# US 2026/0073518 A1 · Method for predicting object state based on dynamic image data and computing device for performing the same > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0073518 A1 - Document ID: US20260073518A1 - Status: Published application - Kind: A1 - Application number: 19/389,431 - Filed: 2025-11-14 - Published: 2026-03-12 - Earliest claimed: 2023-05-17 - Assignees: The Asan Foundation; University of Ulsan Foundation for Industry Cooperation - Applicants: The Asan Foundation; University of Ulsan Foundation for Industry Cooperation - Inventors: Jae Seung KIM; Jungsu OH; Minyoung OH; Dong Yun LEE; Seung Jun OH; Sang Ju LEE; Changhwan SUNG - Web page: https://patentgrep.com/patent/US20260073518A1 ## CPC - G06T 7/0012: {Biomedical image inspection} - A61B 5/00: Measuring for diagnostic purposes (radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00); Identification of persons - A61B 6/03: Computed tomography [CT] - G06T 7/00: Image analysis - G06T 7/0016: {involving temporal comparison} - G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients - G16H 30/00: ICT specially adapted for the handling or processing of medical images (computed tomography A61B6/03) - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/50: for simulation or modelling of medical disorders - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G06T 2207/10104: Positron emission tomography [PET] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - 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 Provided are a dynamic image data-based object state prediction method and a computer device performing the same. The computing device includes a memory and at least one processor communicating with the memory. The processor obtains early dynamic image data corresponding to an early section after a medicine is injected into a learning object until a preset time point, learns a first prediction model for predicting first image data indicating anatomical information about the learning object corresponding to a time point earlier than a first section by using, as an input, early dynamic image data corresponding to the first section of the early section, and learns a second prediction model for predicting second image data indicating disease-specific information about the learning object corresponding to a reference time point after the early section by using, as an input, early dynamic image data corresponding to a second section of the early section. ## Claims ### Claim 1 A method for predicting object state based on dynamic image data, performed by at least one processor, the method comprising: obtaining early dynamic image data corresponding to an early section after a medicine is injected into a learning object until a preset time point; learning a first prediction model for predicting first image data indicating anatomical information or blood flow information about the learning object corresponding to a time point earlier than a first section by using, as an input, early dynamic image data corresponding to the first section of the early section; and learning a second prediction model for predicting second image data indicating disease-specific information about the learning object corresponding to a reference time point after the early section by using, as an input, early dynamic image data corresponding to a second section of the early section. ### Claim 2 (depends on 1) The method of claim 1, further comprising: obtaining early dynamic image data corresponding to the first section after the medicine is injected into a diagnostic object; and predicting first image data indicating anatomical information or blood flow information about the diagnostic object corresponding to the earlier time point, by inputting the early dynamic image data corresponding to the first section of the diagnostic object into the learned first prediction model. ### Claim 3 (depends on 2) The method of claim 2, further comprising: obtaining early dynamic image data corresponding to the second section after the medicine is injected into the diagnostic object; and predicting second image data indicating disease-specific information about the diagnostic object corresponding to the reference time point, by inputting the early dynamic image data corresponding to the second section of the diagnostic object into the learned second prediction model. ### Claim 4 (depends on 3) The method of claim 3, wherein the processor is configured to: when the medicine reduced by a preset amount is injected into the diagnostic object, obtain and process early dynamic image data corresponding to the first section and early dynamic image data corresponding to the second section based on the injection to the diagnostic object, and obtain the processed early dynamic image data corresponding to the first section and the processed early dynamic image data corresponding to the second section; and input the processed early dynamic image data corresponding to the first section into the first prediction model, and input the processed early dynamic image data corresponding to the second section into the second prediction model. ### Claim 5 (depends on 3) The method of claim 3, wherein an amount of the medicine injected into the learning object is an amount reduced from a reference amount, wherein the first prediction model is learned based on first label image data corresponding to the first image data, and the second prediction model is learned based on second label image data corresponding to the second image data, and wherein each of the first label image data and the second label image data is label image data processed according to an medicine injection amount of the learning object. ### Claim 6 (depends on 3) The method of claim 3, further comprising: performing spatial normalization before the early dynamic image data corresponding to the first section of the learning object and the diagnostic object is input to the first prediction model; and performing spatial normalization before the early dynamic image data corresponding to the second section of the learning object and the diagnostic object is input to the second prediction model. ### Claim 7 (depends on 3) The method of claim 3, wherein the processor is configured to: set an acquisition time section of the early dynamic image data corresponding to the first section in the early section of the learning object and the diagnostic object; and set an acquisition time section of the early dynamic image data corresponding to the second section in the early section of the learning object and the diagnostic object. ### Claim 8 (depends on 3) The method of claim 3, wherein the processor is configured to: identify the early section when the medicine is injected into the diagnostic object; and obtain the early dynamic image data corresponding to the first section and the early dynamic image data corresponding to the second section based on the identified early section. ### Claim 9 (depends on 1) The method of claim 1, wherein each of the obtained early dynamic image data, the first image data, and the second image data is positron emission tomography (PET) image data. ### Claim 10 A dynamic image data-based object state prediction device, the device comprising: a memory; and at least one processor configured to communicate with the memory, wherein the processor is configured to: obtain early dynamic image data corresponding to an early section after a medicine is injected into a learning object until a preset time point; learn a first prediction model for predicting first image data indicating anatomical information or blood flow information about the learning object corresponding to a time point earlier than a first section by using, as an input, early dynamic image data corresponding to the first section of the early section; and learn a second prediction model for predicting second image data indicating disease-specific information about the learning object corresponding to a reference time point after the early section by using, as an input, early dynamic image data corresponding to a second section of the early section. ### Claim 11 (depends on 10) The device of claim 10, the processor is configured to: obtain early dynamic image data corresponding to the first section after the medicine is injected into a diagnostic object; and predict first image data indicating anatomical information or blood flow information about the diagnostic object corresponding to the earlier time point, by inputting the early dynamic image data corresponding to the first section of the diagnostic object into the learned first prediction model. ### Claim 12 (depends on 11) The device of claim 11, the processor is configured to: obtain early dynamic image data corresponding to the second section after the medicine is injected into the diagnostic object; and predict second image data indicating disease-specific information about the diagnostic object corresponding to the reference time point, by inputting the early dynamic image data corresponding to the second section of the diagnostic object into the learned second prediction model. ### Claim 13 (depends on 12) The device of claim 12, the processor is configured to: when the medicine reduced by a preset amount is injected into the diagnostic object, obtain and process early dynamic image data corresponding to the first section and early dynamic image data corresponding to the second section based on the injection to the diagnostic object, and obtain the processed early dynamic image data corresponding to the first section and the processed early dynamic image data corresponding to the second section; and input the processed early dynamic image data corresponding to the first section into the first prediction model, and input the processed early dynamic image data corresponding to the second section into the second prediction model. ### Claim 14 (depends on 12) The device of claim 12, wherein an amount of the medicine injected into the learning object is an amount reduced from a reference amount, wherein the first prediction model is learned based on first label image data corresponding to the first image data, wherein the second prediction model is learned based on second label image data corresponding to the second image data, and wherein each of the first label image data and the second label image data is label image data processed according to a medicine injection amount of the learning object. ### Claim 15 (depends on 12) The device of claim 12, where the processor is configured to: perform spatial normalization before the early dynamic image data corresponding to the first section of the learning object and the diagnostic object is input to the first prediction model; and perform spatial normalization before the early dynamic image data corresponding to the second section of the learning object and the diagnostic object is input to the second prediction model.