公开(公告)号：US20240046440A1

公开(公告)日：2024-02-08

申请号：US18546425

申请日：2021-04-14

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Shanshan WANG ,  Hairong ZHENG ,  Kehan QI ,  Chuyu RONG ,  Xin LIU

IPC: G06T7/00 ,  G06T15/00 ,  G06V10/44

CPC classification number: G06T7/0002 ,  G06T15/00 ,  G06V10/44 ,  G06T2207/20084 ,  G06T2207/30168

Abstract: An image data quality evaluation method and apparatus, a terminal device, and a readable storage medium. The image data quality evaluation method includes: inputting three-dimensional image data to be evaluated into a feature extraction network model for processing to obtain a feature matrix; processing the feature matrix on the basis of a plurality, of branch network models in a hypemetwork model to obtain a plurality of parameter matrixes, and respectively adjusting, on the basis of the plurality of parameter matrixes, parameters of a plurality of fully connected layers in a corresponding regression model; and processing the feature matrix on the basis of the adjusted regression model to obtain a quality evaluation score. The dynamic adjustment of parameters of a model is implemented on the basis of content of input data, so that the processing efficiency and the quality evaluation precision for three-dimensional image data are improved.

公开(公告)号：US20200256942A1

公开(公告)日：2020-08-13

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

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：US20240202873A1

公开(公告)日：2024-06-20

申请号：US18555723

申请日：2021-04-19

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Shanshan WANG ,  Haoyun LIANG ,  Hairong ZHENG ,  Xin LIU

IPC: G06T5/20 ,  G06T5/50 ,  G06T5/60

CPC classification number: G06T5/20 ,  G06T5/50 ,  G06T5/60 ,  G06T2207/20084 ,  G06T2207/20221

Abstract: The present application relates to image processing technical field, and provides a method for image reconstruction, an apparatus, a terminal device, and a storage medium. The method first extracts an initial feature map of an original image, then calculates an average value of each column pixel in the initial feature map, and constructs a target row vector and duplicates the target row vector in the column direction after convolution processing, to obtain a feature map. In addition, an average value of the element of each row of pixels in the initial feature map is calculated respectively, and a target column vector is constructed. It is duplicated in a row direction to obtain another feature map, and then the two feature maps are fused. Finally, two-dimensional convolution processing is performed on a fused feature map, and a reconstructed image is generated, thereby the long-distance dependencies of the image can be captured.