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

Stop LCNP: High dose corticosteroid therapy for late radiation-associated lower cranial neuropathy: A report of the phase I dose finding trial and parallel prospective data registry.

medRxiv : the preprint server for health sciences·2026
Same author

Artificial Intelligence in Image Assisted Radiation Oncology.

Cancers·2026
Same author

Targeted autonomic testing for radiation‑induced baroreflex failure in head and neck cancer survivors: index case and early program experience.

Cardio-oncology (London, England)·2026
Same author

Feasibility of longitudinal relaxation rate mapping with non-Cartesian sampling and compressed sensing on a 1.5 T magnetic resonance linear accelerator.

Physics and imaging in radiation oncology·2026
Same author

Neck adiposity on standard oncologic CT predicts radiation-induced carotid disease in oropharyngeal cancer.

Cardio-oncology (London, England)·2026
Same author

Expiratory Muscle Strength Training in Head and Neck Cancer Survivors With Radiation-Associated Dysphagia: Results of a Pilot Prospective Trial.

Head & neck·2026

相关实验视频

Updated: Jul 18, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.2K

一个基于变压器的分层注册框架,用于多模式可变形图像注册.

Yao Zhao1, Xinru Chen1, Brigid McDonald1

  • 1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
|August 25, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了Patch-RegNet,这是一个新的深度学习方法,用于头部和部放射治疗中的可变形图像注册. 它显著提高了CT-MR和MR-MR图像对齐的准确性,这对于适应性治疗至关重要.

关键词:
CT/MR可变形的注册表多模式注册多模式注册基于补丁的注册是基于补丁的注册.视觉变压器 视觉变压器

更多相关视频

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

628
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

8.0K

相关实验视频

Last Updated: Jul 18, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.2K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

628
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

8.0K

科学领域:

  • 医疗成像医学成像
  • 辐射疗法 辐射疗法
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 可变形图像注册 (DIR) 对于适应性放射治疗至关重要.
  • 传统的DIR方法在复杂的头部和部区域中难以准确.
  • 深度学习提供了更快,更强大的注册,但在大型,多个解剖学场所面临挑战.

研究的目的:

  • 开发一个层次化的深度学习框架,Patch-RegNet,用于准确和快速的CT-MR和MR-MR可变形图像注册.
  • 为了提高注册性能,特别是使用MR-Linac治疗的头适应性放射治疗.

主要方法:

  • 开发了Patch-RegNet,这是一个具有全球,基于补丁的刚性和基于补丁的可变形注册步骤的等级框架.
  • 使用ViT-Morph模型 (CNN + 视觉变压器) 进行基于补丁的DIR,并具有模式独立的邻里描述符.
  • 在242个CT-MR和213个MR-MR图像对上训练模型,在24对对上进行测试.

主要成果:

  • 在CT-MR注册方面,Patch-RegNet的表现优于Voxelmorph,6%,在MR-MR注册方面 (DSC测量) 的表现优于Voxelmorph4%.
  • 在CT-MR和MR-MR图像对中,分层方法实现了显著提高DIR精度.
  • 证明了对头部和部地区关键器官的更高的注册准确性.

结论:

  • 补丁-RegNet框架为头部和部自适应性放射治疗的DIR准确性和速度提供了显著的进步.
  • 这种方法在MR引导的适应性治疗中对CT-MR和MR-MR注册特别有益.
  • 层次的方法有效地解决了在注册大型,多个解剖学区域的挑战.