• 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

使用体内氙CT通气数据评估ΔV 4D CT通气计算方法并与其他方法进行比较。

Evaluation of the ΔV 4D CT ventilation calculation method using in vivo xenon CT ventilation data and comparison to other methods.

作者信息

Zhang Geoffrey G, Latifi Kujtim, Du Kaifang, Reinhardt Joseph M, Christensen Gary E, Ding Kai, Feygelman Vladimir, Moros Eduardo G

机构信息

Moffitt Cancer Center.

出版信息

J Appl Clin Med Phys. 2016 Mar 8;17(2):550-560. doi: 10.1120/jacmp.v17i2.5985.

DOI:10.1120/jacmp.v17i2.5985
PMID:27074479
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5874808/
Abstract

Ventilation distribution calculation using 4D CT has shown promising potential in several clinical applications. This study evaluated the direct geometric ventilation calculation method, namely the ΔV method, with xenon-enhanced CT (XeCT) ventilation data from four sheep, and compared it with two other published meth-ods, the Jacobian and the Hounsfield unit (HU) methods. Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used for the evaluation and comparison. The average SCC with one standard deviation was 0.44 ± 0.13 with a range between 0.29 and 0.61 between the XeCT and ΔV ventilation distributions. The average DSC value for lower 30% ventilation volumes between the XeCT and ΔV ventilation distributions was 0.55 ± 0.07 with a range between 0.48 and 0.63. Ventilation difference introduced by deformable image registration errors improved with smoothing. In conclusion, ventilation distributions generated using ΔV-4D CT and deformable image registration are in reasonably agreement with the in vivo XeCT measured ventilation distribution.

摘要

使用4D CT进行通气分布计算在多个临床应用中已显示出有前景的潜力。本研究用来自四只绵羊的氙增强CT（XeCT）通气数据评估了直接几何通气计算方法，即ΔV方法，并将其与另外两种已发表的方法，即雅可比方法和亨氏单位（HU）方法进行比较。使用斯皮尔曼相关系数（SCC）和骰子相似系数（DSC）进行评估和比较。XeCT与ΔV通气分布之间的平均SCC及一个标准差为0.44±0.13，范围在0.29至0.61之间。XeCT与ΔV通气分布之间较低30%通气量的平均DSC值为0.55±0.07，范围在0.48至0.63之间。可变形图像配准误差引入的通气差异通过平滑得到改善。总之，使用ΔV - 4D CT和可变形图像配准生成的通气分布与体内XeCT测量的通气分布相当一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/46eb0344db7e/ACM2-17-550-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/43b4763bb3bc/ACM2-17-550-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/07e4d3166497/ACM2-17-550-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/5d1ab36db4dc/ACM2-17-550-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/46eb0344db7e/ACM2-17-550-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/43b4763bb3bc/ACM2-17-550-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/07e4d3166497/ACM2-17-550-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/5d1ab36db4dc/ACM2-17-550-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57e9/5874808/46eb0344db7e/ACM2-17-550-g004.jpg

相似文献

1
Evaluation of the ΔV 4D CT ventilation calculation method using in vivo xenon CT ventilation data and comparison to other methods.使用体内氙CT通气数据评估ΔV 4D CT通气计算方法并与其他方法进行比较。
J Appl Clin Med Phys. 2016 Mar 8;17(2):550-560. doi: 10.1120/jacmp.v17i2.5985.
2
Dependence of ventilation image derived from 4D CT on deformable image registration and ventilation algorithms.基于四维 CT 的通气图像对形变图像配准和通气算法的依赖性。
J Appl Clin Med Phys. 2013 Jul 8;14(4):4247. doi: 10.1120/jacmp.v14i4.4247.
3
Effects of quantum noise in 4D-CT on deformable image registration and derived ventilation data.4D-CT 中的量子噪声对形变图像配准和衍生通气数据的影响。
Phys Med Biol. 2013 Nov 7;58(21):7661-72. doi: 10.1088/0031-9155/58/21/7661. Epub 2013 Oct 11.
4
The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging.VAMPIRE 挑战赛：CT 通气成像的多机构验证研究。
Med Phys. 2019 Mar;46(3):1198-1217. doi: 10.1002/mp.13346. Epub 2019 Feb 1.
5
Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics.四维计算机断层扫描肺通气图像随可变形图像配准算法和指标而变化。
Med Phys. 2011 Mar;38(3):1348-58. doi: 10.1118/1.3547719.
6
A Feasibility Study of Single-inhalation, Single-energy Xenon-enhanced CT for High-resolution Imaging of Regional Lung Ventilation in Humans.单次吸入氙增强 CT 单能量成像评估人体区域性肺通气高分辨率成像的可行性研究
Acad Radiol. 2019 Jan;26(1):38-49. doi: 10.1016/j.acra.2018.03.006. Epub 2018 Mar 29.
7
Comparison of image registration based measures of regional lung ventilation from dynamic spiral CT with Xe-CT.基于动态螺旋CT的区域肺通气图像配准测量与氙CT的比较。
Med Phys. 2012 Aug;39(8):5084-98. doi: 10.1118/1.4736808.
8
Robust HU-based CT ventilation from an integrated mass conservation formulation.基于整体质量守恒公式的稳健 HU 基 CT 通气成像。
Med Phys. 2019 Nov;46(11):5036-5046. doi: 10.1002/mp.13817. Epub 2019 Sep 28.
9
Robust CT ventilation from the integral formulation of the Jacobian.稳健的 CT 通气从雅可比行列式的整体公式。
Med Phys. 2019 May;46(5):2115-2125. doi: 10.1002/mp.13453. Epub 2019 Mar 22.
10
Validating and improving CT ventilation imaging by correlating with ventilation 4D-PET/CT using 68Ga-labeled nanoparticles.通过使用68Ga标记的纳米颗粒与通气4D-PET/CT相关联来验证和改进CT通气成像。
Med Phys. 2014 Jan;41(1):011910. doi: 10.1118/1.4856055.

引用本文的文献

1
A review on functional lung avoidance radiotherapy plan for lung cancer.肺癌功能性肺避让放疗计划综述
Front Oncol. 2024 Dec 5;14:1429837. doi: 10.3389/fonc.2024.1429837. eCollection 2024.
2
Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features.使用基于 4D-CT 的剂量-函数特征的机器学习预测放射性肺炎。
J Radiat Res. 2022 Jan 20;63(1):71-79. doi: 10.1093/jrr/rrab097.
3
A novel lung-avoidance planning strategy based on 4DCT ventilation imaging and CT density characteristics for stage III non-small-cell lung cancer patients.

本文引用的文献

1
Combined Ventilation and Perfusion Imaging Correlates With the Dosimetric Parameters of Radiation Pneumonitis in Radiation Therapy Planning for Lung Cancer.肺癌放射治疗计划中，联合通气灌注成像与放射性肺炎的剂量学参数相关。
Int J Radiat Oncol Biol Phys. 2015 Nov 15;93(4):778-87. doi: 10.1016/j.ijrobp.2015.08.024. Epub 2015 Aug 19.
2
Analysis of Long-Term 4-Dimensional Computed Tomography Regional Ventilation After Radiation Therapy.放射治疗后长期四维计算机断层扫描区域通气分析
Int J Radiat Oncol Biol Phys. 2015 Jul 1;92(3):683-90. doi: 10.1016/j.ijrobp.2015.02.037. Epub 2015 Apr 28.
3
Dosimetric impact of 4-dimensional computed tomography ventilation imaging-based functional treatment planning for stereotactic body radiation therapy with 3-dimensional conformal radiation therapy.
一种基于 4DCT 通气成像和 CT 密度特征的新型肺回避规划策略，用于治疗 III 期非小细胞肺癌患者。
Strahlenther Onkol. 2021 Dec;197(12):1084-1092. doi: 10.1007/s00066-021-01821-1. Epub 2021 Aug 5.
4
N-Phase Local Expansion Ratio for Characterizing Out-of-Phase Lung Ventilation.用于描述不同时相肺通气的 N 相局部扩张比。
IEEE Trans Med Imaging. 2020 Jun;39(6):2025-2034. doi: 10.1109/TMI.2019.2963083. Epub 2019 Dec 30.
5
Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients.评估胸部癌症患者 CT 通气图像的肺毒性风险。
PLoS One. 2018 Oct 3;13(10):e0204721. doi: 10.1371/journal.pone.0204721. eCollection 2018.
6
Ventilation Series Similarity: A Study for Ventilation Calculation Using Deformable Image Registration and 4DCT to Avoid Motion Artifacts.通气系列相似性：使用可变形图像配准和 4DCT 避免运动伪影的通气计算研究。
Contrast Media Mol Imaging. 2017 Sep 17;2017:9730380. doi: 10.1155/2017/9730380. eCollection 2017.
基于四维计算机断层扫描通气成像的功能治疗计划对立体定向体部放射治疗三维适形放射治疗的剂量学影响。
Pract Radiat Oncol. 2015 Sep-Oct;5(5):e505-e512. doi: 10.1016/j.prro.2015.03.001. Epub 2015 Apr 18.
4
Measuring interfraction and intrafraction lung function changes during radiation therapy using four-dimensional cone beam CT ventilation imaging.使用四维锥形束CT通气成像测量放射治疗期间分次间和分次内肺功能变化。
Med Phys. 2015 Mar;42(3):1255-67. doi: 10.1118/1.4907991.
5
Pulmonary ventilation imaging based on 4-dimensional computed tomography: comparison with pulmonary function tests and SPECT ventilation images.基于 4 维 CT 的肺通气成像：与肺功能测试和 SPECT 通气图像的比较。
Int J Radiat Oncol Biol Phys. 2014 Oct 1;90(2):414-22. doi: 10.1016/j.ijrobp.2014.06.006. Epub 2014 Aug 4.
6
Validating and improving CT ventilation imaging by correlating with ventilation 4D-PET/CT using 68Ga-labeled nanoparticles.通过使用68Ga标记的纳米颗粒与通气4D-PET/CT相关联来验证和改进CT通气成像。
Med Phys. 2014 Jan;41(1):011910. doi: 10.1118/1.4856055.
7
Respiratory effort correction strategies to improve the reproducibility of lung expansion measurements.呼吸努力校正策略可提高肺扩张测量的可重复性。
Med Phys. 2013 Dec;40(12):123504. doi: 10.1118/1.4829519.
8
Effects of quantum noise in 4D-CT on deformable image registration and derived ventilation data.4D-CT 中的量子噪声对形变图像配准和衍生通气数据的影响。
Phys Med Biol. 2013 Nov 7;58(21):7661-72. doi: 10.1088/0031-9155/58/21/7661. Epub 2013 Oct 11.
9
Dependence of ventilation image derived from 4D CT on deformable image registration and ventilation algorithms.基于四维 CT 的通气图像对形变图像配准和通气算法的依赖性。
J Appl Clin Med Phys. 2013 Jul 8;14(4):4247. doi: 10.1120/jacmp.v14i4.4247.
10
Reproducibility of intensity-based estimates of lung ventilation.基于强度的肺通气估计的可重复性。
Med Phys. 2013 Jun;40(6):063504. doi: 10.1118/1.4805106.