A system and method including receiving magnetic resonance (MR) imaging data from a first MR scanner device, the MR imaging data including data for a plurality of MR scans of different structural or anatomical regions; generating, based on the MR imaging data, normalized reference data including statistical information for each MR scan; learning a transformation, based on the normalized reference data, to correlate a set of input MR imaging data to the normalized reference data; and storing a record of the transformed imaging data.

Techniques for correcting gradient non-linearity bias in mean diffusivity measurements by MRI systems are shown and include minimal number of spatial correction terms to achieve sufficient error control using three orthogonal diffusion weighted imaging (DWI) gradients. The correction is based on rotation of system gradient nonlinearity tensor into a DWI gradient frame where spatial bias of b-matrix is described by its Euclidian norm. The techniques obviate time consuming multi-direction acquisition and noise-sensitive mathematical diagonalization of a full diffusion tensor for medium of arbitrary anisotropy.

A technique and associated imaging system is provided that selectively acquires the myelin water signal by utilizing a multiple inversion RF pulses to suppress a range of long T.sub.1 components including those from axonal and extracellular water. This leaves the myelin water, which has been suggested to have short T.sub.1, as the primary source of the image. After long T.sub.1 suppression, the resulting image is dominated by short T.sub.2 in the range of the myelin water (T.sub.2*<20 ms at 3 T). This result confirms that the short T.sub.1 component has short T.sub.2* and, therefore, the resulting image is a myelin water image.

The application provides a method, apparatus and computer program product for denoising a magnetic resonance diffusion tensor, wherein the method comprises: collecting data of K space; calculating a maximum likelihood estimator of a diffusion tensor according to the collected data of K space; calculating a maximum posterior probability estimator of the diffusion tensor by using sparsity of the diffusion tensor and sparsity of a diffusion parameter and taking the calculating maximum likelihood estimator as an initial value; and calculating the diffusion parameter according to the calculated maximum posterior probability estimator. The application solves the technical problem in the prior art of how to realize high precision denoising of diffusion tensor while not increasing scanning time and affecting spatial resolution, achieves the technical effects of effectively suppressing noises in the diffusion tensor and improving the estimation accuracy of the diffusion tensor.

In a method and apparatus for magnetic resonance imaging, in order to improve saturation of magnetic resonance signals during an acquisition sequence, the acquisition sequence includes at least one acquisition cycle, that includes: a preparation pulse set with a number of preparation pulses, a saturation pulse set that is disjoint from the preparation pulse set, with a number of saturation pulses, and a readout block set with a number of readout blocks. The acquisition cycle is temporally divided into a preparation phase and a readout phase, wherein the readout phase is temporally delimited from the preparation phase, and the readout phase follows the preparation phase in the acquisition cycle, and wherein the preparation phase includes at least one preparation pulse of the preparation pulse set, at least one saturation pulse of the saturation pulse set and no readout block of the readout block set, and the readout phase includes at least one saturation pulse of the saturation pulse set and at least one readout block of the readout block set.

A system for quantitative parameter mapping using magnetic resonance (MR) image includes an input for receiving a plurality of weighted MR images of a subject and a corresponding at least one imaging parameter for the plurality of weighted MR images, and a quantitative parameter mapping neural network coupled to the input and configured to estimate at least one tissue parameter and generate at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter. The quantitative parameter mapping neural network can be trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter. The system can further include a display coupled to the quantitative parameter mapping neural network to display the at least one quantitative map.

A magnetic resonance imaging (MRI) system and methods are provided for producing images of a subject. In some aspects, a method includes identifying a point in the cardiac cycle, performing an inversion recovery (IR) pulse at a selected time point from the pre-determined point, and sampling a k-space segment at an inversion time from the IR pulse that is substantially coincident with the pre-determined point. The method also includes repeating the IR pulse and k-space sampling for multiple inversion times, and multiple segments of k-space, in an interleaved manner, to generate datasets having T1-weighted contrasts determined by their respective inversion times. The method further includes reconstructing three-dimensional (3D) spatially-aligned images using the datasets, and generating a T1 recovery map by combining the 3D images. In some aspects, a prospective/retrospective scheme may be used to obtain data fully sampled in the center of k-space and randomly undersampled in the outer regions.

Described herein are systems, methods, and instrumentalities associated with generating multi-contrast MRI images associated with an MRI study. The systems, methods, and instrumentalities utilize an artificial neural network (ANN) trained to jointly determine MRI data sampling patterns for the multiple contrasts based on predetermined quality criteria associated with the MRI study and reconstruct MRI images with the multiple contrasts based on under-sampled MRI data acquired using the sampling patterns. The training of the ANN may be conducted with an objective to improve the quality of the whole MRI study rather than individual contrasts. As such, the ANN may learn to allocate resources among the multiple contrasts in a manner that optimizes the performance of the whole MRI study.

Some aspects of the present disclosure relate a method for magnetic resonance imaging, which can include acquiring, by applying an imaging pulse sequence, magnetic resonance data associated with a region of interest of a subject. The imaging pulse sequence can include a plurality of RF pulses configured to generate a desired image contrast, and an outer-volume suppression (OVS) module to attenuate the signal outside the region of interest. The method can further include reconstructing, from the acquired magnetic resonance data, a plurality of reduced field of view (rFOV) magnetic resonance images corresponding to the region of interest.

Retrospective magnetic resonance imaging (MRI) uses a deep neural network framework [102] to generate from MRI imaging data [100] acquired by an MRI apparatus using a predetermined imaging protocol tissue relaxation parametric maps and magnetic/radiofrequency field maps [104] which are then used to generate using the Bloch equations [106] predicted MRI images [108] corresponding to imaging protocols distinct from the predetermined imaging protocol. This allows obtaining a wide spectrum of tissue contrasts distinct from those of the acquired MRI imaging data.