Medical image generation device, treatment planning preparation device, treatment system, medical image generation method, treatment planning preparation method, medical image generation program, and treatment planning preparation program

Abstract

To provide a medical image generation device, a medical image generation method, and a medical image generation program for reducing a burden of measurement on an observation individual (a patient) and simply generating accurate four-dimensional image data concerning the observation individual, and to provide a treatment planning preparation device, a treatment system, a treatment planning preparation method, and a treatment planning preparation program.SOLUTION: The medical image generation device and the method and program related to the device are provided to acquire image data concerning an observation individual, on the other hand, learn data concerning a four-dimensional image concerning a contrast individual other than the observation individual, deform the acquired image data on the basis of the learned data, and estimate and output the deformed image data as four-dimensional image data concerning the observation individual. There are also provided the treatment planning preparation device, the treatment system, the treatment planning preparation method, and the treatment planning preparation program using the medical image generation device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a medical image generating device, a medical image generating method, and a medical image generating program for generating images used in treatment or diagnosis. The present invention also relates to a treatment planning device, a treatment system, a treatment planning method, and a treatment planning program that use images generated by the medical image generating device. [Background technology]

[0002] In general, images of the affected area are acquired and utilized in the treatment or diagnosis of patients. In particular, in radiation therapy, in which radiation is irradiated to the affected area, it is necessary to accurately grasp the position of the affected area and irradiate the radiation. For this reason, images of the affected area of ​​the patient are acquired in advance as 3D data, and treatment plans are created and the affected area is treated based on these images.

[0003] On the other hand, when the affected area of ​​a patient is located in a place where organ movement occurs due to breathing or heartbeat (chest, abdomen, etc.), it is difficult to grasp the exact location of the affected area using normal 3D images. Therefore, images are acquired taking into account the movement of the affected area due to the patient's breathing.

[0004] For example, Patent Document 1 describes a treatment planning device and method that uses four-dimensional computed tomography images (hereinafter referred to as "four-dimensional CT images") consisting of a collection of three-dimensional images taken for each of the patient's respiratory cycles, cardiac cycles, and arterial pulses. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2008-80131 A Summary of the Invention [Problem to be solved by the invention]

[0006] As described in Patent Document 1, it is known to obtain a four-dimensional CT image of a patient and use the image to create a treatment plan. However, the acquisition of the 4D CT image described in Patent Document 1 requires a longer imaging time than that required for acquiring a normal 3D CT image, and the patient is exposed to a large amount of radiation, particularly when X-rays are used. In addition, there is a technical problem that the accuracy of the 4D CT image is significantly reduced if the patient's breathing becomes disturbed. Furthermore, although measuring devices for acquiring four-dimensional image data of patients are already known, it is difficult to create an environment in which four-dimensional image data of patients can be acquired on a regular basis due to factors such as the cost of the devices themselves and the large amount of data that must be handled.

[0007] On the other hand, 4D image data is very useful for treating and diagnosing patients. Therefore, in order to solve the above-mentioned problems, it is required to reduce the burden on patients involved in measurements and to easily obtain highly accurate 4D image data.

[0008] Therefore, the objective of the present invention is to provide a medical image generation device, a medical image generation method, and a medical image generation program that reduce the burden of measurement on the observed individual (patient) and easily generate highly accurate four-dimensional image data of a specific area of ​​the observed individual. Another object of the present invention is to provide a treatment plan creation device, treatment system, treatment plan creation method, treatment method, and treatment plan creation program that use images created by the above-mentioned medical image generation device to reduce the burden on the observed individual and enable the creation of a highly accurate treatment plan or treatment of the treatment target area. [Means for solving the problem]

[0009] As a result of intensive research into the above-mentioned problems, the inventors have discovered that by using four-dimensional image data relating to a specific area of ​​a control individual other than the observed individual and transforming two-dimensional or three-dimensional image data relating to a specific area of ​​the observed individual, it is possible to reduce the burden on the observed individual and easily obtain highly accurate four-dimensional image data, and have completed the present invention. That is, the present invention provides the following medical image generating apparatus, treatment planning apparatus, treatment system, medical image generating method, treatment planning method, treatment method, medical image generating program, and treatment planning program.

[0010] To solve the above problems, the medical image generating device of the present invention is a medical image generating device that generates four-dimensional image data relating to a specific area of ​​an observed individual, and is characterized in that it comprises an image acquisition unit that acquires two-dimensional or three-dimensional image data relating to the specific area of ​​the observed individual, a learning unit that learns a plurality of data relating to four-dimensional images relating to a specific area of ​​a control individual other than the observed individual, and an image transformation processing unit that transforms the image data acquired by the image acquisition unit based on the data from the learning unit, and estimates and outputs four-dimensional image data relating to the specific area of ​​the observed individual. According to this feature, while acquiring two-dimensional or three-dimensional image data relating to a specific region of the observed individual, image deformation is performed using four-dimensional image data relating to the specific region of a control individual that has already been measured, making it possible to estimate four-dimensional image data relating to the specific region of the observed individual. This reduces the burden of measurement on the observed individual itself. In addition, by performing image deformation using the results of measurement on the control individual, it becomes possible to easily generate highly accurate four-dimensional image data relating to the specific region of the observed individual without being affected by the breathing disturbance of the observed individual, etc.

[0011] In one embodiment of the medical image generating apparatus of the present invention, the image acquiring section acquires three-dimensional image data. This feature makes it possible to reduce the computational load during image transformation compared to when two-dimensional image data is used, making it easier to generate four-dimensional image data.

[0012] Furthermore, one embodiment of the medical image generating device of the present invention is characterized in that it includes a deformation amount calculation unit that sets one two-dimensional or three-dimensional image data as a reference phase for a four-dimensional image relating to a specific region of a control individual, and calculates the deformation amount of the two-dimensional or three-dimensional image data in other phases relative to the reference phase, and the learning unit learns the deformation amount calculated by the deformation amount calculation unit and the image data of the reference phase. According to this feature, it is possible to reduce the required data capacity compared to learning the image itself as data, thereby reducing the computational load.

[0013] In addition, one embodiment of the medical image generating device of the present invention is characterized in that the learning unit creates a model that derives a group of deformation amounts for each phase from the deformation amount calculated by the deformation amount calculation unit and image data of the reference phase, and the image deformation processing unit uses the model to calculate image data for each phase in the image data acquired by the image acquisition unit, and outputs it as four-dimensional image data relating to a specific region of the observed individual. According to this feature, by calculating the amount of deformation in the reference phase image data and other respiratory phases related to the specific region of the control individual and creating a model based on these, it is possible to reduce the amount of data involved in the calculation and reduce the calculation load. In addition, since it is possible to create a model that takes into account the continuity of the data, when deforming the 3D image data related to the specific region of the observed individual, it is possible to output highly accurate 4D image data that ensures the structural continuity of the observed individual.

[0014] In addition, one embodiment of the medical image generating device of the present invention is characterized in that one two-dimensional or three-dimensional image data serving as a reference phase is set for a four-dimensional image relating to a specific region of a control individual, and a comparison calculation unit is provided which compares the image data acquired by the image acquisition unit with image data of the reference phase, and the image transformation processing unit outputs data relating to the four-dimensional image relating to the specific region of the control individual, including image data of the reference phase that is highly similar to the image data acquired by the image acquisition unit, as four-dimensional image data relating to the specific region of the observed individual. According to this feature, the image data of the specific region of the control individual is directly compared with the image data of the specific region of the observed individual, and based on the comparison result, the 4D image data of the specific region of the control individual can be output as it is as the 4D image data of the specific region of the observed individual, which makes it easy to construct an arithmetic formula (program) for image transformation processing.

[0015] To solve the above problem, the treatment planning device of the present invention is characterized by comprising the above-mentioned medical image generation device and a treatment planning unit that uses four-dimensional image data relating to a specific region of the observed individual generated by the medical image generation device to create a treatment plan for the observed individual. According to this feature, by using the images generated by the medical image generating device, it is possible to reduce the burden of measuring the observed individual and create a treatment plan based on highly accurate 4D image data, thereby improving the accuracy of the treatment plan for the observed individual.

[0016] The treatment system of the present invention for solving the above problem is characterized by comprising the above-mentioned medical image generating device and a treatment unit that uses four-dimensional image data relating to a specific region of the observed individual generated by the medical image generating device to perform treatment on the treatment target area of ​​the observed individual. According to this feature, by using the images generated by the medical image generating device, it is possible to reduce the burden of measuring the observed individual and to perform treatment on the treatment target area of ​​the observed individual based on highly accurate 4D image data. This reduces the overall treatment burden on the observed individual and also increases the accuracy of treatment on the treatment target area.

[0017] The medical image generation method of the present invention for solving the above problems is a medical image generation method for generating four-dimensional image data relating to a specific area of ​​an observed individual, and is characterized in that it comprises an image acquisition step of acquiring two-dimensional or three-dimensional image data relating to the specific area of ​​the observed individual, a learning step of learning a plurality of data relating to four-dimensional images relating to a specific area of ​​a control individual other than the observed individual, and an image transformation processing step of transforming the image data acquired by the image acquisition means based on the data learned in the learning step, and estimating and outputting four-dimensional image data relating to the specific area of ​​the observed individual. According to this feature, while acquiring two-dimensional or three-dimensional image data relating to a specific region of the observed individual, image deformation based on data relating to a four-dimensional image relating to the specific region of a control individual that has already been measured is performed, making it possible to estimate four-dimensional image data relating to the specific region of the observed individual. This reduces the burden of measurement on the observed individual itself. In addition, by performing image deformation using the results of measurement on the control individual, it becomes possible to easily generate highly accurate four-dimensional image data relating to the specific region of the observed individual without being affected by disturbances in breathing of the observed individual.

[0018] In one embodiment of the medical image generating method of the present invention, the image acquiring step is characterized in that three-dimensional image data is acquired. This feature makes it possible to reduce the computational load during image transformation compared to when two-dimensional image data is used, making it easier to generate four-dimensional image data.

[0019] Furthermore, one embodiment of the medical image generating method of the present invention includes a deformation amount calculation step in which one two-dimensional or three-dimensional image data is set as a reference phase for a four-dimensional image relating to a specific region of a control individual, and the deformation amount of the two-dimensional or three-dimensional image data in other phases relative to the reference phase is calculated, and the learning step is characterized by learning the deformation amount calculated in the deformation amount calculation step and the image data of the reference phase. According to this feature, it is possible to reduce the required data capacity compared to learning the image itself as data, thereby reducing the computational load.

[0020] Furthermore, in one embodiment of the medical image generation method of the present invention, the learning step creates a model that derives a group of deformation amounts for each phase from the deformation amounts calculated in the deformation amount calculation step and image data of the reference phase, and the image deformation processing step uses the model to calculate image data for each phase in the image data acquired in the image acquisition step, and outputs it as four-dimensional image data relating to a specific region of the observed individual. According to this feature, by calculating the amount of deformation in the reference phase image data and other respiratory phases related to the specific region of the control individual and creating a model based on these, it is possible to reduce the amount of data involved in the calculation and reduce the calculation load. In addition, since it is possible to create a model that takes into account the continuity of the data, when deforming the 3D image data related to the specific region of the observed individual, it is possible to output highly accurate 4D image data that ensures the structural continuity of the observed individual.

[0021] Furthermore, one embodiment of the medical image generating method of the present invention includes a comparison calculation step in which one two-dimensional or three-dimensional image data serving as a reference phase is set for a four-dimensional image relating to a specific region of a control individual, and the image data acquired in the image acquisition step is compared with image data of the reference phase, and the image transformation processing step is characterized in that data relating to the four-dimensional image relating to the specific region of the control individual, which includes image data of the reference phase that is highly similar to the image data acquired in the image acquisition step, is output as four-dimensional image data relating to the specific region of the observed individual. According to this feature, the image data of the specific region of the control individual is directly compared with the image data of the specific region of the observed individual, and based on the comparison result, the 4D image data of the specific region of the control individual can be output as it is as the 4D image data of the specific region of the observed individual, which makes it easy to construct an arithmetic formula (program) for image transformation processing.

[0022] The treatment planning method of the present invention for solving the above problem is characterized by comprising a medical image acquisition step of acquiring four-dimensional image data relating to a specific region of the observed individual by the above-mentioned medical image generation method, and a treatment planning step of using the four-dimensional image data acquired in the medical image acquisition step to develop a treatment plan for the observed individual. According to this feature, by using the images generated by the above medical image generation method, it is possible to reduce the burden of measuring the observed individual and create a treatment plan based on highly accurate 4D image data, thereby improving the accuracy of the treatment plan for the observed individual.

[0023] The treatment method of the present invention for solving the above problem is characterized by comprising a medical image acquisition step of acquiring four-dimensional image data relating to a specific region of an observed individual by the above-mentioned medical image generation method, and a treatment step of performing treatment on a treatment target area of ​​the observed individual using the four-dimensional image data acquired in the medical image acquisition step. According to this feature, by using the images generated by the above medical image generation method, it is possible to reduce the burden of measuring the observed individual and to perform treatment on the treatment target area of ​​the observed individual based on highly accurate 4D image data. This reduces the overall treatment burden on the observed individual and also increases the accuracy of treatment on the treatment target area.

[0024] A medical image generating program according to the present invention for solving the above problem has a feature of executing the above medical image generating method. According to this feature, a large amount of data can be quickly acquired and calculated, and a series of processes related to medical image generation can be smoothly carried out.

[0025] A treatment planning program according to the present invention for solving the above problems is characterized by executing the above treatment planning method. According to this feature, a large amount of data can be quickly acquired and calculated, and a series of processes relating to the creation of a treatment plan can be smoothly carried out. Effect of the Invention

[0026] According to the present invention, it is possible to provide a medical image generation device, a medical image generation method, and a medical image generation program that reduce the burden of measurement on an observed individual (patient) and easily generate highly accurate four-dimensional image data relating to a specific area of ​​the observed individual. Furthermore, according to the present invention, it is possible to provide a treatment plan creation device, a treatment system, a treatment plan creation method, a treatment method, and a treatment plan creation program that use images created by the above-mentioned medical image generation device to reduce the burden on the observed individual and enable the creation of a highly accurate treatment plan or treatment of the treatment target area. [Brief description of the drawings]

[0027] [Figure 1] 1 is a schematic explanatory diagram showing a structure of a medical image generating apparatus according to a first embodiment of the present invention. [Diagram 2] 4 is a flow diagram of deformation amount calculation in a deformation amount calculation unit of the medical image generating apparatus according to the first embodiment of the present invention. FIG. [Diagram 3] FIG. 2 is a diagram showing an example of a neural network constructed in a learning unit of the medical image generating apparatus according to the first embodiment of the present invention. [Figure 4] FIG. 2 is a diagram showing an example of calculation in an image transformation processing unit of the medical image generating apparatus according to the first embodiment of the present invention. [Diagram 5] 2 is a diagram showing an example of image data output from an image transformation processing unit of the medical image generating apparatus according to the first embodiment of the present invention. FIG. [Figure 6] FIG. 2 is a flow diagram of a medical image generating process using the medical image generating apparatus according to the first embodiment of the present invention. [Figure 7] 1 is a schematic explanatory diagram showing a structure of a treatment planning device according to a first embodiment of the present invention. [Figure 8] FIG. 1 is a schematic explanatory diagram showing a structure of a treatment system according to a first embodiment of the present invention. [Figure 9]FIG. 4 is a schematic explanatory diagram showing the structure of a medical image generating apparatus according to a second embodiment of the present invention. [Figure 10] FIG. 11 is a flowchart of calculations in a comparison calculation unit of a medical image generating apparatus according to a second embodiment of the present invention. [Figure 11] FIG. 11 is a flow diagram of a medical image generating process using a medical image generating apparatus according to a second embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] Hereinafter, embodiments of the medical image generating device, treatment plan creating device, treatment system, medical image generating program, and treatment plan creating program of the present invention will be described in detail. The medical image generating method, treatment plan creating method, and treatment method of the present invention will be replaced with the description of the configurations and operations of the medical image generating device, treatment plan creating device, and treatment system. Note that the medical image generating device, treatment plan creation device, treatment system, medical image generating method, treatment plan creation method, treatment method, medical image generating program, and treatment plan creation program described in the embodiments are merely examples used to explain the present invention, and are not limited to these.

[0029] In this specification, the term "observed individual" refers to an object to which the present invention is applied and which requires four-dimensional image data for treatment or diagnosis, and particularly refers to humans and non-human animals. Specific examples of non-human animals include non-human primates, cows, pigs, sheep, goats, rabbits, dogs, cats, guinea pigs, hamsters, mice, rats, and chickens. That is, the device, system, program, and method of the present invention can be applied to humans. They can also be applied to livestock and pets, excluding humans. In addition, in this specification, a "control individual" refers to an entity that is separate from the observed individual and is of the same species as the observed individual. For example, if the observed individual is a human, the control individual is also a human. Furthermore, there is no particular need for a relationship or association between the control individual and the observed individual. For example, there may be many common or similar elements (age, physique, case, etc.) between the control individual and the observed individual, or there may be almost no common or similar elements.

[0030] In this specification, the term "specific region" refers to a location where it is necessary to acquire four-dimensional image data for treatment or diagnosis, and particularly refers to a location where organ movement occurs due to breathing or heartbeat. Specific examples include the chest and abdomen. Note that it is preferable to set a location that includes an affected area (such as a tumor) that is to be treated as the "specific region", but this is not limited to this. It is also possible to set a location where it is determined that image data acquisition is necessary for diagnosis, regardless of whether there is an affected area or not, as the specific region.

[0031] [First embodiment] (Medical image generating device) FIG. 1 is a schematic explanatory diagram showing the structure of a medical image generating apparatus according to a first embodiment of the present invention. The medical image generating device 1A according to this embodiment generates four-dimensional image data relating to a specific region of an observed individual. As shown in Fig. 1, the medical image generating device 1A includes an image acquisition unit 2, a learning unit 3, and an image transformation processing unit 4. The medical image generating device 1A also includes a deformation amount calculation unit 5 as an example of a calculation means in the image transformation processing unit 4. In Fig. 1, the arrows indicated by dashed dotted lines indicate connections that allow control or input.

[0032] The image acquisition unit 2 acquires two-dimensional or three-dimensional image data relating to a specific region of the observed individual. The image data acquired by the image acquisition unit 2 is input to the image transformation processing unit 4 as basic data relating to the specific region of the observed individual.

[0033] The image acquisition unit 2 is not particularly limited as long as it can acquire two-dimensional or three-dimensional image data for a specific region of the observed individual. Here, examples of image data acquired by the image acquisition unit 2 include X-ray fluoroscopic images (X-ray CT images), MRI images, and ultrasound images. Therefore, examples of the image acquisition unit 2 include X-ray fluoroscopic devices, X-ray CT devices, MRI devices, and ultrasound image diagnostic devices as long as it can acquire the above image data. It is more preferable that the image acquisition unit 2 is capable of acquiring three-dimensional image data relating to a specific region of the observed individual, thereby reducing the computational load on the image transformation processing unit 4, which will be described later.

[0034] Here, it is preferable that the image data acquired by the image acquisition unit 2 is acquired in a state where it is not affected by organ movement due to breathing of the observed individual. More specifically, the image data is acquired by the image acquisition unit 2 in a state where the observed individual holds its breath. This makes it possible to improve the image quality of the image data acquired by the image acquisition unit 2.

[0035] In the image acquisition unit 2, only two-dimensional or three-dimensional image data relating to a specific region of the observed individual is acquired. Therefore, it is possible to complete the measurement of the observed individual in a shorter time than the conventional acquisition of four-dimensional image data. This makes it possible to reduce the burden on the observed individual.

[0036] The learning unit 3 learns a plurality of data related to a four-dimensional image of a specific region of a control individual different from the observed individual. Based on the data learned by the learning unit 3, calculations are performed in the image transformation processing unit 4, which will be described later.

[0037] The data learned by the learning unit 3 relates to the measurement results for a specific region of a control individual, and is data acquired prior to the image data of the observed individual acquired by the image acquisition unit 2. The data learned by the learning unit 3 may be obtained by performing measurements on a plurality of control individuals in a facility where an environment for acquiring four-dimensional image data is established. That is, the learning unit 3 collects four-dimensional image data that has been measured on various control individuals in various facilities and stores it in a database. Therefore, the learning unit 3 only needs to be capable of collecting four-dimensional image data, and the means for acquiring the data learned by the learning unit 3 may be disposed in a remote location separate from the medical image generating device 1A. As a result, the medical image generating device 1A itself does not need a function for acquiring data related to four-dimensional images, making it possible to reduce the cost of the device.

[0038] Furthermore, the data learned by the learning unit 3 may be data related to a four-dimensional image of a specific region of a control individual, and the type of data is not particularly limited, but it is preferable that the data be a combination of the type of data acquired by the image acquisition unit 2 and other factors besides dimensions. For example, when the image acquisition unit 2 acquires a three-dimensional X-ray fluoroscopic image (X-ray CT image), the data learned by the learning unit 3 may be a four-dimensional X-ray fluoroscopic image (X-ray CT image). When the image acquisition unit 2 acquires a three-dimensional MRI image, the data learned by the learning unit 3 may be a four-dimensional MRI image.

[0039] The image transformation processing unit 4 transforms the image data acquired by the image acquisition unit 2 based on the data from the learning unit 3, and estimates and outputs four-dimensional image data relating to a specific region of the observed individual. That is, the image transformation processing unit 4 receives the two-dimensional or three-dimensional image data acquired by the image acquisition unit 2 and performs a calculation to output it as four-dimensional image data.

[0040] The calculation means performed by the image transformation processing unit 4 is not particularly limited as long as it performs calculations based on the data from the learning unit 3. For example, it may be possible to grasp a trend in image changes over time from image data relating to a specific region of a control individual and transform the image data acquired by the image acquisition unit 2 based on this trend, or to directly compare the image data acquired by the image acquisition unit 2 with the image data relating to a specific region of a control individual and transform the image data acquired by the image acquisition unit 2 based on the comparison result.

[0041] In the following, in this embodiment, as an example of a calculation means performed by the image transformation processing unit 4, we will explain one that is equipped with a deformation amount calculation unit 5 and grasps trends in image changes over time from image data relating to a specific area of ​​a control individual.

[0042] The deformation amount calculation unit 5 sets one two-dimensional or three-dimensional image data as a reference phase for a four-dimensional image related to a specific region of a control individual, and calculates the deformation amount of the two-dimensional or three-dimensional image data in other phases relative to the reference phase. Note that, in the following, the type of image data will be described using X-ray CT images, but is not limited thereto.

[0043] FIG. 2 shows a flow of calculation of the amount of deformation in the amount of deformation calculation unit 5. As shown in FIG. First, image data (3D image data in FIG. 2) that will be the reference phase is set among the 4D CT images related to the specific region of the control individual learned by the learning unit 3. The 4D CT image is a collection of 3D image data in one respiratory cycle, and in setting the reference phase, one respiratory cycle is divided equally at any interval. For example, one respiratory cycle is divided into 10 equal parts, the beginning and end of the cycle (inhalation phase) are set as T00 and T90, and the midpoint of the cycle (exhalation phase) is set as T50, and the 4D CT image is divided for each respiratory phase (@Tn). Then, one of these is set as the reference phase (@Tref). The reference phase (@Tref) set at this time is preferably matched with the respiratory phase of image data relating to a specific region of the observed individual acquired by the image acquisition unit 2. For example, a point (@T50) corresponding to the expiratory phase in the respiratory cycle is selected as the reference phase (@Tref), and the image acquisition unit 2 acquires an image of the observed individual in a state where it has completely exhaled (expiratory phase).

[0044] Next, the amount of deformation in the image data of other respiratory phases (4DCT@T00 to 4DCT@T90 (excluding 4DCT@T50)) is calculated from the set reference phase image data (4DCT@Tref (=4DCT@T50)) among the 4D image data relating to a specific region of the control individual.

[0045] The amount of deformation can be calculated using the deformable image registration (DIR) method. DIR is a type of image registration that aligns two images, and is a processing method that performs nonlinear image deformation to align one image with the other.

[0046] An example of a process for calculating the amount of deformation using DIR will be described. In two images (image A and image B), the movement vector of the feature points in image B that correspond to the feature points in image A is calculated so that similar parts (feature points) between image A and image B match best. This movement vector is defined as the deformation amount (hereinafter also referred to as "DVF (deformable vector file)") in this embodiment. For example, the deformation amount of 4DCT@T00 with respect to 4DCT@Tref is defined as DVF@T00, and similarly the deformation amounts of 4DCT@T10 to 4DCT@T90 are defined as DVF@T10 to DVF@T90. The algorithm for calculating the amount of deformation by DIR is not particularly limited. For example, the algorithm shown in the "Guidelines for the Use of Non-rigid Image Registration in Radiation Therapy 2018 Edition (abbreviated as DIR Guidelines 2018)," which is an official guideline of the Japanese Society for Radiation Oncology (JASTRO), can be used.

[0047] Then, the deformation amount (DVF@T00-T90) calculated by the deformation amount calculation unit 5 is combined with the reference phase image data (4DCT@Tref) and is trained by the learning unit 3. This allows the data trained by the learning unit 3 in this embodiment to be smaller in volume than inputting the image data itself. This makes it possible to reduce the load on data storage and calculation.

[0048] Here, in the learning unit 3, it is preferable to use a combination of reference phase image data (4DCT@Tref) relating to a specific region of a control individual and deformation amounts at other phases (DVF@T00 to T90) as training data, and create a model that derives a group of deformation amounts for each respiratory phase.

[0049] The calculation means for creating the model is not particularly limited. One example of the calculation means is to construct a neural network and optimize the network parameters so that the relationship between the input data and the output data is closest to the relationship between the reference phase image data (4DCT@Tref) and the deformation amount (DVF@T00-T90) that is the teacher data.

[0050] FIG. 3 shows an example of a neural network constructed in the learning unit 3. As shown in Figure 3, an autoencoder uses one encoder and multiple decoders to optimize the network parameters that connect the relationship between input data and output data. For this optimization, methods such as stochastic gradient descent can be used. Based on the results of this calculation, it is possible to create a model that can derive a group of deformation amounts for each respiratory phase. In addition, calculations can be performed by associating all deformation amounts (DVF@T00-T90) with one reference phase image data (4DCT@Tref). This makes it possible to create a model that takes into account the relationship (continuity) between respiratory phases.

[0051] FIG. 4 shows an example of the calculation in the image transformation processing unit 4. As shown in Fig. 4A, in the image transformation processing unit 4, image data (3DCT) relating to a specific region of the observed individual acquired by the image acquisition unit 2 is input to the model obtained by the learning unit 3, and a transformation amount (DVF@T00-T90) corresponding to this image data (3DCT) is calculated. Then, as shown in Fig. 4B, the image data (3DCT) relating to the specific region of the observed individual is transformed using this transformation amount. As a result, image data (4DCT@T00-T90) for each respiratory phase relating to the specific region of the observed individual is obtained.

[0052] FIG. 5 shows an example of image data output from the image transformation processing unit 4. As shown in FIG. 5 shows image data corresponding to 4DCT@T00, T20, T50, T70, and T90 obtained by the image transformation processing unit 4. At this time, 4DCT@T50 in FIG. 5 corresponds to the image data (3DCT) input to the image transformation processing unit 4. Then, a collection of image data such as that shown in FIG. 5 is output as four-dimensional image data relating to a specific region of the observed individual. The model obtained by the learning unit 3 takes into consideration the relationship between respiratory phases. Therefore, as shown in Fig. 5, the 4D image data output from the image transformation processing unit 4 is output as highly accurate 4D image data in which the image data for each respiratory phase (4DCT@T00 to T90) ensures continuity in the structure of the observed individual (such as the human skeleton and organ shapes).

[0053] The four-dimensional image data relating to the specific region of the observed individual output from the image transformation processing unit 4 is stored in a storage unit (not shown) and is output to the outside as necessary.

[0054] The learning unit 3, image transformation processing unit 4, and transformation amount calculation unit 5 in this embodiment are realized by, for example, a hardware processor such as a CPU executing a program (software). In addition, some or all of these may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by a combination of software and hardware.

[0055] (Medical image generation process) 6 is a flow diagram of a medical image generation process in this embodiment. Note that the steps of this medical image generation process correspond to the medical image generation method in this embodiment.

[0056] As shown in Fig. 6, first, four-dimensional image data (4DCT) relating to a specific region of a control individual is collected and input to the deformation amount calculation unit 5. In the deformation amount calculation unit 5, reference phase image data (4DCT@Tref) of the four-dimensional image data is set, and deformation amounts (DVF@T00 to T90) in other respiratory phases are calculated. This corresponds to a deformation amount calculation step.

[0057] Next, data relating to a combination of the reference phase image data (4DCT@Tref) and the deformation amounts (DVF@T00 to T90) is input from the deformation amount calculation unit 5 to the learning unit 3. This corresponds to a learning step. At this time, the number of data to be input to the learning unit 3 is not particularly limited, but the more the number of data, the higher the generalizability and versatility of the model created by the learning unit 3 will be. The learning unit 3 uses the input data as teacher data and creates a model for deriving a group of deformation amounts for each respiratory phase. At this time, a machine learning method such as a neural network is used to perform learning and calculation while maintaining the association between one piece of input data (reference phase image data) and multiple pieces of output data (deformation amounts), thereby creating a model that takes data continuity into consideration. The model created by the learning unit 3 is then input to the image transformation processing unit 4.

[0058] On the other hand, image data (3DCT) relating to a specific region of the observed individual is acquired by the image acquisition unit 2, and this image data (3DCT) is input to the image transformation processing unit 4. This corresponds to an image acquisition step.

[0059] The image transformation processing unit 4 calculates a transformation amount (DVF@T00-90) corresponding to the image data (3DCT) using the model input from the learning unit 3. Using this transformation amount, the image data (3DCT) relating to the specific region of the observed individual is transformed to obtain image data (4DCT@T00-T90) for each respiratory phase relating to the specific region of the observed individual. The resulting set of image data is then output as four-dimensional image data relating to a specific region of the observed individual, which corresponds to an image transformation processing step. Furthermore, the four-dimensional image data relating to the specific region of the observed individual output from the image transformation processing unit 4 is stored, and the medical image generation process ends.

[0060] It is preferable to create a necessary program for a series of operations related to the medical image generation process based on the flow shown in Figure 6 and execute it by a hardware processor such as a CPU. A large amount of data can be quickly acquired and calculated, and a series of processes related to medical image generation can be smoothly performed. In addition, this program may be stored in a storage device such as an HDD (Hard Disk Drive) or a flash memory in advance, or may be stored in a removable storage medium such as a DVD or a CD-ROM, and installed in the storage device by inserting the storage medium into a drive device.

[0061] As described above, in the medical image generating device 1A in this embodiment, two-dimensional or three-dimensional image data relating to a specific region of the observed individual is acquired, while image deformation is performed using four-dimensional image data relating to a specific region of a control individual that has already been measured, making it possible to estimate four-dimensional image data relating to the specific region of the observed individual. This reduces the burden of measurement on the observed individual itself. In addition, by performing image deformation using the results of measurement on the control individual, it becomes possible to easily generate highly accurate four-dimensional image data relating to the specific region of the observed individual without being affected by the breathing disturbance of the observed individual.

[0062] In particular, in the medical image generating device 1A of this embodiment, by obtaining the reference phase image data related to a specific region of the control individual and the deformation amount in other respiratory phases and creating a model based on these, it is possible to reduce the amount of data related to calculation and reduce the calculation load. In addition, since it is possible to create a model that takes into account the continuity of the data, when deforming the 3D image data related to the specific region of the observed individual, it is possible to output highly accurate 4D image data that ensures the structural continuity of the observed individual.

[0063] (Treatment planning device) FIG. 7 is a schematic explanatory diagram showing the structure of a treatment planning device in the first embodiment of the present invention. As shown in FIG. 7, a treatment planning device 10 in this embodiment includes the above-mentioned medical image generating device 1A and a treatment planning unit 11.

[0064] The treatment planning section 11 uses four-dimensional image data relating to a specific region of the observed individual, generated by the medical image generating apparatus 1A, to generate a treatment plan for the observed individual. The treatment planning unit 11 is not particularly limited as long as it can acquire 4-dimensional image data related to a specific region of the observed individual from the medical image generating device 1A and can plan a treatment plan for the observed individual based on the 4-dimensional image data. For example, when the treatment for the observed individual is radiation therapy, the treatment planning unit 11 includes a target identification means for identifying a treatment target site (target) from the 4-dimensional image data generated by the medical image generating device 1A, a radiation irradiation simulation means for simulating the energy of the radiation to be irradiated, the irradiation direction, the shape of the irradiation range, etc. based on the position and shape of the target, and a treatment plan evaluation means based on the result of the radiation irradiation simulation means, and can plan a treatment plan using these means. In addition, the treatment plan planned by the treatment planning unit 11 is stored in a storage unit (not shown) and output to the outside as necessary. The treatment planning unit 11 is realized by, for example, a hardware processor such as a CPU executing a program (software).

[0065] In the treatment plan creation device 10, it is preferable to create a necessary program and execute it by a hardware processor such as a CPU for a series of operations related to acquisition of 4D image data (corresponding to an image acquisition step) related to a specific region of the observed individual generated by the medical image generation device 1A and the creation of a treatment plan (corresponding to a treatment plan creation step) in the treatment plan creation unit 11. This makes it possible to quickly acquire and calculate a large amount of data, and to smoothly execute a series of processes related to the creation of a treatment plan. In addition, this program may be stored in a storage device such as an HDD (Hard Disk Drive) or a flash memory in advance, or may be stored in a removable storage medium such as a DVD or a CD-ROM, and installed in the storage device by mounting the storage medium in a drive device.

[0066] Moreover, a series of operations in the treatment planning device 10 corresponds to a treatment planning method in this embodiment. For example, acquisition of four-dimensional image data related to a specific region of an observed individual generated by the medical image generating device 1A corresponds to an image acquiring step, and creation of a treatment plan in the treatment planning unit 11 corresponds to a treatment planning step.

[0067] The treatment planning device 10 in this embodiment reduces the load of measuring the observed individual by using the images generated by the medical image generating device 1A, and can create a treatment plan based on highly accurate four-dimensional image data. This makes it possible to improve the accuracy of the treatment plan for the observed individual.

[0068] (Treatment System) FIG. 8 is a schematic explanatory diagram showing the structure of a treatment system according to the first embodiment of the present invention. As shown in FIG. 8, a treatment system 20 in this embodiment includes the above-mentioned medical image generating apparatus 1A and a treatment unit .

[0069] The treatment section 21 uses four-dimensional image data relating to a specific region of the observed individual, generated by the medical image generating apparatus 1A, to perform treatment on a treatment target site of the observed individual. The treatment unit 21 is not particularly limited as long as it can obtain 4D image data related to a specific region of the observed individual from the medical image generating device 1A and perform treatment on the treatment target part of the observed individual based on the 4D image data. For example, when the treatment on the observed individual is radiation therapy, the treatment unit 21 may include a bed for fixing the observed individual, a radiation irradiation device for irradiating radiation, and an output control means for controlling the output of the radiation irradiation device, and perform treatment on the treatment target part of the observed individual based on the 4D image data generated by the medical image generating device 1A. In addition, the treatment unit 21 may be controlled based on the treatment plan by the above-mentioned treatment plan creation unit 11, and the treatment may be performed on the treatment target part of the observed individual.

[0070] Moreover, a series of operations in the treatment system 20 corresponds to the treatment method in this embodiment. For example, acquisition of four-dimensional image data related to a specific region of the observed individual generated by the medical image generating device 1A corresponds to an image acquiring step, and treatment of the treatment target site of the observed individual by the treatment unit 21 corresponds to a treatment step.

[0071] The treatment system 20 in this embodiment reduces the burden of measuring the observed individual by using the images generated by the medical image generating device 1A, and enables treatment of the treatment target area of ​​the observed individual based on highly accurate four-dimensional image data. This reduces the overall treatment burden on the observed individual and also increases the accuracy of treatment of the treatment target area.

[0072] [Second embodiment] FIG. 9 is a schematic explanatory diagram showing the structure of a medical image generating apparatus in the second embodiment of the present invention. As shown in Fig. 9, the medical image generating apparatus 1B according to the second embodiment is provided with a comparison calculation unit 6 instead of the deformation amount calculation unit 5 in the medical image generating apparatus 1A according to the first embodiment. Note that the description of the same structures as those in the first embodiment will be omitted.

[0073] The comparison calculation unit 6 is an example of a calculation means in the image transformation processing unit 4, and sets one two-dimensional or three-dimensional image data that serves as a reference phase for a four-dimensional image relating to a specific region of a control individual, and compares the image data acquired by the image acquisition unit 2 with the image data of the reference phase.

[0074] FIG. 10 shows the calculation flow in the comparison calculation unit 6. First, image data (3D image data in FIG. 10) that will be the reference phase is set among the 4D CT images related to the specific region of the control individual learned by the learning unit 3. The 4D CT image is a collection of 3D image data in one respiratory cycle, and in setting the reference phase, one respiratory cycle is divided equally at any interval. For example, one respiratory cycle is divided into 10 equal parts, the beginning and end of the cycle (inhalation phase) are set as T00 and T90, and the midpoint of the cycle (exhalation phase) is set as T50, and the 4D CT image is divided for each respiratory phase (@Tn). Then, one of these is set as the reference phase (@Tref). The reference phase (@Tref) set at this time is preferably matched with the respiratory phase of image data relating to a specific region of the observed individual acquired by the image acquisition unit 2. For example, a point (@T50) corresponding to the expiratory phase in the respiratory cycle is selected as the reference phase (@Tref), and the image acquisition unit 2 acquires an image of the observed individual in a state where it has completely exhaled (expiratory phase).

[0075] Next, the set reference phase image data (4DCT@Tref (=4DCT@T50)) is compared with three-dimensional image data (3DCT) of the specific region of the observed individual acquired by the image acquisition unit 2. As shown in Fig. 10, the three-dimensional image data (3DCT) of the specific region of the observed individual is compared with the reference phase image data (4DCT@Tref) of the 1st to nth control individuals in order, and the respective differences Δd are calculated.

[0076] The method of calculating the difference Δd is not particularly limited, and may be a method of quantifying the similarity between two images. More specifically, it may be a method of extracting representative points in two images and calculating the difference between their positions, or a method of calculating the difference in signal intensity of each pixel or each voxel between two images.

[0077] Next, the comparison result from the comparison operation unit 6 is input to the image transformation processing unit 4. Then, the image transformation processing unit 4 extracts the reference phase image data (4DCT@Tref) of the 1st to nth control individuals that has the smallest difference Δd value. The number of reference phase image data (4DCT@Tref) of the control individuals to be extracted at this time may be one, or the data having the mth difference Δd value from the smallest value may be extracted. Then, four-dimensional image data (4DCT) relating to a specific region of the control individual, which includes the extracted reference phase image data (4DCT@Tref) of the control individual, is obtained from the data of the learning unit 3, and this is output as four-dimensional image data relating to a specific region of the observed individual. In addition, when reference phase image data (4DCT@Tref) of multiple control individuals are extracted, the four-dimensional image data (4DCT) relating to the specific regions of the corresponding control individuals may be averaged and output as the four-dimensional image data relating to the specific region of the observed individual.

[0078] The four-dimensional image data relating to the specific region of the observed individual output from the image transformation processing unit 4 is stored in a storage unit (not shown) and is output to the outside as necessary.

[0079] The learning unit 3, image transformation processing unit 4, and comparison operation unit 6 in this embodiment are realized by, for example, a hardware processor such as a CPU executing a program (software). In addition, some or all of these may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by a combination of software and hardware.

[0080] (Medical image generation process) 11 is a flow diagram of a medical image generation process in this embodiment. Note that the steps of this medical image generation process correspond to the medical image generation method in this embodiment. As shown in FIG. 11, first, four-dimensional image data (4DCT) relating to a specific region of a control individual is collected and input to the learning unit 3. The learning unit 3 sets reference phase image data (4DCT@Tref) of the four-dimensional image data. This corresponds to the learning step. Then, the set reference phase image data (4DCT@Tref) of the four-dimensional image data is input to the comparison calculation unit 6.

[0081] On the other hand, image data (3DCT) relating to a specific region of the observed individual is acquired by the image acquisition unit 2, and this image data (3DCT) is input to the comparison calculation unit 6. This corresponds to an image acquisition step.

[0082] The comparison calculation unit 6 compares the image data (3DCT) acquired by the image acquisition unit 2 with the reference phase image data (4DCT@Tref) of the four-dimensional image data set by the learning unit 3 to obtain the difference Δd. This is performed for the data of the 1st to nth control individuals. This corresponds to the comparison calculation step. In this case, the number of data of the control individual is not particularly limited, but in order to reduce the calculation load, a predetermined number of control individuals having many elements in common or similar to the observed individual may be selected in advance, and reference phase image data (4DCT@Tref) of the four-dimensional image data relating to a specific region of the selected control individuals may be input to the comparison calculation unit 6.

[0083] Next, from the reference phase image data (4DCT@Tref) of the 1st to nth control individuals, the data with the smallest difference Δd value is extracted. Then, 4D image data (4DCT) of the specific region of the control individual including the extracted reference phase image data (4DCT@Tref) of the control individual is extracted from the data of the learning unit 3, and this is output as 4D image data of the specific region of the observed individual. This corresponds to an image deformation processing step. Furthermore, the four-dimensional image data relating to the specific region of the observed individual output from the image transformation processing unit 4 is stored, and the medical image generation process ends.

[0084] It is preferable to create a necessary program for a series of operations related to the medical image generation process based on the flow shown in Fig. 11 and execute it by a hardware processor such as a CPU. A large amount of data can be quickly acquired and calculated, and a series of processes related to medical image generation can be smoothly performed. In addition, this program may be stored in a storage device such as an HDD (Hard Disk Drive) or a flash memory in advance, or may be stored in a removable storage medium such as a DVD or a CD-ROM, and installed in the storage device by inserting the storage medium into a drive device.

[0085] As described above, in the medical image generating device 1B of this embodiment, as in the medical image generating device 1A shown in the first embodiment, it is possible to estimate the 4D image data of the specific region of the observed individual by performing image deformation of the image data of the specific region of the observed individual using the 4D image data of the specific region of the control individual that has already been measured. This reduces the burden of measurement on the observed individual itself. In addition, by performing image deformation using the results of measurement on the control individual, it is possible to easily generate 4D image data of the specific region of the observed individual with high accuracy without being affected by the breathing disorder of the observed individual.

[0086] In particular, in the medical image generating device 1B, in the image transformation of the image data relating to the specific region of the observed individual, the image data relating to the specific region of the control individual is directly compared with the image data relating to the specific region of the observed individual, and based on the comparison result, the 4D image data relating to the specific region of the control individual can be output as it is as the 4D image data relating to the specific region of the observed individual. This makes it easy to construct an arithmetic formula (program) for the image transformation process.

[0087] Furthermore, the medical image generating device 1B in this embodiment may be used as a medical image generating device in the treatment plan creation device or treatment system shown in the first embodiment. This reduces the load related to the measurement of the observed individual, and makes it possible to create a treatment plan and treat the observed individual based on highly accurate four-dimensional image data. This also makes it possible to improve the accuracy of the treatment plan and treatment for the observed individual.

[0088] The above-mentioned embodiments show examples of the medical image generating device, the treatment plan creating device, the treatment system, the medical image generating method, the treatment plan creating method, the treatment method, the medical image generating program, and the treatment plan creating program. The medical image generating device, the treatment plan creating device, the treatment system, the medical image generating method, the treatment plan creating method, the treatment method, the medical image generating program, and the treatment plan creating program according to the present invention are not limited to the above-mentioned embodiments, and the medical image generating device, the treatment plan creating device, the treatment system, the medical image generating method, the treatment plan creating method, the treatment method, the medical image generating program, and the treatment plan creating program according to the above-mentioned embodiments may be modified within the scope of the gist of the claims. [Industrial Applicability]

[0089] The medical image generating device, the treatment plan creating device, the treatment system, the medical image generating method, the treatment plan creating method, the treatment method, the medical image generating program, and the treatment plan creating program of the present invention are used for the treatment and diagnosis of an observed individual (patient). In particular, they are used for acquiring four-dimensional image data related to a specific region of an observed individual that is useful for treatment and diagnosis, and are suitably used for treatment and diagnosis utilizing this four-dimensional image data. [Explanation of symbols]

[0090] 1A, 1B medical image generating device, 2 image acquisition unit, 3 learning unit, 4 image deformation processing unit, 5 deformation amount calculation unit, 6 comparison calculation unit, 10 treatment plan creation device, 11 treatment plan creation unit, 20 treatment system, 21 treatment unit

Claims

1. 1. A medical image generating apparatus for generating four-dimensional image data relating to a specific region of an observed individual, comprising: an image acquisition unit for acquiring two-dimensional or three-dimensional image data relating to a specific region of the observed individual; A learning unit that learns a plurality of data related to a four-dimensional image relating to a specific region of a control individual other than the observed individual; and an image transformation processing unit that transforms image data acquired by the image acquisition unit based on data from the learning unit, and estimates and outputs four-dimensional image data relating to a specific region of the observed individual.

2. The medical image generating apparatus according to claim 1 , wherein the image acquisition unit acquires three-dimensional image data.

3. a deformation amount calculation unit that sets one two-dimensional or three-dimensional image data as a reference phase for the four-dimensional image related to the specific region of the control individual, and calculates a deformation amount of the two-dimensional or three-dimensional image data in another phase relative to the reference phase; 3. The medical image generating apparatus according to claim 1, wherein the learning unit learns the deformation amount calculated by the deformation amount calculation unit and the image data of the reference phase.

4. the learning unit creates a model for deriving a group of deformation amounts for each phase from the deformation amount calculated by the deformation amount calculation unit and image data of a reference phase; The medical image generating device of claim 3, characterized in that the image transformation processing unit uses the model to calculate image data for each phase in the image data acquired by the image acquisition unit, and outputs it as four-dimensional image data relating to a specific region of the observed individual.

5. A comparison calculation unit is provided for setting one two-dimensional or three-dimensional image data serving as a reference phase for the four-dimensional image relating to the specific region of the control individual, and comparing the image data acquired by the image acquisition unit with the image data of the reference phase; The medical image generating device of claim 1 or 2, characterized in that the image transformation processing unit outputs data regarding a four-dimensional image of a specific area of ​​the control individual, which includes image data of a reference phase that is highly similar to the image data acquired by the image acquisition unit, as four-dimensional image data of a specific area of ​​the observed individual.

6. A medical image generating device according to any one of claims 1 to 5, a treatment planning unit that uses four-dimensional image data relating to a specific region of an observed individual generated by the medical image generating device to generate a treatment plan for the observed individual.

7. A medical image generating device according to any one of claims 1 to 5, a treatment unit that performs treatment on a treatment target site of the observed individual using four-dimensional image data relating to a specific region of the observed individual generated by the medical image generating device.

8. 1. A medical image generating method for generating four-dimensional image data relating to a specific region of an observed individual, comprising: an image acquisition step of acquiring two-dimensional or three-dimensional image data relating to a specific region of the observed individual; A learning step of learning a plurality of data related to a four-dimensional image relating to a specific region of a control individual other than the observed individual; and an image transformation processing step of transforming image data acquired by the image acquisition means based on data learned by the learning step, and estimating and outputting four-dimensional image data relating to a specific region of the observed individual.

9. 9. The medical image generating method according to claim 8, wherein said image acquiring step acquires three-dimensional image data.

10. A deformation amount calculation step is provided for setting one two-dimensional or three-dimensional image data as a reference phase for the four-dimensional image relating to the specific region of the control individual, and calculating a deformation amount of the two-dimensional or three-dimensional image data in another phase relative to the reference phase; 10. The medical image generating method according to claim 8, wherein said learning step learns the image data of the deformation amount and the reference phase calculated in said deformation amount calculation step.

11. The learning step creates a model for deriving a group of deformation amounts for each phase from the deformation amount calculated in the deformation amount calculation step and image data of a reference phase; The medical image generating method according to claim 10, characterized in that the image deformation processing step uses the model to calculate image data for each phase in the image data acquired in the image acquisition step, and outputs the calculated image data as four-dimensional image data relating to a specific region of the observed individual.

12. A comparison calculation step is provided in which one two-dimensional or three-dimensional image data is set as a reference phase for the four-dimensional image relating to the specific region of the control individual, and the image data acquired in the image acquisition step is compared with the image data of the reference phase; The medical image generating method of claim 8 or 9, characterized in that the image transformation processing step outputs data regarding a four-dimensional image of a specific area of ​​the control individual, which includes image data of a reference phase that is highly similar to the image data acquired in the image acquisition step, as four-dimensional image data of a specific area of ​​the observed individual.

13. A medical image acquisition step of acquiring four-dimensional image data relating to a specific region of an observed individual by the medical image generating method according to any one of claims 8 to 12; A treatment planning method comprising: a treatment planning step of formulating a treatment plan for an observed individual by using the four-dimensional image data acquired in the medical image acquiring step.

14. A medical image generating program for executing the medical image generating method according to any one of claims 8 to 12.

15. A treatment planning program for executing the treatment planning method according to claim 13.

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