target object and a dose distribution algorithm. The kernel function of the dose distribution algorithm includes a linear combination of one or more first function terms and one or more second function terms, and the second function terms are not related to the depth information; determining the dose distribution includes: performing a convolution operation based on the treatment-related data, a linear combination of one or more first function terms and one or more second function terms to determine the dose distribution.

Description

Technical Field

[0001] The present invention relates to the field of radiotherapy, and in particular to a method and system for determining dose distribution. Background Art

[0002] Radiotherapy technology is widely used in cancer treatment. Compared with simple radiotherapy technology, complex radiotherapy technology, such as intensity modulated radiotherapy (IMAT), can allow the gantry to rotate continuously during the radiotherapy treatment, and the dose rate of the radiotherapy equipment and the position of the multi-leaf collimator (MLC) can be changed continuously and rapidly, so that the tumor area can reach the specified dose while the radiation dose of the surrounding tissue is controlled within a safe range.

[0003] In radiotherapy, intensity modulated radiotherapy plans have high requirements on the speed and quality of dose calculation. Therefore, it is necessary to provide a method for determining dose distribution to obtain more accurate dose calculation results while ensuring the calculation speed of dose distribution. Summary of the invention

[0004] One aspect of an embodiment of the present specification provides a method for determining a dose distribution. The method for determining a dose distribution includes: acquiring treatment-related data of a target object; determining a dose distribution based on the treatment-related data of the target object and a dose distribution algorithm; wherein the kernel function of the dose distribution algorithm includes a linear combination of one or more first function terms and one or more second function terms, and the second function terms are not related to depth information. Wherein, determining the dose distribution includes: performing a convolution operation based on a linear combination of the treatment-related data, the one or more first function terms, and the one or more second function terms to determine the dose distribution.

[0005] Another aspect of an embodiment of the present specification provides a system for determining a dose distribution. The system includes: an acquisition module, which can be used to acquire treatment-related data of a target object. The determination module can be used to determine the dose distribution based on the treatment-related data of the target object and a dose distribution algorithm; wherein the kernel function of the dose distribution algorithm includes a linear combination of one or more first function terms and one or more second function terms, and the second function terms are not related to the depth information. Wherein, determining the dose distribution includes: performing a convolution operation based on the treatment-related data, the linear combination of the one or more first function terms and the one or more second function terms to determine the dose distribution.

[0006] Another aspect of an embodiment of the present specification provides a device for determining a dose distribution including at least one storage medium and at least one processor, wherein the at least one storage medium is used to store computer instructions; and the at least one processor is used to execute the computer instructions to implement a method for determining a dose distribution.

[0007] Another aspect of the embodiments of the present specification provides a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a method for determining a dose distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0009] Figure 1 is a schematic diagram of an exemplary application scenario of a system for determining a dose distribution according to some embodiments of this specification;

[0010] Figure 2 is an exemplary flow chart of a method for determining a dose distribution according to some embodiments of this specification;

[0011] Figure 3 is an exemplary flow chart for determining dose distribution according to some embodiments of this specification;

[0012] Figure 4 is an exemplary module diagram of a system for determining a dose distribution according to some embodiments of the present specification. DETAILED DESCRIPTION

[0013] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0014] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0015] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" 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.

[0016] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0017] This specification provides systems and components for medical diagnosis and / or treatment. In some embodiments, the diagnosis and treatment system may include a radiotherapy system. The radiotherapy system may include a treatment planning system (TPS, Treatment Planning System), an image guided radiation therapy (Image Guided Radiation Therapy, IGRT) system, etc. As an example only, the image guided radiation therapy system may include a CT (Computed Tomography) guided radiation therapy system, an MRI (Magnetic Resonance Imaging) guided radiation therapy system, etc. In some embodiments, the system may be an imaging system, including a computed tomography (CT) system, an emission computed tomography (Emission Computed Tomography, ECT) system, an X-ray photography system, a positron emission computed tomography (Positron Emission Computed Tomography, PET) system, etc. One or a combination of the above. For ease of understanding, the system and method of radiotherapy are referred to in this specification. The term "image" used in this application may refer to a 2D image, a 3D image, or a 4D image, or may refer to an image of a region of interest (ROI) of a target object. The term "image" in this application may refer to a CT image, an Electronic Portal Imaging Device (EPID) image, a fluoroscopic image, an ultrasound image, a PET image, or the like.

[0018] At present, the methods of dose calculation include a fast dose calculation method based on a convolution kernel and a dose calculation method based on a dose distribution kernel function. The fast dose calculation method based on a convolution kernel has an advantage in calculation speed and can achieve a faster calculation speed, but the calculation accuracy is relatively low; while the dose calculation method based on a dose distribution kernel function, although it can obtain a more accurate dose calculation result, has a slower calculation speed. In the related art, the fast dose calculation method based on a convolution kernel is used to calculate the average convolution kernel parameters within the entire dose calculation depth range. Although a faster dose calculation speed can be obtained under the convolution kernel framework, it will cause a large deviation in the results of the top and bottom layers of the phantom, and the calculation of each depth layer by layer is very time-consuming. On the other hand, although the dose calculation algorithm based on a self-consistent kernel function is more accurate, it has a slower calculation speed and is difficult to meet the needs of clinical use.

[0019] Therefore, the embodiment of this specification discloses a method and system for determining dose distribution, which can use a separate convolution kernel function to calculate the scattering method for each depth while ensuring the calculation speed, so that the accuracy of the dose calculation result at different depths is improved. The technical solution disclosed in this specification is described in detail below through the description of the accompanying drawings.

[0020] Figure 1 It is a schematic diagram of an exemplary application scenario of a system for determining dose distribution according to some embodiments of this specification.

[0021] In some embodiments, the dose distribution determination system 100 can be used to determine a dose distribution associated with a radiotherapy plan. The system 100 can optimize the radiotherapy plan or perform radiotherapy according to the determined dose distribution.

[0022] In a typical application scenario, the system 100 can obtain treatment-related data of the target object; the system 100 can determine the dose distribution based on the treatment-related data of the target object and the dose distribution algorithm. The dose distribution algorithm includes a convolution operation, and the system 100 can convert the kernel function of the dose distribution algorithm into a linear combination of one or more first function terms and one or more second function terms, wherein the second function terms are not related to the depth information; the system 100 can perform a convolution operation based on the treatment-related data, the linear combination of one or more first function terms and one or more second function terms to determine the dose distribution.

[0023] like Figure 1 As shown, the system 100 may include an imaging device 110 , a network 120 , a terminal 130 , a processing device 140 , and a storage device 150 .

[0024] The imaging device 110 can be used to scan the target object to obtain scanning data and image. The imaging device 110 can be a medical imaging device (for example, a computed tomography (CT), a PET imaging device, an MRI imaging device, a single-photon emission computed tomography (SPECT) imaging device, a PET-CT imaging device, a PET-MRI imaging device, etc.). In some embodiments, the imaging device 110 may include a gantry 111, a detector 112, a scanning area 113, and a scanning bed 114. The target object can be placed on the scanning bed 114 to be scanned. The gantry 111 can support the detector 112. In some embodiments, the detector 112 may include one or more detector units. The detector unit may be and / or include a single-row detector and / or a multi-row detector. The detector unit may include a scintillation detector (for example, a cesium iodide detector) and other detectors. In some embodiments, the gantry 111 can rotate, for example, in a CT imaging device, the gantry 111 can rotate clockwise or counterclockwise around the gantry rotation axis. In some embodiments, the imaging device 110 may further include a radiation scanning source, which may rotate together with the gantry 111. The radiation scanning source may emit a radiation beam (e.g., X-ray) to a target object, which is detected by the detector 112 after being attenuated by the target object, thereby generating an image signal. The scan data and / or image may be used to determine a region of interest. For example, the processing device 140 may perform modeling based on the scan data to obtain a geometric model of the tissue and / or organ of the target object, and determine the region of interest based on the established geometric model.

[0025] The processing device 140 can process data and / or information obtained from the imaging device 110, the terminal 130 and / or the storage device 150. For example, the processing device 140 can process the image information detected and generated by the detector 112 to obtain treatment-related data of the target object. For another example, the processing device 140 can determine the dose distribution based on the treatment-related data of the target object and the dose distribution algorithm. In some embodiments, the processing device 140 can be a single server or a server group. The server group can be centralized or distributed. In some embodiments, the processing device 140 can be local or remote. For example, the processing device 140 can access information and / or data from the imaging device 110, the terminal 130 and / or the storage device 150 through the network 120. For another example, the processing device 140 can be directly connected to the imaging device 110, the terminal 130 and / or the storage device 150 to access information and / or data. In some embodiments, the processing device 140 can be implemented on a cloud platform. For example, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, etc., or a combination of these.

[0026] The terminal 130 may include a mobile device 131, a tablet computer 132, a laptop computer 133, etc. or any combination thereof. In some embodiments, the terminal 130 may interact with other components in the system 100 through a network. For example, the terminal 130 may send one or more control instructions to the imaging device 110 to control the imaging device 110 to scan the target object according to the instructions. In some embodiments, the terminal 130 may be part of the processing device 140. In some embodiments, the terminal 130 may be integrated with the processing device 140 as an operating console of the imaging device 110. For example, a user / operator of the system 100 (e.g., a doctor) may control the operation of the imaging device 110 through the operating console, such as scanning the target object, determining a region of interest related to the target object, determining a dose distribution of the region of interest, etc.

[0027] The storage device 150 may store data (e.g., scan data of a target object, a treatment plan, etc.), instructions, and / or any other information. In some embodiments, the storage device 150 may store data obtained from the imaging device 110, the terminal 130, and / or the processing device 140. For example, the storage device 150 may store scan data of a target object obtained from the imaging device 110, etc. In some embodiments, the storage device 150 may store data and / or instructions that the processing device 140 may execute or use to perform the exemplary method described in this application. In some embodiments, the storage device 150 may include a large-capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), etc., one or a combination of several thereof. In some embodiments, the storage device 150 may be implemented by the cloud platform described in this application. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, a cross-cloud, a multi-cloud, etc., one or a combination of several thereof.

[0028] In some embodiments, the storage device 150 may be connected to the network 120 to implement communication with one or more components (e.g., the processing device 140, the terminal 130, etc.) in the system 100. One or more components in the system 100 may read data or instructions in the storage device 150 through the network 120. In some embodiments, the storage device 150 may be part of the processing device 140, or may be independent and directly or indirectly connected to the processing device.

[0029] The network 120 may include any suitable network capable of facilitating information and / or data exchange of the system 100. In some embodiments, one or more components of the system 100 (e.g., the imaging device 110, the terminal 130, the processing device 140, the storage device 150, etc.) may exchange information and / or data with one or more components of the system 100 via the network 120. For example, the processing device 140 may obtain scan data from the imaging device 110 via the network 120. The network 120 may include one or a combination 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., an 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. For example, the network 120 may include a wired network, an optical fiber network, a telecommunication network, a local area network, a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth TM Network, ZigBee TMIn some embodiments, the network 120 may include one or more network access points, such as a wireless network, a near field communication (NFC) network, etc. For example, the network 120 may include a wired and / or wireless network access point, such as a base station and / or an Internet exchange point, through which one or more components of the system 100 may connect to the network 120 to exchange data and / or information.

[0030] Figure 2 is an exemplary flow chart of a method for determining a dose distribution according to some embodiments of the present specification. In some embodiments, process 200 may be executed by a processing device (e.g., processing device 140). For example, process 200 may be stored in a storage device (e.g., a built-in storage unit of a processing device or an external storage device) in the form of a program or instruction, and when the program or instruction is executed, process 200 may be implemented. Process 200 may include the following operations.

[0031] Step 202 , obtaining treatment-related data of the target object. Step 202 may be performed by the obtaining module 410 .

[0032] In some embodiments, the target object may include a patient or other medical experimental subject (e.g., experimental mice or other animals), etc. The target object may also be a part of a patient or other medical experimental subject, such as a region of interest of the target object, including organs and / or tissues, such as the heart, lungs, ribs, abdominal cavity, etc.

[0033] The region of interest refers to an area of ​​concern in radiotherapy, which may be a specific organ, specific tissue or specific part of the target object. For example, the region of interest may include the head, chest, lungs, heart, liver, spleen, pleura, mediastinum, abdomen, large intestine, small intestine, bladder, gallbladder, pelvis, spine, bones, blood vessels or the like of the target object, or any combination thereof.

[0034] In some embodiments, a region of interest related to the target object may be represented by a phantom simulating the target object. The phantom may be a model of a specific organ, specific tissue, or specific part of the target object.

[0035] In some embodiments, the processing device may establish a phantom according to the scan data of the target object to be simulated, the material information of the phantom, and the like.

[0036] Treatment-related data refers to data related to radiotherapy for a target object, and may include scan data (e.g., CT data, PET data, etc.) and treatment plan data of the target object. The treatment plan data may include information on the treatment plan, such as the radiation beams, the dose of each beam, the shape of the beam, the distance of the beam, etc. The spatial density distribution of the region of interest corresponding to the target object may be obtained based on the scan data of the target object.

[0037] In some embodiments, the processing device may obtain treatment-related data by reading from a storage device, a database, a medical device, or by calling a related data interface.

[0038] Step 204 , based on the treatment-related data of the target object and the dose distribution algorithm, determines the dose distribution of the region of interest. Step 204 may be performed by the determination module 420 .

[0039] The dose distribution algorithm may include any linear fast dose algorithm using a convolution kernel, for example, a fast dose algorithm based on a pencil beam convolution kernel, such as a finite size pencil beam algorithm (FSPB), a non-voxel-based broad beam algorithm (NVBB), and the like.

[0040] In some embodiments, the processing device may determine the dose distribution of the region of interest based on the treatment-related data of the target object and a dose distribution algorithm by using the method described in the following embodiments.

[0041] In some embodiments, the kernel function of the dose distribution algorithm includes a linear combination of one or more first function terms and one or more second function terms, and the second function terms are irrelevant to the depth information of the region of interest. The kernel function can be obtained by transforming the kernel function of the dose distribution algorithm.

[0042] The kernel function of the linear fast dose algorithm based on the convolution kernel has the following characteristics: the dependence of the kernel function parameters on the depth and lateral position can be approximately decomposed into two terms, one is the linear superposition coefficient that depends on the depth, and the other is the two-dimensional kernel function that does not depend on the depth. Combined with the linear property of convolution, this feature can be applied to the convolution term of the fast dose algorithm, that is, the kernel function of the dose distribution algorithm is applied to the convolution term in the fast dose algorithm, so that the accuracy of the dose calculation results at different depths is significantly improved while ensuring the speed of the dose algorithm.

[0043] After the kernel function of the dose distribution algorithm is converted, the first function term may be a linear superposition coefficient term that depends on the depth, and the second function term may be a two-dimensional function term that does not depend on the depth. In some embodiments, the number of the first function term and the second function term is the same, for example, the first function term and the second function term are both one term, or both are multiple terms, such as 2 terms, 5 terms, 10 terms, 20 terms, etc.

[0044] For the purpose of illustration only, taking the FSPB algorithm as an example, the self-consistent kernel function of the FSPB algorithm can be shown as formula (1).

[0045]

[0046] Among them, ω is the coefficient of linear superposition, Representation coefficient ω i The sum is equal to 1; represents the two-dimensional spatial distribution of the region of interest, Independent of depth, d represents the depth information of the region of interest, which is related to the depth d. x0, y0 are the parameters of the kernel function, which are related to d is irrelevant. The definition of function f can be shown in formula (2).

[0047]

[0048] Take n = 2, After expansion, it is shown in formula (3).

[0049]

[0050] Among them, the subscripts x and y indicate the x and y directions respectively; the subscripts 1 and 2 distinguish the physical processes of the formation of different types of penumbra, and ω(d) describes the weights of these two types at different depths; the symbol u describes the speed of dose attenuation in the penumbra area, and x0 and y0 describe the cross-sectional dimensions selected in the FSPB model, which are constants.

[0051] In the kernel function shown above, the parameter ω is obviously dependent on the depth d, while the parameter Almost independent of depth. Based on the important characteristics of the kernel function, in the embodiment of the present specification, the kernel function of the dose distribution algorithm is converted into a linear combination of one or more first function terms and one or more second function terms. Among them, the second function term is irrelevant to the depth information of the region of interest (or the second function term has little dependence on the depth of the region of interest), and the first function term is related to the depth information of the region of interest (or the first function term has a greater dependence on the depth of the region of interest).

[0052] The depth information refers to the depth of each layer after the geometric model is divided into a multi-layer structure (for example, after the xy axis direction is determined as the axial direction of the spatial plane, the multi-layer structure is obtained by dividing along the z axis direction of the model or phantom).

[0053] It should be noted that the above examples are only for illustration and are not intended to limit the application algorithm model of the technical solution disclosed in this specification. For example, other similar FSPB convolution kernel analysis models can also be applied if they still maintain the characteristic of being able to decompose deep dependencies.

[0054] Based on the examples described above, it can be seen that based on the dependence of the convolution kernel parameters of the dose calculation algorithm on depth, its kernel function can be converted into a linear combination of multiple function terms. For example, the kernel function of the dose calculation algorithm obtained by converting the kernel function of the dose calculation algorithm into a linear combination of one or more first function terms and one or more second function terms can be as shown in formula (4).

[0055]

[0056] in, represents the kernel function after transformation, represents the spatial plane distribution information of the region of interest, d represents the depth information of the region of interest, ω i (d) represents a first function term, which is a coefficient term related to the depth information of the region of interest, Represents the second function term, which is a two-dimensional kernel function that is irrelevant to depth information.

[0057] In some embodiments, the second function term is related to the spatial plane distribution information of the region of interest. The spatial plane distribution information may refer to the distribution of points of the region of interest on a two-dimensional plane. For example, if the depth information is represented by the z-axis direction, the spatial plane may refer to a plane determined by the x-axis and the y-axis.

[0058] In some embodiments, the greater the number of linear combinations of the first function term and the second function term obtained by transforming the kernel function, the higher the accuracy of subsequent dose calculation.

[0059] In some embodiments, the processing device may determine the dose distribution of the region of interest based on a convolution operation of a linear combination of the treatment-related data, the one or more first function terms, and the one or more second function terms.

[0060] In some embodiments, the processing device may perform a convolution operation on the treatment-related data using a linear combination of the converted one or more first function terms and one or more second function terms to obtain a convolution operation result, and determine the dose distribution of the region of interest based on the convolution operation result.

[0061] Exemplarily, in some embodiments, the processing device may determine a flux map for a convolution operation based on the treatment-related data; and determine the dose distribution of the region of interest by performing a convolution operation based on the flux map and a linear combination of the one or more first function terms and the one or more second function terms.

[0062] The flux map can be understood as the intensity distribution of the radiotherapy ray at each point in the coordinate system. For example, the actual dose can be reflected by the number of ray particles passing through each point per unit time. In some embodiments, the actual dose of the ray passing through the target tissue can be reflected by the pixel value of the image. In some embodiments, the actual dose of the ray passing through the target area is related to the penetration rate of the ray during the treatment process. In some embodiments, the processing device can determine the ray beam, the dose, shape, distance, etc. of the beam used for radiotherapy based on the planning data, and calculate the flux map based on the information such as the ray beam.

[0063] In the embodiment of the present specification, after the kernel function is converted, the amount of calculation when calculating the dose at each depth can be greatly simplified. For the purpose of illustration only, taking the case of n=2 as an example, the kernel function represented by the above formula (4) can be represented as formula (5).

[0064]

[0065] Accordingly, the dose distribution of the region of interest can be calculated based on the following equation (6).

[0066]

[0067] Where D represents the dose distribution in the region of interest, Represents the three-dimensional spatial location of the region of interest, including depth and spatial plane distribution, represents the kernel function, ∝ represents the dose distribution of the region of interest is positively correlated with the result of the subsequent convolution operation, F represents the flux map, represents the convolution operation, ω(d) represents the first function term, and k1 and k2 represent the second function term.

[0068] because It means a The function of the convolution operator is It means that the function f() is convolved with (), and the result after convolution is still function, so we can Written at the end.

[0069] It can represent the result of convolution operation. For more description of convolution operation, please refer to Figure 3 The related descriptions will not be repeated here.

[0070] After the convolution operation result is calculated, other necessary calculations for dose distribution calculation can be performed based on the convolution operation result, and after the calculation is completed, the dose distribution of the region of interest can be determined. The process of other necessary calculations will not be repeated here.

[0071] In the method for determining the dose distribution provided in the embodiment of this specification, the kernel function of the dose distribution algorithm is converted into a linear combination of a first function term and a second function term, wherein the second function term does not depend on the depth information of the region of interest. During the calculation, the converted linear combination is convolved with the flux map. Since the kernel function has been converted into a two-dimensional kernel function (the second function term) and a first function term related to the depth information, the convolution operation is performed using the second function term and the flux map. Compared with the three-dimensional convolution operation of the original kernel function, the calculation speed can be greatly improved. At the same time, the first function term related to the depth information is retained during the calculation process. Therefore, while greatly improving the calculation speed, the accuracy of the dose calculation results at different depths can be significantly improved.

[0072] Figure 3 is an exemplary flow chart for determining the dose distribution of a region of interest according to some embodiments of the present specification. In some embodiments, process 300 may be executed by a processing device. For example, process 300 may be stored in a storage device (such as a built-in storage unit of the processing device or an external storage device) in the form of a program or instruction, and when the program or instruction is executed, process 300 may be implemented. Figure 3 As shown, process 300 may include the following operations.

[0073] Step 302: Perform a convolution operation based on the flux graph and one or more of the second function terms to determine a convolution operation result.

[0074] In some embodiments, when performing a convolution operation, the convolution operation can be appropriately deformed to facilitate calculation. For example, the convolution operation shown in the above formula (6) is still taken as an example. Formula (6) can be deformed, and the deformation result can be shown in formula (7).

[0075]

[0076] The convolution operation result refers to the convolution calculation result of the flux map and the two-dimensional kernel function. For example, When performing calculations, it is only necessary to perform a convolution operation on the second function term and the flux map, thereby greatly reducing the amount of calculations during the convolution operation.

[0077] Step 304: Determine the dose distribution of the region of interest based on a linear combination of the convolution operation result and one or more of the first function terms.

[0078] In some embodiments, the processing device may multiply the convolution operation results by the corresponding first function terms, for example, After the calculation is completed, other necessary dose calculations are performed to determine the dose distribution of the region of interest.

[0079] By using the linear property of convolution to transform formula (6), it can be seen from formula (7) that only one convolution calculation needs to be added to the original dose calculation to calculate the dose for each depth information separately, thereby achieving the purpose of significantly improving the accuracy of dose calculation while ensuring the calculation speed.

[0080] It should be noted that the above description of each process is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, changes to the process steps of this specification, such as adding a pre-processing step and a storage step, etc.

[0081] Figure 4 FIG. 1 is an exemplary module diagram of a system for determining a dose distribution according to some embodiments of the present specification. Figure 4 As shown, the system 400 may include an acquisition module 410 and a determination module 420 .

[0082] The acquisition module 410 may be used to acquire treatment-related data of the target object.

[0083] The determination module 420 may be configured to determine the dose distribution of the region of interest based on the treatment-related data of the target object and a dose distribution algorithm.

[0084] In some embodiments, the determination module 420 may determine the dose distribution of the region of interest based on the treatment-related data of the target object and a dose distribution algorithm.

[0085] In some embodiments, the determination module 420 may convert the kernel function of the dose distribution algorithm into a linear combination of one or more first function terms and one or more second function terms, wherein the second function terms are not related to the depth information of the region of interest. The determination module 420 may perform a convolution operation based on the treatment-related data, the linear combination of the one or more first function terms and the one or more second function terms to determine the dose distribution of the region of interest.

[0086] In some implementations, the determination module 420 may determine a flux map for a convolution operation based on the treatment-related data; and may determine a dose distribution of the region of interest by performing a convolution operation based on a linear combination of the flux map and the one or more first function terms and the one or more second function terms.

[0087] For a detailed description of each module of the dose distribution determination system, please refer to the flowchart section of this specification, for example, Figure 2 and Figure 3 Related instructions.

[0088] It should be understood that Figure 4 The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of this specification. Not only can the hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, it can also be implemented with software such as executed by various types of processors, and it can also be implemented by a combination of the above hardware circuits and software (for example, firmware).

[0089] It should be noted that the above description of the dose distribution determination system and its modules is only for the convenience of description and does not limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various modules, or form a subsystem connected with other modules without deviating from this principle. For example, in some embodiments, the acquisition module 410 and the determination module 420 can be different modules in a system, or a module can implement the functions of two or more of the above modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are within the scope of protection of this specification.

[0090] The embodiment of the present specification also provides a device, which includes at least a processor and a memory. The memory is used to store instructions. When the instructions are executed by the processor, the device implements the aforementioned method for determining the dose distribution. The method may include: acquiring treatment-related data of the target object; determining the dose distribution based on the treatment-related data of the target object and the dose distribution algorithm; wherein the dose distribution algorithm includes a convolution operation, and the convolution operation includes: converting the kernel function of the dose distribution algorithm into a linear combination of one or more first function terms and one or more second function terms, wherein the second function term is not related to the depth information; performing a convolution operation based on the treatment-related data, the one or more first function terms and the linear combination of one or more second function terms to determine the dose distribution.

[0091] The embodiments of this specification also provide a computer-readable storage medium. The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer implements the aforementioned method for determining the dose distribution. The method may include: acquiring treatment-related data of the target object; determining the dose distribution based on the treatment-related data of the target object and the dose distribution algorithm; wherein the dose distribution algorithm includes a convolution operation, and the convolution operation includes: converting the kernel function of the dose distribution algorithm into a linear combination of one or more first function terms and one or more second function terms, wherein the second function term is not related to the depth information; performing a convolution operation based on the treatment-related data, the one or more first function terms and the linear combination of one or more second function terms to determine the dose distribution.

[0092] The beneficial effects that may be brought about by the embodiments of this specification include but are not limited to: applying the kernel function to a fast dose algorithm based on a convolution kernel, and utilizing the characteristics of the kernel function parameters to convert it into a linear combination of a first function term that depends on depth information and a second function term that does not depend on depth information, thereby obtaining a more accurate dose calculation result while maintaining a fast calculation speed.

[0093] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

[0094] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of 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 suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0095] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in 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.

[0096] In addition, it will be understood by those skilled in the art that various aspects of this specification may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of this specification may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of this specification may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0097] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0098] The computer program code required for the operation of each part of this specification can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0099] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with 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 by software solutions, such as installing the described system on an existing server or mobile device.

[0100] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0101] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0102] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0103] 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, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for determining dose distribution, It is characterized in that The method comprises: Obtain treatment-related data of the target subject; Determine the dose distribution based on the treatment-related data of the target object and a dose distribution algorithm; wherein the kernel function of the dose distribution algorithm includes a linear combination of one or more first function terms and one or more second function terms, the first function terms are related to the depth information, and the second function terms are related to the spatial plane distribution information; Wherein, determining the dose distribution includes: performing a convolution operation based on a linear combination of the treatment-related data, the one or more first function terms, and the one or more second function terms to determine the dose distribution.

2. The method according to claim 1, It is characterized in that The step of performing a convolution operation based on the treatment-related data, a linear combination of the one or more first function terms and the one or more second function terms to determine the dose distribution comprises: Determining a flux map for a convolution operation based on the treatment-related data; The dose distribution is determined based on a convolution operation of the flux map with a linear combination of the one or more first function terms and the one or more second function terms.

3. The method according to claim 2, It is characterized in that The step of performing a convolution operation based on the flux map and a linear combination of the one or more first function terms and the one or more second function terms to determine the dose distribution comprises: Performing a convolution operation on the flux map and the one or more second function terms to determine a convolution operation result; The dose distribution is determined based on a linear combination of the convolution operation result and the one or more first function terms. 4 . The method according to claim 1 , wherein the treatment-related data comprises scan data and treatment plan data of the target object. The method according to claim 1 , wherein the dose distribution algorithm comprises a finite pencil beam algorithm and a non-voxel based broad beam algorithm.

6. The method according to claim 1, wherein the kernel function of the linear combination of the one or more first function terms and the one or more second function terms is: in, represents the kernel function, Represents spatial plane distribution information, Represents depth information, represents the first function term, Represents the second function term.

7. A system for determining a dose distribution, It is characterized in that The system comprises: An acquisition module, used to acquire treatment-related data of a target object; A determination module, configured to determine a dose distribution based on the treatment-related data of the target object and a dose distribution algorithm; wherein a kernel function of the dose distribution algorithm comprises a linear combination of one or more first function terms and one or more second function terms, wherein the first function terms are related to the depth information, and the second function terms are related to the spatial plane distribution information; Wherein, determining the dose distribution includes: performing a convolution operation based on a linear combination of the treatment-related data, the one or more first function terms, and the one or more second function terms to determine the dose distribution.

8. A device for determining a dose distribution, comprising at least one storage medium and at least one processor, wherein the at least one storage medium is used to store computer instructions; and the at least one processor is used to execute the computer instructions to implement the method according to any one of claims 1 to 6.

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