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相关概念视频

Computed Tomography01:10

Computed Tomography

4.6K
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...
4.6K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

30
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...
30
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

297
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...
297

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相关实验视频

Updated: Jul 25, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

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基于深度学习的图像噪声量化框架用于计算机断层扫描.

Nathan R Huber1, Jiwoo Kim2, Shuai Leng1

  • 1From the Department of Radiology, Mayo Clinic, Rochester, MN.

Journal of computer assisted tomography
|June 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了单扫描图像局部方差估计器 (SILVER),这是一种用于精确计算断层扫描 (CT) 噪声估计的深度学习工具. 银提供从单个扫描的像素智能噪声地图,帮助图像质量评估和协议优化.

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Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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相关实验视频

Last Updated: Jul 25, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

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Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT
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Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

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科学领域:

  • 医疗成像医学成像
  • 放射学 放射学是一门学科.
  • 医疗保健中的人工智能

背景情况:

  • 精确的噪声量化对于计算机断层扫描 (CT) 图像质量和协议优化至关重要.
  • 传统的噪声评估方法可能耗时且复杂.

研究的目的:

  • 提出和评估一个深度学习框架,单扫描图像局部方差估算器 (SILVER),用于估计CT图像中的局部噪声水平.
  • 从单个CT扫描直接生成像素智能噪声图.

主要方法:

  • 开发了一个U-Net卷积神经网络架构,用于噪声估计的平均平方误差损失.
  • 通过使用12万张幻影CT图像训练模型,并从100次复制扫描中计算出像素智能的噪声图.
  • 在幻影和患者图像上评估SILVER性能,并将结果与手动测量进行比较.

主要成果:

  • 银色准确地预测了虚拟数据上的噪声地图,与计算的噪声地图 (RMSE <8 HU) 密切匹配.
  • 在患者图像上,SILVER与手动感兴趣区域测量相比,显示了5%的低平均百分比误差.

结论:

  • SILVER框架可以直接从患者CT图像中精确地对像素进行噪声水平的估计.
  • 这种在幻影数据上训练的可访问方法在CT图像分析和优化中具有广泛的适用性.