• 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

用于治疗计划的头颈部CT数据的容积可视化。

Volumetric visualization of head and neck CT data for treatment planning.

作者信息

Lee J S, Jani A B, Pelizzari C A, Haraf D J, Vokes E E, Weichselbaum R R, Chen G T

机构信息

Department of Radiation and Cellular Oncology, University of Chicago, IL 60637-9006, USA.

出版信息

Int J Radiat Oncol Biol Phys. 1999 Jun 1;44(3):693-703. doi: 10.1016/s0360-3016(99)00042-5.

DOI:10.1016/s0360-3016(99)00042-5
PMID:10348301
Abstract

PURPOSE

To demonstrate the utility of volume rendering, an alternative visualization technique to surface rendering, in the practice of CT based radiotherapy planning for the head and neck.

METHODS AND MATERIALS

Rendo-avs, a volume visualization tool developed at the University of Chicago, was used to volume render head and neck CT scans from two cases. Rendo-avs is a volume rendering tool operating within the graphical user interface environment of AVS (Application Visualization System). Users adjust the opacity of various tissues by defining the opacity transfer function (OTF), a function which preclassifies voxels by opacity prior to rendering. By defining the opacity map (OTF), the user selectively enhances and suppresses structures of various intensity. Additional graphics tools are available within the AVS network, allowing for the manipulation of perspective, field of view, data orientation. Users may draw directly on volume rendered images, create a partial surface, and thereby correlate objects in the 3D scene to points on original axial slices. Information in volume rendered images is mapped into the original CT slices via a Z buffer, which contains the depth information (Z coordinate) for each pixel in the rendered view. Locally developed software was used to project conventionally designed GTV contours onto volume rendered images.

RESULTS

The lymph nodes, salivary glands, vessels, and airway are visualized in detail without prior manual segmentation. Volume rendering can be used to explore the finer anatomic structures that appear on consecutive axial slices as "points." Rendo-avs allowed for acceptable interactivity, with a processing time of approximately 5 seconds per 256 x 256 pixel output image.

CONCLUSIONS

Volume rendering is a useful alternative to surface rendering, offering high-quality visualization, 3D anatomic delineation, and time savings to the user, due to the elimination of manual segmentation as a preprocessing step. Volume rendered images can be merged with conventional treatment planning images to add anatomic information to the treatment planning process.

摘要

目的

展示体绘制(一种与面绘制不同的可视化技术)在基于CT的头颈部放射治疗计划实践中的效用。

方法与材料

使用在芝加哥大学开发的体可视化工具Rendo-avs对头颈部CT扫描的两个病例进行体绘制。Rendo-avs是一个在AVS(应用可视化系统)的图形用户界面环境中运行的体绘制工具。用户通过定义不透明度传递函数(OTF)来调整各种组织的不透明度,该函数在渲染前按不透明度对体素进行预分类。通过定义不透明度映射(OTF),用户可以有选择地增强和抑制不同强度的结构。AVS网络中还提供了其他图形工具,可用于操作视角、视野、数据方向。用户可以直接在体绘制图像上绘图,创建局部表面,从而将3D场景中的物体与原始轴向切片上的点相关联。体绘制图像中的信息通过Z缓冲区映射到原始CT切片上,Z缓冲区包含渲染视图中每个像素的深度信息(Z坐标)。使用本地开发的软件将传统设计的GTV轮廓投影到体绘制图像上。

结果

无需事先手动分割即可详细显示淋巴结、唾液腺、血管和气道。体绘制可用于探索在连续轴向切片上显示为“点”的更精细的解剖结构。Rendo-avs具有可接受的交互性,每256×256像素输出图像的处理时间约为5秒。

结论

体绘制是面绘制的一种有用替代方法,由于无需将手动分割作为预处理步骤,可为用户提供高质量的可视化、3D解剖描绘并节省时间。体绘制图像可与传统治疗计划图像合并,为治疗计划过程添加解剖信息。

相似文献

1
Volumetric visualization of head and neck CT data for treatment planning.用于治疗计划的头颈部CT数据的容积可视化。
Int J Radiat Oncol Biol Phys. 1999 Jun 1;44(3):693-703. doi: 10.1016/s0360-3016(99)00042-5.
2
Volumetric visualization of anatomy for treatment planning.用于治疗规划的解剖结构容积可视化。
Int J Radiat Oncol Biol Phys. 1996 Jan 1;34(1):205-11. doi: 10.1016/0360-3016(95)00272-3.
3
Prospective feasibility trial of radiotherapy target definition for head and neck cancer using 3-dimensional PET and CT imaging.使用三维正电子发射断层显像(PET)和计算机断层扫描(CT)成像对头颈部癌放疗靶区定义进行的前瞻性可行性试验。
J Nucl Med. 2004 Apr;45(4):543-52.
4
Contouring and dose calculation in head and neck cancer radiotherapy after reduction of metal artifacts in CT images.CT图像中金属伪影减少后对头颈部癌放疗的轮廓勾画与剂量计算
Acta Oncol. 2017 Jun;56(6):874-878. doi: 10.1080/0284186X.2017.1287427. Epub 2017 Feb 13.
5
Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.使用卷积神经网络对头颈部CT图像中的危险器官进行分割。
Med Phys. 2017 Feb;44(2):547-557. doi: 10.1002/mp.12045.
6
Evaluation of two commercial CT metal artifact reduction algorithms for use in proton radiotherapy treatment planning in the head and neck area.评估两种商用 CT 金属伪影降低算法在头颈部质子放射治疗计划中的应用。
Med Phys. 2018 Oct;45(10):4329-4344. doi: 10.1002/mp.13115. Epub 2018 Sep 19.
7
Validation of a Magnetic Resonance Imaging-based Auto-contouring Software Tool for Gross Tumour Delineation in Head and Neck Cancer Radiotherapy Planning.基于磁共振成像的自动轮廓软件工具在头颈癌放疗计划中大体肿瘤轮廓勾画的验证
Clin Oncol (R Coll Radiol). 2017 Jan;29(1):60-67. doi: 10.1016/j.clon.2016.09.016. Epub 2016 Oct 22.
8
AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.AnatomyNet:用于快速和全自动对头颈部解剖结构进行整体体积分割的深度学习方法。
Med Phys. 2019 Feb;46(2):576-589. doi: 10.1002/mp.13300. Epub 2018 Dec 17.
9
Use of deformed intensity distributions for on-line modification of image-guided IMRT to account for interfractional anatomic changes.使用变形强度分布对图像引导的调强放射治疗进行在线修正,以考虑分次间的解剖结构变化。
Int J Radiat Oncol Biol Phys. 2005 Mar 15;61(4):1258-66. doi: 10.1016/j.ijrobp.2004.11.033.
10
Opacity transfer function optimization for volume-rendered computed tomography images of the prostate.前列腺容积再现计算机断层扫描图像的不透明度传递函数优化
Acad Radiol. 2005 Jun;12(6):761-70. doi: 10.1016/j.acra.2005.03.054.

引用本文的文献

1
Application of a 3D volumetric display for radiation therapy treatment planning I: quality assurance procedures.三维容积显示在放射治疗治疗计划中的应用I:质量保证程序。
J Appl Clin Med Phys. 2009 Jul 17;10(3):96-114. doi: 10.1120/jacmp.v10i3.2900.