Multi-resolution Bayesian regression in PET dynamic studies using wavelets

• Published in: NeuroImage (Orlando, Fla.) Vol. 32; no. 1; pp. 111 - 121
• Main Authors: Turkheimer, F.E., Aston, J.A.D., Asselin, M.-C., Hinz, R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-08-2006; Elsevier Limited
• Subjects: Advantages; Bayes Theorem; Bayesian regression; Brain - diagnostic imaging; Computer Simulation; Decomposition; FDG; FDOPA; Fluorodeoxyglucose F18; Humans; Image Processing, Computer-Assisted; Kinetic modeling; Kinetics; Mathematical models; Models, Neurological; Noise; Patlak plot; PET; Physiology; Positron-Emission Tomography - methods; Radiography; Radiopharmaceuticals; Regression Analysis; Sensitivity and Specificity; Wavelets; Wavelets; Kinetic modeling; Bayesian regression; FDG; FDOPA; Patlak plot; PET
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2006.03.002

In the kinetic analysis of dynamic PET data, one usually posits that the variation of the data through one dimension, time, can be described by a mathematical model encapsulating the relevant physiological features of the radioactive tracer. In this work, we posit that the remaining dimension, space, can also be modeled as a physiological feature, and we introduce this concept into a new computational procedure for the production of parametric maps. An organ and, in the instance considered here, the brain presents similarities in the physiological properties of its elements across scales: computationally, this similarity can be implemented in two stages. Firstly, a multi-scale decomposition of the dynamic frames is created through the wavelet transform. Secondly, kinetic analysis is performed in wavelet space and the kinetic parameters estimated at low resolution are used as
priors to inform estimates at higher resolutions.
Kinetic analysis in the above scheme is achieved by extension of the Patlak analysis through Bayesian linear regression that retains the simplicity and speed of the original procedure. Application to artificial and real data (FDG and FDOPA) demonstrates the ability of the procedure to reduce remarkably the variance of parametric maps (up to 4-fold reduction) without introducing sizeable bias. Significance of the methodology and extension of the procedure to other data (fMRI) and models are discussed.