New York University

Siemens Medical Solutions USA, Inc.

Hersh Chandarana

Daniel K Sodickson

Ricardo Otazo

Leon Axel

Li Feng

Tobias Block

Xiaoguang Lu

Tiejun Zhao

A method for real-time assessment of image information employing a computer that includes one or more processors includes obtaining, via a scanner, a magnetic resonance (MR) image of a region-of-interest, and providing data corresponding to at least a portion of the MR image to a deep learning model, where the deep learning model is previously trained based on one or more sets of training data. The method further includes assessing the data using the deep learning model and data obtained from an image quality database to obtain an assessed image quality value, and formulating an image quality classification in response to the assessed image quality value. The method additionally includes outputting the image quality classification within a predetermined time period from an initial scan of the region-of-interest.

Systems and methods for diagnostic oriented image quality assessment

Exemplary method, system and computer-accessible medium can be provided which facilitates an acquisition of radial data, which can be continuous, with an exemplary golden-angle procedure and reconstruction with arbitrary temporal resolution at arbitrary time points. According to such exemplary embodiment, such procedure can be performed with a combination of compressed sensing and parallel imaging to offer a significant improvement, for example in the reconstruction of highly undersampled data. It is also possible to provide an exemplary procedure for highly-accelerated dynamic magnetic resonance imaging using Golden-Angle radial sampling and multicoil compressed sensing reconstruction, called Golden-angle Radial Sparse Parallel MRI (GRASP).

System, method and computer-accessible medium for highly-accelerated dynamic magnetic resonance imaging using golden-angle radial sampling and compressed sensing

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West New York, NJ, United States of America

Hersh Chandarana