Statistical aspects of parallel imaging reconstruction.
A statistical interpretation of existing parallel magnetic resonance imaging methods reveals that the underlying noise model is of additive independent Gaussian noise. In reality MR imaging processes suffer from a variety of noise, errors and other uncertainties. A careful statistical analysis of these uncertainties can potentially allow significant improvement of the reconstruction process. In this paper we present such an analysis and describe a few very recent approaches to handle these statistical models. We show examples of simulation and in vivo reconstructed data which demonstrate the potential of the statistical approach.
Raj
Ashish
A
Kressler
Bryan
B
Singh
Gurmeet
G
Zabih
Ramin
R
Wang
Yi
Y
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted