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).

A magnetic resonance (MR) imaging method of correcting phase errors is provided. The method includes applying, by an MR system, a pulse sequence to acquire the precorrection MR image. The method also includes acquiring, by the MR system, reference k-space data having a field of view (FOV) in a phase-encoding direction that is twice or more greater than an FOV of the precorrection MR image in the phase-encoding direction, wherein the reference k-space data and MR signals of the precorrection MR image are acquired with the same type of pulse sequences. The method further includes splitting the reference k-space data into first k-space data and second k-space data, generating a phase error map based on the first k-space data and the second k-space data, generating a phase-corrected image of the precorrection MR image based on the phase error map, and outputting the phase-corrected image.

A technology is provided for multi-component and/or multi-configuration imaging with coding, signal composition, signal model, structure model, structure model learning, decoding, reconstruction, performance prediction and performance enhancement. A magnetic resonance imaging example comprises acquiring signal samples in accordance with a coding scheme and a k-space sampling scheme, identifying a structure model in a data assembly formed using an extraction operation, and generating a result consistent with both the acquired signal samples and the identified structure model.

According to one embodiment, a magnetic resonance imaging apparatus includes sequence control circuitry and processing circuitry. The sequence control circuitry performs under-sampled data acquisition whose sample points are located at an equal interval in k-space and acquires k-space frames. The processing circuitry generates a plurality of k-space frames related to a plurality of time resolutions based on the k-space frames. In each of the plurality of k-space frames, the sample points are located at an equal interval, and the interval differs for each of the plurality of k-space frames. The processing circuitry generates a time-series image based on the plurality of k-space frames.

A method for marking an entry position for an injection apparatus on a surface of a patient positioned inside a patient receiving region of a magnetic resonance device. The method includes providing a virtual entry position, introducing a marking apparatus into the patient receiving region, acquiring time-resolved MR image data from the marking apparatus by the magnetic resonance device, ascertaining a current position of the marking apparatus based on the time-resolved MR image data, iteratively changing the current position of the marking apparatus using the virtual entry position and the current position, and delivering the liquid from the marking apparatus when the current position matches the virtual entry position.

Disclosed herein is a medical instrument (100, 300, 400, 500) comprising: a memory (110) storing machine executable instructions (120) and a tomographic data assessment module (122) and a processor (106) configured for controlling the medical instrument. Execution of the machine executable instructions causes the processor to receive (200) measured tomographic data (124). The measured tomographic data is configured for being reconstructed into a tomographic image (308) of a subject (418). Execution of the machine executable instructions further causes the processor to receive (202) an image quality indicator (126, 126′, 126″) by inputting the measured tomographic data into the tomographic data assessment module. The tomographic data assessment module is configured for generating the image quality indicator in response to inputting the measured tomographic data. Execution of the machine executable instructions further causes the processor to provide (204) the image quality indicator to an operator using an operator signaling system (108).

In a computer-implemented method of training a reconstruction neural network algorithm used to reconstruct a Magnetic Resonance Imaging (MRI) image, a prediction of training MRI image is determined based on training MRI raw data and using the reconstruction neural network algorithm. A prediction of a presence or absence of the object in the training MRI image is determined based on the prediction of the training MRI image and using an object-detection algorithm. A loss value is determined based on a first difference between the ground truth of the training MRI image and the prediction of the training MRI image, and further based on a second difference between the ground truth of the presence or absence of the object and the prediction of the presence or absence of the object. Weights of the reconstruction neural network algorithm are adjusted based on the loss value and using a training process.

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.

The present disclosure provides a system and method for magnetic resonance imaging. The method may include obtaining a first set of imaging data, the first set of imaging data being sampled in multiple shots, each shot of the multiple shots corresponding to a plurality of echo times, the first set of imaging data including partially sampled data in a first k space; obtaining a second set of imaging data, the second set of imaging data including fully sampled data in a central region of a second k space; determining fitting data in the first k space based on the first set of imaging data and the second set of imaging data; and/or generating a target image based on the fitting data in the first k space and the first set of imaging data in the first k space.

Techniques for suppressing noise in an environment of a magnetic resonance (MR) imaging system having at least one primary coil and at least one auxiliary sensor. The techniques involve estimating a transform, that, when applied to noise received by the at least one auxiliary sensor, provides an estimate of noise received by the at least one primary coil. The transform is estimated from data obtained by the at least one primary coil and the least one auxiliary sensor, with the data being weighted prior to estimation to remove or suppress data in regions with a high signal to noise ratio. In turn, the estimated transform may be applied to noise measured by the at least one auxiliary sensor during imaging of a patient, to estimate and suppress noise present in the MR signals received by the at least one primary coil during imaging.