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
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2018.2885599

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 critical for reconstruction quality, the optimal parameters are subject and dataset specific. Thus, commonly practiced heuristic parameter selection generalizes poorly to independent datasets. Recent studies have proposed to tune parameters by estimating the risk of removing significant image coefficients. Line searches are performed across the parameter space to identify the parameter value that minimizes this risk. Although effective, these line searches yield prolonged reconstruction times. Here, we propose a new self-tuning CS method that uses computationally efficient projections onto epigraph sets of the <inline-formula> <tex-math notation="LaTeX">{\ell }_{{1}} </tex-math></inline-formula> and total-variation norms to simultaneously achieve parameter selection and regularization. In vivo demonstrations are provided for balanced steady-state free precession, time-of-flight, and T1-weighted imaging. The proposed method achieves an order of magnitude improvement in computational efficiency over line-search methods while maintaining near-optimal parameter selection.