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.

The invention provides for a medical imaging system comprising: a memory for storing machine executable instructions; a processor for controlling the medical instrument. Execution of the machine executable instructions causes the processor to: receive MRF magnetic resonance data acquired according to an MRF magnetic resonance imaging protocol of a region of interest; reconstruct an MRF vector for each voxel of a set of voxels descriptive of the region of interest using the MRF magnetic resonance data according to the MRF magnetic resonance imaging protocol; calculate a preprocessed MRF vector (126) for each of the set of voxels by applying a predetermined preprocessing routine to the MRF vector for each voxel, wherein the predetermined preprocessing routine comprises normalizing the preprocessed MRF vector for each voxel; calculate an outlier map for the set of voxels by assigning an outlier score to the preprocessed MRF vector using a machine learning algorithm.

For radial sampling in magnetic resonance imaging (MRI), a rescaling factor is determined from k-space data for each coil. The rescale factor is inversely proportional to the streak energy in the k-space data. The k-space data from the coils is rescaled for reconstruction, such as weighting the k-space data by the rescale factor in a data consistency term of iterative reconstruction. The rescale factor is additionally or alternatively used to determine a correction field for correction of intensity bias applied to intensities in the image-object space after reconstruction. These approaches may result in a diagnostically useful bias-corrected image with reduced streak artifact while benefiting from the efficient computation (i.e., computer operates to reconstruct more quickly).

In a magnetic resonance (MR) method and apparatus for determining an MR image or an MR fat image of an examination subject, first and second MR signal datasets are provided to a computer, respectively obtained at first and second echo times. The computer defines a signal model and determines possible solution candidates for values of parameters of the signal model for each pixel of the two MR signal datasets so that the MR signals thereof are matched as well as possible. A correct solution is selected from the solution candidates, using a calculated phase map, based on predetermined assumptions regarding the calculated phase map. The MR water image or the MR fat image is determined using the correct solution.

In an image acquired by a plurality of receiver coils with the use of MRI, separated images are obtained by separating spatially overlapping signals according to PI method, and noise in the separated images is eliminated with a high degree of precision. A complex image spatially overlapping is measured from nuclear magnetic resonance signals received by a plurality of receiver coils, and spatially overlapping signals are separated and a plurality of separated images are calculated, by using sensitivity information of the plurality of receiver coils. Then, noise is eliminated based on a correlation of noise mixed between the separated images.

Imaging failure of a positioning image due to the difference in the position or the size of a subject placed in the examination space is prevented, and accordingly, the extension of the examination time is prevented. A pre-scan for appropriately setting the imaging position for positioning imaging is automatically performed prior to the positioning imaging and the main imaging of an MRI apparatus, and a region where an examination part of a subject is present (the extent of the examination part) is detected using the measurement data. By using the detected extent of the examination part, it is possible to subsequently determine the imaging position or calculate the scan parameters used for imaging.

Image reconstruction systems and methods include providing sensitivity maps for coils of a magnetic resonance imaging (MRI) system to a neural network. The systems and methods also include providing interleaved k-space data to the neural network, wherein the interleaved k-space data includes partial k-space data interleaved with zeros, or synthesized k-space data, to provide an extended field of view (FOV) different from a FOV utilized during acquisition of the partial k-space data, wherein the partial k-space data were obtained during a scan of a region of interest with the MRI system. The systems and methods further include outputting, from the neural network, a final reconstructed MR image based at least on the sensitivity maps and the interleaved k-space data, wherein the final reconstructed MR image includes the FOV utilized during the acquisition of the partial k-space data.

A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

Techniques are disclosed for acquiring at least two measurement datasets, each consisting of measurement data. The two measurement datasets are recorded at points in time at which spins of a first spin species present in the examination object have different phase positions from spins of a second spin species present in the examination object. Moreover, the two measurement datasets are recorded in each case while switching readout gradients of different polarity, and thus the desired measurement datasets may be recorded faster than conventional approaches.