Dose distribution calculation system, program, and dose distribution calculation method

Abstract

To provide a dose distribution calculation system capable of efficiently obtaining the result of high-accuracy dose distribution calculation, a program, and a dose distribution calculation method.SOLUTION: A dose distribution calculation system 113, which calculates a dose distribution of radiation applied through a multi-leaf collimator, comprises an MLC transport calculation part 120 that calculates a transmission factor of each particle by calculating a passage route of the multi-leaf collimator on two projection planes, for each particle of the radiation, and a dose distribution calculation part 121 that calculates the dose distribution by using the transmission factor.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The present invention relates to a dose distribution calculation system, a program, and a dose distribution calculation method suitably used for a radiation therapy system for treating a diseased part such as a tumor by irradiating it with radiation.

Background Art

[0002] Patent Document 1 describes steps including defining a three-dimensional geometry of a collimator device that defines an aperture configured to pass a radiation beam, projecting the collimator onto a two-dimensional geometry on a plane along the radiation beam, calculating the dose opacity of the collimator device at a position adjacent to the aperture based on the three-dimensional shape of the collimator device, and calculating the transport of the radiation beam passing through the collimator device based on the two-dimensional shape projected onto the plane and using the dose opacity of the collimator device at a position adjacent to the aperture.

[0003] Non-Patent Document 1 describes the influence of the shape of a multi-leaf collimator, such as leaf transmission, inter-leaf leakage, rounded leaf tips, and the effect of the leaf sequence, and taking into account beam divergence and energy fluctuations across the field in the calculation of the dose distribution of intensity-modulated radiation therapy.

[0004] Non-Patent Document 2 describes a transport-based multi-leaf collimator particle transport model that is accurate, fast, and efficient and thus applicable to iterative intensity-modulated radiation therapy dose distribution calculations, which is used in Monte Carlo dose distribution calculations.

Prior Art Documents

Patent Documents

[0005] [[ID=3l]]

Patent Document 1

Non-Patent Documents

[0006] [Non-Patent Document 1] R. F. Aaronson et al., “A Monte Carlo based phase space model for quality assurance of intensity modulated radiotherapy incorporating leaf specific characteristics”, Med. Phys. 29(12), 2952-2958 (2002). [Non-Patent Document 2] J. V. Siebers et al., “A method for photon beam Monte Carlo multileaf collimator particle transport”, Phys. Med. Biol. 47, 3225-3249 (2002). [Summary of the Invention] [Problems to be Solved by the Invention]

[0007] Radiation therapy is a local therapy that irradiates a cancer lesion with radiation to damage the DNA of cancer cells and kill the cancer cells. The types of radiation used for irradiation include particles such as X-rays, protons, and carbon ions. Among these, X-rays are the most commonly used.

[0008] In radiation therapy using X-rays, treatment is performed by a high-precision irradiation method that improves dose concentration by irradiating the target with X-rays from multiple irradiation directions, such as intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), and dynamic wave arc (DWA) that performs continuous non-coplanar irradiation with a wavy trajectory.

[0009] VMAT (Variable Radiation Therapy) allows for continuous rotational irradiation while continuously modulating three parameters: gantry angle, multi-leaf collimator (MLC) aperture shape, and dose rate. This enables the delivery of a high dose to the tumor while reducing the dose to normal tissue.

[0010] In radiation therapy, treatment planning software is used to simulate the dose distribution within the patient's body based on the patient's CT images and the prescribed dose set by the physician. This allows for the determination of irradiation parameters such as the equipment parameters of the treatment device (collimator aperture shape, etc.), irradiation angle, irradiation dose for each irradiation angle, and energy.

[0011] Here, the determined irradiation parameters and the dose distribution that forms the basis for calculating the irradiation parameters are called the treatment plan. By irradiating the area with a beam based on the treatment plan, the irradiation device can create the desired dose distribution in the affected area.

[0012] Before irradiating the patient, the treatment plan used for treatment must be verified to ensure its integrity by confirming that the device can deliver the dose distribution as planned. This is called patient QA. The most common method performed in patient QA is to irradiate a homogeneous phantom, such as a solid phantom, with the treatment plan that will actually be used and measure the dose distribution.

[0013] Through patient QA, clinical staff will (1) verify the calculation accuracy of the dose distribution calculation algorithm of the treatment planning software, (2) verify the impact of irradiation errors of the irradiation device on the dose distribution within a homogeneous phantom, and (3) confirm the validity of the device operation in relation to the treatment plan.

[0014] Items (1) and (2) will be verified by comparing the measured results with the calculation results of the treatment planning software. Each treatment facility sets its own evaluation items and judgment criteria for patient QA, and after confirming that each evaluation item satisfies the judgment criteria, it can be used as a treatment plan. If the judgment criteria are not met, the patient QA will be repeated or the treatment plan will be revised.

[0015] Radiation therapy involves irradiating the affected area once a day for several days to several weeks. Therefore, tumor growth or shrinkage, as well as changes in the surrounding internal tissues, can occur during the treatment period.

[0016] When such changes occur, there is a growing need for adaptive therapy, which involves recreating the treatment plan and using the revised plan for treatment. Among adaptive therapies, online adaptive therapy refers to the process of recreating the treatment plan immediately before treatment while the patient is lying on the treatment bed.

[0017] Online adaptive therapy optimizes the radiation dose to accommodate the patient's body shape, which varies from day to day, including the condition of the nasal cavity and intestines. By optimizing the dose distribution each day, the area outside the target area irradiated can be reduced as a margin, thereby minimizing damage to healthy tissue.

[0018] However, online adaptive therapy presents various development challenges, such as increased treatment time due to a significant increase in processes performed during treatment, including the revision of treatment plans. One of these challenges is patient Q&A during online adaptive therapy.

[0019] In online adaptive therapy, patient quality assurance (QA) of the replanned treatment plan must be conducted while the patient is lying on the treatment table, making it difficult to perform the previously conducted, measurement-based patient QA using actual irradiation.

[0020] Therefore, in recent years, methods have been proposed in patient QA that use independent dose distribution calculation engines employing high-precision dose distribution calculation techniques such as the Monte Carlo method to verify whether the planned dose distribution can be formed within CT images or homogeneous phantoms.

[0021] In the Monte Carlo method, the behavior of individual radiation is probabilistically reproduced using random numbers, and by repeating this a large number of times (millions of times or more), the average behavior of radiation is estimated and the dose distribution is calculated. In calculating the dose distribution for X-ray therapy, if a Monte Carlo simulation is performed in one continuous process, from the stage when electrons accelerated by an accelerator are irradiated onto a tungsten target to generate X-rays, to when the X-rays pass through the MLC to form the irradiation field, and when the X-rays in the irradiated field are irradiated onto the patient to impart a dose, it requires an enormous amount of computation time.

[0022] Therefore, it is common practice to calculate this by dividing it into two processes: (A) the process until the irradiation field is formed by the MLC, and (B) the process by which the X-rays that have formed the irradiation field are transported within the patient's body.

[0023] This invention relates to the calculation of the irradiation field formation process of the former (A)MLC.

[0024] In VMAT irradiation and similar procedures, the leaf shape changes in a complex manner depending on the rotation of the gantry. Therefore, in order to efficiently perform transport calculations for the formation of the irradiation field of MLCs, analytical or semi-analytical methods are used to calculate the irradiation field formation process.

[0025] The aforementioned Patent Document 1 and Non-Patent Document 1 describe calculation methods for the irradiation field formation process using analytical methods. These documents show a method for calculating the dose distribution by calculating a beam transmittance map, which is obtained by projecting a three-dimensional MLC shape from the beam irradiation direction, and then considering the effect of the MLC shape using the transmittance map. When creating a VMAT treatment plan, in order to reduce computational costs, continuous irradiation angles are discretized into a finite number of irradiation angles (for example, gantry angles at 2-degree intervals), and the MLC shape for each irradiation angle is determined by optimization calculation. For this reason, analytical calculation methods are widely used in the treatment plan creation process.

[0026] Non-patent document 2 describes a calculation method for the irradiation field calculation process using a quasi-analytical method. In the quasi-analytical method, particle type information (particle type, position, energy, and propagation direction information) upstream of the MLC is generated in advance using Monte Carlo simulation or modeling. The passage length within the MLC is calculated for each particle using this particle type information as input, and the transmittance calculated from the calculated passage length is set as the particle weight to consider the effect of the MLC shape.

[0027] In this case, the number of particles used in Monte Carlo simulations can exceed several million, and in quasi-analytical methods, it is necessary to calculate the passage length of the MLC several times for each particle and calculate the transmittance for each particle. Therefore, the calculation speed and accuracy of the method for calculating the passage length of the MLC are extremely important.

[0028] Non-patent document 2 employs a method for calculating the path length of an MLC based on the leaf thickness, which is represented by the particle incidence position on the upper and middle surfaces of the leaf, while considering the leaf shape in the driving direction of the MLC leaf. In this method, there is a concern that the calculation accuracy will decrease when particles passing through regions where the leaf positions differ between adjacent leaves exit the leaf between the upper and middle surfaces, or between the middle and lower surfaces.

[0029] One method for calculating the passage length with high accuracy is to use three-dimensional ray tracing calculations. However, this method increases computational costs, making it difficult to calculate the MLC passage length with high accuracy in a short amount of time.

[0030] The present invention provides a dose distribution calculation system, program, and dose distribution calculation method that can efficiently obtain highly accurate dose distribution calculation results. [Means for solving the problem]

[0031] The present invention includes multiple means for solving the above problems, but one example is a dose distribution calculation system for calculating the dose distribution of radiation irradiated after passing through a multi-leaf collimator, comprising: a transport calculation unit that calculates the transmission rate for each particle of radiation by calculating the passage path through the multi-leaf collimator using two projection planes; and a dose distribution calculation unit that calculates the dose distribution using the transmission rate. [Effects of the Invention]

[0032] According to the present invention, highly accurate dose distribution calculation results can be obtained efficiently. Other problems, configurations, and effects will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0033] [Figure 1] This is an overall diagram of the radiotherapy system in this embodiment. [Figure 2] This flowchart shows the dose distribution calculation process using the dose distribution calculation system of this embodiment. [Figure 3] This is a flowchart showing the MLC transport calculation process using the dose distribution calculation system of this embodiment. [Figure 4] This flowchart shows the process of calculating the MLC transmittance for each particle using the dose distribution calculation system of this embodiment. [Figure 5] These are examples of the shapes of each projection plane of the MLC in the dose distribution calculation system of this embodiment. [Figure 6] This is a conceptual diagram illustrating the calculation process for MLC passage length in the dose distribution calculation system of this embodiment. [Figure 7] This flowchart shows another example of the MLC transport calculation process using the dose distribution calculation system of this embodiment. [Modes for carrying out the invention]

[0034] Examples of the dose distribution calculation system, program, and dose distribution calculation method of the present invention will be described with reference to Figures 1 to 7. In the drawings used herein, the same or corresponding components are denoted by the same or similar reference numerals, and repeated descriptions of these components may be omitted.

[0035] The present invention is suitably applicable to X-ray irradiation systems that use X-rays as the radiation source, but is also suitably applicable to particle beam irradiation systems that use particles other than X-rays, such as protons and carbon ions. The following embodiments will be described using an X-ray irradiation system as an example.

[0036] First, the overall configuration of the X-ray therapy system of the present invention will be explained using Figure 1. Figure 1 is a schematic diagram of the overall configuration of the X-ray therapy system.

[0037] The X-ray therapy system shown in Figure 1 comprises an X-ray irradiation system 110, a data server 111, a treatment planning device 112, and a dose distribution calculation system 113.

[0038] The X-ray irradiation system 110 includes an X-ray irradiation device 100, a radiation irradiation control device 104, a communication device 105, a storage device 106, and an input device 107. The X-ray irradiation device 100 includes a ring-type gantry 101, an irradiation nozzle 102, and a patient table 103.

[0039] In the X-ray irradiation system 110, when the radiation irradiation control device 104 outputs an emission start signal, the linear accelerator in the irradiation nozzle 102 accelerates the electron beam, and X-rays are generated by irradiating tungsten with the accelerated electron beam.

[0040] The irradiation nozzle 102 is equipped with a collimator (hereinafter referred to as a multi-leaf collimator (MLC)) consisting of multiple plate-shaped shielding elements (hereinafter referred to as leaves) arranged on the left and right sides. By arbitrarily changing the position of each leaf based on the instruction value from the radiation irradiation control device 104, the generated X-rays are shaped into a desired distribution.

[0041] Furthermore, the irradiation nozzle 102 is equipped with a dose monitor for measuring the amount of X-ray irradiation. The detected measurement value is output to the radiation irradiation control device 104 and used for control during radiation irradiation.

[0042] The bed on which the irradiation target A is placed is called the bed 103. Based on instructions from the radiation irradiation control device 104, the bed 103 can move in the direction of three orthogonal axes and can also rotate around each axis. Through these movements and rotations, the position of the irradiation target A can be moved to a desired position.

[0043] The irradiation nozzle 102 is mounted on a ring-shaped gantry 101. Based on instructions from the radiation irradiation control device 104, the gantry rotation angle and ring rotation angle are set, making it possible to irradiate the target A on the patient bed 103 with X-rays from any angle. The center of rotation for this gantry rotation and ring rotation is called the isocenter.

[0044] The radiation irradiation control device 104 is connected to the ring-type gantry 101, irradiation nozzle 102, patient bed 103, communication device 105, storage device 106, input device 107, etc., and controls the equipment inside the irradiation nozzle 102, the ring-type gantry 101, the patient bed 103, etc.

[0045] The communication device 105 is connected to the data server 111 via the network. Before irradiation, it retrieves treatment plan data from the data server 111, which contains irradiation parameters (gantry angle, irradiation dose, leaf position information, etc.) created by the treatment planning device 112 via the network, and saves the irradiation parameters to the storage device 106.

[0046] The input device 107 is connected to the radiation irradiation control device 104 and displays information on the monitor based on signals acquired from the radiation irradiation control device 104. It also receives input signals from medical personnel operating the X-ray irradiation system 110 and transmits various control signals to the radiation irradiation control device 104. When the radiation irradiation control device 104 receives an instruction to start radiation irradiation via the input device 107, it starts irradiation based on the irradiation parameters stored in the storage device 106.

[0047] The dose distribution calculation system 113 is a system that calculates the dose distribution of radiation irradiated through a multi-leaf collimator, and consists of an MLC transport calculation unit 120, a dose distribution calculation unit 121, a display unit 122, an input unit 123, and a storage unit 124. The dose distribution calculation system 113 is connected to a data server 111 via a network and acquires treatment plan information for calculating the dose distribution.

[0048] The MLC transport calculation unit 120 takes the particle type information at the top surface of the MLC stored in the storage unit 124 as input, calculates the passage length within the MLC for each particle, and sets the transmittance calculated from the passage length as the particle weight, thereby generating particle type data downstream of the MLC. Preferably, this MLC transport calculation unit 120 is the main entity that executes the transport calculation procedure and transport calculation steps.

[0049] In this embodiment, the MLC transport calculation unit 120 calculates the transmission rate for each particle of radiation by calculating the passage path of the multi-leaf collimator using two projection planes. Here, the two projection planes can be the projection plane in the leaf arrangement direction of the multi-leaf collimator and the projection plane in the leaf driving direction. Furthermore, when calculating the transmission rate, the transmission rate can be determined by finding the path length from the common range of the passage paths calculated using the two projection planes for each particle of radiation.

[0050] Furthermore, the MLC transport calculation unit 120 can interpolate the device information at each control point using a common interpolation parameter set for each radiation particle, set different device parameters for each radiation particle, generate particle type information that takes into account the continuously changing device information, and use the generated particle type information to determine the path length.

[0051] Furthermore, the MLC transport calculation unit 120 can generate Compton photons as particles and calculate the transmittance of Compton photons.

[0052] The dose distribution calculation unit 121 takes the particle type data and transmittance downstream of the MLC generated by the MLC transport calculation unit 120 as input and calculates the dose distribution in the patient's body or in the numerical phantom. Here, the particle type data includes position information, propagation vector, energy, particle type (X-ray, electron, positron), and statistical weight for each particle / photon. Preferably, this dose distribution calculation unit 121 is the main entity that executes the dose distribution calculation procedure and dose distribution calculation steps.

[0053] The display unit 122 is a display that shows the dose distribution calculated by the dose distribution calculation unit 121.

[0054] These radiation irradiation control devices 104 and dose distribution calculation systems 113 have a central processing unit (CPU) and memory connected to this CPU.

[0055] Furthermore, the control processes for the actions to be performed may be combined into a single program, divided into multiple programs, or a combination of these.

[0056] Some or all of the programs contained within each device may be implemented using dedicated hardware, or they may be modularized. Furthermore, various programs may be installed on each device via a program distribution server or external storage media, or existing devices may be updated.

[0057] Furthermore, each device may be an independent device connected by a wired or wireless network, or two or more devices may be integrated into a single unit.

[0058] Next, an embodiment of calculating the dose distribution within a patient's body using the dose distribution calculation system 113 of this embodiment will be described. The overall flow of the dose distribution calculation is shown in Figure 2, and the flow of the transport calculation portion within the MLC, which is a characteristic process of the present invention, is shown in Figure 3.

[0059] As shown in Figure 2, first, the dose distribution calculation system 113 obtains treatment plan data for verifying the dose distribution from the data server 111 (S201). At this time, the treatment plan data to be read by the dose distribution calculation system 113 can be selected by the operator from the treatment plan information 111A in the data server 111 using the input unit 123, or the dose distribution calculation system 113 can automatically search the treatment plan information 111A in the data server 111 for treatment plan data read by the radiation irradiation control device 104 and read it.

[0060] Next, the MLC transport calculation unit 120 of the dose distribution calculation system 113 extracts plan parameters used for dose distribution calculation and MLC transport calculation from the treatment plan data read in step S201 and stores them in the storage unit 124 (S202).

[0061] Next, the MLC transport calculation unit 120 calculates the total number of samples at the upstream position of the MLC using the plan parameters of the treatment plan data read in step S202 (S203). Here, the number of samples is the number of particles used for dose distribution calculation, and in the X-ray irradiation device 100, the amount of X-ray irradiation is determined by the MU value of the dose monitor installed in the irradiation nozzle, so the number of particles per MU value is often used. Here, we will explain an example using the number of samples [particles / (MU / Gy)] calculated as the number of samples per unit dose at the normalization point.

[0062] The MLC transport calculation unit 120 obtains dose values ​​and MU value information at the normalization point from the treatment plan information and calculates the MU value per unit dose [MU / Gy] at the normalization point. The number of particles per unit dose MU value [particles / (MU / Gy)] is stored in the storage unit 124 in advance, and the total number of samples is calculated by multiplying the MU value per unit dose at the normalization point by the number of particles per unit dose MU value. Here, the value specified by the operator using the input unit 123 may be used as the number of particles per unit dose MU value [particles / (MU / Gy)].

[0063] Next, the MLC transport calculation unit 120 reads the particle type data upstream of the MLC and performs transport calculations within the MLC to generate particle type data downstream of the MLC (S204).

[0064] After the transport calculation is completed, the dose distribution calculation unit 121 of the dose distribution calculation system 113 reads the MLC downstream particle type data generated by the MLC transport calculation unit 120 and performs the dose distribution calculation (S205). In the dose distribution calculation, the statistical weight of the input particle k is s k d is the dose that particle k imparts to voxel i. i,k Therefore, the dose D of voxel i i It is calculated using the following formula (1). D i =C × Σw k ×d i,k ... (1)

[0065] Here, C is a correction factor used to convert the dose calculated using the number of calculated samples to the dose assigned using the actual number of particles.

[0066] The present invention is characterized by a calculation method for the MLC transport calculation portion in step S204. A flowchart of the MLC transport calculation is shown in Figure 3.

[0067] This section explains the case of performing MLC transport calculations for treatment planning using continuous rotational irradiation, such as VMAT and DWA, as an example.

[0068] Continuous rotational irradiation is an irradiation method that involves continuously modulating three parameters: irradiation angle (gantry angle, irradiation ring angle), MLC shape, and dose rate. In creating a treatment plan for this continuous rotational irradiation, in order to reduce the computational load during treatment plan creation, the irradiation angles are discretized to equally spaced points to generate control points for the irradiation device, and the treatment plan is created. Therefore, it was difficult to take into account device information such as the continuously changing leaf positions in the calculations.

[0069] In contrast, the present invention uses a common quasi-random number set for each particle to interpolate the device information at each control point with the same quasi-random number. This allows for the setting of different device parameters for each particle and the generation of particle data that takes into account continuously changing device information. As a result, it becomes possible to calculate the dose distribution with higher accuracy than in the conventional method. This is one of the features of the present invention.

[0070] The following explanation will describe the use of quasi-random numbers as interpolation parameters, but in addition to quasi-random numbers, random numbers or uniformly random numbers can also be used.

[0071] As shown in Figure 3, first, the MLC transport calculation unit 120 reads the system data of the multi-leaf collimator used in the X-ray irradiation device 100 and constructs the calculation system (S301).

[0072] A multi-leaf collimator consists of multiple plate-shaped shields (hereinafter referred to as "leaves") arranged in a row, each capable of being driven in one axial direction. The direction in which the leaves are driven is called the leaf drive direction, and the direction in which the leaves are arranged is called the leaf arrangement direction.

[0073] In the leaf arrangement direction, a frame is placed outside the leaves, serving to shield the radiation dose outside the irradiation field. As will be described later, in this invention, the transport of each particle through the multi-leaf collimator is calculated using projection plane information of each leaf and frame in the leaf arrangement direction, and projection plane information in the leaf driving direction. Therefore, the system information read here is projection plane information of each leaf and frame in the arrangement direction and driving direction.

[0074] Subsequently, the MLC transport calculation unit 120 reads the particle species information database 125 upstream of the MLC stored in the storage unit 124, and acquires the particle data upstream of the MLC that is an input for the MLC transport calculation (S302).

[0075] Subsequently, the MLC transport calculation unit 120 acquires the plan parameters acquired in step S202 from the storage unit 124 (S302). In the plan parameters for continuous rotation irradiation, the leaf position, gantry angle, irradiation ring angle, and irradiation dose between control points are described for each control point.

[0076] Thereafter, the MLC transport calculation unit 120 acquires the device information for two control points and the device information between two control points from the plan parameters in order to perform MLC transport calculation in units between control points (S303). The number of samples used for the MLC transport calculation between each control point is allocated according to the dose between control points.

[0077] Subsequently, the MLC transport calculation unit 120 reads particles from the particle species data upstream of the MLC read in step S302 (S304), and sets a quasi-random number t for each particle (S305). The t set in this step S305 means a time parameter related to the irradiation timing between two control points, and is given as a uniform random number of 0 < t ≤ 1.

[0078] Subsequently, the MLC transport calculation unit 120 interpolates the device parameters between two control points using this time parameter, sets different device parameters for each particle, and takes into account the continuously changing device information (S306). Let the device parameters at control point A and control point B be ε A , ε B respectively. Then, when the time parameter t j is given to particle j, the device parameter ε j of particle j is given by the following equation (2). ε j = ε A + t j × (ε B - ε A ) ··· (2)

[0079] The device parameters include gantry angle, irradiation ring angle, MLC leaf position, etc., and the same parameters are used across devices. j Using this method, different instrument parameters are set for each particle.

[0080] Next, the MLC transport calculation unit 120 calculates the MLC passage length for each particle and calculates the transmittance (S307). The flow of transmittance calculation is shown in Figure 4. The transmittance calculation method will be explained using Figure 4.

[0081] As shown in Figure 4, the calculation of transmittance is divided into two processes: a process 400 for calculating the passage length within the MLC and a transmittance calculation process 401 for calculating transmittance from the passage length. The main entity executing these two processes is the MLC transport calculation unit 120.

[0082] In this invention, the process 400 for calculating the passage length within the MLC, which is part of the transmittance calculation flow, has a second feature. In this invention, the passage path information of particles within the MLC is calculated on the projection plane in the leaf arrangement direction and the projection plane in the leaf driving direction of the MLC, and the passage length within the MLC is calculated by determining the common range of passage paths on each projection plane. This makes it possible to efficiently calculate the passage length while preventing a decrease in the calculation accuracy for particles passing through regions where the leaf positions differ between adjacent leaves.

[0083] First, in the MLC passage length calculation process 400, leaves and flames that may intersect with the transported particles are extracted from the upper and lower positions of the transported particles (hereinafter referred to as transported particles) of the MLC, which were read in step S304 (S4001).

[0084] More specifically, the propagation vector of the transport particle (v x ,v y ,v zBased on the x, y, z0 position information, the path equations for the projection plane of the leaf's driving direction (x direction) and the projection plane of the leaf's arrangement direction (y direction) are calculated. Since we only consider particles moving in a straight line in the direction of travel, the path equations for the transported particles in the x and y directions are expressed as linear functions and are calculated using the following equations (3) and (4). z=v z / v x ×x+(z0-v z / v x ×x0), ··· (3) z=v z / v y ×y+(z0-v z / v y ×y0), ··· (4)

[0085] Based on the y-coordinates at the MLC upper and lower surface depths calculated using the particle path equation in the y-direction, leaves (hereinafter referred to as "corresponding leaves") and frames that require collision detection with particles are extracted.

[0086] Next, when the aperture region of the MLC is large (the irradiation field size is large), only a portion of the particles collide with the corresponding leaf selected in step S4001, and many particles reach the downstream of the MLC without colliding with the leaf or frame. Therefore, the MLC transport calculation unit 120 performs ray tracing calculations only for particles that are likely to collide, and in order to improve the efficiency of the calculation, it uses the leaf position of the corresponding leaf to determine whether a particle is likely to collide with a leaf (S4002).

[0087] The maximum value of the left leaf position of the corresponding leaf is xL max The minimum value of the right leaf position is xR min In this case, the x-coordinate of the particle on the MLC surface is x up x coordinate of the particle on the lower surface of the MLC down If the following conditions are met, it will be transported without colliding with the Leaf. XL max <x up <x Rmin , and xL max <x down <x Rmin , ··· (5)

[0088] Therefore, if the above conditions are met (no possibility of collision, No in step S402), the path length within the MLC is not calculated, and the statistical weight of the particle is set to 1. On the other hand, if equation (4) is not met (possibility of collision, Yes in step S402), the process proceeds to step S403 to start the path length calculation.

[0089] Next, from the corresponding leaves and frames calculated in step S4001, the leaf or frame to be included in the calculation is selected (S4003), and then the intersection points (excluding contact points) between the particles and the leaves or frames on the projection plane in the leaf arrangement direction are calculated (S4004).

[0090] Figure 5 shows examples of leaf and frame shapes of MLC in each projection plane. Figure 5(a) shows an example of a projection plane in the leaf arrangement direction, and Figure 5(b) shows an example of a projection plane in the leaf driving direction. As can be seen from Figure 5(a), the leaf and frame shapes can be expressed as a linear function for the hypotenuse, similar to the particle path equation, and as a function of z=k for the other sides. Therefore, the intersection points of the particle path equation and the frame and leaves can be easily calculated.

[0091] Next, the intersection points between the particles and leaves in the leaf drive direction projection plane are calculated (S4005).

[0092] The shape of the leaf's projection plane in the driving direction (x direction) is represented by the arc shape of the leaf tip and the function shape of z=k. The equation of this arc at the leaf tip is given by the mid-coordinate of the leaf as z mid Let R0 be the radius of the arc portion, dl be the x-length of the arc portion, and let x be the leaf positions of the i-th left and right leaves in the j-th particle. L,i,j , x R,i,j Therefore, the equation of the arc at the tip of the i-th left leaf in the j-th particle is given by equation (6), and the equation of the arc at the tip of the i-th right leaf is given by equation (7). (x-(x L,i,j -R0)) 2 +(zz mid ) 2 =R02 , x L,i,j -dl <x≦x L,i,j ... (6) (x-(x R,i,j +R0)) 2 +(zz mid ) 2 =R0 2 , x R,i,j ≤x <x R,i,j +dl ··· (7)

[0093] Therefore, it becomes possible to easily calculate the intersection points of the particle path equation and the leaves, even with respect to the xz plane.

[0094] Next, the intersection points and paths with the actual leaves are calculated based on the intersection information calculated from both projection planes (S4006). This procedure is explained using Figure 6.

[0095] Figure 6 shows the appearance of each projection plane, intersection information, and the range of passage within the leaf in the z-axis direction when a particle (particle A or particle B) is incident on the leaf.

[0096] The intersection information for each projection plane created in S4004 and S4005 above is based on the presence of an air layer between each leaf and frame. Therefore, when the intersections are sorted in descending order by z-axis value, odd-numbered intersections correspond to points where particles enter the leaf, while even-numbered intersections correspond to points where particles exit the leaf. This confirms that the range of passage within the leaf in the z-axis direction can be calculated from the intersection information.

[0097] Figures 6(a)-(c) show examples of calculating particle A, whose leaf path is mainly determined by the in-out of the leaf driving direction projection plane, and Figures 6(d)-(f) show examples of calculating particle B, whose in is determined by the leaf driving direction projection plane and out is determined by the leaf arrangement direction projection plane. When the leaf projection plane shape and cross-sectional shape are the same, the intersection point where the leaf and particle actually collide and the path are calculated from the common range of z coordinates of the leaf passage range on both projection planes.

[0098] Steps S4003 through S4006 are repeated until there are no other related leaves / frames (S4007), and the passage length is calculated from the intersection information of all leaves with transport particles (S4008).

[0099] In the transmittance calculation process 401, the transmittance for each particle is calculated using the MLC passage length calculated in the MLC passage length calculation process 400.

[0100] First, the particle type is identified, and it is determined whether the particle is a photon, an electron, or a positron (S4011). If the particle is a photon, the X-ray transmittance is calculated based on the path length within the MLC (S4012), and this is set as the statistical weight of the particle (S4013). The transmittance of a photon is generally expressed using an exponential function. Therefore, the statistical weight of the X-ray photon set in step S4013 is expressed by the following equation (8). w=w 0,j ×exp(-μ / ρ(E)×ρ×t), ··· (8)

[0101] Here, w 0,j This represents the initial weight of the X-ray photons to be transported, and in the case of primary X-rays obtained from PSD, w 0,j = 1. Also, t is the path length within the MLC calculated in the MLC passage length calculation process 400, ρ is the density of the MLC, and μ / ρ represents the material of the MLC, for example, the mass attenuation coefficient of tungsten.

[0102] Furthermore, in the case of electrons and positrons, it is checked whether the path length in the MLC has a value (S4014). If the path length in the MLC has a value (t≠0), the transport particle is removed because the electrons and positrons are shielded by the leaves (S4015). If the path length does not have a value (t=0), the statistical weight of the particle is set to 1 (S4016).

[0103] After the completion of steps S4013, S4015, and S4016, the MLC transport calculation unit 120 proceeds to step S308.

[0104] Returning to Figure 2, the MLC transport calculation unit 120 reads the statistically weighted particle type data created in step S204 (steps S301 to S312 in Figure 3) and calculates the dose distribution using the Monte Carlo method (S205). However, if there are many particles with small statistical weights in the particle type data read in step S205, the efficiency of the dose distribution calculation decreases significantly.

[0105] Therefore, in order to improve the calculation efficiency of step S205, the MLC transport calculation unit 120 includes a step (S308) to reduce low-weight particles using the Russian roulette method, which is one of the dispersion reduction methods shown in Figure 3.

[0106] In the Russian roulette method, the cutoff value is w cut Set the cutoff value w cut For the following particles, use a uniform random number w / w cut The particles are reduced with a certain probability. Meanwhile, the surviving w cut For particles with the following statistical weights, the cutoff value w cut Set this as the statistical weight.

[0107] Next, after calculating the coordinate values ​​of the particles at the downstream position of the MLC, a rotation matrix is ​​created using the irradiation angle information (gantry angle information, irradiation ring angle information, couch angle information) from the instrument parameters obtained in step S306 (S309).

[0108] Next, the rotation matrix created is used to perform coordinate transformations on the propagation vector and position information of the particle type data, thereby considering the effect of the treatment device's rotation. After considering the rotation of the treatment device, the data is written and saved as particle type data downstream of the MLC (S310).

[0109] Steps S304 through S310 are performed for all particles between the control points (S311). This is repeated for all control points (S312) to create particle type data downstream of the MLC.

[0110] The calculated dose distribution is displayed on the display unit 122 as a dose distribution, as in the conventional method, and is also displayed on the display unit 122 in parallel or overlaid on top of it so that it can be compared with the dose distribution used during treatment planning. Furthermore, the display unit 122 is not limited to displaying only the dose distribution; it can also display the gamma index and DVH, either in addition to or instead.

[0111] Furthermore, the display format is not limited to that shown in the display unit 122 within the dose distribution calculation system 113; it can also be displayed in other display units (omitted for illustrative purposes).

[0112] Here, to improve the accuracy of MLC transport calculations, the effects of Compton scattering within the MLC can be considered.

[0113] In this case, as shown in Figure 7, a step is provided after step S310 to generate photons scattered by Compton scattering (Compton photons).

[0114] More specifically, the MLC transport calculation unit 120 checks the particle type and determines whether it is a primary photon or not (S701). If it is determined to be a primary photon, it then calculates the weight of the Compton photon (S702) and generates the Compton photon (S703). After that, the process returns to step S307, and the processing from step S307 onwards is also performed on the Compton photon. If it is not determined to be a primary photon, the process can proceed to step S311.

[0115] Furthermore, in step S205, the dose distribution calculation unit 121 explained the case where statistical weights are used when calculating the dose distribution using transmittance. However, instead of directly using statistical weights in the dose distribution calculation, a Russian roulette method with a cutoff value of 1 can be applied to create particle type data at the downstream position of the MLC by removing low-weight particles, and the dose can be calculated.

[0116] Next, the effects of this embodiment will be described.

[0117] The dose distribution calculation system 113 of the above-described embodiment, which calculates the dose distribution of radiation irradiated through the multi-leaf collimator, comprises an MLC transport calculation unit 120 (transport calculation procedure, transport calculation step) that calculates the transmission rate for each particle of radiation by calculating the passage path of the multi-leaf collimator using two projection planes, and a dose distribution calculation unit 121 (dose distribution calculation procedure, dose distribution calculation step) that calculates the dose distribution using the transmission rate.

[0118] This configuration allows for the efficient calculation of particle type data (phase space data) downstream of the MLC, accurately reproducing the VMAT-DWA irradiation method, which involves continuous beam irradiation from multiple directions. This results in the efficient acquisition of highly accurate dose distribution calculation results.

[0119] Furthermore, by using two projection planes—one for the leaf arrangement direction and the other for the leaf drive direction of the multi-leaf collimator—the calculation of the passage path of the multi-leaf collimator can be simplified, and particle type data can be calculated more efficiently.

[0120] Furthermore, the MLC transport calculation unit 120 can determine the path length for each radiation particle by calculating the common range of the passage paths calculated on two projection planes and then determining the transmittance, thereby reducing the computational load by using a simpler calculation method.

[0121] Furthermore, the MLC transport calculation unit 120 interpolates the device information at each control point using a common interpolation parameter set for each radiation particle, sets different device parameters for each radiation particle, generates particle type information that takes into account the continuously changing device information, and calculates the path length using the generated particle type information. This allows for the calculation of device information that differs for each particle, taking into account not only discrete gantry angles but also the gantry angles in between, as well as the ring angle and the leaf position of the multi-leaf collimator when irradiating at that gantry angle. As a result, dose distribution can be calculated with higher accuracy than before. In addition, the dose distribution can be calculated in a shorter time.

[0122] Furthermore, by using random, uniformly random, or quasi-random numbers as interpolation parameters, it is possible to calculate dose distributions quickly while ensuring accuracy.

[0123] Furthermore, the MLC transport calculation unit 120 generates Compton photons as particles and calculates the transmittance of Compton photons, thereby enabling it to consider the effects of dose in areas outside the irradiation field, and thus improving the accuracy of dose distribution calculations outside the irradiation field.

[0124] Furthermore, when the dose distribution calculation unit 121 calculates the dose distribution using transmittance, it can provide a dose distribution that takes into account the aperture shape of the MLC by directly or by applying statistical weights using the Russian roulette method.

[0125] Furthermore, by adding a display unit 122 that displays the dose distribution calculated by the dose distribution calculation unit 121, it becomes possible to quickly perform dose verification work on the replanned treatment plan based on the dose distribution obtained with high accuracy and in a short time, thereby further improving the accuracy of online adaptive therapy.

[0126] <Other> It should be noted that the present invention is not limited to the embodiments described above, and various modifications and applications are possible. The embodiments described above are explained in detail for the purpose of clearly illustrating the present invention, and are not necessarily limited to those having all the configurations described.

[0127] For example, in another embodiment, as described in Patent Document 1 and Non-Patent Documents 1 and 2 above, calculations considering finer gantry angles, ring angles, and MLR apertures for continuously moving devices are too complex and difficult, and the problem of difficulty in considering continuously changing device information in calculations is addressed by (1) a dose distribution calculation system for calculating the dose distribution of radiation irradiated through a multi-leaf collimator, comprising: a transport calculation unit that calculates the transmittance for each radiation particle; and a dose distribution calculation unit that calculates the dose distribution using the transmittance, wherein the transport calculation unit interpolates the device information of each control point using a common interpolation parameter set for each radiation particle, sets different device parameters for each radiation particle, generates particle type information that takes into account the continuously changing device information, and uses the generated particle type information to determine the path length through which the radiation passes through the multi-leaf collimator.

[0128] By using this dose distribution calculation system (1), it is possible to calculate different device information for each particle, taking into account not only discrete gantry angles but also the ring angle and the leaf position of the multi-leaf collimator when irradiating at that gantry angle, by interpolating the gantry angles in between. This allows for more accurate calculation of the dose distribution than before. Furthermore, since the device information of the control point is interpolated using the same interpolation parameters based on the interpolation parameters set for each particle, the dose distribution can be calculated in a short time.

[0129] (2) In the dose distribution calculation system described in (1), the interpolation parameter is one of a random number, a uniform random number, or a quasi-random number.

[0130] (3) In the dose distribution calculation system described in (1) or (2), the transport calculation unit calculates the transmission rate for each particle of radiation by calculating the passage path of the multi-leaf collimator using two projection planes.

[0131] (4) In the dose distribution calculation system described in (3), the two projection planes are the projection plane in the leaf arrangement direction of the multi-leaf collimator and the projection plane in the leaf driving direction.

[0132] (5) In the dose distribution calculation system described in (3) or (4), the transport calculation unit determines the transmittance for each particle of radiation by determining the path length from the common range of the passage paths calculated on two projection planes.

[0133] (6) In the dose distribution calculation system described in any of (1) to (5), the transport calculation unit generates Compton photons as particles and calculates the transmittance of the Compton photons.

[0134] (7) In the dose distribution calculation system described in any of (1) to (6), the dose distribution calculation unit uses statistical weights directly or by applying the Russian roulette method when calculating the dose distribution using the transmittance.

[0135] (8) The dose distribution calculation system described in any of (1) to (7) further comprises a display unit that displays the dose distribution calculated by the dose distribution calculation unit. [Explanation of Symbols]

[0136] A...Target of irradiation 100...X-ray irradiation device 101... Ring-shaped gantry 102... Irradiation nozzle 103...Bed 104...Radiation irradiation control device 105...Communication device 106...Storage device 107...Input device 110...X-ray irradiation system 111...Data Server 111A…Treatment plan information 112... Treatment planning device 113…Dose distribution calculation system 120…MLC transportation calculation department 121...Dose Distribution Calculation Unit 122...Display section 123...Input section 124...Storage section 125... Particle type information database

Claims

1. A dose distribution calculation system for calculating the dose distribution of radiation irradiated through a multi-leaf collimator, A transport calculation unit calculates the transmission rate for each particle of radiation by calculating the passage path of the multi-leaf collimator using two projection planes, The system includes a dose distribution calculation unit that calculates the dose distribution using the transmittance. Dose distribution calculation system.

2. In the dose distribution calculation system according to claim 1, The two projection planes are defined as the projection plane in the leaf arrangement direction of the multi-leaf collimator and the projection plane in the leaf driving direction. Dose distribution calculation system.

3. In the dose distribution calculation system according to claim 1, The transport calculation unit determines the transmittance for each particle of radiation by finding the path length from the common range of the passage paths calculated on the two projection planes. Dose distribution calculation system.

4. In the dose distribution calculation system described in claim 3, The aforementioned transport calculation unit, Using a common interpolation parameter set for each particle of radiation, the device information of each control point is interpolated using the interpolation parameter. By setting different device parameters for each particle of the aforementioned radiation, Particle type information is generated considering the continuously changing device information, The path length is determined using the generated particle type information. Dose distribution calculation system.

5. In the dose distribution calculation system according to claim 4, The interpolation parameter is either a random number, a uniform random number, or a quasi-random number. Dose distribution calculation system.

6. In the dose distribution calculation system according to any one of claims 1 to 5, The transport calculation unit generates Compton photons as the particles and calculates the transmittance of the Compton photons. Dose distribution calculation system.

7. In the dose distribution calculation system according to any one of claims 1 to 5, The dose distribution calculation unit uses statistical weights directly or by applying the Russian roulette method when calculating the dose distribution using the transmittance. Dose distribution calculation system.

8. In the dose distribution calculation system according to any one of claims 1 to 5, The system further includes a display unit that displays the dose distribution calculated by the dose distribution calculation unit. Dose distribution calculation system.

9. A transport calculation procedure for each radiation particle, which involves calculating the transmission path of a multi-leaf collimator using two projection planes and then calculating the transmittance for each particle, A dose distribution calculation procedure is performed on the calculation system to calculate the dose distribution of the radiation irradiated through a multi-leaf collimator using the aforementioned transmittance. program.

10. A method for calculating the dose distribution of radiation irradiated through a multi-leaf collimator, A transport calculation step involves calculating the transmission rate for each particle of radiation by determining the path through the multi-leaf collimator using two projection planes, and then calculating the transmittance for each particle. The method includes a dose distribution calculation step of calculating the dose distribution using the transmittance. Method for calculating dose distribution.

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