Shanghai United Imaging Intelligence Co., Ltd.

Shanghai United Imaging Healthcare Co., Ltd.

Shu Liao

Xiaoqian Huang

Shuhui Chen

Nan Liu

Guobin Li

Zilin Deng

Liuchun He

Xuyang Lyu

Dongming Wei

Jinguang Zong

Yunhao Ge

A system for PET image reconstruction is provided. The system may obtain PET data of a subject. The PET data may be associated with a plurality of coincidence events, which includes scattering events. The system may also generate a preliminary scatter sinogram relating to the scattering events based on the PET data. The system may also generate a target scatter sinogram relating to the scattering events by applying a scatter sinogram generator based on the preliminary scatter sinogram. The target scatter sinogram may have a higher image quality than the preliminary scatter sinogram. The system may further reconstruct a target PET image of the subject based on the PET data and the target scatter sinogram.

Systems and methods for image reconstruction in positron emission tomography

The present disclosure is related to systems and methods for image processing. The method includes obtaining a first image of a first modality. The method includes generating a second image of a second modality by processing, based on a trained machine learning model, the first image. The second modality may be different from the first modality.

Systems and methods for image processing

A system for Magnetic Resonance Imaging (MRI) is provided. The system may obtain at least one training sample each of which includes full MRI data. The system may also obtain a preliminary subsampling model and a preliminary MRI reconstruction model. The system may further generate a subsampling model corresponding to an MRI reconstruction model by jointly training the preliminary subsampling model and the preliminary MRI reconstruction model using the at least one training sample. The subsampling model may be the trained preliminary subsampling model, and the MRI reconstruction model may be at least a portion of the trained preliminary MRI reconstruction model.

Systems and methods for magnetic resonance imaging

A method for MRI reconstruction is provided. The method may include obtaining a plurality of sub-sampled images of a subject. The plurality of sub-sampled images may include a first sub-sampled image of the subject and one or more second sub-sampled images of the subject. The first sub-sampled image may be generated using a first MRI sequence and a first sub-sampling rate. Each of the one or more second sub-sampled images may be generated using a second MRI sequence and a second sub-sampling rate. The second sub-sampling rate may be smaller than the first sub-sampling rate. The method may include obtaining an image reconstruction model having been trained according to a machine learning technique. The method may further include generating a first full image of the subject corresponding to the first MRI sequence based on the first sub-sampled image, the one or more second sub-sampled images, and the image reconstruction model.

Systems and methods for magnetic resonance imaging reconstruction

The present disclosure is related to systems and methods for image processing. The method may include obtaining a first set of image data. The method may also include generating a second set of image data by processing, based on a trained machine learning model, the first set of image data. The second set of image data may have a relatively high resolution and/or a relatively low level of artifacts with respect to the first set of image data. The method may further include generating a target image by performing a weighted fusion on the first set of image data and the second set of image data.

System and method for removing Gibbs artifact in medical imaging system

A method for magnetic resonance imaging may include acquiring first k-space data that is generated by entering acquired magnetic resonance (MR) data into a plurality of first k-space locations. The method may further include synthesizing second k-space data for a plurality of second k-space locations that are not filled with the acquired MR data. The method may further include reconstructing an image from the first k-space data and the second k-space data by applying a reconstruction algorithm. The reconstruction algorithm is based at least in part on a neural network technique.

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Shanghai, China

Shu Liao