New York University

Siemens Healthineers Ag

Graz University of Technology

Florian Knoll

Daniel K Sodickson

Mariappan S Nadar

Boris Mailhe

Marcel Dominik Nickel

Mahmoud Mostapha

Esther Raithel

Jan Fritz

Ricardo Otazo

Gregor Körzdörfer

Fernando Boada

Thomas Pock

Thomas Koesters

Kerstin Hammernik

Inge Brinkmann

Various techniques of reconstructing multiple Magnetic Resonance Imaging, MRI, images for multiple slices based on an MRI measurement dataset that is acquired using a simultaneous multi-slice protocol and undersampling and K-space are disclosed. A convolutional neural network can be used to implement a regularization operation of an iterative optimization for the reconstruction, i.e., an unrolled neural network or variational neural network. A combination with Dixon imaging, i.e., separation of multiple chemical species, is disclosed.

Simultaneous multi-slice protocol and deep-learning magnetic resonance imaging reconstruction

An exemplary system, method and computer accessible medium for generating an image(s) of a portion(s) of a patient can be provided, which can include, for example, receiving first imaging information related to the portion(s), receiving second information related to modelling information of a further portion(s) of a further patient(s), where the modelling information includes (i) an under sampling procedure, and/or (ii) a learning-based procedure, and generating the image(s) using the first information and the second information. The modelling information can include artifacts present in a further image of the further portion(s). The image(s) can be generated by reducing or minimizing the artifacts. The second information can be generated, for example using a variational network(s).

System, method and computer-accessible medium for learning an optimized variational network for medical image reconstruction

An exemplary system, method and computer-accessible medium for generating a magnetic resonance (MR) image(s) and a positron emission tomography (PET) image(s) of a tissue(s) can be provided, which can include, for example, receiving information related to a combination of MR data and PET data as a single data set, separating the information into at least two dimensions, at least one first of the dimensions corresponding to the MR data and at least one second of the dimensions corresponding to the PET data, and generating the MR image(s) and the PET image(s) based on the separated information.

System, method and computer-accessible medium for joint magnetic resonance-positron emission tomography reconstruction using multi-sensor compressed sensing

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New York, NY, United States of America

Florian Knoll