Medimagemetric LLC

Cornell University

Tian Liu

Yi Jing Wang

Pascal Spincemaille

Yi Wang

Ludovic De Rochefort

Bryan Kressler

Ildar Khalidov

An example method for mapping tissue magnetic susceptibility includes acquiring magnetic resonance (MR) data, where the MR data correspond to a subject, determining a magnetic field based on the MR data, determining a relationship between the magnetic field at a given location to the magnetic susceptibility at that location, and performing a convolution operation on the relationship between the magnetic field at the given location to the magnetic susceptibility at that location to obtain a noise property. The method also includes estimating a magnetic susceptibility distribution of the subject based, at least in part, on the noise property, and generating one or more images of the subject based on the estimated susceptibility distribution of the subject.

Differential approach to quantitative susceptibility mapping without background field removal

An example implementation of a method for mapping tissue magnetic susceptibility includes acquiring magnetic resonance (MR) images acquired at multiple echo times, where the MR images correspond to a subject comprising at least two species of protons, and where each species has a different chemical shift in its respective resonance frequency. The method also includes determining, for each species, an estimated chemical shift value, determining an estimated map of magnetic field inhomogeneity based on the estimated chemical shift values, determining an estimated susceptibility distribution of the subject and an error of each estimated chemical shift value based on the estimated map of magnetic field inhomogeneity and prior information regarding the subject, and generating an image of the subject based on the estimated susceptibility distribution.

Joint estimation of chemical shift and quantitative susceptibility map using MRI signal

For optimal image quality in susceptibility-weighted imaging and accurate quantification of susceptibility, it is necessary to isolate the local field generated by local magnetic sources (such as iron) from the background field that arises from imperfect shimming and variations in magnetic susceptibility of surrounding tissues (including air). We present a nonparametric background field removal method based on projection onto dipole fields in which the background field inside an ROI is decomposed into a field originating from dipoles outside the ROI using the projection theorem in Hilbert space.

Background field removal method for MRI using projection onto dipole fields

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian statistical approach. The likelihood is constructed directly using the complex data. A prior is constructed from matching structures or information content in known morphology. The quantitative susceptibility map can be determined by, e.g., maximizing the posterior. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining information associated with at least one structure. Using such exemplary embodiment, it is possible to receive signals associated with the structure(s), where the signals can include complex data that is in the complex domain.

System, process and computer-accessible medium for providing quantitative susceptibility mapping

A method and apparatus is provided for magnetic source magnetic resonance imaging. The method includes collecting energy signals from an object, providing additional information of characteristics of the object, and generating the image of the object from the energy signals and from the additional information such that the image includes a representation of a quantitative estimation of the characteristics, e.g a quantitative estimation of magnetic susceptibility. The additional information may comprise predetermined characteristics of the object, a magnitude image generated from the object, or magnetic signals collected from different relative orientations between the object and the imaging system. The image is generated by an inversion operation based on the collected signals and the additional information. The inversion operation minimizes a cost function obtained by combining the data extracted from the collected signals and the additional information of the object. Additionally, the image is used to detect a number of diagnostic features including microbleeds, contract agents and the like.

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Tian Liu