Shanghai United Imaging Healthcare Co., Ltd.

Shanghai United Imaging Intelligence Co., Ltd.

Chinese Academy of Sciences

Sungkyunkwan University

Shenzhen United Imaging Healthcare Co., Ltd.

Guobin Li

Chaohong Wang

Zhaopeng Li

Nan Liu

Yang Xin

Xiaoqian Huang

Renkuan Zhai

Jinguang Zong

Qiang Li

Weiguo Zhang

Xin Zhou

Yanli Song

Shu Liao

Xin Liu

Jaeseok Park

Hairong Zheng

Qiang Zhang

Xu Wang

Dong Liang

Wentao Wu

Qiang He

Junyu Chen

Yuhang Shi

Xiangming Hou

Lingzhi Hu

Chao Zou

Shuangyue Zhang

Tuoyu Cao

Weidong Wang

Xinyue Zhang

Rong Zhong

Lixiang Hu

Yanxia Chen

Meiling Ji

Hyunkyung Maeng

Eunji Lim

Zhenkui Wang

The present disclosure is related to systems and methods for image processing. The method includes obtaining an original image. The original image includes at least one blood vessel region and at least one scalp region. The method includes determining an intermediate image by removing the at least one scalp region from the original image. The method includes generating at least one target image by performing a maximum intensity projection operation on the intermediate image. The at least one target image represents the at least one blood vessel region in the original image.

Systems and methods for image processing

Systems and methods for magnetic resonance imaging acceleration. The systems may perform the methods to obtain imaging data of a subject, or a portion thereof, captured by the MRI system according to an undersampling pattern; execute a first iterative procedure; determine that the first iteration number meets a first threshold; execute, in response to the determination that the first iteration number meets the first threshold, a second iterative procedure; determine that a sum of the first iteration number and the second iteration number meets a second threshold; and generate a reconstruction image of the subject, or a portion thereof, according to the processed imaging data, wherein the first threshold is lower than the second threshold.

System and method for magnetic resonance imaging acceleration

A method may include obtaining an image of an object; obtaining a target artifact identification model; determining target artifact feature information by inputting the image into the target artifact identification model, the target artifact feature information indicating a feature of one or more artifacts in the image; obtaining a target artifact extent determination model; determining target artifact extent indication information by inputting the image and the target artifact feature information into the target artifact extent determination model, the target artifact extent indication information indicating an influence extent of the one or more artifacts on an image quality of the image; in response to determining that the influence extent is greater than or equal to the extent threshold, outputting a notice of the one or more artifacts of the image.

The present disclosure provides a noise reduction device. The noise reduction device may include a noise receiving component, a noise reduction component, a processing component, and a housing. The noise receiving component may be configured to receive acoustic noise information of a scanning environment where a medical device is located. The processing component may be configured to control the noise reduction component to generate sound information matching the acoustic noise information received by the noise receiving component. The housing may be configured to support the noise receiving component and the noise reduction component.

Devices, systems, and methods for noise reduction

Systems and methods for image processing are provided in the present disclosure. The systems and methods may obtain an image; determine a current resolution level of the image; determine, based on the current resolution level of the image, from a group of resolution level ranges, a reference resolution level range corresponding to the image; determine a target processing model corresponding to the reference resolution level range; and/or determine a processed image with a target resolution level by processing the image using the target processing model, the target resolution level of the processed image being higher than the current resolution level of the image.

The disclosure relates to systems and methods for magnetic resonance imaging (MRI). A method may include obtaining k-space data associated with MR signals acquired by an MR scanner. The k-space data may corresponding to a first sampling rate. The method may also include generating one or more estimated images based on the k-space data and a target neural network model. The one or more estimated images may correspond to a second sampling rate that exceeds the first sampling rate. The method may further include determining one or more target images based on the one or more estimated images and the k-space data using a compressed sensing model. The compressed sensing model may be constructed based on the one or more estimated images.

Systems and methods for magnetic resonance image reconstruction

Disclosed are information transmission method, apparatus, device and medium for medical imaging application. The method includes: an imaging device corresponding to an application authorization request is determined according to the received application authorization request, and an application permission profile of the imaging device is acquired; and a medical imaging application corresponding to the imaging device is determined according to the application permission profile of the imaging device, and medical imaging application information corresponding to the medical imaging application is transmitted to the imaging device.

Information transmission method, apparatus, device and medium for medical imaging application

The present disclosure relates to systems and methods for magnetic resonance imaging. The method may include obtaining primary imaging data associated with a region of interest (ROI) of a subject and obtaining secondary data associated with the ROI. The method may also include determining secondary imaging data based on the secondary data by using a trained model. The method may further include reconstructing a magnetic resonance image based on the primary imaging data and the secondary imaging data.

Systems and methods for magnetic resonance imaging

A method and a system for image reconstruction are provided. The method may include acquiring raw image data, wherein the raw image data may include a plurality of frequency domain undersampled image data samples. The method may include generating a first reconstruction result based on the raw image data using a first reconstruction method, and generating a second reconstruction result based on the raw image data using a second reconstruction method. The method may further include fusing the first reconstruction result and the second reconstruction result, and generating a reconstructed image based on a result of the fusion.

System and method for image reconstruction

A method may include obtaining a plurality of groups of imaging data. Each group of the plurality of groups of imaging data may be generated based on MR signals acquired by an MR scanner via scanning a subject using a diffusion sequence. The method may also include determining one or more correction coefficients associated with an error caused by the diffusion sequence for each group of the plurality of groups of imaging data. The method may also include determining, based on the one or more correction coefficients corresponding to the each group of the plurality of groups of imaging data, a plurality of groups of corrected imaging data. The method may further include determining averaged imaging data by averaging the plurality of groups of corrected imaging data in a complex domain and generating, based on the averaged imaging data, an MR image.

Method and system for diffusion magnetic resonance imaging

The present disclosure provides systems and methods for magnetic resonance imaging. The method may include obtaining a pulse sequence for scanning an object, obtaining a tolerance parameter associated with the pulse sequence, determining, based on the pulse sequence and the tolerance parameter, a target waveform for scanning the object, and causing an MRI device to scan the object based on the target waveform.

Systems and methods for waveform determination in magnetic resonance imaging

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 system and method for magnetic resonance imaging is provided. The method includes generating a main magnetic field through a region of interest (ROI), applying a slice selection gradient to an slice of the ROI, applying a plurality of RF pulses to the slice to generate a plurality of echoes, applying a first encoding gradient and a second encoding gradient on the echoes, and generating MR images based on the echoes.

System and method for fast imaging in magnetic resonance imaging

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Shanghai, China

Guobin Li