• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

自动、定量且针对具体病例的计算机断层扫描图像中变形图像配准评估。

An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images.

机构信息

Author to whom any correspondence should be addressed.

出版信息

Phys Med Biol. 2018 Feb 21;63(4):045026. doi: 10.1088/1361-6560/aa9dc2.

DOI:10.1088/1361-6560/aa9dc2
PMID:29182154
Abstract

A prerequisite for adaptive dose-tracking in radiotherapy is the assessment of the deformable image registration (DIR) quality. In this work, various metrics that quantify DIR uncertainties are investigated using realistic deformation fields of 26 head and neck and 12 lung cancer patients. Metrics related to the physiologically feasibility (the Jacobian determinant, harmonic energy (HE), and octahedral shear strain (OSS)) and numerically robustness of the deformation (the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM)) were investigated. The deformable registrations were performed using a B-spline transformation model. The DIR error metrics were log-transformed and correlated (Pearson) against the log-transformed ground-truth error on a voxel level. Correlations of r  ⩾  0.5 were found for the DDM and HE. Given a DIR tolerance threshold of 2.0 mm and a negative predictive value of 0.90, the DDM and HE thresholds were 0.49 mm and 0.014, respectively. In conclusion, the log-transformed DDM and HE can be used to identify voxels at risk for large DIR errors with a large negative predictive value. The HE and/or DDM can therefore be used to perform automated quality assurance of each CT-based DIR for head and neck and lung cancer patients.

摘要

自适应剂量跟踪在放射治疗中的一个前提是评估可变形图像配准(DIR)的质量。在这项工作中,使用 26 例头颈部和 12 例肺癌患者的真实变形场研究了各种量化 DIR 不确定性的指标。研究了与变形的生理可行性(雅可比行列式、调和能量(HE)和八面体剪切应变(OSS))和数值稳健性(逆一致性误差(ICE)、传递误差(TE)和距离不一致性度量(DDM))相关的度量。使用 B 样条变换模型进行可变形配准。将 DIR 误差指标进行对数变换,并在体素水平上与地面真实误差的对数变换进行相关(Pearson)。DDM 和 HE 的相关系数 r ⁇ ⁇ ⁇ 0.5。给定 DIR 容限阈值为 2.0mm 和负预测值为 0.90,DDM 和 HE 的阈值分别为 0.49mm 和 0.014。总之,对数变换的 DDM 和 HE 可用于识别存在大 DIR 误差风险的体素,且具有较大的负预测值。因此,HE 和/或 DDM 可用于对头颈部和肺癌患者的每个基于 CT 的 DIR 进行自动质量保证。

相似文献

1
An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images.自动、定量且针对具体病例的计算机断层扫描图像中变形图像配准评估。
Phys Med Biol. 2018 Feb 21;63(4):045026. doi: 10.1088/1361-6560/aa9dc2.
2
The distance discordance metric-a novel approach to quantifying spatial uncertainties in intra- and inter-patient deformable image registration.距离不一致度量——一种量化患者内和患者间可变形图像配准中空间不确定性的新方法。
Phys Med Biol. 2014 Feb 7;59(3):733-46. doi: 10.1088/0031-9155/59/3/733. Epub 2014 Jan 20.
3
A multiple-image-based method to evaluate the performance of deformable image registration in the pelvis.一种基于多图像的方法来评估骨盆中可变形图像配准的性能。
Phys Med Biol. 2016 Aug 21;61(16):6172-80. doi: 10.1088/0031-9155/61/16/6172. Epub 2016 Jul 29.
4
A framework for deformable image registration validation in radiotherapy clinical applications.用于放射治疗临床应用中的可变形图像配准验证的框架。
J Appl Clin Med Phys. 2013 Jan 2;14(1):4066. doi: 10.1120/jacmp.v14i1.4066.
5
Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm.迈向头颈部患者的自适应放射治疗:由于可变形配准算法的选择导致剂量扭曲的不确定性。
Med Phys. 2015 Feb;42(2):760-9. doi: 10.1118/1.4905050.
6
Representing the dosimetric impact of deformable image registration errors.表示可变形图像配准误差的剂量学影响。
Phys Med Biol. 2017 Aug 11;62(17):N391-N403. doi: 10.1088/1361-6560/aa8133.
7
Voxel-based statistical analysis of uncertainties associated with deformable image registration.基于体素的与形变图像配准相关的不确定性的统计分析。
Phys Med Biol. 2013 Sep 21;58(18):6481-94. doi: 10.1088/0031-9155/58/18/6481. Epub 2013 Sep 3.
8
Comprehensive evaluation of ten deformable image registration algorithms for contour propagation between CT and cone-beam CT images in adaptive head & neck radiotherapy.自适应头颈部放疗中用于CT与锥形束CT图像间轮廓传播的十种可变形图像配准算法的综合评估
PLoS One. 2017 Apr 17;12(4):e0175906. doi: 10.1371/journal.pone.0175906. eCollection 2017.
9
A multivariable study of deformable image registration evaluation metrics in 4DCT of thoracic cancer patients.多变量研究在胸部癌症患者 4DCT 中的可变形图像配准评估指标。
Phys Med Biol. 2021 Jan 29;66(3):035019. doi: 10.1088/1361-6560/abcd18.
10
Site-specific deformable imaging registration algorithm selection using patient-based simulated deformations.基于患者模拟变形的特定部位可变形成像配准算法选择。
Med Phys. 2013 Apr;40(4):041911. doi: 10.1118/1.4793723.

引用本文的文献

1
Improving patient specific quality assurance for image registration: clinical use case of target contouring for PET deformable image registration.提高针对图像配准的患者特异性质量保证:PET 可变形图像配准中靶区轮廓勾画的临床应用案例
Phys Eng Sci Med. 2025 May 14. doi: 10.1007/s13246-025-01541-1.
2
Deformable Image Registration Uncertainty-Encompassing Dose Accumulation for Adaptive Radiation Therapy.用于自适应放射治疗的包含可变形图像配准不确定性的剂量累积
Int J Radiat Oncol Biol Phys. 2025 Jul 15;122(4):818-826. doi: 10.1016/j.ijrobp.2025.04.004. Epub 2025 Apr 14.
3
Tools and recommendations for commissioning and quality assurance of deformable image registration in radiotherapy.
放射治疗中可变形图像配准的委托与质量保证工具及建议。
Phys Imaging Radiat Oncol. 2024 Sep 14;32:100647. doi: 10.1016/j.phro.2024.100647. eCollection 2024 Oct.
4
Review and recommendations on deformable image registration uncertainties for radiotherapy applications.放疗应用中形变图像配准不确定性的回顾与建议。
Phys Med Biol. 2023 Dec 13;68(24):24TR01. doi: 10.1088/1361-6560/ad0d8a.
5
Creating patient-specific digital phantoms with a longitudinal atlas for evaluating deformable CT-CBCT registration in adaptive lung radiotherapy.利用纵向图谱创建患者特异性数字体模,以评估自适应肺部放疗中可变形CT-CBCT配准。
Med Phys. 2024 Feb;51(2):1405-1414. doi: 10.1002/mp.16606. Epub 2023 Jul 14.
6
Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer.基于深度学习的快速准确的肺癌 3D CT 可变形图像配准。
Med Phys. 2023 Nov;50(11):6864-6880. doi: 10.1002/mp.16548. Epub 2023 Jun 8.
7
Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer.基于深度学习的肺癌三维CT快速准确可变形图像配准
ArXiv. 2023 Apr 21:arXiv:2304.11135v1.
8
Development of a multi-layer quality assurance program to evaluate the uncertainty of deformable dose accumulation in adaptive radiotherapy.开发一个多层质量保证程序来评估自适应放疗中变形剂量积累的不确定性。
Med Phys. 2023 Mar;50(3):1766-1778. doi: 10.1002/mp.16137. Epub 2022 Dec 10.
9
MIRSIG position paper: the use of image registration and fusion algorithms in radiotherapy.MIRSIG 立场文件:放射治疗中图像配准和融合算法的应用。
Phys Eng Sci Med. 2022 Jun;45(2):421-428. doi: 10.1007/s13246-022-01125-3. Epub 2022 May 6.
10
Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy - the need for guidance and QA tools.放射治疗中可变形图像配准的临床应用、挑战和实施障碍——对指导和 QA 工具的需求。
Br J Radiol. 2021 Jun 1;94(1122):20210001. doi: 10.1259/bjr.20210001. Epub 2021 Apr 29.