An exemplary system, method and computer-accessible medium for determining a breast cancer response(s) for a patient(s) can include, for example, receiving an image(s) of an internal portion(s) of a breast of the patient(s), and determining the breast cancer response(s) by applying a neural network(s) to the image(s). The breast cancer response(s) can be a response to at least one chemotherapy treatment. The breast cancer response(s) can include an Oncotype DX recurrence score. The breast cancer response(s) can be a neoadjuvant axillary response. The image(s) can be a magnetic resonance image(s) (MRI). The MRI(s) can include a dynamic contrast enhanced MRI(s).

An exemplary system, method and computer-accessible medium for classifying a tissue(s) of a patient(s) can include, for example, receiving an image(s) of an internal portion(s) of a breast of the patient(s), and automatically classifying the tissue(s) of the breast by applying a neural(s) network to the image(s). The tissue(s) can include a lymph node(s). The lymph node(s) can be classified as a cancerous tissue or a non-cancerous tissue. The tissue(s) can be classified as a fibroglandular tissue or a background parenchymal enhancement tissue. The tissue(s) can be classified as a cancer molecular subtype. The image(s) can be is a magnetic resonance image.

Apparatus for operating MRI is disclosed. The apparatus comprises: a control for operating an MRI scanner to carry out an MRI scan; an input for receiving first and second MRI scans respectively at the beginning and end of a predetermined time interval post contrast administration; a subtraction map former for forming a subtraction map from the first and the second MRI scans by analyzing the scans to distinguish between a population in which contrast clearance from the tissue is slower than contrast accumulation, and a population in which clearance is faster than accumulation; and an output to provide an indication of distribution of the populations. The control is configured to carry out the first scan at least five minutes and no more than twenty minutes post contrast administration and to carry out the second scan such that the predetermined time period is at least twenty minutes.

An exemplary system, method and computer-accessible medium for classifying a breast tissue(s) a patient(s) can include, for example, receiving an image(s) of an internal portion(s) of a breast of the patient(s), and automatically classifying the breast tissue(s) of the breast by applying a neural network(s) to the image(s). The automatic classification can include a classification as to whether the breast tissue(s) is atypical ductal hyperplasia or ductal carcinoma. The automatic classification can include a classification as to whether the breast tissue(s) is a cancerous tissue or a non-cancerous tissue. The image(s) can be a mammographic image or an optical coherence tomography image.

A method for determining a brain age, the method comprising the following: providing a brain age determining convolutional neural network (CNN) (200); training the CNN (200) to determine the brain age based on a plurality of sets of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least two types of MRI volumes, wherein the at least one type of brain tissue on the first type of the MRI volume is represented by a different contrast with respect to other tissues than on a second type of the MRI volume; and performing an inference process using the trained CNN (200) to determine the brain age based on the set of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least the two types of the MRI volumes as used for the training.

In a method, computer and magnetic resonance (MR) apparatus for normalizing MR contrast images of an examination object that has two chemically different substances (SW, SF), wherein the first substance produces a first image signal and the second substance produces a second image signal, a processor is provided with a complex-valued contrast having pixels with signal contributions from the first and second substances. A phase correction of this contrast image is performed by calculating a real-valued contrast from the amount of the image signals of each pixel of the complex-valued contrast image. A mathematically smooth correction map is determined based on a number of the pixels that have a defined real-valued contrast. The intensity of pixels of the complex-valued contrast image are homogenized with other scans based on the correction map.

The present invention relates to obesity related diseases, such as cancer of non-alcoholic fatty liver disease (NAFLD). Tissue perfusion is currently investigated by using dynamic contrast-enhanced magnetic resonance imaging which is an invasive technique and does not provide enough accuracy. As a result, the inventors worked on post-processing images of subcutaneous adipose tissue or the epididymal adipose tissue taken with a magnetic resonance imaging technique and a multifrequency magnetic resonance elastography technique to obtain parameters such as loss modulus and storage modulus which are more accurate for a diagnosing purpose. This post-processing method may be applied for a method for predicting that a subject is at risk of suffering from said disease, identifying a therapeutic target or a biomarker and screening compounds useful as medicine.

A system and method for automated characterization of solid tumors using medical imaging. The system comprises an interface that is configured to acquire data from medical imaging devices, one or more processors, and an outputting device that reports the characterization of said solid tumor. The method of automated characterization, which is implemented by the system, acquires a sequence of images from the medical imager using a Dynamic Contrast Enhanced (DCE) imaging protocol, performs image registration, detects the contour of the solid tumor, and dividing the contours to segments. For each segment, the method calculating a displacement of the contrast material, fitting the displacement to a flow model and extracting an estimation of the interstitial fluid velocity. The estimated interstitial fluid velocity of the segments provide characterization of the solid tumor and includes an assessment of the tumor interstitial fluid pressure, the tumor drug delivery efficiency, and the tumor prognostic or metastasis risk.

Apparatus for operating MRI is disclosed. The apparatus comprises: a control for operating an MRI scanner to carry out an MRI scan; an input for receiving first and second MRI scans respectively at the beginning and end of a predetermined time interval post contrast administration; a subtraction map former for forming a subtraction map from the first and the second MRI scans by analyzing the scans to distinguish between a population in which contrast clearance from the tissue is slower than contrast accumulation, and a population in which clearance is faster than accumulation; and an output to provide an indication of distribution of the populations. The control is configured to carry out the first scan at least five minutes and no more than twenty minutes post contrast administration and to carry out the second scan such that the predetermined time period is at least twenty minutes.

Method and apparatus for vertebral artery dissection risk analysis using hemodynamic variable based four dimensional magnetic resonance flow imaging, comprising obtaining four-dimensional phase-contrast magnetic resonance imaging data, performing pre-processing of the four-dimensional phase-contrast magnetic resonance imaging data, obtaining at least one blood hemodynamic marker from the four-dimensional phase-contrast magnetic resonance imaging data, classifying the at least one blood hemodynamic marker as a hemodynamic predictor of vertebral artery dissection, and creating a comprehensive risk evaluation of vertebral artery dissection using the hemodynamic predictor.