The impact of data extraction percentage and deep learning-based reconstruction on image quality in gated PET/computed tomography.

Abstract

Background: Respiratory motion artifacts degrade PET/computed tomography (PET/CT) image quality. Data-driven gated (DDG) PET/CT addresses this issue by extracting respiratory signals directly from PET data, eliminating the need for external monitoring devices. This study investigated the effects of data extraction percentage (%count) and deep learning-based reconstruction [Advanced Intelligent Clear-IQ Engine-integrated (AiCE-i)] on image quality in DDG-PET under different respiratory conditions using a phantom model.

Methods: A body phantom containing six spheres (10-37 mm) was imaged using a silicon photomultiplier-based PET/CT system. Four respiratory waveforms (no-motion, sinusoidal, representative patient, and baseline shift) and four %count levels (20, 30, 40, and 50%) were evaluated using AiCE-i reconstruction. Image quality was assessed using background variability (N10 mm), percentage contrast (QH,10 mm), contrast-to-noise ratio (QH,10 mm/N10 mm), and recovery coefficient.

Results: Increasing %count consistently reduced N10 mm across all respiratory waveforms. QH,10 mm and QH,10 mm/N10 mm generally increased with increasing %count; however, at the 50% threshold, significant reductions were observed in the baseline shift and sinusoidal waveforms compared with the no-motion condition. Recovery coefficient analysis demonstrated the partial volume effect in smaller spheres and showed that quantitative performance was maintained across the evaluated gating conditions.

Conclusion: The combination of DDG and AiCE-i maintained stable image quality across the respiratory conditions evaluated. Under the conditions of this phantom study, a %count range of 30-40% provided a favorable balance between image noise and the effects of respiratory motion for standard 120-s acquisitions.