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Magnetic resonance imaging (MRI) is a powerful and highly versatile imaging technique that has had a tremendous impact in both science and medicine. Unfortunately, MRI data acquisition is also time consuming and expensive, which has thus far prevented it from delivering on its full potential. As a result, the MRI field has always been interested in signal processing methods that can generate high-quality images from a small amount of measured data. These methods can increase the comfort of the person being scanned, enable higher-quality assessment of time-varying phenomena, improve scanner throughput, and/or allow more detailed and comprehensive MRI examinations within a fixed total imaging time.

Linear Predictability in Magnetic Resonance Imaging Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging