JP2017202315A - Medical image diagnostic device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a medical image diagnostic device capable of efficiently performing portion detection.SOLUTION: A medical image diagnostic device includes a generation part, a storage part, a detection part, and an output control part. The generation part generates three-dimensional image data of a subject. The storage part stores a plurality of anatomical feature points in the subject in association with a plurality of groups. The detection part selects at least one group of the plurality of groups based on set examination information and a type of scan to be executed, and, based on the anatomical feature point corresponding to the selected group, detects a portion of the subject corresponding to at least one group. The output control part executes control so as to output the information indicating the detected portion.SELECTED DRAWING: Figure 2

Description

  Embodiments described herein relate generally to a medical image diagnostic apparatus.

  Conventionally, in imaging using an X-ray CT apparatus (CT; Computed Tomography), a scan protocol for collecting data is set according to the examination site and examination contents. The scan protocol described above is set by selecting a scan protocol candidate suitable for the examination region and examination contents from a plurality of preset scan protocol candidates and adjusting the preset conditions as appropriate. Here, each scanning protocol candidate is classified according to, for example, physique such as age, adult / child, male / female, weight, height, and inspection purpose, and a positioning image (scan image) for setting an imaging range. ) Shooting range, scan image shooting angle, tube voltage and tube current when shooting the positioning image, scan mode of the main shooting (scan) to collect data used for diagnosis, scan position, scan range, scan Various conditions such as a tube voltage and a tube current and a position and a range of image reconstruction are set in advance.

  Here, a technique is known in which a part included in a scanogram is recognized and used for the various condition settings described above. In such a method, for example, a scanogram is collected in three dimensions by helical scanning, a part is automatically recognized from the geometric features of the collected scanogram, and an imaging condition suitable for the recognized part is set.

JP 2008-012229 A Japanese Patent Laid-Open No. 2005-212514 JP 2000-107169 A

  The problem to be solved by the present invention is to provide a medical image diagnostic apparatus that enables efficient site detection.

  The medical image diagnostic apparatus according to the embodiment includes a generation unit, a storage unit, a detection unit, and an output control unit. The generation unit generates three-dimensional image data of the subject. The storage unit stores a plurality of anatomical feature points in the subject in association with a plurality of groups. The detection unit selects at least one group out of the plurality of groups based on the set examination information and the type of scan to be performed, and based on the anatomical feature points corresponding to the selected group, A part of the subject corresponding to at least one group is detected. The output control unit controls to output information indicating the detected part.

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 unit according to the first embodiment. FIG. 4A is a diagram for explaining an example of a part detection process by the detection function according to the first embodiment. FIG. 4B is a diagram for explaining an example of a part detection process by the detection function according to the first embodiment. FIG. 5 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 illustrating an example of a virtual patient image stored by the storage circuit according to the first embodiment. FIG. 8 is a diagram for explaining an example of collation processing by the position collation function according to the first embodiment. FIG. 9 is a diagram illustrating a scan range conversion example by coordinate conversion according to the first embodiment. FIG. 10A is a diagram illustrating an example of correspondence information according to the first embodiment. FIG. 10B is a diagram illustrating an example of correspondence information according to the first embodiment. FIG. 11 is a diagram for explaining an example of priorities of detection parts according to the first embodiment. FIG. 12A is a diagram illustrating an example of processing by the display control function according to the first embodiment. FIG. 12B is a diagram illustrating an example of processing by the display control function according to the first embodiment. FIG. 12C is a diagram illustrating an example of processing by the display control function according to the first embodiment. FIG. 13 is a diagram illustrating an example of processing by the display control function according to the first embodiment. FIG. 14 is a diagram for explaining an example of adjustment of the scan range according to the first embodiment. FIG. 15 is a flowchart illustrating a processing procedure performed by the X-ray CT apparatus according to the first embodiment. FIG. 16A is a flowchart illustrating a processing procedure by the X-ray CT apparatus according to the first embodiment. FIG. 16B is a flowchart illustrating a processing procedure by the X-ray CT apparatus according to the first embodiment. FIG. 17 is a diagram illustrating an example of processing by the display control function according to the second embodiment. FIG. 18 is a diagram illustrating an example of processing by the display control function according to the second embodiment.

  Hereinafter, a medical image diagnostic apparatus according to the present application will be described with reference to the accompanying drawings. Hereinafter, a medical information processing system including an X-ray CT (Computed Tomography) apparatus as a medical image diagnostic apparatus will be described as an example. In the medical information processing system 100 shown in FIG. 1, only one server device and one terminal device are shown, but actually, a plurality of server devices and terminal devices can be included. The medical information processing system 100 can also include other medical image diagnostic apparatuses such as an X-ray diagnostic apparatus, an MRI (Magnetic Resonance Imaging) apparatus, and an ultrasonic diagnostic apparatus.

(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 illustrated in FIG. 1, the medical information processing system 100 according to the first embodiment includes an X-ray CT apparatus 1, a server apparatus 2, and a terminal apparatus 3. The X-ray CT apparatus 1, the server apparatus 2, and the terminal apparatus 3 are in a state in which they can communicate with each other directly or indirectly through, for example, a hospital LAN (Local Area Network) 4 installed in a hospital. It has become. 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 images and the like according to 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), etc. are introduced to manage various information. For example, the terminal device 3 transmits an inspection order created along the above-described system to the X-ray CT apparatus 1 or the server apparatus 2. The X-ray CT apparatus 1 acquires patient information from an examination order received directly from the terminal apparatus 3 or a patient list (modality work list) for each modality created by the server apparatus 2 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 2. The server apparatus 2 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 3. Data is transmitted to the terminal device 3. The terminal device 3 displays the image data received from the server device 2 on a monitor or the like. Hereinafter, each device will be described.

  The terminal device 3 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 3, medical record information such as a patient's symptom and a doctor's findings is input by a doctor. Further, the terminal device 3 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 2. That is, the doctor in the medical department operates the terminal device 3 to read the reception information of the patient who has visited the hospital and information on the electronic medical record, examines the corresponding patient, and inputs the medical record information to the read electronic medical record. Then, the doctor in the medical department operates the terminal operation 3 according to the necessity of the examination by the X-ray CT apparatus 1 and transmits the examination order.

  The server apparatus 2 stores medical images (for example, X-ray CT image data and image data collected by the X-ray CT apparatus 1) collected by the medical image diagnostic apparatus, and performs various image processing on the medical images. For example, a PACS server or the like. For example, the server device 2 receives a plurality of examination orders from the terminal device 3 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 apparatus 2 receives an examination order for performing an examination by the X-ray CT apparatus 1 from the terminal apparatus 3 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 2 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 terminal device 3, X-ray CT image data and image data are stored in the terminal apparatus. 3 to send.

  The X-ray CT apparatus 1 collects X-ray CT image data for each patient and uses the collected X-ray CT image data and image data generated by performing various image processing on the X-ray CT image data as a server. Transmit to device 2. 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 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 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, 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 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 10 performs a step-and-shoot method in which the position of the top plate 22 is moved at regular intervals and a conventional scan is performed in a plurality of scan areas.

  The console 30 is a device that accepts the operation of the X-ray CT apparatus by the operator and reconstructs 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 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 that is referred to by the operator, and displays image data generated from the X-ray CT image data to the operator under the control of the processing circuit 37, or the operator via the input circuit 31. A GUI (Graphical User Interface) for accepting various instructions, various settings, and the like is displayed. The display 32 displays a plan screen for a scan plan, a screen being scanned, and the like. Further, 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 the photographing for collecting the positioning image (scano image) and the main photographing (scanning) for collecting the image 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 storage circuit 35 stores correspondence information described later. The virtual patient image, the processing result by the processing circuit 37, and the correspondence information will be described later. The storage circuit 35 is an example of a storage unit described in the claims.

  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.

  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 image reconstruction circuit 36 is an example of a generation unit described in the claims.

  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.

  Further, as shown in FIG. 2, the processing circuit 37 executes a detection function 37a, a position matching function 37b, a display control function 37c, and a control function 37d. Here, for example, each processing function executed by the detection function 37a, the position matching function 37b, the display control function 37c, and the control function 37d, which are components of the processing circuit 37 shown in FIG. Is recorded in the memory circuit 35. The processing circuit 37 is a processor that implements a function corresponding to each program by reading each program from the storage circuit 35 and executing the program. In other words, the processing circuit 37 in a state where each program is read has each function shown in the processing circuit 37 of FIG. The detection function 37a described in the present embodiment is an example of a detection unit described in the claims. The display control function 37d is an example of an output control unit described in the claims.

  The term “processor” used in the above description is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an application specific integrated circuit (ASIC), a programmable logic device ( For example, 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 implements a function by reading and executing a program stored in the storage circuit. Instead of storing the program in the storage circuit, the program may be directly incorporated in 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.

  The detection function 37a detects a plurality of parts in the subject included in the three-dimensional image data. Specifically, the detection function 37 a detects a part such as an organ included in the three-dimensional X-ray CT image data (volume data) reconstructed by the image reconstruction circuit 36. For example, the detection function 37a detects a site such as an organ based on an anatomical feature point (Anatomical Landmark) for at least one of the volume data of the positioning image and the volume data of the 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 37a 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 37a 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 bones, organs, blood vessels, nerves, lumens, and the like. Hereinafter, an example of detection of a part by the detection function 37a will be described.

  For example, the detection function 37a 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 37a 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 37a detects each part of the subject included in the volume data. For example, the detection function 37a 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 supervised images in which correct anatomical feature points are manually arranged. For example, a decision forest is used. Is done.

  Then, the detection function 37a optimizes the extracted feature points by comparing a model indicating the three-dimensional positional relationship of 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 is used. That is, the detection function 37a includes a model in which the shape and positional relationship of the 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 the 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 37a will be described with reference to FIGS. 4A, 4B, 5 and 6. FIG. 4A, 4B, 5 and 6 are diagrams for explaining an example of a part detection process by the detection function 37a according to the first embodiment. 4A and 4B, feature points are arranged two-dimensionally. Actually, feature points are arranged three-dimensionally. For example, the detection function 37a 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. 4A. Then, the detection function 37a 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, etc. 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 37a gives 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 shown in FIG. 4B, the detection function 37a gives identification codes such as C1, C2, and C3 to the extracted feature points (voxels). Here, the detection function 37 a attaches an identification code to each data subjected to the detection process, and stores the identification code in the storage circuit 35. Specifically, the detection function 37a 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. 5, the detection function 37a 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 store the storage circuit 35. To store. For example, the detection function 37a extracts the coordinates of the feature points from the volume data of the positioning image, and, as shown in FIG. 5, “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 37a 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.

  Further, for example, as shown in FIG. 5, the detection function 37a attaches to the volume data 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. And stored in the memory circuit 35. Here, the detection function 37a is characterized by 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) in the scan. The coordinates of the point can be extracted, and an identification code can be associated with the extracted coordinates.

For example, the detection function 37a extracts the coordinates of the feature points from the volume data of the non-contrast phase from 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 37a extracts the coordinates of the feature points from the volume data of the contrast phase out of the volume data of the diagnostic image, and as shown in FIG. 5, “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, when feature points are extracted from volume data of contrast phase, feature points that can be extracted by being contrasted are included. For example, when the feature point is extracted from the volume data of the contrast phase, the detection function 37a can extract a blood vessel or the like contrasted with the contrast agent. Therefore, in the case of contrast phase volume data, as shown in FIG. 5, the detection function 37a has coordinates (x ′ ₃₁ , y ′ ₃₁ , z ′ ₃₁ ) to the coordinates of feature 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 37a can identify which feature point is in which position in the volume data of the positioning image or diagnostic image, and based on such information, such as an organ Each site can be detected. For example, the detection function 37a 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 37a 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 37a 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 37a 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 in FIG. As shown in FIG. 5, a region R1 corresponding to “lung” is extracted from the volume data. That is, the detection function 37a extracts the coordinate information of the voxel of the region R1 in the volume data. The detection function 37a 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. 6, the detection function 37a can extract a region R2 corresponding to “heart” or the like in the volume data.

  The detection function 37a 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 chest is defined from the seventh cervical vertebra to the lower end of the lung, the detection function 37a 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. In addition, the detection function 37a can detect a site | part by various methods besides the method using the anatomical feature point mentioned above. For example, the detection function 37a can detect a part included in the volume data by a region expansion method based on a voxel value.

  The position collation function 37b collates the positions of a plurality of parts in the subject included in the three-dimensional image data and the positions of the plurality of parts in the human body included in the virtual patient data. Here, virtual patient data is information representing the standard position of each of a plurality of parts in the human body. In other words, the position matching function 37b matches the position of the subject with the position of the standard part and stores the matching result in the storage circuit 35. For example, the position matching function 37b matches a virtual patient image in which a human body part is arranged at a standard position with 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 corresponding 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 35. That is, the storage circuit 35 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 35. 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 positions and arrangements of these many anatomical feature points in the body are roughly determined according to age, adult / child, male / female, physique such as weight and height.

  In the virtual patient image stored by the storage circuit 35, these many anatomical feature points are detected in advance, and the position data of the detected feature points are attached to the virtual patient image data together with the identification codes of the respective feature points. Or it is stored in association. FIG. 7 is a diagram illustrating an example of a virtual patient image stored by the storage circuit 35 according to the first embodiment. For example, as shown in FIG. 7, the storage circuit 35 has an identification code “V1”, “V2”, and identification codes 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 35 stores the coordinates of the feature points in the coordinate space of the three-dimensional human body image and the corresponding identification code in association with each other. For example, the storage circuit 35 stores the coordinates of the corresponding feature points in association with the identification code “V1” shown in FIG. Similarly, the storage circuit 35 stores the identification code and the feature point coordinates in association with each other. In FIG. 7, only the lung, heart, liver, stomach, kidney, and the like are shown as organs, but actually, the virtual patient image includes a larger number of organs, bones, blood vessels, nerves, and the like. . In FIG. 7, 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 37b matches the feature points in the volume data of the subject detected by the detection function 37a 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. 8 is a diagram for explaining an example of collation processing by the position collation function 37b according to the first embodiment. Here, in FIG. 8, 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 scanogram 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. 8, the position matching function 37b includes feature points indicated by identification codes “V1”, “V2”, and “V3” in the virtual patient image, and identification codes “C1”, “C2” in the scanogram. 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. 8, the position matching function 37b 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 37b can convert the scan range specified on the virtual patient image into the scan range on the positioning image by the obtained coordinate conversion matrix “H”. For example, the position matching function 37b 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. 9 is a diagram illustrating a scan range conversion example by coordinate conversion according to the first embodiment. For example, as shown on the virtual patient image in FIG. 9, when the operator sets the scan range “SRV” on the virtual patient image, the position matching function 37b uses the above-described coordinate transformation matrix to set the scan The range “SRV” is converted into a scan range “SRC” on the scanogram.

  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 scanogram. Is converted and set to a scan range “SRC”. Note that the above-described coordinate transformation matrix “H” may be stored in the storage circuit 35 for each subject and read and used as appropriate, or calculated every time a scanogram 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 time of presetting, and the position / range is planned on the virtual patient image, so that the positioning image (scano image) is captured. It is possible to automatically set numerical values for the position / range on the positioning image corresponding to the planned position / range.

  Returning to FIG. 2, the display control function 37 c controls to display various display information on the display 32. For example, the display control function 37 c controls to display various image data stored in the storage circuit 35 on the display 32. Further, the display control function 37c performs control so that information corresponding to the detection result by the detection function 37a is displayed on the display 32. Details of information displayed by the control of the display control function 37c will be described later. The control function 37d performs overall control of the X-ray CT apparatus 1 by controlling the operations of the gantry 10, the bed apparatus 20, and the console 30. Specifically, the control function 37 d controls the CT scan performed on the gantry 10 by controlling the scan control circuit 33. The control function 37d controls the image reconstruction process and the image generation process in the console 30 by controlling the image reconstruction circuit 36. The control by the control function 37d will be described in detail later.

  The overall configuration of the X-ray CT apparatus 1 according to the first embodiment has been described above. Under such a configuration, the X-ray CT apparatus 1 according to the first embodiment enables efficient site detection. Specifically, the X-ray CT apparatus 1 stores correspondence information in which a plurality of anatomical feature points are associated with a plurality of groups in advance, and selects a group based on examination information and a type of scan. . Then, the X-ray CT apparatus 1 refers to the correspondence information, and detects the part of the feature point corresponding to the selected group, thereby enabling efficient part detection. Here, the X-ray CT apparatus 1 can select a group previously associated with the examination information and the type of scan in selecting a group. The X-ray CT apparatus 1 can also select a group based on the priority of the group for each type of inspection information and scan in selecting a group. Hereinafter, an example in the case of selecting a group previously associated with the inspection information and scan type and an example in the case of selecting a group based on the priority of the group for each inspection information and scan type will be sequentially described. explain.

  An example in the case of selecting a group associated in advance with inspection information and scan type will be described. First, in the X-ray CT apparatus 1, the storage circuit 35 stores correspondence information in which a plurality of anatomical feature points in the subject are associated with a plurality of groups. Specifically, the storage circuit 35 stores correspondence information in which an anatomical feature point of each part is associated with a group based on parts such as a position, an organ, a bone, a blood vessel, and a nerve in the subject. 10A and 10B are diagrams illustrating examples of correspondence information according to the first embodiment. For example, as shown in FIG. 10A, the memory circuit 35 divides anatomical feature points into group 1 (position), group 2 (organ), group 3 (bone), group 4 (blood vessel), and group. Correspondence information assigned to 5 (nerve) is stored.

  For example, the storage circuit 35 stores correspondence information in which “head”, “neck”, “chest”, “abdomen”, “foot”, and the like are classified in group 1 (position). That is, as the group 1 (position), the storage circuit 35 has a feature point for identifying “head”, a feature point for identifying “neck”, and a feature point for identifying “chest”. The feature points for identifying “abdomen” and the feature points for identifying “foot” are stored in association with each other. Similarly, the storage circuit 35 stores correspondence information in which “brain”, “lung”, “heart”, “liver”, and the like are classified into group 2 (organs). In addition, the storage circuit 35 stores correspondence information in which “cranium”, “tibia”, “radius”, “sternum”, “femur”, and the like are classified in group 3 (bone). In addition, the storage circuit 35 stores correspondence information in which “cerebral blood vessel”, “carotid artery”, “aorta”, “femoral artery”, and the like are classified into the group 4 (blood vessels). Further, the storage circuit 35 stores correspondence information in which “optic nerve”, “intercostal nerve”, “abdominal nerve”, “femoral nerve”, and the like are classified into the group 5 (nerve). That is, the storage circuit 35 stores the feature points of each part divided into groups in association with each group.

  The correspondence information is not limited to the above-described example, and can be stored in various forms. For example, as illustrated in FIG. 10B, the storage circuit 35 can store correspondence information indicating a hierarchical structure between groups. For example, the storage circuit 35 may store the correspondence information of the hierarchical structure in which the “chest” of the group 1 is positioned at the top and the group 2 of the organs included in the chest such as “heart” is positioned below the top. it can. Furthermore, in the correspondence information indicating the hierarchical structure, a group 2-1-1 including, for example, “aortic valve”, “mitral valve”, “pulmonary valve”, and “tricuspid valve” below “heart” of group 2 And a group 2-1-2 including a “right coronary artery” and a “left coronary artery main duct”. Further, in the correspondence information indicating the hierarchical structure, a group 2-1-2-1 including, for example, “the left anterior descending coronary artery” and “the left coronary artery rotation branch” is arranged below the group 2-1-2. Is done. Thus, the storage circuit 35 can also store correspondence information indicating a hierarchical structure between groups.

  Note that the correspondence information illustrated in FIGS. 10A and 10B is merely an example, and the embodiment is not limited thereto. That is, the correspondence information stored by the storage circuit 35 can arbitrarily associate the group and the part. For example, the user can operate to generate correspondence information in which a group and a part are associated with each other and store the information in the storage circuit 35.

  As described above, in the X-ray CT apparatus 1, the storage circuit 35 stores the correspondence information. Then, the detection function 37a refers to the correspondence information stored by the storage circuit 35 and detects the part. Here, the detection function 37a selects at least one group out of a plurality of groups based on the set examination information and the type of scan to be executed, and based on the anatomical feature points corresponding to the selected group. Thus, the part of the subject corresponding to at least one group is detected. For example, the detection function 37a selects a group of detection granularity according to the inspection information and the type of scan. As described above, the correspondence information is divided into groups for each part. Here, for example, as shown in FIG. 10A, the parts in the correspondence information include those in a large area such as “head” and “abdomen” and those in a small area such as “blood vessel” and “nerve”. Each is classified. That is, the part in the correspondence information is associated with a group in which a part that is easy to detect in the volume data is different from a part that is difficult to detect. In other words, the parts in the correspondence information include parts that can be detected from the volume data regardless of conditions such as image quality, and parts that are difficult to detect from the volume data depending on conditions such as image quality.

  The detection function 37a selects a group based on examination information and scanning conditions, and detects an anatomical feature point for a feature point of a part corresponding to the selected group. Specifically, the detection function 37a acquires inspection information and scan conditions, and selects a group corresponding to the acquired inspection information and scan conditions. Here, the examination information and the scan conditions are acquired from the subject information and the scan plan. For example, the detection function 37a acquires inspection information and scan conditions from information included in the inspection order and information of a scan protocol selected by the operator. For example, the detection function 37a acquires “chest to pelvis (liver simple imaging + contrast imaging)” as examination information and scanning conditions.

  In addition, the inspection information, the scan condition, and the group are stored in the storage circuit 35 in association with each other in advance. For example, information in which “group 1 (position)”, “group 2 (organ)”, and “group 4 (blood vessel)” are associated with “chest to pelvis (simple liver imaging + contrast imaging)” is stored in the storage circuit. 35. Here, the information described above can be associated with information on a more detailed part in the group. For example, “Group 1 (position): chest, abdomen”, “Group 2 (organ): liver”, and “Group 4 (blood vessel): hepatic artery, Information associated with “hepatic vein” may be stored in the storage circuit 35.

  The detection function 37a acquires examination information and scan conditions from subject information, a scan plan, and the like. Then, the detection function 37a refers to the information indicating the correspondence between the inspection information and the scan condition and the group, and acquires the group information corresponding to the acquired inspection information and the scan condition. Then, the detection function 37a refers to the correspondence information stored in the storage circuit 35, and detects the feature point corresponding to the acquired group, thereby detecting the part of the subject.

  For example, the detection function 37a acquires “chest to pelvis (simple liver imaging + contrast imaging)” as examination information and scanning conditions. Then, the detection function 37a reads group information corresponding to “chest to pelvis (simple liver imaging + contrast imaging)” from the storage circuit 35, and detects feature points of parts included in the read group from the volume data. For example, “Group 1 (position)”, “Group 2 (organ)”, and “Group 4 (blood vessel)” are associated with “chest to pelvis (simple liver imaging + contrast imaging)”, as shown in FIG. 10A. When referring to the correspondence information, the detection function 37a detects feature points classified into “group 1 (position)”, “group 2 (organ)”, and “group 4 (blood vessel)” from the volume data. Here, when the correspondence information has a hierarchical structure as shown in FIG. 10B, the detection function 37 a has “chest” and “corresponding to the positions of“ chest to pelvis ”in“ group 1 (position) ”. It is also possible to detect only the feature points of “Group 2 (organ)” and “Group 4 (blood vessel)” that are located below the “abdomen”.

  Also, for example, “Group 1 (position): chest, abdomen”, “Group 2 (organ): liver”, and “Group 4 (blood vessel): liver with respect to“ chest to pelvis (simple liver imaging + contrast imaging) ” In the case where “artery, hepatic vein” is associated, the detection function 37a refers to the correspondence information, the feature point for identifying “chest, abdomen”, the feature point for identifying “liver”, and “ Feature points for identifying “hepatic artery and hepatic vein” are detected from the volume data. In the feature point detection described above, the blood vessel feature points are detected on the volume data collected by contrast imaging.

  As described above, the detection function 37a selects a group based on examination information and scanning conditions, and detects a feature point corresponding to the selected group, thereby detecting a part based on the examination information and scanning conditions from the volume data. To do. For example, the detection function 37a can perform the above-described detection of the volume data collected by capturing the scanogram and the volume data collected in the main scan.

  As described above, each time a part is detected from the volume data, the detection function 37a sequentially stores the identification code in the storage circuit 35 in association with the coordinates of the feature point corresponding to the detected part. The position matching function 37b reads the correspondence information between the feature point coordinates and the identification code stored in the storage circuit 35, and executes the above-described matching process. Then, the position collation function 37 b stores the collation result (coordinate conversion matrix “H”) in the storage circuit 35. For example, the position matching function 37b performs a matching process every time a part is detected by the detection function 37a, and stores the matching result in the storage circuit 35.

  The display control function 37c controls to output information indicating the detected part each time the part is detected by the detection function 37a. Specifically, the display control function 37c controls to output information indicating the detection result by the detection function 37a to at least one of the display image generated from the volume data and the human body model image (virtual patient image). . For example, the display control function 37c controls to output an image displayed so as to emphasize the part detected by the detection function 37a in comparison with other parts in at least one of the display image and the virtual patient image. To do.

  Next, an example in which a group is selected based on inspection information and group priority for each type of scan will be described. In such a case, for example, the detection function 37a selects a group based on the priority order set in advance according to the examination information and the type of scan. In this case, for example, a priority is given to the group associated with the inspection information and the scan condition. For example, in “Group 1 (position)”, “Group 2 (organ)” and “Group 4 (blood vessel)” associated with “chest to pelvis (simple liver imaging + contrast imaging)”, “group “Priority (1)” is assigned to “2 (organ)”, “Priority: 2” is assigned to “Group 1 (position)”, and “Priority: 3” is assigned to “Group 4 (blood vessel)”. Is done. That is, when referring to the above information, the detection function 37a detects feature points in the order of “group 2 (organ)”, “group 1 (position)”, and “group 4 (blood vessel)” from the volume data. . Similarly, for the groups in the correspondence information having a hierarchical structure, a priority order for detecting feature points can be assigned to each group. Thereby, X-ray CT apparatus 1 can detect a part according to the priority which a user set, and makes it possible to detect a part more efficiently.

  The priorities described above can be arbitrarily set by the user. That is, the user can appropriately set the priority order for detecting feature points for each type of inspection information and scan type. Here, the priority may be set in order from the group to be detected with priority, or may be set in the order of ease of detection. In this case, the X-ray CT apparatus 1 can efficiently detect a site and can grasp the detection status of the site based on anatomical feature points. As described above, in the conventional part detection using the X-ray CT apparatus, the part is automatically recognized from the geometric feature of the scanogram, and the imaging condition suitable for the recognized part is set. Detection of a part using a scanogram is not always successful depending on image quality, physical characteristics of the subject, and the like. In addition, when a region is randomly detected, it may take time to obtain a desired result (such as detection of an organ desired to be detected). Therefore, the X-ray CT apparatus 1 according to the first embodiment detects a part based on an anatomical feature point under the control of the processing circuit 37 (display control function 37c and control function 37d) described in detail below. It is possible to grasp the situation.

  Specifically, in the X-ray CT apparatus 1 according to the first embodiment, the data collection circuit 14 detects X-rays transmitted through the subject and collects projection data, and the detection function 37a re-creates the projection data. A part of the subject is detected by detecting anatomical feature points included in the configured volume data. In the X-ray CT apparatus 1, the display control function 37c is controlled to output information indicating the detection result by the detection function 37a. Here, in the X-ray CT apparatus 1 according to the first embodiment, the parts are detected in the order of priority set in advance in the subject, and the detection result is displayed each time the part is detected. That is, the detection function 37a detects a part of the subject step by step by detecting in advance from feature points with a high priority set in advance among anatomical feature points included in the volume data. The display control function 37c controls to output information indicating the detected part every time the part is detected.

  Here, the priority order of the parts described above will be described. The priority in the detection of the part can be arbitrarily set. For example, the priority order is set so as to be detected in order from a part having a small number of feature points used for detection. That is, priorities are set such that corresponding feature points are detected in order from a part having a small number of feature points for detecting a certain region included in the volume data as a part. Thereby, for example, a part that is easy to detect is detected quickly. Hereinafter, FIG. 11 is a diagram for explaining an example of the priority order of the detection parts according to the first embodiment. Here, FIG. 11 shows an example in which priorities are set for the whole subject. Further, FIG. 11 shows a case where priority is set for the group in the correspondence information shown in FIG. 10, and ease of detection is shown as a recognition level.

  For example, as shown in FIG. 11, the priority order is “recognition level: level 1”, “recognition level: level 2”, “recognition level: level 3”, “recognition level: Level 4 ”and“ recognition level: level 5 ”are set. That is, when the detection process is executed in the priority order shown in FIG. 11, detection is performed sequentially from the site of “recognition level: level 1”. Here, the part of “recognition level: level 1” having the highest priority is “position” in the human body, for example, “head”, “neck”, “chest”, “abdomen”, and “foot”. Is set. The part of “recognition level: level 2” having the next highest priority is “organ” as shown in FIG. 11, for example, “brain” in “head”, “lens”, etc. “Thyroid”, etc. “Esophagus”, “Lung” and “Heart” in “Chest”, “Liver” in “Abdomen”, “Stomach”, “Kidney”, “Colon”, etc. “Achilles tendon” in “Foot”, etc. Is set.

  Similarly, “bone” is set as the site of “recognition level: level 3” of the next priority, “blood vessel” is set as the site of “recognition level: level 4” of the next priority, and the next “Nerve” is set as the site of “recognition level: level 5” of the priority order. The set priority order information is stored in the storage circuit 35 and is appropriately referred to by the detection function 37a. In addition, the example of the recognition level shown in FIG. 11 is an example to the last, and embodiment is not limited to this. For example, it may be a case where a priority order in detecting a part is set according to a scan protocol. In other words, when a scan protocol targeting a predetermined part is selected, the predetermined part may be set to be detected with a high priority. For example, in the MRI apparatus, when a tractography scan protocol targeting “nerve” is selected, information in which “nerve” is set as a site of “recognition level: level 1” is used. It is done. In other words, the storage circuit 35 stores information in which priorities are set according to the scan protocol. The detection function 37a reads the corresponding priority order information from the storage circuit 37a according to the selected scan protocol, and executes the detection process with reference to the read priority order information.

  For example, when the priority information shown in FIG. 11 is referred to, the detection function 37a first detects “head”, “neck”, “chest”, “abdomen”, and “foot” included in “position”. To detect feature points. For example, the detection function 37a preferentially detects the feature point of the “seventh cervical vertebra” and the feature point of the “lower end of the lung” for detecting the “chest”.

  Here, the detection function 37a detects only a part at a position included in a range in which projection data is collected in the scanning image capturing. That is, when the scanogram is collected in the range of “chest” and “abdomen”, the detection function 37a detects feature points for detecting “chest” and “abdomen”. In other words, when the scanogram is collected in the range of “chest” and “abdomen”, feature points for detecting “head”, “neck”, and “foot” are not detected. In such a case, for example, in the detection process, the detection function 37a first refers to the scan protocol and specifies the part to be examined, and then executes the detection process.

  As described above, when the detection process is executed for the part of “position” having “recognition level: level 1” with the highest priority, the detection function 37a then has “recognition level: level 2”. The detection process is executed for the part of “organ”. Here, since the detection function 37a detects the “position” in the previous stage, the “organ” detection process is executed using the detected “position” information, thereby speeding up the process. Can do. As an example, the detection function 37a uses the volume data of the scanogram image to obtain “organs” such as “esophagus”, “lung”, and “heart” from the volume data area corresponding to the position of the “chest” detected in the previous stage. The process is executed so as to detect the feature points. In other words, the detection function 37a detects feature points of organs of “abdomen” such as “liver”, “stomach”, “kidney” and “large intestine” from the volume data area corresponding to the position of “chest”. The process is not executed.

  As described above, the detection function 37a associates the identification code with the coordinates of the feature point corresponding to the detected part each time the part is detected from the volume data in accordance with the preset priority order. Store in order. The position matching function 37b reads the correspondence information between the feature point coordinates and the identification code stored in the storage circuit 35, and executes the above-described matching process. Then, the position collation function 37 b stores the collation result (coordinate conversion matrix “H”) in the storage circuit 35. For example, the position matching function 37b performs a matching process every time a part is detected by the detection function 37a, and stores the matching result in the storage circuit 35.

  The display control function 37c controls to output information indicating the detected part each time the part is detected by the detection function 37a. Specifically, the display control function 37c outputs information indicating the detection result by the detection function 37a to at least one of the display image generated from the three-dimensional image data and the human body model image (virtual patient image). Control. For example, the display control function 37c controls to output an image displayed so as to emphasize the part detected by the detection function 37a in comparison with other parts in at least one of the display image and the virtual patient image. To do.

  12A to 12C are diagrams illustrating an example of processing by the display control function 37c according to the first embodiment. Here, in FIGS. 12A to 12C, a plan screen for a scan plan is shown, and a virtual patient image and a scano image are shown on the plan screen. In FIGS. 12A to 12C, only the virtual patient image or only the virtual patient image and the scano image are shown, but actually, various items displayed on the plan screen of the scan plan are displayed. The For example, when a scanogram is collected, the display control function 37c shows a scan range R11 of the scanogram in the virtual patient image, as shown in the left diagram of FIG. 12A. This range is a range set in the scan protocol or a range after being changed by the operator after the scan protocol is selected. For example, as shown in the diagram on the left side of FIG. 12A, the display control function 37c displays a virtual patient image in which the scan range R11 of the scanogram is shown in gray on the display. Then, when the scanogram collection in the scan range R11 shown on the left side of FIG. 12A is executed, the detection function 37a executes the feature point detection process as described above. For example, the detection function 37a executes detection processing according to the priority order.

  The “chest” is detected from the volume data of the scano image collected in the scan range R11 by the detection function 37a, and the detected feature point information and the coordinate transformation matrix in the chest calculated by the position matching function 37b are stored in the storage circuit 35. The display control function 37c reads the identification code of the detected feature point, and extracts the coordinates of the feature point of the corresponding identification code in the virtual patient image. Then, the display control function 37c calculates a “chest” region “R12” on the virtual patient image by applying a coordinate transformation matrix to the extracted coordinates, and calculates the calculated “chest” region “R12”. As shown in the figure on the right side of FIG. 12A, a virtual patient image that is clarified is displayed on the display 32. In addition, the display control function 37c continuously displays the region “R13” in which the region is not detected as gray as illustrated in the right side of FIG. 12A.

  Here, the display control function 37c can also perform display according to the detection result. For example, the display control function 37c can change the color of a detected region or add a mark according to the detection accuracy of each part by the detection function 37a. For example, the display control function 37c may color-code the regions with different detection accuracy according to the ratio of the feature points detected by the detection function 37a among all the feature points included in the detection target region. Or a mark indicating the detection accuracy. For example, it is assumed that there are “50” feature points for detecting “chest”. For example, the display control function 37c changes the color of the region “R12” according to the ratio of the feature points detected by the detection function 37a among the feature points “50”. For example, in the display control function 37c, the ratio of the feature points detected by the detection function 37a is "80% or more (40 or more)", "60% or more and less than 80% (30 or more and less than 40)", "2 The color of the region “R12” is changed according to each ratio of “more than or equal to 60% (less than or equal to 10 and less than 30)” or “less than 20% (less than 10)”.

  Further, for example, the storage circuit 35 stores information in which marks and characters indicating detection accuracy are associated with the above-described ratios. For example, the memory circuit 35 associates “Excellent” with “80% or more (40 or more)”, “Good” with “60 or more and less than 80% (30 or more and less than 40)”, “ “Poor” is associated with “20% to less than 60% (10 to less than 30)”, and “Bad” is associated with “less than 20% (less than 10)”. The display control function 37c reads out characters corresponding to the detection result by the detection function 37a from the storage circuit 35, adds them to the region “R12”, and displays them. As described above, the display control function 37c changes the color of a detected region or adds a mark according to the detection accuracy of each part by the detection function 37a. Thereby, the observer can confirm the detection accuracy of the detected part at a glance.

  As described above, the display control function 37c causes the display 32 to display the virtual patient image in which the part detected by the detection function 37a is emphasized. Note that the display control function 37c can emphasize and display not only the virtual patient image but also the part detected in the actual scano image. In the above-described example, the case where the region after the position is collated by the position collation function 37b is displayed on the virtual patient image has been described as an example. However, the embodiment is not limited thereto, and collation is performed. It may be a case where the detected part in the virtual patient image is highlighted and displayed without performing the process. In such a case, the display control function 37c reads the identification code stored in the storage circuit 35 and highlights and displays a region defined by the identification code corresponding to the identification code read on the virtual patient image. There may be.

  In this manner, the display control function 37c highlights and displays the part detected by the detection function 37a. However, the detection process by the detection function 37a is not always successful, and there is a case where the part is not successfully detected. Therefore, the display control function 37c can also highlight and display the region on the virtual patient image corresponding to the region set on the display image as a part. In such a case, the input circuit 31 accepts a designation operation for designating the region of the subject with respect to the display image generated from the three-dimensional image data. The display control function 37c controls to output information indicating the part specified by the specifying operation received by the input circuit 31 as the detected part.

  For example, when the “image” button arranged on the scan plan screen is pressed, the display control function 37c displays the collected scanogram on the scan plan screen on which the virtual patient image is shown, as shown in FIG. 12B. Display. Here, as shown in FIG. 12B, the display control function 37c is surrounded by a dotted line so as to be distinguished from the detected region “R12” together with the detected region “R12” on the scanogram. The region “R13” is displayed on the scanned image. Even in this case, the display control function 37c can change the color of a detected region or add a mark in accordance with the detection accuracy of each part by the detection function 37a.

  Here, the operator can set a part via the input circuit 31. For example, the operator sets the region of the part by operating the input circuit 31 and changing the size of the region “R13”. For example, as shown in the diagram on the right side of FIG. 12C, the operator operates the input circuit 31 to change the region “R13” to the region “R14” for setting “abdomen”. As described above, when the region “R14” is set by the operator, the display control function 37c extracts the coordinates of the feature points included in the set region, and in the virtual patient image corresponding to the extracted feature points. By applying the coordinate transformation matrix to the coordinates of the feature points, the region “R14” in the virtual patient image is calculated, and the virtual patient image indicating the calculated region as “R14” is displayed. The coordinate transformation matrix described above may be a case where the coordinate transformation matrix calculated at the chest is used, or a case where the coordinate transformation matrix is calculated again using the feature points included in the region “R14” designated by the operator. May be.

  When the region is detected stepwise by the detection function 37a, the display control function 37c reflects the information on the virtual patient image each time it is detected. FIG. 13 is a diagram illustrating an example of processing by the display control function 37c according to the first embodiment. For example, as shown in the left diagram of FIG. 13, after the chest region “R12” and the abdominal region “R14” are detected, if the detection function 37a detects “heart” from the chest, the display control function 37c As shown in the diagram on the right side of FIG. 13, a virtual patient image in which “heart” is emphasized is displayed. Note that the display control function 37c changes the color of a detected region or adds a mark according to the detection accuracy of each part by the detection function 37a even when the part is detected stepwise. be able to. For example, the display control function 37c can change the color of the heart region according to the detection accuracy of the heart, or can add a mark or a character to the heart.

  In this way, the detection function 37a detects the parts step by step according to the priority order, and the display control function 37c displays the virtual patient image or the scano image in which the detected part is emphasized. It is possible to grasp at a glance the detection status of a part based on anatomical feature points. Accordingly, it is possible to proceed with the inspection by determining at a glance whether or not the range to be scanned in the main scan has been detected.

  It should be noted that the above-described part detection process can be executed not only on the positioning image volume data described above but also on the volume data collected by the main scan. It is expected that the image collected in the main scan compared to the scanogram has high image quality and the accuracy of the detection process by the detection function 37a is increased. In this scan, a contrast medium may be used, and fine blood vessels can be detected. Therefore, when an image is collected by the main scan, the detection function 37a executes detection processing in accordance with the above-described priority order using the collected volume data. Here, in this scan, since the part is set and the scan is executed, for example, the recognition level may start from level 2.

  On the other hand, it is also possible to set the scan range of the main scan in advance and adjust the scan range after the part by the detection function 37a is detected. Specifically, the input circuit 31 accepts setting of a scan range for collecting diagnostic projection data. Then, the control function 37d adjusts the scan range received by the input circuit 31 based on the position of the part of the subject detected by the detection function 37a. That is, the control function 37d controls the scan control circuit 33 to execute scanning. FIG. 14 is a diagram for explaining an example of adjustment of the scan range according to the first embodiment.

  For example, as shown in the upper diagram of FIG. 14, the detection processing for the volume data of the scanogram is executed, the part is not detected (the inside of the region R11 is gray), and the operator inputs the input circuit. The scan range of the main scan is set to “lung” via 31. In such a case, since the lung region in the scanogram has not yet been detected, an accurate scan range cannot be set. For example, as shown in the right diagram in the upper part of FIG. The scan range is set with R15 "deviating from the lung region.

  When the detection function 37a detects “lung” in the volume data, the control function 37d detects the scan range “R16” of the main scan accurately including “lung” as shown in the right diagram in the lower part of FIG. ”And the set information (the information on the position of the bed corresponding to the coordinates of the region R16) is sent to the scan control circuit 33, thereby executing the main scan in the region R16.

  Note that the group information described above can be appropriately displayed on a display. For example, the display control function 37c controls the display 32 to display the correspondence information shown in FIGS. 10A, 10B, 11 and the like. That is, the display control function 37c controls the display 32 to display information indicating the correspondence between the groups and the feature points included in each group. Here, the group information can be displayed in various formats. For example, the display control function 37c can display the correspondence information shown in FIG. 10A, FIG. 10B, or FIG. 11 on the plan screen of the scan plan. Also for example. The display control function 37c can also sequentially display portions corresponding to groups in the virtual patient image. As an example, the display control function 37c can also be controlled to highlight each part from group 1 to group 5 while switching at a constant time interval. The display control function 37c can also be controlled to highlight a group part based on the set scan plan. Thereby, the user can confirm the detected part by referring to the information during the scan plan, and can reset the part to be detected.

  Next, processing of the X-ray CT apparatus 1 according to the first embodiment will be described with reference to FIGS. 15, 16A, and 16B. FIG. 15, FIG. 16A and FIG. 16B are flowcharts showing a processing procedure by the X-ray CT apparatus 1 according to the first embodiment. Here, FIG. 15 shows an example of processing in the case of selecting a group based on examination information and scanning conditions and detecting a part. Moreover, in FIG. 16A and FIG. 16B, an example of the process in the case of detecting a site | part based on a priority is shown.

  Steps S101 and S102 shown in FIG. 15 are steps in which the processing circuit 37 reads out a program corresponding to the control function 37d from the storage circuit 35 and executes it. In step S101, the processing circuit 37 determines whether inspection information and scan conditions have been received. In step S102, the processing circuit 37 controls the scan control circuit 33, the image reconstruction circuit 36, and the like to collect a three-dimensional positioning image.

  Steps S103 to S105 shown in FIG. 15 are steps in which the processing circuit 37 reads a program corresponding to the detection function 37a from the storage circuit 35 and executes it. In step S103, the processing circuit 37 selects a group based on the inspection information and the scan condition. In step S104, the processing circuit 37 detects feature points included in the selected group from the volume data. In step S105, the processing circuit 37 detects a part corresponding to the detected feature point.

  Step S106 illustrated in FIG. 15 is a step in which the processing circuit 37 reads a program corresponding to the display control function 37c from the storage circuit 35 and is executed. In step S106, the processing circuit 37 causes the display 32 to display information on the detected part.

  Step S201 and step S202 shown in FIG. 16A are steps in which the processing circuit 37 reads a program corresponding to the control function 37d from the storage circuit 35 and executes it. In step S201, the processing circuit 37 determines whether or not inspection has been started. In step S202, the processing circuit 37 collects a three-dimensional positioning image by controlling the scan control circuit 33, the image reconstruction circuit 36, and the like.

  Step S203 and step S204 in FIG. 16A are steps in which the processing circuit 37 reads out a program corresponding to the detection function 37a from the storage circuit 35 and executes it. In step S203, the processing circuit 37 sets the recognition level in the detection process to 1. In step S204, the processing circuit 37 detects an anatomical feature point corresponding to the recognition level from the positioning image.

  Step S205 in FIG. 16A is a step in which the processing circuit 37 reads a program corresponding to the display control function 37c from the storage circuit 35 and is executed. In step S205, the processing circuit 37 displays the detection result on the virtual patient image and reflects it in the scan plan.

  Step S206 shown in FIG. 16A is a step in which the processing circuit 37 reads out a program corresponding to the detection function 37a from the storage circuit 35 and executes it. In step S206, the processing circuit 37 determines whether or not a feature point corresponding to the current recognition level has been detected. Step S207 shown in FIG. 16A is a step executed by the input circuit 31. In step S207, when it is determined that the feature point corresponding to the current recognition level is not detected (No in step S206), the input circuit 31 accepts manual designation.

  Steps S208 and S210 shown in FIG. 16A are steps in which the processing circuit 37 reads out a program corresponding to the control function 37d from the storage circuit 35 and executes it. In step S208, the processing circuit 37 determines whether the scan can be executed. In step S210, when it is determined that the scan can be executed (Yes in step S208), the processing circuit 37 executes the main scan.

  Step S209 shown in FIG. 16A is a step in which the processing circuit 37 reads a program corresponding to the detection function 37a from the storage circuit 35 and is executed. If it is determined in step S209 that the scan is not executable (No in step S208), the processing circuit 37 increases the recognition level in the detection process by one, returns to step S204, and the anatomy corresponding to the recognition level. A characteristic feature point is detected from the positioning image.

  Step S211 and step S212 in FIG. 16B are steps after the main scan in step S210 is executed, and the processing circuit 37 reads the program corresponding to the detection function 37a from the storage circuit 35 and is executed. In step S211, the processing circuit 37 sets the recognition level in the detection process to 2. In step S212, the processing circuit 37 detects an anatomical feature point corresponding to the recognition level from the scanned image.

  Step S213 in FIG. 16B is a step in which the processing circuit 37 reads a program corresponding to the display control function 37c from the storage circuit 35 and is executed. In step S213, the processing circuit 37 displays the detection result on the virtual patient image. Step S214 shown in FIG. 16B is a step in which the processing circuit 37 reads the program corresponding to the detection function 37a from the storage circuit 35 and is executed. In step S214, the processing circuit 37 determines whether or not a feature point corresponding to the current recognition level has been detected. Step S215 illustrated in FIG. 16B is a step executed by the input circuit 31. If it is determined in step S215 that no feature point corresponding to the current recognition level has been detected (No in step S214), the input circuit 31 accepts manual designation.

  Step S216 shown in FIG. 16B is a step in which the processing circuit 37 reads out a program corresponding to the control function 37d from the storage circuit 35 and executes it. In step S216, the processing circuit 37 determines whether all feature points have been detected. Here, when it is determined that all feature points have been detected (Yes in step S216), the X-ray CT apparatus 1 ends the process.

  Step S217 illustrated in FIG. 16B is a step in which the processing circuit 37 reads a program corresponding to the detection function 37a from the storage circuit 35 and is executed. If it is determined in step S217 that not all feature points have been detected (No in step S216), the processing circuit 37 increases the recognition level in the detection process by one, and returns to step S212 to deal with the recognition level. An anatomical feature point to be detected is detected from the scanned image.

  As described above, according to the first embodiment, the image reconstruction circuit 36 generates three-dimensional image data of the subject. The storage circuit 35 stores a plurality of anatomical feature points in the subject in association with a plurality of groups. The detection function 37a selects at least one group out of a plurality of groups based on the set examination information and the type of scan to be performed, and at least based on the anatomical feature points corresponding to the selected group A part of the subject corresponding to one group is detected. The display control function 37c controls to output information indicating the detected part. Therefore, the X-ray CT apparatus 1 according to the first embodiment can detect a part corresponding to the examination situation, and can detect the part efficiently.

  Further, according to the first embodiment, the detection function 37a selects a group of detection granularity according to the inspection information and the type of scan. Therefore, the X-ray CT apparatus 1 according to the first embodiment can perform detection processing according to the state of the collected volume data, and can efficiently detect a site.

  According to the first embodiment, the data collection circuit 14 collects projection data by detecting X-rays transmitted through the subject. The detection function 37a detects the region of the subject by detecting the anatomical feature points included in the three-dimensional image data reconstructed from the projection data in order from the preset feature points having the highest priority. Detect in stages. The display control function 37c controls to output information indicating the detected part each time the part is detected by the detection function 37a. Therefore, the X-ray CT apparatus 1 according to the first embodiment makes it possible to grasp the detection status of a part based on anatomical feature points.

  Further, according to the first embodiment, the priority order is set so as to be detected in order from a part having a small number of feature points used for detection. Therefore, the X-ray CT apparatus 1 according to the first embodiment makes it possible to improve detection efficiency.

  Further, according to the first embodiment, the display control unit function 37c shows the detection result by the detection function 37a on at least one of the display image generated from the three-dimensional image data and the human body model image (virtual patient image). Control to output information. In addition, the display control unit function 37c displays an image so as to emphasize the part detected by the detection function 37a in comparison with other parts in at least one of the display image and the human body model image (virtual patient image). Is controlled to output. Therefore, the X-ray CT apparatus 1 according to the first embodiment makes it possible to grasp the detection state more easily.

  Further, according to the first embodiment, the input circuit 31 accepts a designation operation for designating a part of a subject with respect to a display image generated from three-dimensional image data. The display control function 37c controls to output information indicating the part specified by the specifying operation received by the input circuit 31 as the detected part. Therefore, the X-ray CT apparatus 1 according to the first embodiment can easily cope with a case where the detection process is not successful.

  Further, according to the first embodiment, the input circuit 31 accepts setting of a scan range for collecting diagnostic projection data. The control function 37d adjusts the scan range received by the input circuit 31 based on the position of the part of the subject detected by the detection function 37a. Therefore, the X-ray CT apparatus 1 according to the first embodiment makes it possible to set an accurate scan range without waiting for detection processing.

(Second Embodiment)
In the first embodiment described above, a case has been described in which a part is detected from volume data in accordance with a preset priority order. In the second embodiment, a process when the detection result detected from the volume data is different from the standard figure will be described. In the second embodiment, the processing contents of the detection function 37a and the display control function 37c are different from those of the first embodiment. Hereinafter, these will be mainly described.

  The display control function 37c according to the second embodiment includes at least one of information on a part different from a standard form among parts of the subject detected by the detection function 37a and information on a foreign substance contained in the subject. Control to output one information. FIG. 17 is a diagram illustrating an example of processing by the display control function according to the second embodiment. For example, when executing the “lung” detection process, the detection function 37a detects only the feature points corresponding to the right lung and does not detect the feature points corresponding to the left lung. The detection result is stored in the storage circuit 35 as it is without detecting that “lung” is detected.

  That is, the detection function 37a stores the coordinates of the feature point of the right lung from which the feature point is detected and the identification code in the storage circuit 35 in association with each other. When the display control function 37c reads the above-described information from the storage circuit 35, the display control function 37c causes the display 32 to display information reflecting the above-described detection result. For example, the display control function 37c displays a virtual patient image in which only the right lung is emphasized, as shown in the left diagram of FIG. Thereby, it can be shown to the operator that the body shape of the subject is different from the standard. In other words, the operator can be prompted to confirm the actual scanogram by presenting the left virtual patient image in FIG. As a result, the operator can confirm the actual scanogram shown in the diagram on the right side in FIG. 17, recognize that the subject is one lung, and leave it in the record.

  In addition, the display control function 37c can display information for notifying the detected part when it differs greatly from the standard part. For example, the display control function 37c presents such information to the operator when the organ is abnormally enlarged, small, or meandering. For example, when the display control function 37c compares the size of the heart detected by the detection function 37a with the standard heart size to determine whether it is enlarged or not, This information is displayed on the display. Here, the display control function 37c determines whether or not the cardiothoracic ratio exceeds 50% as the determination of cardiac hypertrophy, and if the cardiothoracic ratio exceeds 50%, the display information is displayed as cardiac hypertrophy. Present.

  FIG. 18 is a diagram illustrating an example of processing by the display control function 37c according to the second embodiment. For example, as shown in FIG. 18, the display control function 37c gives information on cardiac hypertrophy to the operator by giving an arrow to the heart, coloring it, lighting it with an animation, or issuing a warning. Present.

  In addition, the display control function 37c can also be controlled to output evaluation information for the part of the subject detected by the detection function 37a. For example, the display control function 37c displays evaluation information based on a calcium score for quantitatively evaluating coronary artery calcification when the detected site is the heart. In such a case, the control function 37d calculates a calcium score based on the CT value. Then, the display control function 37c displays the evaluation information based on the calcium score calculated by the control function 37d. For example, when the calcium score exceeds “600”, the display control function 37c displays a warning for the execution of the cardiac CT (coronary artery CT). As a result, the observer can determine that the heart CT is not appropriate for the examination of the subject.

  As described above, the display control function 37c compares the detected organ with the standard organ, and presents the information when it greatly differs from the standard. In addition, the display control function 37c controls to output evaluation information for the part of the subject detected by the detection function 37a. Here, such information is not only related to the organ, but the same information can be presented when, for example, a metal or a foreign substance is present in the body. Here, for example, metal or the like can be detected by the CT value. For example, the display control function 37c presents the above-described warning information or the like when a CT value different from the CT value of the human body is detected.

  The control function 37d registers information different from the standard as described above in the HIS and RIS. Thereby, this information can be read and used next time when the same subject performs an examination. For example, when there is information on one lung in the information on the subject, the detection function 37a can acquire information on one lung in advance and use it for the detection process.

  Such information can also be used as learning information. For example, when the detection function 37a detects only one lung and the operator confirms the image and registers that it is a single lung, the detection function 37a can detect how feature points are detected and information on one lung. And can learn. As described above, the detection function 37a learns the relationship between a part different from the standard form among the parts of the subject and the feature points corresponding to the different parts, and detects the part of the subject based on the learning result. To do.

  Further, learning by the detection function 37a can be performed not only on the relationship between the part and the feature point but also on a metal or the like. For example, by learning information on the metal used in surgery and how the metal is detected, it is possible to identify the metal contained in the subject at the stage of detection processing for volume data. .

(Third embodiment)
The first embodiment has been described so far, but may be implemented in various different forms other than the first embodiment described above.

  In the first and second embodiments described above, the embodiments for detecting a site have been described. Here, the X-ray CT apparatus 1 according to the present embodiment can also control scan conditions and image reconstruction based on information on detected parts.

  For example, when metal is included in the subject or when the body shape of the subject is abruptly changing locally, noise is included in the collected data. The detection function 37a uses the newly reconstructed volume data by causing the image reconstruction circuit 36 to reconstruct the volume data so as to remove the noise from the projection data when the detection process is not successful due to noise. The detection process is executed. For example, the image reconstruction circuit 36 reconstructs new volume data by performing image reconstruction again by successive approximation reconstruction using the original projection data of the volume data for which detection processing has not succeeded due to noise. To do. The detection function 37a detects a part of the subject included in the newly reconstructed volume data.

  Further, for example, it is possible to control the scan condition based on the detection result by the detection function 37a. For example, when the subject is a single lung, the control function 37d can move the bed so that the single lung is at the center of imaging, or the collimator 12c and the wedge 12b can collect images of the single lung. Or control. For example, the control function 37d modulates the tube current so that the dose is reduced at a rotation angle where the one lung to be imaged is far from the X-ray focal point compared to other angles. For example, when pleural effusion is accumulated in the lung, the control function 37d performs control so as to collect an image with a higher dose.

  In the above-described embodiment, the case where the X-ray CT apparatus executes various processes has been described. However, the embodiment is not limited to this, and may be a case where various processes are executed in another medical image diagnostic apparatus, for example. In such a case, a medical image diagnostic apparatus such as an X-ray diagnostic apparatus, an MRI apparatus, or an ultrasonic diagnostic apparatus has a processing circuit similar to the processing circuit 37, and performs the above-described processing using the collected medical image data. Run.

  In addition, each component of each device illustrated in the first embodiment 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 that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily 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 that is analyzed and executed by the CPU, or may be realized as hardware by wired logic.

  Further, the control method described in the first embodiment can be realized by executing a control program prepared in advance on a computer such as a personal computer or a workstation. This control program can be distributed via a network such as the Internet. The control program can also 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.

  As described above, according to each embodiment, site detection can be performed efficiently.

  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.

1 X-ray CT apparatus 14 Data acquisition circuit 37a Detection function 37b Position matching function 37c Display control function 37d Control function

Claims (16)

A generating unit for generating three-dimensional image data of the subject;
A storage unit that stores a plurality of anatomical feature points in the subject in association with a plurality of groups;
At least one group is selected from the plurality of groups based on the set examination information and the type of scan to be performed, and the at least one group is based on anatomical feature points corresponding to the selected group A detection unit for detecting a portion of the subject corresponding to
An output control unit that controls to output information indicating the detected part;
A medical image diagnostic apparatus comprising:   The medical image diagnostic apparatus according to claim 1, wherein the detection unit selects a group having a detection granularity according to the examination information and a type of scan.   The medical image diagnosis apparatus according to claim 1, wherein the detection unit selects a group based on a priority order set in advance according to the examination information and a type of scan.   The medical image diagnosis apparatus according to claim 3, wherein the priority order is set so as to be detected in order from a part having a small number of feature points used for detection.   The output control unit controls to output information indicating a detection result by the detection unit to at least one of a display image and a human body model image generated from the three-dimensional image data. The medical image diagnostic apparatus according to any one of the above.   The output control unit controls to output an image displayed so as to emphasize a part detected by the detection unit in comparison with other parts in at least one of the display image and the human body model image. The medical image diagnostic apparatus according to claim 5. A reception unit that receives a designation operation for designating a region of the subject with respect to a display image generated from the three-dimensional image data;
The medical output according to any one of claims 1 to 6, wherein the output control unit performs control so as to output information indicating a site specified by the specifying operation received by the receiving unit as a detected site. Diagnostic imaging device. A scan range accepting unit for accepting setting of a scan range for collecting projection data for diagnosis;
A scan control unit that adjusts a scan range received by the scan range reception unit based on a position of a part of the subject detected by the detection unit;
The medical image diagnostic apparatus according to claim 1, further comprising: A reconstruction unit that reconstructs three-dimensional image data so as to remove noise from the projection data collected by the collection unit;
The medical image diagnostic apparatus according to claim 1, wherein the detection unit detects a part of the subject included in the three-dimensional image data reconstructed by the reconstruction unit.   The medical image diagnostic apparatus according to claim 9, wherein the reconstruction unit reconstructs the three-dimensional image data so as to remove noise based on a metal contained in the subject and a body shape of the subject.   The scan control unit according to claim 1, further comprising: a scan control unit configured to control a scan condition for collecting diagnostic projection data based on the position of the part of the subject detected by the detection unit. The medical image diagnostic apparatus described in 1.   The scan control unit includes at least one of an X-ray irradiation condition, an irradiation range, and a bed position when collecting the projection data for diagnosis based on the position of the part of the subject detected by the detection unit. The medical image diagnostic apparatus according to claim 11, which controls one of the two.   The output control unit outputs at least one of information on a part different from a standard form among parts of the subject detected by the detection unit and information on a foreign substance contained in the subject. The medical image diagnostic apparatus according to claim 1, wherein the medical image diagnostic apparatus is controlled so as to perform the control.   The detection unit learns a relationship between a part different from a standard form of the part of the subject and a feature point corresponding to the different part, and detects the part of the subject based on a learning result. The medical image diagnostic apparatus according to any one of claims 1 to 13.   The medical image diagnostic apparatus according to claim 1, wherein the output control unit controls to output information on the plurality of groups.   The medical image diagnostic apparatus according to claim 1, wherein the group corresponds to any one of a position, an organ, a bone, a blood vessel, and a nerve in the subject.

Images