Patent Publication Number: US-2023145523-A1

Title: Medical image processing apparatus, x-ray ct apparatus, medical image processing method and non-volatile storage medium storing program

Description:
FIELD 
     Embodiments described herein relate generally to a medical image processing apparatus, an X-ray CT apparatus, a medical image processing method and a non-volatile storage storing a program. 
     BACKGROUND 
     Various radiographic images that are acquired by a radiographic diagnosis apparatus, such as a computed tomography (CT) apparatus, a positron emission computed tomography (PET) apparatus, a single photon emission computed tomography (SPECT) apparatus, or a chest X-ray or X-ray angiography apparatus, are known. 
     Such a radiographic image may contain streak artifacts due to various factors. For example, in a process of generating a radiographic image or image processing after the generation, high-frequency components are sometimes enhanced for the purpose of increasing a spatial resolution. In such processing, aliasing sometimes occurs and this sometimes appears as streak artifacts in a radiographic image. When generating a radiographic image by reconstruction processing, streak artifacts sometimes appear in the radiographic image due to mechanical accuracy in an imaging unit of a radiographic diagnosis apparatus. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1 A  is a block diagram illustrating an example of a configuration of a medical image processing system according to a first embodiment; 
         FIG.  1 B  is a block diagram illustrating an example of a configuration of a medical image processing apparatus according to the first embodiment; 
         FIG.  2    is a block diagram illustrating an example of a configuration of an X-ray CT apparatus according to the first embodiment; 
         FIG.  3    is a diagram illustrating a process flow according to the first embodiment; 
         FIG.  4    is a diagram illustrating streak artifacts according to the first embodiment; and 
         FIG.  5    is a block diagram illustrating an example of a configuration of an X-ray CT apparatus according to a seventh embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     With reference to the accompanying drawings, an embodiment of a medical image processing apparatus, an X-ray CT apparatus, a medical image processing method and a non-volatile storage storing a program will be described below. 
     In the present embodiment, a medical image processing system  1  illustrated in  FIG.  1 A  and a medical image processing apparatus  30  illustrated in  FIG.  1 B  will be described as an example. A medical image processing apparatus  20  contained in the medical image processing system  1  reduces streak artifacts in a radiographic image using a model M 1  that is generated by the medical image processing apparatus  30 .  FIG.  1 A  is a block diagram illustrating an example of a configuration of the  1  according to a first embodiment.  FIG.  1 B  is a block diagram illustrating an example of a configuration of a medical image processing apparatus according to the first embodiment. 
     As illustrated in  FIG.  1 A , the  1  includes an X-ray CT apparatus  10  and the medical image processing apparatus  20 . In the embodiment, the X-ray CT apparatus  10  will be described as an example of a radiographic diagnosis apparatus. In other words, in the embodiment, an X-ray CT image will be described as an example of a radiographic image. The X-ray CT apparatus  10  and the medical image processing apparatus  20  are connected to each other via a network NW. 
     As long as connection is enabled via the network NW, the X-ray CT apparatus  10  and the medical image processing apparatus  20  are set any locations. For example, the X-ray CT apparatus  10  and the medical image processing apparatus  20  may be set in different facilities. In other words, the NW may consists of a local area network closed in the facility or may be a network via the Internet. Communication between the X-ray CT apparatus  10  and the medical image processing apparatus  20  may be performed via another apparatus, such as an image storage device, or may be performed directly not via any other device. For example, a server of PACS (Picture Archiving and Communication System) is exemplified as an example of such an image storage device. 
     For example, as illustrated in  FIG.  1 A , the medical image processing apparatus  20  includes a memory  21 , a display  22 , an input interface  23  and processing circuitry  24 . 
     The memory  21  is implemented by, for example, a semiconductor memory, such as a random access memory (RAM) or a flash memory, a hard disk or an optical disk. For example, the memory  21  stores a program for circuitry contained in the medical image processing apparatus  20  to implement its function. The memory  21  stores X-ray CT images that are acquired by the X-ray CT apparatus  10 . The memory  21  stores the model M 1  that reduces streak artifacts. The model M 1  will be described below. The memory  21  may be implemented by a server group (cloud) that is connected to the medical image processing apparatus  20  via the network NW. 
     The display  22  displays various types of information. For example, the display  22  displays an X-ray CT image whose streak artifacts are reduced by the processing circuitry  24 . For example, the display  22  is a liquid crystal display or a cathode ray tube (Cathode Ray Tube) display. The display  22  may be a tablet terminal enabling radio communication with the body of the medical image processing apparatus  20 . 
     The input interface  23  receives various input operations from the user, converts the received input operation into an electric signal and outputs the electric signal to the processing circuitry  24 . For example, the input interface  23  is implemented by a mouse and a keyboard, a trackball, a switch, a button, a joystick, a touch pad via which an input operation is made by a contact on an operation screen, a touch screen on which a screen image and a touch pad are integrated, a non-contact input circuit using an optical sensor, or an audio input circuit. The input interface  23  may consist of a tablet terminal enabling radio communication with the medical image processing apparatus  20 . The input interface  23  may be a circuit that receives an input operation from the user by motion capture. For example, the input interface  23  is able to receive the body motion or lines of sight of the user as an input operation by processing signals that are acquired by a tracker and/or images that are acquired from the user. The input interface  23  is not limited to one including physical operational parts, such as a mouse or a keyboard. For example, examples of the input interface  23  includes an electric signal processing circuit that receives an electric signal corresponding to an input operation from an external input device provided independently of the medical image processing apparatus  20  and outputting the electric signal to the processing circuitry. 
     The processing circuitry  24  controls entire operations of the medical image processing apparatus  20  by executing an acquiring function  241 , an image processing function  242 , and an outputting function  243 . The acquiring function  241  is an example of an acquisition unit. The image processing function  242  is an example of an image processor. 
     For example, the processing circuitry  24  reads a program corresponding to the acquiring function  241  from the memory  21  and executes the program, thereby acquiring an X-ray CT image. For example, the acquiring function  241  acquires, via the network NW, X-ray CT images that are acquired by the X-ray CT apparatus  10 . Alternatively, the acquiring function  241  may, via the network NW, acquire projection data that is acquired by the X-ray CT apparatus  10 . In this case, the acquiring function  241  is able to reconstruct an X-ray CT image using the acquired projection data. 
     The processing circuitry  24  reads a program corresponding to the image processing function  242  from the memory  21  and executes the program, thereby applying the model M 1  to a radiographic image and acquires a post-processing image in which streak artifacts are reduced. The processing circuitry  24  reads a program corresponding to the outputting function  243  from the memory  21  and executes the program, thereby displaying the post-processing image in which streak artifacts on the display  22 . Details of each function of the processing circuitry  24  will be described below. 
     In the medical image processing apparatus  20  illustrated in  FIG.  20   , each of the processing functions is stored in a form of a computer-executable program in the memory  21 . The processing circuitry  24  is a processor that reads a program from the memory  21  and executing the program, thereby implementing a function corresponding to each program. In other words, the processing circuitry  24  having read a program includes the function corresponding to the read program. 
       FIG.  1 A  illustrates that the single processing circuitry  24  implements the acquiring function  241 , the image processing function  242  and the outputting function  243 ; however, a plurality of independent processors may be combined to configure the processing circuitry  24  and the respective processors may execute the programs, thereby implementing the functions. Each of the processing functions that the processing circuitry  24  includes may be distributed or integrated to or into a single or a plurality of processing circuits as appropriate and be implemented. 
     The processing circuitry  24  may implement the functions using a processor of an external apparatus that is connected via the network NW. For example, the processing circuitry  24  reads the program corresponding to each of the functions from the memory  21  and executes the program and uses a server group (cloud) that is connected to the medical image processing apparatus  20  via the network NW as a calculation resource, thereby implementing each of the functions illustrated in  FIG.  1 A . 
     The medical image processing apparatus  30  will be described next. As illustrated in  FIG.  1 B , the medical image processing apparatus  30  includes a memory  31  and processing circuitry  32 . 
     The memory  31  can be configured similarly to the memory  21  described above. For example, the memory  31  saves training data to be described below and circuitry contained in the medical image processing apparatus  30  stores a program for implementing a function thereof. 
     The processing circuitry  32  reads a program corresponding to a learning function  321  from a memory  141  and executes the program, thereby generating the model M 1 . The learning function  321  is an example of a learning unit. Details of the learning function  321  will be described below. 
     In the medical image processing apparatus  30  illustrated in  FIG.  1 B , each processing function is stored in a form of a computer-executable program in the memory  31 . The processing circuitry  32  is a processor that reads a program from the memory  31  and executes the program, thereby implementing a function corresponding to each program. In other words, the processing circuitry  32  having read a program includes the function corresponding to the read program. 
       FIG.  1 B  illustrates that the single processing circuitry  32  implements the learning function  321 ; however, a plurality of independent processors may be combined to configure the processing circuitry  32  and the respective processors may execute programs, thereby implementing the function. Processing functions that the processing circuitry  32  includes may be distributed or integrated to or into a single or a plurality of processing circuits and be implemented. 
     The X-ray CT apparatus  10  will be described next using  FIG.  2   .  FIG.  2    is a block diagram illustrating an example of the X-ray CT apparatus  10  according to the first embodiment. For example, the X-ray CT apparatus  10  includes a gantry  110 , a bed  130 , and a console  140 . 
     In  FIG.  2   , a rotational axis of a rotation frame  113  in a non-tilt state or a longitudinal direction of a couch top  133  of the bed  130  is Z-axis direction. An axial direction orthogonal to the Z-axis direction and that is parallel to a floor surface is an X-axis direction. An axial direction that is orthogonal to the Z-axis direction and that is perpendicular to the floor surface is a Y-axis direction.  FIG.  2    is a drawing of the gantry  110  from multiple directions for description and illustrates the case where the X-ray CT apparatus  10  includes the single gantry  110 . 
     The gantry  110  includes an X-ray tube  111 , an X-ray detector  112 , the rotation frame  113 , an X-ray high-voltage device  114 , a control device  115 , a wedge  116 , a collimator  117 , and a data acquisition system (DAS)  118 . 
     The X-ray tube  111  is a vacuum tube including a cathode (filament) that generates thermoelectrons and an anode (target) that generates X-rays in response to collision of the thermoelectrons. The X-ray tube  111  applies thermoelectrons from the cathode to the anode in response to application of a high voltage from the X-ray high voltage device  114 , thereby generating X-rays to be applied to the subject P. 
     The X-ray detector  112  detects X-rays having been applied from the X-ray tube  111  and having passed through the subject P and outputs a signal corresponding to the detected x-ray dosage to the DAS  118 . The X-ray detector  112 , for example, includes a plurality of detection element rows in which a plurality of detection elements are arrayed in a channel direction (channel direction) along an arc about a focal point of the X-ray tube  111 . The X-ray detector  112 , for example, has a configuration in which a plurality of detection element rows in which a plurality of detection elements are arrayed in the channel direction are arrayed in the row direction (slice direction or row direction). 
     For example, the X-ray detector  112  is an indirect transformation detector including a grid, a scintillator array, and an optical sensor array. The scintillator array includes a plurality of scintillators. The scintillator includes a scintillator crystal that outputs light of a photon quantity corresponding to a dosage of incident X-rays. The grid is arranged on a surface of the scintillator array on the side of incidence of X-rays and includes an X-ray shield that absorbs scattering X-rays. The grid is sometimes referred to as a collimator (one-dimensional collimator or a two-dimensional collimator). An optical sensor array has a function of transformation into an electric signal corresponding to the amount of light from the scintillator and includes an optical sensor, such as a photodiode. The X-ray detector  112  may be a direct transformation detector including a semiconductor device that transforms incident X-rays into an electric signal. 
     The rotation frame  113  is an annular frame that supports the X-ray tube  111  and the X-ray detector  112  such that the X-ray tube  111  and the X-ray detector  112  are opposed to each other and that is caused by the control device  115  to rotate the X-ray tube  111  and the X-ray detector  112 . For example, the rotation frame  113  is a cast made of aluminum. The rotation frame  113  is also able to further support the X-ray high-voltage device  114 , the wedge  116 , the collimator  117 , the DAS  118 , etc., in addition to the X-ray tube  111  and the X-ray detector  112 . Furthermore, the rotation frame  113  is capable of further supporting various structures not illustrated in  FIG.  2   . The rotation frame  113  and parts that rotate together with the rotation frame  113  are also referred to as a rotation unit. 
     The X-ray high-voltage device  114  includes electric circuits, such as a transformer and a rectifier, and includes a high-voltage generation device that generates a high voltage to be applied to the X-ray tube  111  and an X-ray control device that performs control on an output voltage corresponding to the X-rays that are generated by the X-ray tube  111 . The high-voltage generation device may be a transformer type or an inverter type. The X-ray high-voltage device  114  may be provided in the rotation frame  113  or  114  or may be provided in a fixed frame not illustrated in the drawing. 
     The control device  115  includes processing circuitry including a central processing unit (CPU), etc., and a drive mechanism, such as a motor, an actuator, etc. The control device  115  receives an input signal from an input interface  143  and controls operations of the gantry  110  and the bed  130 . For example, the control device  115  performs control on rotation of the rotation frame  113 , the tilt of the gantry  110 , and operations of the bed  130 . For example, the control device  115  causes the rotation frame  113  to rotate about an axis parallel to the X-axis direction according to inclination angle (tilt angle) information as the control of tilting the gantry  110 . The control device  115  may be provided in the gantry  110  or may be provided in the console  140 . 
     The wedge  116  is an X-ray filter for adjusting the dosage of X-rays applied from the X-ray tube  111 . Specifically, the wedge  116  is an X-ray filter that attenuates X-rays that are applied from the X-ray tube  111  such that the X-rays applied to the subject P from the X-ray tube  111  have a predetermined distribution. For example, the wedge  116  is a wedge filter or a bow-tie filter and is manufactured by processing aluminum into a given target angle and a given thickness. 
     The collimator  117  includes a lead plate for narrowing the area of radiation with X-rays having been transmitted through the wedge  116  and forms a slit according to combination of a plurality of lead plates, or the like. The collimator  117  is sometimes referred to as an X-ray diaphragm.  FIG.  2    illustrates the case where the wedge  116  is arranged between the X-ray tube  111  and the collimator  117 ; however, the collimator  117  may be arranged between the X-ray tube  111  and the wedge  116 . In this case, the wedge  116  transmits X-rays that are applied from the X-ray tube  111  and whose area of radiation is restricted by the collimator  117  and attenuates the X-rays. 
     The DAS  118  acquires signals of X-rays that are detected by the respective detection elements that the X-ray detector  112  includes. For example, the DAS  118  includes an amplifier that performs amplification processing on the electric signal that is output from each of the detection elements and an A/D converter that converts the electric signal into a digital signal and generates detection data. The DAS  118  is implemented by, for example, a processor. 
     The data that is generated by the DAS  118  is transmitted from a transmitter with a light emitting diode (LED) that is provided in the rotation frame  113  by optical communication to a receiver with a photodiode that is provided in a non-rotation part of the gantry  110  (for example, the fixed frame, or the like, of which illustration in  FIG.  2    is omitted) and is transferred to the console  140 . The non-rotation part is, for example, the fixed frame that supports the rotation frame  113  rotatably. A method of transmitting data from the rotation frame  113  to the gantry  110  is not limited to optical communication, and any non-contact data transmission system may be employed or contact data transmission system may be employed. 
     The bed  130  is a device on which the subject P to be scanned is laid and that moves the subject P and includes a base  131 , a couch drive device  132 , the couch top  133 , and a support frame  134 . The base  131  is a casing that supports the support frame  134  movably in the vertical direction. The couch drive device  132  is a drive mechanism that moves the couch top  133  on which the subject P is laid in a longitudinal direction of the couch top  133  and includes a motor and an actuator. The couch top  133  that is provided on the top surface of the support frame  134  is a board on which the subject P is laid. The couch drive device  132  may move, in addition to the couch top  133 , the support frame  134  may be moved in the longitudinal direction of the couch top  133 . 
     The console  140  includes the memory  141 , a display  142 , the input interface  143 , and processing circuitry  144 . The console  140  is described independently from the gantry  110 ; however, the console  140  or part of each component of the console  140  may be contained in the gantry  110 . 
     The memory  141  can be configured similarly as the memory  21 . For example, the memory  141  saves various types of data acquired from the subject P and stores programs for the circuitry contained in the X-ray CT apparatus  10  to implement the functions of the circuitry. 
     The display  142  can be configured similarly to the display  22  described above. For example, the display  142  is capable of displaying a GUI for receiving various instructions, settings, etc., from the user. 
     The input interface  143  can be configured similarly as the input interface  23  described above. For example, the input interface  143  receives various input operations from the user, transforms the received input operations into electric signals, and outputs the electric signals to the processing circuitry  144 . 
     The processing circuitry  144  executes a controlling function  144   a,  an acquiring function  144   b,  and an outputting function  144   c,  thereby controlling entire operations of the X-ray CT apparatus  10 . 
     For example, the processing circuitry  144  reads a program corresponding to the controlling function  144   a  from the memory  141  and executes the program, thereby controlling various function, such as the acquiring function  144   b  and the outputting function  144   c,  based on various input operations that are received from the user via the input interface  143 . 
     For example, the processing circuitry  144  reads a program corresponding to the acquiring function  144   b  from the memory  141  and executes the program, thereby executing scanning on the subject P. For example, the acquiring function  144   b  controls the X-ray high-voltage device  114 , thereby supplying a high-voltage to the X-ray tube  111 . Thus, the X-ray tube  111  generates X-rays to be applied to the subject P. The acquiring function  144   b  controls the couch drive device  132 , thereby moving the subject P into an imaging port of the gantry  110 . The acquiring function  144   b  adjusts the position of the wedge  116  and the degree of opening and position of the collimator  117 , thereby controlling the distribution of X-rays to be applied to the subject P. The acquiring function  144   b  controls the control device  115 , thereby rotating the rotation unit. While the acquiring function  144   b  is executing scanning, the DAS  118  acquires signals of X-rays from the respective detection elements in the X-ray detector  112  and generates detection data. 
     The acquiring function  144   b  performs pre-processing on the detection data that is output from the DAS  118 . For example, the acquiring function  144   b  performs pre-processing, such as logarithmic transformation and offset correction processing, sensitivity correction processing between channels and beam hardening correction processing, on the detection data that is output from the DAS  118 . The data on which pre-processing has been performed is also referred to as raw data. The detection data before performing of the pre-processing and the raw data after performing of the pre-processing are collectively referred to also as projection data. 
     For example, the processing circuitry  144  reads a program corresponding to the outputting function  144   c  and executes the program, thereby outputting various types of data acquired from the subject P. For example, the outputting function  144   c  transmits data that is acquired by executing scanning on the subject P to the medical image processing apparatus  20  via the network NW. For example, the outputting function  144   c  performs control on display by the display  142 . 
     In the X-ray CT apparatus  10  illustrated in  FIG.  2   , each of the processing functions is stored in a form of a computer-executable program in the memory  141 . The processing circuitry  144  is a processor that reads a program from the memory  141  and executes the program, thereby implementing a function corresponding to each program. In other words, the processing circuitry  144  having read the program includes the function corresponding to the read program. 
       FIG.  2    illustrates that the single processing circuitry  144  implements the controlling function  144   a,  the acquiring function  144   b  and the outputting function  144   c;  however, a plurality of independent processors may be combined to configure the processing circuitry  144  and the respective processors may execute the programs, thereby implementing the functions. Each of the processing functions that the processing circuitry  144  includes may be distributed or integrated to or into a single or a plurality of processing circuits as appropriate and be implemented. 
     The processing circuitry  144  may implement the functions using a processor of an external device that is connected via the network NW. For example, the processing circuitry  144  reads the program corresponding to each of the functions from the memory  141  and executes the program and uses a server group (cloud) that is connected to the X-ray CT apparatus  10  via the network NW as a calculation resource, thereby implementing each of the functions illustrated in  FIG.  2   . 
     Streak artifacts occurring in an X-ray CT image will be described next. There are some factors of generation of streak artifacts and processing of enhancing high-frequency components can be taken as an example. For example, performing reconstruction processing in which high-frequency components are enhanced at the time of generation of an X-ray CT image sometimes causes streak artifacts. For example, according to a reconstruction protocol on bones, or the like, emphasizing a spatial resolution, a reconstruction function that enhances high-frequency components is sometimes used. When an X-ray CT image is reconstructed by MBIR (Model-Based Interactive Reconstruction), a parameter that more emphasizes the spatial resolution than noise is sometimes set. After generation of an X-ray image, image processing (post processing) for increasing the spatial resolution is sometimes performed. Excessively enhancing high-frequency components sometimes causes aliasing, which sometimes appears as streak artifacts in a radiographic image. 
     Mechanical accuracy in an imaging unit of a radiographic diagnosis apparatus is taken as another factor of generation of streak artifacts. The imaging unit of the radiographic diagnosis apparatus is a part of the radiographic diagnosis apparatus that is driven for imaging and, in the case of the X-ray CT apparatus  10 , the gantry  110  containing the X-ray tube  111  and the X-ray detector  112  corresponds. The imaging unit of the radiographic diagnosis apparatus is also referred to as a radiographic imaging system. 
     Specifically, in reconstruction of an X-ray CT image, calculation is performed in each time point during imaging, assuming that the X-ray tube  111  and the X-ray detector  112  are in given positons. When the actual positions of the X-ray tube  111  and the X-ray detector  112  are shifted from the given positions, calculation is performed with the positons of the X-ray tube  111  and the X-ray detector  112  being incorrect and streak artifacts sometimes appear in a radiographic image. 
     Manufacturing the X-ray CT apparatus  10  is performed such that such a positional shift of the imaging unit does not occur and, when the X-ray CT apparatus  10  is conveyed to a hospital or is installed, a positional shift of the imaging unit sometimes occurs. In order to deal with the positional shift of the imaging unit, alignment is sometimes performed on the X-ray tube  111  and the X-ray detector  112 . Specifically, when the X-ray CT apparatus  10  is installed or in regular maintenance, adjustment on the positions of the X-ray tube  111  and the X-ray detector  112  is sometimes performed. Such alignment is however performed by a service staff manually and a shift that is too small to adjust manually tends to remain. Furthermore, because of repeated use of the X-ray CT apparatus  10 , positional shifts of the imaging unit sometimes occur over time or increase. 
     Correcting data such that the positional shift of the imaging unit does not have an effect on an X-ray CT image is also considered. For example, when an X-ray CT image is reconstructed, processing of forward projection and back projection based on acquired projection data is performed. A technique of estimating and correcting a positional shift of the imaging unit during an operation of the forward projection and back projection is known; however, it is not easy to estimate a positional shift of the imaging unit and it is often not possible to correct the positional shift sufficiently. 
     The image processing function  242  in the medical image processing apparatus  20  thus applies the model M 1  to an X-ray CT image, thereby acquiring a post-processing image with reduced streak artifacts. Generation of the model M 1  and application of the model M 1  to an X-ray CT image will be described below using  FIG.  3   .  FIG.  3    is a diagram illustrating a process flow according to the first embodiment. 
     A lower view in  FIG.  3    illustrates a learning phase. Specifically, the lower view in  FIG.  3    illustrates a process of generating the model M 1  that reduces streak artifacts that is performed by the learning function  321 . For example, the learning function  321  first acquires projection data H 1 . The projection data H 1  may be data that is acquired by the X-ray CT apparatus  10  or data that is acquired by another X-ray CT apparatus. The projection data H 1  may be acquired on a phantom imitating a human body. 
     For example, the learning function  321  is capable of acquiring the projection data H 1  via the network NW. The learning function  321  is also able to acquire the projection data H 1  via, for example, any storage medium without connecting to the network NW. 
     The learning function  321  reconstructs the projection data H 1  and generates a target image. A method of reconstruction is not particularly limited. For example, the learning function  321  is capable of generating a target image by performing reconstruction processing using a filter correction back projection method, and a successive approximation reconstruction method. The learning function  321  is able to generate a target image by performing reconstruction processing by AI (Artificial Intelligence). For example, the learning function  321  generates an X-ray CT image by a DLR (Deep learning Reconstruction) method. A target image is an example of a first radiographic image. 
     The learning function  321  performs alignment on the projection data H 1 , reconstructs the projection data H 1  after alignment, and generates an input image. As in the case of a target image, a reconstruction method is not particularly limited. The input image is an example of a second radiographic image. 
     Alignment is an example of an artifact generation processing for generating streak artifacts and is a process of applying components corresponding to a positional shift of the imaging unit to projection data. For example, the projection data H 1  is considered as data containing three-dimensional information in the channel direction, the row direction, and a direction of application of X-rays from the X-ray tube  111  (view direction) in the X-ray detector  112 . The learning function  321  applies components corresponding to a positional shift in at least one of the channel direction, the row direction and the view direction to the projection data H 1 . 
     Hereinafter, “c” is put as a coordinate of the channel direction, “r” is put as a coordinate of the row direction, and “v” is put as a coordinate of the view direction. In the projection data H 1 , the signal that is detected by each detection element of the X-ray detector  112  can be associated with a set of coordinates (c,r,v). Hereinafter, a signal s 1  is put as a signal that is detected by a detection element d1 in the X-ray detector  112  and the signal s 1  is described as one that is associated with a set of coordinates (c1,r1,v1). In this case, by associating the signal s 1  with the setoff coordinates (c2,r1,v1), the learning function  321  is able to apply components corresponding to a positional shift in the channel direction to the projection data H 1 . More specifically, by assuming that the center of each detection element of the X-ray detector  112  is shifted in the channel direction by, for example, few mm, the learning function  321  is able to apply components corresponding to the positional shift in the channel direction to the projection data H 1 . The case where the components corresponding to the positional shift in the channel direction are applied to the projection data H 1  has been described; however, components corresponding to the positional shifts in the row direction and the view direction instead of or in addition to the channel direction may be applied. 
     Reconstruction of the projection data H 1  after alignment is performed and accordingly streak artifacts are generated in the input image. On the other hand, no streak artifact is generated in the target image or the target image has more reduced streak artifacts than in the input image. 
     As illustrated in  FIG.  3   , the learning function  321  generates the model M 1  by machine learning that uses the input image and the training image as a training data pair. For example, the model M 1  consists of a deep convolution neural network (DCNN). In other words, the learning function  321  causes the DCNN to learn using the input image and the target image as the training data pair, thereby generating the model M 1 . 
     For example, the learning function  321  generates the model M 1  by adjusting parameters such that the DCNN that receives an input of the input image is able to output a preferable result. For example, an output image with reduced streak artifacts of the input image is output from an output layer of the DCNN having received the input of the input image. The learning function  321  adjusts the parameters of the DCNN to minimize a function (error function) representing closeness between the output image and the target image. For example, learning of the DCNN is executed off-line. 
     The model M 1  has been described as the DCNN; however, another type of machine learning engine may be employed. For example, the model M 1  may be a neural network, such as a whole binding neural network or a recurrent neural network (RNN). 
     It is preferable that the same reconstruction method and conditions be used between the input image reconstruction processing and the target image reconstruction processing. This enables elements (such as the noise level) other than streak artifacts to be approximately equal between the input image and the target image and consequently enables the model M 1  to learn efficiently. 
     The model M 1  that is generated by the learning function  321  is transmitted to the medical image processing apparatus  20  via the network NW, any storage medium, or the like, and is stored in the memory  21 . The image processing function  242  is capable of reading the model M 1  from the memory  21  and executing processing of reducing streak artifacts. 
     The upper view in  FIG.  3    illustrates a deduction phase. Specifically, the upper view in  FIG.  3    illustrates a series of sets of processing until acquisition of a post-processing image with streak artifacts reduced from the X-ray CT image of the subject P and provision of the image to the user, such as a doctor. 
     First of all, the acquiring function  241  acquires an X-ray CT image of the subject P. Specifically, first of all, the X-ray CT apparatus  10  capture images of the subject P and acquires projection data H 2 . The acquiring function  241  acquires the projection data H 2  via the network NW, performs reconstruction processing, and generates an X-ray CT image of the subject P. As in the case of the target image and the input image described above, a method of reconstruction is not particularly limited. The case where the acquiring function  241  executes the reconstruction processing has been described; however, the acquiring function  241  may acquire an X-ray CT image that is reconstructed by another apparatus, such as the X-ray CT apparatus  10 , via the network NW. 
     The image processing function  242  then applies the model M 1  to the X-ray CT image, thereby acquiring a post-processing image with reduced streak artifacts. The outputting function  243  outputs the post-processing image. For example, the outputting function  243  causes the post-processing image to be displayed on the display  22 . For example, the outputting function  243  transmits the post-processing image to another device via the network NW. In this case, the post-processing image is displayed on the another device and is provided to the user, such as a doctor. For example, the outputting function  243  may transmit the post-processing image to the X-ray CT image and the display  142  may display the post-processing image. 
     As described above, in the first embodiment, the acquiring function  241  acquires an X-ray CT image of the subject P. The image processing function  242  applies the model M 1  that reduces streak artifacts to the X-ray CT image, thereby acquiring a post-processing image with reduced streak artifacts. The model M 1  is generated by machine learning that uses a target image and an input image based on the artifact generation processing for generating streak artifacts as a training data pair. This enables the medical image processing apparatus  20  to reduce streak artifacts in the X-ray CT image. 
     The medical image processing apparatus  20  is able to increase the spatial resolution in the X-ray CT image. In other words, enhancing high-frequency components excessively sometimes generates streak artifacts and thus the processing of generating an X-ray CT image and image processing after the generation are in general controlled such that high-frequency components are not enhanced too much. On the other hand, according to the medical image processing apparatus  20 , because, even when streak artifacts are generated, it is possible to remove or reduce the streak artifacts, it is possible to sufficiently enhance the high-frequency components in the processing of generating an X-ray CT image or the image processing after the generation. 
     In the first embodiment, the case where the input image and the target image for the model M 1  to learn are generated from the single projection data H 1  has been described. In the second embodiment, the case where an input image and a target image are generated from different sets of projection data, respectively, will be described. 
     For example, the learning function  321  acquires a target image based on projection data H 111  and acquires an input image based on projection data H 112  that is different from the projection data H 111 . The projection data H 111  is an example of first projection data. The projection data H 112  is an example of second projection data. The projection data H 111  and the projection data H 112  are, for example, acquired by the X-ray CT apparatus  10  by capturing an image of the same phantom for a plurality of times. 
     In this case, the learning function  321  reconstructs the projection data H 111  and generates a target image. The learning function  321  performs alignment on the projection data H 112 , reconstructs the projection data H 112  after alignment, and generates an input image. The learning function  321  then generates a model M 1  by machine learning that uses the input image and the target image as a training data pair. Thereafter, using the model M 1 , the image processing function  242  is able to perform processing of reducing streak artifacts on an X-ray CT image of the subject P. 
     For the first and second embodiments, the case where streak artifacts are generated in the input image by performing alignment after acquiring the projection data has been described. On the other hand, in the third embodiment, the case where acquiring projection data while performing alignment generates streak artifacts in an input image will be described. 
     In other words, in the first and second embodiments, the case where, assuming that there is a positional shift of the imaging unit, such as the X-ray tube  111  and the X-ray detector  112 , components corresponding to the positional shift are virtually applied after projection data is acquired has been described. In the third embodiment, when projection data is acquired, a positional shift of the imaging unit, such as the X-ray tube  111  and the X-ray detector  112 , is actually generated and thus components corresponding to the positional shift of the imaging unit is applied to the projection data. 
     For example, the learning function  321  acquires a target image based on projection data H 121  and acquires an input image based on projection data H 122  that is different from the projection data H 121 . The projection data H 121  is an example of the first projection data. The projection data H 122  is an example of the second projection data. The projection data H 121  and the projection data H 122  are acquired by the X-ray CT apparatus  10  by capturing an image of the same phantom for multiple times. 
     For example, after the projection data H 121  is acquired, alignment on the imaging unit of the X-ray CT apparatus  10  is performed such that a positional shift is caused. For example, a practitioner, or the like, is able to shift the position in which the X-ray detector  112  is installed from a given position by approximately few mm in the channel direction. Accordingly, components corresponding to the positional shift in the channel direction are applied to the acquired projection data H 122  to be acquired and streak artifacts are generated in an input image that is reconstructed based on the projection data H 122 . The learning function  321  generates the model M 1  by machine learning that uses an input image and a target image as a training data pair. Thereafter, using the model M 1 , the image processing function  242  is able to execute processing of reducing streak artifacts on the X-ray CT image of the subject. The case where components corresponding to the positional shift in the channel direction are applied has been described; however, components corresponding to positional shifts in the row direction and the view direction instead of or in addition to the channel direction may be applied. 
     In the first and second embodiments, the case where alignment is performed after acquisition of the projection data, that is, before the reconstruction process and, in the third embodiment, the case where streak artifacts are generated in the input image by performing alignment at the time of acquisition of the projection data has been described. On the other hand, in a fourth embodiment, the case where streak artifacts are generated in an input image by performing alignment at the time of reconstruction will be described. 
     For example, the learning function  321  acquires a target image by reconstructing projection data H 131  and acquires an input image by reconstructing the projection data while performing alignment on the projection data H 131 . For example, in the reconstruction method such as the filter correction back projection, calculations of forward projection and back projection are performed repeatedly. In forward projection and back projection, a tube position (focal point position in the X-ray tube  111 ) is set. The learning function  321  is able to generate streak artifacts in the input image that is reconstructed by performing forward projection and back projection with the tube position being shifted from a given position. 
     The process of shifting the tube position in forward projection and back projection is described as an example of the process of performing reconstruction while performing alignment has been described; however, examples are not limited thereto. For example, streak artifacts may be generated in an input image by shifting a projection image matrix or a projection path of the detector that is calculated by ray-tracing at the time of reconstruction. 
     The learning function  321  may acquire a target image by reconstructing the projection data H 131  and acquire an input image by reconstructing the projection data H 132  that is acquired from the same imaging subject as that of the projection data H 131  while performing alignment on the projection data H 132 . The projection data H 131  is an example of the first projection data. The projection data H 132  is an example of the second projection data. 
     In the first to fourth embodiments described above, the case where streak artifacts are generated in the input image by performing alignment has been described. On the other hand, in a fifth embodiment, the case where streak artifacts are generated in an input image by reconstruction processing in which high-frequency components are enhanced will be described. 
     For example, the learning function  321  acquires a target image by reconstructing projection data H 141  under a first reconstruction condition. The learning function  321  acquires an input image by reconstructing the projection data H 141  under a second reconstruction condition. The second reconstruction condition is a reconstruction condition under which high-frequency components are enhanced more than under the first reconstruction condition. For example, under the first reconstruction condition, a normal reconstruction function represented by a Ramp function is used and, under the second condition, a reconstruction function that enhances high frequencies excessively compared to the Ramp function is used. Accordingly, the learning function  321  is able to generate streak artifacts in an input image that is reconstructed. 
     The learning function  321  may acquire a target image by reconstructing the projection data H 141  under the first reconstruction condition and acquire an input image by reconstructing the projection data H 142  that is acquired from the same imaging subject as that of the projection data H 141  under the second reconstruction condition under which high frequencies are enhanced more than under the first reconstruction condition. The projection data H 141  is an example of the first projection data. The projection data H 142  is an example of the second projection data. 
     In the fifth embodiment, the case where streak artifacts are generated in the input image by the reconstruction processing in which high-frequency components are enhanced has been described. In other words, in the fifth embodiment, the case where streak artifacts are applied at the time of generation of an input image has been described. On the other hand, in a sixth embodiment, the case where streak artifacts are applied after image generation will be described. 
     For example, the learning function  321  acquires a target image by reconstructing projection data H 151 . The learning function  321  acquires an input image by performing the processing of enhancing high-frequency components on the target image. While the input images described in the first to fifth embodiment are generated by performing the artifact generation processing by the projection data domain, the input image in the sixth embodiment is generated by performing the artifact generation processing by an image data domain. 
     The input images illustrated in the first to fifth embodiments contain streak artifacts like those illustrated in  FIG.  4   . In other words, artifacts are generated such that streaks extend from the vicinity of a structure, such as a bone. On the other hand, the target images illustrated in the first to fifth embodiments are approximately the same image as that of  FIG.  4    except that streak artifacts are not contained. Accordingly, subtraction between the target images and the input images in the first to fifth embodiments makes it possible to generate images presenting streak artifacts. It is also possible to specify a frequency corresponding to the streak artifacts based on the image presenting the streak artifacts. By amplifying frequency components corresponding to streak artifacts in the target image obtained by reconstructing the projection data H 151 , the learning function  321  is able to acquire an input image containing streak artifacts. 
     The learning function  321  may acquire a target image by reconstructing the projection data H 151  under the first reconstruction condition and acquire an input image containing streak artifacts by performing the processing of enhancing high-frequency components on a reconstruction image obtained by reconstructing the projection data H 152  that is acquired from the same imaging subject as that of the projection data H 151 . The projection data H 151  is an example of the first projection data. The projection data H 152  is an example of the second projection data. 
     Various modes may be carried out in addition to the above-described embodiments. 
     For example,  FIGS.  1 A and  1 B  illustrate that the processing circuitry  32  of the medical image processing apparatus  30  executes the learning function  321  and the processing circuitry  24  of the medical image processing apparatus  20  executes the image processing function  242 . In other words,  FIGS.  1 A and  1 B  illustrate the case where the processing of generating the model M 1  and the processing of reducing artifacts are executed in different apparatuses. 
     The processing of generating the model M 1  and the processing of reducing artifacts may be executed in the same apparatus. For example, the processing circuitry  24  of the medical image processing apparatus the processing of generating the model M 1  performed by the learning function  321  and the processing of reducing artifacts performed by the image processing function  242  may be executed in the medical image processing apparatus  20 . 
       FIG.  1 A , illustrates the X-ray CT apparatus  10  and the medical image processing apparatus  20  as independent apparatuses; however, as illustrated in  FIG.  5   , the medical image processing apparatus  20  may be contained in the X-ray CT apparatus  10 .  FIG.  5    is a block diagram illustrating an example of the configuration of the X-ray CT apparatus  10  according to a seventh embodiment. 
     In  FIG.  5   , the console  140  and the medical image processing apparatus  20  may be partly or entirely integrated. For example, any one of the display  142  of the console  140  and the display  22  of the medical image processing apparatus  20  may be omitted. Any one of the processing circuitry  144  of the console  140  and the processing circuitry  24  of the medical image processing apparatus  20  may be omitted. For example, the processing circuitry  24  may be omitted and the processing circuitry  144  may execute the acquiring function  241 , the image processing function  242 , and the outputting function  243  described above. Furthermore, the processing circuitry  144  may execute the learning function  321  described above. 
       FIG.  1 A  illustrates the X-ray CT image as an example of the radiographic image, and various radiographic images, such as a PET image or a SPECT image, apply similarly. Even in the case not associated with the reconstruction processing, such as the case were a two-dimensional X-ray image is acquired by a chest X-ray or X-ray angiography apparatus, the above-described embodiment is partly applicable. For example, it is possible to generate the model M 1  by performing machine learning using an X-ray image that is acquired by chest X-ray as a target image and using an image obtained by performing the processing of enhancing high-frequency components on the target image as an input image. 
     The word “processor” used in the description given above refers to, for example, a circuit, such as a CPU, a GPU (Graphics Processing Unit), an application specific integrated circuit (ASIC), or a programmable logic device (for example, a simple programmable logic device (SPLD), a complex programmable logic device (CPLD) or a field programmable gate array (FPGA)). When the processor is, for example, a CPU, the processor reads programs that are saved in a storage circuit and executes the programs, thereby implementing the functions. On the other hand, when the processor is, for example, an ASIC, instead of saving the programs in the storage circuit, the functions are directly installed as a logic circuit in the circuit of the processor. Each processor of the embodiments is not limited to the case where each processor is configured as a single circuit, and multiple independent circuits may be combined to configure a single processor to implement the functions. Furthermore, the components in each drawing may be integrated into one processor to implement functions thereof. 
       FIG.  1 A  illustrates that the single memory  21  stores the programs corresponding to the respective processing functions of the processing circuitry  24 .  FIG.  1 B  illustrates that the single memory  31  stores the programs corresponding to the respective processing functions of the processing circuitry  32 .  FIG.  2    illustrates that the single memory  141  stores the programs corresponding to the respective processing functions of the processing circuitry  144 . Embodiments however are not limited to this. For example, a plurality of the memories  21  may be arranged in a distributed manner and the processing circuitry  24  may be configured to read a corresponding program from the particular memory  21 . Similarly, a plurality of the memories  31  may be arranged in a distributed manner and the processing circuitry  32  may be configured to read a corresponding program from the particular memory  31 . Similarly, a plurality of the memories  141  may be arranged in a distributed manner and the processing circuitry  144  may be configured to read a corresponding program from the particular memory  141 . Instead of saving the programs in the memory  21 , the memory  31  or the memory  141 , the programs may be directly installed in a circuit of a processor. In this case, the processor reads the programs that are installed in the circuit and executes the programs, thereby implementing the functions. 
     Each of the components of each device according to the above-described embodiments is a functional idea and thus need not necessarily be configured physically as unillustrated in the drawings. In other words, specific modes of distribution and integration of the devices are not limited to those illustrated in the drawings, and all or part of the devices may be configured in a distributed or integrated manner functionally or physically in any unit according to various types of load and the usage. Furthermore, all or any part of the processing functions implemented by the respective devices may be implemented by a CPU and programs that are analyzed and executed by the CPU or may be implemented as hardware using a wired logic. 
     It is possible to implement the medical image processing method described in the above-described embodiments by executing a program that is prepared in advance with a computer, such as a personal computer or a work station. The program can be distributed via a network, such as the Internet. The program may be recorded in a computer-readable and non-transient recording medium, such as a hard disk, a flexible disk (FD), a CD-ROM, a MO or a DVD, and may be read from the recording medium by the computer and thus be executed. 
     According to at least one of the embodiments described above, it is possible to reduce streak artifacts in a radiographic image. 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the inventions.