CN112700841B - Dose calculation modeling method, model, device and storage medium in non-uniform mode - Google Patents
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Abstract
The embodiment of the application discloses a dose calculation modeling method, a model, equipment and a storage medium in a non-uniform function (FFF) mode. The dose calculation modeling method under the non-uniform mode mainly comprises the following steps: acquiring two-dimensional incident intensity distribution; acquiring an irradiation field dose nucleus database comprising a plurality of dose nuclei under different irradiation fields; calculating dose distributions under different radiation fields based on the two-dimensional incident intensity distribution and the radiation field dose nuclear database; and respectively fitting the data of the dose distribution under each different field, recalculating the dose distribution after continuously changing the dose kernel distribution, and comparing the recalculated dose distribution with the actually measured dose distribution to obtain an optimal dose kernel, thereby forming final dose kernel model data. The dose calculation model in the non-uniform f (FFF) mode of the embodiment of the invention can improve the calculation accuracy while quickly realizing the dose calculation.
Description
Technical Field
The invention relates to the technical field related to medical tumor radiotherapy, in particular to a dose calculation modeling method, a model, equipment and a storage medium in a non-uniform f (FFF) mode.
Background
Malignant tumors are the first killer of human beings, the current tumor treatment mode is important to the radiotherapy, chemotherapy and surgery, and the radiotherapy is one of the most common methods in the current tumor treatment.
The conventional radiotherapy requires the dose distribution in the radiation field to be equalized, and the mode is favorable for dose calculation and control in the treatment process, and has the defects that the dose rate is greatly reduced after the equalization of an equalizing block compared with that before the equalization, and the direct result is that the treatment time is too long. The treatment time is an important factor for evaluating the radiotherapy surgical technology, and the excessive treatment time can increase the physical burden of the patient, damage the normal tissue cells of the patient and generate the possibility of secondary carcinogenesis.
Dose calculation is the core content of a radiation therapy planning system, and the accuracy of dose distribution mainly depends on a dose calculation model adopted in the planning system. At present, most of built-in dose algorithms of radiotherapy planning systems are based on a pencil beam convolution superposition technology, and a Monte Carlo algorithm obtains the distribution of deposition energy of particles in human tissues by randomly simulating the interaction between examples and substances, and is considered as the most accurate calculation method in all current calculation methods. The Monte Carlo algorithm is high in precision, but is limited to the calculation speed, and is still difficult to apply to the reverse TMRT plan. On the premise of keeping the characteristic of high precision, how to accelerate the calculation speed is the main subject of the Monte Carlo dose calculation method, and the development of the dose calculation method with higher speed and higher precision is a future research hotspot.
Disclosure of Invention
In view of the deficiencies of the prior art, one aspect of the present invention provides a dose calculation modeling method in a non-uniform mode.
The dose calculation modeling method in the non-uniform mode mainly comprises the following steps:
acquiring two-dimensional incident intensity distribution;
acquiring an irradiation field dose nucleus database comprising a plurality of dose nuclei under different irradiation fields;
calculating dose distributions under different radiation fields based on the two-dimensional incident intensity distribution and the radiation field dose nuclear database;
and respectively fitting the data of the dose distribution under each different field, recalculating the dose distribution after continuously changing the dose kernel distribution, and comparing the recalculated dose distribution with the actually measured dose distribution to obtain an optimal dose kernel, thereby forming final dose kernel model data.
According to a preferred embodiment of the present invention, the two-dimensional incident intensity distribution is obtained by measuring the dose distribution of the maximum field XOY plane of the accelerator when in use according to the intensity of different positions when the rays enter the body surface.
An advantage according to the inventionIn preferred embodiments, the radiation field dose kernel database comprising several different radiation field dose kernels is composed of dose kernels K (F, z) of different depths, the dose kernel being composed of doses of n volume units, i.e. K 0 、K 1 、K 2 ......K n The distance between the volume unit and the center of the dose core is r 0 、r 1 、r 2 ......r n 。
According to a preferred embodiment of the present invention, calculating the dose distribution under different radiation fields based on the two-dimensional incident intensity distribution and the radiation field dose nuclear database comprises the following steps:
according to the setting of the irradiation field, finding out a dose kernel parameter corresponding to the irradiation field in the database model, and using a dose calculation formula of the parameter in the homogeneous phantom as follows:
in the formula (1), the first and second groups,
K i (F, z) is the dose nuclear parameter of the irradiation field F at depth z in the database model, i is the dose nuclear product unit index, r is i Representing the distance of the ith volume element from the center of the dose kernel;
i (x, y) represents the intensity of a point of coordinates (x, y) in the plane XOY;
order to
I.e. all distances from point (x, y) are r i The sum of intensities of the volume units of (c), when point (x + r) i cosθ，y+r i sin θ) in the field, I (x + r) i cosθ，y+r i sin θ) is 0, the formula (1) is simplified to the following formula:
according to a preferred embodiment of the present invention, fitting the data of the dose distribution under each of the different fields, recalculating the dose distribution after continuously changing the dose kernel distribution and comparing the recalculated dose distribution with the actually measured dose distribution to obtain the optimal dose kernel, so as to form the final dose kernel model data includes the following steps:
s401: simulating the dose distribution of the pencil beam by using a Monte Carlo algorithm, taking the distribution as the initial value of the dose core of all the radiation fields, and respectively fitting according to off-axis ratio data under different radiation fields;
s402: calculating the dose d (x) of the corresponding point on the coordinate axis by formula (2) based on off-axis ratio data of a certain depth in the measurement field, wherein,
s403: comparing the calculated data with the measured off-axis ratio data using the following evaluation function:
in the formula (4), the first and second groups,
m represents the number of actually measured off-axis ratio data points;
w j representing the weight of the jth point of off-axis ratio data;
d j representing the dose value calculated by formula (3) at the point corresponding to the measured off-axis ratio data;
x j representing the off-axis distance from the jth point of the axial ratio data;
s404: dose kernel parameter K according to equation (4) j Calculating partial derivatives to obtain formula (5):
s405: equation (5) is analogous to a molecular dynamics equation and uses f j Expressing the intermolecular interaction force yields equation (6):
at this time, K is added k (F, z) is analogous to the position of the kth atom in molecular dynamics, the atomic weight of this atom being in m k Expressed, equation (7) is obtained:
s406: adopting a formula (8) and a formula (9) to carry out iteration, introducing a temporary time variable t in the iteration process, obtaining different dose kernel parameters by continuously changing the value of t,
s407: recalculating the evaluation function of the dose kernel parameter obtained in the step S406 by using a formula (4), and quitting the iteration process when the evaluation function is smaller than a set value or the iteration frequency reaches the set value to obtain a final dose kernel parameter;
s408: obtaining dose kernel data of each measurement depth through steps S402-S407, and obtaining the dose kernel data of the depth which is not measured through interpolation to form a dose kernel model;
s409: and finally, multiplying the dose kernel data by the correction coefficient of each depth to obtain final dose kernel model data.
Another aspect of the invention provides a model for dose calculation in non-uniform mode.
The dose calculation model in the non-uniform mode is formed by a dose calculation modeling method in any non-uniform mode.
Another aspect of the invention provides a dose calculation method in non-uniform mode.
The dose calculation method in the non-averaging mode is realized through the dose calculation model in the non-averaging mode.
Another aspect of the invention provides a dose calculation device in non-leveling mode.
The dose calculation device in the non-averaging mode comprises a processor and a memory; the memory is used for storing instructions, and the instructions, when executed by the processor, cause the dose calculation device in the non-uniform mode to implement the dose calculation modeling method in the non-uniform mode as described in any one of the above or the corresponding operation of the dose calculation method in the non-uniform mode as described above.
Yet another aspect of the invention 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 executes the dose calculation modeling method in the non-uniform mode or the dose calculation method in the non-uniform mode.
Compared with the prior art, the dose calculation modeling method, the model, the device and the storage medium in the non-uniform mode have the following beneficial effects:
the dose calculation model in the non-uniform f (FFF) mode of the embodiment of the invention can improve the calculation accuracy while quickly realizing the dose calculation.
Additional features of the invention will be set forth in the description which follows. Additional features of some aspects of the invention will become apparent to those of ordinary skill in the art upon examination of the following description and accompanying drawings or may be learned by the manufacture or operation of the embodiments. The features of the present disclosure may be realized and attained by practice or use of various methods, instrumentalities and combinations of the specific embodiments described below.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention without limiting the invention. Like reference symbols in the various drawings indicate like elements. Wherein the content of the first and second substances,
FIG. 1 is a schematic illustration of a dose kernel distribution in an embodiment of the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the application and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present application. Thus, the present application is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the claims.
The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to limit the scope of the present application. As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The features and characteristics of the present application, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description of the drawings, which form a part of this application. It is to be understood, however, that the drawings are designed solely for the purposes of illustration and description and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale.
Flowcharts are used herein to illustrate the operations performed by systems according to embodiments of the present application. It should be understood that the operations in the flow diagrams are not necessarily performed exactly in order. Rather, various steps may be processed in reverse order or simultaneously. Further, one or more other operations may be added to the flowchart. One or more operations may also be deleted from the flowchart.
One aspect of the embodiments of the present invention discloses a dose calculation modeling method in a non-uniform mode.
The dose calculation modeling method under the non-uniform mode mainly comprises the following steps:
a two-dimensional incident intensity distribution is obtained.
The two-dimensional incident intensity distribution is obtained by measuring the dose distribution of the maximum field XOY plane of the accelerator when the accelerator is used according to the intensity of different positions when rays enter a body surface.
An irradiation field dose kernel database containing a plurality of dose kernels under different irradiation fields is obtained.
Wherein the irradiation field dose nucleus database comprising several dose nuclei under different irradiation fields consists of dose nuclei K (F, z) of different depths, the dose nuclei consisting of doses of n volume units, namely K 0 、K 1 、K 2 ......K n The distance between the volume unit and the center of the dose core is r 0 、r 1 、r 2 ......r n As shown in fig. 1.
And calculating the dose distribution under different radiation fields based on the two-dimensional incident intensity distribution and the radiation field dose nuclear database.
Calculating the dose distribution under different radiation fields based on the two-dimensional incident intensity distribution and the radiation field dose nuclear database comprises the following steps:
according to the setting of the irradiation field, finding out a dose kernel parameter corresponding to the irradiation field in the database model, and using a dose calculation formula of the parameter in the homogeneous phantom as follows:
in the formula (1), the first and second groups of the compound,
K i (F, z) is the dose nuclear parameter of the irradiation field F at the depth of z in the database model, i is the dose nuclear product unit index, r i Representing the distance of the ith volume element from the center of the dose kernel;
i (x, y) represents the intensity of a point of coordinates (x, y) in the plane XOY;
order to
I.e. all distances from point (x, y) are r i The sum of intensities of the volume units of (c), when point (x + r) i cosθ，y+r i sin θ) in the field, I (x + r) i cosθ，y+r i sin θ) is 0, the formula (1) is simplified to the following formula:
and respectively fitting the data of the dose distribution under each different field, recalculating the dose distribution after continuously changing the dose kernel distribution, and comparing the recalculated dose distribution with the actually measured dose distribution to obtain an optimal dose kernel, thereby forming final dose kernel model data.
The method comprises the following steps of respectively fitting the data of the dose distribution under each different field, recalculating the dose distribution after continuously changing the dose kernel distribution and comparing the recalculated dose distribution with the actually measured dose distribution to obtain an optimal dose kernel, so as to form final dose kernel model data, wherein the method comprises the following steps:
s401: simulating the dose distribution of the pencil beam by using a Monte Carlo algorithm, taking the distribution as the initial value of the dose core of all the radiation fields, and respectively fitting according to off-axis ratio data under different radiation fields;
s402: calculating the dose d (x) of the corresponding point on the coordinate axis by formula (2) based on off-axis ratio data of a certain depth in the measurement field, wherein,
s403: comparing the calculated data with the measured off-axis ratio data using the following evaluation function:
in the formula (4), the first and second groups of the chemical reaction are shown in the specification,
m represents the number of actually measured off-axis ratio data points;
w j representing the weight of the jth point of off-axis ratio data;
d j representing the dose value calculated by formula (3) at the point corresponding to the measured off-axis ratio data;
x j representing the off-axis distance from the jth point of the axial ratio data.
S404: dose kernel parameter K according to equation (4) j Calculating partial derivatives to obtain formula (5):
s405: equation (5) is analogous to a molecular dynamics equation and is expressed in f j Expressing the intermolecular interaction force, equation (6) is obtained:
at this time, K is added k (F, z) is analogous to the position of the kth atom in molecular dynamics, thisAtomic mass of atom is m k Expressed, equation (7) is obtained:
s406: adopting a formula (8) and a formula (9) to carry out iteration, introducing a temporary time variable t in the iteration process, obtaining different dose kernel parameters by continuously changing the value of t,
s407: recalculating the evaluation function of the dose kernel parameter obtained in the step S406 by using a formula (4), and quitting the iteration process when the evaluation function is smaller than a set value or the iteration frequency reaches the set value to obtain a final dose kernel parameter;
s408: obtaining dose kernel data of each measured depth through steps S402-S407, and obtaining the dose kernel data of the depth which is not measured through interpolation to form a dose kernel model;
s409: and finally, multiplying the dose kernel data by the correction coefficient of each depth to obtain final dose kernel model data.
Another aspect of an embodiment of the present invention discloses a dose calculation model in a non-averaging mode.
The dose calculation model in the non-uniform mode is formed by the dose calculation modeling method in the non-uniform mode.
Another aspect of the embodiments of the present invention discloses a dose calculation method in a non-uniform mode.
The dose calculation method in the non-uniform mode is realized through the dose calculation model in the non-uniform mode.
Another aspect of an embodiment of the invention discloses a dose calculation device in a non-leveling mode.
The dose calculation device in the non-leveling mode comprises a processor and a memory; the memory is configured to store instructions, and when executed by the processor, the instructions cause the dose calculation apparatus in the non-averaging mode to implement any one of the dose calculation modeling methods in the non-averaging mode and operations corresponding to the dose calculation method in the non-averaging mode.
Another aspect of an embodiment of the present invention discloses 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 runs the dose calculation modeling method in the non-uniform mode or the dose calculation method in the non-uniform mode.
The dose calculation modeling method, model, device and computer-readable storage medium in the non-uniform mode according to the embodiments of the present invention can bring beneficial effects including, but not limited to, the following:
the dose calculation model in the non-uniform (FFF) mode of the embodiment of the invention can quickly realize dose calculation and simultaneously improve the calculation precision.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such alterations, modifications, and improvements are intended to be suggested herein and are intended to be within the spirit and scope of the exemplary embodiments of this application.
Also, this application uses specific language to describe embodiments of the application. 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 present application is included in at least one embodiment of the present application. 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, certain features, structures, or characteristics may be combined as suitable in one or more embodiments of the application.
Moreover, those skilled in the art will appreciate that aspects of the present application 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 the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in 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 application 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, and the like, 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 the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, a conventional programming language such as C, visualbasic, fortran2003, perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. 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 form of network, 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 using, for example, software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments have been discussed in the foregoing disclosure by way of example, it should 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 foregoing description of embodiments of the application, 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 require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single disclosed embodiment.
Claims (7)
1. A dose calculation modeling method in a non-uniform mode is characterized by comprising the following steps:
acquiring two-dimensional incident intensity distribution;
acquiring an irradiation field dose nucleus database comprising a plurality of dose nuclei under different irradiation fields;
calculating dose distributions under different radiation fields based on the two-dimensional incident intensity distribution and the radiation field dose nuclear database;
respectively fitting the data of the dose distribution under each different field, recalculating the dose distribution after continuously changing the dose kernel distribution and comparing the recalculated dose distribution with the actually measured dose distribution to obtain an optimal dose kernel, thereby forming final dose kernel model data;
calculating the dose distribution under different radiation fields based on the two-dimensional incident intensity distribution and the radiation field dose nuclear database comprises the following steps:
according to the setting of the irradiation field, finding out a dose kernel parameter corresponding to the irradiation field in the database model, and using a dose calculation formula of the parameter in the homogeneous phantom as follows:
in the formula (1), the first and second groups of the compound,
K i (F, z) is the dose nuclear parameter of the irradiation field F at depth z in the database model, i is the dose nuclear product unit index, r is i Represents the distance of the ith volume element from the center of the dose kernel;
i (x, y) represents the intensity of a point of coordinates (x, y) in the plane XOY;
order to
I.e. all distances from point (x, y) are r i The sum of intensities of the volume units of (c), when point (x + r) i cosθ，y+r i sin θ) in the irradiated field, I (x + r) i cosθ，y+r i sin θ) is 0, the formula (1) is simplified to the following formula:
fitting the data of the dose distribution under each different field respectively, recalculating the dose distribution after continuously changing the dose kernel distribution and comparing the recalculated dose distribution with the actually measured dose distribution to obtain the optimal dose kernel, thereby forming the final dose kernel model data, comprising the following steps:
s401: simulating the dose distribution of the pencil beam by using a Monte Carlo algorithm, taking the distribution as the initial value of the dose core of all the radiation fields, and respectively fitting according to off-axis ratio data under different radiation fields;
s402: calculating the dose d (x) of the corresponding point on the coordinate axis by formula (2) based on off-axis ratio data of a certain depth in the measurement field, wherein,
s403: comparing the calculated data with the measured off-axis ratio data using the following evaluation function:
in the formula (4), the first and second groups,
m represents the number of actually measured off-axis ratio data points;
w j representing the weight of the jth point of the off-axis ratio data;
d j representing the dose value calculated by formula (3) at the point corresponding to the measured off-axis ratio data;
x j representing the off-axis distance from the jth point of the axial ratio data;
s404: dose according to equation (4)Nuclear parameter K j Calculating partial derivatives to obtain formula (5):
s405: equation (5) is analogous to a molecular dynamics equation and is expressed in f j Expressing the intermolecular interaction force, equation (6) is obtained:
at this time, K is adjusted k (F, z) is analogous to the position of the kth atom in molecular dynamics, the atomic weight of this atom being in m k Expressed, equation (7) is obtained:
s406: adopting a formula (8) and a formula (9) to carry out iteration, introducing a temporary time variable t in the iteration process, obtaining different dose kernel parameters by continuously changing the value of t,
s407: recalculating the evaluation function of the dose kernel parameter obtained in the step S406 by using a formula (4), and exiting the iteration process when the evaluation function is smaller than a set value or the iteration times reach the set value to obtain a final dose kernel parameter;
s408: obtaining dose kernel data of each measurement depth through steps S402-S407, and obtaining the dose kernel data of the depth which is not measured through interpolation to form a dose kernel model;
s409: and finally, multiplying the dose kernel data by the correction coefficient of each depth to obtain final dose kernel model data.
2. The dose calculation modeling method in non-uniform mode according to claim 1,
the two-dimensional incident intensity distribution is obtained by measuring the dose distribution of the maximum field XOY plane of the accelerator when the accelerator is used according to the intensity of different positions when rays enter a body surface.
3. The dose calculation modeling method in non-uniform mode according to claim 1,
the radiation field dose kernel database comprising several dose kernels under different radiation fields consists of dose kernels K (F, z) of different depths, the dose kernel consisting of a dose of n volume units, i.e. K 0 、K 1 、K 2 ......K n The distance between the volume unit and the center of the dose core is r 0 、r 1 、r 2 ......r n 。
4. A model for calculating a dose in a non-averaging mode, wherein the model for calculating a dose in a non-averaging mode is formed by the method for modeling a dose in a non-averaging mode according to any one of claims 1 to 3.
5. A dose calculation method in a non-averaging mode is realized through the dose calculation model in the non-averaging mode according to claim 4.
6. A dose calculation device in a non-averaging mode is characterized by comprising a processor and a memory; the memory is configured to store instructions, which when executed by the processor, cause the dose calculation apparatus in the non-averaging mode to implement the dose calculation modeling method in the non-averaging mode according to any one of claims 1 to 3 or the corresponding operation of the dose calculation method in the non-averaging mode according to claim 5.
7. A computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the modeling method for dose calculation in non-uniform mode according to any one of claims 1 to 3 or the dose calculation method in non-uniform mode according to claim 5.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110310720A (en) * | 2018-03-20 | 2019-10-08 | 北京连心医疗科技有限公司 | A kind of Monte Carto dosage computing method, equipment and storage medium |
| CN110504016A (en) * | 2018-05-18 | 2019-11-26 | 北京连心医疗科技有限公司 | A kind of Monte Carlo grid parallel computation dose calculation methodology, equipment and storage medium |
| CN111408073A (en) * | 2020-03-24 | 2020-07-14 | 宁波同调医学科技有限公司 | Method and system for calculating dose of radiotherapy ray planar detector |
Family Cites Families (1)
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110310720A (en) * | 2018-03-20 | 2019-10-08 | 北京连心医疗科技有限公司 | A kind of Monte Carto dosage computing method, equipment and storage medium |
| CN110504016A (en) * | 2018-05-18 | 2019-11-26 | 北京连心医疗科技有限公司 | A kind of Monte Carlo grid parallel computation dose calculation methodology, equipment and storage medium |
| CN111408073A (en) * | 2020-03-24 | 2020-07-14 | 宁波同调医学科技有限公司 | Method and system for calculating dose of radiotherapy ray planar detector |
Non-Patent Citations (3)
| Title |
|---|
| Geant4中不同物理列表对硼中子俘获治疗剂量分布的影响;陈昭等;《中国医学物理学杂志》;第36卷(第1期);第1-5页 * |
| GZP3钴-60近距离治疗源剂量学参数的蒙特卡洛计算;龙凤翔等;《中国医学物理学杂志》;第34卷(第2期);第115-120页 * |
| 非均整（FFF）模式下脑转移癌分次立体定向放射治疗的剂量学研究;王敏杰;《中国优秀硕士学位论文全文数据库 医药卫生科技辑》(第12期);E060-133 * |
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