Application of non-linear optical microscopy to lung cancer diagososis and in vivo research
Date Issued
2005
Date
2005
Author(s)
Li, Feng-Chieh
DOI
zh-TW
Abstract
Lung cancer is the number one on the list of ten leading causes of death, for Chinese in the past few decades. To eliminate lung cancer, the first step is to develop a fast and efficient method for early detection and treatment. The core of this study is to develop a reliable system for lung cancer diagnosis. Using data collected from in vivo observation was in combination cancer diagnosis database developed from in vitro observation; we are steps closer to understanding cell growth. Two-photon differs from conventional microscopy in its capability to reinforce images from different depths. Two-photon fluorescence microscopy does not require lengthy histological sample preparation, is to obtain high quality optical images. The time required for complete diagnosis is therefore shorten with two-photon fluorescence microscopy. Furthermore, traditional diagnostic methods emphasize non-quantitative morphological differences. This shortcoming can be compensated by using two-photon fluorescence microscopy in conjunction with spectral analysis and second harmonic generation. Current research and development in the field of molecular biology and bio-medical engineering are progressing rapidly. Most of them are however in vitro experiments. It is more logical to answer questions regarding biological mechanisms through in vivo experiments. During the past few years, observation in vivo has become one of the most active researching fields. The invention need more description is instrumental to researches involving animals and humans. Dorsal skinfold chamber provides an efficient tool for observation in vivo. We developed an improved version of the chamber and used it to obtain images with higher resolutions and for extended period of in vivo observations.
Subjects
非線性光學
雙光子
二倍頻
肺癌診斷
活體觀察
non-liner
two photon
second harmonical generation
lung cancer diagonosis
in vivo research
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-94-R92222033-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):8fa025f68b6a502bed11ae6e943fdc39