Patent Publication Number: US-2023135988-A1

Title: Radiographic image acquiring device, radiographic image acquiring system, and radiographic image acquisition method

Description:
TECHNICAL FIELD 
     One aspect of an embodiment relates to a radiographic image acquiring device, a radiographic image acquiring system, and a radiographic image acquisition method. 
     BACKGROUND ART 
     Since the past, a device that acquires a distribution of electromagnetic waves such as X-rays passing through a target object as image data by providing line sensors of multiple columns disposed orthogonally to the transport direction of the target object and adding detection data output from the line sensors of multiple columns has been used. According to such a device, it is possible to obtain an integrated exposure effect in the image data in which electromagnetic waves passing through the target object are detected. 
     CITATION LIST 
     Patent Literature 
     
         
         [Patent Literature 1] Japanese Unexamined Patent Republication No. WO2019/082276 
         [Patent Literature 2] Japanese Unexamined Patent Publication No. 2019-158663 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     In the device of the related art as described above, the detection data obtained from the line sensors of multiple columns is added, and thus a signal value tends to improve and a noise value also tends to increase in the addition result. Therefore, an S/N ratio may not improve sufficiently in the image data. 
     Consequently, one aspect of an embodiment was contrived in view of such a problem, and an object thereof is to provide a radiographic image acquiring device, a radiographic image acquiring system, and a radiographic image acquisition method that make it possible to effectively improve an S/N ratio in a radiographic image. 
     Solution to Problem 
     According to one aspect of an embodiment, there is provided a radiographic image acquiring device including: an imaging device configured to scan radiation passing through a target object in one direction and capture an image thereof to acquire a radiographic image; a scintillator configured to be provided on the imaging device to convert the radiation into light; and an image processing module configured to input the radiographic image to a trained model constructed through machine learning in advance using image data and execute a noise removal process of removing noise from the radiographic image, wherein the imaging device includes a detection element in which pixel lines each having M (M is an integer equal to or greater than 2) pixels arranged in the one direction are configured to be arranged in N columns (N is an integer equal to or greater than 2) in a direction orthogonal to the one direction and which is configured to output a detection signal related to the light for each of the pixels, and a readout circuit configured to output the radiographic image by adding the detection signals output from at least two of the M pixels for each of the pixel lines of N columns in the detection element and sequentially outputting the added N detection signals. 
     Alternatively, according to another aspect of the embodiment, there is provided a radiographic image acquiring system including: the radiographic image acquiring device; a source configured to radiate radiation to the target object; and a transport device configured to transport the target object to the imaging device in the one direction. 
     Alternatively, according to another aspect of the embodiment, there is provided a radiographic image acquisition method including: a step of scanning scintillation light corresponding to radiation passing through a target object in one direction and capturing an image thereof to acquire a radiographic image; and a step of inputting the radiographic image to a trained model constructed through machine learning in advance using image data and executing a noise removal process of removing noise from the radiographic image, wherein the acquisition step includes using a detection element in which pixel lines each having M (M is an integer equal to or greater than 2) pixels arranged in the one direction are configured to be arranged in N columns (N is an integer equal to or greater than 2) in a direction orthogonal to the one direction and which is configured to output a detection signal related to the scintillation light for each of the pixels, to output the radiographic image by adding the detection signals output from at least two of the M pixels for each of the pixel lines of N columns in the detection element and sequentially output the added N detection signals 
     According to the one aspect or the other aspects, the scintillation light corresponding to the radiation passing through the target object is detected by the detection element in which the pixel lines each having M pixels arranged in the direction of scanning of the target object are arranged in N columns, detection signals of at least two pixels out of detection signals of the M pixels output for each pixel line are added, and a radiographic image is output by the added N detection signals being sequentially output. Additionally, the output radiographic image is input to the trained model constructed through machine learning in advance using image data, and thus the noise removal process is performed on the radiographic image. Thereby, it is possible to remove noise components while increasing signal components in the radiographic image, and to effectively improve an S/N ratio in the radiographic image. 
     Advantageous Effects of Invention 
     According to the embodiment, it is possible to effectively improve an S/N ratio in the radiographic image. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a schematic configuration diagram of an image acquiring device  1  according to a first embodiment. 
         FIG.  2    is a plan view illustrating a configuration of a scan camera  12  of  FIG.  1   . 
         FIG.  3    is a block diagram illustrating an example of a hardware configuration of a control device  20  of  FIG.  1   . 
         FIG.  4    is a block diagram illustrating a functional configuration of the control device  20  of  FIG.  1   . 
         FIG.  5    is a diagram illustrating an example of an X-ray image acquired by an image acquisition unit  203  of  FIG.  4   . 
         FIG.  6    is a diagram illustrating an example of generation of a noise standard deviation map which is performed by a noise map generation unit  204  of  FIG.  4   . 
         FIG.  7    is a diagram illustrating an example of input and output data of trained models  207  of  FIG.  4   . 
         FIG.  8    is a diagram illustrating an example of a training image which is one piece of training data used to construct the trained models  207 . 
         FIG.  9    is a flowchart illustrating a procedure of creating image data which is training data (training data) used for a construction unit  206  to construct the trained models  207 . 
         FIG.  10    is a flowchart illustrating a procedure of observation processing using the image acquiring device  1 . 
         FIG.  11    is a block diagram illustrating a functional configuration of a control device  20 A according to a modification example of the present disclosure. 
         FIG.  12    is a flowchart illustrating a procedure of observation processing using an image acquiring device  1  according to the modification example of the present disclosure. 
         FIG.  13    is a graph illustrating an example of results of simulation calculation of an energy spectrum of transmitted X-rays which is performed by a calculation unit  202 A of  FIG.  11   . 
         FIG.  14    is a chart table illustrating an example of results of simulation calculation of a relationship between the thickness of a target object, average energy, and transmittance which is performed by the calculation unit  202 A of  FIG.  11   . 
         FIG.  15    is a graph illustrating an example of results of simulation calculation of a relationship between the thickness of the target object and the transmittance of X-rays which is performed by the calculation unit  202 A of  FIG.  11   . 
         FIG.  16    is a graph illustrating an example of results of simulation calculation of a relationship between the thickness of the target object and the average energy of transmitted X-rays which is performed by the calculation unit  202 A of  FIG.  11   . 
         FIG.  17    is a graph illustrating an example of results of simulation calculation of a relationship between the pixel value of an X-ray image and the average energy which is performed by the calculation unit  202 A of  FIG.  11   . 
         FIG.  18    is a graph illustrating an example of results of simulation calculation of a relationship between the pixel value of the X-ray image and the standard deviation of noise values. 
         FIG.  19    is a graph illustrating an example of a relationship between the pixel value and the standard deviation of noise values in a case where the material of the target object changes which is derived by the calculation unit  202 A of  FIG.  11   . 
         FIG.  20    is a block diagram illustrating a functional configuration of a control device  20 B according to another modification example of the present disclosure. 
         FIG.  21    is a flowchart illustrating a procedure of observation processing using an image acquiring device  1  according to another modification example of the present disclosure. 
         FIG.  22    is a diagram illustrating an example of generation of a noise standard deviation map which is performed by a noise map generation unit  204 B of  FIG.  20   . 
         FIG.  23    is a perspective view illustrating an example of a structure of a jig used for image capture in the image acquiring device  1  according to another modification example of the present disclosure. 
         FIG.  24    is a diagram illustrating an example of a captured image of the jig of  FIG.  23   . 
         FIG.  25    is a block diagram illustrating a functional configuration of a control device  20 C according to a second embodiment. 
         FIG.  26    is a diagram illustrating an example of image data which is training data used to construct trained models  206 C of  FIG.  25   . 
         FIG.  27    is a diagram illustrating an example of an X-ray transmission image to be analyzed by a selection unit  204 C of  FIG.  25   . 
         FIG.  28    is a diagram illustrating an example of a characteristic graph of thickness and luminance acquired by the selection unit  204 C  FIG.  25   . 
         FIG.  29    is a diagram illustrating an example of a characteristic graph of luminance and SNR acquired by the selection unit  204 C of  FIG.  25   . 
         FIG.  30    is a diagram illustrating a function of selection of a trained model based on image characteristics which is performed by the selection unit  204 C of  FIG.  25   . 
         FIG.  31    is a diagram illustrating an example of an X-ray transmission image used for the evaluation of resolution which is performed by the selection unit  204 C of  FIG.  25   . 
         FIG.  32    is a perspective view illustrating an example of a structure of a jig used for the evaluation of a luminance to noise ratio which is performed by the selection unit  204 C of  FIG.  25   . 
         FIG.  33    is a diagram illustrating an X-ray transmission image after a noise removal process obtained for the jig of  FIG.  32   . 
         FIG.  34    is a flowchart illustrating a procedure of observation processing using an image acquiring device  1  according to the second embodiment. 
         FIG.  35    is a block diagram illustrating a functional configuration of a control device  20 D according to a modification example of the second embodiment. 
         FIG.  36    is a flowchart illustrating a procedure of observation processing using an image acquiring device  1  according to the modification example of the second embodiment. 
         FIG.  37    is a block diagram illustrating a functional configuration of a control device  20 E according to a third embodiment. 
         FIG.  38    is a diagram illustrating an example of image data which is training data used to construct trained models  206 E of  FIG.  37   . 
         FIG.  39    is a diagram illustrating an example of an X-ray transmission image to be analyzed by a specification unit  202 E of  FIG.  37   . 
         FIG.  40    is a diagram illustrating an example of a characteristic graph of thickness and luminance acquired by the specification unit  202 E of  FIG.  37   . 
         FIG.  41    is a diagram illustrating an example of a characteristic graph of luminance and SNR acquired by the specification unit  202 E of  FIG.  37   . 
         FIG.  42    is a diagram illustrating an example of an X-ray transmission image used for the evaluation of resolution which is performed by the specification unit  202 E of  FIG.  37   . 
         FIG.  43    is a diagram illustrating a function of selection of a trained model based on image characteristics which is performed by the selection unit  204 E of  FIG.  37   . 
         FIG.  44    is a perspective view illustrating an example of a structure of a jig used for the evaluation of a luminance to noise ratio which is performed by the selection unit  204 E of  FIG.  37   . 
         FIG.  45    is a diagram illustrating an X-ray transmission image after the noise removal process obtained for the jig of  FIG.  44   . 
         FIG.  46    is a flowchart illustrating a procedure of observation processing using an image acquiring device  1  according to a third embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Meanwhile, in the description, the same elements or elements having the same function are denoted by the same reference signs, and thus duplicate description will be omitted. 
     First Embodiment 
       FIG.  1    is a configuration diagram of an image acquiring device  1  which is a radiographic image acquiring device and a radiographic image acquiring system according to the present embodiment. As shown in  FIG.  1   , the image acquiring device  1  is a device that radiates X-rays (radiation) to a target object F which is transported in a transport direction TD and acquires an X-ray transmission image (radiographic image) obtained by capturing an image of the target object F on the basis of the X-rays passing through the target object F. The image acquiring device  1  performs a foreign substance inspection, a weight inspection, a product inspection, or the like on the target object F using an X-ray transmission image, and examples of the application include food inspection, baggage inspection, substrate inspection, battery inspection, material inspection, and the like. The image acquiring device  1  is configured to include a belt conveyor (transport device)  60 , an X-ray irradiator (radiation source)  50 , an X-ray detection camera (imaging device)  10 , a control device (image processing module)  20 , a display device  30 , and an input device  40  for performing various inputs. Meanwhile, the radiographic image in the embodiment of the present disclosure is not limited to an X-ray image, and may also be an image caused by electromagnetic radiation other than X-rays such as γ-rays. 
     The belt conveyor  60  has a belt portion on which the target object F is placed, and transports the target object F in the transport direction (one direction) TD at a predetermined transport speed by moving the belt portion in the transport direction TD. The transport speed of the target object F is, for example, 48 m/min. The belt conveyor  60  can change the transport speed as necessary to a transport speed such as, for example, 24 m/min or 96 m/min. In addition, the belt conveyor  60  can appropriately change the height position of the belt portion to change a distance between the X-ray irradiator  50  and the target object F. Meanwhile, examples of the target object F transported by the belt conveyor  60  include foodstuffs such as meat, seafood, agricultural products, or confectionery, rubber products such as tires, resin products, metal products, resource materials such as minerals, waste, and various products such as electronic parts or electronic substrates. The X-ray irradiator  50  is a device that radiates (outputs) X-rays to the target object F as an X-ray source. The X-ray irradiator  50  is a point light source, and diffuses and radiates the X-rays in a predetermined angle range in a fixed irradiation direction. The X-ray irradiator  50  is disposed above the belt conveyor  60  at a predetermined distance from the belt conveyor  60  so that the irradiation direction of the X-rays is directed toward the belt conveyor  60  and the diffused X-rays extend in the entire width direction of the target object F (a direction intersecting the transport direction TD). In addition, the X-ray irradiator  50  is configured such that, in the lengthwise direction of the target object F (a direction parallel to the transport direction TD), a predetermined division range in the lengthwise direction is set as an irradiation range, and the X-rays are radiated in the entire lengthwise direction of the target object F by the target object F being transported in the transport direction TD by the belt conveyor  60 . The X-ray irradiator  50  has a tube voltage and a tube current set by the control device  20 , and radiates X-rays having predetermined energy and a radiation dose according to the set tube voltage and tube current toward the belt conveyor  60 . In addition, a filter  51  that transmits a predetermined wavelength region of the X-rays is provided in the vicinity of the X-ray irradiator  50  on the belt conveyor  60  side. 
     The X-ray detection camera  10  detects X-rays passing through the target object F among the X-rays radiated to the target object F by the X-ray irradiator  50 , and acquires and outputs a detection signal based on the X-rays. The image acquiring device  1  according to the present embodiment outputs an X-ray transmission image captured by scanning the X-ray transmission image in the transport direction TD by sequentially outputting the detection signal based on the X-rays passing through the target object F which is transported by the belt conveyor  60 . 
     The X-ray detection camera  10  includes a filter  19 , a scintillator  11 , a scan camera  12  (detection element), a sensor control unit  13 , an amplifier  14 , an AD converter  15 , a correction circuit  16 , an output interface  17 , and an amplifier control unit  18 . The scintillator  11 , the scan camera  12 , the amplifier  14 , the AD converter  15 , the correction circuit  16 , and the output interface  17  are electrically connected to each other. 
     The scintillator  11  is fixed on the scan camera  12  by adhesion or the like, and converts the X-rays passing through the target object F into scintillation light. The scintillator  11  outputs the scintillation light to the scan camera  12 . The filter  19  transmits a predetermined wavelength region of the X-rays toward the scintillator  11 . 
     The scan camera  12  detects the scintillation light from the scintillator  11 , converts the detected light into electric charge, and outputs it as a detection signal (electrical signal) to the amplifier  14 .  FIG.  2    is a plan view illustrating a configuration of the scan camera  12 . As shown in  FIG.  2   , the scan camera  12  includes a plurality of pixels  72  which are photodiodes (photoelectric conversion elements) arranged on a substrate  71  two-dimensionally, a readout circuit  73  that outputs a detection signal output by the plurality of pixels  72  photoelectrically converting the scintillation light to the outside, and a wiring portion W that electrically connects the readout circuit  73  and each of the plurality of pixels  72 . 
     Specifically, the scan camera  12  has a configuration in which pixel lines (pixel groups)  74  composed of M (M is an integer equal to or greater than 2) pixels  72  arranged in the transport direction TD are arranged in N columns (N is an integer equal to or greater than 2) on the substrate  71  in a direction substantially orthogonal to the transport direction TD. For example, the number of pixels M is four, and the number of pixel lines N is any integer equal to or greater than 200 and equal to or less than 30,000. 
     The readout circuit  73  sequentially receives detection signals which are output from M pixels  72  for each pixel line  74  at intervals of a predetermined detection period (the details will be described later) in accordance with control performed by the sensor control unit  13 , performs a process of adding (summing up) detection signals of at least two pixels  72  among the detection signals from the M pixels  72 , combines the detection signals that have undergone the addition process for each pixel line  74 , and outputs the combined signals to the outside as detection signals for one line of the target object F orthogonal to the transport direction TD. In the present embodiment, the readout circuit  73  performs the addition process on all the M detection signals. Additionally, the readout circuit  73  outputs a detection signal for the next line of the target object F orthogonal to the transport direction TD by performing the addition process on the detection signals sequentially output from the M pixels  72  with a predetermined detection period shifted. In the same way, the readout circuit  73  sequentially outputs detection signals for a plurality of lines of the target object F orthogonal to the transport direction TD. 
     The sensor control unit  13  controls the scan camera  12  to repeatedly capture images at a predetermined detection period so that all the pixels  72  within the pixel line  74  in the scan camera  12  can capture an image of X-rays passing through the same region of the target object F. The predetermined detection period may be set on the basis of the pixel width of the pixel  72  within the pixel line  74  in the scan camera  12 . As the predetermined detection period, for example, the deviation (delay time) of the detection timing of the pixel  72  within the pixel line  74  in the scan camera  12  may be specified on the basis of the distance between the pixels  72  within the pixel line  74  in the scan camera  12 , the speed of the belt conveyor  60 , the distance between the X-ray irradiator  50  and the target object F on the belt conveyor  60  (focus object distance (FOD)), and the distance between the X-ray irradiator  50  and the scan camera  12  (focus detector distance (FDD)), and the predetermined detection period may be set on the basis of the deviation. 
     The amplifier  14  amplifies the detection signal at a predetermined set amplification factor to generate an amplified signal, and outputs the amplified signal to the AD converter  15 . The set amplification factor is an amplification factor which is set by the amplifier control unit  18 . The amplifier control unit  18  sets the set amplification factor of the amplifier  14  on the basis of predetermined imaging conditions. 
     The AD converter  15  converts the amplified signal (voltage signal) output by the amplifier  14  into a digital signal, and outputs the converted signal to the correction circuit  16 . The correction circuit  16  performs a predetermined correction such as signal amplification on the digital signal, and outputs the corrected digital signal to the output interface  17 . The output interface  17  outputs the digital signal to the outside of the X-ray detection camera  10 . 
     The control device  20  is a computer such as, for example, a personal computer (PC). The control device  20  generates an X-ray transmission image on the basis of the digital signal (amplified signal) corresponding to detection signals for a plurality of lines which are sequentially output from the X-ray detection camera  10  (more specifically, the output interface  17 ). In the present embodiment, the control device  20  generates one X-ray transmission image on the basis of the digital signals for 128 lines which are output from the output interface  17 . The generated X-ray transmission image is output to the display device  30  after a noise removal process to be described later is performed, and is displayed by the display device  30 . In addition, the control device  20  controls the X-ray irradiator  50 , the amplifier control unit  18 , and the sensor control unit  13 . Meanwhile, the control device  20  of the present embodiment is a device which is independently provided outside the X-ray detection camera  10 , but it may be integrated inside the X-ray detection camera  10 . 
       FIG.  3    shows a hardware configuration of the control device  20 . As shown in  FIG.  3   , the control device  20  is a computer or the like physically including a central processing unit (CPU)  101  and a graphic processing unit (GPU)  105  which are processors, a random access memory (RAM)  102  and a read only memory (ROM)  103  which are recording media, a communication module  104 , an input and output module  106 , and the like, which are electrically connected to each other. Meanwhile, the control device  20  may include a display, a keyboard, a mouse, a touch panel display, and the like as the input device  40  and the display device  30 , or may include a data recording device such as a hard disk drive or a semiconductor memory. In addition, the control device  20  may be constituted by a plurality of computers. 
       FIG.  4    is a block diagram illustrating a functional configuration of the control device  20 . The control device  20  includes an input unit  201 , a calculation unit  202 , an image acquisition unit  203 , a noise map generation unit  204 , a processing unit  205 , and a construction unit  206 . Each functional unit of the control device  20  shown in  FIG.  4    is realized by loading a program (a radiographic image processing program of a first embodiment) on the hardware such as the CPU  101 , the GPU  105 , and the RAM  102  to thereby bring the communication module  104 , the input and output module  106 , and the like into operation under the control of the CPU  101  and the GPU  105  and read out and write data from and to the RANI  102 . The CPU  101  and the GPU  105  of the control device  20  cause the control device  20  to function as each functional unit in  FIG.  4    by executing this computer program, and sequentially execute processing corresponding to a radiographic image acquisition processing method to be described later. Meanwhile, the CPU  101  and the GPU  105  may be a single piece of hardware, or only one of them may be used. In addition, the CPU  101  and the GPU  105  may be implemented in a programmable logic such as an FPGA like a soft processor. The RANI or the ROM may also be a single piece of hardware, or may be built into a programmable logic such as an FPGA. Various types of data required for executing this computer program and various types of data generated by executing this computer program are all stored in a built-in memory such as the ROM  103  or the RANI  102 , or a storage medium such as a hard disk drive. In addition, a built-in memory or a storage medium in the control device  20  stores in advance a trained model  207  that causes the CPU  101  and the GPU  105  to execute the noise removal process for an X-ray image (X-ray transmission image) by being read by the CPU  101  and the GPU  105  (which will be described later). 
     Hereinafter, the details of the function of each functional unit of the control device  20  will be described. 
     The input unit  201  accepts an input of condition information indicating either conditions of a source of radiation or imaging conditions when the radiation is radiated to capture an image of the target object F. Specifically, the input unit  201  accepts an input of condition information indicating the operating conditions of the X-ray irradiator (radiation source)  50  when the X-ray image of the target object F is captured, the imaging conditions of the X-ray detection camera  10 , or the like from a user of the image acquiring device  1 . Examples of the operating conditions include all or some of a tube voltage, a target angle, a target material, and the like. Examples of the condition information indicating the imaging conditions include information indicating the material and thickness of the filters  51  and  19  disposed between the X-ray irradiator  50  and the X-ray detection camera  10 , the distance (FDD) between the X-ray irradiator  50  and the X-ray detection camera  10 , the type of window material of the X-ray detection camera  10 , and the material and thickness of the scintillator  11  of the X-ray detection camera  10 , and all or some of X-ray detection camera information (for example, a gain setting value, a circuit noise value, an amount of saturated charge, a conversion coefficient value (e−/count), and the line rate (Hz) or line speed (m/min) of the camera), information on the target object F, and the like. The input unit  201  may accept an input of the condition information as a direct input of information such as numerical values, or may accept the input as a selective input for information such as numerical values which are set in an internal memory in advance. The input unit  201  accepts the input of the above condition information from a user, but it may acquire some condition information (such as a tube voltage) in accordance with the detection result of the state of control performed by the control device  20 . 
     The calculation unit  202  calculates the average energy related to X-rays (radiation) passing through the target object F on the basis of the condition information. The condition information includes at least any one of a tube voltage of the source, information relating to the target object F, information on a filter included in a camera used to capture an image of the target object F, information on a scintillator included in the camera, and information on a filter included in the X-ray source. Specifically, the calculation unit  202  calculates the value of the average energy of X-rays that pass through the target object F using the image acquiring device  1  and are detected by the X-ray detection camera  10  on the basis of the condition information accepted by the input unit  201 . For example, the calculation unit  202  calculates an X-ray spectrum detected by the X-ray detection camera  10  using, for example, a known approximate expression of Tucker or the like on the basis of information such as a tube voltage, a target angle, a target material, the material and thickness of the filters  51  and  19  and their presence or absence, the type of window material of the X-ray detection camera  10  and its presence or absence, and the material and thickness of the scintillator  11  of the X-ray detection camera  10  which are included in the condition information. The calculation unit  202  further calculates a spectral intensity integration value and a photon number integration value from the spectrum of the X-rays, and calculates the value of the average energy of the X-rays by dividing the spectral intensity integration value by the photon number integration value. 
     A calculation method using a known approximate expression of Tucker will be described. For example, in a case where the target is specified as tungsten and the target angle is specified as 25°, the calculation unit  202  can determine Em: kinetic energy during electron target collision, T: electron kinetic energy in the target, A: proportionality constant determined by the atomic number of the target substance, ρ: the density of the target, μ(E): the linear attenuation coefficient of the target substance, B: the function of Z and T that changes gently, C: Thomson-Whiddington constant, θ: target angle, and c: the speed of light in vacuum. Further, the calculation unit  202  can calculate an irradiation X-ray spectrum by calculating the following Expression (1) on the basis of these values. 
     
       
         
           
             
               
                 
                                    
                   
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     Meanwhile, Em can be determined from information on the tube voltage, A, ρ, and μ(E) can be determined from information on the material of the target object F, and θ can be determined from information on the angle of the target object F. 
     Next, the calculation unit  202  can calculate the X-ray energy spectrum that passes through the filter and the target object F and is absorbed by the scintillator by using the X-ray attenuation expression of the following Expression (2). 
       [Expression 2] 
         I=I   0   e   −μx   (2)
 
     Here, μ is the attenuation coefficient of the target object F, the filter, the scintillator, or the like, and x is the thickness of the target object F, the filter, the scintillator, or the like. In addition, μ can be determined from information on the materials of the target object F, the filter, and the scintillator, and x can be determined from information on the thicknesses of the target object F, the filter, and the scintillator. The X-ray photon number spectrum can be obtained by dividing this X-ray energy spectrum by energy of each X-ray. The calculation unit  202  calculates the average energy of X-rays using the following Expression (3) by dividing the integration value of energy intensity by the integration value of the number of photons. 
       Average energy  E =spectral intensity integration value/photon number integration value  (3)
 
     The calculation unit  202  calculates the average energy of X-rays through the above calculation process. Meanwhile, for the calculation of the X-ray spectrum, a known approximate expression of Kramers or Birch et al. may be used. 
     The image acquisition unit  203  acquires a radiographic image in which radiation is radiated to the target object F and an image of the radiation passing through the target object F is captured. Specifically, the image acquisition unit  203  generates an X-ray image on the basis of the digital signal (amplified signal) output from the X-ray detection camera  10  (more specifically, the output interface  17 ). The image acquisition unit  203  generates one X-ray image on the basis of the digital signal for a plurality of lines output from the output interface  17 .  FIG.  5    is a diagram illustrating an example of an X-ray image acquired by the image acquisition unit  203 . 
     The noise map generation unit  204  derives an evaluation value from the pixel value of each pixel of the radiographic image on the basis of relational data indicating a relationship between the pixel value and the evaluation value obtained by evaluating the spread of the noise value, and generates a noise map which is data obtained by associating the derived evaluation value with each pixel of the radiographic image. In this case, the noise map generation unit  204  derives an evaluation value from the average energy related to the radiation passing through the target object F and the pixel value of each pixel of the radiographic image. Specifically, the noise map generation unit  204  uses the relational expression (relational data) between the pixel value and the standard deviation of the noise value (evaluation value obtained by evaluating the spread of the noise value) to derive the standard deviation of the noise value from the average energy of X-rays calculated by the calculation unit  202  and the pixel value of each pixel of the X-ray image (radiographic image) acquired by the image acquisition unit  203 . The noise map generation unit  204  generates a noise standard deviation map (noise map) by associating the derived standard deviation of the noise value with each pixel of the X-ray image. 
     The relational expression used by the noise map generation unit  204  between the pixel value and the average energy and the standard deviation of the noise value is represented by the following expression (4). 
     
       
         
           
             
               
                 
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     In Expression (4), the variable Noise is the standard deviation of the noise value, the variable Signal is the signal value (pixel value) of a pixel, the constant F is a noise factor, the constant M is the multiplication factor of the scintillator, the constant C is the coupling efficiency between the scan camera  12  and the scintillator  11  in the X-ray detection camera  10 , the constant Q is the quantum efficiency of the scan camera  12 , the constant cf is a conversion coefficient for converting the signal value of a pixel into electric charge in the scan camera  12 , the variable Em is the average energy of X-rays, the constant D is information indicating dark current noise generated by thermal noise in an image sensor, and the constant R is information indicating readout noise in the scan camera  12 . When Expression (4) is used, the noise map generation unit  204  substitutes the pixel value of each pixel of the X-ray image acquired by the image acquisition unit  203  into the variable Signal, and substitutes the numerical value of the average energy calculated by the calculation unit  202  into the variable Em. The noise map generation unit  204  obtains the variable Noise calculated using Expression (4) as the numerical value of standard deviation of the noise value. Meanwhile, other parameters including the average energy may be acquired by the input unit  201  accepting an input, or may be set in advance. 
       FIG.  6    is a diagram illustrating an example of generation of a noise standard deviation map which is performed by the noise map generation unit  204 . The noise map generation unit  204  substitutes various pixel values into the variable Signal and acquires a correspondence relation between the pixel value and the variable Noise using Relational Expression (4) between the pixel value and the standard deviation of the noise value to derive a relational graph G 3  showing the correspondence relation between the pixel value and the standard deviation of the noise value. The noise map generation unit  204  then derives the relational data G 2  indicating the correspondence relation between each pixel position and the pixel value from an X-ray image G 1  acquired by the image acquisition unit  203 . Further, the noise map generation unit  204  derives the standard deviation of the noise value corresponding to a pixel at each pixel position in the X-ray image by applying the correspondence relation indicated by the relational graph G 3  to each pixel value in the relational data G 2 . As a result, the noise map generation unit  204  associates the derived standard deviation of noise with each pixel position, and derives relational data G 4  indicating the correspondence relation between each pixel position and the standard deviation of noise. The noise map generation unit  204  then generates a noise standard deviation map G 5  on the basis of the derived relational data G 4 . 
     The processing unit  205  inputs the radiographic image and the noise map to the trained model  207  constructed through machine learning in advance, and executes image processing for removing noise from the radiographic image. That is, as shown in  FIG.  7   , the processing unit  205  acquires the trained model  207  (which will be described later) constructed by the construction unit  206  from the built-in memory or the storage medium within the control device  20 . The processing unit  205  inputs the X-ray image G 1  acquired by the image acquisition unit  203  and the noise standard deviation map G 5  generated by the noise map generation unit  204  to the trained model  207 . Thereby, the processing unit  205  generates an output image G 6  by executing image processing for removing noise from the X-ray image G 1  using the trained model  207 . The processing unit  205  then outputs the generated output image G 6  to the display device  30  or the like. 
     The construction unit  206  constructs a trained model  207  for outputting noise-removed image data on the basis of the training image and the noise map through machine learning using, as training data, the training image which is a radiographic image, the noise map generated from the training image on the basis of the relational expression between the pixel value and the standard deviation of noise values, and the noise-removed image data which is data obtained by removing noise from the training image. The construction unit  206  stores the constructed trained model  207  in the built-in memory or storage medium within the control device  20 . Examples of machine learning include supervised learning, unsupervised learning, and reinforcement learning, including deep learning, neural network learning, and the like. In the first embodiment, the two-dimensional convolutional neural network described in the paper “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising” authored by Kai Zhang et al. is adopted as an example of a deep learning algorithm Meanwhile, the trained model  207  may be generated by an external computer or the like and downloaded to the control device  20  in addition to being constructed by the construction unit  206 . Meanwhile, the radiographic image used for machine learning includes a radiographic image obtained by capturing an image of a known structure or an image obtained by reproducing the radiographic image. 
       FIG.  8    is an example of a training image which is one piece of training data used to construct the trained models  207 . As the training image, an X-ray image having a pattern of various thicknesses, various materials, and various resolutions as an imaging target can be used. The example shown in  FIG.  8    is a training image G 7  generated for chicken. As the training image G 7 , an X-ray image actually generated for a plurality of types of known structures using the image acquiring device  1  may be used, or an image generated by simulation calculation may be used. The X-ray image may be acquired using a device different from the image acquiring device  1 . 
     As preprocessing for performing machine learning, the construction unit  206  derives the evaluation value from the pixel value of each pixel of the radiographic image on the basis of the relational data indicating the relationship between the pixel value and the evaluation value obtained by evaluating the spread of the noise value, and generates a noise map which is data obtained by associating the derived evaluation value with each pixel of the radiographic image. Specifically, when constructing the trained model  207 , the construction unit  206  acquires a training image generated by actual image capture, simulation calculation, or the like from the image acquisition unit  203  or the like. The construction unit  206  sets, for example, the operating conditions of the X-ray irradiator  50  of the image acquiring device  1 , the imaging conditions of the image acquiring device  1 , or the like. Alternatively, the construction unit  206  sets the imaging conditions or the operating conditions of the X-ray irradiator  50  during simulation calculation. The construction unit  206  uses the same method as the calculation unit  202  to calculate the average energy of X-rays on the basis of the above-described operating conditions or imaging conditions. Further, the construction unit  206  uses the same method as the method performed by the noise map generation unit  204  as shown in  FIG.  6    to generate a noise standard deviation map on the basis of the average energy of X-rays and the training image. That is, the preprocessing method of the machine learning method includes a noise map generation step of deriving an evaluation value from the pixel value of each pixel of the radiographic image on the basis of the relational data indicating the relationship between the pixel value and the evaluation value obtained by evaluating the spread of the noise value and generating a noise map which is data obtained by associating the derived evaluation value with each pixel of the radiographic image. 
     The construction unit  206  constructs the trained model  207  through machine learning using, as training data, the training image, the noise map generated from the training image, and the noise-removed image data which is data in which noise is removed in advance from the training image. Specifically, the construction unit  206  acquires the noise-removed image data in which noise is removed in advance from the training image. In a case where the training image is an X-ray image generated by simulation calculation, the construction unit  206  uses an image before noise is added in a process of generating the training image as the noise-removed image data. On the other hand, in a case where the training image is an X-ray image actually generated for a plurality of types of known structures using the image acquiring device  1 , the construction unit  206  uses an image in which noise is removed from the X-ray image using image processing such as an average value filter, a median filter, a bilateral filter, or an NLM filter as the noise-removed image data. The construction unit  206  constructs a trained model  207  for outputting the noise-removed image data on the basis of the training image and the noise standard deviation map by executing training through machine learning. 
       FIG.  9    is a flowchart illustrating a procedure of creating image data which is training data used by the construction unit  206  to construct the trained models  207 . 
     The image data (also referred to as training image data) which is training data is created by a computer in the following procedure. First, an image of a structural body having a predetermined structure (structure image) is created (step S 301 ). For example, an image of a structure having a predetermined structure may be created by simulation calculation. In addition, an X-ray image of a structure such as a chart having a predetermined structure may be acquired to create a structure image. Next, a sigma value which is a standard deviation of pixel values is calculated for one pixel selected from a plurality of pixels constituting such a structure image (step S 302 ). A normal distribution (Poisson distribution) indicating a noise distribution is then set on the basis of the sigma value obtained in step S 302  (step S 303 ). In this manner, training data for various noise conditions can be generated by setting the normal distribution on the basis of the sigma value. Subsequently, a noise value which is set at random is calculated along the normal distribution which is set on the basis of the sigma value in step S 303  (step S 304 ). Further, the noise value obtained in step S 304  is added to the pixel value of one pixel to generate pixel values constituting the image data which is training data (step S 305 ). The processes of steps S 302  to S 305  are performed for each of a plurality of pixels constituting the structure image (step S 306 ), and training image data serving as training data is generated (step S 307 ). In addition, in a case where the training image data is further required, it is determined that the processes of steps S 301  to S 307  are performed on another structure image (step S 308 ), and another training image data serving as training data is generated. Meanwhile, the other structure image may be an image of a structural body having the same structure, or may be an image of a structural body having another structure. 
     Meanwhile, it is necessary to prepare a large number of pieces of image data which is training data used to construct the trained model  207 . In addition, the structure image is preferably an image with less noise, ideally an image without noise. Therefore, when a structure image is generated through simulation calculation, many images without noise can be generated, and thus it is effective to generate a structure image through simulation calculation. 
     Next, a procedure of observing the X-ray transmission image of the target object F using the image acquiring device  1  according to the first embodiment, that is, a flow of the radiographic image acquisition method according to the first embodiment, will be described.  FIG.  10    is a flowchart illustrating a procedure of observation processing using the image acquiring device  1 . 
     First, the construction unit  206  uses, as training data, the training image, the noise standard deviation map generated from the training image on the basis of the relational expression, and the noise-removed image data to construct a trained model  207  for outputting the noise-removed image data on the basis of the training image and the noise standard deviation map, through machine learning (step S 100 ). Next, the input unit  201  accepts an input of condition information indicating the operating conditions of the X-ray irradiator  50 , the imaging conditions of the X-ray detection camera  10 , or the like from an operator (user) of the image acquiring device  1  (step S 101 ). The calculation unit  202  then calculates the value of the average energy of the X-rays detected by the X-ray detection camera  10  on the basis of the condition information (step S 102 ). 
     Next, the target object F is set in the image acquiring device  1  to capture an image of the target object F, and the control device  20  acquires an X-ray image of the target object F (step S 103 ). Further, the control device  20  derives the standard deviation of the noise value from the average energy of the X-rays and the pixel value of each pixel of the X-ray image on the basis of the relational expression between the pixel value and the standard deviation of the noise value, and the derived standard deviation of noise is associated with each pixel value to generate a noise standard deviation map (step S 104 ). 
     Next, the processing unit  205  inputs an X-ray image of the target object F and the noise standard deviation map to the trained model  207  constructed and stored in advance, and executes the noise removal process for the X-ray image (step S 105 ). Further, the processing unit  205  outputs an output image which is an X-ray image that has undergone the noise removal process to the display device  30  (step S 106 ). 
     According to the image acquiring device  1  described above, the scintillation light corresponding to the X-rays passing through the target object F is detected by the scan camera  12  in which the pixel lines  74  each having M pixels  72  arranged in the direction TD of scanning of the target object F is arranged in N columns, detection signals of at least two pixels  72  out of detection signals of the M pixels  72  output for each pixel line  74  are added, and an X-ray image is output by the added N detection signals being sequentially output. Additionally, the output X-ray image is input to the trained model  207  constructed through machine learning in advance using image data, and thus the noise removal process is performed on the X-ray image. Thereby, it is possible to remove noise components while increasing signal components in the X-ray image, and to effectively improve an S/N ratio in the X-ray image. Specifically, in a case where the noise removal process using the trained model  207  is performed, it can be understood that the contrast to noise ratio (CNR) is improved approximately 6.4 times more than in a case where the noise removal process is not performed, and that the improvement effect is larger than the improvement effect of approximately 1.9 times the CNR based on the noise removal process using the bilateral filter. 
     In addition, in the image acquiring device  1 , the trained model  207  is constructed through machine learning using image data obtained by adding noise values along a normal distribution to an X-ray image of a predetermined structure as training data. Thereby, it becomes easy to prepare image data which is training data used to construct the trained model  207 , and thus it is possible to efficiently construct the trained model  207 . 
     In addition, according to the image acquiring device  1 , the standard deviation of the noise value is derived from the pixel value of each image of the X-ray image using the relational expression between the pixel value and the standard deviation of the noise value, and the noise standard deviation map which is data obtained by associating the derived standard deviation of the noise value with each pixel of the X-ray image is generated. The X-ray image and the noise standard deviation map are input to the trained model  207  constructed through machine learning in advance, and image processing for removing noise from the X-ray image is executed. With such a configuration, the standard deviation of the noise value derived from the pixel value of each pixel of the X-ray image is taken into consideration, and noise in each pixel of the X-ray image is removed through machine learning. Thereby, noise removal corresponding to the relationship between the pixel value and the standard deviation of the noise value in the X-ray image can be realized using the trained model  207 . As a result, it is possible to effectively remove noise from the X-ray image. 
     Particularly, the mode of noise of the X-ray image changes depending on differences in a tube voltage, a filter, a scintillator, conditions of an X-ray detection camera (a gain setting value, a circuit noise value, an amount of saturated charge, a conversion coefficient value (e−/count), and the line rate of the camera), a target object, and the like. For this reason, in a case where noise removal is attempted to be realized through machine learning, the preparation of a learning model trained under various conditions can be considered. That is, as a comparative example, a method of constructing a plurality of learning models according to the conditions during measurement of the X-ray image, selecting a learning model for each condition, and executing the noise removal process can also be adopted. In the case of such a comparative example, for example, a learning model has to be constructed for each noise condition such as the average energy of X-rays, the gain of the X-ray detection camera, and the type of X-ray camera, and it is necessary to generate a huge number of learning models, which may take a lot of time for construction. As an example, when there are ten types of average energy of X-rays, eight gains of the X-ray detection camera, and three types of products, 240 trained models are required, but in a case where it takes one day for each model to construct a trained model, it takes 240 days for machine learning. In this regard, according to the present embodiment, by generating a noise map from the X-ray image and using the noise map as input data for machine learning, it is possible to reduce the noise conditions required to generate a trained model, and a learning time for constructing the trained model  207  is greatly reduced. 
     Modification Example of Control Device  20  of First Embodiment 
       FIG.  11    is a block diagram illustrating a functional configuration of a control device  20 A in a modification example of the first embodiment. The control device  20 A is different from that of the first embodiment described above in that a calculation unit  202 A has a function of deriving the average energy of X-rays from the pixel value of the X-ray image and that a noise map generation unit  204 A has a function of deriving a noise standard deviation map on the basis of the pixel value of the X-ray image and the average energy of the X-rays derived from the X-ray image.  FIG.  12    is a flowchart illustrating a procedure of observation processing using the image acquiring device  1  including the control device  20 A of  FIG.  11   . As shown in  FIG.  12   , in the control device  20 A, the process shown in step S 103  of the control device  20  according to the first embodiment shown in  FIG.  10    is performed immediately after step S 100 . In the control device  20 A, the processes shown in S 102 A and S 104 A are executed in place of the processes of steps S 102  and S 104  of the control device  20 . 
     The calculation unit  202 A calculates the average energy from the pixel value of each pixel of the radiographic image (step S 102 A). Specifically, the calculation unit  202 A derives the relationship between the pixel value and the average energy in advance for each piece of condition information through simulation calculation of the X-ray spectrum or the like. The calculation unit  202 A acquires condition information acquired by the input unit  201 , including at least the tube voltage and the information on the scintillator included in the X-ray detection camera  10 . The calculation unit  202 A then selects a relationship corresponding to the condition information from the relationship between the pixel value and the average energy derived in advance on the basis of the condition information. Further, the calculation unit  202 A derives the average energy for each pixel from the pixel value of each pixel of the X-ray image acquired by the image acquisition unit  203  on the basis of the selected relationship. 
     Hereinafter, the derivation of the relationship between the pixel value and the average energy for each piece of condition information which is performed by the calculation unit  202 A will be described with reference to  FIGS.  13  to  17   . 
     First, the calculation unit  202 A derives a graph G 18  showing the relationship between the thickness of the target object F and the transmittance of X-rays and a graph G 19  showing the relationship between the thickness of the target object F and the average energy of X-rays on the basis of the condition information. Specifically, as shown in the parts (a) to (d) of  FIG.  13   , the calculation unit  202 A calculates energy spectra G 14  to G 17  of X-rays transmitted in a case where the thickness of the target object F is variously changed through simulation calculation on the basis of the condition information including at least the tube voltage and the information on the scintillator included in the X-ray detection camera  10 .  FIG.  13    is a graph illustrating an example of results of simulation calculation of an energy spectrum of X-rays passing through the target object F which is performed by the calculation unit  202 A. Here, the energy spectra G 14  to G 17  of transmitted X-rays in a case where simulation calculation is performed by gradually increasing the thickness of the target object F composed of water are exemplified. Further, the calculation unit  202 A calculates the average energy of X-rays transmitted in a case where the thickness of the target object F is variously changed on the basis of the calculated energy spectra G 14  to G 17 . Meanwhile, in addition to the simulation calculation, the calculation unit  202 A may obtain a relationship between the thickness of the target object F and the average energy on the basis of the X-ray image obtained by capturing an image of a structure of which the thickness is known. 
     Further, the calculation unit  202 A also derives the relationship between the thickness of the target object F and the transmittance of X-rays on the basis of the above simulation result.  FIG.  14    is a chart table illustrating an example of the relationship between the thickness of the target object F, average energy, and transmittance which is derived by the calculation unit  202 A. As shown in  FIG.  14   , the average energy of the transmitted X-rays and the transmittance of X-rays are derived corresponding to each of the energy spectra G 14  to G 17  calculated for each thickness of the target object F. 
     Next, the calculation unit  202 A derives the graph G 18  showing the relationship between the thickness of the target object F and the transmittance of X-rays from the transmittance of the X-rays derived for the target object F having various thicknesses.  FIG.  15    is a graph illustrating the relationship between the thickness of the target object F and the transmittance of X-rays with respect to the target object F which is derived by the calculation unit  202 A. Additionally, the calculation unit  202 A derives the graph G 19  showing the relationship between the thickness of the target object F and the average energy of X-rays from the average energy of the X-rays derived for the target object F having various thicknesses.  FIG.  16    is a graph illustrating the relationship between the thickness of the target object F and the average energy of X-rays passing through the target object F which is derived by the calculation unit  202 A. 
     The calculation unit  202 A derives a graph G 20  showing the relationship between the pixel value of the X-ray image and the average energy as shown in  FIG.  17    for each piece of various types of condition information on the basis of the two graphs G 18  and G 19  derived for each piece of various types of condition information.  FIG.  17    is a graph illustrating the relationship between the pixel value of the X-ray image and the average energy which are derived by the calculation unit  202 A. Specifically, the calculation unit  202 A derives a pixel value I 0  of the X-ray transmission image in a case where the target object F is not present on the basis of the condition information. The calculation unit  202 A then sets a pixel value I of the X-ray image in a case where the target object F is present, and calculates I/I 0  which is the transmittance of X-rays. Further, the calculation unit  202 A derives the thickness of the target object F from I/I 0  which is the calculated transmittance of X-rays on the basis of the graph G 18  of the thickness of the target object F and the transmittance of X-rays with respect to the target object F. Finally, the calculation unit  202 A derives the average energy of the transmitted X-rays corresponding to the thickness the target object F on the basis of the derived thickness of the target object F and the graph G 19  of the thickness of the target object F and the average energy of the transmitted X-rays. Subsequently, the calculation unit  202 A performs the above derivation for each piece of various types of condition information while variously changing the pixel value I of X-ray image to thereby derive the graph G 20  showing the relationship between the pixel value of the X-ray image and the average energy of the transmitted X-rays for each piece of condition information. 
     Here, an example of derivation of average energy based on the pixel value which is performed by the calculation unit  202 A will be described. For example, it is assumed that the calculation unit  202 A derives the pixel value of the X-ray transmission image in a case where the target object F is not present as I 0 =5000 on the basis of the condition information and sets the pixel value of the X-ray image in a case where the target object F is present to be I=500. In this case, the calculation unit  202 A calculates the transmittance of X-rays as I/I 0 =0.1. Subsequently, the calculation unit  202 A derives that the thickness corresponding to the X-ray transmittance of 0.1 is 30 mm on the basis of the graph G 18  showing the relationship between the thickness of the target object F and the transmittance of X-rays with respect to the target object F. Further, the calculation unit  202 A derives that the average energy corresponding to the pixel value of 500 is 27 keV on the basis of the graph G 19  showing the relationship between the thickness of the target object F and the average energy of the transmitted X-rays. Finally, the calculation unit  202 A repeatedly derives the average energy of X-rays for each pixel value, and derives the graph G 20  showing the relationship between the pixel value of the X-ray image and the average energy. 
     Further, the calculation unit  202 A selects a graph G 20  corresponding to the condition information acquired by the input unit  201  from a plurality of graphs G 20  derived in advance in the above procedure. The calculation unit  202 A derives the average energy of the transmitted X-rays corresponding to the pixel value of each pixel of the X-ray image acquired by the image acquisition unit  203  on the basis of the selected graph G 20 . 
     Meanwhile, the calculation unit  202 A does not derive the relationship between the pixel value and the average energy of X-rays in advance for each piece of condition information, and may derive the average energy of X-rays from the condition information acquired by the input unit  201  and the pixel value of each pixel of the X-ray image with reference to the graphs G 18  and G 19 . Specifically, the calculation unit  202 A derives the pixel value I 0  of the X-ray image in a case where the target object is not present on the basis of the condition information. The calculation unit  202 A then calculates the transmittance by obtaining a ratio to the pixel value I 0  for each pixel value I of each pixel of the X-ray image acquired by the image acquisition unit  203 . Further, the calculation unit  202 A derives the thickness on the basis of the graph G 18  showing the relationship between the thickness and the transmittance of X-rays and the calculated transmittance. The calculation unit  202 A then derives the average energy for each pixel value of each pixel of the X-ray image by deriving the average energy on the basis of the graph G 19  showing the relationship between the thickness and the average energy and the derived thickness. 
     The noise map generation unit  204 A generates a noise standard deviation map from the X-ray image acquired by the image acquisition unit  203  and the average energy of X-rays corresponding to each pixel of the X-ray image derived by the calculation unit  202 A (step S 104 A). Specifically, the noise map generation unit  204 A derives the standard deviation of the noise value for each pixel in consideration of the thickness of the target object by substituting the pixel value of each pixel of the X-ray image acquired by the image acquisition unit  203  and the average energy derived for each pixel by the calculation unit  202 A into Relational Expression (4). The noise map generation unit  204 A generates the standard deviation of the noise value corresponding to each pixel of the X-ray image as a noise standard deviation map. 
       FIG.  18    is a graph showing an example of the relationship between the pixel value and the standard deviation of the noise value. This graph shows the relationship between the standard deviation of the noise value derived from the pixel value of the X-ray image by the calculation unit  202 A and the noise map generation unit  204 A according to the present modification example and the pixel value of the X-ray image. In the present modification example, since the standard deviation of the noise value is derived in consideration of the thickness of the target object, the thickness of the target object becomes smaller as the pixel value increases, and the average energy in a pixel decreases. Therefore, as estimated from Relational Expression (4), a change in the standard deviation of the noise value when the pixel value increases differs between the first embodiment and the present modification example. In the example shown in  FIG.  18   , a graph G 22  of the present modification example has a smaller degree of increase in the standard deviation of the noise value when the pixel value increases than a graph G 21  of the first embodiment. 
     In the control device  20 A of the modification example of first embodiment, the average energy is calculated from the pixel value of each pixel of the X-ray image. Here, for example, in a case where a plurality of target objects having different thicknesses and materials are present in the X-ray image, the average energy differs greatly for each target object, and noise cannot be removed sufficiently from the X-ray image. With such a configuration, the average energy of X-rays passing through the target object F is calculated for pixel value of each pixel of the X-ray image, and thus it is possible to realize noise removal corresponding to the relationship between the pixel value of each pixel of the X-ray image and the noise, for example, in consideration of the difference in thickness and material or the like. As a result, it is possible to effectively remove the noise from the X-ray image. 
     Meanwhile, the control device  20 A according to the present modification example derives the average energy from the pixel value of the X-ray image using the graph G 20  derived for each piece of various types of condition information. In this case, the average energy may be derived from the pixel value while ignoring the difference in the material of the target object F.  FIG.  19    is a graph illustrating the relationship between the pixel value of the X-ray image and the standard deviation of the noise value derived by the calculation unit  202 A. Here, a change in the material of the target object F is also taken into consideration as the condition information to derive the relationship. A graph G 24  shows a derivation example in a case where the material is aluminum, a graph G 23  shows a derivation example in a case where the material is polyethylene terephthalate (PET), and a graph G 25  shows a derivation example in a case where the material is copper. In this way, when the tube voltage of the X-ray irradiator  50  and the information on the scintillator included in the X-ray detection camera  10  used for capturing an image of the target object F are the same as each other even in a case where the material of the target object F changes, the relationship between the pixel value and the average energy of the transmitted X-rays does not change greatly, and thus the relationship between the pixel value and the standard deviation of the noise value also does not change greatly. In consideration of such a property, the control device  20 A can derive the average energy from the pixel value of the X-ray image while ignoring the difference in the material of the target object F as the condition information. Even in such a case, according to the control device  20 A of the present modification example, it is possible to realize noise removal corresponding to the relationship between the pixel value and the standard deviation of noise. As a result, it is possible to further effectively remove the noise from the X-ray image. 
     Another Modification Example of Control Device  20  of First Embodiment 
       FIG.  20    is a block diagram illustrating a functional configuration of a control device  20 B according to another modification example of the first embodiment. The control device  20 B is different from the first embodiment described above in that an image acquisition unit  203 B has a function of acquiring an X-ray image of the jig and that a noise map generation unit  204 B has a function of deriving a graph showing the relationship between the pixel value and the standard deviation of the noise value from the X-ray image of the jig.  FIG.  21    is a flowchart illustrating a procedure of observation processing using the image acquiring device  1  including the control device  20 B of  FIG.  20   . As shown in  FIG.  21   , in the control device  20 B according to the present modification example, the processes shown in steps S 201  and S 202  are executed in place of the processes of steps S 101 , S 102 , and S 104  which are performed by the control device  20  according to the first embodiment shown in  FIG.  10   . 
     The image acquisition unit  203 B acquires a radiographic image of the jig obtained by radiating radiation to the jig and capturing an image of the radiation passing through the jig (step S 201 ). Specifically, the image acquisition unit  203 B acquires an X-ray image captured by radiating X-rays to the jig and the target object F using the image acquiring device  1 . As the jig, a flat plate-like member or the like whose thickness and material are known is used. That is, the image acquisition unit  203 B acquires an X-ray image of the jig captured using the image acquiring device  1  in advance of the observation processing of the target object F. The image acquisition unit  203 B then acquires an X-ray image of the target object F captured using the image acquiring device  1 . However, the acquisition timings of the X-ray images of the jig and the target object F are not limited to the above, and may be simultaneous or reverse (step S 103 ). In addition, similarly to the image acquisition unit  203 , the image acquisition unit  203 B acquires an X-ray image obtained by radiating X-rays to the target object F and capturing an image of the X-rays passing through the target object F. 
     In the image acquiring device  1 , the jig is set to capture an image of the jig, and the noise map generation unit  204 B derives relational data indicating the relationship between the pixel value and the evaluation value obtained by evaluating the spread of the noise value from the radiographic image of the jig obtained as a result (step S 202 ). Specifically, the noise map generation unit  204 B derives a noise standard deviation map indicating the relationship between the pixel value and the standard deviation of the noise value from the X-ray image of the jig. 
       FIG.  22    is a diagram illustrating an example of generation of a noise standard deviation map which is performed by the noise map generation unit  204 B. The noise map generation unit  204 B derives a relational graph G 27  showing the correspondence relation between the pixel value and the standard deviation of the noise value from the X-ray image G 26  of the jig. In the same manner as in the first embodiment, the noise map generation unit  204 B then derives the relational data G 2  indicating the correspondence relation between each pixel position and the pixel value from the X-ray image G 1  acquired by the image acquisition unit  203 B. Further, the noise map generation unit  204  derives the standard deviation of the noise value corresponding to a pixel at each pixel position in the X-ray image by applying the correspondence relation indicated by the relational graph G 27  to each pixel in the relational data G 2 . As a result, the noise map generation unit  204  associates the derived standard deviation of noise with each pixel position, and derives relational data G 4  indicating the correspondence relation between each pixel position and the standard deviation of noise. The noise map generation unit  204  then generates a noise standard deviation map G 5  on the basis of the derived relational data G 4 . 
     The derivation of the relational graph G 27  showing the relationship between the pixel value and the standard deviation of the noise value from the X-ray image G 26  of the jig which is performed by the noise map generation unit  204 B will be described.  FIG.  23    shows an example of the structure of the jig used for image capture in the present modification example. As the jig, for example, a member P 1  of which the thickness changes stepwise in one direction can be used.  FIG.  24    shows an example of an X-ray image of the jig of  FIG.  23   . First, in the X-ray image G 26  of the jig, the noise map generation unit  204 B derives a pixel value (hereinafter referred to as a true pixel value) in a case where there is no noise for each step of the jig, and derives the standard deviation of the noise value on the basis of the true pixel value. Specifically, the noise map generation unit  204 B derives the average value of the pixel values in a certain step of the jig. The noise map generation unit  204 B sets the derived average value of the pixel values as a true pixel value in the step. The noise map generation unit  204 B derives a difference between each pixel value and the true pixel value as a noise value in the step. The noise map generation unit  204 B derives the standard deviation of the noise value from the derived noise value for each pixel value. 
     The noise map generation unit  204 B then derives the relationship between the true pixel value and the standard deviation of the noise value as the relationship graph G 27  between the pixel value and the standard deviation of the noise value. Specifically, the noise map generation unit  204 B derives the true pixel value and the standard deviation of the noise value for each step of the jig. The noise map generation unit  204 B derives the relational graph G 27  showing the relationship between the pixel value and the standard deviation of the noise value by plotting the derived relationship between the true pixel value and the standard deviation of the noise value on a graph and drawing an approximate curve. Meanwhile, for the approximate curve, exponential approximation, linear approximation, logarithmic approximation, polynomial approximation, power approximation, or the like is used. 
     In the control device  20 B according to the present modification example, relational data is generated on the basis of a radiographic image obtained by capturing an image of an actual jig. Thereby, the best relational data for noise removal of the radiographic image of the target object F is obtained. As a result, it is possible to more effectively remove noise from the radiographic image. 
     Meanwhile, the noise map generation unit  204 B may derive the relationship between the pixel value and the standard deviation of the noise value from the captured image in a case where the tube current or the exposure time is changed in a state where there is no target object without using a jig. With such a configuration, since the relational data is generated on the basis of the radiographic image obtained by actually performing image capture and the noise map is generated, it is possible to realize noise removal corresponding to the relationship between the pixel value and the spread of noise. As a result, it is possible to more effectively remove the noise from the radiographic image. 
     Specifically, the image acquisition unit  203 B may acquire a plurality of radiographic image captured in a state where there is no target object (step S 201 ), and the noise map generation unit  204 B may derive the relationship between the pixel value and the standard deviation of the noise value from the radiographic image acquired by the image acquisition unit  203 B (step S 202 ). A plurality of radiographic images are a plurality of images in which at least one of the conditions of the source of radiation or imaging conditions is different from each other. As an example, the image acquisition unit  203 B acquires a plurality of X-ray images captured using the image acquiring device  1  in a state where there is no target object F in advance of the observation processing of the target object F while the tube current or the exposure time is changed. The noise map generation unit  204 B then derives a true pixel value for each X-ray image, and derives the standard deviation of noise on the basis of the true pixel value in the same manner as in the present modification example. Further, in the same manner as in the present modification example, the noise map generation unit  204 B derives a relational graph showing the relationship between the pixel value and the standard deviation of the noise value by plotting the relationship between the true pixel value and the standard deviation of noise on a graph and drawing an approximate curve. Finally, in the same manner as in the first embodiment, the noise map generation unit  204 B generates a noise standard deviation map from the X-ray image acquired by the image acquisition unit  203 B on the basis of the derived relational graph. 
     Second Embodiment 
       FIG.  25    is a block diagram illustrating a functional configuration of a control device  20 C according to a second embodiment. The control device  20 C includes an input unit  201 C, a calculation unit  202 C, a narrowing unit  203 C, a selection unit  204 C, and a processing unit  205 C. 
     In addition, a plurality of trained models  206 C for executing the noise removal process for an X-ray transmission image are stored in advance in the control device  20 C. Each of the plurality of trained models  206 C is a learning model based on machine learning constructed in advance using image data as training data. Examples of machine learning include supervised learning, deep learning, reinforcement learning, neural network learning, and the like. In the present embodiment, the two-dimensional convolutional neural network described in the paper “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising” authored by Kai Zhang et al. is adopted as an example of a deep learning algorithm. The plurality of trained models  206 C may be generated by an external computer or the like and downloaded to the control device  20 C, or may be generated in the control device  20 C. 
       FIG.  26    shows an example of image data which is training data used to construct trained models  206 C. As the training data, an X-ray transmission image having a pattern of various thicknesses, various materials, and various resolutions as an imaging target can be used. The example shown in  FIG.  26    is an example of an X-ray transmission image generated for chicken. As the image data, an X-ray transmission image actually generated for a plurality of types of target objects using the image acquiring device  1  may be used, or image data generated by simulation calculation may be used. The X-ray transmission image may be acquired using a device different from the image acquiring device  1 . In addition, the X-ray transmission image and the image data generated by simulation calculation may be used in combination. Each of the plurality of trained models  206 C is constructed in advance using image data obtained for transmitted X-rays having different average energy and having a known noise distribution. The average energy of X-rays in the image data is set to a different value in advance by setting the operating conditions of the X-ray irradiator (radiation source)  50  of the image acquiring device  1 , the imaging conditions of the image acquiring device  1 , or the like, or setting the operating conditions of the X-ray irradiator  50  or imaging conditions during simulation calculation (a method of setting average energy according to the operating conditions or imaging conditions will be described later). That is, the plurality of trained models  206 C are constructed through machine learning using, as training data, a training image which is an X-ray image corresponding to average energy related to X-rays passing through the target object F calculated on the basis of condition information indicating the operating conditions of the X-ray irradiator (radiation source)  50  when the X-ray transmission image of the target object F is captured, the imaging conditions of the X-ray detection camera  10 , or the like (construction step). For example, in the present embodiment, each of the plurality of trained models  206 C is constructed using multiple frames (for example, 20,000 frames) of a plurality of types of image data in which the average energy is 10 keV, 20 keV, 30 keV, . . . and values in increments of 10 keV are set. 
     The image data which is training data used to construct the trained model  206 C is generated by the same creation procedure as the creation procedure in the first embodiment described above. 
     Hereinafter, referring back to  FIG.  25   , the details of the function of each functional unit of the control device  20 C will be described. 
     The input unit  201 C accepts an input of condition information indicating the operating conditions of the X-ray irradiator (radiation source)  50  when the X-ray transmission image of the target object F is captured, the imaging conditions of the X-ray detection camera  10 , or the like from a user of the image acquiring device  1 . Examples of the operating conditions include all or some of a tube voltage, a target angle, a target material, and the like. Examples of the condition information indicating the imaging conditions include information indicating the material and thickness of the filters  51  and  19  (a filter included in a camera used to capture an image of a target object or a filter included in a source) disposed between the X-ray irradiator  50  and the X-ray detection camera  10 , the distance (FDD) between the X-ray irradiator  50  and the X-ray detection camera  10 , the type of window material of the X-ray detection camera  10 , and the material and thickness of the scintillator  11  of the X-ray detection camera  10 , all or some of X-ray detection camera information (for example, a gain setting value, a circuit noise value, an amount of saturated charge, a conversion coefficient value (number of electrons/count), and the line rate (Hz) or line speed (m/min) of the camera), information on the target object, and the like. The input unit  201 C may accept an input of the condition information as a direct input of information such as numerical values, or may accept the input as a selective input for information such as numerical values which are set in an internal memory in advance. The input unit  201 C accepts the input of the above condition information from a user, but it may acquire some condition information (such as a tube voltage) in accordance with the detection result of the state of control performed by the control device  20 C. 
     The calculation unit  202 C calculates the value of the average energy of X-rays (radiation) that pass through the target object F using the image acquiring device  1  and are detected by the X-ray detection camera  10  on the basis of the condition information accepted by the input unit  201 C. For example, the calculation unit  202 C calculates an X-ray spectrum detected by the X-ray detection camera  10  using, for example, a known approximate expression of Tucker or the like on the basis of information such as a tube voltage, a target angle, a target material, the material and thickness of a filter and its presence or absence, the type of a window material and its presence or absence, and the material and thickness of the scintillator  11  of the X-ray detection camera  10  which are included in the condition information. The calculation unit  202 C further calculates a spectral intensity integration value and a photon number integration value from the spectrum of the X-rays, and calculates the value of the average energy of the X-rays by dividing the spectral intensity integration value by the photon number integration value. 
     A calculation method using a known approximate expression of Tucker will be described. For example, in a case where the target is specified as tungsten and the target angle is specified as 25°, the calculation unit  202 C can determine Em: kinetic energy during electron target collision, T: electron kinetic energy in the target, A: proportionality constant determined by the atomic number of the target substance, ρ: the density of the target, μ(E): the linear attenuation coefficient of the target substance, B: the function of Z and T that changes gently, C: Thomson-Whiddington constant, θ: target angle, and c: the speed of light in vacuum. Further, the calculation unit  202 C can calculate an irradiation X-ray spectrum by calculating Expression (1) described above on the basis of these values. 
     Next, the calculation unit  202 C can calculate the X-ray energy spectrum that passes through the filter and the target object F and is absorbed by the scintillator by using the X-ray attenuation expression of Expression (2) described above. The X-ray photon number spectrum can be obtained by dividing this X-ray energy spectrum by energy of each X-ray. The calculation unit  202 C calculates the average energy of X-rays using Expression (3) described above by dividing the integration value of energy intensity by the integration value of the number of photons. The calculation unit  202 C calculates the average energy of X-rays through the above calculation process. Meanwhile, for the calculation of the X-ray spectrum, a known approximate expression of Kramers or Birch et al. may be used. 
     The narrowing unit  203 C narrows down candidates for the trained model from the plurality of trained models  206 C constructed in advance on the basis of the value of the average energy calculated by the calculation unit  202 C. That is, the narrowing unit  203 C compares the calculated average energy value with the value of the X-ray average energy in the image data used to construct the plurality of trained models  206 C, and narrows down a plurality of trained models  206 C constructed by image data having similar average energy values as candidates. More specifically, in a case where the average energy value calculated by the calculation unit  202 C is 53 keV, the narrowing unit  203 C uses trained models  206 C constructed by image data having average energy values of 40 keV, 50 keV, and 60 keV whose difference from the value is less than a predetermined threshold (for example, 15 keV) as candidates for the trained model. 
     The selection unit  204 C selects trained models  206 C to be finally used for a noise removal process of the X-ray transmission image of the target object F from the candidates narrowed down by the narrowing unit  203 C. Specifically, the selection unit  204 C acquires an X-ray transmission image captured by radiating X-rays to a jig in the image acquiring device  1 , and selects trained models  206 C to be finally used on the basis of the image characteristics of the X-ray transmission image. In this case, the selection unit  204 C analyzes energy characteristics, noise characteristics, resolution characteristics, or the like as the image characteristics of the X-ray transmission image, and selects trained models  206 C on the basis of the analysis result. 
     More specifically, the selection unit  204 C acquires an X-ray transmission image for a flat plate-like member as a jig whose thickness and material are known and whose relationship between the average energy of X-rays and the transmittance of X-rays is known, compares the luminance of the X-ray image passing through the jig with the luminance of the X-ray image passing through the air, and calculates the transmittance of X-rays at one point (or the average of a plurality of points) in the jig. For example, in a case where the luminance of the X-ray image passing through the jig is 5,550 and the luminance of the X-ray image passing through the air is 15,000, the transmittance is calculated to be 37%. The selection unit  204 C then specifies the average energy (for example, 50 keV) of transmitted X-rays estimated from the transmittance of 37% as the energy characteristics of the X-ray transmission image of the jig. The selection unit  204 C selects one trained model  206 C constructed by image data of average energy closest to the specified average energy value. 
     In addition, the selection unit  204 C may analyze the characteristics at a plurality of points of the jig whose thickness or material changes as the energy characteristics of the X-ray transmission image of the jig.  FIG.  27    is a diagram illustrating an example of an X-ray transmission image to be analyzed by the selection unit  204 C.  FIG.  27    is an X-ray transmission image for a jig having a shape in which the thickness changes stepwise. The selection unit  204 C selects a plurality of measurement regions (regions of interest (ROI)) having different thicknesses from such an X-ray transmission image, analyzes the luminance average value for each of the plurality of measurement regions, and acquires a characteristic graph of thickness and luminance as energy characteristics.  FIG.  28    shows an example of a characteristic graph of thickness and luminance acquired by the selection unit  204 C. 
     Further, the selection unit  204 C similarly acquires a characteristic graph of thickness and luminance for the image data used to construct the trained model  206 C narrowed down by the narrowing unit  203 C, and selects trained models  206 C constructed by image data having characteristics closest to the characteristic graph acquired for the jig as final trained models  206 C. However, the image characteristics of the image data used to construct the trained models  206 C may refer to those calculated in advance outside the control device  20 C. By setting a plurality of measurement regions in this way, it is possible to select the best trained model for noise removal of the X-ray transmission image of the target object F. Particularly, it is possible to accurately estimate a difference in the X-ray spectrum or a difference in the effect of the filter during measurement of the X-ray transmission image. 
     In addition, the selection unit  204 C can also analyze the luminance value and noise for each of the plurality of measurement regions as the noise characteristics of the X-ray transmission image of the jig, and acquire a characteristic graph of a luminance to noise ratio as the noise characteristics. That is, the selection unit  204 C selects a plurality of measurement regions ROI having different thicknesses or materials from the X-ray transmission image, analyzes the standard deviation of the luminance values of the plurality of measurement regions ROI and the average value of the luminance values thereof, and acquires a characteristic graph of luminance and a SN ratio (SNR) as the noise characteristics. In this case, the selection unit  204 C calculates the SNR for each measurement region ROI using SNR=(average value of luminance values)÷(standard deviation of luminance values).  FIG.  29    shows an example of a characteristic graph of luminance and SNR acquired by the selection unit  204 C. The selection unit  204 C then selects trained model  206 C constructed by image data having the noise characteristics closest to the acquired characteristic graph as final trained model  206 C. 
     Here, the selection unit  204 C may acquire a characteristic graph in which the vertical axis is noise calculated from the standard deviation of the luminance values, as the noise characteristics, instead of the above characteristic graph of luminance and SNR. By using such a characteristic graph of luminance and noise, it is possible to specify a dominant noise factor (such as shot noise or readout noise) from the slope of the graph in the region of each signal amount with respect to each signal amount detected by the X-ray detection camera  10 , and to select trained models  206 C on the basis of the specified result. 
       FIG.  30    is a diagram illustrating a function of selection of a trained model based on image characteristics which is performed by the selection unit  204 C. In  FIG.  30   , the part (a) shows characteristic graphs G 1 , G 2 , and G 3  of luminance and SNR of image data used to construct the plurality of trained models  206 C, and the part (b) shows a characteristic graph G T  of luminance and SNR of the X-ray transmission image obtained by capturing an image of the jig in addition to these characteristic graphs G 1 , G 2 , and G 3 . In a case where such characteristic graphs G 1 , G 2 , G 3 , and G T  are targeted, the selection unit  204 C functions so as to select trained models  206 C constructed by image data of the characteristic graph G 2  closest to the characteristics of the characteristic graph G T . At the time of selection, the selection unit  204 C calculates an SNR error for each luminance value at regular intervals between each of the characteristic graphs G 1 , G 2 , and G 3  and the characteristic graph G T , calculates the root mean squared error (RMSE) of these errors, and selects trained models  206 C corresponding to the characteristic graphs G 1 , G 2 , and G 3  having the smallest root mean squared error. In addition, even in a case where the selection is performed using the energy characteristics, the selection unit  204 C can select trained models  206 C in the same way. 
     The selection unit  204 C can also select trained model  206 C on the basis of the characteristics of an image after a plurality of trained models are applied to the X-ray transmission image of the jig and the noise removal process is executed. 
     For example, the selection unit  204 C uses the X-ray transmission image obtained by capturing an image of the jig having charts of various resolutions to apply a plurality of trained models  206 C to the image and evaluate the resulting image after noise removal. The selection unit  204 C then selects trained model  206 C used for an image having the smallest change in resolution before and after the noise removal process.  FIG.  31    shows an example of an X-ray transmission image used for the evaluation of resolution. In this X-ray transmission image, a chart whose resolution changes stepwise in one direction is used as an imaging target. The resolution of the X-ray transmission image can be measured using a modulation transfer function (MTF) or a contrast transfer function (CTF). 
     In addition to the evaluation of the above change in resolution, the selection unit  204 C may evaluate the characteristics of the luminance to noise ratio of the image after noise removal and select trained model  206 C used to generate an image having the highest characteristics.  FIG.  32    shows an example of the structure of the jig used for the evaluation of the luminance to noise ratio. For example, as the jig, a jig in which foreign substances P 2  having various materials and various sizes are scattered in a member P 1  whose thickness changes stepwise in one direction can be used.  FIG.  33    shows an X-ray transmission image obtained for the jig of  FIG.  32    after the noise removal process. The selection unit  204 C selects an image region R 1  containing an image of the foreign substance P 2  in the X-ray transmission image and an image region R 2  not containing an image of the foreign substance P 2  in the vicinity of the region R 1 , and calculates the minimum value L MIN  of luminance in the image region R 1 , the average value L AVE  of luminance in the image region R 2 , and the standard deviation L SD  of luminance in the image region R 2 . The selection unit  204 C calculates the luminance to noise ratio CNR using the following expression. 
       CNR=( L   AVE   −L   MIN )/ L   SD    
     Further, the selection unit  204 C calculates the luminance to noise ratio CNR for each of the X-ray transmission images after the application of the plurality of trained models  206 C, and selects trained models  206 C used to generate an X-ray transmission image having the highest luminance to noise ratio CNR. 
     Alternatively, the selection unit  204 C may perform the calculation using the following expression on the basis of the average value L AVE_R1  of luminance in the image region R 1 , the average value L AVE_R2  of luminance in the image region R 2 , and the standard deviation L SD  of luminance in the image region R 2 . 
       CNR=( L   AVE_R1   −L   MIN_R2 )/ L   SD    
     The processing unit  205 C applies the trained models  206 C selected by the selection unit  204 C to the X-ray transmission image acquired for the target object F, and generates an output image by executing image processing for removing noise. The processing unit  205 C then outputs the generated output image to the display device  30  or the like. 
     Next, a procedure of observing an X-ray transmission image of the target object F using the image acquiring device  1  according to the second embodiment, that is, a flow of a radiographic image acquisition method according to the second embodiment, will be described.  FIG.  34    is a flowchart illustrating a procedure of observation processing using the image acquiring device  1 . 
     First, the control device  20 C accepts an input of condition information indicating the operating conditions of the X-ray irradiator  50 , the imaging conditions of the X-ray detection camera  10 , or the like from an operator (user) of the image acquiring device  1  (step S 1 ). Next, the control device  20 C calculates the value of the average energy of the X-rays detected by the X-ray detection camera  10  on the basis of the condition information (step S 2 ). 
     Further, the control device  20 C specifies the value of the average energy of the X-rays in the image data used to construct the trained models  206 C stored in the control device  20 C (step S 3 ). Thereafter, the specification of the average energy value of the X-rays is repeated for all the trained models  206 C stored in the control device  20 C (step S 4 ). 
     Next, the control device  20 C compares the calculated average energy values of the X-rays to thereby narrow down candidates for a plurality of trained models  206 C (step S 5 ). Further, in the image acquiring device  1 , a jig is set to capture an image of the jig, and thus an X-ray transmission image of the jig is acquired (step S 6 ). 
     Thereafter, the control device  20 C acquires the image characteristics of the X-ray transmission image of the jig (such as the average energy value of the X-rays, the characteristics of thickness and luminance, the characteristics of the luminance to noise ratio, the characteristics of luminance and noise, and the characteristics of change in resolution) (step S 7 ). The control device  20 C then selects final trained model  206 C on the basis of the acquired image characteristics (step S 8 ). 
     Further, in the image acquiring device  1 , the target object F is set to capture an image of the target object F, and thus an X-ray transmission image of the target object F is acquired (step S 9 ). Next, the control device  20 C applies the finally selected trained model  206 C to the X-ray transmission image of the target object F, and thus the noise removal process is executed for the X-ray transmission image (step S 10 ). Finally, the control device  20 C outputs an output image which is an X-ray transmission image that has undergone the noise removal process to the display device  30  (step S 11 ). 
     With the image acquiring device  1  described above, it is also possible to remove noise components while increasing signal components in the X-ray transmission image, and to effectively improve an S/N ratio in the X-ray transmission image. In addition, the average energy of the X-rays passing through the target object F is calculated on the basis of the operating conditions of the source of the X-rays or the imaging conditions of the X-ray transmission image when the X-ray transmission image of the target object F is acquired. Candidates for the trained model  206 C used for noise removal are narrowed down from the trained models  206 C constructed in advance on the basis of the average energy. Thereby, the trained model  206 C corresponding to the average energy of the X-rays which are a target for imaging is used for noise removal, and thus it is possible to realize noise removal corresponding to the relationship between luminance and noise in the X-ray transmission image. As a result, it is possible to effectively remove noise from the X-ray transmission image, and to improve, for example, foreign substance detection performance. Particularly, the mode of noise of the X-ray transmission image changes depending on differences in a tube voltage, a filter, a scintillator, conditions of an X-ray detection camera (a gain setting value, a circuit noise value, an amount of saturated charge, a conversion coefficient value (e−/count), and the line rate of the camera), a target object, and the like. For this reason, in a case where noise removal is attempted to be realized through machine learning, it is necessary to prepare a plurality of learning models trained under various conditions. In the related art, it has not been realized to select a learning model suitable for the mode of noise from a plurality of learning models in accordance with conditions during measurement of an X-ray transmission image. According to the present embodiment, the trained model  206 C corresponding to the average energy of the X-rays which are a target for imaging is selected, and thus the selection of a learning model that always matches the mode of noise is realized. 
     Generally, an X-ray transmission image contains noise derived from the generation of X-rays. It is also conceivable to increase the X-ray dose in order to improve the SN ratio of the X-ray transmission image. However, in that case, there is a problem in that increasing the X-ray dose increases the exposure of a sensor, shortens the life of the sensor, and shortens the life of the X-ray source, and thus it is difficult to achieve both an improvement in the SN ratio and an increase in life. In the present embodiment, it is not necessary to increase the X-ray dose, and thus it is possible to achieve both an improvement in the SN ratio and an increase in life. 
     In addition, the control device  20 C of the present embodiment has a function of executing image processing for removing noise from the X-ray transmission image of the target object F using the selected trained model  206 C. With such a function, it is possible to realize noise removal corresponding to the relationship between luminance and noise in the X-ray transmission image, and to effectively remove the noise from the X-ray transmission image. 
     In addition, the control device  20 C of the present embodiment has a function of narrowing down candidates for the trained model by comparing the average energy value of the X-rays calculated from selection information with the average energy value specified from the image data used to construct the trained model  206 C. With such a function, it is possible to reliably realize noise removal corresponding to the relationship between luminance and noise in the X-ray transmission image. 
     Further, the control device  20 C of the present embodiment has a function of selecting the trained model  206 C from the candidates on the basis of the image characteristics of the X-ray transmission image of the jig. With such a function, it is possible to select the best trained model  206 C for noise removal of the X-ray transmission image of the target object F. As a result, it is possible to more reliably realize noise removal corresponding to the relationship between luminance and noise in the X-ray transmission image. 
     Modification Example of Second Embodiment 
     The control device  20 C of the second embodiment has selected candidates for the trained model  206 C on the basis of the average energy value of the X-rays calculated from the condition information, but it may have a function corresponding to a degradation in performance of the X-ray detection camera  10  and a fluctuation in output of the X-ray irradiator  50  or a degradation in performance thereof. 
       FIG.  35    is a block diagram illustrating a functional configuration of a control device  20 D according to a modification example of the second embodiment. The control device  20 D is different from the control device  20 C according to the second embodiment in that it has a measurement unit  207 C and has different functions of a calculation unit  202 D and a narrowing unit  203 D. 
     The control device  20 C has no degradation in performance of the X-ray detection camera  10  and no fluctuation in output of the X-ray irradiator  50  or no degradation in performance thereof, and narrows down the trained models  206 C on the premise that the relationship between luminance and noise in the X-ray transmission image can be estimated from the average energy of the X-rays. On the other hand, the control device  20 D according to the present modification example has a function of calculating an X-ray conversion coefficient in consideration of a degradation in performance of the X-ray detection camera  10  and a fluctuation in output of the X-ray irradiator  50  or a degradation in performance thereof, and narrowing down the trained models  206 C on the basis of the X-ray conversion coefficient. The X-ray conversion coefficient is a parameter indicating the efficiency until the X-rays are converted into visible light by a scintillator and then converted into electrons (electrical signal) by a camera sensor. 
     Generally, when the average energy of the X-rays is E [keV], the light emission amount of the scintillator is EM [photon/keV], the coupling efficiency in the sensor is C, and the quantum efficiency of the sensor is QE, the X-ray conversion coefficient F T  can be calculated using the following expression. 
     
       
      
       F 
       T 
       =E×EM×C×QE  
      
     
     In addition, the SN ratio (SNR) in the X-ray transmission image is obtained from the following expression using the X-ray conversion coefficient F T , the X-ray photon number N P , and the readout noise Nr of the camera. 
       SNR= F   T   N   P /{( F   T   N   P   +Nr   2 ) 1/2 } 
     Thus, the relationship between luminance and noise in the X-ray transmission image after considering a degradation in performance of the camera can be estimated on the basis of the X-ray conversion coefficient F T . 
     The measurement unit  207 C of the control device  20 D has a function of measuring the amount of decrease in the light emission amount EM as a degradation in performance of the scintillators  11 , the amount of decrease in the quantum efficiency QE of the sensor as a degradation in performance of the scan camera  12 , and the amount of change in the average energy E as a fluctuation in output of the X-ray irradiator  50  and a degradation in performance thereof. For example, the measurement unit  207 C measures the amount of decrease in the light emission amount between a state where there is no degradation in performance of the scintillator  11  (state when new) and the current scintillators  11 , and estimates the current light emission amount EM from the amount of decrease. In addition, the measurement unit  207 C measures the amount of decrease in luminance between a state where there is no degradation in performance of the scan camera  12  (state when new) and the current scan camera  12 , and estimates the current quantum efficiency QE from the amount of decrease. In addition, the measurement unit  207 C estimates the current average energy E from the amount of change in the average energy between a state where there is no degradation in performance of the X-ray irradiator  50  (state when new) and the current X-ray irradiator  50 . The average energy E may be obtained from imaging data of a flat plate-like member whose thickness and material are known and in which a relationship between the average energy of the X-rays and the transmittance of the X-rays is known, may be obtained from imaging data at a plurality of points of the jig whose thickness or material changes, or the like. 
     The calculation unit  202 D of the control device  20 D calculates the X-ray conversion coefficient F T  using the calculated average energy E of the X-rays and the light emission amount EM and quantum efficiency QE estimated by the measurement unit  207 C. The narrowing unit  203 D of the control device  20 D has a function of narrowing down candidates for the trained model  206 C by comparing the calculated X-ray conversion coefficient F T  with the X-ray conversion coefficient F T  in the image data used to construct the trained model  206 C. 
     In addition, the control device  20 D of the above modification example narrows down candidates for the trained model and then selects the trained model on the basis of the image characteristics obtained by capturing an image of the jig, but it may execute the noise removal process with respect to the X-ray transmission image of the target object without capturing an image of the jig.  FIG.  36    is a flowchart illustrating a procedure of observation processing using the image acquiring device  1  according to another modification example. In this way, it is also possible to omit the processes of steps S 6  to S 8  in  FIG.  34    and to execute the noise removal process using the trained models narrowed down on the basis of the average energy. 
     Third Embodiment 
       FIG.  37    is a block diagram illustrating a functional configuration of a control device  20 E according to a third embodiment. The control device  20 E includes an acquisition unit  201 E, a specification unit  202 E, a selection unit  204 E, and a processing unit  205 E. 
     In addition, a plurality of trained models  206 E for executing the noise removal process for an X-ray transmission image are stored in advance in the control device  20 E. Each of the plurality of trained models  206 E is a learning model based on machine learning constructed in advance using image data as training data. Examples of machine learning include supervised learning, deep learning, reinforcement learning, neural network learning, and the like. In the present embodiment, the two-dimensional convolutional neural network described in the paper “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising” authored by Kai Zhang et al. is adopted as an example of a deep learning algorithm. The plurality of trained models  206 E may be generated by an external computer or the like and downloaded to the control device  20 E, or may be generated in the control device  20 E. 
       FIG.  38    shows an example of image data which is training data used to construct trained models  206 E. As the training data, an X-ray transmission image having a pattern of various thicknesses, various materials, and various resolutions as an imaging target can be used. The example shown in  FIG.  38    is an example of an X-ray transmission image generated for chicken. As the image data, an X-ray transmission image actually generated for a plurality of types of target objects using the image acquiring device  1  may be used, or image data generated by simulation calculation may be used. The X-ray transmission image may be acquired using a device different from the image acquiring device  1 . In addition, the X-ray transmission image and the image data generated by simulation calculation may be used in combination. Each of the plurality of trained models  206 E is constructed in advance using image data obtained for transmitted X-rays having different average energy and having a known noise distribution. The average energy of X-rays in the image data is set to a different value in advance by setting the operating conditions of the X-ray irradiator (radiation source)  50  of the image acquiring device  1 , the imaging conditions of the image acquiring device  1 , or the like, or setting the operating conditions or imaging conditions of the X-ray irradiator  50  during simulation calculation. That is, the plurality of trained models  206 E are constructed through machine learning using, as training data, a training image which is an X-ray image corresponding to average energy related to X-rays passing through the target object F calculated on the basis of condition information indicating the operating conditions of the X-ray irradiator (radiation source)  50  when the X-ray transmission image of the target object F is captured, the imaging conditions of the X-ray detection camera  10 , or the like (construction step). For example, in the present embodiment, each of the plurality of trained models  206 E is constructed using multiple frames (for example, 20,000 frames) of a plurality of types of image data in which the average energy is 10 keV, 20 keV, 30 keV, . . . and values in increments of 10 keV are set. 
     The image data which is training data used to construct the trained model  206 E is generated by the same creation procedure as the creation procedure in the first embodiment described above. 
     Hereinafter, referring back to  FIG.  37   , the details of the function of each functional unit of the control device  20 E will be described. 
     The acquisition unit  201 E acquires an X-ray transmission image captured by radiating X-rays to a jig and the target object F using the image acquiring device  1 . As the jig, a flat plate-like member whose thickness and material are known and in which a relationship between the average energy of X-rays and the transmittance of X-rays is known, or a jig having a chart whose image is captured at various resolutions is used. That is, the acquisition unit  201 E acquires an X-ray transmission image of the jig captured by using the image acquiring device  1  in advance of the observation processing of the target object F. The acquisition unit  201 E acquires an X-ray transmission image of the target object F captured by using the image acquiring device  1  at a timing after the trained model  206 E is selected on the basis of the X-ray transmission image of the jig. However, the acquisition timings of the X-ray transmission images of the jig and the target object F are not limited to the above, and may be simultaneous or reverse timings. 
     The specification unit  202 E specifies the image characteristics of the X-ray transmission image of the jig acquired by the acquisition unit  201 E. Specifically, the selection unit  204 E specifies energy characteristics, noise characteristics, resolution characteristics, frequency characteristics, or the like as the image characteristics of the X-ray transmission image. 
     For example, in a case where a flat plate-like member whose thickness and material are known is used as a jig, the specification unit  202 E compares the luminance of the X-ray image passing through the jig with the luminance of the X-ray image passing through the air, and calculates the transmittance of X-rays at one point (or the average transmittance of a plurality of points) in the jig. For example, in a case where the luminance of the X-ray image passing through the jig is 5,550 and the luminance of the X-ray image passing through the air is 15,000, the transmittance is calculated to be 37%. The specification unit  202 E then specifies the average energy (for example, 50 keV) of transmitted X-rays estimated from the transmittance of 37% as the energy characteristics of the X-ray transmission image of the jig. 
     In addition, the specification unit  202 E may analyze the characteristics at a plurality of points of the jig whose thickness or material changes as the energy characteristics of the X-ray transmission image of the jig.  FIG.  39    is a diagram illustrating an example of an X-ray transmission image to be analyzed by the specification unit  202 E.  FIG.  39    is an X-ray transmission image for a jig having a shape in which the thickness changes stepwise. The specification unit  202 E selects a plurality of measurement regions (regions of interest (ROI)) having different thicknesses from such an X-ray transmission image, analyzes the luminance average value for each of the plurality of measurement regions, and acquires a characteristic graph of thickness and luminance as energy characteristics.  FIG.  40    shows an example of a characteristic graph of thickness and luminance acquired by the specification unit  202 E. 
     In addition, the specification unit  202 E can also analyze the luminance value and noise for each of the plurality of measurement regions as the noise characteristics of the X-ray transmission image of the jig, and acquire a characteristic graph of a luminance to noise ratio as the noise characteristics. That is, the specification unit  202 E selects a plurality of measurement regions ROI having different thicknesses or materials from the X-ray transmission image, analyzes the standard deviation of the luminance values of the plurality of measurement regions ROI and the average value of the luminance values thereof, and acquires a characteristic graph of luminance and a SN ratio (SNR) as the noise characteristics. In this case, the specification unit  202 E calculates the SNR for each measurement region ROI using SNR=(average value of luminance values)÷(standard deviation of luminance values).  FIG.  41    shows an example of a characteristic graph of luminance and SNR acquired by the specification unit  202 E. Here, the specification unit  202 E may acquire a characteristic graph in which the vertical axis is noise calculated from the standard deviation of the luminance values, as the noise characteristics, instead of the above characteristic graph of luminance and SNR. 
     In addition, in a case where a jig having a chart is used, the specification unit  202 E can also acquire the distribution of resolutions in the X-ray transmission image of the jig as the resolution characteristics. Further, the specification unit  202 E also has a function of acquiring the resolution characteristics of an image after the noise removal process is performed by applying a plurality of trained models  206 E to the X-ray transmission image of the jig.  FIG.  42    shows an example of an X-ray transmission image used for the evaluation of resolution. In this X-ray transmission image, a chart whose resolution changes stepwise in one direction is used as an imaging target. The resolution of the X-ray transmission image can be measured using a modulation transfer function (MTF) or a contrast transfer function (CTF). 
     Referring back to  FIG.  37   , the selection unit  204 E finally selects trained models  206 E to be used for the noise removal process of the X-ray transmission image of the target object F from the plurality of trained models  206 E stored in the control device  20 E on the basis of the image characteristics acquired by the specification unit  202 E. That is, the selection unit  204 E compares the image characteristics specified by the specification unit  202 E with the image characteristics specified from the image data used to construct the plurality of trained models  206 E, and selects a trained model  206 E in which both are similar to each other. 
     For example, the selection unit  204 E selects one trained model  206 E constructed by the image data of average energy closest to the value of the average energy of the transmitted X-rays specified by the specification unit  202 E. 
     In addition, the selection unit  204 E acquires a characteristic graph of thickness and luminance for the image data used to construct the plurality of trained models  206 E in the same manner as the method of specification performed by the specification unit  202 E, and selects trained models  206 E constructed by image data having characteristics closest to the characteristic graph of thickness and luminance acquired for the jig as final trained models  206 E. However, the image characteristics of the image data used to construct the trained models  206 E may be referred to those calculated in advance outside the control device  20 E. By using the image characteristics obtained by setting a plurality of measurement regions in this way, it is possible to select the best trained model for noise removal of the X-ray transmission image of the target object F. Particularly, it is possible to accurately estimate a difference in the X-ray spectrum or a difference in the effect of the filter during measurement of the X-ray transmission image. 
     In addition, the selection unit  204 E may select trained models  206 E constructed by image data having the characteristics of the luminance to noise ratio closest to the characteristics of the luminance to noise ratio acquired by the specification unit  202 E as the final trained models  206 E. However, the image characteristics of the image data used to construct the trained models  206 E may be acquired by the selection unit  204 E from the image data, or may be referred to those calculated in advance outside the control device  20 E. Here, the selection unit  204 E may select the trained model  206 E using the characteristics of luminance and noise, as the noise characteristics, instead of the characteristics of the luminance to noise ratio. By using such characteristics of luminance and noise, it is possible to specify a dominant noise factor (such as shot noise or readout noise) from the slope of the graph in the region of each signal amount with respect to each signal amount detected by the X-ray detection camera  10 , and to select trained models  206 E on the basis of the result of specification. 
       FIG.  43    is a diagram illustrating a function of selection of a trained model based on image characteristics which is performed by the selection unit  204 E. In  FIG.  43   , the part (a) shows characteristic graphs G 1 , G 2 , and G 3  of luminance and SNR of image data used to construct the plurality of trained models  206 E, and the part (b) shows a characteristic graph G T  of luminance and SNR of the X-ray transmission image obtained by capturing an image of the jig in addition to these characteristic graphs G 1 , G 2 , and G 3 . In a case where such characteristic graphs G 1 , G 2 , G 3 , and G T  are targeted, the selection unit  204 E functions so as to select trained models  206 E constructed by image data of the characteristic graph G 2  closest to the characteristics of the characteristic graph G T . At the time of selection, the selection unit  204 E calculates an SNR error for each luminance value at regular intervals between each of the characteristic graphs G 1 , G 2 , and G 3  and the characteristic graph G T , calculates the root mean squared error (RMSE) of these errors, and selects trained models  206 E corresponding to the characteristic graphs G 1 , G 2 , and G 3  having the smallest root mean squared error. In addition, even in a case where the selection is performed using the energy characteristics, the selection unit  204 E can select trained models  206 E in the same way. 
     The selection unit  204 E can also select trained models  206 E used to generate an image having relatively excellent characteristics on the basis of the characteristics of an image after a plurality of trained models are applied to the X-ray transmission image of the jig and the noise removal process is executed. 
     For example, the selection unit  204 E uses the X-ray transmission image obtained by capturing an image of the jig having charts of various resolutions to apply a plurality of trained models  206 E to the image and evaluate the resolution characteristics of the resulting image after noise removal. The selection unit  204 E then selects trained model  206 E used for an image having the smallest change in the resolution of each distribution before and after the noise removal process. 
     In addition to the evaluation of the above change in resolution, the selection unit  204 E may evaluate the characteristics of the luminance to noise ratio of the image after noise removal and select trained model  206 E used to generate an image having the highest characteristics.  FIG.  44    shows an example of the structure of the jig used for the evaluation of the luminance to noise ratio. For example, as the jig, a jig in which foreign substances P 2  having various materials and various sizes are scattered in a member P 1  whose thickness changes stepwise in one direction can be used.  FIG.  45    shows an X-ray transmission image obtained for the jig of  FIG.  44    after the noise removal process. The selection unit  204 E selects an image region R 1  containing an image of the foreign substance P 2  in the X-ray transmission image and an image region R 2  not containing an image of the foreign substance P 2  in the vicinity of the region R 1 , and calculates the minimum value L MIN  of luminance in the image region R 1 , the average value L AVE  of luminance in the image region R 2 , and the standard deviation L SD  of luminance in the image region R 2 . The selection unit  204 E calculates the luminance to noise ratio CNR using the following expression. 
       CNR=( L   AVE   −L   MIN )/ L   SD    
     Further, the selection unit  204 E calculates the luminance to noise ratio CNR for each of the X-ray transmission images after the application of the plurality of trained models  206 E, and selects trained models  206 E used to generate an X-ray transmission image having the highest luminance to noise ratio CNR. 
     Alternatively, the selection unit  204 E may perform the calculation using the following expression on the basis of the average value L AVE_R1  of luminance in the image region R 1 , the average value L AVE_R2  of luminance in the image region R 2 , and the standard deviation L SD  of luminance in the image region R 2 . 
       CNR=( L   AVE_R1   −L   MIN_R2 )/ L   SD    
     The processing unit  205 E applies the trained models  206 E selected by the selection unit  204 E to the X-ray transmission image acquired for the target object F, and generates an output image by executing image processing for removing noise. The processing unit  205 E then outputs the generated output image to the display device  30  or the like. 
     Next, a procedure of observing an X-ray transmission image of the target object F using the image acquiring device  1  according to the third embodiment, that is, a flow of a radiographic image acquisition method according to the third embodiment, will be described.  FIG.  46    is a flowchart illustrating a procedure of observation processing using the image acquiring device  1 . 
     First, an operator (user) of the image acquiring device  1  sets the imaging conditions in the image acquiring device  1  such as the tube voltage of the X-ray irradiator  50  or the gain in the X-ray detection camera  10  (step S 1 E). Next, a jig is set in the image acquiring device  1 , and the control device  20 E acquires an X-ray transmission image for the jig (step S 2 E). In this case, X-ray transmission images of a plurality of types of jigs may be sequentially acquired. 
     Accordingly, the control device  20 E specifies the image characteristics (energy characteristics, noise characteristics, and resolution characteristics) of the X-ray transmission image of the jig (step S 3 E). Further, the control device  20 E applies a plurality of trained models  206 E to the X-ray transmission image of the jig, and specifies the image characteristics (such as the resolution characteristics or the value of the luminance to noise ratio) of each X-ray transmission image after the application of the plurality of trained models  206 E (step S 4 E). 
     Next, the control device  20 E selects trained model  206 E on the basis of the result of comparison between the energy characteristics of the X-ray transmission image of the jig and the energy characteristics of the image data used to construct the trained model  206 E, and the degree of change in the resolution characteristics of the X-ray transmission image of the jig before and after the application of the trained model (step S 5 E). Here, the trained model  206 E may be selected on the basis of the result of comparison between the noise characteristics of the X-ray transmission image of the jig and the noise characteristics of the image data used to construct trained models  206 E, and the state of change in the resolution characteristics of the X-ray transmission image of the jig before and after the application of the trained model. In addition, in step S 5 E, a trained model  206 E having the highest luminance to noise ratio CNR after the application of the trained model of the X-ray transmission image of the jig may be selected instead of the above process. 
     Further, in the image acquiring device  1 , the target object F is set to capture an image of the target object F, and thus an X-ray transmission image of the target object F is acquired (step S 7 E). Next, the control device  20 E applies the finally selected trained model  206 E to the X-ray transmission image of the target object F, and thus the noise removal process is executed for the X-ray transmission image (step S 8 E). Finally, the control device  20 E outputs an output image which is an X-ray transmission image that has undergone the noise removal process to the display device  30  (step S 9 E). 
     With the image acquiring device  1  described above, it is also possible to remove noise components while increasing signal components in the X-ray transmission image, and to effectively improve an S/N ratio in the X-ray transmission image. In addition, the image characteristics of the X-ray transmission image of the jig are specified, and a trained model used for noise removal is selected from the trained models constructed in advance on the basis of the image characteristics. Thereby, since the characteristics of the X-ray transmission image changing depending on the operating conditions and the like of the X-ray irradiator  50  in the image acquiring device  1  can be estimated, and the trained model  206 E selected in accordance with the estimation result is used for noise removal, it is possible to realize noise removal corresponding to the relationship between luminance and noise in the X-ray transmission image. As a result, it is possible to effectively remove noise from the X-ray transmission image. 
     Generally, an X-ray transmission image contains noise derived from the generation of X-rays. It is also conceivable to increase the X-ray dose in order to improve the SN ratio of the X-ray transmission image. However, in that case, there is a problem in that increasing the X-ray dose increases the exposure of a sensor, shortens the life of the sensor, and shortens the life of the X-ray source, and thus it is difficult to achieve both an improvement in the SN ratio and an increase in life. In the present embodiment, it is not necessary to increase the X-ray dose, and thus it is possible to achieve both an improvement in the SN ratio and an increase in life. 
     In the present embodiment, in the selection of the trained model, the image characteristics of the X-ray transmission image of the jig and the image characteristics of the image data used to construct the trained model are compared with each other. Thereby, since the trained model  206 E constructed by the image data corresponding to the image characteristics of the X-ray transmission image of the jig is selected, it is possible to effectively remove noise from the X-ray transmission image of the target object F. 
     In addition, in the present embodiment, the trained model is selected using the image characteristics of an image in which a plurality of trained models  206 E are applied to the X-ray transmission image of the jig. In this case, since the trained model  206 E is selected on the basis of the image characteristics of the X-ray transmission image of the jig to which a plurality of trained models  206 E are actually applied, it is possible to effectively remove noise from the X-ray transmission image of the target object F. 
     Particularly, in the present embodiment, energy characteristics or noise characteristics are used as the image characteristics. In this case, the trained model  206 E constructed by an image having characteristics similar to the energy characteristics or noise characteristics of the X-ray transmission image of the jig changing depending on the imaging conditions of the image acquiring device  1  is selected. As a result, it is possible to remove noise from the X-ray transmission image of the target object F corresponding to a change in the conditions of the image acquiring device  1 . 
     In the present embodiment, resolution characteristics or luminance to noise ratio are also used as the image characteristics. According to such a configuration, the selected trained model  206 E is applied, and thus it is possible to obtain an X-ray transmission image having good resolution characteristics or luminance to noise ratio. As a result, it is possible to remove noise from the X-ray transmission image of the target object corresponding to a change in the conditions of the image acquiring device  1 . 
     In the above-described embodiment, it is preferable that the trained model is constructed through machine learning using image data obtained by adding a noise value along a normal distribution to a radiographic image of a predetermined structure as training data. Thereby, it becomes easy to prepare image data which is training data used to construct the trained model, and thus it is possible to efficiently construct the trained model. 
     In addition, in the above-described embodiment, it is also preferable that the image processing module includes a noise map generation unit configured to derive an evaluation value obtained by evaluating a spread of a noise value from a pixel value of each pixel of the radiographic image on the basis of relational data indicating a relationship between the pixel value and the evaluation value and generate a noise map which is data obtained by associating the derived evaluation value with each pixel of the radiographic image, and a processing unit configured to input the radiographic image and the noise map to the trained model and execute the noise removal process of removing noise from the radiographic image. In addition, it is also preferable that the execution step includes deriving an evaluation value obtained by evaluating a spread of a noise value from a pixel value of each pixel of the radiographic image on the basis of relational data indicating a relationship between the pixel value and the evaluation value, generating a noise map which is data obtained by associating the derived evaluation value with each pixel of the radiographic image, inputting the radiographic image and the noise map to the trained model, and executing the noise removal process of removing noise from the radiographic image. In this case, the evaluation value is derived from the pixel value of each image of the radiographic image on the basis of the relational data indicating the relationship between the pixel value and the evaluation value obtained by evaluating the spread of the noise value, and the noise map which is data obtained by associating the derived evaluation value with each pixel of the radiographic image is generated. The radiographic image and the noise map are input to the trained model constructed through machine learning in advance, and the noise removal process of removing noise from the radiographic image is executed. Thereby, in consideration of the spread of the noise value evaluated from the pixel value of each pixel of the radiographic image, noise in each pixel of the radiographic image is removed through machine learning, and thus it is possible to realize noise removal corresponding to the relationship between the pixel value and the spread of noise in the radiographic image using the trained model. As a result, it is possible to effectively remove the noise in the radiographic image. 
     Further, in the above-described embodiment, it is preferable that the image processing module includes an input unit configured to accept an input of condition information indicating either conditions of a source of radiation or imaging conditions when the radiation is radiated to capture an image of a target object, a calculation unit configured to calculate average energy related to the radiation passing through the target object on the basis of the condition information, and a narrowing unit configured to narrow down trained models used for the noise removal process from a plurality of trained models constructed through machine learning in advance using image data on the basis of the average energy. In addition, it is also preferable that the execution step includes accepting an input of condition information indicating either conditions of a source of radiation or imaging conditions when the radiation is radiated to capture an image of a target object, calculating average energy related to the radiation passing through the target object on the basis of the condition information, and narrowing down trained models used for the noise removal process from a plurality of trained models constructed through machine learning in advance using image data on the basis of the average energy. In this case, the average energy of the radiation passing through the target object is calculated on the basis of the conditions of the source of the radiation or the imaging conditions when the radiographic image of the target object is acquired. Candidates for the trained model use for noise removal are narrowed down from the trained models constructed in advance on the basis of the average energy. Thereby, the trained model corresponding to the average energy of the radiation which is a target for imaging is used for noise removal, and thus it is possible to realize noise removal corresponding to the relationship between luminance and noise in the radiographic image. As a result, it is possible to effectively remove the noise in the radiographic image. 
     In addition, in the above-described embodiment, it is also preferable that the image processing module includes a specification unit configured to specify image characteristics of the radiographic image acquired by the imaging device with a jig as a target, a selection unit configured to select a trained model from a plurality of trained models constructed through machine learning in advance using image data on the basis of the image characteristics, and a processing unit configured to execute the noise removal process using the selected trained model. In addition, it is also preferable that the execution step includes specifying image characteristics of the radiographic image acquired with a jig as a target, selecting a trained model from a plurality of trained models constructed through machine learning in advance using image data on the basis of the image characteristics, and executing the noise removal process using the selected trained model. With such a configuration, the image characteristics of the radiographic image of the jig are specified, and a trained model used for noise removal is selected from the trained models constructed in advance on the basis of the image characteristics. Thereby, since the characteristics of the radiographic image changing depending on the conditions of the radiation source and the like in the system can be estimated, and the trained model selected in accordance with the estimation result is used for noise removal, it is possible to realize noise removal corresponding to the relationship between luminance and noise in the radiographic image. As a result, it is possible to effectively remove the noise in the radiographic image. 
     The embodiment uses a radiographic image acquiring device, a radiographic image acquiring system, and a radiographic image acquisition method, thereby allowing an S/N ratio in a radiographic image to be effectively improved. 
     REFERENCE SIGNS LIST 
     
         
         
           
               1  Image acquiring device (radiographic image acquiring device, radiographic image acquiring system) 
               10  X-ray detection camera (imaging device) 
               11  Scintillator 
               12  Scan camera (detection element) 
               20 ,  20 A to  20 E Control device (image processing module) 
               50  X-ray irradiator (radiation source) 
               60  Belt conveyor (transport device) 
               72  Pixel 
               74  Pixel line (pixel group) 
               73  Readout circuit 
               201 ,  201 C Input unit 
               202 ,  202 A,  202 C,  202 D Calculation unit 
               202 E Specification unit 
               203 C,  203 D Narrowing unit 
               204 ,  204 A,  204 B Noise map generation unit 
               204 C,  204 E Selection unit 
               205 ,  205 C,  205 E Processing unit 
               206 C,  206 E,  207  Trained model 
             F Target object 
             TD Transport direction (one direction)