Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.1K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
7.1K
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

29
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...
29

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.

BMJ oncology·2026
Same author

Time-resolved GluCEST MRI of acute glutamate-related signal changes following kainic acid administration.

Journal of the neurological sciences·2026
Same author

SECmeres outperform extracellular vesicles as potential blood RNA biomarkers for Alzheimer's disease.

Nature communications·2026
Same author

Spatially Decoupled Sulfur Redox and Li<sup>+</sup> Transport in Polymer Electrolytes for Solid-State Li-S Batteries.

Nano letters·2026
Same author

Quantitative MRI Assessment of Myotoxin-Induced Skeletal Muscle Damage of mdx Mice.

Muscle & nerve·2026
Same author

Gene regulatory programs of cognitive resilience and pathogenesis in Alzheimer's disease.

medRxiv : the preprint server for health sciences·2026
Same journal

MONTE CARLO STUDY OF NEUTRON SPECTRA UNFOLDING FOR A PROTON BEAM USING MLEM.

Radiation physics and chemistry (Oxford, England : 1993)·2026
Same journal

Fetal Organ-level Specific Absorbed Fractions from Maternal Photon Sources Using Pregnant Female Phantoms.

Radiation physics and chemistry (Oxford, England : 1993)·2025
Same journal

Patient-specific organ dose calculation using automatic segmentation and extension of CT scans.

Radiation physics and chemistry (Oxford, England : 1993)·2025
Same journal

Organ-level radiation dose estimations from cardiac catheterizations in neonates with tetralogy of Fallot.

Radiation physics and chemistry (Oxford, England : 1993)·2025
Same journal

Realistic extension of partial-body pediatric CT for whole-body organ dose estimation in radiotherapy patients.

Radiation physics and chemistry (Oxford, England : 1993)·2025
Same journal

Debye-Waller effects in Bethe-Salpeter calculations: Bridging the gap between XANES and EXAFS.

Radiation physics and chemistry (Oxford, England : 1993)·2024
查看所有相关文章

相关实验视频

Updated: Jul 25, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.9K

基于深度学习的计算机断层扫描图像通过波纹嵌入超分辨率.

Hyeongsub Kim1,2, Haenghwa Lee3, Donghoon Lee4

  • 1School of Interdisciplinary Bioscience and Bioengineering, Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang 37674, Republic of Korea.

Radiation physics and chemistry (Oxford, England : 1993)
|June 29, 2023
PubMed
概括
此摘要是机器生成的。

深度学习超分辨率提高了医疗图像分辨率,显著改善了计算机断层扫描. 这种新的技术提高了图像质量,并有助于降低噪音,而不会扭曲解剖结构.

关键词:
计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.超级分辨率的超级分辨率是什么

更多相关视频

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
08:41

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

11.6K
Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy
07:33

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Published on: March 28, 2025

458

相关实验视频

Last Updated: Jul 25, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.9K
Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
08:41

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

11.6K
Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy
07:33

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Published on: March 28, 2025

458

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 正在努力提高医疗图像分辨率.
  • 基于深度学习的超级分辨率显示了计算机视觉的前景.

研究的目的:

  • 开发一个深度学习模型,以显著提高医疗图像的空间分辨率.
  • 量化证明拟议模型的优越性.

主要方法:

  • 模拟计算机断层扫描 (CT) 图像,探测器像素大小不同 (0.5,0.8,1 mm2).
  • 使用完全卷积神经网络 (CNN) 具有超级分辨率的残余结构.
  • 将低分辨率图像恢复到高分辨率 (0.25 mm2) 的地面真相.

主要成果:

  • 超级分辨率CNN显著提高了图像分辨率.
  • 峰值信号噪声比 (PSNR) 提高了高达38%,调制传输函数 (MTF) 提高了高达65%.
  • 该技术显示了降噪能力,并保持了解剖结构完整性.

结论:

  • 开发了深度学习架构,以提高CT图像分辨率.
  • 量化证实了有效的分辨率改善,没有解剖学扭曲.
  • 该方法的性能在不同的输入图像质量中是一致的.