Jove
Visualize
联系我们
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

相关概念视频

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

Quantification of Imaging Suite-Level Energy Patterns in MRI, CT, and PET/CT to Guide Energy Efficiency.

Radiology·2026
Same author

The end of browsing, continuous publication, and title scrutiny.

Medical physics·2026
Same author

Assessing variations in 3D image quality in chest CT across sites and scanners.

Medical physics·2026
Same author

Multi-x-ray source array for stationary tomosynthesis or multi-cone angle cone beam CT.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Finding the optimal recall rate in breast cancer screening: results from the ROCS study.

European radiology·2026
Same author

Correlation of Automated in Vivo Image Quality With Radiologist's Performance in Abdomen Computed Tomography Across Conventional and Deep Learning Reconstructions.

Journal of computer assisted tomography·2026

相关实验视频

Updated: Jul 6, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

在使用CNN观察员的乳房CT图像中检测到微化.

Su Hyun Lyu1,2, Craig K Abbey3, Andrew M Hernandez2

  • 1Department of Biomedical Engineering, University of California Davis, Davis, California, USA.

Medical physics
|December 28, 2023
PubMed
概括

最大强度投影 (MIP) 显示器通过减少切片厚度而不会牺牲准确度,改善了乳腺CT中微化检测. 这种方法提供了计算上的好处,并有助于在新兴的乳腺成像协议中识别化.

关键词:
胸部CT CT 胸部CT CT 胸部CT在微化过程中,微化模型观察者 模型观察者

更多相关视频

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.5K

相关实验视频

Last Updated: Jul 6, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.5K

科学领域:

  • 放射学和医学成像学 医学成像学
  • 医疗保健中的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 乳房计算机断层扫描 (CT) 是一个不断发展的成像技术,正在进行研究以提高微化检测.
  • 使用混合图像和卷积神经网络 (CNN) 模型观察者的虚拟临床试验被用来评估影响微化检测能力的参数.

研究的目的:

  • 研究各种参数对乳腺CT中微化检测能力的单独和综合影响.
  • 优化乳腺CT成像协议,以提高诊断准确度.

主要方法:

  • 模拟的不同大小和强度的球形微化被嵌入到109名患者的乳腺CT数据集中.
  • 评估的参数包括化大小,对比度,集群密度 (不同集群直径内的化数量) 和图像显示方法 (单片切片,切片平均,MIP).
  • 通过对2D和3D感兴趣的区域进行训练,使用接收器操作特征 (ROC) 分析和曲线下面面积 (AUC) 评估检测性能.

主要成果:

  • 检测性能随着切片厚度的增加而下降;高峰性能是在原生0.2毫米厚度和MIP显示器实现的.
  • MIP显示器提供了与多片原生厚度相当的性能,提供了显著的计算优势.
  • 较小的集群直径和集群内的较高化密度改善了整体检测能力,而较大的集群则减少了检测能力.

结论:

  • 最大强度投影 (MIP) 是乳腺CT中微化集群的有价值的显示方法,可能有利于人类观察者.
  • 该研究提供了对微化集群的模型观察者性能的见解,指导未来研究优化乳腺CT协议的研究.
  • 这些发现有助于开发更有效的乳腺CT成像策略,用于早期发现疾病.