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We propose filtering the PET sinograms with a constraint curvature motion diffusion. The edge-stopping function is computed in terms of edge probability under the assumption of contamination by Poisson noise. We show that the Chi-square is the appropriate prior for finding the edge probability in the sinogram noise-free gradient. Since the sinogram...
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... Recientemente, se han propuesto métodos que además incorporan la información de la intensidad, tal es el caso del filtro bilateral [6] que adiciona al filtro Gaussiano un kernel que depende de la intensidad [5]. En [7], se explora el enfoque de medias no locales, propuesto en [8], y que utiliza información redundante, como son las estructuras repetitivas en la imagen; mientras que en [9] se propone el uso de filtros probabilísticos. Otra tendencia es el uso de transformadas multiresolución, las cuales permiten una representación escasa de la imagen lo que favorece la separación del ruido de la componente que pertenece a la imagen original. ...
In this paper we present an algorithm for the denoising of small animal positron emission images. The proposed algorithm combines a multiresolution transform with robust filtering of regions. The image is processed in the non-subsampled contourlet domain, taking advantage of the transform ability to capture geometric information of important structures like small lesions and borders between tissues. Additionally, in the transform domain, we proposed to apply quasi‑ robust potentials in order to reduce the noise on regions without borders, this is done by estimating an edge map and a set of image regions. Finally the inverse contourlet transform is applied to obtain a denoised image. Quality tests using the NEMA NU4 2008 phantom show that the proposed method reduces the noise in the image while at the same time the average count is preserved on each region. Comparisons with other methods, using a contrast analysis on a simulated lesion show the superiority of our approach to denoise and preserve small structures such as lesions.
... Sinogram based filters have been proposed for PET, CT and SPECT data. In particular for PET data, several algorithms have been proposed (Stearns, 1995), (Demirkaya, 2002), , (Han et al., 2007), (Peltonen, Tuna, & Ruotsalainen, 2012) and (Alrefaya & Sahli, 2013). (Alrefaya & Sahli, 2013) implemented an adaptive probabilistic nonlinear denoising approach where the edge stopping function is based on the edge probability of a pixel under the assumed Poisson noise content. ...
... In particular for PET data, several algorithms have been proposed (Stearns, 1995), (Demirkaya, 2002), , (Han et al., 2007), (Peltonen, Tuna, & Ruotsalainen, 2012) and (Alrefaya & Sahli, 2013). (Alrefaya & Sahli, 2013) implemented an adaptive probabilistic nonlinear denoising approach where the edge stopping function is based on the edge probability of a pixel under the assumed Poisson noise content. The performance of the proposed sinogram filter was tested on iterative OSEM reconstruction method but not FBP reconstruction method. ...
... Then, we define a control parameter at every point in the sinogram that is calculated as: (1) where is the gradient, is the Gaussian kernel with standard deviation and is a constant. The gradient is a 3D gradient of the Gaussian smoothed sinogram calculated as: (2) where , and are the first order derivatives of the Gaussian smoothed sinogram with respect to , and respectively. Note that the complemented value of ( is high at edge regions while it is relatively low at flat regions. ...
Positron Emission Tomography (PET) projection data or sinogram contained poor statistics and randomness that produced noisy PET images. In order to improve the PET image, we proposed an implementation of pre-reconstruction sinogram filtering based on 3D mean-median filter. The proposed filter is designed based on three aims; to minimise angular blurring artifacts, to smooth flat region and to preserve the edges in the reconstructed PET image. The performance of the pre-reconstruction sinogram filter prior to three established reconstruction methods namely filtered-backprojection (FBP), Maximum likelihood expectation maximization–Ordered Subset (OSEM) and OSEM with median root prior (OSEM-MRP) is investigated using simulated NCAT phantom PET sinogram as generated by the PET Analytical Simulator (ASIM). The improvement on the quality of the reconstructed images with and without sinogram filtering is assessed according to visual as well as quantitative evaluation based on global signal to noise ratio (SNR), local SNR, contrast to noise ratio (CNR) and edge preservation capability. Further analysis on the achieved improvement is also carried out specific to iterative OSEM and OSEM-MRP reconstruction methods with and without pre-reconstruction filtering in terms of contrast recovery curve (CRC) versus noise trade off, normalised mean square error versus iteration, local CNR versus iteration and lesion detectability. Overall, satisfactory results are obtained from both visual and quantitative evaluations.
... where x σ is the variance within the 5 Â 5 Â 3 mask and n 2 σ is the noise variance estimated using wavelet decomposition (Alrefaya and Sahli, 2013). The complemented value of σ described as h then becomes the smoothing operator. ...
The integration of physiological (PET) and anatomical (CT) images in cancer delineation requires an accurate spatial registration technique. Although hybrid PET/CT scanner is used to co-register these images, significant misregistrations exist due to patient and respiratory/cardiac motions. This paper proposes a hybrid feature-intensity based registration technique for hybrid PET/CT scanner. First, simulated PET sinogram was filtered with a 3D hybrid mean-median before reconstructing the image. The features were then derived from the segmented structures (lung, heart and tumor) from both images. The registration was performed based on modified multi-modality demon registration with multiresolution scheme. Apart from visual observations improvements, the proposed registration technique increased the normalized mutual information index (NMI) between the PET/CT images after registration. All nine tested datasets show marked improvements in mutual information (MI) index than free form deformation (FFD) registration technique with the highest MI increase is 25%.
In the low-photon imaging regime, noise in the image sensors is dominated by shot noise, best modeled statistically as Poisson distribution. In this work, we show that the Poisson likelihood function is very well matched with the Bayesian estimation of the “difference of log of contrast of pixel intensities.” More specifically, our work is rooted in statistical compositional data analysis, whereby we reinterpret the Aitchison geometry as a multi-resolution analysis in the log-pixel domain.We demonstrate that the difference-log-contrast has wavelet-like properties that correspond well with the human visual system, while being robust to illumination variations. We derive a denoising technique based on an approximate conjugate prior for the latent Aitchison variable that gives rise to an explicit minimum mean squared error estimation. The resulting denoising technique preserves image contrast details that are arguably more meaningful to human vision than the pixel intensity values themselves.
