CN107998519A - A kind of molecular dynamics re-optimization algorithm for IMRT - Google Patents
A kind of molecular dynamics re-optimization algorithm for IMRT Download PDFInfo
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- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1031—Treatment planning systems using a specific method of dose optimization
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- A—HUMAN NECESSITIES
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- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1031—Treatment planning systems using a specific method of dose optimization
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Abstract
The present invention relates to a kind of molecular dynamics re-optimization algorithm for IMRT, IMRT is a kind of advanced high-precision radiotherapy, and the X-ray accelerator whereabouts malignant tumour or the specific region of intra-tumor that its profit is computerizedd control launch accurate dose of radiation.The optimization process of inventive algorithm is：Optimize to obtain the Ziye sequence of each launched field first with Molecular Dynamics method, then according to the requirement of Ziye number, give up the part Ziye of area minimum, finally the primary condition using the result as simulated annealing, position and hop count to the blade of each Ziye are further optimized.By the algorithm, the dose of radiation of higher can be applied by focusing on to the region of intra-tumor, and the normal structure of surrounding is received the dose of radiation of minimum.Solve the problems, such as the intensity error that former algorithm is brought due to discretization, meanwhile, when using simulated annealing, Ziye blade has been in the position of an opposite optimization, which improves the search efficiency of annealing process, accelerates the process of optimization.
Description
Technical Field
The invention relates to a molecular dynamics re-optimization algorithm for IMRT (intensity modulated radiation therapy), belonging to the field of data optimization counting of medical instruments.
Background
Tumor radiotherapy is a local treatment method for treating tumors by utilizing radioactive rays, and forms 3 large means of tumor treatment together with surgical treatment and chemical drug treatment. According to statistical data, the 5-year survival rate of the treated tumor can reach about 45%, wherein about 18% of the tumors are cured by radiotherapy and account for 40% of the total cure rate. The radiotherapy can keep the functions of the treated organs to different degrees, has small side effect of the treatment and can effectively ensure the life quality of the healed patient. Radiation therapy is used in an increasing proportion of clinical treatments and controls of tumours.
Intensity Modulated Radiotherapy (IMRT), i.e., intensity modulated radiation therapy, is one type of three-dimensional conformal radiotherapy, requiring that the dose intensity in the radiation field be adjusted according to certain requirements, called intensity modulated radiotherapy for short. Under the condition that the shapes of the radiation fields and the target areas are consistent, the beam intensity is adjusted according to the three-dimensional shape of the target areas and the specific anatomical relationship between the vital organs and the target areas, the dose distribution in a single radiation field is uneven, but the dose distribution in the whole target area volume is more even than three-dimensional conformal treatment. Strictly speaking, the use of wedge plates and conventional surface curvature compensators is also intensity modulated in reducing the side effects of intensity modulated radiation therapy. However, we refer to intensity modulated radiation therapy as a form of three-dimensional conformal radiation therapy that does not acquire a non-uniform intensity distribution within a single radiation field using computer-aided optimization procedures for certain clinical purposes.
Stereotactic radiotherapy (x (r) -knife) and three-dimensional conformal radiotherapy (3D CRT) mainly rely on image positioning to make the form of high-energy beam consistent or nearly consistent with the projection of tumor (also called beam conformal), thus can greatly increase the tumor dose, improve the tumor control rate, and prevent the surrounding normal tissues from being excessively damaged. The high-energy beam is uniformly finished during accelerator conformal treatment, but most tumors are irregular, and the injection distances between tumor points and the human epidermis are also different, so that the problem of dose uniformity inside the tumors cannot be solved even though the beams are conformal in conformal radiotherapy, and the irradiation scheme required by the final target dose can be achieved by reversely designing and calculating the beam limiting rate of the accelerator beam for the second time according to the requirement of a doctor on the dose of uniform irradiation of a tumor target region and the requirement on the dose of protection of surrounding normal tissues and organs. This process is called inverse Intensity Modulated Radiation Therapy (IMRT)
According to the concept of intensity modulation, first, optimization parameters are input by a planner according to three-dimensional anatomical features of a lesion (target region), surrounding vital organs and normal tissues, and expected dose distribution of the target region and dose tolerance limits of Organs At Risk (OAR), and intensity distribution required in each field direction is calculated through a planning system. After the contour drawing and the determination of the number of radiation fields and the incidence direction are finished, the dosage requirements of each interest area in the CT image are determined. These clinical parameters (i.e., objective functions) are mathematically input by the planner, such as the requirements for target dose range, limits on relevant organs-at-risk dose, etc., and then are automatically optimized by the computer through mathematical methods (such as iterative methods, simulated annealing methods, monte carlo methods, etc.), and the planning scheme that is closest to the objective function and can be implemented is obtained after hundreds or thousands of calculations and comparisons. It is the inverse of conventional treatment planning and is therefore called inverse planning.
The steps of patient image acquisition, contouring, and determining the number and direction of radiation fields are the same, but their optimization processes are different. The former is to calculate the dose first, and to see how the result is, to change the plan without going to the human body and try again, and so on until it is acceptable. The latter is that the planner limits the target area and the main dose distribution of the organs at risk by inputting an objective function, and then the planning system automatically and repeatedly carries out optimization calculation, wherein the number of times of the repetition is determined by the complexity of a case, and at least two or one hundred times is needed.
The molecular dynamics method can quickly and effectively obtain the optimal strength requirement. In practical use, optimizing the resulting intensity distribution requires further sub-field discretization to form the final irradiation field sequence. In the discretization process, errors in intensity distribution are inevitably brought, so that the finally delivered dose has certain difference from the dose distribution defined by optimization, which is reflected in that the Dose Volume Histogram (DVH) of the tumor region is deteriorated (such as maximum dose increase, etc.), and the constraint of Organs At Risk (OAR) cannot meet the requirement (such as exceeding the maximum dose constraint of serial organs, exceeding the DVH constraint of parallel organs, etc.). In addition, in the discretization process, it is not easy to control the number of subfields per irradiation field. The solution is to strengthen the constraint parameters (such as giving lower maximum dose constraint) before optimization, and increase the number of sub-fields in discretization. The former increases the difficulty of optimization and the latter increases the exposure time, so neither is an effective solution.
Disclosure of Invention
In view of the above problems, the present invention provides a molecular dynamics re-optimization algorithm for IMRT. The X-ray accelerator is controlled by the algorithm to emit accurate radiation dose to the malignant tumor or a specific area in the tumor, so that higher radiation dose is applied to the area in the tumor through focusing, and the surrounding normal tissues receive the minimum radiation dose.
The purpose of the invention is realized by the following technical scheme: a molecular dynamics re-optimization algorithm for IMRT is characterized in that a sub-field sequence of each field is obtained by optimization through a molecular dynamics method, then partial sub-fields with the minimum area are abandoned according to the requirement of the number of the sub-fields, and finally the result is used as an initial condition of a simulated annealing algorithm to further optimize the position and hop count of leaves of each sub-field;
the specific process is as follows:
the discretized expression of the objective function PreObj is as follows:
wherein NV is the number of voxels after discretization; NB is the number of discretized bundles; w is a group of j Is the weight of the jth voxel; i is i Is the intensity of the ith pencil beam;is the dose produced by the ith beam on the jth voxel;is the prescribed dose for the jth voxel;
to obtain the optimized result, the target dose is minimized, i.e. the ith beam, has:
obtaining an equation set with the pencil beam intensity as a variable;
the ith equation is:
wherein
After the optimal pencil beam intensity distribution is obtained, a subfield sequence can be obtained through discretization:F i the (i) th sub-field is shown,the kth sub-field, MU, representing the jth angular illumination field i Representing the hop count of the ith sub-field;
by irradiating sub-field F i The obtained intensity distribution is different from the optimized intensity distribution, and at the moment, the leaf position and the hop count of the sub-field obtained by discretization are further optimized by using a simulated annealing algorithm, and the process is as follows:
(1) Calculating the dose distribution by using the discretized subfield sequence, and calculating the current objective function value Preobj;
(2) Obtaining a random variable, and determining to change the leaf or field jump number according to the value of the random variable;
(3) Obtaining a random variable, and determining which field to change according to the value of the random variable;
(4) If the blade is changed, two random variables are obtained, and the blade pair and the position of the blade A or the blade B are determined to be changed;
(5) Obtaining a random variable, determining the magnitude of the change, and changing the position of the blade or the hop count.
(6) Forming a new sub-field sequence after changing, calculating new dose distribution on the basis, and calculating a new objective function value CurObj;
(7) Accepting the change if CurObj < PreObj; if CurObj > PreObj, then abandoning the change;
(8) If the cycle number reaches a set value or the CurObj reaches the requirement, ending; otherwise, preObj = CurObj is set and is repeated from the second step onward.
The invention has the beneficial effects that: on one hand, the optimal solution of the pencil beam intensity is obtained by utilizing a molecular dynamics method and utilizing an iterative method similar to the molecular dynamics; verifying the feasibility of applying the molecular dynamics re-optimization algorithm to an IMRT planning system; obtaining an optimal solution of the pencil beam intensity by using an iterative method similar to molecular dynamics; the problem of intensity error caused by discretization of the original algorithm is solved. On the other hand, the simplified simulated annealing algorithm is adopted to re-optimize the leaf position and the machine hop count of the sub-field obtained by discretization, so that the searching efficiency of the simulated annealing process is improved, the dose distribution meets the prescription requirement of IMRT, and the optimization process is accelerated. The results of the molecular dynamics re-optimization algorithm applied to the test cases showed that each of the planning parameters in the IMRT plans for the simulation of multiple target volumes, the simulation of prostate tumors, the simulation of head and neck tumors, and the simulation of the C-shaped target volume (first case) reached and outperformed the dose target. For a relatively rigid simulated C-shaped target (second case), the optimization results are slightly lower than the prescription requirements. These results indicate that the molecular dynamics re-optimization algorithm can meet the prescription requirements of the emphasis plan after introducing the simulated annealing method. The optimization method enables the IMRT technology to improve the accuracy of target area determination, improves the coverage and dose uniformity of the target area, and effectively avoids normal tissues from being irradiated by high dose.
Drawings
Fig. 1 is a diagram of a network topology used by the present invention.
FIG. 2 is a flow chart of an embodiment of the present invention.
Detailed Description
A molecular dynamics re-optimization algorithm for IMRT is characterized in that a sub-field sequence of each radiation field is obtained by optimizing through a molecular dynamics method, then partial sub-fields with the smallest area are abandoned according to the requirement of the number of the sub-fields, and finally the result is used as an initial condition of a simulated annealing algorithm to further optimize the position and the hop count of leaves of each sub-field;
the specific process and theoretical basis are illustrated as follows:
when the molecular dynamics method is used for optimization, the intensity of the pen beam is regarded as the position of molecules of the molecular dynamics system, the dose response of the pen beam to a human body unit is regarded as the interaction force among the molecules, and the optimal solution of the intensity of the pen beam can be obtained by using an iterative method similar to the molecular dynamics.
The discretized expression of the objective function PreObj is as follows:
wherein NV is the number of voxels after discretization; NB is the number of discretized bundles; w j Is the weight of the jth voxel; i is i Is the intensity of the ith pencil beam;is the dose produced by the ith beam on the jth voxel;is the prescribed dose for the jth voxel;
to obtain the optimized result, the target dose is minimized, i.e. the ith beam, has:
obtaining an equation set with the pencil beam intensity as a variable;
the ith equation is:
wherein
After the optimal pencil beam intensity distribution is obtained, a subfield sequence can be obtained through discretization:F i the (i) th sub-field is shown,the kth sub-field, MU, representing the jth angular illumination field i Representing the hop count of the ith sub-field;
by irradiating the sub-field F due to the influence of discretization i The resulting intensity distribution differs from the optimized intensity distribution in such a way that the dose distribution no longer meets the prescription dose requirements. Therefore, the simulated annealing algorithm is utilized to further optimize the leaf position and the hop count of the sub-field obtained by discretization. Due to the sub-field sequence F i It is a relatively optimal distribution, so the algorithm also improves the simulated annealing algorithm to make it more effective. The process is as follows:
(1) Calculating the dose distribution by using the discretized subfield sequence, and calculating the current objective function value Preobj;
(2) Obtaining a random variable, and determining to change the leaf or field jump number according to the value of the random variable;
(3) Obtaining a random variable, and determining which field to change according to the value of the random variable;
(4) If the blade is changed, two random variables are obtained, and the blade pair and the positions of the A blade or the B blade are determined to be changed;
(5) And obtaining a random variable, determining the magnitude of the change amount, and changing the position of the blade or the hop count.
(6) Forming a new sub-field sequence after changing, calculating new dose distribution on the basis, and calculating a new objective function value CurObj;
(7) Accepting the change if CurObj < PreObj; if CurObj > PreObj, then abandoning the change;
(8) If the cycle number reaches a set value or CurObj reaches the requirement, ending; otherwise, preObj = CurObj is set and is repeated from the second step onward.
The algorithm solves the problem of intensity error caused by discretization of the original algorithm, and meanwhile, when the simulated annealing algorithm is used, the wild leaves are already in a relatively optimized position, so that the searching efficiency in the annealing process is improved, and the optimization process is accelerated.
To verify the feasibility of the molecular dynamics re-optimization algorithm applied to the IMRT planning system, the study performed multiple cases of typical simulation using the molecular dynamics re-optimization algorithm, simulating the clinical conditions common in radiotherapy, including simulating multiple target zones, simulating prostate tumors, simulating head and neck tumors, simulating a type C target zone (first case), and simulating a type C target zone (second case). The simulated cases are provided by computer simulation, with CT data, target volume structures and OARs defined in their test packages using the DICOM-RT format. The optimization process is completed by the Fonics three-dimensional radiation therapy planning system. The re-optimization molecular dynamics algorithm is integrated into the fosics system, the simulated cases are imported into the planning system through the DICOM interface and optimized using the integrated algorithm. The beam placement and prescribed dose of the IMRT are set as required, and the settings of the control parameters in the optimization calculations are set according to the case practice, taking into account the dose objectives in the simulation data. Taking the simulated prostate tumor as an example, the IMRT plan has specific optimization parameters as shown in table 1.
TABLE 1 constraints in IMRT planning optimization for simulation of prostate tumors
IMRT:Intensity-modulated radiotherapy；PTV:Planning target volume；DVH:Dose-volume histogram
The specific application process of the optimization method of the present invention is to install the algorithm in a radiotherapy planning system 100 as shown in fig. 1, and the radiotherapy planning system 100 may include an input unit 101, a template library 102, a template matching unit 103 and a radiotherapy plan output unit 104. The template matching unit 103 is respectively connected with the input unit 101, the template library 102 and the radiotherapy plan output unit 104, wherein: an input unit 101 adapted to acquire a scanned image of the marked tumor site and preliminary radiotherapy planning information for said tumor site. The template library 102 is adapted to store a plurality of radiotherapy plan templates, in which radiotherapy plan information of different tumor portions is stored. The template matching unit 103 is adapted to select a corresponding radiotherapy plan template from a template library according to the acquired scanned image of the marked tumor part and the preliminary radiotherapy plan information of the tumor part. A radiotherapy plan outputting unit 104 adapted to output radiotherapy plan information acquired from the radiotherapy plan template as radiotherapy plan information of the tumor part.
Claims (1)
1. A molecular dynamics re-optimization algorithm for IMRT is characterized in that a sub-field sequence of each field is obtained by optimization through a molecular dynamics method, then partial sub-fields with the minimum area are abandoned according to the requirement of the number of the sub-fields, and finally the result is used as an initial condition of a simulated annealing algorithm to further optimize the position and hop count of leaves of each sub-field;
the specific process is as follows:
the discretized expression of the objective function PreObj is as follows:
wherein NV is the number of voxels after discretization; NB is the number of discretized pencil bundles; w j Is the weight of the jth voxel; I.C. A i Is the intensity of the ith pencil beam;is the dose produced by the ith beam on the jth voxel;is the prescribed dose for the jth voxel;
to obtain the optimized result, the target dose is minimized, i.e. the ith beam, has:
obtaining an equation set with the pencil beam intensity as a variable;
the ith equation is:
wherein
After the optimal pencil beam intensity distribution is obtained, a subfield sequence can be obtained through discretization:F i the (i) th sub-field is shown,the kth sub-field, MU, representing the jth angular illumination field i Representing the hop count of the ith sub-field;
by irradiating sub-field F i The obtained intensity distribution is different from the optimized intensity distribution, and at the moment, the leaf position and the hop count of the sub-field obtained through discretization are further optimized by utilizing a simulated annealing algorithm, and the process is as follows:
(1) Calculating the dose distribution by using the discretized subfield sequence, and calculating the current objective function value Preobj;
(2) Obtaining a random variable, and determining to change the leaf or field jump number according to the value of the random variable;
(3) Obtaining a random variable, and determining which field to change according to the value of the random variable;
(4) If the blade is changed, two random variables are obtained, and the blade pair and the positions of the A blade or the B blade are determined to be changed;
(5) And obtaining a random variable, determining the magnitude of the change amount, and changing the position of the blade or the hop count.
(6) Forming a new subfield sequence after changing, calculating new dose distribution on the basis, and calculating a new objective function value CurObj;
(7) If CurObj < PreObj, then accept the change; if CurObj > PreObj, then abandoning the change;
(8) If the cycle number reaches a set value or the CurObj reaches the requirement, ending; otherwise, preObj = CurObj is set and is repeated from the second step onward.
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| CN111986778A (en) * | 2020-07-31 | 2020-11-24 | 上海联影医疗科技股份有限公司 | Intensity modulated plan optimization system, device and storage medium |
| CN116206695A (en) * | 2023-05-09 | 2023-06-02 | 苏州创腾软件有限公司 | Cross-linking system molecular model construction method and device based on simulated annealing method |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110570923A (en) * | 2018-06-06 | 2019-12-13 | 北京连心医疗科技有限公司 | mixed Monte Carlo radiotherapy reverse optimization method, equipment and storage medium |
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| CN111986778B (en) * | 2020-07-31 | 2024-02-20 | 上海联影医疗科技股份有限公司 | Intensity-modulated plan optimization system, device and storage medium |
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