A method for determining a sensitivity distribution of magnetic resonance (MR) receiving coils may include obtaining a reference image of a region of interest (ROI) of a subject. Contrast information between at least two types of tissues of the ROI may be weakened in the reference image. The method may also include determining, based on the reference image, a preliminary radio frequency (RF) field map corresponding to the ROI. The method may also include obtaining a transmitting field map corresponding to the ROI. The method may also include determining, based on the preliminary RF field map and the transmitting map, a sensitivity distribution of MR receiving coils corresponding to the ROI.

A method for determining a sensitivity distribution of magnetic resonance (MR) receiving coils may include obtaining a reference image of a region of interest (ROI) of a subject. Contrast information between at least two types of tissues of the ROI may be weakened in the reference image. The method may also include determining, based on the reference image, a preliminary radio frequency (RF) field map corresponding to the ROI. The method may also include obtaining a transmitting field map corresponding to the ROI. The method may also include determining, based on the preliminary RF field map and the transmitting map, a sensitivity distribution of MR receiving coils corresponding to the ROI.

The present disclosure relates to quantitative magnetic resonance imaging. A time series of magnetic resonance images of an examination region are assigned to different time points following an excitation is acquired by means of a magnetic resonance device, a signal evolution varying with respect to time is determined from the magnetic resonance images for each pixel from the magnetic resonance data of all of the magnetic resonance images and, by comparison of the signal evolution with comparison evolutions stored in a database, at least one quantitative result value on which the comparison evolution exhibiting the greatest agreement is based is assigned to a respective pixel.

An MR image especially useful for computer-guided diagnostics uses at least one programmed computer to acquire an MR-image of T1 values for a patient volume containing at least one predetermined tissue type having a respectively corresponding predetermined range of expected T1 values. A color-coded T1-image is generated from the MR-image by (a) assigning a first color or spectrum of colors to those pixels having a T1 value falling within a predetermined range of expected T1 values and (b) assigning a second color or spectrum of colors to those pixels having a T1 value falling outside a predetermined range of expected T1 values. The color-coded T1-image is then displayed for use in computer-aided diagnosis of patient tissue.

The disclosure provides a modified EPI sequence for acquiring multi-shot and multi-echo images with interleaved blip-up and blip-down phase encoding; the blip-up and blip-down images are processed by topup in FSL to estimate the inhomogeneous main magnetic field B.sub.0 map that causes image distortions; the B.sub.0 map is then incorporated into the encoding matrix with a low rank constraint to form a joint reconstruction model; the joint reconstruction model is solved to obtain multiple distortion-free images; and the multiple distortion-free images are matched to dictionary to simultaneous acquire the quantitative T.sub.2 (=1/R.sub.2) and T.sub.2* (=1/R.sub.2*) maps. In the phantom and in-vivo measurements, the disclosed method rapidly acquires the comparable quantitative images within one hold-breath (for 20 s) to the conventional mapping method, thus providing important practical application value for evaluation of liver damage, iron level and cancer lesion.

A computer implemented method is usable to detect the presence of abnormal tissue through analysis of magnetic resonance relaxation times T1 and T2. The relaxation times T1 and T2 are determined from a data set obtained from a magnetic resonance apparatus. The method includes: loading the data set from at least one tissue into a computing device; determining a region of interest; determining an average value of the free induction decay signal within the region of interest on each of the scans separately; detecting scans with outlier data in each data series; and, if a scan with outlier data is detected, identifying the scan in the data series; determining the relaxation time within the region of interest based on scans from the corresponding data series that are not identified as having outlier data; classifying the tissue as normal or abnormal based on predefined values, which are determined depending on the type of tissue analyzed.

A computer implemented method is usable to detect the presence of abnormal tissue through analysis of magnetic resonance relaxation times T1 and T2. The relaxation times T1 and T2 are determined from a data set obtained from a magnetic resonance apparatus. The method includes: loading the data set from at least one tissue into a computing device; determining a region of interest; determining an average value of the free induction decay signal within the region of interest on each of the scans separately; detecting scans with outlier data in each data series; and, if a scan with outlier data is detected, identifying the scan in the data series; determining the relaxation time within the region of interest based on scans from the corresponding data series that are not identified as having outlier data; classifying the tissue as normal or abnormal based on predefined values, which are determined depending on the type of tissue analyzed.

A method and apparatus for generating a T1 or T2 map for a three-dimensional (3D) image volume of a subject. The method includes acquiring first, second, and third 3D images of the image volume of the subject. Signal evolutions of voxels through the first to third 3D images by comparing voxel intensity levels of corresponding voxel locations in the first, second, and third 3D images. A simulation dictionary representing the signal evolutions for a number of different tissue parameter combinations is obtained. The T1 or T2 map is generated by comparing the determined signal evolutions to entries in the dictionary and by finding, for each of the determined signal evolutions, the entry in the dictionary that best matches the determined signal evolution.

A method and apparatus for generating a T1 or T2 map for a three-dimensional (3D) image volume of a subject. The method includes acquiring first, second, and third 3D images of the image volume of the subject. Signal evolutions of voxels through the first to third 3D images by comparing voxel intensity levels of corresponding voxel locations in the first, second, and third 3D images. A simulation dictionary representing the signal evolutions for a number of different tissue parameter combinations is obtained. The T1 or T2 map is generated by comparing the determined signal evolutions to entries in the dictionary and by finding, for each of the determined signal evolutions, the entry in the dictionary that best matches the determined signal evolution.

A method may include obtaining a plurality of imaging signals collected by applying a wave encoding gradient to a region of interest (ROI) of a subject. The method may also include obtaining a plurality of auxiliary signals associated with the ROI. The method may also include obtaining a point spread function corresponding to the wave encoding gradient. The method may also include determining, based on the plurality of auxiliary signals, temporal information relating to at least one temporal dimension of the ROI. The method may also include determining, based on the plurality of auxiliary signals, the plurality of imaging signals, and the point spread function, spatial information relating to at least one spatial dimension of the ROI. The method may also include generating at least one target image of the ROI based on the temporal information and the spatial information.