Information processing apparatus, information processing method, and program

Abstract

To provide an information processing apparatus, an information processing method, and a program capable of suppressing generation of an unnecessary image in a CT apparatus and providing an image useful for diagnosis with high time efficiency.SOLUTION: An information processing apparatus includes a processor configured to acquire a first image captured using a CT apparatus, execute analysis processing of the first image using a learner, cause the CT apparatus to execute generation processing of a second image corresponding to a result of the analysis processing, and cause a display device to display at least one of the first image, the result of the analysis processing, and the second image.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a program, and more particularly to an image processing technology for processing images captured by a computed tomography (CT) device and a technology for improving an image diagnosis workflow. [Background technology]

[0002] Patent Document 1 describes an X-ray CT system that uses two or more types of X-ray energy to discriminate between substances in a subject's body, such as kidney stones, fat, or bone. The X-ray CT system described in Patent Document 1 includes a scanning unit that performs a first scan by irradiating a first region of the subject with X-rays to acquire a first subject data set corresponding to the first X-ray energy, and a second scan that performs a second scan by irradiating a second region included in the first region with X-rays to acquire a second subject data set corresponding to the second X-ray energy and a third subject data set corresponding to a third X-ray energy different from the second X-ray energy. It also includes a processing unit that performs material decomposition using multiple reference materials based on a fourth subject data set obtained based on the first subject data set and one of the second and third subject data sets, and the other of the second and third subject data sets. The term "X-ray CT system" corresponds to the term "CT apparatus" in this specification.

[0003] Patent Document 2 describes a photon-counting X-ray CT device capable of performing material decomposition with high accuracy. The photon-counting X-ray CT device described in Patent Document 2 includes a data collection unit that collects first data groups, which are count data for each of the multiple first energy bands, by allocating energy measured by a signal output from a photon-counting detector in response to incidence of X-ray photons to one of multiple first energy bands, a determination unit that determines multiple second energy bands into which the multiple first energy bands are grouped according to decomposition target materials that are materials to be decomposed in the imaging region, an allocation unit that generates second data groups by allocating the first data groups to one of the multiple second energy bands, and a generation unit that generates an image related to the distribution of the decomposition target materials using the second data groups, wherein the widths of the multiple second energy bands are different from one another, and the determination unit selects the widths of the multiple second energy bands so that the integrated values ​​of the X-ray absorption rates in the imaging region for the multiple second energy bands are the same. The term "photon counting X-ray CT device" corresponds to the term "photon counting CT device" in this specification. Hereinafter, the photon counting CT device will be referred to as a "PCCT device." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-10727 [Patent Document 2] Patent No. 6747787 specification [Patent Document 3] Japanese Patent Publication No. 2022-106241 Summary of the Invention [Problem to be solved by the invention]

[0005] Images taken using a CT device in a hospital or other medical institution are sent to a medical image management system called a PACS (Picture Archiving and Communication System) via a communication network such as an in-hospital network, where the images are stored, shared, viewed, etc. In a CT device that can generate other types of images, such as material decomposition images, in addition to normal CT images, it is possible to automatically generate images of multiple image types that can be generated without selecting them, and store all of these images of multiple image types in the PACS.

[0006] While this configuration has the advantage of being able to provide multiple image types necessary for diagnosis in a short time, the amount of image data stored in the PACS is enormous, straining the PACS's storage capacity. Furthermore, even when multiple image types are automatically generated, not all of these images are actually used for analysis or diagnosis, resulting in the generation, transmission, and storage of unnecessary images that are not used. Sending unnecessary images that are not used for analysis, etc., over the network to the PACS increases traffic and strains the network.

[0007] To address the issues associated with automatically generating multiple image types without selecting them, one possible solution would be for the CT system to receive image generation instructions from the user, generate different image types on demand, and then send the requested images to the PACS. However, with this on-demand method, it takes time from receiving the instruction until the reconstructed images are obtained, making efficient interpretation difficult.

[0008] The present disclosure has been made in consideration of the above circumstances, and aims to provide an information processing device, an information processing method, and a program that can suppress the generation of unnecessary images in a CT device and provide images useful for diagnosis in a time-efficient manner. [Means for solving the problem]

[0009] An information processing device according to a first aspect of the present disclosure is an information processing device including a processor, which acquires a first image captured using a CT device, performs an analysis process on the first image using a learning device, causes the CT device to perform a process to generate a second image corresponding to the result of the analysis process, and displays at least one of the first image, the result of the analysis process, and the second image on a display device.

[0010] According to the first aspect, based on the results of the analysis process of the first image by the learning device, a process for generating a second image corresponding to the results of the analysis process is executed. In this way, by using the results of the analysis process by the learning device to control the process for generating a second image in the CT device, it is possible to suppress the generation of unnecessary images. Furthermore, according to the first aspect, since the results of the analysis process of the first image by the learning device can be used to cause the CT device to execute the process for generating a second image, it is possible to display the second image that the user wishes to observe on the display device in a short time. This enables time-efficient interpretation.

[0011] An information processing device according to a second aspect is the information processing device according to the first aspect, wherein the first image is a CT image and the second image is a material decomposition image.

[0012] An information processing device according to a third aspect may be the information processing device according to the first aspect, wherein the second image is an image generated by image reconstruction processing under reconstruction conditions different from those for the first image.

[0013] An information processing device according to a fourth aspect may be configured such that, in an information processing device according to any one of the first to third aspects, the processor outputs an instruction to the CT device to generate a second image based on the results of the analysis processing.

[0014] An information processing device according to a fifth aspect may be configured such that, in the information processing device according to any one of the first to fourth aspects, the processor sets conditions for generating the second image according to the results of the analysis processing.

[0015] An information processing device according to a sixth aspect may be configured such that, in the information processing device according to any one of the first to fifth aspects, the learning device includes a trained model that causes the processor to execute a process of detecting lesions from an input image.

[0016] An information processing device according to a seventh aspect may be configured such that, in the information processing device according to any one of the first to sixth aspects, the learning device includes a trained model that causes the processor to execute a process of detecting at least one of tumors, nodules, stones, fat, and calcified regions from an input image.

[0017] An information processing device according to an eighth aspect may be configured such that, in the information processing device according to any one of the first to seventh aspects, the analysis processing includes at least one of the processing of detecting adrenal tumors, detecting urinary stones, detecting gallstones, and calculating liver fat content.

[0018] An information processing device according to a ninth aspect may be an information processing device according to any one of the first to eighth aspects, wherein the processor is configured to cause the CT device to perform a process of generating a fat density image as a second image when an adrenal tumor is detected by the analysis process.

[0019] An information processing device according to a 10th aspect may be configured such that, in the information processing device according to the 9th aspect, the processor sets the slice thickness of the fat density image generated by the generation process according to the size of the adrenal tumor detected by the analysis process.

[0020] An information processing device according to an eleventh aspect may be configured such that, in the information processing device according to the ninth or tenth aspect, the processor sets the slice position and number of slices of the fat density image generated by the generation process according to the position of the adrenal tumor detected by the analysis process.

[0021] An information processing device according to a twelfth aspect may be configured such that, in the information processing device according to the first to eleventh aspects, the processor receives a request to display a second image via an input device and displays the second image on the display device based on the display request.

[0022] An information processing device according to a thirteenth aspect may be an information processing device according to any one of the first to twelfth aspects, further comprising a storage device including a temporary storage area and a permanent storage area, and the processor may be configured to store a first image in the permanent storage area and a second image in the temporary storage area.

[0023] An information processing device according to a fourteenth aspect may be configured in the information processing device according to the thirteenth aspect, wherein the processor stores the second image displayed on the display device in a persistent storage area.

[0024] An information processing device according to the 15th aspect may be configured such that, in the information processing device according to the 13th or 14th aspect, the processor stores in the permanent storage area only the second image that is displayed on the display device among the second images stored in the temporary storage area.

[0025] An information processing method according to a sixteenth aspect includes a processor performing the steps of acquiring a first image taken using a CT device, performing an analysis process on the first image using a learning device, causing the CT device to perform a process of generating a second image corresponding to the result of the analysis process, and displaying at least one of the first image, the result of the analysis process, and the second image on a display device.

[0026] The processor may automatically execute the processing of each step according to a program, or may accept instructions input from a user for some or all of the steps and execute the processing in accordance with the accepted instructions.

[0027] The information processing method according to the sixteenth aspect may have a configuration including the same specific aspects as the information processing device according to any one of the second to fifteenth aspects.

[0028] The program according to the seventeenth aspect enables a computer to perform the following functions: acquire a first image taken using a CT device; perform an analysis process on the first image using a learning device; cause the CT device to perform a process to generate a second image corresponding to the result of the analysis process; and display at least one of the first image, the result of the analysis process, and the second image on a display device.

[0029] The program according to the seventeenth aspect may be configured to include the same specific aspects as the information processing device according to any one of aspects 2 to 15. The present disclosure also includes a non-transitory, tangible computer-readable storage medium that stores the program according to the seventeenth aspect. [Effects of the Invention]

[0030] According to the present disclosure, it is possible to suppress the generation of unnecessary images in a CT device, and to provide images useful for diagnosis in a time-efficient manner. [Brief explanation of the drawings]

[0031] [Figure 1] FIG. 1 is a block diagram showing an example of a medical information system to which an information processing device according to an embodiment is applied. [Figure 2] FIG. 2 is a block diagram showing an example of the hardware configuration of a computer. [Figure 3] FIG. 3 is an explanatory diagram showing an outline of the operations of the information processing device and the PCCT device according to the embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of the operation of the information processing device and the PCCT device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0032] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings. In this specification, the same components are designated by the same reference numerals, and redundant explanations will be omitted where appropriate.

[0033] <<Example of information processing device>> 1 is a block diagram showing an example of a medical information system including an information processing device 10 according to an embodiment. The information processing device 10 is a computer system including a PACS 12, an artificial intelligence (AI) server 14, and a viewer server 16, and is connected to a PCCT device 30 via a communication network 20. A radiology department PC (personal computer) 18, which is an example of a terminal that can access the viewer server 16, is also connected to the communication network 20. The information processing device 10 may be configured to include the radiology department PC 18.

[0034] The PACS 12 is a computer system that stores and manages various data, including medical images captured using the PCCT device 30 and modality devices (not shown). The PACS 12 is equipped with a large-capacity storage device including a permanent storage area 24 and a temporary storage area 26, and a database management program. The PACS 12 may be configured to include, for example, a DICOM (Digital Imaging and Communications in Medicine) server that operates in accordance with the DICOM specifications. The permanent storage area 24 is an area in which information is stored for a longer period of time than the temporary storage area 26.

[0035] The AI ​​server 14 includes an image processing device that performs image analysis processing using AI technology. The AI ​​server 14 includes one or more AI modules, preferably multiple AI modules, and acquires images to be processed from the PACS 12 and performs processing using the AI ​​modules. The AI ​​modules may be configured using, for example, a convolutional neural network and may be a learning machine trained to perform tasks such as object detection, region classification (semantic segmentation), or numerical prediction of measured values.

[0036] The AI ​​server 14 includes multiple AI modules, such as an adrenal tumor detection AI 41, a urinary tract stone detection AI 42, a gallstone detection AI 43, and a liver fat mass measurement AI 44. The adrenal tumor detection AI 41 includes a trained model trained by machine learning to detect adrenal tumors from input CT images. The urinary tract stone detection AI 42 includes a trained model trained by machine learning to detect urinary tract stones from input CT images. The gallstone detection AI 43 includes a trained model trained by machine learning to detect gallstones from input CT images. The liver fat mass measurement AI 44 includes a trained model trained by machine learning to output a predicted value of liver fat mass from input CT images. Note that the trained models are essentially programs.

[0037] The PACS 12, AI server 14, and viewer server 16 are communicatively connected via a communication network 20. The communication network 20 may be a local area network, a wide area network, or a combination of these. The communication method of the communication network 20 is not limited to wired communication, but may also be wireless communication, or a combination of these.

[0038] The results of the analysis process by the AI ​​server 14 (AI results) are sent to the viewer server 16. The AI ​​results may also be linked to the corresponding CT images and stored in the PACS 12. The AI ​​server 14 of this embodiment instructs the PCCT device 30 to generate a material decomposition image according to the AI ​​results. Details will be described later.

[0039] The PCCT device 30 is a CT device capable of reconstructing an image that has undergone material decomposition by using a photon-counting detector to count X-ray photons (X-ray photons) that have passed through a subject. The term "material decomposition" includes the concept of distinguishing between the type, abundance, density, etc. of a material. Since the configuration of the PCCT device 30 is publicly known, a detailed description thereof will be omitted here. For example, the PCCT device disclosed in Japanese Patent Application Laid-Open No. 2022-106241 may be used as the PCCT device 30. The PCCT device 30 can obtain the X-ray intensity for each energy bin by discriminating the counted individual X-ray photons by energy value. Utilizing this, the PCCT device 30 can extract and image only X-rays within a specific energy range.

[0040] The PCCT device 30 includes a scanner gantry, a bed, and a console (operating table). The console is configured using a computer. The computer constituting the console functions as a control device that controls the imaging operation of the PCCT device 30 and as an image processing device that processes transmitted X-ray data obtained from the scanner gantry and performs calculations for image reconstruction. The raw data acquired by the scanner gantry and the reconstructed image (reconstructed image) are stored in a memory device built into the console. In addition, the image generated by reconstruction is displayed on a display device of the console.

[0041] The PCCT device 30 is equipped with a remote reconstruction function that receives instructions related to image reconstruction processing and the like from outside via the communication network 20 and executes processing in accordance with the received instructions. The PCCT device 30 receives instructions to generate a material decomposition image from the AI ​​server 14 and generates the material decomposition image in accordance with the received instructions. A material decomposition image is an image in which a specific material is emphasized or suppressed. The term material decomposition image includes the concept of a material density image. The PCCT device 30 can generate images in which water, iodine, fat, calcium, etc. can be distinguished from each other. The PCCT device 30 is an example of a "CT device" in this disclosure.

[0042] The viewer server 16 is a server that provides display information including images for interpretation and AI results. By accessing the viewer server 16 from a client terminal such as a radiology department PC 18, an interpretation screen is displayed on a display device such as the radiology department PC 18.

[0043] The radiology PC 18 may be, for example, an image interpretation viewer terminal for the radiology department. Although only one radiology PC 18 is shown in FIG. 1, a plurality of radiology PCs 18 may be connected to the communication network 20.

[0044] 1 is an example, and the specific forms of the PACS 12, AI server 14, viewer server 16, and radiology PC 18 are not important. For example, the PACS 12, AI server 14, viewer server 16, and radiology PC 18 may be configured as an integrated unit, or a system may be constructed by combining some of these multiple elements, or by further distributing processing functions.

[0045] The PACS 12, AI server 14, and viewer server 16 may be physical servers, virtual servers, or a combination of these. In addition, some or all of the PACS 12, AI server 14, and viewer server 16 may be realized by cloud computing.

[0046] <<Example of computer hardware configuration>> 2 is a block diagram showing an example of the hardware configuration of a computer 100 used in the information processing device 10. Each of the PACS 12, AI server 14, viewer server 16, and radiology PC 18 shown in FIG. 1 may have the same configuration as the computer 100, or some or all of the processing functions of the PACS 12, AI server 14, viewer server 16, and radiology PC 18 may be implemented in the computer 100.

[0047] The computer 100 includes a CPU (Central Processing Unit) 102, a non-transitory tangible computer-readable medium 104, a communication interface 106, and an input / output interface 108. The computer 100 may also include a GPU (Graphics Processing Unit) (not shown).

[0048] The CPU 102 is connected to a computer-readable medium 104, a communication interface 106, and an input / output interface 108 via a bus 110. The computer-readable medium 104 includes a memory 112, which is a main storage device, and a storage 114, which is an auxiliary storage device. The computer-readable medium 104 may be, for example, a semiconductor memory, a hard disk drive (HDD) device, a solid state drive (SSD) device, or a combination of these. The computer-readable medium 104 stores various programs, data, etc., including an image processing program and a display control program.

[0049] The computer 100 may also include an input device 122 and a display device 124. The input device 122 and the display device 124 are connected to the bus 110 via the input / output interface 108. The input device 122 may be, for example, a keyboard, a mouse, a multi-touch panel, or other pointing device, or a voice input device, or an appropriate combination of these.

[0050] The display device 124 is configured by, for example, a liquid crystal display, an organic electroluminescence (OEL) display, a projector, or an appropriate combination of these.

[0051] Overview of the operations of the information processing device 10 and the PCCT device 30 3 shows an overview of the operations of the information processing device 10 and the PCCT device 30. The overview of the operations of this system will be explained in the order of the operations shown in [1] to [9] in FIG.

[0052] [1] The PCCT device 30 performs imaging according to the order and transmits the obtained CT images to the PACS 12. The CT images are an example of the "first image" in this disclosure.

[0053] [2] The PACS 12 transfers the CT images received from the PCCT device 30 to the AI ​​server 14. The PACS 12 also stores the CT images received from the PCCT device 30 in the permanent storage area 24.

[0054] [3] The AI ​​server 14 performs multiple AI processes on the CT images acquired via the PACS 12. Specifically, it performs analysis processes including at least one of the following: detection of adrenal tumors, detection of urinary stones, detection of gallstones, and calculation of liver fat content. The analysis processes by the AI ​​server 14 are performed for the purpose of detecting incidental findings.

[0055] [4] Based on the AI ​​results, the AI ​​server 14 instructs the PCCT device 30 to generate a material decomposition image. The AI ​​server 14 selects an image type that is more useful for diagnosis according to the AI ​​results and instructs the PCCT device 30 to generate the image. Image types that are useful for diagnosis include, for example, image types required for detailed image diagnosis, image types recommended for user observation, and image types expected to be observed by the user.

[0056] [5] The PCCT device 30 receives a generation instruction from the AI ​​server 14 and generates a material decomposition image according to the generation instruction. The PCCT device 30 performs reconstruction processing based on raw data stored in a storage device within the device to generate a material decomposition image. The reconstruction processing corresponds to the generation processing that generates a material decomposition image. The relationship between the AI ​​result and the generated image may be, for example, as follows:

[0057] Example 1: When an adrenal tumor is detected by AI, the PCCT device 30 generates a fat density image. The fat density image is an example of a "second image corresponding to the result of the analysis processing" in this disclosure.

[0058] Example 2: The PCCT device 30 may change the slice thickness of the fat density image to be generated depending on the size of the adrenal tumor detected by AI. When the size of the detected adrenal tumor is small, it is preferable to set the slice thickness smaller (thinner) than when the size is large.

[0059] Example 3: The PCCT device 30 may change the slice position and number of slices of the fat density image to be generated depending on the position of the adrenal tumor detected by AI.

[0060] For example, if a total of 400 slices of tomographic images are obtained by imaging a subject's abdomen and an adrenal tumor is detected by AI, it is possible to generate fat density images of 20 slices near the detected adrenal tumor. In this way, by generating fat density images at slice positions limited to the adrenal tumor detected by AI, it is possible to selectively generate material-decomposed images required for observation while suppressing the generation of unnecessary images. A material-decomposed image, such as a fat density image, is an example of a "second image" in this disclosure.

[0061] [6] The PCCT device 30 transmits the generated material decomposition image to the PACS 12.

[0062] [7] The PACS 12 stores the material decomposition images received from the PCCT device 30 in the temporary storage area 26 .

[0063] [8] The viewer server 16 receives CT images and material decomposition images from the PACS 12 and receives AI results for the CT images from the AI ​​server 14. The viewer server 16 performs processing to display the received CT images, material decomposition images, and AI results on the image interpretation screen of the display device. The CT images, material decomposition images, and AI results may all be displayed simultaneously, or only some of them may be displayed selectively. The displayed content may be changed in response to a display request from the radiology department PC 18.

[0064] [9] The radiology department PC 18 can access the viewer server 16, obtain display data from the viewer server 16, and display the CT image, material decomposition image, and AI results on a display device.

[0065] Example of a flowchart Fig. 4 is a flowchart showing an example of the operation of the information processing device 10 according to the embodiment and the PCCT device 30. The information processing method shown in the flowchart of Fig. 4 is an example of the "information processing method" in the present disclosure.

[0066] The PCCT device 30 performs imaging of the subject according to the order (step S11) and stores the raw data obtained by imaging in a storage device (step S12). The PCCT device 30 also reconstructs images from the raw data by applying standard reconstruction conditions to generate CT images (step S13). In step S14, the PCCT device 30 transmits the generated CT images to the PACS 12.

[0067] The PACS 12 receives the CT image from the PCCT device 30 (step S21) and transfers the received CT image to the AI ​​server 14 (step S22). The PACS 12 also stores the received CT image in the permanent storage area 24 (step S23). Note that the order of steps S22 and S23 may be reversed.

[0068] In step S31, the AI ​​server 14 receives the CT image transferred from the PACS 22 and performs AI processing on the CT image. The AI ​​server 14 analyzes the CT image by applying some or all of the multiple AI modules. The AI ​​server 14 may select the AI ​​module to use in analyzing the CT image depending on the body part of the subject related to the order. Performing analysis such as detection or measurement from multiple perspectives using multiple AI modules is useful not only for analysis of the chief complaint, but also for detecting incidental findings.

[0069] In step S32, the AI ​​server 14 instructs the PCCT device 30 to generate a material decomposition image based on the AI ​​analysis result (AI result). There may be cases where generation of a material decomposition image is unnecessary depending on the AI ​​result. The AI ​​server 14 determines whether generation of a material decomposition image is necessary based on the AI ​​result, and if generation is necessary, determines the image type and / or reconstruction conditions to be generated and sends a generation instruction to the PCCT device 30. Note that if generation of a material decomposition image is unnecessary, the AI ​​server 14 does not send a generation instruction to the PCCT device 30, and proceeds to the process of step S33.

[0070] The PCCT device 30 receives an instruction to generate a material decomposition image from the AI ​​server 14 (step S15). When the PCCT device 30 receives the instruction to generate a material decomposition image from the AI ​​server 14, the PCCT device 30 generates a material decomposition image in accordance with the instruction (step S16), and transmits the generated material decomposition image to the PACS 12 (step S17).

[0071] The PACS 12 receives the material decomposition image generated by the PCCT device 30 (step S24), and stores the material decomposition image in the temporary storage area 26 (step S25). Then, the PACS 12 transmits the CT image and the material decomposition image to the viewer server 16 (step S26).

[0072] The AI ​​server 14 also transmits the AI ​​results to the viewer server 16 (step S33). Note that in step S33, the AI ​​server 14 may also transmit the AI ​​results to the PACS 12. The PACS 12 may store the AI ​​results received from the AI ​​server 14 in association with the CT image.

[0073] The viewer server 16 receives the CT image and the material decomposition image from the PACS 12, and receives the AI ​​result from the AI ​​server 14 (step S41). Note that the viewer server 16 may acquire the AI ​​result via the PACS 12. The viewer server 16 can generate display data for an interpretation screen that includes at least one of the acquired CT image, material decomposition image, and AI result.

[0074] In step S42, the viewer server 16 receives a display request from a client terminal such as the radiology department PC 18.

[0075] In step S51, when a display request is sent from the radiology department PC 18 to the viewer server 16, the viewer server 16 receives this display request and provides the radiology department PC 18 with display data for an interpretation screen including at least one of the CT image, material decomposition image, and AI result related to the display request (step S43).

[0076] The radiology department PC 18 receives data from the viewer server 16 (step S52), and displays an interpretation screen including at least one of a CT image, a material decomposition image, and an AI result on the display device 124 (step S53).

[0077] Furthermore, in step S44, the viewer server 16 may transmit display information relating to the image or the like actually displayed on the display device 124 of the radiology PC 18 to the PACS 12. For example, when a display request for a material decomposition image is transmitted from the radiology PC 18 to the viewer server 16 and the material decomposition image is displayed on the display device 124 of the radiology PC 18, the viewer server 16 notifies the PACS 12 of display information indicating that the material decomposition image has been displayed (used for observation).

[0078] The PACS 12 receives the display information from the viewer server 16, moves the material decomposition images displayed on the radiology department PC 18 from the temporary storage area 26 to the permanent storage area 24, and stores the material decomposition images in association with the CT images in the permanent storage area 24 (step S27). Note that, of the material decomposition images stored in the temporary storage area 26, those that were not used for display may be deleted from the temporary storage area 26 at an appropriate time.

[0079] Furthermore, instead of transmitting display information from the viewer server 16 to the PACS 12, a command to move the target material decomposition image from the temporary storage area 26 to the permanent storage area 24 may be transmitted from the viewer server 16 to the PACS 12.

[0080] About the processor In this embodiment, each process is executed by a computer. The computer may execute these processes by a processor, a program, or a combination thereof. The computer may be a general-purpose computer, a computer for specific applications, a system such as a workstation, or other hardware element capable of executing a program.

[0081] The processor may be configured with one or more pieces of hardware, and the type of hardware is not limited. For example, the processor may be configured with hardware such as a programmable logic device such as a central processing unit (CPU), a micro processing unit (MPU), a field programmable gate array (FPGA), a dedicated circuit for executing specific processing such as an application specific integrated circuit (ASIC), a graphic processing unit (GPU), or a neural processing unit (NPU).

[0082] Furthermore, the processor has each unit or each means that executes various processes in this embodiment. Furthermore, the type of hardware may be a combination of different types of hardware. When multiple pieces of hardware are configured to execute one or more processes of a certain processor, the multiple pieces of hardware may exist in devices that are physically separate from each other, or may exist in the same device. Furthermore, in any of the embodiments, the order of each process performed by the processor is not limited to the order described above and may be changed as appropriate. The hardware is configured by an electric circuit or the like that combines circuit elements such as semiconductor elements.

[0083] Furthermore, the present embodiment may be implemented by hardware, software, firmware, microcode, or a combination thereof. Software, firmware, and microcode may be configured by a program. A program may also be, for example, a group of program modules, each function of which may be implemented by a processor configured to perform the respective function. The program may be program code or multiple code segments stored in one or more non-transitory computer-readable media (e.g., storage media or other storages). The program may be stored in multiple non-transitory computer-readable media that reside in physically separate devices. A program code or a code segment may represent a procedure, a function, a subprogram, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A program code or a code segment may be connected to another code segment or a hardware circuit by sending or receiving information, data, arguments, parameters, or memory contents.

[0084] About the programs that run computers A program that causes a computer to realize some or all of the processing functions of the information processing device 10 can be recorded on a computer-readable medium such as an optical disk, a magnetic disk, a semiconductor memory, or other tangible non-transitory information storage medium, and the program can be provided through this information storage medium.

[0085] In addition, instead of providing the program by storing it on such a tangible, non-transitory computer-readable medium, it is also possible to provide the program signal as a download service using a telecommunications line such as the Internet.

[0086] Furthermore, some or all of the processing functions of the information processing device 10 may be realized by cloud computing, and may also be provided as SaaS (Software as a Service).

[0087] Advantages of the embodiment This embodiment has the following advantages.

[0088] [1] According to the information processing device 10, unnecessary material decomposition images are not generated, so that the image capacity of the PACS 12 can be reduced.

[0089] [2] According to the information processing device 10, unnecessary material decomposition images are not generated, so that traffic on the communication network 20 can be reduced.

[0090] [3] According to the information processing device 10, the material decomposition image to be generated is automatically determined using AI, which reduces the work of the service staff, such as setting the conditions for generating the material decomposition image.

[0091] [4] According to the information processing device 10, a material decomposition image useful for diagnosis is automatically selected based on the AI ​​result, and an instruction to generate the image is automatically given to the PCCT device 30. Therefore, the time required for confirmation of the image can be shortened compared to when the material decomposition image that the user wants to observe is generated on demand. This enables time-efficient interpretation.

[0092] Variation 1 The learning device provided in the AI ​​server 14 is not limited to the examples shown in Figures 1 and 3, and may be configured to include a trained model that causes a processor to execute a process to detect lesions from an input image. The learning device provided in the AI ​​server 14 may be configured to include a trained model that causes a processor to execute a process to detect at least one of tumors, nodules, stones, fat, and calcified regions from an input image.

[0093] Variation 2 In the above embodiment, an example has been described in which the PCCT device 30 is instructed to generate a material decomposition image, but the present invention is not limited to the PCCT device 30, and may be configured to instruct a general CT device equipped with a detector that detects X-ray transmission data in integral units to generate a CT image by image reconstruction processing under different reconstruction conditions.

[0094] For example, a CT device replacing the PCCT device 30 generates a first reconstructed image by performing image reconstruction processing applying first reconstruction conditions to raw data obtained by imaging. This first reconstructed image is sent to the PACS 12 and transferred to the AI ​​server 14. Based on the analysis results (AI results) of the first reconstructed image, the AI ​​server 14 instructs the CT device to generate a second reconstructed image applying second reconstruction conditions different from the first reconstruction conditions. In response to this generation instruction, the CT device performs image reconstruction processing applying the second reconstruction conditions to the raw data within the device, and generates the second reconstructed image.

[0095] The second reconstructed image may be, for example, an image having a thinner slice thickness (higher resolution) than the first reconstructed image. That is, the first reconstructed image may be a CT image (simple CT image) having a relatively thick slice thickness, so-called "thick slice," and the second reconstructed image may be a CT image having a relatively thin slice thickness, so-called "thin slice." The "CT image" and "material decomposition image" in the embodiments described with reference to FIGS. 1 to 4 can be understood as the first reconstructed image and the second reconstructed image. Furthermore, two or more types of images of multiple image types may be generated as the second reconstructed image.

[0096] "others" The present disclosure is not limited to the above-described embodiments, and various modifications are possible within the scope of the gist of the technical idea of ​​the present disclosure. [Explanation of symbols]

[0097] 10. Information processing equipment 12 PACS 14 AI Server 16 Viewer Server 18 Radiology PC 20. Communication Networks 24 Persistent Storage 26 Temporary storage area 30 PCCT device 41 Adrenal tumor detection AI 42 Urinary Stone Detection AI 43 Gallstone detection AI 44 Liver fat measurement AI 100 computers 102 CPU 104 Computer-readable medium 106 Communication Interface 108 Input / Output Interface 110 Bus 112 memory 114 Storage 122 Input Device 124 Display device S11~S17 PCCT device operation steps S21~S27 PACS operation steps S31~S33 AI server operation steps S41~S44 Viewer Server Operation Steps S51~S53 Radiology PC operation steps

Claims

1. An information processing device including a processor, The processor: obtaining a first image taken using a CT device; performing an analysis process of the first image using a learning device; causing the CT device to perform a process of generating a second image corresponding to the result of the analysis process; displaying at least one of the first image, the result of the analysis processing, and the second image on a display device; Information processing device.

2. the first image is a CT image; the second image is a material decomposed image. The information processing device according to claim 1 .

3. the second image is an image generated by an image reconstruction process under a reconstruction condition different from that of the first image; The information processing device according to claim 1 .

4. The processor: outputting an instruction to the CT device to generate the second image based on the result of the analysis processing; The information processing device according to claim 1 .

5. The processor: setting a generation condition for the second image according to the result of the analysis processing; The information processing device according to claim 1 .

6. The learning device includes a trained model that causes the processor to execute a process of detecting a lesion from an input image. The information processing device according to claim 1 .

7. The learning device includes a trained model that causes the processor to execute a process of detecting at least one of a tumor, a nodule, a stone, fat, and a calcified region from an input image. The information processing device according to claim 1 .

8. The analysis process includes at least one process of detecting an adrenal tumor, detecting a urinary tract stone, detecting a gallstone, and calculating a liver fat amount. The information processing device according to claim 1 .

9. The processor: If an adrenal tumor is detected by the analysis process, causing the CT device to perform a process of generating a fat density image as the second image; The information processing device according to claim 1 .

10. The processor: setting a slice thickness of the fat density image generated by the generation process according to the size of the adrenal tumor detected by the analysis process; The information processing device according to claim 9 .

11. The processor: a slice position and the number of slices of the fat density image to be generated by the generation process are set according to the position of the adrenal tumor detected by the analysis process; The information processing device according to claim 9 .

12. The processor: receiving a display request for the second image via an input device; displaying the second image on the display device based on the display request; The information processing device according to claim 1 .

13. a storage device including a temporary storage area and a permanent storage area; The processor: storing the first image in the persistent storage area; storing the second image in the temporary storage area; The information processing device according to claim 1 .

14. The processor: storing the second image displayed on the display device in the persistent storage area; The information processing device according to claim 13.

15. The processor: only the image displayed on the display device among the second images stored in the temporary storage area is stored in the permanent storage area; The information processing device according to claim 13.

16. The processor: obtaining a first image taken using a CT device; performing an analysis process on the first image using a learning device; causing the CT device to perform a process of generating a second image corresponding to a result of the analysis process; displaying at least one of the first image, the result of the analysis processing, and the second image on a display device; An information processing method that performs the above.

17. On the computer, a function of acquiring a first image taken using a CT device; a function of performing an analysis process of the first image using a learning device; a function of causing the CT device to execute a process of generating a second image corresponding to a result of the analysis process; a function of displaying at least one of the first image, the result of the analysis processing, and the second image on a display device; A program to make this happen.

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