Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models.
Noise estimation is a challenging task in magnetic resonance imaging (MRI), with applications in quality assessment, filtering or diffusion tensor estimation. Main noise estimators based on the Rician model are revisited and classified in this article, and new useful methods are proposed. Additionally, all the surveyed estimators are extended to the noncentral chi model, which applies to multiple-coil MRI and some important parallel imaging algorithms for accelerated acquisitions. The proposed new noise estimation procedures, based on the distribution of local moments, show better performance in terms of smaller variance and unbiased estimation over a wide range of experiments, with the additional advantage of not needing to explicitly segment the background of the image.
Aja-Fernández
Santiago
S
LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid 47011, Spain. sanaja@tel.uva.es
Tristán-Vega
Antonio
A
Alberola-López
Carlos
C
D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results