Ilovitsh Tali, Danan Yossef, Ilovitsh Asaf, Meiri Amihai, Meir Rinat, Zalevsky Zeev
Faculty of Engineering, Bar Ilan University, Ramat-Gan 5290002, Israel.
Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA.
Biomed Opt Express. 2015 Mar 12;6(4):1262-72. doi: 10.1364/BOE.6.001262. eCollection 2015 Apr 1.
Localization microscopy provides valuable insights into cellular structures and is a rapidly developing field. The precision is mainly limited by additive noise and the requirement for single molecule imaging that dictates a low density of activated emitters in the field of view. In this paper we present a technique aimed for noise reduction and improved localization accuracy. The method has two steps; the first is the imaging of gold nanoparticles that labels targets of interest inside biological cells using a lock-in technique that enables the separation of the signal from the wide spread spectral noise. The second step is the application of the K-factor nonlinear image decomposition algorithm on the obtained image, which improves the localization accuracy that can reach 5nm and enables the localization of overlapping particles at minimal distances that are closer by 65% than conventional methods.
定位显微镜技术为细胞结构提供了有价值的见解,并且是一个快速发展的领域。其精度主要受加性噪声以及单分子成像要求的限制,单分子成像要求视场中激活的发射体密度较低。在本文中,我们提出了一种旨在降低噪声并提高定位精度的技术。该方法有两个步骤;第一步是使用锁相技术对金纳米颗粒进行成像,金纳米颗粒标记生物细胞内的目标物,锁相技术能够将信号与广泛分布的光谱噪声分离。第二步是将K因子非线性图像分解算法应用于所获得的图像,这提高了定位精度,可达到5纳米,并能够以比传统方法近65% 的最小距离对重叠颗粒进行定位。