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The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography

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TLDR
A new algorithm, ordered subsets mirror descent, is developed and implemented, and it is demonstrated that it is well suited for solving the PET reconstruction problem.
Abstract
We describe an optimization problem arising in reconstructing three-dimensional medical images from positron emission tomography (PET). A mathematical model of the problem, based on the maximum likelihood principle, is posed as a problem of minimizing a convex function of several million variables over the standard simplex. To solve a problem of these characteristics, we develop and implement a new algorithm, ordered subsets mirror descent, and demonstrate, theoretically and computationally, that it is well suited for solving the PET reconstruction problem.

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Citations
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Journal ArticleDOI

Mirror descent and nonlinear projected subgradient methods for convex optimization

TL;DR: It is shown that the MDA can be viewed as a nonlinear projected-subgradient type method, derived from using a general distance-like function instead of the usual Euclidean squared distance, and derived in a simple way convergence and efficiency estimates.
Journal ArticleDOI

Primal-dual subgradient methods for convex problems

TL;DR: A new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure that is primal-dual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual problem.
Posted Content

Primal-dual subgradient methods for convex problems

TL;DR: In this paper, a new approach for constructing subgradient schemes for dierent types of nonsmooth problems with convex structure is presented, which are primal-dual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual problem.
Journal ArticleDOI

Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization

TL;DR: In this paper, a randomized stochastic projected gradient (RSPG) algorithm was proposed to solve the convex composite optimization problem, in which proper mini-batch of samples are taken at each iteration depending on the total budget of stochiastic samples allowed, and a post-optimization phase was also proposed to reduce the variance of the solutions returned by the algorithm.
Journal ArticleDOI

Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework

TL;DR: This paper investigates the AC-SA algorithms for solving strongly convex stochastic composite optimization problems in more detail by establishing the large-deviation results associated with the convergence rates and introducing an efficient validation procedure to check the accuracy of the generated solutions.
References
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Book

Nonlinear Programming

Journal ArticleDOI

Maximum Likelihood Reconstruction for Emission Tomography

TL;DR: In this paper, the authors proposed a more accurate general mathematical model for ET where an unknown emission density generates, and is to be reconstructed from, the number of counts n*(d) in each of D detector units d. Within the model, they gave an algorithm for determining an estimate? of? which maximizes the probability p(n*|?) of observing the actual detector count data n* over all possible densities?.
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Accelerated image reconstruction using ordered subsets of projection data

TL;DR: 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).
Book

Problem complexity and method efficiency in optimization

TL;DR: In this article, problem complexity and method efficiency in optimisation are discussed in terms of problem complexity, method efficiency, and method complexity in the context of OO optimization, respectively.
Journal ArticleDOI

The Fourier reconstruction of a head section

TL;DR: The authors compare the Fourier algorithm and a search algorithm using a simulated phantom to speed the search algorithm by using fewer interactions leaves decreased resolution in the region just inside the skull which could mask a subdural hematoma.