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Preconditioned alternating projection algorithm for solving the penalized-likelihood SPECT reconstruction problem.

Si Li1, Jiahan Zhang2, Andrzej Krol3

  • 1School of Data and Computer Science, Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangzhou 510275, China.

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Summary
This summary is machine-generated.

A new preconditioned alternating projection algorithm (PAPA) with total variation (TV) regularization and a forward-backward expectation maximization (EM)-TV algorithm show similar performance for SPECT reconstruction. Both methods outperform conventional EM algorithms in noise suppression and lesion detection.

Keywords:
Fixed-point proximity algorithmsPenalized likelihood SPECT reconstructionPiecewise constant phantomTotal variation regularization

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Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • Single-photon emission computed tomography (SPECT) reconstruction is crucial for diagnostic accuracy.
  • Penalized-likelihood methods offer improved image quality but require efficient algorithms.
  • Total variation (TV) regularization is effective for noise reduction in SPECT.

Purpose of the Study:

  • To compare the performance of a novel preconditioned alternating projection algorithm (PAPA) with total variation (TV) regularization (TV-PAPA) against a forward-backward expectation maximization (EM)-TV algorithm (FB-EM-TV).
  • To evaluate the effectiveness of these algorithms in SPECT reconstruction using simulated data.

Main Methods:

  • Monte Carlo simulations generated SPECT data with varying noise levels.
  • Reconstructions were performed using TV-PAPA, FB-EM-TV, and benchmark algorithms (TVPF-EM, GPF-EM).
  • Evaluated metrics included noise suppression, lesion detectability, contrast, convergence speed, and parameter selection.

Main Results:

  • TV-PAPA and FB-EM-TV demonstrated comparable performance across all evaluated metrics.
  • Both novel algorithms significantly outperformed TVPF-EM in noise suppression, lesion detectability, lesion contrast, and convergence speed.
  • Optimal parameter selection for these methods was found to follow power laws related to information density.

Conclusions:

  • TV-PAPA and FB-EM-TV yield similar qualitative and quantitative results for simulated SPECT data.
  • These advanced algorithms offer superior performance compared to conventional EM-based methods (TVPF-EM, GPF-EM).
  • A minor trade-off in lesion contrast was observed with the advanced algorithms.