The expectation−maximization (EM) algorithm for maximum likelihood image recovery converges very slowly. Thus, the ordered subsets EM (OS−EM) algorithm has been widely used in image reconstruction for tomography due to an order−of−magnitude acceleration over the EM algorithm [1]. However, OS− EM is not guaranteed to converge. The recently proposed ordered subsets, separable paraboloidal surroga...

This paper investigates the possibility of developing a SPECT system that combines the high spatial resolution of position sensitive photomultiplier tubes (PSPMTs) with the excellent performance of iterative reconstruction algorithms. A small field of view (FOV) camera based on a PSPMT and a pixelized scintillation crystal made of CsI(Tl) have been used for the acquisition of the projections. W...

Medical image reconstruction from projections is computationally intensive task that demands solutions for reducing the processing delay in clinical diagnosis applications. This paper analyzes reconstruction methods combined with preand post-filtering for Single Photon Emission Computed Tomography (SPECT) in terms of convergence speed and image quality. The evaluation is performed by means of a...

UNLABELLED Tumor standardized uptake values (SUVs) vary with the interval between 18F-FDG injection and image acquisition. This paper presents a simple method using a single reference point to make appropriate time corrections for tumor SUVs. METHODS The reference point method was algebraically deduced from observations made by Beaulieu et al., who found that tumor SUVs behaved linearly over ...

Iterative maximum-likelihood expectation maximization and ordered-subset expectation maximization algorithms are excellent for image reconstruction and usually provide better images than filtered backprojection (FBP). Recently, an FBP algorithm able to incorporate noise weighting during reconstruction was developed. This paper compares the performance of the noise-weighted FBP algorithm and the...

We propose an algorithm, E-COSEM (enhanced complete-data ordered subsets expectation-maximization), for fast maximum likelihood (ML) reconstruction in emission tomography. E-COSEM is founded on an incremental EM approach. Unlike the familiar OSEM (ordered subsets EM) algorithm which is not convergent, we show that E-COSEM converges to the ML solution. Alternatives to the OSEM include RAMLA, and...

We present new algorithms for penalized-likelihood image reconstruction: modified BSREM (block sequential regularized expectation maximization) and relaxed OS-SPS (ordered subsets separable paraboloidal surrogates). Both of them are globally convergent to the unique solution, easily incorporate convex penalty functions, and are parallelizable—updating all voxels (or pixels) simultaneously. They...

Filtered back projection (FBP) method, maximum likelihood-expectation maximization(ML-EM) method, and ordered subsets-expectation maximization (OS-EM) method are currently used for reconstruction of SPECT images in clinical studies. In the ML-EM method, images of good quality can be reconstructed even with a small sampling number of projection data, when compared with FBP. Shorter acquisition t...

Expectation Maximization (EM) and the Simultaneous Iterative Reconstruction Technique (SIRT) are two iterative computed tomography reconstruction algorithms often used when the data contain a high amount of statistical noise, have been acquired from a limited angular range, or have a limited number of views. A popular mechanism to increase the rate of convergence of these types of algorithms ha...