General Electric Company

Ge Precision Healthcare LLC

Shiyu Li

Yongchuan Lai

Kun Wang

Dan Liu

Ting Ting Zhang

Nan Cao

Xueli Wang

Qingyu Dai

Jiabin Yao

Dan Wu

Bingjie Zhao

Bo Guo

Yongnan Ji

The present application provides a method and device for obtaining a predicted image of a truncated portion, an imaging method and system, and a non-transitory computer-readable storage medium. The method for obtaining a predicted image of a truncated portion comprises preprocessing projection data to obtain, by reconstruction, an initial image of the truncated portion; and calibrating the initial image based on a trained learning network to obtain the predicted image of the truncated portion.

Method and device for obtaining predicted image of truncated portion

A computer aided scanning method for a medical device is provided in the present invention, comprising step: recognizing and analyzing a pre-scan image through a pre-trained neural network to determine and identify a region of interest in the pre-scan image; and step: determining, according to feature information of the identified region of interest, scanning parameters for further scanning of the region of interest. A medical device employing the above method and a computer readable storage medium for performing the method are further provided in the present invention. The method, the medical device, and the readable storage medium provided by the present invention can automatically identify a region of interest, determine a corresponding auxiliary line and subsequent scanning parameters, and improve the scanning efficiency and accuracy of the medical device.

Computer aided scanning method for medical device, medical device, and readable storage medium

Various methods and systems are provided for scanning an image subject along a scan axis. The method includes stitching sectional datasets acquired from different anatomical sections of the image subject based on locations of a landmark in the sectional datasets.

Methods and systems for whole body imaging

Spike noise in a k-space dataset acquired in magnetic resonance imaging may be removed by generating a mask including a set of data points which constitute spike noise in the k-space dataset based on the acquired k-space dataset via a trained deep learning network, the mask corresponding to a location of the spike noise in the acquired k-space dataset. An image reconstructed based on the acquired k-space dataset and the mask may be displayed.

Methods and systems for removing spike noise in magnetic resonance imaging

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Beijing, China

Shiyu Li