The data acquisition device may include a fat-suppression pulse exertion module configured to exert a fat-suppression pulse on an imaging area at set intervals, the fat-suppression pulse being able to suppress an initial fat signal to a negative value and keep the fat signal corresponding to the intermediate echo datum of the echo data collected between two fat-suppression pulses within [0, a], and a being a preset threshold close to 0, and an excitation and acquisition module, configured to exert a radio frequency pulse train and a series of phase encoding gradients after each fat-suppression pulse, collect the corresponding echo data, and fill the echo data into K-space in linear filling mode.

A method for performing magnetic resonance fingerprinting includes acquiring a plurality of MR image datasets using at least two pulse sequence types, the plurality of MR image datasets representing signal evolutions for image elements in a region of interest, comparing the plurality of MR image datasets to a dictionary of signal evolutions to identify at least one parameter of the MR image datasets and generating a report indicating the at least one parameter of the MR image datasets.

According to one embodiment, a data processing apparatus includes processing circuitry. The processing circuitry acquires input data relating to a processing target including a plurality of data segments corresponding respectively to a plurality of imaging contrasts determined by a first pulse sequence. The processing circuitry generates output data relating to the processing target by applying a trained model to input data relating to the processing target. The processing circuitry outputs output data relating to the processing target.

A method for magnetic resonance imaging (MRI) may include cause, based on a pulse sequence, a magnetic resonance (MR) scanner to perform a scan on an object. The pulse sequence may include a steady-state sequence and an acquisition sequence that is different from the steady-state sequence. The steady-state sequence may correspond to a steady-state phase of the scan in which no MR data is acquired. The acquisition sequence may correspond to an acquisition phase of the scan in which MR data of the object is acquired. The method may also include generating one or more images of the object based on the MR data.

A method for motion correction of Magnetic Resonance (MR) images is provided. The method includes acquiring a k-space dataset for an object using an MR scanner, detecting or identifying corrupted k-space data from the acquired k-space dataset, extracting the corrupted k-space data from the acquired k-space dataset, recovering the corrupted k-space data, combining uncorrupted k-space data of the acquired k-space dataset with the recovered k-space data to form a full k-space dataset, and reconstructing an image for the object based on the full k-space dataset. A magnetic resonance imaging system for correcting corrupted k-space data of an entire k-space dataset is also provided.

In a method for reconstructing magnetic resonance (MR) image data from k-space data, k-space data of an image region of a subject are provided to a computer that is also provided with multiple navigator signals for the image region of the subject. The computer sorts the k-space data into multiple bins, the multiple bins representing different motion states of the subject. For each of the multiple bins, the computer executes a compressed sensing procedure to reconstruct the MR image data from the k-space data in the respective bin. Execution of the compressed sensing procedure includes solving an optimization problem comprising a data consistency component and a transform sparsity component. Motion information is incorporated by the computer into at least one of the data consistency component and the transform sparsity component of the optimization problem.

The invention relates to a magnetic resonance imaging system (100) for determining an approximation (150) of an electric conductivity distribution within a three-dimensional anatomical structure of interest. The determining comprises acquiring a first set of (3-n)-dimensional magnetic resonance data (144), reconstructing a (3-n)-dimensional phase distribution (146) using the (3-n)-dimensional magnetic resonance data (144), calculating a (3-n)-dimensional electric conductivity distribution (148) using spatial derivatives within the (3-n) dimensions and applying to the (3-n)-dimensional electric conductivity distribution (148) a scaling factor compensating for the relative reduction of dimensions by n.

The disclosure relates to a method for generating a magnetic resonance image from a measurement dataset. The measurement dataset is initially acquired from k space values. By means of a data processing facility the k space values are then automatically analyzed at least in terms of their size. Furthermore a modified measurement dataset is automatically generated from the measurement dataset by removing k space values whose size satisfies at least one predetermined threshold value criterion. The magnetic resonance image is then generated automatically from the modified measurement dataset.

In a method for generating an image data set and a reference image data set: a first raw data set is provided that is acquired with a MR system and that includes measurement signals at read-out points in k-space that lie on a first k-space trajectory; a second raw data set is provided that is acquired with the same MR system and at the same examination object at read-out points that lie on a second, different k-space trajectory that is different from the first k-space trajectory; image data sets are reconstructed from the first raw data set; a reference image data set is reconstructed from the second raw data set; the reference image data set is compared with each image dataset to generate respective similarity values; and an image data set is selected having a greatest similarity value.

The present invention relates to a magnetic resonance eye imaging method, wherein an eye image is obtained from magnetic resonance image data acquired while the eye is moving, comprising determining eye orientation information data during magnetic resonance image data acquisition; binning the acquired magnetic resonance image data into groups according to eye orientation information data; and constructing a magnetic resonance image eye image from a selection of groups of magnetic resonance image data.