A magnetic resonance imaging apparatus according to an embodiment includes sequence controlling circuitry and processing circuitry. The sequence controlling circuitry is configured to execute (i) a first pulse sequence in which a spatially selective Inversion recovery (IR) pulse and a spatially non-selective IR pulse are applied, and subsequently an acquisition is performed and (ii) a second pulse sequence in which the spatially non-selective IR pulse is applied without applying the spatially selective IR pulse, and subsequently an acquisition is performed, while varying the first TI period, with respect to a plurality of first TI periods. The processing circuitry is configured to calculate a second TI period to be used in a third pulse sequence and a fourth pulse sequence, based on data obtained from the first pulse sequence and the second pulse sequence. The sequence controlling circuitry executes (iii) the third pulse sequence in which the spatially selective IR pulse and the spatially non-selective IR pulse are applied, and subsequently an acquisition is performed and (iv) the fourth pulse sequence in which the spatially non-selective IR pulse is applied without applying the spatially selective IR pulse, and subsequently an acquisition is performed. The processing circuitry generates a magnetic resonance image of an imaged region based on data obtained from the third pulse sequence and the fourth pulse sequence.

The following relates generally to accelerated magnetic resonance imaging (MRI) reconstruction. In some embodiments, a MRI machine learning algorithm is trained based on reference MRI data in non-Cartesian k-space. During the training, at each iteration of a plurality of iterations: (i) a non-Cartesian sampling trajectory ω may be optimized under the physical constraints, and/or (ii) an image reconstructor may be jointly iteratively optimized. Examples of the image reconstructor include a convolutional neural network (CNN) denoiser, a model-based deep learning (MoDL) image reconstructor, iterative image reconstructor, a regularizer, and an invertible neural network.

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 magnetic resonance imaging (MRI) system and method for acquiring magnetic resonance (MR) images using a pulse sequence implementing driven equilibrium and quadratic phase cycling techniques is provided. The method includes, during a pulse repetition period of a pulse sequence and using a quadratic phase cycling scheme, applying a first RF pulse to deflect a net magnetization vector associated with the subject from a longitudinal plane into a transverse plane; after applying the first RF pulse, applying a first sequence of RF pulses each of which flips the net magnetization vector by approximately 180 degrees within the transverse plane; and after applying the first sequence of RF pulses, applying a second RF pulse to deflect the net magnetization vector from the transverse plane to the longitudinal plane.

A magnetic resonance imaging apparatus according to an embodiment includes sequence controlling circuitry and processing circuitry. The sequence controlling circuitry is configured to execute (i) a first pulse sequence in which a spatially selective Inversion recovery (IR) pulse and a spatially non-selective IR pulse are applied, and subsequently an acquisition is performed and (ii) a second pulse sequence in which the spatially non-selective IR pulse is applied without applying the spatially selective IR pulse, and subsequently an acquisition is performed, while varying the first TI period, with respect to a plurality of first TI periods. The processing circuitry is configured to calculate a second TI period to be used in a third pulse sequence and a fourth pulse sequence, based on data obtained from the first pulse sequence and the second pulse sequence. The sequence controlling circuitry executes (iii) the third pulse sequence in which the spatially selective IR pulse and the spatially non-selective IR pulse are applied, and subsequently an acquisition is performed and (iv) the fourth pulse sequence in which the spatially non-selective IR pulse is applied without applying the spatially selective IR pulse, and subsequently an acquisition is performed. The processing circuitry generates a magnetic resonance image of an imaged region based on data obtained from the third pulse sequence and the fourth pulse sequence.

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.

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.

The invention relates to a method of MR imaging of an object (10) placed in an examination volume of an MR apparatus (1). It is an object of the invention to enable fast 3D MR imaging that provides motion-compensation and also allows a precise compensation for system imperfections. The method of the invention comprises the steps of: —subjecting the object (10) to a number of shots (S1-S4) of a 3D imaging sequence, wherein a train of MR signals is generated by each shot (S1-S4), each MR signal representing a k-space profile, wherein the set of k-space profiles of each shot (S1-S4) comprises at least one navigator profile and a number of imaging profiles; —acquiring the MR signals; —deriving motion information from the at least one navigator profile; and —reconstructing an MR image from the imaging profiles, wherein a motion-compensation is applied based on the motion information. Motion-induced phase errors can be derived from the navigator profiles, wherein the motion-compensation involves a corresponding phase-correction. Further, phase errors caused by magnetic field gradient imperfections and/or eddy currents can be derived from the navigator profiles and a corresponding phase-correction can be applied during image reconstruction. Moreover, the invention relates to an MR apparatus (1) for carrying out this method as well as to a computer program to be run on an MR apparatus (1).

The present disclosure is related to systems and methods for magnetic resonance imaging (MRI). The method includes obtaining a plurality of target sets of k-space data by filling target MR signals acquired by a plurality of coils of an MRI device into k-space along a corkscrew trajectory. The method includes obtaining a coil sensitivity of each of the plurality of coils. The method includes obtaining a point spread function corresponding to the corkscrew trajectory. The method includes generating a target image based on an objective function.

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.