The impact of scan time on dynamic [Formula: see text]-FAPI-04 total-body PET parametric imaging generated by deep learning models.

IF 3.5 2区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

EJNMMI Physics Pub Date : 2026-06-03 DOI:10.1186/s40658-026-00895-z

Jidong Han, Yu Liu, Meiyong Huang, Lingxin Chen, Yunlong Gao, Xinlan Yang, Jianjun Liu, Dong Liang, Zhanli Hu, Ruohua Chen, Bo Yuan

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引用次数: 0

Abstract

Purpose: To date, some studies have employed deep learning techniques to directly generate dynamic positron emission tomography (PET) parametric images from static PET. Compared with traditional methods, this approach requires only a single PET/computed tomography (CT) scan. However, current methods tend to employ static PET images captured at fixed scanning times without considering whether static PET images acquired at different scanning times impact the quality of the dynamic PET parametric images generated via deep learning.

Methods: A single-frame image from dynamic [Formula: see text]-FAPI-04 total-body PET can actually be regarded as a static PET image acquired during that specific period of the PET/CT scan. We extracted 5 frames of dynamic [Formula: see text]-FAPI-04 total-body PET at equal intervals, specifically frames 50, 76, 80, 86, and 92. Each frame of the dynamic [Formula: see text]-FAPI-04 total-body PET images was subsequently input into the deep learning model to obtain dynamic [Formula: see text]-FAPI-04 total-body PET parametric images. By comparing and analyzing the quality of dynamic PET parametric images, we attempted to determine the optimal scan time for static PET.

Results: The experimental results revealed that the dynamic [Formula: see text]-FAPI-04 total-body PET parametric image generated from the 58th frame of the dynamic PET image via the deep learning model exhibited the poorest performance. This may be because the diffusion of the radioactive tracer was not stable at the time of PET/CT scanning in frame 58, but it was relatively stable from frames 70-92.

Conclusion: Static PET images acquired at different scanning times do indeed affect the quality of the dynamic [Formula: see text]-FAPI-04 total-body PET parametric images generated via deep learning. Specifically, when the radioactive tracer is unstable during the early stage of scanning, the quality of the dynamic [Formula: see text]-FAPI-04 total-body PET parametric images generated from static PET images via deep learning appears to be inferior.

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扫描时间对动态[公式：见文]-FAPI-04深度学习模型生成的全身PET参数化成像。

目的：迄今为止，一些研究利用深度学习技术直接从静态PET生成动态正电子发射断层扫描（PET）参数图像。与传统方法相比，该方法只需要一次PET/ CT扫描。然而，目前的方法往往采用固定扫描次数捕获的静态PET图像，而没有考虑在不同扫描次数获取的静态PET图像是否会影响通过深度学习生成的动态PET参数图像的质量。方法：动态[公式：见文本]-FAPI-04全身PET的单帧图像实际上可以视为PET/CT扫描特定时段获得的静态PET图像。我们以相等的间隔提取了5帧动态[公式：见文本]-FAPI-04全身PET，具体为第50、76、80、86和92帧。随后将每帧动态[公式：见文]-FAPI-04全身PET图像输入深度学习模型，得到动态[公式：见文]-FAPI-04全身PET参数图像。通过比较和分析动态PET参数图像的质量，我们试图确定静态PET的最佳扫描时间。结果：实验结果显示，通过深度学习模型从动态PET图像的第58帧生成的动态[公式：见文本]-FAPI-04全身PET参数图像表现最差。这可能是由于58帧PET/CT扫描时放射性示踪剂的扩散不稳定，而70-92帧则相对稳定。结论：不同扫描次数下获得的静态PET图像确实会影响深度学习生成的动态[公式：见文]-FAPI-04全身PET参数图像的质量。具体来说，当扫描前期放射性示踪剂不稳定时，通过深度学习从静态PET图像生成的动态[公式：见文]-FAPI-04全身PET参数图像的质量就显得较差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EJNMMI Physics Physics and Astronomy-Radiation

CiteScore

6.70

自引率

10.00%

发文量

审稿时长

13 weeks

期刊介绍： EJNMMI Physics is an international platform for scientists, users and adopters of nuclear medicine with a particular interest in physics matters. As a companion journal to the European Journal of Nuclear Medicine and Molecular Imaging, this journal has a multi-disciplinary approach and welcomes original materials and studies with a focus on applied physics and mathematics as well as imaging systems engineering and prototyping in nuclear medicine. This includes physics-driven approaches or algorithms supported by physics that foster early clinical adoption of nuclear medicine imaging and therapy.