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Abstract #0251

Rapid self-tuning compressed-sensing MRI using projection onto epigraph sets

Mohammad Shahdloo1,2, Efe Ilıcak1,2, Mohammad Tofighi3, Emine U. Sarıtaş1,2,4, A. Enis Cetin1,5, and Tolga Çukur1,2,4

1Electrical and Electronics Engineering Department, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Department of Electrical Engineering, Pennsylvania State University, State College, PA, United States, 4Neuroscience Program, Bilkent University, Ankara, Turkey, 5Electrical & Computer Engineering Department, University of Illinois at Chicago, Chicago, IL, United States

Successful compressed-sensing reconstruction often involves tuning one or more regularization weights. However, tuning the regularization weights is a subject-specific, task-dependent and non-trivial task. Recent studies have proposed to determine the weights by minimizing the statistical risk of removing significant coefficients using line searches across a range of parameters. However, the line-search procedures lead to prolonged reconstruction times. Here, we propose a new self-tuning approach generalized for multi-coil, multi-acquisition CS reconstructions that leverage projection onto epigraph sets of l1 and TV balls. The proposed method yields 7 to 9-fold gain in computational efficiency over conventional methods while enabling further improved image quality.

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