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

4.4K
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...
4.4K

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

A Multi-Component and Multi-Functional Synergistic System for Efficient Viscosity Reduction of Extra-Heavy Oil.

Molecules (Basel, Switzerland)·2025
Same author

Accuracy and reliability of 3D cephalometric landmark detection with deep learning.

European journal of medical research·2025
Same author

Automatically predicting lung tumor invasiveness using deep neural networks.

Medical engineering & physics·2025
Same author

Summary of Best Evidence for Integrated Airway Management in ICU Tracheostomy Patients.

Journal of multidisciplinary healthcare·2025
Same author

Deep learning-assisted comparison of different models for predicting maxillary canine impaction on panoramic radiography.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics·2025
Same author

Evidence Summary of Early Enteral Nutrition Support for Adult Patients with Extracorporeal Membrane Oxygenation (ECMO).

Journal of multidisciplinary healthcare·2025

相关实验视频

Updated: Jun 17, 2025

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
08:19

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Published on: May 17, 2018

9.8K

DiffMAR:一种通用扩散模型用于CT图像中的金属人工物减少.

Tianxiao Cai, Xiang Li, Chenglan Zhong

    IEEE journal of biomedical and health informatics
    |August 7, 2024
    PubMed
    概括
    此摘要是机器生成的。

    一种新的通用扩散模型DiffMAR有效地减少CT扫描中的金属工件. 它最大限度地减少代错误,并增强解剖结构生成,以获得卓越的图像质量和概括性.

    更多相关视频

    DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis
    12:39

    DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

    Published on: September 28, 2021

    3.3K
    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

    28.4K

    相关实验视频

    Last Updated: Jun 17, 2025

    Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
    08:19

    Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

    Published on: May 17, 2018

    9.8K
    DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis
    12:39

    DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

    Published on: September 28, 2021

    3.3K
    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

    28.4K

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 图像处理 图像处理

    背景情况:

    • 计算机断层扫描 (CT) 图像中的金属工件会降低图像质量,特别是金属植入物.
    • 现有的金属工件减少 (MAR) 方法难以通用,并且由于代方法中的累积错误,可以产生较低质量的结果.

    研究的目的:

    • 开发一种通用扩散模型,用于CT图像中有效的金属工件减少 (MAR).
    • 解决目前MAR技术中发现的概括和累积错误的局限性.

    主要方法:

    • 介绍了DiffMAR,这是MAR的通用扩散模型.
    • 使用线性降解过程模拟金属文物形成.
    • 开发了一个时间潜伏调整 (TLA) 模块,以尽量减少代恢复过程中的累积错误.
    • 整合了一个结构信息提取 (SIE) 模块,用于指导使用线性插值数据生成解剖结构.

    主要成果:

    • 与最先进的 MAR 方法相比,DiffMAR 显示出更高的性能.
    • 该方法实现了高质量的图像生成,提高了准确性和稳定性.
    • 对合成和临床数据的验证证实了增强的概括能力.

    结论:

    • 在CT成像中,DiffMAR在金属工件减少方面取得了重大进展.
    • 拟议的模型提供了更准确,更强大,更普遍的无阴影图像生成.
    • DiffMAR克服了现有的代和端到端的 MAR 方法的局限性.