期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 38, 期 7, 页码 1677-1689出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2018.2885599
关键词
Compressed sensing (CS); magnetic resonance imaging (MRI); projection onto epigraph sets; self-tuning; parameter selection; multi-coil; multi-acquisition
类别
资金
- TUBITAK 1001 [117E171]
- European Molecular Biology Organization Installation [IG 3028]
- TUBA GEBIP Fellowship
- BAGEP 2016 Award of the Science Academy
- BAGEP 2017 Award of the Science Academy
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 l(1) 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.
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