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

Predicting Knee osteoarthritis progression using explainable machine learning and clinical imaging data.

Osteoarthritis and cartilage open·2026
Same author

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same authorSame journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

Stretcher: a learning-based framework for deformation-robust keypoint descriptors.

International journal of computer assisted radiology and surgery·2026
Same author

A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration.

IEEE transactions on medical imaging·2026
Same author

Intraoperative Registration by Cross-Modal Inverse Neural Rendering.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation
08:37

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation

Published on: December 22, 2020

4.2K

在无跟踪器脑超声波中对特定患者进行实时细分.

Reuben Dorent1, Erickson Torio1, Nazim Haouchine1

  • 1Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 22, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的患者特异性框架,用于使用手术内超声波 (iUS) 成像进行脑瘤细分. 该方法通过适应个体患者数据和外科医生的目标来提高外科精度,优于现有方法.

关键词:
交叉模式合成 交叉模式合成图像细分 图像细分在外科手术中的超声波.神经外科 神经外科

更多相关视频

Author Spotlight: Advancing Human Brain Modulation – Optimized Protocols for Transcranial Ultrasound Stimulation Experiments
07:52

Author Spotlight: Advancing Human Brain Modulation – Optimized Protocols for Transcranial Ultrasound Stimulation Experiments

Published on: June 28, 2024

1.8K
An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
16:01

An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

Published on: September 24, 2017

10.8K

相关实验视频

Last Updated: Jan 8, 2026

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation
08:37

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation

Published on: December 22, 2020

4.2K
Author Spotlight: Advancing Human Brain Modulation – Optimized Protocols for Transcranial Ultrasound Stimulation Experiments
07:52

Author Spotlight: Advancing Human Brain Modulation – Optimized Protocols for Transcranial Ultrasound Stimulation Experiments

Published on: June 28, 2024

1.8K
An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
16:01

An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

Published on: September 24, 2017

10.8K

科学领域:

  • 神经外科 神经外科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 术内超声波 (iUS) 提供了改善脑外科手术结果的潜力.
  • 对神经外科医生来说,解释iUS是一个挑战.
  • 精确的脑瘤细分对于手术规划和执行至关重要.

研究的目的:

  • 开发第一个针对患者的框架,用于无追踪器的手术内超声波脑瘤细分.
  • 实时调整iUS解释以适应神经外科的目标.
  • 为了提高手术期间脑瘤细分的准确性和可靠性.

主要方法:

  • 一个特定于患者的实时网络被设计用于在无追踪器iUS.US中进行脑瘤细分.
  • 合成超声波数据是通过模拟手术前MRI的虚拟iUS扫描采集生成的.
  • 该网络使用这些合成数据进行了培训,以明确iUS并适应外科目标.

主要成果:

  • 拟议的框架在使用真实手术内超声数据对脑瘤进行细分方面表现出有效性.
  • 这种方法成功地适应了外科医生对手术目标的定义.
  • 针对患者的模型表现优于非针对患者的模型,专家神经外科医生和高端追踪系统.

结论:

  • 开发的患者特定框架显著增强了无追踪器iUS.US.中的脑瘤细分.
  • 这项技术有可能改善手术导航和神经外科手术的结果.
  • 该框架提供了一个可适应和高性能解决方案,用于实时手术指导.