Isocenter optimization selection method for radiotherapy multi-target plan

Abstract

The invention relates to an isocenter optimization selection method for a radiotherapy target plan, and the method comprises the steps: obtaining a CT image, and carrying out the preprocessing of the CT image, and obtaining an initial Mask matrix; based on the initial Mask matrix, calculating to obtain a rotated Mask matrix; establishing and searching an optimal plan center point loss function; and based on a random optimization algorithm, solving to obtain an optimal plan center point. Compared with the prior art, the method has the advantages that potential geometric errors caused by rotation uncertainty in the actual treatment process can be reduced, the optimal isocenter point is determined, and the target coverage rate and the coverage precision are improved.

Description

Technical Field

[0001] The invention relates to the technical field of medical radiotherapy, and in particular to an isocenter optimization selection method for a radiotherapy multi-target plan. Background Art

[0002] Before radiotherapy, doctors will plan the isocenter based on the patient's medical imaging, and then use the movement of the treatment bed in the medical equipment to make the rotation center of the medical equipment gantry coincide with the planned isocenter. Therefore, it is usually necessary to accurately determine the position of the isocenter to ensure the reliability of the radiotherapy operation.

[0003] In current clinical applications, multiple brain metastases are the most common intracranial malignancies, and systematic and comprehensive treatments can effectively prolong the survival of patients. Conventional whole-brain radiotherapy is the standard radiotherapy method for treating multiple brain metastases. With the continuous development of radiotherapy technology, stereotactic radiosurgery (SRS) technology has gradually replaced traditional whole-brain radiotherapy, which has better local control effects and better ability to protect normal tissues, and can reduce the phenomenon of cognitive deterioration in patients.

[0004] Compared with the traditional multi-center SRS treatment plan, the single-center SRS treatment plan has the advantages of shortening the treatment time and improving the accuracy of treatment. However, in the actual treatment process, due to the errors in the rotation accuracy of the treatment bed, the frame, and the small head, mechanical registration and imaging distortion, if the centroid is directly selected as the isocenter of the plan, the sensitivity of the single-center SRS technology to geometric uncertainty will increase, resulting in a decrease in the accuracy of the final target area coverage, and the treatment effect will not meet expectations. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method for optimizing the selection of isocenters for multi-target radiotherapy plans, which can reduce the errors caused by rotation uncertainty during the actual treatment process, determine the optimal isocenter, and improve the target area coverage accuracy.

[0006] The object of the present invention can be achieved by the following technical solution: A method for optimizing the selection of isocenters for radiotherapy target planning, comprising the following steps:

[0007] S1, obtain the CT image, and preprocess it to obtain the initial Mask matrix;

[0008] S2. Based on the initial Mask matrix, the rotated Mask matrix is ​​calculated;

[0009] S3, establish a loss function for searching the center point of the optimal plan;

[0010] S4. Based on the random optimization algorithm, the optimal plan center point is obtained.

[0011] Furthermore, the size of the initial Mask matrix in step S1 is the same as the circumscribed cube containing all GTVs (Gross Tumor Volumes, tumor target volumes).

[0012] Furthermore, the initial Mask matrix in step S1 is specifically:

[0013]

[0014] When position x i When the voxel at is located within the GTV, M(x i ) is equal to 1, otherwise it is equal to 0.

[0015] Furthermore, the step S2 specifically calculates a rotated Mask matrix with geometric uncertainty added based on the initial Mask matrix and the rotation transformation matrix.

[0016] Furthermore, the rotation transformation matrix is ​​specifically:

[0017] T(x,y,z;∝,β,γ)=S -1 RS

[0018] Among them, ∝, β, γ are the rotation angles in the x, y, and z directions, that is, geometric uncertainty, α is the rotation angle around the x-axis, β is the rotation angle around the y-axis, γ is the rotation angle around the z-axis, S is the scaling transformation matrix, R is the rotation transformation matrix, S -1 R represents the uncertainty transformation matrix based on rotation and scaling.

[0019] Furthermore, the rotated Mask matrix is ​​specifically:

[0020] M′(x i )=T(x,y,z;∝,β,γ)M(x i )=S -1 RSM(x i )

[0021] For the x in PTV (Planning Target Volume) i , after the rotation transformation, x i ′=S - 1 RSx i .

[0022] Furthermore, the optimal plan center point loss function in step S3 is specifically:

[0023]

[0024] Among them, M k (x i ) is the Mask of the kth GTV, V 100％ is the relative volume of the kth GTV irradiated with 100% of the prescribed dose after rotation.

[0025] Furthermore, the relative volume of the GTV irradiated with 100% of the prescription dose after rotation is specifically:

[0026]

[0027] The volume of the initial PTV is the volume of the GTV after expanding 1 mm.

[0028] Furthermore, the step S4 specifically constructs a corresponding random problem based on the optimal plan center point loss function, and adopts the sampling average approximation method to solve the random problem to obtain the optimal center point position of the plan.

[0029] Furthermore, the random questions are specifically:

[0030]

[0031] Where N is the total number of random sampling times, 1°≤∝ i ,β i ,γ i ≤1° means the range of rotation uncertainty is 1°.

[0032] Compared with the prior art, the present invention has the following advantages:

[0033] The present invention preprocesses the CT image to obtain the initial Mask matrix, then calculates the rotated Mask matrix, and then establishes a loss function for searching the optimal plan center point, and solves the optimal plan center point based on a random optimization algorithm. This can reduce the potential geometric error caused by rotation uncertainty during the actual treatment process and improve the tumor dose coverage and coverage accuracy.

[0034] The present invention is based on an initial Mask matrix and a rotation transformation matrix to obtain a rotated Mask matrix, wherein the rotation transformation matrix is ​​for the position points within the planned target volume PTV and fully considers the rotation uncertainty in the x, y, and z directions in the three-dimensional space, thereby ensuring the accuracy of the rotated Mask matrix.

[0035] The present invention establishes a loss function for searching the optimal plan center point, and regards the loss function as a random problem. In addition, it also takes into account that the rotation errors of the small head, the frame and the six-dimensional bed during SRS treatment are 0.5° respectively, and the above factors affecting the rotation uncertainty are independent of each other. Therefore, the present invention sets the range of rotation uncertainty to 1°, and then uses the sampling average approximation method to solve the random problem, which can accurately obtain the optimal center point position of the plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0037] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] Example

[0039] like Figure 1 As shown, a method for optimizing the selection of isocenter points for radiotherapy target planning includes the following steps:

[0040] S1, obtain the CT image, and preprocess it to obtain the initial Mask matrix;

[0041] S2. Based on the initial Mask matrix, the rotated Mask matrix is ​​calculated;

[0042] S3, establish a loss function for searching the center point of the optimal plan;

[0043] S4. Based on the random optimization algorithm, the optimal plan center point is obtained.

[0044] Specifically, in step S1, the purpose of preprocessing is to establish an initial Mask matrix of the CT image of multiple brain metastases. The initial Mask size is the same as the circumscribed cube containing all GTVs (Gross Tumor Volume, i.e., the solid part or target area of ​​the tumor), that is:

[0045]

[0046] Among them, M(x i ) is an indicator function, indicating that at position x i Whether the voxel at x is located in the GTV. i is located in GTV, then M(x i ) is equal to 1, otherwise it is equal to 0.

[0047] In step S2, for x in PTV (Planning Target Volume, planning target volume) i , after the rotation transformation, xi ′=S -1 RSx i , then the rotation transformation matrix based on the origin is:

[0048] T(x,y,z;∝,β,γ)=S -1 RS

[0049] Among them, ∝, β, γ are the rotation angles in the x, y, and z directions (i.e., geometric uncertainty), ∝, β, and γ represent the rotation angles, ∝ is the rotation angle around the x-axis, β is the rotation angle around the y-axis, and γ is the rotation angle around the z-axis. They represent the rotation uncertainty in different directions in three-dimensional space;

[0050] S is the scaling transformation matrix, R is the rotation transformation matrix, S -1 R represents the uncertainty transformation matrix based on rotation and scaling.

[0051] Therefore, the initial Mask matrix is ​​transformed into a new matrix after rotation:

[0052] M′(x i )=T(x,y,z;∝,β,γ)M(x i )

[0053] Among them, x, y, and z represent the coordinate axes in the three-dimensional space, which are used to indicate the position of the target in the space.

[0054] In step S3, the optimization loss function is established:

[0055]

[0056] Among them, M k (x i ) is the Mask of the kth GTV, V 100％ V is the relative volume of the kth GTV irradiated with 100% of the prescription dose after rotation. Given the assumption that the overall dose distribution remains unchanged after rotation, V 100％ Defined as:

[0057]

[0058] In step S4, since ∝, β, and γ are random variables, the loss function can be regarded as a random problem:

[0059]

[0060] Among them, N represents the total number of random sampling times. Considering that the rotation errors of the small head, frame and six-dimensional bed during SRS treatment are 0.5° respectively, and the above factors affecting the rotation uncertainty are independent of each other, this scheme sets the range of rotation uncertainty to 1°. Finally, the sample average approximation (SAA) is used to solve the optimal solution of the random problem and obtain the optimal center point position of the plan.

[0061] This embodiment applies the above technical solution to determine the optimal isocenter for the SRS radiotherapy plan for multiple brain metastases. The main process includes:

[0062] 1. Obtain CT scan images of patients with multiple brain metastases;

[0063] 2. Preprocess the CT image to obtain the initial Mask of GTV;

[0064] 3. Expand the GTV by 1 mm to obtain the volume of the corresponding initial PTV;

[0065] 4. Use all GTV circumscribed cubes as the search space and calculate the mask of each point after considering the geometric uncertainty.

[0066] 5. Based on the assumption that the overall dose distribution remains unchanged, calculate the GTV volume after each GTV rotation and calculate:

[0067]

[0068] 6. Use the sampling average approximation method to solve random problems:

[0069]

[0070] Calculate the optimal center point coordinates of the SRS plan.

[0071] In summary, this solution fully considers the rotational uncertainty of mechanical parameters, geometric distortion, etc., and obtains the optimal isocenter of the radiotherapy plan based on the random optimization algorithm, which can obtain the plan center point with the highest target coverage accuracy, thereby achieving the effect of improving the dose coverage of the tumor. This solution solves the problem of decreased treatment accuracy caused by geometric uncertainty during radiotherapy implementation when the center of mass is directly selected as the plan isocenter, and can effectively improve tumor dose coverage and radiotherapy accuracy.

Claims

1. A method for optimizing the selection of isocenters for radiotherapy target planning, characterized in that: The following steps are involved: S1, obtain the CT image, and preprocess it to obtain the initial Mask matrix; S2. Based on the initial Mask matrix, the rotated Mask matrix is ​​calculated; S3, establish a loss function for searching the center point of the optimal plan; S4. Based on the random optimization algorithm, the optimal plan center point is obtained.

2. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 1, characterized in that: The size of the initial Mask matrix in step S1 is the same as the circumscribed cube containing all GTVs.

3. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 2, characterized in that: The initial Mask matrix in step S1 is specifically: When position x i When the voxel at is located within the GTV, M(x i ) is equal to 1, otherwise it is equal to 0.

4. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 3, characterized in that: The step S2 specifically calculates the rotated Mask matrix with geometric uncertainty added based on the initial Mask matrix and the rotation transformation matrix.

5. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 4, characterized in that: The rotation transformation matrix is ​​specifically: T(x,y,z;∝,β,γ)=S -1 RS Among them, ∝, β, γ are the rotation angles in the x, y, and z directions, that is, geometric uncertainty, α is the rotation angle around the x-axis, β is the rotation angle around the y-axis, γ is the rotation angle around the z-axis, S is the scaling transformation matrix, R is the rotation transformation matrix, S -1 R represents the uncertainty transformation matrix based on rotation and scaling.

6. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 5, characterized in that: The rotated Mask matrix is ​​specifically: M′(x i )=T(x,y,z;∝,β,γ)M(x i )=S -1 RSM(x i ) For x in PTV i , after the rotation transformation, x i ′=S -1 RSx i .

7. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 6, It is characterized in that The optimal plan center point loss function in step S3 is specifically: Among them, M k (x i ) is the Mask of the kth GTV, V 100％ is the relative volume of the kth GTV irradiated with 100% of the prescribed dose after rotation.

8. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 7, characterized in that: The relative volume of the GTV irradiated with 100% of the prescription dose after rotation is specifically: The volume of the initial PTV is the volume of the GTV after expanding 1 mm.

9. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 8, characterized in that: The step S4 specifically constructs a corresponding random problem based on the optimal plan center point loss function, and uses the sampling average approximation method to solve the random problem to obtain the optimal center point position of the plan.

10. The method for optimizing the selection of isocenters for radiotherapy target planning according to claim 9, characterized in that: The random questions are specifically: 1°≤∝ i ,b i ,c i ≤1°,N=5000 Where N is the total number of random sampling times, 1°≤∝ i ,β i ,γ i ≤1° means the range of rotation uncertainty is 1°.

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