Abstract: Disclosed is a sealing ring for press-fitting connection structure, which includes a sealing ring body being annular-shaped. The sealing ring body includes an inner curved wall and an outer curved wall. At least one notch is provided and formed axially on the inner curved wall of the sealing ring body, and convex structures are provided at two lateral sides of the notch. By means of setting at least one notch in the inner arc-shaped wall of the sealing ring body, it serves as a gap between the sealing ring and the pipe for fluid to pass therethrough, achieving the purpose of warning the leakage of the press-fitting connection structure and avoiding potential use hazards in the future.

Abstract: A press-fitting connection structure for pipeline defines a first mounting groove for accommodating a retaining ring and a second mounting groove for accommodating a sealing ring, making the retaining ring and sealing ring be respectively mounted in different grooves and not easily to move. The first and second mounting grooves are spaced apart, so that the retaining ring will not come into contact with and scratch the sealing ring when the press-fitting connection structure for pipeline is subjected to the force of a press-fitting tool, thereby eliminating a closing ring originally used to protect the sealing ring, which makes assembly be more labor-saving and efficient. By means of setting a plurality of retaining teeth on the inner surface of the retaining ring, the retaining teeth can be more easily buried into the outer wall of the pipe through tip ends thereof, improving the tensile performance of the retaining ring.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.