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

4.5K
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...
4.5K

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

Low-complexity reconstruction of low-dose spectral CT via double low-rank tensor factorization with adaptive transforms.

Medical image analysis·2026
Same author

Segmentation-Guided Accelerating Diffusion Model for Cardiac CT Motion Artifact Reduction via Limited-Angle Imaging.

IEEE transactions on medical imaging·2026
Same author

Multi-granularity Adversarial Generation Integrated Consistency Representation for Chest Low-Contrast-Enhanced CT Synthesis.

IEEE transactions on medical imaging·2026
Same author

FDA-Recon: Feature and data alignment reconstruction for sparse-view CBCT.

Medical image analysis·2026
Same author

WOADNet: A Wavelet-Inspired Orientational Adaptive Dictionary Network for CT Metal Artifact Reduction.

IEEE journal of biomedical and health informatics·2025
Same author

DECT sparse reconstruction based on hybrid spectrum data generative diffusion model.

Computer methods and programs in biomedicine·2025
Same journal

SynTME: A tumor microenvironment-aware, pharmacology-inspired multi-stage framework for drug synergy prediction.

Computer methods and programs in biomedicine·2026
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

深度卷积字典学习网络用于稀疏视图CT重建,用一个小组稀疏先前的CT重建.

Yanqin Kang1, Jin Liu1, Fan Wu2

  • 1College of Computer and Information, Anhui Polytechnic University, Wuhu, China; Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing, China.

Computer methods and programs in biomedicine
|January 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了DCDL-GS,这是一种可解释的深度学习模型,用于稀疏视图计算断层扫描 (CT) 成像. 这种新的方法提高了图像重建质量,克服了传统方法的局限性.

关键词:
艺术品 文物 文物卷积式的字典学习集团稀有 稀有 集团稀有非本地约束 非本地约束间隔视图 CT CT 的位置.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

556
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

42.6K

相关实验视频

Last Updated: Jul 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

556
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

42.6K

科学领域:

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

背景情况:

  • 稀疏视图计算机断层扫描 (CT) 成像由于投影数据有限而带来挑战,导致错误的重建问题.
  • 现有的深度学习方法,通常基于不透明的卷积神经网络 (CNN),缺乏可解释性,并与非局部自我相似性先验作斗争.
  • 美国有线电视新闻网 (CNN) 专注于本地受感场所限制了他们捕捉高质量重建所必需的全球图像特征的能力.

研究的目的:

  • 提出一种新的,可解释的深度学习模型,DCDL-GS,用于稀疏视图CT成像.
  • 通过结合卷积式字典学习和非局部组稀疏的先验来解决目前不透明的CNN的局限性.
  • 为了提高图像重建质量和诊断价值从稀疏采样投影.

主要方法:

  • 开发了 DCDL-GS 模型,将卷积式字典学习与非本地群体稀疏的先验集成.
  • 在统计代重建框架内使用神经网络来进行增强的图像重建.
  • 引入了一种新的群体值操作,其灵感来源于群体稀疏性先验,以改进特征表示和理论解释.
  • 集成过的反向投影 (FBP),快速滑动窗口非局部自相似操作,以及轻量级的卷积字典学习网络.

主要成果:

  • DCDL-GS模型在LDCT-P和UIH数据集上的边缘保护和恢复特征方面表现出卓越的性能.
  • 观察到量化改善,包括0.6-0.8dB的峰值信号与噪声比率 (PSNR) 的增加,0.005-0.01的结构相似度指数 (SSIM) 的增加,以及调节的Fréchet起始距离 (rFID) 的1-1.3降低.
  • 深度卷积代重建模块和非局部小组稀疏前期的有效性得到了定量验证.

结论:

  • 通过将投影数据和图像的先前知识整合到一个深度代框架中,创建了一个整合和增强的数学模型.
  • 与现有的稀疏视图CT重建方法相比,DCDL-GS模型提供了更大的实用性和可解释性.
  • 实验结果证实了拟议模型的强大性能和优于其他先进技术的优势.