Abstract: A magnetic resonance (MR) imaging acceleration method is provided. The method includes applying, by an MR system, a pulse sequence having a k-space trajectory of a plurality of blades being rotated in k-space, each blade including a plurality of views, wherein the k-space trajectory has an undersampling pattern in the k-space. The method also includes receiving k-space data of a subject acquired by the pulse sequence, reconstructing MR images of the subject based on the k-space data using compressed sensing, and outputting the reconstructed images.

Abstract: A magnetic resonance (MR) imaging acceleration method is provided. The method includes applying, by an MR system, a pulse sequence having a k-space trajectory of a plurality of blades being rotated in k-space, each blade including a plurality of views, wherein the k-space trajectory has an undersampling pattern in the k-space. The method also includes receiving k-space data of a subject acquired by the pulse sequence, reconstructing MR images of the subject based on the k-space data using compressed sensing, and outputting the reconstructed images.

Abstract: Image enhancement systems and methods with variable number of excitation (NEX) acquisitions accelerated using compressed sensing for magnetic resonance (MR) imaging are provided. The MR system comprises a body coil adapted to emit electromagnetic waves onto the anatomy of interest and receive the signals emitted from the anatomy of interest. The system comprises variable number of excitations (NEX) based compressed sensing by acquisition of different points in k-space using the body coil. A first neural network comprising an image enhancement module is provided to reconstruct the body coil images that provides a high-quality image. The high-quality image is stored in an image database. A processor is configured to connect the image database to a second neural network that is a deep learning network trained to assess the image quality and provide feedback to the processor and the first neural network.

Abstract: Image enhancement systems and methods with variable number of excitation (NEX) acquisitions accelerated using compressed sensing for magnetic resonance (MR) imaging are provided. The MR system comprises a body coil adapted to emit electromagnetic waves onto the anatomy of interest and receive the signals emitted from the anatomy of interest. The system comprises variable number of excitations (NEX) based compressed sensing by acquisition of different points in k-space using the body coil. A first neural network comprising an image enhancement module is provided to reconstruct the body coil images that provides a high-quality image. The high-quality image is stored in an image database. A processor is configured to connect the image database to a second neural network that is a deep learning network trained to assess the image quality and provide feedback to the processor and the first neural network.

Abstract: Methods and systems for addressing malfunction of a medical imaging device are disclosed. The method includes classifying a type of an image artifact in a medical image acquired by the medical imaging device by using a trained machine learning model. The method also includes analyzing system data associated with acquisition of the medical image to identify one or more system parameters that might have contributed to the type of image artifact and providing an action for addressing the image artifact based on the identified one or more system parameters.

Abstract: Methods and systems for addressing malfunction of a medical imaging device are disclosed. The method includes classifying a type of an image artifact in a medical image acquired by the medical imaging device by using a trained machine learning model. The method also includes analyzing system data associated with acquisition of the medical image to identify one or more system parameters that might have contributed to the type of image artifact and providing an action for addressing the image artifact based on the identified one or more system parameters.

Abstract: A method for reconstructing a digital image from a set of measurements includes providing a previous image frame in a time series of measurements of an image signal and a current image frame in the time series, calculating an estimated motion vector for a spatial point and current time point between the previous and current image frames, calculating a motion compensated current image frame from the previous image frame, estimating a known support set of a sparse signal estimate of the motion compensated current image frame where the support set comprises indices of non-zero elements of the sparse signal estimate, calculating a sparse signal corresponding to the current image frame whose support contains a smallest number of new additions to the known support set while satisfying a data consistency constraint, and correcting the motion compensated current image frame image frame from the sparse signal.

Abstract: A method reduces multiplicative and additive noise in image pixels by clustering similar patches of the pixels into clusters. The clusters form nodes in an affinity net of nodes and vertices. From each cluster, a dictionary is learned by a sparse combination of corresponding atoms in the dictionaries. The patches are aggregated collaboratively using the dictionaries to construct a denoised image.

Abstract: A method reduces multiplicative and additive noise in image pixels by clustering similar patches of the pixels into clusters. The clusters form nodes in an affinity net of nodes and vertices. From each cluster, a dictionary is learned by a sparse combination of corresponding atoms in the dictionaries. The patches are aggregated collaboratively using the dictionaries to construct a denoised image.

Abstract: A method for reconstructing a digital image from a set of measurements includes providing a previous image frame in a time series of measurements of an image signal and a current image frame in the time series, calculating an estimated motion vector for a spatial point and current time point between the previous and current image frames, calculating a motion compensated current image frame from the previous image frame, estimating a known support set of a sparse signal estimate of the motion compensated current image frame where the support set comprises indices of non-zero elements of the sparse signal estimate, calculating a sparse signal corresponding to the current image frame whose support contains a smallest number of new additions to the known support set while satisfying a data consistency constraint, and correcting the motion compensated current image frame image frame from the sparse signal.