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Optimizing positron emission tomography (PET) image reconstruction with regularization priors requires incorporating prior effects into the preconditioner for faster, stable results. Stochastic Variance Reduced Gradient (SVRG) and SAGA methods show superior performance over SGD.

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Area of Science:

  • Medical Imaging
  • Computational Science
  • Nuclear Medicine

Background:

  • Positron Emission Tomography (PET) image reconstruction is crucial for clinical diagnosis.
  • Relative Difference Prior (RDP) regularization improves reconstruction quality over conventional methods.
  • Fast and stable reconstruction is essential for clinical workflow efficiency.

Purpose of the Study:

  • To investigate and optimize subset-based reconstruction methods for PET using RDP.
  • To evaluate the impact of incorporating prior effects into the preconditioner on convergence speed and stability.
  • To compare the performance of various stochastic optimization algorithms for PET reconstruction.

Main Methods:

  • Evaluated subset-based optimization algorithms including Stochastic Gradient Descent (SGD), SAGA, and SVRG.
  • Utilized simulated PET data and real brain PET scans from the PET Rapid Image Reconstruction Challenge (PETRIC) 2024.
  • Assessed performance across varying noise levels (count statistics) and regularization strengths.

Main Results:

  • Incorporating the prior effect into the preconditioner is critical for fast and stable convergence in RDP-regularized PET reconstruction.
  • SVRG and SAGA algorithms demonstrated superior performance compared to SGD.
  • SVRG exhibited a slight overall advantage in reconstruction speed and stability.

Conclusions:

  • The study highlights the importance of preconditioner design for efficient PET image reconstruction.
  • SVRG and SAGA are effective stochastic optimization methods for RDP-regularized PET reconstruction.
  • The findings contributed to the development of winning algorithms in the PETRIC 2024 challenge.