• 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

基于图谱的磁共振成像(MRI)单模态治疗计划电子密度映射方法及其在自适应 MRI 引导前列腺放射治疗中的应用。

An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy.

机构信息

Australian e-Health Research Center, CSIRO ICT Commonwealth Scientific and Industrial Research Organisation Information and Communication Technologies Centre, Queensland, Australia.

出版信息

Int J Radiat Oncol Biol Phys. 2012 May 1;83(1):e5-11. doi: 10.1016/j.ijrobp.2011.11.056. Epub 2012 Feb 11.

DOI:10.1016/j.ijrobp.2011.11.056
PMID:22330995
Abstract

PURPOSE

Prostate radiation therapy dose planning directly on magnetic resonance imaging (MRI) scans would reduce costs and uncertainties due to multimodality image registration. Adaptive planning using a combined MRI-linear accelerator approach will also require dose calculations to be performed using MRI data. The aim of this work was to develop an atlas-based method to map realistic electron densities to MRI scans for dose calculations and digitally reconstructed radiograph (DRR) generation.

METHODS AND MATERIALS

Whole-pelvis MRI and CT scan data were collected from 39 prostate patients. Scans from 2 patients showed significantly different anatomy from that of the remaining patient population, and these patients were excluded. A whole-pelvis MRI atlas was generated based on the manually delineated MRI scans. In addition, a conjugate electron-density atlas was generated from the coregistered computed tomography (CT)-MRI scans. Pseudo-CT scans for each patient were automatically generated by global and nonrigid registration of the MRI atlas to the patient MRI scan, followed by application of the same transformations to the electron-density atlas. Comparisons were made between organ segmentations by using the Dice similarity coefficient (DSC) and point dose calculations for 26 patients on planning CT and pseudo-CT scans.

RESULTS

The agreement between pseudo-CT and planning CT was quantified by differences in the point dose at isocenter and distance to agreement in corresponding voxels. Dose differences were found to be less than 2%. Chi-squared values indicated that the planning CT and pseudo-CT dose distributions were equivalent. No significant differences (p > 0.9) were found between CT and pseudo-CT Hounsfield units for organs of interest. Mean ± standard deviation DSC scores for the atlas-based segmentation of the pelvic bones were 0.79 ± 0.12, 0.70 ± 0.14 for the prostate, 0.64 ± 0.16 for the bladder, and 0.63 ± 0.16 for the rectum.

CONCLUSIONS

The electron-density atlas method provides the ability to automatically define organs and map realistic electron densities to MRI scans for radiotherapy dose planning and DRR generation. This method provides the necessary tools for MRI-alone treatment planning and adaptive MRI-based prostate radiation therapy.

摘要

目的

在磁共振成像(MRI)扫描上直接进行前列腺放射治疗剂量规划,可减少由于多模态图像配准带来的成本和不确定性。使用结合 MRI-线性加速器的自适应规划也将需要使用 MRI 数据进行剂量计算。本研究的目的是开发一种基于图谱的方法,将真实的电子密度映射到 MRI 扫描上,以进行剂量计算和数字重建射线照相(DRR)生成。

方法和材料

从 39 例前列腺患者中收集了全骨盆 MRI 和 CT 扫描数据。来自 2 名患者的扫描显示出与其余患者群体明显不同的解剖结构,因此排除了这 2 名患者。基于手动勾画的 MRI 扫描生成了全骨盆 MRI 图谱。此外,还从配准的 CT-MRI 扫描中生成了共轭电子密度图谱。通过将 MRI 图谱全局和非刚性配准到患者的 MRI 扫描,自动生成每个患者的伪 CT 扫描,然后将相同的变换应用于电子密度图谱。在计划 CT 和伪 CT 扫描上对 26 名患者的器官分割进行了 Dice 相似系数(DSC)比较和点剂量计算。

结果

通过等中心点处的点剂量差异和相应体素的一致距离来量化伪 CT 和计划 CT 之间的一致性。发现剂量差异小于 2%。卡方值表明计划 CT 和伪 CT 剂量分布等效。对感兴趣的器官,在 CT 和伪 CT 体素的 Hounsfield 单位之间没有发现显著差异(p>0.9)。基于图谱的骨盆骨骼分割的平均±标准偏差 DSC 评分为 0.79±0.12,前列腺为 0.70±0.14,膀胱为 0.64±0.16,直肠为 0.63±0.16。

结论

电子密度图谱方法能够自动定义器官,并将真实的电子密度映射到 MRI 扫描上,用于放射治疗剂量规划和 DRR 生成。该方法为 MRI 单治疗计划和基于 MRI 的自适应前列腺放射治疗提供了必要的工具。

相似文献

1
An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy.基于图谱的磁共振成像(MRI)单模态治疗计划电子密度映射方法及其在自适应 MRI 引导前列腺放射治疗中的应用。
Int J Radiat Oncol Biol Phys. 2012 May 1;83(1):e5-11. doi: 10.1016/j.ijrobp.2011.11.056. Epub 2012 Feb 11.
2
Automatic Substitute Computed Tomography Generation and Contouring for Magnetic Resonance Imaging (MRI)-Alone External Beam Radiation Therapy From Standard MRI Sequences.基于标准 MRI 序列的 MRI 引导外束放射治疗中自动替代 CT 生成和勾画。
Int J Radiat Oncol Biol Phys. 2015 Dec 1;93(5):1144-53. doi: 10.1016/j.ijrobp.2015.08.045. Epub 2015 Sep 5.
3
Magnetic resonance imaging in postprostatectomy radiotherapy planning.磁共振成像在前列腺癌根治术后放疗计划中的应用。
Int J Radiat Oncol Biol Phys. 2012 Feb 1;82(2):911-8. doi: 10.1016/j.ijrobp.2010.11.004. Epub 2011 Mar 21.
4
Regression and statistical shape model based substitute CT generation for MRI alone external beam radiation therapy from standard clinical MRI sequences.基于回归和统计形状模型,从标准临床MRI序列生成替代CT,用于仅MRI的外照射放疗。
Phys Med Biol. 2017 Oct 27;62(22):8566-8580. doi: 10.1088/1361-6560/aa9104.
5
T1/T2*-weighted MRI provides clinically relevant pseudo-CT density data for the pelvic bones in MRI-only based radiotherapy treatment planning.T1/T2*-加权 MRI 可为基于 MRI 的放射治疗计划提供临床相关的骨盆伪 CT 密度数据。
Acta Oncol. 2013 Apr;52(3):612-8. doi: 10.3109/0284186X.2012.692883. Epub 2012 Jun 19.
6
Development of multiorgan finite element-based prostate deformation model enabling registration of endorectal coil magnetic resonance imaging for radiotherapy planning.基于多器官有限元的前列腺变形模型的开发,用于放疗计划中直肠内线圈磁共振成像的配准。
Int J Radiat Oncol Biol Phys. 2007 Aug 1;68(5):1522-8. doi: 10.1016/j.ijrobp.2007.04.004.
7
MR image-based synthetic CT for IMRT prostate treatment planning and CBCT image-guided localization.基于磁共振图像的合成 CT 用于调强适形前列腺治疗计划和锥形束 CT 图像引导定位。
J Appl Clin Med Phys. 2016 May 8;17(3):236-245. doi: 10.1120/jacmp.v17i3.6065.
8
MRI-based treatment planning for radiotherapy: dosimetric verification for prostate IMRT.基于磁共振成像的放射治疗治疗计划:前列腺调强放射治疗的剂量验证
Int J Radiat Oncol Biol Phys. 2004 Oct 1;60(2):636-47. doi: 10.1016/j.ijrobp.2004.05.068.
9
Evaluation of margins in pelvic lymph nodes and prostate radiotherapy and the impact of bladder and rectum on prostate position.评估盆腔淋巴结和前列腺放疗中的边缘以及膀胱和直肠对前列腺位置的影响。
Cancer Radiother. 2021 Apr;25(2):161-168. doi: 10.1016/j.canrad.2020.06.033. Epub 2021 Jan 14.
10
Dose-volume differences for computed tomography and magnetic resonance imaging segmentation and planning for proton prostate cancer therapy.用于质子前列腺癌治疗的计算机断层扫描和磁共振成像分割及治疗计划的剂量体积差异
Int J Radiat Oncol Biol Phys. 2008 Dec 1;72(5):1426-33. doi: 10.1016/j.ijrobp.2008.03.031. Epub 2008 Aug 30.

引用本文的文献

1
Supervised versus unsupervised GAN for pseudo-CT synthesis in brain MR-guided radiotherapy.用于脑部磁共振引导放疗中伪CT合成的监督式与非监督式生成对抗网络
Phys Eng Sci Med. 2025 Jul 22. doi: 10.1007/s13246-025-01606-1.
2
CT synthesis with deep learning for MR-only radiotherapy planning: a review.基于深度学习的CT合成用于仅磁共振放疗计划:综述
Biomed Eng Lett. 2024 Sep 26;14(6):1259-1278. doi: 10.1007/s13534-024-00430-y. eCollection 2024 Nov.
3
Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning.
基于多序列磁共振成像(MR)利用循环生成对抗网络(CycleGAN)生成合成计算机断层扫描(CT)用于仅头部和颈部磁共振成像(MRI)的治疗计划。
Biomed Eng Lett. 2024 Jun 22;14(6):1319-1333. doi: 10.1007/s13534-024-00402-2. eCollection 2024 Nov.
4
MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.基于 MRI 的质子治疗物质质量密度和相对阻止本领的深度学习估算:初步研究。
Sci Rep. 2024 May 15;14(1):11166. doi: 10.1038/s41598-024-61869-8.
5
A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.基于深度学习的 3D Prompt-nnUnet 模型,用于术后子宫内膜癌近距离治疗中的自动分割。
J Appl Clin Med Phys. 2024 Jul;25(7):e14371. doi: 10.1002/acm2.14371. Epub 2024 Apr 29.
6
Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.深度学习法基于 T2 加权磁共振图像合成 CT 图像对宫颈癌患者的临床可行性与 MRCAT 的比较。
Sci Rep. 2024 Apr 12;14(1):8504. doi: 10.1038/s41598-024-59014-6.
7
A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.一项系统的文献综述:用于合成医学图像生成的深度学习技术及其在放射治疗中的应用
Front Radiol. 2024 Mar 27;4:1385742. doi: 10.3389/fradi.2024.1385742. eCollection 2024.
8
"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.“sCT-Feasibility”-一项基于深度学习的仅 MRI 脑放疗可行性研究。
Radiat Oncol. 2024 Mar 8;19(1):33. doi: 10.1186/s13014-024-02428-3.
9
Implementation of a retrofit MRI simulator for radiation therapy planning. retrofit MRI 模拟器在放射治疗计划中的实现。
J Med Radiat Sci. 2023 Dec;70(4):498-508. doi: 10.1002/jmrs.693. Epub 2023 Jun 14.
10
Clinical validation of MR imaging time reduction for substitute/synthetic CT generation for prostate MRI-only treatment planning.临床验证磁共振成像时间缩短用于前列腺 MRI 仅治疗计划的替代/合成 CT 生成。
Phys Eng Sci Med. 2023 Sep;46(3):1015-1021. doi: 10.1007/s13246-023-01268-x. Epub 2023 May 23.