title: STEP: Self-supporting tailored k-space estimation for parallel imaging reconstruction.
abstract: OBJECTIVE
  A new subspace-based iterative reconstruction method, termed Self-supporting Tailored k-space Estimation for Parallel imaging reconstruction (STEP), is presented and evaluated in comparison to the existing autocalibrating method SPIRiT and calibrationless method SAKE.
  
  
  METHODS
  In STEP, two tailored schemes including k-space partition and basis selection are proposed to promote spatially variant signal subspace and incorporated into a self-supporting structured low rank model to enforce properties of locality, sparsity, and rank deficiency, which can be formulated into a constrained optimization problem and solved by an iterative algorithm. Simulated and in vivo datasets were used to investigate the performance of STEP in terms of overall image quality and detail structure preservation.
  
  
  RESULTS
  The advantage of STEP on image quality is demonstrated by retrospectively undersampled multichannel Cartesian data with various patterns. Compared with SPIRiT and SAKE, STEP can provide more accurate reconstruction images with less residual aliasing artifacts and reduced noise amplification in simulation and in vivo experiments. In addition, STEP has the capability of combining compressed sensing with arbitrary sampling trajectory.
  
  
  CONCLUSIONS
  Using k-space partition and basis selection can further improve the performance of parallel imaging reconstruction with or without calibration signals.
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
authors.forename: Zechen
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, Washington, USA.|Philips Research North America, Briarcliff Manor, New York, USA.
authors.forename: Jinnan
authors.initials: J
authors.lastname: Wang
authors.affiliation: Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, Washington, USA.
authors.forename: Niranjan
authors.initials: N
authors.lastname: Balu
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Li
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.|Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, Washington, USA.
authors.forename: Chun
authors.initials: C
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology; D002138:Calibration; D003287:Contrast Media; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D007717:Knee / Q000033:anatomy & histology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio
chemical_list: D003287:Contrast Media
keywords: SAKE; SPIRiT; basis selection; compressed sensing; k-space partition; parallel imaging