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首页> 外文期刊>Optics and Lasers in Engineering >Fast unmixing of multispectral optoacoustic data with vertex component analysis
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Fast unmixing of multispectral optoacoustic data with vertex component analysis

机译：利用顶点分量分析快速分解多光谱光声数据

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摘要

Multispectral optoacoustic tomography enhances the performance of single-wavelength imaging in terms of sensitivity and selectivity in the measurement of the biodistribution of specific chromophores, thus enabling functional and molecular imaging applications. Spectral unmixing algorithms are used to decompose multi-spectral optoacoustic data into a set of images representing distribution of each individual chromophoric component while the particular algorithm employed determines the sensitivity and speed of data visualization. Here we suggest using vertex component analysis (VCA), a method with demonstrated good performance in hyperspectral imaging, as a fast blind unmixing algorithm for multispectral optoacoustic tomography. The performance of the method is subsequently compared with a previously reported blind unmixing procedure in optoacoustic tomography based on a combination of principal component analysis (PCA) and independent component analysis (ICA). As in most practical cases the absorption spectrum of the imaged chromophores and contrast agents are known or can be determined using e.g. a spectrophotometer, we further investigate the so-called semi-blind approach, in which the a priori known spectral profiles are included in a modified version of the algorithm termed constrained VCA. The performance of this approach is also analysed in numerical simulations and experimental measurements. It has been determined that, while the standard version of the VCA algorithm can attain similar sensitivity to the PCA-ICA approach and have a robust and faster performance, using the a priori measured spectral information within the constrained VCA does not generally render improvements in detection sensitivity in experimental optoacoustic measurements.
机译：多光谱光声层析成像技术在测量特定生色团的生物分布方面的灵敏度和选择性方面，提高了单波长成像的性能，从而实现了功能和分子成像应用。光谱解混算法用于将多光谱光声数据分解为代表每个单独发色团成分分布的图像集，同时采用的特定算法确定数据可视化的灵敏度和速度。在这里，我们建议使用顶点分量分析（VCA）（一种在高光谱成像中表现出良好性能的方法）作为用于多光谱光声层析成像的快速盲分解算法。随后将该方法的性能与以前报道的基于主成分分析（PCA）和独立成分分析（ICA）的光声层析成像盲分离方法进行比较。在大多数实际情况下，成像发色团和造影剂的吸收光谱是已知的，或者可以使用例如红外光谱确定。在分光光度计中，我们进一步研究了所谓的半盲法，其中先验已知光谱图包含在算法的受约束VCA的修改版本中。还在数值模拟和实验测量中分析了这种方法的性能。已经确定，虽然VCA算法的标准版本可以达到与PCA-ICA方法相似的灵敏度并具有鲁棒和更快的性能，但是在受约束的VCA中使用先验测量的频谱信息通常不会改善检测效果实验光声测量中的灵敏度。

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  • 来源
    《Optics and Lasers in Engineering》 |2014年第7期|119-125|共7页
  • 作者

    X. Luis Dean-Ben; Nikolaos C. Deliolanis; Vasilis Ntziachristos; Daniel Razansky;

  • 作者单位

    Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingolstaedter Landstrasse 1,85764 Neuherberg, Germany;

    Fraunhofer Institut fuer Produktionstechnik und Automatisierung IPA, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany;

    Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingolstaedter Landstrasse 1,85764 Neuherberg, Germany;

    Institute for Biological and Medical Imaging, Technische Universitaet Muenchen and Helmholtz Zentrum Muenchen, Ingolstaedter Landstrasse 1,85764 Neuherberg, Germany;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Optoacoustic imaging; Photoacoustic imaging; Hyperspectral unmixing; Molecular imaging;

    机译：光声成像;光声成像;高光谱解混;分子成像;

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