Shanghai United Imaging Healthcare Co., Ltd.

Guotao Quan

Yuan Bao

Wenjing Cao

Haining Sun

Xin Wang

Xiang Li

Jiao Tian

Qianlong Zhao

Yi Bin Wang

Yi Wang

Rengcai Yang

Systems and methods for motion correction in medical imaging are provided in the present disclosure. The systems may obtain at least two image sequences relating to a subject. Each of the at least two image sequences may be reconstructed based on image data that is acquired by a medical imaging device during one of at least two time periods. The subject may undergo a physiological motion during the at least two time periods. The systems may generate, based on the at least two image sequences, at least one corrected image sequence relating to the subject by correcting, using a motion correction model, an artifact caused by the physiological motion.

Systems and methods for motion correction in medical imaging

The present disclosure relates to a method, system and non-transitory computer readable medium. In some embodiments, the method includes: acquiring image data of a target subject positioned on a scanning table; determining, by a processor, first position information of the target subject based on the image data; determining, by the processor, second position information related to a scan region of the target subject based on the image data; and scanning, using an imaging device, the target subject based on the first position information and the second position information.

Imaging systems and methods thereof

The disclosure relates to a system and a method for generating a neural network model for image processing by interacting with at least one client terminal. The method may include receiving via a network, a plurality of first training samples from the at least one client terminal. The method may also include training a first neural network model based on the plurality of first training samples to generate a second neural network model. The method may further include transmitting, via the network, the second neural network model to the at least one client terminal.

Systems and methods for generating a neural network model for image processing

A method may include obtaining first image data relating to a region of interest (ROI) of a first subject. The first image data corresponding to a first equivalent dose level may be acquired by a first device. The method may also include obtaining a model for denoising relating to the first image data and determining second image data corresponding to an equivalent dose level higher than the first equivalent dose level based on the first image data and the model for denoising. In some embodiments, the method may further include determining information relating to the ROI of the first subject based on the second image data and ecording the information relating to the ROI of the first subject.

System and method for diagnostic and treatment

The present disclosure relates to a method, system and non-transitory computer readable medium. In some embodiments, the method includes: acquiring image data of a target subject positioned on a scanning table of an imaging device; determining, by a processor, first position information of the target subject by inputting the image data into a first machine learning model, the first position information of the target subject including a posture of the target subject relative to the imaging device; determining, by the processor, second position information related to a scan region of the target subject by inputting the image data into a second machine learning model, the second position information including a position of the scan region relative to the scanning table and the imaging device; and causing the imaging device to scan the target subject based on the first position information and the second position information.

A method may include obtaining first image data relating to a region of interest (ROI) of a first subject. The first image data corresponding to a first equivalent dose level may be acquired by a first device. The method may also include obtaining a model for denoising relating to the first image data and determining second image data corresponding to an equivalent dose level higher than the first equivalent dose level based on the first image data and the model for denoising. In some embodiments, the method may further include determining information relating to the ROI of the first subject based on the second image data and recording the information relating to the ROI of the first subject.

A method may include obtaining a first set of projection data with respect to a first dose level; reconstructing, based on the first set of projection data, a first image; determining a second set of projection data based on the first set of projection data, the second set of projection data relating to a second dose level that is lower than the first dose level; reconstructing a second image based on the second set of projection data; and training a first neural network model based on the first image and the second image. In some embodiments, the trained first neural network model may be configured to convert a third image to a fourth image, the fourth image exhibiting a lower noise level and corresponding to a higher dose level than the third image.

System and method for image conversion

Embodiments of the present disclosure may disclose a method for improving CT image quality. The method for improving the CT image quality may include obtaining SFS data. The SFS data may include SFS scan data or an SFS image. The SFS scan data may be acquired by a CT device in an SFS state. The SFS image may be generated by reconstructing scan data acquired by the CT device via scanning in the SFS state. The method may include generating a corresponding optimized image by processing the SFS data based on an image quality optimization model. The image quality optimization model may be a machine learning model. The present disclosure may simulate the SFS image as an FFS image using a deep neural network model, thereby improving a resolution of the SFS image and reduce artifacts in the SFS image.

Method and system for improving CT image quality

A method for reconstructing an image may include obtaining scan data relating to a subject. The method may also include determining a first field of view (FOV) and determining a second FOV. The method may further include reconstructing a first image based on a first portion of the scan data corresponding to the first field of view, and reconstructing a second image based on a second portion of the scan data corresponding to the second field of view. The method may also include generating a third image based on the first image and the second image.

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Shanghai, China

Guotao Quan