Abstract: A combined physics-based and machine learning framework is used for reconstructing images from k-space data, in which motion artifacts are significantly reduced in the reconstructed images. In general, model-based retrospective motion correction techniques are accelerated using fast machine learning (“ML”) steps, which may be implemented using a trained neural network such as a convolutional neural network. In this way, the confidence of a classical physics-based reconstruction is obtained with the computational benefits of an ML-based network.

Abstract: Electromagnetic interference (“EMI”) is mitigated for portable magnetic resonance imaging (“MRI”) systems using postprocessing interference suppression techniques that make use of EMI detectors external to the MRI system imaging volume to detect EMI signals and remove them from acquired magnetic resonance data. EMI correction models, including static transfer function-based models, dynamic transfer function-based models, correction weight-based models, or parallel imaging kernel-based models can be used to remove the EMI-related artifacts from the magnetic resonance data.

Abstract: A combined physics-based and machine learning framework is used for reconstructing images from k-space data, in which motion artifacts are significantly reduced in the reconstructed images. In general, model-based retrospective motion correction techniques are accelerated using fast machine learning (“ML”) steps, which may be implemented using a trained neural network such as a convolutional neural network. In this way, the confidence of a classical physics-based reconstruction is obtained with the computational benefits of an ML-based network.

Abstract: Described here are systems and methods for producing images of a subject using magnetic resonance imaging (“MRI”) in which data are acquired using a sparse approximate encoding scheme for controlled aliasing techniques. As one example, the sparse approximate encoding can be used for a Wave-CAIPI encoding scheme, which can enable faster image reconstruction using fewer computational resources, in addition to reducing noise in the reconstructed images relative to those reconstructed from data acquired using a Wave-CAIPI encoding scheme without sparse approximate encoding.

Abstract: Described here are systems and methods for retrospectively estimating and correcting for rigid-body motion by using a joint optimization technique to jointly solve for motion parameters and the underlying image. This method is implemented for magnetic resonance imaging (“MRI”), but can also be adapted for other imaging modalities.

Abstract: Systems and methods for performing diffusion-weighted magnetic resonance imaging (“MRI”), including reconstructing and analyzing images, while preserving phase information that is traditionally discarded in such applications, are provided. For instance, background phase variations are eliminated, which enables complex-valued data analysis without the usual noise bias. As a result, the systems and methods described here provide an image reconstruction that enables true signal averaging, which increases signal-to-noise ratio (“SNR”) and allows higher contrast in diffusion model reconstructions without a magnitude bias.

Abstract: Described here are systems and methods for producing images of a subject using magnetic resonance imaging (“MRI”) in which data are acquired using a sparse approximate encoding scheme for controlled aliasing techniques. As one example, the sparse approximate encoding can be used for a Wave-CAIPI encoding scheme, which can enable faster image reconstruction using fewer computational resources, in addition to reducing noise in the reconstructed images relative to those reconstructed from data acquired using a Wave-CAIPI encoding scheme without sparse approximate encoding.

Abstract: Systems and methods for estimating the actual k-space trajectory implemented when acquiring data with a magnetic resonance imaging (“MRI”) system while jointly reconstructing an image from that acquired data are described. An objective function that accounts for deviations between the actual k-space trajectory and a designed k-space trajectory while also accounting for the target image is optimized. To reduce the computational burden of the optimization, a reduced model for the parameters associated with the k-space trajectory deviation and the target image can be implemented.

Abstract: Systems and methods for a hierarchical mapping framework (“HMF”} for coil compression are provided. The HMF-based coil compression can be applied to existing coil compression algorithms to improve their performance. The receive channels associated with a coil array are divided into subgroups based on the strength of their mutual correlation. In each subgroup, one or more virtual channels are produced based on the channels not in the subgroup. The virtual channels are produced using a coil compression algorithm subject to a hierarchically semiseparable channel mixing across the subgroups. Images are reconstructed for the subgroups and then combined to produce the final image of the subject.

Abstract: Described here are systems and methods for retrospectively estimating and correcting for rigid-body motion by using a joint optimization technique to jointly solve for motion parameters and the underlying image. This method is implemented for magnetic resonance imaging (“MRI”), but can also be adapted for other imaging modalities.

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: Methods, apparatus, and other embodiments associated with producing a quantitative parameter map using magnetic resonance fingerprinting (MRF) are described. One example apparatus includes a data store that stores a grouped set of MRF signal evolutions, including a group representative signal and a low-rank representative, a set of logics that collects a received signal evolution from a tissue experiencing nuclear magnetic resonance (NMR) in response to an MRF excitation, a correlation logic that computes a correlation between a portion of the received signal evolution and a portion of a group representative signal, a pruning logic that generates a pruned grouped set, and a matching logic that determines matching quantitative parameters based on the received signal evolution and the low-rank representative.

Abstract: Systems and methods for performing diffusion-weighted magnetic resonance imaging (“MRI”), including reconstructing and analyzing images, while preserving phase information that is traditionally discarded in such applications, are provided. For instance, background phase variations are eliminated, which enables complex-valued data analysis without the usual noise bias. As a result, the systems and methods described here provide an image reconstruction that enables true signal averaging, which increases signal-to-noise ratio (“SNR”) and allows higher contrast in diffusion model reconstructions without a magnitude bias.

Abstract: Systems and methods for estimating the actual k-space trajectory implemented when acquiring data with a magnetic resonance imaging (“MRI”) system while jointly reconstructing an image from that acquired data are described. An objective function that accounts for deviations between the actual k-space trajectory and a designed k-space trajectory while also accounting for the target image is optimized. To reduce the computational burden of the optimization, a reduced model for the parameters associated with the k-space trajectory deviation and the target image can be implemented.

Abstract: Systems and methods for a hierarchical mapping framework (“HMF”} for coil compression are provided. The HMF-based coil compression can be applied to existing coil compression algorithms to improve their performance. The receive channels associated with a coil array are divided into subgroups based on the strength of their mutual correlation. In each subgroup, one or more virtual channels are produced based on the channels not in the subgroup. The virtual channels are produced using a coil compression algorithm subject to a hierarchically semiseparable channel mixing across the subgroups. Images are reconstructed for the subgroups and then combined to produce the final image of the subject.

Abstract: Methods, apparatus, and other embodiments associated with producing a quantitative parameter map using magnetic resonance fingerprinting (MRF) are described. One example apparatus includes a data store that stores a grouped set of MRF signal evolutions, including a group representative signal and a low-rank representative, a set of logics that collects a received signal evolution from a tissue experiencing nuclear magnetic resonance (NMR) in response to an MRF excitation, a correlation logic that computes a correlation between a portion of the received signal evolution and a portion of a group representative signal, a pruning logic that generates a pruned grouped set, and a matching logic that determines matching quantitative parameters based on the received signal evolution and the low-rank representative.

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).