A method for magnetic resonance imaging (MRI) is provided. The method may include obtaining scan data of a subject. The scan data may be acquired by an MR scanner at a time according to a pulse sequence. The method may include obtaining motion data of the subject. The motion data of the subject may be acquired by one or more sensors at the time. The motion data may reflect a motion state of the subject at the time. The method may also include determining, based on the motion data of the subject, a processing strategy indicating whether using the scan data to fill one or more k-space lines corresponding to the pulse sequence in a k-space. The method may further include obtaining k-space data based on the processing strategy.

A system and method is provided for controlling physiological-noise in functional magnetic resonance imaging using raw k-space data to extract physiological noise effects. The method can identify these effects when they are separable and directly reflects the artefactual effects on fMRI data, without the need for external monitoring or recording devices and to be compensated for via rigorous statistical analysis modeling of such noise sources. The physiological fluctuations may be treated as global perturbations presented around the origin point in a k-space 2D slice. Each k-space 2D slice may be acquired at a very short repetition time with an effective sampling rate to sample cardiac and respiratory rhythms through proper reordering and phase-unwarping techniques applied to the raw k-space data.

A magnetic resonance apparatus with a scanner unit, a patient receiving area at least partly surrounded by the scanner unit, and a patient display unit arranged within the patient receiving area, wherein the patient display unit includes a reflective display.

Neural network based systems, methods, and instrumentalities may be used to remove motion artifacts from magnetic resonance (MR) images. Such a neural network based system may be trained to perform the motion artifact removal tasks without reference (e.g., without using paired motion-contaminated and motion-free MR images). Various training techniques are described herein including one that feeds the neural network with pairs of MR images with different levels of motion contamination and forces the neural network learn to correct the motion contamination by transforming a first image of a contaminated pair into a second image of the contaminated pair. Other neural network training techniques are also described with an aim to reduce the reliance on training data that is difficult to obtain.

A method for performing real-time magnetic resonance (MR) imaging on a subject is disclosed. A prep pulse sequence is applied to the subject to obtain a high-quality special subspace, and a direct linear mapping from k-space training data to subspace coordinates. A live pulse sequence is then applied to the subject. During the live pulse sequence, real-time images are constructed using a fast matrix multiplication procedure on a single instance of the k-space training readout (e.g., a single k-space line or trajectory), which can be acquired at a high temporal rate.

For reconstruction of an image in MRI, unsupervised training (i.e., data-driven) based on a scan of a given patient is used to reconstruct model parameters, such as estimating values of a contrast model and a motion model based on fit of images generated by the models for different readouts and times. The models and the estimated values from the scan-specific unsupervised training are then used to generate the patient image for that scan. This may avoid artifacts from binning different readouts together while allowing for scan sequences using multiple readouts.

K-space data obtained from a magnetic resonance imaging scan where motion was detected is split into two parts in accordance with the timing of the motion to produce first and second sets of k-space data corresponding to different poses. Sub-images are reconstructed from the k first and second sets of k-space data, which are used as inputs to a deep neural network which transforms them into a motion-corrected image.

A system includes a machine readable storage medium storing instructions and a processor to execute the instructions. The processor executes the instructions to receive radial k-space magnetic resonance imaging (MRI) data of a patient and determine a series of dipole sources via direct dipole decomposition of the radial k-space MRI data. The processor executes the instructions to identify an activation within the patient based on the series of dipole sources.

Techniques are disclosed for use in magnetic resonance imaging (MRI) for exciting spins of a first material and spins of a second material. A first spin echo signal is acquired when the excited spins include a first phase difference, which is given by Δ, and a second spin echo signal is acquired when the excited spins of the first material and the excited spins of the second material include a second phase difference, which is given by −Δ. An absolute value of Δ lies within the interval ]0,π[. A first image for the first material and/or a second image for the second material is generated by a computing unit depending on the first spin echo signal and the second spin echo signal.

In one embodiment, a magnetic resonance imaging apparatus includes: a scanner that includes a static magnetic field magnet configured to generate a static magnetic field, a gradient coil configured to generate a gradient magnetic field, and a WB (Whole Body) coil configured to apply an RF pulse to an object; and processing circuitry. The processing circuitry is configured to: set (i) a pulse sequence in which a sequence element is repeated, the sequence element including at least an inversion pulse and (ii) a data acquisition sequence executed after a delay time from the inversion pulse; and cause the scanner to execute the pulse sequence by using virtual gating.