Reconstruction of Dynamic PET Data Using Spatio-Temporal Wavelet L_1 Regularization
Jeroen Verhaeghe, Ghent University, Ghent, Belgium

Seminar • 30 May 2007 • BM 5.202

Abstract
Tomographic reconstruction from PET data is an ill-posed problem that requires regularization. Recently, Daubechies et al. proposed an L_1 regularization of the wavelet coefficients that can be optimized using iterative thresholding schemes. In this paper, we extend this approach for the reconstruction of dynamic (spatio-temporal) PET data. Instead of using classical wavelets in the temporal dimension, we introduce exponential-spline wavelets that are specially tailored to model time activity curves (TACs) in PET. We show the usefulness of spatio-temporal regularization and the superior performance of E-spline wavelets over conventional Battle-Lemarie wavelets for a 1-D TAC fitting experiment and a tomographic reconstruction experiment.