Abstract: A system for suppressing radio frequency interference (RFI) or electromagnetic interference (EMI) in a radar or magnetic resonance imaging system that scans target(s) with energy and receives signals that reflect or echo from the target(s). The system involves obtaining RFI/EMI data in the absence of signals of interest in the scanning system using at least one primary antenna and a plurality of reference antennas. The reference antennas are designed and arranged to detect RFI/EMI signals but not signals of interest in the scanning system. Simultaneously a model, e.g. CNN, is trained with the RFI/EMI data to determine the non-linear signal mappings among primary and reference antennas. The trained model is applied to predict the RFI/EMI received by the primary antenna in the presence of signals of interest. Finally, the RFI/EMI signals received by the primary antenna are removed by subtracting out the predicted RFI/EMI.

Abstract: Image reconstruction methods for multi-slice and multi-contrast magnetic resonance imaging with complementary sampling schemes are provided, comprising: data acquisition using complementary sampling schemes between slices or/and contrasts) in spiral imaging or Cartesian acquisition; joint calibrationless reconstruction of multi-slice and multi-contrast data via block-wise Hankel tensor completion.

Abstract: A method for hybrid-domain reconstruction of MRI images includes the steps of (A) extracting null-subspace bases of a calibration matrix from k-space coil calibration data to calculate image-domain spatial null maps (SNMs) and (B) reconstructing multi-channel images by solving an image-domain nulling system formed by SNMs that contain both coil sensitivity and finite image support information, thus circumventing the masking-related procedure and demonstrating a robust reconstruction.

Abstract: Systems and methods for improving magnetic resonance imaging relate to reconstructing multi-slice images based on sharing the strong structural similarities between adjacent image slices. In addition, a joint denoising method exploits these structural similarities. In part the reconstruction is based on use of a residual neural networks and denoising is achieved with a deep learning based strategy. The system and method have proved useful in both simulation and in vivo brain experiments, demonstrating significant noise reduction in all images and revealing more microstructural details in quantitative diffusion maps.

Abstract: Image reconstruction methods for multi-slice and multi-contrast magnetic resonance imaging with complementary sampling schemes are provided, comprising: data acquisition using complementary sampling schemes between slices or/and contrasts) in spiral imaging or Cartesian acquisition; joint calibrationless reconstruction of multi-slice and multi-contrast data via block-wise Hankel tensor completion.

Abstract: Disclosed are deep learning based methods for magnetic resonance imaging (MRI) image reconstruction from partial Fourier-space (i.e., k-space) data, involving: obtaining high-quality complex MRI image data or fully-sampled k-space data as training data; training reconstruction models to predict high-quality complex MRI image data or complete k-space data from incomplete or partial k-space data; and applying trained models to reconstruct high-quality complex MRI image data or complete k-space data from partial k-space data.