2003
DOI: 10.1109/tmi.2003.809597
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A linear wavelet filter for parametric imaging with dynamic pet

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 so… Show more

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Cited by 64 publications
(44 citation statements)
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“…The parameters of filter kernel (length of the wavelet filters) and depth-of-decomposition (optimal number of iterations) have previously been optimized to 16 and 3, respectively [25]. Quantification was performed with Logan's graphical analysis using multi-linear regression to fit the linear part of the curve of the coefficients from the dynamic wavelet transform [26]. The parametric wavelet transform describing the distribution of the total distribution volume (V T ) was then transformed to a three-dimensional (3D) parametric map of V T in normal space.…”
Section: Parametric Imagesmentioning
confidence: 99%
“…The parameters of filter kernel (length of the wavelet filters) and depth-of-decomposition (optimal number of iterations) have previously been optimized to 16 and 3, respectively [25]. Quantification was performed with Logan's graphical analysis using multi-linear regression to fit the linear part of the curve of the coefficients from the dynamic wavelet transform [26]. The parametric wavelet transform describing the distribution of the total distribution volume (V T ) was then transformed to a three-dimensional (3D) parametric map of V T in normal space.…”
Section: Parametric Imagesmentioning
confidence: 99%
“…PET study was performed in three-dimensional acquisition mode as previously reported [19]. A 6 mCi bolus of [ 11 C](R)-PK11195 was injected intravenously into the patients 30 sec after the acquisition scan started.…”
Section: Pet Imaging Acquisition and Analysismentioning
confidence: 99%
“…[19][20][21][22][23][24][25] As HYPR-LR uses temporally integrated data to reduce noise, comparing it to denoising methods that likewise utilize the time domain, such 4D reconstructions, will be particularly important. Iterative reconstructions, wavelet denoising, and HYPR-LR have all demonstrated an ability to substantially reduce noise, but each also has drawbacks.…”
Section: Discussionmentioning
confidence: 99%