JP2017202031A - Medical information processing device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To easily create a control plan for generating radiation.SOLUTION: A medical information processing device includes a detection part, a setting part, and a generation part. The detection part detects respective positions of a plurality of portions in a subject from volume data being three-dimensional medical image data. The setting part sets a region included in the subject. The generation part specifies a position corresponding to the region in the volume data, and, based on the specified position and the respective positions of the plurality of portions detected by the detection part, creates a control plan for controlling a device for generating radiation.SELECTED DRAWING: Figure 4

Description

  Embodiments described herein relate generally to a medical information processing apparatus.

  Radiotherapy (radiotherapy) is known as a treatment method for lesions such as cancer, and various devices for supporting radiotherapy are currently being developed. For example, a radiotherapy device that irradiates a lesion according to a pre-prepared treatment plan in order to irradiate the lesion in a concentrated manner and reduce adverse effects (damage, side effects, etc.) on normal tissue due to the radiation. It has been known.

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

JP 2009-183468 A

  The problem to be solved by the present invention is to provide a medical information processing apparatus that can easily create a control plan for generating radiation.

  The medical information processing apparatus according to the embodiment includes a detection unit, a setting unit, and a generation unit. The detection unit detects positions of a plurality of parts in the subject from volume data that is three-dimensional medical image data. The setting unit sets an area 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 positions of the plurality of parts detected by the detection unit. Generate a control plan.

FIG. 1 is a diagram illustrating an example of a configuration of a medical information processing system according to the first embodiment. FIG. 2 is a diagram illustrating an example of the configuration of the X-ray CT apparatus according to the first embodiment. FIG. 3 is a view for explaining three-dimensional scano image capturing by the scan control circuit according to the first embodiment. FIG. 4 is a diagram illustrating 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 part detection process by the detection function according to the first embodiment. FIG. 5B is a diagram for explaining an example of a part detection process by the detection function according to the first embodiment. FIG. 6 is a diagram for explaining an example of a part detection process by the detection function according to the first embodiment. FIG. 7 is a diagram for explaining an example of a part detection process by the detection function according to the first embodiment. FIG. 8 is a diagram illustrating 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 illustrating an example of scan range conversion by coordinate conversion according to the first embodiment. FIG. 11 is a diagram for explaining processing of the setting function according to the first embodiment. FIG. 12A is a diagram for explaining processing of the setting function according to the first embodiment. FIG. 12B is a diagram for explaining processing of the setting function according to the first embodiment. FIG. 12C is a diagram for explaining processing of the setting function according to the first embodiment. FIG. 13 is a diagram for explaining processing of the generation function according to the first embodiment. FIG. 14 is a flowchart illustrating a processing procedure performed by the treatment planning apparatus according to the first embodiment. FIG. 15 is a diagram illustrating an example of a configuration of a treatment planning apparatus according to the second embodiment.

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

(First embodiment)
FIG. 1 is a diagram illustrating an example of a configuration of a medical information processing system 100 according to the first embodiment. As shown in FIG. 1, a medical information processing system 100 according to the first embodiment includes an X-ray CT (Computed Tomography) apparatus 1, a treatment planning apparatus 2, a radiation therapy apparatus 3, a server apparatus 4, and a terminal. Device 5. The X-ray CT apparatus 1, the treatment planning apparatus 2, the radiation treatment apparatus 3, the server apparatus 4, and the terminal apparatus 5 are, for example, directly or indirectly by a hospital LAN (Local Area Network) 6 installed in the hospital. Are 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 according to the DICOM (Digital Imaging and Communications in Medicine) standard. .

  In the medical information processing system 100, for example, HIS (Hospital Information System), RIS (Radiology Information System), etc. are introduced to manage various information. For example, the terminal device 5 transmits an inspection order created along the above-described system to the X-ray CT apparatus 1 or the server apparatus 4. The X-ray CT apparatus 1 acquires patient information from an examination order received directly from the terminal apparatus 5 or a patient list (modality work list) for each modality created by the server apparatus 4 that has received the examination order. X-ray CT image data is collected every time. Then, the X-ray CT apparatus 1 transmits the collected X-ray CT image data and image data generated by performing various image processing on the X-ray CT image data to the server apparatus 4. The server apparatus 4 stores the X-ray CT image data and image data received from the X-ray CT apparatus 1, generates image data from the X-ray CT image data, and responds to an acquisition request from the terminal apparatus 5. 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 illustrated in FIG. 1, the medical information processing system 100 includes one server device 4 and one terminal device 5, but actually includes a plurality of server devices 4 and terminal devices 5. May be. The medical information processing system 100 is not limited to the X-ray CT apparatus 1, and may include other medical image diagnostic apparatuses such as an X-ray diagnostic apparatus, an MRI (Magnetic Resonance Imaging) apparatus, and an ultrasonic diagnostic apparatus. Good.

  The X-ray CT apparatus 1 is an apparatus 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 that generates a treatment plan that is a plan for performing treatment with radiation, and is, for example, a workstation. For example, the treatment planning device 2 generates a treatment plan for irradiating a patient's lesion with radiation using the X-ray CT image data captured by the X-ray CT device 1, and the generated treatment plan is used as the radiation treatment device 3. Send to. The configuration of the treatment planning device 2 will be described in detail later.

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

  The server apparatus 4 stores medical images collected by the medical image diagnostic apparatus (for example, X-ray CT image data and image data collected by the X-ray CT apparatus 1) and performs various image processing on the medical images. For example, a PACS server or the like. For example, the server device 4 receives a plurality of examination orders from the terminal device 5 arranged in each medical department, creates a patient list for each medical image diagnostic device, and uses the created patient list as each medical image diagnostic device. Send to. For example, the server device 4 receives an examination order for carrying out an examination by the X-ray CT apparatus 1 from the terminal device 5 of each clinical department, creates a patient list, and creates the created patient list as an X-ray. Transmit to the CT apparatus 1. And the server apparatus 4 memorize | stores the X-ray CT image data and image data which were collected by the X-ray CT apparatus 1, and according to the acquisition request from the treatment plan apparatus 2 or the terminal device 5, X-ray CT image data and The image data is transmitted to the treatment planning device 2 or the terminal device 5.

  The terminal device 5 is a device that is arranged in each department in the hospital and is operated by a doctor who works in each department, such as a PC (Personal Computer), a tablet PC, a PDA (Personal Digital Assistant), a mobile phone, etc. It is. For example, in the terminal device 5, medical record information such as a patient's symptom and a doctor's findings is input by a doctor. In addition, the terminal device 5 receives an inspection order for ordering an inspection by the X-ray CT apparatus 1, and transmits the input inspection order to the X-ray CT apparatus 1 and the server apparatus 4. That is, the doctor in the medical department operates the terminal device 5 to read the reception information of the patient who has visited the hospital and the information on the electronic medical record, examines the corresponding patient, and inputs the medical record information to the read electronic medical record. Then, a doctor in the medical department operates the terminal device 5 to transmit an inspection order according to whether or not the inspection by the X-ray CT apparatus 1 is necessary.

  The configuration of the X-ray CT apparatus 1 will be described with reference to FIG. FIG. 2 is a diagram illustrating 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 gantry 10, a bed 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 them to the console 30. The gantry 10 and the X-ray irradiation control circuit 11 generate X-rays. The apparatus 12 includes a detector 13, a data acquisition circuit (DAS) 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 interposed therebetween, and is rotated at a high speed in a circular orbit around the subject P by a gantry driving circuit 16 described later. An annular frame.

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

  The X-ray irradiation control circuit 11 switches the wedge 12b. The X-ray irradiation control circuit 11 adjusts the X-ray irradiation range (fan angle and cone angle) by adjusting the aperture of the collimator 12c. In addition, this embodiment may be a case where an operator manually switches a plurality of types of wedges.

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

  The X-ray tube 12 a is a vacuum tube that irradiates the subject P with an X-ray beam with a high voltage supplied by a high voltage generator (not shown). The X-ray beam is applied to the subject P as the rotating frame 15 rotates. Irradiate. 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 12 a continuously exposes X-rays around the subject P for full reconstruction or exposure that can be reconfigured for half reconstruction. It is possible to continuously expose X-rays in the irradiation range (180 degrees + fan angle). Further, the X-ray irradiation control circuit 11 can control the X-ray tube 12a to intermittently emit X-rays (pulse X-rays) at a preset position (tube position). The X-ray irradiation control circuit 11 can also modulate the intensity of the X-rays emitted 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 from the X-ray tube 12a at a range other than the specific tube position. Reduce the intensity of the emitted X-rays.

  The wedge 12b is an X-ray filter for adjusting the X-ray dose of X-rays emitted 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 irradiated from the X-ray tube 12a to the subject P have a predetermined distribution. Attenuating filter. For example, the wedge 12b is a filter obtained by processing aluminum so as to have a predetermined target angle or 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 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 driving circuit 16 rotates the rotary frame 15 to rotate the X-ray generator 12 and the detector 13 on a circular orbit around the subject P.

  The detector 13 is a two-dimensional array type detector (surface detector) that detects X-rays transmitted through the subject P, and a detection element array 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 includes 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 transmitted through the subject P over a wide range such as a range including the heart and the like.

  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, inter-channel sensitivity correction processing, and the like on the X-ray intensity distribution data detected by the detector 13. The projected data is transmitted to the console 30 described later. For example, when X-rays are continuously emitted from the X-ray tube 12a while the rotary frame 15 is rotating, the data acquisition circuit 14 collects projection data groups for the entire circumference (for 360 degrees). Further, the data collection circuit 14 associates the tube position with each collected projection data and transmits it to the console 30 described later. The tube position is information indicating the projection direction of the projection data. Note that the sensitivity correction processing between channels may be performed by the preprocessing circuit 34 described later.

  The couch device 20 is a device on which the subject P is placed, and includes a couch driving device 21 and a top plate 22 as shown in FIG. The couch driving device 21 moves the subject P into the rotary frame 15 by moving the couchtop 22 in the Z-axis direction. The top plate 22 is a plate on which the subject P is placed.

  For example, the gantry 10 executes a helical scan that rotates the rotating frame 15 while moving the top plate 22 to scan the subject P in a spiral shape. Alternatively, the gantry device 10 performs a conventional scan in which the subject P is scanned in a circular orbit by rotating the rotating frame 15 while the position of the subject P is fixed after the top plate 22 is moved. Alternatively, the gantry device 10 executes a step-and-shoot method in which a conventional scan is performed in a plurality of scan areas by moving the position of the top plate 22 at regular intervals.

  The console 30 is a device that accepts an operation of the X-ray CT apparatus 1 by an operator and reconstructs X-ray CT image data using 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, the display 32, the scan control circuit 33, the preprocessing circuit 34, the storage circuit 35, the image reconstruction circuit 36, and the processing circuit 37 are connected to be communicable with each other.

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

  The display 32 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 37, and gives various instructions from the operator via the input circuit 31. And a GUI (Graphical User Interface) for receiving various settings. The display 32 displays a plan screen for a scan plan, a screen during scanning, and the like. The display 32 displays a virtual patient image, image data, and the like including exposure information. The virtual patient image displayed on 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 driving circuit 16, the data acquisition circuit 14, and the bed driving device 21 under the control of the processing circuit 37, thereby Control the collection process. Specifically, the scan control circuit 33 controls projection data collection processing in positioning imaging for collecting positioning images (scano images) and main imaging (main scanning) for collecting images used for diagnosis. Here, in the X-ray CT apparatus 1 according to the first embodiment, a two-dimensional scanogram and a three-dimensional scanogram can be taken.

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

  The scan control circuit 33 captures a three-dimensional scanogram by collecting projection data for the entire circumference of the subject in the scanogram image capture. FIG. 3 is a view for explaining three-dimensional scano image shooting 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 a helical scan or a non-helical scan. Here, the scan control circuit 33 executes a helical scan or a non-helical scan with a lower dose than the main imaging over a wide range such as the entire chest, abdomen, the entire upper body, and the whole body of the subject. As the non-helical scan, for example, the above-described step-and-shoot scan is executed.

  As described above, the scan control circuit 33 collects projection data for the entire circumference of the subject, so that an image reconstruction circuit 36 described later reconstructs three-dimensional X-ray CT image data (volume data). As shown in FIG. 3, a positioning image can be generated from an arbitrary direction using the reconstructed volume data. Here, whether the positioning image is photographed two-dimensionally or three-dimensionally may be set arbitrarily by the operator, or may be preset according to the examination contents.

  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 acquired by the main photographing, and the storage circuit 35. To store.

  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 diagnostic projection data collected by the main imaging. The storage circuit 35 stores image data and a virtual patient image generated by an image reconstruction circuit 36 described later. Further, the storage circuit 35 appropriately stores a processing result by a 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 X-ray CT image data using the projection data stored in the storage circuit 35. Specifically, the image reconstruction circuit 36 reconstructs 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, for example, back projection processing. Further, as the back projection process, for example, a back projection process by an FBP (Filtered Back Projection) method can be cited. Alternatively, the image reconstruction circuit 36 can reconstruct the X-ray CT image data using a successive approximation method. The image reconstruction circuit 36 is an example of an 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 image data generated by various image processes in the storage circuit 35.

  The processing circuit 37 performs overall control of the X-ray CT apparatus 1 by controlling operations of the gantry 10, the couch device 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. The processing circuit 37 controls the image reconstruction circuit 36 and the image generation process in the console 30 by controlling the image reconstruction circuit 36. In addition, 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 apparatus 2 will be described with reference to FIG. FIG. 4 is a diagram illustrating an example of the configuration of the treatment planning apparatus 2 according to the first embodiment. The treatment planning device 2 is an example of a medical information processing device. Further, the treatment plan generated by the treatment planning apparatus 2 is an example of a control plan for controlling the radiation treatment apparatus 3.

  As illustrated in FIG. 4, the treatment planning apparatus 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 connected to be able to communicate with each other.

  The input circuit 41 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, and the like that are used by the operator of the treatment planning apparatus 2 to input various instructions and settings, and receives instructions and setting information received from the operator. And transferred to the processing circuit 60. For example, the input circuit 41 receives an X-ray CT image data acquisition request, a generation condition for generating a treatment plan, and the like from the operator. Further, the input circuit 41 receives an operation for designating a part on the image.

  The display 42 is a monitor that is referred to by the operator, displays image data generated from X-ray CT image data under the control of the processing circuit 60, and various instructions from the operator via the input circuit 41. Or a GUI for receiving various settings. 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 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.

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

  Note that the volume data 51 stored in the storage circuit 50 and used in the processing in the treatment planning apparatus 2 is volume data of a diagnostic image captured by the main imaging (main scan) of the X-ray CT apparatus 1. Although it is preferable, the same processing described below can be applied to volume data of a positioning image (scano image). Hereinafter, a case where the volume data 51 is volume data of a diagnostic image will be described.

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

  Further, as illustrated in FIG. 4, the processing circuit 60 executes a detection function 61, a position matching function 62, a setting function 63, and a generation function 64. Here, for example, each processing function executed by the detection function 61, the position matching function 62, the setting function 63, and the generation function 64, which are components of the processing circuit 60 shown in FIG. Is recorded in the memory 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 from which each program has been read has the functions shown in the processing circuit 60 of FIG. The detection function 61 is an example of a detection unit.

  The detection function 61 detects a plurality of parts in the subject included in the three-dimensional image data. Specifically, the detection function 61 detects a part such as an organ included in 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 anatomical feature points (Anatomical Landmark) in volume data of a diagnostic image used for diagnosis. Here, the anatomical feature point is a point indicating a feature of a part such as a specific bone, organ, blood vessel, nerve, or lumen. That is, the detection function 61 detects bones, organs, blood vessels, nerves, lumens, and the like included in the volume data by detecting anatomical feature points such as specific organs and bones. The detection function 61 can also detect positions of the head, neck, chest, abdomen, feet, etc. included in the volume data by detecting characteristic feature points of the human body. In addition, the site | part demonstrated by this embodiment means what included these positions in a bone, an organ, a blood vessel, a nerve, a lumen, etc. Hereinafter, an example of detection of a part 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 the textbook with the position of the feature point extracted from the volume data, thereby detecting the feature point extracted from the volume data. The inaccurate feature points are removed from the inside, and the positions of the feature points extracted from the volume data are optimized. Thereby, the detection function 61 detects each part of the subject included in the volume data. For example, the detection function 61 first extracts anatomical feature points included in the volume data using a supervised machine learning algorithm. Here, the above-described supervised machine learning algorithm is constructed using a plurality of teacher images in which correct anatomical feature points are manually arranged. For example, a decision forest is used. Is done.

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

  Hereinafter, an example of the part 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 part detection process by the detection function 61 according to the first embodiment. 5A and 5B, feature points are arranged two-dimensionally, but in actuality, feature points are arranged three-dimensionally. For example, the detection function 61 extracts voxels regarded as anatomical feature points (black dots in the figure) by applying a supervised machine learning algorithm to the volume data, as shown in FIG. 5A. Then, the detection function 61 fits the position of the extracted voxel to a model in which the shape and positional relationship of the part, a point unique to the part, and the like are defined, as shown in FIG. Incorrect 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 point indicating the feature of each part to the extracted feature point (voxel), and the identification code and position (coordinate) information of each feature point Is associated with the image data and stored in the storage circuit 35. For example, as illustrated 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 data subjected to the detection process, and stores it in the storage circuit 35. Specifically, the detection function 61 is obtained from at least one projection data among the projection data of the positioning image, the projection data collected under non-contrast, and the projection data collected in a state of being imaged by the contrast agent. A part of the subject included in the reconstructed volume data is detected.

For example, as shown in FIG. 6, the detection function 61 attaches information in which the identification code is 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 accompany the storage circuit 35. To store. For example, the detection function 61 extracts the coordinates of the marker 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 such information.

  For example, as shown in FIG. 6, the detection function 61 attaches information in which the identification code is associated with the coordinates of each voxel detected from the volume data (scan in the figure) of the diagnostic image to the volume data. And stored in the memory circuit 35. Here, in the scan, the detection function 61 is labeled from volume data (contrast phase in the figure) contrasted with the contrast medium and volume data not contrasted by the contrast medium (non-contrast phase in the figure). The coordinates of the point can be extracted, and an identification code can be associated with the extracted coordinates.

For example, the detection function 61 extracts the coordinates of the marker point from the volume data of the non-contrast phase out of the volume data of the diagnostic image, and as shown in FIG. (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. Further, the detection function 61 extracts the coordinates of the marker points from the volume data of the contrast phase out of the volume data of the diagnostic 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. Here, in the case where the marker points are extracted from the volume data of the contrast phase, the marker points that can be extracted by being contrasted are included. For example, the detection function 61 can extract a blood vessel or the like contrasted with a contrast agent when extracting a marker point from volume data of contrast phase. Therefore, in the case of volume data of contrast phase, the detection function 61, as shown in FIG. 6, has coordinates (x ′ ₃₁ , y ′ ₃₁ , z ′ ₃₁ ) to the coordinates of marker points such as blood vessels extracted by contrasting. Identification codes C31, C32, C33 and C34 for identifying each blood vessel are associated with the coordinates (x ′ ₃₄ , y ′ ₃₄ , z ′ ₃₄ ) and the like.

  As described above, the detection function 61 can identify which position in the volume data of the positioning image or the diagnostic image is at what position, and based on such information, the organ or the like Each part of can be detected. For example, the detection function 61 detects the position of the target part using information on the anatomical positional relationship between the target part to be detected and parts around the target part. For example, when the target region is “lung”, the detection function 61 acquires coordinate information associated with an identification code indicating the characteristics of the lung, and “rib”, “clavicle”, “heart” , Coordinate information associated with an identification code indicating a region around the “lung”, such as “diaphragm”. Then, the detection function 61 uses the information on the anatomical positional relationship between the “lung” and the surrounding site and the acquired coordinate information to extract the “lung” region in the volume data.

  For example, the detection function 61 uses the positional information such as “pulmonary apex: 2 to 3 cm above the clavicle”, “lower end of the lung: height of the seventh rib”, and the coordinate information of each part, as shown in FIG. As shown in FIG. 5, a region R1 corresponding to “lung” is extracted from the volume data. That is, the detection function 61 extracts the coordinate information of the voxel of 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 a region R2 or the like corresponding to “heart” in the volume data.

  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, etc. in the human body. Here, the positions of the head and chest in the human body can be arbitrarily defined. For example, if the seventh cervical vertebra to the lower end of the lung is defined as the chest, the detection function 61 detects from the feature point corresponding to the seventh cervical vertebra to the feature point corresponding to the lower end of the lung as the chest. The detection function 61 can detect a site by various methods other than the method using the anatomical feature points described above. For example, the detection function 61 can detect a part included in the volume data by a region expansion method based on a voxel value or the like.

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

  Here, first, a virtual patient image will be described. The virtual patient image is an actual X-ray image of a human body having a standard physique according to multiple 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 corresponding to the combination of parameters described above. Here, anatomical feature points (feature points) are stored in association with the virtual patient image stored by the storage circuit 50. For example, the human body has many anatomical feature points that can be extracted from an image based on morphological features and the like relatively easily by image processing such as pattern recognition. The position and arrangement of these many anatomical feature points in the body are roughly determined according to age, adult / child, male / female, weight, height, and other physiques.

  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 stored in association. FIG. 8 is a diagram illustrating 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 storage circuit 50 has an identification code “V1”, “V2”, and the like 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 in the three-dimensional human body image and the corresponding identification codes in association with each other. For 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 feature point coordinates in association with each other. In FIG. 8, only the lung, heart, liver, stomach, kidney, and the like are shown as organs, but in reality, the virtual patient image includes a larger number of organs, bones, blood vessels, nerves, and the like. . Further, in FIG. 8, only the feature points corresponding to the identification codes “V1”, “V2”, and “V3” are shown, but actually, more 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 using an identification code, and 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 assigned with identification codes indicating the same feature points 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 an arbitrary set of feature points.

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

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

  The position matching function 62 can convert the scan range specified on the virtual patient image into the scan range on the diagnostic image by the obtained coordinate conversion matrix “H”. For example, the position matching function 62 uses the coordinate transformation matrix “H” to change the scan range “SRV” designated 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 illustrating an example of scan range conversion by coordinate conversion according to the first embodiment. For example, as shown on the virtual patient image in 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 set the scan. The range “SRV” is converted into the scan range “SRC” on the diagnostic image.

  Thereby, for example, the scan range “SRV” set so as to include the feature point corresponding to the identification code “Vn” on the virtual patient image has the identification code “Cn” corresponding to the same feature point on the diagnostic image. Is converted and set to a scan range “SRC”. Note that the coordinate transformation matrix “H” described above may be stored in the storage circuit 50 for each subject and appropriately read 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 the range designation at the preset time, and the position / range is planned on the virtual patient image. It is possible to automatically set numerical values for 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 to perform control for easily creating a treatment plan. Such control will be described in detail later.

  In FIG. 4, the processing function performed by the detection function 61, the position matching function 62, the setting function 63, and the generation function 64 is described as being realized by a single processing circuit 60. A processing circuit may be configured by combining the processors, and the functions may be realized by each processor executing a program.

  The term “processor” used in the above description is, for example, a central preprocess unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a programmable logic device (for example, It means circuits such as a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA). The processor implements a function by reading and executing a program stored in the storage circuit 50. Instead of storing the program in the storage circuit 50, the program may be directly incorporated into the processor circuit. In this case, the processor realizes the function by reading and executing the program incorporated in the circuit. Note that each processor of the present embodiment is not limited to being configured as a single circuit for each processor, but may be configured as a single processor by combining a plurality of independent circuits to realize the function. Good. Furthermore, a 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 designates the position of a lesion or normal tissue with respect to the volume data 51 that is three-dimensional image data, the radiation irradiation path (angle), and the shape of the irradiation field. The work of obtaining information such as the irradiation dose was performed manually. For this reason, it took time and effort to create a treatment plan. Therefore, the treatment planning apparatus 2 according to the first embodiment has the functions described below in order to easily create a treatment plan.

  The storage circuit 50 according to the first embodiment stores a risk organ list 52. The risk organ list 52 is, for example, a list for storing, as risk organs, organs that are highly sensitive to radiation and are susceptible to adverse effects (side effects) due to radiation irradiation. 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.

  The risk organ list 52 may register not only the name of the organ but also, for example, a tolerable dose for each risk organ (a dose allowable amount that is unlikely to cause adverse effects due to radiation). The storage circuit 50 is an example of a storage unit.

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

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

  FIGS. 11 and 12A to 12C are diagrams for explaining 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. 12A to 12C correspond to the axial cross-sectional image, sagittal cross-sectional image, and coronal cross-sectional image of the volume data 51 of the subject P, respectively.

  As shown in FIG. 11, for example, the setting function 63 causes the display 42 to display a display screen including display areas 70, 71, and 72 when the operator starts creating a treatment plan. Here, the display area 70 is an area for displaying a button 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. In addition, when the lesioned part to be irradiated with radiation is not specified, nothing is displayed in the display area 72 as illustrated.

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

  When the prostate is set as a lesioned part, the setting function 63 highlights (black) the prostate region as shown, and also highlights the prostate button displayed in the display region 70 (network). Multiply) 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 part in the virtual patient image and the position of the part in the volume data 51 are matched by the position matching function 62, the lesion area 73 set in the virtual patient image is also displayed in each cross-sectional image. Is done.

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

  As described above, 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 an axial cross-sectional image, a sagittal cross-sectional image, and a coronal cross-sectional image are displayed as a cross-sectional image has been described. However, any MPR (Multi Planar Reconstructions) cross-sectional image may be used. For example, the volume data 51 may be displayed as a stereoscopic image on a three-dimensional display. That is, any image based on the volume data 51 of the subject P (that is, an actual image) may be used.

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

  In the above description, the case where the operator sets the lesion area 73 has been described. However, for example, the lesion area may be set using CAD (Computer Aided Diagnosis) that extracts the lesion area from the X-ray CT image.

  The generation function 64 specifies a position corresponding to the lesion area 73 in the volume data 51, and generates a treatment plan based on the specified position and the positions of the detected plurality of parts.

  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, the electron density conversion process is performed based on, for example, an electron density phantom measurement result obtained by photographing an electron density phantom in advance. And the production | generation function 64 produces | generates the treatment plan demonstrated below using the electron density spatial distribution of the subject P obtained by the electron density conversion process. 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 imaging, 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 radiation therapy apparatus 3. Here, the radiation source is a radiation source that generates radiation, and is installed to be movable with respect to the subject P placed on the bed. The radiation source, for example, irradiates the lesioned region 73 in a concentrated manner by irradiating the lesioned region 73 with radiation while rotating around the subject P around the lesioned region 73. In addition, the adverse effects on the surrounding normal tissue are reduced.

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

  FIG. 13 is a diagram for explaining 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 to be applied to the subject P using the standard value of the irradiation dose for each organ. This standard value is registered in advance by the operator, for example. For this reason, the generation function 64 specifies a standard value corresponding to the organ of the lesion area 73 set by the setting function 63, for example. Then, the generation function 64 obtains, for example, the body type of the subject P from the volume data 51 of the subject P (such as the depth of the lesioned region 73 from the body surface), and the standard value of the irradiation dose according to the obtained body type Is corrected to determine the irradiation dose to be applied to the subject P.

  Subsequently, the generation function 64 determines an irradiation path. For example, the generation function 64 refers to the risk organ list 52 and acquires risk organ information. Then, the generation function 64 determines, for example, an angle at which the risk organ does not exist on the axis 81 connecting the radiation source 80 and the center of the lesion area 73 as the irradiation path. The generation function 64 determines various angles as irradiation paths 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 area 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. For example, the generation function 64 determines the shape of the irradiation field 82 so as to include the lesion area 73 while avoiding the risk organ.

  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 irradiated to the lesion area 73 in each irradiation path.

  Thus, the generation function 64 determines the irradiation dose, irradiation path, irradiation field shape, and irradiation time. The combination of the irradiation dose, the irradiation route, the irradiation field shape, and the irradiation time determined here is a candidate for a treatment plan. The generation function 64 normally generates several patterns of treatment plan candidates. Then, the generation function 64 generates an optimal treatment plan candidate as a treatment plan by obtaining a dose distribution for each of the generated treatment plan candidates.

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

  Thus, the generation function 64 generates a treatment plan for the subject P. Then, the generation function 64 outputs the generated treatment plan for the subject P to the radiation therapy apparatus 3 and stores it in the storage circuit of the radiation therapy apparatus 3. In the above example, the case where the irradiation path and the shape of the irradiation field are determined so as to avoid the risk organ has been described, but it is not always necessary to avoid all the risk organs. For example, even if a risk organ is irradiated with radiation, it can be applied as a treatment plan as long as it is within the allowable dose range. That is, the generation function 64 may generate a treatment plan so that the dose of radiation irradiated to the risk organ becomes a predetermined value or less. In the above example, the case where the operator selects an optimal treatment plan from a plurality of patterns of treatment plan candidates has been described. However, the present invention is not limited to this. For example, when an evaluation value for comparing dose distributions for each treatment plan candidate is set, the generation function 64 automatically selects an appropriate treatment plan candidate (in accordance with the evaluation value). It may be. Further, the generation function 64 may generate only one pattern of treatment plan candidates and use this as a treatment plan for the subject P.

  FIG. 14 is a flowchart illustrating a processing procedure performed by the treatment planning apparatus 2 according to the first embodiment. The processing procedure illustrated 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 to start creating a treatment plan. For example, when the processing circuit 60 receives an instruction to start creating a treatment plan from the operator, the processing circuit 60 starts creating the treatment plan. If step S101 is negative, 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 S <b> 103, the detection function 61 detects a part from the volume data 51. For example, the detection function 61 detects the position of each of a plurality of parts from the volume data 51 of the subject P.

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

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

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

  In step S <b> 107, the setting function 63 receives an operation for setting the shape of the lesion area 73. For example, the setting function 63 adjusts the set lesioned region 73 in the axial sectional image, the sagittal sectional image, and the coronal sectional image that pass through the center of the lesioned 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, and obtains 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 a treatment plan candidate including an irradiation dose, an irradiation route, an irradiation field shape, and an irradiation time. Then, the generation function 64 generates a treatment plan for the subject P from the treatment plan candidates.

  In step S <b> 110, the generation function 64 outputs the generated treatment plan for the subject P to the radiation therapy apparatus 3 and stores it in the storage circuit of the radiation therapy apparatus 3.

  Note that the above description is merely an example, and for example, the above processing procedure does not necessarily have to be executed in the order described above. For example, the processes in steps S101 to S110 described above may be executed by changing the order as appropriate as long as 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 shooting, 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 a plurality of parts in the subject P from the volume data 51. Then, the setting function 63 sets an area included in the subject P as a lesion area 73. Then, the generation function 64 specifies a position corresponding to the lesion area 73 in the volume data 51, and generates a treatment plan based on the specified position and the positions of the plurality of parts detected by the detection function 61. To do. According to this, the treatment planning device 2 according to the first embodiment can easily create a treatment plan.

  For example, the setting function 63 sets the lesion area 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 simply by specifying the lesion area on the virtual patient image.

  In addition, for example, the generation function 64 generates a treatment plan so that the dose of radiation irradiated to a risk organ set in advance is a predetermined value or less. According to this, the operator can create a treatment plan in consideration of risk organs with 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 create a treatment plan more easily (preset). Hereinafter, the treatment planning apparatus 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 illustrating an example of the configuration of the treatment planning apparatus 2 according to the second embodiment. The treatment planning apparatus 2 according to the second embodiment has the same configuration as the treatment planning apparatus 2 shown in FIG. 4, and 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 description will focus on the differences from the first embodiment, and the same functions as those in the first embodiment are the same as those in FIG. Reference numerals are assigned and description is omitted.

  The storage circuit 50 according to the second embodiment stores a standard treatment plan 53. The 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 a standard irradiation dose of radiation irradiated to a certain organ. The irradiation route of the standard treatment plan 53 is a standard irradiation route in which a certain organ is irradiated with radiation. Moreover, 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 for irradiating a certain organ with radiation in each irradiation route. The standard treatment plan 53 is an example of a standard control plan that is a standard plan for controlling an apparatus 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 area 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 parts of the subject P detected from the volume data 51.

  For example, when the prostate is set as the lesion area 73, the generation function 64 acquires the standard treatment plan 53 for the prostate from the storage circuit 50. 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 part in the virtual patient image and the position of each part in the volume data 51 of the subject P. Specifically, the generation function 64 performs standard treatment according to the difference in the lesion area 73 between the virtual patient image and the volume data 51 and the difference in risk organ between the virtual patient image and the volume data 51. The irradiation path of the plan 53 and the shape of the irradiation field are changed. Thereby, the generation function 64 generates a treatment plan.

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

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

  For example, in the above embodiment, the case where the irradiation dose, the irradiation path, 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 thereto. 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 (Bragg peak) that releases energy by collision with electrons and nuclei in the flight process, and releases energy suddenly when the particle stops. . 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 in the vicinity of the lesion.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or a part of the distribution / integration is functionally or physically distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. Further, all or a part of each processing function performed in 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 apparatus 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 matching 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 matching 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 63 Function 64 can be performed.

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

  In addition, among the processes described in the above 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 All or a part of the above can be automatically performed by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above-described 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. The medical information processing method may be executed by being recorded on a computer-readable recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, an MO, and a DVD, and being read from the recording medium by the computer. it can.

  According to at least one embodiment described above, it is possible to easily create a control plan for generating radiation.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example 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 spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

2 treatment planning device 50 storage circuit 51 volume data 52 risk organ list 60 processing circuit 61 detection function 62 position collation function 63 setting function 64 generation function

Claims (7)

A detection unit that detects positions of a plurality of parts in the subject from volume data that is three-dimensional medical image data;
A setting unit for setting an area included in the subject;
A position corresponding to the region is identified in the volume data, and a control plan for controlling a device that generates radiation is generated based on the identified position and the positions of the plurality of parts detected by the detection unit. A medical information processing apparatus comprising: a generating unit that performs processing. The setting unit sets the region in a virtual patient image representing a standard position of each of a plurality of parts in a human body.
The medical information processing apparatus according to claim 1. The generation unit determines, based on the shape of the region, the shape of an irradiation field that is a range irradiated with the radiation for each irradiation route that is a route irradiated with the radiation, and for each determined irradiation route Generating the control plan including the shape of the field;
The medical information processing apparatus according to claim 1 or 2. The generation unit generates the control plan so that a dose of radiation applied to a preset site is a predetermined value or less,
The medical information processing apparatus according to any one of claims 1 to 3. A storage unit that stores a standard control plan that is a standard plan for controlling the device that generates the radiation for each organ;
The generation unit acquires a standard control plan for an organ corresponding to a set region from the storage unit, and changes the acquired standard control plan according to the positions of the plurality of parts detected by the detection unit. To generate the control plan,
The medical information processing apparatus according to any one of claims 1 to 4. The generation unit generates the control plan including a thickness of a water bag attached to a body surface of a subject when a proton beam or a heavy particle beam is used as the radiation.
The medical information processing apparatus 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 apparatus according to any one of claims 1 to 6.

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