The invention relates to a method of MR imaging of an object (10). It is an object of the invention to enable MR imaging in the presence of motion of the imaged object, wherein full use is made of the acquired MR signal and a high-quality MR image essentially free from motion artefacts is obtained. The method of the invention comprises the steps of: generating MR signals by subjecting the object (10) to an imaging sequence comprising RF pulses and switched magnetic field gradients; acquiring the MR signals as signal data over a given period of time (T); subdividing the period of time into a number of successive time segments (SO, S1, S2, . . . Sn); deriving a geometric transformation (DVF1, DVF2, . . . DVFn) in image space for each pair of consecutive time segments (S0, S1, S2, . . . Sn), which geometric transformation (DVF1, DVF2, . . . DVFn) reflects motion occurring between the two time segments of the respective pair; and reconstructing an MR image from the signal data, wherein a motion compensation is applied according to the derived geometric transformations (DVF1, DVF2, . . . DVFn). Moreover, the invention relates to an MR device (1) and to a computer program for an MR device (1).

Disclosed herein is a method of training a neural network (214) to perform a SENSE magnetic resonance imaging reconstruction. The method comprises receiving (100) initial training data, wherein the initial training data comprises sets of initial training complex channel images each paired with a predetermined number of initial ground truth images. The method further comprises generating (102) additional training data by performing data augmentation on the initial training data such that the data augmentation comprises adding a distinct phase offset to each of the set of initial training complex channel images during generation of the sets of additional training complex channel images. The method further comprises inputting (104) the sets of additional training complex channel images into the neural network and receiving in response a predetermined number of output training images and performing deep learning using the output training images.

Distortion generated in an image is effectively corrected in imaging using an EPI sequence such as DWI without extending an imaging time. After one excitation RF pulse of EPI is applied, a navigator scan in which the polarity of the phase encoding is opposite to that of the main scan is performed continuously to the main scan, and the distortion of the image by using the navigator scan data obtained by the navigator scan is corrected. In a case of multi-shot, phase information obtained from the navigator scan data for each shot is used to perform phase correction and multi-shot reconstruction on the main scan data of each shot.

A magnetic resonance imaging apparatus according to an embodiment includes a processing circuit. The processing circuit acquires a plurality of echo signals not to be encoded corresponding to a plurality of respective shots about acquisition of a plurality of echo signals having been encoded by magnetic resonance imaging for a subject, compares the echo signals not to be encoded with each other about the shots, and identifies a shot to be removed about generation of a magnetic resonance image about the subject out of the shots based on a comparison result of the echo signals not to be encoded.

In one embodiment, an MRI apparatus includes: processing circuitry configured to: set a first pulse sequence and a second pulse sequence, wherein, in the first pulse sequence, a first gradient pulse is applied between two adjacent refocusing pulses, and, in the second pulse sequence, a second gradient pulse being different in pulse shape from the first gradient pulse is applied between two adjacent refocusing pulses, wherein: the scanner is configured to acquire first signals and second signals; and the processing circuitry is configured to generate at least one first image and at least one second image; and calculate a T2 value of a body fluid of the object from the at least one first image and the at least one second image in such a manner that influence of movement including diffusion of the body fluid is removed.

The present invention relates to a magnetic resonance imaging system (10), comprising at least one gradient coil (20), and a processing unit (30). The processing unit is configured to control a gradient coil to produce intentional noise for sedation monitoring of a patient.

Motion contaminated magnetic resonance (MR) images for training an artificial neural network to remove motion artifacts from the MR images are difficult to obtain. Described herein are systems, methods, and instrumentalities for injecting motion artifacts into clean MR images and using the artificially contaminated images for machine learning and neural network training. The motion contaminated MR images may be created based on clean source MR images that are associated with multiple physiological cycles of a scanned object, and by deriving MR data segments for the multiple physiological cycles based on the source MR images. The MR data segments thus derived may be combined to obtain a simulated MR data set, from which one or more target MR images may be generated to exhibit a motion artifact. The motion artifact may be created by manipulating the source MR images and/or controlling the manner in which the MR data set or the target MR images are generated.

A magnetic resonance (MR) imaging method of correcting nonuniformity in diffusion-weighted (DW) MR images of a subject is provided. The method includes applying a DW pulse sequence along a plurality of diffusion directions with one or more numbers of excitations (NEX), and acquiring a plurality of DW MR images of the subject along the plurality of diffusion directions with the one or more NEX. The method also includes deriving a reference image and a base image based on the plurality of DW MR images, generating a nonuniformity factor image based on the reference image and the base image, and combining the plurality of DW MR images into a combined image. The method also includes correcting nonuniformity of the combined image using the nonuniformity factor image, and outputting the corrected image.

The present invention is to acquire a multiphase image while avoiding extension of imaging time and excluding an influence of displacement of an image of each multiphase due to a motion. A method for collecting measurement data is to repeat sampling such that low-frequency data and high-frequency data have different densities. At this time, a sampling interval is set shorter than a motion cycle. Motion information is acquired in parallel with imaging, and measurement data obtained in time series is divided into a plurality of time phases based on the motion information so as to obtain a multiphase image. Displacement correction between multiphase images is performed, and then the multiphase images are integrated. Alternatively, measurement data after the displacement correction is used to generate a time-series image.

In one embodiment, a Magnetic Resonance Imaging (MRI) apparatus includes: an RF coil configured to perform A/D conversion on a magnetic resonance (MR) signal received from an object and wirelessly transmit the MR signal; a main body configured to wirelessly receive the MR signal and generate a system clock; first communication circuitry configured to transmit the system clock by surface electric field communication using electric field propagation along a body surface of the object; and second communication circuitry provided in the RF coil and configured to receive the system clock transmitted by the surface electric field communication, wherein the RF coil is configured to operate based on the received system clock.