Jove
Visualize
联系我们

相关概念视频

Computed Tomography01:10

Computed Tomography

6.4K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
6.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Spectral deep learning-based patient and bowtie scatter correction for clinical photon-counting CT.

Medical physics·2026
Same author

Detection of calcified plaques: comparison between coronary CT angiography and thin-slice non-contrast CT with deep learning-aided image registration.

European radiology·2026
Same author

Erratum to: Detection of myeloma-associated osteolytic bone lesions with energy-integrating and photon-counting detector CT.

Radiologie (Heidelberg, Germany)·2026
Same author

Feasibility of opportunistic dental diagnostics in routine photon-counting CT examinations of the cervical spine.

BMC oral health·2026
Same author

Multispectral PCCT and CBCT imaging for high precision radiotherapy through translation of imaging parameters with machine learning validation.

Scientific reports·2026
Same author

Development and characterization of new contrast agents for Photon-Counting CT.

European journal of radiology·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Sep 19, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.1K

基于深度学习的圆束CT运动补偿与单视图时间分辨率.

Joscha Maier1, Stefan Sawall1,2, Marcel Arheit3

  • 1Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Medical physics
|June 4, 2025
PubMed
概括

基于深度单角运动补偿 (SAMoCo) 能够有效地重建4D圆束CT扫描,即使有非周期运动. 这种新的方法可以补偿患者的运动而不需要门,从而提高图像质量和时间分辨率.

关键词:
4D CBCTT 是一个 4D CBCT.深度学习是一种深度学习.动议补偿 补偿 动议补偿

更多相关视频

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.0K

相关实验视频

Last Updated: Sep 19, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.1K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.8K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.0K

科学领域:

  • 医疗成像医学成像
  • 图像重建 图像的重建
  • 计算成像技术的成像

背景情况:

  • 圆束CT (CBCT) 中的运动器件需要补偿策略来实现准确的4D (3D+时间) 成像.
  • 现有的关门策略对于周期性运动是有效的,但对于非周期性的患者运动,如不规则的呼吸,是失败的.
  • 门的局限性包括无法处理非周期性运动和可能减少时间分辨率.

研究的目的:

  • 为了在CBCT中改进运动补偿,引入基于深度单角运动补偿 (SAMoCo).
  • 增强时间分辨率和解决非周期运动的基于门的方法的局限性.

主要方法:

  • 深 SAMoCo 使用类似于U网的网络来预测时间点之间的位移向量场 (DVF),避免门.
  • 该网络通过模拟4D临床CT扫描获得的4D CBCT数据进行训练,学习从投影视图和初始3D重建中预测DVF.
  • 运动补偿重建是通过在任意运动状态或视图之间估计和应用DVF来实现的.

主要成果:

  • 深度SAMOCo成功地为周期性和非周期性呼吸运动生成了高质量的4D CBCT重建.
  • 重建偏离地面真相的平均值低于27 HU,隔膜位置被确定为大约0.75 mm.
  • 真实患者的测量显示出与外部运动监测有很强的相关性,即使在高度不规则的呼吸的情况下也是如此.

结论:

  • 深 SAMoCo 能够随意的运动模式补偿与单视图时间分辨率,适用于不稳定的呼吸和残余运动.
  • 这种方法对快速轮时间和有限的呼吸周期覆盖率的扫描有好处.
  • 消除对关门信号的需求简化了临床工作流程,并减少了患者准备时间.