使用统一的深度学习框架在放射栓塞中90Y SPECT散射估计和voxel剂量测量
Yixuan Jia1, Zongyu Li2, Azadeh Akhavanallaf3
1Department of Electrical Engineering and Computer Science, University of Michigan, 4125 EECS Bldg., 1301 Beal Ave., Ann Arbor, MI, 48109, USA. jiayx@umich.edu.
EJNMMI physics
|December 13, 2023
概括
这项研究引入了一个深度学习框架,用于在放射栓塞后改进90Y剂量测量,与传统方法相比,显著提高准确性和速度.
科学领域:
- 医学物理 医学物理
- 核医学就是核医学.
- 放射学 放射学是一门学科.
背景情况:
- 在肝脏恶性瘤中,基于90Y SPECT的剂量测量是具有挑战性的,因为它具有分散性和低空间分辨率.
- 目前的蒙特卡洛 (MC) 方法面临准确性-效率的权衡,并且对SPECT图像质量敏感.
研究的目的:
- 开发和评估深度学习 (DL) 框架,以准确估计90Y放射栓塞 (RE) 中的吸收剂量率.
- 为了减轻SPECT图像质量差的影响,并提高剂量计计算的效率.
主要方法:
- 一个统一的DL框架,使用卷积神经网络 (CNN) 进行散射估计,并使用残留学习网络 (DblurDoseNet) 进行剂量率映射.
- 培训和测试使用虚拟患者幻影与MC生成的地面真相用于分散和剂量率图.
- 该框架将SPECT预测和CT数据集成到输出吸收剂量-速率图中.
主要成果:
- 该DL框架在分散估计 (NRMSE4.0%) 和SPECT重建与分散校正 (NRMSE9.2%) 中实现了高精度.
- 与MC方法 (24.0%和17.7%) 相比,虚拟患者的剂量率估计显示出明显较低的误差 (NMAE8.6%在病变中,5.4%在健康肝脏中).
- 在DL框架中,NMAE比MC方法的平均改善率为66%,计算时间快 (每患者约21秒).
结论:
- 与MC方法相比,拟议的DL框架为90Y剂量计提供了更高的准确性和效率.
- 这种方法显示出临床应用的潜力,提高了90Y-RE.RE治疗肝脏恶性瘤的剂量测量准确性.
- 该框架的速度和准确性使其对常规临床剂量测量具有高度相关性.
关键词:
90Y 90Y 90Y 90Y 90Y 90Y 90Y 90Y 90Y 90Y 90Y 90Y深度学习是一种深度学习.剂量测量方法 剂量测量方法放射性栓塞是一种放射性栓塞.斯佩克特 (Spectre) 是一个运动场.分散的纠正纠正分散的纠正更多相关视频
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