Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI

• Published in: IEEE transactions on medical imaging Vol. 38; no. 7; pp. 1677 - 1689
• Main Authors: Shahdloo, Mohammad, Ilicak, Efe, Tofighi, Mohammad, Saritas, Emine U., Cetin, A. Enis, Cukur, Tolga
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain - diagnostic imaging; Calibration; Coils; Compressed sensing (CS); Computational efficiency; Computer applications; Data acquisition; Data Compression - methods; Databases, Factual; Datasets; Domains; Humans; Image reconstruction; Magnetic resonance imaging; magnetic resonance imaging (MRI); Magnetic Resonance Imaging - methods; multi-acquisition; multi-coil; Norms; Parameter estimation; Parameter identification; parameter selection; Phantoms, Imaging; projection onto epigraph sets; Regularization; Searching; Self tuning; Signal Processing, Computer-Assisted; Simulation; Sparsity

The compressed sensing (CS) framework leverages the sparsity of MR images to reconstruct from the undersampled acquisitions. CS reconstructions involve one or more regularization parameters that weigh sparsity in transform domains against fidelity to acquired data. While parameter selection is criti...