Medical image diagnostic device and medical image diagnostic method

Abstract

To execute photographic positioning of a subject easily when photographing the subject.SOLUTION: A medical image diagnostic device in an embodiment includes an acquisition unit and a processing unit. The acquisition unit acquires information on a scan object site and a subject positioning image. The processing unit outputs a scan range on the subject related to the acquired information on the scan object site and subject positioning image by inputting the acquired information on the scan object site and subject positioning image to a learned model for outputting a scan range of the subject in scanning based on the information on the scan object site and the subject positioning image.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] FIELD An embodiment of the present invention relates to a medical image diagnostic apparatus and a medical image diagnostic method. [Background technology]

[0002] Conventionally, for imaging diagnostic devices such as X-ray CT devices, there have been disclosed techniques for reducing the processing load at the image analysis stage by using machine learning to set image processing parameters and to learn template images that include the detection target itself (e.g., coronary arteries) and ALs (Anatomical Landmarks) of the detection target within an image.

[0003] Furthermore, there is a conventional technology called ALD (Adaptive Layer Distribution) that automatically sets a scan range by recognizing feature points of the human body (for example, the outline of the head or torso).

[0004] However, with conventional techniques, it was sometimes not easy to align the subject's imaging position during imaging while reflecting the results of machine learning. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Application Publication No. 2016 / 209995 [Patent Document 2] US Patent Application Publication No. 2017 / 347973 [Patent Document 3] US Patent Application Publication No. 2018 / 70908 Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the present invention is to easily perform image capturing position adjustment of a subject during image capturing. [Means for solving the problem]

[0007] A medical image diagnostic apparatus according to an embodiment includes an acquisition unit and a processing unit. The acquisition unit acquires information about a scan target site and an aligned image of a subject. The processing unit inputs the acquired information about the scan target site and the aligned image of the subject to a trained model that outputs a scan range of the subject during scanning based on the information about the scan target site and the aligned image of the subject, and outputs a scan range of the subject related to the acquired information about the scan target site and the aligned image of the subject. [Brief description of the drawings]

[0008] [Figure 1] 1 is a configuration diagram of an X-ray CT apparatus according to a first embodiment. [Diagram 2] FIG. 4 is a diagram showing an example of data stored in a memory 41. [Diagram 3] FIG. 5 is a block diagram of a scan control function 55. [Figure 4] 11 is a diagram for explaining the contents of processing performed by a scan range automatic setting function 55-2. FIG. [Diagram 5] FIG. 4 is a diagram for explaining the processing of a learning function 57. [Figure 6] FIG. 2 is a diagram illustrating an example of a usage environment of the X-ray CT apparatus 1. [Figure 7] FIG. 2 is a diagram illustrating an example of a usage environment of the X-ray CT apparatus 1. [Figure 8] 4 is a flowchart showing an example of the flow of imaging processing by the X-ray CT apparatus 1. [Figure 9] 11 is a flowchart showing an example of the flow of a learning process performed by a learning function 57. [Figure 10] FIG. 11 is a diagram for explaining a gantry device 10A according to a second embodiment. [Figure 11] FIG. 11 is a diagram showing an example of data stored in a memory 41A according to the second embodiment. [Figure 12] FIG. 11 is a configuration diagram of a scan control function 55A according to a second embodiment. [Figure 13] 13 is a diagram for explaining the contents of processing performed by a scan range automatic setting function 55-2A. [Figure 14] FIG. 13 is a diagram for explaining the processing of a learning function 57A. [Figure 15] 11 is a flowchart showing an example of the flow of imaging processing by the X-ray CT apparatus 1A. [Figure 16] 11 is a flowchart showing an example of the flow of an optical image learning process performed by a learning function 57A. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, a medical image diagnostic apparatus and a medical image diagnostic method according to an embodiment will be described with reference to the drawings. The medical image diagnostic apparatus is an apparatus for diagnosing a subject by processing medical images such as an X-ray CT (Computed Tomography) apparatus, an MRI (Magnetic Resonance Imaging) apparatus, or a PET (Positron Emission Tomography) apparatus. In the following description, the medical image diagnostic apparatus will be described as an X-ray CT apparatus, but is not limited thereto.

[0010] (First embodiment) FIG. 1 is a configuration diagram of an X-ray CT apparatus 1 according to the first embodiment. The X-ray CT apparatus 1 includes, for example, a gantry 10, a bed 30, and a console 40. For convenience of explanation, FIG. 1 shows both a view of the gantry 10 from the Z-axis direction and a view of the gantry 10 from the X-axis direction, but in reality, there is only one gantry 10. In this embodiment, the rotation axis of the rotating frame 17 in a non-tilted state or the longitudinal direction of the top board 33 of the bed 30 is defined as the Z-axis direction, an axis perpendicular to the Z-axis direction and horizontal to the floor surface is defined as the X-axis direction, and a direction perpendicular to the Z-axis direction and perpendicular to the floor surface is defined as the Y-axis direction.

[0011] The gantry device 10 includes, for example, an X-ray tube 11, a wedge 12, a collimator 13, an X-ray high voltage device 14, an X-ray detector 15, a data acquisition system (hereinafter, DAS: Data Acquisition System) 16, a rotating frame 17, and a control device 18.

[0012] X-ray tube 11 generates X-rays by irradiating thermoelectrons from a cathode (filament) to an anode (target) when a high voltage is applied from X-ray high voltage device 14. X-ray tube 11 includes a vacuum tube. For example, X-ray tube 11 is a rotating anode type X-ray tube that generates X-rays by irradiating a rotating anode with thermoelectrons.

[0013] The wedge 12 is a filter for adjusting the amount of X-rays irradiated from the X-ray tube 11 to the subject P. The wedge 12 attenuates the X-rays passing through it so that the distribution of the amount of X-rays irradiated from the X-ray tube 11 to the subject P becomes a predetermined distribution. The wedge 12 is also called a wedge filter or a bow-tie filter. The wedge 12 is, for example, made by processing aluminum so as to have a predetermined target angle and a predetermined thickness.

[0014] The collimator 13 is a mechanism for narrowing down the irradiation range of the X-rays transmitted through the wedge 12. The collimator 13 narrows down the irradiation range of the X-rays, for example, by forming a slit by combining multiple lead plates. The collimator 13 is sometimes called an X-ray aperture.

[0015] The X-ray high voltage device 14 has, for example, a high voltage generator and an X-ray control device. The high voltage generator has an electric circuit including a transformer and a rectifier, and generates a high voltage to be applied to the X-ray tube 11. The X-ray control device controls the output voltage of the high voltage generator according to the amount of X-rays to be generated by the X-ray tube 11. The high voltage generator may be one that boosts the voltage using the above-mentioned transformer, or one that boosts the voltage using an inverter. The X-ray high voltage device 14 may be provided on the rotating frame 17, or may be provided on the side of the fixed frame (not shown) of the gantry device 10.

[0016] The X-ray detector 15 detects the intensity of X-rays generated by the X-ray tube 11 and incident upon the subject P. The X-ray detector 15 outputs an electrical signal (which may be an optical signal, etc.) corresponding to the intensity of the detected X-rays to the DAS 18. The X-ray detector 15 has, for example, multiple rows of X-ray detection elements. Each of the multiple rows of X-ray detection elements has multiple X-ray detection elements arranged in the channel direction along an arc centered on the focal point of the X-ray tube 11. The multiple rows of X-ray detection elements are arranged in the slice direction (column direction, row direction).

[0017] The X-ray detector 15 is an indirect detector having, for example, a grid, a scintillator array, and an optical sensor array. The scintillator array has a plurality of scintillators. Each scintillator has a scintillator crystal. The scintillator crystal emits an amount of light according to the intensity of the incident X-ray. The grid is disposed on the surface of the scintillator array on which the X-ray is incident, and has an X-ray shielding plate having a function of absorbing scattered X-rays. The grid may also be called a collimator (one-dimensional collimator or two-dimensional collimator). The optical sensor array has, for example, an optical sensor such as a photomultiplier tube (PMT). The optical sensor array outputs an electric signal according to the amount of light emitted by the scintillator. The X-ray detector 15 may be a direct conversion type detector having a semiconductor element that converts the incident X-ray into an electric signal.

[0018] The DAS 16 includes, for example, an amplifier, an integrator, and an A / D converter. The amplifier performs an amplification process on the electric signal output by each X-ray detection element of the X-ray detector 15. The integrator integrates the amplified electric signal over a view period (described later). The A / D converter converts the electric signal indicating the integration result into a digital signal. The DAS 16 outputs detection data based on the digital signal to the console device 40. The detection data is a digital value of X-ray intensity identified by the channel number and column number of the X-ray detection element that generated the data, and a view number indicating the acquired view. The view number is a number that changes according to the rotation of the rotating frame 17, and is, for example, a number that is incremented according to the rotation of the rotating frame 17. Therefore, the view number is information that indicates the rotation angle of the X-ray tube 11. The view period is a period that falls within the period from a rotation angle corresponding to a certain view number to a rotation angle corresponding to the next view number. The DAS 16 may detect the view switching by a timing signal input from the control device 18, by an internal timer, or by a signal obtained from a sensor (not shown). When performing a full scan and X-rays are continuously irradiated by the X-ray tube 11, the DAS 16 collects a group of detection data for the entire circumference (360 degrees). When performing a half scan and X-rays are continuously irradiated by the X-ray tube 11, the DAS 16 collects detection data for half the circumference (180 degrees).

[0019] The rotating frame 17 is an annular member that supports the X-ray tube 11, the wedge 12, the collimator 13, and the X-ray detector 15 in opposing relation to each other. The rotating frame 17 is supported by a fixed frame so as to be rotatable about the subject P introduced inside. The rotating frame 17 also supports the DAS 16. Detection data output by the DAS 16 is transmitted by optical communication from a transmitter having a light-emitting diode (LED) provided on the rotating frame 17 to a receiver having a photodiode provided on a non-rotating part (e.g., the fixed frame) of the gantry device 10, and is transferred to the console device 40 by the receiver. Note that the method of transmitting detection data from the rotating frame 17 to the non-rotating part is not limited to the above-mentioned method using optical communication, and any non-contact type transmission method may be adopted. The rotating frame 17 is not limited to an annular member, and may be an arm-like member as long as it can support and rotate the X-ray tube 11, etc.

[0020] The X-ray CT device 1 is, for example, a Rotate / Rotate-type X-ray CT device (third generation CT) in which both the X-ray tube 11 and the X-ray detector 15 are supported by a rotating frame 17 and rotate around the subject P, but is not limited to this and may be a Stationary / Rotate-type X-ray CT device (fourth generation CT) in which multiple X-ray detection elements arranged in a circular ring are fixed to a fixed frame and the X-ray tube 11 rotates around the subject P.

[0021] The control device 18 includes, for example, a processing circuit having a processor such as a CPU (Central Processing Unit) and a drive mechanism including a motor, an actuator, etc. The control device 18 receives an input signal from an input interface 43 attached to the console device 40 or the gantry device 10, and controls the operations of the gantry device 10 and the bed device 30.

[0022] The control device 18, for example, rotates the rotating frame 17, tilts the gantry 10, and moves the top plate 33 of the bed 30. When tilting the gantry 10, the control device 18 rotates the rotating frame 17 about an axis parallel to the Z-axis direction based on an inclination angle (tilt angle) input to the input interface 43. The control device 18 grasps the rotation angle of the rotating frame 17 from the output of a sensor (not shown) or the like. The control device 18 also provides the rotation angle of the rotating frame 17 to the processing circuit 50 at any time. The control device 18 may be provided in the gantry 10 or in the console device 40.

[0023] The bed device 30 is a device that places and moves the subject P to be scanned and introduces the subject P into the rotating frame 17 of the gantry device 10. The bed device 30 has, for example, a base 31, a bed driving device 32, a top plate 33, and a support frame 34. The base 31 includes a housing that supports the support frame 34 so that the support frame 34 can move in the vertical direction (Y-axis direction). The bed driving device 32 includes a motor and an actuator. The bed driving device 32 moves the top plate 33, on which the subject P is placed, in the longitudinal direction of the top plate 33 (Z-axis direction) along the support frame 34. The top plate 33 is a plate-shaped member on which the subject P is placed.

[0024] The bed driving device 32 may move not only the tabletop 33 but also the support frame 34 in the longitudinal direction of the tabletop 33. Conversely to the above, the gantry device 10 may be movable in the Z-axis direction, and the rotation frame 17 may be controlled to come to the periphery of the subject P by the movement of the gantry device 10. Also, a configuration in which both the gantry device 10 and the tabletop 33 are movable may be used. The X-ray CT device 1 may be an apparatus in which the subject P is scanned in a standing or sitting position. In this case, the X-ray CT device 1 has a subject support mechanism instead of the bed device 30, and the gantry device 10 rotates the rotation frame 17 around an axial direction perpendicular to the floor surface.

[0025] The console device 40 includes, for example, a memory 41, a display 42, an input interface 43, the memory 41, a network connection circuit 44, and a processing circuit 50. In the embodiment, the console device 40 is described as being separate from the gantry device 10, but the gantry device 10 may include some or all of the components of the console device 40.

[0026] The memory 41 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. The memory 41 stores, for example, detection data, projection data, a reconstructed image, a CT image, etc. These data may be stored in an external memory with which the X-ray CT apparatus 1 can communicate, instead of the memory 41 (or in addition to the memory 41). The external memory is controlled by, for example, a cloud server that manages the external memory, by the cloud server accepting a read / write request. The external memory is realized by, for example, a system called PACS (Picture Archiving and Communication Systems). PACS is a system that systematically stores images captured by various image diagnostic devices, etc.

[0027] Fig. 2 is a diagram showing an example of data stored in the memory 41. As shown in Fig. 2, the memory 41 stores information such as imaging conditions 41-1, detection data 41-2 generated by the processing circuitry 50, projection data 41-3, a reconstructed image 41-4, a scan range 41-7, and a trained model 41-8. The reconstructed image 41-4 can be classified into, for example, a scano image 41-5 and an actual captured image 41-6.

[0028] The display 42 displays various types of information. For example, the display 42 displays medical images (CT images) generated by a processing circuit, GUI (Graphical User Interface) images that accept various operations by an operator, and the like. The display 42 is, for example, a liquid crystal display, a CRT (Cathode Ray Tube), an organic EL (Electroluminescence) display, or the like. The display 42 may be provided on the gantry device 10. The display 42 may be a desktop type, or may be a display device (for example, a tablet terminal) that can wirelessly communicate with the main body of the console device 40.

[0029] The input interface 43 accepts various input operations by an operator and outputs an electric signal indicating the content of the accepted input operation to the processing circuit 50. For example, the input interface 43 accepts input operations such as collection conditions when collecting detection data or projection data (described later), reconstruction conditions when reconstructing a CT image, and image processing conditions when generating a post-processed image from a CT image. For example, the input interface 43 is realized by a mouse, a keyboard, a touch panel, a trackball, a switch, a button, a joystick, a camera, an infrared sensor, a microphone, or the like. The input interface 43 may be provided in the gantry device 10. The input interface 43 may also be realized by a display device (e.g., a tablet terminal) capable of wireless communication with the main body of the console device 40.

[0030] The network connection circuit 44 includes, for example, a network card having a printed circuit board, or a wireless communication module. The network connection circuit 44 implements an information communication protocol according to the type of the network to be connected. The network includes, for example, a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, a cellular network, a dedicated line, etc.

[0031] The processing circuitry 50 controls the overall operation of the X-ray CT apparatus 1. The processing circuitry 50 executes, for example, a system control function 51, a pre-processing function 52, a reconstruction processing function 53, an image processing function 54, a scan control function 55, a display control function 56, a learning function 57, a transfer function 58, and the like. The processing circuitry 50 realizes these functions by, for example, a hardware processor executing a program stored in the memory 41.

[0032] The hardware processor means a circuit such as a CPU, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD) or a complex programmable logic device (CPLD), or a field programmable gate array (FPGA)). Instead of storing a program in the memory 41, the program may be directly built into the circuit of the hardware processor. In this case, the hardware processor realizes a function by reading and executing the program built into the circuit. The hardware processor is not limited to being configured as a single circuit, and may be configured as one hardware processor by combining multiple independent circuits to realize each function. Also, multiple components may be integrated into one hardware processor to realize each function.

[0033] Each component of the console device 40 or the processing circuit 50 may be distributed and realized by multiple pieces of hardware. The processing circuit 50 may be realized by a processing device capable of communicating with the console device 40, rather than being a component of the console device 40. The processing device is, for example, a workstation connected to one X-ray CT device, or a device (for example, a cloud server) connected to multiple X-ray CT devices and collectively executing processing equivalent to that of the processing circuit 50 described below.

[0034] The system control function 51 controls various functions of the processing circuit 50 based on the input operation received by the input interface 43 .

[0035] The pre-processing function 52 performs pre-processing such as logarithmic conversion, offset correction, inter-channel sensitivity correction, and beam hardening correction on the detection data 41-2 output by the DAS 16 to generate projection data 41-3, and stores the generated projection data 41-3 in the memory 41.

[0036] The reconstruction processing function 53 performs reconstruction processing using a filtered back projection method, an iterative reconstruction method, or the like on the projection data generated by the pre-processing function 52 to generate a reconstructed image 41-4, and stores the generated reconstructed image 41-4 in the memory 41.

[0037] The image processing function 54 converts the reconstructed image 41-4 into a three-dimensional image or cross-sectional image data of an arbitrary cross section by a known method based on an input operation received by the input interface 43. The conversion into a three-dimensional image may be performed by the pre-processing function 52.

[0038] The scan control function 55 controls the collection process of detection data in the gantry device 10 by issuing instructions to the X-ray high voltage device 14, the DAS 16, the control device 18, and the bed driving device 32. The scan control function 55 controls the operation of each part when capturing images to collect the scanogram 41-5 or the actual captured image 41-6, and when capturing images to be used for diagnosis.

[0039] The display control function 56 controls the display mode of the display 42 .

[0040] The learning function 57 learns the scan range 41-7. Detailed processing of the learning function 57 will be described later. The learning function 57 is an example of a "learning unit."

[0041] The transfer function 58 transfers a part or the whole of the captured reconstructed image 41-4 to the PACS. The transfer function 58 is an example of a "transfer unit."

[0042] With the above configuration, the X-ray CT device 1 scans the subject P in a manner such as helical scan, conventional scan, or step-and-shoot. Helical scan is a manner in which the subject P is scanned in a spiral manner by rotating the rotating frame 17 while moving the top plate 33. Conventional scan is a manner in which the subject P is scanned in a circular orbit by rotating the rotating frame 17 while keeping the top plate 33 stationary. A conventional scan is executed. Step-and-shoot is a manner in which the position of the top plate 33 is moved at regular intervals to perform a conventional scan in a plurality of scan areas.

[0043] 3 is a configuration diagram of the scan control function 55. The scan control function 55 includes, for example, a scan image acquisition function 55-1, a scan range automatic setting function 55-2, a scan range manual setting function 55-3, and a scan execution function 55-4.

[0044] The scanogram acquisition function 55-1 acquires a scanogram 41-5 of the subject P for determining a scan range. The scanogram is an example of a registration image. Another example of a registration image is a scout image.

[0045] A method for acquiring a scanogram will now be described. The scanogram acquiring function 55-1 controls the control device 18 to fix the position of the X-ray tube 11 at a predetermined rotation angle, and to irradiate the subject P with X-rays from the X-ray tube 11 while moving the top plate 33 of the bed device 30 in the Z-axis direction. The scanogram acquiring function 55-1 acquires a two-dimensional scanogram 41-5 of the subject P from the detection data 41-2 acquired in this manner.

[0046] The method of acquiring the scanogram 41-5 is not limited to the above-mentioned method, and may be, for example, a method of acquiring projection data 41-3 of the entire circumference of the subject P by helical scanning or non-helical scanning. Here, the scanogram acquisition function 55-1 executes a scan at a lower dose than the actual imaging for a wide range such as the entire chest, entire abdomen, entire upper body, and entire body of the subject. The non-helical scan is, for example, a scan of the step-and-shoot method. In this way, the scanogram acquisition function 55-1 acquires detection data 41-2 of the entire circumference of the subject P, and the reconstruction processing function 53 reconstructs a three-dimensional reconstruction image 41-4 (volume data), and may generate a two-dimensional scanogram 41-5 according to an arbitrary direction based on the reconstructed volume data. The scanogram 41-5 can also be treated as a three-dimensional scanogram 41-5 by using the above-mentioned volume data.

[0047] The scano image acquisition function 55-1 outputs a scano image acquired by, for example, one of the methods exemplified above to the scan range automatic setting function 55-2. The scano image acquisition function 55-1 is an example of an "acquisition unit." Note that the "acquisition unit" is not limited to "generating and acquiring by itself" as in the above example, but may also "acquire from another functional unit."

[0048] The automatic scan range setting function 55-2 inputs the scan image output by the scan image acquisition function 55-1 and the shooting conditions into the trained model 41-8, and sets the scan range 41-7 according to the resulting output information and stores it in the memory 41.

[0049] The trained model 41-8 is trained by, for example, the learning function 57, as described later. The trained model 41-8 is trained to output a scan range when, for example, imaging conditions of the subject (for example, some or all of age, sex, body type, disease name, scan target part, examination conditions, and bed position conditions) are input. The scan target part includes, for example, the head, upper body, lungs, liver, etc. Examination conditions include, for example, initial imaging, follow-up observation imaging, etc. In this embodiment, the imaging conditions are input parameters, but the trained model 41-8 for each imaging condition is trained, and the scan range automatic setting function 55-2 may select the trained model 41-8 that matches the imaging conditions of the subject P and use it for the above processing. Furthermore, a trained model 41-8 for each first part of the imaging conditions is trained, and the second part of the imaging conditions is used as an input parameter, and the automatic scan range setting function 55-2 may select the trained model 41-8 that matches the first part of the imaging conditions of the subject P and use it for the above processing.

[0050] 4 is a diagram for explaining the contents of the process by the scan range automatic setting function 55-2. The scan range automatic setting function 55-2 inputs the imaging conditions 41-1 and the scan image 41-5 as parameters to the trained model 41-8, and outputs the scan range 41-7 of the subject P. By performing subsequent processes based on this scan range 41-7, it is possible to reduce the time required for the scan plan from the scan image capture of the subject P to the actual capture, compared to the case where the scan range is manually set from the beginning.

[0051] The automatic scan range setting function 55-2 stores the automatically set scan range 41-7 in the memory 41. The automatic scan range setting function 55-2 is an example of a "processing unit."

[0052] In addition, if there is no suitable trained model 41-8, the automatic scan range setting function 55-2 may omit the setting process of the scan range 41-7 and output the scanned image 41-5 to the manual scan range setting function 55-3.

[0053] The scan range manual setting function 55-3 accepts fine adjustments, redoing, new settings, etc. of the scan range by the operator. For example, the scan range manual setting function 55-3 reflects the contents of fine adjustments input by the operator via the input interface 43 to the scan range 41-7 output by the scan range automatic setting function 55-2, and updates the scan range 41-7 in the memory 41.

[0054] Furthermore, the scan range manual setting function 55-3 redoes the scan range setting (or creates a new one) when the operator performs an operation indicating that the scan range 41-7 output by the scan range automatic setting function 55-2 is not adopted. In this case, the scan range manual setting function 55-3 sets the scan range 41-7 according to an operation input by the operator via the input interface 43 for the scan image 41-5 output by the scan image acquisition function 55-1 (for example, an input operation of displaying a frame indicating the scan range 41-7 on the scan image 41-5 on the display 42, moving the position of the frame, or enlarging or reducing the size of the frame). The scan range manual setting function 55-3 overwrites the scan range 41-7 stored in the memory 41 with the set scan range 41-7. Similarly, even if the scan range automatic setting function 55-2 omits the process of setting the scan range 41-7, the scan range manual setting function 55-3 displays a frame indicating the scan range 41-7 on the scan image 41-5, and sets the scan range 41-7 in response to input operations such as moving the position of the frame, enlarging or reducing the size of the frame, or reducing the size by deleting an arbitrary percentage from the end of the scan image 41-5. The scan range manual setting function 55-3 stores the scan range 41-7 set by fine adjustment, redoing, etc. in the memory 41. The scan range manual setting function 55-3 is an example of an "image processing unit."

[0055] The automatic scan range setting function 55-2 and the manual scan range setting function 55-3 may set a reconstruction range when reconstructing a CT image. In this case, the automatic scan range setting function 55-2 performs a predetermined calculation to convert the scan range 41-7 output by the trained model 41-8 into a reconstruction range, and causes the control device 18 to execute a scan based on the calculation result. At this time, the scan range 41-7 output by the trained model 41-8 may be wider than the reconstruction range. In addition, when the automatic scan range setting function 55-2 and the manual scan range setting function 55-3 set the reconstruction range, and when the trained model 41-8 has a trained model that outputs the reconstruction range, the trained model that outputs the reconstruction range may be used. In this case, the manual scan range setting function 55-3 sets a range that is expanded up, down, left, and right from the reconstruction range as the scan range 41-7.

[0056] The scan execution function 55-4 controls the control device 18 to perform actual photographing within a range defined by the scan range 41-7 stored in the memory 41, and to obtain an actual photographed image 41-6 (scanned image).

[0057] A flag or the like may be set in the scan range 41-7 to identify whether a scan range that is the result of automatic setting by the scan range automatic setting function 55-2 has been adopted, or whether a manual setting has been performed by the scan range manual setting function 55-3.

[0058] The learning function 57 acquires sets of scanograms, imaging conditions, and scan ranges for multiple subjects from the memory 41 or an external device. The learning function 57 uses the acquired scanograms and imaging conditions including information on the parts to be scanned as learning data, and performs machine learning using scanograms set for the same subject as teacher data to generate a trained model 41-8. The learning function 57 is an example of a "model generation unit."

[0059] The learning function 57 may be realized by an external device. In this case, the transfer function 58 may transfer at least the scano image 41-5 to be transferred and the scan range 41-7 corresponding to the scano image 41-5, so as to be used for learning. The transfer function 58 may further transfer the reconstruction range of the reconstructed image 41-4 generated based on the scano image 41-5 to be transferred, so as to be used for learning.

[0060] 5 is a diagram for explaining the processing of the learning function 57. The learning function 57 inputs a plurality of sets of learning data to a machine learning model in which connection information and the like are defined in advance and parameters such as connection coefficients are provisionally set, and adjusts parameters in the machine learning model so that the result approaches teacher data corresponding to the learning data. Shooting conditions and a scano image are input to the machine learning model.

[0061] The shooting conditions input to the machine learning model are set to, for example, the shooting conditions described above.

[0062] The scanogram input to the machine learning model may include, for example, information set during the examination (for example, the body axis direction reconstruction range, setting information of the ROI (region of interest) during image reconstruction), etc.

[0063] The learning function 57 adjusts the parameters of the machine learning model by, for example, backpropagation. The machine learning model is, for example, a DNN (Deep Neural Network) using a CNN (Convolution Neural Network). Note that the machine learning model may perform weighting settings for each piece of learning data so that newer learning data is more easily reflected as output.

[0064] The learning function 57 ends the process when it has completed learning a predetermined number of sets of learning data and corresponding teacher data. The machine learning model at that point is stored as a trained model 41-8.

[0065] A scano image may be captured multiple times from the same or similar direction, for example, when an artifact is detected and re-capture is required. Therefore, the learning function 57 can generate a more suitable trained model by learning only from the scano images (i.e., success cases) transferred by the transfer function 58 and updating the trained model to reflect the processing results of the scan range setting by the scan control function 55. In this case, the learning function 57 may generate a trained model with higher accuracy by extracting the range acquired as the reconstructed image 41-4 from the scano images of the success cases transferred by the transfer function 58 and using it as the learning object.

[0066] In addition, the scan images used in the scan plan may be multiple images taken at two or more different angles, and the learning function 57 may generate a trained model from some of these images, or may generate a trained model from all of the images.

[0067] The trained model 41-8 used by the learning function 57, or the training data and teacher data used by the learning function 57, may be generated by another X-ray CT apparatus 1.

[0068] 6 and 7 are diagrams for explaining an example of a usage environment of the X-ray CT device 1. A trained model generated by another X-ray CT device 1 or a learning device, or training data and teacher data used by the training function 57, is provided to a facility H1 using the X-ray CT device 1 by a service representative MP of the manufacturer of the X-ray CT device 1, for example, as shown in FIG. 6. In addition, a trained model generated by another X-ray CT device 1, or training data and teacher data used by the training function 57 may be shared by a cloud server C or the like via an external network NW by a plurality of facilities H1 to HN (N is any natural number) using the X-ray CT device 1, as shown in FIG. 7. The cloud server C may exclusively perform training similar to that of the training function 57.

[0069] FIG. 8 is a flowchart showing an example of the flow of imaging processing by the X-ray CT apparatus 1.

[0070] First, the console device 40 accepts input of information for identifying the subject P and imaging conditions (step S100), and accepts settings for the imaging (for example, an operation to move the tabletop 33 of the bed device 30 to an imaging start position) (step S102). Next, the control device 18 controls the gantry device 10 to capture a scanogram 41-5 (step S104).

[0071] Next, the imaging conditions and the scan image, which are input parameters, are input to the trained model 41-8 (step S106), and the scan range 41-7, which is the output of the trained model, is displayed on the display 42 (step S108). Note that, if there are multiple trained models 41-8 applied in step S106, multiple patterns of the scan range 41-7 may be displayed in step S108, or the most suitable trained model 41-8 may be selected in step S106.

[0072] Next, the input interface 43 accepts an input of a decision result by the operator of the X-ray CT apparatus 1 as to whether to adopt the scan range 41-7 displayed in step S108, or to have the operator manually redo the scan range setting without adopting the scan range 41-7 (step S110). When the operator manually sets the scan range, the scan range manual setting function 55-3 accepts the scan range setting made via the input interface 43 (step S112). When the scan range displayed in step S110 is adopted, the process proceeds to step S114 described later.

[0073] The scan range manual setting function 55-3 accepts final fine adjustments made manually by the operator (step S114). Next, the scan execution function 55-4 performs actual imaging (step S116).

[0074] Next, the transfer function 58 transfers the actual captured image 41-6, which is the result of the shooting, to a PACS or the like (step S118). Next, the learning function 57 generates a trained model 41-8 based on the scanogram and shooting conditions (e.g., reconstruction range, etc.). Alternatively, the learning function 57 reflects the scanogram and shooting conditions in the trained model (step S120). The details of the process of step S120 will be described later. This concludes the explanation of this flowchart.

[0075] Fig. 9 is a flowchart showing an example of the flow of the learning process by the learning function 57. The process flow shown in Fig. 9 corresponds to the detailed process of step S120 in Fig. 8. The flowchart in Fig. 9 may be performed every time the X-ray CT apparatus 1 finishes imaging one subject, or may be performed every time imaging is performed in which the scan range is redone by the above-mentioned manual operation by the operator (every time step S112 is executed).

[0076] First, the learning function 57 acquires a scano image and a shooting condition, which are one set of learning data (step S200). Next, the learning function 57 inputs the one set of learning data acquired in step S200 to a machine learning model (step S202), and back-propagates an error from the teacher data corresponding to the one set of learning data (step S204).

[0077] Next, the learning function 57 judges whether or not the processing of steps S202 and S204 has been performed for the predetermined number of sets of learning data (step S206). If the processing of steps S202 and S204 has not been performed for the predetermined number of sets of learning data, the learning function 57 returns the processing to step S200. If the processing of steps S202 and S204 has been performed for the predetermined number of sets of learning data, the learning function 57 determines the trained model 41-8 using the parameters at that time (step S208) and ends the processing of this flowchart. Note that in step S206, if the processing cannot be continued because the predetermined number of sets of learning data do not exist, the subsequent processing may be reduced.

[0078] According to the first embodiment of the X-ray CT apparatus 1 described above, the apparatus is equipped with a scan image acquisition function 55-1 that acquires information about the area to be scanned and a scan image of the subject P, and a trained model 41-8 that outputs the scan range of the subject during scanning based on the information about the area to be scanned and the scan image of the subject P. The trained model 41-8 is equipped with a scan range automatic setting function 55-2 that inputs information about the area to be scanned and the scan image of the subject P acquired by the scan image acquisition function 55-1 and outputs the information about the area to be scanned and the scan range 41-7 related to the subject P related to the acquired information about the area to be scanned and the scan image of the subject P, thereby making it possible to easily align the subject's shooting position during shooting.

[0079] Second embodiment The X-ray CT device 1A of the second embodiment will be described below. In the following description, the same configurations and functions as those of the first embodiment are given the same reference numerals as those of the first embodiment, and detailed description will be omitted. Also, those with the same names as those of the first embodiment but different configurations or functions will have "A" added to the end of the reference numerals.

[0080] 10 is a diagram for explaining a gantry 10A according to the second embodiment. The gantry 10A further includes a camera 19, in comparison with the gantry 10 of the first embodiment. The camera 19 photographs a space including the subject P and obtains an optical image 41-9. The camera 19 may be installed in a location other than the gantry 10, for example, in a position where the entire bed device 30 can be photographed (for example, on the ceiling of a room in which the X-ray CT apparatus 1A is installed, or in the vicinity of the bed device 30).

[0081] 11 is a diagram showing an example of data stored in the memory 41A according to the second embodiment. Compared to the memory 41 of the first embodiment, the memory 41A further stores an optical image 41-9.

[0082] 12 is a configuration diagram of a scan control function 55A according to the second embodiment. Compared with the scan control function 55 of the first embodiment, the scan control function 55A further includes an optical image acquisition function 55-5.

[0083] The optical image acquiring function 55-5 acquires the optical image 41-9 captured by the camera 19 and the imaging conditions 41-1 associated with the subject P appearing in the optical image, and outputs them to the scan range automatic setting function 55-2A. The optical image acquiring function 55-5 is another example of the "acquiring unit".

[0084] The automatic scan range setting function 55-2A inputs the optical image output by the optical image acquisition function 55-5 and the shooting conditions into the trained model 41-8A, sets the scan range 41-7 according to the resulting output information, and stores it in the memory 41.

[0085] The automatic scan range setting function 55-2A accepts adjustment, redoing, new setting, etc. of the scan range by the operator. When the automatic scan range setting function 55-2A accepts an operation indicating that the scan range is adopted, the actual shooting is performed after reflecting the contents of the fine adjustment input by the operator via the input interface 43. This fine adjustment is performed by the manual scan range setting function 55-3. When the automatic scan range setting function 55-2A accepts a process indicating redoing, new setting, etc., that is, when it accepts a process indicating that the scan range based on the optical image is not adopted, it may reacquire the optical image or may perform a process for acquiring a scano image. The automatic scan range setting function 55-2A is another example of a "processing unit".

[0086] 13 is a diagram for explaining the contents of the process by the scan range automatic setting function 55-2A. The scan range automatic setting function 55-2A inputs information on the inspection conditions included in the imaging conditions 41-1 and the optical image 41-9 as parameters to the trained model 41-8, and outputs a scan range 41-7 of the subject P. By performing subsequent processes based on this scan range 41-7, it is possible to shorten the time required for the scan plan from the scanogram imaging of the subject P to the actual imaging.

[0087] The learning function 57A generates a trained model using the optical image and the imaging conditions as learning data in addition to a trained model using the scano image and the imaging conditions as learning data. The learning function 57A acquires sets of optical images, imaging conditions, and scan ranges for multiple subjects from the memory 41 or an external device. The learning function 57A generates a trained model 41-8 by performing machine learning using the acquired optical images and imaging conditions including information on the scanned area as learning data and a scan range set for the same subject as teacher data.

[0088] FIG. 14 is a diagram for explaining the processing of the learning function 57A. The learning function 57A inputs a plurality of sets of learning data to a machine learning model in which connection information and the like are defined in advance and parameters such as connection coefficients are provisionally set, and adjusts parameters in the machine learning model so that the result approaches the teacher data corresponding to the learning data. Shooting conditions and optical images are input to the machine learning model. The learning function 57A ends the processing after learning a predetermined number of sets of learning data and the corresponding teacher data. The machine learning model at that point is stored as a trained model 41-8A.

[0089] The generation of the scan range based on the optical image by the scan range automatic setting function 55-2A and the generation of the scan range based on the scan image may be used together. For example, the scan range automatic setting function 55-2A sets the scan range in the X-axis direction based on the output of the trained model of the scan image, and sets the scan range in the Y-axis direction based on the output of the trained model of the optical image as a substitute for the scan image. This can reduce the amount of radiation exposure of the operator of the X-ray CT device 1A and the subject P.

[0090] Fig. 15 is a flowchart showing an example of the flow of imaging processing by the X-ray CT apparatus 1A. Note that steps S300 and S302 in the flowchart of Fig. 15 correspond to steps S100 and S102 in the flowchart of Fig. 7. Also, steps S312 to S328 in the flowchart of Fig. 15 correspond to steps S104 to S120 in the flowchart of Fig. 7.

[0091] First, the console device 40 accepts input of information for identifying the subject P and imaging conditions (step S300), and accepts the settings for the imaging (step S302). Next, the optical image acquisition function 55-5 acquires an optical image captured by the camera 19 (step S304). Next, the optical image is applied to the trained model (step S306), and a scan range based on the trained model is displayed on the display 42 (step S308). Note that, if there are multiple trained models applied in step S306, multiple patterns of the scan range may be displayed in step S308, or the most suitable trained model may be selected in step S36.

[0092] Next, the input interface 43 accepts an input of a decision made by the operator of the X-ray CT apparatus 1A as to whether to adopt the scan range displayed in step S308 or to take a scanogram and set the scan range (step S310). If an operation indicating that the displayed scan range is adopted is accepted, the process proceeds to step S322, which will be described later. If an operation indicating that a scanogram is to be taken is accepted, the control device 18 controls the gantry device 10 to take a scanogram (step S312).

[0093] Next, the scan range automatic setting function 55-2A inputs the imaging conditions and the scan image, which are input parameters, to the trained model (step S314), and displays the scan range, which is the output of the trained model, on the display 42 (step S316). Note that, if there are multiple trained models applied in step S314, multiple patterns of the scan range may be displayed in step S316, or the most suitable trained model may be selected in step S314.

[0094] Next, the input interface 43 accepts an input of a decision result by the operator of the X-ray CT apparatus 1A as to whether to adopt the scan range setting displayed in step S316 or to manually redo the scan range setting (step S318). If the operator manually redoes the scan range setting, the scan range automatic setting function 55-2A accepts the scan range setting made via the input interface 43 (step S320). If the scan range displayed in step S316 is adopted, the process proceeds to step S322 described below.

[0095] The automatic scan range setting function 55-2A accepts final fine adjustments made manually by the operator (step S322). Next, the control device 18 performs main imaging (step S324).

[0096] Next, the transfer function 58 transfers the actual captured image, which is the result of the shooting, to a PACS or the like (step S326). Next, the learning function 57A generates a trained model including the scanogram or optical image, and the shooting conditions. Alternatively, the learning function 57A reflects the scanogram or optical image, and the shooting conditions in the trained model (step S328). The details of the process of step S328 will be described later. This concludes the explanation of this flowchart.

[0097] Fig. 16 is a flowchart showing an example of the flow of the learning process of the optical image by the learning function 57A. The combination of the flowchart shown in Fig. 9 and the flowchart shown in Fig. 16 corresponds to the detailed process of step S328 in Fig. 15. The flowchart in Fig. 16 may be performed every time the X-ray CT apparatus 1 finishes imaging one subject, or may be performed every time imaging is performed in which the scan range is redone by the above-mentioned manual operation by the operator (every time step S320 is executed).

[0098] First, the learning function 57A acquires an optical image and a shooting condition, which are one set of learning data (step S400). Next, the learning function 57A inputs the one set of learning data to a machine learning model (step S402), and back-propagates an error from the teacher data corresponding to the one set of learning data (step S404).

[0099] Next, the learning function 57A judges whether the processing of steps S402 and S404 has been performed for the predetermined number of sets of learning data (step S406). If the processing of steps S402 and S404 has not been performed for the predetermined number of sets of learning data, the learning function 57A returns the processing to step S400. If the processing of steps S402 and S404 has been performed for the predetermined number of sets of learning data, the learning function 57A determines the trained model 41-8 using the parameters at that time (step S408) and ends the processing of this flowchart. Note that in step S406, if the processing cannot be continued because the predetermined number of sets of learning data do not exist, the subsequent processing may be reduced.

[0100] According to the second embodiment described above, in addition to achieving the same effects as the first embodiment, the scan range automatic setting function 55-2A generates a scan range based on an optical image, which increases the possibility of reducing the number of scano images taken compared to the first embodiment, and increases the possibility of reducing the amount of radiation exposure of the operator of the X-ray CT device 1A and the subject P.

[0101] The above-described embodiment can be expressed as follows. A storage device for storing programs; A processor, The processor executes the program, Obtaining information about the area to be scanned and an alignment image of the subject; a trained model that outputs a scan range of the subject during scanning based on information about a scan target portion and a registration image of the subject, and outputs a scan range of the subject related to the acquired information about the scan target portion and the registration image of the subject by inputting the acquired information about the scan target portion and the registration image of the subject into the trained model that outputs a scan range of the subject related to the acquired information about the scan target portion and the registration image of the subject; The medical image diagnostic apparatus is configured as follows.

[0102] According to at least one of the embodiments described above, there is provided an acquisition unit (55-1, 55-5) that acquires imaging condition information (41-1) including information on the part to be scanned and images (41-4, 41-9) for identifying the scan range of the subject, and a processing unit (55-2, 55-2A) that inputs the imaging condition information and the image for identifying the scan range of the subject to a trained model (41-8) based on the imaging condition information and the image for identifying the scan range of the subject, and outputs the scan range of the subject during the scan, thereby facilitating the imaging position adjustment of the subject during imaging.

[0103] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various reductions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0104] 1, 1A...X-ray CT device 10, 10A... Mounting device 11...X-ray tube 12…Wedge 13…Collimator 14…High voltage X-ray device 15…X-ray detector 16…Data Acquisition System 17...Rotating frame 18...Control device 19…Camera 30...Bedroom equipment 31… Foundation 32...Bed drive unit 33…Tabletop 34…Support frame 40...Console device 50...Processing circuit 51...System control function 52…Pre-processing function 53...Reconstruction processing function 54...Image processing function 55, 55A…Scan control function 55-1…Scano image acquisition function 55-2, 55-2A…Scan range automatic setting function 55-3…Manual scan range setting function 55-4…Scan execution function 55-5…Optical image acquisition function 56…Display control function 57, 57A…Learning function 58...Transfer function

Claims

1. an acquisition unit that acquires a scanogram image and an optical image, which are images of the subject being aligned; a processing unit that outputs a scanning range in a first direction of a top plate of a bed device based on a scanogram of a subject, the scanning range being output by inputting an acquired scanogram of the subject to a first trained model, and a scanning range in a second direction being output by inputting an acquired optical image of the subject to a second trained model that outputs a scanning range in a second direction orthogonal to the first direction of the subject based on an optical image of the subject; and A medical image diagnostic apparatus comprising:

2. an acquisition unit that acquires imaging conditions including information about a region to be scanned and a scanogram image and an optical image, which are images of alignment of a subject; a processing unit that outputs a scan range in a first direction of a top plate of a bed device based on imaging conditions including information about a scan target area and a scanogram of the subject, by inputting the acquired imaging conditions and the scanogram of the subject to a first trained model; and outputs a scan range in a second direction of the subject orthogonal to the first direction of the subject based on imaging conditions including information about a scan target area and an optical image of the subject, by inputting the acquired imaging conditions and the acquired optical image of the subject to a second trained model. A medical image diagnostic apparatus comprising:

3. an acquisition unit that acquires a scanogram image and an optical image, which are images of the subject being aligned; a processing unit that outputs a reconstruction range in a first direction of a top plate of a bed device based on a scanogram of a subject, the reconstruction range being output by inputting an acquired scanogram of the subject to a first trained model, and a reconstruction range in a second direction being output by inputting an acquired optical image of the subject to a second trained model that outputs a reconstruction range in a second direction orthogonal to the first direction of the subject based on an optical image of the subject; and A medical image diagnostic apparatus comprising:

4. an acquisition unit that acquires imaging conditions including information about a region to be scanned and a scanogram image and an optical image, which are images of alignment of a subject; a processing unit that outputs a reconstruction range in a first direction of a top board of a bed device based on imaging conditions including information about a region to be scanned and a scanogram of the subject, the reconstruction range being output by inputting the acquired imaging conditions and the scanogram of the subject to a first trained model; and a reconstruction range in a second direction of a second direction of a subject orthogonal to the first direction based on imaging conditions including information about a region to be scanned and the optical image of the subject, the reconstruction range being output by inputting the acquired imaging conditions and the acquired optical image of the subject to a second trained model. A medical image diagnostic apparatus comprising:

5. An image processing unit is further provided that displays the scan ranges in the first direction and the second direction output by the processing unit on the acquired aligned image.

3. The medical image diagnostic apparatus according to claim 1.

6. The image processing unit sets the scan range in response to an operation by an operator on the displayed scan range. The medical image diagnostic apparatus according to claim 5 .

7. An image processing unit is further provided for displaying the reconstruction ranges in the first direction and the second direction output by the processing unit on the acquired aligned image.

5. The medical image diagnostic apparatus according to claim 3.

8. the image processing unit sets the reconstruction range in response to an operation by an operator on the displayed reconstruction range. The medical image diagnostic apparatus according to claim 7.

9. The processing unit converts the scan range into a reconstruction range by performing a predetermined calculation on the scan range in the first direction and the second direction.

3. The medical image diagnostic apparatus according to claim 1.

10. The processing unit sets a range obtained by expanding the reconstruction range in the first direction and the second direction as a scan range.

5. The medical image diagnostic apparatus according to claim 3.

11. The alignment image is a scanogram based on projection data of the entire circumference of the subject acquired by helical scanning or non-helical scanning. The medical image diagnostic apparatus according to claim 1 .

12. The acquisition unit further acquires an inspection condition for the subject, The processing unit includes: A scan range in the first direction output by inputting the acquired inspection conditions of the object and a scanogram of the object to the first trained model; A scan range in the second direction that is output by inputting the acquired inspection conditions of the object and an optical image of the object to the second trained model; Output The medical image diagnostic apparatus according to claim 1 .

13. The acquisition unit further acquires a bed position condition, The processing unit includes: A scan range in the first direction is output by inputting the acquired bed position condition and the scanogram of the subject to the first trained model; A scan range in the second direction that is output by inputting the acquired bed position condition and the optical image of the subject to the second trained model; Output The medical image diagnostic apparatus according to claim 1 .

14. The computer Acquire a scanogram and an optical image, which are aligned images of the subject; a first trained model outputs a scanning range in a first direction of a top plate of a bed device based on a scanogram of the subject, the first scanning range being output by inputting an acquired scanogram of the subject; and a second trained model outputs a scanning range in a second direction orthogonal to the first direction of the subject based on an optical image of the subject, the second scanning range being output by inputting an acquired optical image of the subject. Medical imaging diagnostic methods.

15. The computer Acquire imaging conditions including information about the region to be scanned, and a scanogram image and an optical image, which are images of the subject aligned, a first trained model that outputs a scan range in a first direction of a top board of a bed device based on imaging conditions including information about a region to be scanned and a scanogram of the subject, the first trained model being output by inputting the acquired imaging conditions and the scanogram of the subject; and a second trained model that outputs a scan range in a second direction orthogonal to the first direction of the subject based on imaging conditions including information about a region to be scanned and an optical image of the subject, the second trained model being output by inputting the acquired imaging conditions and the optical image of the subject. Medical imaging diagnostic methods.

16. The computer Acquire a scanogram and an optical image, which are aligned images of the subject; a reconstruction range in a first direction of a table top of a bed device based on a scanogram of a subject, the reconstruction range being output by inputting an acquired scanogram of the subject to a first trained model; and a reconstruction range in a second direction orthogonal to the first direction of the subject based on an optical image of the subject, the reconstruction range being output by inputting an acquired optical image of the subject to a second trained model. Medical imaging diagnostic methods.

17. A computer comprising: Acquire imaging conditions including information about the region to be scanned, and a scanogram image and an optical image, which are images of the subject aligned, a first trained model outputs a reconstruction range in a first direction of a top board of a bed device based on imaging conditions including information about a region to be scanned and a scanogram of the subject, the reconstruction range being output by inputting the acquired imaging conditions and the scanogram of the subject; and a second trained model outputs a reconstruction range in a second direction orthogonal to the first direction of the subject based on imaging conditions including information about a region to be scanned and an optical image of the subject, the reconstruction range being output by inputting the acquired imaging conditions and the acquired optical image of the subject. Medical imaging diagnostic methods.

Images