DOI: 10.1097/mnm.0000000000002222 ISSN: 0143-3636

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

Hiroki Nosaka, Masaya Suda, Noriaki Miyaji, Hiraku Fuse, Kenji Yasue, Norikazu Koori, Shin Miyakawa, Masato Takahashi, Koichi Hanada, Shogo Imai
Show PDF Cite

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 ( N 10 mm ), percentage contrast ( Q H,10 mm ), contrast-to-noise ratio ( Q H,10 mm / N 10 mm ), and recovery coefficient.

Results

Increasing %count consistently reduced N 10 mm across all respiratory waveforms. Q H,10 mm and Q H,10 mm / N 10 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.

More from our Archive