Improved magnetic resonance imaging systems, methods and software are described including a low field strength main magnet, a gradient coil assembly, an RF coil system, and a control system configured for the acquisition and processing of magnetic resonance imaging data from a patient while utilizing a sparse sampling imaging technique.

Reference data relating to a portion of a patient anatomy during patient motion can be acquired from a magnetic resonance imaging system (MRI) to develop a patient motion library. During a time of interest, tracking data is acquired that can be related to the reference data. Partial volumetric data is acquired during the time of interest and at approximately the same time as the acquisition of the tracking data. A volumetric image of patient anatomy that represents a particular motion state can be constructed from the acquired partial volumetric data and acquired tracking data.

Improved magnetic resonance imaging systems, methods and software are described including a low field strength main magnet, a gradient coil assembly, an RF coil system, and a control system configured for the acquisition and processing of magnetic resonance imaging data from a patient while utilizing a sparse sampling imaging technique.

A magnetic resonance imaging apparatus according to an embodiment includes processing circuitry. The processing circuitry sets an imaging region and a labeling region based on at least a blood vessel structure, the imaging region being a region of a matrix of a plurality of divided voxels including different blood vessel regions, respectively, the labeling region being a region to which a labeling pulse for labeling blood flowing into the imaging region is applied. The processing circuitry acquires data of the imaging region by applying the labeling pulse to the labeling region by using an arterial spin labeling (ASL) method. The processing circuitry generates an image based on the data. The processing circuitry determines anomaly of the blood vessel regions by comparing signal values of the voxels included in the image.

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.

Reference data relating to a portion of a patient anatomy during patient motion can be acquired from a magnetic resonance imaging system (MRI) to develop a patient motion library. During a time of interest, tracking data is acquired that can be related to the reference data. Partial volumetric data is acquired during the time of interest and at approximately the same time as the acquisition of the tracking data. A volumetric image of patient anatomy that represents a particular motion state can be constructed from the acquired partial volumetric data and acquired tracking data.

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.

Systems and methods for generating a synthesized contrast enhanced medical image are provided. An input medical image is received. A synthesized contrast enhanced medical image is generated based on the input medical image using a trained machine learning based generator network. The synthesized contrast enhanced medical image includes one or more synthesized contrast enhanced regions of pathological tissue. The synthesized contrast enhanced medical image is output.

A system and method for producing a series of time-resolved magnetic resonance (MR) images is set forth. The system can perform method steps of encoding spatial information into an MRI signal by manipulating a phase of the MRI signal within an MRI system, generating and outputting a phase-encoded MRI signal over time by digitizing a plurality of time points in the MRI signal, repeating the generating and outputting step for a plurality of phase-encoded signals, each phase-encoded signal in synchrony with a trigger, producing a plurality of digitized time points, and reconstructing a series of time resolved MR images, each image of the series of MR images at one specific time point selected from the plurality of digitized time points for each phase-encoded step. Each image in the series of time-resolved MR images corresponding to a specific time point in a cyclic event.

In one aspect the disclosed technology relates to embodiments of a method (e.g., for automatic cine DENSE strain analysis) which includes acquiring magnetic resonance data associated with a physiological activity in an area of interest of a subject where the acquired magnetic resonance data includes one or more phase-encoded data sets. The method also includes determining, from at least the one or more phase-encoded data sets, a data set corresponding to the physiological activity in the area of interest where the reconstruction comprises performing phase unwrapping of the phase-encoded data set using region growing along multiple pathways based on phase predictions.