  Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that
provides excellent soft-tissue contrast without using ionizing radiation. MRI's
clinical application may be limited by long data acquisition time; therefore,
MR image reconstruction from highly under-sampled k-space data has been an
active research area. Calibrationless MRI not only enables a higher
acceleration rate but also increases flexibility for sampling pattern design.
To leverage non-linear machine learning priors, we pair our High-dimensional
Fast Convolutional Framework (HICU) with a plug-in denoiser and demonstrate its
feasibility using 2D brain data.
