Abstract #1604
Rapid CS-Wave MPRAGE acquisition with automated parameter selection
Gabriel Varela-Mattatall1,2,3, Tae Hyung Kim3, Jaejin Cho3, Wei-Ching Lo4, Borjan A. Gagoski5,6, Ravi S. Menon1,2, and Berkin Bilgic3,7
1Centre for Functional and Metabolic Mapping (CFMM) | Robarts Research Institute | Western University, London, ON, Canada, 2Department of Medical Biophysics | Schulich School of Medicine and Dentistry | Western University, London, ON, Canada, 3Department of Radiology | Athinoula A. Martinos Center for Biomedical imaging | Massachusetts General Hospital & Harvard Medical School, Charlestown, MA, United States, 4Siemens Medical Solutions USA, Inc., Charlestown, MA, United States, 5Department of Radiology | Harvard Medical School, Boston, MA, United States, 6Fetal-Neonatal Neuroimaging & Developmental Science Center | Boston Children's Hospital, Boston, MA, United States, 7Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
Synopsis
Wave encoding mitigates g-factor noise amplification in highly accelerated parallel imaging but achieving ultra-high acceleration factors is precluded by the intrinsic “√R” SNR penalty. To overcome this limitation, we propose a compressed sensing-based reconstruction with automatic selection of the regularization weighting. Moreover, we show that CS-Wave is flexible enough to perform well with uniform undersampling. We compare reconstruction performance of CS-Wave against the state-of-art Wave-LORAKS which requires parameter tuning, and evaluate different undersampling patterns at R=12-fold acceleration. Results indicate higher reconstruction quality and showcase the feasibility of ultra-fast Wave-MPRAGE acquisitions.
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