A computer-implemented method is provided for generating correction information for correcting mismatches in magnetic resonance measurements. Magnetic resonance data is received, wherein a generation of the magnetic resonance data includes several partial measurements by a magnetic resonance device. During each partial measurement, a k-space region is sampled at least partially, wherein the k-space regions of different partial measurements differ at least partially in their extent in the readout direction, and wherein the extent in the readout direction depends on prephasing gradients and readout gradients generated by the magnetic resonance device during the partial measurements. A trained function of a machine learning algorithm is applied to the received magnetic resonance data, wherein correction information for correcting a mismatch of the prephasing gradients and readout gradients is generated and output.

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.

Systems and methods for improving calibration of MRI imaging using echo-planar imaging (EPI) include a multi-shot radio frequency (RF) excitation during a calibration phase and a processor that calibrates the k-space for a slice by acquiring k-space data through multi-shot EPI data acquisition for a plurality of interleaved segments in the slice, each divided into a predetermined number of readout lines. Each EPI data acquisition includes providing a series of frequency encoding pulses throughout a readout period equal to the predetermined number of readout lines, providing a series of phase encoding pulses during a middle portion of the readout period corresponding to a middle section of the k-space, capturing magnetic resonance signals during the middle portion. The frequency and phase encoding pulse each include a rewinder pulse before a spoiler pulse after the magnetic resonance signals are captured. The processor creates a calibration model from the acquired k-space data based on the magnetic resonance signals during the middle portion, wherein k-space data corresponding to each segment in the slice is acquired before acquiring data for subsequent slices.

A method can include obtaining a scaling factor for a location proximate a metallic object by optimizing a function of an acquired dataset and a simulated dataset. The simulated dataset can include a first signal from a first pulse having a first excitation flip angle and a first refocusing flip angle. The simulated dataset can include a second signal from a second pulse having a second excitation flip angle and a second refocusing flip angle.

Disclosed herein is a method of medical imaging. The method comprises: receiving (200) an echo planar diffusion weighted magnetic resonance image (122) of a region of interest (309) descriptive of breast tissue; receiving (202) a fat suppressed T2 weighted magnetic resonance image (124) descriptive of the region of interest; segmenting (204) the echo planar diffusion weighted magnetic resonance image to identify high diffusion rate regions (128); segmenting (206) the fat suppressed T2 weighted magnetic resonance image to identify tissue regions (130); identifying (208) a portion of the tissue regions as advisory regions (134) by inputting the high diffusion rate regions and the tissue regions into an image processing module; and providing (210) the advisory regions as a segmentation of the fat suppressed T2 weighted magnetic resonance image.

Embodiments of the present invention provide a method for acquiring and processing magnetic resonance data, a magnetic resonance imaging system and method, and a computer-readable storage medium. The method for acquiring and processing magnetic resonance data comprises: populating, to a K-space, a plurality of sets of echo data acquired from a plurality of excitations of a tissue to be imaged, wherein at least two of the plurality of sets of echo data have opposite K-space populating orders; and reconstructing an image based on the echo data populated to the K-space.

A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

A computer-implemented method for generating an artifact corrected reconstructed contrast image from magnetic resonance imaging (MRI) data is provided. The method includes inputting into a trained deep neural network both a synthesized contrast image derived from multi-delay multi-echo (MDME) scan data or the MDME scan data acquired during a first scan of an object of interest utilizing a MDME sequence and a composite image, wherein the composite image is derived from both the MDME scan data and contrast scan data acquired during a second scan of the object of interest utilizing a contrast MRI sequence. The method also includes utilizing the trained deep neural network to generate the artifact corrected reconstructed contrast image based on both the synthesized contrast image or the MDME scan data and the composite image. The method further includes outputting from the trained deep neural network the artifact corrected reconstructed contrast image.

The present disclosure relates to magnetic resonance imaging technology for simultaneously measuring a plurality of magnetic resonance imaging parameters. According to one embodiment of the present disclosure, a magnetic resonance imaging apparatus includes a data collector for alternately collecting a steady-state-free-precession (SSFP)-FID signal and an SSFP-ECHO signal within a time of repetition to obtain AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data; a data processor for reconstructing a magnitude image and a phase image for each of the SSFP-FID signal and the SSFP-ECHO signal in the AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data and processing the AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data using the reconstructed magnitude images and phase images; and a parameter measuring device for measuring a plurality of magnetic resonance imaging parameters using a plurality of echo data based on the processed AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data.

A computer-implemented method of reducing noise in magnetic resonance (MR) images is provided. The method includes executing a neural network model of analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images. The pristine images are the corrupted images with noise reduced, and target output images of the neural network model are the pristine images. The method also includes receiving first MR signals and second MR signals, reconstructing first and second MR images based on the first MR signals and the second MR signals, and analyzing the first MR image and the second MR image using the neural network model. The method further includes deriving a denoised MR image based on the analysis, wherein the denoised MR image is a combined image based on the first MR image and the second MR image and outputting the denoised MR image.