A magnetic resonance (MR) system configured to acquire MR data from a subject using a set of waveform and pulse sequence commands to prepare a first state of vibration of the one or more hardware elements and/or the subject. Preparing includes generating the vibration matching gradient inducing the first vibrations of the one or more hardware elements and/or the subject, while the net magnetization vector of the subject is aligned along the longitudinal axis of the main magnetic field. The MR system is configured to acquire the MR data by generating at least two spin manipulating gradients for manipulating phases of nuclear spins within the subject. A vibration matching gradient is used for matching with the first state of vibration with the second state of vibration.

A method for phase-contrast magnetic resonance imaging (PC-MRI) acquires undersampled PC-MRI data using a magnetic resonance imaging scanner and reconstructs MRI images from the undersampled PC-MRI data by reconstructing a first flow-encoded image using a first convolutional neural network, reconstructing a complex difference image using a second convolutional neural network, combining the complex difference image and the first flow-encoded image to obtain a second flow-encoded image, and generating a velocity encoded image from the first flow-encoded image and second flow-encoded image using phase difference processing.

Systems and methods for automatic processing of input medical images are provided. A set of input medical images acquired at a plurality of locations on a patient is received. For each respective location of the plurality of locations, an image quality score is determined for each input medical image of the set of input medical images acquired at the respective location and one of the input medical images acquired at the respective location is selected based on the image quality scores. The selected input medical images are processed to correct for errors. One or more regions of interest are segmented from the processed selected input medical images. One or more hemodynamic measures are calculated from the processed selected input medical images based on the segmented one or more regions of interest. The calculated one or more hemodynamic measures are output.

A method and system for automated correction of phase error in MRI-based flow evaluation employs a computer processor programmed to execute a trained convolutional neural network (CNN) to receive and process image data comprising flow velocity data in three directions and magnitude data collected from a region of interest over a scan period from magnetic resonance imaging instrumentation. The image data is processed using the trained CNN to generate three output channels with pixelwise inferred corrections for the flow velocity data which are further smoothed using a regression algorithm. The smoothed corrections are added to the original image data to generate corrected flow data, which may be used for flow visualization and quantization.

A method for phase-contrast magnetic resonance imaging (PC-MRI) acquires undersampled PC-MRI data using a magnetic resonance imaging scanner and reconstructs MRI images from the undersampled PC-MRI data by reconstructing a first flow-encoded image using a first convolutional neural network, reconstructing a complex difference image using a second convolutional neural network, combining the complex difference image and the first flow-encoded image to obtain a second flow-encoded image, and generating a velocity encoded image from the first flow-encoded image and second flow-encoded image using phase difference processing.

A magnetic resonance imaging apparatus according to an embodiment includes sequence control circuitry and processing circuitry. The sequence control circuitry executes a first pulse sequence and a second pulse sequence, the first pulse sequence including a first spoiler pulse serving as a dephasing gradient pulse of a first amount, the second pulse sequence including a second spoiler pulse serving as a dephasing gradient pulse of a second amount being different from the first amount or the second pulse sequence not including a spoiler pulse serving as a dephasing gradient pulse. The processing circuitry performs a subtraction operation between a first data obtained from the first pulse sequence and a second data obtained from the second pulse sequence, thereby generating an image.

Aspects described herein estimate the pressure difference across a hollow region arising from fluid flow within the hollow region, based on an imaged fluid flow. The method utilises a complete description of fluid mechanical behaviour to derive an estimate of relative pressure or pressure difference over arbitrary flow segments. The method uses the concept of a virtual or arbitrary velocity field in the analysis of the work-energy of the fluid flow. Furthermore, the method uses statistical analysis to derive the acquired flow as a mean field and a related covariance quantity and uses this statistical description in the evaluation of virtual work-energy of the fluid flow. This assessment of virtual work-energy of the fluid flow is then used to derive an estimate of the pressure difference across any two given points (relative pressure) in the hollow region.

A variable density Cartesian sampling method that allows retrospective adjustment of temporal resolution, providing added flexibility for real-time applications where optimal temporal resolution may not be known in advance. The methods provide for a computationally efficient sampling methods where a first step includes producing a uniformly random sampling pattern using a golden ratio on a grid, and the second step is applying a nonlinear stretching operation to create a variable density sampling pattern. Diagnostic quality images may be recovered at different temporal resolutions.

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.

A system and method is provided for acquiring flow encoded data from a subject using a magnetic resonance imaging (MRI) system. The method includes acquiring flow encoded (FE) data with alternating encoding polarities and along two of three orthogonal directions through the subject over at least two cycles of the flow within the subject; and separating the FE data into directional FE datasets using a temporal filter that separates the FE data based on temporal modulation of the FE directions caused by the alternating encoding polarities extending over the at least two cycles of the flow within the subject that shift the Fourier spectrum of velocity waveforms corresponding to the FE data. The method also includes using the directional FE datasets to generate an image of the subject showing flow within the subject caused by the at least two cycles of flow within the subject.