CN113536651A - Radiation source intensity reconstruction method based on reverse particle transport - Google Patents
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Abstract
The invention discloses a radiation source intensity reconstruction method based on reverse particle transport, which comprises the following steps: establishing a particle reverse transport nuclear database; acquiring particle energy and intensity distribution information at measuring equipment, and establishing a reverse Monte Carlo radiation transport calculation model by combining initial source spatial distribution information, medium image information and measuring equipment information; sampling to obtain source particles according to the particle energy and intensity distribution information at the measuring equipment, and simulating the particles by using a Monte Carlo reverse transport method until the particles are transported to a radiation source; and performing reverse transport simulation on all the particles, and performing normalization processing on the particle intensity distribution information of the radiation source obtained by statistics to obtain the particle source intensity distribution of the radiation source. The invention can effectively obtain the strong distribution of the particle source and can carry out accurate radiation source inversion.
Description
Technical Field
The invention relates to the application fields of nuclear energy and nuclear technology such as reactor, radiotherapy, radiation protection and the like, in particular to a radiation source intensity reconstruction method based on reverse particle transport.
Background
The intensity distribution of the radiation source directly determines the distribution condition of a radiation dose field, and accurately obtaining the intensity distribution of the radiation source is a key problem in the fields of radiation protection, reactor detection, radiotherapy dose monitoring and the like. In the practical application process, the problems that the radiation source intensity is high, the space is limited, the detection equipment is difficult to place and the like exist, the radiation source intensity is difficult to obtain through direct measurement, the transmission distribution can be formed after rays pass through the medium, the transmission distribution reflects the radiation source intensity distribution to a certain extent, therefore, the method for obtaining the radiation source intensity distribution through reverse reconstruction of the transmission distribution is researched, and the method has important significance on radiation protection, reactor detection, radiotherapy dosage monitoring and the like.
At present, a scattering kernel deconvolution iteration method is mostly adopted for radiation source intensity reconstruction, the method obtains radiation source intensity distribution through repeated iteration optimization based on analysis scattering kernel, although the calculation speed is high, the problem of low reconstruction precision exists, and therefore a set of accurate radiation source intensity reconstruction method needs to be developed.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a radiation source intensity reconstruction method based on reverse particle transport, which aims to solve the problem that a radiation source is difficult to reconstruct accurately in the fields of radiation protection, reactor detection, radiotherapy dosage monitoring and the like.
The technical scheme is as follows: the invention discloses a radiation source intensity reconstruction method based on reverse particle transport, which comprises the following steps:
(1) establishing a particle reverse transport nuclear database;
(2) acquiring particle energy and intensity distribution information at measuring equipment, and establishing a reverse Monte Carlo radiation transport calculation model by combining initial source spatial distribution information, medium image information and measuring equipment information;
(3) sampling to obtain source particles according to the particle energy and intensity distribution information at the measuring equipment, and simulating the particles by using a Monte Carlo reverse transport method until the particles are transported to a radiation source;
(4) and performing reverse transport simulation on all the particles, and performing normalization processing on the particle intensity distribution information of the radiation source obtained by statistics to obtain the particle source intensity distribution of the radiation source.
In the step (1), the particle reverse transport nuclear database comprises incident particle energy and angle distribution information corresponding to particles after reaction with different elements.
In the step (1), the establishing of the particle reverse transport nuclear database specifically comprises the following steps:
(1.1) dividing the particle energy of 0 MeV-20 MeV into N groups, wherein N is an integer greater than 0;
(1.2) building a unit ball, filling different elements in the unit ball, setting a point source at the center of the unit ball, taking each energy group in the step (1.1) as source energy distribution, simulating by using a Monte Carlo method, and counting particle energy/angle distribution after one collision;
(1.3) after all energy groups are simulated, the energy/angle distribution data of the collided particles are processed according to different elements, the energy/angle distribution data of the incident particles corresponding to the energy of the emergent particles are obtained, and a particle reverse transport nuclear database is established.
In the step (2), the establishing of the reverse Monte Carlo radiation transport calculation model specifically comprises the following steps:
(2.1) calibrating the detection data of the measuring equipment by using the calibration information of the measuring data to obtain the energy and intensity distribution of particles at the measuring equipment;
and (2.2) establishing a reverse Monte Carlo radiation transport calculation model by taking the measuring equipment as a reverse particle transport source and the initial source space as a counting space.
In the step (3), the source particles are obtained by sampling according to the particle energy and intensity distribution information at the measuring equipment, and the particles are simulated by using a Monte Carlo reverse transport method until the particle transport reaches the radiation source, which specifically comprises the following steps:
(3.1) sampling to obtain source photon states including positions, energies and angles according to a reverse Monte Carlo radiation transport calculation model;
(3.2) according to the position, the direction and the energy of the particles, combining the information of the medium material, and sampling based on a probability density function to obtain the position and the post-collision state of the next collision point of the particles;
(3.3) simulating the current particles until the particles are transported to the space where the radiation source is located, and adding the weight of the particles transported to the space where the radiation source is located into the intensity distribution of the source particles;
(3.4) if the current particle is not transported to the radiation source space, the current particle weight is evenly distributed to the particle contributions generated by the same count space.
In step (3.2), the expression of the probability density function P (E, Ω, r → E ', Ω ', r ') is:
in the formula, E is the current energy of the particle, omega is the current angle of the particle, r is the current position of the particle, E ' is the energy before the particle collision, omega ' is the angle before the collision, r ' is the position of the last collision point of the particle, and sigmat(r, E') is the total cross-section of the reaction at r for particles of energy Es(r ', E ') is the scattering cross section of the particle with energy E ' at r ', and f (r '; E, omega → E ', omega ') is the inverse transport nuclear data of the particle at r
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: (1) the radiation source intensity is reconstructed by adopting a reverse Monte Carlo method, a particle reverse transport nuclear database is established through Monte Carlo simulation, accurate nuclear data support is provided for particle reverse transport, and the problem that accurate particle reverse transport nuclear data are difficult to obtain is solved; (2) the source intensity reconstruction is carried out based on a reverse Monte Carlo method, and compared with the traditional analytic iteration method, the high-precision radiation source intensity distribution reconstruction can be realized; (3) the invention can effectively improve the reconstruction precision of the intensity distribution of the radiation source in the fields of radiation protection, reactor detection, radiotherapy dosage monitoring and the like.
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FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a comparison graph of input picture and reconstruction source intensity obtained by the method of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail below with reference to the detailed description and the accompanying drawings.
As shown in fig. 1, the present invention includes a radiation source intensity reconstruction method based on inverse particle transport, including the following steps:
(1) establishing a particle reverse transport nuclear database; the particle reverse transport nuclear database comprises incident particle energy and angle distribution information corresponding to particles after reaction with different elements; the method specifically comprises the following steps:
(1.1) dividing the particle energy of 0 MeV-20 MeV into N groups, wherein N is an integer greater than 0;
(1.2) establishing a unit sphere, filling different elements in the unit sphere, setting a point source at the center of the unit sphere, taking each energy group in the step (1.1) as source energy distribution, simulating by using a Monte Carlo method, and counting particle energy/angle distribution f (E → E ', mu) after one collision, wherein E is incident particle energy, E' is emergent particle energy, and mu is the cosine of an emergent angle;
(1.3) after all energy groups are simulated, the energy/angle distribution data of the collided particles are processed aiming at different elements, the energy/angle distribution data of the incident particles corresponding to the energy of the emergent particles are obtained, and a particle reverse transport nuclear database f (E' → E, mu) is established.
(2) Acquiring particle energy and intensity distribution information at measuring equipment, and establishing a reverse Monte Carlo radiation transport calculation model by combining initial source spatial distribution information, medium image information and measuring equipment information; the method for establishing the reverse Monte Carlo radiation transport calculation model specifically comprises the following steps:
(2.1) calibrating the detection data of the measuring equipment by using the calibration information of the measuring data to obtain the energy and intensity distribution of particles at the measuring equipment;
and (2.2) establishing a reverse Monte Carlo radiation transport calculation model by taking the measuring equipment as a reverse particle transport source and the initial source space as a counting space.
(3) Sampling to obtain source particles according to the particle energy and intensity distribution information at the measuring equipment, and simulating the particles by using a Monte Carlo reverse transport method until the particles are transported to a radiation source; the method specifically comprises the following steps:
(3.1) sampling based on a probability density function p (E, omega, r → E ', omega ', r ') according to the position, the direction and the energy of the particle and by combining medium material information to obtain the position and the post-collision state of the next collision point of the particle;
in the formula, E is the current energy of the particle, omega is the current angle of the particle, r is the current position of the particle, E ' is the energy before the particle collision, omega ' is the angle before the collision, r ' is the position of the last collision point of the particle, and sigmat(r, E') is the total cross-section of the reaction at r for particles of energy Es(r ', E') is the scattering cross section of the particle with energy E 'at r', and f (r '; E, omega → E', omega ') is the inverse transport nuclear data of the particle at r';
(3.2) simulating the current particles until the particles are transported to the space where the radiation source is located, and adding the weight of the particles transported to the space where the radiation source is located into the intensity distribution of the source particles;
(3.3) if the current particle is not transported to the radiation source space, the current particle weight is evenly distributed to the particle contributions generated by the same count space.
(4) And performing reverse transport simulation on all the particles, and performing normalization processing on the particle intensity distribution information of the radiation source obtained by statistics to obtain the particle source intensity distribution of the radiation source.
Examples
A typical lung cancer patient is subjected to radiotherapy radiation source intensity reconstruction. The lung cancer patient receives intensity modulated radiotherapy, and a total of 7 beams are irradiated, wherein the beam 1 is subjected to radiation source intensity reconstruction, the beam 1 is vertically incident to the surface of a human body, the distance between a source point and an isocenter of the human body is 100cm, the distance between the source point and a portal imaging device is 140cm, and a measurement image acquired by the portal imaging device is shown in fig. 2 (a). The specific reconstruction steps are as follows:
1. photon reverse transport nuclear database establishment
(1.1) dividing photon energy of 0 MeV-20 MeV into 200 groups, wherein the energy interval of each group is 0.1 MeV; (1.2) establishing a unit sphere, filling different elements (main elements related to human bodies) in the unit sphere, setting a point source at the center of the unit sphere, taking each energy group in the step (1.1) as source energy distribution, simulating by using a Monte Carlo method, and counting particle energy/angle distribution f (E → E ', mu) after one collision, wherein E is incident particle energy, E' is emergent particle energy, and mu is the cosine of an emergent angle;
(1.3) after simulating all energy group photons, processing photon energy/angle distribution data after collision aiming at different elements to obtain incident photon energy/angle distribution data f (E' → E, mu) corresponding to emergent photon energy, and establishing a photon reverse transport nuclear database.
2. Establishment of reverse Monte Carlo radiation transport calculation model
(2.1) calibrating the detection data shown in the figure 2(a) by using the calibration information of the measurement data to obtain photon energy and intensity distribution at the measuring equipment;
and (2.2) establishing a reverse photon transport model by taking the measuring equipment as a reverse photon transport source and the initial source space as a counting space.
3. Monte Carlo reverse photon transport simulation
(3.1) sampling to obtain source photon states including positions, energies and angles according to a reverse Monte Carlo radiation transport calculation model;
(3.2) according to the position, the direction and the energy of the particles, combining the information of the medium material, and sampling based on a probability density function to obtain the position and the post-collision state of the next collision point of the particles; wherein, the expression of the probability density function P (E, Ω, r → E ', Ω ', r ') is:
in the formula, E is the current energy of the particle, omega is the current angle of the particle, r is the current position of the particle, E ' is the energy before the particle collision, omega ' is the angle before the collision, r ' is the position of the last collision point of the particle, and sigmat(r, E ') is the total cross-section of the reaction at r for particles with energy E',∑s(r ', E') is the scattering cross section at r 'of the particle with energy E', and f (r '; E, Ω → E', Ω ') is the inverse transport nuclear data at r' of the particle.
(3.3) simulating the current particles until the particles are transported to the space where the radiation source is located, and adding the weight of the particles transported to the space where the radiation source is located into the intensity distribution of the source particles;
(3.4) if the current particle is not transported to the radiation source space, the current particle weight is evenly distributed to the particle contributions generated by the same count space.
4. Radiation source intensity distribution reconstruction
The photon intensity distribution information at the radiation source obtained by statistics is normalized to obtain the photon source intensity distribution at the radiation source, as shown in fig. 2 (b).
Claims (6)
1. A radiation source intensity reconstruction method based on reverse particle transport is characterized in that: the method comprises the following steps:
(1) establishing a particle reverse transport nuclear database;
(2) acquiring particle energy and intensity distribution information at measuring equipment, and establishing a reverse Monte Carlo radiation transport calculation model by combining initial source spatial distribution information, medium image information and measuring equipment information;
(3) sampling to obtain source particles according to the particle energy and intensity distribution information at the measuring equipment, and simulating the particles by using a Monte Carlo reverse transport method until the particles are transported to a radiation source;
(4) and performing reverse transport simulation on all the particles, and performing normalization processing on the particle intensity distribution information of the radiation source obtained by statistics to obtain the particle source intensity distribution of the radiation source.
2. The radiation source intensity reconstruction method based on reverse particle transport as claimed in claim 1, wherein in step (1), the particle reverse transport kernel database includes incident particle energy and angle distribution information corresponding to particles after reaction with different elements.
3. The radiation source intensity reconstruction method based on reverse particle transport according to claim 1, wherein in the step (1), the establishing of the particle reverse transport nuclear database specifically includes the following steps:
(1.1) dividing the particle energy of 0 MeV-20 MeV into N groups, wherein N is an integer greater than 0;
(1.2) building a unit ball, filling different elements in the unit ball, setting a point source at the center of the unit ball, taking each energy group in the step (1.1) as source energy distribution, simulating by using a Monte Carlo method, and counting particle energy/angle distribution after one collision;
(1.3) after all energy groups are simulated, the energy/angle distribution data of the collided particles are processed according to different elements, the energy/angle distribution data of the incident particles corresponding to the energy of the emergent particles are obtained, and a particle reverse transport nuclear database is established.
4. The radiation source intensity reconstruction method based on inverse particle transport according to claim 1, wherein in the step (2), the establishing of the inverse monte carlo radiation transport calculation model specifically includes the following steps:
(2.1) calibrating the detection data of the measuring equipment by using the calibration information of the measuring data to obtain the energy and intensity distribution of particles at the measuring equipment;
and (2.2) establishing a reverse Monte Carlo radiation transport calculation model by taking the measuring equipment as a reverse particle transport source and the initial source space as a counting space.
5. The radiation source intensity reconstruction method based on reverse particle transport according to claim 1, wherein in the step (3), the source particles are obtained by sampling according to the particle energy and intensity distribution information at the measuring device, and the monte carlo reverse transport method is used to simulate the particles until the particle transport reaches the radiation source, which specifically includes the following steps:
(3.1) sampling to obtain source photon states including positions, energies and angles according to a reverse Monte Carlo radiation transport calculation model;
(3.2) according to the position, the direction and the energy of the particles, combining the information of the medium material, and sampling based on a probability density function to obtain the position and the post-collision state of the next collision point of the particles;
(3.3) simulating the current particles until the particles are transported to the space where the radiation source is located, and adding the weight of the particles transported to the space where the radiation source is located into the intensity distribution of the source particles;
(3.4) if the current particle is not transported to the radiation source space, the current particle weight is evenly distributed to the particle contributions generated by the same count space.
6. The inverse particle transport-based radiation source intensity reconstruction method according to claim 5, wherein in step (3.2), the probability density function P (E, Ω, r → E ', Ω ', r ') is expressed as:
in the formula, E is the current energy of the particle, omega is the current angle of the particle, r is the current position of the particle, E ' is the energy before the particle collision, omega ' is the angle before the collision, r ' is the position of the last collision point of the particle, and sigmat(r, E') is the total cross-section of the reaction at r for particles of energy Es(r ', E') is the scattering cross section at r 'of the particle with energy E', and f (r '; E, Ω → E', Ω ') is the inverse transport nuclear data at r' of the particle.
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