title: A rapid and robust numerical algorithm for sensitivity encoding with sparsity constraints: self-feeding sparse SENSE.
abstract: The method of enforcing sparsity during magnetic resonance imaging reconstruction has been successfully applied to partially parallel imaging (PPI) techniques to reduce noise and artifact levels and hence to achieve even higher acceleration factors. However, there are two major problems in the existing sparsity-constrained PPI techniques: speed and robustness. By introducing an auxiliary variable and decomposing the original minimization problem into two subproblems that are much easier to solve, a fast and robust numerical algorithm for sparsity-constrained PPI technique is developed in this work. The specific implementation for a conventional Cartesian trajectory data set is named self-feeding Sparse Sensitivity Encoding (SENSE). The computational cost for the proposed method is two conventional SENSE reconstructions plus one spatially adaptive image denoising procedure. With reconstruction time approximately doubled, images with a much lower root mean square error (RMSE) can be achieved at high acceleration factors. Using a standard eight-channel head coil, a net acceleration factor of 5 along one dimension can be achieved with low RMSE. Furthermore, the algorithm is insensitive to the choice of parameters. This work improves the clinical applicability of SENSE at high acceleration factors.
authors.affiliation: Advanced Concept Development, Invivo Corporation, Gainesville, Florida, USA. fhuang@invivocorp.com
authors.forename: Feng
authors.initials: F
authors.lastname: Huang
authors.forename: Yunmei
authors.initials: Y
authors.lastname: Chen
authors.forename: Wotao
authors.initials: W
authors.lastname: Yin
authors.forename: Wei
authors.initials: W
authors.lastname: Lin
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Ye
authors.forename: Weihong
authors.initials: W
authors.lastname: Guo
authors.forename: Arne
authors.initials: A
authors.lastname: Reykowski
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity