CN112086173A - Three-dimensional dose calculation method and device, computer equipment and readable medium - Google Patents
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Abstract
The invention discloses a three-dimensional dose calculation method, a three-dimensional dose calculation device, computer equipment and a readable medium, wherein the three-dimensional dose calculation method comprises the following steps: preprocessing the image to obtain a dielectric material distribution diagram, an electron density distribution diagram and a TERMA distribution diagram; the medium material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram are respectively used as a channel to be fused and input into the trained neural network model to obtain the three-dimensional dose distribution.
Description
Technical Field
The invention belongs to the technical field of radiation therapy dose calculation, and particularly relates to a three-dimensional dose calculation method, a three-dimensional dose calculation device, computer equipment and a readable medium.
Background
Modern radiation therapy techniques cannot be implemented with radiation therapy planning, and dose calculation is one of the cornerstones of modern radiation therapy planning, and includes dose optimization for planning, calculation of dose distribution, even independent verification of planning and three-dimensional dose verification.
Currently known dose calculation methods include: 1. an algorithm for obtaining the dose distribution through simulation based on the particle interaction basic principle represented by a Monte Carlo algorithm is highest in accuracy and slower in calculation speed; 2. taking a pencil beam algorithm, a barrel string convolution algorithm and the like as representatives, carrying out dose calculation through the convolution algorithm, wherein the pencil beam algorithm has higher calculation speed but poorer accuracy, and the calculation accuracy and speed of the barrel string convolution algorithm are positioned between the pencil beam algorithm and the Monte Carlo algorithm; 3. based on empirical correction formulas, the root is based on standard data measured in homogeneous water, such as: the method has the advantages that the point Dose is obtained through conversion of equivalent square fields and the like, the method has the fastest calculation speed and is simple to process, but non-uniform correction is carried out due to lack of consideration on specific distribution of media, and subdivision consideration on irregular field shapes is lacked, and the calculation precision is lowest. Therefore, the metering algorithm in the prior art has the defects of low computing speed and low precision.
Disclosure of Invention
In order to overcome the technical defects, the invention provides a three-dimensional dose calculation method, a three-dimensional dose calculation device, a computer device and a readable medium, which can improve the calculation efficiency.
In order to solve the problems, the invention is realized according to the following technical scheme:
a three-dimensional dose calculation method comprising the steps of:
preprocessing the image to obtain a dielectric material distribution diagram, an electron density distribution diagram and a TERMA distribution diagram;
and fusing the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram as a channel, inputting the channel into a trained neural network model to obtain three-dimensional dose distribution, wherein the neural network model is constructed by generating dose data based on different beam conditions and different image data through an existing dose algorithm to serve as training samples and determining parameters through training.
As a further improvement of the present invention, the step of preprocessing the image to obtain a dielectric material distribution map, an electron density distribution map and a TERMA distribution map comprises the steps of:
acquiring a medium material distribution diagram and an electron density distribution diagram according to the image;
calculating the amount TERMA of the interaction of the initial incident photons and the medium according to the beam condition, the medium material distribution diagram and the electron density distribution diagram;
constructing a TERMA distribution diagram by using the TERMA;
carrying out interpolation processing on the medium material distribution map, the electron density distribution map and the TERMA distribution map;
and normalizing the dielectric material distribution map, the electron density distribution map and the TERMA distribution map which are subjected to interpolation processing according to respective corresponding maximum values.
As a further improvement of the invention, the neural network model is of an encoder-decoder structure, the encoder gradually reduces the spatial dimension, identifies the image characteristics, and the decoder gradually restores the details and the spatial dimension of the object, predicts the pixels and finally outputs the pixels by a relu activation function.
As a further improvement of the invention, the invention also comprises a neural network model training step:
acquiring basic image data;
preprocessing the basic image to obtain a plurality of groups of dielectric material distribution maps and electronic density distribution maps which correspond to each other;
randomly setting beam conditions;
calculating a corresponding TERMA distribution diagram and a dose distribution calculated by an existing dose algorithm according to the beam condition, the dielectric material distribution diagram and the electron density distribution diagram;
several sets of dielectric material profiles and electron density profiles, TERMA profiles were trained and optimized using Adam optimizer, where MSE was used as a loss function.
As a further improvement of the present invention, after the step of training the neural network structure, the method further comprises the step of adjusting and correcting:
for the single energy, performing weighted superposition based on the accelerator beam-out energy spectrum;
for the characteristic energy representing the mixed energy spectrum under the mixed energy spectrum, ray hardening correction and polluted electron correction of a built-up area are adopted.
As a further improvement of the present invention, the existing dosage algorithm comprises: pencil beam algorithm, tube string convolution algorithm, monte carlo simulation.
As a further improvement of the present invention, the step of interpolating the dielectric material distribution map, the electron density distribution map and the TERMA distribution map includes the steps of:
aligning physical coordinates of the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram, and interpolating matrixes with the same resolution and the same grid size again;
the matrices are connected.
The invention also provides a three-dimensional dose calculation system comprising:
the preprocessing module is used for preprocessing the image to obtain a dielectric material, an electron density distribution diagram and a TERMA distribution diagram;
and the neural network model is used for calculating the dielectric material, the electron density distribution diagram and the TERMA distribution diagram to obtain three-dimensional dose distribution.
Furthermore, the present invention also provides a computer device comprising a processor and a memory, said memory having stored therein at least one instruction, at least one program, set of codes or set of instructions, which is loaded and executed by said processor to implement the three-dimensional dose calculation method as described above.
Further, the present invention also provides a computer readable storage medium having stored therein at least one instruction, at least one program, code set, or set of instructions, which is loaded and executed by a processor to implement the three-dimensional dose calculation method described above.
Compared with the prior art, the invention has the following beneficial effects: based on the existing dose algorithm calculation result, the neural network model which performs three-dimensional dose calculation by combining the total energy released by the image and the original incident ray in the medium by unit mass is combined, the calculation precision is related to the learned dose algorithm, the calculation time is only related to the learning network and the calculation matrix, the dilemma that the calculation efficiency is sacrificed in order to improve the calculation precision in the traditional dose calculation method is avoided, and the calculation efficiency is improved while the calculation precision is not reduced after the neural network model is trained.
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Embodiments of the invention are described in further detail below with reference to the attached drawing figures, wherein:
fig. 1 is a flowchart of a three-dimensional dose calculation method according to an embodiment.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example one
The embodiment provides a three-dimensional dose calculation method, which comprises the following steps:
and S1, preprocessing the image to obtain a dielectric material distribution diagram, an electron density distribution diagram and a TERMA distribution diagram.
S2, fusing the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram as a channel, inputting the fused material into a trained neural network model to obtain three-dimensional dose distribution, wherein the neural network model is constructed by training and determining parameters through an existing dose algorithm and generating dose data based on different beam conditions and different image data as training samples, and the existing dose algorithm comprises: pencil beam algorithm, tube string convolution algorithm, monte carlo simulation.
Specifically, step S1 includes the steps of:
s11, acquiring the medium material distribution map and the electron density distribution map according to the image, wherein the image is a CT image, and the HU value of the CT image is converted into the electron density distribution image according to the HU-electron density conversion curve of the machine acquiring the CT image.
S12, calculating the TERMA (TERMA amount of interaction between the initial incident photons and the medium according to the beam condition, the medium material distribution diagram and the electron density distribution diagram, wherein the beam condition comprises: energy, type, field shape, isocenter position, and incident angle, the media are different media restored from the image information, and the present embodiment calculates the monoenergetic energy of the photon beam with the monoenergetic energy of E from the source
To the point of computation
The formula for TERMA is:
in the formula
And
are respectively in the grid
And
the attenuation coefficient of photons (attenuation coefficient) of (a), which is related to photon energy and medium, mass attenuation coefficient obtained from National Institute of Standards and Technology (NIST) queries,
is composed of
The density of the medium at the point (b),
is the beam flux distribution;
s13, constructing a TERMA distribution graph by using the TERMA.
And S14, performing interpolation processing on the medium material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram.
S15, normalizing the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram after interpolation according to the respective corresponding maximum values, wherein the step is to enable the training process to have faster convergence speed, to enable the input and output range to be between 0 and 1.0, to normalize the data according to the respective corresponding maximum values, the maximum value of the electron density distribution is 3, and the TERMA and the dose are normalized according to the maximum value of the maximum radiation field.
In the above embodiment, the neural network model is an encoder-decoder structure based on the Unet, the Unet structure is used in a biological image at first and is commonly used in an encoder-decoder structure for image segmentation, the encoder gradually reduces spatial dimensions, identifies image features, and the decoder gradually restores details and spatial dimensions of an object to predict pixels, and is also applied to dose calculation in radiotherapy, and the encoder-decoder is connected through addition calculation considering that dose calculation is a three-dimensional calculation process and adopts three-dimensional convolution processing; the last output layer reduces the characteristic layer to 1 through the convolution layer, and in order to ensure that the final output range is larger than 0, the final output layer adopts a relu activation function.
Further, the embodiment further includes a neural network model training step:
s01, acquiring basic image data, in this embodiment, collecting CT image data including various parts of the human body, such as the head and neck, the chest, the abdomen, and the pelvic cavity, and aiming at 7 incident photons of single energy, i.e., 0.5MeV, 1MeV, 2MeV, 3MeV, 4MeV, 5MeV, and 6MeV, respectively.
And S02, preprocessing the basic image to obtain seven groups of dielectric material distribution maps and electron density distribution maps which correspond to each other.
S03, randomly setting beam conditions, wherein the range of the beam field is the crossline direction, and the X1: -20cm to 15cm, X2: -15cm to 20cm, inline orientation, Y1: -20cm to 10cm, Y2: -10cm to 20cm, and the isocenter is randomly positioned in the die body.
And S04, calculating a corresponding TERMA distribution graph and a dose distribution computed by an existing dose algorithm according to the beam condition, the dielectric material distribution graph and the electron density distribution graph.
S05, training and optimizing seven groups of dielectric material distribution maps, electron density distribution maps and TERMA distribution maps by adopting an Adam optimizer, wherein the optimization parameters are as follows: the learning rate is 0.001, beta _1 is 0.9, beta _2 is 0.999, and epsilon is 1e-8, and Mean Squared Error (MSE) is used as the loss function.
For some brand new data which have no dose and only have CT images and beam conditions, preprocessing is carried out by adopting steps, then seven trained monoenergetic neural networks are input, and three-dimensional dose distribution under corresponding energy is calculated respectively.
In order to make the result of the three-dimensional dose more practical, therefore, after the training of the neural network model is completed, the method further comprises the steps of:
and S05, carrying out weighted superposition on the single-energy based on the accelerator beam-out energy spectrum.
And S06, adopting ray hardening correction and polluted electron correction of the built-up area for the characteristic energy which represents the mixed energy spectrum under the mixed energy spectrum.
More specifically, step S14 includes the steps of:
and S141, aligning the physical coordinates of the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram, and re-interpolating matrixes with the same resolution and the same grid size, such as aligning according to the physical coordinates and re-interpolating matrixes to have a resolution of 0.5cm multiplied by 0.5cm, and a grid size of 80 multiplied by 80.
And S142, connecting the matrixes, wherein the input matrix of the final neural network model is 80 multiplied by 2(slices multiplied by columns multiplied by rows multiplied by channels).
Example two
The present embodiment provides a three-dimensional dose calculation system, including: the device comprises a preprocessing module and a neural network model, wherein the preprocessing module is used for preprocessing an image to obtain a dielectric material distribution map, an electron density distribution map and a TERMA distribution map; the neural network model is used for calculating the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram to obtain three-dimensional dose distribution.
EXAMPLE III
The present embodiments provide a computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by the processor to implement the three-dimensional dose calculation method of the first embodiment.
Example four
The present embodiment provides a computer-readable storage medium, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the computer-readable storage medium, and the at least one instruction, at least one program, a set of codes, or a set of instructions is loaded and executed by a processor to implement the three-dimensional dose calculation method of the first embodiment.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, so that any modification, equivalent change and modification made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (10)
1. A three-dimensional dose calculation method, comprising the steps of:
preprocessing the image to obtain a dielectric material distribution diagram, an electron density distribution diagram and a TERMA distribution diagram;
and fusing the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram as a channel, inputting the channel into a trained neural network model to obtain three-dimensional dose distribution, wherein the neural network model is constructed by generating dose data based on different beam conditions and different image data through an existing dose algorithm to serve as training samples and determining parameters through training.
2. The three-dimensional dose calculation method according to claim 1, wherein the step of preprocessing the image to obtain a dielectric material distribution map, an electron density distribution map, and a TERMA distribution map comprises the steps of:
acquiring a medium material distribution diagram and an electron density distribution diagram according to the image;
calculating the amount TERMA of the interaction of the initial incident photons and the medium according to the beam condition, the medium material distribution diagram and the electron density distribution diagram;
constructing a TERMA distribution diagram by using the TERMA;
carrying out interpolation processing on the medium material distribution map, the electron density distribution map and the TERMA distribution map;
and normalizing the dielectric material distribution map, the electron density distribution map and the TERMA distribution map which are subjected to interpolation processing according to respective corresponding maximum values.
3. The method of claim 1, wherein the neural network model is an encoder-decoder structure, the encoder gradually reduces spatial dimensions, identifies image features, and the decoder gradually restores details and spatial dimensions of the object, predicts pixels, and finally outputs with relu activation function.
4. The three-dimensional dose calculation method of claim 2, further comprising a neural network model training step of:
acquiring basic image data;
preprocessing the basic image to obtain a plurality of groups of dielectric material distribution maps and electronic density distribution maps which correspond to each other;
randomly setting beam conditions;
calculating a corresponding TERMA distribution diagram and a dose distribution calculated by an existing dose algorithm according to the beam condition, the dielectric material distribution diagram and the electron density distribution diagram;
several sets of dielectric material profiles and electron density profiles, TERMA profiles were trained and optimized using Adam optimizer, where MSE was used as a loss function.
5. The three-dimensional dose calculation method of claim 2, further comprising, after the neural network training step, an adjustment correction step of:
for the single energy, performing weighted superposition based on the accelerator beam-out energy spectrum;
for the characteristic energy representing the mixed energy spectrum under the mixed energy spectrum, ray hardening correction and polluted electron correction of a built-up area are adopted.
6. The three-dimensional dose calculation method of claim 3, wherein the pre-existing dose algorithm comprises: pencil beam algorithm, tube string convolution algorithm, monte carlo simulation.
7. The three-dimensional dose calculation method of claim 2, wherein the step of interpolating the dielectric material distribution map, the electron density distribution map, and the TERMA distribution map comprises the steps of:
aligning physical coordinates of the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram, and interpolating matrixes with the same resolution and the same grid size again;
the matrices are connected.
8. A three-dimensional dose calculation system, comprising:
the preprocessing module is used for preprocessing the image to obtain a dielectric material distribution map, an electron density distribution map and a TERMA distribution map;
and the neural network model is used for calculating the dielectric material distribution diagram, the electron density distribution diagram and the TERMA distribution diagram to obtain three-dimensional dose distribution.
9. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the three-dimensional dose calculation method according to any one of claims 1 to 7.
10. A computer readable storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by a processor to carry out a three-dimensional dose calculation method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114300142A (en) * | 2021-12-17 | 2022-04-08 | 广州瑞多思医疗科技有限公司 | Data preprocessing method and device suitable for deep learning dose calculation |
| CN115270588A (en) * | 2022-08-03 | 2022-11-01 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Fluence spectral distribution calculation modeling and fluence spectral distribution calculation method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103149582A (en) * | 2013-02-01 | 2013-06-12 | 沈阳东软医疗系统有限公司 | Method and device for obtaining adsorbed dose of mold body to radioactive wiring harness |
| JP2016223876A (en) * | 2015-05-29 | 2016-12-28 | 東京電力ホールディングス株式会社 | Radiation dose predicting device, radiation dose predicting method, and radiation dose predicting program |
| CN110197709A (en) * | 2019-05-29 | 2019-09-03 | 广州瑞多思医疗科技有限公司 | A kind of 3-dimensional dose prediction technique based on deep learning Yu priori plan |
| CN112086172A (en) * | 2020-09-14 | 2020-12-15 | 广州瑞多思医疗科技有限公司 | Three-dimensional dose calculation method, computer equipment and readable medium |
-
2020
- 2020-09-14 CN CN202010960515.1A patent/CN112086173B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103149582A (en) * | 2013-02-01 | 2013-06-12 | 沈阳东软医疗系统有限公司 | Method and device for obtaining adsorbed dose of mold body to radioactive wiring harness |
| JP2016223876A (en) * | 2015-05-29 | 2016-12-28 | 東京電力ホールディングス株式会社 | Radiation dose predicting device, radiation dose predicting method, and radiation dose predicting program |
| CN110197709A (en) * | 2019-05-29 | 2019-09-03 | 广州瑞多思医疗科技有限公司 | A kind of 3-dimensional dose prediction technique based on deep learning Yu priori plan |
| CN112086172A (en) * | 2020-09-14 | 2020-12-15 | 广州瑞多思医疗科技有限公司 | Three-dimensional dose calculation method, computer equipment and readable medium |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114300142A (en) * | 2021-12-17 | 2022-04-08 | 广州瑞多思医疗科技有限公司 | Data preprocessing method and device suitable for deep learning dose calculation |
| CN115270588A (en) * | 2022-08-03 | 2022-11-01 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Fluence spectral distribution calculation modeling and fluence spectral distribution calculation method and device |
| CN115270588B (en) * | 2022-08-03 | 2023-06-23 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Method and device for calculating and modeling fluence spectrum distribution and calculating fluence spectrum distribution |
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