Systems and methods for magnetic resonance imaging (MRI) are provided. The systems may obtain a sampling pattern associated with an image sequence. The sampling pattern may be associated with a plurality of phase encoding gradient field values. The systems may also obtain k-space data associated with the image sequence using the sampling pattern. The systems may further reconstruct the image sequence based on the k-space data. The sampling pattern may include a plurality of sampling points. Each of the plurality of sampling points may denote a k-space line associated with the k-space data. Each of the plurality of phase encoding gradient field values may correspond to one single sampling point during a time period associated with at least two consecutive images in the image sequence.

Apparatus and techniques are described herein for nuclear magnetic resonance (MR) projection imaging. Such projection imaging may be used to control radiation therapy delivery to a subject, such as including receiving reference imaging information, generating a two-dimensional (2D) projection image using imaging information obtained via nuclear magnetic resonance (MR) imaging, the 2D projection image corresponding to a specified projection direction, the specified projection direction including a path traversing at least a portion of an imaging subject, determining a change between the generated 2D projection image and the reference imaging information, and controlling delivery of the radiation therapy at least in part using the determined change between the obtained 2D projection image and the reference imaging information.

An apparatus to jointly measure oxygen utilization and tissue strain includes an imaging system and a computer processor operatively coupled to the imaging system. The computer processor is configured to control the imaging system to perform a pulse sequence on tissue of a subject. The computer processor also acquires oxygen utilization data and strain data responsive to the pulse sequence. The computer processor further determines an amount of strain on the tissue of the subject based at least in part on the strain data and an amount of oxygen utilization of the tissue of the subject based at least in part on the oxygen utilization data.

A method is disclosed for phase contrast magnetic resonance imaging (MRI) comprising: acquiring phase contrast 3D spatiotemporal MRI image data; inputing the 3D spatiotemporal MRI image data to a three-dimensional spatiotemporal convolutional neural network to produce a phase unwrapping estimate; generating from the phase unwrapping estimate an integer number of wraps per pixel; and combining the integer number of wraps per pixel with the phase contrast 3D spatiotemporal MRI image data to produce final output.

Systems and methods for magnetic resonance imaging (MRI) are provided. The systems may obtain a sampling pattern associated with an image sequence. The sampling pattern may be associated with a plurality of phase encoding gradient field values. The systems may also obtain k-space data associated with the image sequence using the sampling pattern. The systems may further reconstruct the image sequence based on the k-space data. The sampling pattern may include a plurality of sampling points. Each of the plurality of sampling points may denote a k-space line associated with the k-space data. Each of the plurality of phase encoding gradient field values may correspond to one single sampling point during a time period associated with at least two consecutive images in the image sequence.

A method may include acquiring MR signals by an MR scanner and generating image data in a k-space according to the MR signals. The method may also include classifying the image data into a plurality of phases. Each of the plurality of phases may have a first count of spokes. A spoke may be defined by a trajectory for filling the k-space. The method may also include classifying the plurality of phases of the image data into a plurality of groups and determining reference images based on the plurality of groups. Each of the reference images may correspond to the at least one of the phases of the image data. The method may further include reconstructing an image sequence based on the reference images and the plurality of phases of the image data.

An image reconstructing method includes: obtaining pieces of first k-space data acquired from a patient, first acquisition times corresponding to the pieces of first k-space data, and pieces of biological signal information of the patient in a time series, the pieces of first k-space data being sampled with time-varying undersampling pattern; generating pieces of second k-space data by inverse transforming an intermediate data which is generated by transforming the pieces of first k-space data, the pieces of second k-space data is a data that at least part of the undersampled point is filled; generating a pseudo second acquisition time of each of the pieces of second k-space data, based on the first acquisition times; performing a rearranging process on the pieces of second k-space data, based on the second acquisition times and the pieces of biological signal information; and generating images by performing a reconstructing process on the pieces of second k-space data resulting from the rearranging process.

Suppressing artifacts in MRI image acquisition data includes alternatives to phase cycling by using a Convolutional Neural Network to suppress the artifact-generating echos. A U-NET CNN is trained using phase-cycled artifact-free images for ground truth comparison with received displacement encoded stimulated echo (DENSE) images. The DENSE images include data from a single acquisition with both stimulated (STE) and T1-relaxation echoes. The systems and methods of this disclosure are explained as generating artifact-free images in the ultimate output and avoiding the additional data acquisition needed for phase cycling and shortens the scan time in DENSE MRI.

The invention provides a fluid analysis apparatus, a method for operating a fluid analysis apparatus, and a fluid analysis program that display a flow velocity vector such that the tendency of a fluid flow is easily checked. A representative two-dimensional flow velocity vector representing a plurality of two-dimensional flow velocity vectors obtained by projecting three-dimensional flow velocity vectors of a plurality of voxels that overlap each other in a projection direction of a projection plane to the projection plane is acquired from three-dimensional volume data that has information of the three-dimensional flow velocity vector indicating the flow velocity of a fluid in an anatomical structure for each voxel and is displayed.

An image reconstructing method includes: obtaining pieces of first k-space data acquired from a patient, first acquisition times corresponding to the pieces of first k-space data, and pieces of biological signal information of the patient in a time series, the pieces of first k-space data being sampled with time-varying undersampling pattern; generating pieces of second k-space data by inverse transforming an intermediate data which is generated by transforming the pieces of first k-space data, the pieces of second k-space data is a data that at least part of the undersampled point is filled; generating a pseudo second acquisition time of each of the pieces of second k-space data, based on the first acquisition times; performing a rearranging process on the pieces of second k-space data, based on the second acquisition times and the pieces of biological signal information; and generating images by performing a reconstructing process on the pieces of second k-space data resulting from the rearranging process.