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处于风险的胃肠道器官的深关节注册细分 (ProRSeg) 逐步精细化:适用于MRI和形光束CT
Jue Jiang1, Jun Hong1, Kathryn Tringale2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Medical physics
|June 2, 2023
概括
这项研究介绍了ProRSeg,这是一个深度学习网络,用于在胰腺癌适应性辐射治疗中准确细分和注册有风险的胃肠道器官 (OAR). ProRSeg证明了剂量积累的可行性,提高了治疗精度.
科学领域:
- 医疗成像医学成像
- 辐射疗法 辐射疗法
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 针对局部晚期胰腺癌 (LAPC) 的适应性辐射治疗 (ART) 需要精确细分有风险的移动胃肠道器官 (GI OAR).
- 由于可变形图像注册 (DIR) 的局限性,目前的方法大约是剂量积累,妨碍了精确的治疗适应.
研究的目的:
- 开发一个3D渐进精制的联合注册细分 (ProRSeg) 深度网络,以实现基于MRI的精确GI OAR细分和可变形对齐.
- 评估ProRSeg的细分精度,注册一致性和风险器官 (OAR) 剂量积累的可行性.
主要方法:
- 在10名患者的110次T2加权MRI扫描上,ProRSeg使用五倍交叉验证进行了训练,来自五个治疗部分的10名患者.
- 用子相似系数 (DSC) 和豪斯多夫距离 (HD95) 评估细分精度.
- 注册一致性是通过OAR位移的变化系数 (CV) 测量,并与其他方法进行比较.
主要成果:
- 在MRI上,ProRSeg实现了肝脏 (0.94),大肠 (0.88),小肠 (0.78) 和胃双肠 (0.82) 的高细分精度 (DSC).
- 它在3秒内处理了3D卷,并在公开的CBCT数据集上显示了可行性 (小肠DSC:0.72,胃-十二指肠0.76).
- ProRSeg注册表现出最低的位移CV,初步剂量积累表明可能违反器官剂量约束.
结论:
- 与现有方法相比,ProRSeg显著提高了GI OAR在MRI和CBCT中的细分精度和注册一致性.
- 开发的网络显示了适应性放射治疗中准确OAR剂量积累的可行性.
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