3D deconvolution microscopy.
3D deconvolution microscopy.
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DOI：
10.1002/0471142956.cy1219s52
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发表时间：
2010-04-01
影响因子：
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通讯作者：
Biggs, David S C
中科院分区：
文献类型：
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作者：
Biggs, David S C
3D 反卷积显微镜结合了光学和计算技术，用于最大限度地提高生物样本的观察分辨率和信号。数学模型用于预测由仪器固有光学限制引起的离焦光的分布，然后可以使用计算机算法进行补偿。本单元将回顾基于仪器模态和物镜参数的图像形成理论和点扩散函数（PSF）的特性。描述了各种常用的去模糊和反卷积方法，并举例说明了它们在样本数据集上的应用，以显示每种算法的性能。描述了设置图像采集以获取适合反卷积的数据的步骤，并解决了最大化信号水平同时最小化曝光的挑战。显示了宽场落射荧光和激光扫描共焦的反卷积示例，并讨论了其他模式的适用性。
3D deconvolution microscopy is a combination of optical and computational techniques that are used to maximize the observed resolution and signal from a biological specimen. Mathematical models are used to predict the distribution of out-of-focus light caused by the inherent optical limitations of the instrument, which can then be compensated for using computer algorithms. This unit will review the theory of image formation and characteristics of the point spread function (PSF) based on the instrument modality and objective lens parameters. A variety of commonly used deblurring and deconvolution methods are described, and their applications to sample datasets are illustrated to show the performance of each algorithm. Steps for setting up the image acquisition to acquire data suitable for deconvolution are described, and the challenge of maximizing signal levels while minimizing light exposure addressed. Deconvolution examples from widefield epi-fluorescence and laser scanning confocal are shown, and suitability for other modalities discussed.