通过自相关反演重建图像扫描显微镜

IF 4.6 Q1 OPTICS
Daniele Ancora, Alessandro Zunino, Giuseppe Vicidomini and Alvaro H Crevenna
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引用次数: 0

摘要

共焦激光扫描显微镜(CLSM)是应用最广泛的显微镜技术之一,这要归功于它的三维成像能力和亚衍射空间分辨率。然而,针孔也会阻挡有用的光子,突破衍射极限的代价是数据的信噪比(SNR)受到不可挽回的损害。图像扫描显微镜(ISM)的出现是 CLSM 的合理发展,它利用小型阵列探测器取代了针孔和单元素探测器。每个敏感元件都足够小,可以通过共焦效应达到亚衍射分辨率,但整个探测器的尺寸足够大,可以保证出色的收集效率和信噪比。然而,ISM 设置产生的原始数据由 4D 数据集组成,可以看作是一组类似共焦的图像。因此,将数据集融合为单个超分辨图像需要专门的重建算法。传统的方法有多图像解卷积(需要事先了解系统点扩散函数(PSF))或自适应像素重配(APR),后者仅在有限的实验条件下有效。在这项工作中,我们描述并验证了一种基于自相关反演的 ISM 图像重建新概念。我们利用自相关的独特性质摒弃了低频成分,最大限度地提高了重建图像的分辨率,而无需对图像或 PSF 做任何假设。我们的研究结果使 ISM 重建的质量超越了 APR 所提供的水平,为多维图像处理开辟了新的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image scanning microscopy reconstruction by autocorrelation inversion
Confocal laser scanning microscopy (CLSM) stands out as one of the most widely used microscopy techniques thanks to its three-dimensional imaging capability and its sub-diffraction spatial resolution, achieved through the closure of a pinhole in front of a single-element detector. However, the pinhole also rejects useful photons, and beating the diffraction limit comes at the price of irremediably compromising the signal-to-noise ratio (SNR) of the data. Image scanning microscopy (ISM) emerged as the rational evolution of CLSM, exploiting a small array detector in place of the pinhole and the single-element detector. Each sensitive element is small enough to achieve sub-diffraction resolution through the confocal effect, but the size of the whole detector is large enough to guarantee excellent collection efficiency and SNR. However, the raw data produced by an ISM setup consists of a 4D dataset, which can be seen as a set of confocal-like images. Thus, fusing the dataset into a single super-resolved image requires a dedicated reconstruction algorithm. Conventional methods are multi-image deconvolution, which requires prior knowledge of the system point spread functions (PSFs), or adaptive pixel reassignment (APR), which is effective only on a limited range of experimental conditions. In this work, we describe and validate a novel concept for ISM image reconstruction based on autocorrelation inversion. We leverage unique properties of the autocorrelation to discard low-frequency components and maximize the resolution of the reconstructed image without any assumption on the image or any knowledge of the PSF. Our results push the quality of the ISM reconstruction beyond the level provided by APR and open new perspectives for multi-dimensional image processing.
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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