EP-4742164-A2 - SYSTEM AND METHOD FOR MEDICAL IMAGING

EP4742164A2EP 4742164 A2EP4742164 A2EP 4742164A2EP-4742164-A2

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

LYU, Yang

Assignees

Shanghai United Imaging Healthcare Co., Ltd.

Dates

Publication Date: 20260513
Application Date: 20220509

Claims (15)

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 acquired by scanning at least one sample subject using one or more first scanners; obtaining scanning data of a subject acquired by scanning the subject using a second scanner that is different from at least one of the one or more first scanners, wherein the second scanner is a low time-of-flight (TOF) resolution positron emission tomography (PET) scanner; and generating optimized scanning data of the subject by inputting the scanning data into the trained machine learning.
The system of claim 1, wherein the preliminary training data includes at least one of raw data obtained from the one or more first scanners, sinogram data corresponding to the raw data, or image data reconstructed based on the raw data.
The system of claim 1 or claim 2, wherein the training data further includes training input data generated by processing the preliminary training data; wherein the preliminary training data is processed by performing a data rebinning operation.
The system of claim 3, wherein the preliminary training data is further processed by performing at least one of a data splitting operation, or a down-sampling operation.
The system of any one of claims 3-4, wherein 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.
The system of claim 5, wherein the 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.
The system of claim any one of claims 1-6, wherein the trained machine learning model includes a denoising network and an enhancement network.
The system of claim 1, wherein the optimized scanning data is superior to the scanning data with respect to a data quality parameter.
The system of claim 8, wherein the data quality parameter includes at least one of a signal-to-noise ratio (SNR) , a spatial resolution, or an image contrast.
The system of claim 1, wherein the trained machine learning model is trained by: 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 the 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.
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; 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; wherein the training input data is substantially equivalent to or mimics scanning data generated by a low TOF resolution 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 training target data of the machine learning model.
The system of claim 11, wherein the trained machine learning model is configured to generate optimized second scanning data of a subject by inputting into the trained machine learning model second scanning data that are acquired by scanning the subject using a second scanner.
The system of claim 1 or claim 2, wherein the preliminary training data is processed by performing a data rebinning operation.
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; 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; wherein the training input data is substantially equivalent to or mimics scanning data generated by a low TOF resolution 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 training target data of the machine learning model.
A non-transitory readable medium, comprising at least one set of instructions, wherein when executed by at least one processor of a computing device, the at least one set of instructions directs the at least one processor to perform a method, the method comprising: 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; wherein the training input data is substantially equivalent to or mimics scanning data generated by a low TOF resolution 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 training target data of the machine learning model.

Description

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 susupectible 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 training data includes third listmode data generated by a first PET scanner having a noise equivalent count rate (NECR) exceeding a threshold NECR. In some embodiments, the generating training input data by processing t