基于深度学习的低计数全身正子发射断层扫描 (denoising) 结合计算机断层扫描先验
Zhengyu Peng1, Fanwei Zhang2, Han Jiang1,3
1Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China.
Quantitative imaging in medicine and surgery
|December 19, 2024
概括
使用计算机断层扫描 (CT) 的深度学习先验增强低计数 (LC) 定子发射断层扫描 (PET) 无声化. 这种方法可以提高图像质量和病变检测,特别是在减少辐射剂量的情况下.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射化学 放射化学是指辐射化学.
背景情况:
- 深度学习显著提高了低计数 (LC) 定子发射断层扫描 (PET) 中的图像质量和量化.
- 目前用于LC PET denoising的深度学习方法通常只使用单个LC PET图像作为输入.
- 需要进一步降低PET成像中的辐射剂量水平.
研究的目的:
- 为LC PET开发和评估一种基于深度学习的denoising方法,该方法包含计算机断层扫描 (CT) 的先验.
- 评估这种CT前增强方法在进一步降低辐射剂量水平的有效性,同时保持图像质量和诊断准确性.
主要方法:
- 五十名接受二氧化二-[18F]-D-葡萄糖 (18F-FDG) PET/CT扫描的患者被追溯分析.
- 低计数 (LC) PET数据 (LC-10和LC-20) 是通过下方采样全计数 (FC) PET数据生成的.
- 训练了U-Net和cGAN模型,并没有CT图像集成用于消除噪音.
主要成果:
- 结合CT先验 (U-Net-2,cGAN-2) 的深度学习模型在表现方面超过了没有 (U-Net-1,cGAN-1) 的深度学习模型.
- 在平均平方误差和结构相似度指数方面,cGAN-2取得了最好的结果.
- 具有CT先验 (cGAN-2,U-Net-2) 的模型在SUV量化中显示出较低的误差和改善的病变检测能力,成功地检索了传统方法错过的病变.
结论:
- 基于深度学习的LC PET解密,包括CT priors,比使用单个LC PET输入的传统方法更有效.
- 这种方法显著提高了图像质量和病变检测能力,特别是在较低的辐射剂量水平下.
- 整合CT先验为PET成像中的剂量降低提供了一个有希望的策略,而不会影响诊断性能.
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