PT  - JOURNAL ARTICLE
AU  - Takeshi Nii
AU  - Shota Hosokawa
AU  - Tomoya Kotani
AU  - Hiroshi Domoto
AU  - Yasunori Nakamura
AU  - Yasutomo Tanada
AU  - Ryotaro Kondo
AU  - Yasuyuki Takahashi
TI  - Evaluation of data-driven respiratory gating in continuous bed motion in lung lesions
AID  - 10.2967/jnmt.122.264909
DP  - 2023 Feb 01
TA  - Journal of Nuclear Medicine Technology
PG  - jnmt.122.264909
4099  - http://tech.snmjournals.org/content/early/2023/02/07/jnmt.122.264909.short
4100  - http://tech.snmjournals.org/content/early/2023/02/07/jnmt.122.264909.full
AB  - Objectives: Respiratory gating is used in positron emission tomography (PET) to prevent image quality degradation due to respiratory effects. In this study, we evaluated a data-driven respiratory gating for continuous bed motion OncoFreeze AI, which was implemented to improve image quality and accuracy of semiquantitative uptake values affected by respiratory motion. Methods: 18F-fluoro-deoxyglucose PET/ computed tomography was performed on 32 patients with lung lesions. Two types of respiratory gating images (OncoFreeze AI with data-driven respiratory gating, device-based amplitude-based OncoFreeze with elastic motion compensation) and ungated images (Static) were reconstructed. For each image, we calculated standardized uptake value (SUV) and metabolic tumor volume (MTV). The improvement rate (IR) from respiratory gating and the contrast-to-noise ratio (CNR), which indicates the improvement in image noise, were also calculated for these indices. IR was also calculated for the upper and lower lobes of the lung. As OncoFreeze AI assumes the presence of respiratory motion, we examined quantitativity in regions where respiratory motion was not present using a 68Ge cylinder phantom with known quantitativity. Results: OncoFreeze and OncoFreeze AI showed similar values, with a significant increase in SUV and decrease in MTV compared to Static. OncoFreeze and OncoFreeze AI also showed similar values for IR and CNR. OncoFreeze AI increased SUVmax by an average of 18% and decreased MTV by an average of 25% compared to Static. From the IR results, both OncoFreeze and OncoFreeze AI showed a greater improvement rate from Static in the lower lobe than in the upper lobe. OncoFreeze and OncoFreeze AI increased CNR by 17.9% and 18.0%, respectively, compared to Static. The quantitativity of the 68Ge phantom, assuming a region of no respiratory motion, was almost equal for the Static and OncoFreeze AI. Conclusion: OncoFreeze AI improved the influence of respiratory motion in the assessment of lung lesion accumulation to a comparable level to the previously launched OncoFreeze. OncoFreeze AI provides more accurate imaging with significantly larger SUV values and smaller MTV than Static.