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.