[1]唐大洋,胡德斌,齐宏亮,等.基于三维深度分离网络的PET双示踪剂混合图像分离方法[J].中国医学物理学杂志,2025,42(2):160-166.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.004]
　TANG Dayang,HU Debin,et al.Dual-tracer PET image separation using three-dimensional depthwise separable convolution network[J].Chinese Journal of Medical Physics,2025,42(2):160-166.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.004]
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基于三维深度分离网络的PET双示踪剂混合图像分离方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第2期
页码:
160-166
栏目:
医学影像物理
出版日期:
2025-01-20

文章信息/Info

Title:
Dual-tracer PET image separation using three-dimensional depthwise separable convolution network
文章编号:
1005-202X（2025）02-0160-07
作者:
唐大洋1; 2; 胡德斌2; 齐宏亮2; 孙浩1; 韩彦江3; 李翰威2; 张新明1; 2; 潘智林2; 喻文杰1; 2; 路利军1; 陈宏文1; 2
1.南方医科大学生物医学工程学院， 广东 广州 510515； 2.南方医科大学南方医院医学工程科， 广东 广州510515； 3.南方医科大学南方医院PET中心， 广东 广州 510515
Author(s):
TANG Dayang1;  2;  HU Debin2;  QI Hongliang2;  SUN Hao1;  HAN Yanjiang3;  LI Hanwei2;  ZHANG Xinming1;  2;  PAN Zhilin2;  YU Wenjie1;  2;  LU Lijun1;  CHEN Hongwen1;  2
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 2. Department of Clinical Engineering, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China 3. PET Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
关键词:
正电子发射断层成像; 双示踪剂成像; 图像配准; 深度分离网络; 深度学习
Keywords:
Keywords: positron emission computed tomography dual-tracer imaging image registration depthwise separable convolution network deep learning
分类号:
R318；R817
DOI:
DOI:10.3969/j.issn.1005-202X.2025.02.004
文献标志码:
A
摘要:
目的：提出一种基于三维深度分离网络方法用于18F-FDG和18F-FAPI PET双示踪剂混合图像分离成像。方法：收集120例同一患者在不同时间单独扫描的18F-FDG和18F-FAPI PET图像，本研究采用模拟的形式生成PET双示踪剂混合图像，首先对同一患者两种PET示踪剂图像进行配准保证空间位置匹配，然后对配准的PET图像进行前向投影生成弦图数据，将两种弦图数据累加得到混合弦图数据，随后采用最大似然期望法重建得到PET双示踪剂混合图像，输入到基于3D DSN架构的网络进行分离成像，从而得到两种单示踪剂的PET图像。结果：本文提出的方法相较于3D CNN方法，分离得到的18F-FDG图像与真实18F-FDG图像的结构相似性指数（SSIM）提升0.87%，峰值信噪比（PSNR）提升11.8%，归一化均方根误差（NRMSE）减小52%。分离得到的18F-FAPI图像与真实18F-FAPI图像的SSIM提升1.1%，PSNR提升17.0%，NRMSE减小51%。结论：本文方法可以很好地应用在PET双示踪剂同时成像上，减少患者的扫描次数、时间和金钱成本，为临床医生提供更精准和更丰富的诊断信息。
Abstract:
Abstract: Objective To propose a novel method based on three-dimensional depthwise separable convolution network (3D DSN) for the separation of PET images with dual tracers of 18F-FDG and 18F-FAPI. Methods A total of 120 pairs of 18F-FDG and 18F-FAPI PET images of the same patient scanned separately at different time points were collected, and the dual-tracer PET image was generated through simulation. After the image registration of PET images of two tracers for ensuring spatial position matching, the registered PET images were forward-projected to generate sinogram data, and the sinogram data of two tracers were accumulated to obtain mixed sinogram data. Subsequently, the dual-tracer PET image was reconstructed using maximum likelihood expectation maximization and input into a 3D DSN based network for image separation, thereby obtaining PET images of two single tracers. Results Compared with 3D CNN method, the proposed method increased the structure similarity index measure (SSIM) of the separated 18F-FDG images to the real 18F-FDG images by 0.87%, increased the peak signal-to-noise ratio (PSNR) by 11.8%, and reduced the normalized root mean square error (NRMSE) by 52%. The SSIM of the separated 18F-FAPI images to the real 18F-FAPI images increased by 1.1%, PSNR increased by 17.0%, and NRMSE decreased by 51%. Conclusion The proposed method can be effectively applied to simultaneous PET imaging with dual PET tracers, reducing the number of scans and costs in time and money, and providing clinical doctors more accurate and abundant diagnostic information.

相似文献/References:

[1]卢荣辉,陈宗哲,魏晓华,等.一种基于灰关联的PET重建图像评价方法[J].中国医学物理学杂志,2016,33(10):1051.[doi:10.3969/j.issn.1005-202X.2016.10.015]
　[J].Chinese Journal of Medical Physics,2016,33(2):1051.[doi:10.3969/j.issn.1005-202X.2016.10.015]
[2]靳珍怡,王远军,聂生东. 梯度域三维头部PETCT图像融合[J].中国医学物理学杂志,2017,34(3):246.[doi:10.3969/j.issn.1005-202X.2017.03.006]
　[J].Chinese Journal of Medical Physics,2017,34(2):246.[doi:10.3969/j.issn.1005-202X.2017.03.006]
[3]卢昱,彭昭,裴曦,等.基于剂量预测和自动勾画技术的PET/CT器官内照射剂量率快速评估方法[J].中国医学物理学杂志,2023,40(2):149.[doi:DOI:10.3969/j.issn.1005-202X.2023.02.004]
　LU Yu,PENG Zhao,PEI Xi,et al.Fast estimation of internal irradiation dose rate in PET/CT imaging using deep learning-based dose prediction combined with auto-segmentation technique[J].Chinese Journal of Medical Physics,2023,40(2):149.[doi:DOI:10.3969/j.issn.1005-202X.2023.02.004]

备注/Memo

备注/Memo:
【收稿日期】2024-08-15 【基金项目】国家重点研发计划（2023YFC2414601）；广东省医学会医学工程学分会青年委员会基金（2022-GDMAYB-05）；南方医科大学南方医院院长基金（2022B016） 【作者简介】唐大洋，硕士，研究方向：PET成像，E-mail: 2735430380@qq.com 【通信作者】陈宏文，教授，硕士生导师，研究方向：医疗器械质量控制管理，E-mail: chw47922@126.com
更新日期/Last Update: 2025-01-22