System and method for medical imaging
AssigneeShanghai United Imaging Healthcare Co., Ltd.
InventorYang Lyu
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Abstract
The present disclosure provides a medical imaging system and method. The method may include obtaining a machine learning model and preliminary training data of at least one sample subject. The method may also include generating training input data by processing the preliminary training data, the preliminary training data being superior to the training input data with respect to a data quality parameter. The method may further include determining a trained machine learning model by training the machine learning model based on the training input data and the preliminary training data, the preliminary training data being configured as training target data of the machine learning model.
Claims20
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 configured to direct the system to perform operations including:
- obtaining a machine learning model and preliminary training data of at least one sample subject, the preliminary training data being acquired using a first positron emission tomography (PET) scanner;
- generating training input data by processing the preliminary training data, the preliminary training data being superior to the training input data with respect to a data quality parameter, the training input data being configured to simulate first scanning data acquired using a second PET scanner, the first PET scanner being with a higher PET imaging performance than the second PET scanner;
- and
- determining a trained machine learning model by training the machine learning model based on the training input data and the preliminary training data, the preliminary training data being configured as a ground truth for training the machine learning model, the trained machine learning model being configured to optimize second scanning data acquired using the second PET scanner.
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 configured to direct the system to perform operations including:
- obtaining a trained machine learning model, wherein the trained machine learning model is trained using training data that includes preliminary training data and training input data, wherein the preliminary training data is acquired by scanning at least one sample subject using a first positron emission tomography (PET) scanner, the training input data is generated by processing the preliminary training data, the preliminary training data is superior to the training input data with respect to a data quality parameter, the training input data is configured to simulate first scanning data acquired using a second PET scanner, the first PET scanner is with a higher PET imaging performance than the second PET scanner, and the preliminary training data is configured as a ground truth for obtaining the machine learning model;
- obtaining scanning data of a subject acquired by scanning the subject using the second PET scanner;
- and
- generating optimized scanning data of the subject by inputting the scanning data into the trained machine learning.
A method implemented on a computing device having a processor and a computer-readable storage device, the method comprising:
- obtaining a machine learning model and preliminary training data of at least one sample subject, the preliminary training data being acquired using a first positron emission tomography (PET) scanner;
- generating training input data by processing the preliminary training data, the preliminary training data being superior to the training input data with respect to a data quality parameter, the training input data being configured to simulate first scanning data acquired using a second PET scanner, the first PET scanner being with a higher PET imaging performance than the second PET scanner;
- and
- determining a trained machine learning model by training the machine learning model based on the training input data and the preliminary training data, the preliminary training data being configured as a ground truth for training the machine learning model, the trained machine learning model being configured to optimize second scanning data acquired using the second PET scanner.
Dependencies are inferred from the claim text.