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: Anatomical, physiological, instrumental, and other related biases are removed from functional magnetic resonance imaging (“fMRI”) signal data using deep learning algorithms and/or models, such as a neural network. Bias characterization data are used as an auxiliary input to the neural network. The bias characterization data can be subject-specific bias characterization data (e.g., cortical thickness maps, cortical orientation angle maps, vasculature maps), hardware-specific bias characterization data (e.g., coil sensitivity maps, coil transmission profiles), or both. The subject-specific bias characterization data can be extracted from the fMRI signal data using a second neural network. The bias-reduced fMRI signal data can include time-series signals, functional activation maps, functional connectivity maps, or combinations thereof.

Abstract: Anatomical, physiological, instrumental, and other related biases are removed from functional magnetic resonance imaging (“fMRI”) signal data using deep learning algorithms and/or models, such as a neural network. Bias characterization data are used as an auxiliary input to the neural network. The bias characterization data can be subject-specific bias characterization data (e.g., cortical thickness maps, cortical orientation angle maps, vasculature maps), hardware-specific bias characterization data (e.g., coil sensitivity maps, coil transmission profiles), or both. The subject-specific bias characterization data can be extracted from the fMRI signal data using a second neural network. The bias-reduced fMRI signal data can include time-series signals, functional activation maps, functional connectivity maps, or combinations thereof.

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: An imaging-based biomarker that indicates a neurological state of a subject is generated from magnetic resonance imaging data acquired from the subject while the subject was sleeping, or during both a sleep state and wake state. These magnetic resonance imaging data are acquired in such a way so that they simultaneously enable measurement of cerebrospinal fluid (“CSF”) flow and blood-oxygenation-level dependent (“BOLD”) signals. The imaging-based biomarker can be generated based on a correlation between CSF signals and BOLD signals extracted from these magnetic resonance imaging data. Using electroencephalography (“EEG”) data, CSF flow dynamics can also be estimated based on a physiological model in which coherent neural activity is modeled as entraining oscillations in blood volume and CSF.

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: 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: A method and imaging system is provided that can control a magnetic gradient system and an RF system of an MRI system according to a calibration pulse sequence to acquire positive readout gradient (RO+) data and negative readout gradient (RO?) data. The RO+ data and the RQ? data are assembled to form complete image data sets for the RO+ data and the RQ™ data and the RO+ data and the RO? data are combined to generate the calibration data that is ghost-corrected, substantially free of ghost artifacts, or having reduced ghost artifacts compared to traditionally-acquired calibration data. Reconstruction coefficients are derived from the calibration data. The magnetic gradient system and the RF system are controlled according to an imaging pulse sequence to acquire image data and the image data is reconstructed into an image of the subject using the reconstruction coefficients.

Abstract: A method for maximizing the signal-to-noise ratio (“SNR”) in a combined image produced using a parallel magnetic resonance imaging (“MRI”) technique is provided. The image combination used in such techniques require an accurate estimate of the noise covariance. Typically, the thermal noise covariance matrix is used as this estimate; however, in several applications, including accelerated parallel imaging and functional MRI, the noise covariance across the coil channels differs substantially from the thermal noise covariance. By combining the individual channels with more accurate estimates of the channel noise covariance, SNR in the combined data is significantly increased. This improved combination employs a regularization of noise covariance on a per-voxel basis.

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 specifically designed to have a flat magnitude and phase profile across a slice for a range of different flip angles.

Abstract: Systems and methods for combined ghost artifact correction and parallel imaging reconstruction of simultaneous multislice (“SMS”) magnetic resonance imaging (“MRI”) data are provided. Dual-polarity training data are used to generate ghost-free slice data, which are used as target data in a reconstruction kernel training process. The training data are used as source data in the reconstruction kernel training. As a result, reconstruction kernels are computed, which can be used to reconstruct images from SMS data in which slice-specific ghosting artifacts are removed.

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 specifically designed to have a flat magnitude and phase profile across a slice for a range of different flip angles.

Abstract: A system and method for medical imaging using a magnetic resonance imaging system includes performing a segmented echo planar imaging (EPI) pulse sequence. The pulse sequence includes performing multiple radio frequency (RF) excitation pulses designed to excite multiple imaging slices across the subject simultaneously. A gradient encoding scheme applied along the slice-encoding direction is implemented to impart controlled phase shifts to the different imaging slices. Additionally, the multiple RF excitation pulses can be designed to further control an overlap of imaging data acquired from adjacent slices of the multiple imaging slices based on a selected offset. The acquired imaging data is reconstructed using a parallel imaging reconstruction method that separates overlapped slices in the imaging data to provide a series of images with respective images for each of the multiple imaging slices across the subject.

Abstract: A method and imaging system is provided that can control a magnetic gradient system and an RF system of an MRI system according to a calibration pulse sequence to acquire positive readout gradient (RO+) data and negative readout gradient (RO?) data. The RO+ data and the RQ? data are assembled to form complete image data sets for the RO+ data and the RQ™ data and the RO+ data and the RO? data are combined to generate the calibration data that is ghost-corrected, substantially free of ghost artifacts, or having reduced ghost artifacts compared to traditionally-acquired calibration data. Reconstruction coefficients are derived from the calibration data. The magnetic gradient system and the RF system are controlled according to an imaging pulse sequence to acquire image data and the image data is reconstructed into an image of the subject using the reconstruction coefficients.

Abstract: Systems and methods for combined ghost artifact correction and parallel imaging reconstruction of simultaneous multislice (“SMS”) magnetic resonance imaging (“MRI”) data are provided. Dual-polarity training data are used to generate ghost-free slice data, which are used as target data in a reconstruction kernel training process. The training data are used as source data in the reconstruction kernel training. As a result, reconstruction kernels are computed, which can be used to reconstruct images from SMS data in which slice-specific ghosting artifacts are removed.

Abstract: A method for maximizing the signal-to-noise ratio (“SNR”) in a combined image produced using a parallel magnetic resonance imaging (“MRI”) technique is provided. The image combination used in such techniques require an accurate estimate of the noise covariance. Typically, the thermal noise covariance matrix is used as this estimate; however, in several applications, including accelerated parallel imaging and functional MRI, the noise covariance across the coil channels differs substantially from the thermal noise covariance. By combining the individual channels with more accurate estimates of the channel noise covariance, SNR in the combined data is significantly increased. This improved combination employs a regularization of noise covariance on a per-voxel basis.

Abstract: A system and method for medical imaging using a magnetic resonance imaging system includes performing a segmented echo planar imaging (EPI) pulse sequence. The pulse sequence includes performing multiple radio frequency (RF) excitation pulses designed to excite multiple imaging slices across the subject simultaneously. A gradient encoding scheme applied along the slice-encoding direction is implemented to impart controlled phase shifts to the different imaging slices. Additionally, the multiple RF excitation pulses can be designed to further control an overlap of imaging data acquired from adjacent slices of the multiple imaging slices based on a selected offset. The acquired imaging data is reconstructed using a parallel imaging reconstruction method that separates overlapped slices in the imaging data to provide a series of images with respective images for each of the multiple imaging slices across the subject.

Abstract: A parallel MR imaging method that uses a reconstruction algorithm that combines the GRAPPA image reconstruction method and the compressed sensing (CS) image reconstruction method in an iterative approach (200) or joint energy optimization approach (300).

Abstract: The present invention provides a system and method for using a hardware-based compression of signals acquired with an magnetic resonance imaging (MRI) system. This allows a first multi-channel MR signal to be compressed to a second multi-channel MR signal having fewer channels than the first MR signal. This system and method reduces the number of RF receivers needed to achieve the sensitivity encoding benefits associated with highly parallel detection in MRI. Furthermore, the system and method reduces bottlenecks connection an MRI system's RF receiver and reconstruction computer and reduces the computational burden of image reconstruction.

Abstract: A parallel MR imaging method that uses a reconstruction algorithm that combines the GRAPPA image reconstruction method and the compressed sensing (CS) image reconstruction method in an iterative approach (200) or joint energy optimization approach (300).