公开(公告)号：US11918335B2

公开(公告)日：2024-03-05

申请号：US17010870

申请日：2020-09-03

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Haifeng Wang ,  Dong Liang ,  Hairong Zheng ,  Xin Liu ,  Shi Su ,  Zhilang Qiu

IPC: A61B5/055 ,  G01R33/28 ,  G01R33/48

CPC classification number: A61B5/055 ,  G01R33/28 ,  G01R33/48

Abstract: A magnetic resonance imaging method includes: obtaining three-dimensional under-sampling data of a target object based on a first three-dimensional magnetic resonance imaging sequence; obtaining a three-dimensional point spread function based on the three-dimensional under-sampling data or a two-dimensional mapping data of the target object; obtaining a sensitivity map of the target object based on the data collected by three-dimensional low-resolution complete sampling; performing imaging reconstruction to the three-dimensional under-sampling data based on the three-dimensional point spread function and the sensitivity map to obtain a reconstructed magnetic resonance image. The first three-dimensional magnetic resonance imaging sequence has a first sinusoidal gradient field on a phase direction and a second sinusoidal gradient field on a layer selection direction. 0-order moments of the first and the second three-dimensional magnetic resonance imaging sequences are 0. A phase difference between the first and the second three-dimensional magnetic resonance imaging sequence is π/2.

公开(公告)号：US11915401B2

公开(公告)日：2024-02-27

申请号：US17284794

申请日：2020-12-09

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Dong Liang ,  Zhanli Hu ,  Hairong Zheng ,  Xin Liu ,  Qingneng Li ,  Yongfeng Yang

IPC: G06T5/50 ,  G06T7/00 ,  G16H30/40

CPC classification number: G06T5/50 ,  G06T3/4046 ,  G06T7/0012 ,  G16H30/40 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30004

Abstract: An apriori guidance network for multitask medical image synthesis is provided. The apriori guidance network includes a generator and a discriminator, wherein the generator includes an apriori guidance module configured to convert an input feature map into a target modal image pointing to a target domain according to an apriori feature, and the apriori feature is a deep feature of the target modal image. The generator is configured to generate a corresponding target domain image by taking the apriori feature of the target modal image and source modal image data as an input. The discriminator is configured to discriminate an authenticity of the target domain image outputted by the generator.

公开(公告)号：US11699513B2

公开(公告)日：2023-07-11

申请号：US16626661

申请日：2018-12-29

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES ,  SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

Inventor： Dong Liang ,  Chao Zou ,  Qiang He ,  Guobin Li ,  Qiang Zhang ,  Xin Liu ,  Hairong Zheng

IPC: G16H30/20 ,  G16H40/60 ,  G06F21/31 ,  G06F3/12

CPC classification number: G16H30/20 ,  G06F21/31 ,  G16H40/60 ,  G06F3/1204

Abstract: 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.

公开(公告)号：US11210783B2

公开(公告)日：2021-12-28

申请号：US16910074

申请日：2020-06-24

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Hairong Zheng ,  Xin Liu ,  Na Zhang ,  Zhanli Hu ,  Dong Liang ,  Yongfeng Yang

IPC: G06K9/00 ,  G06T7/00 ,  G16H30/40 ,  G06K9/62 ,  G06N3/08 ,  G06T11/00

Abstract: A method of processing plaques in magnetic resonance imaging of vessel wall include: step S101, training a generative adversarial network and a capsule neural network to obtain a trained generator network and a trained capsule neural network; and step S102, cascade-connecting the trained generator network with the capsule neural network into a system to recognize and classify plaques in magnetic resonance imaging of vessel wall. In one aspect, the capsule neural network has more abundant vascular plaques characteristic information represented by vector; in another aspect, when the trained generator network and the capsule neural network are cascaded into the system to recognize and classify the plaques in magnetic resonance imaging of vessel wall, an accuracy of recognition and classification may be greatly improved. A device for processing the method as well as a computer for implementing are also disclosed.

公开(公告)号：US10018698B2

公开(公告)日：2018-07-10

申请号：US15103845

申请日：2014-12-05

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Xi Peng ,  Dong Liang ,  Xin Liu ,  Hairong Zheng

IPC: G01V3/00 ,  G01R33/561 ,  G01R33/56 ,  G01R33/50

CPC classification number: G01R33/561 ,  G01R33/50 ,  G01R33/5608 ,  G01R33/5611

Abstract: Disclosed is a magnetic resonance rapid parameter imaging method and system. The method comprises: obtaining a target undersampled magnetic resonance signal (S10); obtaining prior information of a parameter model (S20); performing sequence reconstruction of a target image according to the undersampled magnetic resonance signal and the prior information to obtain a target image sequence (S30); and substituting the target image sequence into the parameter estimation model to obtain object parameters and to generate parametric images (S40).

公开(公告)号：US11668777B2

公开(公告)日：2023-06-06

申请号：US16760956

申请日：2017-12-01

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Shanshan Wang ,  Dong Liang ,  Sha Tan ,  Xin Liu ,  Hairong Zheng

IPC: G01R33/561 ,  G01R33/56 ,  G06T11/00

CPC classification number: G01R33/5611 ,  G01R33/5608 ,  G06T11/008 ,  G06T2207/10088 ,  G06T2207/20056 ,  G06T2207/20081

Abstract: Provided are a parallel magnetic resonance imaging method and apparatus based on adaptive joint sparse codes and a computer-readable medium. The method includes solving an l2−lF−l2,1 minimization objective, where the l2 norm is a data fitting term, the lF norm is a sparse representation error, and the l2,1 mixed norm is the joint sparsity constraining across multiple channels; separately updating each of a sparse matrix, a dictionary and K-space data with a corresponding algorithm, and obtaining a reconstructed image by a sum of root mean squares of all the channels. The joint sparsity of the channels is developed using the norm l2,1. In this manner, calibration is not required while information sparsity is developed. Moreover, the method is robust.

公开(公告)号：US11579229B2

公开(公告)日：2023-02-14

申请号：US16473806

申请日：2018-07-24

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Haifeng Wang ,  Dong Liang ,  Sen Jia ,  Xin Liu ,  Hairong Zheng

IPC: G01V3/00 ,  G01R33/561 ,  G01R33/56 ,  G01R33/58

Abstract: There are provided a parallel rapid imaging method and device based on a complex number conjugate symmetry of multi-channel coil data and nonlinear GRAPPA image reconstruction, and a medium. The imaging method includes: obtaining virtual conjugate coil data by expanding the actual multi-channel coil data; combining actual multi-channel coil data and virtual multi-channel coil data to obtain a linear data term and a nonlinear data term; calibrating weighting factors of the linear data term and the nonlinear data term by using combined low-frequency full-sampling data (margins of the low-frequency full-sampling data includes parts of high-frequency data); reconstructing data which is under-sampled in a high-frequency region according to the calibrated weighting factors; fusing the low-frequency full-sampling data and the reconstructed data for the high-frequency region.

公开(公告)号：US11514621B2

公开(公告)日：2022-11-29

申请号：US16878633

申请日：2020-05-20

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Zhan Li Hu ,  Dong Liang ,  Hai Rong Zheng ,  Xin Liu ,  Yong Feng Yang ,  Zhen Xing Huang

IPC: G06T11/00 ,  A61N5/10

Abstract: The disclosure provides a low-dose image reconstruction method and system based on prior anatomical structure difference. The method includes: determining the weights of different parts in the low-dose image based on prior information of anatomical structure differences; constructing a generative network being taking the low-dose image as input extract features, and integrating the weights of the different parts in the feature extraction process, outputting a predicted image; constructing a determining network being taking the predicted image and standard-dose image as input, to distinguish the authenticity of the predicted image and standard-dose image as the first optimization goal, and identifying different parts of the predicted image as the second optimization goal, collaboratively training the generative network and the determining network to obtain the mapping relationship between the low-dose image and the standard-dose image; and reconstructing the low-dose image by using the obtained mapping relationship. The disclosure can obtain more accurate high-definition images.

公开(公告)号：US12224057B2

公开(公告)日：2025-02-11

申请号：US17739121

申请日：2022-05-08

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Na Zhang ,  Hairong Zheng ,  Xin Liu ,  Zhanli Hu ,  Zhiyuan Huang ,  Dong Liang

IPC: G16H30/00 ,  G06N3/084 ,  G06T5/70 ,  G06V10/75 ,  G06V10/774 ,  G16H30/40

Abstract: A medical image processing method and processing apparatus, and a computer readable storage medium. The method includes: obtaining a to-be-processed image; performing a feature extraction on the to-be-processed image to obtain a corresponding feature image; and re-determining a pixel value of each pixel in the to-be-processed image based on first information and second information of a corresponding pixel in the feature image, and processing the to-be-processed image; wherein the first information is information of a pixel adjacent to the corresponding pixel in the features image, and the second information is information of a pixel that is not adjacent to and is similar to the corresponding pixel in the features image.

公开(公告)号：US12190568B2

公开(公告)日：2025-01-07

申请号：US17889189

申请日：2022-08-16

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Hairong Zheng ,  Xin Liu ,  Na Zhang ,  Zhanli Hu ,  Qihang Chen ,  Dong Liang ,  Yongfeng Yang

IPC: G06V10/774 ,  A61B5/055 ,  G06T11/00 ,  G06V10/42

Abstract: Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.