Systems and methods to implement Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can exceed four times the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Derivation of the estimator yields explicit probabilities of unwrapping errors and the posterior probability distribution for the velocity estimate; this in turn allows for optimized design of the phase-encoded acquisition.

A flexible no phase wrap (NPW) protocol using outer volume suppression (OVS) for reducing scan time in two-dimensional (2D) magnetic resonance imaging (MRI) is described. According to an example, a method comprises controlling, by a device comprising a processor, acquisition of a signal data associated with a region of interest (ROI) within an anatomical region of a subject using a using a MRI system, wherein the controlling comprises employing a combination of an OVS protocol and a NPW protocol with 2D MRI process. The method further comprises reconstructing, by the device, an image of the ROI from the signal data. Based on employing the combination, the phase field-of-view (FOV) can be reduced while still minimizing or eliminating wrap-around artifacts in the image, thereby reducing the scan time duration.

A magnetic resonance image reconstruction device according to an embodiment is a magnetic resonance image reconstruction device that reconstructs magnetic resonance image data in which an artifact due to undersampling is removed or reduced based on undersampled k-space data, and includes a reconstruction unit reconstructing the magnetic resonance image data using a reconstruction network having a correction module. The correction module includes a regularization block generating second image data by performing a regularization process on first image data using a first neural network, and a data consistency block generating third image data by performing a data consistency process so that k-space data corresponding to the second image data approaches the undersampled k-space data. The correction module further includes at least one of a data consistency adjustment block adjusting the data consistency process and a regularization adjustment block adjusting the regularization process.

A method of processing data by an imaging system is described. The imaging system generates a velocity data set and magnitude data set representative of a fluid. The method includes receiving velocity data set from the imaging system, calculating a phase variation data set from a wrapped phase field data set associated with the velocity data set, calculating a phase difference uncertainty data set from the magnitude data set, using the phase variation-data set and the phase difference uncertainty data set, performing a computational reconstruction of the phase field, data set to generate an unwrapped phase data set, converting the unwrapped phase to a first velocity field data set; and outputting a resultant velocity field set based upon the first velocity field data set.

An magnetic resonance imaging method and system is provided. The method includes: obtaining at least one first K-space dataset from a plurality of K-space datasets corresponding to a plurality of phases of an imaging object; for each of the at least one first K-space dataset, determining a target K-space dataset corresponding to the first K-space dataset by filling, based on at least one second K-space dataset, an undersampled region of the first K-space dataset; and generating a reconstructed image of the imaging object based on the target K-space dataset.

Systems and methods for training a denoising and dealiasing machine-learning model to generate denoised and dealiased image data are provided. The present disclosure provides techniques for training a denoising and dealiasing machine-learning (ML) model to generate denoised and dealiased imaging data. A method includes (1) training a first ML model using a first training dataset comprising first image data to obtain a second ML model; and (2) training (a) the second ML model or (b) a third ML model using a second training dataset to obtain a fourth ML model. The second training dataset includes (i) the first image data and (ii) training image data obtained by applying at least one of the second ML model or the third ML model to second image data. The denoising and dealiasing ML model may be either the fourth ML model or derived from the fourth ML model.

A method for reconstructing MR tomography images from asymmetrically acquired k-space raw data, with symmetrical and asymmetrical parts, may include reconstructing a phase image from the symmetrical k-space data, applying an iterative k-space reconstruction starting with a base image, and forming a working space via k-space transform. A weighting filter may be applied, assigning zero weight where no raw data exists, lower weight to symmetrical data, and non-zero weight to other data. A complex intermediate image is generated by image space transform of weighted k-space data, phase-corrected with the phase image, and the final result image is obtained as the real part of the phase-corrected intermediate image.

Systems and methods of image reconstruction are provided. A system may have a memory and a processor to receive data corresponding to magnetic resonance imaging coils and data corresponding to a region of interest within a field of view of the magnetic resonance imaging machine. By determining different weights to associate with virtualized magnetic resonance imaging coils, images may be reconstructed to favor signals associated with a region of interest and to disfavor interference associated with areas outside the region of interest.

Systems and methods for training a machine-learning model to generate denoised and dealiased image data are provided. The present disclosure provides techniques for training a machine-learning (ML) model to generate denoised and dealiased imaging data. A method includes (1) training a first ML model using a first training dataset comprising first image data to obtain a second ML model; and (2) training (a) the second ML model or (b) a third ML model using a second training dataset to obtain a fourth ML model. The second training dataset includes (i) the first image data and (ii) training image data obtained by applying at least one of the second ML model or the third ML model to second image data. The denoising and dealiasing ML model may be either the fourth ML model or derived from the fourth ML model.

Techniques are described for performing a phase correction for magnetic resonance imaging. The techniques include: for each slice of a target site: using a channel compression algorithm to determine each virtual channel of the slice using a channel compression matrix; determining a main virtual channel of the slice from each virtual channel; using phase correction data of the main virtual channel of the slice of the target site to perform phase correction on multi-segment image data of each virtual channel of the slice of the target site. This results in a reduction in phase errors in multi-segment MR image data.