JP6797555B2 - Medical information processing device - Google Patents

Description

An embodiment of the present invention relates to a medical information processing device.

Radiation therapy (radiation therapy) is known as a treatment method for lesions such as cancer, and various devices for supporting radiation therapy are currently being developed. For example, a radiation therapy device that intensively irradiates a lesion with radiation and irradiates the lesion according to a pre-made treatment plan in order to reduce adverse effects (damage, side effects, etc.) on normal tissues. It has been known.

In addition, a treatment planning device that supports the creation of the treatment plan described above is known. For example, the treatment planning device measures the position and shape of the lesion using a medical image diagnostic device such as an X-ray CT device, and based on the measurement results, the irradiation path of radiation, the shape of the irradiation field, and the irradiation dose. Create a treatment plan that includes such things as. The treatment plan is an example of a control plan for generating radiation.

Japanese Unexamined Patent Publication No. 2009-183468

An object to be solved by the present invention is to provide a medical information processing apparatus capable of easily creating a control plan for generating radiation.

The medical information processing apparatus of the embodiment includes a detection unit, a setting unit, and a generation unit. The detection unit detects the positions of each of the plurality of sites in the subject from the volume data, which is three-dimensional medical image data. The setting unit sets a region included in the subject. The generation unit specifies a position corresponding to the region in the volume data, and controls a device that generates radiation based on the specified position and the position of each of the plurality of parts detected by the detection unit. Generate a control plan.

FIG. 1 is a diagram showing an example of a configuration of a medical information processing system according to the first embodiment. FIG. 2 is a diagram showing an example of the configuration of the X-ray CT apparatus according to the first embodiment. FIG. 3 is a diagram for explaining three-dimensional scanno image capture by the scan control circuit according to the first embodiment. FIG. 4 is a diagram showing an example of the configuration of the treatment planning apparatus according to the first embodiment. FIG. 5A is a diagram for explaining an example of a portion detection process by the detection function according to the first embodiment. FIG. 5B is a diagram for explaining an example of a portion detection process by the detection function according to the first embodiment. FIG. 6 is a diagram for explaining an example of a portion detection process by the detection function according to the first embodiment. FIG. 7 is a diagram for explaining an example of a portion detection process by the detection function according to the first embodiment. FIG. 8 is a diagram showing an example of a virtual patient image stored by the storage circuit according to the first embodiment. FIG. 9 is a diagram for explaining an example of collation processing by the position collation function according to the first embodiment. FIG. 10 is a diagram showing an example of conversion of a scan range by coordinate conversion according to the first embodiment. FIG. 11 is a diagram for explaining the processing of the setting function according to the first embodiment. FIG. 12A is a diagram for explaining the processing of the setting function according to the first embodiment. FIG. 12B is a diagram for explaining the processing of the setting function according to the first embodiment. FIG. 12C is a diagram for explaining the processing of the setting function according to the first embodiment. FIG. 13 is a diagram for explaining the processing of the generation function according to the first embodiment. FIG. 14 is a flowchart showing a processing procedure by the treatment planning apparatus according to the first embodiment. FIG. 15 is a diagram showing an example of the configuration of the treatment planning apparatus according to the second embodiment.

Hereinafter, embodiments of the medical information processing apparatus will be described in detail with reference to the accompanying drawings. In the following embodiment, a medical information processing system including a treatment planning device, which is an example of the medical information processing device, will be described.

(First Embodiment)
FIG. 1 is a diagram showing an example of the configuration of the medical information processing system 100 according to the first embodiment. As shown in FIG. 1, the medical information processing system 100 according to the first embodiment includes an X-ray CT (Computed Tomography) device 1, a treatment planning device 2, a radiotherapy device 3, a server device 4, and a terminal. A device 5 is provided. The X-ray CT device 1, the treatment planning device 2, the radiotherapy device 3, the server device 4, and the terminal device 5 are directly or indirectly connected by, for example, an in-hospital LAN (Local Area Network) 6 installed in the hospital. Connected to each other so that they can communicate with each other. For example, when a PACS (Picture Archiving and Communication System) is introduced in the medical information processing system 100, each device transmits and receives medical image data and the like to and from each other in accordance with the DICOM (Digital Imaging and Communications in Medicine) standard. ..

Further, in the medical information processing system 100, for example, HIS (Hospital Information System), RIS (Radiology Information System) and the like are introduced to manage various information. For example, the terminal device 5 transmits the inspection order created according to the above system to the X-ray CT device 1 and the server device 4. The X-ray CT device 1 acquires patient information from a patient list (modality work list) for each modality created by an examination order directly received from the terminal device 5 or a server device 4 that has received the examination order, and the patient. Collect X-ray CT image data for each. Then, the X-ray CT apparatus 1 transmits the collected X-ray CT image data and the image data generated by performing various image processing on the X-ray CT image data to the server apparatus 4. The server device 4 stores the X-ray CT image data and the image data received from the X-ray CT device 1, generates image data from the X-ray CT image data, and receives an image in response to an acquisition request from the terminal device 5. The data is transmitted to the terminal device 5. The terminal device 5 displays the image data received from the server device 4 on a monitor or the like. Hereinafter, each device will be described.

In the example shown in FIG. 1, the case where the medical information processing system 100 includes one server device 4 and one terminal device 5 is shown, but in reality, a plurality of server devices 4 and terminal devices 5 are further provided. You may. Further, the medical information processing system 100 is not limited to the X-ray CT device 1, and may include other medical image diagnostic devices such as an X-ray diagnostic device, an MRI (Magnetic Resonance Imaging) device, and an ultrasonic diagnostic device. Good.

The X-ray CT device 1 is a device that captures X-ray CT image data of a patient (subject). For example, the X-ray CT apparatus 1 collects X-ray CT image data for each patient, and the collected X-ray CT image data and image data generated by performing various image processing on the X-ray CT image data. Is transmitted to the server device 4. The configuration of the X-ray CT apparatus 1 will be described in detail later.

The treatment planning device 2 is a device for generating a treatment plan, which is a plan for performing treatment with radiation, for example, a workstation. For example, the treatment planning device 2 uses the X-ray CT image data taken by the X-ray CT device 1 to generate a treatment plan for irradiating the lesion portion of the patient with radiation, and the generated treatment plan is used as the radiation therapy device 3 Send to. The configuration of the treatment planning device 2 will be described in detail later.

The radiation therapy device 3 is a device that performs treatment with radiation. For example, the radiotherapy device 3 stores the patient's treatment plan generated by the treatment planning device 2 prior to the radiotherapy. When radiotherapy is performed, the radiotherapy device 3 performs positioning imaging using, for example, X-rays, and aligns the captured image data with the image data (X-ray CT image data) at the time of treatment planning. I do. Then, the radiation therapy device 3 irradiates the lesion portion of the patient with radiation according to the treatment plan after the alignment. The radiation therapy device 3 is an example of a device that generates radiation.

The server device 4 stores medical images (for example, X-ray CT image data and image data collected by the X-ray CT device 1) collected by the medical image diagnostic device, and performs various image processing on the medical image. It is a device for performing, for example, a PACS server or the like. For example, the server device 4 receives a plurality of examination orders from the terminal devices 5 arranged in each clinical department, creates a patient list for each medical image diagnosis device, and creates the created patient list for each medical image diagnosis device. Send to. As an example, the server device 4 receives an examination order for performing an examination by the X-ray CT device 1 from the terminal device 5 of each clinical department, creates a patient list, and X-rays the created patient list. It is transmitted to the CT device 1. Then, the server device 4 stores the X-ray CT image data and the image data collected by the X-ray CT device 1, and receives the X-ray CT image data and the X-ray CT image data in response to the acquisition request from the treatment planning device 2 or the terminal device 5. The image data is transmitted to the treatment planning device 2 or the terminal device 5.

The terminal device 5 is a device arranged in each clinical department in the hospital and operated by a doctor working in each clinical department, and is a PC (Personal Computer), a tablet PC, a PDA (Personal Digital Assistant), a mobile phone, or the like. Is. For example, in the terminal device 5, the doctor inputs medical record information such as the patient's symptoms and the doctor's findings. Further, the terminal device 5 receives an inspection order for ordering the inspection by the X-ray CT device 1, and transmits the input inspection order to the X-ray CT device 1 and the server device 4. That is, the doctor in the clinical department operates the terminal device 5 to read the reception information of the patient who visited the hospital and the information of the electronic medical record, examine the corresponding patient, and input the medical record information into the read electronic medical record. Then, the doctor in the clinical department operates the terminal device 5 to transmit the test order according to the necessity of the test by the X-ray CT device 1.

The configuration of the X-ray CT apparatus 1 will be described with reference to FIG. FIG. 2 is a diagram showing an example of the configuration of the X-ray CT apparatus 1 according to the first embodiment. As shown in FIG. 2, the X-ray CT apparatus 1 according to the first embodiment includes a pedestal 10, a sleeper apparatus 20, and a console 30.

The gantry 10 is a device that irradiates the subject P (patient) with X-rays, detects the X-rays transmitted through the subject P, and outputs the X-rays to the console 30, and includes the X-ray irradiation control circuit 11 and the X-ray generation. It has a device 12, a detector 13, a data acquisition circuit (DAS: Data Acquisition System) 14, a rotating frame 15, and a gantry drive circuit 16.

The rotating frame 15 supports the X-ray generator 12 and the detector 13 so as to face each other with the subject P in between, and rotates at high speed in a circular orbit centered on the subject P by a gantry drive circuit 16 described later. It is an annular frame.

The X-ray irradiation control circuit 11 is a device that supplies a high voltage to the X-ray tube 12a as a high voltage generating unit, and the X-ray tube 12a uses the high voltage supplied from the X-ray irradiation control circuit 11 to X. Generate a line. The X-ray irradiation control circuit 11 adjusts the X-ray dose to be irradiated to the subject P by adjusting the tube voltage and the tube current supplied to the X-ray tube 12a under the control of the scan control circuit 33 described later. ..

Further, the X-ray irradiation control circuit 11 switches the wedge 12b. Further, the X-ray irradiation control circuit 11 adjusts the X-ray irradiation range (fan angle and cone angle) by adjusting the opening degree of the collimator 12c. In this embodiment, the operator may manually switch between a plurality of types of wedges.

The X-ray generator 12 is a device that generates X-rays and irradiates the subject P with the generated X-rays, and has an X-ray tube 12a, a wedge 12b, and a collimator 12c.

The X-ray tube 12a is a vacuum tube that irradiates the subject P with an X-ray beam by a high voltage supplied by a high voltage generator (not shown), and the X-ray beam is sent to the subject P as the rotating frame 15 rotates. Irradiate against. The X-ray tube 12a generates an X-ray beam that spreads with a fan angle and a cone angle. For example, under the control of the X-ray irradiation control circuit 11, the X-ray tube 12a is continuously exposed to X-rays all around the subject P for full reconstruction, or half-reconstructable for half-reconstruction. It is possible to continuously expose X-rays within the range (180 degrees + fan angle). Further, under the control of the X-ray irradiation control circuit 11, the X-ray tube 12a can intermittently irradiate X-rays (pulse X-rays) at a preset position (tube position). The X-ray irradiation control circuit 11 can also modulate the intensity of X-rays exposed from the X-ray tube 12a. For example, the X-ray irradiation control circuit 11 increases the intensity of X-rays emitted from the X-ray tube 12a at a specific tube position, and exposes the X-ray tube 12a to a range other than the specific tube position. Decreases the intensity of the emitted X-rays.

The wedge 12b is an X-ray filter for adjusting the X-ray dose of X-rays exposed from the X-ray tube 12a. Specifically, the wedge 12b transmits the X-rays exposed from the X-ray tube 12a so that the X-rays radiated from the X-ray tube 12a to the subject P have a predetermined distribution. It is a filter that attenuates. For example, the wedge 12b is a filter obtained by processing aluminum so as to have a predetermined target angle and a predetermined thickness. The wedge 12b is also called a wedge filter or a bow-tie filter.

The collimator 12c is a slit for narrowing down the X-ray irradiation range in which the X-ray dose is adjusted by the wedge 12b under the control of the X-ray irradiation control circuit 11 described later.

The gantry drive circuit 16 rotates the rotating frame 15 to rotate the X-ray generator 12 and the detector 13 on a circular orbit centered on the subject P.

The detector 13 is a two-dimensional array type detector (surface detector) that detects X-rays that have passed through the subject P, and the detection element sequence formed by arranging X-ray detection elements for a plurality of channels is the subject P. A plurality of rows are arranged along the body axis direction (Z-axis direction shown in FIG. 2). Specifically, the detector 13 in the first embodiment has X-ray detection elements arranged in multiple rows such as 320 rows along the body axis direction of the subject P, for example, the lungs of the subject P. It is possible to detect X-rays that have passed through the subject P over a wide range, such as the area including the heart and the heart.

The data collection circuit 14 is a DAS and collects projection data from the X-ray detection data detected by the detector 13. For example, the data collection circuit 14 generates projection data by performing amplification processing, A / D conversion processing, sensitivity correction processing between channels, and the like on the X-ray intensity distribution data detected by the detector 13. The projected projection data is transmitted to the console 30 described later. For example, when X-rays are continuously exposed from the X-ray tube 12a during the rotation of the rotating frame 15, the data collection circuit 14 collects the projection data group for the entire circumference (360 degrees). Further, the data collection circuit 14 associates the tube position with each of the collected projection data and transmits the data to the console 30 described later. The tube position is information indicating the projection direction of the projection data. The sensitivity correction processing between channels may be performed by the preprocessing circuit 34, which will be described later.

The bed device 20 is a device on which the subject P is placed, and has a bed drive device 21 and a top plate 22 as shown in FIG. The sleeper drive device 21 moves the top plate 22 in the Z-axis direction to move the subject P into the rotating frame 15. The top plate 22 is a plate on which the subject P is placed.

The gantry 10 executes, for example, a helical scan in which the rotating frame 15 is rotated while the top plate 22 is moved to spirally scan the subject P. Alternatively, the gantry device 10 executes a conventional scan in which the rotating frame 15 is rotated while the position of the subject P is fixed after the top plate 22 is moved to scan the subject P in a circular orbit. Alternatively, the gantry device 10 executes a step-and-shoot method in which the position of the top plate 22 is moved at regular intervals to perform a conventional scan in a plurality of scan areas.

The console 30 is a device that accepts the operation of the X-ray CT device 1 by the operator and reconstructs the X-ray CT image data using the projection data collected by the gantry 10. As shown in FIG. 2, the console 30 includes an input circuit 31, a display 32, a scan control circuit 33, a preprocessing circuit 34, a storage circuit 35, an image reconstruction circuit 36, and a processing circuit 37. .. The input circuit 31, display 32, scan control circuit 33, preprocessing circuit 34, storage circuit 35, image reconstruction circuit 36, and processing circuit 37 are connected to each other so as to be communicable with each other.

The input circuit 31 has a mouse, keyboard, trackball, switch, button, joystick, etc. used by the operator of the X-ray CT device 1 to input various instructions and various settings, and information on the instructions and settings received from the operator. Is transferred to the processing circuit 37. For example, the input circuit 31 receives from the operator the imaging conditions for the X-ray CT image data, the reconstruction conditions for reconstructing the X-ray CT image data, the image processing conditions for the X-ray CT image data, and the like. In addition, the input circuit 31 accepts an operation for selecting a test for a subject. Further, the input circuit 31 accepts a designation operation for designating a portion on the image.

The display 32 is a monitor referenced by the operator, displays image data generated from X-ray CT image data under the control of the processing circuit 37, and gives various instructions from the operator via the input circuit 31. And display GUI (Graphical User Interface) for accepting various settings. In addition, the display 32 displays a plan screen for a scan plan, a screen during scanning, and the like. In addition, the display 32 displays a virtual patient image, image data, and the like including exposure information. The virtual patient image displayed by the display 32 will be described in detail later.

The scan control circuit 33 controls the operations of the X-ray irradiation control circuit 11, the gantry drive circuit 16, the data acquisition circuit 14, and the sleeper drive device 21 under the control of the processing circuit 37, thereby displaying the projected data on the gantry 10. Control the collection process. Specifically, the scan control circuit 33 controls the collection process of projection data in the positioning imaging for collecting the positioning image (scano image) and the main imaging (main scan) for collecting the image used for diagnosis. Here, in the X-ray CT apparatus 1 according to the first embodiment, a two-dimensional scanno image and a three-dimensional scanno image can be taken.

For example, the scan control circuit 33 fixes the X-ray tube 12a at a position of 0 degrees (a position in the front direction with respect to the subject), and continuously shoots while moving the top plate at a constant speed. Take a dimensional scanno image. Alternatively, the scan control circuit 33 fixes the X-ray tube 12a at a position of 0 degrees, moves the top plate intermittently, and intermittently repeats imaging in synchronization with the movement of the top plate to obtain two-dimensional images. Take a scanno image. Here, the scan control circuit 33 can capture a positioning image not only from the front direction with respect to the subject but also from an arbitrary direction (for example, a side surface direction).

In addition, the scan control circuit 33 captures a three-dimensional scanno image by collecting projection data for the entire circumference of the subject when capturing the scanno image. FIG. 3 is a diagram for explaining three-dimensional scanno image capture by the scan control circuit 33 according to the first embodiment. For example, as shown in FIG. 3, the scan control circuit 33 collects projection data for the entire circumference of the subject by helical scan or non-helical scan. Here, the scan control circuit 33 executes a helical scan or a non-helical scan on a wide area such as the entire chest, abdomen, upper body, and whole body of the subject at a lower dose than the main imaging. As the non-helical scan, for example, the above-mentioned step-and-shoot method scan is executed.

In this way, the scan control circuit 33 collects the projection data for the entire circumference of the subject, so that the image reconstruction circuit 36 described later reconstructs the three-dimensional X-ray CT image data (volume data). As shown in FIG. 3, it becomes possible to generate a positioning image from an arbitrary direction using the reconstructed volume data. Here, whether to capture the positioning image in two dimensions or in three dimensions may be arbitrarily set by the operator, or may be preset according to the inspection content.

Returning to FIG. 2, the preprocessing circuit 34 performs logarithmic conversion processing and correction processing such as offset correction, sensitivity correction, and beam hardening correction on the projection data generated by the data acquisition circuit 14. Generate corrected projection data. Specifically, the preprocessing circuit 34 generates corrected projection data for each of the projection data of the positioning image generated by the data acquisition circuit 14 and the projection data collected by the main shooting, and the storage circuit 35. Store in.

The storage circuit 35 stores the projection data generated by the preprocessing circuit 34. Specifically, the storage circuit 35 stores the projection data of the positioning image generated by the preprocessing circuit 34 and the projection data for diagnosis collected by the main imaging. In addition, the storage circuit 35 stores image data and a virtual patient image generated by the image reconstruction circuit 36 described later. Further, the storage circuit 35 appropriately stores the processing result by the processing circuit 37 described later. The virtual patient image and the processing result by the processing circuit 37 will be described later.

The image reconstruction circuit 36 reconstructs the X-ray CT image data using the projection data stored in the storage circuit 35. Specifically, the image reconstruction circuit 36 reconstructs the X-ray CT image data from the projection data of the positioning image and the projection data of the image used for diagnosis. Here, as the reconstruction method, there are various methods, and examples thereof include a back projection process. Further, as the back projection process, for example, a back projection process by the FBP (Filtered Back Projection) method can be mentioned. Alternatively, the image reconstruction circuit 36 can reconstruct the X-ray CT image data by using the successive approximation method. The image reconstruction circuit 36 is an example of the image reconstruction unit.

Further, the image reconstruction circuit 36 generates image data by performing various image processing on the X-ray CT image data. Then, the image reconstruction circuit 36 stores the reconstructed X-ray CT image data and the image data generated by various image processes in the storage circuit 35.

The processing circuit 37 controls the entire operation of the X-ray CT apparatus 1 by controlling the operations of the gantry 10, the sleeper apparatus 20, and the console 30. Specifically, the processing circuit 37 controls the CT scan performed on the gantry 10 by controlling the scan control circuit 33. Further, the processing circuit 37 controls the image reconstruction processing and the image generation processing in the console 30 by controlling the image reconstruction circuit 36. Further, the processing circuit 37 controls the display 32 to display various image data stored in the storage circuit 35.

The configuration of the treatment planning device 2 will be described with reference to FIG. FIG. 4 is a diagram showing an example of the configuration of the treatment planning device 2 according to the first embodiment. The treatment planning device 2 is an example of a medical information processing device. The treatment plan generated by the treatment planning device 2 is an example of a control plan for controlling the radiotherapy device 3.

As shown in FIG. 4, the treatment planning device 2 according to the first embodiment includes an input circuit 41, a display 42, a storage circuit 50, and a processing circuit 60. The input circuit 41, the display 42, the storage circuit 50, and the processing circuit 60 are communicably connected to each other.

The input circuit 41 has a mouse, keyboard, trackball, switch, button, joystick, etc. used by the operator of the treatment planning device 2 to input various instructions and various settings, and receives information on the instructions and settings received from the operator. , Transfer to the processing circuit 60. For example, the input circuit 41 receives a request for acquiring X-ray CT image data, a generation condition for generating a treatment plan, and the like from an operator. Further, the input circuit 41 accepts an operation for designating a portion on the image.

The display 42 is a monitor referred to by the operator, displays image data generated from the X-ray CT image data under the control of the processing circuit 60, and gives various instructions from the operator via the input circuit 41. And display a GUI for accepting various settings. In addition, the display 42 displays a treatment plan that is being generated or has been generated, an image based on the volume data 51, a virtual patient image, and the like. The virtual patient image will be described in detail later.

The storage circuit 50 stores various types of information used for creating a treatment plan. For example, the storage circuit 50 stores various programs for generating a treatment plan and information used by the programs.

Further, the storage circuit 50 stores the volume data 51 and the risk organ list 52. The volume data 51 is three-dimensional medical image data, for example, X-ray CT image data captured by the X-ray CT apparatus 1. For example, the volume data 51 is X-ray CT image data acquired from the server device 4 in response to an acquisition request transmitted to the server device 4 by the operator of the treatment planning device 2. The volume data 51 may be X-ray CT image data directly acquired from the X-ray CT apparatus 1. The risk organ list 52 will be described in detail later.

The volume data 51 stored in the storage circuit 50 and used in the processing in the treatment planning device 2 may be the volume data of the diagnostic image taken by the main imaging (main scanning) of the X-ray CT apparatus 1. Preferably, the same processing described below can be applied to the volume data of the positioning image (scano image). Hereinafter, the case where the volume data 51 is the volume data of the diagnostic image will be described.

The processing circuit 60 controls the entire treatment planning device 2. For example, the processing circuit 60 controls the server device 4 and the X-ray CT device 1 to transmit the acquisition request of the X-ray CT image data input by the operator. Further, the processing circuit 60 controls the display 42 to display various image data stored in the storage circuit 50.

Further, as shown in FIG. 4, the processing circuit 60 executes the detection function 61, the position matching function 62, the setting function 63, and the generation function 64. Here, for example, each processing function executed by the detection function 61, the position collation function 62, the setting function 63, and the generation function 64, which are the components of the processing circuit 60 shown in FIG. 4, is in the form of a program that can be executed by a computer. Is recorded in the storage circuit 50. The processing circuit 60 is a processor that realizes a function corresponding to each program by reading each program from the storage circuit 50 and executing the program. In other words, the processing circuit 60 in the state where each program is read out has each function shown in the processing circuit 60 of FIG. The detection function 61 is an example of a detection unit.

The detection function 61 detects each of a plurality of sites in the subject included in the three-dimensional image data. Specifically, the detection function 61 detects a part such as an organ included in the three-dimensional X-ray CT image data (volume data) taken by the X-ray CT apparatus 1. For example, the detection function 61 detects a site such as an organ based on an anatomical landmark in the volume data of a diagnostic image used for diagnosis. Here, the anatomical feature point is a point showing the feature of a specific part such as a bone, an organ, a blood vessel, a nerve, or a lumen. That is, the detection function 61 detects bones, organs, blood vessels, nerves, lumens, etc. included in the volume data by detecting anatomical feature points such as specific organs and bones. In addition, the detection function 61 can also detect the positions of the head, neck, chest, abdomen, legs, etc. included in the volume data by detecting the characteristic feature points of the human body. The site described in this embodiment means a bone, an organ, a blood vessel, a nerve, a lumen, or the like including these positions. Hereinafter, an example of detecting a portion by the detection function 61 will be described.

For example, the detection function 61 extracts anatomical feature points from the voxel values included in the volume data in the volume data of the positioning image or the volume data of the image used for diagnosis. Then, the detection function 61 compares the three-dimensional position of the anatomical feature point in the information such as a textbook with the position of the feature point extracted from the volume data to obtain the feature point extracted from the volume data. Inaccurate feature points are removed from the inside to optimize the position of the feature points extracted from the volume data. As a result, the detection function 61 detects each part of the subject included in the volume data. As an example, the detection function 61 first extracts anatomical feature points included in the volume data by using a supervised machine learning algorithm. Here, the above-mentioned supervised machine learning algorithm is constructed by using a plurality of supervised images in which correct anatomical feature points are manually arranged, and is used by, for example, a decision forest. Will be done.

Then, the detection function 61 optimizes the extracted feature points by comparing the model showing the three-dimensional positional relationship of the anatomical feature points in the body with the extracted feature points. Here, the above-mentioned model is constructed by using the above-mentioned teacher image, and for example, a point distribution model or the like is used. That is, the detection function 61 is a model in which the shape and positional relationship of the part, points unique to the part, and the like are defined based on a plurality of teacher images in which correct anatomical feature points are manually arranged, and the extracted features. By comparing with points, inaccurate feature points are removed and feature points are optimized.

Hereinafter, an example of the portion detection process by the detection function 61 will be described with reference to FIGS. 5A, 5B, 6 and 7. 5A, 5B, 6 and 7 are diagrams for explaining an example of a portion detection process by the detection function 61 according to the first embodiment. In FIGS. 5A and 5B, the feature points are arranged two-dimensionally, but in reality, the feature points are arranged three-dimensionally. For example, the detection function 61 applies a supervised machine learning algorithm to the volume data to extract voxels regarded as anatomical feature points as shown in FIG. 5A (black dots in the figure). Then, the detection function 61 fits the position of the extracted voxels to a model in which the shape and positional relationship of the parts, points unique to the parts, etc. are defined, and as shown in FIG. 5B, among the extracted voxels. Inaccurate feature points are removed and only voxels corresponding to more accurate feature points are extracted.

Here, the detection function 61 assigns an identification code for identifying the feature points indicating the features of each part to the extracted feature points (voxels), and the identification code and the position (coordinate) information of each feature point. The information associated with and is attached to the image data and stored in the storage circuit 35. For example, as shown in FIG. 5B, the detection function 61 assigns identification codes such as C1, C2, and C3 to the extracted feature points (voxels). Here, the detection function 61 attaches an identification code to each of the data subjected to the detection process and stores the data in the storage circuit 35. Specifically, the detection function 61 is performed from at least one of the projection data of the positioning image, the projection data collected under non-contrast, and the projection data collected in the state of being imaged by the contrast medium. The site of the subject contained in the reconstructed volume data is detected.

For example, as shown in FIG. 6, the detection function 61 attaches information associated with the coordinates of each voxel detected from the volume data (positioning in the figure) of the positioning image to the volume data to the storage circuit 35. Store in. As an example, the detection function 61 extracts the coordinates of the marking point from the volume data of the positioning image, and as shown in FIG. 6, "identification code: C1, coordinates (x ₁ , y ₁ , z ₁ )". , "identification code: C2, coordinates _{(x 2, y 2, z} 2) " and the like are stored in association with the volume data. Thereby, the detection function 61 can identify what kind of feature point is in which position in the volume data of the positioning image, and can detect each part such as an organ based on this information.

Further, as shown in FIG. 6, for example, the detection function 61 attaches information in which an identification code is associated with the coordinates of each voxel detected from the volume data (scan in the figure) of the image for diagnosis to the volume data. Is stored in the storage circuit 35. Here, the detection function 61 labels each of the volume data (contrast-enhanced Phase in the figure) imaged by the contrast medium and the volume data (non-contrast-enhanced Phase in the figure) not imaged by the contrast medium in the scan. The coordinates of the points can be extracted and the identification code can be associated with the extracted coordinates.

As an example, the detection function 61 extracts the coordinates of the marking point from the volume data of the non-contrast phase from the volume data of the image for diagnosis, and as shown in FIG. 6, "identification code: C1, coordinates. _{(x '1, y' 1} , z '1) "," identification code: C2, coordinates _{(x' 2, y '2} , z' 2) "storing in association with such a volume data. The detection function 61, of the volume data of the image for diagnosis, and extracting coordinates of landmark from the volume data of the contrast Phase, as shown in FIG. 6, "identification code: C1, coordinates (x _{'1 , y '1, z' 1} ) "," identification code: C2, coordinates _{(x '2, y' 2} , z '2) "storing in association with such a volume data. Here, when the labeled points are extracted from the volume data of the contrast-enhanced Phase, the labeled points that can be extracted by being contrast-enhanced are included. For example, the detection function 61 can extract blood vessels and the like imaged by a contrast agent when extracting labeled points from the volume data of the contrast-enhanced Phase. Therefore, when the volume data of the contrast Phase, detection 61, as shown in FIG. 6, the coordinates of the landmark, such as a blood vessel extracted by contrast _{(x '31, y' 31} , z '31) ~ such as the coordinate _{(x '34, y' 34} , z '34), the identification code C31 for identifying each vessel, C32, associates and C33 and C34.

As described above, the detection function 61 can identify what kind of marker point is in which position in the volume data of the positioning image or the diagnostic image, and based on this information, the organ or the like can be identified. Each part of the can be detected. For example, the detection function 61 detects the position of the target site by using the information of the anatomical positional relationship between the target site to be detected and the site around the target site. As an example, when the target site is the "lung", the detection function 61 acquires the coordinate information associated with the identification code indicating the characteristics of the lung, and also obtains the "ribs", "clavicle", and "heart". , The coordinate information associated with the identification code indicating the part around the "lung" such as the "diaphragm" is acquired. Then, the detection function 61 extracts the region of the "lung" in the volume data by using the information of the anatomical positional relationship between the "lung" and the surrounding portion and the acquired coordinate information.

For example, the detection function 61 is based on positional relationship information such as “lung apex: 2-3 cm above the clavicle” and “lower end of the lung: height of the 7th rib” and coordinate information of each part, FIG. As shown in, the region R1 corresponding to the “lung” in the volume data is extracted. That is, the detection function 61 extracts the coordinate information of the voxels in the region R1 in the volume data. The detection function 61 associates the extracted coordinate information with the part information, attaches it to the volume data, and stores it in the storage circuit 35. Similarly, as shown in FIG. 7, the detection function 61 can extract the region R2 and the like corresponding to the “heart” in the volume data.

Further, the detection function 61 detects a position included in the volume data based on a feature point that defines the position of the head, chest, or the like in the human body. Here, the positions of the head, chest, etc. in the human body can be arbitrarily defined. For example, if the area from the 7th cervical spine to the lower end of the lung is defined as the chest, the detection function 61 detects from the feature point corresponding to the 7th cervical spine to the feature point corresponding to the lower end of the lung as the chest. The detection function 61 can detect the site by various methods other than the method using the above-mentioned anatomical feature points. For example, the detection function 61 can detect a portion included in the volume data by a region expansion method or the like based on a voxel value.

The position collation function 62 collates the positions of the plurality of parts in the subject included in the three-dimensional image data with the positions of the plurality of parts in the human body included in the virtual patient image. Here, the virtual patient image is information representing a standard position of each of a plurality of parts in the human body. That is, the position collation function 62 collates the part of the subject with the position of the standard part, and stores the collation result in the storage circuit 50. For example, the position matching function 62 matches a virtual patient image in which a part of the human body is arranged at a standard position with a volume data of a subject.

Here, first, a virtual patient image will be described. The virtual patient image is an image actually taken by X-ray of a human body having a standard physique according to a plurality of combinations of parameters related to physique such as age, adult / child, male / female, weight, height, etc. It is generated in advance and stored in the storage circuit 50. That is, the storage circuit 50 stores data of a plurality of virtual patient images according to the combination of the above-mentioned parameters. Here, anatomical feature points (feature points) are associated with and stored in the virtual patient image stored by the storage circuit 50. For example, the human body has a large number of anatomical features that can be relatively easily extracted from an image based on its morphological features and the like by image processing such as pattern recognition. The positions and arrangements of these many anatomical features in the body are roughly determined according to the physique such as age, adult / child, male / female, weight, and height.

In the virtual patient image stored by the storage circuit 50, a large number of these anatomical feature points are detected in advance, and the position data of the detected feature points is attached to the data of the virtual patient image together with the identification code of each feature point. Or it is associated and stored. FIG. 8 is a diagram showing an example of a virtual patient image stored by the storage circuit 50 according to the first embodiment. For example, as shown in FIG. 8, the memory circuit 50 has identification codes “V1”, “V2” and identification codes “V1”, “V2” for identifying anatomical feature points and feature points on a three-dimensional human body including a part such as an organ. A virtual patient image associated with "V3" or the like is stored.

That is, the storage circuit 50 stores the coordinates of the feature points in the coordinate space of the three-dimensional human body image in association with the corresponding identification code. As an example, the storage circuit 50 stores the coordinates of the corresponding feature points in association with the identification code “V1” shown in FIG. Similarly, the storage circuit 50 stores the identification code and the coordinates of the feature point in association with each other. In FIG. 8, only the lungs, heart, liver, stomach, kidneys, etc. are shown as organs, but in reality, the virtual patient image includes a larger number of organs, bones, vasculars, nerves, etc. .. Further, in FIG. 8, only the feature points corresponding to the identification codes “V1”, “V2” and “V3” are shown, but in reality, a larger number of feature points are included.

The position matching function 62 matches the feature points in the volume data of the subject detected by the detection function 61 with the feature points in the virtual patient image described above by using the identification code, and matches the coordinate space of the volume data. Associate with the coordinate space of the virtual patient image. FIG. 9 is a diagram for explaining an example of collation processing by the position collation function 62 according to the first embodiment. Here, in FIG. 9, matching is performed using three sets of feature points to which identification codes indicating the same feature points are assigned between the feature points detected from the diagnostic image and the feature points detected from the virtual patient image. However, the embodiment is not limited to this, and matching can be performed using any set of feature points.

For example, as shown in FIG. 9, the position matching function 62 has feature points indicated by identification codes “V1”, “V2” and “V3” in the virtual patient image, and identification codes “C1” and “C2” in the diagnostic image. When matching with the feature points indicated by "" and "C3", the coordinate space between the images is associated by performing coordinate transformation so that the positional deviation between the same feature points is minimized. For example, as shown in FIG. 9, the position matching function 62 has the same anatomically the same feature points “V1 (x1, y1, z1), C1 (X1, Y1, Z1)” and “V2 (x2, y2, z2). ), C2 (X2, Y2, Z2) ”,“ V3 (x3, y3, z3), C3 (X3, Y3, Z3) ”so as to minimize the total“ LS ”of the positional deviation below. Find the coordinate conversion matrix "H".

LS = ((X1, Y1, Z1) -H (x1, y1, z1))
+ ((X2, Y2, Z2) -H (x2, y2, z2))
+ ((X3, Y3, Z3) -H (x3, y3, z3))

The position collation function 62 can convert the scan range designated on the virtual patient image into the scan range on the diagnostic image by the obtained coordinate transformation matrix “H”. For example, the position collation function 62 uses the coordinate transformation matrix “H” to change the scan range “SRV” specified on the virtual patient image to the scan range “SRC” on the positioning image, as shown in FIG. Can be converted. FIG. 10 is a diagram showing an example of conversion of a scan range by coordinate conversion according to the first embodiment. For example, as shown on the virtual patient image of FIG. 10, when the operator sets the scan range “SRV” on the virtual patient image, the position matching function 62 uses the coordinate transformation matrix described above to perform the set scan. Convert the range "SRV" to the scan range "SRC" on the diagnostic image.

As a result, for example, the scan range "SRV" set to include the feature points corresponding to the identification code "Vn" on the virtual patient image is the identification code "Cn" corresponding to the same feature points on the diagnostic image. Is converted to the scan range "SRC" including the above and set. The coordinate transformation matrix "H" described above may be stored in the storage circuit 50 for each subject and appropriately read out and used, or calculated each time a diagnostic image is collected. It may be the case. As described above, according to the first embodiment, the virtual patient image is displayed for specifying the range at the time of presetting, and the position / range is planned on the virtual patient image, so that it is planned after the diagnostic image is taken. It is possible to automatically set the numerical value of the position / range on the positioning image corresponding to the position / range.

Returning to the description of FIG. 4, the processing circuit 60 executes the setting function 63 and the generation function 64, and controls for easily creating the treatment plan. The control will be described in detail later.

In FIG. 4, it is assumed that the single processing circuit 60 realizes the processing functions performed by the detection function 61, the position matching function 62, the setting function 63, and the generation function 64, but a plurality of independent processing functions are realized. A processing circuit may be formed by combining the above-mentioned processors, and the functions may be realized by each processor executing a program.

The term "processor" used in the above description means, for example, a CPU (central preprocess unit), a GPU (Graphics Processing Unit), an application specific integrated circuit (ASIC), or a programmable logic device (for example, a programmable logic device). It means a circuit such as a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA). The processor realizes the function by reading and executing the program stored in the storage circuit 50. Instead of storing the program in the storage circuit 50, the program may be directly incorporated in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program embedded in the circuit. It should be noted that each processor of the present embodiment is not limited to the case where each processor is configured as a single circuit, and a plurality of independent circuits may be combined to form one processor to realize its function. Good. Further, the plurality of components in FIG. 4 may be integrated into one processor to realize the function.

By the way, conventionally, it has been troublesome to create a treatment plan. For example, when creating a treatment plan, the operator specifies the position of the lesion or normal tissue with respect to the volume data 51, which is three-dimensional image data, and the irradiation path (angle) of radiation and the shape of the irradiation field. , The work of obtaining information such as irradiation dose was performed manually. For this reason, it took time and effort to create a treatment plan. Therefore, the treatment planning device 2 according to the first embodiment has the functions described below in order to make it possible to easily create a treatment plan.

The storage circuit 50 according to the first embodiment stores the risk organ list 52. The risk organ list 52 is a list for storing, for example, an organ that is highly sensitive to radiation and is susceptible to adverse effects (side effects) by irradiation as a risk organ. For example, the names of organs such as the spine, eyeball, genital organs, digestive tract (rectum, etc.) are registered in the risk organ list 52. The risk organ list 52 stored in the storage circuit 50 is set in advance by the operator.

In the risk organ list 52, not only the names of the organs but also, for example, the tolerable dose for each risk organ (allowable amount of dose that is considered to be less likely to cause adverse effects due to radiation) may be registered. The storage circuit 50 is an example of a storage unit.

As described above, the processing circuit 60 according to the first embodiment executes the setting function 63 and the generation function 64. Of these, the setting function 63 is a function realized by the processing circuit 60 reading and executing a program corresponding to the setting function 63 from the storage circuit 50. The generation function 64 is a function realized by the processing circuit 60 reading a program corresponding to the generation function 64 from the storage circuit 50 and executing the program. Hereinafter, each of these functions will be described in detail.

The setting function 63 sets a region included in the subject. For example, the setting function 63 sets the area of the lesion in the virtual patient image displayed on the display 42. In addition, the setting function 63 adjusts the set region of the lesion in the image based on the volume data 51.

11 and 12A to 12C are diagrams for explaining the processing of the setting function 63 according to the first embodiment. FIG. 11 is an example of a display screen displayed on the display 42. Further, FIGS. 12A to 12C correspond to an axial cross-sectional image, a sagittal cross-sectional image, and a coronal cross-sectional image of the volume data 51 of the subject P, respectively.

As shown in FIG. 11, for example, when the operator starts creating a treatment plan, the setting function 63 causes the display 42 to display a display screen including the display areas 70, 71, and 72. Here, the display area 70 is an area for displaying buttons corresponding to each organ on the virtual patient image. For example, the display area 70 includes buttons on which characters such as the head, chest, abdomen, lungs, heart, liver, stomach, prostate, pelvis, etc. are displayed. The display area 71 is an area for displaying a virtual patient image. The display area 72 is an area for displaying a cross-sectional image of the volume data 51. If the lesion to be irradiated with radiation is not specified, nothing is displayed in the display area 72 as shown in the figure.

For example, the setting function 63 accepts the designation of the organ of the lesion on the virtual patient image of the display area 71. As an example, when the operator specifies the region of the prostate on the virtual patient image, the setting function 63 sets the voxels included in the region of the prostate as the lesion region 73. That is, the operator can set a plurality of voxels as the lesion region 73 simply by designating the prostate region on the virtual patient image.

When the prostate is set as a lesion, the setting function 63 highlights (paints black) the prostate area and also highlights the prostate button displayed in the display area 70 (net), as shown in the figure. (Hang). Then, the setting function 63 causes the display area 72 to display an axial cross-sectional image (FIG. 12A), a sagittal cross-sectional image (FIG. 12B), and a coronal cross-sectional image (FIG. 12C) passing through the center of the prostate. Since the position of the site in the virtual patient image and the position of the site in the volume data 51 are matched by the position collation function 62, the lesion region 73 set in the virtual patient image is also displayed in each cross-sectional image. Will be done.

Then, the setting function 63 adjusts the set lesion region 73 in each cross-sectional image of the display region 72. For example, the operator browses each cross-sectional image, determines that the lesion is only a part of the prostate, and performs an operation of reducing the lesion region 73. As an example, the operator selects the contour of the lesion region 73 on the axial cross-sectional image and performs an operation of reducing the selected contour. As a result, the setting function 63 reduces the lesion region 73, and also reduces the contour of the lesion region 73 in the sagittal cross-sectional image and the coronal cross-sectional image. When the lesion has expanded to the tissue surrounding the prostate, the operator performs an operation of expanding the lesion region 73, but this operation is the same as the operation of reducing the lesion, so a detailed explanation will be given. Omit. As a result, the operator can adjust the position and size of the lesion area 73 while viewing the actual state of the lesion on each cross-sectional image. The position and size of the lesion region 73 adjusted on each cross-sectional image are also reflected in the virtual patient image of the display region 71.

In this way, the setting function 63 sets the lesion area 73 on the virtual patient image, and adjusts the set lesion area 73 on each cross-sectional image. In the above example, the case where the axial cross-sectional image, the sagittal cross-sectional image, and the coronal cross-sectional image are displayed as the cross-sectional image has been described, but any MPR (Multi Planar Reconstructions) cross-sectional image may be used. Further, the volume data 51 is not limited to the cross-sectional image, and may be displayed on the three-dimensional display as a stereoscopic image, for example. That is, it may be an image (that is, an actual image) based on the volume data 51 of the subject P.

Further, the lesion region 73 may be adjusted by using not only the volume data 51 which is the X-ray CT image data but also the image data taken by another medical image diagnostic apparatus. For example, volume data taken by another medical diagnostic imaging apparatus such as an MRI apparatus, a PET (Positron Emission Tomography) apparatus, and a SPECT (Single Photon Emission Computed Tomography) apparatus is aligned with the volume data 51. As a result, the lesion region 73 can be adjusted on the image of another medical image diagnostic device or on the superimposed image in which the image of the other medical image diagnostic device is superimposed on the image of the volume data 51. For example, for a mammary gland lesion, the lesion region 73 can be captured more accurately by using an image of an MRI apparatus or an image of a PET apparatus.

Further, in the above description, the case where the operator sets the lesion area 73 has been described, but for example, CAD (Computer Aided Diagnosis) for extracting the lesion from the X-ray CT image may be used for setting.

The generation function 64 identifies a position corresponding to the lesion region 73 in the volume data 51, and generates a treatment plan based on the specified position and the position of each of the detected plurality of sites.

For example, the generation function 64 first performs an electron density conversion process for converting the CT value of each voxel of the volume data 51 into an electron density. Here, this electron density conversion process is performed, for example, based on the electron density phantom measurement result in which the electron density phantom is photographed in advance. Then, the generation function 64 uses the electron density spatial distribution of the subject P obtained by the electron density conversion process to generate the treatment plan described below. Although the case where the electron density spatial distribution is generated by the electron density conversion process has been described here, the present invention is not limited to this. For example, in the case of dual energy photography, the generation function 64 can directly generate an electron density spatial distribution (electron density image) without performing an electron density conversion process.

The generation function 64 generates a treatment plan including the irradiation dose, the irradiation route, the shape of the irradiation field, and the irradiation time for the radiation generated from the radiation source of the radiotherapy apparatus 3. Here, the radiation source is a radiation source that generates radiation, and is movably installed with respect to the subject P placed on the bed. Then, for example, the radiation source irradiates the lesion region 73 intensively by irradiating the lesion region 73 while rotating around the subject P around the lesion region 73. However, it reduces the adverse effects on the normal tissues around it.

In the treatment plan, the irradiation dose indicates the dose of radiation generated from the radiation source of the radiation therapy device 3. In addition, the irradiation path indicates the path of radiation emitted from the radiation source toward the lesion (target), and corresponds to the position (angle) of the radiation source. The shape of the irradiation field indicates the range in which radiation is irradiated in a certain irradiation path. The irradiation time indicates the time during which radiation is irradiated in a certain irradiation path.

FIG. 13 is a diagram for explaining the processing of the generation function 64 according to the first embodiment. FIG. 13 schematically shows various conditions defined by the treatment plan.

In the example shown in FIG. 13, the generation function 64 first determines the irradiation dose. For example, the generation function 64 determines the irradiation dose applied to the subject P by using the standard value of the irradiation dose for each organ. This standard value is registered in advance by the operator, for example. Therefore, the generation function 64 specifies, for example, a standard value corresponding to the organ of the lesion region 73 set in the setting function 63. Then, the generation function 64 obtains, for example, the body shape of the subject P (depth of the lesion region 73 from the body surface, etc.) from the volume data 51 of the subject P, and the standard value of the irradiation dose according to the obtained body shape. Is corrected to determine the irradiation dose applied to the subject P.

Subsequently, the generation function 64 determines the irradiation route. For example, the generation function 64 refers to the risk organ list 52 and acquires information on the risk organ. Then, the generation function 64 determines, for example, an angle at which no risk organ exists on the axis 81 connecting the radiation source 80 and the center of the lesion region 73 as an irradiation path. The generation function 64 determines various angles as the irradiation path by performing the same processing while changing the position of the radiation source 80.

Then, the generation function 64 determines the shape of the irradiation field 82. For example, the generation function 64 determines the shape of the irradiation field 82 for each irradiation path based on the shape of the lesion region 73. Specifically, the generation function 64 determines the shape of the irradiation field 82 in the orthogonal cross section 83 of the axis 81 for each angle determined as the irradiation path. The shape of the irradiation field 82 is controlled by a multi-leaf collimator 84 in which a plurality of diaphragm blades (multi-leaf) are individually movable. The generation function 64 determines the shape of the irradiation field 82 so as to include, for example, the lesion region 73 while avoiding risk organs.

Then, the generation function 64 determines the irradiation time. For example, the generation function 64 determines the irradiation time so that a sufficient dose of radiation is applied to the lesion region 73 in each irradiation route.

In this way, the generation function 64 determines the irradiation dose, the irradiation route, the shape of the irradiation field, and the irradiation time. The combination of the irradiation dose, the irradiation route, the shape of the irradiation field, and the irradiation time determined here is a candidate for the treatment plan. The generation function 64 usually generates several patterns of treatment plan candidates. Then, the generation function 64 generates the optimum treatment plan candidate as the treatment plan by obtaining the dose distribution for each of the generated treatment plan candidates.

For example, the generation function 64 simulates the dose distribution when radiation is applied according to the candidates of each treatment plan by using the Monte Carlo method that stochastically simulates the behavior of electrons in the electron density space distribution. The operator compares the dose distribution for each treatment plan candidate obtained by the simulation and selects the candidate that seems to be the most suitable. As a result, the generation function 64 generates the selected treatment plan candidate as the treatment plan for the subject P.

In this way, the generation function 64 generates a treatment plan for the subject P. Then, the generation function 64 outputs the generated treatment plan of the subject P to the radiotherapy device 3 and stores it in the storage circuit of the radiotherapy device 3. In the above example, the case where the irradiation route and the shape of the irradiation field are determined so as to avoid the risk organs has been described, but it is not always necessary to avoid all the risk organs. For example, even if a risk organ is irradiated, it can be applied as a treatment plan as long as it is within the tolerable dose. That is, the generation function 64 may generate a treatment plan so that the dose of radiation applied to the risk organ is equal to or less than a predetermined value. Further, in the above example, the case where the optimum treatment plan is selected by the operator from the candidates for the treatment plan of a plurality of patterns has been described, but the present invention is not limited to this. For example, when an evaluation value for comparing the dose distribution for each treatment plan candidate is set, the generation function 64 automatically selects an appropriate treatment plan candidate (according to the evaluation value). It may be. Further, the generation function 64 may be a case where only one pattern of treatment plan candidates is generated and this is used as the treatment plan for the subject P.

FIG. 14 is a flowchart showing a processing procedure by the treatment planning apparatus 2 according to the first embodiment. The processing procedure shown in FIG. 14 is started, for example, by receiving an instruction from the operator to start creating a treatment plan.

In step S101, the processing circuit 60 determines whether or not to start creating a treatment plan. For example, when the processing circuit 60 receives an instruction from the operator to start creating the treatment plan, the processing circuit 60 starts creating the treatment plan. If step S101 is denied, the processing circuit 60 does not start creating a treatment plan and is in a standby state.

When step S101 is affirmed, in step S102, the detection function 61 acquires the volume data 51 of the subject P from the storage circuit 50.

In step S103, the detection function 61 detects a site from the volume data 51. For example, the detection function 61 detects the position of each of the plurality of sites from the volume data 51 of the subject P.

In step S104, the setting function 63 causes the virtual patient image to be displayed on the display 42. For example, the setting function 63 causes the display 42 to display a virtual patient image that is matched with the volume data 51.

In step S105, the setting function 63 sets the lesion region on the virtual patient image. For example, the setting function 63 sets the voxels included in the designated area as the lesion area 73 when the area on the virtual patient image is designated by the operator.

In step S106, the setting function 63 displays an image including the lesion region 73 of the subject. For example, the setting function 63 causes the display 42 to display an axial cross-sectional image, a sagittal cross-sectional image, and a coronal cross-sectional image passing through the center of the lesion region 73.

In step S107, the setting function 63 accepts an operation of setting the shape of the lesion region 73. For example, the setting function 63 adjusts the set lesion region 73 in the axial cross-sectional image, the sagittal cross-section image, and the coronal cross-sectional image passing through the center of the lesion region 73.

In step S108, the generation function 64 performs an electron density conversion process. For example, the generation function 64 converts the CT value of each voxel of the volume data 51 into an electron density to obtain an electron density spatial distribution of the subject P.

In step S109, the generation function 64 generates a treatment plan. For example, the generation function 64 generates candidates for a treatment plan including irradiation dose, irradiation route, shape of irradiation field, and irradiation time. Then, the generation function 64 generates a treatment plan for the subject P from the candidates for the treatment plan.

In step S110, the generation function 64 outputs the generated treatment plan of the subject P to the radiotherapy device 3 and stores it in the storage circuit of the radiotherapy device 3.

The above description is merely an example, and for example, the above processing procedures may not necessarily be executed in the above-mentioned order. For example, the processes of steps S101 to S110 may be executed in an appropriate order within a range in which the processing contents do not contradict each other. For example, the electron density conversion process in step S108 may be executed after the process in step S102. Further, for example, in the case of dual energy photography, the generation function 64 may directly generate the electron density spatial distribution without performing the electron density conversion process in step S108.

As described above, in the treatment planning apparatus 2 according to the first embodiment, the detection function 61 detects the position of each of the plurality of sites in the subject P from the volume data 51. Then, the setting function 63 sets the region included in the subject P as the lesion region 73. Then, the generation function 64 identifies a position corresponding to the lesion region 73 in the volume data 51, and generates a treatment plan based on the specified position and the position of each of the plurality of sites detected by the detection function 61. To do. According to this, the treatment planning apparatus 2 according to the first embodiment can easily create a treatment plan.

For example, the setting function 63 sets the lesion region 73 on the virtual patient image. According to this, the operator can set a plurality of voxels included in the lesion area as the lesion area 73 only by designating the lesion area on the virtual patient image.

Further, for example, the generation function 64 generates a treatment plan so that the dose of radiation applied to a preset risk organ is equal to or less than a predetermined value. According to this, the operator can create a treatment plan considering risk organs by a simple operation.

(Second Embodiment)
The treatment planning apparatus 2 according to the second embodiment stores in advance a standard treatment plan, which is a standard treatment plan, in order to more easily create the treatment plan (preset). Hereinafter, the treatment planning device 2 according to the second embodiment will be described.

The configuration of the treatment planning apparatus 2 according to the second embodiment will be described with reference to FIG. FIG. 15 is a diagram showing an example of the configuration of the treatment planning device 2 according to the second embodiment. The treatment planning device 2 according to the second embodiment has the same configuration as the treatment planning device 2 shown in FIG. 4, the point that the storage circuit 50 further stores the standard treatment plan 53, and the processing in the generation function 64. Some are different. Therefore, in the second embodiment, the points different from those in the first embodiment will be mainly described, and the points having the same functions as the configuration described in the first embodiment are the same as those in FIG. The reference numerals are given and the description thereof will be omitted.

The storage circuit 50 according to the second embodiment stores the standard treatment plan 53. This standard treatment plan 53 is a standard treatment plan for each organ, for example, a treatment plan generated on a virtual patient image. That is, the irradiation dose of the standard treatment plan 53 is the standard irradiation dose of the radiation applied to a certain organ. Further, the irradiation route of the standard treatment plan 53 is a standard irradiation route in which a certain organ is irradiated with radiation. Further, the shape of the irradiation field of the standard treatment plan 53 is the shape of the standard irradiation field of the radiation irradiated to a certain organ in each irradiation route. The irradiation time of the standard treatment plan 53 is a standard irradiation time in which a certain organ is irradiated with radiation in each irradiation route. The standard treatment plan 53 is an example of a standard control plan, which is a standard plan for controlling a device that generates radiation.

The generation function 64 according to the second embodiment acquires the standard treatment plan 53 of the organ corresponding to the set lesion region 73 from the storage circuit 50. Then, the generation function 64 generates a treatment plan by changing the acquired standard treatment plan 53 according to the positions of the plurality of sites of the subject P detected from the volume data 51.

For example, the generation function 64 acquires a standard treatment plan 53 for the prostate from the memory circuit 50 when the prostate is set as the lesion region 73. Then, the generation function 64 changes the acquired standard treatment plan 53 based on the volume data 51 of the subject P.

For example, the generation function 64 changes the standard treatment plan 53 based on the positional relationship between the position of each site in the virtual patient image and the position of each site in the volume data 51 of the subject P. Specifically, the generation function 64 is a standard treatment according to the difference in the lesion region 73 between the virtual patient image and the volume data 51 and the difference in the risk organ between the virtual patient image and the volume data 51. The irradiation path and the shape of the irradiation field of Plan 53 are changed. As a result, the generation function 64 generates a treatment plan.

As described above, the treatment planning device 2 according to the second embodiment can more easily create the treatment plan by storing the standard treatment plan 53 in advance.

(Other embodiments)
In addition to the above-described embodiments, various different embodiments may be implemented.

For example, in the above embodiment, the case where the irradiation dose, the irradiation route, the shape of the irradiation field, and the irradiation time are determined as the treatment plan has been described, but the embodiment is not limited to this. For example, the generation function 64 may determine the thickness of the water bag attached to the subject P in addition to the above conditions. For example, when a proton beam or a heavy particle beam is used as radiation, a water bag is attached (built-up) to the body surface of the subject P. This is because the proton beam or heavy particle beam has the property of releasing energy by collision with electrons or atomic nuclei in the flight process, and rapidly releasing energy near the point where the particle stops (Bragg peak). .. In this case, the generation function 64 determines the thickness of the water bag used for build-up so that the maximum energy is released near the lesion.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically distributed in arbitrary units according to various loads and usage conditions. It can be integrated and configured. Further, each processing function performed by each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by wired logic.

For example, in the above embodiment, the case where the processing circuit 60 of the treatment planning device 2 executes the detection function 61 and the position matching function 62 has been described, but the present invention is not limited to this. For example, the detection function 61 and the position collation function 62 may be executed by the processing circuit 37 of the X-ray CT apparatus 1. In this case, the processing circuit 60 of the treatment planning apparatus 2 acquires the volume data after the detection processing and the position collation processing are performed by the processing circuit 37 from the processing circuit 37, and uses the acquired information to generate the setting function 63 and the generation. Function 64 can be executed.

Further, for example, in the above-described embodiment, the case where the process of generating the treatment plan is executed by the treatment planning device 2 has been described, but the embodiment is not limited to this. For example, the process of generating the above treatment plan is executed by another medical image diagnostic device such as an X-ray CT device 1, an MRI device, and an ultrasonic diagnostic device if the setting function 63 and the generation function 64 are provided. It may be executed by another medical information processing device such as a personal computer or a workstation.

Further, among the processes described in the above-described embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed. It is also possible to automatically perform all or part of the above by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified.

Further, the medical information processing method described in the above embodiment can be realized by executing a medical information processing program prepared in advance on a computer such as a personal computer or a workstation. This ultrasonic imaging method can be distributed via a network such as the Internet. Further, this medical information processing method can also be executed by recording on a computer-readable recording medium such as a hard disk, flexible disk (FD), CD-ROM, MO, or DVD, and reading from the recording medium by the computer. it can.

According to at least one embodiment described above, a control plan for generating radiation can be easily created.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, as well as in the scope of the invention described in the claims and the equivalent scope thereof.

2 Treatment planning device 50 Storage circuit 51 Volume data 52 Risk organ list 60 Processing circuit 61 Detection function 62 Position matching function 63 Setting function 64 Generation function

Claims (8)

A detection unit that detects the position of each of a plurality of parts in a subject from volume data, which is three-dimensional medical image data.
When the operation of setting the irradiation target area to be irradiated and adjusting the shape of the irradiation target area corresponding to the part designated by the operator from the plurality of parts is accepted. Further, a setting unit that adjusts the shape of the irradiation target area according to the received operation, and a setting unit.
When the operation of setting the shape of the irradiation target area is not accepted , the position corresponding to the irradiation target area is specified in the volume data.
When the operation of setting the shape of the irradiation target area is accepted, the position corresponding to the irradiation target area of the shape adjusted according to the operation is specified.
A medical information processing device including a generation unit that generates a control plan that controls a device that generates radiation based on a specified position and a position of each of the plurality of parts detected by the detection unit. The setting unit sets the irradiation target area in a virtual patient image showing a standard position of each of a plurality of parts in the human body.
The medical information processing device according to claim 1. Based on the shape of the irradiation target region, the generation unit determines the shape of the irradiation field, which is the range to be irradiated with the radiation, for each irradiation path, which is the path to which the radiation is irradiated, and determines the irradiation path. Generate the control plan, including the shape of each irradiation field,
The medical information processing device according to claim 1 or 2. The generation unit generates the control plan so that the dose of radiation radiated to a preset site is equal to or less than a predetermined value.
The medical information processing device according to any one of claims 1 to 3. For each organ, a storage unit for storing a standard control plan, which is a standard plan for controlling the device for generating the radiation, is further provided.
The generation unit acquires a standard control plan of the organ corresponding to the set irradiation target region from the storage unit, and the acquired standard control plan is used according to the position of each of the plurality of parts detected by the detection unit. By changing the above, the control plan is generated.
The medical information processing device according to any one of claims 1 to 4. The generator generates the control plan including the thickness of the water bag attached to the body surface of the subject when a proton beam or a heavy particle beam is used as the radiation.
The medical information processing device according to any one of claims 1 to 5. The generation unit stores the generated treatment plan in a predetermined storage unit.
The medical information processing device according to any one of claims 1 to 6. The setting unit accepts an operation of adjusting the three-dimensional shape of the irradiation target region in an arbitrary cross-sectional image passing through the center of the irradiation target region.
The medical information processing device according to any one of claims 1 to 7.

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