CN113990443A

CN113990443A - Method and system for determining dose distribution

Info

Publication number: CN113990443A
Application number: CN202111260613.5A
Authority: CN
Inventors: 文理斌; 刘艳芳
Original assignee: Shanghai United Imaging Healthcare Co Ltd
Current assignee: Shanghai United Imaging Healthcare Co Ltd
Priority date: 2021-10-28
Filing date: 2021-10-28
Publication date: 2022-01-28

Abstract

The embodiment of the specification discloses a method and a system for determining dose distribution. Wherein, the method comprises the following steps: acquiring treatment-related data of a target object; the dose distribution is determined based on the treatment-related data of the target subject and a dose distribution algorithm. Wherein the 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, the second function terms being unrelated to the depth information; wherein determining the dose distribution comprises: a dose distribution is determined based on a convolution operation of the treatment-related data, the linear combination of the one or more first function terms and the one or more second function terms.

Description

Method and system for determining dose distribution

Technical Field

The present description relates to the field of radiation therapy, and more particularly, to a method and system for determining a dose distribution.

Background

Radiation therapy techniques are widely used in cancer therapy. Compared with simple Radiotherapy techniques, complex Radiotherapy techniques, such as Intensity Modulated Radiotherapy (IMAT), allow continuous rotation of the gantry during Radiotherapy, continuous and rapid changes in the dose rate of the Radiotherapy device and the position of a Multi-leaf Collimator (MLC), so as to achieve a specified dose in a tumor region and control the irradiation dose of surrounding tissues within a safe range.

In radiation therapy, the intensity modulated radiation therapy plan has high requirements on the speed and quality of dose calculation, so that a method for determining the dose distribution is needed to obtain more accurate dose calculation results while ensuring the calculation speed of the dose distribution.

Disclosure of Invention

One aspect of embodiments of the present specification provides a method of determining a dose distribution. The method for determining the dose distribution comprises the following steps: acquiring treatment-related data of a target object; determining a dose distribution based on the treatment-related data of the target subject and a dose distribution algorithm; wherein the 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, the second function terms being unrelated to depth information. Wherein determining the dose distribution comprises: determining the dose distribution based on a convolution operation of a linear combination of the therapy-related data, the one or more first function terms, and one or more second function terms.

Another aspect of embodiments of the present description provides a system for determining a dose distribution. The system comprises: an acquisition module may be used to acquire treatment related data of a target subject. A determination module operable to determine a dose distribution based on treatment-related data of the target subject and a dose distribution algorithm; wherein the 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, the second function terms being unrelated to depth information. Wherein determining the dose distribution comprises: determining the dose distribution based on a convolution operation of a linear combination of the therapy-related data, the one or more first function terms, and one or more second function terms.

Another aspect of embodiments of the present specification provides a dose distribution determining apparatus comprising at least one storage medium for storing computer instructions and at least one processor; the at least one processor is configured to execute the computer instructions to implement a method of dose distribution determination.

Another aspect of embodiments of the present specification provides a computer-readable storage medium storing computer instructions which, when read by a computer, cause the computer to perform a method for determining a dose distribution.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an exemplary application scenario of a dose distribution determination system according to some embodiments described herein;

FIG. 2 is an exemplary flow chart of a method of determining a dose distribution according to some embodiments shown herein;

FIG. 3 is an exemplary flow chart for determining a dose distribution according to some embodiments described herein;

FIG. 4 is an exemplary block diagram of a system for determining a dose distribution according to some embodiments described herein.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

The present specification provides systems and components for medical diagnosis and/or treatment. In some embodiments, the diagnostic and treatment system may include a radiation therapy system. The Radiation Therapy System may include a Treatment Planning System (TPS), an Image Guided Radiation Therapy (IGRT) System, and the like. By way of example only, image-guided radiation therapy systems may include CT (Computed Tomography) guided radiation therapy systems, MRI (Magnetic Resonance Imaging) guided radiation therapy systems, and the like. In some embodiments, the system may be an imaging system including one or a combination of Computed Tomography (CT) systems, Emission Computed Tomography (ECT) systems, radiography systems, Positron Emission Tomography (PET) systems, and the like. For ease of understanding, the systems and methods of radiation therapy will be referred to herein. The term "image" as used in this application may refer to a 2D image, a 3D image or a 4D image, and may also 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, and the like.

Currently, methods of dose calculation include fast dose calculation methods based on convolution kernels and dose calculation methods based on dose distribution kernel functions. The fast dose calculation method based on the convolution kernel has the advantages of high calculation speed, high calculation speed and relatively low calculation precision; the dose calculation method based on the dose distribution kernel function can obtain a more accurate dose calculation result, but has a slower calculation speed. In the related art, the convolution kernel parameter is calculated by averaging the convolution kernel parameter in the whole calculation depth range of the dose in the fast dose calculation method based on the convolution kernel, although a faster dose calculation speed can be obtained under the framework of the convolution kernel, the result of the top layer and the bottom layer of the phantom can generate larger deviation, and the calculation of each depth layer by layer consumes very much calculation time. On the other hand, although the dose calculation algorithm based on the self-consistent kernel function is more accurate, the calculation speed is slower, and the clinical use requirement is difficult to meet.

Therefore, the embodiments of the present specification disclose a method and a system for determining a dose distribution, which can use a separate convolution kernel function to calculate scattering for each depth under the condition of ensuring the calculation speed, so that the accuracy of dose calculation results at different depths is improved. The technical solutions disclosed in the present specification are explained in detail by the description of the drawings below.

Fig. 1 is a schematic diagram of an exemplary application scenario of a system for determining a dose distribution according to some embodiments described herein.

In some embodiments, the dose distribution determination system 100 may be used to determine a dose distribution associated with a radiation treatment plan. The system 100 may optimize a radiation treatment plan or deliver radiation treatment, etc. based on the determined dose distribution.

In a typical application scenario, the system 100 may acquire treatment-related data of a target subject; the system 100 may determine the dose distribution based on treatment related data of the target subject and a dose distribution algorithm. Wherein the dose distribution algorithm comprises a convolution operation, the system 100 may convert a 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 determine a dose distribution based on a convolution operation of the treatment-related data, the linear combination of the one or more first function terms and the one or more second function terms.

As shown in fig. 1, system 100 may include an imaging device 110, a network 120, a terminal 130, a processing device 140, and a storage device 150.

The imaging device 110 may be used to scan a target object to obtain scan data and image. The imaging device 110 may be a medical imaging device (e.g., a Computed Tomography (CT), PET imaging device, MRI imaging device, Single-Photon Emission Computed Tomography (SPECT) imaging device, PET-CT imaging device, PET-MRI imaging device, etc.). In some embodiments, the imaging device 110 may include a gantry 111, a detector 112, a scan region 113, and a scan bed 114. The target object may be placed on the scanning bed 114 to be scanned. The gantry 111 may support a detector 112. In some embodiments, the detector 112 may include one or more detector cells. The detector unit may be and/or comprise a single row of detectors and/or a plurality of rows of detectors. The detector units may include scintillation detectors (e.g., cesium iodide detectors), other detectors, and the like. In some embodiments, the gantry 111 may rotate, for example, in a CT imaging apparatus, the gantry 111 may rotate clockwise or counterclockwise about a gantry rotation axis. In some embodiments, the imaging device 110 may further include a radiation scanning source, which may rotate with the gantry 111. The radiation scanning source may emit a beam of radiation (e.g., X-rays) toward the object of interest, which is attenuated by the object of interest and detected by the detector 112 to generate an image signal. The scan data and/or the image may be used to determine a region of interest. For example, the processing device 140 may model from the scan data to obtain a geometric model of the tissue and/or organ of the target object and determine the region of interest from the established geometric model.

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

The terminal 130 may include a mobile device 131, a tablet computer 132, a notebook computer 133, and the like, or any combination thereof. In some embodiments, terminal 130 may interact with other components in system 100 over 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 for the imaging device 110. For example, a user/operator of the system 100 (e.g., a physician) may control the operation of the device imaging device 110 via the console, such as scanning a target object, determining a region of interest associated with the target object, determining a dose distribution of the region of interest, and so forth.

The storage device 150 may store data (e.g., scan data for a target object, treatment plan, etc.), instructions, and/or any other information. In some embodiments, storage device 150 may store data obtained from imaging device 110, terminal 130, and/or processing device 140, e.g., storage device 150 may store scan data or the like obtained from imaging device 110 for a target object. In some embodiments, storage device 150 may store data and/or instructions that processing device 140 may execute or use to perform the example methods described herein. In some embodiments, the storage device 150 may include one or a combination of mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like. In some embodiments, the storage device 150 may be implemented by a cloud platform as described herein. For example, the cloud platform may include one or a combination of private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, and the like.

In some embodiments, storage device 150 may be connected to network 120 to enable communication with one or more components in system 100 (e.g., processing device 140, terminal 130, etc.). One or more components in system 100 may read data or instructions from storage device 150 via network 120. In some embodiments, the storage device 150 may be part of the processing device 140 or may be separate and directly or indirectly coupled to the processing device.

Network 120 may include any suitable network capable of facilitating information and/or data exchange for system 100. In some embodiments, one or more components of system 100 (e.g., imaging device 110, terminal 130, processing device 140, storage device 150, etc.) may exchange information and/or data with one or more components of system 100 via network 120. For example, processing device 140 may obtain scan data from imaging device 110 via network 120. The network 120 may include 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. For example, network 120 may include a wireline network, a fiber optic network, a telecommunications network, a local area network, a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), Bluetooth, and/or the like^TMNetwork, ZigBee^TMNetwork, Near Field Communication (NFC) network, and the like. In some embodiments, network 120 may include one or more network access points. For example, network 120 may include wired and/or wireless network access points, such as base stations and/or internet switching points, through which one or more components of system 100 may connect to network 120 to exchange data and/or information.

Fig. 2 is an exemplary flow chart of a method of determining a dose distribution according to some embodiments shown herein. In some embodiments, flow 200 may be performed by a processing device (e.g., processing device 140). For example, the process 200 may be stored in a storage device (e.g., an onboard storage unit of a processing device or an external storage device) in the form of a program or instructions that, when executed, may implement the process 200. The flow 200 may include the following operations.

In step 202, treatment-related data of a target subject is obtained. Step 202 may be performed by the acquisition module 410.

In some embodiments, the target object may include a patient or other medical subject (e.g., other animal such as a laboratory white mouse), or the like. The target object may also be part of a patient or other medical subject, such as a region of interest of the target object, including organs and/or tissues, e.g., heart, lungs, ribs, abdominal cavity, etc.

The region of interest refers to a region of interest in radiation therapy, and may be a specific organ, a specific tissue, or a specific part of a target object. Illustratively, the region of interest may include a head, a chest, a lung, a heart, a liver, a spleen, a pleura, a mediastinum, an abdomen, a large intestine, a small intestine, a bladder, a gall bladder, a pelvis, a spine, a bone, a blood vessel, or the like, or any combination thereof, of the target subject.

In some embodiments, a region of interest associated with the target object may be represented by a phantom that models the target object. A phantom may be a model of a particular organ, a particular tissue, or a particular location of a target subject.

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

The treatment-related data refers to data related to radiation treatment of the 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 of the treatment plan, such as the beam of rays at the time of treatment, the dose of each beam, the shape of the beam, the distance of the beam, and the like. The spatial density distribution of the region of interest corresponding to the target object can be known from the scan data of the target object.

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

Step 204, determining a dose distribution of the region of interest based on the treatment related data of the target object and a dose distribution algorithm. Step 204 may be performed by the determination module 420.

The dose distribution algorithm may comprise any linear fast dose algorithm using a convolution kernel. For example, Pencil-Beam convolution kernel based fast dose algorithms, such as Finite Pencil-Beam algorithm (FSPB), Non-voxel-based broad-Beam algorithm (NVBB), and the like.

In some embodiments, the processing device may determine the dose distribution of the region of interest by the method described in the embodiments below based on the treatment-related data of the target subject and a dose distribution algorithm.

In some embodiments, the 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, the second function terms being unrelated to the depth information of the region of interest. The kernel function may be obtained by transforming a kernel function of a dose distribution algorithm.

The kernel function of the linear fast dose algorithm based on the convolution kernel has the following characteristics: the dependency relationship of the kernel function parameters on the depth and the transverse position can be approximately decomposed into two terms, one term is a linear superposition coefficient depending on the depth, the other term is a two-dimensional kernel function not depending on the depth, and the characteristic can be applied to a convolution term of a rapid dose algorithm by combining the linear property of convolution, namely the kernel function of the dose distribution algorithm is applied to the convolution term in the rapid dose algorithm, so that the accuracy of dose calculation results at different depths is remarkably improved under the condition of ensuring the speed of the dose algorithm.

After transforming the kernel function of the dose distribution algorithm, the first function term may be a depth-dependent linear superposition coefficient term, and the second function term may be a depth-independent two-dimensional function term. In some embodiments, the first function terms and the second function terms are the same in number, for example, the first function terms and the second function terms are both one term, or both are multiple terms, such as 2 terms, 5 terms, 10 terms, 20 terms, etc.

For purposes of example only, taking the FSPB algorithm as an example, the self-consistent kernel function of the FSPB algorithm may be as shown in equation (1).

Wherein, omega is a coefficient of linear superposition,

represents the coefficient ω_iThe sum is equal to 1;

representing a two-dimensional spatial distribution of the region of interest,

independent of depth, d represents depth information of the region of interest, related to depth d, x₀,y₀Is a parameter of a kernel function, and

d is irrelevant. The function f may be defined as shown in formula (2).

Taking n as 2, pair

The expansion is performed as shown in equation (3).

Wherein subscripts x and y represent the x and y directions, respectively; subscripts 1 and 2 distinguish the physical processes of different types of penumbra formation, while ω (d) describes the scaling weights of these two types at different depths; symbol u describes the dose attenuation speed in the penumbra region, x₀And y₀The cross-sectional dimensions chosen in the FSPB model are described as constants.

In the kernel function exemplified above, the dependence of the parameter ω on the depth d is significant, whereas the parameter ω is

Hardly depending on depth. Based on the important characteristics of the kernel function, in the embodiments 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. Wherein the second function term is not related to the depth information of the region of interest (or the second function term has a smaller depth dependence on 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 larger depth dependence on the region of interest).

The depth information is a depth of each layer obtained by dividing the geometric model into a multilayer structure (for example, a multilayer structure obtained by dividing the geometric model along a z-axis direction of a model or a phantom after an xy-axis direction is set as an axis direction of a spatial plane).

It should be noted that the above examples are only examples, and are not intended to limit the application algorithm model of the technical solution disclosed in the present specification, for example, other similar FSPB convolution kernel analysis models may also be applicable if they still maintain the feature of decomposing the depth dependence.

Based on the above-described example, it can be known that, based on the depth-dependent characteristic of the convolution kernel parameter of the dose calculation algorithm, the kernel function thereof can be converted into a linear combination of polynomial function terms, and for example, the kernel function of the linear combination of one or more first function terms and one or more second function terms converted from the kernel function of the dose calculation algorithm can be as shown in formula (4).

Wherein,

the kernel function after the conversion is represented,

representing spatial plane distribution information of the region of interest, d representing depth information of the region of interest, ω_i(d) Representing a first function term being a coefficient term related to depth information of the region of interest,

the second function term is represented as a two-dimensional kernel function that is not correlated with depth information.

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

In some embodiments, the greater the number of linear combinations of the first and second function terms that result from the transformation of the kernel function, the greater the accuracy in subsequent dose calculations.

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

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

For example, in some embodiments, the processing device may determine a flux map for a convolution operation based on the therapy-related data; and performing convolution operation based on the flux map and 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.

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

In the embodiments of the present specification, after the kernel function is converted, the amount of calculation in calculating the dose at each depth can be greatly simplified. For exemplary purposes only, taking the case where n is 2 as an example, the kernel function represented by the above formula (4) may be represented by the formula (5).

Accordingly, the dose distribution of the region of interest can be calculated on the basis of the following equation (6).

Wherein D represents a dose distribution of the region of interest,

representing the three-dimensional spatial position of the region of interest, including depth and spatial plane distribution,

showing a kernel function, oc shows that the dose distribution of the region of interest is positively correlated with the result of the subsequent partial convolution operation, F shows a flux map,

representing a convolution operation, ω (d) representing a first function term, k₁、k₂Representing a second function term.

Due to the fact that

Show that

Before and after the convolution operator

Represents the convolution of the function f () with the result of the convolution remaining

Thus can be

And write at the end.

The result of the convolution operation can be represented. For more description of the convolution operation, refer to fig. 3 and its related description, which are not repeated herein.

After the convolution operation result is obtained through calculation, other necessary calculation of dose distribution calculation can be carried out based on the convolution operation result, and the dose distribution of the region of interest can be determined after the calculation is completed. Further details regarding the process of other necessary calculations are not provided herein.

In the method for determining a dose distribution provided in the embodiments of the present specification, a kernel function of a dose distribution algorithm is converted into a linear combination of a first function term and a second function term, where the second function term is independent of depth information of a region of interest. During calculation, the converted linear combination and the flux map are subjected to convolution operation, and the kernel function is converted into a two-dimensional kernel function (a second function item) and a first function item related to depth information, and the second function item and the flux map are utilized for convolution operation, so that compared with a prokaryotic function for performing three-dimensional convolution operation, the calculation speed can be greatly improved. Meanwhile, the first function item related to the depth information is reserved in the calculation process, so that the accuracy of the dose calculation result at different depths can be remarkably improved while the calculation speed is greatly improved.

Fig. 3 is an exemplary flow chart for determining a dose distribution of a region of interest, shown in accordance with some embodiments herein. In some embodiments, flow 300 may be performed by a processing device. For example, the process 300 may be stored in a storage device (e.g., an onboard storage unit of a processing device or an external storage device) in the form of a program or instructions that, when executed, may implement the process 300. As shown in fig. 3, the process 300 may include the following operations.

Step 302, performing convolution operation on the flux map and one or more second function items to determine a convolution operation result.

In some embodiments, when performing convolution operations, the manner of the convolution operations may be appropriately modified to facilitate the calculation. Illustratively, the convolution operation shown in the above equation (6) is also taken as an example. The formula (6) may be deformed, and the deformation result may be as shown in the formula (7).

The convolution operation result refers to a convolution calculation result of the flux map and the two-dimensional kernel function. For example,

in the calculation, only the second function term needs to be convolved with the flux map, so that the calculation amount in the convolution can be greatly reduced.

Step 304, determining a 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.

In some embodiments, the processing device may multiply the convolution operation results with corresponding first function terms, respectively, for example,

after the calculation is finished, other necessary calculations of dose calculation are carried out, and then the dose distribution of the region of interest can be determined.

After the formula (6) is deformed by using the linear property of convolution, it can be seen from the formula (7) that the dose calculation can be respectively carried out on each depth information only by adding one convolution calculation to the original dose calculation amount, so that the purpose of remarkably improving the accuracy of the dose calculation under the condition of ensuring the calculation speed is achieved.

It should be noted that the above description of the respective flows is only for illustration and description, and does not limit the applicable scope of the present specification. Various modifications and alterations to the flow may occur to those skilled in the art, given the benefit of this description. However, such modifications and variations are intended to be within the scope of the present description. For example, changes to the flow steps described herein, such as the addition of pre-processing steps and storage steps, may be made.

FIG. 4 is an exemplary block diagram of a system for determining a dose distribution according to some embodiments described herein. As shown in fig. 4, the system 400 may include an acquisition module 410 and a determination module 420.

The acquisition module 410 may be used to acquire treatment related data for a target subject.

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

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.

In some embodiments, the determination module 420 may convert a 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 determine a dose distribution of the region of interest based on a convolution operation of the therapy-related data, the one or more first function terms, and one or more second function terms.

In some implementations, the determination module 420 can determine a flux map for a convolution operation based on the treatment-related data; a dose distribution of the region of interest may be 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.

For a detailed description of the modules of the dose distribution determination system, reference may be made to the flow chart section of the present specification, e.g. the associated description of fig. 2 and 3.

It should be understood that the system and its modules shown in FIG. 4 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules in this specification may be implemented not only by hardware circuits such as very 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., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above description of the dose distribution determining system and its modules is merely for convenience of description and should not limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, the obtaining module 410 and the determining module 420 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.

The embodiment of the present specification further provides an apparatus, which at least includes a processor and a memory. The memory is to store instructions. The instructions, when executed by the processor, cause the apparatus to implement the aforementioned method of determining a dose distribution. The method may include: acquiring treatment-related data of a target object; determining a dose distribution based on the treatment-related data of the target subject and a dose distribution algorithm; wherein the dose distribution algorithm comprises a convolution operation, the convolution operation comprising: converting a 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 correlated with depth information; determining the dose distribution based on a convolution operation of a linear combination of the therapy-related data, the one or more first function terms, and one or more second function terms.

The embodiment of the specification also provides a computer readable storage medium. The storage medium stores computer instructions, and after the computer reads the computer instructions in the storage medium, the computer realizes the determination method of the dose distribution. The method may include: acquiring treatment-related data of a target object; determining a dose distribution based on the treatment-related data of the target subject and a dose distribution algorithm; wherein the dose distribution algorithm comprises a convolution operation, the convolution operation comprising: converting a 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 correlated with depth information; determining the dose distribution based on a convolution operation of a linear combination of the therapy-related data, the one or more first function terms, and one or more second function terms.

The beneficial effects that may be brought by the embodiments of the present description include, but are not limited to: the kernel function is applied to a fast dose algorithm based on a convolution kernel, and is converted into a linear combination of a first function item depending on depth information and a second function item independent of the depth information by utilizing the characteristics of kernel function parameters, so that a more accurate dose calculation result is obtained while a fast calculation speed is maintained.

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A method of determining a dose distribution, the method comprising:

acquiring treatment-related data of a target object;

determining a dose distribution based on the treatment-related data of the target subject 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, the second function terms being unrelated to depth information;

wherein determining the dose distribution comprises: determining the dose distribution based on a convolution operation of a linear combination of the therapy-related data, the one or more first function terms, and one or more second function terms.

2. The method of claim 1, wherein determining the dose distribution by performing a convolution operation based on a linear combination of the therapy-related data, the plurality of first function terms, and the second function term comprises:

determining a flux map for a convolution operation based on the treatment-related data;

determining the dose distribution 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 of claim 2, wherein determining the dose distribution based on convolving the flux map with a linear combination of the plurality of first function terms and the second function term comprises:

performing convolution operation on the flux map and one or more second function terms to determine a convolution operation result;

determining the dose distribution based on a linear combination of the convolution operation result and one or more of the first function terms.

4. The method of claim 1, wherein the first function term is related to the depth information.

5. The method of claim 1, wherein the second function term relates to spatial plane distribution information.

6. The method of claim 1, the treatment-related data comprising scan data and treatment plan data of the target object.

7. The method of claim 1, the dose distribution algorithm comprising a limited pencil beam algorithm and a voxel-free wide beam algorithm.

8. The method of claim 1, the kernel function converted to a linear combination of one or more first function terms and one or more second function terms being:

wherein, k represents a kernel function,

representing spatial plane distribution information, d depth information, ω_i(d) The term of the first function is represented as,

representing a second function term.

9. A system for determining a dose distribution, the system comprising:

an acquisition module for acquiring treatment related data of a target object;

a determination module for determining a dose distribution based on treatment related data of the target subject 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, the second function terms being unrelated to depth information;

10. A dose distribution determination apparatus comprising at least one storage medium and at least one processor, the at least one storage medium storing computer instructions; the at least one processor is configured to execute the computer instructions to implement the method of any of claims 1-8.