Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
Eur Radiol. 2024 Sep;34(9):5578-5587. doi: 10.1007/s00330-024-10647-1. Epub 2024 Feb 15.
Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning.
Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements.
The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images.
Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices.
The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up.
• CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious. • The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations. • The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.
具有长轴向视野的全身 PET/CT 扫描仪实现了前所未有的图像质量和定量准确性。然而，CT 产生的电离辐射是 PET 成像中的一个主要问题，在全身 PET/CT 中减少放射性药物剂量时更为明显。因此，我们试图通过深度学习生成无 CT 的衰减校正（CTF-AC）全身 PET 图像。
基于 122 名受试者（29 名女性和 93 名男性）的全身 PET 数据，使用成熟的循环一致生成对抗网络（Cycle-GAN）直接生成 CTF-AC 全身 PET 图像，同时引入部位结构作为先验信息。使用 Pearson 相关系数（PCC）和 t 检验进行相关性测量的统计分析。
生成的 CTF-AC 全身 PET 图像与真实 AC PET 图像非常相似，显示出不同组织结构的噪声降低和对比度良好。获得的峰值信噪比和结构相似性指数测量值分别为 36.92±5.49dB（p<0.01）和 0.980±0.041（p<0.01）。此外，标准化摄取值（SUV）分布与真实 AC PET 图像一致。
我们的方法可以直接生成 CTF-AC 全身 PET 图像，大大降低了冗余解剖检查带来的患者辐射风险。此外，该模型基于多剂量水平 NAC-AC PET 数据集进行了验证，表明我们的方法具有用于低剂量 PET 衰减校正的潜力。在未来的工作中，我们将尝试在更多的临床实践中使用全身 PET/CT 系统验证所提出的方法。
CT 是 PET/CT 成像中的主要辐射源，在全身 PET/CT 设备中更为明显，并且放射性药物剂量的减少使 CT 带来的辐射负担更加明显。我们的无 CT PET 衰减校正方法将有益于广泛的患者群体，特别是儿科检查和需要多次扫描或需要长期随访的患者。
