Optimal data extraction range for deep learning-based gated PET/CT

In a phantom study, a 30-40% data extraction percentage for data-driven gating combined with deep learning PET reconstruction (AiCE-i) yielded optimal balance between image noise and respiratory motion effects.

• Background variability (N10mm) decreased as data extraction percentage increased across all respiratory waveforms.
• At 50% extraction, contrast and contrast-to-noise ratio significantly declined for baseline shift and sinusoidal waveforms compared to no-motion.

Automated summary

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