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

Positron Emission Tomography01:29

Positron Emission Tomography

4.0K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
4.0K
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.0K
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...
5.0K
Computed Tomography01:10

Computed Tomography

4.3K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.3K
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

214
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
214
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

198
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
198

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 authorSame journal

Distinguishing Molecular and Histologic Glioblastomas Using Multiparametric MRI-Based Habitat Analysis.

Korean journal of radiology·2026
Same author

Uncover This Tech Term: Tumor Habitat Analysis.

Korean journal of radiology·2026
Same author

Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast Imaging.

Journal of magnetic resonance imaging : JMRI·2026
Same author

Mixture-of-Skip-Connection Deep Learning Model to Classify Stroke Severity from Diffusion Weighted Imaging Based on NIHSS.

Journal of imaging informatics in medicine·2026
Same author

Uncover This Tech Term: Large Vision-Language Models in Radiology.

Korean journal of radiology·2026
Same author

Generative AI for developing foundation models in radiology and imaging: engineering perspectives.

Biomedical engineering letters·2026

相关实验视频

Updated: Jun 9, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

774

放射学中的基于图像的生成人工智能:全面的更新

Ha Kyung Jung1, Kiduk Kim2, Ji Eun Park3

  • 1Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.

Korean journal of radiology
|October 30, 2024
PubMed
概括

生成型人工智能 (AI) 可以创建用于训练AI模型的医疗图像. 本综述探讨了人工智能图像生成方法,评估技术和放射学中的临床应用,包括幻觉等潜在问题.

关键词:
扩散模型的扩散模型.评估指标评估指标生成性的对抗性网络.生成型的人工智能 (GAI) 是一种人工智能.医学成像医学成像

更多相关视频

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.2K
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

1.1K

相关实验视频

Last Updated: Jun 9, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

774
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.2K
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

1.1K

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:

  • 生成型人工智能 (AI) 在医学成像中越来越多地用于像图像增强和数据增强等任务.
  • 图像生成人工智能为深度学习产生大型数据集,但评估方法和临床实用性需要彻底审查.

研究的目的:

  • 审查图像生成人工智能的基本理论,重点关注生成对抗网络和扩散模型.
  • 讨论评估人工智能产生的医疗图像的方法.
  • 概述这些图像在放射学中的临床和研究实用性,并解决AI幻觉的问题.

主要方法:

  • 医学成像中常用的生成对抗网络和扩散模型的审查.
  • 在临床放射学任务中人工智能生成图像的实用性概述:直接图像的使用,病变检测,细分和诊断.
  • 讨论评估方法和人工智能幻觉现象.

主要成果:

  • 像GAN和扩散模型这样的生成AI模型为医疗图像分析和数据增强提供了巨大的潜力.
  • 放射学中的应用包括图像质量改善,病变检测,细分和诊断支持.
  • 在强大的评估和理解人工智能生成的图像文物,特别是幻觉方面,仍然存在挑战.

结论:

  • 图像生成人工智能为推进放射学研究和实践提供了一个强大的工具.
  • 标准化的评估方法对于确保人工智能生成图像的可靠性和临床翻译至关重要.
  • 解决人工智能幻觉对于安全有效的临床实施至关重要.