Positioning device, radiation therapy device, and positioning method

Abstract

To provide a positioning device, a radiation therapy apparatus and a positioning method which can highly accurately position a patient while reducing a calculation time.SOLUTION: An image acquisition unit 21 acquires a plurality of perspective X-ray images obtained by imaging a patient B along each of a plurality of imaging axes in a direction different from a plurality of movement axes along which a bed 7 on which the patient B is placed translationally moves. A pseudo perspective X-ray image creation unit 22 creates a plurality of pseudo perspective X-ray images obtained by projecting a three-dimensional perspective image of the patient B to each of a plurality of surfaces according to each imaging axis. A similarity calculation unit 24 calculates the similarity between each perspective X-ray image and each pseudo perspective X-ray image. An optimization calculation processing unit 25 calculates a movement amount of the bed 7 such that each perspective X-ray image and each pseudo perspective X-ray image best match with each other for each of the plurality of translation directions respectively along the plurality of optimization axes including the plurality of imaging axes and the plurality of rotation directions with the plurality of rotation axes as the center on the basis of the similarity.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This disclosure relates to a positioning device, a radiotherapy device, and a positioning method. [Background technology]

[0002] Radiation therapy, which involves irradiating the patient with radiation, is a well-known treatment method for cancer. The radiation used in radiation therapy is broadly classified into two types: uncharged particle beams such as X-rays or gamma rays, and charged particle beams such as proton beams or carbon beams. Radiation therapy using the latter type of charged particle beam is generally called particle beam therapy.

[0003] In the case of uncharged particle beams, the dose decreases at a constant rate from shallow to deep within the body. On the other hand, in the case of charged particle beams, a dose distribution (Brack's curve) can be formed with a peak in energy loss at a specific depth. Therefore, by aligning the peak of energy loss of the charged particle beam with the location of the tumor, it is possible to significantly reduce the dose of charged particle beams irradiated to normal tissue located deeper than the tumor.

[0004] Therefore, in radiation therapy, accurately delivering the desired dose of radiation to the target tumor is crucial for improving treatment effectiveness. To achieve accurate radiation delivery to the tumor, it is necessary to position the patient at the same location as the planned location determined by the pre-created treatment plan. This process of positioning the patient is called patient positioning.

[0005] One method for positioning patients in radiation therapy involves using fluoroscopic X-ray images (Digital Radiography: DR) taken from two different directions by two sets of X-ray tubes and flat panel detectors (FPDs) of the patient lying on the treatment table. In this method, the fluoroscopic X-ray image taken of the patient during radiation therapy is compared with a pseudo-fluoroscopic X-ray image created from CT (Computed Tomography) images used to create the treatment plan. The patient is then positioned so that the location of target structures such as bones matches between the fluoroscopic X-ray image and the pseudo-fluoroscopic X-ray image.

[0006] Furthermore, fluoroscopic X-ray images may generally include structures other than the target structure for positioning, such as the patient's fixation devices and soft tissues, or the arrangement of bones, which are the target structures for positioning, may change from the time of treatment planning. In such situations, the structures shown in the fluoroscopic X-ray image and the pseudo-fluoroscopic X-ray image do not match across the entire image. In this case, patient positioning is performed using a region of interest (ROI) set as the area where the target structure for positioning exists on the fluoroscopic X-ray image. The region of interest is usually set by the user, a healthcare professional, drawing the region of interest on the image.

[0007] Automatic patient positioning uses the translational and rotational amounts of the bed on which the patient lies as parameters, and calculates the optimal values ​​of these parameters through optimization calculations. Typically, the translational amount has three components along three mutually orthogonal axes (x, y, z), and the rotational amount has three components (Pitch, Roll, Yaw) with those three axes as the axis of rotation. Therefore, in the optimization calculation, the optimization process is repeatedly performed for each of the six components to calculate the optimal values ​​of the parameters. The three axes that define the translational amount coincide with the movement axes of the bed used to position the patient in the planned location. The x-axis points from right to left (Right-Left direction: RL direction) as viewed from the patient lying supine on the bed, the y-axis points from feet to head (Superior-Inferior direction: SI direction), and the z-axis points from back to abdomen (Anterior-Posterior direction: AP direction).

[0008] However, if the axis of the parameters used in the optimization calculation differs from the imaging axis of the imaging device, the optimization calculation may fail to reach the optimal value of the parameters, or the computational load of the optimization calculation may increase.

[0009] In contrast, Patent Documents 1 and 2 disclose a technique for reducing the number of calculations required to repeat the optimization process in an optimization calculation by adding a one-dimensional optimization process in the direction along the imaging axis from which the fluoroscopic X-ray image is taken, after the optimization process for each component has been completed.

[0010] Furthermore, Patent Document 3 discloses a technique that reduces the number of fluoroscopic X-ray images and shortens the time required for patient positioning by evaluating the optimization of the translation amount in the direction along the imaging axis in only one direction perpendicular to the fluoroscopic imaging axis. [Prior art documents] [Patent Documents]

[0011] [Patent Document 1] Patent No. 6668902 [Patent Document 2] International Publication No. 2018 / 225234 [Patent Document 3] Japanese Patent Publication No. 2013-99431 [Overview of the Initiative] [Problems that the invention aims to solve]

[0012] In the technologies disclosed in Patent Documents 1 and 2, an optimization process for the direction along the imaging axis is added after the normal optimization process for multidimensional components is completed. This increases the number of optimization processes per calculation, resulting in a problem of low reduction in computation time.

[0013] Furthermore, the technology disclosed in Patent Document 3 has a problem in that it becomes difficult to calculate the optimal parameter value depending on the location of the region of interest on the image. For example, if a small region of interest is set at the edge of the image, changing the translation amount in the direction along one shooting axis may cause the target structure within the region of interest to move to the edge or center of the image in an image acquired on another shooting axis perpendicular to that axis, causing it to move outside the region of interest. In this case, it becomes difficult to calculate the optimal value.

[0014] The purpose of this disclosure is to provide a positioning device, a radiotherapy device, and a positioning method that enable highly accurate patient positioning while further reducing computation time. [Means for solving the problem]

[0015] A positioning device according to an aspect of the present disclosure is a positioning device that controls the position of a stretcher on which a subject is mounted, and includes an image acquisition unit that acquires a plurality of fluoroscopic images by photographing the subject along each of a plurality of photographing axes in a direction different from a plurality of moving axes along which the stretcher moves translationally, a creation unit that creates a plurality of pseudo-fluoroscopic images by projecting a three-dimensional fluoroscopic image of the subject onto each of a plurality of planes corresponding to each photographing axis, a calculation unit that calculates the similarity between each fluoroscopic image and each pseudo-fluoroscopic image, and an optimization unit that calculates the amount of movement of the stretcher such that each fluoroscopic image and each pseudo-fluoroscopic image match most closely for each of a plurality of translational directions along each of a plurality of optimization axes including the plurality of photographing axes and each of a plurality of rotational directions around a plurality of rotation axes, based on the similarity.

Advantages of the Invention

[0016] According to the present invention, highly accurate patient positioning can be achieved while further reducing the calculation time.

Brief Description of the Drawings

[0017] [Figure 1] It is a diagram showing the overall configuration of a particle beam therapy system according to an embodiment of the present disclosure. [Figure 2] It is a flowchart for explaining an example of patient positioning processing. [Figure 3] It is a flowchart for explaining the optimization calculation processing in more detail. [Figure 4] It is a diagram showing an example of an optimization axis of a translation parameter. [Figure 5] It is a diagram showing an example of the relationship between an optimization axis and a photographing system. [Figure 6] It is a diagram showing an example of a score map image.

Embodiments for Carrying Out the Invention

[0018] <9000112>Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0019] The following description and drawings are illustrative examples for explaining the present invention, and have been appropriately omitted and simplified for clarity. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. In the diagrams illustrating embodiments, the same reference numeral is used for parts having the same function, and repeated explanations may be omitted. Furthermore, the position, size, shape, and range of each component shown in the drawings may not represent the actual position, size, shape, and range in order to facilitate understanding of the invention. For this reason, the present invention is not limited to the position, size, shape, and range disclosed in the drawings. In addition, when there are multiple identical or similar components, different subscripts may be used for the same reference numeral in the description. However, if it is not necessary to distinguish between these multiple components, the subscript may be omitted in the description.

[0020] Figure 1 is a diagram showing the overall configuration of a particle beam therapy system according to one embodiment of the present disclosure. The particle beam therapy system A shown in Figure 1 is a radiotherapy apparatus having a group of devices for irradiating a patient B, who is a subject of treatment, with a particle beam. The particle beam therapy system A comprises an accelerator 1, a beam transport device 2, a gantry 3, an irradiation nozzle 4, FPDs 5A and 5B, X-ray tubes 6A and 6B, a patient bed 7, a robotic arm 8, a communication device 9, a data server 10, a treatment planning device 11, a fluoroscopic X-ray image acquisition device 12, a patient bed control device 13, and a patient positioning device 20.

[0021] Accelerator 1 is a particle beam generator that produces a particle beam to irradiate patient B. It accelerates and outputs the particle beam until it reaches an energy suitable for treating patient B. Beam transport device 2 transports the particle beam output from accelerator 1 to gantry 3. The type of particle beam is not particularly limited and may be, for example, a proton beam or a carbon beam.

[0022] The gantry 3 and irradiation nozzle 4 are irradiation devices that irradiate patient B with particle beams transported from accelerator 1. The gantry 3 adjusts the irradiation angle at which the particle beams transported from accelerator 1 are irradiated onto patient B. Specifically, the gantry 3 has a rotation mechanism that allows it to rotate 360° around patient B, and adjusts the irradiation angle by rotating. The irradiation nozzle 4 is installed in the gantry 3 and irradiates patient B with particle beams transported to the gantry 3. The irradiation nozzle 4 may incorporate a mechanism to adjust the shape of the particle beam to match the shape of the patient's affected area.

[0023] FPDs 5A and 5B and X-ray tubes 6A and 6B constitute an imaging system for fluoroscopic imaging of patient B. FPDs 5A and 5B are planar detectors that detect X-rays and image patient B. X-ray tubes 6A and 6B emit X-rays. FPDs 5A and X-ray tube 6A are positioned opposite each other so that the X-rays emitted from X-ray tube 6A are detected by FPD 5A, and FPDs 5B and X-ray tube 6B are positioned opposite each other so that the X-rays emitted from X-ray tube 6B are detected by FPD 5B. The axis connecting the center of FPD 5A and X-ray tube 6A, and the axis connecting the center of FPD 5B and X-ray tube 6B become the two imaging axes for imaging the patient. Preferably, the two imaging axes are orthogonal to each other, but they do not have to be orthogonal to each other. Furthermore, particle beam therapy system A may have three or more FPDs and X-ray tubes each. In this case, there will also be three or more imaging axes.

[0024] The bed 7 is a platform on which patient B is placed when irradiating patient B with particle beams. The robotic arm 8 is a device that moves the bed 7. Specifically, the robotic arm 8 performs translational movement in multiple translational directions along multiple movement axes and rotational movement in multiple rotational directions around multiple rotation axes relative to the bed 7. In this embodiment, the movement axis and rotation axis are the same, and there are three movement axes (rotation axes). Furthermore, each movement axis is oriented in the direction from right to left (RL direction) as viewed from patient B lying supine on the bed 7, from patient B's feet to head (SI direction), and from back to abdomen (AP direction).

[0025] The communication device 9 connects the data server 10, the treatment planning device 11, and the patient positioning device 20 so that they can communicate with each other.

[0026] The data server 10 is a storage device that stores various information related to the particle beam therapy of patient B. The data server 10 stores, for example, three-dimensional fluoroscopic image information of patient B and treatment plan information showing the treatment plan for patient B. The three-dimensional fluoroscopic image includes information showing the patient's shape and electron density in voxel units. The three-dimensional fluoroscopic image is, for example, a computed tomography (CT) image and is generated in advance (before creating the treatment plan information for patient B). The treatment plan information is generated based on the three-dimensional fluoroscopic image. The treatment plan information also includes planned placement information showing the planned placement of patient B during treatment. The placement of patient B indicates the position and angle (posture) of patient B, and is determined by the position and angle of the treatment table 7.

[0027] The treatment planning device 11 creates a treatment plan for patient B based on the three-dimensional fluoroscopic image information stored in the data server 10, and stores the treatment plan information representing that treatment plan in the data server 10.

[0028] The fluoroscopic X-ray imaging device 12 controls the FPD 5A and X-ray tube 6A, and the FPD 5B and X-ray tube 6B respectively, to acquire multiple fluoroscopic X-ray images of patient B taken from different angles, and transmits the acquired fluoroscopic X-ray images to the positioning device 20. In this embodiment, there are two fluoroscopic X-ray images.

[0029] The bed control device 13 adjusts the position of patient B by controlling the robot arm 8 to adjust the arrangement of the bed 7.

[0030] The patient positioning device 20 performs the positioning process for patient B based on the three-dimensional fluoroscopic image information and treatment plan information stored in the data server 10, and the fluoroscopic X-ray image acquired by the fluoroscopic X-ray imaging device 12.

[0031] The patient B positioning process is the process of positioning patient B, who is placed on the treatment table 7, to the same position as the planned arrangement indicated in the treatment plan information, before the start of particle beam therapy for patient B. The patient positioning device 20 controls the robot arm 8 via the treatment table control device 13 to adjust the position and angle of the treatment table 7, thereby positioning patient B to the same position as the planned arrangement.

[0032] Once the positioning process is complete, the actual particle beam therapy on patient B is performed. Specifically, the particle beam, accelerated to an energy suitable for treatment by accelerator 1, is transported to gantry 3 via beam transport device 2. The particle beam is deflected in the appropriate direction in gantry 3, passes through irradiation nozzle 4, and is irradiated onto the affected area of ​​patient B.

[0033] The positioning device 20 will be described in more detail below.

[0034] As shown in Figure 1, the positioning device 20 includes an image acquisition unit 21, a pseudo-fluoroscopic X-ray image creation unit 22, an ROI drawing unit 23, a similarity calculation unit 24, an optimization calculation processing unit 25, an image display unit 26, and a control unit 27.

[0035] The image acquisition unit 21 acquires 3D fluoroscopic image information from the data server 10 via the communication device 9 and acquires fluoroscopic X-ray images from the fluoroscopic X-ray image acquisition device 12.

[0036] The pseudo-fluoroscopic X-ray image creation unit 22 is a creation unit that creates multiple pseudo-fluoroscopic X-ray images, which are multiple pseudo-fluoroscopic images created by projecting the 3D fluoroscopic image acquired by the image acquisition unit 21 onto multiple planes corresponding to each shooting axis for capturing fluoroscopic X-ray images. The pseudo-fluoroscopic X-ray image creation unit 22 creates pseudo-fluoroscopic X-ray images by placing the 3D image of patient B in the same virtual space as the shooting system that generated the fluoroscopic X-ray image and performing a projection process. The planes corresponding to the shooting axes are, for example, planes perpendicular to the shooting axes.

[0037] The ROI drawing unit 23 identifies the ROI, which is a region of interest used for positioning the patient in a simulated fluoroscopic X-ray image. Specifically, the ROI drawing unit 23 identifies the ROI by displaying a simulated fluoroscopic X-ray image and having the user draw the ROI on the simulated fluoroscopic X-ray image. The ROI is drawn to include, for example, a positioning target structure such as bone.

[0038] The similarity calculation unit 24 calculates the similarity between the fluoroscopic X-ray image and the pseudo-fluoroscopic X-ray image at the ROI identified by the ROI drawing unit 23. In this embodiment, the similarity calculation unit 24 calculates the sum of the similarities between the fluoroscopic X-ray image and the pseudo-fluoroscopic X-ray image corresponding to each of the two imaging axes.

[0039] Similarity is not particularly limited to any metric that can evaluate the degree of similarity between images, but examples include cross-information or the zero-mean normalized cross-correlation (ZNCC) coefficient. Normalized cross-correlation coefficient S ZNCC This is calculated using equation (1).

number

[0040] The optimization calculation processing unit 25 is an optimization unit that calculates the amount of movement of the bed 7 such that the fluoroscopic X-ray image and the pseudo-fluoroscopic X-ray image best match in the ROI identified by the ROI plotting unit 23, based on the similarity calculated by the similarity calculation unit 24. Specifically, the optimization calculation processing unit 25 calculates the amount of movement of the bed 7 by optimizing the values ​​of the placement parameters corresponding to the translational and rotational movement amounts of the bed 7 using a predetermined optimization calculation method. The placement parameters have three degrees of freedom for translational movement and three degrees of freedom for rotational movement. The optimization calculation method is not particularly limited, but examples include the BFGS (Broyden-Fletcher-Goldfarb-Shanno) method, the Nelder-Mead method, or the Powell method, which belong to the quasi-Newton method.

[0041] The image display unit 26 is a display unit that displays various information and images. For example, the image display unit 26 displays fluoroscopic X-ray images, pseudo-fluoroscopic X-ray images, and ROI images that show ROI regions.

[0042] The control unit 27 adjusts the position of patient B by controlling the bed control device 13 to adjust the arrangement of the bed 7.

[0043] The positioning device 20 having the above functions can be realized by an information processing device capable of various information processing, such as a computer device. The information processing device has, for example, an arithmetic element, a storage medium, and a communication interface, and further, if necessary, an input unit such as a mouse and keyboard, and a display unit such as a display.

[0044] The processing elements are, for example, processors such as CPUs (Central Processing Units) and FPGAs (Field-Programmable Gate Arrays). The storage medium is, for example, magnetic storage medium such as HDDs (Hard Disk Drives), semiconductor storage medium such as RAM (Random Access Memory), ROM (Read Only Memory), and SSDs (Solid State Drives). Alternatively, a combination of optical discs such as DVDs (Digital Versatile Disks) and optical disc drives may be used as the storage medium. Furthermore, other high-value storage media such as magnetic tape media may also be used.

[0045] The storage medium stores programs such as firmware. When the positioning device 20 starts operating (for example, when the power is turned on), the processing element reads the program from the storage medium and executes it, thereby realizing each part 21 to 27 of the positioning device 20 and executing a series of overall controls. In addition to the program, the storage medium also stores data necessary for each process of the positioning device 20.

[0046] Furthermore, the positioning device 20 in this embodiment may be configured in a so-called cloud computing environment in which multiple information processing devices are configured to communicate with each other via a communication network.

[0047] The patient positioning process by the patient positioning device 20 will be explained in more detail below using Figures 2 to 6.

[0048] Figure 2 is a flowchart illustrating an example of the patient positioning process.

[0049] It should be assumed that patient B is positioned in the setup position on bed 7. The setup position is the position that matches the planned placement of patient B. For example, the position of patient B's body surface on bed 7 is measured using an infrared laser installed in the treatment room, and based on that position, patient B is positioned in the setup position on bed 7.

[0050] In the patient positioning process, first, the control unit 27 obtains treatment plan information from the data server 10, and based on the planned placement information contained in the treatment plan information, controls the robot arm 8 via the bed control device 13 to move the bed 7 on which patient B is placed so that patient B is positioned in the planned placement shown in the planned placement information (step S100). At this time, the positioning target structure of patient B placed on the bed 7 is included in the X-ray irradiation area formed by the FPDs 5A and 5B and the X-ray tubes 6A and 6B.

[0051] Subsequently, the image acquisition unit 21 acquires multiple fluoroscopic X-ray image information of patient B taken from multiple different directions via the fluoroscopic X-ray imaging device 12 (step S101). In this embodiment, the image acquisition unit 21 acquires two fluoroscopic X-ray image information taken from two directions along two imaging axes.

[0052] The pseudo-fluoroscopic X-ray image creation unit 22 acquires 3D fluoroscopic image information from the data server 10 and sets initial values ​​for the displacement of the placement parameters based on that 3D fluoroscopic image information (step S102). For example, the pseudo-fluoroscopic X-ray image creation unit 22 displays the 3D fluoroscopic image information on the image display unit 26 and prompts the user to input the initial values. Alternatively, the pseudo-fluoroscopic X-ray image creation unit 22 may set predetermined values ​​as initial values ​​without user intervention.

[0053] The pseudo-fluoroscopic X-ray image creation unit 22 creates two pseudo-fluoroscopic X-ray images by projecting three-dimensional fluoroscopic image information onto a plane, assuming the same imaging system as the imaging system for fluoroscopic X-ray image information (step S103).

[0054] The ROI drawing unit 23 displays pseudo-fluoroscopic X-ray image information to identify the ROI drawn by the user. The similarity calculation unit 24 calculates the similarity between the fluoroscopic X-ray image information and the pseudo-fluoroscopic X-ray image information in that ROI (step S104).

[0055] The optimization calculation processing unit 25 determines whether the similarity satisfies the pre-set convergence conditions (step S105).

[0056] If the similarity does not satisfy the convergence condition (step S105: No), the optimization calculation processing unit 25 optimizes the placement parameters by performing an optimization process to adjust the placement parameters (step S106).

[0057] On the other hand, if the similarity condition is met (step S105: Yes), the control unit 27 moves the bed 7 via the bed control device 13 based on the adjusted placement parameters (step S107), and terminates the patient positioning process. This allows the patient to be moved from the current position to the position planned for treatment, enabling precise positioning. After that, the actual particle beam irradiation is performed.

[0058] Figure 3 is a flowchart that provides a more detailed explanation of the optimization calculation process, which is the process described in steps S102 to S106 of Figure 2.

[0059] In the optimization calculation process, the optimization calculation unit 25 sets the optimization order for the six components of the placement parameters (step S200). Here, the optimization calculation unit 25 sets the optimization order to the three components related to translation and the three components related to rotation. Hereafter, the components related to translation may be called translation parameters and the components related to rotation may be called rotation parameters. Note that the process in step S200 is omitted in Figure 2. Also, in Figure 3, processes performed by other than the optimization calculation unit 25 (for example, the processes in steps S103 and S104) are omitted.

[0060] The optimization calculation processing unit 25 performs a one-dimensional optimization calculation to optimize the values ​​of each of the three components of the translation parameter in order according to the optimization order (step S201). The one-dimensional optimization calculation is a process that uses a known method such as the Brent method to calculate the parameter value that maximizes the similarity between the fluoroscopic X-ray image information and the pseudo-fluoroscopic X-ray image information within the ROI in a one-dimensional direction along the optimal axis to be optimized.

[0061] Figure 4 shows an example of the optimization axes for translational parameters. In the example in Figure 4, the axes of translational movement of the patient bed 7 are the x (RL) axis, the y (SI) axis, and the z (AP) axis. The optimization axes for translational parameters are the FPD1 axis, which is the imaging axis connecting the center of FPD5A and the X-ray tube 6A, the FPD2 axis, which is the imaging axis connecting the center of FPD5B and the X-ray tube 6B, and the y axis.

[0062] In this embodiment, when the FPD1 axis and FPD2 axis and the x axis and z axis are different from each other (in the example in Figure 4, they are arranged at a 45-degree angle to each other), if the x axis and z axis are used as the optimization axes, there is a risk of optimization difficulties occurring. In contrast, in this embodiment, instead of performing optimization calculations in two translational directions using the x axis and z axis as the optimization axes, the optimization axes for the translational parameters are set to the FPD1 axis, FPD2 axis and y axis, thereby making it possible to suppress optimization difficulties. Optimization difficulties occur when the results of optimization calculations in one dimension affect the results of optimization calculations in other directions, leading to an increase in the number of iterations of optimization calculations or the inability to reach the optimal value due to the influence of local optima.

[0063] Figures 5 and 6 are diagrams that provide a more detailed explanation of the phenomenon of difficulty in optimization.

[0064] Figure 5 shows the relationship between the optimized axis and the imaging system (FPDs 5A and 5B, and X-ray tubes 6A and 6B). In the example in Figure 5, the FPD1 axis and FPD2 axis are oriented along a plane perpendicular to the y-axis (the plane formed by the x-axis and z-axis), and are offset by 45 degrees from the x-axis and z-axis, respectively.

[0065] Figure 6 shows a score map image representing the distribution of matching scores, which are the degree of similarity between fluoroscopic X-ray images and pseudo-fluoroscopic X-ray images. Specifically, the score map image shows the matching score for each relative position to the planned placement of the structure to be positioned. The intensity of the score map image represents the magnitude of the matching score, with brighter areas indicating a higher matching score.

[0066] The score map image 100 shown in Figure 6(a) represents the distribution of matching scores in the x-axis and z-axis directions when the optimization axes are the movement axes (x-axis, y-axis, and z-axis) of the bed 7, while the score map image 101 shown in Figure 6(b) represents the distribution in the FPD1 axis and FPD2 axis directions when the optimization axes are the FPD1 axis, FPD2 axis, and y-axis, as in this embodiment.

[0067] In Figure 6(a), the score map image 100 shows a high score band 200 with a high matching score, located diagonally. This is because the high score band 200 appears along the imaging axes (FPD1 axis and FPD2 axis).

[0068] In this case, if the target structure for positioning is located at position A in the treatment plan, but is located at position B, the matching score will be the value corresponding to position B in the score map image 100. In one-dimensional optimization calculations, the parameter values ​​are determined so that the matching score is highest in the one-dimensional direction along the target optimization axis. For example, if optimization calculations in the y-axis and z-axis directions are performed after the x-axis direction optimization calculation, the position of the target structure in the score map image 100 will not immediately become position A, but will be optimized to a high-score zone 200 located on the x-direction side of position B. Subsequently, as the optimization calculation is repeated, the position of the target structure in the score map image 100 will update its x-direction and z-direction values ​​in a zigzag pattern within the high-score zone 200. This increases the number of iterations of the optimization calculation, and in some cases, the parameter values ​​may become locally optimized, preventing the reaching of appropriate values.

[0069] On the other hand, in the score map image 101 when the optimization axes are the FPD1 axis and the FPD2 axis which are the imaging axes as in this embodiment, the high score band 201 will exist along the optimization axes. Therefore, it is possible to suppress the position in the score map image 100 of the positioning target structure as described above from being updated in a zigzag manner, and the value of the parameter can be efficiently optimized to an appropriate value.

[0070] Return to the description of FIG. 3. When step S201 ends, the optimization calculation processing unit 25 performs one-dimensional optimization calculation for each component of the rotation component (step S202). In the optimization calculation of the rotation component, the optimization axis (rotation axis) may be the same as the moving axes (x, y, z) of the bed 7, or may be the same as the optimization axes (FPD1 axis, FPD2 axis, y axis) of the translation component.

[0071] The optimization calculation processing unit 25 determines whether or not the optimization result, which is the result of the optimization calculation of the arrangement parameter, satisfies the convergence condition (step S203).

[0072] For example, when the Powell method is used as the optimization calculation method, the optimization calculation processing unit 25 sets the matching score (similarity) in the current optimization result as f ret and the matching score in the previous optimization result as f p If the formula (2) is satisfied, it is determined that the optimization result satisfies the convergence condition.

Equation

[0073] If the convergence condition is not met (step S203: No), the optimization calculation processing unit 25 determines whether an additional optimization calculation process is required (step S204).

[0074] For example, when Powell's method is used as the optimization calculation method, the optimization calculation processing unit 25 uses the three functions f0, f expressed by equation (3) N and f E This is used to determine whether additional optimization calculations are required.

number

[0075] The optimization calculation unit 25 processes functions f0, f N and f E If at least one of the following equations (4) and (5) is met, it is determined that no additional optimization calculations are necessary.

number

[0076] If equation (4) is true, it indicates that the decrease in the matching score along the mean movement direction depends only on the component in one direction. If equation (5) is true, it indicates that the value of the matching score is already a local optimum.

[0077] If additional optimization calculations are required (step S204: Yes), the optimization calculation processing unit 25 performs the additional optimization calculations (step S205) and returns to the process in step S201. The additional optimization calculations start from the starting point P0 to the optimization point P N This is a one-dimensional optimization calculation with respect to the average movement direction toward [a certain point].

[0078] If no additional optimization calculation processing is required (step S204: No), the optimization calculation processing unit 25 determines whether the number of iterations of the optimization calculation (the number of times the optimization process, including the processes in steps S201 and S202, has been executed) has reached a predetermined upper limit (step S206).

[0079] If the number of repetitions has not reached the upper limit (step S206: No), the process in step S201 is executed again.

[0080] Furthermore, if the convergence conditions are met (step S203: Yes), and if the number of iterations reaches the upper limit (step S206: Yes), the optimization calculation processing unit 25 determines the values ​​of the placement parameters to the optimized values ​​(step S207) and proceeds to step S107 in Figure 2.

[0081] In this embodiment, a particle beam therapy system is used as an example of a radiation therapy device. However, the radiation therapy device is not limited to a particle beam therapy system; it may also be a radiation therapy system using non-particle beams such as X-rays. In this case, accelerator 1 is composed of, for example, an electron beam accelerator that emits X-rays.

[0082] As described above, in this embodiment, the image acquisition unit 21 acquires multiple fluoroscopic X-ray images of patient B along each of the multiple imaging axes which are oriented in a different direction from the multiple movement axes along which the bed 7 on which patient B is mounted moves in translation. The pseudo-fluoroscopic X-ray image creation unit 22 creates multiple pseudo-fluoroscopic X-ray images by projecting the three-dimensional fluoroscopic image of patient B onto each of the multiple planes corresponding to each imaging axis. The similarity calculation unit 24 calculates the similarity between each fluoroscopic X-ray image and each pseudo-fluoroscopic X-ray image. Based on the similarity, the optimization calculation processing unit 25 calculates the amount of movement of the bed 7 that best matches each fluoroscopic X-ray image for each of the multiple translational directions along each of the multiple optimization axes including the multiple imaging axes, and for each of the multiple rotational directions around the multiple rotation axes.

[0083] Therefore, since optimization calculations are performed in directions along multiple imaging axes, it becomes possible to mitigate the possibility of failing to reach the optimal parameter value or an increase in the number of calculations. As a result, highly accurate patient positioning becomes possible while further reducing calculation time.

[0084] Furthermore, in this embodiment, the imaging axes are approximately orthogonal to each other. This makes it possible to optimize the parameters more effectively.

[0085] Furthermore, in this embodiment, there are two imaging axes. Therefore, it is possible to perform optimization calculations for the minimum necessary direction, thereby suppressing the increase in computational load.

[0086] Furthermore, in this embodiment, the imaging axis is oriented along the plane formed by any two of the movement axes. Therefore, optimization calculations can be performed more appropriately.

[0087] Furthermore, in this embodiment, since the axis of rotation is the same as the optimization axis, it becomes possible to perform optimization calculations more appropriately.

[0088] Furthermore, in this embodiment, the amount of movement of the bed 7 is calculated so that the area within the ROI is most closely matched, making it possible to perform optimization calculations more appropriately.

[0089] The embodiments of the Disclosure described above are illustrative for illustrative purposes and are not intended to limit the scope of the Disclosure to those embodiments only. Those skilled in the art can implement the Disclosure in various other forms without departing from the scope of the Disclosure. [Explanation of symbols]

[0090] A...Particle beam therapy system, B...Patient, 1...Accelerator, 2...Beam transport device, 3...Gantry, 4...Irradiation nozzle, 5A...FPD, 5B...FPD, 6A...X-ray tube, 6B...X-ray tube, 7...Patient bed, 8...Robot arm, 9...Communication device for patient, 10...Data server, 11...Treatment planning device, 12...Fluoroscopy X-ray image acquisition device, 13...Patient bed control device, 20...Patient positioning device, 21...Pseudo-fluoroscopy X-ray image creation unit, 22...ROI drawing unit, 23...Similarity calculation unit, 24...Optimization calculation processing unit, 25...Image display unit, 26...Control unit

Claims

1. A positioning device that controls the position of the bed on which the subject is placed, An image acquisition unit acquires multiple fluoroscopic images of the subject taken along each of multiple imaging axes which are oriented in a different direction from the multiple movement axes along which the bed moves in translation. A creation unit that creates multiple pseudo-fluoroscopic images by projecting the three-dimensional fluoroscopic image of the subject onto each of the multiple planes corresponding to each imaging axis, A calculation unit that calculates the similarity between each perspective image and each pseudo-perspective image, The system includes an optimization unit that calculates the amount of movement of the bed such that each fluoroscopic image and each pseudo-fluoroscopic image best match for each of the multiple translational directions along each of the multiple optimization axes including the multiple imaging axes, and each of the multiple rotational directions around the multiple rotation axes, based on the aforementioned similarity. The aforementioned plurality of imaging axes are oriented in a direction along the plane formed by two of the aforementioned movement axes. The optimization axis is a positioning device comprising the plurality of imaging axes and the movement axis perpendicular to the plane.

2. The positioning device according to claim 1, wherein the plurality of imaging axes are substantially orthogonal to each other.

3. The aforementioned imaging axis has two parts, and the aforementioned movement axis has three parts. The positioning device according to claim 1, wherein the two imaging axes are oriented in a direction along a plane perpendicular to the target axis which is one of the three movement axes, and each is offset by 45 degrees from the other movement axes, and the optimization axis consists of the two imaging axes and the target axis.

4. The positioning device according to claim 1, wherein the rotation axis is the same as the optimization axis.

5. The positioning device according to claim 1, wherein the optimization unit calculates the amount of movement of the bed so that the region of interest set on the pseudo-perspective image and the region on the perspective image corresponding to the region of interest best coincide.

6. The positioning device according to claim 1, A bed control device that moves the bed based on the amount of movement calculated by the positioning device, A radiation therapy apparatus comprising: an irradiation device for irradiating a patient mounted on the aforementioned movable bed with radiation.

7. A positioning method using a positioning device that controls the position of the bed on which the subject is placed, Multiple fluoroscopic images are obtained of the subject along each of the multiple imaging axes, which are oriented in a different direction from the multiple movement axes along which the bed moves in translation. Multiple pseudo-fluoroscopic images are created by projecting the three-dimensional fluoroscopic image of the subject onto each of the multiple planes corresponding to each imaging axis. The similarity between each perspective image and each pseudo-perspective image is calculated. Based on the aforementioned similarity, the amount of movement of the bed is calculated such that each fluoroscopic image and each pseudo-fluoroscopic image best match for each of the multiple translational directions along each of the multiple optimization axes including the multiple imaging axes, and for each of the multiple rotational directions around the multiple rotation axes. The aforementioned plurality of imaging axes are oriented in a direction along the plane formed by two of the aforementioned movement axes. The optimization axis is a positioning method comprising the plurality of imaging axes and the movement axis perpendicular to the plane.

Images