Shanghai United Imaging Healthcare Co., Ltd.

Healthcare Co., Ltd.

Shanghai United Imaging

Tuoyu Cao

Lingzhi Hu

Hui Liu

Jiaxu Zheng

Shuangyue Zhang

Yang Lyu

Gang Yang

Yuhang Shi

Xinyuan Xia

Tianyi Zeng

Yiran Li

The present disclosure provides a system and method for motion field generation and image correction. The method may include obtaining a plurality of first sets of magnetic resonance (MR) image data of an object generated based on a plurality of first sets of imaging sequences. The method may include obtaining a motion curve of the object. The method may include obtaining position emission tomography (PET) image data of the object generated in a scanning time period. The method may include generating one or more target motion fields corresponding to the scanning time period based on the plurality of first sets of MR image data and the motion curve. The method may include generating one or more corrected PET images by correcting, based on the one or more target motion fields, the PET image data.

Systems and methods for image correction

A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

Systems and methods for attenuation correction

The present disclosure is related to systems and methods for motion detection. The method includes obtaining, via at least one detection device, detection data of a subject located in a field of view (FOV) of a medical device. The method also includes determining motion data of the subject based on the detection data.

Systems and methods for motion detection

A method for controlling a medical device may be provided. The method may include obtaining, via one or more cameras, first data regarding a first motion of a subject in an examination space of the medical device. The method may include obtaining, via one or more radars, second data regarding a second motion of the subject. The method may further include generating, based on the first data and the second data, a control signal for controlling the medical device to scan at least a part of the subject.

Systems and methods for controlling imaging

A housing for shielding a sensor from a radiofrequency field and an imaging system including the same are provided in the present disclosure. The imaging system may include a magnetic resonance imaging (MRI) device. The housing may include a plurality of walls forming at least a part of a cavity for accommodating a sensor of the imaging system. At least one of the plurality of walls may include a substrate and a multi-layered structure disposed on the substrate. The multi-layered structure may include a plurality of metallic layers. At least one pair of adjacent layers of the plurality of metallic layers may include slits. The slits of the at least one pair of adjacent layers may be staggered.

Housing for shielding a sensor from a radiofrequency field and an imaging system including the same

The present disclosure provides a system and method for image generation. The method may include obtaining a first image of a first modality including a complete representation of a subject, obtaining a second image of a second modality including a partial representation of the subject, obtaining a trained machine learning model, generating an synthesized second image including a complete representation of the subject using the trained machine learning model based on the first image and the second image.

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Houston, TX, United States of America

Tuoyu Cao