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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

7.1K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
7.1K

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

Ensemble CycleGAN for Retrospective Rigid Motion Correction in MRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Intelligent Agent Planning for Optimizing Parallel MRI Reconstruction via A Large Language Model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Improving deep PROPELLER MRI via synthetic blade augmentation and enhanced generalization.

Magnetic resonance imaging·2024
Same author

Suppressing image blurring of PROPELLER MRI via untrained method.

Physics in medicine and biology·2023
Same author

Virtual Conjugate Coil for Improving KerNL Reconstruction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2022
Same author

Interpretable Dimension Reduction for MRI Channel Suppression.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2022

相关实验视频

Updated: Sep 14, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

代理MRI:一种视觉语言模型驱动的人工智能系统,用于具有多个退化的自我调节MRI重建.

Gulfam Ahmed Sajua1, Marjan Akhib2, Yuchou Chang3

  • 1Department of Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, Dartmouth, 02747, MA, USA.

Journal of imaging informatics in medicine
|July 22, 2025
PubMed
概括

使用视觉语言模型的AI系统AgentMRI自主重建磁共振成像 (MRI) 扫描. 它可以在没有人参与的情况下检测和纠正图像损坏,在测试中实现高精度.

关键词:
人工智能代理人AI代理人决策方式 决策方式大型多式联运模式模型磁力共振成像运动校正核磁共振成像 (MRI) 重建的重建推理 推理 推理 推理视觉语言模型 视觉语言模型

更多相关视频

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

175
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

相关实验视频

Last Updated: Sep 14, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

175
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

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 由人工智能 (AI) 驱动的自主代理正在通过统一推理,决策和任务执行来彻底改变各种领域.
  • 在医学成像中,人工智能代理通过尽量减少人类干预和提高图像质量来提供潜在的工作流改善.
  • 目前的磁共振成像 (MRI) 重建方法通常需要手动后处理或依赖静态校正模型.

研究的目的:

  • 引入AgentMRI,这是一个人工智能驱动的系统,利用视觉语言模型 (VLM) 来实现完全自主MRI重建.
  • 开发一种能够动态检测MRI损坏并选择适当的纠正模型,而无需人工干预的系统.
  • 建立一个可扩展和多式联络AI框架,用于自主MRI处理.

主要方法:

  • AgentMRI采用多查询的VLM策略,以进行强大的,基于共识的腐败检测和信任加权推断.
  • 该系统自动选择适当的深度学习模型用于MRI重建,运动校正和无声化.
  • 评估是在零射击和微调设置中使用全面的大脑MRI数据集进行的.

主要成果:

  • AgentMRI在零射击设置中达到73.6%的精度,在MRI重建的微调设置中达到95.1%的精度.
  • 实验结果表明,该系统能够在没有人类干预的情况下准确地执行重建过程.
  • 该框架成功地消除了在MRI后处理中需要人工干预的需求.

结论:

  • 代理MRI代表着向完全自主和智能MR图像重建系统的重大进步.
  • 该系统提供了一个可扩展和多式联络AI框架,用于自主MRI处理,减少对人类输入的依赖.
  • 这种人工智能驱动的方法有可能通过优化效率和图像质量来改变医学成像工作流程.