The impact of deep-inspiration breath-hold total-body PET/CT imaging on thoracic 18F-FDG avid lesions compared with free-breathing.

Journal: European Journal Of Radiology
Published:
Abstract

Objective: To investigate PET/CT registration and quantification accuracy of thoracic lesions of a single 30-second deep-inspiration breath-hold (DIBH) technique with a total-body PET (TB-PET) scanner, and compared with free-breathing (FB) PET/CT.

Methods: 137 of the 145 prospectively enrolled patients finished a routine FB-300 s PET/CT exam and a 30-second DIBH TB-PET with chest to pelvis low dose CT. The total-body FB-300 s, FB-30 s, and DIBH-30 s PET images were reconstructed. Quantitative assessment (SUVmax and SUVmean of lung and other organs), PET/CT registration assessment and lesion analysis (SUVmax, SUVpeak, SUVmean and tumor-background ratio) were compared with Wilcoxon signed-rank tests.

Results: The SUVmax and SUVmean of the lung with DIBH-30 s were significantly lower than those with FB. The distances of the liver dome between PET and CT were significantly smaller with DIBH-30 s than with FB. 195 assessable lesions in 106 patients were included, and the detection sensitivity was 97.9 % and 99.0 % in FB-300 s, and DIBH-30 s, respectively. For both small co-identified lesions (n = 86) and larger co-identified lesions with a diameter ≥ 1 cm (n = 91), the lesion SUVs were significantly greater with DIBH-30 s than with FB-300 s. Regarding lesion location, the differences of the SUVs for the lesions in the lower thorax area (n = 97, p < 0.001) were significant between DIBH-30 s and FB-300 s, while these differences were not statistically significant in the upper thorax (n = 80, p > 0.05). The lesion tumor-to-surrounding-background ratio (TsBR) was significantly increased, both in the upper and lower thorax.

Conclusions: The TB DIBH PET/CT technique is feasible in clinical practice. It reduces the background lung uptake and achieves better registration and lesion quantification, especially in the lower thorax.

Authors
Yingpu Cui, Jin Jia, Qianqian Yan, Xiaoxiao He, Keqing Yuan, Zhijian Li, Weiguang Zhang, Runze Wu, Yumo Zhao, Si Tang, Wei Fan, Yingying Hu
Relevant Conditions

Lung Cancer