Abstract: Images are reconstructed from undersampled k-space data using a residual machine learning algorithm (e.g., a ResNet architecture) to estimate missing k-space lines from acquired k-space data with improved noise resilience. Using a residual machine learning algorithm provides for combining the advantages of both linear and nonlinear k-space reconstructions. The linear residual connection can implement a convolution that estimates most of the energy in k-space, and the multi-layer machine learning algorithm can be implemented with nonlinear activation functions to estimate imperfections, such as noise amplification due to coil geometry, that arise from the linear component.

Abstract: Methods for reconstructing images from undersampled k-space data using a machine learning approach to learn non-linear mapping functions from acquired k-space lines to generate unacquired target points across multiple coils are described.

Abstract: Systems and methods for late gadolinium enhancement (“LGE”) tissue viability imaging in a dynamic (e.g., temporally-resolved) manner using magnetic resonance imaging (“MRI”) are provided. Dynamic LGE images can be generated throughout the entire cardiac cycle at high temporal resolution in a single breath-hold. Dynamic, semi-quantitative longitudinal relaxation maps are acquired and retrospective synthetization of dynamic LGE images is implemented using those semi-quantitative longitudinal relaxation maps.

Abstract: Magnetic resonance imaging (“MRI”) data are corrected from corruptions due to physiological changes using a self-navigated phase correction technique. Unlike motion correction techniques, the effects of physiological changes (e.g., breathing and respiration) are corrected by making the MRI data self-consistent relative to an absolute uncorrupted phase reference. This phase correction information can be extracted from the acquisition itself, thereby eliminating the need for a separate navigator scan, and establishing an accelerated acquisition. This absolute reference can be computed in a data segmented space, and the subsequent data can be corrected relative to this absolute reference with low-resolution phases.

Abstract: A fully sampled calibration data set, which may be Cartesian k-space data, is used to obtain targeted and optimal interpolation kernels for non-regularly sampled data. The calibration data are self-calibration data obtained from a time-averaged image, or re-sampled data. ACS data are resampled for calibration of region-specific kernels. Subsequently, an explicit noise-based regularized solution can be utilized to estimate region-specific kernels for reconstruction.

Abstract: Described here are systems and methods for volumetric excitation in magnetic resonance imaging (“MRI”) using frequency modulated radio frequency (“RF”) pulses. In general, quadratic phase modulation along the slice encoding direction is implemented for additional spatiotemporal encoding, which better distributes signal content in the slice direction and enables higher acceleration rates that are robust to slice-undersampling.

Abstract: Self-supervised training of machine learning (“ML”) algorithms for reconstruction in inverse problems are described. These techniques do not require fully sampled training data. As an example, a physics-based ML reconstruction can be trained without requiring fully-sampled training data. In this way, such ML-based reconstruction algorithms can be trained on existing databases of undersampled images or in a scan-specific manner.

Abstract: Methods for fast magnetic resonance imaging (“MRI”) using a combination of outer volume suppression (“OVS”) and accelerated imaging, which may include simultaneous multislice (“SMS”) imaging, data acquisitions amenable to compressed sensing reconstructions, or combinations thereof. The methods described here do not introduce fold-over artifacts that are otherwise common to reduced field-of-view (“FOV”) techniques.

Abstract: Methods for reconstructing images from undersampled k-space data using a machine learning approach to learn non-linear mapping functions from acquired k-space lines to generate unacquired target points across multiple coils are described.

Abstract: Magnetic resonance imaging (“MRI”) data are corrected from corruptions due to physiological changes using a self-navigated phase correction technique. Unlike motion correction techniques, the effects of physiological changes (e.g., breathing and respiration) are corrected by making the MRI data self-consistent relative to an absolute uncorrupted phase reference. This phase correction information can be extracted from the acquisition itself, thereby eliminating the need for a separate navigator scan, and establishing an accelerated acquisition. This absolute reference can be computed in a data segmented space, and the subsequent data can be corrected relative to this absolute reference with low-resolution phases.

Abstract: Images are reconstructed from undersampled k-space data using a residual machine learning algorithm (e.g., a ResNet architecture) to estimate missing k-space lines from acquired k-space data with improved noise resilience. Using a residual machine learning algorithm provides for combining the advantages of both linear and nonlinear k-space reconstructions. The linear residual connection can implement a convolution that estimates most of the energy in k-space, and the multi-layer machine learning algorithm can be implemented with nonlinear activation functions to estimate imperfections, such as noise amplification due to coil geometry, that arise from the linear component.

Abstract: A fully sampled calibration data set, which may be Cartesian k-space data, is used to obtain targeted and optimal interpolation kernels for non-regularly sampled data. The calibration data are self-calibration data obtained from a time-averaged image, or re-sampled data. ACS data are resampled for calibration of region-specific kernels. Subsequently, an explicit noise-based regularized solution can be utilized to estimate region-specific kernels for reconstruction.

Abstract: Systems and methods for producing quantitative maps of a longitudinal relaxation parameter, such as a longitudinal relaxation time (“T1”), using magnetic resonance imaging (“MRI”) are described. More particularly, a pulse sequence and imaging method for cardiac phase-resolved myocardial T1 mapping are provided.

Abstract: A system and method for controlling noise in magnetic resonance imaging (MRI) are provided. In one aspect, the method includes reconstructing a series of images of the target using the image data, with each image being defined using signal-to-noise (SNR) units, and selecting an image patch corresponding to the series of images. The method also includes forming a matrix by combining vectors generated using the image patch, and applying a local low rank denoising technique using the matrix and the series of images to generate at least one denoised image.

Abstract: Systems and methods for mapping the transmit sensitivity of one or more radio frequency (“RF”) coils for use in magnetic resonance imaging (“MRI”) are described. The transmit RF field (“B1+”) for an RF coil, or an array of RF coils, is mapped using a robust, motion-insensitive technique that implements Bloch-Siegert shifts performed with interleaved positive and negative off-resonance shifts. The motion insensitivity of this technique makes it particularly useful for applications where there is significant motion, such as cardiac imaging, in which previous B1+ mapping techniques are not as accurate or effective.

Abstract: Systems and methods for late gadolinium enhancement (“LGE”) tissue viability imaging in a dynamic (e.g., temporal-ly-resolved) manner using magnetic resonance imaging (“MRI”) are provided. Dynamic LGE images can be generated throughout the entire cardiac cycle at high temporal resolution in a single breath-hold. Dynamic, semi-quantitative longitudinal relaxation maps are acquired and retrospective synthetization of dynamic LGE images is implemented using those semi-quantitative longitudinal relaxation maps.

Abstract: A system and method for controlling a magnetic resonance imaging (MRI) system to acquire images of a subject having inconsistencies in a cardiac cycle of the subject. The process includes receiving an identification of a predetermined point in a cardiac cycle of the subject and, thereupon, performing a saturation module configured to dephase magnetization within a region of interest (ROI) from before the predetermined point. The process also includes performing an inversion module configured to invert spins within the ROI and acquiring medical imaging data from the subject. A delay is inserted between the performance of the saturation module and the performance of the inversion module, wherein a duration of the delay is configured, with the saturation module, to control evidence in the medical imaging data of inconsistencies in the cardiac cycle of the subject by controlling a magnetization history of tissue in the ROI.

Abstract: A system and method for obtaining magnetic resonance images are provided. The system is programmed to control the RF system to apply a saturation pulse at a reference frequency that saturates a selected labile spin species of the subject. The system is programmed to control the RF system to apply an inversion pulse after a variable delay. The system is programmed to control the RF system and the plurality of gradient coils to apply a motion sensitized driven equilibrium (MSDE) preparation pulse. The system is programmed to control the plurality of gradient coils to read imaging data during an acquisition time period. The system is programmed to reconstruct a T1 mapping image of the subject with black-blood contrast.

Abstract: An MRI apparatus includes: a data processor configured to acquire a first set of T2-weighted imaging data and a second set of T2-weighted imaging data; a pulse sequence controller configured to generate a pulse sequence and apply the generated pulse sequence to a gradient coil assembly and RF coil assembly, the generated pulse sequence including: T2-preparation modules and associated imaging modules to acquire the first set of T2-weighted imaging data, and a saturation pulse sequence and an associated saturation imaging module to acquire the second set of T2-weighted imaging data; a curve fitter configured to apply the first and second sets of T2-weighted imaging data to a three-parameter model for T2 decay, to determine a T2 value at a plurality of locations; and an image processor configured to generate a T2 map of the object based on the T2 value determined at the plurality of locations.

Abstract: A system and method for controlling noise in magnetic resonance imaging (MRI) are provided. In one aspect, the method incudes reconstructing a series of images of the target using the image data, with each image being defined using signal-to-noise (SNR) units, and selecting an image patch corresponding to the series of images. The method also includes forming a matrix by combining vectors generated using the image patch, and applying a local low rank denoising technique using the matrix and the series of images to generate at least one denoised image.