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

Year: 2020
Type: article
Abstract: 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.
DOI: 10.1109/msp.2019.2949570
Language: English
Field: Medicine
Open Access status: green
Funders: National Institutes of Health, National Institute of Mental Health, National Cancer Institute, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, Division of Computing and Communication Foundations
Awards: G1066422085, G1066968254, Area B: Precise DCE-MRI Assessment of Brain Tumors, G3048631115, Rapid MRI acquisition for pediatric low-grade gliomas, Faster MRI with Sparse Sampling and Low-Rank Modeling, G8032099392, Next generation in-vivo diffusion imaging at submillimeter resolution
OpenAlex ID: W2999220188
FWCI: 4.032
Cites: 37
Cited by: 70
Related to: 10