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

4.2K
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...
4.2K

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

PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography.

IEEE journal of biomedical and health informatics·2021
Same author

Dexmedetomidine post-conditioning ameliorates long-term neurological outcomes after neonatal hypoxic ischemia: The role of autophagy.

Life sciences·2021
Same author

[Corrigendum] Ski prevents TGF‑β‑induced EMT and cell invasion by repressing SMAD‑dependent signaling in non‑small cell lung cancer.

Oncology reports·2021
Same author

Synergistic Effects of Alpha Olefin Sulfonate and Sodium Alginate on Inkjet Printing of Cotton/Polyamide Fabrics.

Langmuir : the ACS journal of surfaces and colloids·2021
Same author

Diagnostic value of microRNA-25 in patients with non-small cell lung cancer in Chinese population: A systematic review and meta-analysis.

Medicine·2020
Same author

Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study.

BMJ open·2020

相关实验视频

Updated: May 29, 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

2.6K

基于卷积神经网络的稀疏视图CT重建的优化.

Liangliang Lv1, Chang Li1, Wenjing Wei1

  • 1School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China.

Medical physics
|February 2, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了SRII-Net,这是一种深度学习模型,可以显著减少稀疏视图CT图像中的工件. 该网络提高了图像质量,并提供了对文物移除机制的见解,以改善医疗成像.

关键词:
减少人工制造物的减少.深度学习是一种深度学习.图像重建 图像重建稀疏视野CTCT可以使用.

更多相关视频

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

42.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

359

相关实验视频

Last Updated: May 29, 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

2.6K
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

42.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

359

科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 稀疏视图CT减少了扫描时间和辐射剂量,但由于低采样,引入了条纹文物.
  • 深度学习方法在缓解这些文物和提高稀疏视图CT图像质量方面表现有前途.

研究的目的:

  • 通过增强深度学习优化能力来改善稀疏视图CT重建.
  • 提高深度学习方法的可解释性,以删除文物.
  • 促进各种稀疏观点的重建模型的泛化.

主要方法:

  • 开发了SRII-Net,这是一个基于U-Net的网络,具有复制路径和剩余图像输出块.
  • 创建了多样化的网络结构,以分析层次对文物删除和可解释性的贡献.
  • 用多个数据集,不同的抽样视图进行培训和概括测试.

主要成果:

  • SRII-Net显著优于现有网络,通过毫秒级优化改进PSNR和SSIM指标.
  • 分析揭示了浅层 (细节) 和深层 (抽象) 在文物抑制中的关键作用.
  • 使用混合数据集的训练证明了对各种稀疏视图重建的增强优化.

结论:

  • 拟议的SRII-Net有效地抑制了稀疏视图CT图像中的文物,从而提高了概括性.
  • 该研究提供了对深度学习器件移除的更深入的理解,适用于其他成像模式.