title: Direct and accelerated parameter mapping using the unscented Kalman filter.
abstract: OBJECTIVE
  To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem.
  
  
  METHODS
  In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling.
  
  
  RESULTS
  Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies.
  
  
  CONCLUSIONS
  This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping.
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Zhao
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: Xue
authors.initials: X
authors.lastname: Feng
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.|Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
authors.forename: Craig H
authors.initials: CH
authors.lastname: Meyer
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D005583:Fourier Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D011446:Prospective Studies; D012044:Regression Analysis; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio
keywords: accelerated imaging; p-space; parameter mapping; unscented Kalman filter