动态CBCT成像使用以前没有模型的时空隐性神经表示 (PMF-STINR)
Hua-Chieh Shao1, Tielige Mengke1, Tinsu Pan2
1The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Physics in medicine and biology
|May 2, 2024
概括
我们开发了一种用于动态圆束计算机断层扫描 (CBCT) 重建的新型机器学习方法,使放射治疗中精确的运动监测成为可能. 这种方法从没有先前模型的有限投影中重建时间变化的图像.
科学领域:
- 医疗成像医学成像
- 放射治疗 物理 物理
- 医疗保健中的机器学习
背景情况:
- 动态圆束计算断层扫描 (CBCT) 对于放射治疗中的实时运动监测至关重要,但由于采样不足的时空数据,其重建具有挑战性.
- 现有的方法与快速解剖运动作斗争,需要先前的知识或复杂的数据分类,限制了它们的临床适用性.
研究的目的:
- 开发和评估一种新的机器学习技术,即以前没有模型的时空隐性神经表示 (PMF-STINR),用于从有限的X射线投影中重建动态CBCT.
- 为了实现精确的内部扫描运动重建,而不依赖患者特定的先前模型或运动分类.
主要方法:
- 开发了PMF-STINR,一种利用空间和时间隐式神经表示 (INRs) 的联合图像重建和注册方法.
- 采用基于学习的B-spline运动模型与时间INR相结合,以捕捉重建期间的可变形运动.
- 所有组件 (空间INR,时间INR,B-spline模型) 都是以一次性方式在飞行中学习的,从而消除了对事先信息的需求.
主要成果:
- PMF-STINR准确而稳健地在各种数据集中重建了动态CBCT,包括数字幻影,物理幻影和多机构患者数据.
- 该方法成功捕获了具有高时间分辨率 (约0.1秒) 和亚毫米精度的高度不规则的运动.
- 在各种成像协议中验证了性能,证明了稳定性和通用性.
结论:
- PMF-STINR有效地重建动态CBCT,并从传统的3D CBCT扫描中解决扫描内运动,而无需先前的解剖或运动模型.
- 这种一次性学习方法为放射治疗中先进的运动管理提供了有前途的工具,提供比传统的4D-CBCT更丰富的运动信息.
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