Superiorization of EM Algorithm and Its Application in Single-Photon Emission Computed Tomography(SPECT)

  title={Superiorization of EM Algorithm and Its Application in Single-Photon Emission Computed Tomography(SPECT)},
  author={Shousheng Luo and Tie Zhou},
  journal={arXiv: Optimization and Control},
In this paper, we presented an efficient algorithm to implement the regularization reconstruction of SPECT. Image reconstruction with priori assumptions is usually modeled as a constrained optimization problem. However, there is no efficient algorithm to solve it due to the large scale of the problem. In this paper, we used the superiorization of the expectation maximization (EM) iteration to implement the regularization reconstruction of SPECT. We first investigated the convergent conditions… 
XCT image reconstruction by a modified superiorized iteration and theoretical analysis
Experiments show that the proposed algorithm is superior to the classic superiorized iteration and can reconstruct desirable images and prove the convergence of simultaneous iterations without the summable perturbation assumption.
Superiorization of the ML-EM Algorithm
It is shown by a study, using statistical hypothesis testing on simulated collection of PET data from the human head, that for either secondary criterion the superiorized version of ML-EM outperforms MLEM-STOP for the task of estimating activity within neuroanatomical structures.
Superiorization-based multi-energy CT image reconstruction.
This paper proposes a superiorized version of the simultaneous algebraic reconstruction technique (SART) based on the PRISM model, and compares the proposed superiorized algorithm with the Split-Bregman algorithm in numerical experiments.
Linear multispectral absorption tomography based on regularized iterative methods.
A regularization approach of iterative algorithms was proposed to reconstruct the two-dimensional temperature and concentration distributions based on linear multispectral absorption tomography
A framelet algorithm for de-blurring images corrupted by multiplicative noise
A variational model for restoring images from blurry and speckled observations that utilizes the favorable properties of framelet regularization that are well suited for speckle noise reduction is considered.
Projected Subgradient Minimization Versus Superiorization
The two approaches are presented side-by-side and demonstrate their performance on a problem of computerized tomography image reconstruction, posed as a constrained minimization problem aiming at finding a constraint-compatible solution that has a reduced value of the total variation of the reconstructed image.
Superiorized iteration based on proximal point method and its application to XCT image reconstruction
In this paper, we investigate how to determine a better perturbation for superiorized iteration. We propose to seek the perturbation by proximal point method. In our method, the direction and amount
Superior Volumetric Modulated Arc Therapy Planning Solution for Prostate Patients
This work proposes to develop a new innovative inverse planning tool, based on the novel idea of superiorization, to replace the classical constrained optimization approaches employed in clinics today for prostate VMAT cases.
A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise
This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise. Considering the statistical property of the multiplicative noise following Nakagami
Electrical Impedance Tomography – Recent Applications and Developments
Abstract Electrical impedance tomography (EIT) is a low-cost noninvasive imaging method. The main purpose of this paper is to highlight the main aspects of the EIT method and to review the recent


Image reconstruction from truncated data in single-photon emission computed tomography with uniform attenuation
We present a mathematical analysis of the problem of image reconstruction from truncated data in two-dimensional (2D) single-photon emission computed tomography (SPECT). Recent results in classical
A Heuristic Superiorization-Like Approach to Bioluminescence Tomography
It is found that total variation superiorization of BLT can significantly improve the visualization effect of the reconstruction with the sources set as a particular case of radial basis functions.
An accelerated ordered subsets reconstruction algorithm using an accelerating power factor for emission tomography.
A speed-enhanced tomographic reconstruction algorithm, ACOSEM (accelerated complete-data ordered-subset expectation-maximization), to accelerate a convergent OS-type algorithm (COSEM) by applying an accelerating power factor or a bigger step size is proposed.
Accelerated image reconstruction using ordered subsets of projection data
Ordered subsets EM (OS-EM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm, applicable in both single photon (SPECT) and positron emission tomography (PET).
Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm.
The primal-dual optimization algorithm developed in Chambolle and Pock (CP) is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT.
Accurate image reconstruction from few-view and limited-angle data in diffraction tomography.
  • S. LaRoque, E. Sidky, X. Pan
  • Computer Science, Medicine
    Journal of the Optical Society of America. A, Optics, image science, and vision
  • 2008
Overall the results indicate that the TV-minimization algorithm can be successfully applied to DT image reconstruction under a variety of scan configurations and data conditions of practical significance.
A Statistical Model for Positron Emission Tomography
Abstract Positron emission tomography (PET)—still in its research stages—is a technique that promises to open new medical frontiers by enabling physicians to study the metabolic activity of the body
Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT
In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image
Reconstruction from a Few Projections by ℓ(1)-Minimization of the Haar Transform.
In experiments simulating computerized tomography data collection of the head, reconstructions whose Haar transform has a small ℓ(1)-norm are not more efficacious than reconstructions that have a small TV value, suggesting the search for an objective function that provides diagnostically efficacious reconstructions from a few CT projections remains open.
Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?
There is an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing issues of general importance, and some unconventional CT-imaging designs that have the potential to impact on CT applications are sketched.