ScatterNet用于基于投影的4D圆束计算机断层扫描对肺癌患者的强度校正
Henning Schmitz1, Adrian Thummerer1, Maria Kawula1
1Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
Physics and imaging in radiation oncology
|September 8, 2023
概括
基于深度学习的ScatterNet通过精确纠正4D圆束CT (4DCBCT) 图像强度,减少分散校正时间,显著改善了肺癌放射治疗中的4D剂量计算.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 图像处理 图像处理
背景情况:
- 使用4D圆束CT (4DCBCT) 进行放射治疗的精确剂量计算需要对图像强度进行校正.
- 散射校正对于提高基于4DCBCT的剂量计算的准确性至关重要.
研究的目的:
- 将深度学习分散校正工作流 (ScatterNet) 的剂量计算精度与传统方法进行比较.
- 评估ScatterNet在减少4DCBCT的散射校正时间方面的效率.
主要方法:
- 一项涉及26名肺癌患者的回顾性研究.
- 在CBCT投影对上训练了ScatterNet;修正后的投影被重建成4DCBCT.
- 与传统 (4DCBCT_cor) 和基于DIR (4DvCT) 的方法进行了ScatterNet校正的4DCBCT (4DCBCT_SN) 的比较,使用剂量-体积组图参数和马分析.
主要成果:
- 4DCBCT_SN与4DCBCT_cor和4DvCT达成高度一致,平均绝对误差较低.
- 目标中剂量差异的中位数是最小的,这表明剂量计算准确.
- 与两种常规方法相比,ScatterNet实现了高的平均马通过率 (例如,通过3mm/3mm标准的90%点的99.5%).
结论:
- ScatterNet可为接受放射治疗的肺癌患者提供准确的4D剂量计算.
- 深度学习方法显著减少了分散校正时间,提高了临床工作流程的效率.
- 在放射治疗中,ScatterNet显示为4DCBCT散射校正的临床适用性.
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