[18F]-FDG PET和CT图像的基于深度学习的组合,用于生成肺 perfusion 图像
Jiabing Gu1,2, Qingtao Qiu1, Jian Zhu2,3
1Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China.
Medical physics
|June 30, 2023
概括
一种新的深度学习方法结合了18F-FDG-PET和CT扫描来创建精确的肺 perfusion 图像 (PPI). 这一进步通过整合代谢和解剖数据来改善功能性肺避开放射治疗 (FLART) 计划.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- F-FDG-PET和CT扫描对瘤学至关重要,主要是用于瘤的识别和量化.
- 整合PET和CT数据以获得功能性肺回避放射治疗 (FLART) 的肺 perfusion 信息是一个重要的,但具有挑战性的目标.
研究的目的:
- 开发和验证一种基于深度学习 (DL) 的方法,通过结合F-FDG-PET和CT数据来生成肺 perfusion 图像 (PPI).
主要方法:
- 采用了3D U-Net DL架构,将输入从单通道扩展到双通道,以集成多模式F-FDG-PET和CT图像.
- 肺部SPECT (PPISPECT) 作为地面真相,记录图像以对齐PET和CT数据.
- 使用斯皮尔曼相关系数 (rs),多尺度结构相似度指数 (MS-SSIM) 和功能性肺部细分的Dice相似度系数 (DSC) 来评估性能.
主要成果:
- 基于DL的方法 (PPIDLM) 在测试数据集中表现出明显更高的准确性 (rs=0.78,MS-SSIM=0.93) 与单独使用F-FDG-PET (PPIDLPET) (rs=0.55,MS-SSIM=0.90) 相比.
- 与PPIDLPET相比,PPIDLM在高/低功能性肺体积 (HFL/LFL) 中实现了优越的子相似系数,表明细分精度更好.
- 统计分析证实,PPIDLM与PPISPECT产生了更强的相关性和更高的感知相似性,而不是PPIDLPET (p <0.001).
结论:
- 基于DL的方法有效地整合了肺的代谢和解剖信息,以产生准确的PPI.
- 这种方法显著提高了对仅依赖代谢数据的技术的准确性,为肺 perfusion 容量细分提供了有价值的工具.
- 生成的PPIDLM具有优化FLART治疗计划的潜力.
关键词:
在PET成像中使用PET成像.斯佩克特 (Spectre) 是一个运动场.深度学习是一种深度学习.功能性肺部成像 - 功能性肺部成像perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion perfusion 这是一个非常重要的方法,它可以让一个人觉得自己是个好的人,而不是一个好的人,而是一个好的人.相关概念视频
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