肺癌PET/CT中运动校正算法的评估：体模验证与患者研究

Wang Ziyang, Liu Jianjing, Lu Di, Sui Guoqing, Wang Yaya, Tong Lina, Liu Xueyao, Zhang Yan, Fu Jie, Xu Wengui, Dai Dong

Department of Nuclear Medicine, National Clinical Research Center for Cancer, Tianjin Cancer Hospital Airport Hospital, Tianjin, China.

Department of Molecular Imaging and Nuclear Medicine, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for China, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.

Med Phys. 2025 Jul;52(7):e17846. doi: 10.1002/mp.17846. Epub 2025 Apr 25.

BACKGROUND

Data-driven gating (DDG) is an emerging technology that can reduce the respiratory motion artifacts in positron emission tomography (PET) images.

PURPOSE

The aim of this study is to use phantom and patient data to validate the performance of DDG with a motion correction algorithm based on the reconstruct, register, and average (RRA) method.

METHODS

A customized motion platform drove the phantom (five spheres with diameters of 10-28 mm) using a periodic motion that had a duration of 3-5 s and amplitudes of 2-4 cm. Normalized ratio of ungated and RRA PET relative to the ground-truth static PET was calculated for RSUVmax, RSUVmean, RSUVpeak, RVolume, and relative contrast-to-noise ratio (RCNR). Additionally, 30 lung cancer patients with 76 lung lesions less than 3 cm in diameter were prospectively studied. The overall image quality of patient examination was scored using a 5-point scale by two radiologists. SUVmax, SUVmean, SUVpeak, volume, and CNR of lesions measured in ungated and RRA PET were compared, and subgroup analysis was conducted.

RESULTS

In RRA PET images, motion artifacts of the spheres in the phantom were effectively mitigated, regardless of changes in movement amplitudes or duration. For all spheres with different ranges of motion and cycles, RSUVmax, RSUVmean, RSUVpeak, and RCNR increased significantly (p ≤ 0.001) and RVolume decreased significantly (p < 0.001) in RRA PET images. The average radiologist scores of image quality were 3.90 ± 0.86 with RRA PET, and 3.03 ± 1.19 with ungated PET. In RRA PET images, the SUVmax (p < 0.001), SUVmean (p < 0.001), SUVpeak (p < 0.001), and CNR (p < 0.001) of the lesions increased, while the volume (p < 0.001) of the lesions decreased. Δ%SUVmax, Δ%SUVmean, Δ%SUVpeak, and Δ%CNR of the lesions increased by 3.9%, 6.5%, 5.6%, and 4.3%, respectively, while Δ%Volume of the lesions decreased by 18.4%. Subgroup analysis showed that in lesions in the upper and middle lobes, only SUVpeak (p < 0.001) significantly increased by 5.6% in RRA PET, while their volume (p < 0.001) notably decreased by 12.4% (p < 0.001).

CONCLUSION

DDG integrated with RRA motion correction algorithm can effectively mitigate motion artifacts, thus enhancing the quantification accuracy and visual quality of images in lung cancer PET/CT.

背景

数据驱动门控（DDG）是一种新兴技术，可减少正电子发射断层扫描（PET）图像中的呼吸运动伪影。

目的

本研究旨在使用体模和患者数据，通过基于重建、配准和平均（RRA）方法的运动校正算法来验证DDG的性能。

方法

定制的运动平台使用持续时间为3 - 5秒、幅度为2 - 4厘米的周期性运动驱动体模（五个直径为10 - 28毫米的球体）。计算未门控和RRA PET相对于真实静态PET的归一化比值，用于RSUVmax、RSUVmean、RSUVpeak、RVolume和相对对比噪声比（RCNR）。此外，前瞻性研究了30例患有76个直径小于3厘米肺部病变的肺癌患者。由两名放射科医生使用5分制对患者检查的整体图像质量进行评分。比较未门控和RRA PET中测量的病变的SUVmax、SUVmean、SUVpeak、体积和CNR，并进行亚组分析。

结果

在RRA PET图像中，无论运动幅度或持续时间如何变化，体模中球体的运动伪影均得到有效减轻。对于所有具有不同运动范围和周期的球体，RRA PET图像中的RSUVmax、RSUVmean、RSUVpeak和RCNR显著增加（p≤0.001），RVolume显著降低（p<0.001）。RRA PET图像质量的平均放射科医生评分为3.90±0.86，未门控PET为3.03±1.19。在RRA PET图像中，病变的SUVmax（p<0.001）、SUVmean（p<0.001）、SUVpeak（p<0.001）和CNR（p<0.001）增加，而病变体积（p<0.001）减小。病变的Δ%SUVmax、Δ%SUVmean、Δ%SUVpeak和Δ%CNR分别增加3.9%、6.5%、5.6%和4.3%，而病变的Δ%体积减小18.4%。亚组分析表明，在上叶和中叶的病变中，RRA PET中仅SUVpeak（p<0.001）显著增加5.6%，而其体积（p<0.001）显著减小12.4%（p<0.001）。