Abstract: Lipid suppression in magnetic resonance imaging (“MRI”) is provided on a slice-by-slice basis using tailored local field control that is configured for lipid control for each slice in a planned slice prescription. Only those lipid voxels that fall within the bandwidth of the concurrent RF excitation pulse are targeted. Switched B0 offset fields are used to improve lipid suppression pulse performance by pushing water and lipids apart in the frequency domain. Multi-coil B0 shim arrays with rapidly switchable output currents that can be turned on during the lipid suppression pulse may be used. A convex optimization may be used to jointly solve for the shim currents and the lipid suppression pulse center frequency and bandwidth to optimize lipid suppression while minimizing water signal loss.

Abstract: A method for magnetic resonance imaging includes a) performing by an MRI scanner an MRI scan to acquire non-Cartesian k-space MRI acquisition data; b) estimating by the MRI scanner Cartesian k-space data from the non-Cartesian k-space MRI acquisition data, wherein the estimating comprises estimating each Cartesian k-space coordinate in the Cartesian k-space data from multiple neighboring non-Cartesian k-space coordinates using an ensemble of GRAPPA kernels, where each of the GRAPPA kernels is obtained from a non-linear model trained on calibration data from an MRI calibration scan; and c) reconstructing by the MRI scanner an MRI image from the estimated Cartesian k-space data.

Abstract: A method for magnetic resonance imaging with motion estimation comprises: a) performing with a magnetic resonance imaging apparatus a quantitative scout acquisition to produce a data acquisition sequence; b) predicting, based on the data acquisition sequence, navigator data throughout an entirety of the data acquisition sequence; and c) performing joint motion and ?B0 estimation from the navigator data.

Abstract: A method for magnetic resonance imaging acquires time-resolved k-space data by a magnetic resonance imaging apparatus, and generates contrast-weighted images by a multi-task generator G from the time-resolved k-space data. The multi-task generator G comprises a deep learning neural network trained using prospectively under-sampled ground truth images acquired using an acceleration factor of at least 8 without any fully-sampled ground truth images. The multi-task generator G is also trained using a physics guidance model and a semi-supervised loss function.

Abstract: A method for magnetic resonance imaging includes performing a pre-scan using a spin-echo diffusion acquisition with a one-time MRI pulse sequence to measure phase differences between diffusion and non-diffusion acquisitions, which correspond to an estimation of eddy-current-induced phase in a diffusion-prepared sequence; and performing a scan using arbitrary waveform shim currents applied to a shim array during the acquisition, wherein the shim currents are determined from the measured phase differences from the pre-scan to create opposite phase maps to compensate eddy-current or system-imperfection-induced phase differences; wherein the scan and pre-scan are performed using an MRI apparatus with a channel integrated receiver and the shim array.

Abstract: Motion correction in spatiotemporal time-resolving magnetic resonance imaging (“MRI”) include a motion estimation component and a motion correction component. The motion estimation component can include a spatiotemporal time-resolved data acquisition that is configured to acquire navigator data in order to obtain motion parameters and estimate changed in B0 inhomogeneity caused by subject motion. Motion-corrected reconstruction can be used to recover accurate motion-corrected images by modeling the motion into the reconstruction. A subspace reconstruction framework can be used for both navigator reconstruction when estimating motion parameters, and for reconstructing the motion-corrected images.

Abstract: In a method and system for reducing motion artifacts in magnetic resonance image data, a scout scan of the region of the patient is performed, a magnetic resonance (MR) measurement of the region of the patient is performed to acquire MR image data of the region of the patient, and motion correction is performed on the acquired MR image data based on the scout scan to generate corrected MR image data. The motion correction technique advantageously reduces an influence of a patient motion on the magnetic resonance image data.

Abstract: A method for reconstructing a MRI image may include: receiving MRI measurement data sets f1 to fN, each data set being acquired from an examination object based on a different MRI protocol of an MRI system; receiving MRI images u10 to uN0 corresponding to the MRI measurement data sets f1 to fN; performing one or more translation and rotation transformations on the MRI images u10 to uN0; applying one or more trained functions: to the transformed MRI images u10 to uN0, using a neural network, and to the MRI images u10 to uN0, using a forward-sampling operator; performing one or more inverse translation and rotation transformations on an output of the neural network; and generating at least one output MRI image uT based on an output of the forward-sampling operator, the inversely transformed output of the neural network, and the input MRI images u10 to uN0.

Abstract: Signal feature data are efficiently extracted from multi-contrast magnetic resonance images and applied to one or more machine learning algorithms to generate tissue feature data that indicate one or more tissue properties of a tissue depicted in the original multi-contrast images. Compact signal feature map data are extracted from the multi-contrast image data by generating or otherwise constructing subspace bases from prior signal data. and coefficient maps of the subspace bases are generated using a subspace reconstruction. A machine learning algorithm can be implemented to transform the signal feature maps to target tissue property parameters and/or to classify different tissue types.

Abstract: Images are reconstructed from k-space acquired with a magnetic resonance imaging (“MRI”) system using a multi-shot pulse sequence. In each iteration, a phase-aware image reconstruction, a data-consistency update across all shots or subsets of data, and a relative phase estimation across the reconstructed images for each shot are performed. In this way. the reconstruction framework recasts the problem as an iterative relative phase estimation problem, which allows for the use relative phase estimation techniques. Through an iterative search, an artifact-free combined image and the smooth relative phase between each shot in the multi-shot k-space data can be jointly estimated.

Abstract: Described here are systems and methods for producing images with a magnetic resonance imaging (“MRI”) system using a high-resolution, motion-robust, artifact-free segmented echo planar imaging (“EPI”) technique. In particular, a fast low angle excitation echo planar imaging technique (“FLEET”) using variable flip angle (“VFA”) radio frequency (“RF”) excitation pulses that are recursively designed to have a flat magnitude and phase profile across a slice for a range of different flip angles by accounting for longitudinal magnetization remaining after each preceding RF pulse is applied.

Abstract: Lipid suppression in magnetic resonance imaging (“MRI”) is provided on a slice-by-slice basis using tailored local field control that is configured for lipid control for each slice in a planned slice prescription. Only those lipid voxels that fall within the bandwidth of the concurrent RF excitation pulse are targeted. Switched B0 offset fields are used to improve lipid suppression pulse performance by pushing water and lipids apart in the frequency domain. Multi-coil B0 shim arrays with rapidly switchable output currents that can be turned on during the lipid suppression pulse may be used. A convex optimization may be used to jointly solve for the shim currents and the lipid suppression pulse center frequency and bandwidth to optimize lipid suppression while minimizing water signal loss.

Abstract: Techniques are disclosed related to the compensation of phase variations introduced into k-space lines, which cause imaging artifacts. The techniques utilize the detection of motion via an encoding plus motion model, which does not require the use of additional prospective or retrospective motion detection techniques. The techniques described herein use the encoding plus motion model to reconstruct an initial image from a set of motion states, and then calculate phase information from images that are projected form the initial reconstructed image using a projection onto convex sets (POCS). The phase information is incorporated into the encoding plus motion model over several iterations to minimize data consistency error, thereby generating a refined image that compensates for patient motion over the set of motion states.

Abstract: Systems and methods are provided for improving MRI data acquisition efficiency while providing more detailed information with high resolution and isotropic resolution without gaps. Improved data acquisition efficiency may be achieved by implementing a machine learning algorithm with a hardware processor and a memory to estimate imperfections in fast imaging sequences, such as a multi-shot echo planar imaging (MS-EPI) sequence. These imperfections, such as patient motion, physiological noise, and phase variations, may be difficult to model or otherwise estimate using standard physics-based reconstructions.

Abstract: Magnetic resonance imaging (“MRI”) using a PROPELLER echo-planar time-resolved imaging with dynamic encoding (“PEPTIDE”) scheme is described. The PEPTIDE scheme combines a PROPELLER-style trajectory with an echo-planar time-resolved imaging (“EPTI”) acquisition framework, along with dynamic-updating of sensitivity-encoding information.

Abstract: A method includes determining an initial magnetic resonance imaging, MRI, dataset (201) in image domain based on an initial reconstruction of MRI measurement data obtained using an undersampling scheme (400); and determining patches (231-233) of the initial MRI dataset (201) in accordance with a patching scheme, the patching scheme depending on the undersampling scheme (400); and, for each one of the patches (231-233): applying a machine-learned algorithm to obtain a respective patch (231-233) of a reconstructed MRI dataset, the machine-learned algorithm depending on the undersampling scheme (400); and combining the patches (231-233) of the reconstructed MRI dataset.

Abstract: Described here are systems and methods for producing images with a magnetic resonance imaging (“MRI”) system using a high-resolution, motion-robust, artifact-free segmented echo planar imaging (“EPI”) technique. In particular, a fast low angle excitation echo planar imaging technique (“FLEET”) using variable flip angle (“VFA”) radio frequency (“RF”) excitation pulses that are recursively designed to have a flat magnitude and phase profile across a slice for a range of different flip angles by accounting for longitudinal magnetization remaining after each preceding RF pulse is applied.

Abstract: Techniques are disclosed related to the compensation of phase variations introduced into k-space lines, which cause imaging artifacts. The techniques utilize the detection of motion via an encoding plus motion model, which does not require the use of additional prospective or retrospective motion detection techniques. The techniques described herein use the encoding plus motion model to reconstruct an initial image from a set of motion states, and then calculate phase information from images that are projected form the initial reconstructed image using a projection onto convex sets (POCS). The phase information is incorporated into the encoding plus motion model over several iterations to minimize data consistency error, thereby generating a refined image that compensates for patient motion over the set of motion states.

Abstract: Techniques are disclosed related to the compensation of phase offsets introduced into k-space lines as a result of encoding of blip gradients due when motion is present, which may be used for parallel magnetic resonance imaging (MRI) techniques such as blipped SMS or blipped CAIPIRINHA. The compensation of these additional phase offsets may prevent artifacts that would otherwise be present in the reconstructed images as a result of motion during the MRI scanning procedure. The additional phase offsets may be accounted for during the image acquisition phase of the MRI scan or, alternatively, during the image reconstruction phase.

Abstract: Techniques are disclosed related to the compensation of phase offsets introduced into k-space lines as a result of encoding of blip gradients due when motion is present, which may be used for parallel magnetic resonance imaging (MRI) techniques such as blipped SMS or blipped CAIPIRINHA. The compensation of these additional phase offsets may prevent artifacts that would otherwise be present in the reconstructed images as a result of motion during the MRI scanning procedure. The additional phase offsets may be accounted for during the image acquisition phase of the MRI scan or, alternatively, during the image reconstruction phase.