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Related Experiment Videos

Parallelization of the EM algorithm for 3-D PET image reconstruction.

C M Chen1, S Y Lee, Z H Cho

  • 1Sch. of Electr. Eng., Cornell Univ., Ithaca, NY.

IEEE Transactions on Medical Imaging
|January 1, 1991
PubMed
Summary
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Parallelizing the expectation-maximization (EM) algorithm for PET image reconstruction significantly reduces computation time and memory usage. The proposed partition-by-box scheme offers efficient parallelization on multiprocessor systems.

Area of Science:

  • Medical Imaging
  • Computer Science

Background:

  • The expectation-maximization (EM) algorithm is crucial for Positron Emission Tomography (PET) image reconstruction.
  • Routine clinical use of the EM algorithm is hindered by slow convergence and high memory demands.

Purpose of the Study:

  • To address the computational and memory limitations of the EM algorithm for PET image reconstruction.
  • To investigate the efficiency of parallelizing the EM algorithm on multiprocessor systems.

Main Methods:

  • Developed and proposed an efficient data and task partitioning scheme named 'partition-by-box'.
  • Implemented the scheme on message passing (Intel iPSC/2) and shared memory (BBN Butterfly GP1000) systems.
  • Utilized the message passing model for parallelization.

Related Experiment Videos

Main Results:

  • The partition-by-box scheme effectively reduces computation time and memory requirements for EM algorithm-based PET image reconstruction.
  • Message passing systems with complete binary tree interconnections demonstrated performance comparable to hypercube topologies.
  • Efficient parallelization of the EM algorithm was achieved using the proposed scheme.

Conclusions:

  • Parallelizing the EM algorithm using the partition-by-box scheme is a viable solution for overcoming its computational and memory drawbacks.
  • The proposed method enhances the feasibility of routine clinical application of EM algorithm for PET imaging.