# US 12,762,695 B2 · Information processing apparatus, information processing method, program, trained model, and learning model generation method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,695 B2 - Document ID: US12762695B2 - Status: Granted - Kind: B2 - Application number: 18/357,140 - Filed: 2023-07-23 - Published: 2024-01-25 - Granted: 2026-10-13 - Earliest claimed: 2022-07-25 - Assignees: FUJIFILM Corporation - Applicants: FUJIFILM Corporation - Inventors: Deepak Keshwani; Yoshiro Kitamura - Web page: https://patentgrep.com/patent/US12762695B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/482: {involving multiple energy imaging} - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - G06T 7/11: Region-based segmentation - G16H 50/30: for calculating health indices; for individual health risk assessment - A61B 6/541: {involving acquisition triggered by a physiological signal} - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30048: Heart; Cardiac - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract An information processing apparatus includes one or more processors, and one or more storage devices that store a program including an image generation model trained to generate, from a first image, a second image that imitates an image obtained by an imaging protocol different from an imaging protocol of the first image. The image generation model is a model trained, through machine learning using training data in which a training image captured by a first imaging protocol is associated with a correct answer clinical parameter calculated from a corresponding image captured by a second imaging protocol different from the first imaging protocol for the same subject as the training image using a modality of the same type as a modality used to capture the training image, such that a clinical parameter calculated from a generation image output by the image generation model approaches the correct answer clinical parameter. ## Claims ### Claim 1 An information processing apparatus comprising: one or more processors; and one or more storage devices that store a program to be executed by the one or more processors, wherein the program includes an image generation model trained to generate, from a first image which is input, a second image that imitates an image obtained by an imaging protocol different from an imaging protocol of the first image, the image generation model is a model trained, through machine learning using a plurality of training data, wherein the plurality of training data comprises a training image captured by a first imaging protocol for a designated subject using a designated type of modality and a correct answer clinical parameter calculated from a corresponding image captured by a second imaging protocol different from the first imaging protocol for the designated subject using the designated type of modality, such that a clinical parameter calculated from a generation image output by the image generation model in response to input of the training image approaches the correct answer clinical parameter, and wherein the one or more processors input the first image captured by the first imaging protocol to the image generation model, receive the second image output by the image generation model, and calculate the clinical parameter from the second image. ### Claim 2 (depends on 1) The information processing apparatus according to claim 1, wherein the one or more processors divide the first image into anatomical regions, and calculate the clinical parameter for each of the divided anatomical regions. ### Claim 3 (depends on 2) The information processing apparatus according to claim 2, wherein each of the training image and the corresponding image is divided into the anatomical regions, and the correct answer clinical parameter is calculated for each of the divided anatomical regions of the corresponding image, and the image generation model is a model trained such that each clinical parameter calculated from the generation image for each of the divided anatomical regions of the training image approaches the correct answer clinical parameter of each of the divided anatomical regions of the corresponding image. ### Claim 4 (depends on 2) The information processing apparatus according to claim 2, wherein the divided anatomical region is a perfusion region of a coronary artery. ### Claim 5 (depends on 2) The information processing apparatus according to claim 2, wherein the clinical parameter is a calcium volume for each main branch of a coronary artery. ### Claim 6 (depends on 1) The information processing apparatus according to claim 1, wherein the first image is a non-electrocardiogram gated CT image captured by non-electrocardiogram gated imaging, and the second image is an image that imitates an electrocardiogram gated CT image obtained by electrocardiogram gated imaging. ### Claim 7 (depends on 1) The information processing apparatus according to claim 1, wherein the training image is captured under a condition of a lower dose than the corresponding image. ### Claim 8 (depends on 1) The information processing apparatus according to claim 1, wherein the first imaging protocol has a lower dose than the second imaging protocol. ### Claim 9 (depends on 1) The information processing apparatus according to claim 1, wherein the first imaging protocol has a slower scan speed than the second imaging protocol. ### Claim 10 (depends on 1) The information processing apparatus according to claim 1, wherein the clinical parameter is a calcification score. ### Claim 11 (depends on 1) The information processing apparatus according to claim 1, wherein the clinical parameter is a cardiovascular calcium volume. ### Claim 12 (depends on 1) The information processing apparatus according to claim 1, wherein the clinical parameter is severity of coronary artery calcification. ### Claim 13 (depends on 1) The information processing apparatus according to claim 1, wherein the image generation model is configured by a neural network. ### Claim 14 An information processing method executed by one or more processors, the method comprising: via the one or more processors, acquiring a first image captured by a first imaging protocol for a designated subject using a designated type of modality; inputting the first image to an image generation model and receiving a second image, that imitates an image obtained in a case in which imaging is performed by a second imaging protocol different from the first imaging protocol for the designated subject using the designated type of modality, output by the image generation model; and calculating a clinical parameter from the second image, wherein the image generation model is a model trained, by using a plurality of training data, wherein the plurality of training data comprises a training image captured by the first imaging protocol and a correct answer clinical parameter calculated from a corresponding image captured by the second imaging protocol for the designated subject using the designated type of modality, such that the clinical parameter calculated from a generation image output by the image generation model in response to input of the training image approaches the correct answer clinical parameter. ### Claim 15 A non-transitory, computer-readable tangible recording medium which records thereon a program for causing, when read by a computer, the computer to realize: a function of acquiring a first image captured by a first imaging protocol for a designated subject using a designated type of modality; a function of inputting the first image to an image generation model and receiving a second image, that imitates an image obtained in a case in which imaging is performed by a second imaging protocol different from the first imaging protocol for the designated subject using the designated type of modality, output by the image generation model; and a function of calculating a clinical parameter from the second image, wherein the image generation model is a model trained, through machine learning using a plurality of training data, wherein the plurality of training data comprises a training image captured by the first imaging protocol and a correct answer clinical parameter calculated from a corresponding image captured by the second imaging protocol for the designated subject using the designated type of modality, such that the clinical parameter calculated from a generation image output by the image generation model in response to input of the training image approaches the correct answer clinical parameter. ### Claim 16 A learning model generation method of generating a learning model that enables a computer to realize a function of generating, from an image which is input, a pseudo image that imitates an image obtained by an imaging protocol different from an imaging protocol of the image, the method comprising: via a system including one or more processors, performing machine learning using a plurality of training data comprising a training image captured by a first imaging protocol for a designated subject using a designated type of modality and a correct answer clinical parameter calculated from a corresponding image captured by a second imaging protocol different from the first imaging protocol for the designated subject using the designated type of modality; and inputting the training image to the learning model, calculating a clinical parameter from a generation image output by the learning model, and training the learning model such that the clinical parameter calculated from the generation image approaches the correct answer clinical parameter.