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Graph-Enhanced Expectation Maximization for Emission Tomography.

Ryosuke Kasai1, Hideki Otsuka1

  • 1Institute of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8509, Japan.

Journal of Imaging
|January 27, 2026
PubMed
Summary
This summary is machine-generated.

A new Graph-Enhanced Expectation Maximization (GREM) algorithm improves emission tomography image reconstruction. GREM effectively suppresses noise while preserving crucial structural details, outperforming existing methods.

Keywords:
graph laplacianimage reconstructionmaximum-likelihood expectation maximizationsingle-photon emission computed tomography

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Area of Science:

  • Medical Imaging
  • Computational Science
  • Image Reconstruction

Background:

  • Emission tomography (e.g., SPECT) image reconstruction relies on processing noisy projection data.
  • Maximum-likelihood expectation maximization (MLEM) is standard but sensitive to noise, especially at low counts.
  • Total variation (TV) regularization reduces noise but can oversmooth images and requires parameter tuning.

Purpose of the Study:

  • To develop a novel algorithm for enhanced noise suppression in emission tomography.
  • To improve image quality by preserving structural details during reconstruction.
  • To offer a practical, data-driven alternative to existing regularization methods.

Main Methods:

  • Proposed the Graph-Enhanced Expectation Maximization (GREM) algorithm.
  • Integrated graph-based neighborhood information into an MLEM multiplicative reconstruction framework.
  • Utilized a penalized formulation with Kullback-Leibler divergence and graph Laplacian regularization.

Main Results:

  • GREM demonstrated superior performance compared to MLEM and TV-regularized MLEM.
  • Quantitative evaluation showed improvements in Peak Signal-to-Noise Ratio (PSNR) and Multi-Scale Structural Similarity Index Measure (MS-SSIM).
  • Experiments on synthetic phantoms and clinical liver SPECT data validated GREM's effectiveness.

Conclusions:

  • GREM offers effective edge-preserving noise suppression for emission tomography.
  • The algorithm maintains the multiplicative structure and non-negativity of MLEM.
  • GREM provides a practical solution without requiring external training data.