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Penalized maximum likelihood simultaneous longitudinal PET image reconstruction with difference-image priors.

Sam Ellis1, Andrew J Reader1

  • 1School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK.

Medical Physics
|April 27, 2018
PubMed
Summary

This study introduces novel methods for reconstructing longitudinal positron emission tomography (PET) scans by penalizing differences between scans. These techniques reduce noise and improve image quality, potentially allowing for reduced radiation dose or scan times in oncology.

Keywords:
image reconstructionlongitudinal reconstructionlongitudinal studiespositron emission tomographytreatment response

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Image Reconstruction

Background:

  • Longitudinal positron emission tomography (PET) scans are crucial for monitoring treatment response in oncology.
  • Current methods reconstruct each scan independently, failing to leverage inter-scan similarities.
  • Previous work demonstrated improved image quality by penalizing voxel-wise differences in longitudinal PET data.

Purpose of the Study:

  • To introduce and evaluate two novel longitudinal difference-image priors for PET image reconstruction.
  • To assess the performance of these priors against a previously proposed method and standard reconstruction techniques.
  • To investigate the impact of these priors on noise reduction and quantitative accuracy in simulated and real PET data.

Main Methods:

  • Proposed two new difference-image priors: low entropy (DE-PML) and spatial gradient sparsity (DTV-PML).
  • Applied these priors, along with a previously proposed sparse difference prior (DS-PML), to simultaneous penalized maximum likelihood (PML) reconstruction.
  • Evaluated methods using 2D simulated [18F]fluorodeoxyglucose (FDG) brain tumor datasets and a 3D real head and neck cancer patient dataset.

Main Results:

  • All three priors, with appropriate penalty strengths, reduced noise levels comparable to standard reconstructions with increased counts.
  • DTV-PML demonstrated the lowest root mean-squared error (RMSE) due to effective noise reduction within tumors.
  • DE-PML achieved tumor means close to standard maximum likelihood expectation-maximization (MLEM) reconstructions with the lowest bias.
  • Similar improvements were observed in real longitudinal data, though image misalignment presented challenges.

Conclusions:

  • Penalizing difference images in longitudinal PET reconstruction effectively reduces noise across all scans.
  • DE-PML and DTV-PML offer distinct advantages in maintaining quantitative accuracy (mean intensity) and reducing tumor RMSE, respectively.
  • Simultaneous reconstruction of longitudinal PET data holds potential for improving image quality, enabling dose reduction, or shortening scan times.