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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Super-resolution optical fluctuation imaging ： ウィキペディア英語版 Super-resolution optical fluctuation imaging Super-resolution optical fluctuation imaging (SOFI) is a post-processing method for the calculation of super-resolved images from recorded image time series that is based on the temporal correlations of independently fluctuating fluorescent emitters. SOFI has been developed for super-resolution of biological specimen that are labelled with independently fluctuating fluorescent emitters (organic dyes, fluorescent proteins). In comparison to other super-resolution microscopy techniques such as STORM or PALM that rely on single-molecule localization and hence only allow one active molecule per diffraction-limited area (DLA) and timepoint,〔〔 SOFI does not necessitate a controlled photoswitching and/ or photoactivation as well as long imaging times.〔〔 Nevertheless it still requires fluorophores that are cycling through two distinguishable states, either real on-/off-states or states with different fluorescence intensities. In mathematical terms SOFI-imaging relies on the calculation of cumulants, for what two distinguishable ways exist. For one thing an image can be calculated via auto-cumulants〔 that by definition only rely on the information of each pixel itself, and for another thing an improved method utilizes the information of different pixels via the calculation of cross-cumulants.〔 Both methods can increase the final image resolution significantly although the cumulant calculation has its limitations. Actually SOFI is able to increase the resolution in all three dimensions.〔 == Principle == Likewise to other super-resolution methods SOFI is based on recording an image time series on a CCD- or CMOS camera. In contrary to other methods the recorded time series can be substantially shorter, since a precise localization of emitters is not required and therefore a larger quantity of activated fluorophores per diffraction-limited area is allowed. The pixel values of a SOFI-image of the ''n''-th order are calculated from the values of the pixel time series in the form of a ''n''-th order cumulant, whereas the final value assigned to a pixel can be imagined as the integral over a correlation function. The finally assigned pixel value intensities are a measure of the brightness and correlation of the fluorescence signal. Mathematical the ''n''-th order cumulant is related to the ''n''-th order correlation function, but exhibits some advantages concerning the resulting resolution of the image. Since in SOFI several emitters per DLA are allowed, the photon count at each pixel results from the superposition of the signals of all activated nearby emitters. The cumulant calculation now filters the signal and leaves only highly correlated fluctuations. This provides a contrast enhancement and therefore a background reduction for good measure. As it is implied in the figure on the left the fluorescence source distribution: : $$ \sum_^N \delta(\vec-\vec_)\cdot \varepsilon_k \cdot s_k(t) is convolved with the system's point spread function (PSF) ''U''(''r''). Hence the fluorescence signal at time t and position $\backslash vec$ is given by : $$ F(\vec,t)=\sum_^N U(\vec-\vec_)\cdot \varepsilon_k \cdot s_k(t). Within the above equations ''N'' is the amount of emitters, located at the positions $\backslash vec\_$ with a time-dependent molecular brightness $\backslash varepsilon\; \_\backslash cdot\; s\_$ where $\backslash varepsilon\; \_$ is a variable for the constant molecular brightness and $s\_k\; (t)$ is a time-dependent fluctuation function. The molecular brightness is just the average fluorescence count-rate divided by the number of molecules within a specific region. For simplification it has to be assumed that the sample is in a stationary equilibrium and therefore the fluorescence signal can be expressed as a zero-mean fluctuation: : $$ \delta F(\vec,t)=F(\vec,t)-\langle F(\vec,t)\rangle_t where $\backslash langle\; \backslash cdots\; \backslash rangle\_t$ denotes time-averaging. The auto-correlation here e.g. the second-order can then be described deductively as follows for a certain time-lag $\backslash tau$: : $$ \delta F(\vec,t)=\langle \delta F(\vec,t+\tau )\cdot \delta F(\vec,t) \rangle_t From these equations it follows that the PSF of the optical system has to be taken to the power of the order of the correlation. Thus in a second-order correlation the PSF would be reduced along all dimensions by a factor of $\backslash sqrt$. As a result the resolution of the SOFI-images increases according to this factor. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Super-resolution optical fluctuation imaging」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.