公开(公告)号：US12153175B2

公开(公告)日：2024-11-26

申请号：US17824453

申请日：2022-05-25

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Zhanli Hu ,  Yongfeng Yang ,  Chunhui Zhang ,  Zhonghua Kuang ,  Xiaohui Wang ,  San Wu ,  Dong Liang ,  Xin Liu ,  Hairong Zheng

IPC: G01T1/29 ,  A61B6/00 ,  A61B6/03 ,  G06T11/00

Abstract: A method for processing positron emission tomography data is provided, this method includes: obtaining a first coordinate and a second coordinate respectively corresponding to two ends of a response line to be processed; determining corresponding dimensional coordinates of the response line to be processed in a sinogram based on the first coordinate and the second coordinate; and generating the sinogram corresponding to the response line to be processed based on the dimensional coordinates. According to this method, the amount of calculation of system matrix is reduced, the accuracy of position information of the generated response line is improved, and the accuracy of generated sinogram is improved accordingly.

公开(公告)号：US12020351B2

公开(公告)日：2024-06-25

申请号：US17433081

申请日：2020-12-18

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Zhanli Hu ,  Hairong Zheng ,  Dong Liang ,  Xin Liu ,  Yongfeng Yang ,  Dongfang Gao

IPC: G06T11/00 ,  A61B6/03 ,  G06V10/77

CPC classification number: G06T11/006 ,  A61B6/037 ,  G06T11/008 ,  G06V10/7715 ,  G06T2211/40

Abstract: A method, device and equipment for reconstructing a PET image are provided. The method includes acquiring a prior image comprising an anatomical image and an autocorrelation feature image, the autocorrelation feature image being determined based on gray-level co-occurrence matrix of the anatomical image; and acquiring a feature value of the prior image; reconstructing the PET image according to the feature value and an iterative algorithm.

公开(公告)号：US11756191B2

公开(公告)日：2023-09-12

申请号：US16979535

申请日：2018-12-13

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Shanshan Wang ,  Taohui Xiao ,  Hairong Zheng ,  Xin Liu ,  Dong Liang

IPC: G06T7/00 ,  A61B5/00 ,  A61B5/055 ,  G06T11/00 ,  A61B5/02

CPC classification number: G06T7/0012 ,  A61B5/004 ,  A61B5/055 ,  A61B5/7257 ,  A61B5/7267 ,  G06T11/005 ,  A61B5/02007 ,  A61B2576/02 ,  G06T2207/10064 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30101 ,  G06T2210/36 ,  G06T2210/41

Abstract: A method for magnetic resonance imaging and plaque recognition includes: obtaining magnetic resonance undersampled K-space data; transforming the magnetic resonance undersampled K-space data to an image domain through inverse Fourier transform to obtain a preprocessed image; reconstructing the preprocessed image through a pre-established deep learning reconstruction model to obtain a high-resolution imaging image of a blood vessel wall; and recognizing plaques in the high-resolution imaging image of the blood vessel wall through a pre-established deep learning plaque recognition model. A neural network corresponding to the pre-established deep learning reconstruction model is a dense connection network. The magnetic resonance undersampled K-space data is head-and-neck combined magnetic resonance undersampled K-space data of the blood vessel wall.

公开(公告)号：US20210366168A1

公开(公告)日：2021-11-25

申请号：US17287040

申请日：2019-01-15

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

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

IPC: G06T11/00 ,  G06T5/00 ,  G06T7/11 ,  A61B6/03

Abstract: The present invention discloses a PET image reconstruction method, a computer storage medium, and a computer device. The method includes: step 1, obtaining projection data Y and a system matrix P of a PET image; step 2, constructing an imaging model equation Y=PX, in which X is a reconstructed PET image; step 3, obtaining the initial reconstructed image X, and iteratively updating the initial reconstructed image X according to a first objective function to obtain a first reconstructed image; step 4, iteratively updating the first reconstructed image according to the second objective function to obtain the second reconstructed image; and step 5, determining whether an iteration condition is satisfied, if yes, outputting the current round of iteration to obtain the second reconstructed image as a final PET reconstructed image, and if not, returning to step 3 and using the second reconstructed image in the current round of iteration as an initial reconstructed image in the next round of iteration. The reconstruction algorithm of the present invention does not depend on a conformity degree between anatomical structure information and functional information, and can distinguish image edges well regardless of whether there is noise interfering with the image edges.

公开(公告)号：US11830111B2

公开(公告)日：2023-11-28

申请号：US17047062

申请日：2018-05-17

Applicant: Shenzhen Institutes of Advanced Technology

Inventor： Yanjie Zhu ,  Yuanyuan Liu ,  Dong Liang ,  Xin Liu ,  Hairong Zheng

IPC: G06T11/00 ,  G01R33/56 ,  G01R33/561 ,  A61B5/055

CPC classification number: G06T11/005 ,  G01R33/561 ,  G01R33/5608 ,  G06T11/006 ,  A61B5/055

Abstract: Provided are a method and device for magnetic resonance parameter imaging, medical equipment, and a storage medium. The method comprises: performing acceleration sampling with respect to an image to be reconstructed of an observation target in a preset parameter direction to acquire K-space data corresponding to the image to be reconstructed, calculating, on the basis of the K-space data and of a parameter relaxation model, a parameter value and a compensation coefficient of the image to be reconstructed, generating, on the basis of the compensation coefficient, a compensation image corresponding to the image to be reconstructed, calculating, on the basis of the compensation image, respectively a low-rank part and a sparse part of the image to be reconstructed so as to update the compensation image.

公开(公告)号：US11756161B2

公开(公告)日：2023-09-12

申请号：US17340117

申请日：2021-06-07

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

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

IPC: G06T5/00 ,  A61B6/03 ,  G06N3/08 ,  G06N3/045

CPC classification number: G06T5/00 ,  A61B6/037 ,  G06N3/045 ,  G06N3/08 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20081

Abstract: The present application relates to a method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction, and relates to the field of deep learning. The method includes connecting layers of the encoder with layers of the decoder by skip connection to provide a U-Net type picture generator; generating a group of generative adversarial networks by matching a plurality of picture generators with a plurality of discriminators in one-to-one manner; obtaining a first multi-task learning-type generative adversarial network; designing a joint loss function 1 for improving image quality; and training the first multi-task learning-type generative adversarial network according to the joint loss function 1 in combination with an optimizer to provide a second multi-task learning-type generative adversarial network.

公开(公告)号：US11508048B2

公开(公告)日：2022-11-22

申请号：US16976474

申请日：2020-02-10

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

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

IPC: G06T5/50 ,  G06T5/00

Abstract: The present disclosure discloses a method and a system for generating a composite PET-CT image based on a non-attenuation-corrected PET image. The method includes: constructing a first generative adversarial network and a second generative adversarial network; obtaining a mapping relationship between a non-attenuation-corrected PET image and an attenuation-corrected PET image by training the first generative adversarial network; obtaining a mapping relationship between the attenuation-corrected PET image and a CT image by training the second generative adversarial network; and generating the composite PET-CT image by utilizing the obtained mapping relationships. According to the present disclosure, a high-quality PET-CT image can be directly composited from a non-attenuation-corrected PET image, and medical costs can be reduced for patients, and radiation doses applied to the patients in examination processes can be minimized.

公开(公告)号：US11327137B2

公开(公告)日：2022-05-10

申请号：US16761285

申请日：2017-06-06

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Shanshan Wang ,  Dong Liang ,  Ningbo Huang ,  Xin Liu ,  Hairong Zheng

IPC: G01R33/561 ,  G01R33/00 ,  G06N3/08

Abstract: The present disclosure relates to a 1D partial Fourier parallel magnetic resonance imaging method with a deep convolutional network and belongs to the technical field of magnetic resonance imaging. The method includes steps of: creating a sample set and a sample label set for training; constructing an initial deep convolutional network model; inputting a training sample of the sample set to the initial deep convolutional network model for forward process, comparing an output result of the forward process with an expected result in the sample label set, and performing training with a gradient descent method until a parameter of each layer which enables consistency between the output result and the expected result to be maximum is obtained; creating an optimal deep convolutional network model by using the obtained parameter of the each layer; and inputting a multi-coil undersampled image sampled online to the optimal deep convolutional network model, performing the forward process on the optimal deep convolutional network model, and outputting a reconstructed single-channel full-sampled image. The present disclosure can well remove the noise of the reconstructed image, reconstruct a magnetic resonance image with a better visual effect, and has high practical value.