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

Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this principle...

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

Organ preservation in rectal cancer following clinical complete response after short-course radiotherapy-based total neoadjuvant therapy.

Clinical and translational radiation oncology·2026
Same author

Cross-Institutional Validation of a novel LLM-Based Cardiac Event Extraction framework from Electronic Health Records.

International journal of radiation oncology, biology, physics·2026
Same author

Contour and dosimetric evaluation of an in-room mobile CBCT scanner for cylinder based brachytherapy.

Brachytherapy·2026
Same author

Quantitative perfusion imaging from non-contrast micro-ct for pulmonary embolism evaluation in preclinical models.

Physics in medicine and biology·2026
Same author

MAX-SHOCK: A Pragmatic Randomized Controlled Trial Comparing Biphasic Defibrillators Used in Routine Cardioversion of Atrial Fibrillation.

CJC open·2026
Same author

Diagnoses of pulmonary embolism from non-contrast 4DCT using image processing-derived quantitative perfusion scores.

npj biomedical innovations·2026

相关实验视频

Updated: Jun 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

使用自主监督学习和以物理为灵感的U-net变压器架构使用动态非对比计算断层扫描进行 perfusion 估计.

Yi-Kuan Liu1, Jorge Cisneros1, Girish Nair2

  • 1Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.

International journal of computer assisted radiology and surgery
|January 20, 2025
PubMed
概括

这项研究引入了一种新的深度学习方法,用于预测使用非对比吸入和呼出CT扫描的肺 perfusion 图像. 该方法实现了最先进的准确性,可能改善肺功能评估.

关键词:
计算机断层扫描 (CT) 是一种计算机断层扫描.肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion 肺 perfusion自主监督学习学习视觉变压器 视觉变压器

更多相关视频

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

1.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

366

相关实验视频

Last Updated: Jun 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K
Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

1.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

366

科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 肺部诊断 肺部诊断 肺部诊断

背景情况:

  • 肺 perfusion 成像对于肺部健康评估至关重要,但受到当前核医学技术的限制.
  • 现有方法的空间分辨率低,采集时间长,限制临床使用,增加成本.

研究的目的:

  • 开发一种新的深度学习方法,用于预测肺 perfusion 成像.
  • 使用非对比吸入和呼出计算机断层扫描 (IE-CT) 作为预测模型的输入.

主要方法:

  • 开发了一个U-Net变压器架构,修改为姆IE-CT输入.
  • 用523张IE-CT图像的自我监督学习来学习一个低维特征空间.
  • 在44名IE-CT和SPECT/CT输液扫描患者进行了转移学习的监督训练.

主要成果:

  • 深度学习模型实现了0.742 ± 0.037的最先进的空间斯皮尔曼相关性,使用地面真相SPECT perfusion.
  • 观察到0.792 ± 0.036的平均中位相关性,表明预测准确度很高.

结论:

  • 这种新的方法有效地结合了使用深度学习的吸入和呼出CT功能,与物理建模原则保持一致.
  • 这种方法显示了更快,更准确的肺功能成像的潜力,扩大了核医学以外的临床应用.