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相关概念视频

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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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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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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相关实验视频

Updated: May 6, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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轻松转换和优化结构化照明图像重建代码到GPU环境中.

Kwangsung Oh1, Piero R Bianco2

  • 1Department of Computer Science, College of Information Science & Technology, University of Nebraska Omaha, Omaha, NE 68182, USA.

International journal of biomedical imaging
|March 7, 2024
PubMed
概括

超分辨率显微镜重建算法是缓慢的. 我们为图形处理单元 (GPU) 优化了 MATLAB 代码,在不牺牲图像质量的情况下实现了活细胞成像的 4-500 倍加速度.

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科学领域:

  • 生物物理学的生物物理.
  • 计算生物学 计算生物学
  • 显微镜的使用方法

背景情况:

  • 超分辨率结构化照明显微镜 (SIM) 能够以高速度和低光毒性对活细胞进行成像.
  • 图像重建是SIM的一个瓶,通常受到慢算法和低效代码的限制.
  • 目前的算法,经常由非计算机科学家用MATLAB编写,不利用图形处理单元 (GPU) 的功率.

研究的目的:

  • 为了加速SIM图像重建算法.
  • 为SIM数据处理开发高效,GPU优化的代码.
  • 为了提高超分辨率显微镜的计算效率.

主要方法:

  • 修订了现有的 MATLAB 代码,以提高效率.
  • 将MATLAB代码转换为GPU优化的版本.
  • 在支持GPU的计算机上测试了优化的Hessian-SIM算法.

主要成果:

  • 在算法执行速度方面实现了4至500倍的改进.
  • 与原始算法相比,显示出相同的图像质量.
  • 展示了GPU加速对于SIM重建的有效性.

结论:

  • 基于GPU的优化算法显著提高了SIM重建速度.
  • 这种方法使得高速超分辨率的活细胞成像更容易获得.
  • 有效的计算方法对于推进显微镜技术至关重要.