Shanghai United Imaging Healthcare Co., Ltd.

Shanghai United Imaging Intelligence Co., Ltd.

Yang Lyu

Hongdi Li

Yun Dong

Tao Feng

Chen Xi

Gang Yang

Shuhui Chen

Yu Ding

Wentao Zhu

Lingzhi Hu

Defu Yang

Hao Liu

Yong Zhao

Tuoyu Cao

Liuchun He

Yizhang Zhao

Yinghan Qu

Debin Hu

Qinghua Li

Huifang Xie

The present disclosure relates to systems and methods for image processing. The systems may acquire imaging data. The systems may generate multiple intermediate images based on the imaging data by performing image optimization of multiple image optimization dimensions using a plurality of trained machine learning models. The systems may generate a target image based on the intermediate images.

Systems and methods for image processing

A system and method for image reconstruction are provided. A first region of an object may be determined. The first region may correspond to a first voxel. A second region of the object may be determined. The second region may correspond to a second voxel. Scan data of the object may be acquired. A first regional image may be reconstructed based on the scan data. The reconstruction of the first regional image may include a forward projection on the first voxel and the second voxel and a back projection on the first voxel.

System and method for image reconstruction

The present disclosure provides a system and method for image reconstruction. The method may include obtaining training samples, the training samples including at least one sample first image generated based on a first tracer, at least one reference first image each of which corresponds to one of the at least one sample first image and has a higher image quality than the corresponding sample first image, at least one sample second image generated based on a second tracer different from the first tracer, and at least one reference second image each of which corresponds to one of the at least one sample second images and has a higher image quality than the corresponding sample second image; and generating a trained image processing model by training a preliminary model using the training samples.

The present disclosure provides a system and method for image reconstruction and processing. The method may include obtaining image data of an object acquired by an imaging device. The method may include determining one or more regions of interest (ROIs) of the object. The method may also include determining, based on each ROI of the one or more ROIs, a target portion of the image data corresponding to the ROI among the image data. The method may further include reconstructing, based on the target portion of the image data, one or more images of the ROI.

Systems and methods for image reconstruction and processing

An imaging method may include obtaining original imaging data of an object in a raw-data domain including original time of flight (TOF) information. The method may also include gating the original imaging data into a plurality of data sets in the raw-data domain. The method may also include determining a plurality of motion vector fields based on the plurality of data sets. The method may also include generating corrected imaging data in the raw-data domain by performing motion correction on at least one of the plurality of data sets based on the original TOF information and at least one corresponding MVF of the plurality of MVFs. The method may also include generating one or more target images of the object by performing, based on the corrected imaging data, image reconstruction.

Systems and methods for positron emission tomography imaging

The present disclosure relates to systems and methods for image processing. The methods may include obtaining imaging data of a subject, generating a first image based on the imaging data, and generating at least two intermediate images based on the first image. At least one of the at least two intermediate images may be generated based on a machine learning model. And the at least two intermediate images may include a first intermediate image and a second intermediate image. The first intermediate image may include feature information of the first image, and the second intermediate image may have lower noise than the first image. The methods may further include generating, based on the first intermediate image and at least one of the first image or the second intermediate image, a target image of the subject.

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 provides a system and method for medical imaging. The method may include obtaining a preliminary image and scanning data of a subject acquired using a scanner. The method may also include determining a regularization parameter for a regularization item of an objective function based at least in part on the scanning data, wherein the regularization parameter includes at least two of a first component characterizing quality of the scanning data, a second component characterizing the scanner, or a third component characterizing a feature of the subject. The method may further include generating an image of the subject by reconstructing the preliminary image based on the objective function.

System and method for medical imaging

System for image correction in PET is provided. The system may acquire a PET image and a CT image of a subject. The system may generate, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model. The attenuation correction model may be a trained cascaded neural network including a trained first model and at least one trained second model downstream to the trained first model. During the application of the attenuation correction model, an input of each of the at least one trained second model may include the PET image, the CT image, and an output image of a previous trained model that is upstream and connected to the trained second model.

Systems and methods for image correction in positron emission tomography

The present disclosure provides a positron emission tomography (PET) system and an image reconstruction method thereof. The PET system may include a plurality of annular detector units arranged along an axial direction. Each of the detector units may generate a plurality of single event counts. The PET system may further include a plurality of coincidence logic circuits connected to one or more of the detector units. The coincidence logic circuits may be configured to count coincidence events. Single event data generated by each of the detector units may be transmitted to the corresponding coincidence logic circuit. The plurality of coincidence logic circuits may synchronically generate coincidence counts relating to the plurality of detector units.

System and method for image reconstruction in positron emission tomography

The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

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Shanghai, China

Yang Lyu