Abstract: Higher quality diffusion metrics and/or diffusion-weighted images are generated from lower quality input diffusion-weighted images using a suitably trained neural network (or other machine learning algorithm). High-fidelity scalar and orientational diffusion metrics can be extracted using a theoretical minimum of a single non-diffusion-weighted image and six diffusion-weighted images, achieved with data-driven supervised deep learning. As an example, a deep convolutional neural network (“CNN”) is used to map the input non-diffusion-weighted image and diffusion-weighted images sampled along six optimized diffusion-encoding directions to the residuals between the input and output high-quality non-diffusion-weighted image and diffusion-weighted images, which enables residual learning to boost the performance of CNN and full tensor fitting to generate any scalar and orientational diffusion metrics.

Abstract: Super-resolution images are generated from standard-resolution images acquired with a magnetic resonance imaging (“MRI”) system. More particularly, super-resolution (e.g., sub-millimeter isotropic resolution) images are generated from standard-resolution images (e.g., images with 1 mm or coarser isotropic resolution) using a deep learning algorithm, from which accurate cortical surface reconstructions can be generated.

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: Higher quality diffusion metrics and/or diffusion-weighted images are generated from lower quality input diffusion-weighted images using a suitably trained neural network (or other machine learning algorithm). High-fidelity scalar and orientational diffusion metrics can be extracted using a theoretical minimum of a single non-diffusion-weighted image and six diffusion-weighted images, achieved with data-driven supervised deep learning. As an example, a deep convolutional neural network (“CNN”) is used to map the input non-diffusion-weighted image and diffusion-weighted images sampled along six optimized diffusion-encoding directions to the residuals between the input and output high-quality non-diffusion-weighted image and diffusion-weighted images, which enables residual learning to boost the performance of CNN and full tensor fitting to generate any scalar and orientational diffusion metrics.

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: Methods for reducing scan time in magnetic resonance imaging (“MRI”), particularly when imaging three-dimensional image volumes, using a simultaneous time-interleaved multislice (“STIMS”) acquisition are described. The unused time in each repetition time (“TR”) period is exploited to provide an additional reduction in encoding time for a three-dimensional acquisition (e.g., a 3D whole brain coverage). Groups of spatially interleaved slices are excited in a single TR, with the excitation and acquisition of the groups of slices being interleaved in time.

Abstract: Systems and methods for simultaneously acquiring high-resolution images of a subject from multiple different slice locations using magnetic resonance imaging (“MRI”) are described. The present invention overcomes the aforementioned drawbacks by providing method for producing a plurality of images of a subject with a magnetic resonance imaging (“MRI”) system. A radio frequency (RF) excitation field is applied by the MRI system to a portion of a subject that includes a plurality of slice locations. First data are simultaneously acquired from each of the plurality of slice locations by the MRI system.

Abstract: Super-resolution images are generated from standard-resolution images acquired with a magnetic resonance imaging (“MRI”) system. More particularly, super-resolution (e.g., sub-millimeter isotropic resolution) images are generated from standard-resolution images (e.g., images with 1 mm or coarser isotropic resolution) using a deep learning algorithm, from which accurate cortical surface reconstructions can be generated.

Abstract: Systems and methods for simultaneous multislice (“SMS”} magnetic resonance imaging (“MRI”}, in which a random blip gradient encoding scheme is utilized to impart a different phase to each of a plurality of different slice locations. Because of the random blip gradient encoding, the amount of the imparted phase is randomized for each phase encoding step in a Cartesian k-space trajectory. This data acquisition strategy leads to incoherent aliasing artifacts across the simultaneously excited slices. Images of the individual slices can be reconstructed using a compressed sensing framework.

Abstract: Systems and methods for reconstructing images using a hierarchically semiseparable (“HSS”) solver to compactly represent the inverse encoding matrix used in the reconstruction are provided. The reconstruction method includes solving for the actual inverse of the encoding matrix using a direct (i.e., non-iterative) HSS solver. This approach is contrary to conventional reconstruction methods that repetitively evaluate forward models (e.g., compressed sensing or parallel imaging forward models).

Abstract: In a method for controlling a radio-frequency transmitter of a magnetic resonance imaging apparatus to apply an inversion pulse to a sample magnetization, in a multi-shot readout phase, a gradient system of the magnetic resonance imaging apparatus is controlled to apply a steady-state gradient echo readout sequence having at least one first phase-encoding gradient along a first direction, at least one second phase-encoding gradient along a second direction, and a sequence of readout gradients along a readout direction. In the multi-shot readout phase, the gradient system is controlled to apply first AC gradients along the first direction and at least partly contemporaneously with readout gradients of the sequence of readout gradients, and the gradient system is controlled to apply second AC gradients along the second direction and at least partly contemporaneously with the readout gradients of the sequence of readout gradients.

Abstract: Systems and methods for simultaneously acquiring high-resolution images of a subject from multiple different slice locations using magnetic resonance imaging (“MRI”) are described. The present invention overcomes the aforementioned drawbacks by providing method for producing a plurality of images of a subject with a magnetic resonance imaging (“MRI”) system. A radio frequency (RF) excitation field is applied by the MRI system to a portion of a subject that includes a plurality of slice locations. First data are simultaneously acquired from each of the plurality of slice locations by the MRI system.

Abstract: Methods for reducing scan time in magnetic resonance imaging (“MRI”), particularly when imaging three-dimensional image volumes, using a simultaneous time-interleaved multislice (“STIMS”) acquisition are described. The unused time in each repetition time (“TR”) period is exploited to provide an additional reduction in encoding time for a three-dimensional acquisition (e.g., a 3D whole brain coverage). Groups of spatially interleaved slices are excited in a single TR, with the excitation and acquisition of the groups of slices being interleaved in time.

Abstract: Described here are systems and methods for quantitative susceptibility mapping (“QSM”) using magnetic resonance imaging (“MRI”). Susceptibility maps are reconstructed from phase images using an automatic regularization technique based in part on variable splitting. Two different regularization parameters are used, one, ?, that controls the smoothness of the final susceptibility map and one, ?, that controls the convergence speed of the reconstruction. For instance, the regularization parameters can be determined using an L-curve heuristic to find the parameters that yield the maximum curvature on the L-curve. The ? parameter can be determined based on an l2-regularization and the ? parameter can be determined based on the iterative l1-regularization used to reconstruct the susceptibility map.

Abstract: Systems and methods for simultaneous multislice (“SMS”} magnetic resonance imaging (“MRI”}, in which a random blip gradient encoding scheme is utilized to impart a different phase to each of a plurality of different slice locations. Because of the random blip gradient encoding, the amount of the imparted phase is randomized for each phase encoding step in a Cartesian k-space trajectory. This data acquisition strategy leads to incoherent aliasing artifacts across the simultaneously excited slices. Images of the individual slices can be reconstructed using a compressed sensing framework.

Abstract: A method for reconstructing multiple images of a subject depicting multiple different contrast characteristics from medical image data acquired with a medical imaging system is provided. Multiple image data sets are acquired with one or more medical imaging systems and the image data sets used to estimate hyperparameters drawn from a prior distribution, such as a prior distribution of image gradient coefficients. These hyperparameters and the acquired image data sets are utilized to produce a posterior distribution, such as a posterior distribution of image gradients. From this posterior distribution, multiple images with the different contrast characteristics are reconstructed. The medical imaging system may be a magnetic resonance imaging system, an x-ray computed tomography imaging system, an ultrasound system, and so on.

Abstract: Systems and methods for reconstructing images using a hierarchically semiseparable (“HSS”) solver to compactly represent the inverse encoding matrix used in the reconstruction are provided. The reconstruction method includes solving for the actual inverse of the encoding matrix using a direct (i.e., non-iterative) HSS solver. This approach is contrary to conventional reconstruction methods that repetitively evaluate forward models (e.g., compressed sensing or parallel imaging forward models).

Abstract: Described here are systems and methods for quantitative susceptibility mapping (“QSM”) using magnetic resonance imaging (“MRI”). Susceptibility maps are reconstructed from phase images using an automatic regularization technique based in part on variable splitting. Two different regularization parameters are used, one, ?, that controls the smoothness of the final susceptibility map and one, ?, that controls the convergence speed of the reconstruction. For instance, the regularization parameters can be determined using an L-curve heuristic to find the parameters that yield the maximum curvature on the L-curve. The ? parameter can be determined based on an l2-regularization and the ? parameter can be determined based on the iterative l1-regularization used to reconstruct the susceptibility map.

Abstract: A method for reconstructing multiple images of a subject depicting multiple different contrast characteristics from medical image data acquired with a medical imaging system is provided. Multiple image data sets are acquired with one or more medical imaging systems and the image data sets used to estimate hyperparameters drawn from a prior distribution, such as a prior distribution of image gradient coefficients. These hyperparameters and the acquired image data sets are utilized to produce a posterior distribution, such as a posterior distribution of image gradients. From this posterior distribution, multiple images with the different contrast characteristics are reconstructed. The medical imaging system may be a magnetic resonance imaging system, an x-ray computed tomography imaging system, an ultrasound system, and so on.