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相关概念视频

Computed Tomography01:10

Computed Tomography

7.9K
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...
7.9K
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...
764
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
264
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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...
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相关实验视频

Updated: Jan 10, 2026

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

Published on: November 30, 2022

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快速指导的多尺度适应性稀疏表示驱动网络用于低剂量CT MAR.

Baoshun Shi1, Bing Chen2, Shaolei Zhang3

  • 1School of Information Science and Engineering, Yanshan University, Qinhuang Dao, 066004, China.

Medical image analysis
|November 21, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了PMSRNet,这是一个新的深度学习网络,用于低剂量的CT金属工件减少. 它通过一个单一的,可适应的模型,提高了各种辐射剂量的图像质量.

关键词:
可以解释性 解释性低剂量的计算机断层扫描.减少金属工艺品的减少多个尺度的稀疏表示.快速指导即可提供.

更多相关视频

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

Published on: April 13, 2013

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相关实验视频

Last Updated: Jan 10, 2026

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

Published on: November 30, 2022

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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

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算成像技术的成像

背景情况:

  • 低剂量CT (LDCT) 减少了辐射暴露,但降低了图像质量,并从植入物中引入金属工件.
  • 对于LDCT金属工件减少 (LDMAR) 的现有深度学习方法缺乏多层次信息处理,需要剂量特定的模型.

研究的目的:

  • 开发一个统一的深度学习框架,用于同时进行LDCT重建和金属工件减少 (LDMAR),以解决现有方法的局限性.
  • 创建一个能够高效处理各种CT剂量水平的单一模型.

主要方法:

  • 提出了PMSRNet,这是一个由多个尺度的分散框架启发的快速指导的多个尺度的适应性稀疏表示驱动的网络.
  • 引入了一个快速指导规模适应值生成器 (PSATG) 和一个多尺度系数融合模块 (MSFuM) 进行增强的特征处理.
  • 开发了PDuMSRNet,这是一个双域LDMAR框架,使用一个用于剂量级调整的快速指导模块.

主要成果:

  • PMSRNet有效地处理范围内的和跨范围的信息,以改进LDMAR.
  • 快速指导策略使单个模型能够容纳多个CT剂量水平,减少存储要求.
  • 实验结果表明,与最先进的LDMAR方法相比,在各种剂量水平上表现优越.

结论:

  • 拟议的PMSRNet和PDuMSRNet为最不发达国家减少金属工件提供了高效和有效的解决方案.
  • 多尺度适应性稀疏表示方法和快速指导策略显著提高了LDMAR性能和模型适应性.
  • 这项工作为改善在金属植入物存在的情况下CT成像在减少辐射剂量时提供了有前途的方向.