Quantitative wavelet domain image processing of dynamic PET data

Abstract

Neuroreceptor PET studies consisting of long dynamic data acquisitions result in data with low signal-to-noise ratio and limited spatial resolution. To address these problems we have developed a 3D wavelet-based image processing tool (wavelet filter, WF), containing both denoising and enhancement functionality. The filter is based on multi-scale thresholding and cross-scale regularization. These operations are data-driven, which may lead to non-linearity effects and hamper quantification of dynamic PET data. The aim of the present study was to investigate these effects using both phantom and human PET data. A phantom study was performed with a cylindrical phantom, filled with 18F, containing a number of spherical inserts filled with 11C. Human studies were performed on 9 healthy volunteers after injection of the serotonine transporter tracer [11C]DASB. Images from both phantom and human studies were reconstructed with filtered backprojection and post-processed by WF with a series of different denoising and enhancement parameter values. The phantom study was analyzed by computing the insert-to-background ratio as a function of time. The human study was analyzed with a 1-tissue compartment model for a series of brain regions. For the phantom study, linear relations were found between unprocessed and WF processed data for positive contrasts. However, for negative contrast, non-linearity effects were observed. For the human data, good correlation was obtained between results from unprocessed and WF processed data. Our results showed that, although non-linear effects may appear in low-contrast areas, it is possible to achieve accurate quantification with wavelet-based image processing.