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Parallel simulated annealing for emission tomography.

K A Girodias1, H H Barrett, R L Shoemaker

  • 1Optical Sciences Center, University of Arizona, Tucson, 85721.

Physics in Medicine and Biology
|July 1, 1991
PubMed
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This study introduces a parallel simulated annealing method for faster emission tomography (ET) image reconstruction. By reformulating the energy function, it significantly reduces synchronization needs, achieving nearly 50x speedup on a parallel system.

Area of Science:

  • Medical Imaging
  • Computer Science
  • Computational Physics

Background:

  • Emission tomography (ET) image reconstruction is computationally intensive.
  • Parallel processing offers potential speedups but faces challenges with global dependencies and synchronization overhead.

Purpose of the Study:

  • To develop a parallel simulated annealing algorithm for accelerated ET image reconstruction.
  • To reduce communication overhead in parallel ET image reconstruction by minimizing synchronization requirements.

Main Methods:

  • Implemented a parallel-acceptance partitioning strategy for evaluating perturbations in subsets.
  • Reformulated the log-likelihood function to create a new energy function, shifting global dependence from the current to the previous estimate.
  • Utilized 54 Inmos T800 transputers in a ring topology for parallel implementation.

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Main Results:

  • Reduced synchronization from once per acceptance to once per iteration, significantly decreasing communication overhead.
  • Achieved execution times nearly 50 times faster than a sequential VAX 8600 system.
  • Demonstrated a high degree of parallelism through the parallel-acceptance strategy.

Conclusions:

  • The proposed method effectively speeds up ET image reconstruction using parallel simulated annealing.
  • The reformulated energy function successfully mitigates synchronization issues, enabling efficient parallel computation.
  • This approach offers a practical solution for accelerating complex image reconstruction tasks in emission tomography.