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相关概念视频

Computed Tomography01:10

Computed Tomography

4.5K
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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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.0K
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...
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相关实验视频

Updated: Jul 11, 2025

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

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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计算机断层扫描超分辨率的深度学习使用多模式数据训练.

Wai Yan Ryana Fok1,2, Andreas Fieselmann1, Magdalena Herbst1

  • 1X-ray Products, Siemens Healthcare GmbH, Forchheim, Germany.

Medical physics
|November 16, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了超分辨率 (SR) U-Net,以增强低分辨率CT图像,以生成现实的数字放射图 (DRR). SR U-Net显著提高了图像质量,使得更好的AI训练数据能够用于X射线成像.

关键词:
圆束计算机断层扫描技术深度学习是一种深度学习.多式联络 多式联络超级解决方案的超级解决方案

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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

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

Published on: August 16, 2012

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相关实验视频

Last Updated: Jul 11, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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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

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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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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
  • 计算机视觉 计算机视觉

背景情况:

  • 有限的注释数据阻碍了X射线成像中的AI.
  • CT和X射线共享成像物理,使跨领域的数据共享.
  • 超分辨率 (SR) 可以增强CT分辨率,用于生成现实的数字重建放射图 (DRR).

研究的目的:

  • 提出一个新的SR网络,使用基于内核的低分辨率 (LR) 和高分辨率 (HR) 图像进行训练.
  • 从HR圆束CT (CBCT) 扫描中生成现实的多探测器CT (MDCT) 像LR图像.
  • 在医学成像中改善SR的双立方插值.

主要方法:

  • 在CBCT图像切片上使用SR U-Net架构进行LR-HR映射.
  • 训练了两个模型:SRUN (基于内核的LR) 和SRUN (双立方下采样基线).
  • 在未见的CBCT和MDCT数据集上评估模型.

主要成果:

  • 这两种SRUN模型都在未见的CBCT图像上显示了MAE,PSNR和SSIM的显著改善.
  • SRUN (基于内核) 的表现优于SRUN (双立方),MAE减少了14%,PSNR减少了6%,SSIM增加了8%.
  • SRUN产生了更清晰的图像,并证明了LR MDCT数据的跨模式改进.

结论:

  • 拟议的SR方法超越了对未见LR CBCT图像的传统插值.
  • 数据频率特征对于学习SR特征至关重要.
  • 这种方法使得CT生成的高分辨率DRR能够用于深度学习培训.