發(fā)布時(shí)間：2018-03-27 05:36

  本文選題：超分辨定位顯微成像　切入點(diǎn)：數(shù)據(jù)處理　出處：《華中科技大學(xué)》2016年博士論文

【摘要】：超分辨定位顯微成像將熒光顯微成像的空間分辨率提高了超過(guò)一個(gè)數(shù)量級(jí),使生命科學(xué)領(lǐng)域的研究者可以從分子水平觀測(cè)生物精細(xì)結(jié)構(gòu)與功能。為了提升該方法的測(cè)量精確度,拓寬該技術(shù)的應(yīng)用范圍,需要研究進(jìn)一步提高超分辨定位顯微成像空間分辨率的方法。其中,提升超分辨定位成像數(shù)據(jù)處理方法的精度是提高該技術(shù)空間分辨率的直接、有效手段。本文通過(guò)分析超分辨定位成像中的關(guān)鍵數(shù)據(jù)處理步驟,并針對(duì)提升數(shù)據(jù)處理精度所遇到的難點(diǎn),從高密度分子定位、漂移校正、分子篩選以及圖像可視化方面進(jìn)行了研究：(1)高精度快速高密度分子定位方法。本文分析了限制當(dāng)前高密度定位方法計(jì)算性能的瓶頸,通過(guò)優(yōu)化初始模型預(yù)估與多分子擬合的非凸優(yōu)化問(wèn)題求解,開(kāi)發(fā)了高精度、少計(jì)算量的高密度分子定位方法PALMER (PArallel Localization of Multiple Emitters via Bayesian information criterion Recommendation),并基于ImageJ圖像處理軟件開(kāi)發(fā)了此方法的實(shí)用性插件,解決了當(dāng)前高密度分子定位算法無(wú)法兼容快速與高精度性能的問(wèn)題。通過(guò)仿真與實(shí)驗(yàn)驗(yàn)證,證實(shí)PALMER方法具有高的定位精度與分子檢測(cè)率,且定位速度比著名的高密度定位算法DAOSTORM快一個(gè)數(shù)量級(jí)。比起常規(guī)的稀疏分子數(shù)據(jù)采集與定位,通過(guò)高密度分子采集與PALMER方法的結(jié)合,可以將奈奎斯特空間分辨率提升近四倍。(2)高精度漂移校正方法。本文深入分析了互相關(guān)漂移校正方法的數(shù)學(xué)模型,并利用定位數(shù)據(jù)集中不同時(shí)間的子數(shù)據(jù)集均描述同一目標(biāo)結(jié)構(gòu)的冗余性特征,開(kāi)發(fā)了基于冗余互相關(guān)計(jì)算的漂移校正方法(Redundant Cross Correlation, RCC)。仿真與實(shí)驗(yàn)驗(yàn)證的結(jié)果顯示,RCC算法具有高精度、高魯棒性的特點(diǎn)。RCC方法一方面可以在處理此前互相關(guān)漂移校正方法不適用的低信號(hào)數(shù)據(jù)集時(shí),獲得納米量級(jí)的高精度校正結(jié)果;另一方面還可以提升超分辨重建圖像的有效空間分辨率：相比其他的互相關(guān)漂移校正方法,RCC算法將空間分辨率提升了約10%。(3)高性能分子篩選以及圖像可視化方法。本文基于拓展定位數(shù)據(jù)集有效數(shù)據(jù)維度的基本思想,利用對(duì)目標(biāo)結(jié)構(gòu)的先驗(yàn)知識(shí)從定位數(shù)據(jù)集中提取結(jié)構(gòu)各向異性特征,開(kāi)發(fā)了基于各向異性系數(shù)的分子篩選方法。仿真與實(shí)驗(yàn)數(shù)據(jù)分析結(jié)果顯示,該分子篩選方法可以有效濾除定位數(shù)據(jù)集中的背景噪聲,包括其他方法所不能濾除的非特異性簇狀聚合雜點(diǎn),從而顯著提高定位數(shù)據(jù)集的信噪比。同時(shí),本文討論了各向異性特征可以與高斯渲染圖像可視化方法相結(jié)合,用于非線性地增強(qiáng)超分辨重建圖像中的空間結(jié)構(gòu)信息。綜上,本文利用超分辨定位顯微成像的固有特征,發(fā)展了一系列高精度的數(shù)據(jù)處理方法,進(jìn)一步提高了超分辨定位顯微成像的空間分辨率,有望促進(jìn)超分辨定位顯微成像在精細(xì)結(jié)構(gòu)與功能解析中的應(yīng)用。
[Abstract]:Super-resolution localization microscopic imaging improves the spatial resolution of fluorescence microscopic imaging by more than one order of magnitude, enabling researchers in the field of life sciences to observe biological fine structures and functions at the molecular level. In order to widen the scope of application of this technique, we need to study the method of further improving the spatial resolution of micro-imaging of super-resolution positioning, among which, the improvement of the precision of super-resolution positioning imaging data processing method is the direct way to improve the spatial resolution of the technology. In this paper, we analyze the key data processing steps in super-resolution positioning imaging, and aim at the difficulties in improving the accuracy of data processing, from high-density molecular positioning, drift correction, Molecular screening and image visualization are studied. (1) High precision, fast and high density molecular localization methods. This paper analyzes the bottleneck that limits the computational performance of current high density localization methods. By optimizing the initial model prediction and solving the non-convex optimization problem with multi-molecular fitting, a high precision is developed. PALMER (parallel Localization of Multiple Emitters via Bayesian information criterion criterion recommendation), a high-density molecular location method with less computation, is developed based on ImageJ image processing software. The problem that the current high density molecular localization algorithm can not be compatible with fast and high precision is solved. The simulation and experimental results show that the PALMER method has high localization accuracy and molecular detection rate. The localization speed is one order of magnitude faster than the famous high-density localization algorithm DAOSTORM. Compared with the conventional sparse molecular data acquisition and localization, the combination of high-density molecular acquisition and PALMER method, Nyquist spatial resolution can be improved by nearly four times. 2) High precision drift correction method. The mathematical model of cross correlation drift correction method is deeply analyzed in this paper. The redundant features of the same target structure are described by using the sub-datasets of the location dataset at different times. A drift correction method based on redundant cross-correlation calculation is developed. The simulation and experimental results show that the proposed algorithm has high accuracy. The characteristic of high robustness. On the one hand, RCC method can obtain high precision correction results in nanometer order when dealing with low signal data sets which were not applicable to the previous cross-correlation drift correction method. On the other hand, it can improve the effective spatial resolution of super-resolution reconstructed image. Compared with other cross-correlation drift correction methods, RCC algorithm can improve the spatial resolution by about 10%. This paper is based on the basic idea of extending the effective data dimension of location data set. Using the prior knowledge of target structure to extract the anisotropic feature of structure from the location data set, a molecular screening method based on anisotropy coefficient is developed. The results of simulation and experimental data analysis show that, The molecular screening method can effectively filter the background noise in the location dataset, including the non-specific cluster-shaped aggregated clutter that cannot be filtered by other methods, thus significantly improving the signal-to-noise ratio of the location dataset. At the same time, In this paper, we discuss that the anisotropic feature can be combined with the visualization method of Gao Si rendering image to enhance the spatial structure information in the super-resolution reconstruction image. A series of high-precision data processing methods have been developed to further improve the spatial resolution of super-resolution positioning microscopic imaging, which is expected to promote the application of super-resolution positioning microscopic imaging in fine structure and functional analysis.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：Q-336

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