公开(公告)号：US20230320611A1

公开(公告)日：2023-10-12

申请号：US18042163

申请日：2021-08-19

申请人： Cornell University

发明人： Yi Wang ,  Yan Wen ,  Ramin Jafari ,  Thanh Nguyen ,  Pascal Spincemaille ,  Junghun Cho ,  Qihao Zhang

IPC分类号： A61B5/055 ,  A61B5/00

CPC分类号： A61B5/055 ,  A61B5/0042 ,  A61B5/4872 ,  G06T7/00

摘要： Quantitative susceptibility mapping methods, systems and computer-accessible medium generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function comprising of a data fidelity term and regularization terms. The data fidelity term is constructed directly from the multiecho complex magnetic resonance imaging data. The regularization terms include a prior constructed from matching structures or information content in known morphology, and a prior constructed from regions of low susceptibility contrasts characterized on image features. The quantitative susceptibility map can be determined by minimizing the cost function that involves nonlinear functions in modeling the obtained signals, and the corresponding inverse problem is solved using nonconvex optimization using a scaling approach or deep neural network. The nonconvex optimization is also developed for solving other inverse problems of nonlinear signal models in fat-water separation, tissue transport and oxygen extraction fraction.

公开(公告)号：US20240012080A1

公开(公告)日：2024-01-11

申请号：US18474048

申请日：2023-09-25

申请人： Cornell University

发明人： Yi Wang ,  Zhe Liu ,  Jinwei Zhang ,  Qihao Zhang ,  Junghun Cho ,  Pascal Spincemaille

IPC分类号： G01R33/56 ,  A61B5/00 ,  A61B5/055 ,  G01R33/565 ,  G06T7/00

CPC分类号： G01R33/5608 ,  A61B5/0042 ,  A61B5/055 ,  A61B5/7221 ,  A61B5/7267 ,  G01R33/56536 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084

摘要： Exemplary methods for quantitative mapping of physical properties, systems and computer-accessible medium can be provided to generate images of tissue magnetic susceptibility, transport parameters and oxygen consumption from magnetic resonance imaging data using the Bayesian inference approach, which minimizes a data fidelity term under a constraint of a structure prior knowledge. The data fidelity term is constructed directly from the magnetic resonance imaging data. The structure prior knowledge can be characterized from known anatomic images using image feature extraction operation or artificial neural network. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining physical properties associated with at least one structure.

公开(公告)号：US12072403B2

公开(公告)日：2024-08-27

申请号：US18474048

申请日：2023-09-25

申请人： Cornell University

发明人： Yi Wang ,  Zhe Liu ,  Jinwei Zhang ,  Qihao Zhang ,  Junghun Cho ,  Pascal Spincemaille

IPC分类号： G01R33/56 ,  A61B5/00 ,  A61B5/055 ,  G01R33/565 ,  G06T7/00

CPC分类号： G01R33/5608 ,  A61B5/0042 ,  A61B5/055 ,  A61B5/7221 ,  A61B5/7267 ,  G01R33/56536 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084

摘要： Exemplary methods for quantitative mapping of physical properties, systems and computer-accessible medium can be provided to generate images of tissue magnetic susceptibility, transport parameters and oxygen consumption from magnetic resonance imaging data using the Bayesian inference approach, which minimizes a data fidelity term under a constraint of a structure prior knowledge. The data fidelity term is constructed directly from the magnetic resonance imaging data. The structure prior knowledge can be characterized from known anatomic images using image feature extraction operation or artificial neural network. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining physical properties associated with at least one structure.

公开(公告)号：US11782112B2

公开(公告)日：2023-10-10

申请号：US17614277

申请日：2020-05-28

申请人： Cornell University Center for Technology Licensing (CTL)

发明人： Yi Wang ,  Zhe Liu ,  Jinwei Zhang ,  Qihao Zhang ,  Junghun Cho ,  Pascal Spincemaille

IPC分类号： G01R33/56 ,  A61B5/00 ,  A61B5/055 ,  G01R33/565 ,  G06T7/00

CPC分类号： G01R33/5608 ,  A61B5/0042 ,  A61B5/055 ,  A61B5/7221 ,  A61B5/7267 ,  G01R33/56536 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084

摘要： Exemplary methods for quantitative mapping of physical properties, systems and computer-accessible medium can be provided to generate images of tissue magnetic susceptibility, transport parameters and oxygen consumption from magnetic resonance imaging data using the Bayesian inference approach, which minimizes a data fidelity term under a constraint of a structure prior knowledge. The data fidelity term is constructed directly from the magnetic resonance imaging data. The structure prior knowledge can be characterized from known anatomic images using image feature extraction operation or artificial neural network. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining physical properties associated with at least one structure.