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Relaxed ordered subset preconditioned alternating projection algorithm for PET reconstruction with automated penalty

C Ross Schmidtlein1, Yizun Lin2, Si Li3

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1250 First Avenue, New York, NY, 10065, USA.

Medical Physics
|April 25, 2017
PubMed
Summary

The relaxed ordered subsets with higher order total variation (ROS-HOTV-PAPA) algorithm significantly improves positron emission tomography (PET) image quality and allows for a four-fold reduction in radiation dose, making it suitable for clinical use.

Keywords:
image reconstructionmaximum likelihood estimationpositron emission tomographysingle photon emission computed tomographytotal variation

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Positron Emission Tomography (PET)

Background:

  • Positron emission tomography (PET) image reconstruction is crucial for accurate diagnosis.
  • Existing algorithms like OSEM can be limited by noise and artifacts.
  • The development of advanced reconstruction methods is essential for improving image quality and reducing radiation dose.

Purpose of the Study:

  • To investigate the performance of a preconditioned alternating projection algorithm (PAPA) using relaxed ordered subsets (ROS) with a higher order total variation (HOTV) regularizer for PET image reconstruction.
  • To introduce a method for unsupervised selection of penalty weights based on measured data.
  • To evaluate the effectiveness of the ROS-HOTV-PAPA algorithm in improving image quality and reducing noise.

Main Methods:

  • Developed and implemented a ROS version of PAPA with HOTV penalty (ROS-HOTV-PAPA) for PET image reconstruction.
  • Simulated 2D PET data using synthetic phantoms and reconstructed patient data on GE D690 PET/CT.
  • Employed a power-law model for unsupervised selection of penalty weights and evaluated image quality using qualitative and quantitative metrics (RMSE).

Main Results:

  • ROS-HOTV-PAPA demonstrated rapid convergence without limit cycle behavior.
  • Reconstructed image quality was superior to optimally post-filtered OSEM in terms of noise, spatial resolution, and contrast, with no staircase artifacts.
  • Achieved significant RMSE reductions (5%-44%) compared to OSEM, especially in low-count images, and maintained similar RMSE at one-quarter the noise equivalent counts (NEC).

Conclusions:

  • ROS-HOTV-PAPA accelerates HOTV-PAPA, improving RMSE and perceptual image quality over clinical OSEM.
  • This method may enable up to a four-fold reduction in patient radiation dose in PET studies.
  • The unsupervised parameter selection method is effective, and ROS-HOTV-PAPA is a promising candidate for clinical PET applications.