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A linear wavelet filter for parametric imaging with dynamic PET.

Federico E Turkheimer·John A D Aston·Richard B Banati·Cyril Riddell·Vincent J Cunningham
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Abstract

This paper describes a new filter for parametric images obtained from dynamic positron emission tomography (PET) studies. The filter is based on the wavelet transform following the heuristics of a previously published method that are here developed into a rigorous theoretical framework. It is shown that the space-time problem of modeling a dynamic PET sequence reduces to the classical one of estimation of a normal multivariate vector of independent wavelet coefficients that, under least-squares risk, can be solved by straightforward application of well established theory. From the study of the distribution of wavelet coefficients of PET images, it is inferred that a James-Stein linear estimator is more suitable for the problem than traditional nonlinear procedures that are incorporated in standard wavelet filters. This is confirmed by the superior performance of the James-Stein filter in simulation studies compared to a state-of-the-art nonlinear wavelet filter and a nonstationary filter selected from literature. Finally, the formal framework is interpreted for the practitioner's point of view and advantages and limitations of the method are discussed.

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Federico E Turkheimer, John A D Aston, Richard B Banati, Cyril Riddell, Vincent J Cunningham. 2003. A linear wavelet filter for parametric imaging with dynamic PET.. https://doi.org/10.1109/tmi.2003.809597

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