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：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：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.