Abstract: An exemplary system, method, and computer-accessible medium for generating a particular image which can be a quantitative image(s) of at least one section(s) of a patient(s) or (ii) a non-synthetic contrast image(s) of the section(s) of the patient(s), can include, for example, generating a first magnetic resonance (MR) signal and detecting the first MR signal to patient(s), receiving a second MR signal from the patient(s) that can be based on the first MR signal, and generating the particular image(s) based on the second MR signal. The first MR signal can be a configured MR signal. The configured MR signal can be configured for a particular contrast. The first MR signal can have a constant signal intensity. The first MR signal can be generated based on a degree of a plurality of flip angles that maintains the constant signal intensity. A degree of flip angles can be selected for the first MR signal based on the particular contrast.

Abstract: An exemplary system, method, and computer-accessible medium for generating a Cartesian equivalent image(s) of a portion(s) of a patient(s), can include, for example, receiving non-Cartesian sample information based on a magnetic resonance imaging (MRI) procedure of the portion(s) of the patient(s). and automatically generating the Cartesian equivalent image(s) from the non-Cartesian sample information using a deep learning procedure(s). The non-Cartesian sample information can be Fourier domain information. The non-Cartesian sample information can be undersampled non-Cartesian sample information. The MRI procedure can include an ultra-short echo time (UTE) pulse sequence The UTE pulse sequence can include a delay(s) and a spoiling gradient. The Cartesian equivalent image(s) can be generated by reconstructing the Cartesian equivalent image(s).

Abstract: An exemplary system, method, and computer-accessible medium for generating a particular image which can be a quantitative image(s) of at least one section(s) of a patient(s) or (ii) a non-synthetic contrast image(s) of the section(s) of the patient(s), can include, for example, generating a first magnetic resonance (MR) signal and directing the first MR signal to patient(s), receiving a second MR signal from the patient(s) that can be based on the first MR signal, and generating the particular image(s) based on the second MR signal. The first MR signal can be a configured MR signal. The configured MR signal can be configured for a particular contrast. The first MR signal can have a constant signal intensity. The first MR signal can be generated based on a degree of a plurality of flip angles that maintains the constant signal intensity.

Abstract: Exemplary system, method and computer-accessible medium for remotely initiating a medical imaging scan(s) of a patient(s), can include, for example, receiving, over a network, encrypted first information related to first parameters of the patient(s), determining second information related to image acquisition second parameters based on the first information, generating an imaging sequence(s) based on the second information, and initiating, remotely from the patient(s), the medical imaging scan(s) based on the imaging sequence(s). The medical imaging scan(s) can be a magnetic resonance imaging (“MRI”) sequence(s). The image acquisition second parameters can be MRI acquisition parameters, and the imaging sequence(s) can be a gradient recalled echo (“GRE”) pulse sequence(s).

Abstract: Exemplary system, method, and computer-accessible medium for generating a magnetic resonance (MR) tissue fingerprint training network(s) can be provided, using which it is possible to, for example, receive first information related to a MR image(s) of a portion(s) of a phantom(s), partition the first information into a plurality of patches, and generate the MR tissue fingerprint training network(s) by applying a convolutional neural network(s) to the patches. The convolutional neural network(s) can be a fully convolutional neural network(s). Each of the patches can be a same size. The patches can be overlapping patches. A size of the patches can be 3×3 pixels. The MR tissue fingerprint training network can be generated based on float values for each of the patches.

Abstract: Exemplary system, method and computer-accessible medium for estimating a temperature on a portion of a body of an anatomical structure(s) can be provided, using which it is possible to, for example, receive a plurality of magnetic resonance (MR) images for the anatomical structure(s), segment the MR images into a plurality of tissue types, mapping the tissue types to a tissue property(ies), and estimate the temperature on the portion of the body of the patient(s) using a neural network. The tissue property(ies) can include a conductivity, a permittivity, or a density. The density can be a mass cell density. The neural network can be a single neural network. The temperature can be estimated based on a set of vectors between points on the portion of the body and a temperature sensor. Each vector can correspond to a tissue thermal profile for each point.