US-12619908-B2 - System and method for medical imaging

US12619908B2US 12619908 B2US12619908 B2US 12619908B2US-12619908-B2

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.

Inventors

Yang LYU

Assignees

SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

Dates

Publication Date: 20260505
Application Date: 20220513

Claims (20)

1 . 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.
2 . The system of claim 1 , wherein the preliminary training data includes at least one of raw data obtained from the first PET scanner, sinogram data corresponding to the raw data, or image data reconstructed based on the raw data.
3 . The system of claim 1 , wherein the data quality parameter includes at least one of a signal-to-noise ratio (SNR), a spatial resolution, or an image contrast.
4 . The system of claim 1 , wherein the preliminary training data is processed by performing at least one of a data splitting operation, a data rebinning operation, or a down-sampling operation.
5 . The system of claim 1 , wherein the preliminary training data includes third first scanning data generated by the first PET scanner having a larger axial length than the second PET scanner.
6 . The system of claim 5 , wherein the generating training input data by processing the preliminary training data includes: down-sampling the third first scanning data at a preset down-sampling rate; and designating the down-sampled third first scanning data as the training input data.
7 . The system of claim 1 , wherein the preliminary training data includes first listmode data generated by the first PET scanner having a higher time of flight (TOF) resolution than the second PET scanner.
8 . The system of claim 7 , wherein the generating training input data by processing the preliminary training data includes: rebinning the first listmode data according to preset TOF information; and designating the rebinned first listmode data as the training input data.
9 . The system of claim 1 , wherein the preliminary training data includes second listmode data generated by the first PET scanner having a smaller detector unit size than the second PET scanner.
10 . The system of claim 9 , wherein the generating training input data by processing the preliminary training data includes: determining coordinates of virtual detector units based on coordinates of detector units of the first PET scanner and a preset detector unit size, the preset detector unit size being larger than the detector unit size of the first PET scanner; rebinning the second listmode data according to the determined coordinates of the virtual detector units; and designating the rebinned second listmode data as the training input data.
11 . The system of claim 1 , wherein preliminary training data includes third listmode data generated by the first PET scanner having a higher noise equivalent count rate (NECR) than the second PET scanner.
12 . The system of claim 11 , wherein the generating training input data by processing the preliminary training data includes: extracting a data set from delayed coincidence counts of the third listmode data; generating two data sets by duplicating the data set, wherein coincidence marks of one data set remain unchanged, and coincidence marks of the other data set are replaced with prompt coincidence counts; generating fourth listmode data by incorporating the two data sets into the third listmode data; and designating the fourth listmode data as the training input data.
13 . The system of claim 1 , wherein the preliminary training data includes first listmode data generated by the first PET scanner having a higher time of flight (TOF) resolution than the second PET scanner.
14 . 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.
15 . 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.
16 . The method of claim 15 , wherein the preliminary training data includes at least one of raw data obtained from the first PET scanner, sinogram data corresponding to the raw data, or image data reconstructed based on the raw data.
17 . The method of claim 15 , wherein the data quality parameter includes at least one of a signal-to-noise ratio (SNR), a spatial resolution, or an image contrast.
18 . The method of claim 15 , wherein the preliminary training data is processed by performing at least one of a data splitting operation, a data rebinning operation, or a down-sampling operation.
19 . The method of claim 15 , wherein the preliminary training data includes third scanning data generated by the first PET scanner having a larger axial length than the second PET scanner.
20 . The method of claim 19 , wherein the generating training input data by processing the preliminary training data includes: down-sampling the third scanning data at a preset down-sampling rate; and designating the down-sampled third scanning data as the training input data.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to International Patent Application No. PCT/CN2022/091671, filed on May 9, 2022, the content of which is hereby incorporated by reference. TECHNICAL FIELD The present disclosure generally relates to systems and methods for medical imaging, and more particularly, to systems and methods for data and/or image optimization in medical imaging. BACKGROUND At present, machine learning models (e.g., a neural network model) are widely used for data and/or image optimization in medical imaging. For example, a deep neural network may be used for image denoising and/or enhancement so as to improve the efficiency and accuracy of disease diagnosis and/or treatment. A machine learning model may be trained using training data including training input data and training target data, which servers as a target or reference of the output of the machine learning model. The quality of data and/or images output from the machine learning model is ultimately dependent upon the quality of the training data (e.g., the training target data). Conventionally, training data of high quality is obtained by prolonging a scanning time of a subject, increasing a dose of medicine injected into the subject, and/or improving a time-of-flight (TOF) sensitivity of an imaging system used for scanning the subject. However, the conventional methods may increase a radiation dose of the subject, be more susceptible to motion artifact and increase discomfort of the subject due to, e.g., a prolonged scanning time, and/or have a limited improvement on the quality of the training data. Thus, it is desirable for a system and method for providing training data of high quality effectively and conveniently. SUMMARY According to one aspect of the present disclosure, a system is provided. The system may comprise 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. 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; 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; 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 training target data of the machine learning model. In some embodiments, the preliminary training data includes at least one of raw data obtained from one or more first scanners, sinogram data corresponding to the raw data, or image data reconstructed based on the raw data. In some embodiments, the data quality parameter includes at least one of a signal-to-noise ratio (SNR), a spatial resolution, or an image contrast. In some embodiments, the preliminary training data is processed by performing at least one of a data splitting operation, a data rebinning operation, or a down-sampling operation. In some embodiments, the preliminary training data includes first scanning data generated by a first positron emission tomography (PET) scanner having an axial length exceeding a threshold axial length. In some embodiments, the generating training input data by processing the preliminary training data includes: down-sampling the first scanning data at a preset down-sampling rate; and designating the down-sampled first scanning data as the training input data. In some embodiments, the preliminary training data includes first listmode data generated by a first PET scanner having a time of flight (TOF) resolution exceeding a threshold TOF resolution. In some embodiments, the generating training input data by processing the preliminary training data includes: rebinning the first listmode data according to preset TOF information, a TOF resolution corresponding to the preset TOF information being below the threshold TOF resolution; and designating the rebinned first listmode data as the training input data. In some embodiments, the preliminary training data includes second listmode data generated by a first PET scanner having a detector unit size being below a threshold detector unit size. In some embodiments, the generating training input data by processing the preliminary training data includes: determining coordinates of virtual detector units based on coordinates of detector units of the first PET scanner and a preset detector unit size, the preset detector unit size exceeding the threshold detector unit size; rebinning the second listmode data according to the determined coordinates of the virtual detector units; and designating the rebinned second listmode data as the training input data. In some embodiments, preliminary trainin