利用时间扫描进行多分辨率定位,实现荧光的超分辨率漫反射光学成像。

IF 10.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Image Processing Pub Date : 2019-08-12 DOI:10.1109/TIP.2019.2931080
Brian Z Bentz, Dergan Lin, Justin A Patel, Kevin J Webb
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引用次数: 0

摘要

本文介绍了一种超分辨率光学成像方法,该方法依靠与每个荧光光学报告体相关的独特时间信息,通过测量大量散射光来高精度地确定报告体的空间位置。这种多发射器定位方法在成本函数中使用了扩散方程前向模型,有可能在几厘米的组织中实现微米级的空间分辨率。利用报告发射的一定程度的时间分离,使用计算效率高的时间剥离多分辨率算法确定位置和发射强度。这种方法规避了早期使用扩散方程前向模型的光学成像方法所面临的空间分辨率挑战,在体内应用方面大有可为。例如,该方法原则上可用于定位整个啮齿动物大脑中发射的单个神经元,从而实现对神经网络活动的直接成像。
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Multiresolution Localization with Temporal Scanning for Super-Resolution Diffuse Optical Imaging of Fluorescence.

A super-resolution optical imaging method is presented that relies on the distinct temporal information associated with each fluorescent optical reporter to determine its spatial position to high precision with measurements of heavily scattered light. This multiple-emitter localization approach uses a diffusion equation forward model in a cost function, and has the potential to achieve micron-scale spatial resolution through centimeters of tissue. Utilizing some degree of temporal separation for the reporter emissions, position and emission strength are determined using a computationally efficient time stripping multiresolution algorithm. The approach circumvents the spatial resolution challenges faced by earlier optical imaging approaches using a diffusion equation forward model, and is promising for in vivo applications. For example, in principle, the method could be used to localize individual neurons firing throughout a rodent brain, enabling direct imaging of neural network activity.

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来源期刊
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing 工程技术-工程:电子与电气
CiteScore
20.90
自引率
6.60%
发文量
774
审稿时长
7.6 months
期刊介绍: The IEEE Transactions on Image Processing delves into groundbreaking theories, algorithms, and structures concerning the generation, acquisition, manipulation, transmission, scrutiny, and presentation of images, video, and multidimensional signals across diverse applications. Topics span mathematical, statistical, and perceptual aspects, encompassing modeling, representation, formation, coding, filtering, enhancement, restoration, rendering, halftoning, search, and analysis of images, video, and multidimensional signals. Pertinent applications range from image and video communications to electronic imaging, biomedical imaging, image and video systems, and remote sensing.
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