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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...
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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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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...
4.9K
X-ray Imaging01:24

X-ray Imaging

5.6K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
65
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

8.2K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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相关实验视频

Updated: Jul 25, 2025

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

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在间接成像中,成像操作员在间接成像中进行校对绘制.

Muralidhar Madabhushi Balaji, Jiapeng Liu, Danyal Ahsanullah

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    概括
    此摘要是机器生成的。

    间接成像相关图谱 (IIC) 通过重建对象的白度自相对应,使非视线 (NLOS) 成像成为可能. 这项研究模型IICIIC.

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    科学领域:

    • 光学和光子学 在光学和光子学.
    • 计算成像技术的成像
    • 机器学习 机器学习

    背景情况:

    • 间接成像相关图谱 (IIC) 是一种用于非视线 (NLOS) 成像的连贯成像技术.
    • 在大型对峙中,IIC可以恢复模糊物体的亚毫米分辨率图像.
    • 在复杂的NLOS场景中预测IIC分辨率是具有挑战性的,因为对象位置和姿势的变化.

    研究的目的:

    • 在NLOS场景中为IIC成像操作员开发数学模型.
    • 根据场景参数推导和实验验证空间分辨率的表达式.
    • 提出一种自我监督的深度神经网络,用于从自相关数据中重建图像.

    主要方法:

    • 为间接成像相关图像 (IIC) 成像操作员开发数学模型.
    • 导出空间分辨率表达式作为对象位置和姿势的函数.
    • 衍生解决公式的实验验证.
    • 自主监督深度神经网络的实施,用于从自相对应的图像重建.

    主要成果:

    • 一个经过验证的数学模型准确地预测了NLOS场景中的IIC成像操作员行为.
    • 空间分辨率在数量上与物体位置和姿势参数有关.
    • 通过使用深度学习框架,在1米的对峙处成功重建了具有~250μm特征的对象.

    结论:

    • 开发的数学模型增强了在NLOS成像中IIC性能的预测.
    • 该研究提供了一种基于场景几何学的IIC分辨率优化的方法.
    • 拟议的深度学习方法证明了NLOS成像中模糊对象的有效图像重建.