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

Computed Tomography01:10

Computed Tomography

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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

Perfectionism and choice deferral in online shopping: a moderated mediation model of fear of missing out and upward social comparison.

Frontiers in psychology·2026
Same author

Social Comparison Influences the Spreads of Positive and Negative Information About Opponents and Corresponding ERP Responses.

Brain topography·2026
Same author

Fairness during resource allocation influences event-related potential (ERP) responses during memory of receivers' faces.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2026
Same author

Affect Labeling During Pictorial Encoding Enhances Their Recognition and Reduces Amygdalar Responses to Negative Pictures.

Brain and behavior·2026
Same author

Independent effects of working memory loads and facial expressions on event-related potential (ERP) responses: Evidence from mass univariate analysis.

Biological psychology·2025
Same author

Cytokine Storm Induction Linked to Multi-Organ Failure in Fatal Jellyfish Stings.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same journal

Effective contrast-enhanced preprocessing for intracranial artery segmentation in digital subtraction angiography.

Physics in medicine and biology·2026
Same journal

Improving Plan Quality in Adaptive Proton Therapy Using an Interactive Dose Modification Tool.

Physics in medicine and biology·2026
Same journal

Technical Note: Real-Time MLC Control and Latency Measurement Optimization with External Verification.

Physics in medicine and biology·2026
Same journal

Fetus-Specific Hematopoietic Stem Cell Dosimetry Framework for Leukemia-Relevant Target Cells During Prenatal Development.

Physics in medicine and biology·2026
Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026
Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026
查看所有相关文章

相关实验视频

Updated: May 10, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.4K

使用结构保存的扩散模型进行可泛化的医疗图像增强.

Lulu Chen1, Xiangyang Yu2, Haojin Li2

  • 1Peking Union Medical College Hospital, Beijing, People's Republic of China.

Physics in medicine and biology
|June 25, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的扩散模型,用于增强医疗图像,提高诊断准确度. 该方法保留了细结构,并在不同的成像类型中进行了概括.

关键词:
扩散模型的扩散模型.医疗图像增强 医疗图像增强结构的保存 结构的保存

更多相关视频

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K

相关实验视频

Last Updated: May 10, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.4K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 临床医学图像质量对于临床医生和AI的准确诊断至关重要.
  • 现有的基于生成对抗网络 (GAN) 的增强方法面临着诸如人工制品和培训不稳定性等挑战.
  • 扩散模型提供优越的图像生成,但在医疗数据收集和域间隙方面存在困难,特别是在保存细结构方面.

研究的目的:

  • 开发一种可通用的医疗图像增强方法,可以保存细结构.
  • 解决医疗图像增强当前扩散模型的局限性.
  • 提高低质量的临床图像的诊断效用.

主要方法:

  • 建议使用结构保存的扩散模型 (GEDM) 进行可泛化的医疗图像增强.
  • 从增强和细分任务中利用联合监督,以改善结构保存和通用性.
  • 采用合成数据用于配对训练数据收集和拉普拉斯变换,以缩小领域差距并纳入多层次信息.

主要成果:

  • 与最先进的方法相比,GEDM在图像增强方面表现出卓越的表现.
  • 该方法在增强的医学图像中有效地保存了细结构.
  • 提高图像质量导致后续医学分析任务的性能提高.

结论:

  • GEDM为医疗图像增强提供了一个强大的解决方案,其性能优于现有的技术.
  • 联合增强和细分方法有效地保留了结构细节.
  • 拟议的方法显示了改善临床诊断和人工智能辅助医学分析的巨大潜力.