title: 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.
authors.affiliation: Dept. of Psychiatry & Radiol., Columbia Univ., New York, NY 10032, USA. kjell@neuron.cpmc.columbia.edu
authors.forename: Kjell
authors.initials: K
authors.lastname: Erlandsson
authors.forename: Yinpeng
authors.initials: Y
authors.lastname: Jin
authors.forename: Andrew T
authors.initials: AT
authors.lastname: Wong
authors.forename: Peter D
authors.initials: PD
authors.lastname: Esser
authors.forename: Andrew F
authors.initials: AF
authors.lastname: Laine
authors.forename: R Todd
authors.initials: RT
authors.lastname: Ogden
authors.forename: Maria A
authors.initials: MA
authors.lastname: Oquendo
authors.forename: Ronald
authors.initials: R
authors.lastname: van Heertum
authors.forename: J John
authors.initials: JJ
authors.lastname: Mann
authors.forename: Ramin V
authors.initials: RV
authors.lastname: Parsey
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted