计算机断层扫描合成磁共振成像使用循环生成对抗网络与多中心学习
Blanche Texier1, Cédric Hémon1, Pauline Lekieffre1
1Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
Physics and imaging in radiation oncology
|December 11, 2023
概括
多中心培训可以提高合成CT (sCT) 在MRI光照疗中的精度. 在培训中仅包括一个中心的数据显著提高了sCT的概括性,达到与单个中心研究相比的准确性.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 人工智能的人工智能
背景情况:
- 在MRI-only放射治疗中,准确的剂量计算需要从MRI生成合成CT (sCT).
- 深度学习 (DL) 对sCT合成具有前景,但由于中心特定,方法往往缺乏通用性.
- 多中心培训是提议的解决方案,以提高sCT合成模型的通用性.
研究的目的:
- 为基于DL的sCT合成开发和评估一种可通用的多中心方法.
- 评估多中心培训对前列腺癌患者的sCT精度和剂量计算的影响.
主要方法:
- 在四个中心的128名前列腺癌患者的MRI数据中使用2D循环-GAN进行sCT合成.
- 四个培训/测试场景进行了比较:单中心,单中心转移,多中心与看不见中心测试,多中心与看得见中心测试.
- 使用平均绝对误差,平均误差和峰值信号与噪声比率来评估sCT准确性;用马指数和剂量体积组图比较来评估剂量准确性.
主要成果:
- 单中心训练和多中心训练,在测试组中看到中心,产生了可比的sCT准确性.
- 当在一个看不见的中心进行测试时,与单中心方法相比,sCT质量显著降低.
- 在多中心培训中包括来自一个中心的即使是有限的数据也提高了概括性.
结论:
- 可普及的多中心培训对于基于DL的强大的sCT合成在MRI仅放射治疗中至关重要.
- 将来自单个中心的数据纳入多中心培训队伍中,可以达到与单中心研究相当的sCT准确性.
- 这种方法提高了跨不同机构的sCT合成的适用性.
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