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基于3D多焦点散光和压缩感知(3D MACS)的超分辨率重建

3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction.

作者信息

Huang Jiaqing, Sun Mingzhai, Gumpper Kristyn, Chi Yuejie, Ma Jianjie

机构信息

Department of Surgery, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, 43210, USA ; Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, 43210, USA ; These authors contribute equally to this work.

Department of Surgery, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, 43210, USA ; These authors contribute equally to this work.

出版信息

Biomed Opt Express. 2015 Feb 23;6(3):902-17. doi: 10.1364/BOE.6.000902. eCollection 2015 Mar 1.

Abstract

Single molecule based superresolution techniques (STORM/PALM) achieve nanometer spatial resolution by integrating the temporal information of the switching dynamics of fluorophores (emitters). When emitter density is low for each frame, they are located to the nanometer resolution. However, when the emitter density rises, causing significant overlapping, it becomes increasingly difficult to accurately locate individual emitters. This is particularly apparent in three dimensional (3D) localization because of the large effective volume of the 3D point spread function (PSF). The inability to precisely locate the emitters at a high density causes poor temporal resolution of localization-based superresolution technique and significantly limits its application in 3D live cell imaging. To address this problem, we developed a 3D high-density superresolution imaging platform that allows us to precisely locate the positions of emitters, even when they are significantly overlapped in three dimensional space. Our platform involves a multi-focus system in combination with astigmatic optics and an ℓ 1-Homotopy optimization procedure. To reduce the intrinsic bias introduced by the discrete formulation of compressed sensing, we introduced a debiasing step followed by a 3D weighted centroid procedure, which not only increases the localization accuracy, but also increases the computation speed of image reconstruction. We implemented our algorithms on a graphic processing unit (GPU), which speeds up processing 10 times compared with central processing unit (CPU) implementation. We tested our method with both simulated data and experimental data of fluorescently labeled microtubules and were able to reconstruct a 3D microtubule image with 1000 frames (512×512) acquired within 20 seconds.

摘要

基于单分子的超分辨率技术(STORM/PALM)通过整合荧光团(发射体)开关动力学的时间信息来实现纳米级空间分辨率。当每帧发射体密度较低时,它们能够被定位到纳米分辨率。然而,当发射体密度增加,导致显著重叠时,准确地定位单个发射体就变得越来越困难。这在三维(3D)定位中尤为明显,因为三维点扩散函数(PSF)的有效体积较大。无法在高密度下精确地定位发射体导致基于定位的超分辨率技术的时间分辨率较差,并显著限制了其在3D活细胞成像中的应用。为了解决这个问题,我们开发了一个3D高密度超分辨率成像平台,即使发射体在三维空间中显著重叠,该平台也能让我们精确地定位它们的位置。我们的平台涉及一个多焦点系统,结合像散光学和ℓ1同伦优化程序。为了减少压缩感知离散公式引入的固有偏差,我们引入了一个去偏步骤,随后是一个3D加权质心程序,这不仅提高了定位精度,还提高了图像重建的计算速度。我们在图形处理单元(GPU)上实现了我们的算法,与中央处理器(CPU)实现相比,处理速度加快了10倍。我们用荧光标记微管的模拟数据和实验数据测试了我们的方法,并且能够在20秒内重建一个包含1000帧(512×512)的3D微管图像。

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