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

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

Multi-Task Deep Learning on MRI for Tumor Segmentation and Treatment Response Prediction in an Experimental Model of Hepatocellular Carcinoma.

Diagnostics (Basel, Switzerland)·2025
Same author

Combination of Irreversible Electroporation and <i>Clostridium novyi</i>-NT Bacterial Therapy for Colorectal Liver Metastasis.

Cancers·2025
Same author

Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.

Abdominal radiology (New York)·2025
Same author

Therapy Combining Sorafenib and Natural Killer Cells for Hepatocellular Carcinoma: Insights from Magnetic Resonance Imaging and Histological Analyses.

Cancers·2025
Same author

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer.

International journal of molecular sciences·2024
Same author

Sorafenib plus memory-like natural killer cell immunochemotherapy boosts treatment response in liver cancer.

BMC cancer·2024

相关实验视频

Updated: Jul 2, 2025

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
07:01

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

9.6K

在光磁成像中快速图像重建的自导算法:人工智能辅助方法.

Maha Algarawi1,2, Janaki S Saraswatula2, Rajas R Pathare2

  • 1Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.

Bioengineering (Basel, Switzerland)
|February 23, 2024
PubMed
概括
此摘要是机器生成的。

一个新的AI算法通过使用磁共振温度计 (MRT) 数据来增强光磁成像 (PMI) 以改善瘤检测. 这种人工智能驱动的方法可以提高空间分辨率和精度,同时显著减少重建时间.

关键词:
人工智能的人工智能是人工智能.反向问题是反向的问题.线性回归是一种线性回归.神经网络的神经网络的神经网络摄影-磁性成像成像技术

更多相关视频

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

498
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K

相关实验视频

Last Updated: Jul 2, 2025

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
07:01

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

9.6K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

498
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K

科学领域:

  • 生物医学成像技术 生物医学成像技术
  • 医学物理 医学物理
  • 人工智能在医学中的应用

背景情况:

  • 光磁成像 (PMI) 将激光诱导的加热与磁共振温度计 (MRT) 结合起来,用于温度和吸收映射.
  • 在PMI中瘤检测依赖于由异常组织中血红蛋白水平较高引起的温度对比.
  • 现有的PMI重建算法可以在准确性,空间分辨率和速度方面受到限制.

研究的目的:

  • 为PMI开发和评估一种基于人工智能的新型图像重建算法.
  • 提高 PMI 中吸收图的精度,空间分辨率和减少吸收图的恢复时间.
  • 利用机器学习来增强瘤边界检测和PMI中的功能先验信息.

主要方法:

  • 使用监督机器学习方法,直接从MRT温度图中检测瘤边界.
  • 检测到的瘤信息作为软功能先验集成到标准PMI重建算法中.
  • 增强的PMI算法使用类似于组织的幻影,含有模拟瘤的包含物进行了验证.

主要成果:

  • 与标准方法相比,AI增强的PMI算法显著改善了空间分辨率.
  • 吸收回收的精度得到了提高,达到2%的低百分比误差.
  • 图像文物减少了15%,重建过程加速了大约9倍.

结论:

  • 开发的基于AI的图像重建算法大大提高了PMI的性能.
  • 这种人工智能驱动的方法为生物医学成像中的吸收映射提供了更准确,更高分辨率和更快的方法.
  • 这些发现表明,将人工智能与PMI整合起来,可以提高诊断能力的潜力.