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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...
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Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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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...
247

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相关实验视频

Updated: Jul 7, 2025

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

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噪音生成和成像机制启发了低剂量CT重建的隐性规范化学习网络.

Xing Li, Kaili Jing, Yan Yang

    IEEE transactions on medical imaging
    |December 25, 2023
    PubMed
    概括

    这项研究引入了一种全域模型,用于低剂量计算机断层扫描 (LDCT) 重建. 该方法通过考虑噪声特性来提高图像质量,优于当前最先进的技术.

    科学领域:

    • 医疗成像医学成像
    • 计算成像技术的成像
    • 放射学 放射学是一门学科.

    背景情况:

    • 低剂量计算机断层扫描 (LDCT) 旨在尽量减少辐射暴露,同时保持诊断图像质量.
    • 目前用于LDCT重建的深度学习方法往往忽略了投影数据固有的噪声特性,从而限制了性能.
    • 目前的框架将LDCT重建视为一个一般的反向问题,未能充分利用特定领域的信息.

    研究的目的:

    • 为LDCT开发一种新的全域重建模型,考虑噪声产生机制.
    • 整合LDCT内在噪声的统计特性和来自sinogram和图像领域的先前信息.
    • 与现有方法相比,提高LDCT重建的性能和可解释性.

    主要方法:

    • 提出了一种包含噪音生成和成像机制的全域重建模型.
    • 基于近接梯度技术的优化算法被开发来解决该模型.
    • 优化算法被展开到一个深度网络中,通过两个深度神经网络隐式地学习sinogram和图像调节器的近位运算符.

    主要成果:

    • 提出的方法在最先进的LDCT技术中显示出了显著的改进.
    • 在峰值信号与噪声比率 (PSNR) 中实现了>2.9dB的增加.
    • 在结构相似性指数 (SSIM) 测量中显示了> 1.4%的提升,在根平均平方误差 (RMSE) 中显示了> 9 HU的下降.

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    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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    相关实验视频

    Last Updated: Jul 7, 2025

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

    Published on: November 30, 2022

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    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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    Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
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    Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

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    结论:

    • 开发的全域模型有效地解决了LDCT重建中的噪声特征.
    • 无线深度网络为最不发达国家/地区的图像重建提供了可解释和有效的方法.
    • 拟议的方法在定量指标上提供了卓越的性能,推进了低剂量CT成像领域.