Low-dose CT (computed tomography) sinogram data have been shown to be signal-dependent with an analytical relationship between the sample mean and sample variance. Spatially-invariant low-pass linear filters, such as the Butterworth and Hanning filters, could not adequately handle the data noise and statistics-based nonlinear filters may be an alternative choice, in addition to other choices of...

It is known that the process of reconstruction of a Positron Emission Tomography (PET) image from sinogram data is very sensitive to measurement noises; it is still an important research topic to reconstruct PET images with high signal-to-noise ratios. In this paper, we propose a new reconstruction method for a temporal series of PET images from a temporal series of sinogram data. In the propos...

Methods: A uniform Ge-68 phantom (20 cm diameter and length) was imaged on a D-RX PET/CT scanner twice; once with all detectors operational (baseline) and once with 5 detector elements at each of 0, 90, 180 and 270 degrees turned off (partially sampled). The partially sampled (PS) sinogram was first decomposed into two different layers – cartoons and texture using Daubechie wavelets (6rh order)...

An improvement of the trajectory matching algorithm is presented, which is based on the use of the derivative of trajectories and of the projection of experimental sinogram lines in the factor space determined by sinogram lines of projections of a model. The algorithm performance is illustrated by use of different phantom structures, to show the effect of symmetry on trajectory matching. A GroE...

The noise of low-dose computed tomography (CT) sinogram follows approximately a Gaussian distribution with nonlinear dependence between the sample mean and variance. The noise is statistically uncorrelated among detector bins at any view angle. However the correlation coefficient matrix of data signal indicates a strong signal correlation among neighboring views. Based on above observations, Ka...

We present a lightweight and scalable artificial neural network architecture which is used to reconstruct tomographic image from given sinogram. A self-supervised learning approach where the...

4. Single particles and angular reconstitution 323 4.1 Preliminary filtering and centring of data 323 4.2 Alignments using correlation functions 324 4.3 Choice of first reference images 324 4.4 Multi-reference alignment of data 325 4.5 MSA eigenvector/eigenvalue data compression 328 4.6 MSA classification 330 4.7 Euler angle determination (‘ angular reconstitution ’) 332 4.8 Sinograms and sinog...

The expectation maximization algorithm is commonly used to reconstruct images obtained from positron emission tomography sinograms. For images with acceptable signal to noise ratios, iterations are terminated prior to convergence. A new quantitative and reproducible stopping rule is designed and validated on simulations using a Monte-Carlo generated transition matrix with a Poisson noise distri...

This paper presents a total variation (TV) regularized reconstruction algorithm for 3D positron emission tomography (PET). The proposed method first employs the Fourier rebinning algorithm (FORE), rebinning the 3D data into a stack of ordinary 2D data sets as sinogram data. Then, the resulted 2D sinogram are ready to be reconstructed by conventional 2D reconstruction algorithms. Given the local...