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A stopping criterion for iterative proton CT image reconstruction based on correlated noise properties.

Ethan A DeJonghAlexander A PryanichnikovDon F DeJonghReinhard W Schulte
Published in: Journal of applied clinical medical physics (2023)
Iterative algorithms not using a specific metric or rationale for stopping iterations may produce images with an unknown and arbitrary level of convergence or smoothing. We resolve this issue by stopping iterations of a least-squares iterative algorithm when r reaches the range of 0.5-1. This defines a pCT image reconstruction method with consistent statistical properties optimal for clinical use, including for treatment planning with pCT images.
Keyphrases
  • deep learning
  • image quality
  • dual energy
  • convolutional neural network
  • computed tomography
  • machine learning
  • air pollution
  • clinical trial
  • magnetic resonance imaging
  • magnetic resonance
  • pet ct