Optimal image-derived input function models for multi-parameter analysis and acceptably reduced acquisition time in [(18)F]F-FAPI-42 PET total-body dynamic imaging for lung cancer

用于多参数分析的最佳图像衍生输入函数模型,以及在肺癌[(18)F]F-FAPI-42 PET全身动态成像中可接受的采集时间缩短

阅读:2

Abstract

PURPOSE: Lung tumors, which receive dual-blood-supply from the pulmonary and bronchial arteries, may exhibit distinct kinetic parameters compared to other malignancies. This study aimed to investigate the impact of various factors on the kinetic parameter quantification of [(18)F]F-FAPI-42 dynamic PET/CT and to establish an acceptable shortened acquisition time for lung cancer. METHODS: A total of 19 patients with lung tumors underwent 60-minute total-body dynamic [(18)F]F-FAPI-42 PET/CT imaging. Tumor kinetic metrics (K(1) to K(3) and K(i)) were calculated using a two-tissue irreversible comparative (2TiC) model. The effects of different image-derived input function (IDIF) models (derived from the right ventricle [RV], left ventricle [LV], and descending aorta [DA]), as well as tumor location, pathohistological subtype and size on kinetic parameters were evaluated. Additionally, the mean standardized uptake value (SUV(mean)), tumor-to-background ratio (TBR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed to determine an acceptable shortened acquisition time. RESULTS: The time-activity curve (TAC) of the RV exhibited the earliest and highest peak, followed by those of the LV and DA. Impact of IDIF model and tumor size on kinetic parameters of primary tumors was observed. Specifically, in the RVIF model, size of tumor > 3 cm exhibited higher K(2) and K(3) than those with size ≤ 3 cm (P < 0.05). Similar findings were also noted for K(3) in the LVIF model (P < 0.05), but not in the DAIF model. Tumor location and pathohistological subtype had no significant impact on kinetic parameters quantification. Regarding acquisition time, the RVIF model achieved kinetic parameters equivalent to those at 60 min in 26 min, while the LVIF and DAIF models required 36 min. At 26 min, the tumors were clearly visualized, with SUV(mean), SNR, CNR and TBR being equivalent or nearly approaching the values observed at 60 min. CONCLUSION: The RVIF model appears to be more suitable than the DAIF model for quantifying kinetic parameters in [(18)F]F-FAPI-42 PET dynamic imaging of lung cancer, with an acceptable shortened acquisition time of 26 min.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。