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Maximum likelihood SPECT in clinical computation times using mesh-connected parallel computers.

A W McCarthy1, M I Miller

  • 1Dept. of Electr. Eng., Washington Univ., St. Louis, MO.

IEEE Transactions on Medical Imaging
|January 1, 1991
PubMed
Summary
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This study shows that parallel processing enables fast maximum-likelihood reconstructions for single-photon emission computed tomography (SPECT) imaging. These advancements bring Bayesian methods and regularization within clinical computation times.

Area of Science:

  • Medical Imaging
  • Computer Science
  • Computational Science

Background:

  • Previous work by McCarthy et al. (1988) and Miller & Roysam (1991) laid the groundwork for SPECT reconstruction algorithms.
  • Clinical application of advanced SPECT reconstruction methods has been limited by computational time.

Purpose of the Study:

  • To demonstrate a fully parallel implementation of the maximum-likelihood method for SPECT.
  • To achieve clinical time frames for SPECT image reconstruction using massively parallel processors.

Main Methods:

  • Utilized a single-instruction, multiple data (SIMD) distributed array processor with 64^2 processors.
  • Implemented the expectation-maximization (EM) algorithm with Good's smoothing for SPECT imaging on 64x64 grids with 96 view angles.

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Main Results:

  • Achieved a reconstruction rate of 1 iteration per 1.5 seconds for SPECT imaging.
  • Demonstrated that computation times scale roughly linearly with the number of processors.
  • Showcased the feasibility of fully Bayesian reconstructions with regularization in approximately 1 minute per slice.

Conclusions:

  • Parallel processing on massively parallel systolic array processors makes advanced SPECT reconstruction feasible in clinical settings.
  • The linear scaling of computation time with processor count indicates significant potential for further optimization.
  • This work paves the way for faster, more accurate emission tomography reconstructions.