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.

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.

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).

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).

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.

Systems and methods for generative adversarial networks (GANs) to remove artifacts from undersampled magnetic resonance (MR) images are described. The process of training the GAN can include providing undersampled 3D MR images to the generator model, providing the generated example and a real example to the discriminator model, applying adversarial loss, L2 loss, and structural similarity index measure loss to the generator model based on a classification output by the discriminator model, and repeating until the generator model has been trained to remove the artifacts from the undersampled 3D MR images. At runtime, the trained generator model of the GAN can be generate artifact-free images or parameter maps from undersampled MRI data of a patient.

The present application provides a breathing and motion monitoring method for an MRI system, an MRI system and method, and a storage medium. The MRI includes a scanner, a controller, and a signal processor. The scanner includes a radio-frequency transmit chain and a radio-frequency transmit coil, and an object under detection is positioned relative to the radio-frequency transmit coil. The controller is configured to control the scanner to perform a scanning sequence on the object under detection to acquire image data. The scanning sequence includes a radio-frequency excitation stage, a signal acquisition stage, and an idle stage. In the radio-frequency excitation stage, the radio-frequency transmit chain transmits a first radio-frequency pulse to the radio-frequency transmit coil. The signal processor is configured to acquire scattering parameters of the radio-frequency transmit coil in real time, wherein in the radio-frequency excitation stage, a first radio-frequency power signal detected on a line between the radio-frequency transmit chain and the radio-frequency transmit coil is acquired in real time, and the scattering parameters are acquired on the basis of the signal; and at least one of breathing information and motion information of the object under detection is acquired on the basis of the scattering parameters.

Disclosed herein is a medical system (100, 300, 500) comprising a memory (110) storing machine executable instructions (120) and a magnetic resonance reconstruction module (122). The magnetic resonance reconstruction module is configured to reconstruct a motion corrected tracer-kinetic map (126) from measured k-space data (124). The measured k-space data is undersampled. The measured k-space data is T1 weighted. The measured k-space data is dynamic contrast enhanced k-space data. The medical system further comprises a processor (104) configured for controlling the medical system. Execution of the machine executable instructions causes the processor to: receive (200) the measured k-space data; and reconstruct (202) the motion corrected tracer-kinetic map by inputting the measured k-space data into the magnetic resonance reconstruction module. The magnetic resonance reconstruction module (122) is configured to reconstruct the motion corrected tracer-kinetic map as a direct model-based reconstruction from the measured k-space data (124).

The present disclosure provides a system for MRI. The system may obtain a plurality of auxiliary signals and a plurality of imaging signals collected by applying an MRI pulse sequence simultaneously to a plurality of slice locations of a subject. For each of at least one target slice location of the plurality of slice locations, the system may generate at least one target image of the target slice location based on the plurality of auxiliary signals and the plurality of imaging signals. During the application of the MRI pulse sequence, phase modulation may be applied to at least one of the plurality of slice locations so that the plurality of slice locations have different phases during the readout of at least one of the plurality of imaging signals.

An adaptive reconstruction of MR data, including acquired MR data of a core region having core segments and simulated MR data of a peripheral region. The method includes ascertaining a peripheral signal based on the MR data of the peripheral region, determining a scaling factor for each core segment by taking into account the peripheral signal and a mean signal intensity of the MR data for the respective core segment, scaling the MR data of the core region by taking into account the MR data of each core segment and that of the scaling factor corresponding to the respective core segment, generating filtered MR data by combining the scaled MR data of the core region with the MR data of the peripheral region, and reconstructing image data from the filtered MR data.