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

5.2K
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...
5.2K
Brain Imaging01:14

Brain Imaging

234
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
234

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

Gastric cancer-derived exosomal miR-519a-3p promotes liver metastasis by inducing intrahepatic M2-like macrophage-mediated angiogenesis.

Journal of experimental & clinical cancer research : CR·2022
Same author

Effects of different modes of exercise on skeletal muscle mass and function and IGF-1 signaling during early aging in mice.

The Journal of experimental biology·2022
Same author

Optimal Control of False Information Clarification System under Major Emergencies Based on Differential Game Theory.

Computational intelligence and neuroscience·2022
Same author

Ionothermal Synthesis of Fully Conjugated Covalent Organic Frameworks for High-Capacity and Ultrastable Potassium-Ion Batteries.

Advanced materials (Deerfield Beach, Fla.)·2022
Same author

Circ-OMAC drives metastasis in oral squamous cell carcinoma.

Oral diseases·2022
Same author

Biodegradation of Dibutyl Phthalate by the New Strain <i>Acinetobacter baumannii</i> DP-2.

Toxics·2022

相关实验视频

Updated: Jul 6, 2025

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.3K

通过全人工智能促进快速MRI成像管道.

Zhiwen Wang1, Bowen Li1, Hui Yu1

  • 1School of Computer Science, Sichuan University, Chengdu, Sichuan, China.

iScience
|January 4, 2024
PubMed
概括

全式学习 (FSL) 集成了磁共振成像 (MRI) 加速,重建和细分的深度学习. 这种新的方法利用任务依赖性来提高MRI工作流程的效率和诊断准确性.

关键词:
人工智能的人工智能是人工智能.机器学习是机器学习.医学 医学 医学 医学 医学

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

612
A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

4.0K

相关实验视频

Last Updated: Jul 6, 2025

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.3K
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

612
A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

4.0K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 磁共振成像 (MRI) 对于医学诊断,分期和随访至关重要.
  • 深度学习被广泛应用于加速MRI采集,重建图像和细分组织.
  • 当前的深度学习方法经常独立处理这些任务,可能会错过优化机会.

研究的目的:

  • 引入一种全式学习 (FSL) 的新范式,用于同时优化MRI采集,重建和细分.
  • 利用这些任务之间固有的依赖关系来提高整体绩效.
  • 提高实际MRI工作流程的效率和有效性.

主要方法:

  • 开发了一个全学习 (FSL) 框架,同时解决k空间数据采集加速,MR图像重建和组织细分.
  • 在统一的学习模型中利用了这三个核心MRI任务之间的强烈相互依赖.
  • 在多个开放的磁共振成像数据集上验证了方法.

主要成果:

  • 与现有最先进的方法相比,FSL在所有三个任务 (采购加速,重建,细分) 中都表现出优异的表现.
  • 综合方法显著提高了MRI成像过程的效率和有效性.
  • 实验结果证实了考虑整个成像管道的好处.

结论:

  • 全学习 (FSL) 通过统一传统上分开的任务,为MRI数据处理提供了一种强大的新方法.
  • 这种方法具有显著的潜力,可以优化临床MRI工作流程,以改善医疗诊断和放射治疗.
  • FSL代表了对磁共振成像的智能处理的重大进步.