Shanghai United Imaging Healthcare Co., Ltd.

Yuan Bao

Guotao Quan

Wenjing Cao

Haining Sun

Zhicong Yu

Alexander Zamyatin

Dajun Wang

Yi Bin Wang

Xiang Li

Jianwei Fu

Xin Hao

Rengcai Yang

Meili Yang

The present disclosure relates to systems and methods for image generation. The methods may include obtaining projection data generated by a scanner; generating, based on a first weighting function, a first image by back-projecting the projection data, the first image having a first region corresponding to a first part of the object; generating, based on a second weighting function, a second image by back-projecting the projection data, the second image having a second region corresponding to the first part of the object, the second region of the second image presenting a better CT number uniformity than the first region of the first image; and generating a third image based on the first image and the second image.

Systems and methods for image generation

Systems and methods for a parameter adjustment of a multi-energy computed tomography (CT) device is provided. The systems and methods may obtain, by at least one processor, a parameter set associated with a scan to be performed using a multi-energy CT device. The parameter set may include multiple scanning parameters and multiple reconstruction parameters. The systems and methods may also determine, by the at least one processor, a maximum pitch associated with the scan based on a correlation relationship and the parameter set. The correlation relationship may describe a correlation between a pitch of the multi-energy CT device and the parameter set.

Systems and methods for a parameter adjustment of a multi-energy computed tomograph device

Systems and methods for scan preparation are provided. The systems may obtain a first parameter set of a subject to be scanned by a medical device acquired before a scan is performed on the subject. The first parameter set may relate to a physiological motion of the subject acquired before the scan is performed on the subject. The systems may predict, based on the first parameter set and an estimation model, a second parameter set of the subject. The second parameter set may relate to the physiological motion of the subject. The systems may determine at least one scan parameter for the scan based at least in part on the second parameter set. The systems may cause the medical device to perform the scan on the subject based on the at least one scan parameter.

Systems and methods for scan preparation

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

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.

The embodiments of the present disclosure disclose methods and systems for determining a ring artifact. The method for determining the ring artifact may include: obtaining an original image; mapping a plurality of pixels in the original image to a polar coordinate image; determining a protection region in the polar coordinate image; obtaining a smooth image by smoothing at least one region in the polar coordinate image other than the protection region; generating a residual image based on the polar coordinate image and the smooth image; determining a location of the ring artifact in the original image based on the residual image. In the present disclosure, the original image may be mapped to a trapezoidal region or a triangular region in the polar coordinate image, and the gradient angle image may be used for image processing, which may reduce the influence of noise. An accurate location of the ring artifact may be determined, and information for imaging device detection and air correction may be provided.

Systems and methods for determining ring artifact

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 system and method for generating a phase contrast image based on an absorption contrast image are provided. The method may include obtaining an absorption contrast image of a first object. The method may further include obtaining a phase contrast image generation model, wherein the phase contrast image generation model may be associated with at least one sample absorption contrast image and at least one sample phase contrast image of a second object. The method may further include executing the phase contrast image generation model to generate a phase contrast image of the first object based on the absorption contrast image of the first object.

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Shanghai, China

Yuan Bao