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相关概念视频

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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Computed Tomography01:10

Computed Tomography

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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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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
263
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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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...
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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...
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相关实验视频

Updated: Sep 18, 2025

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
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深度学习的概括研究光学连贯性断层扫描图像 denoising.

Yuzeng Xu1, Guangyi Wu1, Zhuoqun Yuan1

  • 1Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Tianjin 300350, People's Republic of China.

Physics in medicine and biology
|June 25, 2025
PubMed
概括
此摘要是机器生成的。

一个新的混合培训策略改进了光学连贯性断层扫描 (OCT) 的深度学习模型. 这种方法提高了适应各种噪声水平的适应性,在各种条件下确保可靠的图像质量.

关键词:
深度学习是一种深度学习.概括的概括是一般化的.图像去色化 图像去色化光学连贯性断层扫描 (optical coherence tomography) 是一种光学连贯性断层扫描.

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科学领域:

  • 医疗成像医学成像
  • 光学一致性断层扫描 (OCT)
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 图像质量在海外被噪音严重影响.
  • 目前的深度学习 (deep learning) 消除噪音的方法在将其推广到未见的噪音水平方面存在困难.

研究的目的:

  • 增强深度学习的适应性,使无声化模型能够适应海外国家和地区的不同噪音条件.
  • 提高了海外国家和地区 (OCT) 无效化模型的普遍化能力.

主要方法:

  • 开发了一种混合培训策略,使用SS-OCT的多噪声级数据集.
  • 使用光学减弱器 (4 dB,6 dB,10 dB) 模拟噪声的构建数据集.
  • 在监督和无监督学习下,将该策略应用于各种拒绝网络 (ResNet,U-Net,DnCNN,AG-DCN).

主要成果:

  • 用混合训练策略训练的模型在不同噪音水平上表现出强的性能,包括未见的4dB噪音.
  • 监督的U-Net模型在4dB的数据上实现了29.233dB的PSNR和0.807的SSIM,与专用模型相比.
  • 混合训练网络有效地抑制了噪音工件,在不匹配的噪音条件下验证了它们的性能.

结论:

  • 多噪声级数据集对于改善深度学习模型概括是有价值的.
  • 拟议的混合培训策略增强了适应能力,并支持在实际应用中可靠的海上和海外国家的分析.