Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic EM methods with Variance Reduction for Penalised PET
  Reconstructions   Expectation-maximization (EM) is a popular and well-established method for
image reconstruction in positron emission tomography (PET) but it often suffers
from slow convergence. Ordered subset EM (OSEM) is an effective reconstruction
algorithm that provides significant acceleration during initial iterations, but
it has been observed to enter a limit cycle. In this work, we investigate two
classes of algorithms for accelerating OSEM based on variance reduction for
penalised PET reconstructions. The first is a stochastic variance reduced EM
algorithm, termed as SVREM, an extension of the classical EM to the stochastic
context, by combining classical OSEM with insights from variance reduction
techniques for gradient descent. The second views OSEM as a preconditioned
stochastic gradient ascent, and applies variance reduction techniques, i.e.,
SAGA and SVRG, to estimate the update direction. We present several numerical
experiments to illustrate the efficiency and accuracy of the approaches. The
numerical results show that these approaches significantly outperform existing
OSEM type methods for penalised PET reconstructions, and hold great potential.

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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph