# US 11,908,046 B2 · Systems and methods for determining processing parameter for medical image processing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,908,046 B2 - Document ID: US11908046B2 - Status: Granted - Kind: B2 - Application number: 17/228,690 - Filed: 2021-04-12 - Published: 2021-09-02 - Granted: 2024-02-20 - Earliest claimed: 2017-06-28 - Assignees: Shanghai United Imaging Healthcare Co., Ltd. - Applicants: Shanghai United Imaging Healthcare Co., Ltd. - Inventors: Yanyan Liu - Web page: https://patentgrep.com/patent/US11908046B2 ## CPC - G06T 12/30: Image post-processing, e.g. metal artefact correction - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06T 5/60: using machine learning, e.g. neural networks - G06T 5/70: Denoising; Smoothing - G06T 12/10: Image preprocessing, e.g. calibration, positioning of sources or scatter correction - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 30/40: for processing medical images, e.g. editing - G16H 40/63: for local operation - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2211/441: AI-based methods, deep learning or artificial neural networks - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A system and method for determine a parameter for medical data processing are provided. The method may include obtaining sample data, the sample data may comprise at least one of projection data or a scanning parameter. The method may also include obtaining a first neural network model. The method may further include determining the parameter based on the sample data and the first neural network model. The parameter may comprise at least one of a correction coefficient or a noise reduction parameter. ## Claims ### Claim 1 A method implemented on at least one computing device, each of which has at least one processor and storage device, the method comprising: obtaining first projection data of a subject; generating second projection data of the subject corresponding to the first projection data of the subject; and obtaining a trained model, by training an initial model with both the first projection data and the second projection data as an input of the initial model, wherein an output of the trained model is a processing parameter, and the processing parameter includes a correction coefficient for correcting errors introduced by a detector. ### Claim 2 (depends on 1) The method of claim 1, wherein the first projection data is raw projection data of the subject. ### Claim 3 (depends on 1) The method of claim 1, wherein the subject comprises a phantom. ### Claim 4 (depends on 1) The method of claim 1, wherein generating the second projection data of the subject corresponding to the first projection data of the subject comprises: reconstructing a first image of the subject from the first projection data of the subject; smoothing the first image of the subject to generate a second image of the subject; and projecting the second image of the subject to generate the second projection data of the subject. ### Claim 5 (depends on 1) The method of claim 1, wherein generating the second projection data of the subject corresponding to the first projection data of the subject comprises: smoothing the first projection data of the subject to generate the second projection data. ### Claim 6 (depends on 1) The method of claim 1, wherein generating the second projection data of the subject corresponding to the first projection data of the subject comprises: reconstructing a first image of the subject from the first projection data of the subject; modelling the subject according to the first image; and calculating analytic equations of an X-ray transmission process to obtain the second projection data of the subject. ### Claim 7 (depends on 1) The method of claim 1, wherein the first projection data of the subject corresponds to a plurality of scanning parameters of a scanner. ### Claim 8 (depends on 7) The method of claim 7, wherein the plurality of scanning parameters of the scanner include at least one of a tube voltage of the scanner or a tube current of the scanner. ### Claim 9 (depends on 1) The method of claim 1, wherein the initial model is generated based on one or more neural network models. ### Claim 10 A system, comprising: at least one storage medium including a set of instructions; and at least one processor configured to communicate with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to perform operations including: obtaining first projection data of a subject; generating second projection data of the subject corresponding to the first projection data of the subject; and obtaining a trained model, by training an initial model with both the first projection data and the second projection data as an input of the initial model, wherein an output of the trained model is a processing parameter-, and the processing parameter includes a correction coefficient for correcting errors introduced by a detector. ### Claim 11 (depends on 10) The system of claim 10, wherein the first projection data is raw projection data of the subject. ### Claim 12 (depends on 10) The system of claim 10, wherein the subject comprises a phantom. ### Claim 13 (depends on 10) The system of claim 10, wherein generating the second projection data of the subject corresponding to the first projection data of the subject comprises: reconstructing a first image of the subject from the first projection data of the subject; smoothing the first image of the subject to generate a second image of the subject; and projecting the second image of the subject to generate the second projection data of the subject. ### Claim 14 (depends on 10) The system of claim 10, wherein generating the second projection data of the subject corresponding to the first projection data of the subject comprises: smoothing the first projection data of the subject to generate the second projection data. ### Claim 15 (depends on 10) The system of claim 10, wherein generating the second projection data of the subject corresponding to the first projection data of the subject comprises: reconstructing a first image of the subject from the first projection data of the subject; modelling the subject according to the first image; and calculating analytic equations of an X-ray transmission process to obtain the second projection data of the subject. ### Claim 16 (depends on 10) The system of claim 10, wherein the first projection data of the subject corresponds to a plurality of scanning parameters of a scanner. ### Claim 17 (depends on 16) The system of claim 16, wherein the plurality of scanning parameters of the scanner include at least one of a tube voltage of the scanner or a tube current of the scanner. ### Claim 18 A non-transitory computer readable medium including executable instructions that, when executed by at least one processor, cause the at least one processor to effectuate a method comprising: obtaining first projection data of a subject; generating second projection data of the subject corresponding to the first projection data of the subject; and obtaining a trained model, by training an initial model with both the first projection data and the second projection data as an input of the initial model, wherein an output of the trained model is a processing parameter, and the processing parameter includes a correction coefficient for correcting errors introduced by a detector. ### Claim 19 (depends on 1) The method of claim 1, wherein the processing parameter further includes a noise reduction parameter. ### Claim 20 (depends on 10) The system of claim 10, wherein the initial model is generated based on one or more neural network models.