Information processing apparatus, information processing method, storage medium, and computer program product

Abstract

The invention provides an information processing apparatus, an information processing method, a storage medium, and a computer program product. An information processing apparatus according to an embodiment of the present disclosure includes a display control unit configured to, upon receiving an instruction for acquiring a first radiation image, control a display unit such that a second radiation image is displayed first and then the first radiation image is displayed. A first radiation image is obtained by performing first image processing on a radiation image obtained by detecting radiation. A second radiation image is obtained by performing second image processing which requires a shorter processing time than the first image processing on the radiation image.

Description

Technical Field

[0001] The present disclosure relates to an information processing device, an information processing method, and a storage medium. Background Art

[0002] Radiographic imaging devices using a flat panel detector (FPD) made of a semiconductor material are widely used for medical image diagnosis or nondestructive inspection. For example, image processing is performed on radiographic images obtained using an FPD for the purpose of reducing noise.

[0003] Japanese Patent Application Laid-Open No. 2023-57871 discloses a device that performs image processing using machine learning in order to reduce noise.

[0004] Image processing using machine learning may require longer processing time than image processing not using machine learning (e.g., image processing using a smoothing filter). As a result, for example, the period from the time a radiographer takes an image to the time a radiographic image is displayed on a display unit may become longer. This may result in an increase in the time required for diagnosis. Summary of the invention

[0005] The present disclosure shortens the time required to display a radiographic image on a display unit when image processing requiring a long processing time is performed.

[0006] An information processing device according to an embodiment of the present disclosure includes a display control unit, which is configured to control a display unit when receiving an instruction for acquiring a first radiographic image so that a second radiographic image is displayed first and then the first radiographic image is displayed. The first radiographic image is obtained by performing a first image processing on a radiographic image obtained by detecting radiation. The second radiographic image is obtained by performing a second image processing on the radiographic image that requires a shorter processing time than the first image processing.

[0007] Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a diagram showing an exemplary configuration of a radiation imaging system according to the first embodiment.

[0009] Figure 2 is a flowchart showing the operation of the controller according to the first embodiment.

[0010] Figure 3A and Figure 3B is a flowchart showing details of the operation of the controller according to the first embodiment.

[0011] Figure 4is a flowchart showing the operation of the controller according to the second embodiment.

[0012] Figure 5 is a diagram showing an exemplary configuration of a radiation imaging system according to a third embodiment.

[0013] FIG. 6A to FIG. 6C is a diagram showing image display according to the third embodiment.

[0014] Figure 7 is a diagram showing an exemplary configuration of a radiation imaging system according to a fourth embodiment.

[0015] Figure 8 is a flowchart showing the operation of the controller according to the fourth embodiment.

[0016] Fig. 9A and Fig. 9B is a diagram showing image display according to the fourth embodiment. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described below with reference to the accompanying drawings. The embodiments described below do not limit the present invention according to the claims. All combinations of features described in the embodiments are not always necessary for the solution according to the present invention.

[0018] (First embodiment)

[0019] Figure 1 1 is a diagram showing an exemplary configuration of a radiation imaging system according to the first embodiment. The radiation imaging system includes a controller 100 , a radiation imaging device 110 , an operation unit 120 , a display unit 130 , a high-definition display unit 135 , and a radiation generator 140 .

[0020] The radiation imaging device 110 is, for example, a radiation imaging device using a flat panel detector (FPD) made of a semiconductor material. The radiation imaging device 110 detects radiation that has passed through a subject (not shown) and outputs image data corresponding to the radiation. The image data may also be referred to as a medical image or a radiographic image. The radiation imaging device 110 converts radiation that has passed through the subject into electric charges.

[0021] For example, a direct conversion sensor is used to convert the radiation into a charge, which directly converts the radiation into a charge using amorphous selenium (a-Se). For example, an indirect sensor is used to convert the radiation into a visible light, and then a scintillator such as cesium iodide (CsI) and a photoelectric conversion element such as amorphous silicon (a-Si) are used to convert the visible light into a charge. The radiation imaging device 110 generates image data by performing analog-to-digital (A / D) conversion on the charge. The radiation imaging device 110 outputs the generated image data to the controller 100.

[0022] The operation unit 120 is, for example, a mouse or a keyboard for receiving instructions from an operator.

[0023] The display unit 130 is, for example, a liquid crystal display. The display unit 130 is controlled by a display control unit 107 included in the controller 100. The display unit 130 may be a separate body from the controller 100, or may be incorporated into the controller 100.

[0024] The high-definition display unit 135 is, for example, a liquid crystal display having a higher resolution than the display unit 130. The high-definition display unit 135 is controlled by the display control unit 107 included in the controller 100. The high-definition display unit 135 may be a separate body from the controller 100, or may be incorporated in the controller 100.

[0025] The display unit 130 and the high-definition display unit 135 do not necessarily have to be independent units. For example, the high-definition display unit 135 may have two display areas.

[0026] The controller 100 is, for example, an information processing device such as a computer. For example, the controller 100 is connected to the radiation imaging device 110 in a communicable manner via a wired network or a wireless network. The communicable manner includes not only direct communication between devices, but also communication between devices via another device. The controller 100 can execute applications. That is, the controller 100 includes one or more processors and a memory, and implements each functional unit to be described below by causing the processor to execute a program stored in the memory. A part or all of each functional unit can be implemented by dedicated hardware. The controller 100 performs image processing on the image data input from the radiation imaging device 110. The controller 100 displays the image data that has been image processed on the display unit 130 or the high-definition display unit 135. The display of the image data on the display unit 130 or the high-definition display unit 135 is performed by the display control unit 107 contained in the controller 100. The display control unit 107 is, for example, a display driver incorporated into a CPU. The display control unit 107 is an example of a display control unit.

[0027] The controller 100 displays a graphical user interface (GUI) on the display unit 130. The controller 100 controls the timing at which the radiation generator 140 generates radiation and the radiation imaging conditions.

[0028] The controller 100 includes an image acquisition unit 101 , an image processing unit 102 , a camera information input unit 104 , a learning result data acquisition unit 105 , a learning result data storage unit 106 , and an output unit 109 .

[0029] The image acquisition unit 101 acquires image data generated by the radiation imaging device 110 .

[0030] The imaging information input unit 104 is, for example, a GUI. The imaging information is input (set) to the imaging information input unit 104 by the operator. The imaging information includes, for example, the type of radiographic device, the type of image to be processed, the image processing method, or the image display method. For example, the type of radiographic device can be divided by, for example, fluorescent materials, the imaging resolution of the radiographic device, and the pixel pitch of the radiographic device. The type of image to be processed includes, for example, a 1 / 8 sparse preview image, a 1 / 4 sparse preview image, a 1 / 2 sparse preview image, an unsparse full-size image, and an output image based on the size and preprocessing of the image. The image processing method includes, for example, image processing using machine learning and image processing not using machine learning. The image display method includes, for example, a display method of only displaying a radiographic image for image processing using machine learning, and a display method of displaying a radiographic image for image processing not using machine learning before displaying a radiographic image for image processing using machine learning. The operator inputs the imaging information to the GUI using the operation unit 120. The imaging information acquired by the image acquisition unit 101 is input to the imaging information input unit 104. The imaging information input to the imaging information input unit 104 is associated with the image data acquired by the image acquisition unit 101 .

[0031] The learning result data storage unit 106 stores learning result data obtained by machine learning using the teacher image.

[0032] The learning result data acquisition unit 105 reads out the learning result data from the learning result data storage unit 106. The learning result data acquisition unit 105 stores information associating various words and word combinations included in the camera information with the learning result data stored in the learning result data storage unit 106. Therefore, the learning result data acquisition unit 105 can acquire the learning result data to be used for processing by the inference processing unit 103 included in the image processing unit 102 based on the words included in the camera information.

[0033] The image processing unit 102 includes an inference processing unit 103 and a display control unit 107 .

[0034] The inference processing unit 103 performs inference processing such as noise reduction processing using the learning result data obtained by machine learning. The inference processing unit 103 may also perform inference processing such as grayscale processing other than noise reduction processing.

[0035] In addition to the inference processing, the image processing unit 102 can also perform image processing such as contrast adjustment on the image data input from the radiation imaging device 110. The image processing unit 102 can also perform image processing such as trimming and rotation on the image data input from the radiation imaging device 110. The image processing unit 102 can perform not only image processing using machine learning (inference processing of the inference processing unit 103), but also image processing not using machine learning (for example, image processing using a smoothing filter). Based on the resolution of the connected display unit or the imaging information input to the imaging information input unit 104, it is determined which of these image processing is to be performed. The processing time required for image processing using machine learning is longer than the processing time required for image processing not using machine learning. Image processing using machine learning is an example of the first image processing. Image processing not using machine learning is an example of the second image processing.

[0036] The display control unit 107 controls the display unit 130 and the high-definition display unit 135. The display control unit 107 can detect whether a display unit is connected thereto. This detection is performed based on, for example, display setting information set by an operator. The display setting information is, for example, information indicating the number of display units (or display areas) connected to the controller 100.

[0037] The display setting information is, for example, information indicating the arrangement relationship between multiple display units (or display areas). For example, detection is performed through electrical communication between the display unit and the controller 100. The electrical communication includes, for example, a connection detection signal sent from the display unit to the controller 100 when the display unit and the controller 100 are connected to each other through a cable. The electrical communication also includes, for example, negotiation communication performed after sending the connection detection signal. The display control unit 107 can obtain the resolution of the display unit connected thereto.

[0038] The resolution is acquired from the above-mentioned display setting information or through the above-mentioned electrical communication. Therefore, the display control unit 107 can detect whether the display unit 130 and the high-definition display unit 135 are connected thereto. The display unit 130 and the high-definition display unit 135 are examples of a display unit. The display unit 130 is an example of a first display area. The high-definition display unit 135 is an example of a second display area different from the first display area. The GUI for setting the imaging conditions is displayed on either one of the display unit 130 and the high-definition display unit 135, but not on the other one of them. In this case, it is assumed that the GUI is displayed on the display unit 130.

[0039] The output unit 109 outputs the image that has been subjected to the image processing by the image processing unit 102 to an external device (eg, a picture archiving and communication system (PACS) or a printer).

[0040] Next, we will refer to Figure 2 The shown flowchart describes the operation of the controller 100 according to the first embodiment.

[0041] In step S201, imaging information is input to the imaging information input unit 104 included in the controller 100. For example, the imaging information is input in such a manner that the operator selects one of a plurality of pieces of imaging information displayed on the display unit 130 in, for example, a list form.

[0042] The imaging information does not necessarily have to be selected by the operator and can be directly input by the operator. These inputs made by the operator are examples of instructions for acquisition. In this case, it is assumed that the acquisition of a radiographic image that has been image processed using machine learning is instructed. The instruction for acquisition may be, for example, an inspection start instruction. Alternatively, the instruction for acquisition may be, for example, an instruction indicating that the inspection is ready to start. That is, the instruction for acquisition may be any instruction input to the controller 100 during the imaging process.

[0043] In step S202, the controller 100 starts the inspection based on the imaging information input in step S201. Specifically, the controller 100 sends an imaging preparation start signal to the radiation imaging device 110. After receiving the imaging preparation start signal, the radiation imaging device 110 uses the main control circuit to control the bias power supply and applies the bias voltage to the imaging pixel. Subsequently, the radiation imaging device 110 performs an initialization operation, that is, uses a drive circuit to read out the charge from the imaging pixel to read out the dark current signal stored in the imaging pixel. After the initialization operation is completed, the radiation imaging device 110 sends status information indicating that the imaging preparation has been completed to the controller 100. The controller 100 sets the operating parameters (e.g., tube voltage) of the radiation generator 140 based on the imaging information selected in step S201. After receiving the status information from the radiation imaging device 110, the controller 100 notifies the radiation generator 140 of exposure permission.

[0044] In step S203, the image acquisition unit 101 included in the controller 100 acquires a radiographic image captured by the radiation camera 110. Specifically, for example, the radiation generator 140 that has been notified of exposure permission emits radiation in response to the operation of an exposure button. The drive circuit of the radiation camera 110 uses a readout circuit to read out an acquired image signal obtained by detecting the emitted radiation and generates a radiographic image. Subsequently, the radiation camera 110 sends the generated radiographic image to the controller 100. The image acquisition unit 101 included in the controller 100 receives the radiographic image sent by the radiation camera 110. In the process from step S201 to step S203, the controller 100 associates the imaging information input to the imaging information input unit 104 with the radiographic image (image data) acquired by the image acquisition unit 101.

[0045] In step S204, the learning result data acquisition unit 105 included in the controller 100 acquires the learning result data from the learning result data storage unit 106 based on the image pickup information selected in step S203. Figure 3A The flowchart shown in describes the processing of step S204 in detail. The processing of step S204 is performed in parallel with the processing of step S203 (acquiring a radiographic image).

[0046] In step S205, the image processing unit 102 included in the controller 100 performs image processing on the radiographic image captured by the radiographic device 110. The display control unit 107 included in the controller 100 displays the radiographic image that has undergone image processing on the display unit. The processing of step S205 is a characteristic part of this embodiment, and thus will be referred to below. Figure 3B The flowchart shown is described in detail.

[0047] After displaying the radiographic image on the display unit, the controller 100 ends the examination in response to the operator's input operation in step S206 .

[0048] Next, we will refer to Figure 3A The illustrated flowchart describes the learning result data acquisition processing (processing of step S204 ) performed by the learning result data acquisition unit 105 included in the controller 100 .

[0049] In step S301 , the learning result data acquisition unit 105 acquires the imaging information determined in step S203 .

[0050] In step S302 , the learning result data acquisition unit 105 selects learning result data based on the imaging information acquired in step S301 .

[0051] In step S303, the learning result data acquisition unit 105 reads out the learning result data selected in step S302 from the learning result data storage unit 106, decompresses the learning result data so that, for example, the inference processing unit 103 can use the learning result data, and stores the learning result data in a storage unit (not shown).

[0052] Next, we will refer to Figure 3B The illustrated flowchart describes the processing of the image processing unit 102 and the display control unit 107 included in the controller 100 (processing of step S205 ).

[0053] In step S311 , the image processing unit 102 performs image processing without using machine learning.

[0054] Subsequently, in step S312, the display control unit 107 displays the radiographic image having undergone the image processing in S311 on the display unit 130. The radiographic image having undergone the image processing in S311 is an example of a second radiographic image.

[0055] Subsequently, in step S313 , the image processing unit 102 performs image processing using machine learning using the learning result data acquired through the process of S204 .

[0056] Subsequently, in step S314, the display control unit 107 displays the radiographic image having undergone the image processing in S313 on the high-definition display unit 135. The radiographic image having undergone the image processing in S313 is an example of a first radiographic image.

[0057] In the method of the prior art, image processing and display (S311 to S312) without machine learning are not performed, but image processing and display (S313 to S314) using machine learning are performed. That is, before the captured radiation image is displayed on the display unit, the processing time required for image processing using machine learning is required.

[0058] In the method according to the present embodiment, image processing and display without machine learning (S311 to S312) are performed before image processing and display with machine learning (S313 to S314). The processing time required for image processing without machine learning is shorter than the processing time required for image processing with machine learning. Therefore, compared with the method using the prior art, the time required to display the captured radiographic image on the display unit can be shortened using the method according to the present embodiment.

[0059] In the case where the controller 100 can perform multiple image processing without using machine learning, the controller 100 can select one of them according to the purpose. For example, the controller 100 can select the image processing with the shortest processing time when giving priority to the time required for display. For example, the controller 100 can select the image processing that obtains the processing result most similar to the processing result of the image processing using machine learning. In this case, not only can the display time be shortened, but the radiographer can appropriately check the imaging results. The selection can be made by the controller 100 or an operator (e.g., a radiographer).

[0060] The radiographic image displayed on the display unit 130 can be used, for example, by a radiographer to check the radiographic image pickup results or adjust image processing parameters. Therefore, by shortening the time required to display the captured radiographic image on the display unit 130, the radiographic technician's operating efficiency can be improved.

[0061] After the radiographer has performed the inspection and adjustment, the radiographic image to be output to the external device is inspected using the high-definition display unit 135. Therefore, by shortening the time required to display the captured radiographic image on the display unit 130, the time required to output the radiographic image to the external device can be shortened.

[0062] The doctor makes a diagnosis after the radiographer has performed the inspection and adjustment. Therefore, by shortening the time required to display the captured radiographic image on the display unit 130, a diagnosis can be made quickly.

[0063] (Second embodiment)

[0064] In the first embodiment, the operation of the controller 100 from imaging to display has been described. Medical images such as captured radiographic images are generally output to an external device for image examination and used, for example, for diagnosis, and the external device is connected to the in-hospital network. In the second embodiment, the operation of the controller 100 when the medical images captured in the first embodiment are output to an external device such as a PACS or a printer will be described. Reference will be made to Figure 4 The flowchart shown in describes the operation of the controller 100 according to the second embodiment.

[0065] In step S401, the inspection completion imaging information is input to the imaging information input unit 104 included in the controller 100. The inspection completion imaging information includes, for example, the imaging date and time of the radiographic image captured, the patient ID, and the patient name. For example, the inspection completion imaging information is input by causing the operator to select one of a plurality of inspection completion imaging information displayed in a list form on the display unit 130. The selected inspection completion imaging information is set as an object to be output to an external device. The inspection completion imaging information does not necessarily have to be selected by the operator and may be directly input by the operator.

[0066] In step S402 , the controller 100 starts output processing based on the inspection completion imaging information input in step S401 .

[0067] In step S403 , the image acquisition unit 101 included in the controller 100 acquires a radiographic image associated with the examination-completed imaging information from an image storage unit (not shown).

[0068] In step S404 , the learning result data acquisition unit 105 included in the controller 100 acquires the learning result data from the learning result data storage unit 106 based on the imaging information associated with the radiographic image acquired in step S403 .

[0069] In step S405, the image processing unit 102 included in the controller 100 performs image processing on the radiographic image acquired by the image acquisition unit 101. At this time, the inference processing unit 103 performs inference processing by machine learning using the learning result data obtained in step S404. The inference processing unit 103 performs, for example, noise reduction processing and processing for determining display contents such as annotations to be superimposed on the output image. In S314 (display) in the first embodiment, a case where a radiographic image that has been image processed using machine learning is displayed on the high-definition display unit 135 has been described. However, in the case of outputting to, for example, a PACS, an image check is performed on the screen of the PACS as the output destination. That is, the image processing in step S405 may be different from the image processing according to the first embodiment because the application is different from that in the first embodiment. For example, in the first embodiment, learning result data that prioritizes speed is used, but the learning result data may also be used differently in the following manner: learning result data that prioritizes image quality is used in the image processing in step S405.

[0070] In step S406 , the controller 100 ends the output.

[0071] As described above, according to the second embodiment, in the output processing, noise reduction processing using the best learning result data suitable for the output processing can also be performed. As a result, the operator can use the high-definition display unit 135 to check the image quality of the radiographic image output to, for example, PACS.

[0072] (Third Embodiment)

[0073] In the first embodiment, the image processing performed on the radiographic images displayed on the display unit 130 and the high-definition display unit 135 has been described. Figure 5 : is a block diagram showing an exemplary configuration of a radiation imaging system according to a third embodiment. Figure 5 Components similar to those according to the first embodiment are denoted by the same reference numerals.

[0074] The radiation imaging system according to the third embodiment further includes an information display unit 108 for displaying image processing information (image processing parameters) applied to each radiation image to be displayed on the display unit 130 and the high-definition display unit 135 .

[0075] Will refer to FIG. 6A to FIG. 6C The information display unit 108 is described. Fig. 6AThe table shown indicates: image processing information set for imaging, image processing applied to the radiographic image to be displayed on the display unit 130 and the high-definition display unit 135, and image processing information displayed by the information display unit 108. The image processing information set for imaging is information indicated by, for example, an operator. For example, No. 1 indicates that noise reduction processing (AI NR) using machine learning has been indicated. For example, No. 2 indicates that noise reduction processing (NR) without machine learning has been indicated. For example, No. 3 indicates that neither noise reduction processing (AI NR) using machine learning nor noise reduction processing (NR) without machine learning has been indicated.

[0076] Figure 6B and Figure 6C Show Fig. 6A An exemplary display of the display unit 130 and the high-definition display unit 135 in case No. 1 is shown. Figure 6B An exemplary display of the display unit 130 is shown. Figure 6C An exemplary display of the high-definition display unit 135 is shown.

[0077] Reference Figure 6B , a radiographic image 601 to which a noise reduction process (NR) not using machine learning is applied is displayed on the display unit 130. On the radiographic image 601, the information display unit 108 displays image processing information 602 (AI NR: ON) indicating that a noise reduction process (AI NR) using machine learning is to be applied. The image processing information 602 is an example of first image processing information.

[0078] Reference Figure 6C , a radiographic image 603 corresponding to the image processing information set for imaging, to which noise reduction processing (AI NR) using machine learning is applied, is displayed on the high-definition display unit 135. On the radiographic image 603, the information display unit 108 displays image processing information 604 (AI NR: ON) indicating that noise reduction processing (AI NR) using machine learning will be applied.

[0079] As described above, with the method according to the third embodiment, as in the first embodiment, image processing and display without using machine learning are performed on the display unit 130. With the method according to the third embodiment, as in the first embodiment, image processing and display using machine learning are performed on the high-definition display unit 135. Therefore, with the method according to the third embodiment, as in the first embodiment, effects such as improving the operating efficiency of the radiographer can be achieved.

[0080] Furthermore, with the method according to the third embodiment, image processing information 604 is displayed, indicating that image processing using machine learning (AI NR) is applied to the radiographic image displayed on the display unit 130, the radiographic image having been subjected to image processing not using machine learning. Thus, an effect of causing the operator to check image processing information to be applied to the radiographic image to be output to, for example, a PACS can be achieved.

[0081] (Fourth embodiment)

[0082] In the fourth embodiment, a case where the high-definition display unit 135 is not connected to the controller 100 will be described.

[0083] Figure 7 is a block diagram showing an exemplary configuration of a radiation imaging system according to a fourth embodiment. Figure 1 The difference is that the high-definition display unit 135 is not connected.

[0084] Figure 8 is a flowchart describing the difference between the operation of the controller 100 according to the fourth embodiment and the operation of the controller 100 according to the first embodiment. The operation of the controller 100 according to the fourth embodiment differs from the operation of the controller 100 according to the first embodiment in that the flowchart used to describe the first embodiment Figure 2 Therefore, Figure 8 The processing details of S205 are shown.

[0085] In step S801 , the image processing unit 102 performs image processing without using machine learning.

[0086] Subsequently, in step S802, the display control unit 107 performs control so that the radiographic image that has been image-processed in S801 can be displayed on the display unit 130. For example, control is performed to make the radiographic image displayable so that the radiographic image can be displayed in response to an operation by the operator (e.g., a mouse click). Control to make the radiographic image displayable does not necessarily have to be performed, and the radiographic image can be displayed on the display unit 130.

[0087] Subsequently, in step S803 , the image processing unit 102 performs image processing using machine learning using the learning result data acquired through the process of S204 .

[0088] Subsequently, in step S804 , the display control unit 107 performs control so that the radiographic image that has been subjected to the image processing in S803 can be displayed on the display unit 130 .

[0089] Fig. 9A and Fig. 9BA display example is shown. Fig. 9A The display state in which the operator has clicked the button indicating NR (image processing without machine learning) on ​​the GUI after completing step S802 is shown. In the state in which step S802 is completed, a radiographic image that has undergone image processing without machine learning can be displayed. Therefore, in response to a mouse click, a radiographic image that has undergone image processing without machine learning is displayed. On the other hand, in the state in which step S802 is completed, a radiographic image that has undergone image processing using machine learning cannot be displayed. Therefore, the display (gray display) indicates that it cannot be selected.

[0090] Fig. 9B The display state in which the operator clicks a button indicating AI NR (image processing using machine learning) on ​​the GUI after completing step S804 is shown. In the state in which step S804 is completed, a radiographic image that has been subjected to image processing using machine learning can be displayed. Therefore, in response to a mouse click, a radiographic image that has been subjected to image processing using machine learning is displayed.

[0091] As described above, using the method according to the fourth embodiment, it is possible to switch the display of a radiographic image that has undergone image processing using machine learning and the display of a radiographic image that has undergone image processing without machine learning even when the high-definition display unit 135 is not connected.

[0092] Although the method of switching between displaying a radiographic image and displaying a radiographic image that has undergone image processing without machine learning is described in the fourth embodiment, the display method is not limited thereto. For example, a radiographic image and a radiographic image that has undergone image processing without machine learning may be displayed side by side.

[0093] In the method according to the fourth embodiment, as in the method according to the first embodiment, image processing and display (S801 to S802) without using machine learning are performed before image processing and display (S803 to S804) using machine learning. The processing time required for image processing without using machine learning is shorter than the processing time required for image processing using machine learning. Therefore, using the method according to this embodiment can shorten the time required to display a captured radiographic image on a display unit, compared with using the method in the prior art.

[0094] According to the present disclosure, when image processing that takes a long processing time is performed, the time required to display a radiographic image on a display unit can be shortened.

[0095] Other embodiments

[0096] The embodiments of the present invention may also be implemented by reading and executing computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be more completely referred to as a "non-transitory computer-readable storage medium") to perform one or more functions in the above-mentioned embodiments, and / or a computer of a system or device including one or more circuits (e.g., an application-specific integrated circuit (ASIC)) for performing one or more functions in the above-mentioned embodiments, and the embodiments of the present invention may be implemented by, for example, reading and executing the computer executable instructions from the storage medium by the computer of the system or device to perform one or more functions in the above-mentioned embodiments, and / or controlling the one or more circuits to perform one or more functions in the above-mentioned embodiments. The computer may include one or more processors (e.g., a central processing unit (CPU), a microprocessing unit (MPU)), and may include a network of separate computers or separate processors to read and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, a hard disk, a random access memory (RAM), a read-only memory (ROM), a memory of a distributed computing system, an optical disk (such as a compact disc (CD), a digital versatile disc (DVD), or a Blu-ray disc (BD) TM ), flash memory devices, memory cards, etc.

[0097] The embodiments of the present invention may also be implemented by providing software (program) for performing the functions of the above-described embodiments to a system or device via a network or various storage media, and a computer or a central processing unit (CPU) or a microprocessing unit (MPU) of the system or device reads and executes the program.

[0098] While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims

1. An information processing device, comprising a display control unit, wherein the display control unit is configured to control the display unit when receiving an instruction for acquiring a first radiographic image, so that a second radiographic image is displayed first and then the first radiographic image is displayed, the first radiographic image being obtained by performing a first image processing on a radiographic image obtained by detecting radiation, and the second radiographic image being obtained by performing a second image processing on the radiographic image that requires a shorter processing time than the first image processing.

2. The information processing device according to claim 1, wherein: The display control unit controls the display unit so that the second radiographic image is displayed instead of the first radiographic image when receiving the instruction for acquiring the second radiographic image.

3. The information processing device according to claim 1, wherein: The display control unit controls the display unit so that the second radiographic image and first image processing information indicating the first image processing are displayed, and the display control unit controls the display unit so that the first radiographic image and the first image processing information are displayed.

4. The information processing device according to claim 1, wherein: The display control unit controls the display unit so that display of the first radiographic image and display of the second radiographic image are switched in response to an instruction from an operator.

5. The information processing device according to claim 1, wherein: When receiving an instruction for acquiring a first radiographic image and making a setting for displaying a second radiographic image before the first radiographic image, the display control unit controls the display unit so that the second radiographic image is displayed before the first radiographic image.

6. The information processing device according to claim 1, wherein: When receiving an instruction for acquiring a first radiographic image and making settings for displaying the first radiographic image, the display control unit controls the display unit so that the first radiographic image is displayed.

7. The information processing device according to claim 5, wherein: The display control unit controls the display unit so that the second radiation image is displayed in a display area for setting, which is one of a first display area of ​​the display unit and a second display area of ​​the display unit that is different from the first display area, and the display control unit controls the display unit so that the first radiation image is displayed in the other of the first display area and the second display area.

8. The information processing device according to claim 1, wherein: The first image processing is image processing using machine learning.

9. The information processing device according to claim 1, wherein: The first image processing is a noise reduction processing using machine learning, and the second image processing is a noise reduction processing not using machine learning.

10. The information processing device according to claim 1, wherein: The instruction is given by the operator before acquiring the radiographic image.

11. A radiation imaging system, comprising: a radiation imaging device configured to detect radiation; as well as The information processing apparatus according to any one of claims 1 to 10, the information processing apparatus being communicably connected to a radiation imaging apparatus.

12. An information processing device, comprising a display control unit, which is configured to display the first radiographic image on a first display unit and display the second radiographic image on a second display unit different from the first display unit when an instruction for acquiring a first radiographic image is received, wherein the first radiographic image is obtained by performing a first image processing on a radiographic image obtained by detecting radiation, and the second radiographic image is obtained by performing a second image processing on the radiographic image that requires a shorter processing time than the first image processing.

13. The information processing device according to claim 12, wherein: The first display unit has a higher resolution than the second display unit.

14. An information processing method, comprising: A display control step, when receiving an instruction for acquiring a first radiographic image, controls the display unit so that the second radiographic image is displayed first and then the first radiographic image, the first radiographic image being obtained by performing a first image processing on a radiographic image obtained by detecting radiation, and the second radiographic image being obtained by performing a second image processing on the radiographic image that requires a shorter processing time than the first image processing. 15 . A storage medium storing a program for causing a computer to execute the information processing method according to claim 14 .

16. A computer program product, configured to cause a computer to execute the information processing method according to claim 14.

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