Smoothing dynamic positron emission tomography time courses using functional principal components

• Published in: NeuroImage (Orlando, Fla.) Vol. 47; no. 1; pp. 184 - 193
• Main Authors: Jiang, Ci-Ren, Aston, John A.D., Wang, Jane-Ling
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2009; Elsevier Limited
• Subjects: Advantages; Algorithms; Brain - physiology; Carbon Radioisotopes; Computer Simulation; Diprenorphine; Economic models; Functional data analysis; Humans; Mathematical functions; Medical imaging; Methods; Models, Biological; Neuroreceptor ligands; Noise; Nonparametric smoothing; Parameter estimation; Phantoms, Imaging; Positron-Emission Tomography - methods; Principal Component Analysis; Principal components analysis; Signal Processing, Computer-Assisted; Statistics, Nonparametric; Studies; Time Factors; Neuroreceptor ligands; Nonparametric smoothing; Functional data analysis; Principal component analysis
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2009.03.051

A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows subsequent analysis by methods such as Spectral Analysis to be substantially improved in terms of their mean squared error.