Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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 undersampling 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).

Abstract: A method of acquiring PET and MR images simultaneously includes obtaining raw k-space data for continuous MR volumes and acquiring PET information. The method further includes performing a joint multi-modality image reconstruction and generating a set of PET and MR images. The method additionally includes generating a set of MR fingerprints from the reconstructed MR images and using the MR fingerprints to generate a set of parameter maps.

Abstract: 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.