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aDiner:用于动态CBCT成像的自适应动态隐含神经表示
IEEE transactions on medical imaging
|January 20, 2026
概括
本研究介绍了自适应的动态隐性神经表示 (aDiner),这是一种自我监督的方法,用于在肺癌放射治疗中重建动态圆束计算机断层扫描 (CBCT) 图像. aDiner准确地捕捉肺部运动,以改善图像引导治疗.
科学领域:
- 医疗成像医学成像
- 放射治疗 物理 物理
- 人工智能在医学中的应用
背景情况:
- 圆束计算机断层扫描 (CBCT) 对肺癌放射治疗至关重要,但动态运动重建具有挑战性.
- 现有的监督方法需要配对数据,并在离散阶段重建图像,限制时间分辨率.
- 从单个投影进行动态重建是一个错误的反向问题.
研究的目的:
- 开发一种自我监督的学习方法,用于每一个时间点的高保真性动态CBCT重建.
- 为了提高图像指导放射治疗中的肺成像的准确性和稳定性.
- 在动态CBCT重建中解决现有监督方法的局限性.
主要方法:
- 拟议的适应性动态隐性神经表示 (aDiner),一种自我监督的方法.
- 引入了自适应复合表示 (ACR) 与周期间和周期内编码 (iICE) 以实现时间连贯性.
- 开发了基于概率的特征融合 (PFF) 和运动引导的粗微采样 (MCS),包括自适应投射射射线采样 (APS) 和自适应变形点采样 (ADS).
主要成果:
- aDiner在模拟,幻影和临床数据集中的每个时间点实现了动态CBCT的准确和强大的重建.
- 该方法有效地捕捉了正常和不正常呼吸周期期间的肺部运动.
- 通过单独估计静态和动态区域并利用运动相似性,证明了优越的重建质量.
结论:
- aDiner为肺放射治疗中的动态CBCT重建提供了一种新的自我监督解决方案.
- 拟议的ACR和MCS策略增强了时间连贯性和运动估计准确性.
- 这种方法有可能通过提供连续运动信息来改善肺癌患者的图像导向放射治疗.
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